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Despite decades of research and an enormity of resultant data, cancer remains a significant public health problem. New tools and fresh perspectives are needed to obtain fundamental insights, to develop better prognostic and predictive tools, and to identify improved therapeutic interventions. With increasingly common genome-scale data, one suite of algorithms and concepts with potential to shed light on cancer biology is phylogenetics, a scientific discipline used in diverse fields. From grouping subsets of cancer samples to tracing subclonal evolution during cancer progression and metastasis, the use of phylogenetics is a powerful systems biology approach. Well-developed phylogenetic applications provide fast, robust approaches to analyze high-dimensional, heterogeneous cancer data sets. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Mutations detected in cancers are often divided into "drivers" and "passengers." We suggest that this classification is potentially misleading for purposes of early detection and prevention. Specifically, some mutations are frequent in tumors and thus appear to be drivers, but are poor predictors of cancer; other mutations are individually rare and thus appear to be passengers, but may collectively explain a large proportion of risk. The assumptions bundled into the terms "driver" and "passenger" can lead to misunderstandings of neoplastic progression, with unintended consequences including overdiagnosis, overtreatment, and failure to identify the true sources of risk. We argue that samples from healthy, benign, or neoplastic tissues are critical for evaluating the risk of future cancer posed by mutations in a given gene. Cancer Prev Res; 9(5); 335-8. ©2016 AACR., (©2016 American Association for Cancer Research.)

When multiple samples are taken from the neoplastic tissues of a single patient, it is natural to compare their mutation content. This is often done by bulk genotyping of whole biopsies, but the chance that a mutation will be detected in bulk genotyping depends on its local frequency in the sample. When the underlying mutation count per cell is equal, homogenous biopsies will have more high-frequency mutations, and thus more detectable mutations, than heterogeneous ones. Using simulations, we show that bulk genotyping of data simulated under a neutral model of somatic evolution generates strong spurious evidence for non-neutrality, because the pattern of tissue growth systematically generates differences in biopsy heterogeneity. Any experiment which compares mutation content across bulk-genotyped biopsies may therefore suggest mutation rate or selection intensity variation even when these forces are absent. We discuss computational and experimental approaches for resolving this problem.