Differences and similarities in breast cancer risk assessment models in clinical practice: which model to choose?

Plasma metabolomics profiles and breast cancer risk

BACKGROUND

Breast cancer (BC) is the most common cancer in women and incidence rates are increasing; metabolomics may be a promising approach for identifying the drivers of the increasing trends that cannot be explained by changes in known BC risk factors.

METHODS

We conducted a nested case-control study (median followup 6.3 years) within the New York site of the Breast Cancer Family Registry (BCFR) (n = 40 cases and 70 age-matched controls). We conducted a metabolome-wide association study using untargeted metabolomics coupling hydrophilic interaction liquid chromatography (HILIC) and C18 chromatography with high-resolution mass spectrometry (LC-HRMS) to identify BC-related metabolic features.

RESULTS

We found eight metabolic features associated with BC risk. For the four metabolites negatively associated with risk, the adjusted odds ratios (ORs) ranged from 0.31 (95% confidence interval (CI): 0.14, 0.66) (L-Histidine) to 0.65 (95% CI: 0.43, 0.98) (N-Acetylgalactosamine), and for the four metabolites positively associated with risk, ORs ranged from 1.61 (95% CI: 1.04, 2.51, (m/z: 101.5813, RT: 90.4, 1,3-dibutyl-1-nitrosourea, a potential carcinogen)) to 2.20 (95% CI: 1.15, 4.23) (11-cis-Eicosenic acid). These results were no longer statistically significant after adjusting for multiple comparisons. Adding the BC-related metabolic features to a model, including age, the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk score improved the accuracy of BC prediction from an area under the curve (AUC) of 66% to 83%.

CONCLUSIONS

If replicated in larger prospective cohorts, these findings offer promising new ways to identify exposures related to BC and improve BC risk prediction.

Deep Learning vs Traditional Breast Cancer Risk Models to Support Risk-Based Mammography Screening

BACKGROUND

Deep learning breast cancer risk models demonstrate improved accuracy compared with traditional risk models but have not been prospectively tested. We compared the accuracy of a deep learning risk score derived from the patient's prior mammogram to traditional risk scores to prospectively identify patients with cancer in a cohort due for screening.

METHODS

We collected data on 119 139 bilateral screening mammograms in 57 617 consecutive patients screened at 5 facilities between September 18, 2017, and February 1, 2021. Patient demographics were retrieved from electronic medical records, cancer outcomes determined through regional tumor registry linkage, and comparisons made across risk models using Wilcoxon and Pearson χ2 2-sided tests. Deep learning, Tyrer-Cuzick, and National Cancer Institute Breast Cancer Risk Assessment Tool (NCI BCRAT) risk models were compared with respect to performance metrics and area under the receiver operating characteristic curves.

RESULTS

Cancers detected per thousand patients screened were higher in patients at increased risk by the deep learning model (8.6, 95% confidence interval [CI] = 7.9 to 9.4) compared with Tyrer-Cuzick (4.4, 95% CI = 3.9 to 4.9) and NCI BCRAT (3.8, 95% CI = 3.3 to 4.3) models (P < .001). Area under the receiver operating characteristic curves of the deep learning model (0.68, 95% CI = 0.66 to 0.70) was higher compared with Tyrer-Cuzick (0.57, 95% CI = 0.54 to 0.60) and NCI BCRAT (0.57, 95% CI = 0.54 to 0.60) models. Simulated screening of the top 50th percentile risk by the deep learning model captured statistically significantly more patients with cancer compared with Tyrer-Cuzick and NCI BCRAT models (P < .001).

CONCLUSIONS

A deep learning model to assess breast cancer risk can support feasible and effective risk-based screening and is superior to traditional models to identify patients destined to develop cancer in large screening cohorts.

Diagnostic Accuracy of Nipple Aspirate Fluid Cytology in Asymptomatic Patients: A Meta-analysis and Systematic Review of the Literature

PURPOSE

To calculate the diagnostic accuracy of nipple aspirate fluid (NAF) cytology.

BACKGROUND

Evaluation of NAF cytology in asymptomatic patients conceptually offers a non-invasive method for either screening for breast cancer or else predicting or stratifying future cancer risk.

METHODS

Studies were identified by performing electronic searches up to August 2019. A meta-analysis was conducted to attain an overall pooled sensitivity and specificity of NAF for breast cancer detection.

RESULTS

A search through 938 studies yielded a total of 19 studies. Overall, 9308 patients were examined, with cytology results from 10,147 breasts [age (years), mean ± SD = 49.73 ± 4.09 years]. Diagnostic accuracy meta-analysis of NAF revealed a pooled specificity of 0.97 (95% CI 0.97-0.98), and sensitivity of 0.64 (95% CI 0.62-0.66).

CONCLUSIONS

The diagnostic accuracy of nipple smear cytology is limited by poor sensitivity. If nipple fluid assessment is to be used for diagnosis, then emerging technologies for fluid biomarker analysis must supersede the current diagnostic accuracy of NAF cytology.

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.

Breast Cancer Risk Assessment and Chemoprevention Use Among Veterans Affairs Primary Care Providers: A National Online Survey

INTRODUCTION

Breast cancer is the most common cancer diagnosed among women and the second most common cause of cancer death among women. There are ways to reduce a woman's risk of breast cancer; however, most eligible women in the United States are neither offered personalized screening nor chemoprevention. Surveys have found that primary care providers are largely unaware of breast cancer risk assessment models or chemoprevention. This survey aims to investigate Veterans Health Administration primary care providers' comfort level, practice patterns, and knowledge of breast cancer risk assessment and chemoprevention.

MATERIALS AND METHODS

An online, Research Electronic Data Capture-generated survey was distributed to VHA providers in internal medicine, family medicine, and obstetrics/gynecology. Survey domains were provider demographics, women's health experience, comfort level, practice patterns, barriers to using risk models and chemoprevention, and knowledge of chemoprevention.

RESULTS

Of the 167 respondents, 33.1% used the Gail model monthly or more often and only 2.4% prescribed chemoprevention in the past 2 years. Most VHA primary care providers did not answer chemoprevention knowledge questions correctly. Designated women's health providers were more comfortable with risk assessment (P < 0.018) and chemoprevention (P < 0.011) and used both breast cancer risk models (P < 0.0045) and chemoprevention more often (P < 0.153). Reported barriers to chemoprevention were lack of education and provider time.

CONCLUSIONS

VHA providers and women Veterans would benefit from a system to ensure that women at increased risk of breast cancer are identified with risk modeling and that risk reduction options, such as chemoprevention, are offered when appropriate. VHA providers requested risk reduction education, which could improve primary care provider comfort level with chemoprevention.

