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Cited by 2,546Cancer Genomics and DiagnosticsGene expression and cancer classificationEpigenetics and DNA Methylation

The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.

Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer

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Hoadley, Katherine A., Yau, Christina, Hinoue, Toshinori, Wolf, Denise M., Lazar, Alexander J., Drill, Esther, Shen, Ronglai, Taylor, Alison M., Cherniack, Andrew D., Thorsson, Vésteinn, Akbani, Rehan, Bowlby, Reanne, Wong, Christopher K., Wiznerowicz, Maciej, Sanchez-Vega, Francisco, Robertson, A. 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Mora, Quintero-Aguilo, Mario, Carlotti, Carlos Gilberto, Dos Santos, Jose Sebastião, Kemp, Rafael, Sankarankuty, Ajith, Tirapelli, Daniela, Catto, James, Agnew, Kathy, Swisher, Elizabeth, Creaney, Jenette, Robinson, Bruce, Shelley, Carl Simon, Godwin, Eryn M., Kendall, Sara, Shipman, Cassaundra, Bradford, Carol, Carey, Thomas, Haddad, Andrea, Moyer, Jeffey, Peterson, Lisa, Prince, Mark, Rozek, Laura, Wolf, Gregory, Bowman, Rayleen, Fong, Kwun M., Yang, Ian, Korst, Robert, Rathmell, W. Kimryn, Fantacone-Campbell, J. Leigh, Hooke, Jeffrey A., Kovatich, Albert J., Shriver, Craig D., DiPersio, John, Drake, Bettina, Govindan, Ramaswamy, Heath, Sharon, Ley, Timothy, Van Tine, Brian, Westervelt, Peter, Rubin, Mark A., Lee, Jung Il, Aredes, Natália D., Mariamidze, Armaz, Stuart, Joshua M., Benz, Christopher C., Laird, Peter W.·2018·Cell
DOI 10.1016/j.cell.2018.03.022PMC PMC5957518
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Abstract

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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Figures

Figure 1 Platform-Specific Classification of 10,000 TCGA Cancer Tumor Samples across 33 Cancer Types (A) Aneuploidy (AN). Unsupervised consensus clustering of 10,522 tumors and chromosomal arm-level amplifications or deletions. (B) DNA hypermethylation (METH). Clustering of cancer-associated DNA methylation profiles in 10,814 tumors at 1,035 CpG sites lacking DNA methylation in normal tissues (left) and leukocytes (right). DNA methylation β-values are represented as a color gradient from low (blue) to high (red). (C) mRNA (MRNA). Unsupervised consensus clustering of 10,165 tumors and variably expressed genes. (D) microRNA (MIR). Unsupervised hierarchical clustering of 743 expressed mature strands in 10,170 tumors. (E) Protein (P). Unsupervised hierarchical clustering of 7,858 tumor samples from 32 cancer types across 216 cancer-relevant proteins and phosphoproteins. Tumor types are color-coded as shown in the lower-right corner. See also Tables S1–S5.
Figure 1 Platform-Specific Classification of 10,000 TCGA Cancer Tumor Samples across 33 Cancer Types (A) Aneuploidy (AN). Unsupervised consensus clustering of 10,522 tumors and chromosomal arm-level amplifications or deletions. (B) DNA hypermethylation (METH). Clustering of cancer-associated DNA methylation profiles in 10,814 tumors at 1,035 CpG sites lacking DNA methylation in normal tissues (left) and leukocytes (right). DNA methylation β-values are represented as a color gradient from low (blue) to high (red). (C) mRNA (MRNA). Unsupervised consensus clustering of 10,165 tumors and variably expressed genes. (D) microRNA (MIR). Unsupervised hierarchical clustering of 743 expressed mature strands in 10,170 tumors. (E) Protein (P). Unsupervised hierarchical clustering of 7,858 tumor samples from 32 cancer types across 216 cancer-relevant proteins and phosphoproteins. Tumor types are color-coded as shown in the lower-right corner. See also Tables S1–S5.CC BY-NC-ND · PubMed Central
Figure 2 Cross-Platform Classification Revealed Genomic, Epigenomic, and Transcriptomic Similarities and Differences across Cancer Types (A) COCA clusters. Membership for individual clusters for each of the five molecular platforms—aneuploidy (AN), methylation (Meth), miRNA expression (miR), mRNA, and RPPA—is displayed as a separate binary membership variable in a distinct row. For the mRNA platform, only clusters containing >40 samples were considered. Samples are labeled for membership of each platform-specific cluster (red, member; white, non-member; gray, not evaluated on the platform). Order of samples and platform-specific clusters were determined by hierarchical clustering using a binary distance matrix and average linkage. Column annotation shows cancer type and tissue organ systems of each sample; row annotations reflect the platform for each classification (bright pink, AN; purple, Meth; light turquoise, miR; dark turquoise, mRNA; orange, RPPA). (B) iCluster. Data used for integrated analysis of iClusters. RPPA data are also included in the heatmap to visualize proteomic patterns across the integrated clusters. (C) iCluster robustness versus composition. Pie charts show the cancer-type composition within each iCluster and the size is proportional to the membership size. The cancer type accounting for the highest proportion of members within the iCluster was considered the dominant cancer type. They coordinate of each pie center reflects this dominant cancer-type proportion; the x coordinate was determined by the iCluster silhouette width. (D) Relationship of TCGA tumor type, iCluster, and Pan-Organ system. The Sankey diagram demonstrates the tumor-type composition of each iCluster. The pan-cancer designations are shown on the right. See also Tables S6 and S7.
