Melbourne School of Population and Global Health - Research Publications

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    Weight is More Informative than Body Mass Index for Predicting Postmenopausal Breast Cancer Risk: Prospective Family Study Cohort (ProF-SC)
    Ye, Z ; Li, S ; Dite, GS ; Nguyen, TL ; MacInnis, RJ ; Andrulis, IL ; Buys, SS ; Daly, MB ; John, EM ; Kurian, AW ; Genkinger, JM ; Chung, WK ; Phillips, K-A ; Thorne, H ; Winship, IM ; Milne, RL ; Dugue, P-A ; Southey, MC ; Giles, GG ; Terry, MB ; Hopper, JL (AMER ASSOC CANCER RESEARCH, 2022-03)
    UNLABELLED: We considered whether weight is more informative than body mass index (BMI) = weight/height2 when predicting breast cancer risk for postmenopausal women, and if the weight association differs by underlying familial risk. We studied 6,761 women postmenopausal at baseline with a wide range of familial risk from 2,364 families in the Prospective Family Study Cohort. Participants were followed for on average 11.45 years and there were 416 incident breast cancers. We used Cox regression to estimate risk associations with log-transformed weight and BMI after adjusting for underlying familial risk. We compared model fits using the Akaike information criterion (AIC) and nested models using the likelihood ratio test. The AIC for the weight-only model was 6.22 units lower than for the BMI-only model, and the log risk gradient was 23% greater. Adding BMI or height to weight did not improve fit (Ī”AIC = 0.90 and 0.83, respectively; both P = 0.3). Conversely, adding weight to BMI or height gave better fits (Ī”AIC = 5.32 and 11.64; P = 0.007 and 0.0002, respectively). Adding height improved only the BMI model (Ī”AIC = 5.47; P = 0.006). There was no evidence that the BMI or weight associations differed by underlying familial risk (P > 0.2). Weight is more informative than BMI for predicting breast cancer risk, consistent with nonadipose as well as adipose tissue being etiologically relevant. The independent but multiplicative associations of weight and familial risk suggest that, in terms of absolute breast cancer risk, the association with weight is more important the greater a woman's underlying familial risk. PREVENTION RELEVANCE: Our results suggest that the relationship between BMI and breast cancer could be due to a relationship between weight and breast cancer, downgraded by inappropriately adjusting for height; potential importance of anthropometric measures other than total body fat; breast cancer risk associations with BMI and weight are across a continuum.
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    Segregation analysis of 17,425 population-based breast cancer families: Evidence for genetic susceptibility and risk prediction
    Li, S ; MacInnis, RJ ; Lee, A ; Nguyen-Dumont, T ; Dorling, L ; Carvalho, S ; Dite, GS ; Shah, M ; Luccarini, C ; Wang, Q ; Milne, RL ; Jenkins, MA ; Giles, GG ; Dunning, AM ; Pharoah, PDP ; Southey, MC ; Easton, DF ; Hopper, JL ; Antoniou, AC (CELL PRESS, 2022-10-06)
    Rare pathogenic variants in known breast cancer-susceptibility genes and known common susceptibility variants do not fully explain the familial aggregation of breast cancer. To investigate plausible genetic models for the residual familial aggregation, we studied 17,425 families ascertained through population-based probands, 86% of whom were screened for pathogenic variants in BRCA1, BRCA2, PALB2, CHEK2, ATM, and TP53 via gene-panel sequencing. We conducted complex segregation analyses and fitted genetic models in which breast cancer incidence depended on the effects of known susceptibility genes and other unidentified major genes and a normally distributed polygenic component. The proportion of familial variance explained by the six genes was 46% at age 20-29 years and decreased steadily with age thereafter. After allowing for these genes, the best fitting model for the residual familial variance included a recessive risk component with a combined genotype frequency of 1.7% (95% CI: 0.3%-5.4%) and a penetrance to age 80 years of 69% (95% CI: 38%-95%) for homozygotes, which may reflect the combined effects of multiple variants acting in a recessive manner, and a polygenic variance of 1.27 (95% CI: 0.94%-1.65), which did not vary with age. The proportion of the residual familial variance explained by the recessive risk component was 40% at age 20-29 years and decreased with age thereafter. The model predicted age-specific familial relative risks consistent with those observed by large epidemiological studies. The findings have implications for strategies to identify new breast cancer-susceptibility genes and improve disease-risk prediction, especially at a young age.
