Clonal status of actionable driver events and the timing of mutational processes in cancer evolution

By Nicholas McGranahan, F. Favero, E. D. de Bruin, Nicolai J. Birkbak, Zoltan Szallasi, Charles Swanton. Science Translational Medicine, 2015

630 citations recorded by the source corpus.

Pan-cancer analysis of tumor subclonal structures uncovers widespread intratumor heterogeneity in actionable driver events and reveals mutational processes fueling their selection. Uncovering a tumor’s family tree In the era of targeted anticancer drugs, correctly identifying the mutations in a tumor becomes an essential part of optimizing cancer treatment. This is not necessarily straightforward because tumors can contain both “driver” mutations, which control tumor growth and therefore should be blocked with specific drugs, and “passenger” mutations, which, as their name suggests, may not contribute to the progression of a tumor and are unlikely to be useful therapeutic targets. McGranahan et al. identified patterns of driver event evolution in a wide variety of tumor types, revealing specific patterns of mutations that will be important in the design of future therapeutic regimens for cancer. Deciphering whether actionable driver mutations are found in all or a subset of tumor cells will likely be required to improve drug development and precision medicine strategies. We analyzed nine cancer types to determine the subclonal frequencies of driver events, to time mutational processes during cancer evolution, and to identify drivers of subclonal expansions. Although mutations in known driver genes typically occurred early in cancer evolution, we also identified later subclonal “actionable” mutations, including BRAF (V600E), IDH1 (R132H), PIK3CA (E545K), EGFR (L858R), and KRAS (G12D), which may compromise the efficacy of targeted therapy approaches. More than 20% of IDH1 mutations in glioblastomas, and 15% of mutations in genes in the PI3K (phosphatidylinositol 3-kinase)–AKT–mTOR (mammalian target of rapamycin) signaling axis across all tumor types were subclonal. Mutations in the RAS–MEK (mitogen-activated protein kinase kinase) signaling axis were less likely to be subclonal than mutations in genes associated with PI3K-AKT-mTOR signaling. Analysis of late mutations revealed a link between APOBEC-mediated mutagenesis and the acquisition of subclonal driver mutations and uncovered putative cancer genes involved in subclonal expansions, including CTNNA2 and ATXN1. Our results provide a pan-cancer census of driver events within the context of intratumor heterogeneity and reveal patterns of tumor evolution across cancers. The frequent presence of subclonal driver mutations suggests the need to stratify targeted therapy response according to the proportion of tumor cells in which the driver is identified.

Subfields: Cancer Research, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/145970fefdefa99ccc9efcd9f2708f1c2e4505c4

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Citations in the corpus

The PI3K pathway in human disease (2017, positive): “…ﬁndings, a study spanning nine cancer subtypes demonstrated that subclonal mutations in the PI3K-AKT-mTOR pathway were considerably more frequent than subclonal mutations in the RAS-MAPK pathway, which tended to be ubiquitously expressed in all sub-clones in the tumor (McGranahan et al., 2015).”
The long tail of oncogenic drivers in prostate cancer (2018, positive): “Clonality of mutations was estimated as cancer cell fraction (CCF) 40 , and implemented in the FACETS algorithm 36 .”
The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes (2016, neutral): “To characterize the clonal states of Mut-driver gene mutations across the IntClusts, we used variant allele fractions (VAF) and copy number data to obtain estimates of the fraction of cancer cells harbouring mutations 40 (Fig.”
Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future. (2017, positive): “Studies exploring ITH within solid tumors have demonstrated a tendency for established cancer genes to harbor clonal mutations (McGranahan et al., 2015).”
Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab (2017, methodology): “As in previous work, we considered mutations to be clonal if the 95% confidence interval (CI) of the CCF contained 95% (McGranahan et al., 2015).”
Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic (2018, background): “deaminase family and can also occur following cytotoxic chemotherapy in resistant emergent subclones [54-57].”
Comprehensive Characterization of Cancer Driver Genes and Mutations (2018, background): “…of driver gene sets across cancer types (Hoadley et al., 2014), mutation order and timing (founder vs. progression mutations) (Ding et al., 2012; McGranahan et al., 2015), interactions among mutations (Raimondi et al., 2016), the consequences of different mutations affecting the same gene…”