Dataset Information

The Prognosis Predictive Score around Neo Adjuvant Chemotherapy (PPSN) Improves Diagnostic Efficacy in Predicting the Prognosis of Epithelial Ovarian Cancer Patients.

ABSTRACT:

Background

Recent studies have shown that pretreatment inflammatory responses can predict prognosis. However, no reports have analyzed the combined effect of the inflammatory response with pre-treatment and post-neo adjuvant chemotherapy (NACT). This retrospective study aims to identify factors predicting prognosis and create a novel predictive scoring system.

Methods

The study was conducted at our institution between June 2006 and March 2020. Demographic and clinicopathological data were collected from patients with advanced epithelial ovarian cancer who underwent neoadjuvant chemotherapy after sample collection by laparoscopic or laparotomy surgery, followed by interval debulking surgery. We created a scoring system, called the Predictive Prognosis Score around NACT (PPSN), using factors extracted from a receiver operating characteristic curve analysis. Univariate and multivariate analyses were conducted to assess the efficacy of PPSN in predicting progression-free survival and overall survival. Kaplan-Meier and log-rank tests were used to compare the PFS or OS rate.

Results

Our study included 72 patients, with a cut-off value of four for the scoring system. Our analysis showed that high PPSN (≥4) significantly predicts poor prognosis. Moreover, CD3⁺ and CD8⁺ tumor-infiltrating lymphocytes with low PPSN (<4) showed higher aggregation than those with high PPSN (≥4) cases.

Conclusion

Our study shows that PPSN could be a useful prognostic tool for advanced EOC patients who undergo NACT followed by IDS.

SUBMITTER: Kawahara N

PROVIDER: S-EPMC10605019 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Publications

The Prognosis Predictive Score around Neo Adjuvant Chemotherapy (PPSN) Improves Diagnostic Efficacy in Predicting the Prognosis of Epithelial Ovarian Cancer Patients.

Kawahara Naoki N Yamanaka Shoichiro S Sugimoto Sumire S Kamibayashi Junya J Nishikawa Kyohei K Kawaguchi Ryuji R Kimura Fuminori F

Cancers 20231019 20

<h4>Background</h4>Recent studies have shown that pretreatment inflammatory responses can predict prognosis. However, no reports have analyzed the combined effect of the inflammatory response with pre-treatment and post-neo adjuvant chemotherapy (NACT). This retrospective study aims to identify factors predicting prognosis and create a novel predictive scoring system.<h4>Methods</h4>The study was conducted at our institution between June 2006 and March 2020. Demographic and clinicopathological d ...[more]

PMID: 37894429

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Project description:Purpose: Despite advances in radical surgery and chemotherapy delivery, ovarian cancer is the most lethal gynecologic malignancy. Most of these patients are treated with platinum-based chemotherapies, but there is no biomarker model to guide their responses to these therapeutic agents. We have developed and independently tested our novel multivariate molecular predictors for forecasting patients' responses to individual drugs on a cohort of 58 ovarian cancer patients. Experimental Design: We adapted and applied the previously-published COXEN algorithm to develop molecular predictors for therapeutic responses of patients' tumors based on expression signatures derived from the NCI-60 in vitro drug activities and genomic expression data. Genome-wide candidate biomarkers were first triaged by examining expression patterns of frozen and formalin-fixed paraffin embedded (FFPE) tissue samples. We then identify initial drug sensitivity biomarkers for carboplatin and paclitaxel, respectively. These biomarkers were further narrowed by examining concordant expression patterns between cell lines and a historical set of ovarian cancer patients. Multivariate predictors were obtained from the NCI-60 cell lines and refined using historical patient cohorts. To independent validate these molecular predictors, we performed genome-wide profiling on FFPE samples of 58 ovarian cancer patients obtained prior to adjuvant chemotherapy. Results: Carboplatin predictor significantly stratified platinum sensitive and resistant patients (p = 0.019) with sensitivity = 93%, specificity = 33%, PPV = 65%, and NPV = 78%. Paclitaxel predictor also significantly stratified patients' responses (p = 0.033) with sensitivity = 96%, specificity = 26%, PPV = 61%, and NPV = 86%. The combination predictor for platinum-taxane combination demonstrated a significant survival difference between the predicted responders and nonresponders with median survival of 12.9 months vs. 8.1 months (p = 0.045). Conclusions: COXEN predictors successfully stratified platinum resistance and taxane response in this retrospective cohort, especially based on their FFPE tumor samples. Accurate prediction of chemotherapeutic response, especially to platinum agents is highly clinically relevant and could alter primary management of ovarian cancer. Gene expression data from 58 stage III-IV ovarian cancer patients treated with Carboplatin and Taxol agents

Dataset Information

The Prognosis Predictive Score around Neo Adjuvant Chemotherapy (PPSN) Improves Diagnostic Efficacy in Predicting the Prognosis of Epithelial Ovarian Cancer Patients.

Background

Methods

Results

Conclusion

Publications

The Prognosis Predictive Score around Neo Adjuvant Chemotherapy (PPSN) Improves Diagnostic Efficacy in Predicting the Prognosis of Epithelial Ovarian Cancer Patients.

Similar Datasets

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