1
|
Bai S, Song D, Chen M, Lai X, Xu J, Dong F. The association between mammographic density and breast cancer molecular subtypes: a systematic review and meta-analysis. Clin Radiol 2023; 78:622-632. [PMID: 37230842 DOI: 10.1016/j.crad.2023.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/12/2023] [Accepted: 04/21/2023] [Indexed: 05/27/2023]
Abstract
AIM To conduct a systematic review and meta-analysis to evaluate the whether high mammographic density (MD) is differentially associated with all subtypes of breast cancer. MATERIALS AND METHODS The PubMed, Cochrane Library, and Embase databases were searched systematically in October 2022 to include all studies that investigated the association between MD and breast cancer subtype. Aggregate data of 17,193 breast cancer cases from 23 studies were selected, including five cohort/case-control and 18 case-only studies. The relative risk (RR) of MD were combined using random/fixed effects models for case-control studies, and for case-only studies, relative risk ratios (RRRs) were a combination of luminal A, luminal B, and HER2-positive versus triple-negative tumours. RESULTS Women in the highest density category in case-control/cohort studies had a 2.24-fold (95% confidence interval [CI] 1.53, 3.28), 1.81-fold (95% CI 1.15, 2.85), 1.44-fold (95% CI 1.14, 1.81), and 1.59-fold (95% CI 0.89, 2.85) higher risk of triple-negative, HER-2 (human epidermal growth factor receptor 2) positive, luminal A, and luminal B breast cancer compared to women in the lowest density category. RRRs for breast tumours being luminal A, luminal B, and HER-2 positive versus triple-negative in case-only studies were 1.62 (95% CI 1.14, 2.31), 1.81 (95% CI 1.22, 2.71) and 2.58 (95% CI 1.63, 4.08), respectively, for BIRADS 4 versus BIRADS 1. CONCLUSION The evidence indicates MD is a potent risk factor for the majority of breast cancer subtypes to different degrees. Increased MD is more strongly linked to HER-2-positive cancers compared to other breast cancer subtypes. The application of MD as a subtype-specific risk marker may facilitate the creation of personalised risk prediction models and screening procedures.
Collapse
Affiliation(s)
- S Bai
- Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, Guangdong, China
| | - D Song
- Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, Guangdong, China
| | - M Chen
- Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, Guangdong, China
| | - X Lai
- Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, Guangdong, China
| | - J Xu
- Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, Guangdong, China.
| | - F Dong
- Department of Ultrasound, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, Guangdong, China.
| |
Collapse
|
2
|
Mao X, Omeogu C, Karanth S, Joshi A, Meernik C, Wilson L, Clark A, Deveaux A, He C, Johnson T, Barton K, Kaplan S, Akinyemiju T. Association of reproductive risk factors and breast cancer molecular subtypes: a systematic review and meta-analysis. BMC Cancer 2023; 23:644. [PMID: 37430191 DOI: 10.1186/s12885-023-11049-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 06/08/2023] [Indexed: 07/12/2023] Open
Abstract
BACKGROUND Associations between reproductive factors and breast cancer (BC) risk vary by molecular subtype (i.e., luminal A, luminal B, HER2, and triple negative/basal-like [TNBC]). In this systematic review and meta-analysis, we summarized the associations between reproductive factors and BC subtypes. METHODS Studies from 2000 to 2021 were included if BC subtype was examined in relation to one of 11 reproductive risk factors: age at menarche, age at menopause, age at first birth, menopausal status, parity, breastfeeding, oral contraceptive (OC) use, hormone replacement therapy (HRT), pregnancy, years since last birth and abortion. For each reproductive risk factor, BC subtype, and study design (case-control/cohort or case-case), random-effects models were used to estimate pooled relative risks and 95% confidence intervals. RESULTS A total of 75 studies met the inclusion criteria for systematic review. Among the case-control/cohort studies, later age at menarche and breastfeeding were consistently associated with decreased risk of BC across all subtypes, while later age at menopause, later age of first childbirth, and nulliparity/low parity were associated with increased risk of luminal A, luminal B, and HER2 subtypes. In the case-only analysis, compared to luminal A, postmenopausal status increased the risk of HER2 and TNBC. Associations were less consistent across subtypes for OC and HRT use. CONCLUSION Identifying common risk factors across BC subtypes can enhance the tailoring of prevention strategies, and risk stratification models can benefit from subtype specificity. Adding breastfeeding status to current BC risk prediction models can enhance predictive ability, given the consistency of the associations across subtypes.
