Hereditary breast cancer: new genetic developments, new therapeutic avenues

High-throughput resequencing in the diagnosis of BRCA1/2 mutations using oligonucleotide resequencing microarrays

Breast cancer is the most frequent form of carcinoma in European females (incidence 65 per 100,000). In about 10% of all cases, pedigree analysis predicts a hereditary breast-ovarian cancer syndrome (HBOC) to be causative for the disease. Frequently, mutations in two genes, BRCA1 (Chr. 17q21) and BRCA2 (Chr. 13q12), are associated with HBOC. In females, mutations in these genes result in a lifetime risk of 80-85% for breast cancer and 54% (BRCA1) or 23% (BRCA2) for ovarian cancer. Current genetic diagnostic tools for BRCA1 and BRCA2 remain laborious and expensive. Here, we present the first oligonucleotide resequencing microarray covering the complete coding sequence of both genes. In total, 36 previously characterized DNAs were resequenced; all 11 patients with single-nucleotide mutations and, due to a special mutational design, eight patients with heterozygous deletions were detected correctly. In total, 47 different single-nucleotide variants (SNVs) were found. A newly developed software, SeqC, reduced the number of ambiguous calls with the help of a statistical module comparing the acquired data to an online-database. SeqC improved the average call rate to 99% (GSeq: 97%) and reduced time and efforts for manual analysis. SeqC confirmed the results obtained by GSeq and found an additional 33 sequences changes representing 14 SNVs. In total, 945 kb were screened and the overall turnaround time for each patient took approximately 3 days, including analysis.

Screening and prevention of breast cancer in primary care

Mammography remains the mainstay of breast cancer screening. There is little controversy that mammography reduces the risk of dying from breast cancer by about 23% among women between the ages of 50 and 69 years, although the harms associated with false-positive results and overdiagnosis limit the net benefit of mammography. Women in their 70s may have a small benefit from screening mammography, but overdiagnosis increases in this age group as do competing causes of death. While new data support a 16% reduction in breast cancer mortality for 40- to 49-year-old women after 10 years of screening, the net benefit is less compelling in part because of the lower incidence of breast cancer in this age group and because mammography is less sensitive and specific in women younger than 50 years. Digital mammography is more sensitive than film mammography in young women with similar specificity, but no improvements in breast cancer outcomes have been demonstrated. Magnetic resonance imaging may benefit the highest risk women. Randomized trials suggest that self-breast examination does more harm than good. Primary prevention with currently approved medications will have a negligible effect on breast cancer incidence. Public health efforts aimed at increasing mammography screening rates, promoting regular exercise in all women, maintaining a healthy weight, limiting alcohol intake, and limiting postmenopausal hormone therapy may help to continue the recent trend of lower breast cancer incidence and mortality among American women.

Learning to predict relapse in invasive ductal carcinomas based on the subcellular localization of junctional proteins

The complexity of breast cancer biology makes it challenging to analyze large datasets of clinicopathologic and molecular attributes, toward identifying the key prognostic features and producing systems capable of predicting which patients are likely to relapse. We applied machine-learning techniques to analyze a set of well-characterized primary breast cancers, which specified the abundance and localization of various junctional proteins. We hypothesized that disruption of junctional complexes would lead to the cytoplasmic/nuclear redistribution of the protein components and their potential interactions with growth-regulating molecules, which would promote relapse, and that machine-learning techniques could use the subcellular locations of these proteins, together with standard clinicopathological data, to produce an efficient prognostic classifier. We used immunohistochemistry to assess the expression and subcellular distribution of six junctional proteins, in addition to a panel of eight standard clinical features and concentrations of four "growth-regulating" proteins, to produce a database involving 36 features, over 66 primary invasive ductal breast carcinomas. A machine-learning system was applied to this clinicopathologic dataset to produce a decision-tree classifier that could predict whether a novel breast cancer patient would relapse. We show that this decision-tree classifier, which incorporates a combination of only four features (nuclear alpha- and beta-catenin levels, the total level of PTEN and the number of involved axillary lymph nodes), is able to correctly classify patient outcomes essentially 80% of the time. Further, this classifier is significantly better than classifiers based on any subgroup of these 36 features. This study demonstrates that autonomous machine-learning techniques are able to generate simple and efficient decision-tree prognostic classifiers from a wide variety of clinical, pathologic and biomarker data, and unlike other analytic methods, suggest testable biologic relationships among explicitly identified key variables. The decision-tree classifier resulting from these analytic methods is sufficiently simple and should be widely applicable to a spectrum of clinical cancer settings. Further, the subcellular distribution of junctional proteins, which influences growth regulatory pathways involved in locoregional and metastatic relapse of breast cancer, helped to identify which patients would relapse while their total concentration did not. This emphasizes the need to evaluate the subcellular distribution of junctional proteins in assessing their contribution to tumor progression.

High incidence of protein-truncating TP53 mutations in BRCA1-related breast cancer

