Breast cancer risk prediction in women aged 35-50 years: impact of including sex hormone concentrations in the Gail model

History of infertility and anti-Müllerian hormone levels among participants in the Nurses' Health Study II

OBJECTIVES

To better understand whether history of infertility is associated with anti-Müllerian hormone (AMH) levels later in life, outside of reproduction.

METHODS

Among 1,758 premenopausal women in the Nurses' Health Study II with measured AMH, we used multivariable generalized linear models to compare log-transformed plasma AMH for women with a history of infertility compared with fertile women. We investigated AMH levels by cause of infertility and effect modification by menstrual cycle regularity. Lastly, we investigated AMH levels by history of primary and secondary infertility and age at reported infertility.

RESULTS

Mean age at blood collection was 40 years. We observed no association between overall history of infertility and AMH levels (% difference AMH: -8.1% [CI, -19.4 to 4.8]). The association between overall infertility and AMH was strongest among women who first reported infertility at >30 years (-17.7% [CI, -32.1 to -0.3]).

CONCLUSIONS

Overall, we observed no association between the history of infertility and AMH levels later in life. However, specific subgroups of women with a history of infertility may have lower AMH levels throughout life compared with fertile women. This association was observed among subgroups, such as those who first experienced infertility at >30 years. These findings have implications for mechanisms through which infertility may be associated with premature menopause and chronic disease risk.

Heavy Metals and Trajectories of Anti-Müllerian Hormone During the Menopausal Transition

BACKGROUND

Experimental and epidemiological studies have linked metals with women's reproductive aging, but the mechanisms are not well understood. Disrupted ovarian folliculogenesis and diminished ovarian reserve could be a pathway through which metals impact reproductive hormones and outcomes.

OBJECTIVE

The study aimed to evaluate the associations of heavy metals with anti-Müllerian hormone (AMH), a marker of ovarian reserve.

METHODS

The study included 549 women from the Study of Women's Health Across the Nation with 2252 repeated AMH measurements from 10 to 0 years before the final menstrual period (FMP). Serum AMH concentrations were measured using picoAMH ELISA. Urinary concentrations of arsenic, cadmium, mercury, and lead were measured using high-resolution inductively coupled plasma mass spectrometry. Multivariable linear mixed regressions modeled AMH as a function of time before the FMP interaction terms between metals and time to the FMP were also included.

RESULTS

Adjusting for confounders, compared with those in the lowest tertile, women in the highest tertile of urinary arsenic or mercury concentrations had lower AMH concentrations at the FMP (percent change: -32.1%; 95% CI, -52.9 to -2.2, P-trend = .03 for arsenic; percent change: -40.7%; 95% CI, -58.9 to -14.5, P-trend = .005 for mercury). Higher cadmium and mercury were also associated with accelerated rates of decline in AMH over time (percent change per year: -9.0%; 95% CI, -15.5 to -1.9, P-trend = .01 for cadmium; -7.3%; 95% CI, -14.0 to -0.1, P-trend = .04 for mercury).

CONCLUSION

Heavy metals including arsenic, cadmium, and mercury may act as ovarian toxicants by diminishing ovarian reserve in women approaching the FMP.

Age‑integrated breast imaging reporting and data system assessment model to improve the accuracy of breast cancer diagnosis

Early diagnosis is an effective strategy for decreasing breast cancer mortality. Ultrasonography is one of the most predominant imaging modalities for breast cancer owing to its convenience and non-invasiveness. The present study aimed to develop a model that integrates age with Breast Imaging Reporting and Data System (BI-RADS) lexicon to improve diagnostic accuracy of ultrasonography in breast cancer. This retrospective study comprised two cohorts: A training cohort with 975 female patients from Renmin Hospital of Wuhan University (Wuhan, China) and a validation cohort with 500 female patients from Maternal and Child Health Hospital of Hubei Province (Wuhan, China). Logistic regression was used to construct a model combining BI-RADS score with age and to determine the age-based prevalence of breast cancer to predict a cut-off age. The model that integrated age with BI-RADS scores demonstrated the best performance compared with models based solely on age or BI-RADS scores, with an area under the curve (AUC) of 0.872 (95% CI: 0.850-0.894, P<0.001). Furthermore, among participants aged <30 years, the prevalence of breast cancer was lower than the lower limit of the reference range (2%) for BI-RADS subcategory 4A lesions but within the reference range for BI-RADS category 3 lesions, as indicated by linear regression analysis. Therefore, it is recommended that management for this subset of participants are categorized as BI-RADS category 3, meaning that biopsies typically indicated could be replaced with short-term follow-up. In conclusion, the integrated assessment model based on age and BI-RADS may enhance accuracy of ultrasonography in diagnosing breast lesions and young patients with BI-RADS subcategory 4A lesions may be exempted from biopsy.

