Integrating imaging and RNA-seq improves outcome prediction in cervical cancer

Jin Zhang, Ramachandran Rashmi, Matthew Inkman, Kay Jayachandran, Fiona Ruiz, Michael R. Waters, Perry W. Grigsby, Stephanie Markovina, Julie K. Schwarz

Research output: Contribution to journalArticlepeer-review

Abstract

Approaches using a single type of data have been applied to classify human tumors. Here we integrate imaging features and transcriptomic data using a prospectively collected tumor bank. We demonstrate that increased maximum standardized uptake value on pretreatment 18F-fluorodeoxyglucose-positron emission tomography correlates with epithelial-to-mesenchymal transition (EMT) gene expression. We derived and validated 3 major molecular groups, namely squamous epithelial, squamous mesenchymal, and adenocarcinoma, using prospectively collected institutional (n = 67) and publicly available (n = 304) data sets. Patients with tumors of the squamous mesenchymal subtype showed inferior survival outcomes compared with the other 2 molecular groups. High mesenchymal gene expression in cervical cancer cells positively correlated with the capacity to form spheroids and with resistance to radiation. CaSki organoids were radiation-resistant but sensitive to the glycolysis inhibitor, 2-DG. These experiments provide a strategy for response prediction by integrating large data sets, and highlight the potential for metabolic therapy to influence EMT phenotypes in cervical cancer.

Original languageEnglish
Article numbere139232
JournalJournal of Clinical Investigation
Volume131
Issue number5
DOIs
StatePublished - Mar 1 2021

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