The Cancer Genome Atlas
The Cancer Genome Atlas (TCGA) is a project, begun in 2005, to catalogue genetic mutations responsible for cancer, using genome sequencing and bioinformatics. TCGA applies high-throughput genome analysis techniques to improve the ability to diagnose, treat, and prevent cancer through a better understanding of the genetic basis of this disease.
TCGA is supervised by the National Cancer Institute's Center for Cancer Genomics and the National Human Genome Research Institute funded by the US government. A three-year pilot project, begun in 2006, focused on characterization of three types of human cancers: glioblastoma multiforme, lung, and ovarian cancer. In 2009, it expanded into phase II, which planned to complete the genomic characterization and sequence analysis of 20–25 different tumor types by 2014. TCGA surpassed that goal, characterizing 33 cancer types including 10 rare cancers. Funding is split between genome characterization centers (GCCs), which perform the sequencing, and genome data analysis centers (GDACs), which perform the bioinformatic analyses.
The project scheduled 500 patient samples, more than most genomics studies, and used different techniques to analyze the patient samples. Techniques include gene expression profiling, copy number variation profiling, SNP genotyping, genome wide DNA methylation profiling, microRNA profiling, and exon sequencing of at least 1,200 genes. TCGA was sequencing the entire genomes of some tumors, including at least 6,000 candidate genes and microRNA sequences. This targeted sequencing is being performed by all three sequencing centers using hybrid-capture technology. In phase II, TCGA was performing whole exome and whole transcriptome sequencing on 100% of the cases and whole genome sequencing on 10% of the cases used in the project.
The goal of the pilot project was to demonstrate that advanced genomic technologies could be utilized by a team of scientists from various institutions to generate statistically and biologically significant conclusions from the genomic data set generated. Two tumor types were explored during the pilot phase, Glioblastoma Multiforma (GBM) and Cystadenocarcinoma of the Ovary. The goal of TCGA Phase II is to expand the success experienced in the pilot project to more cancer types, providing a large, statistically significant data set for further discovery.
TCGA is co-managed by scientists and managers from the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). With the expansion of TCGA from the pilot phase to Phase II in October, 2009, the NCI created a TCGA Program Office. Dr. Jean Claude Zenklusen has been the director of the office since August 2013. This office is responsible for the operation of six Genome Characterization Centers, seven Genome Analysis Centers, the Biospecimen Core Resource, the Data Coordination Center, and approximately one third of the sequencing done for the project by the three Genome Sequencing Centers. In addition, the TCGA Project Office was responsible for coordinating the accrual of tissues for TCGA. Dr. Carolyn Hutter, project manager for NHGRI, directs two thirds of the sequencing at the Genome Sequencing Centers.
The project is managed by a project team composed of members from the NCI and the NHGRI. This team, along with principal investigators funded by the project, makes up the Steering Committee. The Steering Committee is tasked with overseeing the scientific validity of the project while the NCI/NHGRI project team ensures that the scientific progress and goals of the project are met, the project is completed on time and on budget and the coordination of the various components of the project.
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Tissue requirements varied from tissue type to tissue type and from cancer type to cancer type. Disease experts from the project's Disease Working Groups helped to define the characteristics of the typical tissue samples accrued as "standard of care" in the United States and how TCGA can best utilize the tissue. For example, the Brain Disease Working Group determined that samples containing more than 50% necrosis would not be suitable for TCGA and that 80% tumor nuclei were required in the viable portion of the tumor. TCGA followed some general guidelines as a starting point for collecting samples from any type of tumor. These include a minimum of 200 mg in size, no less than 80% tumor nuclei and a matched source of germline DNA (such as blood or purified DNA). In addition, institutions submitting tissues to TCGA must have a minimal clinical data set as defined by the Disease Working Group, signed consents which have been approved by their institution's IRB as well as a material transfer agreement with TCGA.
In 2009, the NCI removed approximately $130 million of ARRA from the NCI's "Prime Contract" with Science Applications International Corporation (SAIC) to fund tissue accrual and a variety of other activities through the NCI Office of Acquisition. $42 million was available for tissue accrual through the NCI using "Requests for Quotations" (RFQs) and "Requests for Proposals" (RFPs) to generate purchase orders and contracts, respectively. RFQs were primarily used for the collection of retrospective samples from established banks while RFPs are used for the prospective collection of samples.TCGA finalized sample collection in December, 2013, with nearly 20,000 biospecimens.
