Bib CSL mapping

@article{Garcia-Alonso01082019, author = {Garcia-Alonso, Luz and Holland, Christian H. and Ibrahim, Mahmoud M. and Turei, Denes and Saez-Rodriguez, Julio}, title = {Benchmark and integration of resources for the estimation of human transcription factor activities}, volume = {29}, number = {8}, pages = {1363-1375}, year = {2019}, doi = {10.1101/gr.240663.118}, abstract ={The prediction of transcription factor (TF) activities from the gene expression of their targets (i.e., TF regulon) is becoming a widely used approach to characterize the functional status of transcriptional regulatory circuits. Several strategies and data sets have been proposed to link the target genes likely regulated by a TF, each one providing a different level of evidence. The most established ones are (1) manually curated repositories, (2) interactions derived from ChIP-seq binding data, (3) in silico prediction of TF binding on gene promoters, and (4) reverse-engineered regulons from large gene expression data sets. However, it is not known how these different sources of regulons affect the TF activity estimations and, thereby, downstream analysis and interpretation. Here we compared the accuracy and biases of these strategies to define human TF regulons by means of their ability to predict changes in TF activities in three reference benchmark data sets. We assembled a collection of TF–target interactions for 1541 human TFs and evaluated how different molecular and regulatory properties of the TFs, such as the DNA-binding domain, specificities, or mode of interaction with the chromatin, affect the predictions of TF activity. We assessed their coverage and found little overlap on the regulons derived from each strategy and better performance by literature-curated information followed by ChIP-seq data. We provide an integrated resource of all TF–target interactions derived through these strategies, with confidence scores, as a resource for enhanced prediction of TF activities.}, URL = {http://genome.cshlp.org/content/29/8/1363.abstract}, eprint = {http://genome.cshlp.org/content/29/8/1363.full.pdf+html}, journal = {Genome Research} }

@Article{Schubert2018, author={Schubert, Michael and Klinger, Bertram and Kl{"u}nemann, Martina and Sieber, Anja and Uhlitz, Florian and Sauer, Sascha and Garnett, Mathew J. and Bl{"u}thgen, Nils and Saez-Rodriguez, Julio}, title={Perturbation-response genes reveal signaling footprints in cancer gene expression}, journal={Nature Communications}, year={2018}, volume={9}, number={1}, pages={20}, abstract={Aberrant cell signaling can cause cancer and other diseases and is a focal point of drug research. A common approach is to infer signaling activity of pathways from gene expression. However, mapping gene expression to pathway components disregards the effect of post-translational modifications, and downstream signatures represent very specific experimental conditions. Here we present PROGENy, a method that overcomes both limitations by leveraging a large compendium of publicly available perturbation experiments to yield a common core of Pathway RespOnsive GENes. Unlike pathway mapping methods, PROGENy can (i) recover the effect of known driver mutations, (ii) provide or improve strong markers for drug indications, and (iii) distinguish between oncogenic and tumor suppressor pathways for patient survival. Collectively, these results show that PROGENy accurately infers pathway activity from gene expression in a wide range of conditions.}, issn={2041-1723}, doi={10.1038/s41467-017-02391-6}, url={https://doi.org/10.1038/s41467-017-02391-6} }}

@misc{Edelman2012, abstract = {Among the most intensively studied systems in molecular biology is the eukaryotic transcriptional apparatus, which expresses genes in a regulated manner across hundreds of different cell types. Several studies over the past few years have added weight to the concept that transcription takes place within discrete 'transcription factories' assembled inside the cell nucleus. These studies apply innovative technical approaches to gain insights into the molecular constituents, dynamical behaviour and organizational regulators of transcription factories, providing exciting insights into the spatial dimension of transcriptional control. {\textcopyright} 2012 Elsevier Ltd.}, author = {Edelman, Lucas Brandon and Fraser, Peter}, booktitle = {Current Opinion in Genetics and Development}, doi = {10.1016/j.gde.2012.01.010}, issn = {0959437X}, pmid = {22365496}, title = {{Transcription factories: Genetic programming in three dimensions}}, year = {2012} }

@article{Agarwal8493, abstract = {Increased expression of wild-type p53 in response to DNA damage arrests cells late in the G1 stage of the cell cycle by stimulating the synthesis of inhibitors of cyclin-dependent kinases, such as p21/WAF1. To study the effects of p53 without the complication of DNA damage, we used tetracycline to regulate its expression in MDAH041 human fibroblasts that lack endogenous p53. When p53 is expressed at a level comparable to that induced by DNA damage in other cells, most MDAH041 cells arrested in G1, but a significant fraction also arrested in G2/M. Cells released from a mimosine block early in S phase stopped predominantly in G2/M in the presence of p53, confirming that p53 can mediate arrest at this stage, as well as in G1. In these cells, there was appreciable induction of p21/WAF1. MDAH041 cells arrested by tetracycline-regulated p53 for as long as 20 days resumed growth when the p53 level was lowered, in striking contrast to the irreversible arrest mediated by DNA damage. Therefore, irreversible arrest must involve processes other than or in addition to the interaction of p53-induced p21/WAF1 with G1 and G2 cyclin-dependent kinases.}, author = {Agarwal, M L and Agarwal, A and Taylor, W R and Stark, G R}, doi = {10.1073/pnas.92.18.8493}, issn = {0027-8424}, journal = {Proceedings of the National Academy of Sciences}, number = {18}, pages = {8493--8497}, publisher = {National Academy of Sciences}, title = {{p53 controls both the G2/M and the G1 cell cycle checkpoints and mediates reversible growth arrest in human fibroblasts}}, url = {https://www.pnas.org/content/92/18/8493}, volume = {92}, year = {1995} }

@misc{Levine2019, author = {Levine, Arnold J.}, booktitle = {Journal of Molecular Cell Biology}, doi = {10.1093/jmcb/mjz026}, issn = {17594685}, pmid = {30925588}, title = {{The many faces of p53: Something for everyone}}, year = {2019} }

@article{Kim2018, abstract = {Mutant p53 proteins impart changes in cellular behavior and function through interactions with proteins that alter gene expression. The milieu of intracellular proteins available to interact with mutant p53 is context specific and changes with disease, cell type, and environmental conditions. Varying conformations of mutant p53 largely dictate protein-protein interactions as different point mutations within protein-coding regions greatly alter the extent and array of gain-of-function (GOF) activities. Given such variables, how can knowledge regarding p53 missense mutations be translated into predicting or altering biologic activity for therapy? How may knowledge regarding mutant p53 functions within certain disease contexts be harnessed to blunt or ablate mutant p53 GOF for therapy? In this article, we review known proteins that interact withmutant p53 and result in the activation of genes that contribute to p53 GOF with particular emphasis on context dependency and an evolving appreciation of GOF mechanisms.}, author = {Kim, Michael P. and Lozano, Guillermina}, doi = {10.1038/cdd.2017.185}, issn = {14765403}, journal = {Cell Death and Differentiation}, pmid = {29099488}, title = {{Mutant p53 partners in crime}}, year = {2018} }

@article{Willis2004, abstract = {Mutation of the p53 tumor suppressor gene is the most common genetic alteration in human cancer. A majority of these mutations are missense mutations in the DNA-binding domain. As a result, the mutated p53 gene encodes a full-length protein incapable of transactivating its target genes. In addition to this loss of function, mutant p53 can have a dominant negative effect over wild-type p53 and/or gain of function activity independently of the wild-type protein. To better understand the nature of the tumorigenic activity of mutant p53, we have investigated the mechanism by which mutant p53 can exert a dominant negative effect. We have established several stable cell lines capable of inducibly expressing a p53 mutant alone, wild-type p53 alone, or both proteins concurrently. In this context, we have used chromatin immunoprecipitation to determine the ability of wild-type p53 to bind to its endogenous target genes in the presence of various p53 mutants. We have found that p53 missense mutants markedly reduce the binding of wild-type p53 to the p53 responsive element in the target genes of p21, MDM2, and PIG3. These findings correlate with the reduced ability of wild-type p53 in inducing these and other endogenous target genes and growth suppression in the presence of mutant p53. We also showed that mutant p53 suppresses the ability of wild-type p53 in inducing cell cycle arrest. This highlights the sensitivity and utility of the dual inducible expression system because in previous studies, p53-mediated cell cycle arrest is not affected by transiently overexpressed p53 mutants. Together, our data showed that mutant p53 exerts its dominant negative activity by abrogating the DNA binding, and subsequently the growth suppression, functions of wild-type p53.}, author = {Willis, Amy and Jung, Eun Joo and Wakefield, Therese and Chen, Xinbin}, doi = {10.1038/sj.onc.1207396}, issn = {09509232}, journal = {Oncogene}, keywords = {Cell cycle arrest,Mutant p53,Transcription,p53}, pmid = {14743206}, title = {{Mutant p53 exerts a dominant negative effect by preventing wild-type p53 from binding to the promoter of its target genes}}, year = {2004} }

@article{Kandoth2013, abstract = {The Cancer Genome Atlas (TCGA) has used the latest sequencing and analysis methods to identify somatic variants across thousands of tumours. Here we present data and analytical results for point mutations and small insertions/deletions from 3,281 tumours across 12 tumour types as part of the TCGA Pan-Cancer effort. We illustrate the distributions of mutation frequencies, types and contexts across tumour types, and establish their links to tissues of origin, environmental/carcinogen influences, and DNA repair defects. Using the integrated data sets, we identified 127 significantly mutated genes from well-known (for example, mitogen-activated protein kinase, phosphatidylinositol- 3-OH kinase, Wnt/$\beta$-catenin and receptor tyrosine kinase signalling pathways, and cell cycle control) and emerging (for example, histone, histone modification, splicing, metabolism and proteolysis) cellular processes in cancer. The average number of mutations in these significantly mutated genes varies across tumour types; most tumours have two to six, indicating that the number of driver mutations required during oncogenesis is relatively small. Mutations in transcriptional factors/regulators show tissue specificity, whereas histone modifiers are often mutated across several cancer types. Clinical association analysis identifies genes having a significant effect on survival, and investigations of mutations with respect to clonal/subclonal architecture delineate their temporal orders during tumorigenesis. Taken together, these results lay the groundwork for developing new diagnostics and individualizing cancer treatment. {\textcopyright} 2013 Macmillan Publishers Limited. All rights reserved.}, author = {Kandoth, Cyriac and McLellan, Michael D. and Vandin, Fabio and Ye, Kai and Niu, Beifang and Lu, Charles and Xie, Mingchao and Zhang, Qunyuan and McMichael, Joshua F. and Wyczalkowski, Matthew A. and Leiserson, Mark D.M. and Miller, Christopher A. and Welch, John S. and Walter, Matthew J. and Wendl, Michael C. and Ley, Timothy J. and Wilson, Richard K. and Raphael, Benjamin J. and Ding, Li}, doi = {10.1038/nature12634}, issn = {14764687}, journal = {Nature}, pmid = {24132290}, title = {{Mutational landscape and significance across 12 major cancer types}}, year = {2013} }

@misc{Futreal2004, abstract = {A central aim of cancer research has been to identify the mutated genes that are causally implicated in oncogenesis ('cancer genes'). After two decades of searching, how many have been identified and how do they compare to the complete gene set that has been revealed by the human genome sequence? We have conducted a 'census' of cancer genes that indicates that mutations in more than 1{%} of genes contribute to human cancer. The census illustrates striking features in the types of sequence alteration, cancer classes in which oncogenic mutations have been identified and protein domains that are encoded by cancer genes.}, author = {Futreal, P. Andrew and Coin, Lachlan and Marshall, Mhairi and Down, Thomas and Hubbard, Timothy and Wooster, Richard and Rahman, Nazneen and Stratton, Michael R.}, booktitle = {Nature Reviews Cancer}, doi = {10.1038/nrc1299}, issn = {1474175X}, pmid = {14993899}, title = {{A census of human cancer genes}}, year = {2004} }

@Article{Liu2019, author={Liu, Anika and Trairatphisan, Panuwat and Gjerga, Enio and Didangelos, Athanasios and Barratt, Jonathan and Saez-Rodriguez, Julio}, title={From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL}, journal={npj Systems Biology and Applications}, year={2019}, volume={5}, number={1}, pages={40}, abstract={While gene expression profiling is commonly used to gain an overview of cellular processes, the identification of upstream processes that drive expression changes remains a challenge. To address this issue, we introduce CARNIVAL, a causal network contextualization tool which derives network architectures from gene expression footprints. CARNIVAL (CAusal Reasoning pipeline for Network identification using Integer VALue programming) integrates different sources of prior knowledge including signed and directed protein-protein interactions, transcription factor targets, and pathway signatures. The use of prior knowledge in CARNIVAL enables capturing a broad set of upstream cellular processes and regulators, leading to a higher accuracy when benchmarked against related tools. Implementation as an integer linear programming (ILP) problem guarantees efficient computation. As a case study, we applied CARNIVAL to contextualize signaling networks from gene expression data in IgA nephropathy (IgAN), a condition that can lead to chronic kidney disease. CARNIVAL identified specific signaling pathways and associated mediators dysregulated in IgAN including Wnt and TGF-b, which we subsequently validated experimentally. These results demonstrated how CARNIVAL generates hypotheses on potential upstream alterations that propagate through signaling networks, providing insights into diseases.}, issn={2056-7189}, doi={10.1038/s41540-019-0118-z}, url={https://doi.org/10.1038/s41540-019-0118-z} }

@Article{Staaf2019, author={Staaf, Johan and Glodzik, Dominik and Bosch, Ana and Vallon-Christersson, Johan and Reutersw{"a}rd, Christel and H{"a}kkinen, Jari and Degasperi, Andrea and Amarante, Tauanne Dias and Saal, Lao H. and Hegardt, Cecilia and Stobart, Hilary and Ehinger, Anna and Larsson, Christer and Ryd{'e}n, Lisa and Loman, Niklas and Malmberg, Martin and Kvist, Anders and Ehrencrona, Hans and Davies, Helen R. and Borg, {\AA}ke and Nik-Zainal, Serena}, title={Whole-genome sequencing of triple-negative breast cancers in a population-based clinical study}, journal={Nature Medicine}, year={2019}, volume={25}, number={10}, pages={1526-1533}, abstract={Whole-genome sequencing (WGS) brings comprehensive insights to cancer genome interpretation. To explore the clinical value of WGS, we sequenced 254 triple-negative breast cancers (TNBCs) for which associated treatment and outcome data were collected between 2010 and 2015 via the population-based Sweden Cancerome Analysis Network-Breast (SCAN-B) project (ClinicalTrials.gov ID:NCT02306096). Applying the HRDetect mutational-signature-based algorithm to classify tumors, 59% were predicted to have homologous-recombination-repair deficiency (HRDetect-high): 67% explained by germline/somatic mutations of BRCA1/BRCA2, BRCA1 promoter hypermethylation, RAD51C hypermethylation or biallelic loss of PALB2. A novel mechanism of BRCA1 abrogation was discovered via germline SINE-VNTR-Alu retrotransposition. HRDetect provided independent prognostic information, with HRDetect-high patients having better outcome on adjuvant chemotherapy for invasive disease-free survival (hazard ratio (HR) = 0.42; 95% confidence interval (CI) = 0.2-0.87) and distant relapse-free interval (HR = 0.31, CI = 0.13-0.76) compared to HRDetect-low, regardless of whether a genetic/epigenetic cause was identified. HRDetect-intermediate, some possessing potentially targetable biological abnormalities, had the poorest outcomes. HRDetect-low cancers also had inadequate outcomes: {\textasciitilde}4.7% were mismatch-repair-deficient (another targetable defect, not typically sought) and they were enriched for (but not restricted to) PIK3CA/AKT1 pathway abnormalities. New treatment options need to be considered for now-discernible HRDetect-intermediate and HRDetect-low categories. This population-based study advocates for WGS of TNBC to better inform trial stratification and improve clinical decision-making.}, issn={1546-170X}, doi={10.1038/s41591-019-0582-4}, url={https://doi.org/10.1038/s41591-019-0582-4} }

@Article{Costa2018, author={Costa, Ricardo L. B. and Han, Hyo Sook and Gradishar, William J.}, title={Targeting the PI3K/AKT/mTOR pathway in triple-negative breast cancer: a review}, journal={Breast Cancer Research and Treatment}, year={2018}, volume={169}, number={3}, pages={397-406}, abstract={Triple-negative breast cancer (TNBC) accounts for approximately 20% of breast cancer cases. Although there have been advances in the treatment of hormone receptor-positive and human epidermal growth factor receptor 2-positive breast cancers, targeted therapies for TNBC remain unavailable. In this narrative review, we summarize recent discoveries related to the underlying biology of the phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT)/mechanistic target of rapamycin (mTOR) pathway in TNBC, examine clinical progress to date, and suggest rational future approaches for investigational therapies in TNBC.}, issn={1573-7217}, doi={10.1007/s10549-018-4697-y}, url={https://doi.org/10.1007/s10549-018-4697-y} }

@Article{Nair2018, author={Nair, Amritha and Chung, Hsiang-Ching and Sun, Tingting and Tyagi, Siddhartha and Dobrolecki, Lacey E. and Dominguez-Vidana, Rocio and Kurley, Sarah J. and Orellana, Mayra and Renwick, Alexander and Henke, David M. and Katsonis, Panagiotis and Schmitt, Earlene and Chan, Doug W. and Li, Hui and Mao, Sufeng and Petrovic, Ivana and Creighton, Chad J. and Gutierrez, Carolina and Dubrulle, Julien and Stossi, Fabio and Tyner, Jeffrey W. and Lichtarge, Olivier and Lin, Charles Y. and Zhang, Bing and Scott, Kenneth L. and Hilsenbeck, Susan G. and Sun, Jinpeng and Yu, Xiao and Osborne, C. Kent and Schiff, Rachel and Christensen, James G. and Shields, David J. and Rimawi, Mothaffar F. and Ellis, Matthew J. and Shaw, Chad A. and Lewis, Michael T. and Westbrook, Thomas F.}, title={Combinatorial inhibition of PTPN12-regulated receptors leads to a broadly effective therapeutic strategy in triple-negative breast cancer}, journal={Nature Medicine}, year={2018}, volume={24}, number={4}, pages={505-511}, abstract={Targeting tyrosine kinase receptors that share the feedback inhibitor PTPN12 leads to broad spectrum therapeutic suppression of triple-negative breast cancer.}, issn={1546-170X}, doi={10.1038/nm.4507}, url={https://doi.org/10.1038/nm.4507} }

@article{10.1093/annonc/mdy024, author = {Bareche, Y and Venet, D and Ignatiadis, M and Aftimos, P and Piccart, M and Rothe, F and Sotiriou, C}, title = "{Unravelling triple-negative breast cancer molecular heterogeneity using an integrative multiomic analysis}", journal = {Annals of Oncology}, volume = {29}, number = {4}, pages = {895-902}, year = {2018}, month = {01}, abstract = "{Recent efforts of genome-wide gene expression profiling analyses have improved our understanding of the biological complexity and diversity of triple-negative breast cancers (TNBCs) reporting, at least six different molecular subtypes of TNBC namely Basal-like 1 (BL1), basal-like 2 (BL2), immunomodulatory (IM), mesenchymal (M), mesenchymal stem-like (MSL) and luminal androgen receptor (LAR). However, little is known regarding the potential driving molecular events within each subtype, their difference in survival and response to therapy. Further insight into the underlying genomic alterations is therefore needed.This study was carried out using copy-number aberrations, somatic mutations and gene expression data derived from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and The Cancer Genome Atlas. TNBC samples (n = 550) were classified according to Lehmann’s molecular subtypes using the TNBCtype online subtyping tool (http://cbc.mc.vanderbilt.edu/tnbc/).Each subtype showed significant clinic-pathological characteristic differences. Using a multivariate model, IM subtype showed to be associated with a better prognosis (HR = 0.68; CI = 0.46–0.99; P = 0.043) whereas LAR subtype was associated with a worst prognosis (HR = 1.47; CI = 1.0–2.14; P = 0.046). BL1 subtype was found to be most genomically instable subtype with high TP53 mutation (92%) and copy-number deletion in genes involved in DNA repair mechanism (BRCA2, MDM2, PTEN, RB1 and TP53). LAR tumours were associated with higher mutational burden with significantly enriched mutations in PI3KCA (55%), AKT1 (13%) and CDH1 (13%) genes. M and MSL subtypes were associated with higher signature score for angiogenesis. Finally, IM showed high expression levels of immune signatures and check-point inhibitor genes such as PD1, PDL1 and CTLA4.Our findings highlight for the first time the substantial genomic heterogeneity that characterize TNBC molecular subtypes, allowing for a better understanding of the disease biology as well as the identification of several candidate targets paving novel approaches for the development of anticancer therapeutics for TNBC.}", issn = {0923-7534}, doi = {10.1093/annonc/mdy024}, url = {https://doi.org/10.1093/annonc/mdy024}, eprint = {http://oup.prod.sis.lan/annonc/article-pdf/29/4/895/25082538/mdy024.pdf}, }