Towards a more precise and individualized assessment of breast cancer risk

Many clinically based models are available for breast cancer risk assessment; however, these models are not particularly useful at the individual level, despite being designed with that intent. There is, therefore, a significant need for improved, precise individualized risk assessment. In this Research Perspective, we highlight commonly used clinical risk assessment models and recent scientific advances to individualize risk assessment using precision biomarkers. Genome-wide association studies have identified >100 single nucleotide polymorphisms (SNPs) associated with breast cancer risk, and polygenic risk scores (PRS) have been developed by several groups using this information. The ability of a PRS to improve risk assessment is promising; however, validation in both genetically and ethnically diverse populations is needed. Additionally, novel classes of biomarkers, such as microRNAs, may capture clinically relevant information based on epigenetic regulation of gene expression. Our group has recently identified a circulating-microRNA signature predictive of long-term breast cancer in a prospective cohort of high-risk women. While progress has been made, the importance of accurate risk assessment cannot be understated. Precision risk assessment will identify those women at greatest risk of developing breast cancer, thus avoiding overtreatment of women at average risk and identifying the most appropriate candidates for chemoprevention or surgical prevention.

Performance of the Gail and Tyrer-Cuzick breast cancer risk assessment models in women screened in a primary care setting with the FHS-7 questionnaire

Breast cancer (BC) risk assessment models base their estimations on different aspects of a woman's personal and familial history. The Gail and Tyrer-Cuzick models are the most commonly used, and BC risks assigned by them vary considerably especially concerning familial history. In this study, our aim was to compare the Gail and Tyrer-Cuzick models after initial screening for familial history of cancer in primary care using the FHS-7 questionnaire. We compared 846 unrelated women with at least one positive answer to any of the seven FHS-7 questions (positive group) and 892 unrelated women that answered negatively (negative group). Concordance between BC risk estimates was compared by Bland-Altman graphics. Mean BC risk estimates were higher using the Tyrer-Cuzick Model in women from the positive group, while women from the negative group had higher BC risk estimates using the Gail model. With increasing estimates, discordance also increased, mainly in the FHS-7 positive group. Our results show that in women with a familial history of cancer, the Gail model underestimates risk and the Tyrer-Cuzick seems to be more appropriate. FHS-7 can be a useful tool for the identification of women with higher breast cancer risks in the primary care setting.

Use of Receiver Operating Characteristic (ROC) Curve Analysis for Tyrer-Cuzick and Gail in Breast Cancer Screening in Jiangxi Province, China

BACKGROUND Breast cancer is a malignant tumor derived from breast gland epithelium. The screening and early diagnosis of breast cancer in high-risk populations can effectively suppress its threat to women's health and improve treatment efficiency, and thus has critical importance. Using various evaluation models, the present study evaluated cancer risk in 35-69-year-old women, and the usefulness of models in breast cancer prevention was compared. MATERIAL AND METHODS A total of 150 infiltrative breast cancer patients who were diagnosed with breast cancer at our hospital were recruited, along with 130 healthy women as the control group. A retrospective study was performed to collect information. The 5-year risk of breast cancer was evaluated using the Gail and Tyrer-Cuzick models. Diagnostic results were analyzed to plot ROC curves for comparing the value for screening between Gail and Tyrer-Cuzick models. RESULTS The Gail model has 53.33% sensitivity and 77.69% specificity, with 73.39% positive prediction value, 59.06% negative prediction value, 64.64% accuracy, and 0.31 Jordon index. The Tyrer-Cuzick model had 66.00% sensitivity, 86.92% specificity, 85.34% positive prediction value, 68.90% negative prediction value, 75.71% accuracy, and 0.53 Jordon index. The area under the curve (AUC) was 0.665 for the Gail model (95% CI: 0.629~0.701) and 0.786 for the Tyrer-Cuzick model (95% CI: 0.757~0.815). CONCLUSIONS Both Gail model and Tyrer-Cuzick models can be used to evaluate breast cancer risk. The Gail model has relatively lower accuracy in evaluating breast cancer risk in Jiangxi province of China and the Tyrer-Cuzick model had relatively higher accuracy.

Validation of the IBIS breast cancer risk evaluator for women with lobular carcinoma in-situ

BACKGROUND

Management advice for women with lobular carcinoma in situ (LCIS) is hampered by the lack of accurate personalised risk estimates for subsequent invasive breast cancer (BC). Prospective validation of the only tool that estimates individual BC risk for a woman with LCIS, the International Breast Cancer Intervention Study Risk Evaluation Tool (IBIS-RET), is lacking.

METHODS

Using population-based cancer registry data for 732 women with LCIS, the calibration and discrimination accuracy of IBIS-RET Version 7.2 were assessed.

RESULTS

The mean observed 10-year risk of invasive BC was 14.1% (95% CI:11.3%-17.5%). IBIS-RET overestimated invasive BC risk (p = 0.0003) and demonstrated poor discriminatory accuracy (AUC 0.54, 95% CI: 0.48 - 0.62).

CONCLUSIONS

Clinicians should understand that IBIS-RET Version 7.2 may overestimate 10-year invasive BC risk for Australian women with LCIS. The newer IBIS-RET Version 8.0, released September 2017, includes mammographic density and may perform better, but validation is needed.

Comparison between CaGene 5.1 and 6.0 for BRCA1/2 mutation prediction: a retrospective study of 150 BRCA1/2 genetic tests in 517 families with breast/ovarian cancer

During the past years, several empirical and statistical models have been developed to discriminate between carriers and non-carriers of germline BRCA1/BRCA2 (breast cancer 1, early onset/breast cancer 2, early onset) mutations in families with hereditary breast or ovarian cancer. Among these, the BRCAPRO or CaGene model is commonly used during genetic counseling, and plays a central role in the identification of potential carriers of BRCA1/2 mutations. We compared performance and clinical applicability of BRCAPRO version 5.1 vs version 6.0 in order to assess diagnostic accuracy of updated version. The study was carried out on 517 pedigrees of patients with familial history of breast or ovarian cancer, 150 of which were submitted to BRCA1/2 mutation screening, according to BRCAPRO evaluation or to criteria based on familial history. In our study, CaGene 5.1 was more sensitive than CaGene 6.0, although the latter showed a higher specificity. Both BRCAPRO versions better discriminate BRCA1 than BRCA2 mutations. This study evidenced similar performances in the two BRCAPRO versions even if the CaGene 6.0 has underestimated the genetic risk prediction in some BRCA mutation-positive families. Genetic counselors should recognize this limitation and during genetic counseling would be advisable to use a set of criteria in order to improve mutation carrier prediction.

