Comparing 5-Year and Lifetime Risks of Breast Cancer using the Prospective Family Study Cohort

Clinical implications of incorporating genetic and non-genetic risk factors in CanRisk-based breast cancer risk prediction

BACKGROUND

Breast cancer (BC) risk prediction models consider cancer family history (FH) and germline pathogenic variants (PVs) in risk genes. It remains elusive to what extent complementation with polygenic risk score (PRS) and non-genetic risk factor (NGRFs) data affects individual intensified breast surveillance (IBS) recommendations according to European guidelines.

METHODS

For 425 cancer-free women with cancer FH (mean age 40·6 years, range 21-74), recruited in France, Germany and the Netherlands, germline PV status, NGRFs, and a 306 variant-based PRS (PRS306) were assessed to calculate estimated lifetime risks (eLTR) and estimated 10-year risks (e10YR) using CanRisk. The proportions of women changing country-specific European risk categories for IBS recommendations, i.e. ≥20 % and ≥30 % eLTR, or ≥5 % e10YR were determined.

FINDINGS

Of the women with non-informative PV status, including PRS306 and NGRFs changed clinical recommendations for 31·0 %, (57/184, 20 % eLTR), 15·8 % (29/184, 30 % eLTR) and 22·4 % (41/183, 5 % e10YR), respectively whereas of the women tested negative for a PV observed in their family, clinical recommendations changed for 16·7 % (25/150), 1·3 % (2/150) and 9·5 % (14/147). No change was observed for 82 women with PVs in high-risk genes (BRCA1/2, PALB2). Combined consideration of eLTRs and e10YRs identified BRCA1/2 PV carriers benefitting from IBS <30 years, and women tested non-informative/negative for whom IBS may be postponed.

INTERPRETATION

For women who tested non-informative/negative, PRS and NGRFs have a considerable impact on IBS recommendations. Combined consideration of eLTRs and e10YRs allows personalizing IBS starting age.

FUNDING

Horizon 2020, German Cancer Aid, Federal Ministry of Education and Research, Köln Fortune.

Validation of a Genetic-Enhanced Risk Prediction Model for Colorectal Cancer in a Large Community-Based Cohort

BACKGROUND

Polygenic risk scores (PRS) which summarize individuals' genetic risk profile may enhance targeted colorectal cancer screening. A critical step towards clinical implementation is rigorous external validations in large community-based cohorts. This study externally validated a PRS-enhanced colorectal cancer risk model comprising 140 known colorectal cancer loci to provide a comprehensive assessment on prediction performance.

METHODS

The model was developed using 20,338 individuals and externally validated in a community-based cohort (n = 85,221). We validated predicted 5-year absolute colorectal cancer risk, including calibration using expected-to-observed case ratios (E/O) and calibration plots, and discriminatory accuracy using time-dependent AUC. The PRS-related improvement in AUC, sensitivity and specificity were assessed in individuals of age 45 to 74 years (screening-eligible age group) and 40 to 49 years with no endoscopy history (younger-age group).

RESULTS

In European-ancestral individuals, the predicted 5-year risk calibrated well [E/O = 1.01; 95% confidence interval (CI), 0.91-1.13] and had high discriminatory accuracy (AUC = 0.73; 95% CI, 0.71-0.76). Adding the PRS to a model with age, sex, family and endoscopy history improved the 5-year AUC by 0.06 (P < 0.001) and 0.14 (P = 0.05) in the screening-eligible age and younger-age groups, respectively. Using a risk-threshold of 5-year SEER colorectal cancer incidence rate at age 50 years, adding the PRS had a similar sensitivity but improved the specificity by 11% (P < 0.001) in the screening-eligible age group. In the younger-age group it improved the sensitivity by 27% (P = 0.04) with similar specificity.

CONCLUSIONS

The proposed PRS-enhanced model provides a well-calibrated 5-year colorectal cancer risk prediction and improves discriminatory accuracy in the external cohort.

IMPACT

The proposed model has potential utility in risk-stratified colorectal cancer prevention.

Breast cancer risk predictions by birth cohort and ethnicity in a population-based screening mammography program

OBJECTIVES

To examine whether birth cohorts affect the risk of breast cancer for East Asian, First Nations, African, South Asian and Caucasian ethnicities in British Columbia (BC).

METHODS

We used Cox PH models adjusted for well-known risk factors, such as age, breast density, mammographic features on false positives, and family history, to examine risk of breast cancer among East Asian, First Nations, African and South Asian ethnicities, relative to Caucasian, across three birth cohorts.

RESULTS

There were 813,280 participants and 11,166 in situ and invasive breast cancer diagnoses. East Asians screened in BC were found to have a lower risk of breast cancer in the birth cohort born pre-1946 compared to Caucasian, but there was no statistically significant decrease for East Asians born after 1946. First Nations had an increased risk of breast cancer compared with Caucasian for all birth cohorts ranging from 1.1 to 2.0x the risk, which was statistically significant for those born after 1965. South Asians showed a statistically significant decrease in risk ranging from 0.58 to 0.81x lower compared with Caucasians for all birth cohorts.