DDIT3 is associated with breast cancer prognosis and immune microenvironment: an integrative bioinformatic and immunohistochemical analysis

DNA damage-inducible transcript 3 (DDIT3) is a transcription factor central to apoptosis, differentiation, and stress response. DDIT3 has been extensively studied in cancer biology. However, its precise implications in breast cancer progression and its interaction with the immune microenvironment are unclear. In this study, we utilized a novel multi-omics integration strategy, combining bulk RNA sequencing, single-cell sequencing, spatial transcriptomics and immunohistochemistry, to explore the role of DDIT3 in breast cancer and establish the correlation between DDIT3 and poor prognosis in breast cancer patients. We identified a robust prognostic signature, including six genes (unc-93 homolog B1, TLR signaling regulator, anti-Mullerian hormone, DCTP pyrophosphatase 1, mitochondrial ribosomal protein L36, nuclear factor erythroid 2, and Rho GTPase activating protein 39), associated with DDIT3. This signature stratified the high-risk patient groups, characterized by increased infiltration of the regulatory T cells and M2-like macrophages and fibroblast growth factor (FGF)/FGF receptor signaling activation. Notably, the high-risk patient group demonstrated enhanced sensitivity to immunotherapy, presenting novel therapeutic opportunities. Integrating multi-omics data helped determine the spatial expression pattern of DDIT3 in the tumor microenvironment and its correlation with immune cell infiltration. This multi-dimensional analysis provided a comprehensive understanding of the intricate interplay between DDIT3 and the immune microenvironment in breast cancer. Overall, our study not only facilitates understanding the role of DDIT3 in breast cancer but also offers innovative insights for developing prognostic models and therapeutic strategies. Identifying the DDIT3-related prognostic signature and its association with the immune microenvironment provided a promising avenue for personalized breast cancer treatment.

Hematological and biochemical markers influencing breast cancer risk and mortality: Prospective cohort study in the UK Biobank by multi-state models

BACKGROUND

Breast cancer is the most common cancer and the leading cause of cancer-related death among women. However, evidence concerning hematological and biochemical markers influencing the natural history of breast cancer from in situ breast cancer to mortality is limited.

METHODS

In the UK Biobank cohort, 260,079 women were enrolled during 2006-2010 and were followed up until 2019 to test the 59 hematological and biochemical markers associated with breast cancer risk and mortality. The strengths of these associations were evaluated using the multivariable Cox regression models. To understand the natural history of breast cancer, multi-state survival models were further applied to examine the effects of biomarkers on transitions between different states of breast cancer.

RESULTS

Eleven biomarkers were found to be significantly associated with the risk of invasive breast cancer, including mainly inflammatory-related biomarkers and endogenous hormones, while serum testosterone was also associated with the risk of in-situ breast cancer. Among them, C-reactive protein (CRP) was more likely to be associated with invasive breast cancer and its transition to death from breast cancer (HR for the highest quartile = 1.46, 95 % CI = 1.07-1.97), while testosterone and insulin-like growth factor-1 (IGF-1) were more likely to impact the early state of breast cancer development (Testosterone: HR for the highest quartile = 1.31, 95 % CI = 1.12-1.53; IGF-1: HR for the highest quartile = 1.17, 95 % CI = 1.00-1.38).