@article {Foidart1838, author = {Foidart, Pierre and Yip, Cassandre and Radermacher, Jean and Blacher, Silvia and Lienard, Mehdi and Montero-Ruiz, Laetitia and Maquoi, Erik and Montaudon, Elodie and Ch{^a}teau-Joubert, Sophie and Collignon, Jo{"e}lle and Coibion, Michel and Jossa, V{'e}ronique and Marangoni, Elisabetta and No{"e}l, Agn{`e}s and Sounni, Nor Eddine and Jerusalem, Guy}, title = {Expression of MT4-MMP, EGFR, and RB in Triple-Negative Breast Cancer Strongly Sensitizes Tumors to Erlotinib and Palbociclib Combination Therapy}, volume = {25}, number = {6}, pages = {1838--1850}, year = {2019}, doi = {10.1158/1078-0432.CCR-18-1880}, publisher = {American Association for Cancer Research}, abstract = {Purpose: Here, we investigated the clinical relevance of an unprecedented combination of three biomarkers in triple-negative breast cancer (TNBC), both in human samples and in patient-derived xenografts of TNBC (PDX-TNBC): EGFR, its recently identified partner (MT4-MMP), and retinoblastoma protein (RB).Experimental Design: IHC analyses were conducted on human and PDX-TNBC samples to evaluate the production of the three biomarkers. The sensitivity of cancer cells expressing or not MT4-MMP to anti-EGFR (erlotinib) or anti-CDK4/6 inhibitor (palbociclib) was evaluated in vitro in 2D and 3D proliferation assays and in vivo using xenografts and PDX-TNBC displaying different RB, MT4-MMP, and EGFR status after single (erlotinib or palbociclib) or combined (erlotinib + palbociclib) treatments.Results: EGFR and MT4-MMP were coexpressed in >70% of TNBC samples and PDX-TNBC, among which approximately 60% maintained RB expression. Notably, approximately 50% of all TNBC and PDX-TNBC expressed the three biomarkers. Single erlotinib and palbociclib treatments drastically reduced the in vitro proliferation of cells expressing EGFR and MT4-MMP when compared with control cells. Both TNBC xenografts and PDX expressing MT4-MMP, EGFR, and RB, but not PDX-TNBC with RB loss, were sensitive to erlotinib and palbociclib with an additive effect of combination therapy. Moreover, this combination was efficient in another PDX-TNBC expressing the three biomarkers and resistant to erlotinib alone.Conclusions: We defined a new association of three biomarkers (MT4-MMP/EGFR/RB) expressed together in 50% of TNBC and demonstrated its usefulness to predict the TNBC response to anti-EGFR and anti-CDK4/6 drugs used in single or combined therapy.This article is featured in Highlights of This Issue, p. 1691}, issn = {1078-0432}, URL = {https://clincancerres.aacrjournals.org/content/25/6/1838}, eprint = {https://clincancerres.aacrjournals.org/content/25/6/1838.full.pdf}, journal = {Clinical Cancer Research} }

@article{doi:10.1080/0284186X.2017.1400180, author = {Elisabeth Specht Stovgaard and Dorte Nielsen and Estrid Hogdall and Eva Balslev}, title = {Triple negative breast cancer – prognostic role of immune-related factors: a systematic review}, journal = {Acta Oncologica}, volume = {57}, number = {1}, pages = {74-82}, year = {2018}, publisher = {Taylor & Francis}, doi = {10.1080/0284186X.2017.1400180}, note ={PMID: 29168430},

URL = { https://doi.org/10.1080/0284186X.2017.1400180

}, eprint = { https://doi.org/10.1080/0284186X.2017.1400180

}

@Article{Diana2018, author={Diana, Anna and Franzese, Elisena and Centonze, Sara and Carlino, Francesca and Della Corte, Carminia Maria and Ventriglia, Jole and Petrillo, Angelica and De Vita, Ferdinando and Alfano, Roberto and Ciardiello, Fortunato and Orditura, Michele}, title={Triple-Negative Breast Cancers: Systematic Review of the Literature on Molecular and Clinical Features with a Focus on Treatment with Innovative Drugs}, journal={Current Oncology Reports}, year={2018}, volume={20}, number={10}, pages={76}, abstract={Triple-negative breast cancer (TNBC) accounts for 15-20% of diagnosed breast tumours, with higher incidence in young and African-American women, and it is frequently associated with BRCA germline mutations. Chemotherapy is the only well-established therapeutic option in both early- and advanced-stages of the disease. TNBC tumours relapse earlier after standard anthracycline- and/or taxane-based chemotherapy treatments, generally within 1-3?years after the diagnosis, and often develop visceral metastases, representing the subtype with a worse prognosis among all breast cancers. In the present review, we will provide an updated overview of the available results of recent clinical trials for this disease and we will describe the implications of the known molecular pathways representing novel targets for development of future therapies for TNBC patients.}, issn={1534-6269}, doi={10.1007/s11912-018-0726-6}, url={https://doi.org/10.1007/s11912-018-0726-6} }

@article{LEE2018110, title = "Triple negative breast cancer: Emerging therapeutic modalities and novel combination therapies", journal = "Cancer Treatment Reviews", volume = "62", pages = "110 - 122", year = "2018", issn = "0305-7372", doi = "https://doi.org/10.1016/j.ctrv.2017.11.003", url = "http://www.sciencedirect.com/science/article/pii/S0305737217301883", author = "Alice Lee and Mustafa B.A. Djamgoz", keywords = "Triple-negative breast cancer, Targeted therapy, Combination therapy, Biomarkers", abstract = "Triple negative breast cancer (TNBC) is a complex and aggressive subtype of breast cancer which lacks oestrogen receptors, progesterone receptors and HER2 amplification, thereby making it difficult to target therapeutically. In addition, TNBC has the highest rates of metastatic disease and the poorest overall survival of all breast cancer subtypes. Resultantly, development of targeted therapies for TNBC is urgently needed. Recent efforts aimed at molecular characterisation of TNBCs have revealed various emerging therapeutic targets including PARP1, receptor and non-receptor tyrosine kinases, immune-checkpoints, androgen receptor and epigenetic proteins. Key successes include that of the PARP inhibitor, olaparib, which prolonged progression-free survival in a trial of BRCA-mutated breast cancer and for which clinical approval (in this setting) appears imminent. Nevertheless, the heterogeneity of TNBC has limited the clinical benefits of many trialled therapies in ‘unselected’ patients. Further, drug resistance develops following use of many targeted monotherapies due to upregulation of compensatory signalling pathways. In this review, we evaluate the current status of investigational targeted treatments and present evidence for the role of novel biomarkers and combination therapies in increasing response rates and circumventing drug-induced resistance. Additionally, we discuss promising novel targets in metastatic TNBC identified through preclinical and/or epidemiological studies." }

@article{doi:10.1080/15384047.2019.1595285, author = {Chen Khuan Wong and Christopher Gromisch and Sait Ozturk and Panagiotis Papageorgis and Hamid Mostafavi Abdolmaleky and Björn M. Reinhard and Arunthathi Thiagalingam and Sam Thiagalingam}, title = {MicroRNA-4417 is a tumor suppressor and prognostic biomarker for triple-negative breast cancer}, journal = {Cancer Biology & Therapy}, volume = {20}, number = {8}, pages = {1113-1120}, year = {2019}, publisher = {Taylor & Francis}, doi = {10.1080/15384047.2019.1595285}, note ={PMID: 30922194},

URL = { https://doi.org/10.1080/15384047.2019.1595285

}, eprint = { https://doi.org/10.1080/15384047.2019.1595285

} , abstract = { ABSTRACTTriple-negative breast cancer (TNBC) is the most aggressive form of breast cancer with poor prognosis due to lack of druggable targets such as hormone and growth factor receptors. Therefore, identification of targetable regulators such as miRNAs could provide new avenues for therapeutic applications. Here, we report that the expression of miR-4417 is suppressed during the progression of TNBC cells from non-malignant to the malignant stage. MiR-4417 is localized to chromosome 1p36, a region with high frequency of loss of heterozygosity in multiple cancers, and its biogenesis is DICER-dependent. Low expression of miR-4417 is significantly associated with worse prognosis in TNBC patients, while overexpression of miR-4417 is sufficient to inhibit migration and mammosphere formation of TNBC cells in vitro. Overall, our findings suggest miR-4417 exerts a tumor suppressive effect and thereby could serve as a prognostic biomarker and therapeutic tool against TNBC. } }

@article{doi:10.1096/fj.201800120R, author = {Turashvili, Gulisa and Lightbody, Elizabeth D. and Tyryshkin, Kathrin and SenGupta, Sandip K. and Elliott, Bruce E. and Madarnas, Yolanda and Ghaffari, Abdi and Day, Andrew and Nicol, Christopher J. B.}, title = {Novel prognostic and predictive microRNA targets for triple-negative breast cancer}, journal = {The FASEB Journal}, volume = {32}, number = {11}, pages = {5937-5954}, year = {2018}, doi = {10.1096/fj.201800120R},

URL = { https://doi.org/10.1096/fj.201800120R

}, eprint = { https://doi.org/10.1096/fj.201800120R

} , abstract = { Triple-negative breast cancers (TNBCs) account for ∼25% of all invasive carcinomas and represent a large subset of aggressive, high-grade tumors. Despite current research focused on understanding the genetic landscape of TNBCs, reliable prognostic and predictive biomarkers remain limited. Although dysregulated microRNAs (miRNAs) have emerged as key players in many cancer types, the role of miRNAs in TNBC disease progression is unclear. We performed miRNA profiling of 51 TNBCs by next-generation sequencing to reveal differentially expressed miRNAs. A total of 228 miRNAs were identified. Three miRNAs (miR-224-5p, miR-375, and miR-205-5p) separated the tumors based on basal status. Six miRNAs (high let-7d-3p, miR-203b-5p, and miR-324-5p; low miR-30a-3p, miR-30a-5p, and miR-199a-5p) were significantly associated with decreased overall survival (OS) and 5 miRNAs (high let-7d-3p; low miR-30a-3p, miR-30a-5p, miR-30c-5p, and miR-128-3p) with decreased relapse-free survival (RFS). On multivariate analysis, high expression of let-7d-3p and low expression of miR-30a were independent predictors of decreased OS and RFS. High expression of miR-95-3p was significantly associated with decreased OS and RFS in patients treated with anthracycline-based chemotherapy. Five miRNAs (let-7d-3p, miR-30a-3p, miR-30c-5p, miR-128-3p, and miR-95-3p) were validated by quantitative RT-PCR. Our findings unveil novel prognostic and predictive miRNA targets for TNBC, including a miRNA signature that predicts patient response to anthracycline-based chemotherapy. This may improve clinical management and/or lead to the development of novel therapies.—Turashvili, G., Lightbody, E. D., Tyryshkin, K., SenGupta, S. K., Elliott, B. E., Madarnas, Y., Ghaffari, A., Day, A., Nicol, C. J. B. Novel prognostic and predictive microRNA targets for triple-negative breast cancer. } }

@Article{Ghandi2019, author={Ghandi, Mahmoud and Huang, Franklin W. and Jan{'e}-Valbuena, Judit and Kryukov, Gregory V. and Lo, Christopher C. and McDonald, E. Robert and Barretina, Jordi and Gelfand, Ellen T. and Bielski, Craig M. and Li, Haoxin and Hu, Kevin and Andreev-Drakhlin, Alexander Y. and Kim, Jaegil and Hess, Julian M. and Haas, Brian J. and Aguet, Fran{\c{c}}ois and Weir, Barbara A. and Rothberg, Michael V. and Paolella, Brenton R. and Lawrence, Michael S. and Akbani, Rehan and Lu, Yiling and Tiv, Hong L. and Gokhale, Prafulla C. and de Weck, Antoine and Mansour, Ali Amin and Oh, Coyin and Shih, Juliann and Hadi, Kevin and Rosen, Yanay and Bistline, Jonathan and Venkatesan, Kavitha and Reddy, Anupama and Sonkin, Dmitriy and Liu, Manway and Lehar, Joseph and Korn, Joshua M. and Porter, Dale A. and Jones, Michael D. and Golji, Javad and Caponigro, Giordano and Taylor, Jordan E. and Dunning, Caitlin M. and Creech, Amanda L. and Warren, Allison C. and McFarland, James M. and Zamanighomi, Mahdi and Kauffmann, Audrey and Stransky, Nicolas and Imielinski, Marcin and Maruvka, Yosef E. and Cherniack, Andrew D. and Tsherniak, Aviad and Vazquez, Francisca and Jaffe, Jacob D. and Lane, Andrew A. and Weinstock, David M. and Johannessen, Cory M. and Morrissey, Michael P. and Stegmeier, Frank and Schlegel, Robert and Hahn, William C. and Getz, Gad and Mills, Gordon B. and Boehm, Jesse S. and Golub, Todd R. and Garraway, Levi A. and Sellers, William R.}, title={Next-generation characterization of the Cancer Cell Line Encyclopedia}, journal={Nature}, year={2019}, volume={569}, number={7757}, pages={503-508}, abstract={Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR-Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines.}, issn={1476-4687}, doi={10.1038/s41586-019-1186-3}, url={https://doi.org/10.1038/s41586-019-1186-3} } @article{Lang2004, abstract = {Individuals with Li-Fraumeni syndrome carry inherited mutations in the p53 tumor suppressor gene and are predisposed to tumor development. To examine the mechanistic nature of these p53 missense mutations, we generated mice harboring a G-to-A substitution at nucleotide 515 of p53 (p53+/515A) corresponding to the p53R175H hot spot mutation in human cancers. Although p53+/515A mice display a similar tumor spectrum and survival curve as p53+/- mice, tumors from p53+/515A mice metastasized with high frequency. Correspondingly, the embryonic fibroblasts from the p53 515A/515A mutant mice displayed enhanced cell proliferation, DNA synthesis, and transformation potential. The disruption of p63 and p73 in p53-/- cells increased transformation capacity and reinitiated DNA synthesis to levels observed in p53515A/515A cells. Additionally, p63 and p73 were functionally inactivated in p53515A cells. These results provide in vivo validation for the gain-of-function properties of certain p53 missense mutations and suggest a mechanistic basis for these phenotypes.}, author = {Lang, Gene A. and Iwakuma, Tomoo and Suh, Young Ah and Liu, Geng and Rao, V. Ashutosh and Parant, John M. and Valentin-Vega, Yasmine A. and Terzian, Tamara and Caldwell, Lisa C. and Strong, Louise C. and El-Naggar, Adel K. and Lozano, Guillermina}, doi = {10.1016/j.cell.2004.11.006}, issn = {00928674}, journal = {Cell}, pmid = {15607981}, title = {{Gain of function of a p53 hot spot mutation in a mouse model of Li-Fraumeni syndrome}}, year = {2004} }

@misc{Robles2010, abstract = {The initial observation that p53 accumulation might serve as a surrogate biomarker for TP53 mutation has been the cornerstone for vast translational efforts aimed at validating its clinical use for the diagnosis, prognosis, and treatment of cancer. Early on, it was realized that accurate evaluation of p53 status and function could not be achieved through protein-expression analysis only. As our understanding of the p53 pathway has evolved and more sophisticated methods for assessment of p53 functional integrity have become available, the clinical and molecular epidemiological implications of p53 abnormalities in cancers are being revealed. They include diagnostic testing for germline p53 mutations, and the assessment of selected p53 mutations as biomarkers of carcinogen exposure and cancer risk and prognosis. Here, we describe the strengths and limitations of the most frequently used techniques for determination of p53 status in tumors, as well as the most remarkable latest findings relating to its clinical and epidemiological value.}, author = {Robles, Ana I. and Harris, Curtis C.}, booktitle = {Cold Spring Harbor perspectives in biology}, doi = {10.1101/cshperspect.a001016}, issn = {19430264}, pmid = {20300207}, title = {{Clinical outcomes and correlates of TP53 mutations and cancer.}}, year = {2010} }

@article{DONEHOWER20191370, title = "Integrated Analysis of TP53 Gene and Pathway Alterations in The Cancer Genome Atlas", journal = "Cell Reports", volume = "28", number = "5", pages = "1370 - 1384.e5", year = "2019", issn = "2211-1247", doi = "https://doi.org/10.1016/j.celrep.2019.07.001", url = "http://www.sciencedirect.com/science/article/pii/S221112471930885X", author = "Lawrence A. Donehower and Thierry Soussi and Anil Korkut and Yuexin Liu and Andre Schultz and Maria Cardenas and Xubin Li and Ozgun Babur and Teng-Kuei Hsu and Olivier Lichtarge and John N. Weinstein and Rehan Akbani and David A. Wheeler", keywords = ", mutation, p53, TCGA, The Cancer Genome Atlas, PanCanAtlas, p53 signaling pathway, chromosomal instability, p53 signature, p53 targets", abstract = "Summary The TP53 tumor suppressor gene is frequently mutated in human cancers. An analysis of five data platforms in 10,225 patient samples from 32 cancers reported by The Cancer Genome Atlas (TCGA) enables comprehensive assessment of p53 pathway involvement in these cancers. More than 91% of TP53-mutant cancers exhibit second allele loss by mutation, chromosomal deletion, or copy-neutral loss of heterozygosity. TP53 mutations are associated with enhanced chromosomal instability, including increased amplification of oncogenes and deep deletion of tumor suppressor genes. Tumors with TP53 mutations differ from their non-mutated counterparts in RNA, miRNA, and protein expression patterns, with mutant TP53 tumors displaying enhanced expression of cell cycle progression genes and proteins. A mutant TP53 RNA expression signature shows significant correlation with reduced survival in 11 cancer types. Thus, TP53 mutation has profound effects on tumor cell genomic structure, expression, and clinical outlook." }

@Article{Darb-Esfahani2016, author={Darb-Esfahani, Silvia and Denkert, Carsten and Stenzinger, Albrecht and Salat, Christoph and Sinn, Bruno and Schem, Christian and Endris, Volker and Klare, Peter and Schmitt, Wolfgang and Blohmer, Jens-Uwe and Weichert, Wilko and M{"o}bs, Markus and Tesch, Hans and K{"u}mmel, Sherko and Sinn, Peter and Jackisch, Christian and Dietel, Manfred and Reimer, Toralf and Loi, Sherene and Untch, Michael and von Minckwitz, Gunter and Nekljudova, Valentina and Loibl, Sibylle}, title={Role of TP53 mutations in triple negative and HER2-positive breast cancer treated with neoadjuvant anthracycline/taxane-based chemotherapy}, journal={Oncotarget}, year={2016}, month={Oct}, day={18}, publisher={Impact Journals LLC}, volume={7}, number={42}, pages={67686-67698}, keywords={HER2}, keywords={TP53}, keywords={mutation}, keywords={pathological complete response}, keywords={triple negative breast cancer}, keywords={Anthracyclines/administration & dosage}, keywords={Antineoplastic Combined Chemotherapy Protocols/*therapeutic use}, keywords={Bevacizumab/administration & dosage}, keywords={Breast Neoplasms/*drug therapy/metabolism/pathology}, keywords={Bridged-Ring Compounds/administration & dosage}, keywords={Carboplatin/administration & dosage}, keywords={Chemotherapy, Adjuvant}, keywords={Disease-Free Survival}, keywords={Female}, keywords={Humans}, keywords={Lapatinib}, keywords={Middle Aged}, keywords={*Mutation}, keywords={Neoadjuvant Therapy}, keywords={Quinazolines/administration & dosage}, keywords={Receptor, ErbB-2/*metabolism}, keywords={Taxoids/administration & dosage}, keywords={Trastuzumab/administration & dosage}, keywords={Triple Negative Breast Neoplasms/*drug therapy/genetics/pathology}, keywords={Tumor Suppressor Protein p53/*genetics/metabolism}, abstract={BACKGROUND: TP53 mutations are frequent in breast cancer, however their clinical relevance in terms of response to chemotherapy is controversial. METHODS: 450 pre-therapeutic, formalin-fixed, paraffin-embedded core biopsies from the phase II neoadjuvant GeparSixto trial that included HER2-positive and triple negative breast cancer (TNBC) were subjected to Sanger sequencing of exons 5-8 of the TP53 gene. TP53 status was correlated to response to neoadjuvant anthracycline/taxane-based chemotherapy with or without carboplatin and trastuzumab/lapatinib in HER2-positive and bevacizumab in TNBC. p53 protein expression was evaluated by immunohistochemistry in the TNBC subgroup. RESULTS: Of 450 breast cancer samples 297 (66.0%) were TP53 mutant. Mutations were significantly more frequent in TNBC (74.8%) compared to HER2-positive cancers (55.4%, P < 0.0001). Neither mutations nor different mutation types and effects were associated with pCR neither in the whole study group nor in molecular subtypes (P > 0.05 each). Missense mutations tended to be associated with a better survival compared to all other types of mutations in TNBC (P = 0.093) and in HER2-positive cancers (P = 0.071). In TNBC, missense mutations were also linked to higher numbers of tumor-infiltrating lymphocytes (TILs, P = 0.028). p53 protein overexpression was also linked with imporved survival (P = 0.019). CONCLUSIONS: Our study confirms high TP53 mutation rates in TNBC and HER2-positive breast cancer. Mutations did not predict the response to an intense neoadjuvant chemotherapy in these two molecular breast cancer subtypes.}, note={27611952[pmid]}, note={PMC5356512[pmcid]}, note={11891[PII]}, issn={1949-2553}, url={https://www.ncbi.nlm.nih.gov/pubmed/27611952} }

