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Watt GP, Keshavamurthy KN, Nguyen TL, Lobbes MBI, Jochelson MS, Sung JS, Moskowitz CS, Patel P, Liang X, Woods M, Hopper JL, Pike MC, Bernstein JL. Association of breast cancer with quantitative mammographic density measures for women receiving contrast-enhanced mammography. JNCI Cancer Spectr 2024; 8:pkae026. [PMID: 38565262 PMCID: PMC11060476 DOI: 10.1093/jncics/pkae026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 02/21/2024] [Accepted: 03/25/2024] [Indexed: 04/04/2024] Open
Abstract
Women with high mammographic density have an increased risk of breast cancer. They may be offered contrast-enhanced mammography to improve breast cancer screening performance. Using a cohort of women receiving contrast-enhanced mammography, we evaluated whether conventional and modified mammographic density measures were associated with breast cancer. Sixty-six patients with newly diagnosed unilateral breast cancer were frequency matched on the basis of age to 133 cancer-free control individuals. On low-energy craniocaudal contrast-enhanced mammograms (equivalent to standard mammograms), we measured quantitative mammographic density using CUMULUS software at the conventional intensity threshold ("Cumulus") and higher-than-conventional thresholds ("Altocumulus," "Cirrocumulus"). The measures were standardized to enable estimation of odds ratio per adjusted standard deviation (OPERA). In multivariable logistic regression of case-control status, only the highest-intensity measure (Cirrocumulus) was statistically significantly associated with breast cancer (OPERA = 1.40, 95% confidence interval = 1.04 to 1.89). Conventional Cumulus did not contribute to model fit. For women receiving contrast-enhanced mammography, Cirrocumulus mammographic density may better predict breast cancer than conventional quantitative mammographic density.
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Affiliation(s)
- Gordon P Watt
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Tuong L Nguyen
- Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Marc B I Lobbes
- Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, The Netherlands
| | - Maxine S Jochelson
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Janice S Sung
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Chaya S Moskowitz
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Prusha Patel
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Xiaolin Liang
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Meghan Woods
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - John L Hopper
- Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Malcolm C Pike
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jonine L Bernstein
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Hopper JL, Li S, MacInnis RJ, Dowty JG, Nguyen TL, Bui M, Dite GS, Esser VFC, Ye Z, Makalic E, Schmidt DF, Goudey B, Alpen K, Kapuscinski M, Win AK, Dugué PA, Milne RL, Jayasekara H, Brooks JD, Malta S, Calais-Ferreira L, Campbell AC, Young JT, Nguyen-Dumont T, Sung J, Giles GG, Buchanan D, Winship I, Terry MB, Southey MC, Jenkins MA. Breast and bowel cancers diagnosed in people 'too young to have cancer': A blueprint for research using family and twin studies. Genet Epidemiol 2024. [PMID: 38504141 DOI: 10.1002/gepi.22555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/29/2024] [Accepted: 02/23/2024] [Indexed: 03/21/2024]
Abstract
Young breast and bowel cancers (e.g., those diagnosed before age 40 or 50 years) have far greater morbidity and mortality in terms of years of life lost, and are increasing in incidence, but have been less studied. For breast and bowel cancers, the familial relative risks, and therefore the familial variances in age-specific log(incidence), are much greater at younger ages, but little of these familial variances has been explained. Studies of families and twins can address questions not easily answered by studies of unrelated individuals alone. We describe existing and emerging family and twin data that can provide special opportunities for discovery. We present designs and statistical analyses, including novel ideas such as the VALID (Variance in Age-specific Log Incidence Decomposition) model for causes of variation in risk, the DEPTH (DEPendency of association on the number of Top Hits) and other approaches to analyse genome-wide association study data, and the within-pair, ICE FALCON (Inference about Causation from Examining FAmiliaL CONfounding) and ICE CRISTAL (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLysis) approaches to causation and familial confounding. Example applications to breast and colorectal cancer are presented. Motivated by the availability of the resources of the Breast and Colon Cancer Family Registries, we also present some ideas for future studies that could be applied to, and compared with, cancers diagnosed at older ages and address the challenges posed by young breast and bowel cancers.
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Affiliation(s)
- John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Minh Bui
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Genetic Technologies Ltd., Fitzroy, Victoria, Australia
| | - Vivienne F C Esser
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Daniel F Schmidt
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia
| | - Benjamin Goudey
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, Victoria, Australia
- The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Karen Alpen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Miroslaw Kapuscinski
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Aung Ko Win
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne, Victoria, Australia
- Genetic Medicine, Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Pierre-Antoine Dugué
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Harindra Jayasekara
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Jennifer D Brooks
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Sue Malta
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Lucas Calais-Ferreira
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Alexander C Campbell
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia
| | - Jesse T Young
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Centre for Adolescent Health, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia
- Justice Health Group, Curtin School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - Tu Nguyen-Dumont
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Joohon Sung
- Department of Public Health Sciences, Division of Genome and Health Big Data, Graduate School of Public Health, Seoul National University, Seoul, South Korea
- Genome Medicine Institute, Seoul National University, Seoul, South Korea
- Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Daniel Buchanan
- Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - Ingrid Winship
- Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne, Victoria, Australia
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3
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Ye Z, Dite GS, Nguyen TL, MacInnis RJ, Schmidt DF, Makalic E, Al-Qershi OM, Nguyen-Dumont T, Goudey B, Stone J, Dowty JG, Giles GG, Southey MC, Hopper JL, Li S. Genetic and Environmental Causes of Variation in an Automated Breast Cancer Risk Factor Based on Mammographic Textures. Cancer Epidemiol Biomarkers Prev 2024; 33:306-313. [PMID: 38059829 DOI: 10.1158/1055-9965.epi-23-1012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 10/24/2023] [Accepted: 12/05/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND Cirrus is an automated risk predictor for breast cancer that comprises texture-based mammographic features and is mostly independent of mammographic density. We investigated genetic and environmental variance of variation in Cirrus. METHODS We measured Cirrus for 3,195 breast cancer-free participants, including 527 pairs of monozygotic (MZ) twins, 271 pairs of dizygotic (DZ) twins, and 1,599 siblings of twins. Multivariate normal models were used to estimate the variance and familial correlations of age-adjusted Cirrus as a function of age. The classic twin model was expanded to allow the shared environment effects to differ by zygosity. The SNP-based heritability was estimated for a subset of 2,356 participants. RESULTS There was no evidence that the variance or familial correlations depended on age. The familial correlations were 0.52 (SE, 0.03) for MZ pairs and 0.16(SE, 0.03) for DZ and non-twin sister pairs combined. Shared environmental factors specific to MZ pairs accounted for 20% of the variance. Additive genetic factors accounted for 32% (SE = 5%) of the variance, consistent with the SNP-based heritability of 36% (SE = 16%). CONCLUSION Cirrus is substantially familial due to genetic factors and an influence of shared environmental factors that was evident for MZ twin pairs only. The latter could be due to nongenetic factors operating in utero or in early life that are shared by MZ twins. IMPACT Early-life factors, shared more by MZ pairs than DZ/non-twin sister pairs, could play a role in the variation in Cirrus, consistent with early life being recognized as a critical window of vulnerability to breast carcinogens.
