Quality of Life and Adverse Events: Prognostic Relationships in Long-Term Ovarian Cancer Survival

Background: There is a critical need to identify patient characteristics associated with long-term ovarian cancer survival. Methods: Quality of life (QOL), measured by the Functional Assessment of Cancer Therapy-Ovarian-Trial Outcome Index (FACT-O-TOI), including physical, functional, and ovarian-specific subscales, was compared between long-term survivors (LTS) (8+ years) and short-term survivors (STS) (<5 years) of GOG 218 at baseline; before cycles 4, 7, 13, 21; and 6 months post-treatment using linear and longitudinal mixed models adjusted for covariates. Adverse events (AEs) were compared between survivor groups at each assessment using generalized linear models. All P values are 2-sided. Results: QOL differed statistically significantly between STS (N = 1115) and LTS (N = 260) (P < .001). Baseline FACT-O-TOI and FACT-O-TOI change were independently associated with long-term survival (odds ratio = 1.05, 95% confidence interval = 1.03 to 1.06 and odds ratio = 1.06, 95% confidence interval = 1.05 to 1.07, respectively). A 7-point increase in baseline QOL was associated with a 38.0% increase in probability of LTS, and a 9-point increase in QOL change was associated with a 67.0% increase in odds for LTS. QOL decreased statistically significantly with increasing AE quartiles (cycle 4 quartiles: 0-5 vs 6-8 vs 9-11 vs ≥12 AEs, P = .01; cycle 21 quartiles: 0-2 vs 3 vs 4-5 vs ≥6 AEs, P = .001). Further, LTS reported statistically significantly better QOL compared with STS (P = .03 and P = .01, cycles 4 and 21, respectively), with similar findings across higher AE grades. Conclusions: Baseline and longitudinal QOL change scores distinguished LTS vs STS and are robust prognosticators for long-term survival. Results have trial design and supportive care implications, providing meaningful prognostic value in this understudied population

Improving Risk Assessment for Metastatic Disease in Endometrioid Endometrial Cancer Patients Using Molecular and Clinical Features: An NRG Oncology/Gynecologic Oncology Group Study

Objectives: A risk assessment model for metastasis in endometrioid endometrial cancer (EEC) was developed using molecular and clinical features, and prognostic association was examined. Methods: Patients had stage I, IIIC, or IV EEC with tumor-derived RNA-sequencing or microarray-based data. Metastasis-associated transcripts and platform-centric diagnostic algorithms were selected and evaluated using regression modeling and receiver operating characteristic curves. Results: Seven metastasis-associated transcripts were selected from analysis in the training cohorts using 10-fold cross validation and incorporated into an MS7 classifier using platform-specific coefficients. The predictive accuracy of the MS7 classifier in Training-1 was superior to that of other clinical and molecular features, with an area under the curve (95% confidence interval) of 0.89 (0.80–0.98) for MS7 compared with 0.69 (0.59–0.80) and 0.71 (0.58–0.83) for the top evaluated clinical and molecular features, respectively. The performance of MS7 was independently validated in 245 patients using RNA sequencing and in 81 patients using microarray-based data. MS7 + MI (myometrial invasion) was preferrable to individual features and exhibited 100% sensitivity and negative predictive value. The MS7 classifier was associated with lower progression-free and overall survival (p ≤ 0.003). Conclusion: A risk assessment classifier for metastasis and prognosis in EEC patients with primary tumor derived MS7 + MI is available for further development and optimization as a companion clinical support tool