Assessing women at high risk of breast cancer: a review of risk assessment models

Breast Density: Where Are We Now?

Breast density refers to the amount of fibroglandular tissue relative to fat on mammography and is determined either qualitatively through visual assessment or quantitatively. It is a heritable and dynamic trait associated with age, race/ethnicity, body mass index, and hormonal factors. Increased breast density has important clinical implications including the potential to mask malignancy and as an independent risk factor for the development of breast cancer. Breast density has been incorporated into breast cancer risk models. Given the impact of dense breasts on the interpretation of mammography, supplemental screening may be indicated.

Quantitative assessment of background parenchymal enhancement is associated with lifetime breast cancer risk in screening MRI

OBJECTIVES

To compare the quantitative background parenchymal enhancement (BPE) in women with different lifetime risks and BRCA mutation status of breast cancer using screening MRI.

MATERIALS AND METHODS

This study included screening MRI of 535 women divided into three groups based on lifetime risk: nonhigh-risk women, high-risk women without BRCA mutation, and BRCA1/2 mutation carriers. Six quantitative BPE measurements, including percent enhancement (PE) and signal enhancement ratio (SER), were calculated on DCE-MRI after segmentation of the whole breast and fibroglandular tissue (FGT). The associations between lifetime risk factors and BPE were analyzed via linear regression analysis. We adjusted for risk factors influencing BPE using propensity score matching (PSM) and compared the BPE between different groups. A two-sided Mann-Whitney U-test was used to compare the BPE with a threshold of 0.1 for multiple testing issue-adjusted p values.

RESULTS

Age, BMI, menopausal status, and FGT level were significantly correlated with quantitative BPE based on the univariate and multivariable linear regression analyses. After adjusting for age, BMI, menopausal status, hormonal treatment history, and FGT level using PSM, significant differences were observed between high-risk non-BRCA and BRCA groups in PEFGT (11.5 vs. 8.0%, adjusted p = 0.018) and SERFGT (7.2 vs. 9.3%, adjusted p = 0.066).

CONCLUSION

Quantitative BPE varies in women with different lifetime breast cancer risks and BRCA mutation status. These differences may be due to the influence of multiple lifetime risk factors. Quantitative BPE differences remained between groups with and without BRCA mutations after adjusting for known risk factors associated with BPE.

CLINICAL RELEVANCE STATEMENT

BRCA germline mutations may be associated with quantitative background parenchymal enhancement, excluding the effects of known confounding factors. This finding can provide potential insights into the cancer pathophysiological mechanisms behind lifetime risk models.

KEY POINTS

Expanding understanding of breast cancer pathophysiology allows for improved risk stratification and optimized screening protocols. Quantitative BPE is significantly associated with lifetime risk factors and differs between BRCA mutation carriers and noncarriers. This research offers a possible understanding of the physiological mechanisms underlying quantitative BPE and BRCA germline mutations.

Breast cancer risk prediction using machine learning: a systematic review

Background

Breast cancer is the leading cause of cancer-related fatalities among women worldwide. Conventional screening and risk prediction models primarily rely on demographic and patient clinical history to devise policies and estimate likelihood. However, recent advancements in artificial intelligence (AI) techniques, particularly deep learning (DL), have shown promise in the development of personalized risk models. These models leverage individual patient information obtained from medical imaging and associated reports. In this systematic review, we thoroughly investigated the existing literature on the application of DL to digital mammography, radiomics, genomics, and clinical information for breast cancer risk assessment. We critically analyzed these studies and discussed their findings, highlighting the promising prospects of DL techniques for breast cancer risk prediction. Additionally, we explored ongoing research initiatives and potential future applications of AI-driven approaches to further improve breast cancer risk prediction, thereby facilitating more effective screening and personalized risk management strategies.

Objective and methods

This study presents a comprehensive overview of imaging and non-imaging features used in breast cancer risk prediction using traditional and AI models. The features reviewed in this study included imaging, radiomics, genomics, and clinical features. Furthermore, this survey systematically presented DL methods developed for breast cancer risk prediction, aiming to be useful for both beginners and advanced-level researchers.

Results

A total of 600 articles were identified, 20 of which met the set criteria and were selected. Parallel benchmarking of DL models, along with natural language processing (NLP) applied to imaging and non-imaging features, could allow clinicians and researchers to gain greater awareness as they consider the clinical deployment or development of new models. This review provides a comprehensive guide for understanding the current status of breast cancer risk assessment using AI.

Conclusion

This study offers investigators a different perspective on the use of AI for breast cancer risk prediction, incorporating numerous imaging and non-imaging features.

Disparities in genetic testing for breast cancer among black and Hispanic women in the United States

Women from racial and ethnic minorities are at a higher risk for developing breast cancer. Despite significant advancements in breast cancer screening, treatment, and overall survival rates, disparities persist among Black and Hispanic women. These disparities manifest as breast cancer at an earlier age with worse prognosis, lower rates of genetic screening, higher rates of advanced-stage diagnosis, and higher rates of breast cancer mortality compared to Caucasian women. The underutilization of available resources, such as genetic testing, counseling, and risk assessment tools, by Black and Hispanic women is one of many reasons contributing to these disparities. This review aims to explore the racial disparities that exist in genetic testing among Black and Hispanic women. Barriers that contribute to racial disparities include limited access to resources, insufficient knowledge and awareness, inconsistent care management, and slow progression of incorporation of genetic data and information from women of racial/ethnic minorities into risk assessment models and genetic databases. These barriers continue to impede rates of genetic testing and counseling among Black and Hispanic mothers. Consequently, it is imperative to address these barriers to promote early risk assessment, genetic testing and counseling, early detection rates, and ultimately, lower mortality rates among women belonging to racial and ethnic minorities.

Genetic Basis of Breast and Ovarian Cancer: Approaches and Lessons Learnt from Three Decades of Inherited Predisposition Testing

Germline variants occurring in BRCA1 and BRCA2 give rise to hereditary breast and ovarian cancer (HBOC) syndrome, predisposing to breast, ovarian, fallopian tube, and peritoneal cancers marked by elevated incidences of genomic aberrations that correspond to poor prognoses. These genes are in fact involved in genetic integrity, particularly in the process of homologous recombination (HR) DNA repair, a high-fidelity repair system for mending DNA double-strand breaks. In addition to its implication in HBOC pathogenesis, the impairment of HR has become a prime target for therapeutic intervention utilizing poly (ADP-ribose) polymerase (PARP) inhibitors. In the present review, we introduce the molecular roles of HR orchestrated by BRCA1 and BRCA2 within the framework of sensitivity to PARP inhibitors. We examine the genetic architecture underneath breast and ovarian cancer ranging from high- and mid- to low-penetrant predisposing genes and taking into account both germline and somatic variations. Finally, we consider higher levels of complexity of the genomic landscape such as polygenic risk scores and other approaches aiming to optimize therapeutic and preventive strategies for breast and ovarian cancer.

Rates of high-risk screening prior to a breast cancer diagnosis in patients under age 40

BACKGROUND

NCCN Guidelines recommend screening young women with an increased breast cancer risk (>20 ​% lifetime risk). We sought to evaluate our institutional rates of high-risk screening in young breast cancer patients prior to their diagnoses."

METHODS

A single-institution retrospective review (2013-2018) was performed investigating risk scores (Tyrer-Cuzick model) and characteristics of breast cancer patients (age <40 ​y) prior to diagnosis.

RESULTS

92 breast cancer patients age <40 ​y were identified (average age 34.5). Only 3.3 ​% (n ​= ​3) underwent appropriate screening, despite 35.8 ​% meeting high-risk criteria. Nearly all patients underwent genetic testing (98.9 ​%) with pathogenic mutations identified in 36.5 ​%, including 15.3 ​% with BRCA1/2 mutations.