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Affiliation(s)
- Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Genetic Technologies Limited, Fitzroy, Victoria, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Daniel F Schmidt
- Department of Data Science and AI, Faculty of IT, Monash University, Melbourne, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Osamah M Al-Qershi
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Tu Nguyen-Dumont
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
| | - Benjamin Goudey
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, Victoria, Australia
- The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Jennifer Stone
- Genetic Epidemiology Group, School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
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4
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Ye Z, Nguyen TL, Dite GS, MacInnis RJ, Schmidt DF, Makalic E, Al-Qershi OM, Bui M, Esser VFC, Dowty JG, Trinh HN, Evans CF, Tan M, Sung J, Jenkins MA, Giles GG, Southey MC, Hopper JL, Li S. Causal relationships between breast cancer risk factors based on mammographic features. Breast Cancer Res 2023; 25:127. [PMID: 37880807 PMCID: PMC10598934 DOI: 10.1186/s13058-023-01733-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 10/17/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND Mammogram risk scores based on texture and density defined by different brightness thresholds are associated with breast cancer risk differently and could reveal distinct information about breast cancer risk. We aimed to investigate causal relationships between these intercorrelated mammogram risk scores to determine their relevance to breast cancer aetiology. METHODS We used digitised mammograms for 371 monozygotic twin pairs, aged 40-70 years without a prior diagnosis of breast cancer at the time of mammography, from the Australian Mammographic Density Twins and Sisters Study. We generated normalised, age-adjusted, and standardised risk scores based on textures using the Cirrus algorithm and on three spatially independent dense areas defined by increasing brightness threshold: light areas, bright areas, and brightest areas. Causal inference was made using the Inference about Causation from Examination of FAmilial CONfounding (ICE FALCON) method. RESULTS The mammogram risk scores were correlated within twin pairs and with each other (r = 0.22-0.81; all P < 0.005). We estimated that 28-92% of the associations between the risk scores could be attributed to causal relationships between the scores, with the rest attributed to familial confounders shared by the scores. There was consistent evidence for positive causal effects: of Cirrus, light areas, and bright areas on the brightest areas (accounting for 34%, 55%, and 85% of the associations, respectively); and of light areas and bright areas on Cirrus (accounting for 37% and 28%, respectively). CONCLUSIONS In a mammogram, the lighter (less dense) areas have a causal effect on the brightest (highly dense) areas, including through a causal pathway via textural features. These causal relationships help us gain insight into the relative aetiological importance of different mammographic features in breast cancer. For example our findings are consistent with the brightest areas being more aetiologically important than lighter areas for screen-detected breast cancer; conversely, light areas being more aetiologically important for interval breast cancer. Additionally, specific textural features capture aetiologically independent breast cancer risk information from dense areas. These findings highlight the utility of ICE FALCON and family data in decomposing the associations between intercorrelated disease biomarkers into distinct biological pathways.
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Affiliation(s)
- Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
- Genetic Technologies Limited, Fitzroy, VIC, 3065, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, 3004, Australia
| | - Daniel F Schmidt
- Department of Data Science and AI, Faculty of IT, Monash University, Melbourne, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Osamah M Al-Qershi
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Minh Bui
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Vivienne F C Esser
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Ho N Trinh
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Christopher F Evans
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Maxine Tan
- Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, 47500, Sunway City, Malaysia
- School of Electrical and Computer Engineering, The University of Oklahoma, Norman, OK, 73019, USA
| | - Joohon Sung
- Department of Public Health Sciences, Division of Genome and Health Big Data, Graduate School of Public Health, Seoul National University, Seoul, 08826, Korea
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, 3004, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, 3168, Australia
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, 3004, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, 3168, Australia
- Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia.
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, 3168, Australia.
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, CB1 8RN, UK.
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC, 3051, Australia.
