Risk prediction models for familial breast cancer

Machine Learning Model Construction and Testing: Anticipating Cancer Incidence and Mortality

In recent years, the escalating environmental challenges have contributed to a rising incidence of cancer. The precise anticipation of cancer incidence and mortality rates has emerged as a pivotal focus in scientific inquiry, exerting a profound impact on the formulation of public health policies. This investigation adopts a pioneering machine learning framework to address this critical issue, utilizing a dataset encompassing 72,591 comprehensive records that include essential variables such as age, case count, population size, race, gender, site, and year of diagnosis. Diverse machine learning algorithms, including decision trees, random forests, logistic regression, support vector machines, and neural networks, were employed in this study. The ensuing analysis revealed testing accuracies of 62.17%, 61.92%, 54.53%, 55.72%, and 62.30% for the respective models. This state-of-the-art model not only enhances our understanding of cancer dynamics but also equips researchers and policymakers with the capability of making meticulous projections concerning forthcoming cancer incidence and mortality rates. Considering sustainability, the application of this advanced machine learning framework emphasizes the importance of judiciously utilizing extensive and intricate databases. By doing so, it facilitates a more sustainable approach to healthcare planning, allowing for informed decision-making that takes into account the long-term ecological and societal impacts of cancer-related policies. This integrative perspective underscores the broader commitment to sustainable practices in both health research and public policy formulation.

Familial history and prevalence of BRCA1, BRCA2 and TP53 pathogenic variants in HBOC Brazilian patients from a public healthcare service

Several studies have demonstrated the cost-effectiveness of genetic testing for surveillance and treatment of carriers of germline pathogenic variants associated with hereditary breast/ovarian cancer syndrome (HBOC). In Brazil, seventy percent of the population is assisted by the public Unified Health System (SUS), where genetic testing is still unavailable. And few studies were performed regarding the prevalence of HBOC pathogenic variants in this context. Here, we estimated the prevalence of germline pathogenic variants in BRCA1, BRCA2 and TP53 genes in Brazilian patients suspected of HBOC and referred to public healthcare service. Predictive power of risk prediction models for detecting mutation carriers was also evaluated. We found that 41 out of 257 tested patients (15.9%) were carriers of pathogenic variants in the analyzed genes. Most frequent pathogenic variant was the founder Brazilian mutation TP53 c.1010G > A (p.Arg337His), adding to the accumulated evidence that supports inclusion of TP53 in routine testing of Brazilian HBOC patients. Surprisingly, BRCA1 c.5266dupC (p.Gln1756fs), a frequently reported pathogenic variant in Brazilian HBOC patients, was not observed. Regarding the use of predictive models, we found that familial history of cancer might be used to improve selection or prioritization of patients for genetic testing, especially in a context of limited resources.

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.

Differences in breast cancer risk after benign breast disease by type of screening diagnosis

Introduction

We aimed to assess differences in breast cancer risk across benign breast disease diagnosed at prevalent or incident screens.

Materials and methods

We conducted a retrospective cohort study with data from 629,087 women participating in a long-standing population-based breast cancer screening program in Spain. Each benign breast disease was classified as non-proliferative, proliferative without atypia, or proliferative with atypia, and whether it was diagnosed in a prevalent or incident screen. We used partly conditional Cox hazard regression to estimate the adjusted hazard ratios of the risk of breast cancer.

Results

Compared with women without benign breast disease, the risk of breast cancer was significantly higher (p-value = 0.005) in women with benign breast disease diagnosed in an incident screen (aHR, 2.67; 95%CI: 2.24–3.19) than in those with benign breast disease diagnosed in a prevalent screen (aHR, 1.87; 95%CI: 1.57–2.24). The highest risk was found in women with a proliferative benign breast disease with atypia (aHR, 4.35; 95%CI: 2.09–9.08, and 3.35; 95%CI: 1.51–7.40 for those diagnosed at incident and prevalent screens, respectively), while the lowest was found in women with non-proliferative benign breast disease (aHR, 2.39; 95%CI: 1.95–2.93, and 1.63; 95%CI: 1.32–2.02 for those diagnosed at incident and prevalent screens, respectively).

Conclusion

Our study showed that the risk of breast cancer conferred by a benign breast disease differed according to type of screen (prevalent or incident). To our knowledge, this is the first study to analyse the impact of the screening type on benign breast disease prognosis.

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Breast cancer risk after a benign breast disease varied with the screening type.

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Incident benign breast disease had a higher breast cancer risk than prevalent.

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The risk remained increased regardless of benign breast disease subtype.

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.

Associations between XRCC2 rs3218536 and ERCC2 rs13181 polymorphisms and ovarian cancer

Recent studies explored XRCC2 rs3218536 and ERCC2 rs13181 polymorphisms and ovarian cancer (OC) risk. However, the association between these two single nucleotide polymorphisms and OC risk remains conflicting. Thus, we conducted a comprehensive systematic review and meta-analysis to investigate the association. We searched the databases of PubMed, and Embase. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by using fixed-effect or random-effect models. 15 case-control studies published in 11 papers including 4,757 cases and 8,431 controls were included in this meta-analysis. No associations were obtained between XRCC2 rs3218536 and ERCC2 rs13181 polymorphisms and OC risk. Stratification analyses of Hardy–Weinberg equilibrium status indicated that rs3218536 polymorphism was associated with the decreased risk of OC when in analysis of combined HWE positive studies. In conclusion, this meta-analysis indicates that XRCC2 rs3218536 and ERCC2 rs13181 polymorphisms may not be associated with the risk of OC.

High prevalence and predominance of BRCA1 germline mutations in Pakistani triple-negative breast cancer patients

BACKGROUND

Women harboring BRCA1/2 germline mutations have high lifetime risk of developing breast/ovarian cancer. The recommendation to pursue BRCA1/2 testing is based on patient's family history of breast/ovarian cancer, age of disease-onset and/or pathologic parameters of breast tumors. Here, we investigated if diagnosis of triple-negative breast cancer (TNBC) independently increases risk of carrying a BRCA1/2 mutation in Pakistan.

METHODS

Five hundred and twenty-three breast cancer patients including 237 diagnosed ≤ 30 years of age and 286 with a family history of breast/ovarian cancer were screened for BRCA1/2 small-range mutations and large genomic rearrangements. Immunohistochemical analyses were performed at one center. Univariate and multiple logistic regression models were used to investigate possible differences in prevalence of BRCA1/2 mutations according to patient and tumor characteristics.