Institutions that contribute samples to TCGA are paid, and have access to molecular data generated on their samples, while maintaining a link between the TCGA unique identifier and their own unique identifier. This permits contributing institutions to link back to the clinical data for their samples and to enter into collaborations with other institutions that have similar data on TCGA samples, thus increasing the power of outcome analysis.
TCGA has a number of different types of centers that are funded to generate and analyze data. TCGA is the first large-scale genomics project funded by the NIH to include significant resources to bioinformatic discovery. The NCI has devoted 50% of TCGA appropriated funds, approximately $12M/year, to fund bioinformatic discovery. Genome Characterization Centers and Genome Sequencing Centers generate data. Two types of Genome Data Analysis Centers utilize the data for bioinformatic discovery. Two centers are funded to isolate biomolecules from patient samples and one center is funded to store the data. For more information on TCGA project organization, see http://cancergenome.nih.gov/newsevents/multimedialibrary/interactives/howitworks.
Biospecimen core resource
The Biospecimen Core Resource (BCR) is responsible for verifying the quality and quantity of tissue shipped by tissue source sites, the isolation of DNA and RNA from the samples, quality control of these biomolecules and the shipment of samples to the GSCs and GCCs. The International Genomics Consortium was awarded the contract to initiate the BCR for the pilot project. There were two BCRs funded by the NCI at the start of the full project: Nationwide Children's Hospital and the International Genomics Consortium. The BCRs were recompeted with due date for proposals June 4, 2010 and Nationwide Children's Hospital was awarded the contract.
Genome sequencing centers
Three Genome Sequencing Centers were co-funded by the NCI and NHGRI: the Broad Institute, McDonnell Genome Institute at Washington University and Baylor College of Medicine. All three of these sequencing centers have shifted from Sanger sequencing to next-generation sequencing (NGS), although a variety of NGS technologies are being implemented simultaneously.
Genome characterization centers
The NCI funded seven Genome characterization centers: the Broad Institute, Harvard, University of North Carolina, MD Anderson Cancer Center, Van Andel Institute, Baylor College of Medicine and the British Columbia Cancer Center.
Data coordinating center
The data coordinating center is the central repository for TCGA data. It is also responsible for the quality control of data entering the TCGA database. The DCC also maintains the TCGA Data Portal which is where users access TCGA data. This work is performed under contract by bioinformatics scientists and developers from SRA International, Inc. The DCC does not host lower levels of sequence data. NCI's Cancer Genomics Hub (CGHub) is the secure repository for storing, cataloging, and accessing sequence-related data. This work is performed by scientists and staff at the University of California, Santa Cruz Genomics Institute.
Genome data analysis centers
Seven Genome data analysis centers funded by the NCI/NHGRI are responsible for the integration of data across all characterization and sequencing centers as well as biological interpretation of TCGA data. The GDACs include The Broad Institute, University of North Carolina, Oregon Health and Science University, University of California, Santa Cruz, MD Anderson Cancer Center, Memorial Sloan Kettering Cancer Center, and The Institute for Systems Biology. All seven GDACs work together to develop an analysis pipeline for automated data analysis.
A preliminary list of tumors for TCGA to study was generated by compiling incidence and survival statistics from the SEER Cancer Statistic website. In addition, U.S. current “Standard of Care” was considered when choosing the top 25 tumor types, as TCGA is targeting tumor types where resection prior to adjunct therapy is the standard of care. Availability of samples also plays a critical role in determining which tumor types to study and the order in which tumor projects are started. The more common the tumor is, the more likely that samples will be accrued quickly, resulting in common tumor types, such as colon, lung and breast cancer becoming the first tumor types entered into the project, before rare tumor types.
