Revealing the inherent heterogeneity of human malignancies by variant consensus strategies coupled with cancer clonal analysis

Abstract

Tumors are heterogeneous in composition. They are composed of cancer cells proper, along with stromal elements that collectively form a microenvironment, all of which are necessary to nurture the malignant process. In addition, many of the stromal cells are modified to support the unique needs of the malignant state. Tumors are composed of a variety of clones or subpopulations of cancer cells, which may differ in karyotype, growth rate, expression of cell surface markers, sensitivity to therapeutics, etc. New tools and methods to provide an improved understanding of tumor clonal architecture are needed to guide therapy. The subclonal structure and transcription status of underlying somatic mutations reveal the trajectory of tumor progression in patients with cancer. Approaching the analysis of tumors to reveal clonal complexity in a quantitative manner should facilitate better characterization and therapeutic assignments. The challenge is the interpretation of massive amounts of data from next generation sequencing (NGS) experiments to find what is truly meaningful for improving the understanding of basic cancer biology, as well as therapeutic assignments and outcomes. To meet this need, a methodology named CloneViz was developed and utilized for the identification of serial clonal mutations. Whole exome sequencing (WES) on an Illumina HiSeq 2500 was performed on paired tumor and normal samples from a Multiple Myeloma (MM) patient at presentation, then first and second relapse. Following alignment, a consensus strategy for variant selection was employed along with computational linkage to a formal tumor clonality analysis based on visualization and quantitative methods.