Breast Cancer Risk Assessment at the Time of Screening Mammography: Perceptions and Clinical Management Outcomes for Women at High Risk

The purpose of this study was to evaluate the utility of a breast cancer risk assessment (BCRA) at the time of screening mammogram. Women whose BCRA indicated a high risk for cancer received a letter with instructions for breast health care and genetic counseling if appropriate. After 6 months this group received surveys to evaluate their risk perception and their recall of, and compliance with, recommendations. We also explored the impact of other variables such as a recommendation for genetic counseling and physician communication with the women. After the BCRA, the majority of high risk women reported no change in their perceived risk of cancer. A woman's perceived risk of cancer after a BCRA was significantly associated with her recall of recommendations for breast health care, but not with compliance. A recommendation for genetic counseling was not significantly related to women's perceived risk of cancer after the BCRA. Ten percent of women who should have obtained genetic counseling actually completed an appointment. Women who discussed their BCRA results with their physicians were more compliant with a six month breast exam with a doctor (53% vs 17%, p = 0.018). Overall, women felt that the BCRA was helpful and did not cause undue stress or anxiety. Although the cohort's compliance with recommendations was suboptimal, physicians' interactions with their patients may have a positive influence on their compliance.

Assessment of Breast Cancer Risk in an Iranian Female Population Using Bayesian Networks with Varying Node Number

Objective:

As a source of information, medical data can feature hidden relationships. However, the high volume of datasets and complexity of decision-making in medicine introduce difficulties for analysis and interpretation and processing steps may be needed before the data can be used by clinicians in their work. This study focused on the use of Bayesian models with different numbers of nodes to aid clinicians in breast cancer risk estimation.

Methods:

Bayesian networks (BNs) with a retrospectively collected dataset including mammographic details, risk factor exposure, and clinical findings was assessed for prediction of the probability of breast cancer in individual patients. Area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were used to evaluate discriminative performance.

Result:

A network incorporating selected features performed better (AUC = 0.94) than that incorporating all the features (AUC = 0.93). The results revealed no significant difference among 3 models regarding performance indices at the 5% significance level.

Conclusion:

BNs could effectively discriminate malignant from benign abnormalities and accurately predict the risk of breast cancer in individuals. Moreover, the overall performance of the 9-node BN was better, and due to the lower number of nodes it might be more readily be applied in clinical settings.

Improvement in risk prediction, early detection and prevention of breast cancer in the NHS Breast Screening Programme and family history clinics: a dual cohort study

Background

In the UK, women are invited for 3-yearly mammography screening, through the NHS Breast Screening Programme (NHSBSP), from the ages of 47–50 years to the ages of 69–73 years. Women with family histories of breast cancer can, from the age of 40 years, obtain enhanced surveillance and, in exceptionally high-risk cases, magnetic resonance imaging. However, no NHSBSP risk assessment is undertaken. Risk prediction models are able to categorise women by risk using known risk factors, although accurate individual risk prediction remains elusive. The identification of mammographic breast density (MD) and common genetic risk variants [single nucleotide polymorphisms (SNPs)] has presaged the improved precision of risk models.

Objectives

To (1) identify the best performing model to assess breast cancer risk in family history clinic (FHC) and population settings; (2) use information from MD/SNPs to improve risk prediction; (3) assess the acceptability and feasibility of offering risk assessment in the NHSBSP; and (4) identify the incremental costs and benefits of risk stratified screening in a preliminary cost-effectiveness analysis.

Design

Two cohort studies assessing breast cancer incidence.

Setting

High-risk FHC and the NHSBSP Greater Manchester, UK.

Participants

A total of 10,000 women aged 20–79 years [Family History Risk Study (FH-Risk); UK Clinical Research Network identification number (UKCRN-ID) 8611] and 53,000 women from the NHSBSP [aged 46–73 years; Predicting the Risk of Cancer At Screening (PROCAS) study; UKCRN-ID 8080].

Interventions

Questionnaires collected standard risk information, and mammograms were assessed for breast density by a number of techniques. All FH-Risk and 10,000 PROCAS participants participated in deoxyribonucleic acid (DNA) studies. The risk prediction models Manual method, Tyrer–Cuzick (TC), BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) and Gail were used to assess risk, with modelling based on MD and SNPs. A preliminary model-based cost-effectiveness analysis of risk stratified screening was conducted.

Main outcome measures

Breast cancer incidence.

Data sources

The NHSBSP; cancer registration.

Results

A total of 446 women developed incident breast cancers in FH-Risk in 97,958 years of follow-up. All risk models accurately stratified women into risk categories. TC had better risk precision than Gail, and BOADICEA accurately predicted risk in the 6268 single probands. The Manual model was also accurate in the whole cohort. In PROCAS, TC had better risk precision than Gail [area under the curve (AUC) 0.58 vs. 0.54], identifying 547 prospective breast cancers. The addition of SNPs in the FH-Risk case–control study improved risk precision but was not useful inBRCA1(breast cancer 1 gene) families. Risk modelling of SNPs in PROCAS showed an incremental improvement from using SNP18 used in PROCAS to SNP67. MD measured by visual assessment score provided better risk stratification than automatic measures, despite wide intra- and inter-reader variability. Using a MD-adjusted TC model in PROCAS improved risk stratification (AUC = 0.6) and identified significantly higher rates (4.7 per 10,000 vs. 1.3 per 10,000;p < 0.001) of high-stage cancers in women with above-average breast cancer risks. It is not possible to provide estimates of the incremental costs and benefits of risk stratified screening because of lack of data inputs for key parameters in the model-based cost-effectiveness analysis.

Conclusions

Risk precision can be improved by using DNA and MD, and can potentially be used to stratify NHSBSP screening. It may also identify those at greater risk of high-stage cancers for enhanced screening. The cost-effectiveness of risk stratified screening is currently associated with extensive uncertainty. Additional research is needed to identify data needed for key inputs into model-based cost-effectiveness analyses to identify the impact on health-care resource use and patient benefits.

Future work

A pilot of real-time NHSBSP risk prediction to identify women for chemoprevention and enhanced screening is required.

Funding

The National Institute for Health Research Programme Grants for Applied Research programme. The DNA saliva collection for SNP analysis for PROCAS was funded by the Genesis Breast Cancer Prevention Appeal.

The Genetics of Breast Cancer: What the Surgical Oncologist Needs to Know

This article summarizes the impact of germline predisposition to breast cancer on the surgical management of breast cancer and breast cancer risk. Surgical implications of germline predisposition to breast cancer are now more nuanced due to the application of increasingly more complicated next-generation sequencing-based tests. The rapid pace of change will continue to challenge paradigms for genetic cancer risk assessment, which can influence the medical and surgical management of breast cancer risk as well as strategies for screening and for risk reduction.

Assessing the Effects of Participant Preference and Demographics in the Usage of Web-based Survey Questionnaires by Women Attending Screening Mammography in British Columbia

BACKGROUND

Increased usage of Internet applications has allowed for the collection of patient reported outcomes (PROs) and other health data through Web-based communication and questionnaires. While these Web platforms allow for increased speed and scope of communication delivery, there are certain limitations associated with this technology, as survey mode preferences vary across demographic groups.