CONCLUSION

Serum CRP, testosterone, and IGF-1 have different impacts on the transitions of different breast cancer states, confirming the role of chronic inflammation and endogenous hormones in breast cancer progression. This study further highlights the need of closer surveillance for these biomarkers during the breast cancer development course.

Addition of polygenic risk score to a risk calculator for prediction of breast cancer in US Black women

BACKGROUND

Previous work in European ancestry populations has shown that adding a polygenic risk score (PRS) to breast cancer risk prediction models based on epidemiologic factors results in better discriminatory performance as measured by the AUC (area under the curve). Following publication of the first PRS to perform well in women of African ancestry (AA-PRS), we conducted an external validation of the AA-PRS and then evaluated the addition of the AA-PRS to a risk calculator for incident breast cancer in Black women based on epidemiologic factors (BWHS model).

METHODS

Data from the Black Women's Health Study, an ongoing prospective cohort study of 59,000 US Black women followed by biennial questionnaire since 1995, were used to calculate AUCs and 95% confidence intervals (CIs) for discriminatory accuracy of the BWHS model, the AA-PRS alone, and a new model that combined them. Analyses were based on data from 922 women with invasive breast cancer and 1844 age-matched controls.

RESULTS

AUCs were 0.577 (95% CI 0.556-0.598) for the BWHS model and 0.584 (95% CI 0.563-0.605) for the AA-PRS. For a model that combined estimates from the questionnaire-based BWHS model with the PRS, the AUC increased to 0.623 (95% CI 0.603-0.644).

CONCLUSIONS

This combined model represents a step forward for personalized breast cancer preventive care for US Black women, as its performance metrics are similar to those from models in other populations. Use of this new model may mitigate exacerbation of breast cancer disparities if and when it becomes feasible to include a PRS in routine health care decision-making.

Prediabetes and the risk of breast cancer: a meta-analysis

Background

Diabetes has been related to a higher risk of breast cancer (BC) in women. However, it remains unknown whether the incidence of BC is increased in women with prediabetes. A systematic review and meta-analysis was therefore performed to evaluate the relationship between prediabetes and risk of BC.

Methods

Observational studies with longitudinal follow-up relevant to the objective were found via searching Medline, Embase, Cochrane Library, and Web of Science. A fixed- or random-effects model was used to pool the results depending on heterogeneity.

Results

Eight prospective cohort studies and two nest case-control studies were included. A total of 1069079 community women were involved, and 72136 (6.7%) of them had prediabetes at baseline. During a mean duration follow-up of 9.6 years, 9960 (0.93%) patients were diagnosed as BC. Pooled results with a fixed-effects model showed that women with prediabetes were not associated with a higher incidence of BC as compared to those with normoglycemia (risk ratio: 0.99, 95% confidence interval: 0.93 to 1.05, p = 0.72) with mild heterogeneity (p for Cochrane Q test = 0.42, I2 = 3%). Subgroup analyses showed that study characteristics such as study design, menopausal status of the women, follow-up duration, diagnostic criteria for prediabetes, methods for validation of BC cases, and study quality scores did not significantly affect the results (p for subgroup analyses all > 0.05).

Conclusion

Women with prediabetes may not be associated with an increased risk of BC as compared to women with normoglycemia.

Improved prediction of breast cancer risk based on phenotypic DNA damage repair capacity in peripheral blood B cells

Background

Standard Breast Cancer (BC) risk prediction models based only on epidemiologic factors generally have quite poor performance, and there have been a number of risk scores proposed to improve them, such as AI-based mammographic information, polygenic risk scores and pathogenic variants. Even with these additions BC risk prediction performance is still at best moderate. In that decreased DNA repair capacity (DRC) is a major risk factor for development of cancer, we investigated the potential to improve BC risk prediction models by including a measured phenotypic DRC assay.

Methods

Using blood samples from the Breast Cancer Family Registry we assessed the performance of phenotypic markers of DRC in 46 matched pairs of individuals, one from each pair with BC (with blood drawn before BC diagnosis) and the other from controls matched by age and time since blood draw. We assessed DRC in thawed cryopreserved peripheral blood mononuclear cells (PBMCs) by measuring γ-H2AX yields (a marker for DNA double-strand breaks) at multiple times from 1 to 20 hrs after a radiation challenge. The studies were performed using surface markers to discriminate between different PBMC subtypes.

