The cancer genome atlas pan-cancer analysis project
Open accessAbstract
Current clinical practice is organized according to tissue or organ of origin of tumors. Now, The Cancer Genome Atlas (TCGA) Research Network has started to identify genomic and other molecular commonalities among a dozen different types of cancer. Emerging similarities and contrasts will form the basis for targeted therapies of the future and for repurposing existing therapies by molecular rather than histological similarities of the diseases. The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The large number of applications based on omics can be ascribed to The Cancer Genome Atlas Program (TCGA), a publicly available collection of genomes developed mainly for cancer-related research.
“the large number based on omics can be ascribed to The Cancer Genome Atlas Program (TCGA), a publicly available collection of genomes that was developed mainly for cancer-related research42.”
From A scoping review of robustness concepts for machine learning in healthcare (2025) · cites this paper as [42]
The Pan-Cancer Project section describes TCGA’s cancer-focused datasets across genomic, epigenomic, transcriptional and proteomic platforms. From Many Tumours to the Individual Patient says the datasets are publicly available and a resource for cancer analysis. It does not establish that TCGA accounts for the large number of omics-based applications. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe genetics and genomics communities have created large reference datasets, including the human genome project, HapMap, TCGA, ENCODE, GTEx, HPA, HCA, and population-scale biobank efforts.
“The genetics and genomics communities have created large reference datasets, such as the human genome project,23 HapMap,122 the Cancer Genome Atlas (TCGA),123 ENCODE,124 the Genotype-Tissue Expression (GTEx) project,125 the Human Protein Atlas (HPA),64,126 the Human Cell Atlas (HCA),24 and a growing number of deeply phenotyped, population-scale biobank efforts.127”
From How to build the virtual cell with artificial intelligence: Priorities and opportunities (2024) · cites this paper as [123]
CTransPath is a lightweight (30 million parameters) Swin Transformer trained at a resolution of 1.0 micron per pixel on over 32,000 WSIs from TCGA and PAIP using a semantically guided contrastive learning algorithm.
“The former is a lightweight (30 million parameters) Swin Transformer15 trained at a resolution of 1.0 micron per pixel (mpp) on over 32,000 WSIs from TCGA16 and PAIP19 using a semantically guided contrastive learning algorithm20.”
From Generating dermatopathology reports from gigapixel whole slide images with HistoGPT (2025) · cites this paper as [16]
Generative vision language models such as Med-PaLM M, LLaVA-Med, or PathChat can output text, but only at the patch level for small image regions up to 1024 × 1024 pixels.
“Generative vision language models such as Med-PaLM M14, LLaVA-Med15, or PathChat16 can output text, but only at the patch level for small image regions up to 1024 × 1024 pixels.”
From Generating dermatopathology reports from gigapixel whole slide images with HistoGPT (2025) · cites this paper as [16]
The Pan-Cancer Project and Limitations of Pan-Cancer Analysis sections discuss cancer-genomics data and integration challenges, not generative vision-language models. Searches for Med-PaLM M, LLaVA-Med, PathChat, and the 1024 × 1024-pixel patch limit found no passage addressing the claim. (LLM, conf=0.78)
Experimental — accuracy not yet measuredIn patients with HCC, high PROX1 expression is associated with increased survival.
“in patients with HCC, high PROX1 expression or chromosomal amplifications including PROX1 were both associated with increased survival25–32”
From Active repression of cell fate plasticity by PROX1 safeguards hepatocyte identity and prevents liver tumorigenesis (2025) · cites this paper as [30]
The Pan-Cancer Project section discusses predicting patient outcomes across tumour types, while Limitations of Pan-Cancer Analysis mentions hepatocellular carcinoma only in relation to viral causes (page numbers unavailable). Searches for PROX1, HCC survival and chromosomal amplification found no passage linking PROX1 expression to survival in HCC patients. (LLM, conf=0.77)
Experimental — accuracy not yet measured
In patients with HCC, chromosomal amplifications including PROX1 are associated with increased survival.
“in patients with HCC, high PROX1 expression or chromosomal amplifications including PROX1 were both associated with increased survival25–32”
From Active repression of cell fate plasticity by PROX1 safeguards hepatocyte identity and prevents liver tumorigenesis (2025) · cites this paper as [30]
The Limitations of Pan-Cancer Analysis section mentions hepatocellular carcinoma only in connection with viral causes. Searches for PROX1, HCC copy-number amplification, and survival found no passage linking amplifications including PROX1 to increased survival in HCC patients. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe study uses lung cancer images from The Cancer Genome Atlas to construct a simulated environment of several institutions to validate the approach.
“Finally, we use lung cancer images from The Cancer Genome Atlas (TCGA) dataset 5 to construct a simulated environment of several institutions to validate our approach.”
From Federated Learning and Differential Privacy for Medical Image Analysis (2021) · cites this paper as [5]
117 de novo mutations occurred at 76 recurrent somatic mutations observed in at least three TCGA patients.
“We observe 117 DNMs at 76 recurrent somatic mutations observed in at least three patients in The Cancer Genome Atlas (TCGA)22.”
From Evidence for 28 genetic disorders discovered by combining healthcare and research data (2020) · cites this paper as [22]
Analysis of genomic alterations in glioblastoma according to TCGA identified the q13-15 region of chromosome 12 as one of the regions most often rearranged in the disease.
“Our analysis of genomic alterations in glioblastoma according to data in the Cancer Genome Atlas (TCGA) identified the q13–15 region of chromosome 12 as one of the regions that most often rearranged in the disease (Fig. 1A and B)5, 6, 7.”
Combining prostate cancer RNA-seq and SNP data from TCGA identified risk-modulating non-coding RNA.
“One group combined prostate cancer RNA-seq and SNP data to identify risk-modulating non-coding RNA, demonstrating the application of multi-omics TCGA data integration146,147.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [147]
TCGA is a cancer-specific multi-omics resource containing over 20,000 primary cancer and matched normal samples from 33 cancer types, with genomic, epigenomic, transcriptomic and proteomic data.
“The Cancer Genome Atlas (TCGA) is a cancer-specific multi-omics data resource characterizing over 20,000 primary cancer and corresponding normal samples from 33 cancer types, representing data from genomic, epigenomic, transcriptomic and proteomic modalities.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [147]
TCGA is a large-scale landmark cancer genomics database providing whole-slide images for normal controls and cancers for histology and microscopy research.
