Assessment of spatial transcriptomics for oncology discovery

• DOI: 10.1016/j.crmeth.2022.100340
• Publication Date: 2022-11-16T05:07:15Z
• Authors: Anna Lyubetskaya; Brian Rabe; Andrew Fisher; Anne Lewin; Isaac Neuhaus; Constance Brett; Todd Brett; Ethel Pereira; Ryan Golhar; Sami Kebede; Alba Font-Tello; Kathy Mosure; Nicholas Van Witt...; Konstantinos J. M...; Kenzie MacIsaac; Benjamin J. Chen; Eugene Drokhlyansky
• Keywords: Precision oncology; Leverage (statistics)

Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape key to identifying new targets impactful model systems. Here, we test utility transcriptomics (ST) by profiling 40 tissue sections 80,024 capture spots across diverse set types, sample formats, RNA chemistries. We verify accuracy fidelity ST leveraging matched pathology analysis, which provides ground truth section composition. then use data demonstrate depth features, hypoxia, necrosis, vasculature, extracellular matrix variation. also leverage context identify relative cell-type locations showing anti-correlation immune cells in syngeneic cancer models. Lastly, target identification approaches clinical pancreatic adenocarcinoma samples, highlighting intrinsic biomarkers paracrine signaling.