Evaluation of mathematical models for breast cancer risk assessment in routine clinical use.

Chemoprevention, prophylactic surgery, and intensified screening can be offered to patients with an increased lifetime risk, p(life), for breast cancer. Estimation of p(life) includes BRCA analysis and risk estimation based on individual risk factors and family history. MENDEL and BRCAPRO are models that estimate the probability of BRCA1/2-mutations, p(mut), and p(life). In this study, the models are compared with Ford and Claus penetrance/frequency functions. The results were compared with the Tyrer-Cuzick model. Genetic analysis of 111 breast cancer-affected patients from 103 kindreds with a family history of breast and/or ovarian cancer (German Consortium for Hereditary Breast and Ovarian Cancer) was carried out by sequencing BRCA1 and BRCA2. p(life) and p(mut) were calculated with MENDEL, BRCAPRO(Claus), BRCAPRO(Ford), as well as the Tyrer-Cuzick model. The accuracy of p(mut) was analyzed by receiver operating characteristics, and p(life) of each model was compared. The strongest correlation of p(life) was shown by BRCAPRO(Ford)/MENDEL, at r=0.69; no correlation was shown by BRCAPRO(Claus)/MENDEL, at r=0.018. The Tyrer-Cuzick model had the strongest correlations with MENDEL and BRCAPRO(Ford). For MENDEL and BRCAPRO, low correlation or p(mut)-prediction was improved by excluding kindreds with ovarian cancer. p(mut) showed the best accuracy for BRCAPRO(Ford) and MENDEL. BRCAPRO and MENDEL are useful tools for calculating p(mut). They can provide support in decision-making for or against genetic analysis. Estimations of p(life) and p(mut) based on a mathematical model should use algorithms and penetrance/frequency data appropriate to the population counseled. Reproductive/hormonal data, should be incorporated as Tyrer-Cuzick does.     

Telethermography and breast cancer risk prediction

The authors report on 4,624 noncancer women classified by telethermography (TH1-2 vs TH3) and followed for an average of 6.6 years (range, 2-12). Breast cancer occurring beyond the sixth month from TH were recorded according to a Cancer Registry, and the association between breast cancer incidence and thermographic class or patient age was evaluated. Univariate analysis showed a significant association of age and thermography with further cancer incidence, but multivariate analysis (Cox's model) confirmed a significant association only for age. Thermography (TH3) showed a nonsignificant odds ratio of 1.6 with respect to TH1-2 cases. Thus thermography did not show any practical role as a breast cancer risk indicator. Possible biases affecting previous reports suggesting the use of thermography as a breast cancer risk indicator are discussed.     

Risk prediction models for familial breast cancer

A positive family history of breast cancer, reflecting genetic susceptibility, is one of the strongest risk factors for the disease. A number of breast cancer susceptibility genes have been identified to date, with the most important being BRCA1 and BRCA2. Risk prediction models can be used to identify individuals likely to carry BRCA1 and BRCA2 mutations and individuals at high risk of developing the disease. This information can then be used to target genetic testing, screening and interventions more effectively. In this article, the authors review the risk models that have been developed for familial breast cancer and discuss their applicability, strengths and weaknesses, and present examples of classifying women into risk categories according to the predictions by the various models. The review concludes with a discussion of the ways in which risk models could be improved in the immediate- and long-term future.     

Dynamic prediction in breast cancer: proving feasibility in clinical practice using the TEAM trial

