About Our Data

File Formats

St. Jude Cloud hosts both raw genomic data files and processed results files:

File Type Short Description Details
BAM HG38 aligned BAM files produced by Microsoft Genomics Service (DNA-Seq) or STAR 2-pass mapping (RNA-Seq). Click here
gVCF Genomic VCF files produced by Microsoft Genomics Service. Click here
Somatic VCF Curated list of somatic variants produced by the St. Jude somatic variant analysis pipeline. Click here
CNV List of somatic copy number alterations produced by St. Jude CONSERTING pipeline. Click here
Feature Counts Curated list of read counts mapped to each gene produced by HTSeq Click here

BAM files

In St. Jude Cloud, we store aligned sequence reads in BAM file format for whole genome sequencing, whole exome sequencing, and RNA-seq. For more information on SAM/BAM files, please refer to the SAM/BAM specification. For research samples, we require the standard 30X coverage for whole genome and 100X for whole exome sequencing. For clinical samples, we require higher coverage, 45X, for whole genome sequencing due to tumor purity issues found in clinical tumor specimens. For RNA-Seq, since only a subset of genes are expressed in a specific tissue, we require 30% of the exons to have 20X coverage in order to ensure that at least 30% of the expressed genes have sufficient coverage.

gVCF files

We provide gVCF files produced by the Microsoft Genomics Service. gVCF files are derived from the BAM files produced above as called by GATK's haplotype caller. Today, we defer to the official specification document from the Broad Institute, as well as this discussion on the difference between VCF and gVCF files. For more information about how Microsoft Genomics produces gVCF files or any other questions regarding data generation, please refer to the official Microsoft Genomics whitepaper.

Somatic VCF files

Somatic VCF files contain HG38 based SNV/Indel variant calls from the St. Jude somatic variant analysis pipeline as follows. Broadly speaking:

  1. Reads were aligned to HG19 using bwa backtrack (bwa aln + bwa sampe) using default parameters.
  2. Post processing of aligned reads was performed using Picard CleanSam and MarkDuplicates.
  3. Variants were called using the Bambino variant caller (you can download Bambino here or by navigating to the Zhang Lab page where the "Bambino package" is listed as a dependency under the CONSERTING section).
  4. Variants were post-processed using an in-house post-processing pipeline that cleans and annotates variants. This pipeline is not currently publicly available.
  5. Variants were manually reviewed by analysts and published with the relevant Pediatric Cancer Genome Project (PCGP) paper.
  6. Post-publication, variants were lifted over to HG38 (the original HG19 coordinates are stored in the HG19 INFO field.).

Note

Our Somatic VCF files were designed specifically for St. Jude Cloud visualization purposes. Variants in these files were manually curated from analyses across multiple sequencing types including WGS and WES.
For more information on variants for each of the individuals, please refer to the relevant PCGP paper. For more information on the variant calling format (VCF), please see the latest specification for VCF document listed here.

CNV files

CNV files contain copy number alteration (CNA) analysis results for paired tumor-normal WGS samples. Files are produced by running paired tumor-normal BAM files through the CONSERTING pipeline which identifies CNA through iterative analysis of (i) local segmentation by read depth within boundaries identified by structural variation (SV) breakpoints followed by (ii) segment merging and local SV analysis. CREST was used to identify local SV breakpoints. CNV files contain the following information:

Field Description
chrom chromosome
loc.start start of segment
loc.end end of segment
num.mark number of windows retained in the segment (gaps and windows with low mappability are excluded)
length.ratio The ratio between the length of the used windows to the genomic length
seg.mean The estimated GC corrected difference signal (2 copy gain will have a seg.mean of 1)
GMean The mean coverage in the germline sample (a value of 1 represents diploid)
DMean The mean coverage in the tumor sample
LogRatio Log2 ratio between tumor and normal coverage
Quality score A empirical score used in merging
SV_Matching Whether the boundary of the segments were supported by SVs (3: both ends supported, 2: right end supported, 1: left end supported, 0: neither end supported)

Feature Counts files

Feature counts are text files that contain counts of reads aligned to genomic features. St. Jude Cloud feature files are generated using HTSeq. The detailed command is documented in our RNA-Seq V2 RFC. The files contain a count of the number of reads overlapping each genomic feature, in this case, genes as specified in GENCODE V31. St. Jude Cloud uses the gene name as feature key. The files are tab-delimited text and contain the feature key and read count for that feature.