TCGA Targeted Tumors: lung squamous cell carcinoma, kidney papillary carcinoma, clear cell kidney carcinoma, breast ductal carcinoma, renal cell carcinoma, cervical cancer (squamous), colon adenocarcinoma, stomach adenocarcinoma, rectal carcinoma, hepatocellular carcinoma, Head and neck (oral) squamous cell carcinoma, thyroid carcinoma, bladder urothelial carcinoma – nonpapillary, uterine corpus (endometrial carcinoma), pancreatic ductal adenocarcinoma, acute myeloid leukemia, prostate adenocarcinoma, lung adenocarcinoma, cutaneous melanoma, breast lobular carcinoma and lower grade glioma, esophageal carcinoma, ovarian serous cystadenocarcinoma, lung squamous cell carcinoma, adrenocortical carcinoma, Diffuse Large B-cell lymphoma, paraganglioma & pheochromocytoma, cholangiocarcinoma, uterine carcinosarcoma, uveal melanoma, thymoma, sarcoma, mesothelioma, and testicular germ cell cancer.
TCGA accrued samples for all of these tumor types simultaneously. As samples became available, the tumor types with the most samples accrued were entered into production. For more rare tumor types, tumor types where samples are difficult to accrue and for tumor types where TCGA cannot identify a source of high quality samples, these types of cancer entered the “TCGA production pipeline” in the second year of the project. This gave the TCGA Program Office additional time to accrue sufficient samples for the project.
|Cancer Type Studied||Final
Number Analyzed in Original Marker Paper
|Data Publicly Available||TCGA Analysis Findings|
|Glioblastoma Multiforme||206||X||GBM subtypes Classical, Mesenchymal and Proneural are defined by EGFR, NF1, and PDGFRA/IDH1 mutations respectively; over 40% of tumors have mutations in chromatin-modifier genes; other frequently mutated genes include TP53, PlK3R1, PIK3CA, IDH1, PTEN, RB1, LZTR1|
|Lower Grade Glioma||293||X||Defined three subtypes correlating with patient outcomes: IDH1 mutant with 1p/19q deletion, IDH mutant without 1p/19q deletion, and IDH wildtype; IDH wildtype is genomically similar to glioblastoma|
|Breast Lobular Carcinoma||203||X||Lobular carcinoma distinct from ductal carcinoma; FOXA1 elevated in lobular carcinoma, GATA3 elevated in ductal carcinoma; lobular carcinoma enriched for PTEN loss and Akt activation|
|Breast Ductal Carcinoma||784||X||Four distinct genomic subtypes: basal, Her2, luminal A, luminal B; most common driver mutations TP53, PIK3CA, GATA3; basal subtype similar to serous ovarian cancer|
|Colorectal Adenocarcinoma||276||X||Colon and rectal cancers have similar genomic profiles; hypermutated subtype (16% of samples) mostly found in right colon and associated with favorable prognosis; new potential drivers: ARlD1A, SOX9, FAM123B/WTX; overexpression of: ERBB2, IGF2; mutations in the WNT pathway|
|Stomach Adenocarcinoma||295||X||Identified four subtypes: EBV characterized by Epstein-Barr virus infection, MSI (microsatellite instability) characterized by hypermutation, GS characterized by genomic stability, CIN characterized by chromosomal instability; CIN enriched for mutations in tyrosine kinases|
|Esophageal Carcinoma||164||X||Squamous cell and adenocarcinoma are molecularly distinct; squamous cell carcinomas were similar to head and neck squamous cell carcinomas and had frequent amplifications of CCND1, SOX2 and TP63; adenocarcinomas were similar to chromosomally unstable gastric adenocarcinoma and had frequent amplifications in ERBB2, VEGFA, GATA4, and GATA6|
|Ovarian Serous Cystadenocarcinoma||489||X||Mutations in TP53 occurred in 96% of the cases studied; mutations in BRCA1 and BRCA2 occurred in 21% of the cases and were associated with more favorable outcomes|
|Uterine Corpus Endometrial Carcinoma||373||X||Classified endometrial cancers into four categories: POLE ultramutated, MSI (microsatellite instability) hypermutated, copy-number low, and copy-number high; uterine serous carcinomas were similar to ovarian serous and basal-like Breast carcinomas and had less favorable prognoses than uterine endometrioid carcinomas|
|Cervical Squamous Cell Carcinoma and Adenocarcinoma||228||X||Identification of HPV-negative, endometrial-like cervical cancers with mutations in KRAS, ARID1A, and PTEN genes; amplification of CD274 and PDCD1LG2 immune checkpoint genes; alterations to genes including MED1, ERBB3, CASP8, HLA-A, and TGFBR2 and fusions involving lncRNA BCAR4; nearly three-quarters of samples had alterations in either one or both of the PI3K/MAPK and TGF-beta signaling pathways|
