diff --git a/content/2.cc4k/1.index.md b/content/2.cc4k/1.index.md
index 140d1da..8350290 100644
--- a/content/2.cc4k/1.index.md
+++ b/content/2.cc4k/1.index.md
@@ -4,6 +4,112 @@ navigation:
title: Overview
---
+CC4K (Cancer Classifications for Kids) provides a harmonized, computable pediatric cancer classification framework used across [St. Jude Cloud](https://stjude.cloud) and by the [CCDI Federated API](https://github.com/CBIIT/ccdi-federation-api).
+
+## v0.5
+
+Submitted: March 11, 2026
+
+Click [here](/files/cc4k/Official_CC4K_v0.5.xlsx){target="_blank"} to download.
+
+Version 0.5 delivers a major revision to the solid tumor tree, replacing the prior bespoke tissue-of-origin model with a pediatric-specific structure grounded in the WHO 5th Edition Classification of Tumours: Paediatric Tumours (WHO pediatric book).
+For an informative summary of the WHO pediatric book, see Pfister et al.1.
+
+By organizing the updated tree around the WHO pediatric book, the hierarchy reflects a pediatric-focused framework and improves consistency for pediatric entities that are often scattered across adult-oriented organ-system classifications.
+Outside of CNS and hematological malignancies, the WHO organizes non-CNS solid tumor diagnoses into multiple tissue-specific blue books.
+Directly translating this structure into previous versions of CC4K resulted in duplicated pediatric diagnoses appearing under multiple anatomic contexts, with examples shown below in Table 1.
+Using the pediatric book’s organization as the basis for our tree structure in this update resolves these duplication issues by providing a single pediatric-oriented context or by explicitly omitting entities not yet defined for pediatric disease, while preserving extensibility for future updates.
+
+| **Tumor Classification Terms** | **Number of WHO Books Referenced** | **Number of References in Pediatric Book** |
+|:----------------------------------:|:----------------------------------:|:------------------------------------------:|
+| Ewing Sarcoma | 7 | 2* |
+| Inflammatory Myofibroblastic Tumor | 7 | 1 |
+| Adenoid Cystic Carcinoma | 7 | 0 |
+| Angiosarcoma | 10 | 2* |
+| Tubular Adenoma | 4 | 0 |
+| Rhabdoid Tumor | 2 | 2* |
+
+**Table 1:** Redundant terms across WHO Books
+
+*Includes references such as renal Ewing Sarcoma, Hepatic Angiosarcoma, and Atypical Teratoid/Rhabdoid Tumor.
+Histogenic consistency was enforced across the tree, with Angiosarcoma aligned to Vascular Tumors, Inflammatory Myofibroblastic Tumor to Fibroblastic/Myofibroblastic Tumors, and Adenoid Cystic Carcinoma placed under Head and Neck Tumors, while anatomic site variation is captured through metadata attributes to deduplicate Ewing Sarcoma and Angiosarcoma.
+Rhabdoid Tumor was elevated to its own entity, described below.
+
+### Comprehensive Updates
+
+The prior CC4K solid tumor hierarchy contained 256 total nodes (236 leaf nodes across 21 parent nodes) and was organized using a tissue-of-origin framework, initially aligned to OncoTree, but over-time, developing more into a bespoke framework.
+In contrast, the updated v0.5 hierarchy contains 218 total nodes with 187 leaf nodes (Δ = −49) distributed across 31 parent nodes.
+Tree depth remains four layers; however, the organization has shifted from site-centric to aligning with 11 WHO pediatric book chapters, introducing targeted subdivision where necessary, as described below.
+Eight chapters were mapped directly (column A), while the structure for three chapters was intentionally modified (column B).
+Using this updated hierarchy, approximately 9,474 solid tumor samples were reclassified.
+
+| | **A** | **B** | **C** | **D** | **E** |
+|:------:|:---------------------------------------------------:|:-----------------------:|:------------------:|:------------------------------:|:------------------:|
+| | **WHO Chapters aligned (Under the Pediatric Book)** | **Subchapter detail** | **Levels (depth)** | **CC4K (under Solid Tumor)** | **Levels (depth)** |
+| **1** | Endocrine Tumors | | 3 | Endocrine Tumors | 2 |
+| **2** | Peripheral Neuroblastic Tumors | | 3 | Peripheral Neuroblastic Tumors | 1 |
+| **3** | Soft Tissue and Bone Tumors | | 3 | | |
+| | | Soft Tissue Sarcomas | 2 | Soft Tissue Tumors | 2 |
+| | | Bone Tumors | 2 | Bone Tumors | 1 |
+| | | Undifferentiated Tumors | 2 | Undifferentiated Tumors | 1 |
+| **4** | Germ Cell Tumors | | 3 | Germ Cell Tumors | 1 |
+| **5** | Head and Neck Tumors | | 3 | Head and Neck Tumors | 1 |
+| **6** | Skin Tumors | | 3 | Skin Tumors | 1 |
+| **7** | Eye Tumors | | 3 | Eye Tumors | 1 |
+| **8** | Thoracic Tumors | | 3 | Thoracic Tumors | 1 |
+| **9** | Digestive System Tumors | | 3 | Digestive System Tumors | 2 |
+| **10** | Renal and Male Genital Tumors | | 3 | | |
+| | | Renal | 2 | Renal Tumors | 1 |
+| | | Male Genital | 2 | Genital Tumors | 1 |
+| **11** | Female Genital Tumors | | 3 | Genital Tumors | 1 |
+
+**Table 2:** Mapping of Pediatric WHO chapters to CC4K solid tumor domains
+
+For example, we split the WHO chapters “Soft Tissue and Bone Tumors” and “Renal and Male Genital Tumors” because these chapters function primarily as aggregations of distinct groups.
+In the Soft Tissue and Bone chapter, tumors are subdivided into soft tissue, bone, and undifferentiated subchapters, each of which is organized according to different underlying principles.
+In this update, we elevated these subchapters to top-level nodes rather than retaining the aggregate chapter as a single branch, as seen in Table 2, column D.
+
+This change also enabled alignment with contemporary guidance to organize tumors by histogenetic lineage rather than by prior distinctions such as rhabdomyosarcoma versus non-rhabdomyosarcoma.