BackgroundPredictive models are an integral part of current clinical practice and help determine optimal treatment strategies for individual patients. A drawback is that covariates are assumed to have constant effects on overall survival (OS), when in fact, these effects may change during follow-up (FU). Furthermore, breast cancer (BC) patients may experience events that alter their prognosis from that time onwards. We investigated the ‘dynamic’ effects of different covariates on OS and developed a nomogram to calculate 5-year dynamic OS (DOS) probability at different prediction timepoints (t_P) during FU.MethodsDutch and Belgian postmenopausal, endocrine-sensitive, early BC patients enrolled in the TEAM trial were included. We assessed time-varying effects of specific covariates and obtained 5-year DOS predictions using a proportional baselines landmark supermodel. Covariates included age, histological grade, hormone receptor and HER2 status, T- and N-stage, locoregional recurrence (LRR), distant recurrence, and treatment compliance. A nomogram was designed to calculate 5-year DOS based on individual characteristics.ResultsA total of 2602 patients were included (mean FU 6.2 years). N-stage, LRR, and HER2 status demonstrated time-varying effects on 5-year DOS. Hazard ratio (HR) functions for LRR, high-risk N-stage (N2/3), and HER2 positivity were HR = (8.427 × 0.583^t_P, HR = (3.621 × 0.816^t_P, and HR = (1.235 × 0.851^t_P, respectively. Treatment discontinuation was associated with a higher mortality risk, but without a time-varying effect [HR 1.263 (0.867–1.841)]. All other covariates were time-constant.DiscussionThe current nomogram accounts for elapsed time since starting adjuvant endocrine treatment and optimizes prediction of individual 5-year DOS during FU for postmenopausal, endocrine-sensitive BC patients. The nomogram can facilitate in determining whether further therapy will benefit an individual patient, although validation in an independent dataset is still needed.     

Mammographic density,breast cancer risk and risk prediction

In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models.     

Optimized outcome prediction in breast cancer by combining the 70-gene signature with clinical risk prediction algorithms

Clinical guidelines for breast cancer treatment differ in their selection of patients at a high risk of recurrence who are eligible to receive adjuvant systemic treatment (AST). The 70-gene signature is a molecular tool to better guide AST decisions. The aim of this study was to evaluate whether adding the 70-gene signature to clinical risk prediction algorithms can optimize outcome prediction and consequently treatment decisions in early stage, node-negative breast cancer patients. A 70-gene signature was available for 427 patients participating in the RASTER study (cT1-3N0M0). Median follow-up was 61.6 months. Based on 5-year distant-recurrence free interval (DRFI) probabilities survival areas under the curve (AUC) were calculated and compared for risk estimations based on the six clinical risk prediction algorithms: Adjuvant! Online (AOL), Nottingham Prognostic Index (NPI), St. Gallen (2003), the Dutch National guidelines (CBO 2004 and NABON 2012), and PREDICT plus. Also, survival AUC were calculated after adding the 70-gene signature to these clinical risk estimations. Systemically untreated patients with a high clinical risk estimation but a low risk 70-gene signature had an excellent 5-year DRFI varying between 97.1 and 100 %, depending on the clinical risk prediction algorithms used in the comparison. The best risk estimation was obtained in this cohort by adding the 70-gene signature to CBO 2012 (AUC: 0.644) and PREDICT (AUC: 0.662). Clinical risk estimations by all clinical algorithms improved by adding the 70-gene signature. Patients with a low risk 70-gene signature have an excellent survival, independent of their clinical risk estimation. Adding the 70-gene signature to clinical risk prediction algorithms improves risk estimations and therefore might improve the identification of early stage node-negative breast cancer patients for whom AST has limited value. In this cohort, the PREDICT plus tool in combination with the 70-gene signature provided the best risk prediction.     

A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance

A risk prediction model is a statistical tool for estimating the probability that a currently healthy individual with specific risk factors will develop a condition in the future such as breast cancer. Reliably accurate prediction models can inform future disease burdens, health policies and individual decisions. Breast cancer prediction models containing modifiable risk factors, such as alcohol consumption, BMI or weight, condom use, exogenous hormone use and physical activity, are of particular interest to women who might be considering how to reduce their risk of breast cancer and clinicians developing health policies to reduce population incidence rates. We performed a systematic review to identify and evaluate the performance of prediction models for breast cancer that contain modifiable factors. A protocol was developed and a sensitive search in databases including MEDLINE and EMBASE was conducted in June 2010. Extensive use was made of reference lists. Included were any articles proposing or validating a breast cancer prediction model in a general female population, with no language restrictions. Duplicate data extraction and quality assessment were conducted. Results were summarised qualitatively, and where possible meta-analysis of model performance statistics was undertaken. The systematic review found 17 breast cancer models, each containing a different but often overlapping set of modifiable and other risk factors, combined with an estimated baseline risk that was also often different. Quality of reporting was generally poor, with characteristics of included participants and fitted model results often missing. Only four models received independent validation in external data, most notably the ‘Gail 2’ model with 12 validations. None of the models demonstrated consistently outstanding ability to accurately discriminate between those who did and those who did not develop breast cancer. For example, random-effects meta-analyses of the performance of the ‘Gail 2’ model showed the average C statistic was 0.63 (95% CI 0.59–0.67), and the expected/observed ratio of events varied considerably across studies (95% prediction interval for E/O ratio when the model was applied in practice was 0.75–1.19). There is a need for models with better predictive performance but, given the large amount of work already conducted, further improvement of existing models based on conventional risk factors is perhaps unlikely. Research to identify new risk factors with large additionally predictive ability is therefore needed, alongside clearer reporting and continual validation of new models as they develop.     

