Germline mutation in the RAD51B gene confers predisposition to breast cancer.

BACKGROUND: Most currently known breast cancer predisposition genes play a role in DNA repair by homologous recombination. Recent studies conducted on RAD51 paralogs, involved in the same DNA repair pathway, have identified rare germline mutations conferring breast and/or ovarian cancer predisposition in the RAD51C, RAD51D and XRCC2 genes. The present study analysed the five RAD51 paralogs (RAD51B, RAD51C, RAD51D, XRCC2, XRCC3) to estimate their contribution to breast and ovarian cancer predisposition. METHODS: The study was conducted on 142 unrelated patients with breast and/or ovarian cancer either with early onset or with a breast/ovarian cancer family history. Patients were referred to a French family cancer clinic and had been previously tested negative for a BRCA1/2 mutation. Coding sequences of the five genes were analysed by EMMA (Enhanced Mismatch Mutation Analysis). Detected variants were characterized by Sanger sequencing analysis. RESULTS: Three splicing mutations and two likely deleterious missense variants were identified: RAD51B c.452 + 3A > G, RAD51C c.706-2A > G, RAD51C c.1026 + 5_1026 + 7del, RAD51B c.475C > T/p.Arg159Cys and XRCC3 c.448C > T/p.Arg150Cys. No RAD51D and XRCC2 gene mutations were detected. These mutations and variants were detected in families with both breast and ovarian cancers, except for the RAD51B c.475C > T/p.Arg159Cys variant that occurred in a family with 3 breast cancer cases. CONCLUSIONS: This study identified the first RAD51B mutation in a breast and ovarian cancer family and is the first report of XRCC3 mutation analysis in breast and ovarian cancer. It confirms that RAD51 paralog mutations confer breast and ovarian cancer predisposition and are rare events. In view of the low frequency of RAD51 paralog mutations, international collaboration of family cancer clinics will be required to more accurately estimate their penetrance and establish clinical guidelines in carrier individuals.

Importance of pre-analytical steps for transcriptome and RT-qPCR analyses in the context of the phase II randomised multicentre trial REMAGUS02 of neoadjuvant chemotherapy in breast cancer patients.

BACKGROUND: Identification of predictive markers of response to treatment is a major objective in breast cancer. A major problem in clinical sampling is the variability of RNA templates, requiring accurate management of tumour material and subsequent analyses for future translation in clinical practice. Our aim was to establish the feasibility and reliability of high throughput RNA analysis in a prospective trial. METHODS: This study was conducted on RNA from initial biopsies, in a prospective trial of neoadjuvant chemotherapy in 327 patients with inoperable breast cancer. Four independent centres included patients and samples. Human U133 GeneChips plus 2.0 arrays for transcriptome analysis and quantitative RT-qPCR of 45 target genes and 6 reference genes were analysed on total RNA. RESULTS: Thirty seven samples were excluded because i) they contained less than 30% malignant cells, or ii) they provided RNA Integrity Number (RIN) of poor quality. Among the 290 remaining cases, taking into account strict quality control criteria initially defined to ensure good quality of sampling, 78% and 82% samples were eligible for transcriptome and RT-qPCR analyses, respectively. For RT-qPCR, efficiency was corrected by using standard curves for each gene and each plate. It was greater than 90% for all genes. Clustering analysis highlighted relevant breast cancer phenotypes for both techniques (ER+, PR+, HER2+, triple negative). Interestingly, clustering on trancriptome data also demonstrated a "centre effect", probably due to the sampling or extraction methods used in on of the centres. Conversely, the calibration of RT-qPCR analysis led to the centre effect withdrawing, allowing multicentre analysis of gene transcripts with high accuracy. CONCLUSIONS: Our data showed that strict quality criteria for RNA integrity assessment and well calibrated and standardized RT-qPCR allows multicentre analysis of genes transcripts with high accuracy in the clinical context. More stringent criteria are needed for transcriptome analysis for clinical applications.

Tumor aromatase expression as a prognostic factor for local control in young breast cancer patients after breast-conserving treatment.

INTRODUCTION: We sought to determine whether the levels of expression of 17 candidate genes were associated with locoregional control after breast-conserving treatments of early-stage breast cancers in young, premenopausal women. METHODS: Gene expression was measured by using RT-PCR in the breast tumors of a series of 53 young (younger than 40 years), premenopausal patients. All treatments consisted of primary breast-conserving surgery followed by whole-breast radiotherapy (+/- regional lymph nodes) with or without systemic treatments (chemotherapy +/- hormone therapy). The median follow-up was 10 years. RESULTS: The 10-year locoregional control rate was 70% (95% CI, 57% to 87%). In univariate analysis, no clinical/pathologic prognostic factors were found to be significantly associated with decreased locoregional control. Expression of three genes was found to be significantly associated with an increased locoregional recurrence rate: low estrogen-receptor beta, low aromatase, and high GATA3. Two others were associated with only a trend (P < 0.10): low HER1 and SKP2. In multivariate analysis, only the absence of aromatase was significantly associated with an increased locoregional recurrence rate (P = 0.003; relative risk = 0.49; 95% CI 0.29 to 0.82). CONCLUSIONS: Recent data give credit to the fact that breast cancer in young women is a distinct biologic entity driven by special oncogenic pathways. Our results highlight the role of estrogen-signaling pathways (mainly CYP19/aromatase, GATA3, and ER-beta) in the risk of locoregional recurrence of breast cancer in young women. Confirmation in larger prospective studies is needed.

Quantitation of estradiol receptors alpha and beta and progesterone receptors in human breast tumors by real-time reverse transcription-polymerase chain reaction. Correlation with protein assays.

Hormone receptor content is the most useful parameter for predicting hormone response therapy in breast cancer. The high frequency of primary and secondary resistance to treatment makes it necessary to find out other parameters in order to improve the prediction of response to treatment. The newly described estrogen receptor beta (ERbeta) is a potential candidate. Using real-time quantitative RT-PCR, we evaluated estrogen receptor alpha (ERalpha), ERbeta, and progesterone receptors (PR) in comparison with ERalpha and PR protein content measured with the enzyme immunoassay (EIA), in a retrospective series of 98 breast tumors. We obtained a highly significant correlation between mRNA and EIA assays for ERalpha and PR (r=0.79 and r=0.71, respectively; P<0.001). We confirmed the low level of ERbeta mRNA transcripts in comparison to ERalpha in breast tumors. We did not find any statistically significant correlation between the absolute ERbeta mRNA level and ERalpha or PR mRNA level, and ERalpha or PR-EIA. We found a significant correlation between ERalpha mRNA and PR mRNA expressions. We did not find any correlation between ERbeta mRNA and clinical features of the patients (age, menopausal status, tumor size, and nodal status), nor with the histological type of the tumor. In conclusion, the accuracy of the present quantitative RT-PCR assay makes it suitable for a routine clinical use. In addition, the present results suggest that, ERbeta mRNA expression is independent of the classical parameters.