+While the broad distinction is useful to categorize samples, this is not useful to delineate as a framework for a sample classification tree.
+Bone and undifferentiated tumors are now represented as flat lists of leaf nodes to simplify handling of singleton entities and to avoid unnecessary nesting of diagnoses such as Ewing sarcoma and osteosarcoma.
+Notably, rhabdoid tumors were extracted as a distinct entity under Soft Tissue Tumors.
+Although rhabdoid tumors arise across multiple tissues of origin, their shared biology supports representation as a single queryable node, reducing redundancy across child leaves.
+
+Similarly, the Renal and Male Genital Tumors chapter was restructured because many male genital tumor entities overlap with those in the female genital book.
+These were consolidated under a unified Genital Tumors node, with patient sex retained as an attribute if needed to stratify.
+
+### Challenges
+
+Certain pediatric tumor entities span multiple anatomic sites, histogenic lineages, or fall outside the current scope of the WHO pediatric book.
+In CC4K v0.5, tubular adenoma, observed in the colon, gastrointestinal tract, and breast, was assigned a single canonical placement under Digestive System Tumors → Gastrointestinal → Tubular Adenoma.
+Breast cases (n=1) are classified as Adenoma, NOS with anatomic site captured via metadata, preventing duplication while preserving query flexibility. Canonical placements are periodically re-evaluated as sample volume and distribution evolve.
+
+In total, 43 child leaf nodes in v0.5 fall outside the pediatric volume but were retained by evaluating where they might exist in a non-pediatric book.
+For example, adrenal cortical adenoma and carcinoma are not explicitly defined in the WHO pediatric book but appear across other WHO books which now belong under Endocrine Tumors → Adrenal Tumors.
+To see which nodes exist in non-pediatric books, we annotated this in column I in our downloadable tree for transparency.
+
+To address over-fragmentation, five low-frequency NEC nodes were consolidated into existing NOS categories.
+This decision was informed by experience with NEC/NOS usage in the CNS tumor tree and by review of WHO solid tumor classifications, which are largely not molecularly driven and do not consistently support NEC distinctions.
+Given the limited sample counts and lack of defining molecular features, maintaining separate NEC nodes was not justified. These categories were therefore consolidated, pending future evidence or novel biomarkers.
+
+Boundary conditions between the solid tumor (ST) and CNS trees were also clarified for germ cell tumors (GCTs).
+Tumors explicitly defined as brain-specific germ cell entities are now placed under the CNS tree (e.g., Germ Cell Tumor, Brain).
+In contrast, mixed germ cell tumors, yolk sac tumors, and germinomas with brain as the anatomic site remain under the solid tumor GCT branch.
+This distinction reflects differences between WHO CNS5, which includes a single unstratified “germ cell tumor, brain” node, and the WHO pediatric book, which defines germ cell tumors as a universal chapter with resolved subtypes.
+CC4K v0.5 integrates both frameworks to maintain consistency with each specific case.
+
+Finally, the CNS tumor tree was refined to reduce overlapping vocabulary across roots.
+This included adding explicit CNS prefixes to CNS terms that would otherwise overlap with solid tumor terms (e.g., CNS Soft Tissue Tumors, CNS Tumors of Uncertain Differentiation, CNS CIC-rearranged Sarcoma), as well as identification of CNS Histiocytic Tumors and CNS Vascular Tumors as distinct domains.
+These changes eliminate ambiguity while preserving the historical and commonly used distinction between CNS and non-CNS solid tumors.
+
+### Future Updates
+
+Next, we will focus on the remaining branches under Heme (e.g., Hodgkin and Non-Hodgkin Lymphoma and Other Heme Malignancies).
+
+### References
+
+1. Pfister, S. M. et al. A Summary of the Inaugural WHO Classification of Pediatric Tumors: Transitioning from the Optical into the Molecular Era. Cancer Discov. 12, 331–355 (2022). [https://doi.org/10.1158/2159-8290.CD-21-1094](https://doi.org/10.1158/2159-8290.CD-21-1094)
+
## v0.4
Submitted: September 3, 2025
diff --git a/content/3.genomics-platform/1.getting-started/2.accounts-and-billing.md b/content/3.genomics-platform/1.getting-started/2.accounts-and-billing.md
index 3bf5ebb..cea1f33 100644
--- a/content/3.genomics-platform/1.getting-started/2.accounts-and-billing.md
+++ b/content/3.genomics-platform/1.getting-started/2.accounts-and-billing.md
@@ -28,7 +28,7 @@ Once you have created an account through St. Jude Cloud your DNAnexus account an
If you are a St. Jude employee, go to the [Bioinformatics Self Service page](https://sjcrh.sharepoint.com/sites/computational-biology/SitePages/Bioinformatics-Self-Service-on-St.-Jude-Cloud.aspx) on the Intranet for information on how to set up your billing account.
If you are not a St. Jude employee, you will need to contact DNAnexus directly to setup billing.
-To complete DNAnexus billing account setup, email DNAnexus support [support@dnanexus.com](support@dnanexus.com).
+To complete DNAnexus billing account setup, email DNAnexus support ().
DNAnexus Support will reach out directly once the email is received.
Use the template below, adding in the required information to request billing assistance.
Your DNAnexus account ID and email can be found listed on your User Account page on DNAnexus.
diff --git a/content/3.genomics-platform/2.about-our-data/1.data-sets-and-data-access-units.md b/content/3.genomics-platform/2.about-our-data/1.data-sets-and-data-access-units.md
index 5d02540..1eafd49 100644
--- a/content/3.genomics-platform/2.about-our-data/1.data-sets-and-data-access-units.md
+++ b/content/3.genomics-platform/2.about-our-data/1.data-sets-and-data-access-units.md
@@ -2,20 +2,34 @@
title: Data Sets and Data Access Units
---
-## Data Set
+## Table of Contents
+
+- [Key Concepts](#key-concepts)
+ - [Data Set](#data-set)
+ - [Data Access Unit (DAU)](#data-access-unit)
+ - [Data Access Committee (DAC)](#data-access-committee)
+ - [Embargo Date](#embargo-date)
+- [List of DAUs](#list-of-daus)
+- [List of Data Sets](#list-of-data-sets)
+
+---
+
+## Key Concepts
+
+### Data Set
A St. Jude Cloud Data Set is a grouping of data which has been curated by St. Jude and can correspond to a study, project, or specific disease.