“The Cancer Genome Atlas (TCGA) [116] 14 is a large-scale landmark cancer genomics database.”
From Federated learning for medical image analysis: A survey (2024) · cites this paper as [116]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Hallmarks of Cancer: The Next Generation
Hanahan, D., & Weinberg, R. A. (2011) · Cell · doi:10.1016/j.cell.2011.02.013 · Cited by 66,590 · see citing works
Hanahan, D., & Weinberg, R. A. (2000) · Cell · doi:10.1016/s0092-8674(00)81683-9 · Cited by 29,219 · see citing works
Molecular portraits of human breast tumours
Perou, C., Sørlie, T., Eisen, M., van de Rijn, M., Jeffrey, S., Rees, C. A., Pollack, J., Ross, D., Johnsen, H., Akslen, L., et al. (2000) · Nature · doi:10.1038/35021093 · Cited by 16,551 · see citing works
Comprehensive molecular portraits of human breast tumours
The Cancer Genome Atlas Network (2012) · Nature · doi:10.1038/nature11412 · Cited by 12,129 · see citing works
Self-citationMutations of the BRAF gene in human cancer
Davies, H., Bignell, G. R., Cox, C., Stephens, P., Edkins, S., Clegg, S., Teague, J., Woffendin, H., Garnett, M. J., Bottomley, W., et al. (2002) · Nature · doi:10.1038/nature00766 · Cited by 10,674 · see citing works
Signatures of mutational processes in human cancer
Initiative, A. P. C. G., Alexandrov, L. B., ICGC Breast Cancer Consortium, ICGC MMML-Seq Consortium, PedBrain, I., Nik-Zainal, S., Wedge, D. C., Aparicio, S. A. J. R., Behjati, S., Biankin, A. V., et al. (2013) · Nature · doi:10.1038/nature12477 · Cited by 9,660 · see citing works
<i>EGFR</i> Mutations in Lung Cancer: Correlation with Clinical Response to Gefitinib Therapy
Paez, J. G., Jänne, P. A., Lee, J. C., Tracy, S., Greulich, H., Gabriel, S., Herman, P., Kaye, F. J., Lindeman, N., Boggon, T. J., et al. (2004) · Science · doi:10.1126/science.1099314 · Cited by 9,368 · see citing works
Comprehensive molecular characterization of human colon and rectal cancer
The Cancer Genome Atlas Network (2012) · Nature · doi:10.1038/nature11252 · Cited by 8,499 · see citing works
The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) · Nature · doi:10.1038/nature11003 · Cited by 7,950 · see citing works
Integrated genomic analyses of ovarian carcinoma
Perou, C. M. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/hvp3-wg08 · Cited by 7,927 · see citing works
Vogelstein, B., Papadopoulos, N., Velculescu, V. E., Zhou, S., Diaz, L. A., & Kinzler, K. W. (2013) · Science · doi:10.1126/science.1235122 · Cited by 7,863 · see citing works
Comprehensive genomic characterization defines human glioblastoma genes and core pathways
The Cancer Genome Atlas Research Network (2008) · Nature · doi:10.1038/nature07385 · Cited by 7,735 · see citing works
Self-citationImproved survival with vemurafenib in melanoma with BRAF V600E mutation
Chapman, P. B., Hauschild, A., Robert, C., Haanen, J. B., Ascierto, P., Larkin, J., Dummer, R., Garbe, C., Testori, A., Maio, M., et al. (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1103782 · Cited by 7,720 · see citing works
Integrated genomic characterization of endometrial carcinoma
The Cancer Genome Atlas Research Network, & Levine, D. A. (2013) · Nature · doi:10.1038/nature12113 · Cited by 5,784 · see citing works
Self-citationMutational heterogeneity in cancer and the search for new cancer-associated genes
Lawrence, M. S., Stojanov, P., Polak, P., Kryukov, G. V., Cibulskis, K., Sivachenko, A., Carter, S. L., Stewart, C., Mermel, C. H., Roberts, S. A., et al. (2013) · Nature · doi:10.1038/nature12213 · Cited by 5,461 · see citing works
Identification of the transforming EML4–ALK fusion gene in non-small-cell lung cancer
Soda, M., Choi, Y. L., Enomoto, M., Takada, S., Yamashita, Y., Ishikawa, S., Fujiwara, S.-I., Watanabe, H., Kurashina, K., Hatanaka, H., et al. (2007) · Nature · doi:10.1038/nature05945 · Cited by 5,439 · see citing works
Comprehensive genomic characterization of squamous cell lung cancers
The Cancer Genome Atlas Research Network (2012) · Nature · doi:10.1038/nature11404 · Cited by 3,994 · see citing works
Recurrent Fusion of <i>TMPRSS2</i> and ETS Transcription Factor Genes in Prostate Cancer
Tomlins, S. A., Rhodes, D. R., Perner, S., Dhanasekaran, S. M., Mehra, R., Sun, X.-W., Varambally, S., Cao, X., Tchinda, J., Kuefer, R., et al. (2005) · Science · doi:10.1126/science.1117679 · Cited by 3,846 · see citing works
Comprehensive molecular characterization of clear cell renal cell carcinoma
The Cancer Genome Atlas Research Network (2013) · Nature · doi:10.1038/nature12222 · Cited by 3,485 · see citing works
Self-citationActivating Mutations of NOTCH1 in Human T Cell Acute Lymphoblastic Leukemia
Weng, A. P., Ferrando, A. A., Lee, W., Morris, J. P., Silverman, L. B., Sanchez-Irizarry, C., Blacklow, S. C., Look, A. T., & Aster, J. C. (2004) · Science · doi:10.1126/science.1102160 · Cited by 2,706 · see citing works
The Mutational Landscape of Head and Neck Squamous Cell Carcinoma