Figure 2 Cross-Platform Classification Revealed Genomic, Epigenomic, and Transcriptomic Similarities and Differences across Cancer Types (A) COCA clusters. Membership for individual clusters for each of the five molecular platforms—aneuploidy (AN), methylation (Meth), miRNA expression (miR), mRNA, and RPPA—is displayed as a separate binary membership variable in a distinct row. For the mRNA platform, only clusters containing >40 samples were considered. Samples are labeled for membership of each platform-specific cluster (red, member; white, non-member; gray, not evaluated on the platform). Order of samples and platform-specific clusters were determined by hierarchical clustering using a binary distance matrix and average linkage. Column annotation shows cancer type and tissue organ systems of each sample; row annotations reflect the platform for each classification (bright pink, AN; purple, Meth; light turquoise, miR; dark turquoise, mRNA; orange, RPPA). (B) iCluster. Data used for integrated analysis of iClusters. RPPA data are also included in the heatmap to visualize proteomic patterns across the integrated clusters. (C) iCluster robustness versus composition. Pie charts show the cancer-type composition within each iCluster and the size is proportional to the membership size. The cancer type accounting for the highest proportion of members within the iCluster was considered the dominant cancer type. They coordinate of each pie center reflects this dominant cancer-type proportion; the x coordinate was determined by the iCluster silhouette width. (D) Relationship of TCGA tumor type, iCluster, and Pan-Organ system. The Sankey diagram demonstrates the tumor-type composition of each iCluster. The pan-cancer designations are shown on the right. See also Tables S6 and S7.CC BY-NC-ND · PubMed Central
Figure 3 Cellularity of the Tumor Microenvironment among iCluster Samples (A) Stromal fraction of tumor samples. The stromal fraction, defined by subtracting tumor purity (estimated by ABSOLUTE) from one, is shown for 9,057 TCGA tumor samples, segregated by iCluster membership. (B) Leukocyte fraction. Leukocyte fraction, estimated from DNA methylation arrays, for 9,417 tumor samples, for each iCluster, with the exception of C24:LAML and C21:DLBC. (C) Leukocyte fraction versus stromal fraction. Points near the diagonal correspond to tumor samples in which non-tumor stromal cells are nearly all immune cells, and points away from the diagonal correspond to a more mixed or a non-immune stromal tumor microenvironment. Points in the upper-left triangle of each plot are estimation artifacts.
