Lindsay J Collin, Kara L Cushing-Haugen, Kathryn L Terry, Ellen L Goode, Anna H Wu, Holly R Harris, Naoko Sasamoto, Daniel W Cramer, Francesmary Modugno, Esther Elishaev, Zhuxuan Fu, Kirsten B Moysich, Peter A Fasching, Celeste Leigh Pearce, Usha Menon, Aleksandra Gentry-Maharaj, Simon A Gayther, Nicolas Wentzensen, Marc T Goodman, Joshy George, Aline Talhouk, Michael S Anglesio, Susan J Ramus, David D L Bowtell, Shelley S Tworoger, Joellen M Schildkraut, Penelope M Webb, Jennifer A Doherty
{"title":"高级别浆液性卵巢癌基因表达亚型与流行病学因素的关联模式","authors":"Lindsay J Collin, Kara L Cushing-Haugen, Kathryn L Terry, Ellen L Goode, Anna H Wu, Holly R Harris, Naoko Sasamoto, Daniel W Cramer, Francesmary Modugno, Esther Elishaev, Zhuxuan Fu, Kirsten B Moysich, Peter A Fasching, Celeste Leigh Pearce, Usha Menon, Aleksandra Gentry-Maharaj, Simon A Gayther, Nicolas Wentzensen, Marc T Goodman, Joshy George, Aline Talhouk, Michael S Anglesio, Susan J Ramus, David D L Bowtell, Shelley S Tworoger, Joellen M Schildkraut, Penelope M Webb, Jennifer A Doherty","doi":"10.1158/1055-9965.EPI-24-1143","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Ovarian high-grade serous carcinomas (HGSC) comprise four distinct molecular subtypes based on mRNA expression patterns, with differential survival. Understanding risk factor associations is important to elucidate the etiology of HGSC. We investigated associations between different epidemiologic risk factors and HGSC molecular subtypes.</p><p><strong>Methods: </strong>We pooled data from 11 case-control studies with epidemiologic and tumor gene expression data from custom NanoString CodeSets developed through a collaboration within the Ovarian Tumor Tissue Analysis consortium. The PrOTYPE-validated NanoString-based 55-gene classifier was used to assign HGSC gene expression subtypes. We examined associations between epidemiologic factors and HGSC subtypes in 2,070 cases and 16,633 controls using multivariable-adjusted polytomous regression models.</p><p><strong>Results: </strong>Among the 2,070 HGSC cases, 556 (27%) were classified as C1.MES, 340 (16%) as C5.PRO, 538 (26%) as C2.IMM, and 636 (31%) as C4.DIF. The key factors, including oral contraceptive use, parity, breastfeeding, and family history of ovarian cancer, were similarly associated with all subtypes. Heterogeneity was observed for several factors. Former smoking [OR = 1.25; 95% confidence interval (CI) = 1.03, 1.51] and genital powder use (OR = 1.42; 95% CI = 1.08, 1.86) were uniquely associated with C2.IMM. History of endometriosis was associated with C5.PRO (OR = 1.46; 95% CI = 0.98, 2.16) and C4.DIF (OR = 1.27; 95% CI = 0.94, 1.71) only. Family history of breast cancer (OR = 1.44; 95% CI = 1.16, 1.78) and current smoking (OR = 1.40; 95% CI = 1.11, 1.76) were associated with C4.DIF only.</p><p><strong>Conclusions: </strong>This study observed heterogeneous associations of epidemiologic and modifiable factors with HGSC molecular subtypes.</p><p><strong>Impact: </strong>The different patterns of associations may provide key information about the etiology of the four subtypes.</p>","PeriodicalId":9458,"journal":{"name":"Cancer Epidemiology Biomarkers & Prevention","volume":" ","pages":"762-773"},"PeriodicalIF":3.7000,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12046315/pdf/","citationCount":"0","resultStr":"{\"title\":\"Patterns of Associations with Epidemiologic Factors by High-Grade Serous Ovarian Cancer Gene Expression Subtypes.\",\"authors\":\"Lindsay J Collin, Kara L Cushing-Haugen, Kathryn L Terry, Ellen L Goode, Anna H Wu, Holly R Harris, Naoko Sasamoto, Daniel W Cramer, Francesmary Modugno, Esther Elishaev, Zhuxuan Fu, Kirsten B Moysich, Peter A Fasching, Celeste Leigh Pearce, Usha Menon, Aleksandra Gentry-Maharaj, Simon A Gayther, Nicolas Wentzensen, Marc T Goodman, Joshy George, Aline Talhouk, Michael S Anglesio, Susan J Ramus, David D L Bowtell, Shelley S Tworoger, Joellen M Schildkraut, Penelope M Webb, Jennifer A Doherty\",\"doi\":\"10.1158/1055-9965.EPI-24-1143\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Ovarian high-grade serous carcinomas (HGSC) comprise four distinct molecular subtypes based on mRNA expression patterns, with differential survival. Understanding risk factor associations is important to elucidate the etiology of HGSC. We investigated associations between different epidemiologic risk factors and HGSC molecular subtypes.</p><p><strong>Methods: </strong>We pooled data from 11 case-control studies with epidemiologic and tumor gene expression data from custom NanoString CodeSets developed through a collaboration within the Ovarian Tumor Tissue Analysis consortium. The PrOTYPE-validated NanoString-based 55-gene classifier was used to assign HGSC gene expression subtypes. We examined associations between epidemiologic factors and HGSC subtypes in 2,070 cases and 16,633 controls using multivariable-adjusted polytomous regression models.