CONCLUSIONS

This analysis highlights a significant discrepancy between those meeting criteria for high-risk screening and those who underwent appropriate screening. We identified that this cohort carries significant genetic burden. Future analysis should investigate these findings on a broader scale and strategies to improve screening.

Potential therapeutic efficacy of photodynamic therapy on female hormonal-dependent cancers in a hormonal simulated microenvironment

BACKGROUND

Photodynamic Therapy (PDT) is a clinically approved cancer treatment. Sex hormones, the key drivers for the development of female hormonal dependent cancers, might affect cancer treatment. There are seldom studies to evaluate the effect of sex hormones mimicked the menstrual cycle on the PDT mediated by prodrug 5-aminolevulinic acid (ALA) and its ester derivatives to the hormonal dependent cancers.

AIMS

To evaluate the efficacy of sex hormones on Hexyl-ALA-PDT in hormonal dependent cancers and the effect of the sex hormones on heme biosynthetic pathway.

METHODS

Cell culture system that mimicked the fluctuation of sex hormones 17β-estradiol (E2) and progesterone (PG) in the menstrual cycle was developed. Two pairs of hormonal-independent and hormonal dependent uterine sarcoma and breast cancer cell lines were used as cell models. Hexyl-ALA induced PpIX production and intracellular localization were examined. Key enzymes for PpIX synthesis were analysed. Hexyl-ALA-PDT mediated phototoxicity was evaluated.

RESULTS

The PpIX generation was increased in the hormonal-dependent cells (28-50 %) when cultured in the hormonal microenvironment with long incubation of Hexyl-ALA for 15 and 24 h compared to that cultured without hormones; whereas only slight difference in PpIX generation in their hormonal-independent counterpart. The PpIX generation was in a time-dependent manner. The CPOX, PPOX and FECH expressions were significantly enhanced by Hexyl-ALA-PDT in uterine sarcoma cells in hormonal microenvironment. Hexyl-ALA-PDT triggered significant increase of PPOX expression in breast cancer cells in hormonal microenvironment. The Hexyl-ALA-PDT phototoxicity was enhanced by 18-40 % in cells cultured in the hormonal system in a dose-dependent manner.

CONCLUSION

The PpIX generation and the efficacy of Hexyl-ALA-PDT in both uterine sarcoma and breast cancer cells was significantly enhanced by the sex hormones via cultured in the hormonal microenvironment.

Talking numbers: how women and providers use risk scores during and after risk counseling - a qualitative investigation from the NRG Oncology/NSABP DMP-1 study

OBJECTIVES

Little research exists on how risk scores are used in counselling. We examined (a) how Breast Cancer Risk Assessment Tool (BCRAT) scores are presented during counselling; (b) how women react and (c) discuss them afterwards.

DESIGN

Consultations were video-recorded and participants were interviewed after the consultation as part of the NRG Oncology/National Surgical Adjuvant Breast and Bowel Project Decision-Making Project 1 (NSABP DMP-1).

SETTING

Two NSABP DMP-1 breast cancer care centres in the USA: one large comprehensive cancer centre serving a high-risk population and an academic safety-net medical centre in an urban setting.

PARTICIPANTS

Thirty women evaluated for breast cancer risk and their counselling providers were included.

METHODS

Participants who were identified as at increased risk of breast cancer were recruited to participate in qualitative study with a video-recorded consultation and subsequent semi-structured interview that included giving feedback and input after viewing their own consultation. Consultation videos were summarised jointly and inductively as a team.tThe interview material was searched deductively for text segments that contained the inductively derived themes related to risk assessment. Subgroup analysis according to demographic variables such as age and Gail score were conducted, investigating reactions to risk scores and contrasting and comparing them with the pertinent video analysis data. From this, four descriptive categories of reactions to risk scores emerged. The descriptive categories were clearly defined after 19 interviews; all 30 interviews fit principally into one of the four descriptive categories.

RESULTS

Risk scores were individualised and given meaning by providers through: (a) presenting thresholds, (b) making comparisons and (c) emphasising or minimising the calculated risk. The risk score information elicited little reaction from participants during consultations, though some added to, agreed with or qualified the provider's information. During interviews, participants reacted to the numbers in four primary ways: (a) engaging easily with numbers; (b) expressing greater anxiety after discussing the risk score; (c) accepting the risk score and (d) not talking about the risk score.

CONCLUSIONS

Our study highlights the necessity that patients' experiences must be understood and put into relation to risk assessment information to become a meaningful treatment decision-making tool, for instance by categorising patients' information engagement into types.

TRIAL REGISTRATION NUMBER

NCT01399359.

The cost-effectiveness of risk-stratified breast cancer screening in the UK

BACKGROUND

There has been growing interest in the UK and internationally of risk-stratified breast screening whereby individualised risk assessment may inform screening frequency, starting age, screening instrument used, or even decisions not to screen. This study evaluates the cost-effectiveness of eight proposals for risk-stratified screening regimens compared to both the current UK screening programme and no national screening.

METHODS

A person-level microsimulation model was developed to estimate health-related quality of life, cancer survival and NHS costs over the lifetime of the female population eligible for screening in the UK.

RESULTS

Compared with both the current screening programme and no screening, risk-stratified regimens generated additional costs and QALYs, and had a larger net health benefit. The likelihood of the current screening programme being the optimal scenario was less than 1%. No screening amongst the lowest risk group, and triannual, biennial and annual screening amongst the three higher risk groups was the optimal screening strategy from those evaluated.

CONCLUSIONS

We found that risk-stratified breast cancer screening has the potential to be beneficial for women at the population level, but the net health benefit will depend on the particular risk-based strategy.

Effect of smoking, hypertension and lifestyle factors on kidney cancer - perspectives for prevention and screening programmes

Renal cell carcinoma (RCC) incidence has doubled over the past few decades. However, death rates have remained stable as the number of incidental renal mass diagnoses peaked. RCC has been recognized as a European health care issue, but to date, no screening programmes have been introduced. Well-known modifiable risk factors for RCC are smoking, obesity and hypertension. A direct association between cigarette consumption and increased RCC incidence and RCC-related death has been reported, but the underlying mechanistic pathways for this association are still unclear. Obesity is associated with an increased risk of RCC, but interestingly, improved survival outcomes have been reported in obese patients, a phenomenon known as the obesity paradox. Data on the association between other modifiable risk factors such as diet, dyslipidaemia and physical activity with RCC incidence are conflicting, and potential mechanisms underlying these associations remain to be elucidated.

Risk Analysis of Breast Cancer by Using Bilateral Mammographic Density Differences: A Case-Control Study

The identification of risk factors helps radiologists assess the risk of breast cancer. Quantitative factors such as age and mammographic density are established risk factors for breast cancer. Asymmetric breast findings are frequently encountered during diagnostic mammography. The asymmetric area may indicate a developing mass in the early stage, causing a difference in mammographic density between the left and right sides. Therefore, this paper aims to propose a quantitative parameter named bilateral mammographic density difference (BMDD) for the quantification of breast asymmetry and to verify BMDD as a risk factor for breast cancer. To quantitatively evaluate breast asymmetry, we developed a semi-automatic method to estimate mammographic densities and calculate BMDD as the absolute difference between the left and right mammographic densities. And then, a retrospective case-control study, covering the period from July 2006 to October 2014, was conducted to analyse breast cancer risk in association with BMDD. The study included 364 women diagnosed with breast cancer and 364 matched control patients. As a result, a significant difference in BMDD was found between cases and controls (P < 0.001) and the case-control study demonstrated that women with BMDD > 10% had a 2.4-fold higher risk of breast cancer (odds ratio, 2.4; 95% confidence interval, 1.3-4.5) than women with BMDD ≤ 10%. In addition, we also demonstrated the positive association between BMDD and breast cancer risk among the subgroups with different ages and the Breast Imaging Reporting and Data System (BI-RADS) mammographic density categories. This study demonstrated that BMDD could be a potential risk factor for breast cancer.