Stransky, N., Egloff, A. M., Tward, A. D., Kostic, A. D., Cibulskis, K., Sivachenko, A., Kryukov, G. V., Lawrence, M. S., Sougnez, C. L., McKenna, A., et al. (2011) · Science · doi:10.1126/science.1208130 · Cited by 2,500 · see citing works
Systematic identification of genomic markers of drug sensitivity in cancer cells
Garnett, M. J., Edelman, E. J., Heidorn, S. J., Greenman, C. D., Dastur, A., Lau, K. W., Greninger, P., Thompson, I. R., Luo, X., Soares, J., et al. (2012) · Nature · doi:10.1038/nature11005 · Cited by 2,456 · see citing works
Massive genomic rearrangement acquired in a single catastrophic event during cancer development
Stephens, P. J., Greenman, C. D., Fu, B., Yang, F., Bignell, G. R., Mudie, L. J., Pleasance, E. D., Lau, K. W., Beare, D., Stebbings, L. A., et al. (2011) · Cell · doi:10.1016/j.cell.2010.11.055 · Cited by 2,414 · see citing works
Recurring mutations found by sequencing an acute myeloid leukemia genome
Mardis, E. R., Ding, L., Dooling, D. J., Larson, D. E., McLellan, M. D., Chen, K., Koboldt, D. C., Fulton, R. S., Delehaunty, K. D., McGrath, S. D., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0903840 · Cited by 2,219 · see citing works
Chromosome 17 Deletions and p53 Gene Mutations in Colorectal Carcinomas
Baker, S. J., Fearon, E. R., Nigro, J. M., Hamilton, S. R., Preisinger, A. C., Jessup, J. M., vanTuinen, P., Ledbetter, D. H., Barker, D. F., Nakamura, Y., et al. (1989) · Science · doi:10.1126/science.2649981 · Cited by 2,067 · see citing works
Genomic analysis identifies association of <i>Fusobacterium</i> with colorectal carcinoma
Kostic, A. D., Gevers, D., Pedamallu, C. S., Michaud, M., Duke, F., Earl, A. M., Ojesina, A. I., Jung, J., Bass, A. J., Tabernero, J., et al. (2012) · Genome Research · doi:10.1101/gr.126573.111 · Cited by 1,960 · see citing works
Pan-cancer patterns of somatic copy number alteration
Zack, T. I., Schumacher, S. E., Carter, S. L., Cherniack, A. D., Saksena, G., Tabak, B., Lawrence, M. S., Zhang, C., Wala, J. A., Mermel, C. H., et al. (2013) · Nature Genetics · doi:10.1038/ng.2760 · Cited by 1,843 · see citing works
Lessons from the Cancer Genome
Garraway, L. A., & Lander, E. S. (2013) · Cell · doi:10.1016/j.cell.2013.03.002 · Cited by 1,322 · see citing works
RET, ROS1 and ALK fusions in lung cancer
Takeuchi, K., Soda, M., Togashi, Y., Suzuki, R., Sakata, S., Hatano, S., Asaka, R., Hamanaka, W., Ninomiya, H., Uehara, H., et al. (2012) · Nature Medicine · doi:10.1038/nm.2658 · Cited by 1,320 · see citing works
Punctuated Evolution of Prostate Cancer Genomes
Baca, S. C., Prandi, D., Lawrence, M. S., Mosquera, J. M., Romanel, A., Drier, Y., Park, K., Kitabayashi, N., MacDonald, T. Y., Ghandi, M., et al. (2013) · Cell · doi:10.1016/j.cell.2013.03.021 · Cited by 1,300 · see citing works
Palles, C., Cazier, J., Howarth, K. M., Domingo, E., Jones, A. M., Broderick, P., Kemp, Z., Spain, S. L., Guarino, E., Salguero, I., et al. (2012) · Nature Genetics · doi:10.1038/ng.2503 · Cited by 995 · see citing works
Human EJ bladder carcinoma oncogene is homologue of Harvey sarcoma virus ras gene
Parada, L. F., Tabin, C. J., Shih, C., & Weinberg, R. A. (1982) · Nature · doi:10.1038/297474a0 · Cited by 947 · see citing works
Network-based stratification of tumor mutations
Hofree, M., Shen, J. P., Carter, H. K., Gross, A. M., & Ideker, T. E. (2013) · Nature Methods · doi:10.1038/nmeth.2651 · Cited by 726 · see citing works
Multiple arrangements of viral DNA and an activated host oncogene in bursal lymphomas
Payne, G. S., Bishop, J. M., & VARMUS, H. E. (1982) · Nature · doi:10.1038/295209a0 · Cited by 625 · see citing works
TCPA: a resource for cancer functional proteomics data
Li, J., Lu, Y., Akbani, R., Ju, Z., Roebuck, P. L., Liu, W., Yang, J.-Y., Broom, B. M., Verhaak, R. G. W., Kane, D. W., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2650 · Cited by 513 · see citing works
Self-citationComprehensive identification of mutational cancer driver genes across 12 tumor types
Tamborero, D., González-Pérez, A., Perez-Llamas, C., Deu-Pons, J., Kandoth, C., Reimand, J., Lawrence, M. S., Getz, G. A., Bader, G. D., Li, J., et al. (2013) · Scientific Reports · doi:10.1038/srep02650 · Cited by 475 · see citing works
Subtype and pathway specific responses to anticancer compounds in breast cancer
Heiser, L. M., Sadanandam, A., Kuo, W., Benz, S. C., Goldstein, T. C., Ng, S., Gibb, W. J., Wang, N. J., Ziyad, S., Tong, F. K., et al. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1018854108 · Cited by 464 · see citing works
Self-citationLoss-of-function mutations in Notch receptors in cutaneous and lung squamous cell carcinoma
Wang, N. J., Sanborn, Z., Arnett, K. L., Bayston, L. J., Liao, W. J., Proby, C. M., Leigh, I. M., Collisson, E. A., Gordon, P. B., Jakkula, L. R., et al. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1114669108 · Cited by 460 · see citing works
Self-citationAlterations in Notch signaling in neoplastic lesions of the human cervix.