Results

The parameter F res , the residual damage signal in PBMC B cells at 20 hrs post challenge, was the strongest predictor of breast cancer with an AUC (Area Under receiver-operator Curve) of 0.89 [95% Confidence Interval: 0.84-0.93] and a BC status prediction accuracy of 0.80. To illustrate the combined use of a phenotypic predictor with standard BC predictors, we combined F res in B cells with age at blood draw, and found that the combination resulted in significantly greater BC predictive power (AUC of 0.97 [95% CI: 0.94-0.99]), an increase of 13 percentage points over age alone.

Conclusions

If replicated in larger studies, these results suggest that inclusion of a fingerstick-based phenotypic DRC blood test has the potential to markedly improve BC risk prediction.

Goodness-of-fit two-phase sampling designs for time-to-event outcomes: a simulation study based on New York University Women's Health Study for breast cancer

BACKGROUND

Sub-cohort sampling designs such as a case-cohort study play a key role in studying biomarker-disease associations due to their cost effectiveness. Time-to-event outcome is often the focus in cohort studies, and the research goal is to assess the association between the event risk and risk factors. In this paper, we propose a novel goodness-of-fit two-phase sampling design for time-to-event outcomes when some covariates (e.g., biomarkers) can only be measured on a subgroup of study subjects.

METHODS

Assuming that an external model, which can be the well-established risk models such as the Gail model for breast cancer, Gleason score for prostate cancer, and Framingham risk models for heart diseases, or built from preliminary data, is available to relate the outcome and complete covariates, we propose to oversample subjects with worse goodness-of-fit (GOF) based on an external survival model and time-to-event. With the cases and controls sampled using the GOF two-phase design, the inverse sampling probability weighting method is used to estimate the log hazard ratio of both incomplete and complete covariates. We conducted extensive simulations to evaluate the efficiency gain of our proposed GOF two-phase sampling designs over case-cohort study designs.

RESULTS

Through extensive simulations based on a dataset from the New York University Women's Health Study, we showed that the proposed GOF two-phase sampling designs were unbiased and generally had higher efficiency compared to the standard case-cohort study designs.

CONCLUSION

In cohort studies with rare outcomes, an important design question is how to select informative subjects to reduce sampling costs while maintaining statistical efficiency. Our proposed goodness-of-fit two-phase design provides efficient alternatives to standard case-cohort designs for assessing the association between time-to-event outcome and risk factors. This method is conveniently implemented in standard software.

Development and validation of a nomogram to predict Chinese breast cancer risk based on clinical serum biomarkers

Background: This study investigated and compared clinical serum biomarkers and developed a diagnostic nomogram for breast cancer. Methods: A total of 1224 breast cancer and 1280 healthy controls were enrolled. Univariate and multivariate analyses were performed to identify factors and a nomogram was developed. Discrimination, accuracy and clinical utility values were evaluated by receiver operating characteristic, Hosmer-Lemeshow, calibration plots, decision curve analysis and clinical impact plots. Results: carcinoembryonic antigen, CA125, CA153, lymphocyte-to-monocyte ratio, platelet-to-lymphocyte ratio, fibrinogen and platelet distributing width were effectively identified to predict breast cancer. The nomogram showed the area under the curve of 0.708 and 0.710 in the training and validation set. Calibration plots, Hosmer-Lemeshow, decision curve analysis and clinical impact plots confirmed great accuracy and clinical utility. Conclusion: We developed and validated a nomogram that is effectively used for risk prediction of Chinese breast cancer.