Sequencing Information

Whole Genome and Whole Exome

Whole Genome Sequence (WGS) and Whole Exome Sequence (WES) BAM files were produced by the Microsoft Genomics Service aligned to HG38 (GRCh38 no alt analysis set). For more information about how Microsoft Genomics produces BAM files or any other questions regarding data generation, please refer to the official Microsoft Genomics whitepaper.

RNA-Seq

RNA-Seq BAM files are mapped to HG38. For alignment, STAR v2.7.1a 2-pass mapping is used. Below is the STAR command used during alignment. For more information about any of the parameters used, please refer to the STAR manual for v2.7.1a. The complete RNA-Seq WDL pipeline is available on GitHub. The STAR alignment parameters are also available on GitHub. 

    STAR \
             --readFilesIn $(cat read_one_fastqs_sorted.txt) $(cat read_two_fastqs_sorted.txt) \
             --genomeDir ~{stardb_dir} \
             --runThreadN $n_cores \
             --outSAMunmapped Within \
             --outSAMstrandField intronMotif \
             --outSAMtype BAM Unsorted \
             --outSAMattributes NH HI AS nM NM MD XS \
             --outFilterMultimapScoreRange 1 \
             --outFilterMultimapNmax 20 \
             --outFilterMismatchNmax 10 \
             --alignIntronMax 500000 \
             --alignMatesGapMax 1000000 \
             --sjdbScore 2 \
             --alignSJDBoverhangMin 1 \
             --outFilterMatchNminOverLread 0.66 \
             --outFilterScoreMinOverLread 0.66 \
             --outFileNamePrefix ~{output_prefix + "."} \
             --twopassMode Basic \
             --limitBAMsortRAM ~{(memory_gb - 2) + "000000000"} \
             --outSAMattrRGline $(cat read_groups_sorted.txt)

Data Access Units

We currently have the five Data Access Units (DAU) listed below. Basic clinical data is available for relevant subjects in each DAU. Click on the DAU's abbreviation below to navigate directly to that DAU's Study page for more detailed information.

Pediatric Cancer Genome Project (PCGP)

PCGP is a paired-tumor normal dataset focused on discovering the genetic origins of pediatric cancer. The Pediatric Cancer Genome Project 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.

St. Jude Lifetime (SJLIFE)

SJLIFE is a germline-only dataset focused on studying the long-term adverse outcomes associated with cancer and cancer-related therapy. 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.

Clinical Genomics (Clinical Pilot, G4K, and RTCG)

Clinical Genomics is a paired tumor-normal dataset focused on identifying variants that influence the development and behavior of childhood tumors. Clinical Genomics is a cohort from St. Jude Children's Research Hospital, comprised of three studies: Clinical Pilot, Genomes4Kids, and Real-time Clinical Genomics. Clinical Pilot is a smaller, pilot study generated to asses the validity and accuracy of moving forward with the G4K study. The RTCG study aims to release Clinical Genomics data in real time to the research community. The goal of these studies is to identify all inherited and tumor-acquired (somatic) genome sequence and structural variants influencing the development and behavior of childhood tumors.

Sickle Cell Genome Project (SGP)

SGP is a germline-only dataset 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. Additional objectives include, but are not limited to, developing accurate methods to characterize germline structural variants in highly homologous globin locus and blood typing.

Childhood Cancer Survivor Study (CCSS)

CCSS is a germline-only dataset 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. The primary purpose of this sequencing of CCSS participants 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.