They are available for free to researchers and access to a Data Set can be requested from the data browser.
However, access is not granted at the Data Set level, but rather the Data Access Unit.
-A single Data Set may belong to only one DAU, or it could belong in multiple if it contains data came from different groups.
+A single Data Set may belong to only one DAU, or it could belong in multiple if it contains data that came from different groups.
An approved Data Access Request grants access to a particular Data Access Unit which includes specific Data Sets that can be selected from the Data Browser.
An approved DAR would give access not only to the data selected at the time, but also any additional data that is included in the DAUs which were approved.
Only the data initially selected will be vended to a project folder upon approval but returning to the data browser and selecting additional data which falls under the approved DAUs will not require another Data Access Request.
-See [the list of Data Sets.](#list-of-data-sets)
+See [the list of Data Sets.](#list-of-data-sets)
-## Data Access Unit
+### Data Access Unit (DAU)
A St. Jude Cloud **Data Access Unit (DAU)** is a grouping of data that typically corresponds to a project, study, or Data Set generated at the same time at the same institution.
Each DAU has its own governing body of researchers, the [Data Access Committee](#data-access-committee), who preside over the data and who may grant or deny access.
@@ -27,7 +41,7 @@ For a more detailed description please see the respective [Schedule 1(s)](/overv
See [the list of Data Access Units.](#list-of-daus)
-## Data Access Committee
+### Data Access Committee (DAC)
A St. Jude Cloud **Data Access Committee (DAC)** is a group of St. Jude researchers who oversee access to a particular [Data Access Unit (DAU)](#data-access-unit) and evaluate incoming data requests.
@@ -42,19 +56,31 @@ Once you place a request, your application will be routed to the corresponding t
Since each DAC is made up of different individuals using different criteria for evaluation, you may or may not be approved for access to all of the files.
::
-## Embargo Date
+### Embargo Date
The **Embargo Date** specifies the date that a publishing embargo on the file in question has been lifted.
Publishing using any of the files _before_ the embargo date has passed is strictly prohibited as outlined in section 1.15 of the [Data Access Agreement (DAA)](/genomics-platform/getting-started/making-a-data-request).
Some Data, including Data funded by the NIH, are not subject to embargo.
Applicable Embargo Dates can be found in [Genomics Platform Metadata](https://platform.stjude.cloud/api/v1/manifest.tsv){target="_blank"} in the `SJ_Embargo_Date` column.
+---
+
## List of DAUs
-We currently have the six [Data Access Units (DAU)](#data-access-unit) listed below.
+We currently have the seven [Data Access Units (DAU)](#data-access-unit) listed below.
Basic clinical data is available for relevant subjects in each DAU.
Click on the name below to navigate directly to that DAU's Study page for more detailed information.
-The [Data Sets](#data-set) included in each DAU are listed below, note that some Data Sets are a part of multiple DAU's.
+The [Data Sets](#data-set) included in each DAU are listed below; note that some Data Sets are a part of multiple DAUs.
+
+| DAU | Focus | Data Type |
+|---------------------------------------------------------------------------------------------------------|--------------------------------------------------|---------------------------------------|
+| [CCSS](#childhood-cancer-survivor-study-ccss) | Long-term outcomes in childhood cancer survivors | Germline WGS |
+| [Clinical Genomics](#clinical-genomics) | Variants influencing childhood tumor development | Paired tumor-normal WGS, WES, RNA-Seq |
+| [CReATe](#clinical-research-in-als-and-related-disorders-for-therapeutic-development-consortium-create) | ALS and related disorders | WGS |
+| [PanALL](#pan-acute-lymphoblastic-leukemia-panall) | ALL subtypes across the age continuum | Tumor-only RNA-Seq |
+| [PCGP](#pediatric-cancer-genome-project-pcgp) | Genetic origins of pediatric cancer | Paired tumor-normal WGS, WES, RNA-Seq |
+| [SGP](#sickle-cell-genome-project-sgp) | Genetic modifiers in Sickle Cell Disease | Germline WGS |
+| [SJLIFE](#st-jude-life-sjlife) | Long-term adverse outcomes of cancer therapy | Germline WGS, WES |
### Childhood Cancer Survivor Study (CCSS)
@@ -73,6 +99,8 @@ The following data set(s) are included within Clinical Genomics:
- [Clinical Pilot](#clinical-pilot)
- [Genome 4 Kids (G4K)](#genome-4-kids)
- [Real-Time Clinical Genomics (RTCG)](#real-time-clinical-genomics)
+- [Genomics and Transcriptomics of Relapsed Pediatric AML (RPAML)](#genomics-and-transcriptomics-of-relapsed-pediatric-aml)
+- [Pediatric Brain Tumor Program (PBTP)](#pediatric-brain-tumor-program)
### Clinical Research in ALS and Related Disorders for Therapeutic Development Consortium (CReATe)
@@ -99,6 +127,15 @@ The following data set(s) are included within PCGP:
- [Pan-Acute Lymphoblastic Leukemia (PanALL)](#pan-acute-lymphoblastic-leukemia)
- [Pediatric Cancer Genome Project (PCGP)](#pediatric-cancer-genome-project)
- [Pediatric therapy-related Myeloid Neoplasms (tMN)](#pediatric-therapy-related-myeloid-neoplasms)
+- [Genomics and Transcriptomics of Relapsed Pediatric AML (RPAML)](#genomics-and-transcriptomics-of-relapsed-pediatric-aml)
+- [Pediatric Brain Tumor Program (PBTP)](#pediatric-brain-tumor-program)
+- [Landscape of Pediatric Acute Myeloid Leukemia (PanpAML)](#landscape-of-pediatric-acute-myeloid-leukemia)
+- [DMG-H3K27a Clonal Evolution (H3K27A_EVOLUTION)](#dmg-h3k27a-clonal-evolution)
+- [Atypical Teratoid/Rhabdoid Tumor-derived Tumoroid Models (ATRT_TM)](#atypical-teratoidrhabdoid-tumor-derived-tumoroid-models)
+- [Pediatric Acute Myeloid Leukemia (PedAML)](#pediatric-acute-myeloid-leukemia)
+- [Medulloblastoma Preclinical Ribociclib and Gemcitabine (MBPRG)](#medulloblastoma-preclinical-ribociclib-and-gemcitabine)
+- [Medulloblastoma Preclinical Ribociclib and Paxalisib (MBPRP)](#medulloblastoma-preclinical-ribociclib-and-paxalisib)
+- [St. Jude Life Clonal Hematopoiesis (SJLIFE_ClonalHematopoiesis)](#st-jude-life-clonal-hematopoiesis)
### Sickle Cell Genome Project (SGP)
@@ -114,14 +151,47 @@ The following data set(s) are included within SJLIFE:
- [St. Jude Life (SJLIFE)](#st-jude-life)
+---
+
## List of Data Sets
-We currently have the ten [Data Sets](#data-set) listed below.