OBJECTIVE

To investigate the impact of demographic factors and participant preferences on the use of a Web-based questionnaire in comparison with more traditional methods (mail and phone) for women participating in screening mammography in British Columbia, Canada.

METHODS

A sample of women attending the Screening Mammography Program of British Columbia (SMPBC) participated in a breast cancer risk assessment project. The study questionnaire was administered through one of three modes (ie, telephone, mail, or website platform). Survey mode preferences and actual methods of response were analyzed for participants recruited from Victoria General Hospital. Both univariate and multivariate analyses were used to investigate the association of demographic factors (ie, age, education level, and ethnicity) with certain survey response types.

RESULTS

A total of 1192 women successfully completed the study questionnaire at Victoria General Hospital. Mail was stated as the most preferred survey mode (509/1192, 42.70%), followed by website platform (422/1192, 35.40%), and telephone (147/1192, 12.33%). Over 80% (955/1192) of participants completed the questionnaire in the mode previously specified as their most preferred; mail was the most common method of response (688/1192, 57.72%). Mail was also the most preferred type of questionnaire response method when participants responded in a mode other than their original preference. The average age of participants who responded via the Web-based platform (age 52.9, 95% confidence interval [CI] 52.1-53.7) was significantly lower than those who used mail and telephone methods (age 55.9, 95% CI 55.2-56.5; P<.001); each decade of increased age was associated with a 0.97-fold decrease in the odds of using the website platform (P<.001). Web-based participation was more likely for those who completed higher levels of education; each interval increase leading to a 1.83 increase in the odds of website platform usage (P<.001). Ethnicity was not shown to play a role in participant preference for the website platform (P=.96).

CONCLUSIONS

It is beneficial to consider participant survey mode preference when planning to collect PROs and other patient health data. Younger participants and those of higher education level were more likely to use the website platform questionnaire; Web-based participation failed to vary across ethnic group. Because mail questionnaires were still the most preferred survey mode, it will be important to employ strategies, such as user-friendly design and Web-based support, to ensure that the patient feedback being collected is representative of the population being served.

Cancer screening in the United States, 2016: A review of current American Cancer Society guidelines and current issues in cancer screening

Each year the American Cancer Society (ACS) publishes a summary of its guidelines for early cancer detection, data and trends in cancer screening rates, and select issues related to cancer screening. In this issue of the journal, we summarize current ACS cancer screening guidelines, including the update of the breast cancer screening guideline, discuss quality issues in colorectal cancer screening and new developments in lung cancer screening, and provide the latest data on utilization of cancer screening from the National Health Interview Survey.

Survival benefit in women with BRCA1 mutation or familial risk in the MRI screening study (MRISC)

Adding MRI to annual mammography screening improves early breast cancer detection in women with familial risk or BRCA1/2 mutation, but breast cancer specific metastasis free survival (MFS) remains unknown. We compared MFS of patients from the largest prospective MRI Screening Study (MRISC) with 1:1 matched controls. Controls, unscreened if<50 years, and screened with biennial mammography if ≥50 years, were matched on risk category (BRCA1, BRCA2, familial risk), year and age of diagnosis. Of 2,308 MRISC participants, breast cancer was detected in 93 (97 breast cancers), who received MRI <2 years before breast cancer diagnosis; 33 BRCA1 mutation carriers, 18 BRCA2 mutation carriers, and 42 with familial risk. MRISC patients had smaller (87% vs. 52% <T2, p < 0.001), more often node negative (69% vs. 44%, p = 0.001) tumors and received less chemotherapy (39% vs. 77%, p < 0.001) and hormonal therapy (14% vs. 47%, p < 0.001) than controls. Median follow-up time was 9 years (range 0-14). Breast cancer metastasized in 9% (8/93) of MRISC patients and in 23% (21/93) of controls (p = 0.009). MFS was better in MRISC patients overall (log-rank p = 0.008, HR 0.36, 95% CI 0.16-0.80), with familial risk (log-rank p = 0.024, HR: 0.21, 95% CI 0.04-0.95), and in BRCA1 mutation carriers (log-rank p = 0.055, HR 0.30, 95% CI 0.08-1.13). MFS remained better in MRISC patients after lead time correction (log-rank p = 0.020, HR 0.40, 95% CI 0.18-0.90). Overall survival was non-significantly better in MRISC patients (log-rank p = 0.064, HR 0.51, CI 0.24-1.06). Annual screening with MRI and mammography improves metastasis free survival in women with BRCA1 mutation or familial predisposition.

Breast cancer risk assessment in 8,824 women attending a family history evaluation and screening programme

Accurate individualized breast cancer risk assessment is essential to provide risk-benefit analysis prior to initiating interventions designed to lower breast cancer risk and start surveillance. We have previously shown that a manual adaptation of Claus tables was as accurate as the Tyrer-Cuzick model and more accurate at predicting breast cancer than the Gail, Claus model and Ford models. Here we reassess the manual model with longer follow up and higher numbers of cancers. Calibration of the manual model was assessed using data from 8,824 women attending the family history evaluation and screening programme in Manchester UK, with a mean follow up of 9.71 years. After exclusion of 40 prevalent cancers, 406 incident breast cancers occurred, and 385.1 were predicted (O/E = 1.05, 95 % CI 0.95-1.16) using the manual model. Predictions were close to that of observed cancers in all risk categories and in all age groups, including women in their forties (O/E = 0.99, 95 % CI 0.83-1.16). Manual risk prediction with use of adjusted Claus tables and curves with modest adjustment for hormonal and reproductive factors was a well-calibrated approach to breast cancer risk estimation in a UK family history clinic.

Cancer screening in the United States, 2015: a review of current American cancer society guidelines and current issues in cancer screening

Each year, the American Cancer Society (ACS) publishes a summary of its guidelines for early cancer detection along with a report on data and trends in cancer screening rates and select issues related to cancer screening. In this issue of the journal, we summarize current ACS cancer screening guidelines. The latest data on utilization of cancer screening from the National Health Interview Survey (NHIS) also is described, as are several issues related to screening coverage under the Affordable Care Act, including the expansion of the Medicaid program.