Lightweight Separable Convolution Network for Breast Cancer Histopathological Identification

Breast cancer is one of the leading causes of death among women worldwide. Histopathological images have proven to be a reliable way to find out if someone has breast cancer over time, however, it could be time consuming and require much resources when observed physically. In order to lessen the burden on the pathologists and save lives, there is need for an automated system to effectively analysis and predict the disease diagnostic. In this paper, a lightweight separable convolution network (LWSC) is proposed to automatically learn and classify breast cancer from histopathological images. The proposed architecture aims to treat the problem of low quality by extracting the visual trainable features of the histopathological image using a contrast enhancement algorithm. LWSC model implements separable convolution layers stacked in parallel with multiple filters of different sizes in order to obtain wider receptive fields. Additionally, the factorization and the utilization of bottleneck convolution layers to reduce model dimension were introduced. These methods reduce the number of trainable parameters as well as the computational cost sufficiently with greater non-linear expressive capacity than plain convolutional networks. The evaluation results depict that the proposed LWSC model performs optimally, obtaining 97.23% accuracy, 97.71% sensitivity, and 97.93% specificity on multi-class categories. Compared with other models, the proposed LWSC obtains comparable performance.

Towards implementation of comprehensive breast cancer risk prediction tools in health care for personalised prevention

Advances in knowledge about breast cancer risk factors have led to the development of more comprehensive risk models. These integrate information on a variety of risk factors such as lifestyle, genetics, family history, and breast density. These risk models have the potential to deliver more personalised breast cancer prevention. This is through improving accuracy of risk estimates, enabling more effective targeting of preventive options and creating novel prevention pathways through enabling risk estimation in a wider variety of populations than currently possible. The systematic use of risk tools as part of population screening programmes is one such example. A clear understanding of how such tools can contribute to the goal of personalised prevention can aid in understanding and addressing barriers to implementation. In this paper we describe how emerging models, and their associated tools can contribute to the goal of personalised healthcare for breast cancer through health promotion, early disease detection (screening) and improved management of women at higher risk of disease. We outline how addressing specific challenges on the level of communication, evidence, evaluation, regulation, and acceptance, can facilitate implementation and uptake.

Prospective evaluation of a breast-cancer risk model integrating classical risk factors and polygenic risk in 15 cohorts from six countries

BACKGROUND

Rigorous evaluation of the calibration and discrimination of breast-cancer risk-prediction models in prospective cohorts is critical for applications under clinical guidelines. We comprehensively evaluated an integrated model incorporating classical risk factors and a 313-variant polygenic risk score (PRS) to predict breast-cancer risk.

METHODS

Fifteen prospective cohorts from six countries with 239 340 women (7646 incident breast-cancer cases) of European ancestry aged 19-75 years were included. Calibration of 5-year risk was assessed by comparing expected and observed proportions of cases overall and within risk categories. Risk stratification for women of European ancestry aged 50-70 years in those countries was evaluated by the proportion of women and future cases crossing clinically relevant risk thresholds.

RESULTS

Among women <50 years old, the median (range) expected-to-observed ratio for the integrated model across 15 cohorts was 0.9 (0.7-1.0) overall and 0.9 (0.7-1.4) at the highest-risk decile; among women ≥50 years old, these were 1.0 (0.7-1.3) and 1.2 (0.7-1.6), respectively. The proportion of women identified above a 3% 5-year risk threshold (used for recommending risk-reducing medications in the USA) ranged from 7.0% in Germany (∼841 000 of 12 million) to 17.7% in the USA (∼5.3 of 30 million). At this threshold, 14.7% of US women were reclassified by adding the PRS to classical risk factors, with identification of 12.2% of additional future cases.

CONCLUSION

Integrating a 313-variant PRS with classical risk factors can improve the identification of European-ancestry women at elevated risk who could benefit from targeted risk-reducing strategies under current clinical guidelines.

Identification of a novel immune signature for optimizing prognosis and treatment prediction in colorectal cancer

BACKGROUND

Globally, colorectal cancer (CRC) is one of the most lethal malignant diseases. However, the currently approved therapeutic options for CRC failed to acquire satisfactory treatment efficacy. Tailoring therapeutic strategies for CRC individuals can provide new insights into personalized prediction approaches and thus maximize clinical benefits.