RESULTS

Thirty-seven percent of patients presented with TNBC. The prevalence of BRCA1 mutations was higher in patients with TNBC than non-TNBC (37 % vs. 10 %, P < 0.0001). 1 % of TNBC patients were observed to have BRCA2 mutations. Subgroup analyses revealed a larger proportion of BRCA1 mutations in TNBC than non-TNBC among patients 1) diagnosed at early-age with no family history of breast/ovarian cancer (14 % vs. 5 %, P = 0.03), 2) diagnosed at early-age irrespective of family history (28 % vs. 11 %, P = 0.0003), 3) had a family history of breast cancer (49 % vs. 12 %, P < 0.0001), and 4) those with family history of breast and ovarian cancer (81 % vs. 28 %, P = 0.0005). TNBC patients harboring BRCA1 mutations were diagnosed at a later age than non-carriers (median age at diagnosis: 30 years (range 22-53) vs. 28 years (range 18-67), P = 0.002). The association between TNBC status and presence of BRCA1 mutations was independent of the simultaneous consideration of family phenotype, tumor histology and grade in a multiple logistic regression model (Ratio of the probability of carrying BRCA1/2 mutations for TNBC vs. non-TNBC 4.23; 95 % CI 2.50-7.14; P < 0.0001).

CONCLUSION

Genetic BRCA1 testing should be considered for Pakistani women diagnosed with TNBC.

Exome genotyping arrays to identify rare and low frequency variants associated with epithelial ovarian cancer risk

Rare and low frequency variants are not well covered in most germline genotyping arrays and are understudied in relation to epithelial ovarian cancer (EOC) risk. To address this gap, we used genotyping arrays targeting rarer protein-coding variation in 8,165 EOC cases and 11,619 controls from the international Ovarian Cancer Association Consortium (OCAC). Pooled association analyses were conducted at the variant and gene level for 98,543 variants directly genotyped through two exome genotyping projects. Only common variants that represent or are in strong linkage disequilibrium (LD) with previously-identified signals at established loci reached traditional thresholds for exome-wide significance (P < 5.0 × 10 -  7). One of the most significant signals (Pall histologies = 1.01 × 10 -  13;Pserous = 3.54 × 10 -  14) occurred at 3q25.31 for rs62273959, a missense variant mapping to the LEKR1 gene that is in LD (r2 = 0.90) with a previously identified 'best hit' (rs7651446) mapping to an intron of TIPARP. Suggestive associations (5.0 × 10 -  5 > P≥5.0 ×10 -  7) were detected for rare and low-frequency variants at 16 novel loci. Four rare missense variants were identified (ACTBL2 rs73757391 (5q11.2), BTD rs200337373 (3p25.1), KRT13 rs150321809 (17q21.2) and MC2R rs104894658 (18p11.21)), but only MC2R rs104894668 had a large effect size (OR = 9.66). Genes most strongly associated with EOC risk included ACTBL2 (PAML = 3.23 × 10 -  5; PSKAT-o = 9.23 × 10 -  4) and KRT13 (PAML = 1.67 × 10 -  4; PSKAT-o = 1.07 × 10 -  5), reaffirming variant-level analysis. In summary, this large study identified several rare and low-frequency variants and genes that may contribute to EOC susceptibility, albeit with possible small effects. Future studies that integrate epidemiology, sequencing, and functional assays are needed to further unravel the unexplained heritability and biology of this disease.

Mutation detection rates associated with specific selection criteria for BRCA1/2 testing in 1854 high-risk families: A monocentric Italian study

BACKGROUND

BRCA mutation screening is frequently offered on the basis of the fulfillment of empirical selection criteria, thought to be indicative of a genetic predisposition to breast/ovarian cancer (BrCa/OvCa). This study aimed to evaluate, in a large cohort of BrCa/OvCa families, the mutation detection rate (DR) associated with specific clinical features and the relative performance of the employed selection criteria.

METHODS

BRCA gene analysis was performed on 1854 family probands. The Fisher exact test was used to compare the DRs associated with different clinical features. In a subset of families fulfilling only mutually exclusive criteria, odds ratios and 95% CI were estimated to test the relative effectiveness of each criterion.

RESULTS

The overall DR was 29.3%. Among BrCa-only families, the DRs were significantly higher in the presence of early-onset compared with late-onset cases, and of bilateral compared with unilateral cases. In families with bilateral cases, ages at diagnosis of both the first and second tumour were significantly lower in mutation carriers. In families fulfilling mutually exclusive criteria, OvCa was the best predictor of BRCA mutations, with DRs (range: 31.8%-80.0%) significantly higher compared with the other criteria. Conversely, isolated early-onset BrCa and three or more late-onset BrCa displayed significantly lower predictive values (7.9% and 7.2%, respectively).

CONCLUSIONS

The observed estimates, albeit confirming a DR above 10% for most of the considered criteria, highlighted some relevant differences among them. Such differences should be taken into account in the identification of patients who might benefit from genetic counselling and subsequent testing.

Family history predictors of BRCA1/BRCA2 mutation status among Tunisian breast/ovarian cancer families

BACKGROUND

With the increasing request for BRCA1/BRCA2 mutation tests, several risk models have been developed to predict the presence of mutation in these genes; in this study, we have developed an efficient BRCA genetic testing strategy.

METHOD

As first step, to identify predictor variables associated with BRCA status, we have undertaken a cumulative mutation analysis including data from three Tunisian studies. Then, we have developed a logistic regression model for predicting the likelihood of harboring a BRCA mutation. Using receiver operating characteristic curves (ROC), an effective evaluation was performed. A total of 92 Tunisian families were included. Overall, 27 women were positive for BRCA1/BRCA2 deleterious mutations.

RESULTS

Tow recurrent mutations (c.211dupA and c.5266dupC) explained 76 % of BRCA1-related families and three recurrent mutations (c.1310_1313del, c.1542_1547delAAGA and c.7887_7888insA) explained 90 % of BRCA2-related families. Early age at diagnosis of breast cancer, ovarian cancer, bilateral breast cancer were associated with BRCA1, whereas male breast cancer and four or more breast cancer cases in the family were associated with BRCA2. The area under the receiver operating characteristic curve of the risk score was 0.802 (95 % confidence interval = 0.0699-0. 905).

CONCLUSION

Logistic regression reported particular profiles related to BRCA germline mutation carriers in our population, as well as an efficient prediction model that may be a useful tool for increasing the cost-effectiveness of genetic testing strategy.

Refining Breast Cancer Risk Stratification: Additional Genes, Additional Information

Recent advances in genomic technology have enabled far more rapid, less expensive sequencing of multiple genes than was possible only a few years ago. Advances in bioinformatics also facilitate the interpretation of large amounts of genomic data. New strategies for cancer genetic risk assessment include multiplex sequencing panels of 5 to more than 100 genes (in which rare mutations are often associated with at least two times the average risk of developing breast cancer) and panels of common single-nucleotide polymorphisms (SNPs), combinations of which are generally associated with more modest cancer risks (more than twofold). Although these new multiple-gene panel tests are used in oncology practice, questions remain about the clinical validity and the clinical utility of their results. To translate this increasingly complex genetic information for clinical use, cancer risk prediction tools are under development that consider the joint effects of all susceptibility genes, together with other established breast cancer risk factors. Risk-adapted screening and prevention protocols are underway, with ongoing refinement as genetic knowledge grows. Priority areas for future research include the clinical validity and clinical utility of emerging genetic tests; the accuracy of developing cancer risk prediction models; and the long-term outcomes of risk-adapted screening and prevention protocols, in terms of patients' experiences and survival.

