Genomic Predictions in Uterine Cancers

Abstract

Introduction: Current uterine cancer classification provides suboptimal treatment stratification and often groups together patients with significant differences in survival outcome and/or response. We used transcriptomic information to devise genomic scores for improved prediction of uterine cancer patient outcomes and validated these scores in our institutional cohorts. Project 1: In an early iteration of our gene signature discovery pipeline, we developed USC73, a genomic score for uterine serous carcinoma patients, which grouped patients into a low score (lower 66.7 percentile), good prognosis group and a high score (upper 33.3 percentile), poor prognosis group (5-year overall survival: 83.3% and 13.3%, respectively). USC73 predicts survival independently of stage, and can be combined with stage for further resolution of patient survival. Poor survivors have faster-growing tumors and lower rates of complete response to primary therapy. Project 2: We applied our pipeline to uterine endometrioid carcinoma, the most common histotype of uterine cancer, and developed UEC_IGS, an immune gene score that separates early stage patients into a high lymphocytic infiltration, good prognosis group (IGS 1) and a low lymphocytic infiltration, poor prognosis group (IGS 2). UEC_IGS predicts overall survival independent of grade and treatment. IGS 1 patients have higher levels of CD8+ tumor infiltrating lymphocytes (TILs), more CD45RO+/CD3+ memory T cells, and lower levels of FOXP3+ Tregs compared to IGS 2. Conclusion: Using transcriptomic data, we can reliably stratify uterine cancer patients into good and poor survival groups. This information can be used to facilitate recruitment of only poor prognosis patients into clinical trials, mitigating some heterogeneity in patient response and allowing clinicians to better identify treatments for patients who will not survive on the current therapy. Additionally, biological functions (e.g. cellular proliferation or immune infiltration) are associated with each genomic score, and these can serve as potential pathways to target for improving the outcome of poor survival groups.