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Metastatic pancreatic ductal adenocarcinoma (PDAC) is an aggressive and lethal malignancy with few therapeutic options. Tumor transcriptional state is a strong predictor of clinical outcome in PDAC, with two primary cell states, basal-like and classical, identified by bulk transcriptional profiling. Basal-like tumors carry a worse prognosis, but the mechanisms underlying this survival difference, the degree of cellular heterogeneity within a given tumor, and the subtype-specific contributions from the local immune microenvironment are not well understood. In addition, there are ongoing efforts to use patient-derived organoid models as functional surrogates for an individual patient’s disease, but the degree to which patient transcriptional phenotypes are preserved in their matched organoid models remains unclear. Here, we describe a pipeline that enables both direct characterization of the liver metastatic niche via single-cell RNA-sequencing and functional assessment of PDAC tumor biology in patient-matched organoid models. Starting from core needle biopsies of metastatic PDAC lesions, we applied this approach to profile 22 patient samples and their matched organoid models using single-cell RNA-sequencing with Seq-Well. We demonstrate significant heterogeneity at the single-cell level across the basal-like to classical transcriptional spectrum. Basal-like cells expressed more mesenchymal and stem-like features, while classical cells expressed features of epithelial and pancreatic progenitor transcriptional programs. A population of “hybrid” malignant cells co-expressed markers of both basal-like and classical states, suggesting that these phenotypes lie on a continuum rather than as discrete entities. Microenvironmental composition also differed by subtype across T/NK and macrophage populations. Specifically, basal-like tumors exhibited tumor cell crosstalk with specific macrophage subsets, while classical tumors harbored greater immune infiltration and a relatively pro-angiogenic microenvironment, raising important considerations for subtype-specific microenvironmental directed therapy. Finally, we found that matched organoids exhibited transcriptional drift along the basal-like to classical axis relative to their parent tumors, with evidence for selection against basal-like phenotypes in vitro. However, tumor cells in organoid culture exhibited remarkable plasticity and could recover in vivo basal-like phenotypes in response to changes in their growth conditions. Taken together, our work provides a framework for the analysis of human cancers and their matched models using single-cell methods to dissect tumor-intrinsic and extrinsic contributions, and reveals novel insights into the transcriptional heterogeneity and plasticity of PDAC. Citation Format: Srivatsan Raghavan, Peter S. Winter, Andrew W. Navia, Hannah L. Williams, Alan DenAdel, Radha L. Kalekar, Jennyfer Galvez-Reyes, Kristen E. Lowder, Nolawit Mulugeta, Manisha S. Raghavan, Ashir A. Borah, Sara A. Vayrynen, Andressa Dias Costa, Junning Wang, Emma Reilly, Dorisanne Y. Ragon, Lauren K. Brais, Alex M. Jaeger, James M. Cleary, Lorin Crawford, Jonathan A. Nowak, Brian M. Wolpin, William C. Hahn, Andrew J. Aguirre, Alex K. Shalek. Transcriptional subtype-specific microenvironmental crosstalk and tumor cell plasticity in metastatic pancreatic cancer [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2020 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2020;80(22 Suppl):Abstract nr PO-058.