Core needle biopsy of breast cancer tumors increases distant metastases in a mouse model

INTRODUCTION: Incisional biopsies, including the diagnostic core needle biopsy (CNB), routinely performed before surgical excision of breast cancer tumors are hypothesized to increase the risk of metastatic disease. In this study, we experimentally determined whether CNB of breast cancer tumors results in increased distant metastases and examine important resultant changes in the primary tumor and tumor microenvironment associated with this outcome. METHOD: To evaluate the effect of CNB on metastasis development, we implanted murine mammary 4T1 tumor cells in BALB/c mice and performed CNB on palpable tumors in half the mice. Subsequently, emulating the human scenario, all mice underwent complete tumor excision and were allowed to recover, with attendant metastasis development. Tumor growth, lung metastasis, circulating tumor cell (CTC) levels, variation in gene expression, composition of the tumor microenvironment, and changes in immunologic markers were compared in biopsied and non-biopsied mice. RESULTS: Mice with biopsied tumors developed significantly more lung metastases compared to non-biopsied mice. Tumors from biopsied mice contained a higher frequency of myeloid-derived suppressor cells (MDSCs) accompanied by reduced CD4 + T cells, CD8 + T cells, and macrophages, suggesting biopsy-mediated development of an increasingly immunosuppressive tumor microenvironment. We also observed a CNB-dependent up-regulation in the expression of SOX4, Ezh2, and other key epithelial-mesenchymal transition (EMT) genes, as well as increased CTC levels among the biopsy group. CONCLUSION: CNB creates an immunosuppressive tumor microenvironment, increases EMT, and facilitates release of CTCs, all of which likely contribute to the observed increase in development of distant metastases.

Obesity is a significant risk factor for breast cancer in Arab women

Background

Breast cancer (BC) is the most common malignancy and the leading cause of cancer-related death amongst women worldwide. The risk factors of this disease are numerous, and their prevalence varies between racial and ethnic groups as well as geographical regions. Therefore, we sought to delineate the association of socio-demographic, reproductive and life-style related risk factors with breast cancer in the Arab population.

Methods

Unmatched case-control study was conducted in the kingdom of Saudi Arabia using 534 cases of histologically confirmed breast cancer and 638 controls. Controls were randomly selected from primary health care visits and were free of breast cancer. Unconditional logistic regression analysis was performed to estimate odds ratios (ORs) and to examine the predictive effect of each factor on risk for BC. All study participants were interviewed by trained interviewers at hospital (cases) or at primary health care centers (controls).

Results

A total of 1172 women were eligible for this study, of which 281 (24.0%) were aged ≤35 years, 22.9% illiterate, 43.6% employed, 89.5% married, and 38.1% were obese. Grade III tumors constituted 38.4% of cases. Tumor stage I was 7.5%; II, 50.7%; II, 30.9%; IV, 11.1%. We have shown strong association between breast cancer among Arab females and obesity (OR =2.29, 95% CI 1.68-3.13), positive family history of breast cancer (OR =2.31, 95% CI 1.60 – 3.32), the use of hormonal replacement therapy (OR =2.25, 95% CI 1.65 – 3.08), post-menopause (OR =1.72, 95% CI 1.25 – 2.38), lack of education (OR =9.09, 95% CI 5.88 – 14.29), and never breastfeed (OR =1.89, 95% CI 1.19 – 2.94).

Conclusion

These results indicate the presence of classical risk factors established in the western countries, and also some specific ones, which may result from genetic and/or environmental factors. Thereby, these findings will be of great value to establish adequate evidence-based awareness and preventative measures in the Arab world.

Pathogenesis, prevention, diagnosis and treatment of breast cancer

Breast cancer is the most common cancer affecting women worldwide. Prediction models stratify a woman's risk for developing cancer and can guide screening recommendations based on the presence of known and quantifiable hormonal, environmental, personal, or genetic risk factors. Mammography remains the mainstay breast cancer screening and detection but magnetic resonance imaging and ultrasound have become useful diagnostic adjuncts in select patient populations. The management of breast cancer has seen much refinement with increased specialization and collaboration with multidisciplinary teams that include surgeons, oncologists, radiation oncologists, nurses, geneticist, reconstructive surgeons and patients. Evidence supports a less invasive surgical approach to the staging and management of the axilla in select patients. In the era of patient/tumor specific management, the advent of molecular and genomic profiling is a paradigm shift in the treatment of a biologically heterogenous disease.

Breast cancer risk prediction accuracy in Jewish Israeli high-risk women using the BOADICEA and IBIS risk models

Several breast cancer risk prediction models have been validated in ethnically diverse populations, but none in Israeli high-risk women. To validate the accuracy of the IBIS and BOADICEA risk prediction models in Israeli high-risk women, the 10-year and lifetime risk for developing breast cancer were calculated using both BOADICEA and IBIS models for high-risk, cancer-free women, counselled at the Sheba Medical Center from 1 June 1996-31 May 2000. Women diagnosed with breast cancer by 31 May 2011 were identified from the Israeli National Cancer Registry. The observed to expected breast cancer ratios were calculated to evaluate the predictive value of both algorithms. Overall, 358 mostly (N = 205, 57·2%) Ashkenazi women, were eligible, age range at counselling was 20-75 years (mean 46·76 ± 9·8 years). Over 13·6 ± 1·45 years (range 11-16 years), 15 women (4·19%) were diagnosed with breast cancer, at a mean age of 57 ± 8·6 years. The 10-year risks assigned by BOADICEA and IBIS ranged from 0·2 to 12·6% and 0·89 to 21·7%, respectively. The observed:expected breast cancer ratio was 15/18·6 (0·8-95% CI 0·48-1·33) and 15/28·6 (0·52-95% CI 0·32-0·87), using both models, respectively. In Jewish Israeli high-risk women the BOADICEA model has a better predictive value and accuracy in determining 10-year breast cancer risk than the IBIS model.

Cancer screening in the United States, 2014: a review of current American Cancer Society guidelines and current issues in cancer screening

Answer questions and earn CME/CNE Each year the American Cancer Society publishes a summary of its guidelines for early cancer detection, a report on data and trends in cancer screening rates, and select issues related to cancer screening. In this issue of the journal, we summarize current American Cancer Society cancer screening guidelines. In addition, the latest data on the use of cancer screening from the National Health Interview Survey is described, as are several issues related to screening coverage under the Patient Protection and Affordable Care Act, including the expansion of the Medicaid program.

Identification, Evaluation, and Treatment of Patients with Hereditary Cancer Risk within the United States

Recognizing the importance of identifying patients at high risk for inherited cancer predisposition, the United States Preventive Services Task Force (USPSTF) has outlined specific family history patterns associated with an increased risk for BRCA mutations. However, national data indicate a need to facilitate the ability of primary care providers to appropriately identify high risk patients. Once a patient is identified as high risk, it is necessary for the patient to undergo a detailed genetics evaluation to generate a differential diagnosis, determine a cost-effective genetic testing strategy, and interpret results of testing. With identification of inherited predisposition, risk management strategies in line with national guidelines can be implemented to improve patient outcomes through cancer risk reduction and early detection. As use of genetic testing increasingly impacts patient outcomes, the role of primary care providers in the identification and care of individuals at high risk for hereditary cancer becomes even more important. Nevertheless it should be acknowledged that primary care providers face many competing demands and challenges to identify high risk patients. Therefore initiatives which promote multidisciplinary and coordinated care, potentially through academic-community partnerships, may provide an opportunity to enhance care of these patients.

