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Genetic interactions between INPP4B and RAD50 is prognostic of breast cancer survival.

ABSTRACT: RAD50 is commonly depleted in basal-like breast cancer with concomitant absence of INPP4B and several tumor suppressors such as BRCA1 and TP53. Our previous study revealed that INPP4B and RAD50 interact and such an interaction is associated with breast cancer survival at the transcriptional, translational and genomic levels. In the present study, we explored single nucleotide polymorphisms (SNPs) of these two genes that have synergistic effects on breast cancer survival to decipher mechanisms driving their interactions at the genetic level. The Cox's proportional hazards model was used to test whether SNPs of these two genes are interactively associated with breast cancer survival, following expression quantitative trait loci (eQTL) analysis and functional investigations. Our study revealed two disease-associating blocks, each encompassing five and two non-linkage disequilibrium linked SNPs of INPP4B and RAD50, respectively. Concomitant presence of any rare homozygote from each disease-associating block is synergistically prognostic of poor breast cancer survival. Such synergy is mediated via bypassing pathways controlling cell proliferation and DNA damage repair, which are represented by INPP4B and RAD50. Our study provided genetic evidence of interactions between INPP4B and RAD50, and deepened our understandings on the orchestrated genetic machinery governing tumor progression.

SUBMITTER: Chen X

PROVIDER: S-EPMC6954369 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Genetic interactions between INPP4B and RAD50 is prognostic of breast cancer survival.

Chen Xiao X Theobard Rutaganda R Zhang Jianying J Dai Xiaofeng X

Bioscience reports 20200101 1

RAD50 is commonly depleted in basal-like breast cancer with concomitant absence of INPP4B and several tumor suppressors such as BRCA1 and TP53. Our previous study revealed that INPP4B and RAD50 interact and such an interaction is associated with breast cancer survival at the transcriptional, translational and genomic levels. In the present study, we explored single nucleotide polymorphisms (SNPs) of these two genes that have synergistic effects on breast cancer survival to decipher mechanisms dr ...[more]

PMID: 31872854

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Project description:Introduction: BRIP1 (BRCA1-interacting protein 1) is one of the major interacting partners of BRCA1, which plays an important role in repair by homologous recombination (HR). This gene is mutated in around 4% of cases of breast cancer; however, its mechanism of action is unclear. In this study, we presented the fundamental role of BRCA1 interactors BRIP1 and RAD50 in the development of differential severity in triple-negative breast cancer (TNBC) among various affected individuals. Methods: We have analyzed the expression of DNA repair-related genes in different BC cells using Real-time PCR and western blotting analysis and assessed changes in stemness property and proliferation through Immunophenotyping. We have performed cell cycle analysis to see the defect in checkpoints and also immunofluorescence assay to confirm the accumulation of gamma-H2AX and BRCA1 foci and subsequent incidence. We have performed a severity analysis using TCGA data sets for comparing the expression in MDA-MB-468 MDA-MB-231 and MCF7 cell line. Results: We showed that in some TNBC cell lines such as MDA-MB-231, the functioning of both BRCA1/TP53 is compromised. Furthermore, the sensing of DNA damage is affected. Due to less damage-sensing capability and low availability of BRCA1 at the damage sites, the repair by HR becomes inefficient, leading to more damage. Accumulation of damage sends a signal for over activation of NHEJ repair pathways. Over expressed NHEJ molecules with compromised HR and checkpoint conditions lead to higher proliferation and error-prone repair, which increases the mutation rate and corresponding tumour severity. The in-silico analysis of the TCGA datasets with gene expression in the deceased population showed a significant correlation of BRCA1 expression with overall survival (OS) in TNBCs (0.0272). The association of BRCA1 with OS became stronger with the addition of BRIP1 expression (0.000876**). Conclusion: The severity phenotypes were more in cells having compromised BRCA1-BRIP1 functioning. Since the OS is directly proportional to the extent of severity, the data analysis hints at the role of BRIP1 in controlling the severity of TNBC.

