Prognostic gene expression signature for high-grade serous ovarian cancer.

BACKGROUND: Median overall survival (OS) for women with high-grade serous ovarian cancer (HGSOC) is ∼4 years, yet survival varies widely between patients. There are no well-established, gene expression signatures associated with prognosis. The aim of this study was to develop a robust prognostic signature for OS in patients with HGSOC. PATIENTS AND METHODS: Expression of 513 genes, selected from a meta-analysis of 1455 tumours and other candidates, was measured using NanoString technology from formalin-fixed paraffin-embedded tumour tissue collected from 3769 women with HGSOC from multiple studies. Elastic net regularization for survival analysis was applied to develop a prognostic model for 5-year OS, trained on 2702 tumours from 15 studies and evaluated on an independent set of 1067 tumours from six studies. RESULTS: Expression levels of 276 genes were associated with OS (false discovery rate \u3c 0.05) in covariate-adjusted single-gene analyses. The top five genes were TAP1, ZFHX4, CXCL9, FBN1 and PTGER3 (P \u3c 0.001). The best performing prognostic signature included 101 genes enriched in pathways with treatment implications. Each gain of one standard deviation in the gene expression score conferred a greater than twofold increase in risk of death [hazard ratio (HR) 2.35, 95% confidence interval (CI) 2.02-2.71; P \u3c 0.001]. Median survival [HR (95% CI)] by gene expression score quintile was 9.5 (8.3 to -), 5.4 (4.6-7.0), 3.8 (3.3-4.6), 3.2 (2.9-3.7) and 2.3 (2.1-2.6) years. CONCLUSION: The OTTA-SPOT (Ovarian Tumor Tissue Analysis consortium - Stratified Prognosis of Ovarian Tumours) gene expression signature may improve risk stratification in clinical trials by identifying patients who are least likely to achieve 5-year survival. The identified novel genes associated with the outcome may also yield opportunities for the development of targeted therapeutic approaches

Prognostic gene expression signature for high-grade serous ovarian cancer

BACKGROUND:Median overall survival (OS) for women with high-grade serous ovarian cancer (HGSOC) is ∼4 years, yet survival varies widely between patients. There are no well-established, gene expression signatures associated with prognosis. The aim of this study was to develop a robust prognostic signature for OS in patients with HGSOC. PATIENTS AND METHODS:Expression of 513 genes, selected from a meta-analysis of 1455 tumours and other candidates, was measured using NanoString technology from formalin-fixed paraffin-embedded tumour tissue collected from 3769 women with HGSOC from multiple studies. Elastic net regularization for survival analysis was applied to develop a prognostic model for 5-year OS, trained on 2702 tumours from 15 studies and evaluated on an independent set of 1067 tumours from six studies. RESULTS:Expression levels of 276 genes were associated with OS (false discovery rate < 0.05) in covariate-adjusted single-gene analyses. The top five genes were TAP1, ZFHX4, CXCL9, FBN1 and PTGER3 (P < 0.001). The best performing prognostic signature included 101 genes enriched in pathways with treatment implications. Each gain of one standard deviation in the gene expression score conferred a greater than twofold increase in risk of death [hazard ratio (HR) 2.35, 95% confidence interval (CI) 2.02-2.71; P < 0.001]. Median survival [HR (95% CI)] by gene expression score quintile was 9.5 (8.3 to -), 5.4 (4.6-7.0), 3.8 (3.3-4.6), 3.2 (2.9-3.7) and 2.3 (2.1-2.6) years. CONCLUSION:The OTTA-SPOT (Ovarian Tumor Tissue Analysis consortium - Stratified Prognosis of Ovarian Tumours) gene expression signature may improve risk stratification in clinical trials by identifying patients who are least likely to achieve 5-year survival. The identified novel genes associated with the outcome may also yield opportunities for the development of targeted therapeutic approaches

Prognostic gene expression signature for high-grade serous ovarian cancer.

BACKGROUND: Median overall survival (OS) for women with high-grade serous ovarian cancer (HGSOC) is ∼4 years, yet survival varies widely between patients. There are no well-established, gene expression signatures associated with prognosis. The aim of this study was to develop a robust prognostic signature for OS in patients with HGSOC. PATIENTS AND METHODS: Expression of 513 genes, selected from a meta-analysis of 1455 tumours and other candidates, was measured using NanoString technology from formalin-fixed paraffin-embedded tumour tissue collected from 3769 women with HGSOC from multiple studies. Elastic net regularization for survival analysis was applied to develop a prognostic model for 5-year OS, trained on 2702 tumours from 15 studies and evaluated on an independent set of 1067 tumours from six studies. RESULTS: Expression levels of 276 genes were associated with OS (false discovery rate < 0.05) in covariate-adjusted single-gene analyses. The top five genes were TAP1, ZFHX4, CXCL9, FBN1 and PTGER3 (P < 0.001). The best performing prognostic signature included 101 genes enriched in pathways with treatment implications. Each gain of one standard deviation in the gene expression score conferred a greater than twofold increase in risk of death [hazard ratio (HR) 2.35, 95% confidence interval (CI) 2.02-2.71; P < 0.001]. Median survival [HR (95% CI)] by gene expression score quintile was 9.5 (8.3 to -), 5.4 (4.6-7.0), 3.8 (3.3-4.6), 3.2 (2.9-3.7) and 2.3 (2.1-2.6) years. CONCLUSION: The OTTA-SPOT (Ovarian Tumor Tissue Analysis consortium - Stratified Prognosis of Ovarian Tumours) gene expression signature may improve risk stratification in clinical trials by identifying patients who are least likely to achieve 5-year survival. The identified novel genes associated with the outcome may also yield opportunities for the development of targeted therapeutic approaches

