Assessment of spatial transcriptomics for oncology discovery. Academic Article uri icon

Overview

abstract

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

authors

  • Lyubetskaya, Anna
  • Rabe, Brian
  • Fisher, Andrew
  • Lewin, Anne
  • Neuhaus, Isaac
  • Brett, Constance
  • Brett, Todd
  • Pereira, Ethel
  • Golhar, Ryan
  • Kebede, Sami
  • Font-Tello, Alba
  • Mosure, Kathy
  • Van Wittenberghe, Nicholas
  • Mavrakis, Konstantinos J
  • MacIsaac, Kenzie
  • Chen, Benjamin J
  • Drokhlyansky, Eugene

publication date

  • November 15, 2022

Identity

PubMed Central ID

  • PMC9701619

Scopus Document Identifier

  • 85130636354

Digital Object Identifier (DOI)

  • 10.1126/science.abl4290

PubMed ID

  • 36452860

Additional Document Info

volume

  • 2

issue

  • 11