Breast cancer risk stratification using genetic and non-genetic risk assessment tools for 246,142 women in the UK Biobank

PURPOSE

The benefit of using individual risk prediction tools to identify high-risk individuals for breast cancer (BC) screening is uncertain, despite the personalized approach of risk-based screening.

METHODS

We studied the overlap of predicted high-risk individuals among 246,142 women enrolled in the UK Biobank. Risk predictors assessed include the Gail model (Gail), BC family history (FH, binary), BC polygenic risk score (PRS), and presence of loss-of-function (LoF) variants in BC predisposition genes. Youden J-index was used to select optimal thresholds for defining high-risk.

RESULTS

In total, 147,399 were considered at high risk for developing BC within the next 2 years by at least 1 of the 4 risk prediction tools examined (Gail2-year > 0.5%: 47%, PRS2-yea r > 0.7%: 30%, FH: 6%, and LoF: 1%); 92,851 (38%) were flagged by only 1 risk predictor. The overlap between individuals flagged as high-risk because of genetic (PRS) and Gail model risk factors was 30%. The best-performing combinatorial model comprises a union of high-risk women identified by PRS, FH, and, LoF (AUC2-year [95% CI]: 62.2 [60.8 to 63.6]). Assigning individual weights to each risk prediction tool increased discriminatory ability.

CONCLUSION

Risk-based BC screening may require a multipronged approach that includes PRS, predisposition genes, FH, and other recognized risk factors.

Breast Cancer Screening for Women at Higher-Than-Average Risk: Updated Recommendations From the ACR

Early detection decreases breast cancer death. The ACR recommends annual screening beginning at age 40 for women of average risk and earlier and/or more intensive screening for women at higher-than-average risk. For most women at higher-than-average risk, the supplemental screening method of choice is breast MRI. Women with genetics-based increased risk, those with a calculated lifetime risk of 20% or more, and those exposed to chest radiation at young ages are recommended to undergo MRI surveillance starting at ages 25 to 30 and annual mammography (with a variable starting age between 25 and 40, depending on the type of risk). Mutation carriers can delay mammographic screening until age 40 if annual screening breast MRI is performed as recommended. Women diagnosed with breast cancer before age 50 or with personal histories of breast cancer and dense breasts should undergo annual supplemental breast MRI. Others with personal histories, and those with atypia at biopsy, should strongly consider MRI screening, especially if other risk factors are present. For women with dense breasts who desire supplemental screening, breast MRI is recommended. For those who qualify for but cannot undergo breast MRI, contrast-enhanced mammography or ultrasound could be considered. All women should undergo risk assessment by age 25, especially Black women and women of Ashkenazi Jewish heritage, so that those at higher-than-average risk can be identified and appropriate screening initiated.

Variability Among Breast Cancer Risk Classification Models When Applied at the Level of the Individual Woman

BACKGROUND

Breast cancer risk models guide screening and chemoprevention decisions, but the extent and effect of variability among models, particularly at the individual level, is uncertain.

OBJECTIVE

To quantify the accuracy and disagreement between commonly used risk models in categorizing individual women as average vs. high risk for developing invasive breast cancer.

DESIGN

Comparison of three risk prediction models: Breast Cancer Risk Assessment Tool (BCRAT), Breast Cancer Surveillance Consortium (BCSC) model, and International Breast Intervention Study (IBIS) model.

SUBJECTS

Women 40 to 74 years of age presenting for screening mammography at a multisite health system between 2011 and 2015, with 5-year follow-up for cancer outcome.

MAIN MEASURES

Comparison of model discrimination and calibration at the population level and inter-model agreement for 5-year breast cancer risk at the individual level using two cutoffs (≥ 1.67% and ≥ 3.0%).

KEY RESULTS

A total of 31,115 women were included. When using the ≥ 1.67% threshold, more than 21% of women were classified as high risk for developing breast cancer in the next 5 years by one model, but average risk by another model. When using the ≥ 3.0% threshold, more than 5% of women had disagreements in risk severity between models. Almost half of the women (46.6%) were classified as high risk by at least one of the three models (e.g., if all three models were applied) for the threshold of ≥ 1.67%, and 11.1% were classified as high risk for ≥ 3.0%. All three models had similar accuracy at the population level.

CONCLUSIONS

Breast cancer risk estimates for individual women vary substantially, depending on which risk assessment model is used. The choice of cutoff used to define high risk can lead to adverse effects for screening, preventive care, and quality of life for misidentified individuals. Clinicians need to be aware of the high false-positive and false-negative rates and variation between models when talking with patients.

Biobank-scale methods and projections for sparse polygenic prediction from machine learning

In this paper we characterize the performance of linear models trained via widely-used sparse machine learning algorithms. We build polygenic scores and examine performance as a function of training set size, genetic ancestral background, and training method. We show that predictor performance is most strongly dependent on size of training data, with smaller gains from algorithmic improvements. We find that LASSO generally performs as well as the best methods, judged by a variety of metrics. We also investigate performance characteristics of predictors trained on one genetic ancestry group when applied to another. Using LASSO, we develop a novel method for projecting AUC and correlation as a function of data size (i.e., for new biobanks) and characterize the asymptotic limit of performance. Additionally, for LASSO (compressed sensing) we show that performance metrics and predictor sparsity are in agreement with theoretical predictions from the Donoho-Tanner phase transition. Specifically, a future predictor trained in the Taiwan Precision Medicine Initiative for asthma can achieve an AUC of [Formula: see text] and for height a correlation of [Formula: see text] for a Taiwanese population. This is above the measured values of [Formula: see text] and [Formula: see text], respectively, for UK Biobank trained predictors applied to a European population.

Deep Learning Analysis of Mammography for Breast Cancer Risk Prediction in Asian Women

The purpose of this study was to develop a mammography-based deep learning (DL) model for predicting the risk of breast cancer in Asian women. This retrospective study included 287 examinations in 153 women in the cancer group and 736 examinations in 447 women in the negative group, obtained from the databases of two tertiary hospitals between November 2012 and March 2022. All examinations were labeled as either dense breast or nondense breast, and then randomly assigned to either training, validation, or test sets. DL models, referred to as image-level and examination-level models, were developed. Both models were trained to predict whether or not the breast would develop breast cancer with two datasets: the whole dataset and the dense-only dataset. The performance of DL models was evaluated using the accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). On a test set, performance metrics for the four scenarios were obtained: image-level model with whole dataset, image-level model with dense-only dataset, examination-level model with whole dataset, and examination-level model with dense-only dataset with AUCs of 0.71, 0.75, 0.66, and 0.67, respectively. Our DL models using mammograms have the potential to predict breast cancer risk in Asian women.

Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review

Breast cancer remains the leading cause of cancer-related deaths in women worldwide. Current screening regimens and clinical breast cancer risk assessment models use risk factors such as demographics and patient history to guide policy and assess risk. Applications of artificial intelligence methods (AI) such as deep learning (DL) and convolutional neural networks (CNNs) to evaluate individual patient information and imaging showed promise as personalized risk models. We reviewed the current literature for studies related to deep learning and convolutional neural networks with digital mammography for assessing breast cancer risk. We discussed the literature and examined the ongoing and future applications of deep learning techniques in breast cancer risk modeling.