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Hopper JL, Dowty JG, Nguyen TL, Li S, Dite GS, MacInnis RJ, Makalic E, Schmidt DF, Bui M, Stone J, Sung J, Jenkins MA, Giles GG, Southey MC, Mathews JD. Variance of age-specific log incidence decomposition (VALID): a unifying model of measured and unmeasured genetic and non-genetic risks. Int J Epidemiol 2023; 52:1557-1568. [PMID: 37349888 PMCID: PMC10655167 DOI: 10.1093/ije/dyad086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 06/16/2023] [Indexed: 06/24/2023] Open
Abstract
BACKGROUND The extent to which known and unknown factors explain how much people of the same age differ in disease risk is fundamental to epidemiology. Risk factors can be correlated in relatives, so familial aspects of risk (genetic and non-genetic) must be considered. DEVELOPMENT We present a unifying model (VALID) for variance in risk, with risk defined as log(incidence) or logit(cumulative incidence). Consider a normally distributed risk score with incidence increasing exponentially as the risk increases. VALID's building block is variance in risk, Δ2, where Δ = log(OPERA) is the difference in mean between cases and controls and OPERA is the odds ratio per standard deviation. A risk score correlated r between a pair of relatives generates a familial odds ratio of exp(rΔ2). Familial risk ratios, therefore, can be converted into variance components of risk, extending Fisher's classic decomposition of familial variation to binary traits. Under VALID, there is a natural upper limit to variance in risk caused by genetic factors, determined by the familial odds ratio for genetically identical twin pairs, but not to variation caused by non-genetic factors. APPLICATION For female breast cancer, VALID quantified how much variance in risk is explained-at different ages-by known and unknown major genes and polygenes, non-genomic risk factors correlated in relatives, and known individual-specific factors. CONCLUSION VALID has shown that, while substantial genetic risk factors have been discovered, much is unknown about genetic and familial aspects of breast cancer risk especially for young women, and little is known about individual-specific variance in risk.
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Affiliation(s)
- John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Genetic Technologies Ltd., Fitzroy, VIC, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Daniel F Schmidt
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - Minh Bui
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Jennifer Stone
- School of Population and Global Health, University of Western Australia, Perth, WA, Australia
| | - Joohon Sung
- Division of Genome and Health Big Data, Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - John D Mathews
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
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6
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Kou K, Cameron J, Youl P, Pyke C, Chambers S, Dunn J, Aitken JF, Baade PD. Severity and risk factors of interval breast cancer in Queensland, Australia: a population-based study. Breast Cancer 2023; 30:466-477. [PMID: 36809492 PMCID: PMC10119209 DOI: 10.1007/s12282-023-01439-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 02/15/2023] [Indexed: 02/23/2023]
Abstract
BACKGROUND Interval breast cancers (BC) are those diagnosed within 24 months of a negative mammogram. This study estimates the odds of being diagnosed with high-severity BC among screen-detected, interval, and other symptom-detected BC (no screening history within 2 years); and explores factors associated with being diagnosed with interval BC. METHODS Telephone interviews and self-administered questionnaires were conducted among women (n = 3,326) diagnosed with BC in 2010-2013 in Queensland. Respondents were categorised into screen-detected, interval, and other symptom-detected BCs. Data were analysed using logistic regressions with multiple imputation. RESULTS Compared with screen-detected BC, interval BC had higher odds of late-stage (OR = 3.50, 2.9-4.3), high-grade (OR = 2.36, 1.9-2.9) and triple-negative cancers (OR = 2.55, 1.9-3.5). Compared with other symptom-detected BC, interval BC had lower odds of late stage (OR = 0.75, 0.6-0.9), but higher odds of triple-negative cancers (OR = 1.68, 1.2-2.3). Among women who had a negative mammogram (n = 2,145), 69.8% were diagnosed at their next mammogram, while 30.2% were diagnosed with an interval cancer. Those with an interval cancer were more likely to have healthy weight (OR = 1.37, 1.1-1.7), received hormone replacement therapy (2-10 years: OR = 1.33, 1.0-1.7; > 10 years: OR = 1.55, 1.1-2.2), conducted monthly breast self-examinations (BSE) (OR = 1.66, 1.2-2.3) and had previous mammogram in a public facility (OR = 1.52, 1.2-2.0). CONCLUSION These results highlight the benefits of screening even among those with an interval cancer. Women-conducted BSE were more likely to have interval BC which may reflect their increased ability to notice symptoms between screening intervals.
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Affiliation(s)
- Kou Kou
- Cancer Council Queensland, Spring Hill, PO Box 201, Brisbane, QLD, 4001, Australia
| | - Jessica Cameron
- Cancer Council Queensland, Spring Hill, PO Box 201, Brisbane, QLD, 4001, Australia.,School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Philippa Youl
- Cancer Alliance Queensland, Metro South Hospital and Health Service, Woolloongabba, Australia
| | - Chris Pyke
- Mater Hospitals South Brisbane, Brisbane, Australia
| | - Suzanne Chambers
- Faculty of Health, University of Technology Sydney, Sydney, Australia
| | - Jeff Dunn
- Prostate Cancer Foundation of Australia, Sydney, Australia
| | - Joanne F Aitken
- Cancer Council Queensland, Spring Hill, PO Box 201, Brisbane, QLD, 4001, Australia.,School of Public Health, The University of Queensland, Brisbane, Australia.,School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.,Institute for Resilient Regions, University of Southern Queensland, Brisbane, Australia
| | - Peter D Baade
- Cancer Council Queensland, Spring Hill, PO Box 201, Brisbane, QLD, 4001, Australia. .,Centre for Data Science, Faculty of Science, Queensland University of Technology, Brisbane, Australia. .,Menzies Health Institute Queensland, Griffith University, Gold Coast Campus, Parklands Drive, Southport, QLD, Australia.