@Article{Bianchini2016, author={Bianchini, Giampaolo and Balko, Justin M. and Mayer, Ingrid A. and Sanders, Melinda E. and Gianni, Luca}, title={Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease}, journal={Nature Reviews Clinical Oncology}, year={2016}, volume={13}, number={11}, pages={674-690}, abstract={The routine diagnosis of triple-negative breast cancer (TNBC) depends on the accurate assessment of the status of the oestrogen receptor (ER), progesterone receptor (PgR) and HER2Chemotherapy remains the standard therapeutic approach for TNBC at all stages, with platinum compounds having a relevant role, especially in patients harbouring BRCA1/2 mutations or 'BRCAness''Omics' technologies have provided unprecedented insights into the molecular complexity and heterogeneous clinical behaviour of TNBC but, to date, none of the newly developed molecular classifications has demonstrated clinical utilitySeveral potentially actionable molecular alterations, frequently affecting PI3K/mTOR or RAS/RAF/MEK, have been found in TNBC, but none have been confirmed as a 'driver alteration', nor have any TNBC subsets been shown to be 'addicted' to themTargeted agents currently under clinical investigation in TNBC include PARP inhibitors, PI3K inhibitors, MEK inhibitors, anti-androgen therapies, heat shock protein 90 inhibitors, histone deacetylase inhibitors, and their combinationsTNBC is remarkably heterogeneous in terms of the tumour microenvironment; tumour lymphocyte infiltration is associated with good prognosis and a response to chemotherapy, which provides a strong rationale for testing immunotherapies in TNBC}, issn={1759-4782}, doi={10.1038/nrclinonc.2016.66}, url={https://doi.org/10.1038/nrclinonc.2016.66} }

@article{BOSCH2010206, title = "Triple-negative breast cancer: Molecular features, pathogenesis, treatment and current lines of research", journal = "Cancer Treatment Reviews", volume = "36", number = "3", pages = "206 - 215", year = "2010", issn = "0305-7372", doi = "https://doi.org/10.1016/j.ctrv.2009.12.002", url = "http://www.sciencedirect.com/science/article/pii/S0305737209001844", author = "Ana Bosch and Pilar Eroles and Rosa Zaragoza and Juan R. Viña and Ana Lluch", keywords = "Breast cancer, Triple negative, Basal-like, Molecular features, Breast epithelium, Molecular targets, Platinum compounds, PARP inhibitors, EGFR inhibitors, Antiangiogenics", abstract = "Summary Breast cancer is a heterogeneous disease with different morphologies, molecular profiles, clinical behaviour and response to therapy. The triple negative is a particular type of breast cancer defined by absence of oestrogen and progesterone receptor expression as well as absence of ERBB2 amplification. It is characterized by its biological aggressiveness, worse prognosis and lack of a therapeutic target in contrast with hormonal receptor positive and ERBB2+ breast cancers. Given these characteristics, triple-negative breast cancer is a challenge in today’s clinical practice. A new breast cancer classification emerged recently in the scientific scene based in gene expression profiles. The new subgroups (luminal, ERBB2, normal breast and basal-like) have distinct gene expression patterns and phenotypical characteristics. Triple-negative breast cancer shares phenotypical features with basal-like breast cancer, which is in turn the most aggressive and with worse outcome. Since microarray gene-expression assays are only used in the research setting, clinicians use the triple-negative definition as a surrogate of basal-like breast cancer. The aim of this review, that focuses on triple-negative breast cancer, is to summarize the most relevant knowledge on this particular type of cancer in terms of molecular features, pathogenesis, clinical characteristics, current treatments and the new therapeutic options that include the use of platinum compounds, EGFR antagonists, antiangiogenics and PARP inhibitors. Advances in research are promising and new types of active drugs will become a reality in the near future, making possible a better outcome for this subgroup of breast cancer patients." }

@Article{Weinberg2019, author={Weinberg, Robert A.}, title={How TP53 (almost) became an oncogene}, journal={Journal of molecular cell biology}, year={2019}, month={Jul}, day={19}, publisher={Oxford University Press}, volume={11}, number={7}, pages={531-533}, note={31282927[pmid]}, note={PMC6735798[pmcid]}, note={5529696[PII]}, issn={1759-4685}, doi={10.1093/jmcb/mjz061}, url={https://www.ncbi.nlm.nih.gov/pubmed/31282927} }

@article{Blondel_2008, doi = {10.1088/1742-5468/2008/10/p10008}, url = {https://doi.org/10.1088%2F1742-5468%2F2008%2F10%2Fp10008}, year = 2008, month = {oct}, publisher = {{IOP} Publishing}, volume = {2008}, number = {10}, pages = {P10008}, author = {Vincent D Blondel and Jean-Loup Guillaume and Renaud Lambiotte and Etienne Lefebvre}, title = {Fast unfolding of communities in large networks}, journal = {Journal of Statistical Mechanics: Theory and Experiment}, abstract = {We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2 million customers and by analysing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad hoc modular networks.} }

@Article{Alvarez2016, author={Alvarez, Mariano J. and Shen, Yao and Giorgi, Federico M. and Lachmann, Alexander and Ding, B. Belinda and Ye, B. Hilda and Califano, Andrea}, title={Functional characterization of somatic mutations in cancer using network-based inference of protein activity}, journal={Nature genetics}, year={2016}, month={Aug}, volume={48}, number={8}, pages={838-847}, keywords={*Algorithms}, keywords={Antineoplastic Agents/pharmacology}, keywords={Computational Biology/*methods}, keywords={Databases, Pharmaceutical}, keywords={Databases, Protein}, keywords={Gene Expression Regulation, Neoplastic/drug effects}, keywords={Gene Regulatory Networks}, keywords={Humans}, keywords={Mutation/*genetics}, keywords={Neoplasm Proteins/*genetics/*metabolism}, keywords={Neoplasms/drug therapy/*genetics/*metabolism}, keywords={Protein Interaction Maps/drug effects}, keywords={Protein Structure, Tertiary}, abstract={Identifying the multiple dysregulated oncoproteins that contribute to tumorigenesis in a given patient is crucial for developing personalized treatment plans. However, accurate inference of aberrant protein activity in biological samples is still challenging as genetic alterations are only partially predictive and direct measurements of protein activity are generally not feasible. To address this problem we introduce and experimentally validate a new algorithm, virtual inference of protein activity by enriched regulon analysis (VIPER), for accurate assessment of protein activity from gene expression data. We used VIPER to evaluate the functional relevance of genetic alterations in regulatory proteins across all samples in The Cancer Genome Atlas (TCGA). In addition to accurately infer aberrant protein activity induced by established mutations, we also identified a fraction of tumors with aberrant activity of druggable oncoproteins despite a lack of mutations, and vice versa. In vitro assays confirmed that VIPER-inferred protein activity outperformed mutational analysis in predicting sensitivity to targeted inhibitors.}, note={27322546[pmid]}, note={PMC5040167[pmcid]}, note={ng.3593[PII]}, issn={1546-1718}, doi={10.1038/ng.3593}, url={https://www.ncbi.nlm.nih.gov/pubmed/27322546} }

@article{hotspots, author = {Baugh, Evan and Ke, Hua and Levine, Arnold and Bonneau, Richard and Chan, Chang}, year = {2017}, month = {11}, pages = {}, title = {Why are there hotspot mutations in the TP53 gene in human cancers?}, volume = {25}, journal = {Cell Death & Differentiation}, doi = {10.1038/cdd.2017.180} }

@article{omnipath, author = {Turei, Denes and Korcsmaros, Tamas and Saez-Rodriguez, Julio}, year = {2016}, month = {11}, pages = {966-967}, title = {OmniPath: Guidelines and gateway for literature-curated signaling pathway resources}, volume = {13}, journal = {Nature Methods}, doi = {10.1038/nmeth.4077} }

@article{10.1371/journal.pone.0157368, author = {Lehmann, Brian D. AND Jovanović, Bojana AND Chen, Xi AND Estrada, Monica V. AND Johnson, Kimberly N. AND Shyr, Yu AND Moses, Harold L. AND Sanders, Melinda E. AND Pietenpol, Jennifer A.}, journal = {PLOS ONE}, publisher = {Public Library of Science}, title = {Refinement of Triple-Negative Breast Cancer Molecular Subtypes: Implications for Neoadjuvant Chemotherapy Selection}, year = {2016}, month = {06}, volume = {11}, url = {https://doi.org/10.1371/journal.pone.0157368}, pages = {1-22}, abstract = {Triple-negative breast cancer (TNBC) is a heterogeneous disease that can be classified into distinct molecular subtypes by gene expression profiling. Considered a difficult-to-treat cancer, a fraction of TNBC patients benefit significantly from neoadjuvant chemotherapy and have far better overall survival. Outside of BRCA1/2 mutation status, biomarkers do not exist to identify patients most likely to respond to current chemotherapy; and, to date, no FDA-approved targeted therapies are available for TNBC patients. Previously, we developed an approach to identify six molecular subtypes TNBC (TNBCtype), with each subtype displaying unique ontologies and differential response to standard-of-care chemotherapy. Given the complexity of the varying histological landscape of tumor specimens, we used histopathological quantification and laser-capture microdissection to determine that transcripts in the previously described immunomodulatory (IM) and mesenchymal stem-like (MSL) subtypes were contributed from infiltrating lymphocytes and tumor-associated stromal cells, respectively. Therefore, we refined TNBC molecular subtypes from six (TNBCtype) into four (TNBCtype-4) tumor-specific subtypes (BL1, BL2, M and LAR) and demonstrate differences in diagnosis age, grade, local and distant disease progression and histopathology. Using five publicly available, neoadjuvant chemotherapy breast cancer gene expression datasets, we retrospectively evaluated chemotherapy response of over 300 TNBC patients from pretreatment biopsies subtyped using either the intrinsic (PAM50) or TNBCtype approaches. Combined analysis of TNBC patients demonstrated that TNBC subtypes significantly differ in response to similar neoadjuvant chemotherapy with 41% of BL1 patients achieving a pathological complete response compared to 18% for BL2 and 29% for LAR with 95% confidence intervals (CIs; [33, 51], [9, 28], [17, 41], respectively). Collectively, we provide pre-clinical data that could inform clinical trials designed to test the hypothesis that improved outcomes can be achieved for TNBC patients, if selection and combination of existing chemotherapies is directed by knowledge of molecular TNBC subtypes.}, number = {6}, doi = {10.1371/journal.pone.0157368} }

@article{DASILVA2020102855, title = "Triple negative breast cancer: A thorough review of biomarkers", journal = "Critical Reviews in Oncology/Hematology", volume = "145", pages = "102855", year = "2020", issn = "1040-8428", doi = "https://doi.org/10.1016/j.critrevonc.2019.102855", url = "http://www.sciencedirect.com/science/article/pii/S1040842819302410", author = "Jesse Lopes {da Silva} and Natalia Cristina {Cardoso Nunes} and Patricia Izetti and Guilherme Gomes {de Mesquita} and Andreia Cristina {de Melo}", keywords = "Triple-negative breast cancer, Biomarkers, Tumor-infiltrating lymphocytes, PD-L1, BRCA mutation, Molecular target therapy", abstract = "Triple-negative breast cancer (TNBC) is defined as a type of breast cancer with lack of expression of estrogen receptor (ER), progesterone receptor (PR) and HER2 protein. The tumorigenesis is not likely to be driven by hormonal or HER2 pathway. In comparison to other types of breast cancer, TNBC stands out for its aggressive behavior, more prone to early recurrence. Historically, TNBC has been considered a disease with poor response to molecular target therapy, requiring better validation of biomarkers. Recent issues related to tumor heterogeneity have been widely discussed suggesting the subdivision of TNBC into different molecular subtypes. Through a complete research on the main published trials databases and platforms of ongoing clinical studies, the current manuscript was carried out in order to present a critical view of the role of immunohistochemical and molecular biomarkers for the prognosis and response prediction of TNBC to traditional therapy and new molecular target agents." }

@article{10.1093/gigascience/giz010, author = {Voukantsis, Dimitrios and Kahn, Kenneth and Hadley, Martin and Wilson, Rowan and Buffa, Francesca M}, title = "{Modeling genotypes in their microenvironment to predict single- and multi-cellular behavior}", journal = {GigaScience}, volume = {8}, number = {3}, year = {2019}, month = {01}, abstract = "{A cell's phenotype is the set of observable characteristics resulting from the interaction of the genotype with the surrounding environment, determining cell behavior. Deciphering genotype-phenotype relationships has been crucial to understanding normal and disease biology. Analysis of molecular pathways has provided an invaluable tool to such understanding; however, typically it does not consider the physical microenvironment, which is a key determinant of phenotype.In this study, we present a novel modeling framework that enables the study of the link between genotype, signaling networks, and cell behavior in a three-dimensional microenvironment. To achieve this, we bring together Agent-Based Modeling, a powerful computational modeling technique, and gene networks. This combination allows biological hypotheses to be tested in a controlled stepwise fashion, and it lends itself naturally to model a heterogeneous population of cells acting and evolving in a dynamic microenvironment, which is needed to predict the evolution of complex multi-cellular dynamics. Importantly, this enables modeling co-occurring intrinsic perturbations, such as mutations, and extrinsic perturbations, such as nutrient availability, and their interactions.Using cancer as a model system, we illustrate how this framework delivers a unique opportunity to identify determinants of single-cell behavior, while uncovering emerging properties of multi-cellular growth.This framework is freely available at http://www.microc.org.}", issn = {2047-217X}, doi = {10.1093/gigascience/giz010}, url = {https://doi.org/10.1093/gigascience/giz010}, note = {giz010}, eprint = {https://academic.oup.com/gigascience/article-pdf/8/3/giz010/28096115/giz010.pdf}, }

@Article{Suzuki2011, author={Suzuki, Kazufumi and Matsubara, Hisahiro}, title={Recent advances in p53 research and cancer treatment}, journal={Journal of biomedicine {&} biotechnology}, year={2011}, edition={2011/06/16}, publisher={Hindawi Publishing Corporation}, volume={2011}, pages={978312-978312}, keywords={Apoptosis/*genetics; Cell Cycle/*genetics; DNA Damage; Gene Expression Regulation, Neoplastic; Genes, p53/*genetics; Genetic Therapy; Humans; Immunotherapy; Mutation/*genetics; Neoplasms/*genetics/*therapy; Tumor Suppressor Protein p53/genetics/metabolism/physiology}, abstract={TP53, encoding p53, is one of the most famous tumor suppressor genes. The majority of human cancers demonstrate the inactivation of the p53 pathway. Mutant p53 not only, no longer, functions as a tumor suppressor but can also exert tumor-promoting effects. The basic function of p53 is to respond to cellular stress. We herein review the recent advances in p53 research and focus on apoptosis, cell cycle arrest, and senescence in response to stress. We also review the clinical applications of p53-based therapy for human cancer.}, note={21765642[pmid]}, note={PMC3134396[pmcid]}, issn={1110-7251}, doi={10.1155/2011/978312}, url={https://pubmed.ncbi.nlm.nih.gov/21765642}, url={https://doi.org/10.1155/2011/978312}, language={eng} }

@misc {PPR:PPR13396, Title = {ShinyGO: a graphical enrichment tool for ani-mals and plants}, Author = {Ge, Steven Xijin and Jung, Dongmin}, DOI = {10.1101/315150}, Publisher = {bioRxiv}, Year = {2018}, URL = {https://doi.org/10.1101/315150}, }

@article{10.1093/jmcb/mjz026, author = {Levine, Arnold J}, title = "{The many faces of p53: something for everyone}", journal = {Journal of Molecular Cell Biology}, volume = {11}, number = {7}, pages = {524-530}, year = {2019}, month = {08}, issn = {1759-4685}, doi = {10.1093/jmcb/mjz026}, url = {https://doi.org/10.1093/jmcb/mjz026}, eprint = {https://academic.oup.com/jmcb/article-pdf/11/7/524/29310269/mjz026.pdf}, }

@Article{Giacomelli2018, author={Giacomelli, Andrew O. and Yang, Xiaoping and Lintner, Robert E. and McFarland, James M. and Duby, Marc and Kim, Jaegil and Howard, Thomas P. and Takeda, David Y. and Ly, Seav Huong and Kim, Eejung and Gannon, Hugh S. and Hurhula, Brian and Sharpe, Ted and Goodale, Amy and Fritchman, Briana and Steelman, Scott and Vazquez, Francisca and Tsherniak, Aviad and Aguirre, Andrew J. and Doench, John G. and Piccioni, Federica and Roberts, Charles W. M. and Meyerson, Matthew and Getz, Gad and Johannessen, Cory M. and Root, David E. and Hahn, William C.}, title={Mutational processes shape the landscape of TP53 mutations in human cancer}, journal={Nature Genetics}, year={2018}, month={Oct}, day={01}, volume={50}, number={10}, pages={1381-1387}, abstract={Unlike most tumor suppressor genes, the most common genetic alterations in tumor protein p53 (TP53) are missense mutations1,2. Mutant p53 protein is often abundantly expressed in cancers and specific allelic variants exhibit dominant-negative or gain-of-function activities in experimental models3--8. To gain a systematic view of p53 function, we interrogated loss-of-function screens conducted in hundreds of human cancer cell lines and performed TP53 saturation mutagenesis screens in an isogenic pair of TP53 wild-type and null cell lines. We found that loss or dominant-negative inhibition of wild-type p53 function reliably enhanced cellular fitness. By integrating these data with the Catalog of Somatic Mutations in Cancer (COSMIC) mutational signatures database9,10, we developed a statistical model that describes the TP53 mutational spectrum as a function of the baseline probability of acquiring each mutation and the fitness advantage conferred by attenuation of p53 activity. Collectively, these observations show that widely-acting and tissue-specific mutational processes combine with phenotypic selection to dictate the frequencies of recurrent TP53 mutations.}, issn={1546-1718}, doi={10.1038/s41588-018-0204-y}, url={https://doi.org/10.1038/s41588-018-0204-y} }