+We currently have 21 [Data Sets](#data-set) listed below.
Additional information can also be seen including which [Data Access Units (DAU)](#data-access-unit) the Data Set belongs to, tissue type, sequencing type, number of samples, additional links, and a brief description.
+| Data Set | DAU(s) | Tissue Type | Sequencing | Samples |
+|--------------------------------------------------------------------------------------------------|-------------------------|---------------------|--------------------------|--------:|
+| [ATRT_TM](#atypical-teratoidrhabdoid-tumor-derived-tumoroid-models) | PCGP | — | WES, WGS, RNA-Seq | 8 |
+| [CCSS](#childhood-cancer-survivor-study) | CCSS | Germline Only | WGS | 2,912 |
+| [Cicero Benchmark](#cicero-benchmark) | PCGP, Clinical Genomics | Paired Tumor-Normal | RNA-Seq | 124 |
+| [Clinical Pilot](#clinical-pilot) | PCGP, Clinical Genomics | Paired Tumor-Normal | WGS, WES, RNA-Seq | 155 |
+| [CReATe](#clinical-research-in-als-and-related-disorders-for-therapeutic-development-consortium) | CReATe | PBMC Germline DNA | WGS | 705 |
+| [CSTN](#childhood-solid-tumor-network) | PCGP, Clinical Genomics | Paired Tumor-Normal | WGS, WES, RNA-Seq | 143 |
+| [G4K](#genome-4-kids) | Clinical Genomics | Paired Tumor-Normal | WGS, WES, RNA-Seq | 571 |
+| [H3K27A_EVOLUTION](#dmg-h3k27a-clonal-evolution) | PCGP | — | — | 70 |
+| [MBPRG](#medulloblastoma-preclinical-ribociclib-and-gemcitabine) | PCGP | — | RNA-Seq | 70 |
+| [MBPRP](#medulloblastoma-preclinical-ribociclib-and-paxalisib) | PCGP | — | RNA-Seq | 39 |
+| [PanALL](#pan-acute-lymphoblastic-leukemia) | PanALL | Paired Tumor-Normal | RNA-Seq | 735 |
+| [PanpAML](#landscape-of-pediatric-acute-myeloid-leukemia) | PCGP | — | — | 272 |
+| [PBTP](#pediatric-brain-tumor-program) | Clinical Genomics, PCGP | — | WES, WGS, RNA-Seq | 97 |
+| [PCGP](#pediatric-cancer-genome-project) | PCGP | Paired Tumor-Normal | WGS, WES, RNA-Seq | 3,031 |
+| [PedAML](#pediatric-acute-myeloid-leukemia) | PCGP | — | WES, WGS, RNA-Seq | 275 |
+| [RPAML](#genomics-and-transcriptomics-of-relapsed-pediatric-aml) | Clinical Genomics, PCGP | — | — | — |
+| [RTCG](#real-time-clinical-genomics) | Clinical Genomics | Paired Tumor-Normal | WGS, WES, RNA-Seq | 2,371 |
+| [SGP](#sickle-cell-genome-project) | SGP | Germline Only | WGS | 807 |
+| [SJLIFE](#st-jude-life) | SJLIFE | Germline Only | WGS, WES | 4,838 |
+| [SJLIFE_ClonalHematopoiesis](#st-jude-life-clonal-hematopoiesis) | PCGP | — | SingleCell-WGS, Targeted | 3,192 |
+| [tMN](#pediatric-therapy-related-myeloid-neoplasms) | PCGP | Paired Tumor-Normal | WGS, WES, RNA-Seq | 206 |
+
+### Atypical Teratoid/Rhabdoid Tumor-derived Tumoroid Models
+
+**DAU**: PCGP | **Tissue Type**: — | **Sequencing Type**: WES, WGS, RNA-Seq | **Samples**: 8
+
+The ATRT-TM dataset comprises atypical teratoid/rhabdoid tumors (ATRT) of the Sonic hedgehog (ATRT-SHH) and Myc (ATRT-MYC) subgroups. ATRT-SHH and ATRT-MYC patient-derived orthotopic xenografts (PDOX) were used to generate pre-clinical in vitro tumoroid models. The key objective of this dataset is to validate the tumoroid models by comparing them to their parental PDOX at the molecular level, including gene alterations (whole genome/whole exome sequencing), gene expression (RNA-seq), and DNA methylation (Illumina EPIC array).
+The findings of the project were published in [Oncogene](https://doi.org/10.1038/s41388-023-02681-y).
+
### Childhood Cancer Survivor Study
-**DAU**: CCSS | **Tissue Type**: Germline Only | **Sequencing Type**: WGS | **Samples**: 2912 | **[Additional Information About CCSS](https://ccss.stjude.org/)**
+**DAU**: CCSS | **Tissue Type**: Germline Only | **Sequencing Type**: WGS | **Samples**: 2,912 | **[Additional Information About CCSS](https://ccss.stjude.org/)**
Childhood Cancer Survivor Study (CCSS) is a germline-only Data Set consisting of whole genome sequencing of childhood cancer survivors. CCSS is a multi-institutional, multi-disciplinary, NCI funded collaborative resource established to evaluate long-term outcomes among survivors of childhood cancer.