CCSS: Potential Bacterial Contamination

Samples for the Childhood Cancer Survivorship Study were collected by sending out Buccal swab kits to enrolled participants and having them complete the kits at home. This mechanism of collecting saliva and buccal cells for sequencing is highly desirable because of its non-invasive nature and ease of execution. However, collection of samples in this manner also has higher probability of contamination from external sources (as compared to, say, samples collected using blood). We have observed some samples in this cohort which suffer from bacterial contamination. To address this issue, we have taken the following steps:

  1. We have estimated the bacterial contamination rate and annotated each of the samples in the CCSS cohort. For each sample, you will find the estimated contamination rate in the Description field of the SAMPLE_INFO.txt file that is vended with your data (and as a property on the DNAnexus file). For information on this field, see the Metadata specification.
  2. Using this estimated contamination rate, we have removed 82 samples which exhibited large rates of bacterial contamination.
  3. For the remaining samples, we have provided the BAM file as aligned with bwa mem with default parameters. We have observed that there are instances of reads originating from bacterial contamination that are erroneously mapped to the human genome and display a very low mapping quality. Please be advised that we have kept these reads as they were aligned and have not yet made any attempt to unmap these reads. Any analysis you perform on these samples will need to take this into account!
  4. Last, we will be working over the coming months to unmap the reads originating from bacterial contamination and release updated BAM files along with the associated gVCF files from Microsoft Genomics Service.

With any questions on the nature or implications of this warning, please contact us at support@stjude.cloud.

Pan-Acute Lymphoblastic Leukemia (PanALL)

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.

Metadata

Each data request includes a text file called SAMPLE_INFO.txt that provides a number of file level properties (sample identifiers, clinical attributes, etc).

Standard Metadata

Below are the set of tags which may exist for any given file in St. Jude Cloud. All optional metadata will have sj_ prepended to their tag name.

Property Description
file_path The path to the file in your St. Jude Cloud project.
subject_name A unique subject identifier assigned internally at St. Jude.
sample_name A unique sample identifier assigned internally at St. Jude.
sample_type One of Autopsy, Cell line, Diagnosis, Germline, Metastasis, Relapse, or Xenograft.
sequencing_type Whether the file was generated from Whole Genome (WGS), Whole Exome (WES), or RNA-Seq.
file_type One of the file types available in St. Jude Cloud.
description Optional field that may contain additional file information.
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 (keyly, 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.
sj_datasets If present, the datasets in the data browser which this file is associated with.
sj_pmid_accessions If the file was associated with a paper, the related Pubmed accession number.
sj_ega_accessions If the file was associated with a paper, the related EGA accession number.
sj_dataset_accession If present, the permanent accession number assigned in St. Jude Cloud.
sj_embargo_date The embargo date, which specifies the first date which the files can be used in a publication.

Clinical and Phenotypic Information

Also included is a set of phenotypic information queried from the physician or research team's records at the time of sample submission to St. Jude Cloud. These are all considered to be optional, as the level of information gathered for each sample varies. If empty, the physician or research team did not indicate a value for the field. All basic clinical or phenotypic information will have attr_ prepended to their tag name.

Property Description
attr_age_at_diagnosis Age at first diagnosis. This field is normalized as a decimal value. If empty, the physician or research team did not indicate a value for this field.
attr_diagnosis Unharmonized primary diagnosis as reported by the lab or PI upon submission of data to St. Jude Cloud.
attr_ethnicity Self-reported ethnicity. Values are normalized according to the US Census Bureau classifications.
attr_race Self-reported race. Values are normalized according to the US Census Bureau classifications.
attr_sex Self-reported sex.
attr_oncotree_disease_code The disease code (assigned at the time of genomic sequencing) as specified by Oncotree Version 2019-03-01.

Diagnosis Codes

Note

During the release of the St. Jude Cloud paper, we undertook a massive effort to curate and harmonize diagnosis values within St. Jude Cloud. We provide two values for diagnosis, and you should select carefully which value you use based on your use case:

  1. sj_diseases, which, since August 18, 2020, represents the harmonized diagnosis value curated by scientists on the St. Jude Cloud team (before that time it represented the diagnosis known at time of sequencing).
  2. attr_diagnosis, which contains the unharmonized diagnosis value directly as it was submitted to us from the lab or PI.

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 field. 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 value in attr_diagnosis.

The SAMPLE_INFO.txt file that comes with your data request will contain the list of associated harmonized diagnosis codes (sj_diseases) for each sample. These codes represent the harmonized diagnosis values curated by the St. Jude Cloud team and reflect the most up to date information about the sample. Below, we include the full set of diagnosis values in St. Jude Cloud Genomics Platform, the category of the tumor, and the closest available OncoTree term. We regularly work with the OncoTree committee to update new subtypes, so any discrepancies between our diagnosis code and the OncoTree term can be interpreted as more granular classifications that OncoTree does not yet account for.

