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.[1][2] TCGA applies high-throughput genome analysis techniques to improve our 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.[3] 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.[4][5] 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 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 is 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 is performing whole exon sequencing on 80% of the cases and whole genome sequencing on 80% of the cases used in the project.

Goals

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.[6] 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. More information about TCGA is available at the TCGA home page (http://cancergenome.nih.gov/) and TCGA data can be accessed through the TCGA Data Portal (http://tcga-data.nci.nih.gov/tcga/).

Management

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. This office, directed by Jean Claude Zenklusen 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.[7] In addition, the TCGA Project Office is responsible for coordinating the accrual of tissues for TCGA. 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.

Tissue accrual

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 wereprimarily 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.[8]

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.

Funding

The NCI and NHGRI equally co-funded the Pilot Project with $50M for the first three years. The NCI has committed $25M/year of appropriated funds for five years for TCGA Phase II. The NHGRI has committed $25M/year of appropriated funds for two years. The beginning of the second phase of the project coincided with the American Recovery and Reinvestment Act of 2009 (ARRA), providing $153.5M of additional funding to the NCI beyond their appropriated funds. The Office of the Director of the NIH has provided another $25M of ARRA funds dedicated to sequence analysis and another $25M of ARRA funds in the second year of Phase II if substantial progress is made during year 1. In all, $150M will be spent on sequencing. Another $70M will be spent on tissue accrual, sample QC and biomolecule (DNA and RNA) isolation.

Organization

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.[9]

Genome sequencing centers

Three Genome Sequencing Centers were co-funded by the NCI and NHGRI: the Broad Institute, The Genome Center 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 under contract by scientists and staff at the University of California, Santa Cruz.

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 at 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.

Tumors

A preliminary list of tumors for TCGA to study was generated by compiling incidence and survival statistics from the SEER Cancer Statistic website (http://seer.cancer.gov/). 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.

Publications

Progress as of October 25, 2015
Cancer Type Studied Final

Number of Cases [10]

 Data Publicly Available TCGA Analysis Findings
Glioblastoma Multiforme 528 X GBM subtypes Classical, Mesenchymal and Proneural are defined by EGFR, NF1, and PDGFRA/IDH1 mutations respectively;[11] Over 40% of tumors have mutations in chromatin-modifier genes;[12] Other frequently mutated genes include TP53, PlK3R1, PIK3CA, IDH1, PTEN, RB1, LZTR1[13]
Lower Grade Glioma 516 X Defined three subtypes the correlate 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[14]
Breast Lobular Carcinoma 127[15] X Lobular Carcinoma distinct from Ductal Carcinoma; FOXA1 elevated in Lobular Carcinoma, GATA3 in Ductal Carcinoma; Lobular Carcinoma enriched for PTEN loss and Akt activation[16]