CONCLUSION

Risk of breast cancer for South Asians living in BC was found to be lower than Caucasians for each birth cohort examined, while East Asians had a comparable risk of breast cancer, First Nations had a consistently higher risk than Caucasians.

ADVANCES IN KNOWLEDGE

When accounting for birth cohort, compared to Caucasians, South Asians have a decreased risk, First Nations have an increased risk, and East Asians have a similar risk of breast cancer.

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 &lt; .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 &lt; .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.

Cumulative Advanced Breast Cancer Risk Prediction Model Developed in a Screening Mammography Population

BACKGROUND

Estimating advanced breast cancer risk in women undergoing annual or biennial mammography could identify women who may benefit from less or more intensive screening. We developed an actionable model to predict cumulative 6-year advanced cancer (prognostic pathologic stage II or higher) risk according to screening interval.

METHODS

We included 931 186 women aged 40-74 years in the Breast Cancer Surveillance Consortium undergoing 2 542 382 annual (prior mammogram within 11-18 months) or 752 049 biennial (prior within 19-30 months) screening mammograms. The prediction model includes age, race and ethnicity, body mass index, breast density, family history of breast cancer, and prior breast biopsy subdivided by menopausal status and screening interval. We used fivefold cross-validation to internally validate model performance. We defined higher than 95th percentile as high risk (>0.658%), higher than 75th percentile to 95th or less percentile as intermediate risk (0.380%-0.658%), and 75th or less percentile as low to average risk (<0.380%).

RESULTS

Obesity, high breast density, and proliferative disease with atypia were strongly associated with advanced cancer. The model is well calibrated and has an area under the receiver operating characteristics curve of 0.682 (95% confidence interval = 0.670 to 0.694). Based on women's predicted advanced cancer risk under annual and biennial screening, 69.1% had low or average risk regardless of screening interval, 12.4% intermediate risk with biennial screening and average risk with annual screening, and 17.4% intermediate or high risk regardless of screening interval.

CONCLUSION

Most women have low or average advanced cancer risk and can undergo biennial screening. Intermediate-risk women may consider annual screening, and high-risk women may consider supplemental imaging in addition to annual screening.

Intention to Inform Relatives, Rates of Cascade Testing, and Preference for Patient-Mediated Communication in Families Concerned with Hereditary Breast and Ovarian Cancer and Lynch Syndrome: The Swiss CASCADE Cohort

Cascade screening for Tier 1 cancer genetic conditions is a significant public health intervention because it identifies untested relatives of individuals known to carry pathogenic variants associated with hereditary breast and ovarian cancer (HBOC) and Lynch syndrome (LS). The Swiss CASCADE is a family-based, open-ended cohort, including carriers of HBOC- and LS-associated pathogenic variants and their relatives. This paper describes rates of cascade screening in relatives from HBOC- and LS- harboring families, examines carriers' preferences for communication of testing results, and describes theory-based predictors of intention to invite relatives to a cascade screening program. Information has been provided by 304 index cases and 115 relatives recruited from September 2017 to December 2021. On average, 10 relatives per index case were potentially eligible for cascade screening. Approximately 65% of respondents wanted to invite relatives to the cohort, and approximately 50% indicated a preference for patient-mediated communication of testing results, possibly with the assistance of digital technology. Intention to invite relatives was higher for first- compared to second- and third-degree relatives, but was not different between syndromes or based on relatives' gender. The family environment and carrying pathogenic variants predicts intention to invite relatives. Information helps optimize delivery of tailored genetic services.

Recreational Physical Activity and Outcomes After Breast Cancer in Women at High Familial Risk

Background

Recreational physical activity (RPA) is associated with improved survival after breast cancer (BC) in average-risk women, but evidence is limited for women who are at increased familial risk because of a BC family history or BRCA1 and BRCA2 pathogenic variants (BRCA1/2 PVs).

Methods

We estimated associations of RPA (self-reported average hours per week within 3 years of BC diagnosis) with all-cause mortality and second BC events (recurrence or new primary) after first invasive BC in women in the Prospective Family Study Cohort (n = 4610, diagnosed 1993-2011, aged 22-79 years at diagnosis). We fitted Cox proportional hazards regression models adjusted for age at diagnosis, demographics, and lifestyle factors. We tested for multiplicative interactions (Wald test statistic for cross-product terms) and additive interactions (relative excess risk due to interaction) by age at diagnosis, body mass index, estrogen receptor status, stage at diagnosis, BRCA1/2 PVs, and familial risk score estimated from multigenerational pedigree data. Statistical tests were 2-sided.