METHODS

In this study, a multi-step process was used to construct an immune-related genes (IRGs) based signature leveraging the expression profiles and clinical characteristics of CRC from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database. An integrated immunogenomic analysis was performed to determine the association between IRGs with prognostic significance and cancer genotypes in the tumor immune microenvironment (TIME). Moreover, we performed a comprehensive in silico therapeutics screening to identify agents with subclass-specific efficacy.

RESULTS

The established signature was shown to be a promising biomarker for evaluating clinical outcomes in CRC. The immune risk score as calculated by this classifier was significantly correlated with over-riding malignant phenotypes and immunophenotypes. Further analyses demonstrated that CRCs with low immune risk scores achieved better therapeutic benefits from immunotherapy, while AZD4547, Cytochalasin B and S-crizotinib might have potential therapeutic implications in the immune risk score-high CRCs.

CONCLUSIONS

Overall, this IRGs-based signature not only afforded a useful tool for determining the prognosis and evaluating the TIME features of CRCs, but also shed new light on tailoring CRCs with precise treatment.

Endogenous hormones and risk of invasive breast cancer in pre- and post-menopausal women: findings from the UK Biobank

BACKGROUND

Some endogenous hormones have been associated with breast cancer risk, but the nature of these relationships is not fully understood.

METHODS

UK Biobank was used. Hormone concentrations were measured in serum collected in 2006-2010, and in a repeat subsample (N ~ 5000) in 2012-13. Incident cancers were identified through data linkage. Cox regression models were used, and hazard ratios (HRs) corrected for regression dilution bias.

RESULTS

Among 30,565 pre-menopausal and 133,294 post-menopausal women, 527 and 2,997, respectively, were diagnosed with invasive breast cancer during a median follow-up of 7.1 years. Cancer risk was positively associated with testosterone in post-menopausal women (HR per 0.5 nmol/L increment: 1.18; 95% CI: 1.14, 1.23) but not in pre-menopausal women (pheterogeneity = 0.03), and with IGF-1 (insulin-like growth factor-1) (HR per 5 nmol/L increment: 1.18; 1.02, 1.35 (pre-menopausal) and 1.07; 1.01, 1.12 (post-menopausal); pheterogeneity = 0.2), and inversely associated with SHBG (sex hormone-binding globulin) (HR per 30 nmol/L increment: 0.96; 0.79, 1.15 (pre-menopausal) and 0.89; 0.84, 0.94 (post-menopausal); pheterogeneity = 0.4). Oestradiol, assessed only in pre-menopausal women, was not associated with risk, but there were study limitations for this hormone.

CONCLUSIONS

This study confirms associations of testosterone, IGF-1 and SHBG with breast cancer risk, with heterogeneity by menopausal status for testosterone.

Longitudinal Risk Management for Patients with Increased Risk for Breast Cancer

INTRODUCTION

This study aims to characterize longitudinal care management and evaluate the relationship between various patient factors and the likelihood of choosing risk-reducing behaviors in women with increased risk of developing breast cancer.

METHODS

A retrospective study was conducted to evaluate all adult female patients who had at least one clinic visit with a surgical provider for discussion of breast cancer risk assessment between January, 2017 to July, 2020 at an academic center. Patients with prior history of breast cancer were excluded. Patient details and strategies pursued at clinic visits were recorded. A time-to-event analysis was performed, and hazard ratios were determined to characterize associations between patient characteristics and time to pursuing risk-reducing care management.

RESULTS

There were 283 participants with at least one follow-up visit and 48 (17.0%) ultimately changed their initial strategy to either chemoprevention or prophylactic mastectomy. Patients with gene mutations were 6 times more likely to engage in risk-reducing management compared to those without (hazard ratio (HR) 5.99, P < 0.001). Those with histories of high-risk proliferative changes (HR 7.62, P < 0.001) and hysterectomy (HR 2.99, P = 0.019) were also more likely to engage in risk-reducing management. Age, race, and increased predicted risk of developing breast cancer (estimated by various calculators) were not associated with increased likelihood of engaging in risk-reducing strategies.

CONCLUSION

Known gene mutations, history of high-risk proliferative changes, and prior hysterectomy were factors associated with women who were more likely to engage in risk-reducing strategies. These findings, when paired with patient reported outcome measures, may help guide shared decision-making.