Breast Ductal Carcinoma > 800 X Four subtypes Basal, Her2, Luminal A, Luminal B differed in genomic profile; most common driver mutations TP53, PIK3CA, GATA3; Basal subtype similar to Serous Ovarian Cancer[15]
Colorectal Adenocarcinoma 632 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[17]
Stomach Adenocarcinoma 443 X Identified four subytpes: EBV characterized by Epstein-Bar 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[18]
Esophageal Carcinoma 185 X
Ovarian Serous Cystadenocarcinoma 586 X Mutations in TP53 occurred in 96% of the cases studied;[19] Mutations in BRCA1 and BRCA2 occurred in 21% of the cases and were associated with more favorable outcomes[20]
Uterine Corpus Endometrial Carcinoma 548 X Classified endometrial cancers into four categories: POLE ultramutated, MSI (microsatellite instability) hypermutated, Copy-number low, Copy-number high; Uterine serous carcinomas have similar genomic profiles to Ovarian serous and Basal-like Breast carcinomas and less favorable prognoses than Uterine endometriod carcniomas[21]
Cervical Squamous Cell Carcinoma and Adenocarcinoma 308 X
Head and Neck Squamous Cell Carcinoma 528 X Identified genomic features of HPV related 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, copy number alterations[22]
Thyroid Carcinoma 507 X Majority driven by RAS or BRAFV600E mutations; tumors driven by these mutations are distinct[23]
Acute Myeloid Leukemia 200 X AML tumors contained very few mutations compared to other cancer types, 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 470XEstablished four subtypes of cutaneous melanoma, 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 521 X Lung adenocarcinomas contain a very high average number of mutations; 76 percent of lung adenocarcinoma tumors studied demonstrated activation of receptor tyrosine kinase pathways[24]
Lung Squamous Cell Carcinoma 504 X Lung Squamous Cell Carcinomas contain a high average number of mutations and copy number aberrations; like Ovarian Serous Cystadenocarcinoma almost all Lung Squamous Cell Carcinoma tumors studied contained a mutation in TP53; Many tumors contained inactivating mutations in HLA-A that may help the cancer avoid immune detection[25]
Clear Cell Carcinoma 536 X Commonly mutated genes included VHL involved in oxygen sensing, SED2 involved in epigenetic modification resulting in global hypomethylation, and genes of the PI3K/AKT/mTOR pathway; Metabolic shift similar to the Warburg effect correlates with a poor prognosis[26]
Kidney Papillary Carcinoma 291 X
Invasive Urothelial Bladder Cancer 412XSmoking is associated with risk of Urothelial Bladder Carcinoma; Frequently mutated genes included TP53 which was inactivated in 76 percent of tumors studied, ERBB2 (HER2), genes in the receptor tyrosine kinase (RTK)/RAS pathways altered in 44 percent;[27]
Prostate Adenocarcinoma 498X
Chromophobe Renal Cell Carcinoma 66 X Chromophobe Renal Cell Carcinoma has a low rate of mutation compared to most cancers including Clear Cell Carcinoma; Chromophobe Renal Cell Carcinoma originates from more distal regions of the kidney compared to Clear Cell Carcinoma which is primarily from proximal regions; Metabolic shift in Chromophobe Renal Cell Carcinoma is distinct from the Warburg effect- like shift observed in Clear Cell Carcinoma; TP53 and PTEN tumor suppressor genes were frequently mutated; TERT gene promoter was frequently altered[28]
Adrenocortical Carcinoma 80 X
Paraganglioma & Pheochromocytoma 179 X
Cholangiocarcinoma 36 X
Liver Hepatocellular Carcinoma 377 X
Pancreatic Ductal Adenocarcinoma 185 X
Uterine Carcinosarcoma 57 X
Uveal Melanoma 80 X
Thymoma 124 X
Sarcoma 261 X
Mesothelioma 87 X
Testicular Germ Cell Cancer 150 X

