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.
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.
|Yamamoto, Kenta; Wang, Jiguang; Sprinzen, Lisa et al. (2016) Kinase-dead ATM protein is highly oncogenic and can be preferentially targeted by Topo-isomerase I inhibitors. Elife 5:|
|Del Giudice, Ilaria; Marinelli, Marilisa; Wang, Jiguang et al. (2016) Inter- and intra-patient clonal and subclonal heterogeneity of chronic lymphocytic leukaemia: evidences from circulating and lymph nodal compartments. Br J Haematol 172:371-83|
|Ceccarelli, Michele; Barthel, Floris P; Malta, Tathiane M et al. (2016) Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma. Cell 164:550-63|
|Oshima, Koichi; Khiabanian, Hossein; da Silva-Almeida, Ana C et al. (2016) Mutational landscape, clonal evolution patterns, and role of RAS mutations in relapsed acute lymphoblastic leukemia. Proc Natl Acad Sci U S A 113:11306-11311|
|Pefanis, Evangelos; Wang, Jiguang; Rothschild, Gerson et al. (2015) RNA exosome-regulated long non-coding RNA transcription controls super-enhancer activity. Cell 161:774-89|
|Crescenzo, Ramona; Abate, Francesco; Lasorsa, Elena et al. (2015) Convergent mutations and kinase fusions lead to oncogenic STAT3 activation in anaplastic large cell lymphoma. Cancer Cell 27:516-32|
|Melamed, Rachel D; Emmett, Kevin J; Madubata, Chioma et al. (2015) Genetic similarity between cancers and comorbid Mendelian diseases identifies candidate driver genes. Nat Commun 6:7033|
|Melamed, Rachel D; Wang, Jiguang; Iavarone, Antonio et al. (2015) An information theoretic method to identify combinations of genomic alterations that promote glioblastoma. J Mol Cell Biol 7:203-13|
|Wang, Jiguang; Khiabanian, Hossein; Rossi, Davide et al. (2014) Tumor evolutionary directed graphs and the history of chronic lymphocytic leukemia. Elife 3:|
|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|