Breast density quantification with cone-beam CT: a post-mortem study

Forty post-mortem breasts were imaged with a flat-panel based cone-beam x-ray CT system at 50 kVp. The feasibility of breast density quantification has been investigated using standard histogram thresholding and an automatic segmentation method based on the fuzzy c-means algorithm (FCM). The breasts were chemically decomposed into water, lipid, and protein immediately after image acquisition was completed. The per cent fibroglandular volume (%FGV) from chemical analysis was used as the gold standard for breast density comparison. Both image-based segmentation techniques showed good precision in breast density quantification with high linear coefficients between the right and left breast of each pair. When comparing with the gold standard using %FGV from chemical analysis, Pearson's r-values were estimated to be 0.983 and 0.968 for the FCM clustering and the histogram thresholding techniques, respectively. The standard error of the estimate was also reduced from 3.92% to 2.45% by applying the automatic clustering technique. The results of the postmortem study suggested that breast tissue can be characterized in terms of water, lipid and protein contents with high accuracy by using chemical analysis, which offers a gold standard for breast density studies comparing different techniques. In the investigated image segmentation techniques, the FCM algorithm had high precision and accuracy in breast density quantification. In comparison to conventional histogram thresholding, it was more efficient and reduced inter-observer variation.

Breast cancer in lesbians and bisexual women: systematic review of incidence, prevalence and risk studies

BACKGROUND

The UK Parliamentary Enquiry and USA Institute of Medicine state that lesbians may be at a higher risk of breast cancer but there is insufficient information. Lesbians and bisexual (LB) women have behavioural risk-factors at higher rates compared to heterosexuals such as increased alcohol intake and higher stress levels. Conversely, breast cancer rates are higher in more affluent women yet income levels in LB women are relatively low. This systematic review investigated all evidence on whether there is, or likely to be, higher rates of breast cancer in LB women.

METHODS

Cochrane library (CDSR, CENTRAL, HTA, DARE, NHSEED), MEDLINE, EMBASE, PsychINFO, CAB abstracts, Web of Science (SCI, SSCI), SIGLE and Social Care Online databases were searched to October 2013. Unpublished research and specific lesbian, gay and bisexual websites were checked, as were citation lists of relevant papers. Included were studies in LB populations reporting breast cancer incidence or prevalence rates, risk model results or risk-factor estimates. Inclusions, data-extraction and quality assessment were by two reviewers with disagreements resolved by discussion.

RESULTS

Searches found 198 references. No incidence rates were found. Nine studies gave prevalence estimates - two showed higher, four showed no differences, one showed mixed results depending on definitions, one had no comparison group and one gave no sample size. All studies were small with poor methodological and/or reporting quality. One incidence modelling study suggested a higher rate.Four risk modelling studies were found, one Rosner-Colditz and three Gail models. Three suggested higher and one lower rate in LB compared to heterosexual women. Six risk-factor estimates suggested higher risk and one no difference between LB and heterosexual women.

CONCLUSIONS

The only realistic way to establish rates in LB women would be to collect sexual orientation within routine statistics, including cancer registry data, or from large cohort studies.

Assessing breast cancer risk models in Marin County, a population with high rates of delayed childbirth

INTRODUCTION

This study was designed to compare the Breast Cancer Risk Assessment Tool (BCRAT; Gail), International Breast Intervention Study (IBIS; Tyrer-Cuzick), and BRCAPRO breast cancer risk assessment models using data from the Marin Women's Study, a cohort of women within Marin County, California, with high rates of breast cancer, nulliparity, and delayed childbirth. Existing models have not been well-validated in these high-risk populations.

METHODS

Discrimination was assessed using the area under the receiver operating characteristic curve (AUC) and calibration by estimating the ratio of expected-to-observed (E/O) cases. The models were assessed using data from 12,843 participants, of whom 203 had developed cancer during a 5-year period. All tests of statistical significance were 2-sided.

RESULTS

The IBIS model achieved an AUC of 0.65 (95% confidence interval [CI], 0.61-0.68) compared with 0.62 (95% CI, 0.59-0.66) for BCRAT and 0.60 (95% CI, 0.56-0.63) for BRCAPRO. The corresponding estimated E/O ratios for the models were 1.08 (95% CI, 0.95-1.25), 0.81 (95% CI, 0.71-0.93), and 0.59 (95% CI, 0.52-0.68). In women with age at first birth > 30 years, the AUC for the IBIS, BCRAT, and BRCAPRO models was 0.69 (95% CI, 0.62-0.75), 0.63 (95% CI, 0.56-0.70), and 0.62 (95% CI, 0.56-0.68) and the E/O ratio was 1.15 (95% CI, 0.89-1.47), 0.81 (95% CI, 0.63-1.05), and 0.53 (95% CI, 0.41-0.68), respectively.

CONCLUSIONS

The IBIS model was well calibrated for the high-risk Marin mammography population and demonstrated the best calibration of the 3 models in nulliparous women. The IBIS model also achieved the greatest overall discrimination and displayed superior discrimination for women with age at first birth > 30 years.

Cost-effectiveness of screening women with familial risk for breast cancer with magnetic resonance imaging

BACKGROUND

To reduce mortality, women with a family history of breast cancer are often screened with mammography before age 50 years. Additional magnetic resonance imaging (MRI) improves sensitivity and is cost-effective for BRCA1/2 mutation carriers. However, for women with a family history without a proven mutation, cost-effectiveness is unclear.

METHODS

We evaluated data of the largest prospective MRI screening study (MRISC). Between 1999 and 2007, 1597 women (8370 woman-years at risk) aged 25 to 70 years with an estimated cumulative lifetime risk of 15% to 50% for breast cancer were screened with clinical breast examination every 6 months and with annual mammography and MRI. We calculated the cost per detected and treated breast cancer. After incorporating MRISC data into a microsimulation screening analysis model (MISCAN), different schemes were evaluated, and cost per life-year gained (LYG) was estimated in comparison with the Dutch nationwide breast cancer screening program (biennial mammography from age 50 to 75 years). All statistical tests were two-sided.

RESULTS

Forty-seven breast cancers (9 ductal carcinoma in situ) were detected. Screening with additional MRI costs $123 672 (€93 639) per detected breast cancer. In increasing age-cohorts, costs per detected and treated breast cancer decreased, but, unexpectedly, the percentage of MRI-only detected cancers increased. Screening under the MRISC-scheme from age 35 to 50 years was estimated to reduce breast cancer mortality by 25% at $134 932 (€102 164) per LYG (3.5% discounting) compared with 17% mortality reduction at $54 665 (€41 390) per LYG with mammography only.

CONCLUSIONS

Screening with MRI may improve survival for women with familial risk for breast cancer but is expensive, especially in the youngest age categories.