It is a retrospective cohort consisting of >24,000 five-year survivors of childhood cancer who were diagnosed between 1970-1999 at one of 31 participating centers in the U.S. and Canada.
@@ -150,32 +220,39 @@ With any questions on the nature or implications of this warning, please contact
### Childhood Solid Tumor Network
-**DAU**: PCGP and Clinical Genomics | **Tissue Type**: Paired Tumor-Normal | **Sequencing Type**: WGS, WES, RNA-Seq | **Samples**: 143 | **[Additional Information About CSTN](https://www.stjude.org/research/resources-data/childhood-solid-tumor-network.html)**
+**DAU**: PCGP, Clinical Genomics | **Tissue Type**: Paired Tumor-Normal | **Sequencing Type**: WGS, WES, RNA-Seq | **Samples**: 143 | **[Additional Information About CSTN](https://www.stjude.org/research/resources-data/childhood-solid-tumor-network.html)**
The Childhood Solid Tumor Network (CSTN) is a St. Jude Children's Research Hospital initiative to disseminate its childhood solid tumor resources and data.
The raw Data Sets from this initiative are made available via St. Jude Cloud.
### Cicero Benchmark
-**DAU**: PCGP and Clinical Genomics | **Tissue Type**: Paired Tumor-Normal | **Sequencing Type**: RNA-Seq | **Samples**: 124
+**DAU**: PCGP, Clinical Genomics | **Tissue Type**: Paired Tumor-Normal | **Sequencing Type**: RNA-Seq | **Samples**: 124
The CICERO Data Set contains the samples which were selected for use in the [CICERO Paper](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02043-x).
+### Clinical Pilot
+
+**DAU**: PCGP, Clinical Genomics | **Tissue Type**: Paired Tumor-Normal | **Sequencing Type**: WGS, WES, RNA-Seq | **Samples**: 155 | **[Additional Information About Clinical Genomics](https://www.stjude.cloud/studies/clinical-genomics)**
+
+The Clinical Pilot project was a retrospective study that evaluated the accuracy and demonstrated the feasibility of three-platform sequencing in a CAP/CLIA setting.
+The findings of this project were published in [Nature Communications](https://pubmed.ncbi.nlm.nih.gov/30262806/).
+
### Clinical Research in ALS and Related Disorders for Therapeutic Development Consortium
-**DAU**: CReATe | **Tissue Type**: PBMC germline DNA | **Sequencing Type**: WGS | **Samples**: 705
+**DAU**: CReATe | **Tissue Type**: PBMC Germline DNA | **Sequencing Type**: WGS | **Samples**: 705
The Phenotype-Genotype-Biomarker (PGB, or PGB1) study (NCT02327845) of the Clinical Research in ALS and Related Disorders for Therapeutic Development (CReATe) Consortium was a natural history and biomarker study of patients with amyotrophic lateral sclerosis (ALS) or a related disorder, including but not limited to ALS-frontotemporal dementia (ALS-FTD), progressive muscular atrophy (PMA), primary lateral sclerosis (PLS), hereditary spastic paraplegia (HSP), and multisystem proteinopathy (MSP).
In addition to patients enrolled in the PGB1 Cohort (primary participants), the study also enrolled family members for limited data collection (secondary participants).
This dataset includes WGS data from N=705 in PGB1, including N=472 ALS/ALS-FTD, N=20 PMA, N=47 PLS, N=162 HSP, and N=4 with other related disorders.
The findings of the project were published in [Translational Neurodegeneration](https://translationalneurodegeneration.biomedcentral.com/articles/10.1186/s40035-025-00516-2).
-### Clinical Pilot
+### DMG-H3K27a Clonal Evolution
-**DAU**: PCGP and Clinical Genomics | **Tissue Type**: Paired Tumor-Normal | **Sequencing Type**: WGS, WES, RNA-Seq | **Samples**: 155 | **[Additional Information About Clinical Genomics](https://www.stjude.cloud/studies/clinical-genomics)**
+**DAU**: PCGP | **Tissue Type**: — | **Sequencing Type**: — | **Samples**: 70
-The Clinical Pilot project was a retrospective study that evaluated the accuracy and demonstrated the feasibility of three-platform sequencing in a CAP/CLIA setting.
-The findings of this project were published in [Nature Communications](https://pubmed.ncbi.nlm.nih.gov/30262806/).
+The primary purpose of the DMG-H3K27a Clonal Evolution (H3K27A_EVOLUTION) project is to understand how clonal evolution contributes to tumor invasive spread. The study performed exome sequencing and SNP array profiling on 49 multi-region autopsy samples from 11 patients with pontine DMG-H3 K27-a enrolled in a phase I clinical trial of PDGFR inhibitor crenolanib. Additional objectives include deconvoluting subclonal composition and prevalence at each tumor region to study convergent evolution and invasion patterns.
+For more information see: [http://permalinks.stjude.cloud/permalinks/h3k27a_evolution](http://permalinks.stjude.cloud/permalinks/h3k27a_evolution)
### Genome 4 Kids
@@ -184,16 +261,55 @@ The findings of this project were published in [Nature Communications](https://p
The goal of the Genomes 4 Kids (G4K) prospective study was to determine whether the three-platform sequencing protocol laid out in the Clinical Pilot project could generate results on a clinical timeline in practice and to evaluate the prevalence of actionable findings.
The study concluded with just over 300 patients, and the publication is currently in review.
+### Genomics and Transcriptomics of Relapsed Pediatric AML
+
+**DAU**: Clinical Genomics, PCGP | **Tissue Type**: — | **Sequencing Type**: — | **Samples**: —
+
+The primary purpose of the Relapsed Pediatric AML Dataset (RPAML) is to identify the tumor-acquired (somatic) genome sequence and structural variants in pediatric AML at the time of disease relapse. Additional objectives include the acquisition and analysis of additional genomic data, including gene expression data, mutational signatures, and germline variants that may predispose to AML or other bone marrow disorders.
+The findings of the project were published in [Blood Cancer Discovery](https://doi.org/10.1158/2643-3230.BCD-21-0160).