Tumor Category Diagnosis Diagnosis Code Corresponding Oncotree Code
Brain Tumor Astrocytoma, NOS ASTR ASTR
Brain Tumor Atypical Meningioma ATM ATM
Brain Tumor Atypical Teratoid/Rhabdoid Tumor ATRT ATRT
Brain Tumor Central Neurocytoma CNC CNC
Brain Tumor Choroid Plexus Carcinoma CPC CPC
Brain Tumor Craniopharyngioma, Adamantinomatous Type ACPG ACPG
Brain Tumor Craniopharyngioma, NOS CPG SELT
Brain Tumor Desmoplastic/Nodular Medulloblastoma DMBL DMBL
Brain Tumor Dysembryoplastic Neuroepithelial Tumor DNT DNT
Brain Tumor Embryonal Tumor with Multilayered Rosettes, Brain ETMR EMBT
Brain Tumor Embryonal Tumor, Brain EBMT EMBT
Brain Tumor Ependymomal Tumor EPMT EPMT
Brain Tumor Ependymomal Tumor, Posterior Fossa EPMTPF EPMT
Brain Tumor Ependymomal Tumor, Spinal Tumor EPMTST EPMT
Brain Tumor Ependymomal Tumor, Supratentorial EPMTSU EPMT
Brain Tumor Fibrillary Astrocytoma FASTR DASTR
Brain Tumor Gangliocytoma GNG GNG
Brain Tumor Glioblastoma GB GB
Brain Tumor Glioma, NOS GNOS GNOS
Brain Tumor High-Grade Glioma, NOS HGGNOS HGGNOS
Brain Tumor High-Grade Neuroepithelial Tumor HGNET HGNET
Brain Tumor Low-Grade Glioma, NOS LGGNOS LGGNOS
Brain Tumor Medulloblastoma MBL MBL
Brain Tumor Medulloblastoma, Group 3 MBLG3 MBL
Brain Tumor Medulloblastoma, Group 4 MBLG4 MBL
Brain Tumor Medulloblastoma, SHH subtype MBLSHH MBL
Brain Tumor Medulloblastoma, WNT subtype MBLWNT MBL
Brain Tumor Medulloepithelioma MDEP MDEP
Brain Tumor Meningioma MNG MNG
Brain Tumor Miscellaneous Brain Tumor MBT MBT
Brain Tumor Mixed Myxopapillary Anaplastic Ependymoma, Spinal Tumor MEPMST EPMT
Brain Tumor Myxopapillary Ependymoma MPE MPE
Brain Tumor Myxopapillary Ependymoma, Fourth Ventrice MPEFV MPE
Brain Tumor Myxopapillary Ependymoma, Posterior Fossa MPEPF MPE
Brain Tumor Neuroepithelioma NEP
Brain Tumor Oligodendroglioma ODG ODG
Brain Tumor Papillary Ependymoma, NOS PEPNOS EPMT
Brain Tumor Peripheral Primitive Neuroectodermal Tumor PPNET PNET
Brain Tumor Pilocytic Astrocytoma PAST PAST
Brain Tumor Pineoblastoma PBL PBL
Brain Tumor Pleomorphic Xanthoastrocytoma PXA PXA
Brain Tumor Primitive Neuroectodermal Tumor PNET PNET
Brain Tumor Subependymal Giant Cell Astrocytoma SEGA
Germ Cell Tumor Choriocarcinoma CCA
Germ Cell Tumor Dysgerminoma DYS
Germ Cell Tumor Dysgerminoma, Ovarian ODYS ODYS
Germ Cell Tumor Dysgerminoma, Pelvis PDYS
Germ Cell Tumor Embryonal Carcinoma, NOS ECNOS
Germ Cell Tumor Germ Cell Tumor, Brain BGCT BGCT
Germ Cell Tumor Germ Cell Tumor, NOS GCT
Germ Cell Tumor Germinoma GMN GMN
Germ Cell Tumor Mixed Germ Cell Tumor, Brain BMGCT BMGCT
Germ Cell Tumor Mixed Germ Cell Tumor, NOS MGCTNOS
Germ Cell Tumor Mixed Germ Cell Tumor, Ovary and Lymph Node OMGCT OMGCT
Germ Cell Tumor Mixed Germ Cell Tumor, Testis MGCT MGCT