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    Common variants in breast cancer risk loci predispose to distinct tumor subtypes
    Ahearn, TU ; Zhang, H ; Michailidou, K ; Milne, RL ; Bolla, MK ; Dennis, J ; Dunning, AM ; Lush, M ; Wang, Q ; Andrulis, IL ; Anton-Culver, H ; Arndt, V ; Aronson, KJ ; Auer, PL ; Augustinsson, A ; Baten, A ; Becher, H ; Behrens, S ; Benitez, J ; Bermisheva, M ; Blomqvist, C ; Bojesen, SE ; Bonanni, B ; Borresen-Dale, A-L ; Brauch, H ; Brenner, H ; Brooks-Wilson, A ; Bruening, T ; Burwinkel, B ; Buys, SS ; Canzian, F ; Castelao, JE ; Chang-Claude, J ; Chanock, SJ ; Chenevix-Trench, G ; Clarke, CL ; Collee, JM ; Cox, A ; Cross, SS ; Czene, K ; Daly, MB ; Devilee, P ; Dork, T ; Dwek, M ; Eccles, DM ; Evans, DG ; Fasching, PA ; Figueroa, J ; Floris, G ; Gago-Dominguez, M ; Gapstur, SM ; Garcia-Saenz, JA ; Gaudet, MM ; Giles, GG ; Goldberg, MS ; Gonzalez-Neira, A ; Alnaes, GIG ; Grip, M ; Guenel, P ; Haiman, CA ; Hall, P ; Hamann, U ; Harkness, EF ; Heemskerk-Gerritsen, BAM ; Holleczek, B ; Hollestelle, A ; Hooning, MJ ; Hoover, RN ; Hopper, JL ; Howell, A ; Jakimovska, M ; Jakubowska, A ; John, EM ; Jones, ME ; Jung, A ; Kaaks, R ; Kauppila, S ; Keeman, R ; Khusnutdinova, E ; Kitahara, CM ; Ko, Y-D ; Koutros, S ; Kristensen, VN ; Kruger, U ; Kubelka-Sabit, K ; Kurian, AW ; Kyriacou, K ; Lambrechts, D ; Lee, DG ; Lindblom, A ; Linet, M ; Lissowska, J ; Llaneza, A ; Lo, W-Y ; MacInnis, RJ ; Mannermaa, A ; Manoochehri, M ; Margolin, S ; Martinez, ME ; McLean, C ; Meindl, A ; Menon, U ; Nevanlinna, H ; Newman, WG ; Nodora, J ; Offit, K ; Olsson, H ; Orr, N ; Park-Simon, T-W ; Patel, A ; Peto, J ; Pita, G ; Plaseska-Karanfilska, D ; Prentice, R ; Punie, K ; Pylkas, K ; Radice, P ; Rennert, G ; Romero, A ; Ruediger, T ; Saloustros, E ; Sampson, S ; Sandler, DP ; Sawyer, EJ ; Schmutzler, RK ; Schoemaker, MJ ; Schottker, B ; Sherman, ME ; Shu, X-O ; Smichkoska, S ; Southey, MC ; Spinelli, JJ ; Swerdlow, AJ ; Tamimi, RM ; Tapper, WJ ; Taylor, JA ; Teras, LR ; Terry, MB ; Torres, D ; Troester, MA ; Vachon, CM ; van Deurzen, CHM ; van Veen, EM ; Wagner, P ; Weinberg, CR ; Wendt, C ; Wesseling, J ; Winqvist, R ; Wolk, A ; Yang, XR ; Zheng, W ; Couch, FJ ; Simard, J ; Kraft, P ; Easton, DF ; Pharoah, PDP ; Schmidt, MK ; Garcia-Closas, M ; Chatterjee, N (BMC, 2022-01-04)
    BACKGROUND: Genome-wide association studies (GWAS) have identified multiple common breast cancer susceptibility variants. Many of these variants have differential associations by estrogen receptor (ER) status, but how these variants relate with other tumor features and intrinsic molecular subtypes is unclear. METHODS: Among 106,571 invasive breast cancer cases and 95,762 controls of European ancestry with data on 173 breast cancer variants identified in previous GWAS, we used novel two-stage polytomous logistic regression models to evaluate variants in relation to multiple tumor features (ER, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and grade) adjusting for each other, and to intrinsic-like subtypes. RESULTS: Eighty-five of 173 variants were associated with at least one tumor feature (false discovery