+
+### Landscape of Pediatric Acute Myeloid Leukemia
+
+**DAU**: PCGP | **Tissue Type**: — | **Sequencing Type**: — | **Samples**: 272
+
+Recent studies on pediatric acute myeloid leukemia (pAML) have revealed pediatric-specific driver alterations, many of which are underrepresented in the current classification schemas. The PanpAML study systematically categorized 887 pAML cases into 23 mutually distinct molecular categories, including new major entities such as UBTF or BCL11B, covering 91.4% of the cohort. These molecular categories were associated with unique expression profiles and mutational patterns, and were strongly associated with clinical outcomes, leading to the establishment of a new prognostic framework for pAML based on updated molecular categories and minimal residual disease.
+The findings of the project were published in [Nature Genetics](https://doi.org/10.1038/s41588-023-01640-3).
+
+### Medulloblastoma Preclinical Ribociclib and Gemcitabine
+
+**DAU**: PCGP | **Tissue Type**: — | **Sequencing Type**: RNA-Seq | **Samples**: 70
+
+The MBPRG dataset comprises medulloblastoma group 3 (G3 MB) patient-derived orthotopic xenografts (PDOX) and mouse G3 MB tumor models. Both human (PDOX) and mouse tumor models were treated with either ribociclib (CDK4/6 inhibitor), gemcitabine (metabolic inhibitor of DNA synthesis), or the combination of these two drugs in comparison to control (vehicle). The key objective of this dataset is to evaluate the impact of this treatment and identify perturbation of gene expression/pathways at the transcriptional level in G3 MB.
+
+### Medulloblastoma Preclinical Ribociclib and Paxalisib
+
+**DAU**: PCGP | **Tissue Type**: — | **Sequencing Type**: RNA-Seq | **Samples**: 39
+
+The MBPRP dataset comprises medulloblastoma group 3 (G3 MB) and medulloblastoma Sonic hedgehog (SHH MB) patient-derived orthotopic xenografts (PDOX). These human tumor models were treated with either ribociclib (CDK4/6 inhibitor), paxalisib (PI3K/mTOR inhibitor), or the combination of these two drugs in comparison to control (vehicle). The key objective of this dataset is to validate the synergistic effect of the combination treatment observed in vitro, and evaluate the impact of these treatments on gene expression/pathways at the transcriptional level in MB.
+
### Pan-Acute Lymphoblastic Leukemia
**DAU**: PanALL | **Tissue Type**: Paired Tumor-Normal | **Sequencing Type**: RNA-Seq | **Samples**: 735
Pan-Acute Lymphoblastic Leukemia (PanALL) comprises cases of B-progenitor and T-lineage ALL encompassing the spectrum of ALL subtypes across the age continuum.
-Samples sequenced were obtained from multiple sites, centers and cooperative groups including St. Jude Children’s Research Hospital, The Children’s Oncology Group, The Alliance – Cancer and Leukemia Group B, the Eastern Cooperative Oncology Group, The Southwestern Oncology group, MD Anderson Cancer Center, City of Hope National Medical Center, Princess Margaret Cancer Center, Northern Italy Leukemia Group, and UKALL.
+Samples sequenced were obtained from multiple sites, centers and cooperative groups including St. Jude Children's Research Hospital, The Children's Oncology Group, The Alliance – Cancer and Leukemia Group B, the Eastern Cooperative Oncology Group, The Southwestern Oncology group, MD Anderson Cancer Center, City of Hope National Medical Center, Princess Margaret Cancer Center, Northern Italy Leukemia Group, and UKALL.
+
+### Pediatric Acute Myeloid Leukemia
+
+**DAU**: PCGP | **Tissue Type**: — | **Sequencing Type**: WES, WGS, RNA-Seq | **Samples**: 275
+
+The primary purpose of the Pediatric AML (PedAML) Data Set is to identify the genome sequence and structural variants that define the different molecular subtypes of pediatric AML (pAML). Additional objectives include, but are not limited to, the acquisition and analysis of additional genomic data, including gene expression data and patterns of mutational cooperativity.
+
+### Pediatric Brain Tumor Program
+
+**DAU**: Clinical Genomics, PCGP | **Tissue Type**: — | **Sequencing Type**: WES, WGS, RNA-Seq | **Samples**: 97
+
+The Pediatric Brain Tumor Portal (PBTP) is organized by the St. Jude Children's Research Hospital Neurobiology and Brain Tumor Program. Investigators have access to specialized resources, such as an integrated support structure for preclinical modeling, including patient-derived xenograft samples. The program consists of clinicians, radiation oncologists, neurobiologists, medicinal chemists, and other research faculty and staff. PBTP features molecular characterization for patient-derived orthotopic xenograft (PDOX) models of pediatric CNS tumors and reflects close to 10 years of effort to generate and extensively characterize in vivo models that faithfully recapitulate pediatric brain cancer diseases. The portal offers visualization tools that allow users to interrogate curated datasets and access models from our library of PDOX for functional studies of tumorigenesis or preclinical testing.
+The findings of the project were published in [Acta Neuropathol](https://doi.org/10.1007/s00401-020-02171-5).
### Pediatric Cancer Genome Project
-**DAU**: PCGP | **Tissue Type**: Paired Tumor-Normal | **Sequencing Type**: WGS, WES, RNA-Seq | **Samples**: 3031 | **[Additional Information About PCGP](https://stjude.cloud/studies/pediatric-cancer-genome-project)**
+**DAU**: PCGP | **Tissue Type**: Paired Tumor-Normal | **Sequencing Type**: WGS, WES, RNA-Seq | **Samples**: 3,031 | **[Additional Information About PCGP](https://stjude.cloud/studies/pediatric-cancer-genome-project)**
The Pediatric Cancer Genome Project (PCGP) is a collaboration between St. Jude Children's Research Hospital and the McDonnell Genome Institute at Washington University School of Medicine that sequenced the genomes of over 600 pediatric cancer patients.
@@ -216,12 +332,18 @@ Real-time Clinical Genomics (RTCG) is a [first of its kind initiative](https://w
**DAU**: SGP | **Tissue Type**: Germline Only | **Sequencing Type**: WGS | **Samples**: 807 | **[Additional Information About SGP](https://sickle-cell.stjude.cloud/)**
SGP is a germline-only Data Set of Sickle Cell Disease (SCD) patients from birth to young adulthood.