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

Women from high-risk families consider preventive measures for breast cancer including screening. Guidelines on screening differ considerably regarding starting age. We investigated whether age at diagnosis in affected relatives is predictive for age at diagnosis. We analyzed the age of breast cancer detection of 1,304 first- and second-degree relatives of 314 BRCA1, 164 BRCA2 and 244 high-risk participants of the Dutch MRI-SCreening study. The within- and between-family variance in the relative's age at diagnosis was analyzed with a random effect linear regression model. We compared the starting age of screening based on risk-group (25 years for BRCA1, 30 years for BRCA2 and 35 years for familial risk), on family history, and on the model, which combines both. The findings were validated in 63 families from the UK-MARIBS study. Mean age at diagnosis in the relatives varied between families; 95% range of mean family ages was 35-55 in BRCA1-, 41-57 in BRCA2- and 44-60 in high-risk families. In all, 14% of the variance in age at diagnosis, in BRCA1 even 23%, was explained by family history, 7% by risk group. Determining start of screening based on the model and on risk-group gave similar results in terms of cancers missed and years of screening. The approach based on familial history only, missed more cancers and required more screening years in both the Dutch and the United Kingdom data sets. Age at breast cancer diagnosis is partly dependent on family history which may assist planning starting age for preventive measures.

Practical aspects of genetic counseling in breast cancer: lights and shadows

In unselected populations, less than 10% of breast cancers are associated with germline mutations in predisposing genes. Breast cancer type 1 and 2 (BRCA1 and BRCA2) susceptibility genes are the most common involved genes and confer a 10-30 times higher risk of developing the disease compared to the general population. A personal or family history suggestive of inherited breast cancer syndrome may be further evaluated to assess the risk of genetic predisposition and the presence of a genetic mutation. Breast cancer genetic counseling should include a careful risk assessment with associated psychosocial evaluation and support, possible molecular testing, personalized discussion of results. Knowledge of BRCA status can influence individualized cancer risk-reduction strategies. i.e. active surveillance, prophylactic surgery and/or pharmacoprevention.

Intrafamilial disclosure of risk for hereditary breast and ovarian cancer: points to consider

The primary goal of breast and ovarian cancer screening is to minimize the cases of advanced disease and therefore its mortality rate. For hereditary breast and ovarian cancer, one method to reach this goal is to disseminate genetic risk information among family members. However, experience tells us that this information does not always reach family members in a timely manner, if at all. There are many moving parts to a decision to disclose genetic risk information within a family, and the lack of detail and cohesion in current guidelines do a disservice to hereditary breast cancer prevention. Utilizing legal, medical, and policy databases for literature, case law and policy documents relating to communication of genetic test results within families, as well as a consultative process with representative stakeholders, a points to consider has been developed to address a number of issues that might impact the ability and willingness of patients to inform family members of genetic risk. These include: what is "genetic information"; who is the "family"; why should patients inform their family members; and how should health professionals be involved in this process? This represents only an initial step towards fostering better communication within families. Additional research is needed to determine the best methods for encouraging this communication and motivations for disclosing or not and to promote the development of a solution, considering the complexity of human relationships and the probabilistic nature of genetic information.

Cancer screening in the United States, 2013: a review of current American Cancer Society guidelines, current issues in cancer screening, and new guidance on cervical cancer screening and lung cancer screening

Each year the American Cancer Society (ACS) publishes a summary of its recommendations for early cancer detection, a report on data and trends in cancer screening rates, and select issues related to cancer screening. In this issue of the journal, current ACS cancer screening guidelines are summarized, as are updated guidelines on cervical cancer screening and lung cancer screening with low-dose helical computed tomography. The latest data on the use of cancer screening from the National Health Interview Survey also are described, as are several issues related to screening coverage under the Patient Protection and Affordable Care Act of 2010.

Personalized medicine and access to health care: potential for inequitable access?

Personalized medicine promises that an individual's genetic information will be increasingly used to prioritize access to health care. Use of genetic information to inform medical decision making, however, raises questions as to whether such use could be inequitable. Using breast cancer genetic risk prediction models as an example, on the surface clinical use of genetic information is consistent with the tools provided by evidence-based medicine, representing a means to equitably distribute limited health-care resources. However, at present, given limitations inherent to the tools themselves, and the mechanisms surrounding their implementation, it becomes clear that reliance on an individual's genetic information as part of medical decision making could serve as a vehicle through which disparities are perpetuated under public and private health-care delivery models. The potential for inequities arising from using genetic information to determine access to health care has been rarely discussed. Yet, it raises legal and ethical questions distinct from those raised surrounding genetic discrimination in employment or access to private insurance. Given the increasing role personalized medicine is forecast to play in the provision of health care, addressing a broader view of what constitutes genetic discrimination, one that occurs along a continuum and includes inequitable access, will be needed during the implementation of new applications based on individual genetic profiles. Only by anticipating and addressing the potential for inequitable access to health care occurring from using genetic information will we move closer to realizing the goal of personalized medicine: to improve the health of individuals.

Can the Gail model increase the predictive value of a positive mammogram in a European population screening setting? Results from a Spanish cohort

AIMS OF THE STUDY

The Gail Model (GM) is the most well-known model to assess the individual risk of breast cancer (BC). Although its discriminatory accuracy is low in the clinical context, its usefulness in the screening setting is not well known. The aim of this study is to assess the utility of the GM in a European screening program.

METHODS

Retrospective cohort study of 2200 reassessed women with information on the GM available in a BC screening program in Barcelona, Spain. The 5 year-risk of BC applying the GM right after the screening mammogram was compared first with the actual woman's risk of BC in the same screening round and second with the BC risk during the next 5 years.

RESULTS

The curves of BC Gail risk overlapped for women with and without BC, both in the same screening episode as well as 5 years afterward. Overall sensitivity and specificity in the same screening episode were 22.3 and 86.5%, respectively, and 46.2 and 72.1% 5 years afterward. ROC curves were barely over the diagonal and the concordance statistics were 0.59 and 0.61, respectively.

CONCLUSION

The GM has very low accuracy among women with a positive mammogram result, predicting BC both in the concomitant episode and 5 years later. Our results do not encourage the use of the GM in the screening context to aid the referral decision or the type of procedures after a positive mammogram or to identify women at high risk among those with a false-positive outcome.

Breast cancer risk assessment across the risk continuum: genetic and nongenetic risk factors contributing to differential model performance

Introduction

Clinicians use different breast cancer risk models for patients considered at average and above-average risk, based largely on their family histories and genetic factors. We used longitudinal cohort data from women whose breast cancer risks span the full spectrum to determine the genetic and nongenetic covariates that differentiate the performance of two commonly used models that include nongenetic factors - BCRAT, also called Gail model, generally used for patients with average risk and IBIS, also called Tyrer Cuzick model, generally used for patients with above-average risk.

