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Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival
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// Luc G.T. Morris 1,2,* , Nadeem Riaz 1,3* , Alexis Desrichard 1 , Yasin Şenbabaoğlu 4 , A. Ari Hakimi 2 , Vladimir Makarov 1 , Jorge S. Reis-Filho 1,5 and Timothy A. Chan 1,3 1 Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA 2 Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA 3 Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA 4 Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA 5 Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA * These authors have contributed equally to this work Correspondence to: Luc G.T. Morris, email: // Timothy A. Chan, email: // Keywords : heterogeneity, evolution, survival, cancer, immune surveillance Received : January 14, 2016 Accepted : January 21, 2016 Published : January 28, 2016 Abstract As tumors accumulate genetic alterations, an evolutionary process occurs in which genetically distinct subclonal populations of cells co-exist, resulting in intratumor genetic heterogeneity (ITH). The clinical implications of ITH remain poorly defined. Data are limited with respect to whether ITH is an independent determinant of patient survival outcomes, across different cancer types. Here, we report the results of a pan-cancer analysis of over 3300 tumors, showing a varied landscape of ITH across 9 cancer types. While some gene mutations are subclonal, the majority of driver gene mutations are clonal events, present in nearly all cancer cells. Strikingly, high levels of ITH are associated with poorer survival across diverse types of cancer. The adverse impact of high ITH is independent of other clinical, pathologic and molecular factors. High ITH tends to be associated with lower levels of tumor-infiltrating immune cells, but this association is not able to explain the observed survival differences. Together, these data show that ITH is a prognostic marker in multiple cancers. These results illuminate the natural history of cancer evolution, indicating that tumor heterogeneity represents a significant obstacle to cancer control.
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Cites
Inferring tumour purity and stromal and immune cell admixture from expression data
Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., Treviño, V., Shen, H., Laird, P. W., Levine, D. A., et al. (2013) · Nature Communications · doi:10.1038/ncomms3612 · Cited by 9,778
Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer
Rizvi, N. A., Hellmann, M. D., Snyder, A., Kvistborg, P., Makarov, V., Havel, J. J., Lee, W., Yuan, J., Wong, P., Ho, T. S., et al. (2015) · Science · doi:10.1126/science.aaa1348 · Cited by 7,802
Self-citationIntratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing
Gerlinger, M., Rowan, A. J., Horswell, S., Larkin, J., Endesfelder, D., Gronroos, E., Martinez, P., Matthews, N., Stewart, A., Tarpey, P., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1113205 · Cited by 7,774 · cited 2×
“These limitations would tend to attenuate the strength of associations in this study: the measures of ITH in this study are likely to be conservative, potentially underestimating the true extent of heterogeneity in tumors, as evidenced by studies that have sequenced multiple regions of tumors [3-5, 40, 41].”
BackgroundResultNowell, P. C. (1976) · Science · doi:10.1126/science.959840 · Cited by 6,471
Greaves, M., & Maley, C. C. (2012) · Nature · doi:10.1038/nature10762 · Cited by 3,162
Distant metastasis occurs late during the genetic evolution of pancreatic cancer
Yachida, S., Jones, S., Bozic, I., Antal, T., Leary, R., Fu, B., Kamiyama, M., Hruban, R. H., Eshleman, J. R., Nowak, M. A., et al. (2010) · Nature · doi:10.1038/nature09515 · Cited by 2,502
Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK)
McShane, L. M., Altman, D. G., Sauerbrei, W., Taube, S. E., Gion, M., & Clark, G. M. (2005) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/dji237 · Cited by 2,162
Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing
