Intratumor genetic and transcriptional heterogeneity is a common feature across diverse cancer types, including. CLL is a particular cancer that exhibits genetic and transcriptional heterogeneity along with a highly variable disease course among patients that remains poorly understood. Previous research has established that the presence of particular subclonal mutations in CLL can be linked with adverse clinical outcomes and that these subclonal mutations change over time in response to therapy. Therefore, genetic and transcriptional characterization of these subclonal populations will be paramount to enabling precision medicine and synergistic treatment combinations that target subclonal drivers and eliminate aggressive subpopulations thereby improving clinical outcome. While bulk measurements and analysis has provided key insights into cancer biology, etiology, and prognosis in the past, this approach does not provide the resolution that is critical for understanding the interactions between different genetic events within the same environmental and genetic backgrounds to drive metastatic disease, drug resistance and disease progression. Single cell measurements are uniquely able to definitively unravel and connect these relationships. However, simultaneous extraction of DNA and RNA from the same single cells is currently not reliable. Therefore, new statistical methods and computational approaches are needed to identify and resolve genetic subpopulations using single cell transcriptional data alone. In this proposed research, I will develop statistical methods and computational software to analyze single cell RNA-seq data derived from CLL patient samples. Specifically, I will develop methods to identify aspects of genetic heterogeneity, such as the presence of small single nucleotide mutations and regions of copy number variation, in single cells. I will then reconstruct the genetic subclonal architecture and characterize the gene expression profiles of identified subclonal populations. The proposed work will yield innovative statistical methods to enable the identification and characterization of subclonal populations in cancer and yield opensource software that can be tailored and applied to diverse cancer types. Ultimately, application of these developed methods to CLL will provide a better understanding of CLL development and progression.

Public Health Relevance

Intratumor genetic and transcriptional heterogeneity is a common feature across diverse cancer types, including, chronic lymphocytic leukemia (CLL). Understanding how this heterogeneity impacts clinical outcome and shapes therapeutic resistance is paramount to improving treatment strategies and enabling more personalized cancer treatments. This research proposal will develop statistical methods and computational software to analyze and connect these different aspects of heterogeneity to provide a better understanding of cancer development and progression, using CLL as a primary focus.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Predoctoral Individual National Research Service Award (F31)
Project #
1F31CA206236-01
Application #
9121235
Study Section
Special Emphasis Panel (ZRG1-F09A-D (20)L)
Program Officer
Mcguirl, Michele
Project Start
2016-07-01
Project End
2019-06-30
Budget Start
2016-07-01
Budget End
2017-06-30
Support Year
1
Fiscal Year
2016
Total Cost
$34,102
Indirect Cost
Name
Harvard Medical School
Department
Biology
Type
Schools of Medicine
DUNS #
047006379
City
Boston
State
MA
Country
United States
Zip Code
02115
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Wang, Lili; Brooks, Angela N; Fan, Jean et al. (2016) Transcriptomic Characterization of SF3B1 Mutation Reveals Its Pleiotropic Effects in Chronic Lymphocytic Leukemia. Cancer Cell 30:750-763
Zhang, Xiaochang; Chen, Ming Hui; Wu, Xuebing et al. (2016) Cell-Type-Specific Alternative Splicing Governs Cell Fate in the Developing Cerebral Cortex. Cell 166:1147-1162.e15