rateā€‰<ā€‰5%), most commonly ER and grade, followed by PR and HER2. Models for intrinsic-like subtypes found nearly all of these variants (83 of 85) associated at pā€‰<ā€‰0.05 with risk for at least one luminal-like subtype, and approximately half (41 of 85) of the variants were associated with risk of at least one non-luminal subtype, including 32 variants associated with triple-negative (TN) disease. Ten variants were associated with risk of all subtypes in different magnitude. Five variants were associated with risk of luminal A-like and TN subtypes in opposite directions. CONCLUSION: This report demonstrates a high level of complexity in the etiology heterogeneity of breast cancer susceptibility variants and can inform investigations of subtype-specific risk prediction.
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    Recreational Physical Activity and Outcomes After Breast Cancer in Women at High Familial Risk
    Kehm, RD ; MacInnis, RJ ; John, EM ; Liao, Y ; Kurian, AW ; Genkinger, JM ; Knight, JA ; Colonna, S ; Chung, WK ; Milne, R ; Zeinomar, N ; Dite, GS ; Southey, MC ; Giles, GG ; Mclachlan, S-A ; Whitaker, KD ; Friedlander, ML ; Weideman, PC ; Glendon, G ; Nesci, S ; Investigators, K ; Phillips, K-A ; Andrulis, IL ; Buys, SS ; Daly, MB ; Hopper, JL ; Terry, MB (OXFORD UNIV PRESS, 2021-12)
    BACKGROUND: Recreational physical activity (RPA) is associated with improved survival after breast cancer (BC) in average-risk women, but evidence is limited for women who are at increased familial risk because of a BC family history or BRCA1 and BRCA2 pathogenic variants (BRCA1/2 PVs). METHODS: We estimated associations of RPA (self-reported average hours per week within 3 years of BC diagnosis) with all-cause mortality and second BC events (recurrence or new primary) after first invasive BC in women in the Prospective Family Study Cohort (n = 4610, diagnosed 1993-2011, aged 22-79 years at diagnosis). We fitted Cox proportional hazards regression models adjusted for age at diagnosis, demographics, and lifestyle factors. We tested for multiplicative interactions (Wald test statistic for cross-product terms) and additive interactions (relative excess risk due to interaction) by age at diagnosis, body mass index, estrogen receptor status, stage at diagnosis, BRCA1/2 PVs, and familial risk score estimated from multigenerational pedigree data. Statistical tests were 2-sided. RESULTS: We observed 1212 deaths and 473 second BC events over a median follow-up from study enrollment of 11.0 and 10.5 years, respectively. After adjusting for covariates, RPA (any vs none) was associated with lower all-cause mortality of 16.1% (95% confidence interval [CI] = 2.4% to 27.9%) overall, 11.8% (95% CI = -3.6% to 24.9%) in women without BRCA1/2 PVs, and 47.5% (95% CI = 17.4% to 66.6%) in women with BRCA1/2 PVs (RPA*BRCA1/2 multiplicative interaction P = .005; relative excess risk due to interaction = 0.87, 95% CI = 0.01 to 1.74). RPA was not associated with risk of second BC events. CONCLUSION: Findings support that RPA is associated with lower all-cause mortality in women with BC, particularly in women with BRCA1/2 PVs.