Figure 3 Cellularity of the Tumor Microenvironment among iCluster Samples (A) Stromal fraction of tumor samples. The stromal fraction, defined by subtracting tumor purity (estimated by ABSOLUTE) from one, is shown for 9,057 TCGA tumor samples, segregated by iCluster membership. (B) Leukocyte fraction. Leukocyte fraction, estimated from DNA methylation arrays, for 9,417 tumor samples, for each iCluster, with the exception of C24:LAML and C21:DLBC. (C) Leukocyte fraction versus stromal fraction. Points near the diagonal correspond to tumor samples in which non-tumor stromal cells are nearly all immune cells, and points away from the diagonal correspond to a more mixed or a non-immune stromal tumor microenvironment. Points in the upper-left triangle of each plot are estimation artifacts.CC BY-NC-ND · PubMed Central
Figure 4 The iCluster TumorMap (A–F) The map layout was computed from sample Euclidean similarity in the iCluster latent space, and similar samples are positioned in close proximity to each other. Each spot represents a single sample and is colored to represent attributes as described for each panel including (A) iCluster, (B) disease type, and (C) organ system. Organ systems highlighted include pan-kidney, red; pan-gyn, orange; pan-GI, blue; pan-squamous, purple; and those that overlap pan-gyn and pan-squamous, light purple. (D) Subtypes from the pan-kidney analysis (Ricketts et al., 2018). Clear cell renal cell carcinoma (ccRCC), green; papillary renal cell carcinoma type 1 (PRCC T1), blue; papillary renal cell carcinoma type 2(PRCC T2), yellow; unclassified papillary renal cell carcinoma (PRCC Unc.), dark gray; CpG island methylator phenotype renal cell carcinoma (RCC-CIMP), red; and chromophobe renal cell carcinoma (ChRCC), purple. (E) Subtypes from the pan-gyn group (Berger et al., 2018). Not hypermutated, with low copy-number changes (non-HM CNV low), red; hypermutated, with low copy-number changes (HM), blue; high levels of leukocyte infiltration (immune), green; low AR or PR expression (AR/PR low), orange; and high androgen receptor (AR) or progesterone receptor (PR) expression (AR/PR high), dark gray. (F) Subtypes from the pan-GI group (Liu et al., 2018). High Epstein-Barr virus (EBV) burden, red; microsatellite instability (MSI), blue; hypermutated without MSI (HM-SNV), gold; chromosomal instability tumors (CIN), purple; and genome stable (GS) with low aneuploidy, green. The gray dots represent non-highlighted diseases.
Figure 4 The iCluster TumorMap (A–F) The map layout was computed from sample Euclidean similarity in the iCluster latent space, and similar samples are positioned in close proximity to each other. Each spot represents a single sample and is colored to represent attributes as described for each panel including (A) iCluster, (B) disease type, and (C) organ system. Organ systems highlighted include pan-kidney, red; pan-gyn, orange; pan-GI, blue; pan-squamous, purple; and those that overlap pan-gyn and pan-squamous, light purple. (D) Subtypes from the pan-kidney analysis (Ricketts et al., 2018). Clear cell renal cell carcinoma (ccRCC), green; papillary renal cell carcinoma type 1 (PRCC T1), blue; papillary renal cell carcinoma type 2(PRCC T2), yellow; unclassified papillary renal cell carcinoma (PRCC Unc.), dark gray; CpG island methylator phenotype renal cell carcinoma (RCC-CIMP), red; and chromophobe renal cell carcinoma (ChRCC), purple. (E) Subtypes from the pan-gyn group (Berger et al., 2018). Not hypermutated, with low copy-number changes (non-HM CNV low), red; hypermutated, with low copy-number changes (HM), blue; high levels of leukocyte infiltration (immune), green; low AR or PR expression (AR/PR low), orange; and high androgen receptor (AR) or progesterone receptor (PR) expression (AR/PR high), dark gray. (F) Subtypes from the pan-GI group (Liu et al., 2018). High Epstein-Barr virus (EBV) burden, red; microsatellite instability (MSI), blue; hypermutated without MSI (HM-SNV), gold; chromosomal instability tumors (CIN), purple; and genome stable (GS) with low aneuploidy, green. The gray dots represent non-highlighted diseases.CC BY-NC-ND · PubMed Central
Figure 5 Sample Characteristics in the Context of the iCluster TumorMap (A–D) The TumorMap layout is as described for Figure 4. (A) Histopathology. Colors indicate major histopathology types. Adenocarcinoma, yellow; squamous cell carcinoma, purple; other carcinomas, green; sarcomas, light blue; leukemias, dark blue; lymphomas, magenta; and other, red. (B) Immune subtypes. Wound-healing group, red; IFN-gamma, yellow; inflammatory group, green; lymphocyte-depleted, light blue; immunologically quiescent, dark blue; and transforming growth factor (TGF)-beta activity, magenta. (C and D) Stemness signatures for (C) mRNA and (D) DNA methylation from Malta et al. (2018) are displayed. Increasing red colors indicate increasing stemness index.