Germ Cell Tumor Teratocarcinoma TTC
Germ Cell Tumor Teratoma, NOS TTNOS
Germ Cell Tumor Yolk Sac Tumor, Brain BYST BYST
Germ Cell Tumor Yolk Sac Tumor, NOS YSTNOS
Hematologic Malignancy Acute Leukemias of Ambiguous Lineage ALAL ALAL
Hematologic Malignancy Acute Lymphoblastic Leukemia, NOS ALL LNM
Hematologic Malignancy Acute Megakaryoblastic Leukemia AMKL AMKL
Hematologic Malignancy Acute Myeloid Leukemia AML AML
Hematologic Malignancy Acute Myeloid Leukemia, Core Binding Factor CBF AMLRGA
Hematologic Malignancy Acute Myeloid Leukemia, KMT2A rearrangement AML AMLRGA
Hematologic Malignancy Acute Promyelocytic Leukemia APLPMLRARA APLPMLRARA
Hematologic Malignancy Acute Undifferentiated Leukemia, KMT2A rearrangement AULKMT2A AUL
Hematologic Malignancy Anaplastic Large Cell Lymphoma ALCL ALCL
Hematologic Malignancy B-cell Acute Lymphoblastic Leukemia, BCR-ABL1 BALLBCRABL1 BLLBCRABL1
Hematologic Malignancy B-cell Acute Lymphoblastic Leukemia, BCR-ABL1 like BALLBCRABL1L BLLBCRABL1L
Hematologic Malignancy B-cell Acute Lymphoblastic Leukemia, DUX4-IGH BALLDUX4IGH BLLRGA
Hematologic Malignancy B-cell Acute Lymphoblastic Leukemia, DUX4-IGH like BALLDUX4IGHL BLLRGA
Hematologic Malignancy B-cell Acute Lymphoblastic Leukemia, ETV6-RUNX1 BALLETV6RUNX1 BLLETV6RUNX1
Hematologic Malignancy B-cell Acute Lymphoblastic Leukemia, ETV6-RUNX1 like BALLETV6RUNX1L BLLRGA
Hematologic Malignancy B-cell Acute Lymphoblastic Leukemia, HLF rearrangement BALLHLF BLLRGA
Hematologic Malignancy B-cell Acute Lymphoblastic Leukemia, Hyperdiploidy BALLHYPER BLLHYPER
Hematologic Malignancy B-cell Acute Lymphoblastic Leukemia, Hypodiploidy BALLHYPO BLLHYPO
Hematologic Malignancy B-cell Acute Lymphoblastic Leukemia, iAMP21 BALLIAMP21 BLLIAMP21
Hematologic Malignancy B-cell Acute Lymphoblastic Leukemia, IGH-CEBPD BALLIGHCEBPD BLLRGA
Hematologic Malignancy B-cell Acute Lymphoblastic Leukemia, KMT2A rearrangement BALLKMT2A BLLKMT2A
Hematologic Malignancy B-cell Acute Lymphoblastic Leukemia, MEF2D rearrangement BALLMEF2D BLLRGA
Hematologic Malignancy B-cell Acute Lymphoblastic Leukemia, MYC rearrangement BALLMYC BLLRGA
Hematologic Malignancy B-cell Acute Lymphoblastic Leukemia, NOS BALLNOS BLLNOS
Hematologic Malignancy B-cell Acute Lymphoblastic Leukemia, NUTM1 rearrangement BALLNUTM1 BLLRGA
Hematologic Malignancy B-cell Acute Lymphoblastic Leukemia, PAX5 Alteration BALLPAX5 BLLRGA
Hematologic Malignancy B-cell Acute Lymphoblastic Leukemia, PAX5 P80R BALLPAX5P80R BLLRGA
Hematologic Malignancy B-cell Acute Lymphoblastic Leukemia, TCF3-PBX1 BALLTCF3PBX1 BLLTCF3PBX1
Hematologic Malignancy B-cell Acute Lymphoblastic Leukemia, ZNF384 rearrangement BALLZNF384 BLLRGA