The role of catastrophizing in chronic cyclical pelvic pain: A systematic review and meta-analysis

Background: Dysmenorrhea (painful menstrual cramps) is one of the most common gynecological complaints in women and girls. Dysmenorrhea may be a condition itself or a result of another medical condition, including endometriosis and chronic pelvic pain. Research examining the relationship between menstrual pain ratings and catastrophizing has produced mixed results. Objective: To review and meta-analyze the relationship between catastrophizing and pain ratings of chronic cyclical pelvic pain. Design: Cross-sectional, longitudinal, and intervention studies that reported the relationship between menstrual/pelvic pain and catastrophizing were included. Study populations had to include healthy menstruating persons or persons with a condition associated with cyclical pelvic pain including primary dysmenorrhea, endometriosis, and/or chronic pelvic pain. Data sources and methods: A systematic search of articles published since 2012 on PubMed, PsychInfo, CINHAL, and Medline was conducted in January and rerun in November of 2022. Search terms included cyclical pelvic pain, dysmenorrhea, endometriosis, pelvic pain, and catastrophizing. Data extraction was completed independently by two extractors and cross-checked for errors. A random-effects meta-regression was used to synthesize the data using restricted maximum likelihood. Results: Twenty-five studies examining 4,540 participants were included. A random effects model found a meta-correlation between catastrophizing and pain of r  = .31 (95% confidence interval: .23–.40) p  < .001. Heterogeneity was large and significant ( I 2  = 84.5%, Q (24) = 155.16, p  < .001). Studies that measured general pelvic pain rather than cyclical pelvic pain specifically and those that used multi-item rather than single-item measures of pain had significantly higher correlations. Age and depression did not moderate the relationship between catastrophizing and pain. Conclusion: A systematic review and meta-analysis found that catastrophizing had a small but significant positive association with pain ratings. Patients experiencing cyclical pelvic pain may benefit from interventions targeting the psychological management of pain. Registration: This meta-analysis was registered in PROSPERO on 14 January 2022. Registration number: CRD42022295328

sj-docx-2-whe-10.1177_17455057231199949 – Supplemental material for The role of catastrophizing in chronic cyclical pelvic pain: A systematic review and meta-analysis

Supplemental material, sj-docx-2-whe-10.1177_17455057231199949 for The role of catastrophizing in chronic cyclical pelvic pain: A systematic review and meta-analysis by Emily P Rabinowitz, MacKenzie A Sayer and Douglas L Delahanty in Women’s Health</p

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Supplemental material, sj-jpg-3-whe-10.1177_17455057231199949 for The role of catastrophizing in chronic cyclical pelvic pain: A systematic review and meta-analysis by Emily P Rabinowitz, MacKenzie A Sayer and Douglas L Delahanty in Women’s Health</p

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Supplemental material, sj-docx-4-whe-10.1177_17455057231199949 for The role of catastrophizing in chronic cyclical pelvic pain: A systematic review and meta-analysis by Emily P Rabinowitz, MacKenzie A Sayer and Douglas L Delahanty in Women’s Health</p

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Supplemental material, sj-docx-6-whe-10.1177_17455057231199949 for The role of catastrophizing in chronic cyclical pelvic pain: A systematic review and meta-analysis by Emily P Rabinowitz, MacKenzie A Sayer and Douglas L Delahanty in Women’s Health</p

sj-doc-1-whe-10.1177_17455057231199949 – Supplemental material for The role of catastrophizing in chronic cyclical pelvic pain: A systematic review and meta-analysis

Supplemental material, sj-doc-1-whe-10.1177_17455057231199949 for The role of catastrophizing in chronic cyclical pelvic pain: A systematic review and meta-analysis by Emily P Rabinowitz, MacKenzie A Sayer and Douglas L Delahanty in Women’s Health</p

sj-docx-5-whe-10.1177_17455057231199949 – Supplemental material for The role of catastrophizing in chronic cyclical pelvic pain: A systematic review and meta-analysis

Supplemental material, sj-docx-5-whe-10.1177_17455057231199949 for The role of catastrophizing in chronic cyclical pelvic pain: A systematic review and meta-analysis by Emily P Rabinowitz, MacKenzie A Sayer and Douglas L Delahanty in Women’s Health</p