Methods

We evaluated the performance of the BCRAT and IBIS models as currently applied in clinical settings for 10-year absolute risk of breast cancer, using prospective data from 1,857 women over a mean follow-up length of 8.1 years, of whom 83 developed cancer. This cohort spans the continuum of breast cancer risk, with some subjects at lower than average population risk. Therefore, the wide variation in individual risk makes it an interesting population to examine model performance across subgroups of women. For model calibration, we divided the cohort into quartiles of model-assigned risk and compared differences between assigned and observed risks using the Hosmer-Lemeshow (HL) chi-squared statistic. For model discrimination, we computed the area under the receiver operator curve (AUC) and the case risk percentiles (CRPs).

Results

The 10-year risks assigned by BCRAT and IBIS differed (range of difference 0.001 to 79.5). The mean BCRAT- and IBIS-assigned risks of 3.18% and 5.49%, respectively, were lower than the cohort's 10-year cumulative probability of developing breast cancer (6.25%; 95% confidence interval (CI) = 5.0 to 7.8%). Agreement between assigned and observed risks was better for IBIS (HL X₄² = 7.2, P value 0.13) than BCRAT (HL X₄² = 22.0, P value <0.001). The IBIS model also showed better discrimination (AUC = 69.5%, CI = 63.8% to 75.2%) than did the BCRAT model (AUC = 63.2%, CI = 57.6% to 68.9%). In almost all covariate-specific subgroups, BCRAT mean risks were significantly lower than the observed risks, while IBIS risks showed generally good agreement with observed risks, even in the subgroups of women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative).

Conclusions

Models developed using extended family history and genetic data, such as the IBIS model, also perform well in women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative). Extending such models to include additional nongenetic information may improve performance in women across the breast cancer risk continuum.

A case-control study on risk factors of breast cancer in China

INTRODUCTION

To screen the risk factors associated with breast cancer among Chinese women in order to evaluate the individual risk of developing breast cancer among women in China.

MATERIAL AND METHODS

A case-control study on 416 breast cancer patients and 1156 matched controls was conducted in 14 hospitals in 8 provinces of China in 2008. Controls were age- and region-matched to the cases. Clinicians conducted in-person interviews with the subjects to collect information on demographics and suspected risk factors for breast cancer that are known worldwide. Conditional logistic regression was used to derive odds ratios (OR) and 95% confidence intervals (CI) for the associations between risk factors and breast cancer.

RESULTS

Compared with matched controls, women with breast cancer were significantly more likely to have higher body mass index (BMI, OR = 4.07, 95% CI: 2.98-5.55), history of benign breast disease (BBD) biopsy (OR = 1.68, 95% CI: 1.19-2.38), older age of menarche (AOM) (OR = 1.41, 95% CI: 1.07-1.87), stress anticipation (SA), for grade 1-4, OR = 2.15, 95% CI: 1.26-3.66; for grade 5-9, OR = 3.48, 95% CI: 2.03-5.95) and menopause (OR = 2.22, 95% CI: 1.50-3.282) at the level of p < 0.05. Family history of breast cancer (FHBC) in first-degree relatives (OR = 1.66, 95% CI: 0.77-3.59) and use of oral contraceptives (OC) (OR = 1.59, 95% CI: 0.83-3.05) were associated with an increased risk of breast cancer at the level of p < 0.20.

CONCLUSIONS

Our results showed that BMI, history of BBD biopsy, older AOM, SA and menopause were associated with increased risk of breast cancer among Chinese women. The findings derived from the study provided some suggestions for population-based prevention and control of breast cancer in China.

Prevention of breast cancer in the context of a national breast screening programme

Breast cancer is not only increasing in the west but also particularly rapidly in eastern countries where traditionally the incidence has been low. The rise in incidence is mainly related to changes in reproductive patterns and lifestyle. These trends could potentially be reversed by defining women at greatest risk and offering appropriate preventive measures. A model for this approach was the establishment of Family History Clinics (FHCs), which have resulted in improved survival in younger women at high risk. New predictive models of risk that include reproductive and lifestyle factors, mammographic density and measurement of risk-associated single nucleotide polymorphisms (SNPs) may give more precise information concerning risk and enable better targeting for mammographic screening programmes and of preventive measures. Endocrine prevention using anti-oestrogens and aromatase inhibitors is effective, and observational studies suggest lifestyle modification may also be effective. However, referral to FHCs is opportunistic and predominantly includes younger women. A better approach for identifying older women at risk may be to use national breast screening programmes. Here were described pilot studies to assess whether the routine assessment of breast cancer risk is feasible within a population-based screening programme, whether the feedback and advice on risk-reducing interventions would be welcomed and taken up, and to consider whether the screening interval should be modified according to breast cancer risk.

Economic evaluation of targeted cancer interventions: critical review and recommendations

Scientific advances have improved our ability to target cancer interventions to individuals who will benefit most and spare the risks and costs to those who will derive little benefit or even be harmed. Several approaches are currently used for targeting interventions for cancer risk reduction, screening, and treatment, including risk prediction algorithms for identifying high-risk subgroups and diagnostic tests for tumor markers and germline genetic mutations. Economic evaluation can inform decisions about the use of targeted interventions, which may be more costly than traditional strategies. However, assessing the impact of a targeted intervention on costs and health outcomes requires explicit consideration of the method of targeting. In this study, we describe the importance of this principle by reviewing published cost-effectiveness analyses of targeted interventions in breast cancer. Few studies we identified explicitly evaluated the relationships among the method of targeting, the accuracy of the targeting test, and outcomes of the targeted intervention. Those that did found that characteristics of targeting tests had a substantial impact on outcomes. We posit that the method of targeting and the outcomes of a targeted intervention are inextricably linked and recommend that cost-effectiveness analyses of targeted interventions explicitly consider costs and outcomes of the method of targeting.

Genetics, genomics, and cancer risk assessment: State of the Art and Future Directions in the Era of Personalized Medicine

Scientific and technologic advances are revolutionizing our approach to genetic cancer risk assessment, cancer screening and prevention, and targeted therapy, fulfilling the promise of personalized medicine. In this monograph, we review the evolution of scientific discovery in cancer genetics and genomics, and describe current approaches, benefits, and barriers to the translation of this information to the practice of preventive medicine. Summaries of known hereditary cancer syndromes and highly penetrant genes are provided and contrasted with recently discovered genomic variants associated with modest increases in cancer risk. We describe the scope of knowledge, tools, and expertise required for the translation of complex genetic and genomic test information into clinical practice. The challenges of genomic counseling include the need for genetics and genomics professional education and multidisciplinary team training, the need for evidence-based information regarding the clinical utility of testing for genomic variants, the potential dangers posed by premature marketing of first-generation genomic profiles, and the need for new clinical models to improve access to and responsible communication of complex disease risk information. We conclude that given the experiences and lessons learned in the genetics era, the multidisciplinary model of genetic cancer risk assessment and management will serve as a solid foundation to support the integration of personalized genomic information into the practice of cancer medicine.