PhyloOncology: Understanding cancer through phylogenetic analysis

Abstract

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.

Keywords: Cancer stratification; Cancer types; Clonal evolution; Tumor heterogeneity; Tumor trees.

Fig. 1.

Phylogenetics reveals evolutionary relationships between states. A. Characteristics from various species under study can be transformed into a binary character state matrix. A species that is known to possess the ancestral state of the given characters (e.g. here, the lamprey) can be included as an “outgroup” as a means by which to polarize the resulting tree. B. An unrooted most parsimonious tree obtained by choosing the topology requiring the fewest number of character changes. C. The unrooted tree is converted to a rooted tree by assuming that jawed vertebrates share a more recent common ancestor than the most recent common ancestor (MRCA) of the entire group. C. As a cancer research tool, phylogenetic analyses can be used strictly as a clustering algorithm to segregate individual patients by their progression status. D. Samples are collected from individual patients, a matrix of characters is constructed using gene expression, mutation status, or some other information, and a phylogenetic tree is generated. E. In a more direct application of phylogenetic methods, they can be used to analyze phenotypic/genotypic heterogeneity within a patient or disease location. In this example, samples are collected at different sites to construct a matrix and tree of progression F. Depending on the question being asked, samples can be collected longitudinally or from neighboring areas of a tissue a single site (e.g. primary tumor and metastatic nodules) to reconstruct the evolutionary history of the disease progression.

Fig. 2.

Reconstructing the chronology of metastatic lineages using phylogenetics. Timings of the first genetic divergence from normal tissue sequence (blue circle), of the first genetic divergence of metastases (blue dashes) and of diagnosis (red dashes) during tumor progression. A. A patient with renal clear cell carcinoma exemplifies late metastasis in which diagnosis of the primary tumor and metastases occurred after the first genetic divergence of metastasis. B. Probability density for the occurrence of the first genetic divergence of metastases and for the time of diagnosis. The x axis is scaled from 0 (the first genetic divergence of primary tumor tissue from normal tissue) to 1 (death). In the set of 40 lethal cancers analyzed in the study, the first genetic divergences of metastatic lineages (blue triangles) are distributed so as to often occur earlier than diagnosis time (red triangles). Figure reproduced from [28]. Early and multiple origins of metastatic lineages within primary tumors. 113:8, 2140–2145. doi:10.1073/pnas.1525677113. Copyright Proceedings of the National Academy of Sciences.