乳腺癌风险评估模型的研究进展

具有乳腺癌高危因素的女性可通过风险评估模型计算乳腺癌的发生风险,以BRCA基因突变检测为基础的基因模型准确性更高,经验模型也有其不可替代的优点而被广泛应用.     

二代测序检测乳腺癌易感基因在乳腺癌发病风险预测及临床治疗中的应用

  目的  通过二代测序技术筛选乳腺癌易感基因突变位点,探讨其对乳腺癌风险预测、临床干预及预后指导的意义。  方法  采取2013年11月至2015年7月272例就诊天津医科大学肿瘤医院的乳腺癌患者146例、高危人群71例及健康者55例3组样本的外周血,采用二代测序技术进行DNA检测。采用Amplicon方法筛选BRCA1、BRCA2、PTEN、STK11、TP53及BAP1全外显子区域有价值的突变位点,分析3组的基因突变发生率及乳腺癌患者易感基因突变与临床病理特征之间的相关性。  结果  经筛选获得177个突变位点,去重后共得到67个突变位点,其中包括50个单核苷酸突变（single nucleotide variants,SNVs）、8个无义突变（non-sense mutation）和9个插入缺失突变（insertion-deletions,InDels）。3组突变位点中31个突变位点收录于ExAC数据库,40个突变位点收录于Clin Var数据库。本研究中21个新发现的突变位点在ExAC、Clin Var或db SNP数据库中均未提及。统计分析发现85.1%（57/67）的突变发生于乳腺癌患者及高危人群中,且突变阳性乳腺癌患者有较高的淋巴结转移率（P=0.010）及病理分期（P=0.002）,致病性突变的乳腺癌患者的肿瘤家族史（P=0.005）及三阴性乳腺癌比例（P=0.009）均高于非致病性突变者。  结论  乳腺癌易感基因突变位点在乳腺癌风险预测、临床治疗及预后评价方面具有重要意义。      

循证医学证据对乳腺癌临床实践的影响

循证医学是遵循证据的医学,提倡将医生的个人临床实践和经验与从外部得到的最好的临床证据以及患者的意愿和要求结合起来,为患者的诊治做出最佳决策。循证医学越来越深刻地影响乳腺癌诊治的临床实践。来自于大型随机对照研究的循证医学证据可以改变临床实践,并为其提供更多选择。但是,也可能仅仅是证据,并不改变临床实践。     

A model for individualized risk prediction of contralateral breast cancer

Purpose

Patients diagnosed with invasive breast cancer (BC) or ductal carcinoma in situ are increasingly choosing to undergo contralateral prophylactic mastectomy (CPM) to reduce their risk of contralateral BC (CBC). This is a particularly disturbing trend as a large proportion of these CPMs are believed to be medically unnecessary. Many BC patients tend to substantially overestimate their CBC risk. Thus, there is a pressing need to educate patients effectively on their CBC risk. We develop a CBC risk prediction model to aid physicians in this task.

Methods

We used data from two sources: Breast Cancer Surveillance Consortium and Surveillance, Epidemiology, and End Results to build the model. The model building steps are similar to those used in developing the BC risk assessment tool (popularly known as Gail model) for counseling women on their BC risk. Our model, named CBCRisk, is exclusively designed for counseling women diagnosed with unilateral BC on the risk of developing CBC.

Results

We identified eight factors to be significantly associated with CBC—age at first BC diagnosis, anti-estrogen therapy, family history of BC, high-risk pre-neoplasia status, estrogen receptor status, breast density, type of first BC, and age at first birth. Combining the relative risk estimates with the relevant hazard rates, CBCRisk projects absolute risk of developing CBC over a given period.