Figure 5 Sample Characteristics in the Context of the iCluster TumorMap (A–D) The TumorMap layout is as described for Figure 4. (A) Histopathology. Colors indicate major histopathology types. Adenocarcinoma, yellow; squamous cell carcinoma, purple; other carcinomas, green; sarcomas, light blue; leukemias, dark blue; lymphomas, magenta; and other, red. (B) Immune subtypes. Wound-healing group, red; IFN-gamma, yellow; inflammatory group, green; lymphocyte-depleted, light blue; immunologically quiescent, dark blue; and transforming growth factor (TGF)-beta activity, magenta. (C and D) Stemness signatures for (C) mRNA and (D) DNA methylation from Malta et al. (2018) are displayed. Increasing red colors indicate increasing stemness index.CC BY-NC-ND · PubMed Central
Figure 6 Mutation Patterns of iClusters (A) Somatic mutation frequency (log10) per iCluster sorted by median mutations per megabase. Somatic mutation frequencies were calculated using a filtered MC3 mutation annotation file to determine the total number of mutations per sample, normalized by whole-exome sequencing coverage as described in Knijnenburg et al. (2018). Bars represent median mutation frequency for each iCluster. (B) Mutational signatures (Covington et al., 2016) enriched in iClusters. Mutational signature scores were scaled per sample by the overall mutation rate. The means of scaled signature scores were calculated for each iCluster and log10-transformed. Hierarchical clustered data are displayed in the heatmap (blue, low; red, high).
Figure 6 Mutation Patterns of iClusters (A) Somatic mutation frequency (log10) per iCluster sorted by median mutations per megabase. Somatic mutation frequencies were calculated using a filtered MC3 mutation annotation file to determine the total number of mutations per sample, normalized by whole-exome sequencing coverage as described in Knijnenburg et al. (2018). Bars represent median mutation frequency for each iCluster. (B) Mutational signatures (Covington et al., 2016) enriched in iClusters. Mutational signature scores were scaled per sample by the overall mutation rate. The means of scaled signature scores were calculated for each iCluster and log10-transformed. Hierarchical clustered data are displayed in the heatmap (blue, low; red, high).CC BY-NC-ND · PubMed Central
Figure 7 Pathway Features Characterizing the PanCancer-33 iCluster Subtypes (A) PARADIGM pathway heatmap. Regulatory nodes with differential PARADIGM-inferred pathway levels (IPL) with at least 15 downstream regulatory targets with differential inferred activities between iClusters are shown for one versus rest comparisons. Samples are arranged by iCluster order; regulatory nodes are hierarchically clustered using 1-Pearson correlation as distance and average linkage. Red-blue intensities represent median-centered IPLs from low (blue) to high (red). (B) Gene programs and canonical pathway values. The 22 Gene Programs (Hoadley et al., 2014) and 20 pathway signatures reflecting drug targets and canonical pathways (found in Table S4 of Hoadley et al. [2014]) were hierarchically clustered using 1-Pearson distance and complete linkage and are shown with samples arranged by iCluster subtypes in numerical order. Red-blue intensities represent signature scores from low (blue) to high (red). See also Tables S8 and S9.