Network-Based Integration of GWAS and Gene Expression Identifies a HOX-Centric Network Associated with Serous Ovarian Cancer Risk

BACKGROUND

Genome-wide association studies (GWAS) have so far reported 12 loci associated with serous epithelial ovarian cancer (EOC) risk. We hypothesized that some of these loci function through nearby transcription factor (TF) genes and that putative target genes of these TFs as identified by coexpression may also be enriched for additional EOC risk associations.

METHODS

We selected TF genes within 1 Mb of the top signal at the 12 genome-wide significant risk loci. Mutual information, a form of correlation, was used to build networks of genes strongly coexpressed with each selected TF gene in the unified microarray dataset of 489 serous EOC tumors from The Cancer Genome Atlas. Genes represented in this dataset were subsequently ranked using a gene-level test based on results for germline SNPs from a serous EOC GWAS meta-analysis (2,196 cases/4,396 controls).

RESULTS

Gene set enrichment analysis identified six networks centered on TF genes (HOXB2, HOXB5, HOXB6, HOXB7 at 17q21.32 and HOXD1, HOXD3 at 2q31) that were significantly enriched for genes from the risk-associated end of the ranked list (P < 0.05 and FDR < 0.05). These results were replicated (P < 0.05) using an independent association study (7,035 cases/21,693 controls). Genes underlying enrichment in the six networks were pooled into a combined network.

CONCLUSION

We identified a HOX-centric network associated with serous EOC risk containing several genes with known or emerging roles in serous EOC development.

IMPACT

Network analysis integrating large, context-specific datasets has the potential to offer mechanistic insights into cancer susceptibility and prioritize genes for experimental characterization.

Contribution of Germline Mutations in the RAD51B, RAD51C, and RAD51D Genes to Ovarian Cancer in the Population

PURPOSE

The aim of this study was to estimate the contribution of deleterious mutations in the RAD51B, RAD51C, and RAD51D genes to invasive epithelial ovarian cancer (EOC) in the population and in a screening trial of individuals at high risk of ovarian cancer.

PATIENTS AND METHODS

The coding sequence and splice site boundaries of the three RAD51 genes were sequenced and analyzed in germline DNA from a case-control study of 3,429 patients with invasive EOC and 2,772 controls as well as in 2,000 unaffected women who were BRCA1/BRCA2 negative from the United Kingdom Familial Ovarian Cancer Screening Study (UK_FOCSS) after quality-control analysis.

RESULTS

In the case-control study, we identified predicted deleterious mutations in 28 EOC cases (0.82%) compared with three controls (0.11%; P < .001). Mutations in EOC cases were more frequent in RAD51C (14 occurrences, 0.41%) and RAD51D (12 occurrences, 0.35%) than in RAD51B (two occurrences, 0.06%). RAD51C mutations were associated with an odds ratio of 5.2 (95% CI, 1.1 to 24; P = .035), and RAD51D mutations conferred an odds ratio of 12 (95% CI, 1.5 to 90; P = .019). We identified 13 RAD51 mutations (0.65%) in unaffected UK_FOCSS participants (RAD51C, n = 7; RAD51D, n = 5; and RAD51B, n = 1), which was a significantly greater rate than in controls (P < .001); furthermore, RAD51 mutation carriers were more likely than noncarriers to have a family history of ovarian cancer (P < .001).

CONCLUSION

These results confirm that RAD51C and RAD51D are moderate ovarian cancer susceptibility genes and suggest that they confer levels of risk of EOC that may warrant their use alongside BRCA1 and BRCA2 in routine clinical genetic testing.

A targeted approach to genetic counseling in breast cancer patients: the experience of an Italian local project

AIMS AND BACKGROUND

Patients with hereditary breast cancer (BC) may benefit from genetic counseling and testing for detection of causative mutations, definition of therapeutic and preventive strategies, and identification of at-risk relatives. Italy has few oncogenetic centers and genetic evaluation of all patients with BC is not feasible. Moreover, lack of uniformity in the selection of patients generates inappropriate referral to the geneticist. We designed a model that may represent a reproducible way to select patients at risk for hereditary BC, with the aims of rationalizing access to genetic centers and improving clinical management and surveillance.

METHODS

The genetic unit of a Cancer Center and the Departments of Oncology from 2 public Hospitals in Milan were involved in the project. After training sessions at the genetic unit, operators from the 2 hospitals evaluated all patients with BC attending a first oncologic visit, through a specific interview. Patients considered at risk of hereditary BC attended counseling at the genetic unit.

RESULTS

Of 419 patients, 61 (14.5%) were eligible for genetic counseling after the interview. Of these, 46 (10.9%) strictly met testing criteria. Overall, 52 (12.4%) patients underwent genetic counseling and 47 were tested for BRCA1/BRCA2 mutation. After genetic test results, the available options for treatment/surveillance were discussed by a multidisciplinary team, according to the level of genetic risk.

CONCLUSIONS

It is possible to improve the process of referring patients with suspected hereditary BC for genetic risk assessment. The application of clinical screening reduced the genetics unit's workload and enabled optimization of time and resources.

Prostate Cancer and Li-Fraumeni Syndrome: Implications for Screening and Therapy

Li-Fraumeni Syndrome (LFS) is an autosomal dominant genetic disorder associated with mutations in the TP53 gene and characterized by a propensity to develop a variety of malignancies resulting in a shortened lifespan. We report a case of prostate cancer in a 50 year old male with LFS. Experimental studies suggest that TP53 mutations in prostate cancer are associated with therapeutic resistance to radiation, chemotherapy, and anti-androgens, implying that LFS men may experience more aggressive cancer biology with implications for therapeutic decisions. The potential of prostate cancer to develop earlier in LFS favors institution of screening at earlier ages.

Evaluation of the Dutch BRCA1/2 clinical genetic center referral criteria in an unselected early breast cancer population

In this study, we evaluated the diagnostic value of the Dutch Clinical Genetic Center (CGC) referral guidelines for BRCA1/2 mutation testing in 903 early breast cancer patients, unselected for family history, diagnosed in a cancer hospital before the age of 50 years in 1974-2002; most prevalent Dutch pathogenic BRCA1/2 mutations had been analyzed on coded DNA in a research setting. Forty-nine (5.4%) of the patients were proven to be BRCA1/2 mutation carriers. We found that 78% and 69% of BRCA1 and BRCA2 mutation carriers identified met the criteria for referral to the CGC based on age, family history and synchronous multiple tumors; reflected by a combined sensitivity of 75.5% and specificity of 63.2%. More than half of the BRCA1 mutation carriers, that is, 58% had a triple-negative tumor. The highest AUC was obtained by shifting the age at diagnosis threshold criterion from 40 to 35 years and by adding a 'triple-negative breast cancer' criterion with an age threshold of 45 years; the specificity increased to 71.2%, whereas the sensitivity remained the same; that is, a referral of fewer patients will lead to the identification of at least the same number of BRCA1/2 mutation carriers. Two-thirds of the BRCA1/2 mutation carriers identified in this research setting had been referred for counseling and testing. Our results indicate that, awaiting a possibly more extended mutation screening of all breast cancer patients, the triple-negative status of a breast cancer should be added to the CGC referral criteria.