@article{10.1093/jmcb/mjz070, author = {Oren, Moshe}, title = "{p53: not just a tumor suppressor}", journal = {Journal of Molecular Cell Biology}, volume = {11}, number = {7}, pages = {539-543}, year = {2019}, month = {08}, abstract = "{However, in 1980, the bright future of p53 was still far from being within sight. The subsequent several years were rather frustrating for the Levine p53 team. As a matter of fact, they were rough years for the entire (very small!) international p53 community. For quite a while, no significant progress seemed to be made in understanding what p53 was good for and why it was accumulated in many cancer cell types. In particular, our efforts to clone p53, eventually expanded to a team of three post-docs (Kaoru Segawa, myself, and my spouse Rachel), repeatedly met with failure. Likewise, none of the other p53 projects in the lab managed to really take off and fly high. I recall vividly a day in 1981 (at that time already in StonyBrook, to where Arnie moved from Princeton to become Chair of the Department of Microbiology) when Arnie assembled all of us in his office. With an unusually sad face, he brought up for discussion the question whether we should abandon p53 research altogether, because it seemed to be going nowhere. Luckily, not only for us in Arnie’s lab but also for the entire p53 field, Arnie’s conclusion was that we should not give up. And indeed, the rest is history.}", issn = {1759-4685}, doi = {10.1093/jmcb/mjz070}, url = {https://doi.org/10.1093/jmcb/mjz070}, eprint = {https://academic.oup.com/jmcb/article-pdf/11/7/539/29310298/mjz070.pdf}, }

@Article{Hanahan2011, author={Hanahan, Douglas and Weinberg, Robert A.}, title={Hallmarks of Cancer: The Next Generation}, journal={Cell}, year={2011}, month={Mar}, day={04}, publisher={Elsevier}, volume={144}, number={5}, pages={646-674}, issn={0092-8674}, doi={10.1016/j.cell.2011.02.013}, url={https://doi.org/10.1016/j.cell.2011.02.013} }

@Article{Davies2010, author={Davies, M. A. and Samuels, Y.}, title={Analysis of the genome to personalize therapy for melanoma}, journal={Oncogene}, year={2010}, month={Oct}, day={01}, volume={29}, number={41}, pages={5545-5555}, abstract={The treatment of cancer is being revolutionized by an improved understanding of the genetic events that occur in tumors. Advances in the understanding of the prevalence and patterns of mutations in melanoma have recently led to impressive results in trials of personalized, targeted therapies for this disease. In this review, we will discuss the molecular targets that have been validated clinically, additional genetic events that are candidates for future trials, and the challenges that remain to improve outcomes further in this aggressive disease.}, issn={1476-5594}, doi={10.1038/onc.2010.323}, url={https://doi.org/10.1038/onc.2010.323} }

@Article{sec2020, author={Se{\c{c}}ilmi{\c{s}}, Deniz and Hillerton, Thomas and Morgan, Daniel and Tj{"a}rnberg, Andreas and Nelander, Sven and Nordling, Torbj{"o}rn E. M. and Sonnhammer, Erik L. L.}, title={Uncovering cancer gene regulation by accurate regulatory network inference from uninformative data}, journal={npj Systems Biology and Applications}, year={2020}, month={Nov}, day={09}, volume={6}, number={1}, pages={37}, abstract={The interactions among the components of a living cell that constitute the gene regulatory network (GRN) can be inferred from perturbation-based gene expression data. Such networks are useful for providing mechanistic insights of a biological system. In order to explore the feasibility and quality of GRN inference at a large scale, we used the L1000 data where {\textasciitilde}1000 genes have been perturbed and their expression levels have been quantified in 9 cancer cell lines. We found that these datasets have a very low signal-to-noise ratio (SNR) level causing them to be too uninformative to infer accurate GRNs. We developed a gene reduction pipeline in which we eliminate uninformative genes from the system using a selection criterion based on SNR, until reaching an informative subset. The results show that our pipeline can identify an informative subset in an overall uninformative dataset, allowing inference of accurate subset GRNs. The accurate GRNs were functionally characterized and potential novel cancer-related regulatory interactions were identified.}, issn={2056-7189}, doi={10.1038/s41540-020-00154-6}, url={https://doi.org/10.1038/s41540-020-00154-6} }

@Article{Reyna2020, author={Reyna, Matthew A. and Haan, David and Paczkowska, Marta and Verbeke, Lieven P. C. and Vazquez, Miguel and Kahraman, Abdullah and Pulido-Tamayo, Sergio and Barenboim, Jonathan and Wadi, Lina and Dhingra, Priyanka and Shrestha, Raunak and Getz, Gad and Lawrence, Michael S. and Pedersen, Jakob Skou and Rubin, Mark A. and Wheeler, David A. and Brunak, S{\o}ren and Izarzugaza, Jose M. G. and Khurana, Ekta and Marchal, Kathleen and von Mering, Christian and Sahinalp, S. Cenk and Valencia, Alfonso and Abascal, Federico and Amin, Samirkumar B. and Bader, Gary D. and Bandopadhayay, Pratiti and Beroukhim, Rameen and Bertl, Johanna and Boroevich, Keith A. and Busanovich, John and Campbell, Peter J. and Carlevaro-Fita, Joana and Chakravarty, Dimple and Chan, Calvin Wing Yiu and Chen, Ken and Choi, Jung Kyoon and Deu-Pons, Jordi and Diamanti, Klev and Feuerbach, Lars and Fink, J. Lynn and Fonseca, Nuno A. and Frigola, Joan and Gambacorti-Passerini, Carlo and Garsed, Dale W. and Gerstein, Mark and Guo, Qianyun and Gut, Ivo G. and Hamilton, Mark P. and Haradhvala, Nicholas J. and Harmanci, Arif O. and Helmy, Mohamed and Herrmann, Carl and Hess, Julian M. and Hobolth, Asger and Hodzic, Ermin and Hong, Chen and Hornsh{\o}j, Henrik and Isaev, Keren and Johnson, Rory and Johnson, Todd A. and Juul, Malene and Juul, Randi Istrup and Kahles, Andre and Kellis, Manolis and Kim, Jaegil and Kim, Jong K. and Kim, Youngwook and Komorowski, Jan and Korbel, Jan O. and Kumar, Sushant and Lanz{'o}s, Andr{'e}s and Larsson, Erik and Lee, Donghoon and Lehmann, Kjong-Van and Li, Shantao and Li, Xiaotong and Lin, Ziao and Liu, Eric Minwei and Lochovsky, Lucas and Lou, Shaoke and Madsen, Tobias and Martincorena, I{~{n}}igo and Martinez-Fundichely, Alexander and Maruvka, Yosef E. and McGillivray, Patrick D. and Meyerson, William and Mui{~{n}}os, Ferran and Mularoni, Loris and Nakagawa, Hidewaki and Nielsen, Morten Muhlig and Park, Keunchil and Park, Kiejung and Pons, Tirso and Reyes-Salazar, Iker and Rheinbay, Esther and Rubio-Perez, Carlota and Saksena, Gordon and Salichos, Leonidas and Sander, Chris and Schumacher, Steven E. and Shackleton, Mark and Shapira, Ofer and Shen, Ciyue and Shuai, Shimin and Sidiropoulos, Nikos and Sieverling, Lina and Sinnott-Armstrong, Nasa and Stein, Lincoln D. and Tamborero, David and Tiao, Grace and Tsunoda, Tatsuhiko and Umer, Husen M. and Uusk{"u}la-Reimand, Liis and Wadelius, Claes and Wang, Jiayin and Warrell, Jonathan and Waszak, Sebastian M. and Weischenfeldt, Joachim and Wu, Guanming and Yu, Jun and Zhang, Jing and Zhang, Xuanping and Zhang, Yan and Zhao, Zhongming and Zou, Lihua and Reimand, J{"u}ri and Stuart, Joshua M. and Raphael, Benjamin J. and Drivers, P. C. A. W. G. and Group, Functional Interpretation Working and Consortium, P. C. A. W. G.}, title={Pathway and network analysis of more than 2500 whole cancer genomes}, journal={Nature Communications}, year={2020}, month={Feb}, day={05}, volume={11}, number={1}, pages={729}, abstract={The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We find that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.}, issn={2041-1723}, doi={10.1038/s41467-020-14367-0}, url={https://doi.org/10.1038/s41467-020-14367-0} }

@Article{Yan2016, author={Yan, Wenying and Xue, Wenjin and Chen, Jiajia and Hu, Guang}, title={Biological Networks for Cancer Candidate Biomarkers Discovery}, journal={Cancer informatics}, year={2016}, month={Sep}, day={04}, publisher={Libertas Academica}, volume={15}, number={Suppl 3}, pages={1-7}, keywords={biomarker; cancer; network; omics}, abstract={Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field.}, note={27625573[pmid]}, note={PMC5012434[pmcid]}, note={cin-suppl.3-2016-001[PII]}, issn={1176-9351}, doi={10.4137/CIN.S39458}, url={https://pubmed.ncbi.nlm.nih.gov/27625573}, url={https://doi.org/10.4137/CIN.S39458}, language={eng} }

@Article{Zhou2012, author={Zhou, Ting-Ting}, title={Network systems biology for targeted cancer therapies}, journal={Chinese journal of cancer}, year={2012}, month={Mar}, edition={2011/12/16}, publisher={Sun Yat-sen University Cancer Center}, volume={31}, number={3}, pages={134-141}, keywords={Antineoplastic Agents/therapeutic use; Drug Discovery/*methods; *Drug Resistance, Neoplasm; Humans; Models, Biological; *Molecular Targeted Therapy; Neoplasms/*drug therapy; *Systems Biology/methods}, abstract={The era of targeted cancer therapies has arrived. However, due to the complexity of biological systems, the current progress is far from enough. From biological network modeling to structural/dynamic network analysis, network systems biology provides unique insight into the potential mechanisms underlying the growth and progression of cancer cells. It has also introduced great changes into the research paradigm of cancer-associated drug discovery and drug resistance.}, note={22176777[pmid]}, note={PMC3777487[pmcid]}, note={cjc.011.10282[PII]}, issn={1000-467X}, doi={10.5732/cjc.011.10282}, url={https://pubmed.ncbi.nlm.nih.gov/22176777}, url={https://doi.org/10.5732/cjc.011.10282}, language={eng} }

@article {Benstead-Hume751776, author = {Benstead-Hume, Graeme and Wooller, Sarah K. and Dias, Samantha and Woodbine, Lisa and Carr, Anthony M. and Pearl, Frances M. G.}, title = {Biological network topology features predict gene dependencies in cancer cell lines}, elocation-id = {751776}, year = {2019}, doi = {10.1101/751776}, publisher = {Cold Spring Harbor Laboratory}, abstract = {In this paper we explore computational approaches that enable us to identify genes that have become essential in individual cancer cell lines. Using recently published experimental cancer cell line gene essentiality data, human protein-protein interaction (PPI) network data and individual cell-line genomic alteration data we have built a range of machine learning classification models to predict cell line specific acquired essential genes. Genetic alterations found in each individual cell line were modelled by removing protein nodes to reflect loss of function mutations and changing the weights of edges in each PPI to reflect gain of function mutations and gene expression changes.We found that PPI networks can be used to successfully classify human cell line specific acquired essential genes within individual cell lines and between cell lines, even across tissue types with AUC ROC scores of between 0.75 and 0.85. Our novel perturbed PPI network models further improved prediction power compared to the base PPI model and are shown to be more sensitive to genes on which the cell becomes dependent as a result of other changes. These improvements offer opportunities for personalised therapy with each individual{\textquoteright}s cancer cell dependencies presenting a potential tailored drug target.The overriding motivation for predicting cancer cell line specific acquired essential genes is to provide a low-cost approach to identifying personalised cancer drug targets without the cost of exhaustive loss of function screening.}, URL = {https://www.biorxiv.org/content/early/2019/08/31/751776}, eprint = {https://www.biorxiv.org/content/early/2019/08/31/751776.full.pdf}, journal = {bioRxiv} }

@article {Albert4947, author = {Albert, R{'e}ka}, title = {Scale-free networks in cell biology}, volume = {118}, number = {21}, pages = {4947--4957}, year = {2005}, doi = {10.1242/jcs.02714}, publisher = {The Company of Biologists Ltd}, abstract = {A cell{\textquoteright}s behavior is a consequence of the complex interactions between its numerous constituents, such as DNA, RNA, proteins and small molecules. Cells use signaling pathways and regulatory mechanisms to coordinate multiple processes, allowing them to respond to and adapt to an ever-changing environment. The large number of components, the degree of interconnectivity and the complex control of cellular networks are becoming evident in the integrated genomic and proteomic analyses that are emerging. It is increasingly recognized that the understanding of properties that arise from whole-cell function require integrated, theoretical descriptions of the relationships between different cellular components. Recent theoretical advances allow us to describe cellular network structure with graph concepts and have revealed organizational features shared with numerous non-biological networks. We now have the opportunity to describe quantitatively a network of hundreds or thousands of interacting components. Moreover, the observed topologies of cellular networks give us clues about their evolution and how their organization influences their function and dynamic responses.}, issn = {0021-9533}, URL = {https://jcs.biologists.org/content/118/21/4947}, eprint = {https://jcs.biologists.org/content/118/21/4947.full.pdf}, journal = {Journal of Cell Science} }

@Article{Willis2004, author={Willis, Amy and Jung, Eun Joo and Wakefield, Therese and Chen, Xinbin}, title={Mutant p53 exerts a dominant negative effect by preventing wild-type p53 from binding to the promoter of its target genes}, journal={Oncogene}, year={2004}, month={Mar}, day={01}, volume={23}, number={13}, pages={2330-2338}, abstract={Mutation of the p53 tumor suppressor gene is the most common genetic alteration in human cancer. A majority of these mutations are missense mutations in the DNA-binding domain. As a result, the mutated p53 gene encodes a full-length protein incapable of transactivating its target genes. In addition to this loss of function, mutant p53 can have a dominant negative effect over wild-type p53 and/or gain of function activity independently of the wild-type protein. To better understand the nature of the tumorigenic activity of mutant p53, we have investigated the mechanism by which mutant p53 can exert a dominant negative effect. We have established several stable cell lines capable of inducibly expressing a p53 mutant alone, wild-type p53 alone, or both proteins concurrently. In this context, we have used chromatin immunoprecipitation to determine the ability of wild-type p53 to bind to its endogenous target genes in the presence of various p53 mutants. We have found that p53 missense mutants markedly reduce the binding of wild-type p53 to the p53 responsive element in the target genes of p21, MDM2, and PIG3. These findings correlate with the reduced ability of wild-type p53 in inducing these and other endogenous target genes and growth suppression in the presence of mutant p53. We also showed that mutant p53 suppresses the ability of wild-type p53 in inducing cell cycle arrest. This highlights the sensitivity and utility of the dual inducible expression system because in previous studies, p53-mediated cell cycle arrest is not affected by transiently overexpressed p53 mutants. Together, our data showed that mutant p53 exerts its dominant negative activity by abrogating the DNA binding, and subsequently the growth suppression, functions of wild-type p53.}, issn={1476-5594}, doi={10.1038/sj.onc.1207396}, url={https://doi.org/10.1038/sj.onc.1207396} }

@Article{Olivier2010, author={Olivier, Magali and Hollstein, Monica and Hainaut, Pierre}, title={TP53 mutations in human cancers: origins, consequences, and clinical use}, journal={Cold Spring Harbor perspectives in biology}, year={2010}, month={Jan}, publisher={Cold Spring Harbor Laboratory Press}, volume={2}, number={1}, pages={a001008-a001008}, keywords={Animals; Carcinogens; CpG Islands; *Gene Expression Regulation, Neoplastic; *Genes, p53; Humans; Mice; Mice, Transgenic; Models, Biological; Mutagenesis; *Mutation; Neoplasms/*genetics; Prognosis}, abstract={Somatic mutations in the TP53 gene are one of the most frequent alterations in human cancers, and germline mutations are the underlying cause of Li-Fraumeni syndrome, which predisposes to a wide spectrum of early-onset cancers. Most mutations are single-base substitutions distributed throughout the coding sequence. Their diverse types and positions may inform on the nature of mutagenic mechanisms involved in cancer etiology. TP53 mutations are also potential prognostic and predictive markers, as well as targets for pharmacological intervention. All mutations found in human cancers are compiled in the IARC TP53 Database (http://www-p53.iarc.fr/). A human TP53 knockin mouse model (Hupki mouse) provides an experimental model to study mutagenesis in the context of a human TP53 sequence. Here, we summarize current knowledge on TP53 gene variations observed in human cancers and populations, and current clinical applications derived from this knowledge.}, note={20182602[pmid]}, note={PMC2827900[pmcid]}, issn={1943-0264}, doi={10.1101/cshperspect.a001008}, url={https://pubmed.ncbi.nlm.nih.gov/20182602}, url={https://doi.org/10.1101/cshperspect.a001008}, language={eng} }

@Article{Petitjean2007, author={Petitjean, A. and Achatz, M. I. W. and Borresen-Dale, A. L. and Hainaut, P. and Olivier, M.}, title={TP53 mutations in human cancers: functional selection and impact on cancer prognosis and outcomes}, journal={Oncogene}, year={2007}, month={Apr}, day={01}, volume={26}, number={15}, pages={2157-2165}, abstract={A large amount of data is available on the functional impact of missense mutations in TP53 and on mutation patterns in many different cancers. New data on mutant p53 protein function, cancer phenotype and prognosis have recently been integrated in the International Agency for Research on Cancer TP53 database (http://www-p53.iarc.fr/). Based on these data, we summarize here current knowledge on the respective roles of mutagenesis and biological selection of mutations with specific functional characteristic in shaping the patterns and phenotypes of mutations observed in human cancers. The main conclusion is that intrinsic mutagenicity rates, loss of transactivation activities, and to a lesser extent, dominant-negative activities are the main driving forces that determine TP53 mutation patterns and influence tumor phenotype. In contrast, current experimental data on the acquisition of oncogenic activities (gain of function) by p53 mutants are too scarce and heterogenous to assess whether this property has an impact on tumor development and outcome. In the case of inherited TP53 mutations causing Li--Fraumeni and related syndromes, the age at onset of some tumor types is in direct relation with the degree of loss of transactivation capacity of missense mutations. Finally, studies on large case series demonstrate that TP53 mutations are independent markers of bad prognosis in breast and several other cancers, and that the exact type and position of the mutation influences disease outcome. Further studies are needed to determine how TP53 haplotypes or loss of alleles interact with mutations to modulate their impact on cancer development and prognosis.}, issn={1476-5594}, doi={10.1038/sj.onc.1210302}, url={https://doi.org/10.1038/sj.onc.1210302} }

@Article{Shahbandi2020, author={Shahbandi, Ashkan and Nguyen, Hoang D. and Jackson, James G.}, title={TP53 Mutations and Outcomes in Breast Cancer: Reading beyond the Headlines}, journal={Trends in Cancer}, year={2020}, month={Feb}, day={01}, publisher={Elsevier}, volume={6}, number={2}, pages={98-110}, issn={2405-8033}, doi={10.1016/j.trecan.2020.01.007}, url={https://doi.org/10.1016/j.trecan.2020.01.007} }

@Article{Kotler2018, author={Kotler, Eran and Shani, Odem and Goldfeld, Guy and Lotan-Pompan, Maya and Tarcic, Ohad and Gershoni, Anat and Hopf, Thomas A. and Marks, Debora S. and Oren, Moshe and Segal, Eran}, title={A Systematic p53 Mutation Library Links Differential Functional Impact to Cancer Mutation Pattern and Evolutionary Conservation}, journal={Molecular Cell}, year={2018}, month={Jul}, day={05}, publisher={Elsevier}, volume={71}, number={1}, pages={178-190.e8}, issn={1097-2765}, doi={10.1016/j.molcel.2018.06.012}, url={https://doi.org/10.1016/j.molcel.2018.06.012} }

@Article{Walerych2016, author={Walerych, Dawid and Lisek, Kamil and Sommaggio, Roberta and Piazza, Silvano and Ciani, Yari and Dalla, Emiliano and Rajkowska, Katarzyna and Gaweda-Walerych, Katarzyna and Ingallina, Eleonora and Tonelli, Claudia and Morelli, Marco J. and Amato, Angela and Eterno, Vincenzo and Zambelli, Alberto and Rosato, Antonio and Amati, Bruno and Wi{'{s}}niewski, Jacek R. and Del Sal, Giannino}, title={Proteasome machinery is instrumental in a common gain-of-function program of the p53 missense mutants in cancer}, journal={Nature Cell Biology}, year={2016}, month={Aug}, day={01}, volume={18}, number={8}, pages={897-909}, abstract={In cancer, the tumour suppressor gene TP53 undergoes frequent missense mutations that endow mutant p53 proteins with oncogenic properties. Until now, a universal mutant p53 gain-of-function program has not been defined. By means of multi-omics: proteome, DNA interactome (chromatin immunoprecipitation followed by sequencing) and transcriptome (RNA sequencing/microarray) analyses, we identified the proteasome machinery as a common target of p53 missense mutants. The mutant p53--proteasome axis globally affects protein homeostasis, inhibiting multiple tumour-suppressive pathways, including the anti-oncogenic KSRP--microRNA pathway. In cancer cells, p53 missense mutants cooperate with Nrf2 (NFE2L2) to activate proteasome gene transcription, resulting in resistance to the proteasome inhibitor carfilzomib. Combining the mutant p53-inactivating agent APR-246 (PRIMA-1MET) with the proteasome inhibitor carfilzomib is effective in overcoming chemoresistance in triple-negative breast cancer cells, creating a therapeutic opportunity for treatment of solid tumours and metastasis with mutant p53.}, issn={1476-4679}, doi={10.1038/ncb3380}, url={https://doi.org/10.1038/ncb3380} }