Hematologic Malignancy B-cell Acute Lymphoblastic Leukemia, ZNF384 rearrangement like BALLZNF384L BLLRGA
Hematologic Malignancy Blood Cancer of Unknown Primary BCUP CUP
Hematologic Malignancy Burkitt Lymphoma BL BL
Hematologic Malignancy Chronic Myeloid Leukemia CML CML
Hematologic Malignancy Diffuse Large B-cell Lymphoma, NOS DLBCLNOS DLBCLNOS
Hematologic Malignancy General Leukemia SJGENLK
Hematologic Malignancy Hodgkin Lymphoma HL HL
Hematologic Malignancy Langerhans Cell Histiocytosis LCH LCH
Hematologic Malignancy Lymphocyte-Depleted Classical Hodgkin Lymphoma LDCHL LDCHL
Hematologic Malignancy Lymphocyte-Rich Classical Hodgkin Lymphoma LRCHL LRCHL
Hematologic Malignancy Mixed Cellularity Classical Hodgkin Lymphoma MCCHL MCCHL
Hematologic Malignancy Mycosis Fungoides MYCF MYCF
Hematologic Malignancy Myelodysplastic Syndromes MDS MDS
Hematologic Malignancy Myeloid Sarcoma MS MS
Hematologic Malignancy Nodular Lymphocyte-Predominant Hodgkin Lymphoma NLPHL NLPHL
Hematologic Malignancy Nodular Sclerosis Classical Hodgkin Lymphoma NSCHL NSCHL
Hematologic Malignancy Non-Hodgkin Lymphoma NHL NHL
Hematologic Malignancy T-cell Acute Lymphoblastic Leukemia TALL TLL
Hematologic Malignancy T-cell Acute Lymphoblastic Leukemia, KMT2A rearrangement TALLKMT2A TLL
Hematologic Malignancy T-cell Lymphoblastic Lymphoma TLL TLL
Non-Malignancy Non-Malignancy SJNM
Normal SJLIFE Normal Control SJNORM
Sickle Cell Disease Sickle Cell Disease SJSCD
Solid Tumor Acinar Cell Carcinoma ACN ACN
Solid Tumor Adenocarcinoma, NOS ADNOS ADNOS
Solid Tumor Adrenocortical Carcinoma ACC ACC
Solid Tumor Alveolar Rhabdomyosarcoma ARMS ARMS
Solid Tumor Alveolar Soft Part Sarcoma ASPS ASPS
Solid Tumor Angiomatoid Fibrous Histiocytoma AFH AFH
Solid Tumor Basal Cell Carcinoma BCC BCC
Solid Tumor Botryoid Type Embryonal Rhabdomyosarcoma BERMS ERMS
Solid Tumor Carcinoma, NOS CNOS CUP
Solid Tumor Chondroblastic Osteosarcoma CHOS CHOS
Solid Tumor Chondrosarcoma CHS CHS
Solid Tumor Chordoma CHDM CHDM
Solid Tumor Clear Cell Sarcoma of Kidney CCSK CCSK
Solid Tumor Congenital Mesoblastic Nephroma CMN RCC
Solid Tumor Dermatofibrosarcoma Protuberans DFSP DFSP
Solid Tumor Desmoid/Aggressive Fibromatosis DES DES
Solid Tumor Desmoplastic Small Round Cell Tumor DSRCT DSRCT
Solid Tumor Embryonal Rhabdomyosarcoma ERMS ERMS
Solid Tumor Endometrioid Adenocarcinoma, NOS EACNOS ADNOS
Solid Tumor Epithelioid Hemangioendothelioma EHAE EHAE
Solid Tumor Epithelioid Sarcoma EPIS EPIS
Solid Tumor Ewing Sarcoma EWS ES
Solid Tumor Fibroblastic Osteosarcoma FIOS FIOS
Solid Tumor Fibromyxoid Sarcoma FMS
Solid Tumor Fibrosarcoma, NOS FIBS FIBS
Solid Tumor Follicular Thyroid Cancer THFO THFO
Solid Tumor Ganglioneuroblastoma GNBL GNBL
Solid Tumor Ganglioneuroma GN GN
