Women carrying germ-line mutations in BRCA1 are strongly predisposed to developing breast cancers with characteristic features also observed in sporadic basal-like breast cancers. They appear as high-grade tumors with high proliferation rates and pushing borders. On the molecular level, they are negative for hormone receptors and ERBB2, display frequent TP53 mutations, and express basal epithelial markers. To study the role of BRCA1 and P53 loss of function in breast cancer development, we generated conditional mouse models with tissue-specific mutation of Brca1 and/or p53 in basal epithelial cells. Somatic loss of both BRCA1 and p53 resulted in the rapid and efficient formation of highly proliferative, poorly differentiated, estrogen receptor-negative mammary carcinomas with pushing borders and increased expression of basal epithelial markers, reminiscent of human basal-like breast cancer. BRCA1-and p53-deficient mouse mammary tumors exhibit dramatic genomic instability, and their molecular signatures resemble those of human BRCA1-mutated breast cancers. Thus, these tumors display important hallmarks of hereditary breast cancers in BRCA1-mutation carriers.mouse models ͉ conditional knockout G erm-line mutations in the human breast cancer susceptibility gene BRCA1 are responsible for 40% to 50% of hereditary breast cancers and confer increased risk for development of ovarian, colon, and prostate cancers (1, 2). BRCA1 has been implicated in various cellular processes, including maintenance of genome integrity, DNA replication and repair, chromatin remodeling, and transcriptional regulation (3, 4). Although the exact mechanism of mammary tumor suppression by BRCA1 remains largely unknown, cells with dysfunctional BRCA1 show defects in survival and proliferation, increased radiosensitivity, chromosomal abnormalities, G 2 /M checkpoint loss, and impaired homologous recombination repair (5).BRCA1-mutated breast cancers that arise in women with germline mutations in BRCA1 are high-grade, hormone receptornegative breast carcinomas with frequent mutation of TP53 (4, 6). They also possess a basal-like phenotype as defined by the expression of markers that are typical for basal/myoepithelial cells, such as the basal cytokeratins (CKs) CK5/6 and CK14 (7). Indeed, strong molecular similarities are observed between hereditary BRCA1-mutated breast cancers and sporadic basal-like breast carcinomas (8,9). This phenotypic overlap has led to the hypothesis that sporadic basal-like cancers may have defects in BRCA1-related pathways, such as the amplification of EMSY and the methylation of BRCA1 and FANCF (10).Despite the fact that several mouse strains with conventional or conditional mutations in Brca1 have been generated (11), no good mouse model for BRCA1-mutated basal-like breast cancer has been developed so far. Most conventional Brca1 knockouts are embryonic-lethal when bred to homozygosity, yet heterozygous ⌬11 allele, which encodes BRCA1-⌬11, a naturally occurring splice variant of Brca1 (19). Mouse mammary tumor models ba...
There is a strong need to better predict the survival of patients with newly diagnosed multiple myeloma (MM). As gene expression profiles (GEPs) reflect the biology of MM in individual patients, we built a prognostic signature based on GEPs. GEPs obtained from newly diagnosed MM patients included in the HOVON65/GMMG-HD4 trial (n ¼ 290) were used as training data. Using this set, a prognostic signature of 92 genes (EMC-92-gene signature) was generated by supervised principal component analysis combined with simulated annealing. Performance of the EMC-92-gene signature was confirmed in independent validation sets of newly diagnosed (total therapy (TT)2, n ¼ 351; TT3, n ¼ 142; MRC-IX, n ¼ 247) and relapsed patients (APEX, n ¼ 264). In all the sets, patients defined as high-risk by the EMC-92-gene signature show a clearly reduced overall survival (OS) with a hazard ratio (HR) of 3.40 (95% confidence interval (CI): 2.19-5.29) for the TT2 study, 5.23 (95% CI: 2.46-11.13) for the TT3 study, 2.38 (95% CI: 1.65-3.43) for the MRC-IX study and 3.01 (95% CI: 2.06-4.39) for the APEX study (Po0.0001 in all studies). In multivariate analyses this signature was proven to be independent of the currently used prognostic factors. The EMC-92-gene signature is better or comparable to previously published signatures. This signature contributes to risk assessment in clinical trials and could provide a tool for treatment choices in high-risk MM patients.
Introduction Several gene expression signatures have been proposed and demonstrated to be predictive of outcome in breast cancer. In the present article we address the following issues: Do these signatures perform similarly? Are there (common) molecular processes reported by these signatures? Can better prognostic predictors be constructed based on these identified molecular processes? Methods We performed a comprehensive analysis of the performance of nine gene expression signatures on seven different breast cancer datasets. To better characterize the functional processes associated with these signatures, we enlarged each signature by including all probes with a significant correlation to at least one of the genes in the original signature. The enrichment of functional groups was assessed using four ontology databases. Results The classification performance of the nine gene expression signatures is very similar in terms of assigning a sample to either a poor outcome group or a good outcome group. Nevertheless the concordance in classification at the sample level is low, with only 50% of the breast cancer samples classified in the same outcome group by all classifiers. The predictive accuracy decreases with the number of poor outcome assignments given to a sample. The best classification performance was obtained for the group of patients with only good outcome assignments. Enrichment analysis of the enlarged signatures revealed 11 functional modules with prognostic ability. The combination of the RNA-splicing and immune modules resulted in a classifier with high prognostic performance on an independent validation set. Conclusions The study revealed that the nine signatures perform similarly but exhibit a large degree of discordance in prognostic group assignment. Functional analyses indicate that proliferation is a common cellular process, but that other functional categories are also enriched and show independent prognostic ability. We provide new evidence of the potentially promising prognostic impact of immunity and RNA-splicing processes in breast cancer.
PURPOSE More than 80% of patients who undergo sentinel lymph node (SLN) biopsy have no nodal metastasis. Here, we describe a model that combines clinicopathologic and molecular variables to identify patients with thin- and intermediate-thickness melanomas who may forgo the SLN biopsy procedure because of their low risk of nodal metastasis. PATIENTS AND METHODS Genes with functional roles in melanoma metastasis were discovered by analysis of next-generation sequencing data and case-control studies. We then used polymerase chain reaction to quantify gene expression in diagnostic biopsy tissue across a prospectively designed archival cohort of 754 consecutive thin- and intermediate-thickness primary cutaneous melanomas. Outcome of interest was SLN biopsy metastasis within 90 days of melanoma diagnosis. A penalized maximum likelihood estimation algorithm was used to train logistic regression models in a repeated cross-validation scheme to predict the presence of SLN metastasis from molecular, clinical, and histologic variables. RESULTS Expression of genes with roles in epithelial-to-mesenchymal transition (glia-derived nexin, growth differentiation factor 15, integrin-β3, interleukin 8, lysyl oxidase homolog 4, transforming growth factor-β receptor type 1, and tissue-type plasminogen activator) and melanosome function (melanoma antigen recognized by T cells 1) were associated with SLN metastasis. The predictive ability of a model that only considered clinicopathologic or gene expression variables was outperformed by a model that included molecular variables in combination with the clinicopathologic predictors Breslow thickness and patient age (area under the receiver operating characteristic curve, 0.82; 95% CI, 0.78 to 0.86; SLN biopsy reduction rate, 42%; negative predictive value, 96%). CONCLUSION A combined model that included clinicopathologic and gene expression variables improved the identification of patients with melanoma who may forgo the SLN biopsy procedure because of their low risk of nodal metastasis.
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