Clonal evolution in hematological malignancies and therapeutic implications

Abstract

The ability of cancer to evolve and adapt is a principal challenge to therapy in general and to the paradigm of targeted therapy in particular. This ability is fueled by the co-existence of multiple, genetically heterogeneous subpopulations within the cancer cell population. Increasing evidence has supported the idea that these subpopulations are selected in a Darwinian fashion, by which the genetic landscape of the tumor is continuously reshaped. Massively parallel sequencing has enabled a recent surge in our ability to study this process, adding to previous efforts using cytogenetic methods and targeted sequencing. Altogether, these studies reveal the complex evolutionary trajectories occurring across individual hematological malignancies. They also suggest that while clonal evolution may contribute to resistance to therapy, treatment may also hasten the evolutionary process. New insights into this process challenge us to understand the impact of treatment on clonal evolution and inspire the development of novel prognostic and therapeutic strategies.

Figure 1

inferring the size of a subpopulation affected by somatic mutations from genomic data. Massively parallel sequencing provides an estimate of variant allelic fraction (VAF), which is calculated by counting the number of reads with the variant alleles and dividing it by the total number of reading from the specific location. The certainty of the estimate is a function of the depth of coverage, using binomial distribution (panel A). Subsequently, the VAF estimates are integrated with the purity and local copy number information (panel B) to yield cancer cell fractions (CCF, panel C). In the example provided a somatic mutation with a VAF of 0.125, a local copy number of 3 and a purity of 67% yields CCF estimates of 0.5.

Figure 2. Three models of how cancer therapy may accelerate clonal evolution

First, cancer therapy, particularly containing genotoxic agents, can induce novel mutagenesis (A). Second, therapy can accelerate clonal evolution by selecting a clone (here illustrated in red) containing a mutation that confers resistance to the therapeutic agent used (B). The resistance of the selected clone is reflected in the depiction of the cell population after cytoreduction, composed almost entirely of the resistant clone (in red). A third model postulates similar sensitivity to treatment of the different subpopulations, reflected in similar proportions before and after cytoreduction. The clearing niche alters the dynamic evolutionary landscape allowing a faster rise of a fitter clone.

Figure 3. Translating clonal heterogeneity insights to the clinic

Possible prognostic and therapeutic implications of clonal heterogeneity are outlined. Image courtesy Broad Institute/Lauren Solomon; hourglass photo © iStockphoto/Dominik Pabis.