A new scoring system in cancer genetics: application to criteria for BRCA1 and BRCA2 mutation screening

BACKGROUND

In hereditary forms of cancer due to mutations of genes such as BRCA1 and BRCA2, methods have been proposed to predict the presence of a mutation in a family.

METHODS

Relying on carriage probability computation is the most predictive, but scores are a good proxy and avoid using computer software. An empirical method, the Manchester scoring system, has been elaborated for BRCA1 and BRCA2 mutation identification. We propose a general scoring system based on a transformation of the carriage probability. Up to an approximation, the transformed carriage probability becomes an additive score. We applied this new scoring system to the diagnosis of BRCA1-associated and BRCA2-associated breast-ovarian cancer predisposition. Using simulations, its performance was evaluated and compared with that of the Manchester scoring system and of the exact probability. Finally, the score system was used on a sample of 4563 families screened for BRCA1 and BRCA2 mutations.

RESULTS

The performance of the new scoring system was superior to the Manchester scoring system, but the probability computation remained the most predictive. The better performance of the new scoring system was attributed to accounting for unaffected family members and for the degree of kinship of relatives with the proband.

CONCLUSIONS

The new scoring system has a theoretical basis and may be applied to any cancer family syndrome and, more generally, to any disease with monogenic subentities, in which the causal gene mutations have been identified. It will be easily modified when additional predictive factors are found.

Development and validation of risk models and molecular diagnostics to permit personalized management of cancer

Despite the advances made in cancer management over the past few decades, improvements in cancer diagnosis and prognosis are still poor, highlighting the need for individualized strategies. Toward this goal, risk prediction models and molecular diagnostic tools have been developed, tailoring each step of risk assessment from diagnosis to treatment and clinical outcomes based on the individual's clinical, epidemiological, and molecular profiles. These approaches hold increasing promise for delivering a new paradigm to maximize the efficiency of cancer surveillance and efficacy of treatment. However, they require stringent study design, methodology development, comprehensive assessment of biomarkers and risk factors, and extensive validation to ensure their overall usefulness for clinical translation. In the current study, the authors conducted a systematic review using breast cancer as an example and provide general guidelines for risk prediction models and molecular diagnostic tools, including development, assessment, and validation.

Establishing a program for individuals at high risk for breast cancer

Our need to create a program for individuals at high risk for breast cancer development led us to research the available data on such programs. In this paper, we summarize our findings and our thinking process as we developed our own program. Breast cancer incidence is increasing worldwide. Even though there are known risk factors for breast cancer development, approximately 60% of patients with breast cancer have no known risk factor, although this situation will probably change with further research, especially in genetics. For patients with risk factors based on personal or family history, different models are available for assessing and quantifying risk. Assignment of risk levels permits tailored screening and risk reduction strategies. Potential benefits of specialized programs for women with high breast cancer risk include more cost -effective interventions as a result of patient stratification on the basis of risk; generation of valuable data to advance science; and differentiation of breast programs from other breast cancer units, which can result in increased revenue that can be directed to further improvements in patient care. Guidelines for care of patients at high risk for breast cancer are available from various groups. However, running a high-risk breast program involves much more than applying a guideline. Each high-risk program needs to be designed by its institution with consideration of local resources and country legislation, especially related to genetic issues. Development of a successful high-risk program includes identifying strengths, weaknesses, opportunities, and threats; developing a promotion plan; choosing a risk assessment tool; defining "high risk"; and planning screening and risk reduction strategies for the specific population served by the program. The information in this article may be useful for other institutions considering creation of programs for patients with high breast cancer risk.

Combined and interactive effects of environmental and GWAS-identified risk factors in ovarian cancer

BACKGROUND

There are several well-established environmental risk factors for ovarian cancer, and recent genome-wide association studies have also identified six variants that influence disease risk. However, the interplay between such risk factors and susceptibility loci has not been studied.

METHODS

Data from 14 ovarian cancer case-control studies were pooled, and stratified analyses by each environmental risk factor with tests for heterogeneity were conducted to determine the presence of interactions for all histologic subtypes. A genetic "risk score" was created to consider the effects of all six variants simultaneously. A multivariate model was fit to examine the association between all environmental risk factors and genetic risk score on ovarian cancer risk.

RESULTS

Among 7,374 controls and 5,566 cases, there was no statistical evidence of interaction between the six SNPs or genetic risk score and the environmental risk factors on ovarian cancer risk. In a main effects model, women in the highest genetic risk score quartile had a 65% increased risk of ovarian cancer compared with women in the lowest [95% confidence interval (CI), 1.48-1.84]. Analyses by histologic subtype yielded risk differences across subtype for endometriosis (Phet < 0.001), parity (Phet < 0.01), and tubal ligation (Phet = 0.041).

CONCLUSIONS

The lack of interactions suggests that a multiplicative model is the best fit for these data. Under such a model, we provide a robust estimate of the effect of each risk factor that sets the stage for absolute risk prediction modeling that considers both environmental and genetic risk factors. Further research into the observed differences in risk across histologic subtype is warranted.

GWAS meta-analysis and replication identifies three new susceptibility loci for ovarian cancer

Genome-wide association studies (GWAS) have identified four susceptibility loci for epithelial ovarian cancer (EOC), with another two suggestive loci reaching near genome-wide significance. We pooled data from a GWAS conducted in North America with another GWAS from the UK. We selected the top 24,551 SNPs for inclusion on the iCOGS custom genotyping array. We performed follow-up genotyping in 18,174 individuals with EOC (cases) and 26,134 controls from 43 studies from the Ovarian Cancer Association Consortium. We validated the two loci at 3q25 and 17q21 that were previously found to have associations close to genome-wide significance and identified three loci newly associated with risk: two loci associated with all EOC subtypes at 8q21 (rs11782652, P = 5.5 × 10(-9)) and 10p12 (rs1243180, P = 1.8 × 10(-8)) and another locus specific to the serous subtype at 17q12 (rs757210, P = 8.1 × 10(-10)). An integrated molecular analysis of genes and regulatory regions at these loci provided evidence for functional mechanisms underlying susceptibility and implicated CHMP4C in the pathogenesis of ovarian cancer.

