Our goal is to investigate the utility of cancer molecular phylogeny, a novel technological concept, to predict clinical outcomes. Rather than use novel high-throughput technologies to identify a molecular marker or signature to predict survival, we propose to use a molecular measure of cancer age. Estimation of cancer age is complicated by the fact that we do not observe the initial transformation and clonal expansion. What we do observe is a population of cells that are descendants of the original transformed cell. What we can measure is the molecular diversity of the population. Population genetics dictates that cells from an older population will show more diversity than cells from a younger population. Thus a molecular measure of tumor diversity should capture tumor age. We propose that tumor age will help predict patient outcomes. Thus, we challenge the current paradigm of finding a common pathway of cancer development that leads to poor survival. Instead we propose that older tumors are more diverse, regardless of the sequence of mutations accumulated, and that it is this diversity that makes them resistant to chemotherapy and prone to dissemination, thus leading to poorer outcomes. We propose to calibrate this novel technology using cell lines, in order to characterize the association between diversity and number of passages of cell division at a large number of (epi)genomic regions. We hypothesize that this basic, but presently unstudied, tumor characteristic of aging, will predict clinical outcome. We propose to demonstrate this for the special case of cancer patients with Stage III colon cancers. If successful, the approach has promise for predicting clinical outcome for solid tumors of many different cancer sites (e.g. breast, lung, prostate). This application has two Specific Aims:
Aim 1 : Characterize 70 somatic cell cancer molecular clocks to measure progression histories;
Aim 2 : Test whether the timing of metastases correlates with Stage III colorectal cancer disease-free survival.
Our hypothesis is that older cancers are more diverse cell populations and more resistant to treatment than younger cancers. However, this simple hypothesis has never been tested because we do not observe the initial transformation and clonal expansion of a cancer, and therefore cannot measure tumor age directly. We resolve this fundamental challenge by apply concepts from the field of population genetics to infer tumor age. In this project we apply tools for molecular phylogeny to predict clinical outcomes in cancer research.
|Sottoriva, Andrea; Kang, Haeyoun; Ma, Zhicheng et al. (2015) A Big Bang model of human colorectal tumor growth. Nat Genet 47:209-16|