Approximately half of all hereditary breast cancers are compromised in their DNA repair mechanisms due to loss of BRCA1 or BRCA2 function. Previous research has found a strong correlation between BRCA mutation and TP53 mutation. However, TP53 mutation status is often indirectly assessed by immunohistochemical staining of accumulated p53 protein. We sequenced TP53 exons 2 to 9 in 21 BRCA1-related breast cancers and 37 sporadic breast tumors. Strikingly, all BRCA1-related breast tumors contained TP53 mutations, whereas only half of these tumors stained positive for p53 accumulation. Positive p53 staining correlates with the presence of TP53 hotspot mutations in both BRCA1-related and sporadic breast tumors. However, whereas the majority of sporadic breast tumors that stained negative for p53 accumulation had wild-type TP53, the majority of BRCA1-associated breast tumors that stained negative for p53 accumulation had protein-truncating TP53 mutations (nonsense, frameshift, and splice mutations). Therefore, the strong selection for p53 loss in BRCA1-related tumors is achieved by an increase of protein-truncating TP53 mutations rather than hotspot mutations. Hence, immunohistochemical detection of TP53 mutation could lead to misdiagnosis in approximately half of all BRCA1-related tumors. The presence of deleterious TP53 mutations in most, if not all, BRCA1-related breast cancers suggests that p53 loss of function is essential for BRCA1-associated tumorigenesis. BRCA1-related tumors may therefore be treated not only with drugs that target BRCA1 deficiency [e.g., poly(ADP-ribose) polymerase inhibitors] but also with drugs that selectively target p53-deficient cells. This raises interesting possibilities for combination therapies against BRCA1-deficient breast cancers and BRCA1-like tumors with homologous recombination deficiency.

The influence of genetic variation in 30 selected genes on the clinical characteristics of early onset breast cancer

Introduction

Common variants that alter breast cancer risk are being discovered. Here, we determine how these variants influence breast cancer prognosis, risk and tumour characteristics.

Methods

We selected 1,001 women with early onset nonfamilial invasive breast cancer from the Prospective study of Outcomes in Sporadic versus Hereditary breast cancer (POSH) cohort and genotyped 206 single nucleotide polymorphisms (SNPs) across 30 candidate genes. After quality control, 899 cases and 133 SNPs remained. Survival analyses were used to identify SNPs associated with prognosis and determine their interdependency with recognized prognostic factors. To identify SNPs that alter breast cancer risk, association tests were used to compare cases with controls from the Wellcome Trust Case Control Consortium. To search for SNPs affecting tumour biology, cases were stratified into subgroups according to oestrogen receptor (ER) status and grade and tested for association.

Results

We confirmed previous associations between increased breast cancer risk and SNPs in CASP8, TOX3 (previously known as TNRC9) and ESR1. Analysis of prognosis identified eight SNPs in six genes (MAP3K1, DAPK1, LSP1, MMP7, TOX3 and ESR1) and one region without genes on 8q24 that are associated with survival. For MMP7, TOX3 and MAP3K1 the effects on survival are independent of the main recognized clinical prognostic factors. The SNP in 8q24 is more weakly associated with independent effects on survival. Once grade and pathological nodal status (pN stage) were taken into account, SNPs in ESR1 and LSP1 showed no independent survival difference, whereas the effects of the DAPK1 SNP were removed when correcting for ER status. Interestingly, effects on survival for SNPs in ESR1 were most significant when only ER-positive tumours were examined. Stratifying POSH cases by tumour characteristics identified SNPs in FGFR2 and TOX3 associated with ER-positive disease and SNPs in ATM associated with ER-negative disease.

Conclusions

We have demonstrated that several SNPs are associated with survival. In some cases this appears to be due to an effect on tumour characteristics known to have a bearing on prognosis; in other cases the effect appears to be independent of these prognostic factors. These findings require validatation by further studies in similar patient groups.

Identification of a novel CHEK2 variant and assessment of its contribution to the risk of breast cancer in French Canadian women

BACKGROUND

BRCA1 and BRCA2 account for the majority of the known familial breast cancer risk, however, the impact of other cancer susceptibility genes largely remains to be elucidated. Checkpoint Kinase 2 (CHEK2) is an important signal transducer of cellular responses to DNA damage, whose defects have been associated with an increase in breast cancer risk. Previous studies have identified low penetrance CHEK2 alleles such as 1100delC and I157T, as well as variants such as S428F in the Ashkenazi Jewish population and IVS2 + 1G>A in the Polish population. No founder allele has been specifically identified in the French Canadian population.

METHODS

The 14 coding exons of CHEK2 were fully sequenced for variant alleles in a panel of 25 affected French Canadian women and 25 healthy controls. Two variants were identified of which one novel variant was further screened for in an additional panel of 667 breast cancer patients and 6548 healthy controls. Additional genotyping was conducted using allele specific PCR and a restriction digest assay. Significance of amino acid substitutions were deduced by employing comparative analysis techniques.

RESULTS

Two variants were identified: the previously reported silent substitution 252A>G (E84E) and the novel missense variant, 1217G>A (R406H). No significant difference in allele distribution between French Canadian women with breast cancer and healthy controls was observed (3/692, 0.43% vs. 22/6573, 0.33%, respectively, P = 0.73).

CONCLUSION

The novel CHEK2 missense variant identified in this study, R406H, is unlikely to contribute to breast cancer risk in French Canadian women.

Analysis of the genes coding for the BRCA1-interacting proteins, RAP80 and Abraxas (CCDC98), in high-risk, non-BRCA1/2, multiethnic breast cancer cases

Background Around half of familial breast cancer cases are caused by germ-line mutations in genes which are critically involved in the maintenance of genome stability. Mutations in related genes functioning in DNA repair may account for currently unattributed cases. Two such genes, RAP80 and Abraxas, have recently been identified to be in a complex with BRCA1, and are required for the localization of BRCA1 to DNA damage foci. Methods RAP80 and Abraxas variants were screened for in a cohort of 95 high risk, non-BRCA1/2 breast cancer cases of varying ethnicity: those of Ashkenazi Jewish (n = 35), mixed Canadian (n = 34) and Swiss descent (n = 26). Results We have identified four missense variants, four silent SNPs, three SNPs in the UTRs and seven intronic variants in RAP80. Two of the previously reported RAP80 variants were further investigated. In Abraxas, we have identified two missense, nine intronic and two variants in the 3' UTR. Conclusions Overall, it seems unlikely that moderate to highly penetrant alleles of either RAP80 or Abraxas, confer a significantly high relative risk of breast cancer.