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    Alcohol and tobacco use and risk of multiple myeloma: A caseā€control study
    Cheah, S ; Bassett, JK ; Bruinsma, FJ ; Cozen, W ; Hopper, JL ; Jayasekara, H ; Joshua, D ; MacInnis, RJ ; Prince, HM ; Vajdic, CM ; van Leeuwen, MT ; Doo, NW ; Harrison, SJ ; English, DR ; Giles, GG ; Milne, RL (Wiley, 2022-02)
    Abstract Multiple myeloma (MM) is the second most common hematological cancer and causes significant mortality and morbidity. Knowledge regarding modifiable risk factors for MM remains limited. This analysis of an Australian populationā€based caseā€“control family study investigates whether smoking or alcohol consumption is associated with risk of MM and related diseases. Incident cases (n = 789) of MM were recruited via cancer registries in Victoria and New South Wales. Controls (n = 1,113) were either family members of cases (n = 696) or controls recruited for a similarly designed study of renal cancers (n = 417). Adjusted odds ratios (OR) and 95% confidence intervals (CI) were estimated using unconditional multivariable logistic regression. Heavy intake (>20 g ethanol/day) of alcohol had a lower risk of MM compared with nondrinkers (OR = 0.68, 95% CI: 0.50ā€“0.93), and there was an inverse doseā€“response relationship for average daily alcohol intake (OR per 10 g ethanol per day = 0.92, 95% CI: 0.86ā€“0.99); there was no evidence of an interaction with sex. There was no evidence of an association with MM risk for smokingā€related exposures (p > 0.18). The associations between smoking and alcohol with MM are similar to those with nonā€Hodgkin lymphoma. Further research into potential underlying mechanisms is warranted.
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    Prospective Evaluation over 15 Years of Six Breast Cancer Risk Models
    Li, SX ; Milne, RL ; Nguyen-Dumont, T ; English, DR ; Giles, GG ; Southey, MC ; Antoniou, AC ; Lee, A ; Winship, I ; Hopper, JL ; Terry, MB ; MacInnis, RJ (MDPI, 2021-10)
    Prospective validation of risk models is needed to assess their clinical utility, particularly over the longer term. We evaluated the performance of six commonly used breast cancer risk models (IBIS, BOADICEA, BRCAPRO, BRCAPRO-BCRAT, BCRAT, and iCARE-lit). 15-year risk scores were estimated using lifestyle factors and family history measures from 7608 women in the Melbourne Collaborative Cohort Study who were aged 50-65 years and unaffected at commencement of follow-up two (conducted in 2003-2007), of whom 351 subsequently developed breast cancer. Risk discrimination was assessed using the C-statistic and calibration using the expected/observed number of incident cases across the spectrum of risk by age group (50-54, 55-59, 60-65 years) and family history of breast cancer. C-statistics were higher for BOADICEA (0.59, 95% confidence interval (CI) 0.56-0.62) and IBIS (0.57, 95% CI 0.54-0.61) than the other models (p-difference ā‰¤ 0.04). No model except BOADICEA calibrated well across the spectrum of 15-year risk (p-value < 0.03). The performance of BOADICEA and IBIS was similar across age groups and for women with or without a family history. For middle-aged Australian women, BOADICEA and IBIS had the highest discriminatory accuracy of the six risk models, but apart from BOADICEA, no model was well-calibrated across the risk spectrum.
