Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer
Open accessAbstract
We conducted comprehensive integrative molecular analyses of the complete set of tumors in The Cancer Genome Atlas (TCGA), consisting of approximately 10,000 specimens and representing 33 types of cancer. We performed molecular clustering using data on chromosome-arm-level aneuploidy, DNA hypermethylation, mRNA, and miRNA expression levels and reverse-phase protein arrays, of which all, except for aneuploidy, revealed clustering primarily organized by histology, tissue type, or anatomic origin. The influence of cell type was evident in DNA-methylation-based clustering, even after excluding sites with known preexisting tissue-type-specific methylation. Integrative clustering further emphasized the dominant role of cell-of-origin patterns. Molecular similarities among histologically or anatomically related cancer types provide a basis for focused pan-cancer analyses, such as pan-gastrointestinal, pan-gynecological, pan-kidney, and pan-squamous cancers, and those related by stemness features, which in turn may inform strategies for future therapeutic development.
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The nature of mutations differs for each cancer and in each individual.
“The nature of these mutations differs for each cancer [90] and in each individual [90,91,92].”
From The Tumor-on-Chip: Recent Advances in the Development of Microfluidic Systems to Recapitulate the Physiology of Solid Tumors (2019) · cites this paper as [92]
The Introduction describes diverse genomic aberrations across cancer types, and Results > Mutational Assessment of iClusters reports differences in mutational burden and signatures among tumor groups. It also reports widely varying mutation frequencies within two clusters, but does not establish that the nature of mutations differs in every individual. (LLM, conf=0.83)
Experimental — accuracy not yet measuredTumors keep transcriptomic features of their parental tissue, so tumors from the same organ resemble each other more than tumors from different organs.
“First, although tumors are readily recognized through gene expression signatures, they retain transcriptomic features of the parental tissue so that tumors arising in the same organ are more similar to each other than tumors arising in different organs.”
From Metabolic reprogramming and cancer progression (2020) · cites this paper as [3]
Tissue of origin is a major factor that defines DNA methylation and gene expression patterns in human tumors.
“A study that classified thousands of human tumors found that tissue of origin was a major factor defining DNA methylation and gene expression patterns (3).”
From Metabolic reprogramming and cancer progression (2020) · cites this paper as [3]
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MethodInfluentialSelf-citationIntegrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma
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“Consequently, these four major cell-of-origin patterns are the subject of separate in-depth reports detailing their distinguishing genomic and molecular features (Berger et al., 2018; Campbell et al., 2018; Liu et al., 2018; Malta et al., 2018; Ricketts et al., 2018).”
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Self-citationComprehensive Pan-Genomic Characterization of Adrenocortical Carcinoma
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Campbell, J. D., Yau, C. S. F., Bowlby, R., Liu, Y., Brennan, K., Fan, H., Taylor, A. M., Wang, C., Walter, V., Akbani, R., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.063 · Cited by 341 · cited 5× · see citing works
“Consequently, these four major cell-of-origin patterns are the subject of separate in-depth reports detailing their distinguishing genomic and molecular features (Berger et al., 2018; Campbell et al., 2018; Liu et al., 2018; Malta et al., 2018; Ricketts et al., 2018).”
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Shen, R., Mo, Q., Schultz, N. D., Seshan, V., Olshen, A. B., Huse, J. T., Ladanyi, M., & Sander, C. (2012) · PLoS ONE · doi:10.1371/journal.pone.0035236 · Cited by 258 · cited 2× · see citing works
“We present a new PanCancer Atlas integrative analysis using iCluster (Shen et al., 2009, 2012) identifying 28 distinct molecular subtypes arising from the 33 different tumor types analyzed across at least four different TCGA platforms.”
MethodSelf-citationThe Macrophage Colony-Stimulating Factor 1 Response Signature in Breast Carcinoma
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“Based on the WGCNA analysis, five immuno-oncology-related immune expression signatures: activation of macrophages/monocytes (Beck et al., 2009), overall lymphocyte infiltration (dominated by T and B cells) (Calabrò et al., 2009), TGF-β response (Teschendorff et al., 2010), IFN-γ response (Wolf et…”
MethodTeschendorff, A. E., Gómez, S., Arenas, À., El‐Ashry, D., Schmidt, M., Gehrmann, M. C., & Caldas, C. (2010) · BMC Cancer · doi:10.1186/1471-2407-10-604 · Cited by 152 · cited 2× · see citing works
“…of macrophages/monocytes (Beck et al., 2009), overall lymphocyte infiltration (dominated by T and B cells) (Calabrò et al., 2009), TGF-β response (Teschendorff et al., 2010), IFN-γ response (Wolf et al., 2014), and wound healing (Chang et al., 2004), robustly reproduced co-clustering of the…”
BackgroundIntegrated genomic analyses of ovarian carcinoma
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“…of stemness, attesting to their underlying molecular heterogeneity, as previously reported (Cancer Genome Atlas Network, 2015; Cancer Genome Atlas Research Network, 2011, 2012, 2014a, 2014b, 2015a, 2015b, 2017; Cancer Genome Atlas Research Network et al., 2013a, 2013b; Robertson et al., 2017).”
