Transcriptomic and functional pathway features were associated with survival after pathological complete response to neoadjuvant chemotherapy in breast cancer.

Pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) has been proposed as a surrogate endpoint for the prediction of long-term survival in breast cancer (BC); however, an increased pCR rate has not clearly correlated with improved survival. We hypothesized that some transcriptomic and functional pathway features correlate with survival after pCR in BC. We utilized 2 published NAC cohorts, 105 women with gene expression data before, "Baseline", and that changed during NAC, "Delta", and TCGA database with 1068 BC patients to investigate the relationship between the efficacy of NAC and survival utilizing differentially expressed-mRNAs, construction and analysis of the mRNA-hub gene network, and functional pathway analysis. In mRNA expression profiling, S100A8 was a gene involved in survival after pCR in Baseline and NDP was a gene involved in recurrence after pCR in Delta. In functional pathway analysis, we found multiple pathways involved in survival after pCR. In mRNA-hub gene analysis, HSP90AA1, EEF1A1, APP, and HSPA4 were related to recurrence in BC patients with pCR due to NAC. TP53, EGFR, CTNNB1, ERBB2, and HSPB1 may play a significant role in survival for patients with pCR. Interestingly, high HSP90AA1, HSPA4, S100A8, and TP53, and low EEF1A1, EGFR, and CTNNB1 expressing tumors have significantly worse overall survival in TCGA BC cohort. We demonstrated the genes and functional pathway features associated with pCR and survival utilizing the bioinformatics approach to public BC cohorts. Some genes involved in recurrence after pCR due to NAC also served as prognostic factors in primary BC.

Involvement of Chromatin Remodeling Genes and the Rho GTPases RhoB and CDC42 in Ovarian Clear Cell Carcinoma.

OBJECTIVE: Ovarian clear cell carcinomas (OCCCs) constitute a rare ovarian cancer subtype with distinct clinical features, but may nonetheless be difficult to distinguish morphologically from other subtypes. There is limited knowledge of genetic events driving OCCC tumorigenesis beyond ARID1A, which is reportedly mutated in 30-50% of OCCCs. We aimed to further characterize OCCCs by combined global transcriptional profiling and targeted deep sequencing of a panel of well-established cancer genes. Increased knowledge of OCCC-specific genetic aberrations may help in guiding development of targeted treatments and ultimately improve patient outcome. METHODS: Gene expression profiling of formalin-fixed, paraffin-embedded (FFPE) tissue from a cohort of the major ovarian cancer subtypes (cohort 1; n = 67) was performed using whole-genome cDNA-mediated Annealing, Selection, extension and Ligation (WG-DASL) bead arrays, followed by pathway, gene module score, and gene ontology analyses, respectively. A second FFPE cohort of 10 primary OCCCs was analyzed by targeted DNA sequencing of a panel of 60 cancer-related genes (cohort 2). Non-synonymous and non-sense variants affecting single-nucleotide variations and insertions or deletions were further analyzed. A tissue microarray of 43 OCCCs (cohort 3) was used for validation by immunohistochemistry and chromogenic in situ hybridization. RESULTS: Gene expression analyses revealed a distinct OCCC profile compared to other histological subtypes, with, e.g., ERBB2, TFAP2A, and genes related to cytoskeletal actin regulation being overexpressed in OCCC. ERBB2 was, however, not overexpressed on the protein level and ERBB2 amplification was rare in the validation cohort. Targeted deep sequencing revealed non-synonymous variants or insertions/deletions in 11/60 cancer-related genes. Genes involved in chromatin remodeling, including ARID1A, SPOP, and KMT2D were frequently mutated across OCCC tumors. CONCLUSION: OCCCs appear genetically heterogeneous, but harbor frequent alterations in chromatin remodeling genes. Overexpression of TFAP2A and ERBB2 was observed on the mRNA level in relation to other ovarian cancer subtypes. However, overexpression of ERBB2 was not reflected by HER2 amplification or protein overexpression in the OCCC validation cohort. In addition, Rho GTPase-dependent actin organization may also play a role in OCCC pathogenesis and warrants further investigation. The distinct biological features of OCCC discovered here may provide a basis for novel targeted treatment strategies.

A Case-Matched Gender Comparison Transcriptomic Screen Identifies eIF4E and eIF5 as Potential Prognostic Markers in Male Breast Cancer.

