High-dimensional deconstruction of pancreatic cancer identifies tumor microenvironmental and developmental stemness features that predict survival

dc.contributor.authorStorrs, Erik P.
dc.contributor.authorChati, Prathamesh
dc.contributor.authorUsmani, Abul
dc.contributor.authorSloan, Ian
dc.contributor.authorKrasnick, Bradley A.
dc.contributor.authorBabbra, Ramandeep
dc.contributor.authorHarris, Peter K.
dc.contributor.authorSachs, Chloe M.
dc.contributor.authorQaium, Faridi
dc.contributor.authorChatterjee, Deyali
dc.contributor.authorWetzel, Chris
dc.contributor.authorGoedegebuure, Peter
dc.contributor.authorHollander, Thomas
dc.contributor.authorAnthony, Hephzibah
dc.contributor.authorPonce, Jennifer
dc.contributor.authorKhaliq, Ateeq M.
dc.contributor.authorBadiyan, Shahed
dc.contributor.authorKim, Hyun
dc.contributor.authorDenardo, David G.
dc.contributor.authorLang, Gabriel D.
dc.contributor.authorCosgrove, Natalie D.
dc.contributor.authorKushnir, Vladimir M.
dc.contributor.authorEarly, Dayna S.
dc.contributor.authorMasood, Ashiq
dc.contributor.authorLim, Kian-Huat
dc.contributor.authorHawkins, William G.
dc.contributor.authorDing, Li
dc.contributor.authorFields, Ryan C.
dc.contributor.authorDas, Koushik K.
dc.contributor.authorChaudhuri, Aadel A.
dc.contributor.departmentMedicine, School of Medicine
dc.date.accessioned2024-03-27T17:10:27Z
dc.date.available2024-03-27T17:10:27Z
dc.date.issued2023-10-19
dc.description.abstractNumerous cell states are known to comprise the pancreatic ductal adenocarcinoma (PDAC) tumor microenvironment (TME). However, the developmental stemness and co-occurrence of these cell states remain poorly defined. Here, we performed single-cell RNA sequencing (scRNA-seq) on a cohort of treatment-naive PDAC time-of-diagnosis endoscopic ultrasound-guided fine needle biopsy (EUS-FNB) samples (n = 25). We then combined these samples with surgical resection (n = 6) and publicly available samples to increase statistical power (n = 80). Following annotation into 25 distinct cell states, cells were scored for developmental stemness, and a customized version of the Ecotyper tool was used to identify communities of co-occurring cell states in bulk RNA-seq samples (n = 268). We discovered a tumor microenvironmental community comprised of aggressive basal-like malignant cells, tumor-promoting SPP1+ macrophages, and myofibroblastic cancer-associated fibroblasts associated with especially poor prognosis. We also found a developmental stemness continuum with implications for survival that is present in both malignant cells and cancer-associated fibroblasts (CAFs). We further demonstrated that high-dimensional analyses predictive of survival are feasible using standard-of-care, time-of-diagnosis EUS-FNB specimens. In summary, we identified tumor microenvironmental and developmental stemness characteristics from a high-dimensional gene expression analysis of PDAC using human tissue specimens, including time-of-diagnosis EUS-FNB samples. These reveal new connections between tumor microenvironmental composition, CAF and malignant cell stemness, and patient survival that could lead to better upfront risk stratification and more personalized upfront clinical decision-making.
dc.eprint.versionFinal published version
dc.identifier.citationStorrs EP, Chati P, Usmani A, et al. High-dimensional deconstruction of pancreatic cancer identifies tumor microenvironmental and developmental stemness features that predict survival. NPJ Precis Oncol. 2023;7(1):105. Published 2023 Oct 19. doi:10.1038/s41698-023-00455-z
dc.identifier.urihttps://hdl.handle.net/1805/39558
dc.language.isoen_US
dc.publisherSpringer Nature
dc.relation.isversionof10.1038/s41698-023-00455-z
dc.relation.journalNPJ Precision Oncology
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.sourcePMC
dc.subjectCancer microenvironment
dc.subjectPancreatic cancer
dc.titleHigh-dimensional deconstruction of pancreatic cancer identifies tumor microenvironmental and developmental stemness features that predict survival
dc.typeArticle
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