Breast cancer genetics and risk assessment: an overview for the clinician

Breast cancer is the most common cancer in women globally with enormous associated morbidity, mortality and economic impact. Prevention of breast cancer is a global public health imperative. To date, most of our global efforts have been directed at expanding population breast cancer screening programs for early cancer detection and not at breast cancer prevention efforts. It is imperative that we change the paradigm. As with other diseases, prevention of breast cancer starts with identification of individuals at high risk, and for breast cancer this requires improved identification of individuals who carry a hereditary cancer mutation associated with an elevated risk of breast cancer, and identification of others who are at high risk due to non-genetic, established modifiable and non-modifiable factors. This article will review basic breast cancer genetics and the most common hereditary breast cancer mutations associated with increased risk. We will also discuss the other non-genetic modifiable and non-modifiable breast cancer risk factors, available risk assessment models and an approach to incorporating screening for genetic mutation carriers and identifying high-risk women in clinical practice. A discussion of guidelines for enhanced screening, chemoprevention and surgical management of high-risk women is beyond the scope of this review.

Clinical characteristics, risk factors, and outcomes in Chilean triple negative breast cancer patients: a real-world study

BACKGROUND

Latin American (LA) studies on triple-negative breast cancer (TNBC) and their characteristics are scarce. This forces physicians to make clinical decisions based on data obtained from studies that include non-Hispanic patients. Our study sought to obtain local epidemiological data, including risk factors and clinical outcomes from a Chilean BC registry.

METHODS

This was a retrospective population-cohort study that included patients treated at a community hospital (mid-low income) or an academic private center (high income), in the 2010-2021 period. Univariate and multivariate analyses were performed to identify prognostic factors associated with survival.

RESULTS

647 out of 5,806 BC patients (11.1%) were TNBC. These patients were younger (p = 0.0001) and displayed lower rates of screening-detected cases (p = 0.0001) compared to non-TNBC counterparts. Among TNBC patients, lower income (i. e., receiving treatment at a community hospital) was associated with poorer overall survival (HR: 1.53; p = 0.0001) and poorer BC specific survival (HR: 1.29; p = 0.004). Other risk factors showed no significant differences between TNBC and non-TNBC. As expected, 5-year OS was significantly shorter on TNBC versus non-TNBC patients (p = 0.00001). In our multivariate analyses TNBC subtype (HR: 2.30), locally advanced stage (HR: 7.04 for stage III), lower income (HR: 1.64), or non-screening detected BC (HR: 1.32) were associated with poorer OS.

CONCLUSION

To the best of our knowledge, this is the largest LA cohort of TNBC patients. Interestingly, the proportion of TNBC among Chileans was smaller compared to similar studies within LA. As expected, TNBC patients had poorer survival and higher risk for early recurrence versus non-TNBC. Other relevant findings include a higher proportion of premenopausal patients among TNBC. Also, mid/low-income patients that received medical attention at a community hospital displayed lower survival versus private health center counterparts.

"It Will Lead You to Make Better Decisions about Your Health"-A Focus Group and Survey Study on Women's Attitudes towards Risk-Based Breast Cancer Screening and Personalised Risk Assessments

Singapore launched a population-based organised mammography screening (MAM) programme in 2002. However, uptake is low. A better understanding of breast cancer (BC) risk factors has generated interest in shifting from a one-size-fits-all to a risk-based screening approach. However, public acceptability of the change is lacking. Focus group discussions (FGD) were conducted with 54 women (median age 37.5 years) with no BC history. Eight online sessions were transcribed, coded, and thematically analysed. Additionally, we surveyed 993 participants in a risk-based MAM study on how they felt in anticipation of receiving their risk profiles. Attitudes towards MAM (e.g., fear, low perceived risk) have remained unchanged for ~25 years. However, FGD participants reported that they would be more likely to attend routine mammography after having their BC risks assessed, despite uncertainty and concerns about risk-based screening. This insight was reinforced by the survey participants reporting more positive than negative feelings before receiving their risk reports. There is enthusiasm in knowing personal disease risk but concerns about the level of support for individuals learning they are at higher risk for breast cancer. Our results support the empowering of Singaporean women with personal health information to improve MAM uptake.

Polygenic Health Index, General Health, and Pleiotropy: Sibling Analysis and Disease Risk Reduction

We construct a polygenic health index as a weighted sum of polygenic risk scores for 20 major disease conditions, including, e.g., coronary artery disease, type 1 and 2 diabetes, schizophrenia, etc. Individual weights are determined by population-level estimates of impact on life expectancy. We validate this index in odds ratios and selection experiments using unrelated individuals and siblings (pairs and trios) from the UK Biobank. Individuals with higher index scores have decreased disease risk across almost all 20 diseases (no significant risk increases), and longer calculated life expectancy. When estimated Disability Adjusted Life Years (DALYs) are used as the performance metric, the gain from selection among ten individuals (highest index score vs average) is found to be roughly 4 DALYs. We find no statistical evidence for antagonistic trade-offs in risk reduction across these diseases. Correlations between genetic disease risks are found to be mostly positive and generally mild. These results have important implications for public health and also for fundamental issues such as pleiotropy and genetic architecture of human disease conditions.

External validation of models for predicting risk of colorectal cancer using the China Kadoorie Biobank

BACKGROUND

In China, colorectal cancer (CRC) incidence and mortality have been steadily increasing over the last decades. Risk models to predict incident CRC have been developed in various populations, but they have not been systematically externally validated in a Chinese population.  This study aimed to assess the performance of risk scores in predicting CRC using the China Kadoorie Biobank (CKB), one of the largest and geographically diverse prospective cohort studies in China.

METHODS

Nine models were externally validated in 512,415 participants in CKB and included 2976 cases of CRC. Model discrimination was assessed, overall and by sex, age, site, and geographic location, using the area under the receiver operating characteristic curve (AUC). Model discrimination of these nine models was compared to a model using age alone. Calibration was assessed for five models, and they were re-calibrated in CKB.

RESULTS

The three models with the highest discrimination (Ma (Cox model) AUC 0.70 [95% CI 0.69-0.71]; Aleksandrova 0.70 [0.69-0.71]; Hong 0.69 [0.67-0.71]) included the variables age, smoking, and alcohol. These models performed significantly better than using a model based on age alone (AUC of 0.65 [95% CI 0.64-0.66]). Model discrimination was generally higher in younger participants, males, urban environments, and for colon cancer. The two models (Guo and Chen) developed in Chinese populations did not perform better than the others. Among the 10% of participants with the highest risk, the three best performing models identified 24-26% of participants that went on to develop CRC.

CONCLUSIONS

Several risk models based on easily obtainable demographic and modifiable lifestyle factor have good discrimination in a Chinese population. The three best performing models have a higher discrimination than using a model based on age alone.

Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction

Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.

Impact of COVID-19 and Socioeconomic Factors on Delays in High-Risk MRI Breast Cancer Screening

The purpose of this study is to investigate if there was a delay in high-risk MRI breast cancer screening in our local region, if this delay is ongoing despite COVID-19 vaccinations, and if demographic and socioeconomic factors are associated with these delays. Six-hundred and sixty-five high-risk breast patients from 23 January 2018–30 September 2021 were included. Delays were determined by comparing the time in between each patients’ MRI screening exams prior to the COVID-19 pandemic to the time in between MRI screening exams during the height of the COVID-19 pandemic as well as the time in between exams when our patients started receiving vaccinations. Delays were analyzed via logistical regression with demographic and socioeconomic factors to determine if there was an association between these factors and delays. Significant time delays in between MRI screening exams were found between the pre-COVID timeframe compared to during the height of COVID. Significant time delays also persisted during the timeframe after patients started getting vaccinations. There were no associations with delays and socioeconomic or demographic factors. Significant time delays were found in between MRI high-risk breast cancer screening examinations due to the COVID-19 pandemic. These delays were not exacerbated by demographic or socioeconomic factors.

