4.4.23-Health-Shalek

Conference Video|Duration: 36:38
April 4, 2023
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    Recent advances in high throughput genomic sequencing technologies have led to a detailed understanding of the genetic alterations that underlie human tumors. However, evidence increasingly understanding of the genetic alterations that underlie human tumors. However, evidence increasingly indicates that using mutations alone to assign therapies has its limitations, even for cancers with actionable mutational heterogeneity. The advent of single-cell genomic technologies has confirmed extensive mutational heterogeneity in human tumors but also revealed that the complexity of cancer extends to variation in cell transcriptional state. Deciphering whether transcriptional variation informs treatment response heterogeneity represents a new but poorly understood frontier in cancer therapeutics. In pancreatic ductal adenocarcinoma (PDAC), clinically relevant RNA expression states exist but our understanding of their drivers, stability, and relationship to therapeutic response is limited. To examine these attributes systematically, we profiled metastatic biopsies and matched organoid models at single-cell resolution. We identify a new intermediate PDAC transcriptional cell state and uncover distinct site- and state-specific tumor microenvironments. Moreover, we reveal strong organoid culture-specific biases in cancer cell transcriptional state representation and nominate critical factors missing from the ex vivo microenvironment. By adding back specific factors, we restore in vivo expression state heterogeneity and show plasticity in culture models, demonstrating that microenvironmental signals are critical regulators of cell state. Importantly, we prove that non-genetic modulation of cell state can significantly influence drug responses and uncover state-specific vulnerabilities. Our work provides a broadly applicable framework for mapping cell states across in vivo and ex vivo settings, identifying drivers of transcriptional plasticity, and manipulating cell state to target its associated vulnerabilities. 
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  • Video details
    Recent advances in high throughput genomic sequencing technologies have led to a detailed understanding of the genetic alterations that underlie human tumors. However, evidence increasingly understanding of the genetic alterations that underlie human tumors. However, evidence increasingly indicates that using mutations alone to assign therapies has its limitations, even for cancers with actionable mutational heterogeneity. The advent of single-cell genomic technologies has confirmed extensive mutational heterogeneity in human tumors but also revealed that the complexity of cancer extends to variation in cell transcriptional state. Deciphering whether transcriptional variation informs treatment response heterogeneity represents a new but poorly understood frontier in cancer therapeutics. In pancreatic ductal adenocarcinoma (PDAC), clinically relevant RNA expression states exist but our understanding of their drivers, stability, and relationship to therapeutic response is limited. To examine these attributes systematically, we profiled metastatic biopsies and matched organoid models at single-cell resolution. We identify a new intermediate PDAC transcriptional cell state and uncover distinct site- and state-specific tumor microenvironments. Moreover, we reveal strong organoid culture-specific biases in cancer cell transcriptional state representation and nominate critical factors missing from the ex vivo microenvironment. By adding back specific factors, we restore in vivo expression state heterogeneity and show plasticity in culture models, demonstrating that microenvironmental signals are critical regulators of cell state. Importantly, we prove that non-genetic modulation of cell state can significantly influence drug responses and uncover state-specific vulnerabilities. Our work provides a broadly applicable framework for mapping cell states across in vivo and ex vivo settings, identifying drivers of transcriptional plasticity, and manipulating cell state to target its associated vulnerabilities. 
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