Targeted inhibitors of essential oncogenic kinases induce high rates of clinical response but cure few patients due to the persistence of minimal residual disease (MRD). BCR-ABL mutant leukemias are a classic example of this paradigm where patients usually achieve deep remissions followed by near inevitable relapses. Multiple factors have been shown to influence how an individual patient’s leukemic cells will navigate treatment including differentiation state, mutational background, and communication with the microenvironment. Here, we use BCR-ABL-rearranged acute lymphoblastic leukemia (BCR-ABL ALL) to interrogate cell-autonomous features leading to therapeutic resistance using low-input single-cell assays. Specifically, we use a combination of primary samples and PDX models to dissect aberrant developmental hierarchies and monitor leukemic cell transcriptional and biophysical phenotype at pretreatment, MRD, and relapse. Using machine learning, we relate malignant B cells to normal development, allowing us to define leukemic developmental programs and demonstrate that these have consequences for the time to progression as well as the genetic alterations seen at relapse. Further, we determine that there are unique biophysical features tied to leukemic developmental states and that these integrative properties co-evolve with transcriptional state over the course of treatment. Finally, we demonstrate in PDX studies that it may be possible to intercept relapse by targeting specific features of MRD cells. Together, these data suggest that significant developmental hierarchies exist in ALL, tumor subpopulations can be identified directly within MRD, and their phenotypic and molecular characterization can be exploited to therapeutic effect. Citation Format: Peter S. Winter, Andrew Navia, Haley Strouf, Mahnoor Mirza, Jennyfer Galvez-Reyes, Nolawit Mulugeta, Laura Bilal, Nezha Senhaji, Peter Dennis, Catharine S. Leahy, Kay Shigemori, Foster Powers, Alejandro Gupta, Nicholas Calistri, Alex Van Scoyk, Kristen Jones, Huiyun Liu, Kristen E. Stevenson, Robert Kimmerling, Mark Stevens, David M. Weinstock, Scott R. Manalis, Mark A. Murakami, Alex K. Shalek. Aberrant leukemic developmental hierarchies and MRD-specific targeting informed by single-cell biophysical and molecular profiling [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr IA20.

The majority of patients with pancreatic ductal adenocarcinoma (PDAC) present with metastatic disease at diagnosis and have median survival times of less than 12 months. Recent studies have demonstrated that PDAC tumors with distinct transcriptional signatures are associated with different clinical outcomes, and that the tumor microenvironment may contribute to PDAC pathogenesis. In parallel, there are ongoing efforts to understand if patient-derived organoid models can be used as functional surrogates for an individual patient’s disease. However, it remains unclear if patient transcriptional phenotypes are preserved in their matched organoid models. Here, we describe a pipeline that permits both direct characterization of the PDAC liver metastatic niche via single-cell RNA-sequencing and functional assessment of PDAC tumor biology in patient-matched organoid models. Starting from core needle biopsies of metastatic PDAC lesions containing 50-100k viable cells, we simultaneously perform (1) low-input single-cell RNA-sequencing using Seq-Well and (2) three-dimensional organoid culture generation. We have applied this approach to profile 21 patients and their matched early passage organoid models. Our pipeline yields high-quality single-cell measurements across diverse cell types—both tumor and nontumor stromal—enabling a principled dissection of tumor intrinsic and extrinsic factors. Evaluation of clinically relevant transcriptional signatures (e.g., basal-like vs. classical) revealed extensive heterogeneity at the single-cell level and identified new, hybrid expression states. We also observed evidence of significant subtype-specific crosstalk between immune populations and tumor cells—specifically between T cells and tumor cells originating from basal-like tumors. Serial sampling at different stages of treatment revealed transcriptional shifts in tumor cells suggestive of significant plasticity. We similarly found that organoids derived from basal-like tumors exhibited considerable plasticity in vitro and had decreased fitness in standard organoid culture conditions, suggesting the need for distinct environments to support specific transcriptional subtypes. Overall, our approach provides actionable insights into the heterogeneity and plasticity of human PDAC, as well as a pipeline and framework for the analysis of PDAC and other cancers. This abstract is also being presented as Poster A50. Citation Format: Peter S. Winter, Srivatsan Raghavan, Andrew W. Navia, Hannah Williams, Jennyfer Galvez-Reyes, Radha Kalekar, Ashir Borah, Alan DenAdel, Manisha Raghavan, Kristen Lowder, Nolawit Mulugeta, Junning Wang, Emma Reilly, Lauren Brais, Lorin Crawford, James McFarland, James M. Cleary, Jonathan Nowak, Brian M. Wolpin, Andrew J. Aguirre, William C. Hahn, Alex K. Shalek. Matched metastatic pancreatic ductal adenocarcinoma biopsies and organoid models reveal tumor cell transcriptional plasticity and subtype-specific microenvironmental crosstalk [abstract]. In: Proceedings of the AACR Special Conference on the Evolving Landscape of Cancer Modeling; 2020 Mar 2-5; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2020;80(11 Suppl):Abstract nr PR02.