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Purpose Breast cancer (BC) risk prediction allows systematic identification of individuals at highest and lowest risk. We extend the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk model to incorporate the effects of polygenic risk scores (PRS) and other risk factors (RFs). Methods BOADICEA incorporates the effects of truncating variants in BRCA1 , BRCA2 , PALB2 , CHEK2 , and ATM ; a PRS based on 313 single-nucleotide polymorphisms (SNPs) explaining 20% of BC polygenic variance; a residual polygenic component accounting for other genetic/familial effects; known lifestyle/hormonal/reproductive RFs; and mammographic density, while allowing for missing information. Results Among all factors considered, the predicted UK BC risk distribution is widest for the PRS, followed by mammographic density. The highest BC risk stratification is achieved when all genetic and lifestyle/hormonal/reproductive/anthropomorphic factors are considered jointly. With all factors, the predicted lifetime risks for women in the UK population vary from 2.8% for the 1st percentile to 30.6% for the 99th percentile, with 14.7% of women predicted to have a lifetime risk of ≥17–<30% (moderate risk according to National Institute for Health and Care Excellence [NICE] guidelines) and 1.1% a lifetime risk of ≥30% (high risk). Conclusion This comprehensive model should enable high levels of BC risk stratification in the general population and women with family history, and facilitate individualized, informed decision-making on prevention therapies and screening.

Background Observational studies have investigated the association of risk factors with breast cancer prognosis. However, the results have been conflicting and it has been challenging to establish causality due to potential residual confounding. Using a Mendelian randomisation (MR) approach, we aimed to examine the potential causal association between breast cancer-specific survival and nine established risk factors for breast cancer: alcohol consumption, body mass index, height, physical activity, mammographic density, age at menarche or menopause, smoking, and type 2 diabetes mellitus (T2DM). Methods We conducted a two-sample MR analysis on data from the Breast Cancer Association Consortium (BCAC) and risk factor summary estimates from the GWAS Catalog. The BCAC data included 86,627 female patients of European ancestry with 7054 breast cancer-specific deaths during 15 years of follow-up. Of these, 59,378 were estrogen receptor (ER)-positive and 13,692 were ER-negative breast cancer patients. For the significant association, we used sensitivity analyses and a multivariable MR model. All risk factor associations were also examined in a model adjusted by other prognostic factors. Results Increased genetic liability to T2DM was significantly associated with worse breast cancer-specific survival (hazard ratio [HR] = 1.10, 95% confidence interval [CI] = 1.03–1.17, P value [ P ] = 0.003). There were no significant associations after multiple testing correction for any of the risk factors in the ER-status subtypes. For the reported significant association with T2DM, the sensitivity analyses did not show evidence for violation of the MR assumptions nor that the association was due to increased BMI. The association remained significant when adjusting by other prognostic factors. Conclusions This extensive MR analysis suggests that T2DM may be causally associated with worse breast cancer-specific survival and therefore that treating T2DM may improve prognosis.

Background PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in ‘step’ changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. Methods Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. Results In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age of 40. Conclusions The PREDICT v2 is an improved prognostication and treatment benefit model compared with v1. The online version should continue to aid clinical decision making in women with early breast cancer.