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7
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Watt GP, Knight JA, Nguyen TL, Reiner AS, Malone KE, John EM, Lynch CF, Brooks JD, Woods M, Liang X, Bernstein L, Pike MC, Hopper JL, Bernstein JL. Association of contralateral breast cancer risk with mammographic density defined at higher-than-conventional intensity thresholds. Int J Cancer 2022; 151:1304-1309. [PMID: 35315524 PMCID: PMC9420749 DOI: 10.1002/ijc.34001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 02/25/2022] [Accepted: 03/04/2022] [Indexed: 11/06/2022]
Abstract
Mammographic dense area (MDA) is an established predictor of future breast cancer risk. Recent studies have found that risk prediction might be improved by redefining MDA in effect at higher-than-conventional intensity thresholds. We assessed whether such higher-intensity MDA measures gave stronger prediction of subsequent contralateral breast cancer (CBC) risk using the Women's Environment, Cancer, and Radiation Epidemiology (WECARE) Study, a population-based CBC case-control study of ≥1 year survivors of unilateral breast cancer diagnosed between 1990 and 2008. Three measures of MDA for the unaffected contralateral breast were made at the conventional intensity threshold ("Cumulus") and at two sequentially higher-intensity thresholds ("Altocumulus" and "Cirrocumulus") using the CUMULUS software and mammograms taken up to 3 years prior to the first breast cancer diagnosis. The measures were fitted separately and together in multivariable-adjusted logistic regression models of CBC (252 CBC cases and 271 unilateral breast cancer controls). The strongest association with CBC was MDA defined using the highest intensity threshold, Cirrocumulus (odds ratio per adjusted SD [OPERA] 1.40, 95% CI 1.13-1.73); and the weakest association was MDA defined at the conventional threshold, Cumulus (1.32, 95% CI 1.05-1.66). In a model fitting the three measures together, the association of CBC with Cirrocumulus was unchanged (1.40, 95% CI 0.97-2.05), and the lower brightness measures did not contribute to the CBC model fit. These results suggest that MDA defined at a high-intensity threshold is a better predictor of CBC risk and has the potential to improve CBC risk stratification beyond conventional MDA measures.
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Affiliation(s)
- Gordon P. Watt
- Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Julia A. Knight
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
- Dalla Lana School of Public Health Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Tuong L. Nguyen
- Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Anne S. Reiner
- Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Kathleen E. Malone
- Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Esther M. John
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, United States of America
| | | | - Jennifer D. Brooks
- Dalla Lana School of Public Health Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Meghan Woods
- Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Xiaolin Liang
- Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Leslie Bernstein
- Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, California, United States of America
| | - Malcolm C. Pike
- Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - John L. Hopper
- Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Jonine L. Bernstein
- Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
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8
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Li S, Nguyen TL, Nguyen-Dumont T, Dowty JG, Dite GS, Ye Z, Trinh HN, Evans CF, Tan M, Sung J, Jenkins MA, Giles GG, Hopper JL, Southey MC. Genetic Aspects of Mammographic Density Measures Associated with Breast Cancer Risk. Cancers (Basel) 2022; 14:cancers14112767. [PMID: 35681745 PMCID: PMC9179294 DOI: 10.3390/cancers14112767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 04/27/2022] [Accepted: 05/30/2022] [Indexed: 11/26/2022] Open
Abstract
Cumulus, Altocumulus, and Cirrocumulus are measures of mammographic density defined at increasing pixel brightness thresholds, which, when converted to mammogram risk scores (MRSs), predict breast cancer risk. Twin and family studies suggest substantial variance in the MRSs could be explained by genetic factors. For 2559 women aged 30 to 80 years (mean 54 years), we measured the MRSs from digitized film mammograms and estimated the associations of the MRSs with a 313-SNP breast cancer polygenic risk score (PRS) and 202 individual SNPs associated with breast cancer risk. The PRS was weakly positively correlated (correlation coefficients ranged 0.05−0.08; all p < 0.04) with all the MRSs except the Cumulus-white MRS based on the “white but not bright area” (correlation coefficient = 0.04; p = 0.06). After adjusting for its association with the Altocumulus MRS, the PRS was not associated with the Cumulus MRS. There were MRS associations (Bonferroni-adjusted p < 0.04) with one SNP in the ATXN1 gene and nominally with some ESR1 SNPs. Less than 1% of the variance of the MRSs is explained by the genetic markers currently known to be associated with breast cancer risk. Discovering the genetic determinants of the bright, not white, regions of the mammogram could reveal substantial new genetic causes of breast cancer.
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Affiliation(s)
- Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (T.N.-D.); (M.C.S.)
| | - Tuong L. Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Tu Nguyen-Dumont
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (T.N.-D.); (M.C.S.)
- Department of Clinical Pathology, The University of Melbourne, Parkville, VIC 3051, Australia
| | - James G. Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Gillian S. Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
- Genetic Technologies Limited, Fitzroy, VIC 3065, Australia
| | - Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Ho N. Trinh
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Christopher F. Evans
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Maxine Tan
- Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, Bandar Sunway 47500, Malaysia;
- School of Electrical and Computer Engineering, The University of Oklahoma, Norman, OK 73019, USA
| | - Joohon Sung
- Department of Public Health Sciences, Division of Genome and Health Big Data, Graduate School of Public Health, Seoul National University, Seoul 08826, Korea;
| | - Mark A. Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Graham G. Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (T.N.-D.); (M.C.S.)
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia
| | - John L. Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
- Correspondence:
| | - Melissa C. Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (T.N.-D.); (M.C.S.)
- Department of Clinical Pathology, The University of Melbourne, Parkville, VIC 3051, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia
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9
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Sneider A, Kiemen A, Kim JH, Wu PH, Habibi M, White M, Phillip JM, Gu L, Wirtz D. Deep learning identification of stiffness markers in breast cancer. Biomaterials 2022; 285:121540. [PMID: 35537336 PMCID: PMC9873266 DOI: 10.1016/j.biomaterials.2022.121540] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 04/12/2022] [Accepted: 04/21/2022] [Indexed: 02/07/2023]
Abstract
While essential to our understanding of solid tumor progression, the study of cell and tissue mechanics has yet to find traction in the clinic. Determining tissue stiffness, a mechanical property known to promote a malignant phenotype in vitro and in vivo, is not part of the standard algorithm for the diagnosis and treatment of breast cancer. Instead, clinicians routinely use mammograms to identify malignant lesions and radiographically dense breast tissue is associated with an increased risk of developing cancer. Whether breast density is related to tumor tissue stiffness, and what cellular and non-cellular components of the tumor contribute the most to its stiffness are not well understood. Through training of a deep learning network and mechanical measurements of fresh patient tissue, we create a bridge in understanding between clinical and mechanical markers. The automatic identification of cellular and extracellular features from hematoxylin and eosin (H&E)-stained slides reveals that global and local breast tissue stiffness best correlate with the percentage of straight collagen. Importantly, the percentage of dense breast tissue does not directly correlate with tissue stiffness or straight collagen content.