BackgroundGene Co-Expression Modules as Clinically Relevant Hallmarks of Breast Cancer Diversity
Wolf, D. M., Lenburg, M. E., Yau, C. S. F., Boudreau, A., & Veer, L. V. ‘. (2014) · PLoS ONE · doi:10.1371/journal.pone.0088309 · Cited by 128 · cited 1× · see citing works
“…overall lymphocyte infiltration (dominated by T and B cells) (Calabrò et al., 2009), TGF-β response (Teschendorff et al., 2010), IFN-γ response (Wolf et al., 2014), and wound healing (Chang et al., 2004), robustly reproduced co-clustering of the immune signature sets, and were selected to…”
BackgroundSelf-citationCalabrò, A., Beißbarth, T., Kuner, R., Stojanov, M., Benner, A., Asslaber, M., Ploner, F., Zatloukal, K., Samonigg, H., Poustka, A., et al. (2008) · Breast Cancer Research and Treatment · doi:10.1007/s10549-008-0105-3 · Cited by 127 · cited 2× · see citing works
“…immune expression signatures: activation of macrophages/monocytes (Beck et al., 2009), overall lymphocyte infiltration (dominated by T and B cells) (Calabrò et al., 2009), TGF-β response (Teschendorff et al., 2010), IFN-γ response (Wolf et al., 2014), and wound healing (Chang et al., 2004),…”
BackgroundChu, J., Sadeghi, S., Raymond, A., Jackman, S. D., Nip, K. M., Mar, R., Mohamadi, H., Butterfield, Y. S. N., Robertson, A. G., & Birol, İ. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu558 · Cited by 123 · cited 2× · see citing works
“The PARADIGM algorithm with the interaction-learning update (Chu et al., 2014; Vaske et al., 2010) was used to infer protein activities in the context of gene regulatory pathways, based on gene expression and copy-number data.”
MethodSelf-citationCancer stem cells and differentiation therapy
Jin, X. J., Jin, X., & Kim, H. (2017) · Tumor Biology · doi:10.1177/1010428317729933 · Cited by 101 · see citing works
Cluster stability and the use of noise in interpretation of clustering
Davidson, G. S., Wylie, B., & Boyack, K. W. (2005) · IEEE Symposium on Information Visualization · doi:10.1109/infvis.2001.963275 · Cited by 76 · cited 1× · see citing works
“The distances were used as input to generate a 2D layout of the samples using the physics-based Distributed Recursive (Graph) Layout method (Alencar and Polley, 2011), previously known as VxOrd (Davidson et al., 2001).”
MethodTumorMap: Exploring the Molecular Similarities of Cancer Samples in an Interactive Portal
Newton, Y., Novak, A. M., Swatloski, T., McColl, D., Chopra, S., Graim, K. S., Weinstein, A., Baertsch, R., Salama, S. R., Ellrott, K., et al. (2017) · Cancer Research · doi:10.1158/0008-5472.can-17-0580 · Cited by 66 · cited 2× · see citing works
“We employed a new 2D visualization approach, TumorMap (Newton et al., 2017), to intepret the relationships between the samples and iClusters.”
MethodSelf-citationMutation signatures reveal biological processes in human cancerPreprint
Covington, K. R., Shinbrot, E., & Wheeler, D. (2016) · bioRxiv · doi:10.1101/036541 · Cited by 25 · cited 1× · see citing works
“Mutational signatures (Covington et al., 2016) also varied among iClusters.”
Self-citationAbstract LB-373: Comprehensive analysis of cancer stemness
Malta, T. M., Sokolov, A., Gentles, A. J., Burzykowski, T., Poisson, L., Weinstein, J. N., Kamińska, B., Huelsken, J., Omberg, L., Gevaert, O., et al. (2018) · Cancer Research · doi:10.1158/1538-7445.am2018-lb-373 · Cited by 2 · see citing works
Self-citation
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Visualizing and interpreting cancer genomics data via the Xena platform
Goldman, M. J., Craft, B., Hastie, M., Repečka, K., McDade, F., Kamath, A., Banerjee, A., Luo, Y., Rogers, D., Brooks, A. N., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0546-8 · Cited by 4,481 · 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
“The existence of shared genomic features across histologies has been highlighted by several studies (Ciriello et al., 2013; Hoadley et al., 2014, 2018), but these studies traditionally used a gene-centric, as opposed to pathway-centric, approach.”