Epigenetic analysis leads to identification of HNF1B as a subtype-specific susceptibility gene for ovarian cancer

HNF1B is overexpressed in clear cell epithelial ovarian cancer, and we observed epigenetic silencing in serous epithelial ovarian cancer, leading us to hypothesize that variation in this gene differentially associates with epithelial ovarian cancer risk according to histological subtype. Here we comprehensively map variation in HNF1B with respect to epithelial ovarian cancer risk and analyse DNA methylation and expression profiles across histological subtypes. Different single-nucleotide polymorphisms associate with invasive serous (rs7405776 odds ratio (OR)=1.13, P=3.1 × 10(-10)) and clear cell (rs11651755 OR=0.77, P=1.6 × 10(-8)) epithelial ovarian cancer. Risk alleles for the serous subtype associate with higher HNF1B-promoter methylation in these tumours. Unmethylated, expressed HNF1B, primarily present in clear cell tumours, coincides with a CpG island methylator phenotype affecting numerous other promoters throughout the genome. Different variants in HNF1B associate with risk of serous and clear cell epithelial ovarian cancer; DNA methylation and expression patterns are also notably distinct between these subtypes. These findings underscore distinct mechanisms driving different epithelial ovarian cancer histological subtypes.

Genome-wide association analysis identifies three new breast cancer susceptibility loci

Breast cancer is the most common cancer among women. To date, 22 common breast cancer susceptibility loci have been identified accounting for ∼8% of the heritability of the disease. We attempted to replicate 72 promising associations from two independent genome-wide association studies (GWAS) in ∼70,000 cases and ∼68,000 controls from 41 case-control studies and 9 breast cancer GWAS. We identified three new breast cancer risk loci at 12p11 (rs10771399; P = 2.7 × 10(-35)), 12q24 (rs1292011; P = 4.3 × 10(-19)) and 21q21 (rs2823093; P = 1.1 × 10(-12)). rs10771399 was associated with similar relative risks for both estrogen receptor (ER)-negative and ER-positive breast cancer, whereas the other two loci were associated only with ER-positive disease. Two of the loci lie in regions that contain strong plausible candidate genes: PTHLH (12p11) has a crucial role in mammary gland development and the establishment of bone metastasis in breast cancer, and NRIP1 (21q21) encodes an ER cofactor and has a role in the regulation of breast cancer cell growth.

A risk prediction algorithm based on family history and common genetic variants: application to prostate cancer with potential clinical impact

Genome wide association studies have identified several single nucleotide polymorphisms (SNPs) that are independently associated with small increments in risk of prostate cancer, opening up the possibility for using such variants in risk prediction. Using segregation analysis of population-based samples of 4,390 families of prostate cancer patients from the UK and Australia, and assuming all familial aggregation has genetic causes, we previously found that the best model for the genetic susceptibility to prostate cancer was a mixed model of inheritance that included both a recessive major gene component and a polygenic component (P) that represents the effect of a large number of genetic variants each of small effect, where . Based on published studies of 26 SNPs that are currently known to be associated with prostate cancer, we have extended our model to incorporate these SNPs by decomposing the polygenic component into two parts: a polygenic component due to the known susceptibility SNPs, , and the residual polygenic component due to the postulated but as yet unknown genetic variants, . The resulting algorithm can be used for predicting the probability of developing prostate cancer in the future based on both SNP profiles and explicit family history information. This approach can be applied to other diseases for which population-based family data and established risk variants exist.

Genetic variation in insulin-like growth factor 2 may play a role in ovarian cancer risk

The insulin-like growth factor (IGF) signaling axis plays an important role in cancer biology. We hypothesized that genetic variation in this pathway may influence risk of ovarian cancer. A three-center study of non-Hispanic whites including 1880 control women, 1135 women with invasive epithelial ovarian cancer and 321 women with borderline epithelial ovarian tumors was carried out to test the association between tag single-nucleotide polymorphisms (tSNPs) (n=58) in this pathway and risk of ovarian cancer. We found no association between variation in IGF1, IGFBP1 or IGFBP3 and risk of invasive disease, whereas five tSNPs in IGF2 were associated with risk of invasive epithelial ovarian cancer at P<0.05 and followed-up one of the associated SNPs. We conducted genotyping in 3216 additional non-Hispanic white cases and 5382 additional controls and were able to independently replicate our initial findings. In the combined set of studies, rs4320932 was associated with a 13% decreased risk of ovarian cancer per copy of the minor allele carried (95% confidence interval 0.81-0.93, P-trend=7.4 × 10(-5)). No heterogeneity of effect across study centers was observed (p(het)=0.25). IGF2 is emerging as an important gene for ovarian cancer; additional genotyping is warranted to further confirm these associations with IGF2 and to narrow down the region harboring the causal SNP.

Genetic testing for familial/hereditary breast cancer-comparison of guidelines and recommendations from the UK, France, the Netherlands and Germany

In this review, the national guidelines and recommendations for genetic testing for familial/hereditary breast cancer from the UK, France, the Netherlands and Germany were evaluated as to the inclusion criteria for genetic testing. In all four countries, access to genetic testing relies basically on the family history of breast and ovarian cancer. Similarities are obvious for most selection criteria. All four guidelines recommend embedding genetic testing within a framework of genetic counselling, and all agree to perform genetic testing first in an affected person. However, there are differences regarding the thresholds based on certain familial constellations, detailed description of selection criteria, the degree of relatedness between affected individuals and the counsellee, the age of diagnosis, the individual history of early onset breast cancer, bilateral breast cancer, the tumour morphology or the access to intensified surveillance. These differences and open questions not covered by the guidelines, e.g. on how to deal with phenocopies, unclassified variants, genetic variants in newly identified breast cancer susceptibility genes or with family constellations not fitting the criteria, are discussed. New evidence is usually slowly integrated into the guidelines. An exchange process towards the harmonization of the guidelines will ensure high quality health care across Europe.

Multicenter surveillance of women at high genetic breast cancer risk using mammography, ultrasonography, and contrast-enhanced magnetic resonance imaging (the high breast cancer risk italian 1 study): final results

OBJECTIVES

: To prospectively compare clinical breast examination, mammography, ultrasonography, and contrast-enhanced magnetic resonance imaging (MRI) in a multicenter surveillance of high-risk women.

MATERIALS AND METHODS

: We enrolled asymptomatic women aged ≥ 25: BRCA mutation carriers; first-degree relatives of BRCA mutation carriers, and women with strong family history of breast/ovarian cancer, including those with previous personal breast cancer.

RESULTS

: A total of 18 centers enrolled 501 women and performed 1592 rounds (3.2 rounds/woman). Forty-nine screen-detected and 3 interval cancers were diagnosed: 44 invasive, 8 ductal carcinoma in situ; only 4 pT2 stage; 32 G3 grade. Of 39 patients explored for nodal status, 28 (72%) were negative. Incidence per year-woman resulted 3.3% overall, 2.1% <50, and 5.4% ≥ 50 years (P < 0.001), 4.3% in women with previous personal breast cancer and 2.5% in those without (P = 0.045). MRI was more sensitive (91%) than clinical breast examination (18%), mammography (50%), ultrasonography (52%), or mammography plus ultrasonography (63%) (P < 0.001). Specificity ranged 96% to 99%, positive predictive value 53% to 71%, positive likelihood ratio 24 to 52 (P not significant). MRI showed significantly better negative predictive value (99.6) and negative likelihood ratio (0.09) than those of the other modalities. At receiver operating characteristic analysis, the area under the curve of MRI (0.97) was significantly higher than that of mammography (0.83) or ultrasonography (0.82) and not significantly increased when MRI was combined with mammography and/or ultrasonography. Of 52 cancers, 16 (31%) were diagnosed only by MRI, 8 of 21 (38%) in women <50, and 8 of 31 (26%) in women ≥ 50 years of age.

