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Intratumoral Heterogeneity in Cancer Progression and Response to Immunotherapy

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Journal Nat Med
Date 2021 Feb 12
PMID 33574607
Citations 290
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Abstract

Most (if not all) tumors emerge and progress under a strong evolutionary pressure imposed by trophic, metabolic, immunological, and therapeutic factors. The relative impact of these factors on tumor evolution changes over space and time, ultimately favoring the establishment of a neoplastic microenvironment that exhibits considerable genetic, phenotypic, and behavioral heterogeneity in all its components. Here, we discuss the main sources of intratumoral heterogeneity and its impact on the natural history of the disease, including sensitivity to treatment, as we delineate potential strategies to target such a detrimental feature of aggressive malignancies.

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References
1.
Vitale I, Sistigu A, Manic G, Rudqvist N, Trajanoski Z, Galluzzi L . Mutational and Antigenic Landscape in Tumor Progression and Cancer Immunotherapy. Trends Cell Biol. 2019; 29(5):396-416. DOI: 10.1016/j.tcb.2019.01.003. View

2.
Wang J, Cazzato E, Ladewig E, Frattini V, Rosenbloom D, Zairis S . Clonal evolution of glioblastoma under therapy. Nat Genet. 2016; 48(7):768-76. PMC: 5627776. DOI: 10.1038/ng.3590. View

3.
Teixeira V, Pipinikas C, Pennycuick A, Lee-Six H, Chandrasekharan D, Beane J . Deciphering the genomic, epigenomic, and transcriptomic landscapes of pre-invasive lung cancer lesions. Nat Med. 2019; 25(3):517-525. PMC: 7614970. DOI: 10.1038/s41591-018-0323-0. View

4.
Sharma A, Merritt E, Hu X, Cruz A, Jiang C, Sarkodie H . Non-Genetic Intra-Tumor Heterogeneity Is a Major Predictor of Phenotypic Heterogeneity and Ongoing Evolutionary Dynamics in Lung Tumors. Cell Rep. 2019; 29(8):2164-2174.e5. PMC: 6952742. DOI: 10.1016/j.celrep.2019.10.045. View

5.
van Galen P, Hovestadt V, Wadsworth Ii M, Hughes T, Griffin G, Battaglia S . Single-Cell RNA-Seq Reveals AML Hierarchies Relevant to Disease Progression and Immunity. Cell. 2019; 176(6):1265-1281.e24. PMC: 6515904. DOI: 10.1016/j.cell.2019.01.031. View