Project description:BackgroundRecently, several genome-wide association studies have identified various genetic susceptibility loci for breast cancer. Relatively little is known about the possible interactions between these loci and the established risk factors for breast cancer.MethodsTo assess interactions between single-nucleotide polymorphisms (SNPs) and established risk factors, we prospectively collected DNA samples and questionnaire data from 8576 breast cancer case subjects and 11 892 control subjects nested within the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium (BPC3). We genotyped 17 germline SNPs (FGFR2-rs2981582, FGFR2-rs3750817, TNRC9-rs3803662, 2q35-rs13387042, MAP3K1-rs889312, 8q24-rs13281615, CASP8-rs1045485, LSP1-rs3817198, COL1A1-rs2075555, COX11-rs6504950, RNF146-rs2180341, 6q25-rs2046210, SLC4A7-rs4973768, NOTCH2-rs11249433, 5p12-rs4415084, 5p12-rs10941679, RAD51L1-rs999737), and odds ratios were estimated by logistic regression to confirm previously reported associations with breast cancer risk. We performed likelihood ratio test to assess interactions between 17 SNPs and nine established risk factors (age at menarche, parity, age at menopause, use of hormone replacement therapy, family history, height, body mass index, smoking status, and alcohol consumption), and a correction for multiple testing of 153 tests (adjusted P value threshold = .05/153 = 3 × 10(-4)) was done. Case-case comparisons were performed for possible differential associations of polymorphisms by subgroups of tumor stage, estrogen and progesterone receptor status, and age at diagnosis. All statistical tests were two-sided.ResultsWe confirmed the association of 14 SNPs with breast cancer risk (P(trend) = 2.57 × 10(-3) -3.96 × 10(-19)). Three SNPs (LSP1-rs3817198, COL1A1-rs2075555, and RNF146-rs2180341) did not show association with breast cancer risk. After accounting for multiple testing, no statistically significant interactions were detected between the 17 SNPs and the nine risk factors. We also confirmed that SNPs in FGFR2 and TNRC9 were associated with greater risk of estrogen receptor-positive than estrogen receptor-negative breast cancer (P(heterogeneity) = .0016 for FGFR2-rs2981582 and P(heterogeneity) = .0053 for TNRC9-rs3803662). SNP 5p12-rs10941679 was statistically significantly associated with greater risk of progesterone receptor-positive than progesterone receptor-negative breast cancer (P(heterogeneity) = .0028).ConclusionThis study does not support the hypothesis that known common breast cancer susceptibility loci strongly modify the associations between established risk factors and breast cancer.

Project description:Breast cancer is the second largest cause of cancer death among U.S. women and the leading cause of cancer death among women worldwide. Genome-wide association studies (GWAS) have identified several genetic variants associated with susceptibility to breast cancer, but these still explain less than half of the estimated genetic contribution to the disease. Combinations of variants (i.e. genetic interactions) may play an important role in breast cancer susceptibility. However, due to a lack of statistical power, the current tests for genetic interactions from GWAS data mainly leverage prior knowledge to focus on small sets of genes or SNPs that are known to have an association with breast cancer. Thus, many genetic interactions, particularly among novel variants, remain understudied. Reverse-genetic interaction screens in model organisms have shown that genetic interactions frequently cluster into highly structured motifs, where members of the same pathway share similar patterns of genetic interactions. Based on this key observation, we recently developed a method called BridGE to search for such structured motifs in genetic networks derived from GWAS studies and identify pathway-level genetic interactions in human populations. We applied BridGE to six independent breast cancer cohorts and identified significant pathway-level interactions in five cohorts. Joint analysis across all five cohorts revealed a high confidence consensus set of genetic interactions with support in multiple cohorts. The discovered interactions implicated the glutathione conjugation, vitamin D receptor, purine metabolism, mitotic prometaphase, and steroid hormone biosynthesis pathways as major modifiers of breast cancer risk. Notably, while many of the pathways identified by BridGE show clear relevance to breast cancer, variants in these pathways had not been previously discovered by traditional single variant association tests, or single pathway enrichment analysis that does not consider SNP-SNP interactions.

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Genetic interactions between INPP4B and RAD50 is prognostic of breast cancer survival.

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Genetic interactions between INPP4B and RAD50 is prognostic of breast cancer survival.

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