CONCLUSION

: MRI largely outperformed mammography, ultrasonography, and their combination for screening high-risk women below and over 50.

Common alleles in candidate susceptibility genes associated with risk and development of epithelial ovarian cancer

Common germline genetic variation in the population is associated with susceptibility to epithelial ovarian cancer. Microcell-mediated chromosome transfer and expression microarray analysis identified nine genes associated with functional suppression of tumorogenicity in ovarian cancer cell lines; AIFM2, AKTIP, AXIN2, CASP5, FILIP1L, RBBP8, RGC32, RUVBL1 and STAG3. Sixty-three tagging single nucleotide polymorphisms (tSNPs) in these genes were genotyped in 1,799 invasive ovarian cancer cases and 3,045 controls to look for associations with disease risk. Two SNPs in RUVBL1, rs13063604 and rs7650365, were associated with increased risk of serous ovarian cancer [HetOR = 1.42 (1.15-1.74) and the HomOR = 1.63 (1.10-1.42), p-trend = 0.0002] and [HetOR = 0.97 (0.80-1.17), HomOR = 0.74 (0.58-0.93), p-trend = 0.009], respectively. We genotyped rs13063604 and rs7650365 in an additional 4,590 cases and 6,031 controls from ten sites from the United States, Europe and Australia; however, neither SNP was significant in Stage 2. We also evaluated the potential role of tSNPs in these nine genes in ovarian cancer development by testing for allele-specific loss of heterozygosity (LOH) in 286 primary ovarian tumours. We found frequent LOH for tSNPs in AXIN2, AKTIP and RGC32 (64, 46 and 34%, respectively) and one SNP, rs1637001, in STAG3 showed significant allele-specific LOH with loss of the common allele in 94% of informative tumours (p = 0.015). Array comparative genomic hybridisation indicated that this nonrandom allelic imbalance was due to amplification of the rare allele. In conclusion, we show evidence for the involvement of a common allele of STAG3 in the development of epithelial ovarian cancer.

Breast cancer susceptibility variants alter risk in familial ovarian cancer

Recent candidate gene and genome wide association studies have revealed novel loci associated with an increased risk of breast cancer. We evaluated the effect of these breast cancer associated variants on ovarian cancer risk in individuals with familial ovarian cancer both with and without BRCA1 or BRCA2 mutations. A total of 158 unrelated white British women (54 BRCA1/2 mutation positive and 104 BRCA1/2 mutation negative) with familial ovarian cancer were genotyped for FGFR2, TNRC9/TOX3 and CASP8 variants. The p.Asp302His CASP8 variant was associated with reduced ovarian cancer risk in the familial BRCA1/2 mutation negative ovarian cancer cases (P = 0.016). The synonymous TNRC9/TOX3 (Ser51) variant was present at a significantly lower frequency than in patients with familial BRCA1/2 positive breast cancer (P = 0.0002). Our results indicate that variants in CASP8 and TNRC9/TOX3 alter the risk of disease in individuals affected with familial ovarian cancer.

Genomics of the NF-κB signaling pathway: hypothesized role in ovarian cancer

OBJECTIVE

We sought to review evidence linking nuclear factor-kappa B (NF-κB) to ovarian cancer and to identify genetic variants involved in NF-κB signaling.

METHODS

PubMed was reviewed to inform on ovarian cancer biology and NF-κB signaling and to identify key genes. Public linkage disequilibrium (LD) data were analyzed to identify informative inherited variants (tagSNPs) using ldSelect.

RESULTS

We identified 319 key NF-κB genes including five NF-κB subunits, 167 activating genes, and 55 inhibiting genes. We found that the 1000 Genomes Project was the most informative LD source for most genes (92.8%), and we identified 13,027 LD bins (r (2) ≥ 0.9, minor allele frequency ≥ 0.05) and 1,018 putative-functional variants worthy of investigation. We also report that reliance on a commonly used genome-wide SNP array and genotype imputation with HapMap Phase II data provides data on only 74% of the common inherited NF-κB SNPs of interest.

CONCLUSIONS

Compelling evidence suggests that NF-κB plays a critical role in ovarian cancer, yet inherited variation in these genes has not been thoroughly assessed in relation to disease risk or outcome. We present a collection of variants in key genes and suggest creation of a custom genotyping array as an optimal approach.

A KRAS-variant in ovarian cancer acts as a genetic marker of cancer risk

Ovarian cancer (OC) is the single most deadly form of women's cancer, typically presenting as an advanced disease at diagnosis in part due to a lack of known risk factors or genetic markers of risk. The KRAS oncogene and altered levels of the microRNA (miRNA) let-7 are associated with an increased risk of developing solid tumors. In this study, we investigated a hypothesized association between an increased risk of OC and a variant allele of KRAS at rs61764370, referred to as the KRAS-variant, which disrupts a let-7 miRNA binding site in this oncogene. Specimens obtained were tested for the presence of the KRAS-variant from nonselected OC patients in three independent cohorts, two independent ovarian case-control studies, and OC patients with hereditary breast and ovarian cancer syndrome (HBOC) as well as their family members. Our results indicate that the KRAS-variant is associated with more than 25% of nonselected OC cases. Further, we found that it is a marker for a significant increased risk of developing OC, as confirmed by two independent case-control analyses. Lastly, we determined that the KRAS-variant was present in 61% of HBOC patients without BRCA1 or BRCA2 mutations, previously considered uninformative, as well as in their family members with cancer. Our findings strongly support the hypothesis that the KRAS-variant is a genetic marker for increased risk of developing OC, and they suggest that the KRAS-variant may be a new genetic marker of cancer risk for HBOC families without other known genetic abnormalities.

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.

METHODS

We extended BOADICEA by treating breast cancer subtypes as distinct disease end points. Age-specific expression of phenotypic markers in a series of tumours from 182 BRCA1 mutation carriers, 62 BRCA2 mutation carriers and 109 controls from the Breast Cancer Linkage Consortium, and over 300,000 tumours from the general population obtained from the Surveillance Epidemiology, and End Results database, were used to calculate age-specific and genotype-specific incidences of each disease end point. The probability that an individual carries a BRCA1 or BRCA2 mutation given their family history and tumour marker status of family members was computed in sample pedigrees.