BackgroundSelf-citationPan-cancer analysis of whole genomes
The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium, Aaltonen, L. A., Abascal, F., Abeshouse, A., Aburatani, H., Adams, D. J., Agrawal, N., Ahn, K. S., Ahn, S.-M., Aikata, H., et al. (2020) · Nature · doi:10.1038/s41586-020-1969-6 · Cited by 3,064 · see citing works
Self-citationMetabolic reprogramming and cancer progression
Faubert, B., Solmonson, A., & DeBerardinis, R. J. (2020) · Science · doi:10.1126/science.aaw5473 · Cited by 2,740 · see citing works
“A study that classified thousands of human tumors found that tissue of origin was a major factor defining DNAmethylation and gene expression patterns (3).”
BackgroundRegulation of apoptosis in health and disease: the balancing act of BCL-2 family proteins
Singh, R., Letai, A., & Sarosiek, K. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-018-0089-8 · Cited by 2,111 · see citing works
A Consensus Molecular Classification of Muscle-invasive Bladder Cancer
Kamoun, A., de Reyniès, A., Allory, Y., Sjödahl, G., Robertson, A. G., Seiler, R., Hoadley, K. A., Groeneveld, C. S., Al-Ahmadie, H., Choi, W., et al. (2020) · European Urology · doi:10.1016/j.eururo.2019.09.006 · Cited by 1,359 · see citing works
“We compared the consensus classes with the TCGA PanCancer Atlas integrative classification [23] (Supplementary Fig.”
ResultSelf-citationA compendium of mutational cancer driver genes
Martínez-Jiménez, F., Muiños, F., Sentís, I., Deu-Pons, J., Reyes-Salazar, I., Arnedo-Pac, C., Mularoni, L., Pich, O., Bonet, J., Kranas, H., et al. (2020) · Nature reviews. Cancer · doi:10.1038/s41568-020-0290-x · Cited by 1,278 · see citing works
The Frequency of Ras Mutations in Cancer
Prior, I. A., Hood, F. E., & Hartley, J. L. (2020) · Cancer Research · doi:10.1158/0008-5472.can-19-3682 · Cited by 1,130 · see citing works
“The Pan-Cancer Atlas used TCGA data to define the molecular subtypes of cancer and to generate deep understanding of the genetic programmes associated with driving different cancer types (45).”
MethodThe chromatin accessibility landscape of primary human cancers
Corces, M. R., Granja, J. M., Shams, S., Louie, B. H., Seoane, J. A., Zhou, W., Silva, T. C., Groeneveld, C., Wong, C. K., Cho, S. W., et al. (2018) · Science · doi:10.1126/science.aav1898 · Cited by 1,230 · see citing works
“We found strong concordance between this ATAC-seq–based clustering and the published multiomic iCluster scheme using TCGA mRNA-seq, microRNA (miRNA)–seq, DNAmethylation, reverse-phase protein array (RPPA), and DNA copy number data (28) (Fig.”
BackgroundMethodSelf-citationRegulatory T Cells and Human Disease
Sakaguchi, S., Mikami, N., Wing, J. B., Tanaka, A., Ichiyama, K., & Ohkura, N. (2020) · Annual Review of Immunology · doi:10.1146/annurev-immunol-042718-041717 · Cited by 1,133 · see citing works
“The immune signature of a tumor is linked to its tissue of origin, suggesting a link between tumor- and tissue-resident Tregs (154, 155).”
