Associations of Breast Cancer Risk Factors With Tumor Subtypes: A Pooled Analysis From the Breast Cancer Association Consortium Studies

Yang, Xiaohong R.; Chang‐Claude, Jenny; Goode, Ellen L.; Couch, Fergus J.; Nevanlinna, Heli; Milne, Roger L.; Gaudet, Mia M.; Schmidt, Marjanka K.; Broeks, Annegien; Cox, Angela; Fasching, Peter A.; Hein, Rebecca; Spurdle, Amanda B.; Blows, Fiona M.; Driver, Kristy; Flesch-Janys, Dieter; Heinz, Judith; Sinn, Hans‐Peter; Vrieling, Alina; Heikkinen, Tuomas; Aittomäki, Kristiina; Heikkilä, Päivi; Blomqvist, Carl; Lissowska, Jolanta; Pepłońska, Beata; Chanock, Stephen J.; Figueroa, Jonine D.; Brinton, Louise A.; Hall, Per; Czene, Kamila; Humphreys, Keith; Darabi, Hatef; Li, Jianjun; Veer, Laura J. van ’t; Leeuwen, Flora E. van; Andrulis, Irene L.; Glendon, Gord; Knight, Julia A.; Mulligan, Anna Marie; O’Malley, Frances P.; Weerasooriya, Nayana; John, Esther M.; Beckmann, Matthias W.; Hartmann, Arndt; Weihbrecht, Sebastian; Wachter, David; Jud, Sebastian M.; Loehberg, Christian R.; Baglietto, Laura; English, Dallas R.; Giles, Graham G.; McLean, Catriona; Severi, Gianluca; Lambrechts, Diether; Vandorpe, Thijs; Weltens, Caroline; Paridaens, Robert; Smeets, Ann; Neven, Patrick; Wildiers, Hans; Wang, Xianshu; Olson, Janet E.; Cafourek, Victoria; Fredericksen, Zachary S.; Kosel, Matthew L.; Vachon, Celine M.; Cramp, Helen; Connley, Daniel; Cross, Simon S.; Balasubramanian, Sabapathy P; Reed, Malcolm; Dörk, Thilo; Bremer, Michael; Meyer, Andreas; Karstens, Johann H.; Ay, Aysun; Park-Simon, Tjoung Won; Hillemanns, Peter; Peŕez, José Ignacio Arias; Rodríguez, Primitiva Menéndez; Zamora, Pilar; Benı́tez, Javier; Ko, Yon; Fischer, Hans Peter; Hamann, Ute; Pesch, Beate; Brüning, Thomas; Justenhoven, Christina; Brauch, Hiltrud; Eccles, Diana; Tapper, William; Gerty, Sue; Sawyer, Elinor J.; Tomlinson, Ian; Jones, Angela; Kerin, Michael J.; Miller, Nicola; McInerney, Niall; Anton‐Culver, Hoda; Ziogas, Argyrios; Shen, Chen; Hsiung, Chia Ni; Wu, Pei Ei; Yang, Shan; Yu, Jyh Cherng; Chen, Shou Tung; Hsu, Giu Cheng; Haiman, Christopher A.; Henderson, Brian E.; Marchand, Loı̈c Le; Kolonel, Laurence N.; Lindblom, Annika; Margolin, Sara; Jakubowska, Anna; Lubiński, Jan; Huzarski, Tomasz; Byrski, Tomasz; Górski, Bohdan; Gronwald, Jacek; Hooning, Maartje J.; Hollestelle, Antoinette; Ouweland, Ans M.W. van den; Jager, Agnes; Kriege, Mieke; Tilanus-Linthorst, Madeleine M.A.; Collée, Margriet; Wang-Gohrke, Shan; Pylkäs, Katri; Jukkola-Vuorinen, Arja; Mononen, Kari; Grip, Mervi; Hirvikoski, Pasi; Winqvist, Robert; Mannermaa, Arto; Kosma, Veli Matti; Kauppinen, Jaana M.; Kataja, Vesa; Auvinen, Päivi; Soini, Ylermi; Sironen, Reijo; Bojesen, Stig E.; Ørsted, David D.; Kaur-Knudsen, Diljit; Flyger, Henrik; Nordestgaard, Børge G.; Holland, Helene; Chenevix-Trench, Georgia; Manoukian, Siranoush; Barile, Mônica; Radice, Paolo; Hankinson, Susan E.; Hunter, David J.; Tamimi, Rulla M.; Sangrajrang, Suleeporn; Brennan, Paul; McKay, James; Odefrey, Fabrice; Gaborieau, Valérie; Devilee, Peter; Huijts, Petra E A; Tollenaar, R.A.E.M.; Seynaeve, Caroline; Dite, Gillian S.; Apicella, Carmel; Hopper, John L.; Hammet, Fleur; Tsimiklis, Helen; Smith, Letitia; Southey, Melissa C.; Humphreys, Manjeet K.; Easton, Douglas F.; Pharoah, Paul D.P.; Sherman, Mark E.; García-Closas, Montserrat

doi:10.1093/jnci/djq526

Cited by 620 publications

(624 citation statements)

