Proteogenomic analysis of chemo-refractory high-grade serous ovarian cancer

Shrabanti Chowdhury, Jacob J. Kennedy, Richard G. Ivey, Oscar D. Murillo, Noshad Hosseini, Xiaoyu Song, Francesca Petralia, Anna Calinawan, Sara R. Savage, Anna B. Berry, Boris Reva, Umut Ozbek, Azra Krek, Weiping Ma, Felipe da Veiga Leprevost, Jiayi Ji, Seungyeul Yoo, Chenwei Lin, Uliana J. Voytovich, Yajue HuangSun Hee Lee, Lindsay Bergan, Travis D. Lorentzen, Mehdi Mesri, Henry Rodriguez, Andrew N. Hoofnagle, Zachary T. Herbert, Alexey I. Nesvizhskii, Bing Zhang, Jeffrey R. Whiteaker, David Fenyo, Wilson McKerrow, Joshua Wang, Stephan C. Schürer, Vasileios Stathias, X. Steven Chen, Mary Helen Barcellos-Hoff, Timothy K. Starr, Boris J. Winterhoff, Andrew C. Nelson, Samuel C. Mok, Scott H. Kaufmann, Charles Drescher, Marcin Cieslik, Pei Wang, Michael J. Birrer, Amanda G. Paulovich

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


To improve the understanding of chemo-refractory high-grade serous ovarian cancers (HGSOCs), we characterized the proteogenomic landscape of 242 (refractory and sensitive) HGSOCs, representing one discovery and two validation cohorts across two biospecimen types (formalin-fixed paraffin-embedded and frozen). We identified a 64-protein signature that predicts with high specificity a subset of HGSOCs refractory to initial platinum-based therapy and is validated in two independent patient cohorts. We detected significant association between lack of Ch17 loss of heterozygosity (LOH) and chemo-refractoriness. Based on pathway protein expression, we identified 5 clusters of HGSOC, which validated across two independent patient cohorts and patient-derived xenograft (PDX) models. These clusters may represent different mechanisms of refractoriness and implicate putative therapeutic vulnerabilities.

Original languageEnglish (US)
Pages (from-to)3476-3498.e35
Issue number16
StatePublished - Aug 3 2023

Bibliographical note

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© 2023 The Authors


  • chemorefractory
  • high-grade serous ovarian cancer
  • machine learning
  • mass spectrometry
  • multiple reaction monitoring
  • platinum
  • precision oncology
  • predictive biomarker
  • prognostic biomarker
  • proteogenomic


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