BackgroundLiquid biopsy enters the clinic — implementation issues and future challenges
Ignatiadis, M., Sledge, G. W., & Jeffrey, S. S. (2021) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-020-00457-x · Cited by 1,191 · 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
RetractedCo-occurring genomic alterations in non-small-cell lung cancer biology and therapy
Skoulidis, F., & Heymach, J. V. (2019) · Nature Reviews Cancer · doi:10.1038/s41568-019-0179-8 · Cited by 1,019 · see citing works
de Bruijn, I., Kundra, R., Mastrogiacomo, B., Tran, T. N., Sikina, L., Mazor, T., Li, X., Ochoa, A., Zhao, G., Lai, B., et al. (2023) · Cancer Research · doi:10.1158/0008-5472.can-23-0816 · Cited by 886 · see citing works
Self-citationTriple negative breast cancer: Pitfalls and progress
Zagami, P., & Carey, L. (2022) · npj Breast Cancer · doi:10.1038/s41523-022-00468-0 · Cited by 899 · see citing works
Intratumoral heterogeneity in cancer progression and response to immunotherapy
Vitale, I., Shema, E., Loi, S., & Galluzzi, L. (2021) · Nature Medicine · doi:10.1038/s41591-021-01233-9 · Cited by 823 · see citing works
hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data
Morabito, S., Reese, F., Rahimzadeh, N., Miyoshi, E., & Swarup, V. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100498 · Cited by 773 · see citing works
“To date, it remains cost-prohibitive for most researchers to perform high-dimensional -omics studies of large patient cohorts, but there are numerous large-scale disease-relevant bulk RNA-seq datasets containing thousands of samples from consortia such as the Encyclopedia of DNA Elements (ENCODE),(60) the Genotype-Tis…”
BackgroundAdvances in bladder cancer biology and therapy
Tran, L. L., Xiao, J.-F., Agarwal, N., Duex, J. E., & Theodorescu, D. (2020) · Nature reviews. Cancer · doi:10.1038/s41568-020-00313-1 · Cited by 742 · see citing works
Nanopore native RNA sequencing of a human poly(A) transcriptome
Workman, R. E., Tang, A. D., Tang, P. S., Jain, M., Tyson, J. R., Razaghi, R., Zuzarte, P. C., Gilpatrick, T., Payne, A., Quick, J., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0617-2 · Cited by 708 · see citing works
Human FOXP3+ Regulatory T Cell Heterogeneity and Function in Autoimmunity and Cancer
Wing, J. B., Tanaka, A., & Sakaguchi, S. (2019) · Immunity · doi:10.1016/j.immuni.2019.01.020 · Cited by 726 · see citing works
Context is everything: aneuploidy in cancer
Ben‐David, U., & Amon, A. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0171-x · Cited by 651 · see citing works
Stem cell fate in cancer growth, progression and therapy resistance
Lytle, N. K., Barber, A. G., & Reya, T. (2018) · Nature reviews. Cancer · doi:10.1038/s41568-018-0056-x · Cited by 645 · see citing works
The PI3K-AKT-mTOR Pathway and Prostate Cancer: At the Crossroads of AR, MAPK, and WNT Signaling
Shorning, B. Y., Dass, M. S., Smalley, M. J., & Pearson, H. B. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21124507 · Cited by 638 · see citing works
A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers
Berger, A. C., Korkut, A., Kanchi, R. S., Hegde, A. M., Lenoir, W. F., Liu, W., Liu, Y., Fan, H., Shen, H., Ravikumar, V., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.03.014 · Cited by 606 · see citing works
“similarities at the molecular level have been identified across gynecologic and breast cancers in a comprehensive analysis of all 33 TCGA tumor types (Hoadley et al., 2018).”
Self-citationComparative Molecular Analysis of Gastrointestinal Adenocarcinomas
Liu, Y., Sethi, N. S., Hinoue, T., Schneider, B., Cherniack, A., Sánchez-Vega, F., Seoane, J., Farshidfar, F., Bowlby, R., Islam, M., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.03.010 · Cited by 504 · see citing works
“These results are consistent with integrated clustering analysis across multiple platforms of 10,000 TCGA tumors, which identified GIACs as a distinct group (Hoadley et al., 2018).”
Self-citationThe UCSC Xena platform for public and private cancer genomics data visualization and interpretationPreprint
Goldman, M., Craft, B., Hastie, M., Repečka, K., Kamath, A., McDade, F., Rogers, D., Brooks, A. N., Zhu, J., & Haussler, D. H. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/326470 · Cited by 604 · see citing works
Circulating tumor DNA and liquid biopsy in oncology
Cescon, D. W., Bratman, S. V., Chan, S. M., & Siu, L. L. (2020) · Nature Cancer · doi:10.1038/s43018-020-0043-5 · Cited by 600 · see citing works
Luo, H., Xia, X., Huang, L., An, H., Cao, M., Kim, G. D., Chen, H., Zhang, W., Shu, Y., Kong, X., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34395-2 · Cited by 585 · see citing works
Self-citationMulti-omic and multi-view clustering algorithms: review and cancer benchmark
Rappoport, N., & Ron Shamir, A. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky889 · Cited by 549 · see citing works
“iCluster was also recently used to cluster more than ten thousand tumors from 33 cancers in a pancancer analysis (96).”
MethodPan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions
Narunsky-Haziza, L., Sepich-Poore, G. D., Livyatan, I., Asraf, O., Martino, C., Nejman, D., Gavert, N., Stajich, J. E., Amit, G., González, A., et al. (2022) · Cell · doi:10.1016/j.cell.2022.09.005 · Cited by 538 · see citing works
SeSAMe: reducing artifactual detection of DNA methylation by Infinium BeadChips in genomic deletions
Zhou, W., Triche, T. J., Laird, P. W., & Shen, H. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky691 · Cited by 536 · see citing works
“We use the order of the matched samples from a previous study based on SNP array platform (41).”