Glioblastoma multiforme

In 2008, the TCGA published its first results on Glioblastoma multiforme (GBM) in Nature.[29] 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.[30] 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.[31] 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, Katie Hoadley, PhD, and Neil Hayes, MD, successfully correlated glioma gene expression subtypes with genomic abnormalities.[32] 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.[33][34]

Serous ovarian

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.[35]

Colorectal carcinoma

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.[36] Recently, a database known as Colorectal Cancer Atlas (http://colonatlas.org) integrating genomic and proteomic data pertaining to colorectal cancer tissues and cell lines have 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 whole-genome sequencing of 3,281 cancer genomes 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.[37]

See also

References

  1. ↑ "The Cancer Genome Atlas homepage". NCI and the NHGRI. Retrieved 2009-04-28.
  2. ↑ NIH Launches Cancer Genome Project Washington Post Dec 14, 2005
  3. ↑ Daniela S. Gerhard (2008-05-27). "TCGA Moving Molecular Oncology Forward". NCI cancer Bulletin, Director's Update. National Cancer Institute. Retrieved 2009-08-27.
  4. ↑ "Cancers Selected for Study". The Cancer Genome Atlas - National Cancer Institute. Retrieved 2015-11-02.
  5. ↑ "Rare Tumor Characterization Projects". The Cancer Genome Atlas - National Cancer Institute. Retrieved 2015-11-02.
  6. ↑ McLendon, R.; Friedman, Allan; Bigner, Darrell; Van Meir, Erwin G.; Brat, Daniel J.; M. Mastrogianakis, Gena; Olson, Jeffrey J.; Mikkelsen, Tom; et al. (2008-10-23). "Comprehensive genomic characterization defines human glioblastoma genes and core pathways". Nature 455 (7216): 1061–1068. doi:10.1038/nature07385. PMC 2671642. PMID 18772890.
  7. ↑ "2015 Sammies Winner: People's Choice Award". Service to America Medals. Retrieved 2015-10-15.
  8. ↑ "History and Timeline". The Cancer Genome Atlas - National Cancer Institute. Retrieved 2015-11-02.
  9. ↑ "The Cancer Genome Atlas Data Portal: Biospecimen Core Resource". NCI and the NHGRI. Retrieved 2014-01-24.
  10. ↑ "The Cancer Genome Atlas - Data Portal". tcga-data.nci.nih.gov. Retrieved 2015-10-27.
  11. ↑ Verhaak, Roel G.W.; Hoadley, Katherine A.; Purdom, Elizabeth; Wang, Victoria; Qi, Yuan; Wilkerson, Matthew D.; Miller, C. Ryan; Ding, Li; Golub, Todd (2010-01-19). "An integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR and NF1". Cancer Cell 17 (1): 98–110. doi:10.1016/j.ccr.2009.12.020. ISSN 1535-6108. PMC 2818769. PMID 20129251.
  12. ↑ Brennan, Cameron W.; Verhaak, Roel G. W.; McKenna, Aaron; Campos, Benito; Noushmehr, Houtan; Salama, Sofie R.; Zheng, Siyuan; Chakravarty, Debyani; Sanborn, J. Zachary (2013-10-10). "The somatic genomic landscape of glioblastoma". Cell 155 (2): 462–477. doi:10.1016/j.cell.2013.09.034. ISSN 1097-4172. PMC 3910500. PMID 24120142.
  13. ↑ McLendon, Roger; Friedman, Allan; Bigner, Darrell; Meir, Erwin G. Van; Brat, Daniel J.; Mastrogianakis, Gena M.; Olson, Jeffrey J.; Mikkelsen, Tom; Lehman, Norman (2008-10-23). "Comprehensive genomic characterization defines human glioblastoma genes and core pathways". Nature 455 (7216): 1061–1068. doi:10.1038/nature07385. ISSN 0028-0836. PMC 2671642. PMID 18772890.
  14. ↑ "Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas". New England Journal of Medicine 372 (26): 2481–2498. 2015-06-25. doi:10.1056/NEJMoa1402121. ISSN 0028-4793. PMC 4530011. PMID 26061751.
  15. 1 2 Network, The Cancer Genome Atlas (2012-10-04). "Comprehensive molecular portraits of human breast tumours". Nature 490 (7418): 61–70. doi:10.1038/nature11412. ISSN 0028-0836. PMC 3465532. PMID 23000897.
  16. ↑ Ciriello, Giovanni; Gatza, Michael L.; Beck, Andrew H.; Wilkerson, Matthew D.; Rhie, Suhn K.; Pastore, Alessandro; Zhang, Hailei; McLellan, Michael; Yau, Christina (2015-08-10). "Comprehensive Molecular Portraits of Invasive Lobular Breast Cancer". Cell 163 (2): 506–519. doi:10.1016/j.cell.2015.09.033. ISSN 0092-8674. PMC 4603750. PMID 26451490.
  17. ↑ Network, The Cancer Genome Atlas (2012-07-19). "Comprehensive molecular characterization of human colon and rectal cancer". Nature 487 (7407): 330–337. doi:10.1038/nature11252. ISSN 0028-0836. PMC 3401966. PMID 22810696.