Figure 7 Pathway Features Characterizing the PanCancer-33 iCluster Subtypes (A) PARADIGM pathway heatmap. Regulatory nodes with differential PARADIGM-inferred pathway levels (IPL) with at least 15 downstream regulatory targets with differential inferred activities between iClusters are shown for one versus rest comparisons. Samples are arranged by iCluster order; regulatory nodes are hierarchically clustered using 1-Pearson correlation as distance and average linkage. Red-blue intensities represent median-centered IPLs from low (blue) to high (red). (B) Gene programs and canonical pathway values. The 22 Gene Programs (Hoadley et al., 2014) and 20 pathway signatures reflecting drug targets and canonical pathways (found in Table S4 of Hoadley et al. [2014]) were hierarchically clustered using 1-Pearson distance and complete linkage and are shown with samples arranged by iCluster subtypes in numerical order. Red-blue intensities represent signature scores from low (blue) to high (red). See also Tables S8 and S9.CC BY-NC-ND · PubMed Central

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Hoadley, K. A., Yau, C., Hinoue, T., Wolf, D. M., Lazar, A. J., Drill, E., Shen, R., Taylor, A. M., Cherniack, A. D., Thorsson, V., Akbani, R., Bowlby, R., Wong, C. K., Wiznerowicz, M., Sanchez-Vega, F., Robertson, A. G., Schneider, B. G., Lawrence, M. S., Noushmehr, H., Malta, T. M., Caesar-Johnson, S. J., Demchok, J. A., Felau, I., Kasapi, M., Ferguson, M. L., Hutter, C. M., Sofia, H. J., Tarnuzzer, R., Wang, Z., Yang, L., Zenklusen, J. C., Zhang, J. (., Chudamani, S., Liu, J., Lolla, L., Naresh, R., Pihl, T., Sun, Q., Wan, Y., Wu, Y., Cho, J., DeFreitas, T., Frazer, S., Gehlenborg, N., Getz, G., Heiman, D. I., Kim, J., Lawrence, M. S., Lin, P., Meier, S., Noble, M. S., Saksena, G., Voet, D., Zhang, H., Bernard, B., Chambwe, N., Dhankani, V., Knijnenburg, T., Kramer, R., Leinonen, K., Liu, Y., Miller, M., Reynolds, S., Shmulevich, I., Thorsson, V., Zhang, W., Akbani, R., Broom, B. M., Hegde, A. M., Ju, Z., Kanchi, R. 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. B., Gao, G. F., Ha, G., Meyerson, M., Schumacher, S. E., Shih, J., Kucherlapati, M. H., Kucherlapati, R. S., Baylin, S., Cope, L., Danilova, L., Bootwalla, M. S., Lai, P. H., Maglinte, D. T., Van Den Berg, D. J., Weisenberger, D. J., Auman, J. T., Balu, S., Bodenheimer, T., Fan, C., Hoadley, K. A., Hoyle, A. P., Jefferys, S. R., Jones, C. D., Meng, S., Mieczkowski, P. A., Mose, L. E., Perou, A. H., Perou, C. M., Roach, J., Shi, Y., Simons, J. V., Skelly, T., Soloway, M. G., Tan, D., Veluvolu, U., Fan, H., Hinoue, T., Laird, P. W., Shen, H., Zhou, W., Bellair, M., Chang, K., Covington, K., Creighton, C. J., Dinh, H., Doddapaneni, H., Donehower, L. A., Drummond, J., Gibbs, R. A., Glenn, R., Hale, W., Han, Y., Hu, J., Korchina, V., Lee, S., Lewis, L., Li, W., Liu, X., Morgan, M., Morton, D., Muzny, D., Santibanez, J., Sheth, M., Shinbrot, E., Wang, L., Wang, M., Wheeler, D. A., Xi, L., Zhao, F., Hess, J., Appelbaum, E. L., Bailey, M., Cordes, M. G., Ding, L., Fronick, C. C., Fulton, L. A., Fulton, R. S., Kandoth, C., Mardis, E. R., McLellan, M. D., Miller, C. A., Schmidt, H. K., Wilson, R. K., Crain, D., Curley, E., Gardner, J., Lau, K., Mallery, D., Morris, S., Paulauskis, J., Penny, R., Shelton, C., Shelton, T., Sherman, M., Thompson, E., Yena, P., Bowen, J., Gastier-Foster, J. M., Gerken, M., Leraas, K. M., Lichtenberg, T. M., Ramirez, N. C., Wise, L., Zmuda, E., Corcoran, N., Costello, T., Hovens, C., Carvalho, A. L., de