Ding, L., Ley, T. J., Larson, D. E., Miller, C. A., Koboldt, D. C., Welch, J. S., Ritchey, J. K., Young, M. A., Lamprecht, T., McLellan, M. D., et al. (2012) · Nature · doi:10.1038/nature10738 · Cited by 1,993
The clonal and mutational evolution spectrum of primary triple-negative breast cancers
Shah, S. P., Roth, A., Goya, R., Oloumi, A., Ha, G., Zhao, Y., Turashvili, G., Ding, J., Tse, K., Haffari, G., et al. (2012) · Nature · doi:10.1038/nature10933 · Cited by 1,967
Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer
Misale, S., Yaeger, R., Hobor, S., Scala, E., Janakiraman, M., Liska, D., Valtorta, E., Schiavo, R., Buscarino, M., Siravegna, G., et al. (2012) · Nature · doi:10.1038/nature11156 · Cited by 1,889
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Thorsson, V., Gibbs, D. L., Brown, S. D., Wolf, D., Bortone, D. S., Ou Yang, T.-H., Porta-Pardo, E., Gao, G. F., Plaisier, C. L., Eddy, J. A., et al. (2018) · Immunity · doi:10.1016/j.immuni.2018.03.023 · Cited by 5,870
Self-citationDevelopment of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic
Chan, T. A., Yarchoan, M., Jaffee, E., Swanton, C., Quezada, S. A., Stenzinger, A., & Peters, S. (2019) · Annals of Oncology · doi:10.1093/annonc/mdy495 · Cited by 2,683
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Riaz, N., Havel, J. J., Makarov, V., Desrichard, A., Urba, W. J., Sims, J. S., Hodi, F. S., Martín-Algarra, S., Mandal, R., Sharfman, W. H., et al. (2017) · Cell · doi:10.1016/j.cell.2017.09.028 · Cited by 2,401
Self-citationThe evolving landscape of biomarkers for checkpoint inhibitor immunotherapy
Havel, J. J., Chowell, D., & Chan, T. A. (2019) · Nature Reviews Cancer · doi:10.1038/s41568-019-0116-x · Cited by 2,286
Self-citationMayerhoefer, M. E., Materka, A., Langs, G., Häggström, I., Szczypiński, P., Gibbs, P., & Cook, G. (2020) · Journal of Nuclear Medicine · doi:10.2967/jnumed.118.222893 · Cited by 1,777
Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., & Madabhushi, A. (2019) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-019-0252-y · Cited by 1,698
Liu, D., Schilling, B., Liu, D., Sucker, A., Livingstone, E., Jerby-Arnon, L., Zimmer, L., Gutzmer, R., Satzger, I., Loquai, C., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0654-5 · Cited by 1,039
Intratumor Heterogeneity: The Rosetta Stone of Therapy Resistance
Marusyk, A., Janiszewska, M., & Polyak, K. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.03.007 · Cited by 1,009
Ahronian, L. G., & Corcoran, R. B. (2017) · Genome Medicine · doi:10.1186/s13073-017-0431-3 · Cited by 67
Intratumor CMS heterogeneity impacts patient prognosis in localized colon cancer
Marisa, L., Blum, Y., Taieb, J., Ayadi, M., Pilati, C., Le Malicot, K., Lepage, C., Salazar, R., Aust, D., Duval, A., et al. (2021) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-21-0529 · Cited by 68
Jammihal, T., Saliby, R. M., Labaki, C., Soulati, H., Gallegos, J., Peris, A., McCurry, D., Yu, C., Shah, V., Poduval, D., et al. (2025) · Nature Cancer · doi:10.1038/s43018-024-00896-w · Cited by 38
Mitra, A., Andrews, M. C., Roh, W., De Macedo, M. P., Hudgens, C. W., Carapeto, F., Singh, S., Reuben, A., Wang, F., Mao, X., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15538-9 · Cited by 27
Seligson, N. D., Chen, J. L., Goodrich, A. C., Van Tine, B. A., Campbell, J. D., Richards, A. L., Antonescu, C. R., Liebner, D. A., Milhem, M. M., Streicher, H., et al. (2024) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2024-009472 · Cited by 21
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Zhang, J. Q., & Plitas, G. (2021) · Journal of Surgical Oncology · doi:10.1002/jso.26287 · Cited by 6
Metadata
- Authors
- Morris, L. G. T., Riaz, N., Desrichard, A., Şenbabaoğlu, Y., Hakimi, A. A., Makarov, V., Reis-Filho, J. S., & Chan, T. A.
- Venue
- Oncotarget
- Publisher
- Impact Journals, LLC
- Published
- 2016-01-28
- Volume
- 7
- Issue
- 9
- Pages
- 10051-10063
- ISSN
- 1949-2553 (ISSN-L)
- OpenAlex
- W2292534871
- Cited by
- 299
- References
- 20
- FWCI
- 11.77
- Type
- journal article
- Language
- en
- Open access
- diamond
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