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Affiliation(s)
- Alexandra Sneider
- Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Ashley Kiemen
- Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Joo Ho Kim
- Department of Materials Science and Engineering, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Pei-Hsun Wu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Mehran Habibi
- Johns Hopkins Breast Center, Johns Hopkins Bayview Medical Center, 4940 Eastern Ave, Baltimore, MD, 21224, USA
| | - Marissa White
- Department of Pathology, Johns Hopkins School of Medicine, 401 N Broadway, Baltimore, MD, 21231, USA
| | - Jude M. Phillip
- Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA,Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Luo Gu
- Department of Materials Science and Engineering, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Denis Wirtz
- Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA,Department of Pathology, Johns Hopkins School of Medicine, 401 N Broadway, Baltimore, MD, 21231, USA,Department of Oncology, Johns Hopkins School of Medicine, 1800 Orleans St, Baltimore, MD, 21205, USA,Corresponding author. Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, and Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA., (D. Wirtz)
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10
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Colditz GA, Bennett DL, Tappenden J, Beers C, Ackermann N, Wu N, Luo J, Humble S, Linnenbringer E, Davis K, Jiang S, Toriola AT. Joanne Knight Breast Health Cohort at Siteman Cancer Center. Cancer Causes Control 2022; 33:623-629. [PMID: 35059919 PMCID: PMC8904336 DOI: 10.1007/s10552-022-01554-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 01/11/2022] [Indexed: 12/01/2022]
Abstract
PURPOSE The Joanne Knight Breast Health Cohort was established to link breast cancer risk factors, mammographic breast density, benign breast biopsies and associated tissue markers, and blood markers in a diverse population of women undergoing routine mammographic screening to study risk factors and validate models for breast cancer risk prediction. METHODS Women were recruited from November 2008 to April 2012 through the mammography service at the Joanne Knight Breast Health Center at Washington University in St. Louis, Missouri. Baseline questionnaire risk factors, blood, and screening mammograms were collected from 12,153 women. Of these, 1,672 were excluded for prior history of any cancer (except non-melanoma skin) or diagnosis of breast cancer within 6 months of blood draw/registration for the study, for a total of 10,481 women. Follow-up is through linking to electronic health records, tumor registry, and death register. Routine screening mammograms are collected every 1-2 years and incident benign breast biopsies and cancers are identified through record linkage to pathology and tumor registries. Formal fixed tissue samples are retrieved and stored for analysis. County-level measures of structural inequality were derived from publicly available resources. RESULTS Cohort Composition: median age at entry was 54.8 years and 26.7% are African American. Through 2020, 74% of participants have had a medical center visit within the past year and 80% within the past 2 years representing an average of 9.7 person-years of follow-up from date of blood draw per participant. 9,997 women are continuing in follow-up. Data collected at baseline include breast cancer risk factors, plasma and white blood cells, and mammograms prior to baseline, at baseline, and during follow-up. CONCLUSION This cohort assembled and followed in a routine mammography screening and care setting that serves a diverse population of women in the St. Louis region now provides opportunities to integrate study of questionnaire measures, plasma and DNA markers, benign and malignant tissue markers, and repeated breast image features into prospective evaluation for breast cancer etiology and outcomes.
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Affiliation(s)
- Graham A Colditz
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA.
| | - Debbie L Bennett
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA
| | - Jennifer Tappenden
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA
| | - Courtney Beers
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA
| | - Nicole Ackermann
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA
| | - Ningying Wu
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA
| | - Jingqin Luo
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA
| | - Sarah Humble
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA
| | - Erin Linnenbringer
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA
| | - Kia Davis
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA
| | - Shu Jiang
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA
| | - Adetunji T Toriola
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA
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11
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Familial Aspects of Mammographic Density Measures Associated with Breast Cancer Risk. Cancers (Basel) 2022; 14:cancers14061483. [PMID: 35326633 PMCID: PMC8946826 DOI: 10.3390/cancers14061483] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 02/22/2022] [Accepted: 03/08/2022] [Indexed: 01/10/2023] Open
Abstract
Simple Summary Cumulus, Cumulus-percent, Altocumulus, Cirrocumulus, and Cumulus-white are mammogram risk scores (MRSs) that predict a woman’s risk of breast cancer based on mammographically dense areas when defined by different levels of brightness. We measured these MRS for 593 monozygotic (MZ) and 326 dizygotic (DZ) female twin pairs and 1592 of their sisters. We estimated how much these MRSs were correlated in relatives (ρ), how much of the differences between women were due to genetic factors (heritability), and how much these MRS explained why breast cancer runs in families. The ρ estimates ranged from: 0.41 to 0.60 for MZ pairs, 0.16 to 0.26 for DZ pairs, and 0.19 to 0.29 sister pairs, respectively. Heritability estimates were 36% to 69%. Genetic factors explain most of why twins and sisters are similar in their MRS, and these genetic factors explain one-quarter to one-half as much breast cancer risk as to the current best genetic risk score. Abstract Cumulus, Cumulus-percent, Altocumulus, Cirrocumulus, and Cumulus-white are mammogram risk scores (MRSs) for breast cancer based on mammographic density defined in effect by different levels of pixel brightness and adjusted for age and body mass index. We measured these MRS from digitized film mammograms for 593 monozygotic (MZ) and 326 dizygotic (DZ) female twin pairs and 1592 of their sisters. We estimated the correlations in relatives (r) and the proportion of variance due to genetic factors (heritability) using the software FISHER and predicted the familial risk ratio (FRR) associated with each MRS. The ρ estimates ranged from: 0.41 to 0.60 (standard error [SE] 0.02) for MZ pairs, 0.16 to 0.26 (SE 0.05) for DZ pairs, and 0.19 to 0.29 (SE 0.02) for sister pairs (including pairs of a twin and her non-twin sister), respectively. Heritability estimates were 39% to 69% under the classic twin model and 36% to 56% when allowing for shared non-genetic factors specific to MZ pairs. The FRRs were 1.08 to 1.17. These MRSs are substantially familial, due mostly to genetic factors that explain one-quarter to one-half as much of the familial aggregation of breast cancer that is explained by the current best polygenic risk score.