Solid Tumor Gastrointestinal Stromal Tumor GIST GIST
Solid Tumor General Bone Tumor SJGENBN
Solid Tumor Giant Cell Tumor, NOS GICT GCTB
Solid Tumor Granulosa Cell Tumor GRCT GRCT
Solid Tumor Hepatoblastoma HB LIHB
Solid Tumor Hepatocellular Carcinoma HCC HCC
Solid Tumor Infantile Fibrosarcoma IFS IFS
Solid Tumor Leiomyosarcoma, NOS LMS LMS
Solid Tumor Liposarcoma LIPO LIPO
Solid Tumor Liver Malignancy, NOS LMNOS
Solid Tumor Malignant Fibrous Histiocytoma MFH MFH
Solid Tumor Malignant Mesenchymoma MME
Solid Tumor Malignant Mesenchymoma of the Liver MMEL
Solid Tumor Malignant Peripheral Nerve Sheath Tumor MPNST MPNST
Solid Tumor Malignant Rhabdoid Tumor of the Liver MRTL MRTL
Solid Tumor Melanoma MEL MEL
Solid Tumor Mesenchymal Chondrosarcoma MCHS MCHS
Solid Tumor Mixed Spindle Cell and Embryonal Rhabdomyosarcoma MSCERMS RMS
Solid Tumor Mucinous Adenocarcinoma of the Colon and Rectum MACR MACR
Solid Tumor Mucinous Adenocarcinoma, NOS MACNOS
Solid Tumor Mucoepidermoid Carcinoma MUCC MUCC
Solid Tumor Nasopharyngeal Carcinoma NPC NPC
Solid Tumor Neuroblastoma NBL NBL
Solid Tumor Neurofibroma NFIB NFIB
Solid Tumor Osteosarcoma OS OS
Solid Tumor Pancreatic Neuroendocrine Tumor PANET PANET
Solid Tumor Papillary Renal Cell Carcinoma PRCC PRCC
Solid Tumor Papillary Thyroid Cancer THPA THPA
Solid Tumor Paraganglioma PGNG PGNG
Solid Tumor Parosteal Osteosarcoma PAOS PAOS
Solid Tumor Periosteal Osteosarcoma PEOS PEOS
Solid Tumor Pleuropulmonary Blastoma PPB PPB
Solid Tumor Renal Cell Carcinoma RCC RCC
Solid Tumor Renal Clear Cell Carcinoma CCRCC CCRCC
Solid Tumor Retinoblastoma RBL RBL
Solid Tumor Rhabdoid Cancer, Kidney MRT MRT
Solid Tumor Rhabdomyosarcoma RMS RMS
Solid Tumor Round Cell Sarcoma, NOS RCSNOS RCSNOS
Solid Tumor Serous Surface Papillary Carcinoma SSPC
Solid Tumor Small Cell Osteosarcoma SCOS SCOS
Solid Tumor Soft Tissue Sarcoma, NOS STSNOS CUP
Solid Tumor Solid Cancer of Unknown Primary SCUP CUP
Solid Tumor Solitary Fibrous Tumor/Hemangiopericytoma SFT SFT
Solid Tumor Spindle Cell Rhabdomyosarcoma SCRMS SCRMS
Solid Tumor Spindle Cell Sarcoma, NOS SCSNOS
Solid Tumor Spindle Cell/Sclerosing Rhabdomyosarcoma SCSRMS SCSRMS
Solid Tumor Spindle Epithelial Tumor with Thymus-Like Differentiation SETTLE
Solid Tumor Squamous Cell Carcinoma, NOS SCCNOS SCCNOS
Solid Tumor Stomach Inflammatory Pseudotumor SIPT
Solid Tumor Synovial Sarcoma SYNS SYNS
Solid Tumor Telangiectatic Osteosarcoma TEOS TEOS
Solid Tumor Undifferentiated Embryonal Sarcoma of the Liver UESL UESL
Solid Tumor Wilms WT WT
Solid Tumor Wilms, Bilateral WTB WT

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About our Decision Process & Terminology
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