RESULTS

The cumulative risk of ER-negative breast cancer by age 70 for BRCA1 mutation carriers was estimated to be 55% and the risk of ER-positive disease was 18%. The corresponding risks for BRCA2 mutation carriers were 21% and 44% for ER-negative and ER-positive disease, respectively. The predicted BRCA1 carrier probabilities among ER-positive breast cancer cases were less than 1% at all ages. For women diagnosed with breast cancer below age 50 years, these probabilities rose to more than 5% in ER-negative breast cancer, 7% in TN disease and 24% in TN breast cancer expressing both CK5/6 and CK14 cytokeratins. Large differences in mutation probabilities were observed by combining ER status and other informative markers with family history.

CONCLUSIONS

This approach combines both full pedigree and tumour subtype data to predict BRCA1/2 carrier probabilities. Prediction of BRCA1/2 carrier status, and hence selection of women for mutation screening, may be substantially improved by combining tumour pathology with family history of cancer.

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

Women who are at high risk of breast cancer can be offered more intensive surveillance or prophylactic measures, such as surgery or chemoprevention. Central to decisions regarding the level of prevention is accurate and individualized risk assessment. This review aims to distill the diverse literature and provide practicing clinicians with an overview of the available risk assessment methods. Risk assessments fall into two groups: the risk of carrying a mutation in a high-risk gene such as BRCA1 or BRCA2 and the risk of developing breast cancer with or without such a mutation. Knowledge of breast cancer risks, taken together with the risks and benefits of the intervention, is needed to choose an appropriate disease management strategy. A number of models have been developed for assessing these risks, but independent validation of such models has produced variable results. Some models are able to predict both mutation carriage risks and breast cancer risk; however, to date, all are limited by only moderate discriminatory accuracy. Further improvements in the knowledge of how to best integrate both new risk factors and newly discovered genetic variants into these models will allow clinicians to more accurately determine which women are most likely to develop breast cancer. These steady and incremental improvements in models will need to undergo revalidation.

The inherited genetics of ovarian and endometrial cancer

Endometrial and epithelial ovarian cancers are the fourth and fifth most common cancers in women in developed countries, after breast, lung, and colorectal cancer. In the United States alone, in 2008 there were about 40,000 new diagnoses of endometrial cancer and 7500 disease-related deaths. For ovarian cancer, there were about 22,000 new diagnoses and 15,000 deaths over the same period. The purpose of this article is to review the recent developments in the inherited genetics of ovarian and endometrial cancer, with particular attention to recent progress in identifying common low-penetrance susceptibility genes and their clinical implications.

Clinical application of breast cancer risk assessment models

With the evolving availability of testing for genetic cancer syndromes, oncologists now are increasingly expected to review family histories and to give a genetic risk assessment as part of their care for breast cancer. The most important of these breast cancer genetic syndromes identified to date have been those associated with the BRCA1 and BRCA2 genes. Therefore, the proper identification of potentially affected families and providing risk assessment estimates will be ever more essential. This review outlines several different available breast cancer risk assessment models. Risk models for the development of breast cancer as well as risk models that estimate the chance of having a genetic cancer syndrome are discussed. Their clinical applications are also outlined and clinical situations appropriate for each model are reviewed.

Prospective multicenter cohort study to refine management recommendations for women at elevated familial risk of breast cancer: the EVA trial

PURPOSE

We investigated the respective contribution (in terms of cancer yield and stage at diagnosis) of clinical breast examination (CBE), mammography, ultrasound, and quality-assured breast magnetic resonance imaging (MRI), used alone or in different combination, for screening women at elevated risk for breast cancer.

METHODS

Prospective multicenter observational cohort study. Six hundred eighty-seven asymptomatic women at elevated familial risk (> or = 20% lifetime) underwent 1,679 annual screening rounds consisting of CBE, mammography, ultrasound, and MRI, read independently and in different combinations. In a subgroup of 371 women, additional half-yearly ultrasound and CBE was performed more than 869 screening rounds. Mean and median follow-up was 29.18 and 29.09 months.

RESULTS

Twenty-seven women were diagnosed with breast cancer: 11 ductal carcinoma in situ (41%) and 16 invasive cancers (59%). Three (11%) of 27 were node positive. All cancers were detected during annual screening; no interval cancer occurred; no cancer was identified during half-yearly ultrasound. The cancer yield of ultrasound (6.0 of 1,000) and mammography (5.4 of 1,000) was equivalent; it increased nonsignificantly (7.7 of 1,000) if both methods were combined. Cancer yield achieved by MRI alone (14.9 of 1,000) was significantly higher; it was not significantly improved by adding mammography (MRI plus mammography: 16.0 of 1,000) and did not change by adding ultrasound (MRI plus ultrasound: 14.9 of 1,000). Positive predictive value was 39% for mammography, 36% for ultrasound, and 48% for MRI.

CONCLUSION

In women at elevated familial risk, quality-assured MRI screening shifts the distribution of screen-detected breast cancers toward the preinvasive stage. In women undergoing quality-assured MRI annually, neither mammography, nor annual or half-yearly ultrasound or CBE will add to the cancer yield achieved by MRI alone.

A genome-wide association study identifies a new ovarian cancer susceptibility locus on 9p22.2

Epithelial ovarian cancer has a major heritable component, but the known susceptibility genes explain less than half the excess familial risk. We performed a genome-wide association study (GWAS) to identify common ovarian cancer susceptibility alleles. We evaluated 507,094 SNPs genotyped in 1,817 cases and 2,353 controls from the UK and approximately 2 million imputed SNPs. We genotyped the 22,790 top ranked SNPs in 4,274 cases and 4,809 controls of European ancestry from Europe, USA and Australia. We identified 12 SNPs at 9p22 associated with disease risk (P < 10(-8)). The most significant SNP (rs3814113; P = 2.5 x 10(-17)) was genotyped in a further 2,670 ovarian cancer cases and 4,668 controls, confirming its association (combined data odds ratio (OR) = 0.82, 95% confidence interval (CI) 0.79-0.86, P(trend) = 5.1 x 10(-19)). The association differs by histological subtype, being strongest for serous ovarian cancers (OR 0.77, 95% CI 0.73-0.81, P(trend) = 4.1 x 10(-21)).

Association between common germline genetic variation in 94 candidate genes or regions and risks of invasive epithelial ovarian cancer

Background

Recent studies have identified several single nucleotide polymorphisms (SNPs) in the population that are associated with variations in the risks of many different diseases including cancers such as breast, prostate and colorectal. For ovarian cancer, the known highly penetrant susceptibility genes (BRCA1 and BRCA2) are probably responsible for only 40% of the excess familial ovarian cancer risks, suggesting that other susceptibility genes of lower penetrance exist.

Methods

We have taken a candidate approach to identifying moderate risk susceptibility alleles for ovarian cancer. To date, we have genotyped 340 SNPs from 94 candidate genes or regions, in up to 1,491 invasive epithelial ovarian cancer cases and 3,145 unaffected controls from three different population based studies from the UK, Denmark and USA.

