Werba G, Weissinger D, Kawaler EA, Zhao E, Kalfakakou D, Dhara S, Wang L, Lim HB, Oh G, Jing X, Beri N, Khanna L, Gonda T, Oberstein P, Hajdu C, Loomis C, Heguy A, Sherman MH, Lund AW, Welling TH, Dolgalev I, Tsirigos A, Simeone DM. Single-cell RNA sequencing reveals the effects of chemotherapy on human pancreatic adenocarcinoma and its tumor microenvironment. Nat Commun. 2023 Feb 13;14(1):797. doi: 10.1038/s41467-023-36296-4. PMID: 36781852; PMCID: PMC9925748.
scSeq Analysis of Human Pancreatic Cancer
A deeper understanding of the complex composition, cellular states and interactions of cancer cells and the tumor microenvironment (TME) in PDAC is needed to develop better therapeutics and improve outcomes. In our lab we have established a pipeline for the collection of tissue from human primary and metastatic pancreatic tumors in a routine clinical setting and subsequent real-time single-cell RNA sequencing (scSeq) of these samples. Our growing collection of samples in a real world setting enables us to address multiple questions about cancer cell and TME biology. In an initial study, we performed scSeq on freshly collected human PDAC samples either before or after chemotherapy. Overall, we found a heterogeneous mixture of basal and classical cancer cell subtypes, along with distinct cancer-associated fibroblast and macrophage subpopulations. Strikingly, classical and basal-like cancer cells exhibited similar transcriptional responses to chemotherapy and did not demonstrate a shift towards a basal-like transcriptional program among treated samples. We observed decreased ligand-receptor interactions in treated samples, particularly TIGIT on CD8+ T cells and its receptor on cancer cells, and identified TIGIT – not PD1 – as the major inhibitory checkpoint molecule of CD8+ T cells. Our results suggest that chemotherapy profoundly impacts the PDAC TME and may actually promote resistance to immunotherapy.
Our ongoing projects focus on the differences between the tumor microenvironment in primary PDAC and liver metastases, CNV analysis of cancer cells to assesses subclonal growth patterns and interacting neighborhoods within tumors, and using matched pre- and on-treatment samples from the same patient to get a deeper understanding of response to therapy and identify predictive biomarkers for clinical applications. In these studies, we are incorporating multiplex IHC and spatial transcriptomic analysis to define the spatial relationships of our findings.