A population genetics perspective on the determinants of intra-tumor heterogeneity

Highlights

• Population genetics principles provide insight into the forces that influence intra-tumor heterogeneity.

• Distinct modes of evolution are operative during different stages of tumor progression and in different tumor types.

• Spatial information obtained via multi-region sequencing can help to resolve tumor evolutionary dynamics.

• A quantitative understanding of tumor dynamics may inform the detection and treatment of cancer.

Abstract

Cancer results from the acquisition of somatic alterations in a microevolutionary process that typically occurs over many years, much of which is occult. Understanding the evolutionary dynamics that are operative at different stages of progression in individual tumors might inform the earlier detection, diagnosis, and treatment of cancer. Although these processes cannot be directly observed, the resultant spatiotemporal patterns of genetic variation amongst tumor cells encode their evolutionary histories. Such intra-tumor heterogeneity is pervasive not only at the genomic level, but also at the transcriptomic, phenotypic, and cellular levels. Given the implications for precision medicine, the accurate quantification of heterogeneity within and between tumors has become a major focus of current research. In this review, we provide a population genetics perspective on the determinants of intra-tumor heterogeneity and approaches to quantify genetic diversity. We summarize evidence for different modes of evolution based on recent cancer genome sequencing studies and discuss emerging evolutionary strategies to therapeutically exploit tumor heterogeneity. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

Introduction

Cancer results from the acquisition of alterations during somatic cell division in a microevolutionary process that typically occurs over decades, much of which is occult and with extended clinically latent intervals. For example, more than half of all detectable somatic mutations in colorectal cancers occur prior to transformation [1], [2]. A set of initiating (epi)genetic events (so called drivers) provide a selective fitness advantage (resulting in higher proliferation or lower apoptosis for example) in the target cell relative to other pre-malignant cells, resulting in clonal expansion. These initiating events in the founding tumor cell are often specific to certain tissue types or cells of origin as is the case for APC in colon and other gastrointestinal tumors [3], [4], [5] or DNMT3 in leukemias [6]. Hence, the fitness benefit that the mutation confers may depend on the microenvironment or cellular context in which it occurs. However, a single mutation is seldom sufficient to evoke a fully malignant phenotype and the pre-malignant clones likely maintain functionality until additional ‘hits’ accrue, eventually leading to transformation and uncontrolled cell growth (Fig. 1).

Given that tumor cells continuously accrue mutations and are subject to ever changing microenvironments, genetic and phenotypic heterogeneity is expected. Indeed, according to the clonal evolution theory, heterogeneity is attributed to continued genetic and heritable epigenetic alterations and selection in diverse microenvironments within tumors [7], [8], [9]. Hence mutation rates, genetic drift, population structure, microenvironment and selection impact the extent of tumor heterogeneity. As tumors progress, expanding into an environment that is resource limited, cells that are better able to replicate, utilize energy, and migrate will have a competitive advantage and these ‘hallmarks’ are noted in most advanced cancers [10]. At this stage, the initiating genetic lesions may no longer ensure cell survival. As such, it is essential to develop an understanding of the phenotypic consequences of putative oncogenes and tumor suppressors in a context-dependent fashion.

Although intra-tumor heterogeneity (ITH) has been appreciated for decades [11], the advent of high-throughput technologies has enabled the characterization of (epi)genomic, transcriptomic, phenotypic, and cellular heterogeneity at enhanced resolution [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25]. The presence of pervasive ITH poses significant challenges for precision medicine. For example, sampling bias due to solid tumor spatial structure within lesions can obscure the interpretation of genomic profiles for patient stratification and therapeutic decision-making. Elevated ITH may also be associated with disease progression [26] and poor prognosis [27], [28]. Given the numerous clinical implications, the accurate quantification of heterogeneity within and between tumors from the same patient is a major focus of current research. However, to date, these analyses have largely been descriptive in nature.

While human tumor evolution cannot be directly observed, spatiotemporal patterns of genetic variation amongst tumor cells are stably inherited during cell division, and hence surreptitiously encode their ancestries. As such, the resultant patterns of ITH can be ‘read’ via multi-region sequencing (MRS) of spatially and/or temporally separated tumor regions or single-cell sequencing (SCS) and used to reconstruct the evolutionary relationships amongst the distinct cell populations (clones) that comprise a tumor, as has now been performed in a variety of tumor types [13], [16], [17], [18], [19], [21], [22], [25], [29], [30], [31], [32], [33], [34]. Intriguingly, these and other cancer sequencing studies suggest that distinct modes of evolution are operative during tumor progression and in different tumor types. However, this has yet to be systematically evaluated. More generally, while the elements of somatic evolution have been defined [7], [8], [35] and include mutation, genetic drift, migration, population structure, and selection, the evolutionary dynamics that are operative at different stages of progression in individual tumors are poorly characterized. A quantitative understanding of tumor dynamics has the potential to define cancers' evolutionary playbook and might hold clues to as to how to better prevent, detect, and treat cancers. For example, the earlier the initial clonal expansion is detected, the less diverse and likely less fit the cell population, and the better the prognosis. Hence, knowledge of the initiating events and their relative temporal ordering may inform strategies for earlier intervention. Likewise, computational and mathematical modeling can provide insights into mechanisms of progression and enable the inference of patient-specific tumor dynamics. Such information may ultimately inform the design of rational treatment strategies.

In this review, we provide a population genetic perspective on the origins of tumor heterogeneity and summarize approaches to quantify genetic diversity, drawing from established theory for studying genetic variation within and among natural populations in light of the forces of mutation, genetic drift, selection, and demography. We summarize evidence for different modes of clonal evolution based on recent cancer genome sequencing studies and the potential clinical relevance of the resultant heterogeneity. Finally, we discuss emerging evolutionary strategies to therapeutically exploit tumor heterogeneity and steer clonal dynamics.