MethodSelf-citationSingle-cell multiomics: technologies and data analysis methods
Lee, J., Hyeon, D. Y., & Hwang, D. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-0420-2 · Cited by 524 · see citing works
Self-citationSignatures of copy number alterations in human cancer
Steele, C. D., Abbasi, A., Islam, S. M. A., Bowes, A. L., Khandekar, A., Haase, K., Hames-Fathi, S., Ajayi, D., Verfaillie, A., Dhami, P., et al. (2022) · Nature · doi:10.1038/s41586-022-04738-6 · Cited by 487 · see citing works
RNA sequence analysis reveals macroscopic somatic clonal expansion across normal tissues
Yizhak, K., Aguet, F., Kim, J., Hess, J. M., Kübler, K., Grimsby, J., Frazer, R., Zhang, H., Haradhvala, N. J., Rosebrock, D., et al. (2019) · Science · doi:10.1126/science.aaw0726 · Cited by 500 · see citing works
Self-citationClinical epigenetics: seizing opportunities for translation
Berdasco, M., & Esteller, M. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0074-2 · Cited by 501 · see citing works
Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia
Granja, J. M., Klemm, S., McGinnis, L. M., Kathiria, A. S., Mezger, A., Corces, M. R., Parks, B., Gars, E., Liedtke, M., Zheng, G. X. Y., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0332-7 · Cited by 498 · see citing works
Synthetic lethality as an engine for cancer drug target discovery
Huang, A., Garraway, L. A., Ashworth, A., & Weber, B. L. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0046-z · Cited by 470 · see citing works
DNA Methylation Cancer Biomarkers: Translation to the Clinic
Locke, W. J., Guanzon, D., Ma, C., Liew, Y. J., Duesing, K., Fung, K. Y. C., & Ross, J. P. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.01150 · Cited by 472 · see citing works
From Big Data to Precision Medicine
Hulsen, T., Jamuar, S. S., Moody, A. R., Karnes, J. H., Varga, O., Hedensted, S., Spreafico, R., Hafler, D. A., & McKinney, E. F. (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00034 · Cited by 466 · see citing works
Machine learning for precision medicine
MacEachern, S. J., & Forkert, N. D. (2020) · Genome · doi:10.1139/gen-2020-0131 · Cited by 464 · see citing works
“…1900s revolutionized blood Page 3 of 32 © The Author(s) or their Institution(s) Genome Draft 4 1 transfusions, allowing for matching of donor and recipient blood types, and avoiding 2 complications associated with mismatched donor and recipient blood (Dance, 2016; 3 Giangrande, 2000; Hodson, 2016).”
BackgroundA clinically applicable integrative molecular classification of meningiomas
Nassiri, F., Liu, J., Patil, V. S., Mamatjan, Y., Wang, J. Z., Hugh-White, R., Macklin, A. M., Khan, S., Singh, O., Karimi, S., et al. (2021) · Nature · doi:10.1038/s41586-021-03850-3 · Cited by 459 · see citing works
Self-citationPerspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics
Ding, L., Bailey, M. H., Porta-Pardo, E., Thorsson, V., Colaprico, A., Bertrand, D., Gibbs, D. L., Weerasinghe, A., Huang, K.-L., Tokheim, C., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.033 · Cited by 399 · see citing works
“The TCGA PanCancer Atlas project has a special focus on the oncogenic processes governing cancer development and progression, with its ten analysis working groups (AWGs) presenting their findings.”
InfluentialSelf-citationMolecular characterization and clinical relevance of m6A regulators across 33 cancer types
Li, Y., Xiao, J. Y., Bai, J., Tian, Y., Qu, Y., Chen, X., Wang, Q., Li, X., Zhang, Y., & Xu, J. (2019) · Molecular Cancer · doi:10.1186/s12943-019-1066-3 · Cited by 399 · see citing works
Dong, L., Lu, D., Chen, R., Lin, Y., Zhu, H., Zhang, Z., Cai, S., Cui, P., Song, G., Rao, D., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.12.006 · Cited by 400 · see citing works
Self-citationContext-dependent roles of complement in cancer
Roumenina, L. T., Daugan, M. V., Petitprez, F., Sautès‐Fridman, C., & Fridman, W. H. (2019) · Nature reviews. Cancer · doi:10.1038/s41568-019-0210-0 · Cited by 387 · see citing works
Hornbeck, P. V., Kornhauser, J. M., Latham, V. M., Murray, B., Nandhikonda, V., Nord, A., Skrzypek, E., Wheeler, T. J., Zhang, B., & Gnad, F. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1159 · Cited by 348 · see citing works
“For example, we may incorporate a subset of missense mutations from 18 additional cancer types in the TCGA PanCancer Atlas (22).”