  18. ↑ Bass, Adam J.; Thorsson, Vesteinn; Shmulevich, Ilya; Reynolds, Sheila M.; Miller, Michael; Bernard, Brady; Hinoue, Toshinori; Laird, Peter W.; Curtis, Christina (2014-07-23). "Comprehensive molecular characterization of gastric adenocarcinoma". Nature 513 (7517): 202–209. doi:10.1038/nature13480. PMC 4170219. PMID 25079317.
  19. ↑ "Integrated Genomic Analyses of Ovarian Carcinoma". Nature 474 (7353): 609–615. 2011-06-29. doi:10.1038/nature10166. ISSN 0028-0836. PMC 3163504. PMID 21720365.
  20. ↑ "ASsociation between brca1 and brca2 mutations and survival in women with invasive epithelial ovarian cancer". JAMA 307 (4): 382–389. 2012-01-25. doi:10.1001/jama.2012.20. ISSN 0098-7484. PMC 3727895. PMID 22274685.
  21. ↑ Network, The Cancer Genome Atlas Research (2013-05-02). "Integrated genomic characterization of endometrial carcinoma". Nature 497 (7447): 67–73. doi:10.1038/nature12113. ISSN 0028-0836. PMC 3704730. PMID 23636398.
  22. ↑ "Comprehensive genomic characterization of head and neck squamous cell carcinomas". Nature 517 (7536): 576–582. 2015-01-29. doi:10.1038/nature14129. ISSN 0028-0836. PMC 4311405. PMID 25631445.
  23. ↑ Agrawal, Nishant; Akbani, Rehan; Aksoy, B. Arman; Ally, Adrian; Arachchi, Harindra; Asa, Sylvia L.; Auman, J. Todd; Balasundaram, Miruna; Balu, Saianand. "Integrated Genomic Characterization of Papillary Thyroid Carcinoma". Cell 159 (3): 676–690. doi:10.1016/j.cell.2014.09.050. ISSN 0092-8674. PMC 4243044. PMID 25417114.
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  25. ↑ Network, The Cancer Genome Atlas Research (2012-09-27). "Comprehensive genomic characterization of squamous cell lung cancers". Nature 489 (7417): 519–525. doi:10.1038/nature11404. ISSN 0028-0836. PMC 3466113. PMID 22960745.
  26. ↑ "Comprehensive molecular characterization of clear cell renal cell carcinoma". Nature 499 (7456): 43–49. 2013-07-04. doi:10.1038/nature12222. ISSN 0028-0836. PMC 3771322. PMID 23792563.
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  28. ↑ Davis, Caleb F.; Ricketts, Christopher J.; Wang, Min; Yang, Lixing; Cherniack, Andrew D.; Shen, Hui; Buhay, Christian; Kang, Hyojin; Kim, Sang Cheol (2014-09-08). "The Somatic Genomic Landscape of Chromophobe Renal Cell Carcinoma". Cancer Cell 26 (3): 319–330. doi:10.1016/j.ccr.2014.07.014. PMC 4160352. PMID 25155756.
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  30. ↑ "The Cancer Genome Atlas Data Portal". NCI and the NHGRI. Retrieved 2009-04-28.
  31. ↑ "The Cancer Genome Atlas Data Portal". National Institute of Health. Retrieved 2 November 2010.
  32. ↑ Verhaak, Roel G.W.; Hoadley, Katherine A.; Purdom, Elizabeth; Wang, Victoria; Qi, Yuan; Wilkerson, Matthew D.; Miller, C. Ryan; Ding, Li; Golub, Todd; Mesirov, Jill P.; Alexe, Gabriele; Lawrence, Michael; O'Kelly, Michael; Tamayo, Pablo; Weir, Barbara A.; Gabriel, Stacey; Winckler, Wendy; Gupta, Supriya; Jakkula, Lakshmi; Feiler, Heidi S.; Hodgson, J. Graeme; James, C. David; Sarkaria, Jann N.; Brennan, Cameron; Kahn, Ari; Spellman, Paul T.; Wilson, Richard K.; Speed, Terence P.; Gray, Joe W.; Meyerson, Matthew; Getz, Gad; Perou, Charles M.; Hayes, D. Neil (2010). "Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized by Abnormalities in PDGFRA, IDH1, EGFR, and NF1". Cancer Cell 17 (1): 98–110. doi:10.1016/j.ccr.2009.12.020. PMC 2818769. PMID 20129251.
  33. ↑ Noushmehr H; Weisenberger DJ; Diefes K; et al. (May 2010). "Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma". Cancer Cell (Cancer Cell) 17 (5): 510–22. doi:10.1016/j.ccr.2010.03.017. PMC 2872684. PMID 20399149.
  34. ↑ "Glioma subtype with less severe outcome.". Retrieved 6 March 2011.
  35. ↑ "Integrated genomic analyses of ovarian carcinoma". Macmillan Publishers Limited. line feed character in |publisher= at position 10 (help)
  36. ↑ "Comprehensive molecular characterization of human colon and rectal cancer". Macmillan Publishers Limited. Retrieved August 18, 2012. line feed character in |publisher= at position 10 (help)
  37. ↑ Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, Xie M, Zhang Q, McMichael JF, Wyczalkowski MA, Leiserson MD, Miller CA, Welch JS, Walter MJ, Wendl MC, Ley TJ, Wilson RK, Raphael BJ, Ding L (2013). "Mutational landscape and significance across 12 major cancer types". Nature 502 (7471): 333–9. doi:10.1038/nature12634. PMID 24132290.

External links

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