@Article{Tan2009, author={Tan, Yuhong and Luo, Ray}, title={Structural and functional implications of p53 missense cancer mutations}, journal={PMC biophysics}, year={2009}, month={Jun}, day={26}, publisher={BioMed Central}, volume={2}, number={1}, pages={5-5}, abstract={Most human cancers contain mutations in the transcription factor p53 and majority of these are missense and located in the DNA binding core domain. In this study, the stabilities of all core domain missense mutations are predicted and are used to infer their likely inactivation mechanisms. Overall, 47.0{%} non-PRO/GLY mutants are stable (DeltaDeltaG < 1.0 kT) and 36.3{%} mutants are unstable (DeltaDeltaG > 3.0 kT), 12.2{%} mutants are with 1.0 kT < DeltaDeltaG < 3.0 kT. Only 4.5{%} mutants are with no conclusive predictions. Certain types of either stable or unstable mutations are found not to depend on their local structures. Y, I, C, V, F and W (W, R and F) are the most common residues before (after) mutation in unstable mutants. Q, N, K, D, A, S and T (I, T, L and V) are the most common residues before (after) mutation in stable mutants. The stability correlations with sequence, structure, and molecular contacts are also analyzed. No direct correlation between secondary structure and stability is apparent, but a strong correlation between solvent exposure and stability is noticeable. Our correlation analysis shows that loss of protein-protein contacts may be an alternative cause for p53 inactivation. Correlation with clinical data shows that loss of stability and loss of DNA contacts are the two main inactivation mechanisms. Finally, correlation with functional data shows that most mutations which retain functions are stable, and most mutations that gain functions are unstable, indicating destabilized and deformed p53 proteins are more likely to find new binding partners.PACS codes: 87.14.E-}, note={19558684[pmid]}, note={PMC2709103[pmcid]}, note={1757-5036-2-5[PII]}, issn={1757-5036}, doi={10.1186/1757-5036-2-5}, url={https://pubmed.ncbi.nlm.nih.gov/19558684}, url={https://doi.org/10.1186/1757-5036-2-5}, language={eng} }

@Article{Ozaki2011, author={Ozaki, Toshinori and Nakagawara, Akira}, title={Role of p53 in Cell Death and Human Cancers}, journal={Cancers}, year={2011}, month={Mar}, day={03}, publisher={Molecular Diversity Preservation International (MDPI)}, volume={3}, number={1}, pages={994-1013}, abstract={p53 is a nuclear transcription factor with a pro-apoptotic function. Since over 50{%} of human cancers carry loss of function mutations in p53 gene, p53 has been considered to be one of the classical type tumor suppressors. Mutant p53 acts as the dominant-negative inhibitor toward wild-type p53. Indeed, mutant p53 has an oncogenic potential. In some cases, malignant cancer cells bearing p53 mutations display a chemo-resistant phenotype. In response to a variety of cellular stresses such as DNA damage, p53 is induced to accumulate in cell nucleus to exert its pro-apoptotic function. Activated p53 promotes cell cycle arrest to allow DNA repair and/or apoptosis to prevent the propagation of cells with serious DNA damage through the transactivation of its target genes implicated in the induction of cell cycle arrest and/or apoptosis. Thus, the DNA-binding activity of p53 is tightly linked to its tumor suppressive function. In the present review article, we describe the regulatory mechanisms of p53 and also p53-mediated therapeutic strategies to cure malignant cancers.}, note={24212651[pmid]}, note={PMC3756401[pmcid]}, note={cancers3010994[PII]}, issn={2072-6694}, doi={10.3390/cancers3010994}, url={https://pubmed.ncbi.nlm.nih.gov/24212651}, url={https://doi.org/10.3390/cancers3010994}, language={eng} }

@Article{Muller2014, author={Muller, Patricia A. J. and Vousden, Karen H.}, title={Mutant p53 in cancer: new functions and therapeutic opportunities}, journal={Cancer cell}, year={2014}, month={Mar}, day={17}, publisher={Cell Press}, volume={25}, number={3}, pages={304-317}, keywords={Animals; Apoptosis/genetics; Gene Expression Regulation, Neoplastic; Humans; Mice; Mutation; Neoplasms/drug therapy/genetics/*metabolism; Signal Transduction/genetics; Tumor Suppressor Protein p53/*genetics/*metabolism}, abstract={Many different types of cancer show a high incidence of TP53 mutations, leading to the expression of mutant p53 proteins. There is growing evidence that these mutant p53s have both lost wild-type p53 tumor suppressor activity and gained functions that help to contribute to malignant progression. Understanding the functions of mutant p53 will help in the development of new therapeutic approaches that may be useful in a broad range of cancer types.}, note={24651012[pmid]}, note={PMC3970583[pmcid]}, note={S1535-6108(14)00037-3[PII]}, issn={1878-3686}, doi={10.1016/j.ccr.2014.01.021}, url={https://pubmed.ncbi.nlm.nih.gov/24651012}, url={https://doi.org/10.1016/j.ccr.2014.01.021}, language={eng} }

@Article{Mantovani2019, author={Mantovani, Fiamma and Collavin, Licio and Del Sal, Giannino}, title={Mutant p53 as a guardian of the cancer cell}, journal={Cell Death {&} Differentiation}, year={2019}, month={Feb}, day={01}, volume={26}, number={2}, pages={199-212}, abstract={Forty years of research have established that the p53 tumor suppressor provides a major barrier to neoplastic transformation and tumor progression by its unique ability to act as an extremely sensitive collector of stress inputs, and to coordinate a complex framework of diverse effector pathways and processes that protect cellular homeostasis and genome stability. Missense mutations in the TP53 gene are extremely widespread in human cancers and give rise to mutant p53 proteins that lose tumor suppressive activities, and some of which exert trans-dominant repression over the wild-type counterpart. Cancer cells acquire selective advantages by retaining mutant forms of the protein, which radically subvert the nature of the p53 pathway by promoting invasion, metastasis and chemoresistance. In this review, we consider available evidence suggesting that mutant p53 proteins can favor cancer cell survival and tumor progression by acting as homeostatic factors that sense and protect cancer cells from transformation-related stress stimuli, including DNA lesions, oxidative and proteotoxic stress, metabolic inbalance, interaction with the tumor microenvironment, and the immune system. These activities of mutant p53 may explain cancer cell addiction to this particular oncogene, and their study may disclose tumor vulnerabilities and synthetic lethalities that could be exploited for hitting tumors bearing missense TP53 mutations.}, issn={1476-5403}, doi={10.1038/s41418-018-0246-9}, url={https://doi.org/10.1038/s41418-018-0246-9} }

@article{10.1093/jmcb/mjz063, author = {Lozano, Guillermina}, title = "{Restoring p53 in cancer: the promises and the challenges}", journal = {Journal of Molecular Cell Biology}, volume = {11}, number = {7}, pages = {615-619}, year = {2019}, month = {08}, issn = {1759-4685}, doi = {10.1093/jmcb/mjz063}, url = {https://doi.org/10.1093/jmcb/mjz063}, eprint = {https://academic.oup.com/jmcb/article-pdf/11/7/615/29310252/mjz063.pdf}, }

@article {Blagihjcs237453, author = {Blagih, Julianna and Buck, Michael D. and Vousden, Karen H.}, title = {p53, cancer and the immune response}, volume = {133}, number = {5}, elocation-id = {jcs237453}, year = {2020}, doi = {10.1242/jcs.237453}, publisher = {The Company of Biologists Ltd}, abstract = {The importance of cancer-cell-autonomous functions of the tumour suppressor p53 (encoded by TP53) has been established in many studies, but it is now clear that the p53 status of the cancer cell also has a profound impact on the immune response. Loss or mutation of p53 in cancers can affect the recruitment and activity of myeloid and T cells, allowing immune evasion and promoting cancer progression. p53 can also function in immune cells, resulting in various outcomes that can impede or support tumour development. Understanding the role of p53 in tumour and immune cells will help in the development of therapeutic approaches that can harness the differential p53 status of cancers compared with most normal tissue.}, issn = {0021-9533}, URL = {https://jcs.biologists.org/content/133/5/jcs237453}, eprint = {https://jcs.biologists.org/content/133/5/jcs237453.full.pdf}, journal = {Journal of Cell Science} }

@article{STEELE2005197, title = {p53 in cancer: A paradigm for modern management of cancer}, journal = {The Surgeon}, volume = {3}, number = {3}, pages = {197-205}, year = {2005}, issn = {1479-666X}, doi = {https://doi.org/10.1016/S1479-666X(05)80041-1}, url = {https://www.sciencedirect.com/science/article/pii/S1479666X05800411}, author = {R.J.C. Steele and D.P. Lane}, abstract = {The p53 tumour suppressor gene is thought to be central in protecting against the development of cancer, and this article reviews current understanding of its function and potential clinical significance. Information for this review was obtained from previous review articles, references cited in original papers, a Pubmed search of the last twelve months' literature and by scanning the latest issues of relevant journals. P53 can be described as a stress response gene, its product (the p53 protein) acting to induce apoptosis or cell-cycle arrest in response to DNA damage, thereby maintaining genetic stability in the organism. These functions are realised by a series of steps known as the “p53 pathway” involving induction of the expression of a number of other genes. As p53 is the most commonly mutated gene in human cancer, it has attracted a great deal of interest in the areas of prognosis, diagnosis and therapy, and p53 gene therapy is becoming established as a useful adjunct to conventional cancer treatment} }

@article {Klimovich22288, author = {Klimovich, Boris and Mutlu, Samet and Schneikert, Jean and Elmsh{"a}user, Sabrina and Klimovich, Maria and Nist, Andrea and Mernberger, Marco and Timofeev, Oleg and Stiewe, Thorsten}, title = {Loss of p53 function at late stages of tumorigenesis confers ARF-dependent vulnerability to p53 reactivation therapy}, volume = {116}, number = {44}, pages = {22288--22293}, year = {2019}, doi = {10.1073/pnas.1910255116}, publisher = {National Academy of Sciences}, abstract = {Mouse studies demonstrating regression of p53-null tumors following reinstatement of functional p53 have fueled the development of p53 reactivating drugs. However, successful p53 reactivation responses have only been formally demonstrated in tumor models where p53 inactivation served as the initiating event. Our study provides the first proof-of-principle evidence that p53 inactivation at late stages of tumorigenesis can also generate a vulnerability to p53 reactivation. However, this is dependent on intact ARF function highlighting ARF as a potential biomarker for p53 reactivation responses in tumors with late-stage p53 inactivation. It furthermore suggests the use of Mdm2 inhibitors as ARF mimetics for sensitizing ARF-deficient tumors to p53-reactivating drugs.Cancer development is driven by activated oncogenes and loss of tumor suppressors. While oncogene inhibitors have entered routine clinical practice, tumor suppressor reactivation therapy remains to be established. For the most frequently inactivated tumor suppressor p53, genetic mouse models have demonstrated regression of p53-null tumors upon p53 reactivation. While this was shown in tumor models driven by p53 loss as the initiating lesion, many human tumors initially develop in the presence of wild-type p53, acquire aberrations in the p53 pathway to bypass p53-mediated tumor suppression, and inactivate p53 itself only at later stages during metastatic progression or therapy. To explore the efficacy of p53 reactivation in this scenario, we used a reversibly switchable p53 (p53ERTAM) mouse allele to generate E{\textmu}-Myc{\textendash}driven lymphomas in the presence of active p53 and, after full lymphoma establishment, switched off p53 to model late-stage p53 inactivation. Although these lymphomas had evolved in the presence of active p53, later loss and subsequent p53 reactivation surprisingly activated p53 target genes triggering massive apoptosis, tumor regression, and long-term cure of the majority of animals. Mechanistically, the reactivation response was dependent on Cdkn2a/p19Arf, which is commonly silenced in p53 wild-type lymphomas, but became reexpressed upon late-stage p53 inactivation. Likewise, human p53 wild-type tumor cells with CRISPR-engineered switchable p53ERTAM alleles responded to p53 reactivation when CDKN2A/p14ARF function was restored or mimicked with Mdm2 inhibitors. Together, these experiments provide genetic proof of concept that tumors can respond, in an ARF-dependent manner, to p53 reactivation even if p53 inactivation has occurred late during tumor evolution.}, issn = {0027-8424}, URL = {https://www.pnas.org/content/116/44/22288}, eprint = {https://www.pnas.org/content/116/44/22288.full.pdf}, journal = {Proceedings of the National Academy of Sciences} }

@article{doi:10.1146/annurev-biochem-060815-014710, author = {Joerger, Andreas C. and Fersht, Alan R.}, title = {The p53 Pathway: Origins, Inactivation in Cancer, and Emerging Therapeutic Approaches}, journal = {Annual Review of Biochemistry}, volume = {85}, number = {1}, pages = {375-404}, year = {2016}, doi = {10.1146/annurev-biochem-060815-014710}, note ={PMID: 27145840},

URL = { https://doi.org/10.1146/annurev-biochem-060815-014710

}, eprint = { https://doi.org/10.1146/annurev-biochem-060815-014710

} , abstract = { Inactivation of the transcription factor p53, through either direct mutation or aberrations in one of its many regulatory pathways, is a hallmark of virtually every tumor. In recent years, screening for p53 activators and a better understanding of the molecular mechanisms of oncogenic perturbations of p53 function have opened up a host of novel avenues for therapeutic intervention in cancer: from the structure-guided design of chemical chaperones to restore the function of conformationally unstable p53 cancer mutants, to the development of potent antagonists of the negative regulators MDM2 and MDMX and other modulators of the p53 pathway for the treatment of cancers with wild-type p53. Some of these compounds have now moved from proof-of-concept studies into clinical trials, with prospects for further, personalized anticancer medicines. We trace the structural evolution of the p53 pathway, from germ-line surveillance in simple multicellular organisms to its pluripotential role in humans. } }

@article {Soussi1777, author = {Soussi, Thierry}, title = {p53 Antibodies in the Sera of Patients with Various Types of Cancer: A Review}, volume = {60}, number = {7}, pages = {1777--1788}, year = {2000}, publisher = {American Association for Cancer Research}, abstract = {p53 antibodies (p53-Abs) were discovered 20 years ago during the course of tumor-associated antigens screening. The discovery of p53 mutation and accumulation of p53 in human tumors shed new light on the p53 humoral response. In the present review, we have compiled more than 130 papers published in this specific field since 1992. We demonstrate that p53-Abs are found predominantly in human cancer patients with a specificity of 96%. Such antibodies are predominantly associated with p53 gene missense mutations and p53 accumulation in the tumor, but the sensitivity of such detection is only 30%. It has been demonstrated that this immune response is due to a self-immunization process linked to the strong immunogenicity of the p53 protein. The clinical value of these antibodies remains subject to debate, but consistent results have been observed in breast, colon, oral, and gastric cancers, in which they have been associated with high-grade tumors and poor survival. The finding of p53-Abs in the sera of individuals who are at high risk of cancer, such as exposed workers or heavy smokers, indicates that they have promising potential in the early detection of cancer. {\textcopyright}2000 American Association for Cancer Research.}, issn = {0008-5472}, URL = {https://cancerres.aacrjournals.org/content/60/7/1777}, eprint = {https://cancerres.aacrjournals.org/content/60/7/1777.full.pdf}, journal = {Cancer Research} }

@Article{Melling2019, author={Melling, Nathaniel and Norrenbrock, Sonja and Kluth, Martina and Simon, Ronald and Hube‑Magg, Claudia and Steurer, Stefan and Hinsch, Andrea and Burandt, Eike and Jacobsen, Frank and Wilczak, Waldemar and Quaas, Alexander and Bockhorn, Maximillian and Grupp, Katharina and Tachezy, Michael and Izbicki, Jakob and Sauter, Guido and Gebauer, Florian}, title={p53 overexpression is a prognosticator of poor outcome in esophageal cancer}, journal={Oncol Lett}, year={2019}, month={Apr}, day={01}, volume={17}, number={4}, pages={3826-3834}, keywords={p53 expression outcome esophageal cancer}, abstract={Immunohistochemistry studies on p53 inactivation in esophageal cancer are available with inconclusive results. Data on the combined effect of p53 protein accumulation and TP53 genomic deactivation in large scale studies for esophageal cancer are currently lacking. A tissue microarray with 691 esophageal cancer samples was analyzed by p53 immunohistochemistry and fluorescence in situ hybridization (FISH). Nuclear p53 accumulation was observed in 45.9{%} of patients with adenocarcinoma (AC) and in 40.0{%} in squamous cell carcinoma (SCC). Heterozygous TP53 deletions occurred in 40.9{%} in AC and in 19.4{%} in SCC. Homozygous deletions did not occur at all. High‑level p53 immunostaining was associated with shortened overall survival in AC and SCC while TP53 deletions alone showed no correlation with survival. High‑level p53 immunostaining in patients with AC was associated with advanced tumor (P=0.019) and Union for International Cancer Control stages (P=0.004), grading (P=0.027) and the resection margin status (P=0.006). Associations between p53 immunostaining and SCC were not found. TP53 deletions were found to be associated with advanced tumor stages (P=0.028) and the presence of lymph node metastasis (P=0.009) in SCC. In conclusion, strong p53 immunostaining, but not TP53 deletion alone, is associated with unfavorable outcomes and may therefore represent a clinically useful molecular marker in esophageal cancer.}, doi={10.3892/ol.2019.10020}, url={https://doi.org/10.3892/ol.2019.10020} }

@article{ATM13010, author = {Francesco Perri and Salvatore Pisconti and Giuseppina Della Vittoria Scarpati}, title = {P53 mutations and cancer: a tight linkage}, journal = {Annals of Translational Medicine}, volume = {4}, number = {24}, year = {2016}, keywords = {}, abstract = {P53 is often mutated in solid tumors, in fact, somatic changes involving the gene encoding for p53 (TP53) have been discovered in more than 50% of human malignancies and several data confirmed that p53 mutations represent an early event in cancerogenesis. Main p53 functions consist in cell cycle arrest, DNA repair, senescence and apoptosis induction in response to mutagenic stimuli, and, to exert those functions, p53 acts as transcriptional factor. Recent data have highlighted another very important role of p53, consisting in regulate cell metabolism and cell response to oxidative stress. Majority of tumor suppressor genes, such as adenomatous polyposis coli (APC), retinoblastoma-associated protein (RB) and Von-Hippel-Lindau (VHL) are inactivated by deletion or early truncation mutations in tumors, resulting in the decreased or loss of expression of their proteins. Differently, most p53 mutations in human cancer are missense mutations, which result in the production of full-length mutant p53 proteins. It has been reported that mutant p53 proteins and wild type p53 proteins often regulate same cellular biological processes with opposite effects. So, mutant p53 has been reported to supply the cancer cells of glucose and nutrients, and, to avoid reactive oxygen species (ROS) mediated damage during oxidative stress. These last features are able to render tumor cells resistant to ionizing radiations and chemotherapy. A future therapeutic approach in tumors bearing p53 mutations may be to deplete cancer cells of their energy reserves and antioxidants.}, issn = {2305-5847}, url = {https://atm.amegroups.com/article/view/13010} }