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Watt GP, Knight JA, Lin C, Lynch CF, Malone KE, John EM, Bernstein L, Brooks JD, Reiner AS, Liang X, Woods M, Nguyen TL, Hopper JL, Pike MC, Bernstein JL. Mammographic texture features associated with contralateral breast cancer in the WECARE Study. NPJ Breast Cancer 2021; 7:146. [PMID: 34845211 PMCID: PMC8630158 DOI: 10.1038/s41523-021-00354-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 11/01/2021] [Indexed: 01/12/2023] Open
Abstract
To evaluate whether mammographic texture features were associated with second primary contralateral breast cancer (CBC) risk, we created a "texture risk score" using pre-treatment mammograms in a case-control study of 212 women with CBC and 223 controls with unilateral breast cancer. The texture risk score was associated with CBC (odds per adjusted standard deviation = 1.25, 95% CI 1.01-1.56) after adjustment for mammographic percent density and confounders. These results support the potential of texture features for CBC risk assessment of breast cancer survivors.
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Affiliation(s)
- Gordon P. Watt
- grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Julia A. Knight
- grid.250674.20000 0004 0626 6184Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Division of Epidemiology, Dalla Lana School of Public Health, Toronto, ON Canada
| | - Christine Lin
- grid.240473.60000 0004 0543 9901Penn State College of Medicine, Hershey, PA USA
| | - Charles F. Lynch
- grid.214572.70000 0004 1936 8294 Department of Epidemiology, University of Iowa, Iowa City, IA USA
| | - Kathleen E. Malone
- grid.270240.30000 0001 2180 1622Fred Hutchinson Cancer Research Center, Seattle, WA USA
| | - Esther M. John
- grid.168010.e0000000419368956Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Department of Medicine, Stanford University School of Medicine, Stanford, CA USA
| | - Leslie Bernstein
- grid.410425.60000 0004 0421 8357Beckman Research Institute, City of Hope National Medical Center, Duarte, CA USA
| | - Jennifer D. Brooks
- grid.17063.330000 0001 2157 2938Division of Epidemiology, Dalla Lana School of Public Health, Toronto, ON Canada
| | - Anne S. Reiner
- grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Xiaolin Liang
- grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Meghan Woods
- grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Tuong L. Nguyen
- grid.1008.90000 0001 2179 088XMelbourne School of Population and Global Health, University of Melbourne, Parkville, VIC Australia
| | - John L. Hopper
- grid.1008.90000 0001 2179 088XMelbourne School of Population and Global Health, University of Melbourne, Parkville, VIC Australia
| | - Malcolm C. Pike
- grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Jonine L. Bernstein
- grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA
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Hopper JL, Nguyen TL, Li S. RE: Chemopreventive Agents to Reduce Mammographic Breast Density in Premenopausal Women: A Systematic Review of Clinical Trials. JNCI Cancer Spectr 2021; 5:pkab051. [PMID: 34377932 PMCID: PMC8346692 DOI: 10.1093/jncics/pkab051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 03/12/2021] [Indexed: 11/26/2022] Open
Affiliation(s)
- John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
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14
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Nguyen TL, Schmidt DF, Makalic E, Maskarinec G, Li S, Dite GS, Aung YK, Evans CF, Trinh HN, Baglietto L, Stone J, Song YM, Sung J, MacInnis RJ, Dugué PA, Dowty JG, Jenkins MA, Milne RL, Southey MC, Giles GG, Hopper JL. Novel mammogram-based measures improve breast cancer risk prediction beyond an established mammographic density measure. Int J Cancer 2020; 148:2193-2202. [PMID: 33197272 DOI: 10.1002/ijc.33396] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 10/28/2020] [Accepted: 11/02/2020] [Indexed: 12/11/2022]
Abstract
Mammograms contain information that predicts breast cancer risk. We developed two novel mammogram-based breast cancer risk measures based on image brightness (Cirrocumulus) and texture (Cirrus). Their risk prediction when fitted together, and with an established measure of conventional mammographic density (Cumulus), is not known. We used three studies consisting of: 168 interval cases and 498 matched controls; 422 screen-detected cases and 1197 matched controls; and 354 younger-diagnosis cases and 944 controls frequency-matched for age at mammogram. We conducted conditional and unconditional logistic regression analyses of individually- and frequency-matched studies, respectively. We estimated measure-specific risk gradients as the change in odds per standard deviation of controls after adjusting for age and body mass index (OPERA) and calculated the area under the receiver operating characteristic curve (AUC). For interval, screen-detected and younger-diagnosis cancer risks, the best fitting models (OPERAs [95% confidence intervals]) involved: Cumulus (1.81 [1.41-2.31]) and Cirrus (1.72 [1.38-2.14]); Cirrus (1.49 [1.32-1.67]) and Cirrocumulus (1.16 [1.03 to 1.31]); and Cirrus (1.70 [1.48 to 1.94]) and Cirrocumulus (1.46 [1.27-1.68]), respectively. The AUCs were: 0.73 [0.68-0.77], 0.63 [0.60-0.66], and 0.72 [0.69-0.75], respectively. Combined, our new mammogram-based measures have twice the risk gradient for screen-detected and younger-diagnosis breast cancer (P ≤ 10-12 ), have at least the same discriminatory power as the current polygenic risk score, and are more correlated with causal factors than conventional mammographic density. Discovering more information about breast cancer risk from mammograms could help enable risk-based personalised breast screening.