Metabolic Links to Socioeconomic Stresses Uniquely Affecting Ancestry in Normal Breast Tissue at Risk for Breast Cancer

A primary difference between black women (BW) and white women (WW) diagnosed with breast cancer is aggressiveness of the tumor. Black women have higher mortalities with similar incidence of breast cancer compared to other race/ethnicities, and they are diagnosed at a younger age with more advanced tumors with double the rate of lethal, triple negative breast cancers. One hypothesis is that chronic social and economic stressors result in ancestry-dependent molecular responses that create a tumor permissive tissue microenvironment in normal breast tissue. Altered regulation of N-glycosylation of proteins, a glucose metabolism-linked post-translational modification attached to an asparagine (N) residue, has been associated with two strong independent risk factors for breast cancer: increased breast density and body mass index (BMI). Interestingly, high body mass index (BMI) levels have been reported to associate with increases of cancer-associated N-glycan signatures. In this study, we used matrix assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) to investigate molecular pattern changes of N-glycosylation in ancestry defined normal breast tissue from BW and WW with significant 5-year risk of breast cancer by Gail score. N-glycosylation was tested against social stressors including marital status, single, education, economic status (income), personal reproductive history, the risk factors BMI and age. Normal breast tissue microarrays from the Susan G. Komen tissue bank (BW=43; WW= 43) were used to evaluate glycosylation against socioeconomic stress and risk factors. One specific N-glycan (2158 m/z) appeared dependent on ancestry with high sensitivity and specificity (AUC 0.77, Brown/Wilson p-value<0.0001). Application of a linear regression model with ancestry as group variable and socioeconomic covariates as predictors identified a specific N-glycan signature associated with different socioeconomic stresses. For WW, household income was strongly associated to certain N-glycans, while for BW, marital status (married and single) was strongly associated with the same N-glycan signature. Current work focuses on understanding if combined N-glycan biosignatures can further help understand normal breast tissue at risk. This study lays the foundation for understanding the complexities linking socioeconomic stresses and molecular factors to their role in ancestry dependent breast cancer risk.

Surgical Outcome Measures in a Cohort of Patients at High Risk of Breast Cancer Treated by Bilateral Risk Reducing Mastectomy and Breast Reconstruction

INTRODUCTION

Women with breast cancer related genetic pathogenic variants (e.g. BRCA1, BRCA2) or with a strong family history carry lifetime risks of developing breast cancer of up to 80-90%. A significant proportion of these women proceed to bilateral risk reducing mastectomy (RRM). We aimed to document the surgical morbidity of RRM and establish whether a diagnosis of breast cancer at the time of surgery impacted on outcomes.

METHODS

Clinical details of 445 women identified as having >25% lifetime risk of developing breast cancer who underwent RRM and breast reconstruction were interrogated for surgical outcomes such as planned, unplanned and emergency procedures, complication rates, length of stay and longevity of breast reconstruction. These outcome measures were recorded in women diagnosed with breast cancer perioperatively (cancer group, CG) and those without malignancy (benign group, BG).

RESULTS

Median follow up was similar in both groups (BG, 70months; CG 73 months). Patients were older in the CG than BG (43y v 39y; p<0.001). Women in the CG required more planned procedures to complete reconstruction than those in the BG (4 v 2; p=0.002). Emergency procedures, unplanned surgical interventions (e.g. capsulectomy) and post reconstruction complication rates were similar between groups.One in five women overall required revisional surgery. Patients with autologous reconstructions had a revision rate of 1.24/1000 person years compared with 2.52 in the implant reconstruction group.

CONCLUSION

Women contemplating RRM can be reassured that this a safe and effective procedure but will likely take multiple interventions. This knowledge should be integral to obtaining informed consent.

Validation and public health modelling of risk prediction models for kidney cancer using the UK Biobank

OBJECTIVES

To externally validate risk models for the detection of kidney cancer, as early detection of kidney cancer improves survival and stratifying the population using risk models could enable an individually tailored screening programme.

METHODS

We validated the performance of 30 existing phenotypic models predicting the risk of kidney cancer in the UK Biobank cohort (n = 450 687). We compared the discrimination and calibration of models for men, women, and a mixed-sex cohort. Population level data were used to estimate model performance in a screening scenario for a range of risk thresholds (6-year risk: 0.1-1.0%).

RESULTS

In all, 10 models had reasonable discrimination (area under the receiver-operating characteristic curve >0.60), although some had poor calibration. Modelling demonstrated similar performance of the best models over a range of thresholds. The models showed an improvement in ability to identify cases compared to age- and sex-based screening. All the models performed less well in women than men.

CONCLUSIONS

The present study is the first comprehensive external validation of risk models for kidney cancer. The best-performing models are better at identifying individuals at high risk of kidney cancer than age and sex alone; however, the benefits are relatively small. Feasibility studies are required to determine applicability to a screening programme.

Prediction of Short-Term Breast Cancer Risk with Fusion of CC- and MLO-Based Risk Models in Four-View Mammograms

This study performed and assessed a novel program to improve the accuracy of short-term breast cancer risk prediction by using information from craniocaudal (CC) and mediolateral-oblique (MLO) views of two breasts. An age-matched dataset of 556 patients with at least two sequential full-field digital mammography examinations was applied. In the second examination, 278 cases were diagnosed and pathologically verified as cancer, and 278 were negative, while all cases in the first examination were negative (not recalled). Two generalized linear-model-based risk prediction models were established with global- and local-based bilateral asymmetry features for CC and MLO views first. Then, a new fusion risk model was developed by fusing prediction results of the CC- and MLO-based risk models with an adaptive alpha-integration-based fusion method. The AUC of the fusion risk model was 0.72 ± 0.02, which was significantly higher than the AUC of CC- or MLO-based risk model (P < 0.05). The maximum odds ratio for CC- and MLO-based risk models were 8.09 and 5.25, respectively, and increased to 11.99 for the fusion risk model. For subgroups of patients aged 37-49 years, 50-65 years, and 66-87 years, the AUCs of 0.73, 0.71, and 0.75 for the fusion risk model were higher than AUC for CC- and MLO-based risk models. For the BIRADS 2 and 3 subgroups, the AUC values were 0.72 and 0.71 respectively for the fusion risk model which were higher than the AUC for the CC- and MLO-based risk models. This study demonstrated that the fusion risk model we established could effectively derive and integrate supplementary and useful information extracted from both CC and MLO view images and adaptively fuse them to increase the predictive power of the short-term breast cancer risk assessment model.

Are English-language online patient education materials related to breast cancer risk assessment understandable, readable, and actionable?

PURPOSE

To evaluate the readability, understandability, and actionability of online patient education materials (OPEM) related to breast cancer risk assessment.

MATERIAL AND METHODS

We queried seven English-language search terms related to breast cancer risk assessment: breast cancer high-risk, breast cancer risk factors, breast cancer family history, BRCA, breast cancer risk assessment, Tyrer-Cuzick, and Gail model. Websites were categorized as: academic/hospital-based, commercial, government, non-profit or academic based on the organization hosting the site. Grade-level readability of qualifying websites and categories was determined using readability metrics and generalized estimating equations based on written content only. Readability scores were compared to the recommended parameters set by the American Medical Association (AMA). Understandability and actionability of OPEM related to breast cancer high-risk were evaluated using the Patient Education Materials Assessment Tool (PEMAT) and compared to criteria set at ≥70%. Descriptive statistics and inter-rater reliability analysis were utilized.