@Article{Sermeus2011, author={Sermeus, A. and Michiels, C.}, title={Reciprocal influence of the p53 and the hypoxic pathways}, journal={Cell Death {&} Disease}, year={2011}, month={May}, day={01}, volume={2}, number={5}, pages={e164-e164}, abstract={When cells sense a decrease in oxygen availability (hypoxia), they develop adaptive responses in order to sustain this condition and survive. If hypoxia lasts too long or is too severe, the cells eventually die. Hypoxia is also known to modulate the p53 pathway, in a manner dependent or not of HIF-1 (hypoxia-inducible factor-1), the main transcription factor activated by hypoxia. The p53 protein is a transcription factor, which is rapidly stabilised by cellular stresses and which has a major role in the cell responses to these stresses. The aim of this review is to compile what has been reported until now about the interconnection between these two important pathways. Indeed, according to the cell line, the severity and the duration of hypoxia, oxygen deficiency influences very differently p53 protein level and activity. Conversely, p53 is also described to affect HIF-1$\alpha$ stability, one of the two subunits of HIF-1, and HIF-1 activity. The direct and indirect interactions between HIF-1$\alpha$ and p53 are described as well as the involvement in this complex network of their respective ubiquitin ligases von Hippel Lindau protein and murine double minute 2. Finally, the synergistic or antagonistic effects of p53 and HIF-1 on some important cellular pathways are discussed.}, issn={2041-4889}, doi={10.1038/cddis.2011.48}, url={https://doi.org/10.1038/cddis.2011.48} }

@Article{Puzio-Kuter2011, author={Puzio-Kuter, Anna M.}, title={The Role of p53 in Metabolic Regulation}, journal={Genes {&} cancer}, year={2011}, month={Apr}, publisher={SAGE Publications}, volume={2}, number={4}, pages={385-391}, keywords={aerobic glycolysis; oxidative phosphorylation; oxidative stress; p53}, abstract={The metabolic changes that occur in a cancer cell have been studied for a few decades, but our appreciation of the complexity and importance of those changes is now being realized. The metabolic switch from oxidative phosphorylation to aerobic glycolysis provides intermediates for cell growth and division and is regulated by both oncogenes and tumor suppressor genes. The p53 tumor suppressor gene has long been shown to play key roles in responding to DNA damage, hypoxia, and oncogenic activation. However, now p53 has added the ability to mediate metabolic changes in cells through the regulation of energy metabolism and oxidative stress to its repertoire of activities. It is therefore the focus of this review to discuss the metabolic pathways regulated by p53 and their cooperation in controlling cancer cell metabolism.}, note={21779507[pmid]}, note={PMC3135642[pmcid]}, note={10.1177{_}1947601911409738[PII]}, issn={1947-6027}, doi={10.1177/1947601911409738}, url={https://pubmed.ncbi.nlm.nih.gov/21779507}, url={https://doi.org/10.1177/1947601911409738}, language={eng} }

@Article{Liu2020, author={Liu, Mengqi and Liu, Wensheng and Qin, Yi and Xu, Xiaowu and Yu, Xianjun and Zhuo, Qifeng and Ji, Shunrong}, title={Regulation of metabolic reprogramming by tumor suppressor genes in pancreatic cancer}, journal={Experimental Hematology {&} Oncology}, year={2020}, month={Sep}, day={03}, volume={9}, number={1}, pages={23}, abstract={Pancreatic cancer continues to be one of the most aggressive malignant tumors. Work in recent years in cancer molecular biology has revealed that metabolic reprogramming is an additional hallmark of cancer that is involved in the pathogenesis of cancers, and is intricately linked to gene mutations.}, issn={2162-3619}, doi={10.1186/s40164-020-00179-x}, url={https://doi.org/10.1186/s40164-020-00179-x} }

@article{10.1093/bioinformatics/btab167, author = {Belyaeva, Anastasiya and Squires, Chandler and Uhler, Caroline}, title = "{DCI: learning causal differences between gene regulatory networks}", journal = {Bioinformatics}, year = {2021}, month = {03}, abstract = "{Designing interventions to control gene regulation necessitates modeling a gene regulatory network by a causal graph. Currently, large-scale gene expression datasets from different conditions, cell types, disease states, and developmental time points are being collected. However, application of classical causal inference algorithms to infer gene regulatory networks based on such data is still challenging, requiring high sample sizes and computational resources. Here, we describe an algorithm that efficiently learns the differences in gene regulatory mechanisms between different conditions. Our difference causal inference (DCI) algorithm infers changes (i.e. edges that appeared, disappeared, or changed weight) between two causal graphs given gene expression data from the two conditions. This algorithm is efficient in its use of samples and computation since it infers the differences between causal graphs directly without estimating each possibly large causal graph separately. We provide a user-friendly Python implementation of DCI and also enable the user to learn the most robust difference causal graph across different tuning parameters via stability selection. Finally, we show how to apply DCI to single-cell RNA-seq data from different conditions and cell states, and we also validate our algorithm by predicting the effects of interventions.Python package freely available at http://uhlerlab.github.io/causaldag/dci.Supplementary data are available at Bioinformatics online.}", issn = {1367-4803}, doi = {10.1093/bioinformatics/btab167}, url = {https://doi.org/10.1093/bioinformatics/btab167}, note = {btab167}, eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab167/36666576/btab167.pdf}, }

@article{10.1093/gigascience/giz010, author = {Voukantsis, Dimitrios and Kahn, Kenneth and Hadley, Martin and Wilson, Rowan and Buffa, Francesca M}, title = "{Modeling genotypes in their microenvironment to predict single- and multi-cellular behavior}", journal = {GigaScience}, volume = {8}, number = {3}, year = {2019}, month = {01}, abstract = "{A cell's phenotype is the set of observable characteristics resulting from the interaction of the genotype with the surrounding environment, determining cell behavior. Deciphering genotype-phenotype relationships has been crucial to understanding normal and disease biology. Analysis of molecular pathways has provided an invaluable tool to such understanding; however, typically it does not consider the physical microenvironment, which is a key determinant of phenotype.In this study, we present a novel modeling framework that enables the study of the link between genotype, signaling networks, and cell behavior in a three-dimensional microenvironment. To achieve this, we bring together Agent-Based Modeling, a powerful computational modeling technique, and gene networks. This combination allows biological hypotheses to be tested in a controlled stepwise fashion, and it lends itself naturally to model a heterogeneous population of cells acting and evolving in a dynamic microenvironment, which is needed to predict the evolution of complex multi-cellular dynamics. Importantly, this enables modeling co-occurring intrinsic perturbations, such as mutations, and extrinsic perturbations, such as nutrient availability, and their interactions.Using cancer as a model system, we illustrate how this framework delivers a unique opportunity to identify determinants of single-cell behavior, while uncovering emerging properties of multi-cellular growth.This framework is freely available at http://www.microc.org.}", issn = {2047-217X}, doi = {10.1093/gigascience/giz010}, url = {https://doi.org/10.1093/gigascience/giz010}, note = {giz010}, eprint = {https://academic.oup.com/gigascience/article-pdf/8/3/giz010/28096120/giz010\_response\_to\_reviewer\_comments\_revision\_1.pdf}, }

@article{10.1371/journal.pcbi.1003204, doi = {10.1371/journal.pcbi.1003204}, author = {Melas, Ioannis N. AND Samaga, Regina AND Alexopoulos, Leonidas G. AND Klamt, Steffen}, journal = {PLOS Computational Biology}, publisher = {Public Library of Science}, title = {Detecting and Removing Inconsistencies between Experimental Data and Signaling Network Topologies Using Integer Linear Programming on Interaction Graphs}, year = {2013}, month = {09}, volume = {9}, url = {https://doi.org/10.1371/journal.pcbi.1003204}, pages = {1-19}, abstract = {Cross-referencing experimental data with our current knowledge of signaling network topologies is one central goal of mathematical modeling of cellular signal transduction networks. We present a new methodology for data-driven interrogation and training of signaling networks. While most published methods for signaling network inference operate on Bayesian, Boolean, or ODE models, our approach uses integer linear programming (ILP) on interaction graphs to encode constraints on the qualitative behavior of the nodes. These constraints are posed by the network topology and their formulation as ILP allows us to predict the possible qualitative changes (up, down, no effect) of the activation levels of the nodes for a given stimulus. We provide four basic operations to detect and remove inconsistencies between measurements and predicted behavior: (i) find a topology-consistent explanation for responses of signaling nodes measured in a stimulus-response experiment (if none exists, find the closest explanation); (ii) determine a minimal set of nodes that need to be corrected to make an inconsistent scenario consistent; (iii) determine the optimal subgraph of the given network topology which can best reflect measurements from a set of experimental scenarios; (iv) find possibly missing edges that would improve the consistency of the graph with respect to a set of experimental scenarios the most. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGFR/ErbB signaling against a library of high-throughput phosphoproteomic data measured in primary hepatocytes. Our methods detect interactions that are likely to be inactive in hepatocytes and provide suggestions for new interactions that, if included, would significantly improve the goodness of fit. Our framework is highly flexible and the underlying model requires only easily accessible biological knowledge. All related algorithms were implemented in a freely available toolbox SigNetTrainer making it an appealing approach for various applications.}, number = {9},

}

@Article{Li2014, author={Li, H. and Zhang, Y. and Str{"o}se, A. and Tedesco, D. and Gurova, K. and Selivanova, G.}, title={Integrated high-throughput analysis identifies Sp1 as a crucial determinant of p53-mediated apoptosis}, journal={Cell death and differentiation}, year={2014}, month={Sep}, edition={2014/06/27}, publisher={Nature Publishing Group}, volume={21}, number={9}, pages={1493-1502}, keywords={*Apoptosis; HCT116 Cells; *High-Throughput Screening Assays; Humans; Sp1 Transcription Factor/*metabolism; Tumor Suppressor Protein p53/*metabolism}, abstract={The restoration of p53 tumor suppressor function is a promising therapeutic strategy to combat cancer. However, the biological outcomes of p53 activation, ranging from the promotion of growth arrest to the induction of cell death, are hard to predict, which limits the clinical application of p53-based therapies. In the present study, we performed an integrated analysis of genome-wide short hairpin RNA screen and gene expression data and uncovered a previously unrecognized role of Sp1 as a central modulator of the transcriptional response induced by p53 that leads to robust induction of apoptosis. Sp1 is indispensable for the pro-apoptotic transcriptional repression by p53, but not for the induction of pro-apoptotic genes. Furthermore, the p53-dependent pro-apoptotic transcriptional repression required the co-binding of Sp1 to p53 target genes. Our results also highlight that Sp1 shares with p53 a common regulator, MDM2, which targets Sp1 for proteasomal degradation. This uncovers a new mechanism of the tight control of apoptosis in cells. Our study advances the understanding of the molecular basis of p53-mediated apoptosis and implicates Sp1 as one of its key modulators. We found that small molecules reactivating p53 can differentially modulate Sp1, thus providing insights into how to manipulate p53 response in a controlled way.}, note={24971482[pmid]}, note={PMC4131181[pmcid]}, note={cdd201469[PII]}, issn={1476-5403}, doi={10.1038/cdd.2014.69}, url={https://pubmed.ncbi.nlm.nih.gov/24971482}, url={https://doi.org/10.1038/cdd.2014.69}, language={eng} }

@Article{Sun2014, author={Sun, Kai and Gon{\c{c}}alves, Joana P. and Larminie, Chris and Pr{\v{z}}ulj, Nata{\v{s}}a}, title={Predicting disease associations via biological network analysis}, journal={BMC Bioinformatics}, year={2014}, month={Sep}, day={17}, volume={15}, number={1}, pages={304}, abstract={Understanding the relationship between diseases based on the underlying biological mechanisms is one of the greatest challenges in modern biology and medicine. Exploring disease-disease associations by using system-level biological data is expected to improve our current knowledge of disease relationships, which may lead to further improvements in disease diagnosis, prognosis and treatment.}, issn={1471-2105}, doi={10.1186/1471-2105-15-304}, url={https://doi.org/10.1186/1471-2105-15-304} }

@Article{Masiero2013, author={Masiero, Massimo and Sim{~o}es, Filipa Costa and Han, Hee Dong and Snell, Cameron and Peterkin, Tessa and Bridges, Esther and Mangala, Lingegowda S. and Wu, Sherry Yen-Yao and Pradeep, Sunila and Li, Demin and Han, Cheng and Dalton, Heather and Lopez-Berestein, Gabriel and Tuynman, Jurriaan B. and Mortensen, Neil and Li, Ji-Liang and Patient, Roger and Sood, Anil K. and Banham, Alison H. and Harris, Adrian L. and Buffa, Francesca M.}, title={A core human primary tumor angiogenesis signature identifies the endothelial orphan receptor ELTD1 as a key regulator of angiogenesis}, journal={Cancer cell}, year={2013}, month={Aug}, day={12}, edition={2013/07/18}, publisher={Cell Press}, volume={24}, number={2}, pages={229-241}, keywords={Animals; Cell Growth Processes/physiology; Endothelial Cells/*metabolism/pathology; Female; Genetic Predisposition to Disease; HCT116 Cells; Humans; Mice; Mice, Nude; Neoplasms/*blood supply/*metabolism/pathology; Neovascularization, Pathologic/metabolism/pathology; Receptors, G-Protein-Coupled/genetics/*metabolism; Signal Transduction}, abstract={Limited clinical benefits derived from anti-VEGF therapy have driven the identification of new targets involved in tumor angiogenesis. Here, we report an integrative meta-analysis to define the transcriptional program underlying angiogenesis in human cancer. This approach identified ELTD1, an orphan G-protein-coupled receptor whose expression is induced by VEGF/bFGF and repressed by DLL4 signaling. Extensive analysis of multiple cancer types demonstrates significant upregulation of ELTD1 in tumor-associated endothelial cells, with a higher expression correlating with favorable prognosis. Importantly, ELTD1 silencing impairs endothelial sprouting and vessel formation in vitro and in vivo, drastically reducing tumor growth and greatly improving survival. Collectively, these results provide insight into the regulation of tumor angiogenesis and highlight ELTD1 as key player in blood vessel formation.}, note={23871637[pmid]}, note={PMC3743050[pmcid]}, note={S1535-6108(13)00280-8[PII]}, issn={1878-3686}, doi={10.1016/j.ccr.2013.06.004}, url={https://pubmed.ncbi.nlm.nih.gov/23871637}, url={https://doi.org/10.1016/j.ccr.2013.06.004}, language={eng} }

@Article{Buffa2010, author={Buffa, F. M. and Harris, A. L. and West, C. M. and Miller, C. J.}, title={Large meta-analysis of multiple cancers reveals a common, compact and highly prognostic hypoxia metagene}, journal={British Journal of Cancer}, year={2010}, month={Jan}, day={01}, volume={102}, number={2}, pages={428-435}, abstract={There is a need to develop robust and clinically applicable gene expression signatures. Hypoxia is a key factor promoting solid tumour progression and resistance to therapy; a hypoxia signature has the potential to be not only prognostic but also to predict benefit from particular interventions.}, issn={1532-1827}, doi={10.1038/sj.bjc.6605450}, url={https://doi.org/10.1038/sj.bjc.6605450} }

