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Large-scale genomic studies have detailed the molecular landscape of tumors from patients with high-grade serous ovarian cancers (HGSC) who underwent primary debulking surgery and correlated the identified subgroups to survival. In recent years, there is increased use of neoadjuvant chemotherapy (NACT) for patients with HGSC and while abundant data exist for patients who underwent primary debulking, little data are available on the cancer cells remaining after NACT that could lead to recurrences. We aimed to analyze gene expression profiles of NACT-treated HGSC tumor samples, and correlate them to treatment response and outcome. Tumor samples were collected from patients with stage III or IV HGSC (NACT cohort, N = 57) at the time of surgery and diagnosis (biopsy samples N = 8). Tumor content was validated by histologic examination and bioinformatics. Gene expression analysis was performed using a tailored NanoString-based assay, while sequencing was performed using MiSeq. A cross-validated survival classifier revealed patient clusters with either a "Better" or "Worse" prognostic outcome. The association with overall survival remained significant after controlling for clinical variables, and differential gene expression, gene set enrichment analyses, and the appropriate survival models were used to assess the associations between alterations in gene expression in cancer cells remaining after NACT and outcome. Pathway-based analysis of the differentially expressed genes revealed comparatively high levels of cell cycle and DNA repair gene expression in the poor outcome group. IMPLICATIONS: Our work suggests mRNA expression patterns in key genes following NACT may reflect response to treatment and outcome in patient with HGSC., (©2019 American Association for Cancer Research.)

Intense efforts are currently being directed toward profiling gene expression in the hope of developing better cancer markers and identifying potential drug targets. Here, we present a sensitive new approach for the identification of cancer signatures based on direct high-throughput reverse transcription-PCR validation of alternative splicing events. This layered and integrated system for splicing annotation (LISA) fills a gap between high-throughput microarray studies and high-sensitivity individual gene investigations, and was created to monitor the splicing of 600 cancer-associated genes in 25 normal and 21 serous ovarian cancer tissues. Out of >4,700 alternative splicing events screened, the LISA identified 48 events that were significantly associated with serous ovarian tumor tissues. In a further screen directed at 39 ovarian tissues containing cancer pathologies of various origins, our ovarian cancer splicing signature successfully distinguished all normal tissues from cancer. High-volume identification of cancer-associated splice forms by the LISA paves the way for the use of alternative splicing profiling to diagnose subtypes of cancer.