RESULTS

343 websites were identified, of which 162 met study inclusion criteria. The average grade readability score was 12.1 across all websites (range 10.8-13.4). No website met the AMA recommendation. Commercial websites demonstrated the highest overall average readability of 13.1. Of the 26 websites related to the search term breast cancer high-risk, the average understandability and actionability scores were 62% and 34% respectively, both below criteria.

CONCLUSIONS

OPEM on breast cancer risk assessment available to the general public do not meet criteria for readability, understandability, or actionability. To ensure patient comprehension of medical information online, future information should be published in simpler, more appropriate terms.

From Genotype to Phenotype: Polygenic Prediction of Complex Human Traits

Decoding the genome confers the capability to predict characteristics of the organism (phenotype) from DNA (genotype). We describe the present status and future prospects of genomic prediction of complex traits in humans. Some highly heritable complex phenotypes such as height and other quantitative traits can already be predicted with reasonable accuracy from DNA alone. For many diseases, including important common conditions such as coronary artery disease, breast cancer, type I and II diabetes, individuals with outlier polygenic scores (e.g., top few percent) have been shown to have 5 or even 10 times higher risk than average. Several psychiatric conditions such as schizophrenia and autism also fall into this category. We discuss related topics such as the genetic architecture of complex traits, sibling validation of polygenic scores, and applications to adult health, in vitro fertilization (embryo selection), and genetic engineering.

Improving Performance of Breast Cancer Risk Prediction by Incorporating Optical Density Image Feature Analysis: An Assessment

RATIONALE AND OBJECTIVES

The purpose of this study is to improve accuracy of near-term breast cancer risk prediction by applying a new mammographic image conversion method combined with a two-stage artificial neural network (ANN)-based classification scheme.

MATERIALS AND METHODS

The dataset included 168 negative mammography screening cases. In developing and testing our new risk model, we first converted the original grayscale value (GV)-based mammographic images into optical density (OD)-based images. For each case, our computer-aided scheme then computed two types of image features representing bilateral asymmetry and the maximum of the image features computed from GV and OD images, respectively. A two-stage classification scheme consisting of three ANNs was developed. The first stage included two ANNs trained using features computed separately from GV and OD images of 138 cases. The second stage included another ANN to fuse the prediction scores produced by two ANNs in the first stage. The risk prediction performance was tested using the rest 30 cases.

RESULTS

With the two-stage classification scheme, the computed area under the receiver operating characteristic curve (AUC) was  0.816 ± 0.071, which was significantly higher than the AUC values of 0.669 ± 0.099 and 0.646 ± 0.099 achieved using two ANNs trained using GV features and OD features, respectively (P < .05).

CONCLUSION

This study demonstrated that applying an OD image conversion method can acquire new complimentary information to those acquired from the original images. As a result, fusion image features computed from these two types of images yielded significantly higher performance in near-term breast cancer risk prediction.

Familial Breast Cancer: Disease Related Gene Mutations and Screening Strategies for Chinese Population

BACKGROUND

About 5%-10% of the breast cancer cases have a hereditary background, and this subset is referred to as familial breast cancer (FBC). In this review, we summarize the susceptibility genes and genetic syndromes associated with FBC and discuss the FBC screening and high-risk patient consulting strategies for the Chinese population.

METHODS

We searched the PubMed database for articles published between January 2000 and August 2021. Finally, 380 pieces of literature addressing the genes and genetic syndromes related to FBC were included and reviewed.

RESULTS

We identified 16 FBC-related genes and divided them into three types (high-, medium-, and low-penetrance) of genes according to their relative risk ratios. In addition, six genetic syndromes were found to be associated with FBC. We then summarized the currently available screening strategies for FBC and discussed those available for high-risk Chinese populations.

CONCLUSION

Multiple gene mutations and genetic disorders are closely related to FBC. The National Comprehensive Cancer Network (NCCN) guidelines recommend corresponding screening strategies for these genetic diseases. However, such guidelines for the Chinese population are still lacking. For screening high-risk groups in the Chinese population, genetic testing is recommended after genetic counseling.

Controversies in Breast Cancer Surgery

Breast surgical oncology is a rapidly evolving field with significant advances shaped by practice-changing research. Three areas of ongoing controversy are (1) high rates of contralateral prophylactic mastectomy (CPM) in the United States despite uncertain benefit, (2) indications for and use of neoadjuvant chemotherapy (NACT) and endocrine therapy (NET), and (3) staging and treatment of the axilla, particularly after neoadjuvant systemic therapy. We discuss the patient populations for whom CPM may or may not be beneficial, indications for NACT and NET, and the trend toward de-escalation of locoregional axillary treatment.

Supplemental Cancer Screening for Women With Dense Breasts: Guidance for Health Care Professionals

Mammography is the standard for breast cancer screening. The sensitivity of mammography in identifying breast cancer, however, is reduced for women with dense breasts. Thirty-eight states have passed laws requiring that all women be notified of breast tissue density results in their mammogram report. The notification includes a statement that differs by state, encouraging women to discuss supplemental screening options with their health care professionals (HCPs). Several supplemental screening tests are available for women with dense breast tissue, but no established guidelines exist to direct HCPs in their recommendation of preferred supplemental screening test. Tailored screening, which takes into consideration the patient's mammographic breast density and lifetime breast cancer risk, can guide breast cancer screening strategies that are more comprehensive. This review describes the benefits and limitations of the various available supplemental screening tests to guide HCPs and patients in choosing the appropriate breast cancer screening.

Identifying women with increased risk of breast cancer and implementing risk-reducing strategies and supplemental imaging

Breast cancer (BC) is the second most common cancer in women, affecting 1 in 8 women in the United States (12.5%) in their lifetime. However, some women have a higher lifetime risk of BC because of genetic and lifestyle factors, mammographic breast density, and reproductive and hormonal factors. Because BC risk is variable, screening and prevention strategies should be individualized after considering patient-specific risk factors. Thus, health care professionals need to be able to assess risk profiles, identify high-risk women, and individualize screening and prevention strategies through a shared decision-making process. In this article, we review the risk factors for BC, risk-assessment models that identify high-risk patients, and preventive medications and lifestyle modifications that may decrease risk. We also discuss the benefits and limitations of various supplemental screening methods.

Distinct genetic landscape and a low response to doxorubicin in a luminal-A breast cancer cell line of Pakistani origin

BACKGROUND

Breast cancers exhibit genetic heterogeneity which causes differential responses to various chemotherapy agents. Given the unique demographic and genomic background in South Asia, genetic architecture in breast cancers is not fully explored.

METHODS AND RESULTS

In this study, we determined the genetic landscape of our previously established luminal-A subtype breast cancer cell line (BC-PAK1), and compared it with a Caucasian origin MCF7 breast cancer cell line of the same molecular subtype. Deep whole-exome sequencing (100X) was performed from early passages of the primary cancer cells using the Illumina NextSeq500. Data analysis with in silico tools showed novel non-silent somatic mutations previously not described in breast cancers, including a frameshift insertion (p.Ala1591AlafsTer28) in CIC, and a frameshift deletion (p.Lys333LysfsTer21) in PABPC1. Five genes CDC27, PIK3CG, ARAP3, RAPGEF1, and EFNA3, related with cell cycle pathway (hsa04110), ErbB signaling pathway (hsa04012), Ras signaling pathway (hsa04014), and Rap1 signaling pathway (hsa04015) were found to have recurrent non-silent somatic mutations. Further, the major contribution of COSMIC signatures 3 (failure of DNA double-strand break repair by homologous recombination), and 12 (transcriptional strand-bias for T>C substitutions) was observed. Also, the somatic mutations landscape in BC-PAK1 was found to be different as compared to the MCF7 cell line. The unique genetic landscape of BC-PAK1 might be responsible for significantly reduced response to doxorubicin than the MCF7 cell line.