Zagouras, P., Stifani, S., Blaumueller, C. M., Carcangiu, M. L., & Artavanis‐Tsakonas, S. (1995) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.92.14.6414 · Cited by 352 · see citing works
Wen, Y., Liu, Y., Xu, Y., Zhao, Y., Hua, R., Wang, K., Sun, M., Li, Y.-H., Yang, S., Zhang, X.-J., et al. (2009) · Nature Genetics · doi:10.1038/ng.276 · Cited by 209 · see citing works
From human genome to cancer genome: The first decadePreprint
Wheeler, D. A., & Wang, L. (2013) · Genome Research · doi:10.1101/gr.157602.113 · Cited by 176 · see citing works
Integrated genomic analyses of ovarian carcinoma
The Cancer Genome Atlas Research Network (2011) · Nature · doi:10.1038/nature10166 · Cited by 129 · see citing works
Self-citationMcDermott, U., & Settleman, J. (2009) · Journal of Clinical Oncology · doi:10.1200/jco.2009.22.9054 · Cited by 126 · see citing works
Weinstein, J. N. (2012) · Nature · doi:10.1038/483544a · Cited by 107 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 3
- [36]Gonzalez A Ding L Bader GD Bigas-Lopes N Identification of cancer driver genes using complementary criteria. Nature Scientific Reports Under review 2013
- [38]Verhaak RG Estimating the presence of tumor-associated normal cells relates the tumor microenvironment to genomic correlates. Nature Communications Under review 2013
- [39]Yuan Y How much can we gain in predicting patient prognosis by integrating cancer genomics data? Nature biotechnology Under review 2013
Cited by
Sorted by citations
Salmon provides fast and bias-aware quantification of transcript expression
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017) · Nature Methods · doi:10.1038/nmeth.4197 · Cited by 12,700 · see citing works
GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses
Tang, Z., Li, C., Kang, B., Gao, G., Li, C., & Zhang, Z. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx247 · Cited by 9,849 · see citing works
Fagerberg, L., Hallström, B. M., Oksvold, P., Kampf, C., Djureinovic, D., Odeberg, J., Habuka, M., Tahmasebpoor, S., Danielsson, A., Edlund, K., et al. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.035600 · Cited by 3,665 · see citing works
Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden
Chalmers, Z. R., Connelly, C. F., Fabrizio, D., Gay, L., Ali, S. M., Ennis, R., Schrock, A., Campbell, B., Shlien, A., Chmielecki, J., et al. (2017) · Genome Medicine · doi:10.1186/s13073-017-0424-2 · Cited by 3,519 · see citing works
A pathology atlas of the human cancer transcriptome
Uhlen, M., Zhang, C., Lee, S., Sjöstedt, E., Fagerberg, L., Bidkhori, G., Benfeitas, R., Arif, M., Liu, Z., Edfors, F., et al. (2017) · Science · doi:10.1126/science.aan2507 · Cited by 3,371 · see citing works
Oncogenic Signaling Pathways in The Cancer Genome Atlas
Sanchez-Vega, F., Mina, M., Armenia, J., Chatila, W. K., Luna, A., La, K. C., Dimitriadoy, S., Liu, D. L., Kantheti, H. S., Saghafinia, S., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.035 · Cited by 3,201 · see citing works
Self-citationOpportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310 · see citing works
Vemurafenib in Multiple Nonmelanoma Cancers with <i>BRAF</i> V600 Mutations
Hyman, D. M., Puzanov, I., Subbiah, V., Faris, J. E., Chau, I., Blay, J.-Y., Wolf, J., Raje, N. S., Diamond, E. L., Hollebecque, A., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1502309 · Cited by 1,764 · see citing works
Current and future perspectives of liquid biopsies in genomics-driven oncology
Heitzer, E., Haque, I. S., Roberts, C. E. S., & Speicher, M. R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-018-0071-5 · Cited by 1,557 · see citing works
Towards a general-purpose foundation model for computational pathology
Chen, R. J., Ding, T., Lu, M. Y., Williamson, D. F. K., Jaume, G., Song, A. H., Chen, B., Zhang, A., Shao, D., Shaban, M., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02857-3 · Cited by 1,560 · see citing works
Single-cell genome sequencing: current state of the science
Gawad, C., Koh, W., & Quake, S. R. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2015.16 · Cited by 1,334 · see citing works
Toil enables reproducible, open source, big biomedical data analyses
Vivian, J., Rao, A. A., Nothaft, F. A., Ketchum, C., Armstrong, J., Novak, A., Pfeil, J., Narkizian, J., Deran, A. D., Musselman-Brown, A., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.3772 · Cited by 1,365 · see citing works
The human protein atlas: a spatial map of the human proteome
Thul, P. J., & Lindskog, C. (2018) · Protein Science · doi:10.1002/pro.3307 · Cited by 1,299 · see citing works
Broz, M. L., Binnewies, M., Boldajipour, B., Nelson, A. E., Pollack, J. L., Erle, D. J., Barczak, A., Rosenblum, M. D., Daud, A., Barber, D. L., et al. (2014) · Cancer Cell · doi:10.1016/j.ccell.2014.09.007 · Cited by 1,207 · see citing works
Digital pathology and artificial intelligence
Niazi, M. K. K., Parwani, A. V., & Gurcan, M. N. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30154-8 · Cited by 1,232 · see citing works
Pan-cancer whole-genome analyses of metastatic solid tumours
Priestley, P., Baber, J., Lolkema, M. P., Steeghs, N., de Bruijn, E., Shale, C., Duyvesteyn, K., Haidari, S., van Hoeck, A., Onstenk, W., et al. (2019) · Nature · doi:10.1038/s41586-019-1689-y · Cited by 1,106 · see citing works