The Expression of Anti-Müllerian Hormone Type II Receptor (AMHRII) in Non-Gynecological Solid Tumors Offers Potential for Broad Therapeutic Intervention in Cancer

Simple Summary

Until now, only a few studies have examined the AMHRII expression in tumors. Here, with more than 1000 tumor samples and using several complementary techniques we confirmed AMHRII expression in gynecological cancer and demonstrated AMHRII expression in certain non-gynecological cancers such as colorectal cancers. These findings open the way for new therapeutic approaches targeting AMHRII and emphasize the need to better understand the role of AMH/AMHRII in cancer.

Abstract

The anti-Müllerian hormone (AMH) belongs to the TGF-β family and plays a key role during fetal sexual development. Various reports have described the expression of AMH type II receptor (AMHRII) in human gynecological cancers including ovarian tumors. According to qRT-PCR results confirmed by specific In-Situ Hybridization (ISH) experiments, AMHRII mRNA is expressed in an extremely restricted number of normal tissues. By performing ISH on tissue microarray of solid tumor samples AMHRII mRNA was unexpectedly detected in several non-gynecological primary cancers including lung, breast, head and neck, and colorectal cancers. AMHRII protein expression, evaluated by immunohistochemistry (IHC) was detected in approximately 70% of epithelial ovarian cancers. Using the same IHC protocol on more than 900 frozen samples covering 18 different cancer types we detected AMHRII expression in more than 50% of hepato-carcinomas, colorectal, lung, and renal cancer samples. AMHRII expression was not observed in neuroendocrine lung tumor samples nor in non-Hodgkin lymphoma samples. Complementary analyses by immunofluorescence and flow cytometry confirmed the detection of AMHRII on a panel of ovarian and colorectal cancers displaying comparable expression levels with mean values of 39,000 and 50,000 AMHRII receptors per cell, respectively. Overall, our results suggest that this embryonic receptor could be a suitable target for treating AMHRII-expressing tumors with an anti-AMHRII selective agent such as murlentamab, also named 3C23K or GM102. This potential therapeutic intervention was confirmed in vivo by showing antitumor activity of murlentamab against AMHRII-expressing colorectal cancer and hepatocarcinoma Patient-Derived tumor Xenografts (PDX) models.

Sedentary behavior and risk of breast cancer: a dose-response meta-analysis from prospective studies

BACKGROUND

Emerging studies examined the association between sedentary behavior and risk of breast cancer, however, the dose-response relationship remained unclear. We aim to explore dose-response relationship of sedentary behavior and breast cancer risk based on relevant cohort studies.

METHODS

Online database (PubMed, Web of Science, EMBASE and Cochrane Library) were searched up to March 29, 2019. Overall relative risk (RR) with 95% confidence interval (CI) were pooled, and generalized least squares (GLS) method and restricted cubic splines were applied to evaluate the linear or nonlinear relation. Attributable risk proportion (ARP) was used to assess the health hazards of sedentary behavior in different countries.

RESULTS

Eight prospective studies were included in the meta-analysis, containing 17 048 breast cancer cases and 426 506 participants. The borderline statistical association was detected between prolonged sedentary behavior and risk of breast cancer (RR 1.08, 95% CI 0.99-1.19). Linear association between sedentary and breast cancer was observed (P_nonlinearity = 0.262), and for 1 h/d increment of sedentary behavior, there was 1% increase of breast cancer risk (RR 1.01, 95% CI1.00-1.02). Similar results were also found between TV viewing and risk of breast cancer (P_nonlinearity = 0.551), with 1 h/day increment of TV viewing daily attributing to 2% increase of breast cancer risk (RR 1.02, 95% CI 1.00-1.04). Moreover, sedentary behavior may statistically increase the risk of breast cancer by 21.6% for Asian countries, 8.26% for North America.

CONCLUSIONS

Sedentary behavior was validated as a risk factor of breast cancer through dose-response analysis, especially TV viewing.