Results

We observed 1212 deaths and 473 second BC events over a median follow-up from study enrollment of 11.0 and 10.5 years, respectively. After adjusting for covariates, RPA (any vs none) was associated with lower all-cause mortality of 16.1% (95% confidence interval [CI] = 2.4% to 27.9%) overall, 11.8% (95% CI = -3.6% to 24.9%) in women without BRCA1/2 PVs, and 47.5% (95% CI = 17.4% to 66.6%) in women with BRCA1/2 PVs (RPA*BRCA1/2 multiplicative interaction P = .005; relative excess risk due to interaction = 0.87, 95% CI = 0.01 to 1.74). RPA was not associated with risk of second BC events.

Conclusion

Findings support that RPA is associated with lower all-cause mortality in women with BC, particularly in women with BRCA1/2 PVs.

Prospective Evaluation over 15 Years of Six Breast Cancer Risk Models

Prospective validation of risk models is needed to assess their clinical utility, particularly over the longer term. We evaluated the performance of six commonly used breast cancer risk models (IBIS, BOADICEA, BRCAPRO, BRCAPRO-BCRAT, BCRAT, and iCARE-lit). 15-year risk scores were estimated using lifestyle factors and family history measures from 7608 women in the Melbourne Collaborative Cohort Study who were aged 50-65 years and unaffected at commencement of follow-up two (conducted in 2003-2007), of whom 351 subsequently developed breast cancer. Risk discrimination was assessed using the C-statistic and calibration using the expected/observed number of incident cases across the spectrum of risk by age group (50-54, 55-59, 60-65 years) and family history of breast cancer. C-statistics were higher for BOADICEA (0.59, 95% confidence interval (CI) 0.56-0.62) and IBIS (0.57, 95% CI 0.54-0.61) than the other models (p-difference ≤ 0.04). No model except BOADICEA calibrated well across the spectrum of 15-year risk (p-value < 0.03). The performance of BOADICEA and IBIS was similar across age groups and for women with or without a family history. For middle-aged Australian women, BOADICEA and IBIS had the highest discriminatory accuracy of the six risk models, but apart from BOADICEA, no model was well-calibrated across the risk spectrum.

Role of Routine Peritoneal Biopsies During Risk Reducing Salpingo-Oophorectomy (RRSO)

Objective The objective of this retrospective study was to assess the role of routine peritoneal biopsies during risk reducing salpingo-oophorectomy (RRSO).

Methods Data of 204 women who underwent RRSO between January 1, 2014 and February 20, 2020 at Charité – Universitätsmedizin Berlin, Campus Mitte were retrospectively analyzed. RRSO was done according to the standard operating procedures of the German Consortium Hereditary Breast and Ovarian Cancer (GC-HBOC) with peritoneal washing and several peritoneal biopsies. Specimen collected during RRSO were analyzed using the protocol for Sectioning and Extensively Examining the FIMbria (SEE-FIM). Perioperative complications were classified using the Clavien-Dindo-Classification.

Results 147 women who underwent RRSO had peritoneal biopsies and pelvic washing, 44 women had none of that. 123 patients (64.4%) carried a pathologic variant in gBRCA1 , 53 (27.7%) carried a pathologic variant in gBRCA2 . Histopathological evaluation identified four patients (2.1%) with pathological findings. Neither peritoneal biopsies nor pelvic washings revealed additional information after histological examination. There was no statistically significant difference in complication rate between the two groups. The mean surgery time for RRSO without peritoneal biopsies was 64.3 minutes compared to 77.8 minutes with peritoneal biopsies. That shows a statistically significant prolongation of 16% (13.5 minutes, p = 0.0383).

Conclusions The routine use of peritoneal biopsies during RRSO does not improve detection of occult ovarian cancer or STIC but prolongs the operation time significantly. By omitting peritoneal biopsies in RRSO not only perioperative risks are diminished but also costs could be reduced by shortening of surgery time as well as decreased number of pathological samples.

Understanding the Clinical Implications of Low Penetrant Genes and Breast Cancer Risk

OPINION STATEMENT

Since the 2013 Supreme Court declaration, panel testing for hereditary cancer syndromes has evolved into the gold standard for oncology germline genetic testing. With the advent of next-generation sequencing, competitive pricing, and developing therapeutic options, panel testing is now well integrated into breast cancer management and surveillance. Although many established syndromes have well-defined cancer risks and management strategies, several breast cancer genes are currently classified as limited-evidence genes by the National Comprehensive Cancer Network (NCCN). Follow-up for individuals with mutations in these genes is a point of contention due to conflicting information in the literature. The most recent NCCN guidelines have stratified management based on gene-specific cancer risks indicating that expanding data will allow for better recommendations as research progresses. The evolving management for these genes emphasizes the clinicians' need for evidence-based understanding of low penetrance breast cancer genes and their implications for patient care. This article reviews current literature for limited evidence genes, detailing cancer risks, association with triple-negative breast cancer, and recommendations for surveillance. A brief review of the challenges and future directions is outlined to discuss the evolving nature of cancer genetics and the exciting opportunities that can impact management.