-The Sickle Cell Genome Project (SGP) is a collaboration between St. Jude Children’s Research Hospital and Baylor College of Medicine focused on identifying genetic modifiers that contribute to various health complications in SCD patients.
+The Sickle Cell Genome Project (SGP) is a collaboration between St. Jude Children's Research Hospital and Baylor College of Medicine focused on identifying genetic modifiers that contribute to various health complications in SCD patients.
Additional objectives include, but are not limited to, developing accurate methods to characterize germline structural variants in highly homologous globin locus and blood typing.
### St. Jude Life
-**DAU**: SJLIFE | **Tissue Type**: Germline Only | **Sequencing Type**: WGS, WES | **Samples**: 4838 | **[Additional Information About SJLIFE](https://sjlife.stjude.org/)**
+**DAU**: SJLIFE | **Tissue Type**: Germline Only | **Sequencing Type**: WGS, WES | **Samples**: 4,838 | **[Additional Information About SJLIFE](https://sjlife.stjude.org/)**
St. Jude Lifetime (SJLIFE) is a longevity study from St. Jude Children's Research Hospital that aims to identify all inherited genome sequence and structural variants influencing the development of childhood cancer and occurrence of long-term adverse outcomes associated with cancer and cancer-related therapy.
This cohort contains unpaired germline samples and does not contain tumor samples.
+
+### St. Jude Life Clonal Hematopoiesis
+
+**DAU**: PCGP | **Tissue Type**: — | **Sequencing Type**: SingleCell-WGS, Targeted | **Samples**: 3,192
+
+The primary purpose of the St. Jude Lifetime Cohort Study (SJLIFE) Clonal Hematopoiesis dataset is to identify all inherited genome sequence and structural variants influencing the development of childhood cancer and occurrence of long-term adverse outcomes associated with cancer and cancer-related therapy. Additional objectives include, but are not limited to, the acquisition and analysis of additional genomic data, including epigenetic and gene expression data, data integration, and the development and validation of informatic and analytical solutions appropriate to the scale and nature of the project, as well as use of the data generated to answer important methodological and biological questions as specifically related to childhood malignancies.
diff --git a/content/3.genomics-platform/2.about-our-data/3.metadata-and-clinical-information.md b/content/3.genomics-platform/2.about-our-data/3.metadata-and-clinical-information.md
index 61aa2e1..b701467 100644
--- a/content/3.genomics-platform/2.about-our-data/3.metadata-and-clinical-information.md
+++ b/content/3.genomics-platform/2.about-our-data/3.metadata-and-clinical-information.md
@@ -38,12 +38,13 @@ Properties with `attr` prepended are information queried from the physician or r
| `attr_read_type` | The sequencing read type, if available. |
| `attr_inferred_strandedness` | Computationally determined strandedness of RNA-seq data, if applicable. |
| `sj_publication_titles` | The title of associated publications(s), if the file was associated with a paper(s). |
+| `ped_cancer_diagnosis_category` | The disease group which is associated with the pediatric cancer diagnosis. For more details see [here](https://cadsr.cancer.gov/onedata/Home.jsp). |
| `sj_pub_accessions` | The related St. Jude Cloud accession number(s), if the file was associated with a paper(s). These group the files into publications as displayed on the Genomics Platform data browser. |
| `sj_pmid_accessions` | The related [Pubmed][pubmed] accession number, if the file was associated with a paper. |
| `attr_subtype_biomarkers` | A molecular mutation, SV or fusion event associated with a particular disease subtype that is used to define membership in that subtype. |
| `sj_associated_diagnoses` | List of all available associated diagnoses for the subject (from the tumor samples or from a patient's clinical history). |
| `attr_germline_sample` | The paired germline sample that was used when creating the Somatic VCF file, if applicable. |
-| `attr_diagnosis_group` | Each file is categorized into one of five diagnosis groups based on the type of tumor - hematologic malignancy, solid tumor, brain tumor, germ cell tumor, or not applicable (for germline samples). |
+| `attr_diagnosis_group` | Each file is categorized into one of four diagnosis groups based on the type of tumor - hematologic malignancy, solid tumor, brain tumor, or not applicable (for germline samples). |
| `sj_ega_accessions` | The related [EGA][ega] accession number, if the file was associated with a paper. |
| `sj_access_unit` | Lists which Data Access Unit (DAU) the file belongs to. For more on Data Access Units, see [here](https://university.stjude.cloud/docs/genomics-platform/about-our-data/dau-and-datasets/#data-access-unit). |
| `sj_diseases` | If your data request was process after August 18, 2020, the field should be interpreted as the harmonized St. Jude Cloud diagnosis based on the best available information (data provided by the lab or PI and followup by scientists on the St. Jude Cloud team). If your data request was processed before August 18, 2020, this field should be interpreted as the disease identifier assigned at the time of genomic sequencing (namely, the diagnosis known at the time of genomic testing may not be the best available information). **If your data request was processed after August 18, 2020 and you'd like to use the most up to date, harmonized diagnosis**, we recommend using `sj_diseases` when including diagnosis in your analysis. If your data request was made before this time *or* if you wish to use the values exactly as provided by the lab or PI, we recommend using the lab-provided value in `attr_diagnosis`. For more information about our disease ontology go [here](https://university.stjude.cloud/docs/genomics-platform/about-our-data/disease-ontology). |