BackgroundTranscription regulation by long non-coding RNAs: mechanisms and disease relevance
Ferrer, J. P., & Dimitrova, N. (2024) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-023-00694-9 · Cited by 346 · see citing works
“Examples include PVT1 structural mutations in multiple cancer types 74,219,220 , amplification of FAL1 in approximately 10% of liver cancer 221 , amplification of SAMMSON in 10% of melanoma 222 and loss of ANRIL in >50% of glioblastomas 223 .”
Comprehensive Analysis of Genetic Ancestry and Its Molecular Correlates in Cancer
Carrot‐Zhang, J., Chambwe, N., Damrauer, J. S., Knijnenburg, T., Robertson, A. G., Yau, C. S. F., Zhou, W., Berger, A. C., Huang, K., Newberg, J. Y., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.04.012 · Cited by 328 · see citing works
Self-citationMetastasis and Immune Evasion from Extracellular cGAMP Hydrolysis
Li, J., Duran, M. A., Dhanota, N., Chatila, W. K., Bettigole, S. E., Kwon, J., Sriram, R. K., Humphries, M. P., Salto-Tellez, M., James, J. A., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-0387 · Cited by 312 · see citing works
“gov/about-data/publications/pancanatlas) (43).”
Self-citationLung cancer in patients who have never smoked — an emerging disease
LoPiccolo, J., Gusev, A., Christiani, D. C., & Janne, P. A. (2024) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-023-00844-0 · Cited by 307 · see citing works
Estrogen Signaling in Endometrial Cancer: a Key Oncogenic Pathway with Several Open Questions
Rodriguez, A. C., Blanchard, Z., Maurer, K. A., & Gertz, J. (2019) · Hormones and Cancer · doi:10.1007/s12672-019-0358-9 · Cited by 291 · see citing works
“8% of tumors harboring an ER mutation [132], representing approximately 3500 new ER mutant endometrial cancer diagnoses in the USA each year.”
BackgroundFreed-Pastor, W. A., Lambert, L. J., Ely, Z. A., Pattada, N. B., Bhutkar, A., Eng, G., Mercer, K. L., Garcia, A. P., Lin, L., Rideout, W. M., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.07.007 · Cited by 273 · see citing works
“Patients within each cohort were limited to those included in the TCGA Pan-Cancer Atlas study (Hoadley et al., 2018) for which mutational profiles were available on cBioPortal (cBioPortal for Cancer Genomics, cbioportal.”
MethodPan-cancer analysis of transcriptional metabolic dysregulation using The Cancer Genome Atlas
Rosario, S. R., Long, M. D., Affronti, H. C., Rowsam, A. M., Eng, K. H., & Smiraglia, D. J. (2018) · Nature Communications · doi:10.1038/s41467-018-07232-8 · Cited by 268 · see citing works
DNA methylation in mammalian development and disease
Smith, Z. D., Hetzel, S., & Meissner, A. (2024) · Nature Reviews Genetics · doi:10.1038/s41576-024-00760-8 · Cited by 280 · see citing works
Targeted therapy for hepatocellular carcinoma: Challenges and opportunities
Chen, S., Cao, Q., Wen, W., & Wang, H. (2019) · Cancer Letters · doi:10.1016/j.canlet.2019.114428 · Cited by 253 · see citing works
Identification of cancer driver genes based on nucleotide context
Dietlein, F., Weghorn, D., Taylor-Weiner, A. N., Richters, A., Reardon, B. M., Liu, D., Lander, E. S., Allen, E. M. V., & Sunyaev, S. R. (2020) · Nature Genetics · doi:10.1038/s41588-019-0572-y · Cited by 241 · see citing works
“Most marker papers from TCGA employ MutSigCV 10,11 or MuSiC 48 to discover cancer genes 14–16 .”
BackgroundMethodResultInfluentialA Pan-cancer Transcriptome Analysis Reveals Pervasive Regulation through Alternative Promoters
Demircioğlu, D., Cukuroglu, E., Kindermans, M., Nandi, T., Calabrese, C., Fonseca, N. A., Kahles, A., Lehmann, K.-V., Stegle, O., Brazma, A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.08.018 · Cited by 250 · see citing works
White blood cell and cell-free DNA analyses for detection of residual disease in gastric cancer
Leal, A., van Grieken, N. C. T., Palsgrove, D. N., Phallen, J. A., Medina, J. E., Hruban, C., Broeckaert, M. A. M., Anagnostou, V., Adleff, V., Bruhm, D. C., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14310-3 · Cited by 242 · see citing works
Estrogen Receptor Function: Impact on the Human Endometrium
Yu, K., Huang, Z., Xu, X.-L., Li, J., Fu, X., & Deng, S. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.827724 · Cited by 236 · see citing works
Self-citationApplication of Next Generation Sequencing in Laboratory Medicine
Zhong, Y., Xu, F., Wu, J., Schubert, J. A., & Li, M. M. (2020) · Annals of Laboratory Medicine · doi:10.3343/alm.2021.41.1.25 · Cited by 216 · see citing works
“Recently, the US FDA issued draft guidelines on the use of MRD assessment in trials involving patients with hematologic malignancies [6].”