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Affiliation(s)
- Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Daniel F Schmidt
- Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | | | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.,Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.,Genetic Technologies Ltd., Fitzroy, Victoria, Australia
| | - Ye K Aung
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Christopher F Evans
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Ho N Trinh
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Laura Baglietto
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Jennifer Stone
- Genetic Epidemiology Group, School of Population and Global Health, University of Western Australia, Perth, Western Australia, Australia
| | - Yun-Mi Song
- Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Joohon Sung
- Department of Epidemiology School of Public Health, Seoul National University, Seoul, South Korea.,Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Pierre-Antoine Dugué
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Melissa C Southey
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
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15
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Pashayan N, Antoniou AC, Ivanus U, Esserman LJ, Easton DF, French D, Sroczynski G, Hall P, Cuzick J, Evans DG, Simard J, Garcia-Closas M, Schmutzler R, Wegwarth O, Pharoah P, Moorthie S, De Montgolfier S, Baron C, Herceg Z, Turnbull C, Balleyguier C, Rossi PG, Wesseling J, Ritchie D, Tischkowitz M, Broeders M, Reisel D, Metspalu A, Callender T, de Koning H, Devilee P, Delaloge S, Schmidt MK, Widschwendter M. Personalized early detection and prevention of breast cancer: ENVISION consensus statement. Nat Rev Clin Oncol 2020; 17:687-705. [PMID: 32555420 PMCID: PMC7567644 DOI: 10.1038/s41571-020-0388-9] [Citation(s) in RCA: 154] [Impact Index Per Article: 38.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/06/2020] [Indexed: 02/07/2023]
Abstract
The European Collaborative on Personalized Early Detection and Prevention of Breast Cancer (ENVISION) brings together several international research consortia working on different aspects of the personalized early detection and prevention of breast cancer. In a consensus conference held in 2019, the members of this network identified research areas requiring development to enable evidence-based personalized interventions that might improve the benefits and reduce the harms of existing breast cancer screening and prevention programmes. The priority areas identified were: 1) breast cancer subtype-specific risk assessment tools applicable to women of all ancestries; 2) intermediate surrogate markers of response to preventive measures; 3) novel non-surgical preventive measures to reduce the incidence of breast cancer of poor prognosis; and 4) hybrid effectiveness-implementation research combined with modelling studies to evaluate the long-term population outcomes of risk-based early detection strategies. The implementation of such programmes would require health-care systems to be open to learning and adapting, the engagement of a diverse range of stakeholders and tailoring to societal norms and values, while also addressing the ethical and legal issues. In this Consensus Statement, we discuss the current state of breast cancer risk prediction, risk-stratified prevention and early detection strategies, and their implementation. Throughout, we highlight priorities for advancing each of these areas.
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Affiliation(s)
- Nora Pashayan
- Department of Applied Health Research, Institute of Epidemiology and Healthcare, University College London, London, UK
| | - Antonis C Antoniou
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Urska Ivanus
- Epidemiology and Cancer Registry, Institute of Oncology Ljubljana, Ljubljana, Slovenia
| | - Laura J Esserman
- Carol Franc Buck Breast Care Center, University of California, San Francisco, CA, USA
| | - Douglas F Easton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - David French
- Division of Psychology & Mental Health, School of Health Sciences, University of Manchester, Manchester, UK
| | - Gaby Sroczynski
- Department of Public Health, Health Services Research and Health Technology Assessment, Institute of Public Health, Medical Decision Making and Health Technology Assessment, UMIT-University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria
- Division of Health Technology Assessment, Oncotyrol - Center for Personalized Cancer Medicine, Innsbruck, Austria
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Jack Cuzick
- Wolfson Institute of Preventive Medicine, Barts and The London, Centre for Cancer Prevention, Queen Mary University of London, London, UK
| | - D Gareth Evans
- Division of Evolution and Genomic Sciences, University of Manchester, Manchester, UK
| | - Jacques Simard
- Genomics Center, CHU de Québec - Université Laval Research Center, Québec, Canada
| | | | - Rita Schmutzler
- Center of Family Breast and Ovarian Cancer, University Hospital Cologne, Cologne, Germany
| | - Odette Wegwarth
- Max Planck Institute for Human Development, Center for Adaptive Rationality, Harding Center for Risk Literacy, Berlin, Germany
| | - Paul Pharoah
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
| | | | | | | | - Zdenko Herceg
- Epigenetic Group, International Agency for Research on Cancer (IARC), WHO, Lyon, France
| | - Clare Turnbull
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | | | - Paolo Giorgi Rossi
- Epidemiology Unit, Azienda USL di Reggio Emilia - IRCCS, Reggio Emilia, Italy
| | - Jelle Wesseling
- Division of Molecular Pathology, Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands
| | - David Ritchie
- Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Marc Tischkowitz
- Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK
| | - Mireille Broeders
- Department for Health Evidence, Radboud University Medical Center, Nijmegen, Netherlands
| | - Dan Reisel
- Department of Women's Cancer, Institute for Women's Health, University College London, London, UK
| | - Andres Metspalu
- The Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Thomas Callender
- Department of Applied Health Research, Institute of Epidemiology and Healthcare, University College London, London, UK
| | - Harry de Koning
- Department of Public Health, Erasmus MC, Rotterdam, Netherlands
| | - Peter Devilee
- Department of Human Genetics, Department of Pathology, Leiden University Medical Centre, Leiden, Netherlands
| | - Suzette Delaloge
- Breast Cancer Department, Gustave Roussy Institute, Paris, France
| | - Marjanka K Schmidt
- Division of Molecular Pathology, Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands
| | - Martin Widschwendter
- Department of Women's Cancer, Institute for Women's Health, University College London, London, UK.