@Article{Liu2018, author={Liu, Jianfang and Lichtenberg, Tara and Hoadley, Katherine A. and Poisson, Laila M. and Lazar, Alexander J. and Cherniack, Andrew D. and Kovatich, Albert J. and Benz, Christopher C. and Levine, Douglas A. and Lee, Adrian V. and Omberg, Larsson and Wolf, Denise M. and Shriver, Craig D. and Thorsson, Vesteinn and Caesar-Johnson, Samantha J. and Demchok, John A. and Felau, Ina and Kasapi, Melpomeni and Ferguson, Martin L. and Hutter, Carolyn M. and Sofia, Heidi J. and Tarnuzzer, Roy and Wang, Zhining and Yang, Liming and Zenklusen, Jean C. and Zhang, Jiashan (Julia) and Chudamani, Sudha and Liu, Jia and Lolla, Laxmi and Naresh, Rashi and Pihl, Todd and Sun, Qiang and Wan, Yunhu and Wu, Ye and Cho, Juok and DeFreitas, Timothy and Frazer, Scott and Gehlenborg, Nils and Getz, Gad and Heiman, David I. and Kim, Jaegil and Lawrence, Michael S. and Lin, Pei and Meier, Sam and Noble, Michael S. and Saksena, Gordon and Voet, Doug and Zhang, Hailei and Bernard, Brady and Chambwe, Nyasha and Dhankani, Varsha and Knijnenburg, Theo and Kramer, Roger and Leinonen, Kalle and Liu, Yuexin and Miller, Michael and Reynolds, Sheila and Shmulevich, Ilya and Zhang, Wei and Akbani, Rehan and Broom, Bradley M. and Hegde, Apurva M. and Ju, Zhenlin and Kanchi, Rupa S. and Korkut, Anil and Li, Jun and Liang, Han and Ling, Shiyun and Liu, Wenbin and Lu, Yiling and Mills, Gordon B. and Ng, Kwok-Shing and Rao, Arvind and Ryan, Michael and Wang, Jing and Weinstein, John N. and Zhang, Jiexin and Abeshouse, Adam and Armenia, Joshua and Chakravarty, Debyani and Chatila, Walid K. and de Bruijn, Ino and Gao, Jianjiong and Gross, Benjamin E. and Heins, Zachary J. and Kundra, Ritika and La, Konnor and Ladanyi, Marc and Luna, Augustin and Nissan, Moriah G. and Ochoa, Angelica and Phillips, Sarah M. and Reznik, Ed and Sanchez-Vega, Francisco and Sander, Chris and Schultz, Nikolaus and Sheridan, Robert and Sumer, S. Onur and Sun, Yichao and Taylor, Barry S. and Wang, Jioajiao and Zhang, Hongxin and Anur, Pavana and Peto, Myron and Spellman, Paul and Benz, Christopher and Stuart, Joshua M. and Wong, Christopher K. and Yau, Christina and Hayes, D. Neil and Parker, Joel S. and Wilkerson, Matthew D. and Ally, Adrian and Balasundaram, Miruna and Bowlby, Reanne and Brooks, Denise and Carlsen, Rebecca and Chuah, Eric and Dhalla, Noreen and Holt, Robert and Jones, Steven J.M. and Kasaian, Katayoon and Lee, Darlene and Ma, Yussanne and Marra, Marco A. and Mayo, Michael and Moore, Richard A. and Mungall, Andrew J. and Mungall, Karen and Robertson, A. Gordon and Sadeghi, Sara and Schein, Jacqueline E. and Sipahimalani, Payal and Tam, Angela and Thiessen, Nina and Tse, Kane and Wong, Tina and Berger, Ashton C. and Beroukhim, Rameen and Cibulskis, Carrie and Gabriel, Stacey B. and Gao, Galen F. and Ha, Gavin and Meyerson, Matthew and Schumacher, Steven E. and Shih, Juliann and Kucherlapati, Melanie H. and Kucherlapati, Raju S. and Baylin, Stephen and Cope, Leslie and Danilova, Ludmila and Bootwalla, Moiz S. and Lai, Phillip H. and Maglinte, Dennis T. and Van Den Berg, David J. and Weisenberger, Daniel J. and Auman, J. Todd and Balu, Saianand and Bodenheimer, Tom and Fan, Cheng and Hoyle, Alan P. and Jefferys, Stuart R. and Jones, Corbin D. and Meng, Shaowu and Mieczkowski, Piotr A. and Mose, Lisle E. and Perou, Amy H. and Perou, Charles M. and Roach, Jeffrey and Shi, Yan and Simons, Janae V. and Skelly, Tara and Soloway, Matthew G. and Tan, Donghui and Veluvolu, Umadevi and Fan, Huihui and Hinoue, Toshinori and Laird, Peter W. and Shen, Hui and Zhou, Wanding and Bellair, Michelle and Chang, Kyle and Covington, Kyle and Creighton, Chad J. and Dinh, Huyen and Doddapaneni, HarshaVardhan and Donehower, Lawrence A. and Drummond, Jennifer and Gibbs, Richard A. and Glenn, Robert and Hale, Walker and Han, Yi and Hu, Jianhong and Korchina, Viktoriya and Lee, Sandra and Lewis, Lora and Li, Wei and Liu, Xiuping and Morgan, Margaret and Morton, Donna and Muzny, Donna and Santibanez, Jireh and Sheth, Margi and Shinbro, Eve and Wang, Linghua and Wang, Min and Wheeler, David A. and Xi, Liu and Zhao, Fengmei and Hess, Julian and Appelbaum, Elizabeth L. and Bailey, Matthew and Cordes, Matthew G. and Ding, Li and Fronick, Catrina C. and Fulton, Lucinda A. and Fulton, Robert S. and Kandoth, Cyriac and Mardis, Elaine R. and McLellan, Michael D. and Miller, Christopher A. and Schmidt, Heather K. and Wilson, Richard K. and Crain, Daniel and Curley, Erin and Gardner, Johanna and Lau, Kevin and Mallery, David and Morris, Scott and Paulauskis, Joseph and Penny, Robert and Shelton, Candace and Shelton, Troy and Sherman, Mark and Thompson, Eric and Yena, Peggy and Bowen, Jay and Gastier-Foster, Julie M. and Gerken, Mark and Leraas, Kristen M. and Lichtenberg, Tara M. and Ramirez, Nilsa C. and Wise, Lisa and Zmuda, Erik and Corcoran, Niall and Costello, Tony and Hovens, Christopher and Carvalho, Andre L. and de Carvalho, Ana C. and Fregnani, Jos{'e} H. and Longatto-Filho, Adhemar and Reis, Rui M. and Scapulatempo-Neto, Cristovam and Silveira, Henrique C.S. and Vidal, Daniel O. and Burnette, Andrew and Eschbacher, Jennifer and Hermes, Beth and Noss, Ardene and Singh, Rosy and Anderson, Matthew L. and Castro, Patricia D. and Ittmann, Michael and Huntsman, David and Kohl, Bernard and Le, Xuan and Thorp, Richard and Andry, Chris and Duffy, Elizabeth R. and Lyadov, Vladimir and Paklina, Oxana and Setdikova, Galiya and Shabunin, Alexey and Tavobilov, Mikhail and McPherson, Christopher and Warnick, Ronald and Berkowitz, Ross and Cramer, Daniel and Feltmate, Colleen and Horowitz, Neil and Kibel, Adam and Muto, Michael and Raut, Chandrajit P. and Malykh, Andrei and Barnholtz-Sloan, Jill S. and Barrett, Wendi and Devine, Karen and Fulop, Jordonna and Ostrom, Quinn T. and Shimmel, Kristen and Wolinsky, Yingli and Sloan, Andrew E. and De Rose, Agostino and Giuliante, Felice and Goodman, Marc and Karlan, Beth Y. and Hagedorn, Curt H. and Eckman, John and Harr, Jodi and Myers, Jerome and Tucker, Kelinda and Zach, Leigh Anne and Deyarmin, Brenda and Hu, Hai and Kvecher, Leonid and Larson, Caroline and Mural, Richard J. and Somiari, Stella and Vicha, Ales and Zelinka, Tomas and Bennett, Joseph and Iacocca, Mary and Rabeno, Brenda and Swanson, Patricia and Latour, Mathieu and Lacombe, Louis and T{^e}tu, Bernard and Bergeron, Alain and McGraw, Mary and Staugaitis, Susan M. and Chabot, John and Hibshoosh, Hanina and Sepulveda, Antonia and Su, Tao and Wang, Timothy and Potapova, Olga and Voronina, Olga and Desjardins, Laurence and Mariani, Odette and Roman-Roman, Sergio and Sastre, Xavier and Stern, Marc-Henri and Cheng, Feixiong and Signoretti, Sabina and Berchuck, Andrew and Bigner, Darell and Lipp, Eric and Marks, Jeffrey and McCall, Shannon and McLendon, Roger and Secord, Angeles and Sharp, Alexis and Behera, Madhusmita and Brat, Daniel J. and Chen, Amy and Delman, Keith and Force, Seth and Khuri, Fadlo and Magliocca, Kelly and Maithel, Shishir and Olson, Jeffrey J. and Owonikoko, Taofeek and Pickens, Alan and Ramalingam, Suresh and Shin, Dong M. and Sica, Gabriel and Van Meir, Erwin G. and Zhang, Hongzheng and Eijckenboom, Wil and Gillis, Ad and Korpershoek, Esther and Looijenga, Leendert and Oosterhuis, Wolter and Stoop, Hans and van Kessel, Kim E. and Zwarthoff, Ellen C. and Calatozzolo, Chiara and Cuppini, Lucia and Cuzzubbo, Stefania and DiMeco, Francesco and Finocchiaro, Gaetano and Mattei, Luca and Perin, Alessandro and Pollo, Bianca and Chen, Chu and Houck, John and Lohavanichbutr, Pawadee and Hartmann, Arndt and Stoehr, Christine and Stoehr, Robert and Taubert, Helge and Wach, Sven and Wullich, Bernd and Kycler, Witold and Murawa, Dawid and Wiznerowicz, Maciej and Chung, Ki and Edenfield, W. Jeffrey and Martin, Julie and Baudin, Eric and Bubley, Glenn and Bueno, Raphael and De Rienzo, Assunta and Richards, William G. and Kalkanis, Steven and Mikkelsen, Tom and Noushmehr, Houtan and Scarpace, Lisa and Girard, Nicolas and Aymerich, Marta and Campo, Elias and Gin{'e}, Eva and Guillermo, Armando L{'o}pez and Van Bang, Nguyen and Hanh, Phan Thi and Phu, Bui Duc and Tang, Yufang and Colman, Howard and Evason, Kimberley and Dottino, Peter R. and Martignetti, John A. and Gabra, Hani and Juhl, Hartmut and Akeredolu, Teniola and Stepa, Serghei and Hoon, Dave and Ahn, Keunsoo and Kang, Koo Jeong and Beuschlein, Felix and Breggia, Anne and Birrer, Michael and Bell, Debra and Borad, Mitesh and Bryce, Alan H. and Castle, Erik and Chandan, Vishal and Cheville, John and Copland, John A. and Farnell, Michael and Flotte, Thomas and Giama, Nasra and Ho, Thai and Kendrick, Michael and Kocher, Jean-Pierre and Kopp, Karla and Moser, Catherine and Nagorney, David and O'Brien, Daniel and O'Neill, Brian Patrick and Patel, Tushar and Petersen, Gloria and Que, Florencia and Rivera, Michael and Roberts, Lewis and Smallridge, Robert and Smyrk, Thomas and Stanton, Melissa and Thompson, R. Houston and Torbenson, Michael and Yang, Ju Dong and Zhang, Lizhi and Brimo, Fadi and Ajani, Jaffer A. and Angulo Gonzalez, Ana Maria and Behrens, Carmen and Bondaruk, Jolanta and Broaddus, Russell and Czerniak, Bogdan and Esmaeli, Bita and Fujimoto, Junya and Gershenwald, Jeffrey and Guo, Charles and Logothetis, Christopher and Meric-Bernstam, Funda and Moran, Cesar and Ramondetta, Lois and Rice, David and Sood, Anil and Tamboli, Pheroze and Thompson, Timothy and Troncoso, Patricia and Tsao, Anne and Wistuba, Ignacio and Carter, Candace and Haydu, Lauren and Hersey, Peter and Jakrot, Valerie and Kakavand, Hojabr and Kefford, Richard and Lee, Kenneth and Long, Georgina and Mann, Graham and Quinn, Michael and Saw, Robyn and Scolyer, Richard and Shannon, Kerwin and Spillane, Andrew and Stretch, Jonathan and Synott, Maria and Thompson, John and Wilmott, James and Al-Ahmadie, Hikmat and Chan, Timothy A. and Ghossein, Ronald and Gopalan, Anuradha and Reuter, Victor and Singer, Samuel and Singh, Bhuvanesh and Tien, Nguyen Viet and Broudy, Thomas and Mirsaidi, Cyrus and Nair, Praveen and Drwiega, Paul and Miller, Judy and Smith, Jennifer and Zaren, Howard and Park, Joong-Won and Hung, Nguyen Phi and Kebebew, Electron and Linehan, W. Marston and Metwalli, Adam R. and Pacak, Karel and Pinto, Peter A. and Schiffman, Mark and Schmidt, Laura S. and Vocke, Cathy D. and Wentzensen, Nicolas and Worrell, Robert and Yang, Hannah and Moncrieff, Marc and Goparaju, Chandra and Melamed, Jonathan and Pass, Harvey and Botnariuc, Natalia and Caraman, Irina and Cernat, Mircea and Chemencedji, Inga and Clipca, Adrian and Doruc, Serghei and Gorincioi, Ghenadie and Mura, Sergiu and Pirtac, Maria and Stancul, Irina and Tcaciuc, Diana and Albert, Monique and Alexopoulou, Iakovina and Arnaout, Angel and Bartlett, John and Engel, Jay and Gilbert, Sebastien and Parfitt, Jeremy and Sekhon, Harman and Thomas, George and Rassl, Doris M. and Rintoul, Robert C. and Bifulco, Carlo and Tamakawa, Raina and Urba, Walter and Hayward, Nicholas and Timmers, Henri and Antenucci, Anna and Facciolo, Francesco and Grazi, Gianluca and Marino, Mirella and Merola, Roberta and de Krijger, Ronald and Gimenez-Roqueplo, Anne-Paule and Pich{'e}, Alain and Chevalier, Simone and McKercher, Ginette and Birsoy, Kivanc and Barnett, Gene and Brewer, Cathy and Farver, Carol and Naska, Theresa and Pennell, Nathan A. and Raymond, Daniel and Schilero, Cathy and Smolenski, Kathy and Williams, Felicia and Morrison, Carl and Borgia, Jeffrey A. and Liptay, Michael J. and Pool, Mark and Seder, Christopher W. and Junker, Kerstin and Dinkin, Mikhail and Manikhas, George and Alvaro, Domenico and Bragazzi, Maria Consiglia and Cardinale, Vincenzo and Carpino, Guido and Gaudio, Eugenio and Chesla, David and Cottingham, Sandra and Dubina, Michael and Moiseenko, Fedor and Dhanasekaran, Renumathy and Becker, Karl-Friedrich and Janssen, Klaus-Peter and Slotta-Huspenina, Julia and Abdel-Rahman, Mohamed H. and Aziz, Dina and Bell, Sue and Cebulla, Colleen M. and Davis, Amy and Duell, Rebecca and Elder, J. Bradley and Hilty, Joe and Kumar, Bahavna and Lang, James and Lehman, Norman L. and Mandt, Randy and Nguyen, Phuong and Pilarski, Robert and Rai, Karan and Schoenfield, Lynn and Senecal, Kelly and Wakely, Paul and Hansen, Paul and Lechan, Ronald and Powers, James and Tischler, Arthur and Grizzle, William E. and Sexton, Katherine C. and Kastl, Alison and Henderson, Joel and Porten, Sima and Waldmann, Jens and Fassnacht, Martin and Asa, Sylvia L. and Schadendorf, Dirk and Couce, Marta and Graefen, Markus and Huland, Hartwig and Sauter, Guido and Schlomm, Thorsten and Simon, Ronald and Tennstedt, Pierre and Olabode, Oluwole and Nelson, Mark and Bathe, Oliver and Carroll, Peter R. and Chan, June M. and Disaia, Philip and Glenn, Pat and Kelley, Robin K. and Landen, Charles N. and Phillips, Joanna and Prados, Michael and Simko, Jeffry and Smith-McCune, Karen and VandenBerg, Scott and Roggin, Kevin and Fehrenbach, Ashley and Kendler, Ady and Sifri, Suzanne and Steele, Ruth and Jimeno, Antonio and Carey, Francis and Forgie, Ian and Mannelli, Massimo and Carney, Michael and Hernandez, Brenda and Campos, Benito and Herold-Mende, Christel and Jungk, Christin and Unterberg, Andreas and von Deimling, Andreas and Bossler, Aaron and Galbraith, Joseph and Jacobus, Laura and Knudson, Michael and Knutson, Tina and Ma, Deqin and Milhem, Mohammed and Sigmund, Rita and Godwin, Andrew K. and Madan, Rashna and Rosenthal, Howard G. and Adebamowo, Clement and Adebamowo, Sally N. and Boussioutas, Alex and Beer, David and Giordano, Thomas and Mes-Masson, Anne-Marie and Saad, Fred and Bocklage, Therese and Landrum, Lisa and Mannel, Robert and Moore, Kathleen and Moxley, Katherine and Postier, Russel and Walker, Joan and Zuna, Rosemary and Feldman, Michael and Valdivieso, Federico and Dhir, Rajiv and Luketich, James and Mora Pinero, Edna M. and Quintero-Aguilo, Mario and {Carlotti} and Jr, Carlos Gilberto and Dos Santos, Jose Sebasti{~a}o and Kemp, Rafael and Sankarankuty, Ajith and Tirapelli, Daniela and Catto, James and Agnew, Kathy and Swisher, Elizabeth and Creaney, Jenette and Robinson, Bruce and Shelley, Carl Simon and Godwin, Eryn M. and Kendall, Sara and Shipman, Cassaundra and Bradford, Carol and Carey, Thomas and Haddad, Andrea and Moyer, Jeffey and Peterson, Lisa and Prince, Mark and Rozek, Laura and Wolf, Gregory and Bowman, Rayleen and Fong, Kwun M. and Yang, Ian and Korst, Robert and Rathmell, W. Kimryn and Fantacone-Campbell, J. Leigh and Hooke, Jeffrey A. and DiPersio, John and Drake, Bettina and Govindan, Ramaswamy and Heath, Sharon and Ley, Timothy and Van Tine, Brian and Westervelt, Peter and Rubin, Mark A. and Lee, Jung Il and Aredes, Nat{'a}lia D. and Mariamidze, Armaz}, title={An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics}, journal={Cell}, year={2018}, month={Apr}, day={05}, publisher={Elsevier}, volume={173}, number={2}, pages={400-416.e11}, issn={0092-8674}, doi={10.1016/j.cell.2018.02.052}, url={https://doi.org/10.1016/j.cell.2018.02.052} }

@Article{Olivier2009, author={Olivier, M. and Petitjean, A. and Marcel, V. and P{'e}tr{'e}, A. and Mounawar, M. and Plymoth, A. and de Fromentel, C. C. and Hainaut, P.}, title={Recent advances in p53 research: an interdisciplinary perspective}, journal={Cancer Gene Therapy}, year={2009}, month={Jan}, day={01}, volume={16}, number={1}, pages={1-12}, abstract={The TP53 gene is one of the most studied genes in human cancer. In recent years, considerable interest was focused on mutant p53, the abnormal protein product of TP53 somatic or germline alleles with missense mutations that often accumulate in cancer cells. There is now compelling experimental evidence that many mutations can exert mutant-specific, gain-of-function effects by perturbing the regulation of expression of multiple genes. This notion is supported by the observation that targeted mutant p53 expression enhances the formation of specific cancers in the mouse even in the absence of wild-type p53 expression. In addition, clinical studies are producing a wealth of functional pathway data demonstrating correlations between specific TP53 mutations and gene expression patterns identified by transcriptome studies. These correlations imply that alteration of p53 function is critical in shaping gene expression patterns in cancer. Finally, progress is being made in the development of new therapeutic approaches targeting p53 alterations. Key advances regarding the structural, biochemical and functional properties of normal and mutant p53 proteins, their abnormal regulation and distribution in human cancers, and their associations with clinical and pathological cancer characteristics are reviewed. New opportunities for translational research for improving cancer detection, prognosis, prevention and therapy based upon the integration of this knowledge are described.}, issn={1476-5500}, doi={10.1038/cgt.2008.69}, url={https://doi.org/10.1038/cgt.2008.69} }

@Article{Chene2003, author={Ch{`e}ne, Patrick}, title={Inhibiting the p53--MDM2 interaction: an important target for cancer therapy}, journal={Nature Reviews Cancer}, year={2003}, month={Feb}, day={01}, volume={3}, number={2}, pages={102-109}, abstract={The tumour suppressor p53 induces cell death by apoptosis in response to various stress conditions, such as oncogene activation or DNA damage. The loss of p53 tumour-suppressor activity --- either by mutation/deletion of the TP53 gene or by inhibition of the p53 protein --- favours the development of cancer. The MDM2 protein is a negative regulator of p53. After binding to p53, it inhibits its transcriptional activity, favours its nuclear export and stimulates its degradation. The overexpression of MDM2 in various tumours inhibits p53, therefore favouring uncontrolled cell proliferation. The inhibition of the p53--MDM2 interaction is an attractive strategy to activate p53-mediated apoptosis in tumours with overexpressed MDM2, but wild-type p53. Several low-molecular-weight compounds and peptides that inhibit the p53--MDM2 interaction have been obtained. The peptidic inhibitors show an antiproliferative effect in tumour cells overexpressing MDM2.}, issn={1474-1768}, doi={10.1038/nrc991}, url={https://doi.org/10.1038/nrc991} }

@Article{Nigro1989, author={Nigro, Janice M. and Baker, Suzanne J. and Preisinger, Antonette C. and Jessup, J. Milburn and Hosteller, Richard and Cleary, Karen and Signer, Sandra H. and Davidson, Nancy and Baylin, Stephen and Devilee, Peter and Glover, Thomas and Collins, Francis S. and Weslon, Ainsley and Modali, Rama and Harris, Curtis C. and Vogelstein, Bert}, title={Mutations in the p53 gene occur in diverse human tumour types}, journal={Nature}, year={1989}, month={Dec}, day={01}, volume={342}, number={6250}, pages={705-708}, abstract={THE p53 gene has been a constant source of fascination since its discovery nearly a decade ago1,2. Originally considered to be an oncogene, several convergent lines of research have indicated that the wild-type gene product actually functions as a tumour suppressor gene3--9. For example, expression of the neoplastic phenotype is inhibited, rather than promoted, when rat cells are transfected with the murine wild-type p53 gene together with mutant p53 genes and/or other oncogenes3,4. Moreover, in human tumours, the short arm of chromosome 17 is often deleted (reviewed in ref. 10). In colorectal cancers, the smallest common region of deletion is centred at 17pl3.1 (ref. 9); this region harbours the p53 gene, and in two tumours examined in detail, the remaining (non-deleted) p53 alleles were found to contain mutations9. This result was provocative because allelic deletion coupled with mutation of the remaining allele is a theoretical hallmark of tumour-suppressor genes11. In the present report, we have attempted to determine the generality of this observation; that is, whether tumours with allelic deletions of chromosome 17p contain mutant p53 genes in the allele that is retained. Our results suggest that (1) most tumours with such allelic deletions contain p53 point mutations resulting in amino-acid substitutions, (2) such mutations are not confined to tumours with allelic deletion, but also occur in at least some tumours that have retained both parental 17p alleles, and (3) p53 gene mutations are clustered in four 'hot-spots' which exactly coincide with the four most highly conserved regions of the gene. These results suggest that p53 mutations play a role in the development of many common human malignancies.}, issn={1476-4687}, doi={10.1038/342705a0}, url={https://doi.org/10.1038/342705a0} }

@article{doi:10.1126/science.2144057, author = {Suzanne J. Baker and Sanford Markowitz and Eric R. Fearon and James K. V. Willson and Bert Vogelstein }, title = {Suppression of Human Colorectal Carcinoma Cell Growth by Wild-Type p53}, journal = {Science}, volume = {249}, number = {4971}, pages = {912-915}, year = {1990}, doi = {10.1126/science.2144057}, URL = {https://www.science.org/doi/abs/10.1126/science.2144057}, eprint = {https://www.science.org/doi/pdf/10.1126/science.2144057}, abstract = {Mutations of the p53 gene occur commonly in colorectal carcinomas and the wild-type p53 allele is often concomitantly deleted. These findings suggest that the wild-type gene may act as a suppressor of colorectal carcinoma cell growth. To test this hypothesis, wild-type or mutant human p53 genes were transfected into human colorectal carcinoma cell lines. Cells transfected with the wild-type gene formed colonies five- to tenfold less efficiently than those transfected with a mutant p53 gene. In those colonies that did form after wild-type gene transfection, the p53 sequences were found to be deleted or rearranged, or both, and no exogenous p53 messenger RNA expression was observed. In contrast, transfection with the wild-type gene had no apparent effect on the growth of epithelial cells derived from a benign colorectal tumor that had only wild-type p53 alleles. Immunocytochemical techniques demonstrated that carcinoma cells expressing the wild-type gene did not progress through the cell cycle, as evidenced by their failure to incorporate thymidine into DNA. These studies show that the wild-type gene can specifically suppress the growth of human colorectal carcinoma cells in vitro and that an in vivo-derived mutation resulting in a single conservative amino acid substitution in the p53 gene product abrogates this suppressive ability.}}

