TY - JOUR AU - Zhang, Jin AU - Rashmi, Ramachandran AU - Inkman, Matthew AU - Jayachandran, Kay AU - Ruiz, Fiona AU - Waters, Michael R. AU - Grigsby, Perry W. AU - Markovina, Stephanie AU - Schwarz, Julie K. T1 - Integrating imaging and RNA-seq improves outcome prediction in cervical cancer PY - 2021/03/01/ AB - 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. JF - The Journal of Clinical Investigation JA - J Clin Invest SN - 0021-9738 DO - 10.1172/JCI139232 VL - 131 IS - 5 UR - https://doi.org/10.1172/JCI139232 PB - The American Society for Clinical Investigation ER -