Introduction The presence of tumor-infiltrating lymphocytes (TILs) is correlated with good prognosis and outcome after (immuno)therapy in triple-negative and HER2-positive breast cancer. However, the role of TILs in luminal breast cancer is less clear. Emerging evidence has now demonstrated that genetic aberrations in malignant cells influence the immune landscape of tumors. Phosphatidylinositol 3-kinase (PI3K) is the most common altered pathway in ER-positive breast cancer. It is unknown whether changes in the PI3K pathway result in a different composition of the breast tumor microenvironment. Here we present the retrospective analysis of a prospective randomized trial in ER-positive breast cancer on the prognostic and predictive value of specific tumor-associated lymphocytes in the context of PI3K alterations. Methods We included 563 ER-positive tumors from a multicenter trial for stage I to III postmenopausal breast cancer patients, who were randomized to tamoxifen or no adjuvant therapy. The amount of CD8-, CD4-, and FOXP3-positive cells was evaluated by immunohistochemistry and quantified by imaging-analysis software. We analyzed the associations between PIK3CA hotspot mutations, PTEN expression, phosphorylated proteins of the PI3K and MAPK pathway (p-AKT, p-ERK1/2, p-4EBP1, p-p70S6K), and recurrence-free interval after adjuvant tamoxifen or no adjuvant treatment. Results CD8-positive lymphocytes were significantly more abundant in PIK3CA -mutated tumors (OR = 1.65; 95% CI 1.03–2.68). While CD4 and FOXP3 were not significantly associated with prognosis, patients with tumors classified as CD8-high had increased risk of recurrence (HR = 1.98; 95% CI 1.14–3.41; multivariable model including PIK3CA status, treatment arm, and other standard clinicopathological variables). Lymphocytes were more often present in tumors with increased PI3K downstream phosphorylation. This was most pronounced for FOXP3-positive cells. Conclusion These exploratory analyses of a prospective trial in luminal breast cancer suggest high CD8 infiltration is associated with unfavorable outcome and that PI3K pathway alterations might be associated with the composition of the tumor microenvironment.

Conflicting conclusions have been published regarding breast cancer survival of BRCA1/2 mutation carriers. Here we provide an evidence-based systematic literature review. Eligible publications were observational studies assessing the survival of breast cancer patients carrying a BRCA1/2 mutation compared to non-carriers or the general breast cancer population. We performed meta-analyses and best-evidence syntheses for survival outcomes taking into account study quality assessed by selection bias, misclassification bias and confounding. Sixty-six relevant studies were identified. Moderate evidence for a worse unadjusted recurrence-free survival for BRCA1 mutation carriers was found. For BRCA1 and BRCA2 there was a tendency towards a worse breast cancer-specific and overall survival, however, results were heterogeneous and the evidence was judged to be indecisive. Surprisingly, only 8 studies considered adjuvant treatment as a confounder or effect modifier while only two studies took prophylactic surgery into account. Adjustment for tumour characteristics tended to shift the observed risk estimates towards a relatively more favourable survival. In contrast to currently held beliefs of some oncologists, current evidence does not support worse breast cancer survival of BRCA1/2 mutation carriers in the adjuvant setting; differences if any are likely to be small. More well-designed studies are awaited.

Women with diabetes have a worse survival after breast cancer diagnosis compared to women without diabetes. This may be due to a different etiological profile, leading to the development of more aggressive breast cancer subtypes. Our aim was to investigate whether insulin and non-insulin treated women with diabetes develop specific clinicopathological breast cancer subtypes compared to women without diabetes. This cross-sectional study included randomly selected patients with invasive breast cancer diagnosed in 2000-2010. Stratified by age at breast cancer diagnosis (≤50 and >50 years), women with diabetes were 2:1 frequency-matched on year of birth and age at breast cancer diagnosis (both in 10-year categories) to women without diabetes, to select ~300 patients with tumor tissue available. Tumor MicroArrays were stained by immunohistochemistry for estrogen and progesterone receptor (ER, PR), HER2, Ki67, CK5/6, CK14, and p63. A pathologist scored all stains and revised morphology and grade. Associations between diabetes/insulin treatment and clinicopathological subtypes were analyzed using multivariable logistic regression. Morphology and grade were not significantly different between women with diabetes (n = 211) and women without diabetes (n = 101), irrespective of menopausal status. Premenopausal women with diabetes tended to have more often PR-negative (OR = 2.44(95%CI:1.07-5.55)), HER2-negative (OR = 2.84(95%CI:1.11-7.22)), and basal-like (OR = 3.14(95%CI:1.03-9.60) tumors than the women without diabetes, with non-significantly increased frequencies of ER-negative (OR = 2.48(95%CI:0.95-6.45)) and triple negative (OR = 2.60(95%CI:0.88-7.67) tumors. After adjustment for age and BMI, the associations remained similar in size but less significant. We observed no evidence for associations of clinicopathological subtypes with diabetes in postmenopausal women, or with insulin treatment in general. We found no compelling evidence that women with diabetes, treated with or without insulin, develop different breast cancer subtypes than women without diabetes. However, premenopausal women with diabetes tended to develop breast tumors that do not express hormonal receptors, which are typically associated with poor prognosis.