|Head and Neck Squamous Cell Carcinoma||279||X||Identified genomic features of HPV- and smoking-related cancers: HPV-positive characterized by shortened or deleted TRAF3, HPV-negative characterized by co-amplification of 11q13 and 11q22, smoking-related characterized by TP53 mutations, CDKN2A inactivation, and copy number alterations|
|Thyroid Carcinoma||496||X||Majority driven by RAS or BRAFV600E mutations; tumors driven by these mutations are distinct|
|Acute Myeloid Leukemia||200||X||Low mutation burden, with only 13 coding mutations on average per tumor; classified driver events into nine categories including transcription factor fusions, histone modifier mutations, spliceosome mutations and others|
|Cutaneous Melanoma||331||X||Established four subtypes: BRAF mutant, RAS mutant, NF1 mutant, and triple wild-type based on driver mutations; higher levels of immune lymphocyte infiltration correlated with better patient survival|
|Lung Adenocarcinoma||230||X||High mutation burden; 76% of tumors demonstrated activation of receptor tyrosine kinase pathways|
|Lung Squamous Cell Carcinoma||178||X||High average number of mutations and copy number aberrations; like ovarian serous cystadenocarcinoma, almost all lung squamous cell carcinomas contained a mutation in TP53; many tumors contained inactivating mutations in HLA-A that may help the cancer avoid immune detection|
|Clear Cell Renal Cell Carcinoma||446||X||Commonly mutated genes included VHL involved in oxygen sensing, SED2 involved in epigenetic modifications resulting in global hypomethylation, and genes of the PI3K/AKT/mTOR pathway; metabolic shift similar to the 'Warburg effect' correlates with a poor prognosis|
|Kidney Papillary Carcinoma||161||X||81% of type 1 tumors contained an alteration to MET; genomic profiles of type 2 tumors were heterogeneous, with alterations to CDKN2A, SETD2, TFE3, or increased expression of the NRF2–ARE pathway; loss of expression of CDKN2A and CpG island methylation phenotype were associated with poor outcome|
|Invasive Urothelial Bladder Cancer||131||X||Smoking is associated with increased risk; frequently mutated genes include TP53, which was inactivated in 76% of tumors and ERBB2 (HER2), genes in the receptor tyrosine kinase (RTK)/RAS pathways altered in 44% of tumors;|
|Prostate Adenocarcinoma||333||X||Highly heterogeneous with 26% of samples driven by unknown molecular alterations; 7 subtypes defined by ETS transcription factor gene fusions or mutations in SPOP, FOXA1, or IDH1; actionable lesions in the PI3K, MAPK, and DNA repair pathways|
|Chromophobe Renal Cell Carcinoma||66||X||Extremely low mutation burden; the carcinoma originates from more distal regions of the kidney compared to clear cell carcinoma, which is primarily from proximal regions; metabolic shift distinct from the 'Warburg effect' shift observed in clear cell carcinoma; TP53 and PTEN tumor suppressor genes were frequently mutated; TERT gene promoter was frequently altered|
|Adrenocortical Carcinoma||91||X||Overexpression of IGF2, mutations in TP53, PRKAR1A and other genes, and copy number alterations were common hallmarks; hypoploidy followed by whole genome doubling may be a driving mechanism of tumor development|
|Paraganglioma & Pheochromocytoma||173||X||Four distinct subtypes: Wnt-altered, cortical admixture, pseudohypoxia and kinase signaling; MAML3 fusion gene and CSDE1 somatic mutation define and drive the poor prognosis, Wnt-altered subtype|
|Cholangiocarcinoma||38||X||Low expression of CDKN2, BAP1, and ARID1 genes and overexpression of FGFR2 and IDH1/2 genes; four subtypes, one subtype characterized by alterations in IDH, silencing of ARID1A and low expression of other chromatin modifiers, and high mitochondrial gene expression; another subtype characterized by BAP1 mutations and FGFR2 gene fusions; the cancer may exist on a continuous spectrum with a subset of liver carcinomas with IDH or FGFR mutations|
|Liver Hepatocellular Carcinoma||363||X||TERT promoter mutations, identified in 44% of tumors, associated with increased elongation of telomeres and silencing of CDKN2A; TP53 commonly mutated or under-expressed; CTNNBB1 significantly mutated; many tumors with high levels of lymphocyte infiltration or overexpressed immune checkpoint genes CTLA4, PD-1, and PD-L1|