Purpose: Breast cancer affects both genders, but is understudied in men. Although still rare, male breast cancer (MBC) is being diagnosed more frequently. Treatments are wholly informed by clinical studies conducted in women, based on assumptions that underlying biology is similar.Experimental Design: A transcriptomic investigation of male and female breast cancer was performed, confirming transcriptomic data in silico Biomarkers were immunohistochemically assessed in 697 MBCs (n = 477, training; n = 220, validation set) and quantified in pre- and posttreatment samples from an MBC patient receiving everolimus and PI3K/mTOR inhibitor.Results: Gender-specific gene expression patterns were identified. eIF transcripts were upregulated in MBC. eIF4E and eIF5 were negatively prognostic for overall survival alone (log-rank P = 0.013; HR = 1.77, 1.12-2.8 and P = 0.035; HR = 1.68, 1.03-2.74, respectively), or when coexpressed (P = 0.01; HR = 2.66, 1.26-5.63), confirmed in the validation set. This remained upon multivariate Cox regression analysis [eIF4E P = 0.016; HR = 2.38 (1.18-4.8), eIF5 P = 0.022; HR = 2.55 (1.14-5.7); coexpression P = 0.001; HR = 7.04 (2.22-22.26)]. Marked reduction in eIF4E and eIF5 expression was seen post BEZ235/everolimus, with extended survival.Conclusions: Translational initiation pathway inhibition could be of clinical utility in MBC patients overexpressing eIF4E and eIF5. With mTOR inhibitors that target this pathway now in the clinic, these biomarkers may represent new targets for therapeutic intervention, although further independent validation is required. Clin Cancer Res; 23(10); 2575-83. ©2016 AACR.

Microarrays in breast cancer research and clinical practice--the future lies ahead.

Molecular profiling for classification and prognostic purposes has demonstrated that the genetic signatures of tumors contain information regarding biological properties as well as clinical behavior. This review highlights the progress that has been made in the field of gene expression profiling of human breast cancer. Breast cancer has become one of the most intensely studied human malignancies in the genomic era; several hundred papers over the last few years have investigated various clinical and biological aspects of human breast cancer using high-throughput molecular profiling techniques. Given the grossly heterogeneous nature of the disease and the lack of robust conventional markers for disease prediction, prognosis, and response to treatment, the notion that a transcriptional profile comprising multiple genes, rather than any single gene or other parameter, will be more predictive of tumor behavior is both appealing and reasonable. Promising results have emerged from these studies, correlating gene expression profiles with prognosis, recurrence, metastatic potential, therapeutic response, as well as biological and functional aspects of the disease. Clearly, the integration of genomic approaches into the clinic lies in the near future, but prospective studies based on larger patient cohorts representing the whole spectrum of breast cancer, oncogenic pathway-based studies, attendant care in bioinformatic analyses and validation studies are needed before the full promise of gene expression profiling can be realized in the clinical setting.

Characterization of a novel breast carcinoma xenograft and cell line derived from a BRCA1 germ-line mutation carrier.

A human tumor xenograft (L56Br-X1) was established from a breast cancer axillary lymph node metastasis of a 53-year-old woman with a BRCA1 germ-line nonsense mutation (1806C>T; Q563X), and a cell line (L56Br-C1) was subsequently derived from the xenograft. The xenograft carries only the mutant BRCA1 allele and expresses mutant BRCA1 mRNA but no BRCA1 protein as determined by immunoprecipitation or Western blotting. The primary tumor, lymph node metastasis, and xenograft were hypodiploid by DNA flow cytometry, whereas the cell line displayed an aneuploidy apparently developed via polyploidization. Cytogenetic analysis, spectral karyotyping, and comparative genomic hybridization of the cell line revealed a highly complex karyotype with numerous unbalanced translocations. The xenograft and cell line had retained a somatic TP53 missense mutation (S215I) originating from the primary tumors, as well as a lack of immunohistochemically detectable expression of steroid hormone receptors, epidermal growth factor receptor, human epidermal growth factor receptor 2 (HER-2), and keratin 8. Global gene expression analysis by cDNA microarrays supported a correlation between the expression profiles of the primary tumor, lymph node metastasis, xenograft, and cell line. We conclude that L56Br-X1 and L56Br-C1 are useful model systems for studies of the pathogenesis and new therapeutic modalities of BRCA1-induced human breast cancer.

Gene expression in inherited breast cancer.

Large proportions of hereditary breast cancers are due to mutations in the two breast cancer susceptibility genes BRCA1 and BRCA2. Considerable effort has gone into studying the function(s) of these tumor suppressor genes, both in attempts to better understand why individuals with these inherited mutations acquire breast (and ovarian) cancer and to potentially develop better treatment strategies. The advent of tools such as cDNA microarrays has enabled researchers to study global gene expression patterns in, for example, primary tumors, thus providing more comprehensive overviews of tumor development and progression. Our recent study (Hedenfalk et al., 2001) strongly supports the principle that genomic approaches to classification of hereditary breast cancers are possible, and that further studies will likely identify the most significant genes that discriminate between subgroups and may influence prognosis and treatment. A large number of hereditary breast cancer cases cannot be accounted for by mutations in these two genes and are believed to be due to as yet unidentified breast cancer predisposition genes (BRCAx). Subclassification of these non-BRCA1/2 breast cancers using cDNA microarray-based gene expression profiling, followed by linkage analysis and/or investigation of genomic alterations, may help in the recognition of novel breast cancer predisposition loci. To summarize, gene expression-based analysis of hereditary breast cancer can potentially be used for classification purposes, as well as to expand upon our knowledge of differences between different forms of hereditary breast cancer. Initial studies indicate that a patient's genotype does in fact leave an identifiable trace on her/his cancer's gene expression profile.