Background Patients are generally willing to contribute to research with routinely collected health data and residual biosamples, but transparency and being able to (to some extent) have control over data are important conditions. A broad consent procedure ensures that patients are informed, without overloading the patients with too many or repeated study-specific consents. In the context of implementation of broad consent, we investigated five aspects: response rates, whether patients felt informed and were able to reach a decision, whether the registered consent was in line with their desired consent, reasons for giving (no) consent or not responding, and suggestions to improve the procedure. Methods We analyzed consent decisions of 31,894 patients, recorded between May 2018 and December 2020 in a specialized cancer hospital. We also interviewed 64 patients selected from first-time visiting patients between October and November 2018 (25 with consent, 16 with no consent and 23 with no response). Results Consent rates were: 85.2% consented, 3.8% did not consent and 11% did not respond. The majority of the interviewees, who recalled that consent was asked, felt sufficiently informed. Those that needed more information, mostly had not (yet) read the information given to them, due to the hectic and emotional period. For the majority of our interviewees the desired consent decision matched with what was registered in the hospitals’ system. Reasons for giving consent were mostly motivational, e.g., altruism and solidarity. Reasons for not giving consent or not responding yet were mostly contextual, e.g., insufficient headspace and needing more time. Privacy concerns, e.g. mistakes resulting in data being publicly accessible, data linkage and hacking, were mentioned as well. Sometimes the reason to not give consent or not respond was based on misunderstanding, e.g. that consenting would require bureaucratic entanglements. Conclusions For high quality research with patient data and samples, broad consent from a large and representative patient population is essential, and patients must feel informed and be able to register their consent decision easily. Our novel consent procedure led to an 85.2% consent rate and desired consent decisions were mostly registered correctly. In addition, patients felt sufficiently informed.

Annexin A1 (AnxA1) is a candidate regulator of the epithelial- to mesenchymal (EMT)-like phenotypic switch, a pivotal event in breast cancer progression. We show here that AnxA1 expression is associated with a highly invasive basal-like breast cancer subtype both in a panel of human breast cancer cell lines as in breast cancer patients and that AnxA1 is functionally related to breast cancer progression. AnxA1 knockdown in invasive basal-like breast cancer cells reduced the number of spontaneous lung metastasis, whereas additional expression of AnxA1 enhanced metastatic spread. AnxA1 promotes metastasis formation by enhancing TGFβ/Smad signaling and actin reorganization, which facilitates an EMT-like switch, thereby allowing efficient cell migration and invasion of metastatic breast cancer cells.

Hereditary, genetic factors as well as lifestyle and environmental factors, for example, parity and body mass index, predict breast cancer development. Gene–environment interaction studies may help to identify subgroups of women at high-risk of breast cancer and can be leveraged to discover new genetic risk factors. A few interesting results in studies including over 30 000 breast cancer cases and healthy controls indicate that such interactions exist. Explorative gene–environment interaction studies aiming to identify new genetic or environmental factors are scarce and still underpowered. Gene–environment interactions might be stronger for rare genetic variants, but data are lacking. Ongoing initiatives to genotype larger sample sets in combination with comprehensive epidemiologic databases will provide further opportunities to study gene–environment interactions in breast cancer. However, based on the available evidence, we conclude that associations between the common genetic variants known today and breast cancer risk are only weakly modified by environmental factors, if at all.