BackgroundComprehensive Benchmarking and Integration of Tumor Microenvironment Cell Estimation Methods
Jiménez-Sánchez, A., Cast, O. M., & Miller, M. L. (2019) · Cancer Research · doi:10.1158/0008-5472.can-18-3560 · Cited by 212 · see citing works
Self-citationMulti-omic tumor data reveal diversity of molecular mechanisms that correlate with survival
Ramazzotti, D., Lal, A., Wang, B., Batzoglou, S., & Sidow, A. (2018) · Nature Communications · doi:10.1038/s41467-018-06921-8 · Cited by 211 · see citing works
Molecular pathogenesis, targeted therapies, and future perspectives for gastric cancer
Zeng, Y., & Jin, R. U. (2021) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2021.12.004 · Cited by 214 · see citing works
Liang, N., Li, B., Jia, Z., Wang, C., Wu, P., Zheng, T., Wang, Y., Qiu, F., Wu, Y., Su, J., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00746-5 · Cited by 209 · see citing works
Self-citationNoorbakhsh, J., Farahmand, S., Foroughi pour, A., Namburi, S., Caruana, D., Rimm, D., Soltanieh-ha, M., Zarringhalam, K., & Chuang, J. H. (2020) · Nature Communications · doi:10.1038/s41467-020-20030-5 · Cited by 206 · see citing works
Pan-Cancer Analyses Reveal Genomic Features of FOXM1 Overexpression in Cancer
Barger, C. J., Branick, C., Chee, L., & Karpf, A. R. (2019) · Cancers · doi:10.3390/cancers11020251 · Cited by 210 · see citing works
DNA damage repair as a target in pancreatic cancer: state-of-the-art and future perspectives
Perkhofer, L., Gout, J., Roger, É., de Almeida, F. K., Simões, C., Wiesmüller, L., Seufferlein, T. T. W., & Kleger, A. (2020) · Gut · doi:10.1136/gutjnl-2019-319984 · Cited by 204 · see citing works
Macroscopic somatic clonal expansion in morphologically normal human urothelium
Li, R., Du, Y., Chen, Z., Xu, D., Lin, T., Jin, S., Wang, G., Liu, Z., Lu, M., Chen, X., et al. (2020) · Science · doi:10.1126/science.aba7300 · Cited by 206 · see citing works
Competing Endogenous RNAs, Non-Coding RNAs and Diseases: An Intertwined Story
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The Immune Subtypes and Landscape of Squamous Cell Carcinoma
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BackgroundSelf-citationSyngeneic animal models of tobacco-associated oral cancer reveal the activity of in situ anti-CTLA-4
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InfluentialClassification of Cancer Types Using Graph Convolutional Neural Networks
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BackgroundOpposing Wnt signals regulate cervical squamocolumnar homeostasis and emergence of metaplasia
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Self-citation
- Authors
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S., Korkut, A., Li, J., Liang, H., Ling, S., Liu, W., Lu, Y., Mills, G. B., Ng, K.-S., Rao, A., Ryan, M., Wang, J., Weinstein, J. N., Zhang, J., Abeshouse, A., Armenia, J., Chakravarty, D., Chatila, W. K., de Bruijn, I., Gao, J., Gross, B. E., Heins, Z. J., Kundra, R., La, K., Ladanyi, M., Luna, A., Nissan, M. G., Ochoa, A., Phillips, S. M., Reznik, E., Sanchez-Vega, F., Sander, C., Schultz, N., Sheridan, R., Sumer, S. O., Sun, Y., Taylor, B. S., Wang, J., Zhang, H., Anur, P., Peto, M., Spellman, P., Benz, C., Stuart, J. M., Wong, C. K., Yau, C., Hayes, D. N., Parker, J. S., Wilkerson, M. D., Ally, A., Balasundaram, M., Bowlby, R., Brooks, D., Carlsen, R., Chuah, E., Dhalla, N., Holt, R., Jones, S. J. M., Kasaian, K., Lee, D., Ma, Y., Marra, M. A., Mayo, M., Moore, R. A., Mungall, A. J., Mungall, K., Robertson, A. G., Sadeghi, S., Schein, J. E., Sipahimalani, P., Tam, A., Thiessen, N., Tse, K., Wong, T., Berger, A. C., Beroukhim, R., Cherniack, A. D., Cibulskis, C., Gabriel, S. 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- Affiliations
- University of California, San Francisco (US)
- Memorial Sloan Kettering Cancer Center (US)
- Broad Institute (US)
- Vanderbilt University Medical Center (US)
- Henry Ford Health (US)
- Buck Institute for Research on Aging (US)
- Van Andel Institute (US)
- BC Cancer Agency (CA)
- Institute for Systems Biology (US)
- The University of Texas MD Anderson Cancer Center (US)
- University of California, Santa Cruz (US)
- University of North Carolina at Chapel Hill (US)
- Poznan University of Medical Sciences (PL)
- Corresponding author
- Felau, Ina
- Nissan, Moriah G.
