Both healthy and diseased tissues are composed of multiple cell types whose interplay underpins their functions. Even within a given cell type, cells differ in their transcriptional state due to external influences, the history of that cell, or stocastic events. The impact of such heterogeneity is particularly notable in the context of cancer therapy, where presence of phenotypically distinct subclonal populations fuels relapse and resistance to treatment. As part of an ongoing collaboration with the laboratory of Catherine Wu (DFCI), we are applying single-cell genomic assays to investigate subpopulation dynamics in leukemia cells. Examining samples from patients with chronic lymphocytic leukemia (CLL), we find notable transcriptional and epigenetic heterogeneity (Landau, Cancer Cell in press), and are aiming to characterize transcriptional subpopulations associated with therapeutic resistance and establish their relationship to better-studied genetic subclones. While single-cell assays provide direct means to dissect heterogeneous tissues, their application is currently limited by the lack of sensitive statistical tools for their analysis. Here we propose the development and application of novel statistical methods for the identification and characterization of biologically distinct subsets of cells from regular and spatially-resolved single-cell transcriptome measurements. Building on our approach for statistical modeling of single-cell transcriptome data (Kharchenko, Nature Methods 2014) we propose to: 1) use sensitive model-based factor analysis to capture the structure of transcriptional variability; 2) implement a framework to explore all statistically significant aspects of heterogeneity within cell population, enabling a focused analysis of biologically relevant heterogeneity; 3) develop integrative approach to align transcriptional and genetic tumor subpopulations on a single-cell level; 4) combine error models with statistical wavelet analysis and Markov Random Field methods to identify spatial patterns of heterogeneity from spatially- resolved RNA-seq data, and model tissue microarchitecture in tumor and normal tissue. Given the clear clinical need to advance our understanding of intra-tumor heterogeneity in cancer and its impact on therapy response and resistance, we focus the application of these methods on the analysis of subclonal populations in tumors of leukemia patients. Using single-cell genomic assays, we will examine samples collected at serial time points from CLL patients undergoing therapy, for which matched bulk DNA and RNA sequencing data have been collected as part of a separate effort by the Wu lab. Applying the proposed methods, we will 1) characterize transcriptional heterogeneity within and between the subclonal populations, 2) search for functional features linked to subclonal expansion rates and resistance to therapies targeting B-cell receptor pathway in peripheral blood; 3) apply spatially-resolved RNA-seq methods to investigate focused lymph node reservoirs of drug-resistant leukemic cells. We expect these studies to provide valuable insights into cell characteristics associated with therapy resistance, and yield widely applicable computational tools.

Public Health Relevance

A key challenge in cancer therapy is prevention of recurrence, which occurs because a subset of cancer cells is able to adapt and resume growth despite therapeutic interventions. Our ability to identify subsets of cancer cells that pose greater risk t patient's health has been very limited, but a set of biological assays developed in the past several years are posed to change this by enabling detailed analysis of individual cells within a given sample. The goal of this proposal is to develop the statistical tools necessary to identify and characterize biologically distinct subsets of cells from such single-cell data, and apply them to investigate what distinguishes the subpopulations of rapidly-growing and therapy-resistant cancer cells in chronic lymphocytic leukemia patients.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
1R01HL131768-01A1
Application #
8984955
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Hanspal, Manjit
Project Start
2015-08-15
Project End
2020-05-31
Budget Start
2015-08-15
Budget End
2016-05-31
Support Year
1
Fiscal Year
2015
Total Cost
Indirect Cost
Name
Harvard Medical School
Department
Miscellaneous
Type
Schools of Medicine
DUNS #
047006379
City
Boston
State
MA
Country
United States
Zip Code
Hoggatt, Jonathan; Singh, Pratibha; Tate, Tiffany A et al. (2018) Rapid Mobilization Reveals a Highly Engraftable Hematopoietic Stem Cell. Cell 172:191-204.e10
Lake, Blue B; Chen, Song; Sos, Brandon C et al. (2018) Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat Biotechnol 36:70-80
Fan, Jean; Lee, Hae-Ock; Lee, Soohyun et al. (2018) Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data. Genome Res 28:1217-1227
La Manno, Gioele; Soldatov, Ruslan; Zeisel, Amit et al. (2018) RNA velocity of single cells. Nature 560:494-498
Petukhov, Viktor; Guo, Jimin; Baryawno, Ninib et al. (2018) dropEst: pipeline for accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments. Genome Biol 19:78
Wang, Lili; Livak, Kenneth J; Wu, Catherine J (2018) High-dimension single-cell analysis applied to cancer. Mol Aspects Med 59:70-84
Wang, Lili; Fan, Jean; Francis, Joshua M et al. (2017) Integrated single-cell genetic and transcriptional analysis suggests novel drivers of chronic lymphocytic leukemia. Genome Res 27:1300-1311
Lake, Blue B; Codeluppi, Simone; Yung, Yun C et al. (2017) A comparative strategy for single-nucleus and single-cell transcriptomes confirms accuracy in predicted cell-type expression from nuclear RNA. Sci Rep 7:6031
Landau, Dan A; Sun, Clare; Rosebrock, Daniel et al. (2017) The evolutionary landscape of chronic lymphocytic leukemia treated with ibrutinib targeted therapy. Nat Commun 8:2185
Furlan, Alessandro; Dyachuk, Vyacheslav; Kastriti, Maria Eleni et al. (2017) Multipotent peripheral glial cells generate neuroendocrine cells of the adrenal medulla. Science 357:

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