Results

After adjusting for population stratification by genomic control, 18 SNPs (5.3%) were significant at the 5% level, and 5 SNPs (1.5%) were significant at the 1% level. The most significant association was for the SNP rs2107425, located on chromosome 11p15.5, which has previously been identified as a susceptibility allele for breast cancer from a genome wide association study (P-trend = 0.0012). When SNPs/genes were stratified into 7 different pathways or groups of validation SNPs, the breast cancer associated SNPs were the only group of SNPs that were significantly associated with ovarian cancer risk (P-heterogeneity = 0.0003; P-trend = 0.0028; adjusted (for population stratification) P-trend = 0.006). We did not find statistically significant associations when the combined data for all SNPs were analysed using an admixture maximum likelihood (AML) experiment-wise test for association (P-heterogeneity = 0.051; P-trend = 0.068).

Conclusion

These data suggest that a proportion of the SNPs we evaluated were associated with ovarian cancer risk, but that the effect sizes were too small to detect associations with individual SNPs.

Polymorphisms in NF-kappaB inhibitors and risk of epithelial ovarian cancer

Background

The nuclear factor-κB (NF-κB) family is a set of transcription factors with key roles in the induction of the inflammatory response and may be the link between inflammation and cancer development. This pathway has been shown to influence ovarian epithelial tissue repair. Inhibitors of κB (IκB) prevent NF-κB activation by sequestering NF-κB proteins in the cytoplasm until IκB proteins are phosphorylated and degraded.

Methods

We used a case-control study to evaluate the association between single nucleotide polymorphisms (SNPs) in NFKBIA and NFKBIB (the genes encoding IκBα and IκBβ, respectively) and risk of epithelial ovarian cancer. We queried 19 tagSNPs and putative-functional SNPs among 930 epithelial ovarian cancer cases and 1,037 controls from two studies.

Results

The minor allele for one synonymous SNP in NFKBIA, rs1957106, was associated with decreased risk (p = 0.03).

Conclusion

Considering the number of single-SNP tests performed and null gene-level results, we conclude that NFKBIA and NFKBIB are not likely to harbor ovarian cancer risk alleles. Due to its biological significance in ovarian cancer, additional genes encoding NF-κB subunits, activating and inhibiting molecules, and signaling molecules warrant interrogation.

Tagging single-nucleotide polymorphisms in candidate oncogenes and susceptibility to ovarian cancer

Low-moderate risk alleles that are relatively common in the population may explain a significant proportion of the excess familial risk of ovarian cancer (OC) not attributed to highly penetrant genes. In this study, we evaluated the risks of OC associated with common germline variants in five oncogenes (BRAF, ERBB2, KRAS, NMI and PIK3CA) known to be involved in OC development. Thirty-four tagging SNPs in these genes were genotyped in approximately 1800 invasive OC cases and 3000 controls from population-based studies in Denmark, the United Kingdom and the United States. We found no evidence of disease association for SNPs in BRAF, KRAS, ERBB2 and PIK3CA when OC was considered as a single disease phenotype; but after stratification by histological subtype, we found borderline evidence of association for SNPs in KRAS and BRAF with mucinous OC and in ERBB2 and PIK3CA with endometrioid OC. For NMI, we identified a SNP (rs11683487) that was associated with a decreased risk of OC (unadjusted P(dominant)=0.004). We then genotyped rs11683487 in another 1097 cases and 1792 controls from an additional three case-control studies from the United States. The combined odds ratio was 0.89 (95% confidence interval (CI): 0.80-0.99) and remained statistically significant (P(dominant)=0.032). We also identified two haplotypes in ERBB2 associated with an increased OC risk (P(global)=0.034) and a haplotype in BRAF that had a protective effect (P(global)=0.005). In conclusion, these data provide borderline evidence of association for common allelic variation in the NMI with risk of epithelial OC.

Validating genetic risk associations for ovarian cancer through the international Ovarian Cancer Association Consortium

The search for genetic variants associated with ovarian cancer risk has focused on pathways including sex steroid hormones, DNA repair, and cell cycle control. The Ovarian Cancer Association Consortium (OCAC) identified 10 single-nucleotide polymorphisms (SNPs) in genes in these pathways, which had been genotyped by Consortium members and a pooled analysis of these data was conducted. Three of the 10 SNPs showed evidence of an association with ovarian cancer at P< or =0.10 in a log-additive model: rs2740574 in CYP3A4 (P=0.011), rs1805386 in LIG4 (P=0.007), and rs3218536 in XRCC2 (P=0.095). Additional genotyping in other OCAC studies was undertaken and only the variant in CYP3A4, rs2740574, continued to show an association in the replication data among homozygous carriers: OR(homozygous(hom))=2.50 (95% CI 0.54-11.57, P=0.24) with 1406 cases and 2827 controls. Overall, in the combined data the odds ratio was 2.81 among carriers of two copies of the minor allele (95% CI 1.20-6.56, P=0.017, p(het) across studies=0.42) with 1969 cases and 3491 controls. There was no association among heterozygous carriers. CYP3A4 encodes a key enzyme in oestrogen metabolism and our finding between rs2740574 and risk of ovarian cancer suggests that this pathway may be involved in ovarian carcinogenesis. Additional follow-up is warranted.

Consortium analysis of 7 candidate SNPs for ovarian cancer

The Ovarian Cancer Association Consortium selected 7 candidate single nucleotide polymorphisms (SNPs), for which there is evidence from previous studies of an association with variation in ovarian cancer or breast cancer risks. The SNPs selected for analysis were F31I (rs2273535) in AURKA, N372H (rs144848) in BRCA2, rs2854344 in intron 17 of RB1, rs2811712 5' flanking CDKN2A, rs523349 in the 3' UTR of SRD5A2, D302H (rs1045485) in CASP8 and L10P (rs1982073) in TGFB1. Fourteen studies genotyped 4,624 invasive epithelial ovarian cancer cases and 8,113 controls of white non-Hispanic origin. A marginally significant association was found for RB1 when all studies were included [ordinal odds ratio (OR) 0.88 (95% confidence interval (CI) 0.79-1.00) p = 0.041 and dominant OR 0.87 (95% CI 0.76-0.98) p = 0.025]; when the studies that originally suggested an association were excluded, the result was suggestive although no longer statistically significant (ordinal OR 0.92, 95% CI 0.79-1.06). This SNP has also been shown to have an association with decreased risk in breast cancer. There was a suggestion of an association for AURKA, when one study that caused significant study heterogeneity was excluded [ordinal OR 1.10 (95% CI 1.01-1.20) p = 0.027; dominant OR 1.12 (95% CI 1.01-1.24) p = 0.03]. The other 5 SNPs in BRCA2, CDKN2A, SRD5A2, CASP8 and TGFB1 showed no association with ovarian cancer risk; given the large sample size, these results can also be considered to be informative. These null results for SNPs identified from relatively large initial studies shows the importance of replicating associations by a consortium approach.