- Weisenberger, Daniel J.
- Jones, Corbin D.
- Bellair, Michelle
- Mallery, David
- Oosterhuis, Wolter
- Kefford, Richard
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- ORCID
- Wolf, Denise M.
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- Sun, Yichao
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- Carroll, Peter R.
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- Kelley, Robin K.
- Landen, Charles N.
- Prados, Michael
- VandenBerg, Scott
- Roggin, Kevin
- Kendler, Ady
- Steele, Ruth
- Jimeno, Antonio
- Forgie, Ian
- Mannelli, Massimo
- Carney, Michael
- Campos, Benito
- Unterberg, Andreas
- Bossler, Aaron
- Ma, Deqin
- Milhem, Mohammed
- Godwin, Andrew K.
- Madan, Rashna
- Adebamowo, Clement
- Adebamowo, Sally N.
- Boussioutas, Alex
- Beer, David
- Giordano, Thomas
- Bocklage, Therese
- Landrum, Lisa
- Moore, Kathleen
- Moxley, Katherine
- Zuna, Rosemary
- Feldman, Michael
- Luketich, James
- Carlotti, Carlos Gilberto
- Kemp, Rafael
- Tirapelli, Daniela
- Catto, James
- Swisher, Elizabeth
- Creaney, Jenette
- Robinson, Bruce
- Shelley, Carl Simon
- Bradford, Carol
- Carey, Thomas
- Prince, Mark
- Rozek, Laura
- Wolf, Gregory
- Fong, Kwun M.
- Yang, Ian
- Rathmell, W. Kimryn
- Kovatich, Albert J.
- Shriver, Craig D.
- DiPersio, John
- Drake, Bettina
- Govindan, Ramaswamy
- Heath, Sharon
- Ley, Timothy
- Rubin, Mark A.
- Lee, Jung Il
- Aredes, Natália D.
- Mariamidze, Armaz
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 173
- Issue
- 2
- Pages
- 291-304.e6
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC5957518
- OpenAlex
- W2796153844
- Cited by
- 2,546
- References
- 58
- Citations by year
- 2026: 284 · 2025: 294 · 2024: 296 · 2023: 355 · 2022: 375 · 2021: 374 · 2020: 330 · 2019: 178 · 2018: 56 · 2013: 1
- FWCI
- 71.94
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cell (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (acceptedVersion, cc-by-nc-nd)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by-nc-nd)
- UWA Profiles and Research Repository (UWA) (submittedVersion, cc-by)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- Bern Open Repository and Information System (University of Bern) (submittedVersion, cc-by-nc-nd)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- eScholarship (California Digital Library) (submittedVersion)
- Florence Research (University of Florence) (submittedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion, cc-by-nc-nd)
- mediaTUM (Technical University of Munich) (submittedVersion, other-oa)
- Institutional Research Information System University of Ferrara (University of Ferrara) (submittedVersion, cc-by-nc-nd)
- Spiral (Imperial College London) (submittedVersion, cc-by-nc-nd)
- Funders
- National Human Genome Research Institute
- National Cancer Institute
- National Institutes of Health (U24 CA143866, U24 CA143843, P30 CA016672, U24 CA143867, U24 CA143840, U24 CA143882, U24 CA143858, U24 CA143835, U54 HG003273, U24 CA143883, U24 CA143848, U24 CA144025, U24 CA143845, U54 HG003079, U24 CA143799, U54 HG003067)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Keywords
- cell of origin, molecular classification of cancer, pan-cancer analysis, TCGA, aneuploidy, DNA methylation, mRNA expression, microRNA expression, reverse phase protein array, histological subtypes, tissue type, stemness, molecular clustering
- MeSH terms
- Aneuploidy, Chromosomes, Humans, Mutation, Neoplasm Proteins, Neoplasms, RNA, Messenger, Cluster Analysis, Databases, Factual, CpG Islands, DNA Methylation, MicroRNAs
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