Carvalho, A. C., Fregnani, J. H., Longatto-Filho, A., Reis, R. M., Scapulatempo-Neto, C., Silveira, H. C. S., Vidal, D. O., Burnette, A., Eschbacher, J., Hermes, B., Noss, A., Singh, R., Anderson, M. L., Castro, P. D., Ittmann, M., Huntsman, D., Kohl, B., Le, X., Thorp, R., Andry, C., Duffy, E. R., Lyadov, V., Paklina, O., Setdikova, G., Shabunin, A., Tavobilov, M., McPherson, C., Warnick, R., Berkowitz, R., Cramer, D., Feltmate, C., Horowitz, N., Kibel, A., Muto, M., Raut, C. P., Malykh, A., Barnholtz-Sloan, J. S., Barrett, W., Devine, K., Fulop, J., Ostrom, Q. T., Shimmel, K., Wolinsky, Y., Sloan, A. E., De Rose, A., Giuliante, F., Goodman, M., Karlan, B. Y., Hagedorn, C. H., Eckman, J., Harr, J., Myers, J., Tucker, K., Zach, L. A., Deyarmin, B., Hu, H., Kvecher, L., Larson, C., Mural, R. J., Somiari, S., Vicha, A., Zelinka, T., Bennett, J., Iacocca, M., Rabeno, B., Swanson, P., Latour, M., Lacombe, L., Têtu, B., Bergeron, A., McGraw, M., Staugaitis, S. M., Chabot, J., Hibshoosh, H., Sepulveda, A., Su, T., Wang, T., Potapova, O., Voronina, O., Desjardins, L., Mariani, O., Roman-Roman, S., Sastre, X., Stern, M.-H., Cheng, F., Signoretti, S., Berchuck, A., Bigner, D., Lipp, E., Marks, J., McCall, S., McLendon, R., Secord, A., Sharp, A., Behera, M., Brat, D. J., Chen, A., Delman, K., Force, S., Khuri, F., Magliocca, K., Maithel, S., Olson, J. J., Owonikoko, T., Pickens, A., Ramalingam, S., Shin, D. M., Sica, G., Van Meir, E. G., Zhang, H., Eijckenboom, W., Gillis, A., Korpershoek, E., Looijenga, L., Oosterhuis, W., Stoop, H., van Kessel, K. E., Zwarthoff, E. C., Calatozzolo, C., Cuppini, L., Cuzzubbo, S., DiMeco, F., Finocchiaro, G., Mattei, L., Perin, A., Pollo, B., Chen, C., Houck, J., Lohavanichbutr, P., Hartmann, A., Stoehr, C., Stoehr, R., Taubert, H., Wach, S., Wullich, B., Kycler, W., Murawa, D., Wiznerowicz, M., Chung, K., Edenfield, W. J., Martin, J., Baudin, E., Bubley, G., Bueno, R., De Rienzo, A., Richards, W. G., Kalkanis, S., Mikkelsen, T., Noushmehr, H., Scarpace, L., Girard, N., Aymerich, M., Campo, E., Giné, E., Guillermo, A. L., Van Bang, N., Hanh, P. T., Phu, B. D., Tang, Y., Colman, H., Evason, K., Dottino, P. R., Martignetti, J. A., Gabra, H., Juhl, H., Akeredolu, T., Stepa, S., Hoon, D., Ahn, K., Kang, K. J., Beuschlein, F., Breggia, A., Birrer, M., Bell, D., Borad, M., Bryce, A. H., Castle, E., Chandan, V., Cheville, J., Copland, J. A., Farnell, M., Flotte, T., Giama, N., Ho, T., Kendrick, M., Kocher, J.-P., Kopp, K., Moser, C., Nagorney, D., O’Brien, D., O’Neill, B. P., Patel, T., Petersen, G., Que, F., Rivera, M., Roberts, L., Smallridge, R., Smyrk, T., Stanton, M., Thompson, R. H., Torbenson, M., Yang, J. D., Zhang, L., Brimo, F., Ajani, J. A., Gonzalez, A. M. A., Behrens, C., Bondaruk, O., Broaddus, R., Czerniak, B., Esmaeli, B., Fujimoto, J., Gershenwald, J., Guo, C., Lazar, A. J., Logothetis, C., Meric-Bernstam, F., Moran, C., Ramondetta, L., Rice, D., Sood, A., Tamboli, P., Thompson, T., Troncoso, P., Tsao, A., Wistuba, I., Carter, C., Haydu, L., Hersey, P., Jakrot, V., Kakavand, H., Kefford, R., Lee, K., Long, G., Mann, G., Quinn, M., Saw, R., Scolyer, R., Shannon, K., Spillane, A., Stretch, J., Synott, M., Thompson, J., Wilmott, J., Al-Ahmadie, H., Chan, T. A., Ghossein, R., Gopalan, A., Levine, D. A., Reuter, V., Singer, S., Singh, B., Tien, N. V., Broudy, T., Mirsaidi, C., Nair, P., Drwiega, P., Miller, J., Smith, J., Zaren, H., Park, J.