</p><p><strong>Results: </strong>Among the 2,070 HGSC cases, 556 (27%) were classified as C1.MES, 340 (16%) as C5.PRO, 538 (26%) as C2.IMM, and 636 (31%) as C4.DIF. The key factors, including oral contraceptive use, parity, breastfeeding, and family history of ovarian cancer, were similarly associated with all subtypes. Heterogeneity was observed for several factors. Former smoking [OR = 1.25; 95% confidence interval (CI) = 1.03, 1.51] and genital powder use (OR = 1.42; 95% CI = 1.08, 1.86) were uniquely associated with C2.IMM. History of endometriosis was associated with C5.PRO (OR = 1.46; 95% CI = 0.98, 2.16) and C4.DIF (OR = 1.27; 95% CI = 0.94, 1.71) only. Family history of breast cancer (OR = 1.44; 95% CI = 1.16, 1.78) and current smoking (OR = 1.40; 95% CI = 1.11, 1.76) were associated with C4.DIF only.</p><p><strong>Conclusions: </strong>This study observed heterogeneous associations of epidemiologic and modifiable factors with HGSC molecular subtypes.</p><p><strong>Impact: </strong>The different patterns of associations may provide key information about the etiology of the four subtypes.</p>\",\"PeriodicalId\":9458,\"journal\":{\"name\":\"Cancer Epidemiology Biomarkers & Prevention\",\"volume\":\" \",\"pages\":\"762-773\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2025-05-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12046315/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cancer Epidemiology Biomarkers & Prevention\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1158/1055-9965.EPI-24-1143\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Epidemiology Biomarkers & Prevention","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1158/1055-9965.EPI-24-1143","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
Patterns of Associations with Epidemiologic Factors by High-Grade Serous Ovarian Cancer Gene Expression Subtypes.
Background: Ovarian high-grade serous carcinomas (HGSC) comprise four distinct molecular subtypes based on mRNA expression patterns, with differential survival. Understanding risk factor associations is important to elucidate the etiology of HGSC. We investigated associations between different epidemiologic risk factors and HGSC molecular subtypes.
Methods: We pooled data from 11 case-control studies with epidemiologic and tumor gene expression data from custom NanoString CodeSets developed through a collaboration within the Ovarian Tumor Tissue Analysis consortium. The PrOTYPE-validated NanoString-based 55-gene classifier was used to assign HGSC gene expression subtypes. We examined associations between epidemiologic factors and HGSC subtypes in 2,070 cases and 16,633 controls using multivariable-adjusted polytomous regression models.
Results: Among the 2,070 HGSC cases, 556 (27%) were classified as C1.MES, 340 (16%) as C5.PRO, 538 (26%) as C2.IMM, and 636 (31%) as C4.DIF. The key factors, including oral contraceptive use, parity, breastfeeding, and family history of ovarian cancer, were similarly associated with all subtypes. Heterogeneity was observed for several factors. Former smoking [OR = 1.25; 95% confidence interval (CI) = 1.03, 1.51] and genital powder use (OR = 1.42; 95% CI = 1.08, 1.86) were uniquely associated with C2.IMM. History of endometriosis was associated with C5.PRO (OR = 1.46; 95% CI = 0.98, 2.16) and C4.DIF (OR = 1.27; 95% CI = 0.94, 1.71) only. Family history of breast cancer (OR = 1.44; 95% CI = 1.16, 1.78) and current smoking (OR = 1.40; 95% CI = 1.11, 1.76) were associated with C4.DIF only.
Conclusions: This study observed heterogeneous associations of epidemiologic and modifiable factors with HGSC molecular subtypes.
Impact: The different patterns of associations may provide key information about the etiology of the four subtypes.
期刊介绍：
Cancer Epidemiology, Biomarkers & Prevention publishes original peer-reviewed, population-based research on cancer etiology, prevention, surveillance, and survivorship. The following topics are of special interest: descriptive, analytical, and molecular epidemiology; biomarkers including assay development, validation, and application; chemoprevention and other types of prevention research in the context of descriptive and observational studies; the role of behavioral factors in cancer etiology and prevention; survivorship studies; risk factors; implementation science and cancer care delivery; and the science of cancer health disparities. Besides welcoming manuscripts that address individual subjects in any of the relevant disciplines, CEBP editors encourage the submission of manuscripts with a transdisciplinary approach.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