References 66 publications

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“…Increasingly, risk exposure is linked with specific molecular signatures-for example, BMI is associated with increased risk of postmenopausal breast cancer [41], but we now appreciate that this is in the main limited to oestrogen receptor/progesterone receptor positive breast tumours [75,76]. Similarly, BMI is associated with increased risk of colon cancer [41], but this is mainly linked to microsatellite stable tumours [77].…”

Section: Differences In Tumour Biologymentioning

confidence: 99%

Diabetes and cancer (2): evaluating the impact of diabetes on mortality in patients with cancer

et al. 2012

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In this paper we address methodological aspects of aetiological importance in the link between diabetes and mortality in patients with cancer. We identified nine key points on the cancer pathway at which confounding may arise-cancer screening use, stage at diagnosis, cancer treatment selection, cancer treatment complications and failures, peri-treatment mortality, competing risks for long-term mortality, effects of type 2 diabetes on anti-cancer therapies, effects of glucoselowering treatments on cancer outcome and differences in tumour biology. Two types of mortality studies were identified: (1) inception cohort studies that evaluate the effect of baseline diabetes on cancer-related mortality in general populations, and (2) cohorts of patients with a cancer diagnosis and preexisting type 2 diabetes. We demonstrate, with multiple examples from the literature, that pre-existing diabetes affects presentation, cancer treatment, and outcome of several common cancer types, often to varying extents. Diabetes is associated with increased all-cause mortality in cancer patients, but the evidence that it influences cancer-specific mortality is inconsistent. In the absence of data that address the potential biases and confounders outlined in the above framework, we caution against the reporting of cancer-related mortality as a main endpoint in analyses determining the impact of diabetes and glucose-lowering medications on risk of cancer.

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Section: Differences In Tumour Biologymentioning

confidence: 99%

Diabetes and cancer (2): evaluating the impact of diabetes on mortality in patients with cancer

et al. 2012

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“…( 4 Interestingly, those molecular subtypes have also been demonstrated in the breast cancerprecursor lesion ductal carcinoma in situ (DCIS). 5,6 Importantly, the molecular subtypes can be reliably determined with immunohistochemical stains.…”

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confidence: 99%

Characterization of Molecular Subtypes of Paget Disease of the Breast Using Immunohistochemistry and In Situ Hybridization

Wachter

Fasching

et al. 2018

Archives of Pathology &Amp; Laboratory Medicine

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Context.— Paget disease of the breast, in most cases, represents intraepidermal spread of ductal carcinoma in situ. Molecular subtypes of invasive carcinoma of the breast have prognostic and therapeutic significance and show characteristic distribution. Little is known about the distribution of molecular subtypes in Paget disease of the breast. Objectives.— To examine the distribution of molecular subtypes in Paget disease of the breast and to compare them to concurrent invasive carcinoma of the breast, if present. Design.— We examined 48 cases of Paget disease of the breast with immunohistochemistry and antibodies against estrogen and progesterone receptors, human epidermal growth factor receptor 2 (HER2), and Ki-67, as well as HER2 chromogenic in situ hybridization, to classify the cases into molecular subtypes. Then, we compared the results to the molecular subtypes of associated invasive carcinoma of the breast, if present. Results.— The HER2 subtype was the most common found in Paget disease of the breast, followed by the luminal B subtype and 2 cases of the triple-negative subtype. The associated invasive carcinoma cases were most often of the luminal B subtype, followed by the HER2 subtype and the triple-negative subtype. The molecular subtype of Paget disease and invasive carcinoma was congruent in most of the cases. Conclusions.— Molecular subtypes of invasive carcinoma of the breast can already be detected in Paget disease. The distribution of molecular subtypes of Paget disease and of Paget disease–associated invasive carcinoma differs from invasive carcinoma without associated Paget disease, with the HER2 subtype overrepresented in Paget disease and associated invasive carcinoma and the luminal and triple-negative subtypes underrepresented.

