Cancer is a dynamic process that proceeds through the accumulation of genomic alterations. Large sequencing projects have illuminated the complex static landscapes of alterations across a large number of tumors. However, these studies have failed to address the dynamic nature of cancers. Understanding how tumors are shaped by selective pressures bears implications in therapies and prognoses. To address this issue we propose PITCH (Parsimony Inference of Tumor Clone Heterogeneity), a computational model that aims to uncover the evolutionary history of tumors using high throughput genomic data from cross-sectional studies. PITCH identifies traces of older clones and reconstructs possible histories of lesions. By combining the data from different patients, PITCH is able to capture statistically robust historical relationships between driver alterations in tumors and to represent these relationships as an evolutionary network. We will calibrate the approach in a longitudinal cohort of nearly 1,500 Chronic Lymphocytic Leukemia patients spanning a period of 12 years, along with 20 Glioblastoma Multiforme samples.
We aim to extend and thus experimentally validate the approach using the large collection of Glioblastoma Multiforme and Low Grade Glioma in The Cancer Genome Atlas. We will be able to provide a robust computational approach that can be easily extended to any other tumor type where large cross-sectional data is available. Discovery of alterations associated to different phases, stages or therapeutic strategies could provide invaluable biomarkers for personalized approaches based on genomic data.

Public Health Relevance

Tumors are dynamic biological entities that evolve through the accumulation of genetic alterations. In this proposal, we aim to uncover the history of tumors through the analysis of high throughput genomic data from cross-sectional studies. This work will provide a strategy for the identification of alterations that mark the different stages of the evolution of a tumor.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
1R01CA185486-01
Application #
8687270
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Li, Jerry
Project Start
Project End
Budget Start
Budget End
Support Year
1
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Columbia University (N.Y.)
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
City
New York
State
NY
Country
United States
Zip Code
10032
Pefanis, Evangelos; Wang, Jiguang; Rothschild, Gerson et al. (2014) Noncoding RNA transcription targets AID to divergently transcribed loci in B cells. Nature 514:389-93