CONCLUSION

This study presents a distinct genetic architecture in luminal-A breast cancer potentially responsible for differential response to chemotherapy. Further studies on large cohorts of breast cancer patients are suggested for implementation in personalized medicine.

Breast Cancer Incidence Trends in Isfahan Province Compared with those in England over the Period 2001-2013

Background:

Figures from Iranian cancer registries indicate that Isfahan ranks first in female breast cancer incidence. Although few previous studies have examined whether the breast cancer incidence trend in Isfahan province has increased over a given period of time, this study employed a joint point regression analysis to answer the same question. Moreover, it compared the data of Isfahan province, from a developing country, with those of England, as a representative of developed countries, and tried to explain the causes of the differences observed between the trends.

Methods:

This repeated cross-sectional study was conducted on the data of 6057 women in Isfahan province and of 141,011 women in England with breast cancer over the years 2001–2013. The incidence rates were calculated using direct standardization method and based on the 2013 standard European population. For an analysis of the trends in breast cancer incidence rates, Joint Point Regression program, version 4.3.1.0, released in April 2016, was employed.

Results:

The mean age-standardized incidence rate (ASR) was calculated to be 34.7 per100,000 population over the years 2001 to 2013, which indicated an increase from 22 to 68 in Isfahan province. The corresponding mean ASR for England has also risen from 147.5 to 170.1 per 100,000 women during the same time period. The average annual percentage changes (AAPCs) for Isfahan and England were also calculated to be 9.6 and 1.1, respectively. This indicated an increasing trend in breast cancer incidence rates for Isfahan province over the period in question.

Conclusions:

The drastic discrepancy in breast cancer incidence rates between these two regions may be attributed to differences in an improved cancer registry system in Iran and women's developing awareness of the cancer over time.

Genetic epidemiology of BRCA1- and BRCA2-associated cancer across Latin America

The prevalence and contribution of BRCA1/2 (BRCA) pathogenic variants (PVs) to the cancer burden in Latin America are not well understood. This study aims to address this disparity. BRCA analyses were performed on prospectively enrolled Latin American Clinical Cancer Genomics Community Research Network participants via a combination of methods: a Hispanic Mutation Panel (HISPANEL) on MassARRAY; semiconductor sequencing; and copy number variant (CNV) detection. BRCA PV probability was calculated using BRCAPRO. Among 1,627 participants (95.2% with cancer), we detected 236 (14.5%) BRCA PVs; 160 BRCA1 (31% CNVs); 76 BRCA2 PV frequency varied by country: 26% Brazil, 9% Colombia, 13% Peru, and 17% Mexico. Recurrent PVs (seen ≥3 times), some region-specific, represented 42.8% (101/236) of PVs. There was no ClinVar entry for 14% (17/125) of unique PVs, and 57% (111/196) of unique VUS. The area under the ROC curve for BRCAPRO was 0.76. In summary, we implemented a low-cost BRCA testing strategy and documented a significant burden of non-ClinVar reported BRCA PVs among Latin Americans. There are recurrent, population-specific PVs and CNVs, and we note that the BRCAPRO mutation probability model performs adequately. This study helps address the gap in our understanding of BRCA-associated cancer in Latin America.

Embryo Screening for Polygenic Disease Risk: Recent Advances and Ethical Considerations

Machine learning methods applied to large genomic datasets (such as those used in GWAS) have led to the creation of polygenic risk scores (PRSs) that can be used identify individuals who are at highly elevated risk for important disease conditions, such as coronary artery disease (CAD), diabetes, hypertension, breast cancer, and many more. PRSs have been validated in large population groups across multiple continents and are under evaluation for widespread clinical use in adult health. It has been shown that PRSs can be used to identify which of two individuals is at a lower disease risk, even when these two individuals are siblings from a shared family environment. The relative risk reduction (RRR) from choosing an embryo with a lower PRS (with respect to one chosen at random) can be quantified by using these sibling results. New technology for precise embryo genotyping allows more sophisticated preimplantation ranking with better results than the current method of selection that is based on morphology. We review the advances described above and discuss related ethical considerations.

Initial Findings from a High Genetic Risk Prostate Cancer Clinic

OBJECTIVE

To improve prostate cancer screening for high-risk men, we developed an early detection clinic for patients at high genetic risk of developing prostate cancer. Despite the rapidly growing understanding of germline variants in driving aggressive prostate cancer and the increased availability of genetic testing, there is little evidence surrounding how best to screen these men.

METHODS

We are reporting on the first 45 patients enrolled, men between the ages of 35-75, primarily with known pathogenic germline variants in prostate cancer susceptibility genes. Screening consists of an intake lifestyle survey, PSA, DRE, and SelectMDx urine assay. A biopsy was recommended for any of the following indications: 1) abnormal DRE, 2) PSA above threshold, or 3) SelectMDx above threshold. The primary outcomes were number needed to screen, and number needed to biopsy to diagnose a patient with prostate cancer.

RESULTS

Patients enrolled in the clinic included those with BRCA1 (n=7), BRCA2 (n=16), Lynch Syndrome (n=6), and CHEK2 (n = 4) known pathogenic germline variants. The median age and PSA were 58 (range 35-71) and 1.4 ng/ml (range 0.1-11.4 ng/ml), respectively. 12 patients underwent a prostate needle biopsy and there were 4positive biopsies for prostate cancer.

CONCLUSION

These early data support the feasibility of opening a dedicated clinic for men at high genetic risk of prostate cancer. This early report on the initial enrollment of our long-term study will help optimize early detection protocols and provide evidence for personalized prostate cancer screening in men with key pathogenic germline variants.

A Decade in Review after Idiopathic Scoliosis Was First Called a Complex Trait-A Tribute to the Late Dr. Yves Cotrel for His Support in Studies of Etiology of Scoliosis

Adolescent Idiopathic Scoliosis (AIS) is a prevalent and important spine disorder in the pediatric age group. An increased family tendency was observed for a long time, but the underlying genetic mechanism was uncertain. In 1999, Dr. Yves Cotrel founded the Cotrel Foundation in the Institut de France, which supported collaboration of international researchers to work together to better understand the etiology of AIS. This new concept of AIS as a complex trait evolved in this setting among researchers who joined the annual Cotrel meetings. It is now over a decade since the first proposal of the complex trait genetic model for AIS. Here, we review in detail the vast information about the genetic and environmental factors in AIS pathogenesis gathered to date. More importantly, new insights into AIS etiology were brought to us through new research data under the perspective of a complex trait. Hopefully, future research directions may lead to better management of AIS, which has a tremendous impact on affected adolescents in terms of both physical growth and psychological development.

Machine Learning Prediction of Biomarkers from SNPs and of Disease Risk from Biomarkers in the UK Biobank

We use UK Biobank data to train predictors for 65 blood and urine markers such as HDL, LDL, lipoprotein A, glycated haemoglobin, etc. from SNP genotype. For example, our Polygenic Score (PGS) predictor correlates ∼0.76 with lipoprotein A level, which is highly heritable and an independent risk factor for heart disease. This may be the most accurate genomic prediction of a quantitative trait that has yet been produced (specifically, for European ancestry groups). We also train predictors of common disease risk using blood and urine biomarkers alone (no DNA information); we call these predictors biomarker risk scores, BMRS. Individuals who are at high risk (e.g., odds ratio of >5× population average) can be identified for conditions such as coronary artery disease (AUC∼0.75), diabetes (AUC∼0.95), hypertension, liver and kidney problems, and cancer using biomarkers alone. Our atherosclerotic cardiovascular disease (ASCVD) predictor uses ∼10 biomarkers and performs in UKB evaluation as well as or better than the American College of Cardiology ASCVD Risk Estimator, which uses quite different inputs (age, diagnostic history, BMI, smoking status, statin usage, etc.). We compare polygenic risk scores (risk conditional on genotype: PRS) for common diseases to the risk predictors which result from the concatenation of learned functions BMRS and PGS, i.e., applying the BMRS predictors to the PGS output.

