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Center for Cancer Systems Biology

Project 1: Modeling the SCLC Phenotype Space

Leaders: Vito Quaranta, Carlos F. Lopez
Co IS: Christine M. Lovly, Jonathan M. Lehman

The overarching goal of this project is to develop a global blueprint of SCLC phenotypic space, clarifying bias imposed by genomic alterations, and epigenetic basis for phenotype transition or selection in response to drugs. We leverage our expertise in modeling the origin of heterogeneous phenotypes from transcription factor (TF) networks inferred from transcriptomics data. Simulations of TF network dynamics via logic-based models enable the identification of attractors, which roughly correspond to SCLC differentiation states defined by profiles of activated or silenced TFs. We define a gene ontology metric to identify biological similarities and differences between phenotypes across a variety of experimental systems including human cell lines, patient-derived xenografts and primary tumors, as well as tumors from SCLC genetically engineered mouse models (GEMMs). The resulting phenotype map informs studies aimed at connecting model systems to patients.

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Figure1: Potential Sources and Dynamics of Tumor Heterogeneity in the SCLC Phenotypic Landscape. Diversity of tumor cell phenotypic states can occur based on genetic or epigenetic changes that allow cells to adapt to various circumstances, including the microenvironment. Additional features, such as stochasticity of molecular states and cellular interactions (e.g. through secretion) will also contribute to transitions between phenotypic states and differential biological functions of tumors, such as altered ratios of drug resistant, sensitive, and quiescent cell populations.

A limitation of SCLC is the scarcity of patient specimens, since biopsies or surgery are rarely performed beyond initial diagnosis. We circumvent this barrier by using instead liquid biopsies of circulating, cell-free DNA as a clinical proxy for the primary tumor, allowing a connection between genomic alterations and phenotypic space states of SCLC tumors. To evaluate the relative role of state transitions vs. selection underneath SCLC plasticity and drug treatment evasion, we use DNA barcoding and information theory techniques to quantify rates of diversification of SCLC phenotypes in response perturbations. Specifically, we map trajectories of cells within the SCLC phenotype space as cells adapt and evade treatment. In summary, we propose to develop a comprehensive view of SCLC phenotypic heterogeneity, linking transcriptomic, genomic, and functional features of SCLC cells across diverse experimental model systems and patient primary tumor specimens. We will link these observations to clinically measurable variables, develop a unified map of phenotypic response dynamics in response to therapy, and analyze the role of plasticity in phenotype shifting and evolution of drug resistance., providing possible novel avenues to SCLC treatment strategies.

Figure 2: Mutational analysis of plasma cfDNA from 27 patients with SCLC. Summary of mutations identified by patient at any time point. Patients are separated by stage of disease at diagnosis (LS-limited stage; ES- extensive stage). Alterations are color coded per the figure legend below the image. The mutation frequencies for each gene are plotted on the right panel.
Figure 2: Mutational analysis of plasma cfDNA from 27 patients with SCLC. Summary of mutations identified by patient at any time point. Patients are separated by stage of disease at diagnosis (LS-limited stage; ES- extensive stage). Alterations are color coded per the figure legend below the image. The mutation frequencies for each gene are plotted on the right panel.