Tumor Growth Rate Estimates Are Independently Predictive of Therapy Response and Survival in Recurrent High-Grade Serous Ovarian Cancer Patients

Abstract

This study aimed to assess the predictive value of tumor growth rate estimates based on serial cancer antigen-125 (CA-125) levels on therapy response and survival of patients with recurrent high-grade serous ovarian cancer (HGSOC). In total, 301 consecutive patients with advanced HGSOC (exploratory cohort: n = 155, treated at the Medical University of Vienna; external validation cohort: n = 146, from the Ovarian Cancer Therapy–Innovative Models Prolong Survival (OCTIPS) consortium) were enrolled. Tumor growth estimates were obtained using a validated two-phase equation model involving serial CA-125 levels, and their predictive value with respect to treatment response to the next chemotherapy and the prognostic value with respect to disease-specific survival and overall survival were assessed. Tumor growth estimates were an independent predictor for response to second-line chemotherapy and an independent prognostic factor for second-line chemotherapy use in both univariate and multivariable analyses, outperforming both the predictive (second line: p = 0.003, HR 5.19 [1.73–15.58] vs. p = 0.453, HR 1.95 [0.34–11.17]) and prognostic values (second line: p = 0.042, HR 1.53 [1.02–2.31] vs. p = 0.331, HR 1.39 [0.71–2.27]) of a therapy-free interval (TFI) < 6 months. Tumor growth estimates were a predictive factor for response to third- and fourth-line chemotherapy and a prognostic factor for third- and fourth-line chemotherapy use in the univariate analysis. The CA-125-derived tumor growth rate estimate may be a quantifiable and easily assessable surrogate to TFI in treatment decision making for patients with recurrent HGSOC.

Single‐cell isogrowth profiling: Uniform inhibition uncovers non‐uniform drug responses

“…Uncovering the mechanisms of non‐genetic diversity, and understanding how it interacts with genetic diversity, 8 how it is induced by various drugs, and how we can prevent it or use it to our advantage is therefore instrumental for the advancement of therapies. While both genetic 9 and transcriptional 10 heterogeneity of cancer are intensively investigated and a connection between growth rate and chemosensitivity has been demonstrated, 11 studies of drug‐induced heterogeneity in a setting that controls for growth rate of cancer cells are lacking. This limits our ability to characterise the drug effects on genes that are strongly dependent on growth rate, such as ribosomal protein genes, which may be critical for establishing phenotypically relevant heterogeneity.…”

CA-125 Early Dynamics to Predict Overall Survival in Women with Newly Diagnosed Advanced Ovarian Cancer Based on Meta-Analysis Data

“…Alternatively, statistical techniques such as mixed-effect modeling have been successfully developed in the case of prostate cancer to model the prostate-specific antigen dynamic over time [ 17 , 21 ]. A recent study proposed a CA-125 rate estimate that is easy to quantify in order to aid in decision making regarding second-line treatment for patients with recurrent high-grade serous ovarian cancer [ 22 ]. These statistical methods for longitudinal data are thus central to the assessment of the prognostic value of the trajectory estimated over various timeframes.…”

HER2 Expression in Peritoneal Dissemination of High-Grade Serous Ovarian Carcinoma: A Comparative Study of Immunohistochemical Reactivity Using Four HER2 Antibodies

“…The mortality of HGSOC is high, accounting for >70% of ovarian cancer deaths [ 2 , 3 ]. Platinum-based chemotherapy is a cornerstone of HGSOC therapy, and >80% of patients achieve a primary response; however, most patients will relapse and develop resistance to platinum-based therapies [ 4 , 5 ].…”