diff --git a/deployment/preview/pr131/app.yaml b/deployment/preview/pr131/app.yaml
new file mode 100644
index 0000000..b762022
--- /dev/null
+++ b/deployment/preview/pr131/app.yaml
@@ -0,0 +1,87 @@
+apiVersion: helm.toolkit.fluxcd.io/v2beta1
+kind: HelmRelease
+metadata:
+ name: docs
+ namespace: docs-pr131
+spec:
+ interval: 30m
+ chart:
+ spec:
+ chart: generic
+ version: 1.1.x
+ sourceRef:
+ kind: HelmRepository
+ name: stjudecloud
+ namespace: flux-system
+ interval: 1h
+ values:
+ nameOverride: docs
+ extraDeploy:
+ - |
+ apiVersion: v1
+ kind: Service
+ metadata:
+ name: {{ template "common.names.fullname" . }}-oauth-bridge
+ labels: {{- include "common.labels.standard" . | nindent 4 }}
+ {{- if .Values.commonLabels }}
+ {{- include "common.tplvalues.render" ( dict "value" .Values.commonLabels "context" $ ) | nindent 4 }}
+ {{- end }}
+ {{- if .Values.commonAnnotations }}
+ annotations: {{- include "common.tplvalues.render" ( dict "value" .Values.commonAnnotations "context" $ ) | nindent 4 }}
+ {{- end }}
+ spec:
+ type: ExternalName
+ externalName: oauth2-proxy.oauth2-proxy
+ - |
+ ---
+ apiVersion: networking.k8s.io/v1
+ kind: Ingress
+ metadata:
+ name: {{ .Release.Name }}-oauth
+ spec:
+ ingressClassName: nginx
+ rules:
+ - host: {{ .Values.ingress.hostname }}
+ http:
+ paths:
+ - backend:
+ service:
+ name: {{ template "common.names.fullname" . }}-oauth-bridge
+ port:
+ number: 80
+ path: /oauth2
+ pathType: ImplementationSpecific
+ tls:
+ - hosts:
+ - {{ .Values.ingress.hostname }}
+ secretName: {{ .Values.ingress.hostname }}-tls
+ image:
+ repository: stjudecloud/docs
+ tag: pr131-e3b768a-231 # {"$imagepolicy": "flux-system:docs-pr131:tag"}
+ podAnnotations:
+ linkerd.io/inject: enabled
+ config.linkerd.io/proxy-cpu-request: 20m
+ containerPorts:
+ http: 3000
+ service:
+ ports:
+ - name: http
+ protocol: TCP
+ port: 3000
+ targetPort: http
+ ingress:
+ enabled: true
+ hostname: docs-pr131.staging.stjude.cloud
+ path: /
+ annotations:
+ cert-manager.io/cluster-issuer: letsencrypt-prod
+ linkerd.io/inject: ingress
+ nginx.ingress.kubernetes.io/auth-signin: https://$host/oauth2/start?rd=$escaped_request_uri
+ nginx.ingress.kubernetes.io/auth-url: https://$host/oauth2/auth
+ nginx.ingress.kubernetes.io/service-upstream: "true"
+ nginx.ingress.kubernetes.io/enable-modsecurity: "true"
+ nginx.ingress.kubernetes.io/enable-owasp-core-rules: "true"
+ tls:
+ enabled: true
+ datadog:
+ enabled: false
diff --git a/deployment/preview/pr148/app.yaml b/deployment/preview/pr148/app.yaml
new file mode 100644
index 0000000..76c6ef3
--- /dev/null
+++ b/deployment/preview/pr148/app.yaml
@@ -0,0 +1,88 @@
+---
+apiVersion: helm.toolkit.fluxcd.io/v2beta1
+kind: HelmRelease
+metadata:
+ name: docs
+ namespace: docs-pr148
+spec:
+ interval: 30m
+ chart:
+ spec:
+ chart: generic
+ version: 1.1.x
+ sourceRef:
+ kind: HelmRepository
+ name: stjudecloud
+ namespace: flux-system
+ interval: 1h
+ values:
+ nameOverride: docs
+ extraDeploy:
+ - |
+ apiVersion: v1
+ kind: Service
+ metadata:
+ name: {{ template "common.names.fullname" . }}-oauth-bridge
+ labels: {{- include "common.labels.standard" . | nindent 4 }}
+ {{- if .Values.commonLabels }}
+ {{- include "common.tplvalues.render" ( dict "value" .Values.commonLabels "context" $ ) | nindent 4 }}
+ {{- end }}
+ {{- if .Values.commonAnnotations }}
+ annotations: {{- include "common.tplvalues.render" ( dict "value" .Values.commonAnnotations "context" $ ) | nindent 4 }}
+ {{- end }}
+ spec:
+ type: ExternalName
+ externalName: oauth2-proxy.oauth2-proxy
+ - |
+ ---
+ apiVersion: networking.k8s.io/v1
+ kind: Ingress
+ metadata:
+ name: {{ .Release.Name }}-oauth
+ spec:
+ ingressClassName: nginx
+ rules:
+ - host: {{ .Values.ingress.hostname }}
+ http:
+ paths:
+ - backend:
+ service:
+ name: {{ template "common.names.fullname" . }}-oauth-bridge
+ port:
+ number: 80
+ path: /oauth2
+ pathType: ImplementationSpecific
+ tls:
+ - hosts:
+ - {{ .Values.ingress.hostname }}
+ secretName: {{ .Values.ingress.hostname }}-tls
+ image:
+ repository: stjudecloud/docs
+ tag: latest # {"$imagepolicy": "flux-system:docs-pr148:tag"}
+ podAnnotations:
+ linkerd.io/inject: enabled
+ config.linkerd.io/proxy-cpu-request: 20m
+ containerPorts:
+ http: 3000
+ service:
+ ports:
+ - name: http
+ protocol: TCP
+ port: 3000
+ targetPort: http
+ ingress:
+ enabled: true
+ hostname: docs-pr148.staging.stjude.cloud
+ path: /
+ annotations:
+ cert-manager.io/cluster-issuer: letsencrypt-prod
+ linkerd.io/inject: ingress
+ nginx.ingress.kubernetes.io/auth-signin: https://$host/oauth2/start?rd=$escaped_request_uri
+ nginx.ingress.kubernetes.io/auth-url: https://$host/oauth2/auth
+ nginx.ingress.kubernetes.io/service-upstream: "true"
+ nginx.ingress.kubernetes.io/enable-modsecurity: "true"
+ nginx.ingress.kubernetes.io/enable-owasp-core-rules: "true"
+ tls:
+ enabled: true
+ datadog:
+ enabled: false
diff --git a/public/files/cc4k/Official_CC4K_v0.5.xlsx b/public/files/cc4k/Official_CC4K_v0.5.xlsx
new file mode 100644
index 0000000..b18c05b
Binary files /dev/null and b/public/files/cc4k/Official_CC4K_v0.5.xlsx differ