Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network modeling strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or 'omic') data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR, encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer-associated gene BRCA1. Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network modeling strategy should be useful for the discovery of additional cancer-associated genes.

No abstract

The importance of the p53 stress response pathway in the suppression of tumor formation is well documented. In a previous report, a single nucleotide polymorphism (SNP309 T/G) was found in the promoter of the MDM2 gene resulting in higher levels of MDM2 RNA and protein and, consequently, in the attenuation of the p53 pathway both in vitro and in vivo. As the SNP309 locus is found in a region of the MDM2 promoter, which is regulated by hormonal signaling pathways, and the G-allele of SNP309 increases the affinity of a welldescribed cotranscriptional activator of nuclear hormone receptors (i.e., Sp1), the hypothesis that the SNP309 locus could alter the effects of hormones on tumorigenesis was tested in vivo in humans. Data obtained from patients with three different sporadic cancers, from four independent case studies, support this hypothesis, providing an example for the genetic basis of gender differences in cancer and showing that the genotype at a specific locus can affect how hormones, like estrogen, affect tumorigenesis in humans.

Germline BRCA1 mutations predispose to breast cancer. To identify genetic modifiers of this risk, we performed a genome-wide association study in 1,193 individuals with BRCA1 mutations who were diagnosed with invasive breast cancer under age 40 and 1,190 BRCA1 carriers without breast cancer diagnosis over age 35. We took forward 96 SNPs for replication in another 5,986 BRCA1 carriers (2,974 individuals with breast cancer and 3,012 unaffected individuals). Five SNPs on 19p13 were associated with breast cancer risk (Ptrend = 2.3 × 10−9 to Ptrend = 3.9 × 10−7), two of which showed independent associations (rs8170, hazard ratio (HR) = 1.26, 95% CI 1.17–1.35; rs2363956 HR = 0.84, 95% CI 0.80–0.89). Genotyping these SNPs in 6,800 population-based breast cancer cases and 6,613 controls identified a similar association with estrogen receptor–negative breast cancer (rs2363956 per-allele odds ratio (OR) = 0.83, 95% CI 0.75–0.92, Ptrend = 0.0003) and an association with estrogen receptor–positive disease in the opposite direction (OR = 1.07, 95% CI 1.01–1.14, Ptrend = 0.016). The five SNPs were also associated with triple-negative breast cancer in a separate study of 2,301 triple-negative cases and 3,949 controls (Ptrend = 1 × 10−7 to Ptrend = 8 × 10−5; rs2363956 per-allele OR = 0.80, 95% CI 0.74–0.87, Ptrend = 1.1 × 10−7).