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    Repeatability of methylation measures using a QIAseq targeted methyl panel and comparison with the Illumina HumanMethylation450 assay
    Yu, C ; Dugue, P-A ; Dowty, JG ; Hammet, F ; Joo, JE ; Wong, EM ; Hosseinpour, M ; Giles, GG ; Hopper, JL ; Tu, N-D ; MacInnis, RJ ; Southey, MC (SPRINGERNATURE, 2021-10-24)
    OBJECTIVE: In previous studies using Illumina Infinium methylation arrays, we have identified DNA methylation marks associated with cancer predisposition and progression. In the present study, we have sought to find appropriate technology to both technically validate our data and expand our understanding of DNA methylation in these genomic regions. Here, we aimed to assess the repeatability of methylation measures made using QIAseq targeted methyl panel and to compare them with those obtained from the Illumina HumanMethylation450 (HM450K) assay. We included in the analysis high molecular weight DNA extracted from whole blood (WB) and DNA extracted from formalin-fixed paraffin-embedded tissues (FFPE). RESULTS: The repeatability of QIAseq-methylation measures was assessed at 40 CpGs, using the Intraclass Correlation Coefficient (ICC). The mean ICCs and 95% confidence intervals (CI) were 0.72 (0.62-0.81), 0.59 (0.47-0.71) and 0.80 (0.73-0.88) for WB, FFPE and both sample types combined, respectively. For technical replicates measured using QIAseq and HM450K, the mean ICCs (95% CI) were 0.53 (0.39-0.68), 0.43 (0.31-0.56) and 0.70 (0.59-0.80), respectively. Bland-Altman plots indicated good agreement between QIAseq and HM450K measurements. These results demonstrate that the QIAseq targeted methyl panel produces reliable and reproducible methylation measurements across the 40 CpGs that were examined.
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    Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses
    Zhan, H ; Ahearn, TU ; Lecarpentier, J ; Barnes, D ; Beesley, J ; Qi, G ; Jiang, X ; O'Mara, TA ; Zhao, N ; Bolla, MK ; Dunning, AM ; Dennis, J ; Wang, Q ; Abu Ful, Z ; Aittomaki, K ; Andrulis, IL ; Anton-Culver, H ; Arndt, V ; Aronson, KJ ; Arun, BK ; Auer, PL ; Azzollini, J ; Barrowdale, D ; Becher, H ; Beckmann, MW ; Behrens, S ; Benitez, J ; Bermisheva, M ; Bialkowska, K ; Blanco, A ; Blomqvist, C ; Bogdanova, N ; Bojesen, SE ; Bonanni, B ; Bondavalli, D ; Borg, A ; Brauch, H ; Brenner, H ; Briceno, I ; Broeks, A ; Brucker, SY ; Bruening, T ; Burwinkel, B ; Buys, SS ; Byers, H ; Caldes, T ; Caligo, MA ; Calvello, M ; Campa, D ; Castelao, JE ; Chang-Claude, J ; Chanock, SJ ; Christiaens, M ; Christiansen, H ; Chung, WK ; Claes, KBM ; Clarke, CL ; Cornelissen, S ; Couch, FJ ; Cox, A ; Cross, SS ; Czene, K ; Daly, MB ; Devilee, P ; Diez, O ; Domchek, SM ; Doerk, T ; Dwek, M ; Eccles, DM ; Ekici, AB ; Evans, DG ; Fasching, PA ; Figueroa, J ; Foretova, L ; Fostira, F ; Friedman, E ; Frost, D ; Gago-Dominguez, M ; Gapstur, SM ; Garber, J ; Garcia-Saenz, JA ; Gaudet, MM ; Gayther, SA ; Giles, GG ; Godwin, AK ; Goldberg, MS ; Goldgar, DE ; Gonzalez-Neira, A ; Greene, MH ; Gronwald, J ; Guenel, P ; Haeberle, L ; Hahnen, E ; Haiman, CA ; Hake, CR ; Hall, P ; Hamann, U ; Harkness, EF ; Heemskerk-Gerritsen, BAM ; Hillemanns, P ; Hogervorst, FBL ; Holleczek, B ; Hollestelle, A ; Hooning, MJ ; Hoover, RN ; Hopper, JL ; Howell, A ; Huebner, H ; Hulick, PJ ; Imyanitov, EN ; Isaacs, C ; Izatt, L ; Jager, A ; Jakimovska, M ; Jakubowska, A ; James, P ; Janavicius, R ; Janni, W ; John, EM ; Jones, ME ; Jung, A ; Kaaks, R ; Kapoor, PM ; Karlan, BY ; Keeman, R ; Khan, S ; Khusnutdinova, E ; Kitahara, CM ; Ko, Y-D ; Konstantopoulou, I ; Koppert, LB ; Koutros, S ; Kristensen, VN ; Laenkholm, A-V ; Lambrechts, D ; Larsson, SC ; Laurent-Puig, P ; Lazaro, C ; Lazarova, E ; Lejbkowicz, F ; Leslie, G ; Lesueur, F ; Lindblom, A ; Lissowska, J ; Lo, W-Y ; Loud, JT ; Lubinski, J ; Lukomska, A ; MacInnis, RJ ; Mannermaa, A ; Manoochehri, M ; Manoukian, S ; Margolin, S ; Martinez, ME ; Matricardi, L ; McGuffog, L ; McLean, C ; Mebirouk, N ; Meindl, A ; Menon, U ; Miller, A ; Mingazheva, E ; Montagna, M ; Mulligan, AM ; Mulot, C ; Muranen, TA ; Nathanson, KL ; Neuhausen, SL ; Nevanlinna, H ; Neven, P ; Newman, WG ; Nielsens, FC ; Nikitina-Zake, L ; Nodora, J ; Offit, K ; Olah, E ; Olopade, O ; Olsson, H ; Orr, N ; Papi, L ; Papp, J ; Park-Simon, T-W ; Parsons, MT ; Peissel, B ; Peixoto, A ; Peshkin, B ; Peterlongo, P ; Peto, J ; Phillips, K-A ; Piedmonte, M ; Plaseska-Karanfilska, D ; Prajzendanc, K ; Prentice, R ; Prokofyeva, D ; Rack, B ; Radice, P ; Ramus, SJ ; Rantala, J ; Rashid, MU ; Rennert, G ; Rennert, HS ; Risch, HA ; Romero, A ; Rookus, MA ; Ruebner, M ; Ruediger, T ; Saloustros, E ; Sampson, S ; Sandler, DP ; Sawyer, EJ ; Scheuner, MT ; Schmutzler, RK ; Schneeweiss, A ; Schoemaker, MJ ; Schoettker, B ; Schuermann, P ; Senter, L ; Sharma, P ; Sherman, ME ; Shu, X-O ; Singer, CF ; Smichkoska, S ; Soucy, P ; Southey, MC ; Spinelli, JJ ; Stone, J ; Stoppa-Lyonnet, D ; Swerdlow, AJ ; Szabo, C ; Tamimi, RM ; Tapper, WJ ; Taylor, JA ; Teixeira, MR ; Terry, M ; Thomassen, M ; Thull, DL ; Tischkowitz, M ; Toland, AE ; Tollenaar, RAEM ; Tomlinson, I ; Torres, D ; Troester, MA ; Truong, T ; Tung, N ; Untch, M ; Vachon, CM ; van den Ouweland, AMW ; van der Kolk, LE ; van Veen, EM ; vanRensburg, EJ ; Vega, A ; Wappenschmidt, B ; Weinberg, CR ; Weitzel, JN ; Wildiers, H ; Winqvist, R ; Wolk, A ; Yang, XR ; Yannoukakos, D ; Zheng, W ; Zorn, KK ; Milne, RL ; Kraft, P ; Simard, J ; Pharoah, PDP ; Michailidou, K ; Antoniou, AC ; Schmidt, MK ; Chenevix-Trench, G ; Easton, DF ; Chatterjee, N ; Garcia-Closas, M (NATURE RESEARCH, 2020-06)
    Breast cancer susceptibility variants frequently show heterogeneity in associations by tumor subtype1-3. To identify novel loci, we performed a genome-wide association study including 133,384 breast cancer cases and 113,789 controls, plus 18,908 BRCA1 mutation carriers (9,414 with breast cancer) of European ancestry, using both standard and novel methodologies that account for underlying tumor heterogeneity by estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 status and tumor grade. We identified 32 novel susceptibility loci (Pā€‰<ā€‰5.0ā€‰Ć—ā€‰10-8), 15 of which showed evidence for associations with at least one tumor feature (false discovery rateā€‰<ā€‰0.05). Five loci showed associations (Pā€‰<ā€‰0.05) in opposite directions between luminal and non-luminal subtypes. In silico analyses showed that these five loci contained cell-specific enhancers that differed between normal luminal and basal mammary cells. The genetic correlations between five intrinsic-like subtypes ranged from 0.35 to 0.80. The proportion of genome-wide chip heritability explained by all known susceptibility loci was 54.2% for luminal A-like disease and 37.6% for triple-negative disease. The odds ratios of polygenic risk scores, which included 330 variants, for the highest 1% of quantiles compared with middle quantiles were 5.63 and 3.02 for luminal A-like and triple-negative disease, respectively. These findings provide an improved understanding of genetic predisposition to breast cancer subtypes and will inform the development of subtype-specific polygenic risk scores.
