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Clinically relevant subtypes exist for pancreatic ductal adenocarcinoma (PDAC), but molecular characterization is not yet standard in clinical care. We implemented a biopsy protocol to perform time-sensitive whole-exome sequencing and RNA sequencing for patients with advanced PDAC. Therapeutically relevant genomic alterations were identified in 48% (34/71) and pathogenic/likely pathogenic germline alterations in 18% (13/71) of patients. Overall, 30% (21/71) of enrolled patients experienced a change in clinical management as a result of genomic data. Twenty-six patients had germline and/or somatic alterations in DNA-damage repair genes, and 5 additional patients had mutational signatures of homologous recombination deficiency but no identified causal genomic alteration. Two patients had oncogenic in-frame BRAF deletions, and we report the first clinical evidence that this alteration confers sensitivity to MAPK pathway inhibition. Moreover, we identified tumor/stroma gene expression signatures with clinical relevance. Collectively, these data demonstrate the feasibility and value of real-time genomic characterization of advanced PDAC. Significance: Molecular analyses of metastatic PDAC tumors are challenging due to the heterogeneous cellular composition of biopsy specimens and rapid progression of the disease. Using an integrated multidisciplinary biopsy program, we demonstrate that real-time genomic characterization of advanced PDAC can identify clinically relevant alterations that inform management of this difficult disease. Cancer Discov; 8(9); 1096–111. ©2018 AACR. See related commentary by Collisson, p. 1062. This article is highlighted in the In This Issue feature, p. 1047

Prognostically relevant RNA expression states exist in pancreatic ductal adenocarcinoma (PDAC), 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. In vivo, we identify a new intermediate PDAC transcriptional cell state and uncover distinct site- and state-specific tumor microenvironments (TMEs). Benchmarking models against this reference map, we reveal strong culture-specific biases in cancer cell transcriptional state representation driven by altered TME signals. We restore expression state heterogeneity by adding back in vivo-relevant factors and show plasticity in culture models. Further, we prove that non-genetic modulation of cell state can strongly influence drug responses, uncovering state-specific vulnerabilities. This work provides a broadly applicable framework for aligning cell states across in vivo and ex vivo settings, identifying drivers of transcriptional plasticity and manipulating cell state to target associated vulnerabilities.

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.

Various biodiesel feedstocks were evaluated using gas chromatography–mass spectrometry data combined with unsupervised chemometric methods of analysis. Peak areas of the fatty acid methyl esters (FAMEs) present in the biodiesel feedstocks (soybean oil, canola oil, waste grease, animal tallow, etc.) were utilized. The importance of chromatographic parameters, such as temperature program and column polarity, was examined with respect to the clustering that was observed using principal component analysis (PCA) and hierarchical cluster analysis (HCA). Biodiesels in this study clustered based on feedstock type regardless of temperature program or column type, as long as FAME isomers were resolved from one another. As such, the number and type of FAME components required to observe this clustering was investigated further. In general, the minor components in the sample did not provide improved clustering and thus did not need to be included. In addition, data from various temperature programs or column types were combined to yield similar clustering, showing potential versatility in analyzing similar samples across laboratories using different columns and column properties. Overall, we determined that (1) minor FAME components are non-essential for feedstock identification and (2) PCA and HCA clustering is based on feedstock, regardless of column selection, so long as resolution of FAME isomers is achieved.

Pancreatic ductal adenocarcinoma (PDAC) is currently the fourth-leading cause of cancer-related death in the United States and is projected to become the second leading cause by 2030. Most patients present with advanced disease and die within 12 months of diagnosis. Recent genomic studies of primary pancreatic cancer resection specimens have identified several molecular alterations and genomic subtypes of the disease that may guide precision medicine approaches to clinical management. However, the molecular landscape of metastatic PDAC has been less well characterized. Moreover, biopsy-driven studies in metastatic PDAC have been historically very challenging due to the aggressive course of this disease as well as the low-volume and heterogeneous nature of biopsies that makes deep molecular characterization difficult. Insufficient genomic analysis of a patient’s tumor early in their disease course is a major barrier to enrollment on clinical trials of targeted therapies. To address these limitations, we have implemented a multi-disciplinary clinical and research biopsy protocol to enable real time comprehensive molecular characterization of metastatic PDAC biopsy specimens. We have performed core needle biopsies of metastatic lesions in the liver or peritoneal cavity in 42 patients at the time of initial presentation. A low rate of complications was observed, with only a single patient having a self-limited hemorrhagic complication after liver biopsy. On average, 4-6 separate biopsy specimens were collected from each patient for histopathology and genomic analysis. Whole exome sequencing (WES) was performed in a CLIA-certified laboratory and a comprehensive molecular report of somatic alterations and selected pathogenic germline variants was returned to the referring clinician with a typical turn-around time of 3-5 weeks. We observed a striking incidence of recurrent germline and somatic alterations in DNA-damage repair genes, such as BRCA2, ATM and CHEK2. We also observed alterations in genes with known therapeutic implications, such as BRAF, RNF43, STK11 and ROS, and in select cases, these results guided choice of second or third line therapy. In parallel to WES, we performed RNA sequencing on bulk tumor tissue and readily identified expression signatures defining multiple subtypes of tumor and stroma that may have prognostic or therapeutic implications for tumor- or stroma-directed therapies. Collectively, these results demonstrate the feasibility and value of real-time genomic characterization of metastatic PDAC and provide a path forward for improved stratification and enrollment of PDAC patients on molecularly defined clinical trials. Citation Format: Andrew J. Aguirre, Scott Carter, Nicholas Camarda, Arezou Ghazani, Jonathan Nowak, Annacarolina Da Silva, Lauren Brais, Dorisanne Ragon, Devin McCabe, Lori Marini, Kristin Anderka, Karla Helvie, Nelly Oliver, Ana Babic, Paul Shyn, Douglas Rubinson, Anuj Patel, James Cleary, Nadine McCleary, Matthew Kulke, Thomas Clancy, Leona Doyle, Jason Hornick, Christine Ardito-Abraham, Ruth Yu, Michael Downes, Ronald Evans, Richard A. Moffitt, Jen Jen Yeh, William C. Hahn, Charles Fuchs, Robert Mayer, Nikhil Wagle, David Tuveson, Levi A. Garraway, Brian M. Wolpin. Real-time genomic characterization of metastatic pancreatic cancer to enable precision medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3036. doi:10.1158/1538-7445.AM2017-3036

The fatty acid methyl ester (FAME) content of biodiesel fuels has traditionally been determined using gas chromatography with a polar stationary phase. In this study, a direct comparison of the separation of FAMEs present in various biodiesel samples on three polar stationary phases and one moderately polar stationary phase (with comparable column dimensions) was performed. Retention on each column was based on solubility in and polarity of the phase. Quantitative metrics describing the resolution of important FAME pairs indicate high resolution on all polar columns, yet the best resolution, particularly of geometric isomers, is achieved on the cyanopropyl column. In addition, the separation of four C18 monounsaturated isomers was optimized and the elution order determined on each column. FAME composition of various biodiesel fuel types was determined on each column to illustrate (1) chemical differences in biodiesels produced from different feedstocks and (2) chemical similarities in biodiesels of the same feedstock type produced in different locations and harvest seasons.