Conclusions

By providing individualized CBC risk estimates, CBCRisk may help in counseling of BC patients. In turn, this may potentially help alleviate the rate of medically unnecessary CPMs.

     

Breast self-examination practice and clinical stage of breast cancer

The relationship between breast self-examination (BSE) and pre-treatment clinical stage of breast cancer was assessed in female breast cancer patients consisting of 30 patients practicing BSE monthly, 60 patients practicing occasionally, and 60 patients who had rarely or never practiced BSE. These patients were matched by age, residence, and hospital. More frequent practice of BSE was associated with more favorable clinical stage. The percentage of stage I patients was 33% for monthly performers, and this was significantly higher than the value of 14% for those who had rarely or never practiced. BSE practice was significantly related to tumor size measured before treatment in a dose-response manner. The average values of maximum tumor diameter were 2.5 cm for monthly performers, 3.0 cm for occasional performers, and 3.5 cm for those who had rarely or never practiced. The results suggest that BSE practice increases the probability of detecting smaller cancers at an early stage, and this may lead to a more favorable prognosis.     

Chemotherapy dose reduction and delay in clinical practice. evaluating the risk to patient outcome in adjuvant chemotherapy for breast cancer

Randomised clinical trials demonstrate the importance of maintaining chemotherapy dose and dose intensity in the systemic adjuvant treatment of breast cancer, and show that the strategies of dose delay and dose reduction carry the risk of suboptimal outcome. Such dose modifications are usually necessitated by the myelosuppressive effects, specifically neutropenia, thrombocytopenia and anaemia, resulting from the previous cycle of chemotherapy. The Canadian Database Initiative was designed to determine the incidence of neutropenic complications (an episode of febrile neutropenia or dose delay or reduction) and the frequency of complications by cycle of therapy using data from patients with breast cancer treated at centres across Canada. The centres used a variety of adjuvant chemotherapy regimens and the database covered the treatment of 444 patients, average age 47.7 years, who were treated between 1991 and 1996. Across all chemotherapy regimens, 42% of patients experienced at least one complication. Of those, 72% went on to have additional complications in subsequent cycles. The neutropenic complications usually occurred early in the treatment schedule.     

Incorporating tumour pathology information into breast cancer risk prediction algorithms

Introduction  

Mutations in BRCA1 and BRCA2 confer high risks of breast cancer and ovarian cancer. The risk prediction algorithm BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) may be used to compute the probabilities of carrying mutations in BRCA1 and BRCA2 and help to target mutation screening. Tumours from BRCA1 and BRCA2 mutation carriers display distinctive pathological features that could be used to better discriminate between BRCA1 mutation carriers, BRCA2 mutation carriers and noncarriers. In particular, oestrogen receptor (ER)-negative status, triple-negative (TN) status, and expression of basal markers are predictive of BRCA1 mutation carrier status.     

Impact of biomarkers on clinical trial risk in breast cancer

We determined the success rate of new drug approval by the US FDA in two breast cancer indications, one of which used a biomarker. This allowed us to assess if biomarkers improved clinical trial risk in breast cancer. We performed a retrospective screening of industry-sponsored drug development programs registered on clinicaltrials.gov from 1998 to 2012 for HER2-positive patients compared to patients that had either failed or had been exposed to anthracycline or taxane, whose first phase I in this indication occurred no earlier than 1998. Compounds not registered on clinicaltrials.gov and studied exclusively outside the US were excluded. Twenty-nine drugs for HER2-positive patients and 28 drugs for anthracycline/taxane-exposed patients met our screening criteria. The overall success rate of new drug development in anthracycline/taxane patients was only 15?%, while in HER2-positive patients it was 23?%. However, HER2-targeted therapies underperformed compared to broad acting agents. The cost for clinical trial testing alone, when adjusted for the risk of failure, for HER2-positive breast cancer patients was $199 million, significantly lower than the cost of $274 million for anthracycline/taxane-experienced patients. The use of a validated biomarker, such as HER2, reduced clinical trial risk by as much as 50?% resulting in cost savings of 27?% in advanced and metastatic breast cancer. However, these data have to be evaluated in a context in which studies combining a novel drug with a novel biomarker not yet recognized by the FDA may actually increase clinical trial risk.