Extensive three-dimensional intratumor proteomic heterogeneity revealed by multiregion sampling in high-grade serous ovarian tumor specimens

Allison L. Hunt, Nicholas W. Bateman, Waleed Barakat, Sasha Makohon-Moore, Brian L. Hood, Kelly A. Conrads, Ming Zhou, Valerie Calvert, Mariaelena Pierobon, Jeremy Loffredo, Tracy J. Litzi, Julie Oliver, Dave Mitchell, Glenn Gist, Christine Rojas, Brian Blanton, Emma L. Robinson, Kunle Odunsi, Anil K. Sood, Yovanni CasablancaKathleen M. Darcy, Craig D. Shriver, Emanuel F. Petricoin, Uma N.M. Rao, G. Larry Maxwell*, Thomas P. Conrads*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Enriched tumor epithelium, tumor-associated stroma, and whole tissue were collected by laser microdissection from thin sections across spatially separated levels of ten high-grade serous ovarian carcinomas (HGSOCs) and analyzed by mass spectrometry, reverse phase protein arrays, and RNA sequencing. Unsupervised analyses of protein abundance data revealed independent clustering of an enriched stroma and enriched tumor epithelium, with whole tumor tissue clustering driven by overall tumor “purity.” Comparing these data to previously defined prognostic HGSOC molecular subtypes revealed protein and transcript expression from tumor epithelium correlated with the differentiated subtype, whereas stromal proteins (and transcripts) correlated with the mesenchymal subtype. Protein and transcript abundance in the tumor epithelium and stroma exhibited decreased correlation in samples collected just hundreds of microns apart. These data reveal substantial tumor microenvironment protein heterogeneity that directly bears on prognostic signatures, biomarker discovery, and cancer pathophysiology and underscore the need to enrich cellular subpopulations for expression profiling.

Original languageEnglish
Article number102757
JournaliScience
Volume24
Issue number7
DOIs
StatePublished - 23 Jul 2021
Externally publishedYes

Keywords

  • Cancer systems biology
  • Oncology
  • Proteomics
  • Transcriptomics

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