Building better biomarkers: brain models in translational neuroimaging
Woo, C.-W., Chang, L. J., Lindquist, M. A., & Wager, T. D. (2017) · Nature Neuroscience · doi:10.1038/nn.4478 · Cited by 1,056 · see citing works
Comprehensive functional genomic resource and integrative model for the human brain
Wang, D., Liu, S., Warrell, J., Won, H., Shi, X., Navarro, F. C. P., Clarke, D., Gu, M., Emani, P., Yang, Y. T., et al. (2018) · Science · doi:10.1126/science.aat8464 · Cited by 965 · see citing works
RETRACTED ARTICLE: Microbiome analyses of blood and tissues suggest cancer diagnostic approach
Poore, G. D., Kopylova, E., Zhu, Q., Carpenter, C., Fraraccio, S., Wandro, S., Kosciolek, T., Janssen, S., Metcalf, J., Song, S. J., et al. (2020) · Nature · doi:10.1038/s41586-020-2095-1 · Cited by 989 · see citing works
RetractedThe technological landscape and applications of single-cell multi-omics
Baysoy, A., Bai, Z., Satija, R., & Fan, R. (2023) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-023-00615-w · Cited by 1,037 · see citing works
Machine Learning Methods for Histopathological Image Analysis
Komura, D., & Ishikawa, S. (2018) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2018.01.001 · Cited by 937 · see citing works
Massive mining of publicly available RNA-seq data from human and mouse
Lachmann, A., Torre, D., Keenan, A. B., Jagodnik, K. M., Lee, H. J., Wang, L., Silverstein, M. C., & Ma’ayan, A. (2018) · Nature Communications · doi:10.1038/s41467-018-03751-6 · Cited by 701 · see citing works
In Vitro Tumor Models: Advantages, Disadvantages, Variables, and Selecting the Right Platform
Katt, M. E., Placone, A. L., Wong, A. D., Xu, Z. S., & Searson, P. C. (2016) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2016.00012 · Cited by 754 · see citing works
McDonald, E. R., de Weck, A., Schlabach, M. R., Billy, E., Mavrakis, K. J., Hoffman, G. R., Belur, D., Castelletti, D., Frias, E., Gampa, K., et al. (2017) · Cell · doi:10.1016/j.cell.2017.07.005 · Cited by 658 · see citing works
Evidence for 28 genetic disorders discovered by combining healthcare and research data
Kaplanis, J., Samocha, K. E., Wiel, L., Zhang, Z., Arvai, K. J., Eberhardt, R. Y., Gallone, G., Lelieveld, S. H., Martin, H. C., McRae, J. F., et al. (2020) · Nature · doi:10.1038/s41586-020-2832-5 · Cited by 615 · see citing works
A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme
Lao, J., Chen, Y., Li, Z.-C., Li, Q., Zhang, J., Liu, J., & Zhai, G. (2017) · Scientific Reports · doi:10.1038/s41598-017-10649-8 · Cited by 630 · see citing works
Tumour hypoxia causes DNA hypermethylation by reducing TET activity
Thienpont, B., Steinbacher, J., Zhao, H., D’Anna, F., Kuchnio, A., Ploumakis, A., Ghesquière, B., Van Dyck, L., Boeckx, B., Schoonjans, L., et al. (2016) · Nature · doi:10.1038/nature19081 · Cited by 635 · see citing works
Microenvironment drives cell state, plasticity, and drug response in pancreatic cancer
Raghavan, S., Winter, P. S., Navia, A. W., Williams, H. L., DenAdel, A., Lowder, K. E., Galvez-Reyes, J., Kalekar, R. L., Mulugeta, N., Kapner, K. S., et al. (2021) · Cell · doi:10.1016/j.cell.2021.11.017 · Cited by 592 · see citing works
Therapeutic opportunities within the DNA damage response
Pearl, L. H., Schierz, A. C., Ward, S. E., Al-Lazikani, B., & Pearl, F. M. G. (2015) · Nature Reviews Cancer · doi:10.1038/nrc3891 · Cited by 587 · see citing works
Metabolic landscape of the tumor microenvironment at single cell resolution
Xiao, Z., Dai, Z., & Locasale, J. W. (2019) · Nature Communications · doi:10.1038/s41467-019-11738-0 · Cited by 524 · see citing works
Genetic Mechanisms of Immune Evasion in Colorectal Cancer
Grasso, C. S., Giannakis, M., Wells, D. K., Hamada, T., Mu, X. J., Quist, M., Nowak, J. A., Nishihara, R., Qian, Z. R., Inamura, K., et al. (2018) · Cancer Discovery · doi:10.1158/2159-8290.cd-17-1327 · Cited by 535 · see citing works
“Precision medicine advances have been fueled by technological advances, such as targeted sequencing, and also by multi-omic sequencing efforts, such as The Cancer Genome Atlas (TCGA)(3), which are powered to provide guidance for targeting molecular observations in the form of estimates of the frequencies of drivers to…”
BackgroundA pathology foundation model for cancer diagnosis and prognosis prediction
Wang, X., Zhao, J., Marostica, E., Yuan, W., Jin, J., Zhang, J., Li, R., Tang, H., Wang, K., Li, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07894-z · Cited by 580 · see citing works
Gene‐specific correlation of RNA and protein levels in human cells and tissues
Edfors, F., Danielsson, F., Hallström, B. M., Käll, L., Lundberg, E., Pontén, F., Forsström, B., & Uhlén, M. (2016) · Molecular Systems Biology · doi:10.15252/msb.20167144 · Cited by 493 · see citing works
Multimodal machine learning in precision health: A scoping review
Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., Wang, F., Cheng, F., & Luo, Y. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00712-8 · Cited by 528 · see citing works
“This is likely fostered by curated databases that lend themselves to multi-modality predictions such as Alzheimer ’ s Disease Neuroimaging Initiative 165 and The Cancer Genome Atlas Program 166 .”