Design, synthesis, in vitro and in silico studies of some novel thiazole-dihydrofuran derivatives as aromatase inhibitors

Aromatase inhibitors used against hormone-dependent breast cancer, especially in post-menopausal women, are very susceptible to the development of resistance due to their limited number and long-term use. In this study, it is aimed to obtain new aromatase inhibitors including thiazole and dihydrofuran ring systems. Synthesis of compounds (2a-2l) were performed according to literature methods. Their structures were elucidated by ¹H NMR, ¹³C NMR and APCI-MS spectroscopic methods. MTT test was carried out to assess the cell proliferation effects of the different compounds on two different pulmonary cell lines (A549, CCD-19Lu) and mammary cell line (MCF7). According to MTT assay, it was observed that the calculated IC₅₀ values of some compounds for the CCD-19Lu cell line were found higher than for the A549 and MCF7 cell lines. Considering the viability results, it was found that the selected compounds (2a, 2c, 2e, 2g, 2h, 2l) showed favourable safety profile and have anticancer activities. Apoptotic activities of the selected compounds were investigated by flow cytometry analysis. And were found that have apoptotic effects on cancerous cell lines. In the light of this information, the aromatase inhibition potentials of 2g and 2l compounds, which are the most active derivatives, were examined in vitro and it was determined that they showed a similar inhibition profile with letrazole. Interaction modes between aromatase enzyme and compounds 2g and 2l were investigated by docking studies. In conclusion, findings of these study indicate that compounds 2g and 2l possess significant anticancer activity.

Utilization of breast cancer risk prediction models by cancer genetic counselors in clinical practice predominantly in the United States

Risk assessment in cancer genetic counseling is essential in identifying individuals at high risk for developing breast cancer to recommend appropriate screening and management options. Historically, many breast cancer risk prediction models were developed to calculate an individual's risk to develop breast cancer or to carry a pathogenic variant in the BRCA1 or BRCA2 genes. However, how or when genetic counselors use these models in clinical settings is currently unknown. We explored genetic counselors' breast cancer risk model usage patterns including frequency of use, reasons for using or not using models, and change in usage since the adoption of multi-gene panel testing. An online survey was developed and sent to members of the National Society of Genetic Counselors; board-certified genetic counselors whose practice included cancer genetic counseling were eligible to participate in the study. The response rate was estimated at 23% (243/1,058), and respondents were predominantly working in the United States. The results showed that 93% of all respondents use at least one breast cancer risk prediction model in their clinical practice. Among the six risk models selected for the study, the Tyrer-Cuzick (IBIS) model was used most frequently (95%), and the BOADICEA model was used least (40%). Determining increased or decreased surveillance and breast MRI eligibility were the two most common reasons for most model usage, while time consumption and difficulty in navigation were the two most common reasons for not using models. This study provides insight into perceived benefits and limitations of risk models in clinical use in the United States, which may be useful information for software developers, genetic counseling program curriculum developers, and currently practicing cancer genetic counselors.

Distribution of Estimated Lifetime Breast Cancer Risk Among Women Undergoing Screening Mammography

OBJECTIVE. Supplemental screening breast MRI is recommended for women with an estimated lifetime risk of breast cancer of greater than 20-25%. The performance of risk prediction models varies for each individual and across groups of women. The present study investigates the concordance of three breast cancer risk prediction models among women presenting for screening mammography. SUBJECTS AND METHODS. In this prospective study, we calculated the estimated lifetime risk of breast cancer using the modified Gail, Tyrer-Cuzick version 7, and BRCAPRO models for each woman who presented for screening mammography. Per American Cancer Society guidelines, for each woman the risk was categorized as less than 20% or 20% or greater as well as less than 25% or 25% or greater with use of each model. Venn diagrams were constructed to evaluate concordance across models. The McNemar test was used to test differences in risk group allocations between models, with p ≤ .05 considered to denote statistical significance. RESULTS. Of 3503 screening mammography patients who underwent risk stratification, 3219 (91.9%) were eligible for risk estimation using all three models. Using at least one model, 440 (13.7%) women had a lifetime risk of 20% or greater, including 390 women (12.1%) according to the Tyrer-Cuzick version 7 model, 18 (0.6%) according to the BRCAPRO model, and 141 (4.4%) according to the modified Gail model. Six women (0.2%) had a risk of 20% or greater according to all three models. Women were significantly more likely to be classified as having a high lifetime breast cancer risk by the Tyrer-Cuzick version 7 model compared with the modified Gail model, with thresholds of 20% or greater (odds ratio, 6.4; 95% CI, 4.7-8.7) or 25% or greater (odds ratio, 7.4; 95% CI, 4.7-11.9) used for both models. CONCLUSION. To identify women with a high lifetime breast cancer risk, practices should use estimates of lifetime breast cancer risk derived from multiple risk prediction models.

Long-term antihypertensive drug use and risk of cancer: The Japan Public Health Center-based prospective study

Antihypertensive drugs have been reported as both promotors and suppressors of cancers and this relationship has been known for several decades. We examined a large‐scale prospective cohort study in Japan to assess the relationship between long‐term antihypertensive drug use, for 10 y, and carcinogenesis. We divided participants into 4 categories according to the period of antihypertensive drug use, and calculated the hazard ratios (HRs), 95% confidence intervals (CIs), and P trends using the Cox proportional hazard model. In all cancers, there was a significant difference in the medication period and the adjusted HR, as well as a significant difference in the P trend. Furthermore, more than 10 y use of antihypertensive drugs significantly increased the adjusted HR in colorectal cancer (multivariable HR: 1.18, 95% CI: 1.01‐1.37 in the >10 y use group; P for trend = .033) and renal cancer (multivariable HR: 3.76, 95% CI: 2.32‐6.10 in the 5‐10 y use group; multivariable HR: 2.14, 95% CI: 1.29‐3.56 in the >10 y use group; P for trend < .001). The highest adjusted HR in renal cancer among antihypertensive drug users was observed in the analysis performed on patients in which the outcomes were calculated from 3 y after the 10‐y follow‐up survey and by sex. A large‐scale cohort study in Japan suggested that long‐term use of antihypertensive drugs may be associated with an increased incidence of colorectal and renal cancer.

Assessing Risk of Breast Cancer: A Review of Risk Prediction Models

Accurate and individualized breast cancer risk assessment can be used to guide personalized screening and prevention recommendations. Existing risk prediction models use genetic and nongenetic risk factors to provide an estimate of a woman's breast cancer risk and/or the likelihood that she has a BRCA1 or BRCA2 mutation. Each model is best suited for specific clinical scenarios and may have limited applicability in certain types of patients. For example, the Breast Cancer Risk Assessment Tool, which identifies women who would benefit from chemoprevention, is readily accessible and user-friendly but cannot be used in women under 35 years of age or those with prior breast cancer or lobular carcinoma in situ. Emerging research on deep learning-based artificial intelligence (AI) models suggests that mammographic images contain risk indicators that could be used to strengthen existing risk prediction models. This article reviews breast cancer risk factors, describes the appropriate use, strengths, and limitations of each risk prediction model, and discusses the emerging role of AI for risk assessment.