-W., Hung, N. P., Kebebew, E., Linehan, W. M., Metwalli, A. R., Pacak, K., Pinto, P. A., Schiffman, M., Schmidt, L. S., Vocke, C. D., Wentzensen, N., Worrell, R., Yang, H., Moncrieff, M., Goparaju, C., Melamed, J., Pass, H., Botnariuc, N., Caraman, I., Cernat, M., Chemencedji, I., Clipca, A., Doruc, S., Gorincioi, G., Mura, S., Pirtac, M., Stancul, I., Tcaciuc, D., Albert, M., Alexopoulou, I., Arnaout, A., Bartlett, J., Engel, J., Gilbert, S., Parfitt, J., Sekhon, H., Thomas, G., Rassl, D. M., Rintoul, R. C., Bifulco, C., Tamakawa, R., Urba, W., Hayward, N., Timmers, H., Antenucci, A., Facciolo, F., Grazi, G., Marino, M., Merola, R., de Krijger, R., Gimenez-Roqueplo, A.-P., Piché, A., Chevalier, S., McKercher, G., Birsoy, K., Barnett, G., Brewer, C., Farver, C., Naska, T., Pennell, N. A., Raymond, D., Schilero, C., Smolenski, K., Williams, F., Morrison, C., Borgia, J. A., Liptay, M. J., Pool, M., Seder, C. W., Junker, K., Omberg, L., Dinkin, M., Manikhas, G., Alvaro, D., Bragazzi, M. C., Cardinale, V., Carpino, G., Gaudio, E., Chesla, D., Cottingham, S., Dubina, M., Moiseenko, F., Dhanasekaran, R., Becker, K.-F., Janssen, K.-P., Slotta-Huspenina, J., Abdel-Rahman, M. H., Aziz, D., Bell, S., Cebulla, C. M., Davis, A., Duell, R., Elder, J. B., Hilty, J., Kumar, B., Lang, J., Lehman, N. L., Mandt, R., Nguyen, P., Pilarski, R., Rai, K., Schoenfield, L., Senecal, K., Wakely, P., Hansen, P., Lechan, R., Powers, J., Tischler, A., Grizzle, W. E., Sexton, K. C., Kastl, A., Henderson, J., Porten, S., Waldmann, J., Fassnacht, M., Asa, S. L., Schadendorf, D., Couce, M., Graefen, M., Huland, H., Sauter, G., Schlomm, T., Simon, R., Tennstedt, P., Olabode, O., Nelson, M., Bathe, O., Carroll, P. R., Chan, J. M., Disaia, P., Glenn, P., Kelley, R. K., Landen, C. N., Phillips, J., Prados, M., Simko, J., Smith-McCune, K., VandenBerg, S., Roggin, K., Fehrenbach, A., Kendler, A., Sifri, S., Steele, R., Jimeno, A., Carey, F., Forgie, I., Mannelli, M., Carney, M., Hernandez, B., Campos, B., Herold-Mende, C., Jungk, C., Unterberg, A., von Deimling, A., Bossler, A., Galbraith, J., Jacobus, L., Knudson, M., Knutson, T., Ma, D., Milhem, M., Sigmund, R., Godwin, A. K., Madan, R., Rosenthal, H. G., Adebamowo, C., Adebamowo, S. N., Boussioutas, A., Beer, D., Giordano, T., Mes-Masson, A.-M., Saad, F., Bocklage, T., Landrum, L., Mannel, R., Moore, K., Moxley, K., Postier, R., Walker, J., Zuna, R., Feldman, M., Valdivieso, F., Dhir, R., Luketich, J., Pinero, E. M. M., Quintero-Aguilo, M., Carlotti, C. G., Dos Santos, J. S., Kemp, R., Sankarankuty, A., Tirapelli, D., Catto, J., Agnew, K., Swisher, E., Creaney, J., Robinson, B., Shelley, C. S., Godwin, E. M., Kendall, S., Shipman, C., Bradford, C., Carey, T., Haddad, A., Moyer, J., Peterson, L., Prince, M., Rozek, L., Wolf, G., Bowman, R., Fong, K. M., Yang, I., Korst, R., Rathmell, W. K., Fantacone-Campbell, J. L., Hooke, J. A., Kovatich, A. J., Shriver, C. D., DiPersio, J., Drake, B., Govindan, R., Heath, S., Ley, T., Van Tine, B., Westervelt, P., Rubin, M. A., Lee, J. I., Aredes, N. D., Mariamidze, A., Stuart, J. M., Benz, C. C., & Laird, P. W.
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
  • Vocke, Cathy D.
  • Zuna, Rosemary
  • Rubin, Mark A.
  • Aredes, Natália D.
ORCID
Venue
Cell
Publisher
Elsevier BV
Published
2018-04
Volume
173
Issue
2
Pages
291-304.e6
ISSN
0092-8674 (ISSN-L)
DOI
10.1016/j.cell.2018.03.022
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
FWCI
71.94
Type
journal article
Language
en
Open access
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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