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“…We do not yet know the extent to which this family influence on age at onset is caused by inherited factors such as the specific BRCA1 or 2 mutations in that family or single-nucleotide polymorphisms (SNPs) that also influence cancer risk in carriers, by lifestyle or by other factors. 24,25 Overall, determining starting age of screening based on our model or starting at 25 for BRCA1, 30 for BRCA2 and 35 for women with familial risk, as suggested in several guidelines, resulted in optimal balance between missed tumors and "unnecessary" screening years. Per risk group, however, there were differences between the two approaches.…”

Section: Early Detection and Diagnosismentioning

confidence: 91%

“…This finding is compatible with the large amount of unexplained variance in age at onset in the normal risk population and the numerous lifestyle factors and SNPs that influence age-related penetrance in BRCA1/2 families. 24,25 The prediction of age of onset might be further improved by indentifying individual factors that contribute to the development of breast cancer.…”

Section: Early Detection and Diagnosismentioning

confidence: 99%

“…[8][9][10] National guidelines that advise screening for highrisk groups rely for the starting age mainly on expert opinion, and differ between countries. Most guidelines advise either starting at a fixed age depending only on risk group (e.g., 25 or 30 years in BRCA1 or BRCA2 carriers), [11][12][13] or take into account the age of the youngest affected relative. 14,15 Aside from financial burden, risk reduction strategies have documented side effects, such as false-positive screening results and anxiety.…”

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confidence: 99%

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Optimal age to start preventive measures in women with BRCA1/2 mutations or high familial breast cancer risk

Tilanus-Linthorst

Lingsma

Evans

et al. 2013

Intl Journal of Cancer

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Women from high-risk families consider preventive measures for breast cancer including screening. Guidelines on screening differ considerably regarding starting age. We investigated whether age at diagnosis in affected relatives is predictive for age at diagnosis. We analyzed the age of breast cancer detection of 1,304 first-and second-degree relatives of 314 BRCA1, 164 BRCA2 and 244 highrisk participants of the Dutch MRI-SCreening study. The within-and between-family variance in the relative's age at diagnosis was analyzed with a random effect linear regression model. We compared the starting age of screening based on risk-group (25 years for BRCA1, 30 years for BRCA2 and 35 years for familial risk), on family history, and on the model, which combines both. The findings were validated in 63 families from the UK-MARIBS study. Mean age at diagnosis in the relatives varied between families; 95% range of mean family ages was 35-55 in BRCA1-, 41-57 in BRCA2-and 44-60 in high-risk families. In all, 14% of the variance in age at diagnosis, in BRCA1 even 23%, was explained by family history, 7% by risk group. Determining start of screening based on the model and on risk-group gave similar results in terms of cancers missed and years of screening. The approach based on familial history only, missed more cancers and required more screening years in both the Dutch and the United Kingdom data sets. Age at breast cancer diagnosis is partly dependent on family history which may assist planning starting age for preventive measures.The average risk of detecting breast cancer between 50 and 70 years of age is 5-6% for women in the United States and Europe. A woman's risk of developing breast cancer increases when a first-degree relative has had breast cancer progressively with decreasing age at onset in the relative 1 and with an increasing number of family members with breast and/or ovarian cancer. 1-3 Very high risk and young age at onset are seen in women with a BRCA1 or BRCA2 mutation, who have a cumulative lifetime risk of 45-80%. [2][3][4][5] Screening with MRI or other preventive measures such as preventive mastectomy are considered appropriate in these groups from 25 to 30 years onward. However, in about 80% of families that meet the criteria for BRCA testing no causative gene mutation can be detected currently. 6 These women are considered as having an increased familial risk.Screening and preventive surgery should ideally be tailored to the expected age of breast cancer onset. The estimated risk at a given age differs considerably between studies, both for BRCA1/2 mutation carriers and for women

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Associations of Breast Cancer Risk Factors With Tumor Subtypes: A Pooled Analysis From the Breast Cancer Association Consortium Studies

Abstract: This study shows that reproductive factors and BMI are most clearly associated with hormone receptor-positive tumors and suggest that triple-negative or CBP tumors may have distinct etiology.

Cited by 620 publications

References 66 publications

Diabetes and cancer (2): evaluating the impact of diabetes on mortality in patients with cancer

Diabetes and cancer (2): evaluating the impact of diabetes on mortality in patients with cancer

Characterization of Molecular Subtypes of Paget Disease of the Breast Using Immunohistochemistry and In Situ Hybridization

Optimal age to start preventive measures in women with BRCA1/2 mutations or high familial breast cancer risk

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