Collapse
Affiliation(s)
- Xihua Mao
- Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, KY, USA
| | - Chioma Omeogu
- Department of Population Health Sciences, School of Medicine, Duke University, Durham, NC, USA
| | - Shama Karanth
- UF Health Cancer Canter, University of Florida, Gainesville, FL, USA
| | - Ashwini Joshi
- Department of Population Health Sciences, School of Medicine, Duke University, Durham, NC, USA
| | - Clare Meernik
- Department of Population Health Sciences, School of Medicine, Duke University, Durham, NC, USA
| | - Lauren Wilson
- Department of Population Health Sciences, School of Medicine, Duke University, Durham, NC, USA
| | - Amy Clark
- Department of Population Health Sciences, School of Medicine, Duke University, Durham, NC, USA
| | - April Deveaux
- Department of Population Health Sciences, School of Medicine, Duke University, Durham, NC, USA
| | - Chunyan He
- The Cancer Prevention and Control Research Program, University of Kentucky Markey Cancer Center, Lexington, KY, USA
| | - Tisha Johnson
- Department of Preventive Medicine and Environmental Health, College of Public Health, University of Kentucky, Lexington, KY, USA
| | - Karen Barton
- Duke University Medical Center Library & Archives, Duke University School of Medicine, Durham, NC, USA
| | - Samantha Kaplan
- Duke University Medical Center Library & Archives, Duke University School of Medicine, Durham, NC, USA
| | - Tomi Akinyemiju
- Department of Population Health Sciences, School of Medicine, Duke University, Durham, NC, USA.
- Duke Cancer Institute, Duke University, Durham, NC, USA.
| |
Collapse
|
3
|
Kleinstern G, Scott CG, Tamimi RM, Jensen MR, Pankratz VS, Bertrand KA, Norman AD, Visscher DW, Couch FJ, Brandt K, Shepherd J, Wu FF, Chen YY, Cummings SR, Winham S, Kerlikowske K, Vachon CM. Association of mammographic density measures and breast cancer "intrinsic" molecular subtypes. Breast Cancer Res Treat 2021; 187:215-224. [PMID: 33392844 DOI: 10.1007/s10549-020-06049-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 12/07/2020] [Indexed: 01/29/2023]
Abstract
PURPOSE We evaluated the association of percent mammographic density (PMD), absolute dense area (DA), and non-dense area (NDA) with risk of "intrinsic" molecular breast cancer (BC) subtypes. METHODS We pooled 3492 invasive BC and 10,148 controls across six studies with density measures from prediagnostic, digitized film-screen mammograms. We classified BC tumors into subtypes [63% Luminal A, 21% Luminal B, 5% HER2 expressing, and 11% as triple negative (TN)] using information on estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and tumor grade. We used polytomous logistic regression to calculate odds ratio (OR) and 95% confidence intervals (CI) for density measures (per SD) across the subtypes compared to controls, adjusting for age, body mass index and study, and examined differences by age group. RESULTS All density measures were similarly associated with BC risk across subtypes. Significant interaction of PMD by age (P = 0.001) was observed for Luminal A tumors, with stronger effect sizes seen for younger women < 45 years (OR = 1.69 per SD PMD) relative to women of older ages (OR = 1.53, ages 65-74, OR = 1.44 ages 75 +). Similar but opposite trends were seen for NDA by age for risk of Luminal A: risk for women: < 45 years (OR = 0.71 per SD NDA) was lower than older women (OR = 0.83 and OR = 0.84 for ages 65-74 and 75 + , respectively) (P < 0.001). Although not significant, similar patterns of associations were seen by age for TN cancers. CONCLUSIONS Mammographic density measures were associated with risk of all "intrinsic" molecular subtypes. However, findings of significant interactions between age and density measures may have implications for subtype-specific risk models.
Collapse
Affiliation(s)
- Geffen Kleinstern
- School of Public Health, University of Haifa, Haifa, Israel
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Christopher G Scott
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Rulla M Tamimi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Matthew R Jensen
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA
| | | | - Kimberly A Bertrand
- Slone Epidemiology Center, Boston University School of Medicine, Boston, MA, USA
| | - Aaron D Norman
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Daniel W Visscher
- Department of Anatomic Pathology, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Kathleen Brandt
- Department of Radiology, Mayo Clinic College of Medicine, Rochester, MN, USA
| | | | - Fang-Fang Wu
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First St. SW, Rochester, MN, 55905, USA
| | - Yunn-Yi Chen
- Department of Pathology and Laboratory Services, University of California, San Francisco, CA, USA
| | - Steven R Cummings
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - Stacey Winham
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Karla Kerlikowske
- Departments of Epidemiology and Biostatistics and General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, CA, USA
| | - Celine M Vachon
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First St. SW, Rochester, MN, 55905, USA.
| |
Collapse
|