BackgroundGenome-wide analysis of noncoding regulatory mutations in cancer
Weinhold, N., Jacobsen, A., Schultz, N., Sander, C., & Lee, W. (2014) · Nature Genetics · doi:10.1038/ng.3101 · Cited by 484 · see citing works
Self-citationNagpal, K., Foote, D., Liu, Y., Chen, P.-H. C., Wulczyn, E., Tan, F., Olson, N., Smith, J. L., Mohtashamian, A., Wren, J. H., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0112-2 · Cited by 495 · see citing works
Federated learning for medical image analysis: A survey
Guan, H., Yap, P.-T., Bozoki, A., & Liu, M. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110424 · Cited by 518 · see citing works
Mutations and mechanisms of WNT pathway tumour suppressors in cancer
Bugter, J. M., Fenderico, N., & Maurice, M. M. (2021) · Nature Reviews Cancer · doi:10.1038/s41568-020-00307-z · Cited by 466 · see citing works
Deep learning with multimodal representation for pancancer prognosis prediction
Cheerla, A., & Gevaert, O. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz342 · Cited by 421 · see citing works
MOLI: multi-omics late integration with deep neural networks for drug response prediction
Sharifi-Noghabi, H., Zolotareva, O., Collins, C. C., & Ester, M. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz318 · Cited by 421 · see citing works
The genomics of oxidative DNA damage, repair, and resulting mutagenesis
Poetsch, A. R. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.12.013 · Cited by 420 · see citing works
Suda, K., Nakaoka, H., Yoshihara, K., Ishiguro, T., Tamura, R., Mori, Y., Yamawaki, K., Adachi, S., Takahashi, T., Kase, H., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.07.037 · Cited by 414 · see citing works
How to build the virtual cell with artificial intelligence: Priorities and opportunities
Bunne, C., Roohani, Y., Rosen, Y., Gupta, A., Zhang, X., Roed, M., Alexandrov, T., AlQuraishi, M., Brennan, P., Burkhardt, D. B., et al. (2024) · Cell · doi:10.1016/j.cell.2024.11.015 · Cited by 393 · see citing works
Tumour-infiltrating Gr-1+ myeloid cells antagonize senescence in cancer
Di Mitri, D., Toso, A., Chen, J. J., Sarti, M., Pinton, S., Jost, T. R., D’Antuono, R., Montani, E., Garcia-Escudero, R., Guccini, I., et al. (2014) · Nature · doi:10.1038/nature13638 · Cited by 388 · see citing works
Matson, V., Chervin, C. S., & Gajewski, T. F. (2021) · Gastroenterology · doi:10.1053/j.gastro.2020.11.041 · Cited by 385 · see citing works
Gene expression analysis identifies global gene dosage sensitivity in cancer
Fehrmann, R. S. N., Karjalainen, J. M., Krajewska, M., Westra, H.-J., Maloney, D., Simeonov, A., Pers, T. H., Hirschhorn, J. N., Jansen, R. C., Schultes, E. A., et al. (2015) · Nature Genetics · doi:10.1038/ng.3173 · Cited by 347 · see citing works
Guo, T., Kouvonen, P., Koh, C. C., Gillet, L. C., Wolski, W. E., Röst, H. L., Rosenberger, G., Collins, B. C., Blum, L. C., Gillessen, S., et al. (2015) · Nature Medicine · doi:10.1038/nm.3807 · Cited by 376 · see citing works
Chen, K., Chen, Z., Wu, D., Zhang, L., Lin, X., Su, J., Rodriguez, B., Xi, Y., Xia, Z., Chen, X., et al. (2015) · Nature Genetics · doi:10.1038/ng.3385 · Cited by 373 · see citing works
Federated Learning and Differential Privacy for Medical Image Analysis
Adnan, M., Kalra, S., Cresswell, J. C., Taylor, G. W., & Tizhoosh, H. (2021) · doi:10.21203/rs.3.rs-1005694/v1 · Cited by 464 · see citing works
Li, Y., Ge, X., Peng, F., Li, W., & Li, J. J. (2022) · Genome Biology · doi:10.1186/s13059-022-02648-4 · Cited by 303 · see citing works
Cloud computing for genomic data analysis and collaboration
Langmead, B., & Nellore, A. (2018) · Nature Reviews Genetics · doi:10.1038/nrg.2017.113 · Cited by 310 · see citing works
Correlation of an epigenetic mitotic clock with cancer risk
Yang, Z., Wong, A., Kuh, D., Paul, D. S., Rakyan, V. K., Leslie, R. D., Zheng, S. C., Widschwendter, M., Beck, S., & Teschendorff, A. E. (2016) · Genome Biology · doi:10.1186/s13059-016-1064-3 · Cited by 325 · see citing works
SHP2 is required for growth of KRAS-mutant non-small-cell lung cancer in vivo
Mainardi, S., Mulero-Sánchez, A., Prahallad, A., Germano, G., Bosma, A., Krimpenfort, P., Lieftink, C., Steinberg, J. D., de Wit, N., Gonçalves-Ribeiro, S., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0023-9 · Cited by 320 · see citing works
Xu, J., Yang, P., Xue, S., Sharma, B., Sanchez-Martin, M., Wang, F., Beaty, K. A., Dehan, E., & Parikh, B. (2019) · Human Genetics · doi:10.1007/s00439-019-01970-5 · Cited by 305 · see citing works
HER2 Activating Mutations Are Targets for Colorectal Cancer Treatment
Kavuri, S. M., Jain, N., Galimi, F., Cottino, F., Leto, S. M., Migliardi, G., Searleman, A. C., Shen, W., Monsey, J., Trusolino, L., et al. (2015) · Cancer Discovery · doi:10.1158/2159-8290.cd-14-1211 · Cited by 302 · see citing works
The therapeutic potential of targeting tryptophan catabolism in cancer
Opitz, C. A., Somarribas Patterson, L. F., Mohapatra, S. R., Dewi, D. L., Sadik, A., Platten, M., & Trump, S. (2020) · British Journal of Cancer · doi:10.1038/s41416-019-0664-6 · Cited by 300 · see citing works
Rubio-Perez, C., Tamborero, D., Schroeder, M. P., Antolín, A. A., Deu-Pons, J., Perez-Llamas, C., Mestres, J., Gonzalez-Perez, A., & Lopez-Bigas, N. (2015) · Cancer Cell · doi:10.1016/j.ccell.2015.02.007 · Cited by 296 · see citing works
Flaherty, K. T., Gray, R. J., Chen, A. P., Li, S., McShane, L. M., Patton, D., Hamilton, S. R., Williams, P. M., Iafrate, A. J., Sklar, J., et al. (2020) · Journal of Clinical Oncology · doi:10.1200/jco.19.03010 · Cited by 294 · see citing works
Tellez, D., Balkenhol, M., Otte-Höller, I., van de Loo, R., Vogels, R., Bult, P., Wauters, C., Vreuls, W., Mol, S., Karssemeijer, N., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2820199 · Cited by 288 · see citing works
Genomic Instability of iPSCs: Challenges Towards Their Clinical Applications
Yoshihara, M., Hayashizaki, Y., & Murakawa, Y. (2017) · Stem Cell Reviews and Reports · doi:10.1007/s12015-016-9680-6 · Cited by 283 · see citing works
Deep learning-based survival prediction for multiple cancer types using histopathology images
Wulczyn, E., Steiner, D. F., Xu, Z., Sadhwani, A., Wang, H., Flament-Auvigne, I., Mermel, C. H., Chen, P.-H. C., Liu, Y., & Stumpe, M. C. (2020) · PLOS ONE · doi:10.1371/journal.pone.0233678 · Cited by 266 · see citing works
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518 · see citing works
“…leading to potential subpopulation shift issues, e.g,. a rare cancer subtype in one context might be more common in another; or even if it remains rare, we would seek to leverage the greater quantity of data from other subtypes to improve model accuracy on the rare subtype (Weinstein et al., 2013).”