|Pancreatic Ductal Adenocarcinoma||150||X||Used deep and targeted sequencing to better analyze low neoplastic cellularity; KRAS mutations present in 93% of tumors; mutations in RREB1 or other members of RAS-MAPK signaling pathway|
|Uterine Carcinosarcoma||57||X||Identified a strong and varied degree of epithelial-mesenchymal transition; TP53 mutations present in 91% of samples; alterations in PI3K present in half of samples|
|Uveal Melanoma||80||X||Complex mutations in BAP1; identified distinct subdivisions of disomy 3 (D3) and monosomy 3 (M3) subtypes; in M3, mutually exclusive EIF1AX and SRSF2/SF3B1 mutations have distinct methylation profiles and prognoses|
|Sarcoma||206||X||TP53, ATRX, and RB1 among the few genes recurrently mutated across sarcoma types; copy number alterations frequently occurred in complex karyotype sarcomas, affecting p53 and RB1 cell cycle and other pathways; synovial sarcoma sarcomas expressed fusions in SSX1 or SSX2 and TERT; For dedifferentiated liposarcoma, JUN amplification associates with worse survival; altered PI3K-AKT-mTOR pathway in leiomyosarcoma; undifferentiated pleomorphic sarcoma and myxofibrosarcoma may be driven by alterations in the Hippo pathway|
|Testicular Germ Cell Cancer||150||X|
In 2008, the TCGA published its first results on Glioblastoma multiforme (GBM) in Nature. These first results published on 91 tumor-normal matched pairs. While 587 biospecimens were collected for the study, most were rejected during quality control: the tumor samples needed to contain at least 80% tumor nuclei and no more than 50% necrosis, and a secondary pathology assessment had to agree that the original diagnosis of GBM was accurate. A last batch of samples were excluded because the DNA or RNA collected was not of sufficient quality or quantity to be analyzed by all of the different platforms used in this study.
All of the data from the paper, as well as data that has been collected since the publication are publicly available at the Data Coordinating Center (DCC) for public access. Most of the TCGA data is completely open access, except for data that could potentially identify specific patients. This Clinically Controlled-Access data can be accessed through application to the Data Access Committee (DAC), which evaluates whether the end user is a bona fide researcher and is asking a legitimate scientific question that merits access to individual-level data. This process is similar to that of other NIH-funded programs, including dbGAP.
Since the publication of the first marker paper, several analysis groups within the TCGA Network have presented more detailed analysis of the glioblastoma data. An analysis group led by Roel Verhaak, PhD, Katherine A. Hoadley, PhD, and D. Neil Hayes, MD, successfully correlated glioma gene expression subtypes with genomic abnormalities. The DNA methylation data analysis team, led by Houtan Noushmehr, PhD and Peter Laird, PhD, identified a distinct subset of glioma samples which displays concerted hypermethylation at a large number of loci, indicating the existence of a glioma-CpG island methylator phenotype (G-CIMP). G-CIMP tumors belong to the proneural subgroup and were tightly associated with IDH1 somatic mutations.
Starting a new era in cancer genome sequencing, TCGA reported on the exome sequencing of 316 tumor samples of high grade serous ovarian cancer in Nature in June 2011.
TCGA reported on the exome sequencing and gene expression analysis of 276 tumor samples of colon and rectal cancers, including whole genome sequencing of 97 samples, in Nature in July 2012. Recently, a database known as Colorectal Cancer Atlas (http://colonatlas.org) integrating genomic and proteomic data pertaining to colorectal cancer tissues from TCGA and cell lines has been developed.
Status as of 2013: mutational landscape of 12 common cancer subtypes
In 2013, TCGA published a description of the "mutational landscape" defined as frequently recurring mutations identified from exome sequencing of 3,281 tumours from 12 commonly occurring cancer subtypes. The twelve subtypes studied were breast adenocarcinoma, lung adenocarcinoma, lung squamous cell carcinoma, endometrial carcinoma, glioblastoma multiforme, squamous cell carcinoma of the head and neck, colon cancer, rectal cancer, bladder cancer, kidney clear cell carcinoma, ovarian carcinoma and acute myeloid leukaemia.
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