Burges, Alexander

Abstract Purpose: Predicting surgical outcome could improve individualizing treatment strategies for patients with advanced ovarian cancer. It has been suggested earlier that gene expression signatures (GES) might harbor the potential to predict surgical outcome. Experimental Design: Data derived from high-grade serous tumor tissue of FIGO stage IIIC/IV patients of AGO-OVAR11 trial were used to generate a transcriptome profiling. Previously identified molecular signatures were tested. A theoretical model was implemented to evaluate the impact of medically associated factors for residual disease (RD) on the performance of GES that predicts RD status. Results: A total of 266 patients met inclusion criteria, of those, 39.1% underwent complete resection. Previously reported GES did not predict RD in this cohort. Similarly, The Cancer Genome Atlas molecular subtypes, an independent de novo signature and the total gene expression dataset using all 21,000 genes were not able to predict RD status. Medical reasons for RD were identified as potential limiting factors that impact the ability to use GES to predict RD. In a center with high complete resection rates, a GES which would perfectly predict tumor biological RD would have a performance of only AUC 0.83, due to reasons other than tumor biology. Conclusions: Previously identified GES cannot be generalized. Medically associated factors for RD may be the main obstacle to predict surgical outcome in an all-comer population of patients with advanced ovarian cancer. If biomarkers derived from tumor tissue are used to predict outcome of patients with cancer, selection bias should be focused on to prevent overestimation of the power of such a biomarker. See related commentary by Handley and Sood, p. 9

TPS5602 Background: Primary cytoreductive surgery (PDS) followed by chemotherapy has been considered as standard management for advanced ovarian cancer patients (pts) over decades. An alternative approach of interval debulking surgery (IDS) following neoadjuvant chemotherapy (NACT) was subsequently reported by two randomized phase III trials (EORTC‐GCG, CHORUS). Owing to important limitations of these studies, especially regarding surgical quality, optimal timing of surgical therapy in advanced ovarian cancer is still unclear. Methods: TRUST is an international open, randomized, controlled multicenter trial investigating overall survival (OS; primary endpoint) after PDS vs NACT and subsequent IDS in pts with FIGO stage IIIB‐IVB ovarian, tubal, and peritoneal carcinoma. Secondary objectives are safety of complete tumor resection, progression‐free survival and quality of life (QoL) as well as surgical morbidity. In order to guarantee adequate surgical quality, participating centers need to fulfill specific quality assurance criteria (e.g. ≥50% complete resection rate in upfront surgery for FIGO IIIB-IV pts, ≥36 debulking-surgeries/year) and agree to independent audits by TRUST Quality committee delegates. A 1:1 randomization to PDS or NACT followed by IDS stratified by center and age‐ECOG combination (ECOG 0 and age ≤65 years vs ECOG > 0 or age > 65 years) is performed. Pts in the PDS arm will undergo surgery followed by 6 cycles of platinum-based chemotherapy, whereas pts in the IDS arm will be treated with 3 cycles of NACT after histologic confirmation of the disease, followed by IDS and subsequently 3 cycles of platinum-based chemotherapy. Intention of surgery for both groups will be complete tumor resection as per guideline recommendations. Health related QoL will be assessed using the EORTC QLQ‐C30, QLQ‐OV28, and EQ‐5D‐3L questionnaires. For sample size planning, we considered a prolongation of median OS from 45 months in the IDS arm to 60 months in the PDS arm (HR 0.75) as clinically relevant. 380 events are needed to obtain a power of 80% in two‐sided log-rank test with significance level of 0.05. The primary analysis will be done in the ITT‐population of 686 randomized pts. By Feb 3 2017, 46 pts were randomized. Clinical trial information: NCT02828618.

Abstract Purpose: Recent progress in understanding the molecular biology of epithelial ovarian cancer has not yet translated into individualized treatment for these women or improvements in their disease outcome. Gene expression has been utilized to identify distinct molecular subtypes, but there have been no reports investigating whether or not molecular subtyping is predictive of response to bevacizumab in ovarian cancer. Experimental Design: DASL gene expression arrays were performed on FFPE tissue from patients enrolled on the ICON7 trial. Patients were stratified into four TCGA molecular subtypes. Associations between molecular subtype and the efficacy of randomly assigned therapy with bevacizumab were assessed. Results: Molecular subtypes were assigned as follows: 122 immunoreactive (34%), 96 proliferative (27%), 73 differentiated (20%), and 68 mesenchymal (19%). In univariate analysis patients with tumors of proliferative subtype obtained the greatest benefit from bevacizumab with a median PFS improvement of 10.1 months [HR, 0.55 (95% CI, 0.34–0.90), P = 0.016]. For the mesenchymal subtype, bevacizumab conferred a nonsignificant improvement in PFS of 8.2 months [HR 0.78 (95% CI, 0.44–1.40), P = 0.41]. Bevacizumab conferred modest improvements in PFS for patients with immunoreactive subtype (3.8 months; P = 0.08) or differentiated subtype (3.7 months; P = 0.61). Multivariate analysis demonstrated significant PFS improvement in proliferative subtype patients only [HR, 0.45 (95% CI, 0.27–0.74), P = 0.0015]. Conclusions: Ovarian carcinoma molecular subtypes with the poorest survival (proliferative and mesenchymal) derive a comparably greater benefit from treatment that includes bevacizumab. Validation of our findings in an independent cohort could enable the use of bevacizumab for those patients most likely to benefit, thereby reducing side effects and healthcare cost. Clin Cancer Res; 23(14); 3794–801. ©2017 AACR.