Li, Y., El Habib Daho, M., Conze, P.-H., Zeghlache, R., Le Boité, H., Tadayoni, R., Cochener, B., Lamard, M., & Quellec, G. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108635 · Cited by 274 · see citing works
Garg, A. D., Vandenberk, L., Koks, C., Verschuere, T., Boon, L., Van Gool, S. W., & Agostinis, P. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aae0105 · Cited by 270 · see citing works
Long-term cancer survival prediction using multimodal deep learning
Vale-Silva, L. A., & Rohr, K. (2021) · Scientific Reports · doi:10.1038/s41598-021-92799-4 · Cited by 268 · see citing works
Hammerl, D., Martens, J. W. M., Timmermans, M., Smid, M., Trapman-Jansen, A. M., Foekens, R., Isaeva, O. I., Voorwerk, L., Balcioglu, H. E., Wijers, R., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25962-0 · Cited by 262 · see citing works
Taftaf, R., Liu, X., Singh, S., Jia, Y., Dashzeveg, N. K., Hoffmann, A. D., El-Shennawy, L., Ramos, E. K., Adorno-Cruz, V., Schuster, E. J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25189-z · Cited by 261 · see citing works
Big data in basic and translational cancer research
Jiang, P., Sinha, S., Aldape, K., Hannenhalli, S., Sahinalp, C., & Ruppin, E. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00502-0 · Cited by 254 · see citing works
Modified penetrance of coding variants by cis-regulatory variation contributes to disease risk
Castel, S. E., Cervera, A., Mohammadi, P., Aguet, F., Reverter, F., Wolman, A., Guigo, R., Iossifov, I., Vasileva, A., & Lappalainen, T. (2018) · Nature Genetics · doi:10.1038/s41588-018-0192-y · Cited by 217 · see citing works
A Vision-Language Foundation Model for Precision Oncology
Xiang, J., Wang, X., Zhang, X., Xi, Y., Eweje, F., Chen, Y., Li, Y., Bergstrom, C., Gopaulchan, M., Kim, T., et al. (2025) · Nature · doi:10.1038/s41586-024-08378-w · Cited by 256 · see citing works
Feng, X., Arang, N., Rigiracciolo, D. C., Lee, J. S., Yeerna, H., Wang, Z., Lubrano, S., Kishore, A., Pachter, J. A., König, G. M., et al. (2019) · Cancer Cell · doi:10.1016/j.ccell.2019.01.009 · Cited by 237 · see citing works
An, X., Zhu, Y., Zheng, T., Wang, G., Zhang, M., Li, J., Ji, H., Li, S., Yang, S., Xu, D., et al. (2019) · Molecular Therapy Nucleic Acids · doi:10.1016/j.omtn.2018.11.003 · Cited by 188 · see citing works
CancerMine: a literature-mined resource for drivers, oncogenes and tumor suppressors in cancer
Lever, J., Zhao, E. Y., Grewal, J., Jones, M. R., & Jones, S. J. M. (2019) · Nature Methods · doi:10.1038/s41592-019-0422-y · Cited by 205 · see citing works
Park, C., Ha, J., & Park, S. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112873 · Cited by 184 · see citing works
Benchmarking Self-Supervised Learning on Diverse Pathology Datasets
Kang, M., Song, H., Park, S., Yoo, D., & Pereira, S. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00326 · Cited by 187 · see citing works
Is There a Clinical Future for IDO1 Inhibitors After the Failure of Epacadostat in Melanoma?
Van den Eynde, B. J., van Baren, N., & Baurain, J.-F. (2020) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-030419-033635 · Cited by 175 · see citing works
Machine learning: a powerful tool for gene function prediction in plants
Mahood, E. H., Kruse, L. H., & Moghe, G. D. (2020) · Applications in Plant Sciences · doi:10.1002/aps3.11376 · Cited by 153 · see citing works
The HUSH complex is a gatekeeper of type I interferon through epigenetic regulation of LINE-1s
Tunbak, H., Enriquez-Gasca, R., Tie, C. H. C., Gould, P. A., Mlcochova, P., Gupta, R. K., Fernandes, L., Holt, J., van der Veen, A. G., Giampazolias, E., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19170-5 · Cited by 162 · see citing works
Myeloid cell heterogeneity in cancer: not a single cell alike
Kiss, M., Van Gassen, S., Movahedi, K., Saeys, Y., & Laoui, D. (2018) · Cellular Immunology · doi:10.1016/j.cellimm.2018.02.008 · Cited by 159 · see citing works
Variational autoencoders for cancer data integration: design principles and computational practice
Simidjievski, N., Bodnar, C., Tariq, I., Scherer, P., Andres Terre, H., Shams, Z., Jamnik, M., & Liò, P. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.01205 · Cited by 145 · see citing works
A multimodal whole-slide foundation model for pathology
Ding, T., Wagner, S. J., Song, A. H., Chen, R. J., Lu, M. Y., Zhang, A., Vaidya, A. J., Jaume, G., Shaban, M., Kim, A., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03982-3 · Cited by 171 · see citing works
Guo, W., Li, H., Zhu, Y., Lan, L., Yang, S., Drukker, K., Morris, E. A., Burnside, E. S., Whitman, G. J., Giger, M. L., et al. (2015) · Journal of Medical Imaging · doi:10.1117/1.jmi.2.4.041007 · Cited by 148 · see citing works
Dolezal, J. M., Srisuwananukorn, A., Karpeyev, D., Ramesh, S., Kochanny, S., Cody, B., Mansfield, A. S., Rakshit, S., Bansal, R., Bois, M. C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34025-x · Cited by 147 · see citing works
Predicting drug-target interaction network using deep learning model
You, J., McLeod, R. D., & Hu, P. (2019) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2019.03.016 · Cited by 150 · see citing works
Romero, J. M., Grünwald, B., Jang, G.-H., Bavi, P. P., Jhaveri, A., Masoomian, M., Fischer, S. E., Zhang, A., Denroche, R. E., Lungu, I. M., et al. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-2803 · Cited by 146 · see citing works
Precise detection of low-level somatic mutation in resected epilepsy brain tissue