Assessment of the risk of developing breast cancer using the Gail model in Asian females: A systematic review

Introduction

Currently, the Breast Cancer Risk Assessment Tool (BCRAT), also known as the Gail model (GM) has been widely recognized and adapted for to study disparity in racial and ethnic groups in America including Asian and Pacific Islander American females. However, its applicability outside America remains uncertain due to diversity in epidemiology and risk factors of breast cancer in populations especially in Asian females. We sought to evaluate the performance of the GM to predict breast cancer risk in Asian countries.

Material and methods

This study identified articles published from 2010 by searching PubMed, MEDLINE, Scopus, Web of Science, Google Scholar and gray literature. The initial search terms were breast cancer, mammary, carcinoma, tumor, neoplasm, risk assessment tool, BCRAT, breast cancer prediction, Gail model, Asia, and Asian.

Results

The search yielded 20 articles, with 7 articles addressing the AUC and/or the expected (E) to observed (O) ratio of predicted breast cancer risk, representing the accuracy of the GM in the Asian population. One publication reported the sensitivity and specificity but no AUC. None of the studies were accepted as the standard for reporting prognostic models. Several studies reported good prognostic testing and likely developed a new model modifying the items in the instrument.

Conclusion

The results are not strong enough to develop breast cancer risk in the setting of Asian countries. Involving the breast cancer risk of the Asian population in developing a prognostic model with good statistical understanding is particularly important and can reduce flawed or biased models. Identifying the best methods to achieve well-suited prognostic models in the Asian population should be a priority.

Predicting breast cancer risk using personal health data and machine learning models

Among women, breast cancer is a leading cause of death. Breast cancer risk predictions can inform screening and preventative actions. Previous works found that adding inputs to the widely-used Gail model improved its ability to predict breast cancer risk. However, these models used simple statistical architectures and the additional inputs were derived from costly and / or invasive procedures. By contrast, we developed machine learning models that used highly accessible personal health data to predict five-year breast cancer risk. We created machine learning models using only the Gail model inputs and models using both Gail model inputs and additional personal health data relevant to breast cancer risk. For both sets of inputs, six machine learning models were trained and evaluated on the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial data set. The area under the receiver operating characteristic curve metric quantified each model’s performance. Since this data set has a small percentage of positive breast cancer cases, we also reported sensitivity, specificity, and precision. We used Delong tests (p < 0.05) to compare the testing data set performance of each machine learning model to that of the Breast Cancer Risk Prediction Tool (BCRAT), an implementation of the Gail model. None of the machine learning models with only BCRAT inputs were significantly stronger than the BCRAT. However, the logistic regression, linear discriminant analysis, and neural network models with the broader set of inputs were all significantly stronger than the BCRAT. These results suggest that relative to the BCRAT, additional easy-to-obtain personal health inputs can improve five-year breast cancer risk prediction. Our models could be used as non-invasive and cost-effective risk stratification tools to increase early breast cancer detection and prevention, motivating both immediate actions like screening and long-term preventative measures such as hormone replacement therapy and chemoprevention.

Breast cancer prevention in high-risk women

Women at high risk of developing breast cancer are a heterogeneous group of women including those with and without high-risk germline mutation/s. Prevention in these women requires a personalised and multidisciplinary approach. Preventive therapy with selective oestrogen receptor modulators (SERMs) like tamoxifen and aromatase inhibitors (AIs) substantially reduces breast cancer risk well beyond the active treatment period. The importance of benign breast disease as a marker of increased breast cancer risk remains underappreciated, and although the benefit of preventive therapy may be greater in such women, preventive therapy remains underutilised in these and other high-risk women. Bilateral Risk-Reducing Mastectomy (BRRM) reduces the risk of developing breast cancer by 90% in high-risk women such as carriers of BRCA mutations. It also improves breast cancer-specific survival in BRCA1 carriers. Bilateral risk-reducing salpingo-oophorectomy may also reduce risk in premenopausal BRCA2 carriers. Further research to improve risk models, to identify surrogate biomarkers of preventive therapy benefit and to develop newer preventive agents is needed.