- Universität Innsbruck, Innsbruck, Austria.
- European Translational Oncology Prevention and Screening (EUTOPS) Institute, Hall in Tirol, Austria.
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16
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Hopper JL, Nguyen TL, Schmidt DF, Makalic E, Song YM, Sung J, Dite GS, Dowty JG, Li S. Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk. J Clin Med 2020; 9:jcm9030627. [PMID: 32110975 PMCID: PMC7141100 DOI: 10.3390/jcm9030627] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 02/15/2020] [Accepted: 02/17/2020] [Indexed: 12/12/2022] Open
Abstract
This commentary is about predicting a woman’s breast cancer risk from her mammogram, building on the work of Wolfe, Boyd and Yaffe on mammographic density. We summarise our efforts at finding new mammogram-based risk predictors, and how they combine with the conventional mammographic density, in predicting risk for interval cancers and screen-detected breast cancers across different ages at diagnosis and for both Caucasian and Asian women. Using the OPERA (odds ratio per adjusted standard deviation) concept, in which the risk gradient is measured on an appropriate scale that takes into account other factors adjusted for by design or analysis, we show that our new mammogram-based measures are the strongest of all currently known breast cancer risk factors in terms of risk discrimination on a population-basis. We summarise our findings graphically using a path diagram in which conventional mammographic density predicts interval cancer due to its role in masking, while the new mammogram-based risk measures could have a causal effect on both interval and screen-detected breast cancer. We discuss attempts by others to pursue this line of investigation, the measurement challenge that allows different measures to be compared in an open and transparent manner on the same datasets, as well as the biological and public health consequences.
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17
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Nguyen TL, Li S, Dite GS, Aung YK, Evans CF, Trinh HN, Baglietto L, Stone J, Song YM, Sung J, English DR, Jenkins MA, Dugué PA, Milne RL, Southey MC, Giles GG, Pike MC, Hopper JL. Interval breast cancer risk associations with breast density, family history and breast tissue aging. Int J Cancer 2019; 147:375-382. [PMID: 31609476 PMCID: PMC7318124 DOI: 10.1002/ijc.32731] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 09/16/2019] [Accepted: 09/27/2019] [Indexed: 01/04/2023]
Abstract
Interval breast cancers (those diagnosed between recommended mammography screens) generally have poorer outcomes and are more common among women with dense breasts. We aimed to develop a risk model for interval breast cancer. We conducted a nested case-control study within the Melbourne Collaborative Cohort Study involving 168 interval breast cancer patients and 498 matched control subjects. We measured breast density using the CUMULUS software. We recorded first-degree family history by questionnaire, measured body mass index (BMI) and calculated age-adjusted breast tissue aging, a novel measure of exposure to estrogen and progesterone based on the Pike model. We fitted conditional logistic regression to estimate odds ratio (OR) or odds ratio per adjusted standard deviation (OPERA) and calculated the area under the receiver operating characteristic curve (AUC). The stronger risk associations were for unadjusted percent breast density (OPERA = 1.99; AUC = 0.66), more so after adjusting for age and BMI (OPERA = 2.26; AUC = 0.70), and for family history (OR = 2.70; AUC = 0.56). When the latter two factors and their multiplicative interactions with age-adjusted breast tissue aging (p = 0.01 and 0.02, respectively) were fitted, the AUC was 0.73 (95% CI 0.69-0.77), equivalent to a ninefold interquartile risk ratio. In summary, compared with using dense breasts alone, risk discrimination for interval breast cancers could be doubled by instead using breast density, BMI, family history and hormonal exposure. This would also give women with dense breasts, and their physicians, more information about the major consequence of having dense breasts-an increased risk of developing an interval breast cancer.
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Affiliation(s)
- Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Ye K Aung
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Christopher F Evans
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Ho N Trinh
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Laura Baglietto
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Jennifer Stone
- Centre for Genetic Origins of Health and Disease, University of Western Australia, Perth, WA, Australia
| | - Yun-Mi Song
- Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Joohon Sung
- Department of Epidemiology School of Public Health, Seoul National University, Seoul, South Korea.,Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Dallas R English
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Pierre-Antoine Dugué
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Malcolm C Pike
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
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18
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A review of the influence of mammographic density on breast cancer clinical and pathological phenotype. Breast Cancer Res Treat 2019; 177:251-276. [PMID: 31177342 DOI: 10.1007/s10549-019-05300-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 05/27/2019] [Indexed: 10/26/2022]
Abstract
PURPOSE It is well established that high mammographic density (MD), when adjusted for age and body mass index, is one of the strongest known risk factors for breast cancer (BC), and also associates with higher incidence of interval cancers in screening due to the masking of early mammographic abnormalities. Increasing research is being undertaken to determine the underlying histological and biochemical determinants of MD and their consequences for BC pathogenesis, anticipating that improved mechanistic insights may lead to novel preventative or treatment interventions. At the same time, technological advances in digital and contrast mammography are such that the validity of well-established relationships needs to be re-examined in this context. METHODS With attention to old versus new technologies, we conducted a literature review to summarise the relationships between clinicopathologic features of BC and the density of the surrounding breast tissue on mammography, including the associations with BC biological features inclusive of subtype, and implications for the clinical disease course encompassing relapse, progression, treatment response and survival. RESULTS AND CONCLUSIONS There is reasonable evidence to support positive relationships between high MD (HMD) and tumour size, lymph node positivity and local relapse in the absence of radiotherapy, but not between HMD and LVI, regional relapse or distant metastasis. Conflicting data exist for associations of HMD with tumour location, grade, intrinsic subtype, receptor status, second primary incidence and survival, which need further confirmatory studies. We did not identify any relationships that did not hold up when data involving newer imaging techniques were employed in analysis.
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