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    Prospective Evaluation of the Addition of Polygenic Risk Scores to Breast Cancer Risk Models
    Li, SX ; Milne, RL ; Nguyen-Dumont, T ; Wang, X ; English, DR ; Giles, GG ; Southey, MC ; Antoniou, AC ; Lee, A ; Li, S ; Winship, I ; Hopper, JL ; Terry, MB ; MacInnis, RJ (OXFORD UNIV PRESS, 2021-06)
    BACKGROUND: The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm and the International Breast Cancer Intervention Study breast cancer risk models are used to provide advice on screening intervals and chemoprevention. We evaluated the performance of these models, which now incorporate polygenic risk scores (PRSs), using a prospective cohort study. METHODS: We used a case-cohort design, involving women in the Melbourne Collaborative Cohort Study aged 50-75ā€‰years when surveyed in 2003-2007, of whom 408 had a first primary breast cancer diagnosed within 10ā€‰years (cases), and 2783 were from the subcohort. Ten-year risks were calculated based on lifestyle factors, family history data, and a 313-variant PRS. Discrimination was assessed using a C-statistic compared with 0.50 and calibration using the ratio of expected to observed number of cases (E/O). RESULTS: When the PRS was added to models with lifestyle factors and family history, the C-statistic (95% confidence interval [CI]) increased from 0.57 (0.54 to 0.60) to 0.62 (0.60 to 0.65) using IBIS and from 0.56 (0.53 to 0.59) to 0.62 (0.59 to 0.64) using BOADICEA. IBIS underpredicted risk (E/Oā€‰=ā€‰0.62, 95% CI = 0.48 to 0.80) for women in the lowest risk category (<1.7%) and overpredicted risk (E/Oā€‰=ā€‰1.40, 95% CI = 1.18 to 1.67) in the highest risk category (ā‰„5%), using the Hosmer-Lemeshow test for calibration in quantiles of risk and a 2-sided P value less thanā€‰ā€‰.001. BOADICEA underpredicted risk (E/Oā€‰=ā€‰0.82, 95% CI = 0.67 to 0.99) in the second highest risk category (3.4%-5%); the Hosmer-Lemeshow test and a 2-sided P valueā€‰was equal to .02. CONCLUSIONS: Although the inclusion of a 313 genetic variant PRS doubles discriminatory accuracy (relative to reference 0.50), models with and without this PRS have relatively modest discrimination and might require recalibration before their clinical and wider use are promoted.
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    Novel mammogram-based measures improve breast cancer risk prediction beyond an established mammographic density measure
    Nguyen, TL ; Schmidt, DF ; Makalic, E ; Maskarinec, G ; Li, S ; Dite, GS ; Aung, YK ; Evans, CF ; Trinh, HN ; Baglietto, L ; Stone, J ; Song, Y-M ; Sung, J ; MacInnis, RJ ; Dugue, P-A ; Dowty, JG ; Jenkins, MA ; Milne, RL ; Southey, MC ; Giles, GG ; Hopper, JL (WILEY, 2021-05-01)
    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 (Pā€‰ā‰¤ā€‰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.