Sim, N. S., Ko, A., Kim, W. K., Kim, S. H., Kim, J. S., Shim, K.-W., Aronica, E., Mijnsbergen, C., Spliet, W. G. M., Koh, H. Y., et al. (2019) · Acta Neuropathologica · doi:10.1007/s00401-019-02052-6 · Cited by 137 · see citing works
Yottixel – An Image Search Engine for Large Archives of Histopathology Whole Slide Images
Kalra, S., Tizhoosh, H. R., Choi, C., Shah, S., Diamandis, P., Campbell, C. J. V., & Pantanowitz, L. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101757 · Cited by 139 · see citing works
Application of Artificial Intelligence in Lung Cancer
Chiu, H.-Y., Chao, H.-S., & Chen, Y.-M. (2022) · Cancers · doi:10.3390/cancers14061370 · Cited by 138 · see citing works
Katsonis, P., & Lichtarge, O. (2014) · Genome Research · doi:10.1101/gr.176214.114 · Cited by 137 · see citing works
Conditional Selection of Genomic Alterations Dictates Cancer Evolution and Oncogenic Dependencies
Mina, M., Raynaud, F., Tavernari, D., Battistello, E., Sungalee, S., Saghafinia, S., Laessle, T., Sanchez-Vega, F., Schultz, N., Oricchio, E., et al. (2017) · Cancer Cell · doi:10.1016/j.ccell.2017.06.010 · Cited by 129 · see citing works
Liu, M., Thomas, S. L., DeWitt, A. K., Zhou, W., Madaj, Z. B., Ohtani, H., Baylin, S. B., Liang, G., & Jones, P. A. (2018) · Cancer Research · doi:10.1158/0008-5472.can-17-3953 · Cited by 127 · see citing works
Johnson, G. L., Stuhlmiller, T. J., Angus, S. P., Zawistowski, J. S., & Graves, L. M. (2014) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-13-1081 · Cited by 129 · see citing works
Casein kinase 1α: biological mechanisms and theranostic potential
Jiang, S., Zhang, M., Sun, J., & Yang, X. (2018) · Cell Communication and Signaling · doi:10.1186/s12964-018-0236-z · Cited by 130 · see citing works
Fast and scalable search of whole-slide images via self-supervised deep learning
Chen, C., Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Schaumberg, A. J., & Mahmood, F. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00929-8 · Cited by 130 · see citing works
A clinical benchmark of public self-supervised pathology foundation models
Campanella, G., Chen, S., Singh, M., Verma, R., Muehlstedt, S., Zeng, J., Stock, A., Croken, M., Veremis, B., Elmas, A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58796-1 · Cited by 126 · see citing works
Determining sequencing depth in a single-cell RNA-seq experiment
Zhang, M. J., Ntranos, V., & Tse, D. (2020) · Nature Communications · doi:10.1038/s41467-020-14482-y · Cited by 112 · see citing works
Efficient Generation of Transcriptomic Profiles by Random Composite Measurements
Cleary, B., Cong, L., Cheung, A., Lander, E. S., & Regev, A. (2017) · Cell · doi:10.1016/j.cell.2017.10.023 · Cited by 114 · see citing works
Steps to avoid overuse and misuse of machine learning in clinical research
Volovici, V., Syn, N. L., Ercole, A., Zhao, J. J., & Liu, N. (2022) · Nature Medicine · doi:10.1038/s41591-022-01961-6 · Cited by 110 · see citing works
NOTCH-mediated non-cell autonomous regulation of chromatin structure during senescence
Parry, A. J., Hoare, M., Bihary, D., Hänsel-Hertsch, R., Smith, S., Tomimatsu, K., Mannion, E., Smith, A., D’Santos, P., Russell, I. A., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04283-9 · Cited by 102 · see citing works
Schouten, D., Nicoletti, G., Dille, B., Chia, C., Vendittelli, P., Schuurmans, M., Litjens, G., & Khalili, N. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103621 · Cited by 108 · see citing works
- Authors
- The Cancer Genome Atlas Research Network, Weinstein, J. N., Collisson, E. A., Mills, G. B., Shaw, K. R. M., Ozenberger, B. A., Ellrott, K., Shmulevich, I., Sander, C., & Stuart, J. M.
- Affiliations
- The University of Texas MD Anderson Cancer Center (US)
- University of California, San Francisco (US)
- National Institutes of Health (US)
- University of California, Santa Cruz (US)
- Memorial Sloan Kettering Cancer Center (US)
- Corresponding author
- Stuart, Joshua M
- ORCID
- Venue
- Nature Genetics
- Published
- 2013-09-26
- Volume
- 45
- Issue
- 10
- Pages
- 1113-1120
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC3919969
- OpenAlex
- W2158485828
- Cited by
- 9,092
- References
- 44
- Citations by year
- 2026: 884 · 2025: 1024 · 2024: 1102 · 2023: 948 · 2022: 908 · 2021: 949 · 2020: 842 · 2019: 675 · 2018: 538 · 2017: 442 · 2016: 360 · 2015: 260 · 2014: 137 · 2013: 15
- FWCI
- 90.66
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Genetics (publishedVersion, cc-by-nc-sa)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eSpace (Curtin University) (submittedVersion)
- Chalmers Publication Library (Chalmers University of Technology) (submittedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by-nc-sa)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- Scholarworks@UNIST (Ulsan National Institute of Science and Technology) (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by-nc-sa)
- Funders
- National Human Genome Research Institute (R01HG005690)
- National Cancer Institute (P50CA098258, U24CA143882, U24CA143840, U24CA143858, K08CA137153, R01CA180778, U01CA168394, U24CA143883, U24CA143835, P50CA083639, P01CA101937, P30CA008748, P30CA016672)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- pan-cancer analysis, TCGA, genetic abnormalities, targeted cancer therapy, drug repurposing, tumor lineage, epigenetic modifications, RNA expression profiling, protein expression profiling
- MeSH terms
- Humans, Neoplasms, Genome, Gene Expression Profiling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