@article{ANDERSSON2005743, title = {Worse survival for TP53 (p53)-mutated breast cancer patients receiving adjuvant CMF}, journal = {Annals of Oncology}, volume = {16}, number = {5}, pages = {743-748}, year = {2005}, issn = {0923-7534}, doi = {https://doi.org/10.1093/annonc/mdi150}, url = {https://www.sciencedirect.com/science/article/pii/S0923753419505087}, author = {J. Andersson and L. Larsson and S. Klaar and L. Holmberg and J. Nilsson and M. Inganäs and G. Carlsson and J. Öhd and C.-M. Rudenstam and B. Gustavsson and J. Bergh}, keywords = {adjuvant therapy, CMF, p53, sequence-based analysis, tamoxifen, TP53} }

@Article{Shahbandi2020, author={Shahbandi, Ashkan and Nguyen, Hoang D. and Jackson, James G.}, title={TP53 Mutations and Outcomes in Breast Cancer: Reading beyond the Headlines}, journal={Trends in Cancer}, year={2020}, month={Feb}, day={01}, publisher={Elsevier}, volume={6}, number={2}, pages={98-110}, issn={2405-8033}, doi={10.1016/j.trecan.2020.01.007}, url={https://doi.org/10.1016/j.trecan.2020.01.007} }

@Article{Li2017, author={Li, Yong and Li, Wenguo and Tan, Yi and Liu, Fang and Cao, Yijia and Lee, Kwang Y.}, title={Hierarchical Decomposition for Betweenness Centrality Measure of Complex Networks}, journal={Scientific Reports}, year={2017}, month={Apr}, day={20}, volume={7}, number={1}, pages={46491}, abstract={Betweenness centrality is an indicator of a node's centrality in a network. It is equal to the number of shortest paths from all vertices to all others that pass through that node. Most of real-world large networks display a hierarchical community structure, and their betweenness computation possesses rather high complexity. Here we propose a new hierarchical decomposition approach to speed up the betweenness computation of complex networks. The advantage of this new method is its effective utilization of the local structural information from the hierarchical community. The presented method can significantly speed up the betweenness calculation. This improvement is much more evident in those networks with numerous homogeneous communities. Furthermore, the proposed method features a parallel structure, which is very suitable for parallel computation. Moreover, only a small amount of additional computation is required by our method, when small changes in the network structure are restricted to some local communities. The effectiveness of the proposed method is validated via the examples of two real-world power grids and one artificial network, which demonstrates that the performance of the proposed method is superior to that of the traditional method.}, issn={2045-2322}, doi={10.1038/srep46491}, url={https://doi.org/10.1038/srep46491} }

@Article{Barthelemy2004, author={Barth{'e}lemy, M.}, title={Betweenness centrality in large complex networks}, journal={The European Physical Journal B}, year={2004}, month={Mar}, day={01}, volume={38}, number={2}, pages={163-168}, abstract={We analyze the betweenness centrality (BC) of nodes in large complex networks. In general, the BC is increasing with connectivity as a power law with an exponent {$}{\backslash}eta{$}. We find that for trees or networks with a small loop density {$}{\backslash}eta = 2{$}while a larger density of loops leads to {$}{\backslash}eta < 2{$}. For scale-free networks characterized by an exponent {$}{\backslash}gamma{$}which describes the connectivity distribution decay, the BC is also distributed according to a power law with a non universal exponent {$}{\backslash}delta{$}. We show that this exponent {$}{\backslash}delta{$}must satisfy the exact bound {$}{\backslash}delta{\backslash}geq ({\backslash}gamma + 1)/2{$}. If the scale free network is a tree, then we have the equality {$}{\backslash}delta = ({\backslash}gamma + 1)/2{$}.}, issn={1434-6036}, doi={10.1140/epjb/e2004-00111-4}, url={https://doi.org/10.1140/epjb/e2004-00111-4} }

@Article{VandeVoorde2019, author={Vande Voorde, Johan and Ackermann, Tobias and Pfetzer, Nadja and Sumpton, David and Mackay, Gillian and Kalna, Gabriela and Nixon, Colin and Blyth, Karen and Gottlieb, Eyal and Tardito, Saverio}, title={Improving the metabolic fidelity of cancer models with a physiological cell culture medium}, journal={Science advances}, year={2019}, month={Jan}, day={02}, publisher={American Association for the Advancement of Science}, volume={5}, number={1}, pages={eaau7314-eaau7314}, keywords={Arginine/metabolism; Argininosuccinate Lyase/metabolism; Cell Line, Tumor; Cell Proliferation/drug effects; *Culture Media; Female; Ferroptosis/drug effects; Humans; Hypoxia-Inducible Factor 1, alpha Subunit/metabolism; Lipid Peroxidation/drug effects; *Models, Biological; Pyruvic Acid/metabolism; Sodium Selenite/pharmacology; Spheroids, Cellular/metabolism; Triple Negative Breast Neoplasms/*metabolism/*pathology; Tumor Microenvironment/*physiology; Urea/metabolism}, abstract={Currently available cell culture media may not reproduce the in vivo metabolic environment of tumors. To demonstrate this, we compared the effects of a new physiological medium, Plasmax, with commercial media. We prove that the disproportionate nutrient composition of commercial media imposes metabolic artifacts on cancer cells. Their supraphysiological concentrations of pyruvate stabilize hypoxia-inducible factor 1$\alpha$ in normoxia, thereby inducing a pseudohypoxic transcriptional program. In addition, their arginine concentrations reverse the urea cycle reaction catalyzed by argininosuccinate lyase, an effect not observed in vivo, and prevented by Plasmax in vitro. The capacity of cancer cells to form colonies in commercial media was impaired by lipid peroxidation and ferroptosis and was rescued by selenium present in Plasmax. Last, an untargeted metabolic comparison revealed that breast cancer spheroids grown in Plasmax approximate the metabolic profile of mammary tumors better. In conclusion, a physiological medium improves the metabolic fidelity and biological relevance of in vitro cancer models.}, note={30613774[pmid]}, note={PMC6314821[pmcid]}, note={aau7314[PII]}, issn={2375-2548}, doi={10.1126/sciadv.aau7314}, url={https://pubmed.ncbi.nlm.nih.gov/30613774}, url={https://doi.org/10.1126/sciadv.aau7314}, language={eng} }

@Article{decoupleR, author = {Pau Badia-i-Mompel and Jesús Vélez Santiago and Jana Braunger and Celina Geiss and Daniel Dimitrov and Sophia Müller-Dott and Petr Taus and Aurelien Dugourd and Christian H. Holland and Ricardo O. Ramirez Flores and Julio Saez-Rodriguez}, title = {decoupleR: ensemble of computational methods to infer biological activities from omics data}, journal = {Bioinformatics Advances}, year = {2022}, doi = {https://doi.org/10.1093/bioadv/vbac016}, }

@article{10.1158/0008-5472.CAN-17-1679, author = {Garcia-Alonso, Luz and Iorio, Francesco and Matchan, Angela and Fonseca, Nuno and Jaaks, Patricia and Peat, Gareth and Pignatelli, Miguel and Falcone, Fiammetta and Benes, Cyril H. and Dunham, Ian and Bignell, Graham and McDade, Simon S. and Garnett, Mathew J. and Saez-Rodriguez, Julio}, title = "{Transcription Factor Activities Enhance Markers of Drug Sensitivity in Cancer}", journal = {Cancer Research}, volume = {78}, number = {3}, pages = {769-780}, year = {2018}, month = {01}, abstract = "{Transcriptional dysregulation induced by aberrant transcription factors (TF) is a key feature of cancer, but its global influence on drug sensitivity has not been examined. Here, we infer the transcriptional activity of 127 TFs through analysis of RNA-seq gene expression data newly generated for 448 cancer cell lines, combined with publicly available datasets to survey a total of 1,056 cancer cell lines and 9,250 primary tumors. Predicted TF activities are supported by their agreement with independent shRNA essentiality profiles and homozygous gene deletions, and recapitulate mutant-specific mechanisms of transcriptional dysregulation in cancer. By analyzing cell line responses to 265 compounds, we uncovered numerous TFs whose activity interacts with anticancer drugs. Importantly, combining existing pharmacogenomic markers with TF activities often improves the stratification of cell lines in response to drug treatment. Our results, which can be queried freely at dorothea.opentargets.io, offer a broad foundation for discovering opportunities to refine personalized cancer therapies.Significance: Systematic analysis of transcriptional dysregulation in cancer cell lines and patient tumor specimens offers a publicly searchable foundation to discover new opportunities to refine personalized cancer therapies. Cancer Res; 78(3); 769–80. ©2017 AACR.}", issn = {0008-5472}, doi = {10.1158/0008-5472.CAN-17-1679}, url = {https://doi.org/10.1158/0008-5472.CAN-17-1679}, eprint = {https://aacrjournals.org/cancerres/article-pdf/78/3/769/2777418/769.pdf}, }

@article{Blondel_2008, doi = {10.1088/1742-5468/2008/10/p10008}, url = {https://doi.org/10.1088/1742-5468/2008/10/p10008}, year = 2008, month = {oct}, publisher = {{IOP} Publishing}, volume = {2008}, number = {10}, pages = {P10008}, author = {Vincent D Blondel and Jean-Loup Guillaume and Renaud Lambiotte and Etienne Lefebvre}, title = {Fast unfolding of communities in large networks}, journal = {Journal of Statistical Mechanics: Theory and Experiment}, abstract = {We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2 million customers and by analysing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad hoc modular networks.} }

@misc {PPR:PPR13396, Title = {ShinyGO: a graphical enrichment tool for ani-mals and plants}, Author = {Ge, Steven Xijin and Jung, Dongmin}, DOI = {10.1101/315150}, Publisher = {bioRxiv}, Year = {2018}, URL = {https://doi.org/10.1101/315150}, }

@article{10.1093/bioinformatics/btx364, author = {Conway, Jake R and Lex, Alexander and Gehlenborg, Nils}, title = "{UpSetR: an R package for the visualization of intersecting sets and their properties}", journal = {Bioinformatics}, volume = {33}, number = {18}, pages = {2938-2940}, year = {2017}, month = {06}, abstract = "{Venn and Euler diagrams are a popular yet inadequate solution for quantitative visualization of set intersections. A scalable alternative to Venn and Euler diagrams for visualizing intersecting sets and their properties is needed.We developed UpSetR, an open source R package that employs a scalable matrix-based visualization to show intersections of sets, their size, and other properties.UpSetR is available at https://github.com/hms-dbmi/UpSetR/ and released under the MIT License. A Shiny app is available at https://gehlenborglab.shinyapps.io/upsetr/.Supplementary data are available at Bioinformatics online.}", issn = {1367-4803}, doi = {10.1093/bioinformatics/btx364}, url = {https://doi.org/10.1093/bioinformatics/btx364}, eprint = {https://academic.oup.com/bioinformatics/article-pdf/33/18/2938/25164302/btx364.pdf}, }

@ARTICLE{Jeter2015-ln, title = "Concise Review: {NANOG} in Cancer Stem Cells and Tumor Development: An Update and Outstanding Questions", author = "Jeter, Collene R and Yang, Tao and Wang, Junchen and Chao, Hsueh-Ping and Tang, Dean G", abstract = "The homeobox domain transcription factor NANOG, a key regulator of embryonic development and cellular reprogramming, has been reported to be broadly expressed in human cancers. Functional studies have provided strong evidence that NANOG possesses protumorigenic attributes. In addition to promoting self-renewal and long-term proliferative potential of stem-like cancer cells, NANOG-mediated oncogenic reprogramming may underlie clinical manifestations of malignant disease. In this review, we examine the molecular origin, expression, biological activities, and mechanisms of action of NANOG in various malignancies. We also consider clinical implications such as correlations between NANOG expression and cancer prognosis and/or response to therapy. We surmise that NANOG potentiates the molecular circuitry of tumorigenesis, and thus may represent a novel therapeutic target or biomarker for the diagnosis, prognosis, and treatment outcome of cancer. Finally, we present critical pending questions relating NANOG to cancer stem cells and tumor development.", journal = "Stem Cells", volume = 33, number = 8, pages = "2381--2390", month = may, year = 2015, address = "England", keywords = "Cancer stem cells; NANOG; Self-renewal; Tumor development", language = "en" }

@article{Maeda2006-rt, author = {Maeda, Yutaka and Hwang-Verslues, Wendy W. and Wei, Gang and Fukazawa, Takuya and Durbin, Mary L. and Owen, Laurie B. and Liu, Xuan and Sladek, Frances M.}, title = "{Tumour suppressor p53 down-regulates the expression of the human hepatocyte nuclear factor 4α (HNF4α) gene}", journal = {Biochemical Journal}, volume = {400}, number = {2}, pages = {303-313}, year = {2006}, month = {11}, abstract = "{The liver is exposed to a wide variety of toxic agents, many of which damage DNA and result in increased levels of the tumour suppressor protein p53. We have previously shown that p53 inhibits the transactivation function of HNF (hepatocyte nuclear factor) 4α1, a nuclear receptor known to be critical for early development and liver differentiation. In the present study we demonstrate that p53 also down-regulates expression of the human HNF4α gene via the proximal P1 promoter. Overexpression of wild-type p53 down-regulated endogenous levels of both HNF4α protein and mRNA in Hep3B cells. This decrease was also observed when HepG2 cells were exposed to UV irradiation or doxorubicin, both of which increased endogenous p53 protein levels. Ectopically expressed p53, but not a mutant p53 defective in DNA binding (R249S), down-regulated HNF4α P1 promoter activity. Chromatin immunoprecipitation also showed that endogenous p53 bound the HNF4α P1 promoter in vivo after doxorubicin treatment. The mechanism by which p53 down-regulates the P1 promoter appears to be multifaceted. The down-regulation was partially recovered by inhibition of HDAC activity and appears to involve the positive regulator HNF6α. p53 bound HNF6α in vivo and in vitro and prevented HNF6α from binding DNA in vitro. p53 also repressed stimulation of the P1 promoter by HNF6α in vivo. However, since the R249S p53 mutant also bound HNF6α, binding HNF6α is apparently not sufficient for the repression. Implications of the p53-mediated repression of HNF4α expression in response to cellular stress are discussed.}", issn = {0264-6021}, doi = {10.1042/BJ20060614}, url = {https://doi.org/10.1042/BJ20060614}, eprint = {https://portlandpress.com/biochemj/article-pdf/400/2/303/645065/bj4000303.pdf}, }

@Article{Lv2021, author={Lv, Duo-Duo and Zhou, Ling-Yun and Tang, Hong}, title={Hepatocyte nuclear factor 4$\alpha$ and cancer-related cell signaling pathways: a promising insight into cancer treatment}, journal={Experimental {&} Molecular Medicine}, year={2021}, month={Jan}, day={01}, volume={53}, number={1}, pages={8-18}, abstract={Hepatocyte nuclear factor 4$\alpha$ (HNF4$\alpha$), a member of the nuclear receptor superfamily, is described as a protein that binds to the promoters of specific genes. It controls the expression of functional genes and is also involved in the regulation of numerous cellular processes. A large number of studies have demonstrated that HNF4$\alpha$ is involved in many human malignancies. Abnormal expression of HNF4$\alpha$ is emerging as a critical factor in cancer cell proliferation, apoptosis, invasion, dedifferentiation, and metastasis. In this review, we present emerging insights into the roles of HNF4$\alpha$ in the occurrence, progression, and treatment of cancer; reveal various mechanisms of HNF4$\alpha$ in cancer (e.g., the Wnt/$\beta$-catenin, nuclear factor-$\kappa$B, signal transducer and activator of transcription 3, and transforming growth factor $\beta$ signaling pathways); and highlight potential clinical uses of HNF4$\alpha$ as a biomarker and therapeutic target for cancer.}, issn={2092-6413}, doi={10.1038/s12276-020-00551-1}, url={https://doi.org/10.1038/s12276-020-00551-1} }

@article{ Zhang2011-ig, author = {Xiao-Peng Zhang and Feng Liu and Wei Wang }, title = {Two-phase dynamics of p53 in the DNA damage response}, journal = {Proceedings of the National Academy of Sciences}, volume = {108}, number = {22}, pages = {8990-8995}, year = {2011}, doi = {10.1073/pnas.1100600108}, URL = {https://www.pnas.org/doi/abs/10.1073/pnas.1100600108}, eprint = {https://www.pnas.org/doi/pdf/10.1073/pnas.1100600108}, abstract = {The tumor suppressor p53 mainly induces cell cycle arrest/DNA repair or apoptosis in the DNA damage response. How to choose between these two outcomes is not fully understood. We proposed a four-module model of the p53 signaling network and associated the network dynamics with cellular outcomes after ionizing radiation. We found that the cellular response is mediated by both the level and posttranslational modifications of p53 and that p53 is activated in a progressive manner. First, p53 is partially activated by primary modifications such as phosphorylation at Ser-15/20 to induce cell cycle arrest, with its level varying in a series of pulses. If the damage cannot be fixed after a critical number of p53 pulses, then p53 is fully activated by further modifications such as phosphorylation at Ser-46 to trigger apoptosis, with its concentration switching to rather high levels. Thus, p53 undergoes a two-phase response in irreparably damaged cells. Such combinations of pulsatile and switch-like behaviors of p53 may represent a flexible and efficient control mode, avoiding the premature apoptosis and promoting the execution of apoptosis. In our model, p53 pulses are recurrently driven by ataxia telangiectasia mutated (ATM) pulses triggered by DNA damage. The p53-Mdm2 and ATM-p53-Wip1 negative feedback loops are responsible for p53 pulses, whereas the switching behavior occurs when the p53-PTEN-Akt-Mdm2 positive feedback loop becomes dominant. Our results suggest that a sequential predominance of distinct feedback loops may elicit multiple-phase dynamical behaviors. This work provides a new mechanism for p53 dynamics and cell fate decision.}}

@article{Gupta2020-kf, title = {Towards DNA-damage induced autophagy: A Boolean model of p53-induced cell fate mechanisms}, journal = {DNA Repair}, volume = {96}, pages = {102971}, year = {2020}, issn = {1568-7864}, doi = {https://doi.org/10.1016/j.dnarep.2020.102971}, url = {https://www.sciencedirect.com/science/article/pii/S1568786420302202}, author = {Shantanu Gupta and Daner A. Silveira and José Carlos M. Mombach}, keywords = {DNA damage, p53, miR-16, Boolean model, G1/S Checkpoint, Autophagy, U87}, abstract = {How a cell determines a given phenotype upon damaged DNA is an open problem. Cell fate decisions happen at cell cycle checkpoints and it is becoming clearer that the p53 pathway is a major regulator of cell fate decisions involving apoptosis or senescence upon DNA damage, especially at G1/S. However, recent results suggest that this pathway is also involved in autophagy induction upon DNA damage. To our knowledge, in this work we propose the first model of the DNA damage-induced G1/S checkpoint contemplating the decision between three phenotypes: apoptosis, senescence, and autophagy. The Boolean model is proposed based on experiments with U87 glioblastoma cells using the transfection of miR-16 that can induce a DNA damage response. The wild-type case of the model shows that DNA damage induces the checkpoint and the coexistence of the three phenotypes (tristable dynamics), each with a different probability. We also predict that the positive feedback involving ATM, miR-16, and Wip1 has an influence on the tristable state. The model predictions were compared to experiments of gain and loss of function in other three different cell lines (MCF-7, A549, and U2OS) presenting agreement. For p53-deficient cell lines such as HeLa, H1299, and PC-3, our model contemplates the experimental observation that the alternative AMPK pathway can compensate this deficiency. We conclude that at the G1/S checkpoint the p53 pathway (or, in its absence, the AMPK pathway) can regulate the induction of different phenotypes in a stochastic manner in the U87 cell line and others.} }

@Article{Mermel2011, author={Mermel, Craig H. and Schumacher, Steven E. and Hill, Barbara and Meyerson, Matthew L. and Beroukhim, Rameen and Getz, Gad}, title={GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers}, journal={Genome Biology}, year={2011}, month={Apr}, day={28}, volume={12}, number={4}, pages={R41}, abstract={We describe methods with enhanced power and specificity to identify genes targeted by somatic copy-number alterations (SCNAs) that drive cancer growth. By separating SCNA profiles into underlying arm-level and focal alterations, we improve the estimation of background rates for each category. We additionally describe a probabilistic method for defining the boundaries of selected-for SCNA regions with user-defined confidence. Here we detail this revised computational approach, GISTIC2.0, and validate its performance in real and simulated datasets.}, issn={1474-760X}, doi={10.1186/gb-2011-12-4-r41}, url={https://doi.org/10.1186/gb-2011-12-4-r41} }

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Bib CSL mapping

Clone this wiki locally