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Improving Pathological Assessment of Breast Cancer by Employing Array-Based Transcriptome Analysis.

ABSTRACT: Breast cancer research has paved the way of personalized oncology with the introduction of hormonal therapy and the measurement of estrogen receptor as the first widely accepted clinical biomarker. The expression of another receptor-HER2/ERBB2/neu-was initially a sign of worse prognosis, but targeted therapy has granted improved outcome for these patients so that today HER2 positive patients have better prognosis than HER2 negative patients. Later, the introduction of multigene assays provided the pathologists with an unbiased assessment of the tumors' molecular fingerprint. The recent FDA approval of complete microarray pipelines has opened new possibilities for the objective classification of breast cancer samples. Here we review the applications of microarrays for determining ER and HER2 status, molecular subtypes as well as predicting prognosis and grade for breast cancer patients. An open question remains the role of single genes within such signatures. Openly available microarray datasets enable the execution of an independent cross-validation of new marker and signature candidates. In summary, we review the current state regarding clinical applications of microarrays in breast cancer molecular pathology.

SUBMITTER: Mihaly Z

PROVIDER: S-EPMC5003464 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature

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Improving Pathological Assessment of Breast Cancer by Employing Array-Based Transcriptome Analysis.

Mihály Zsuzsanna Z Győrffy Balázs B

Microarrays (Basel, Switzerland) 20130829 3

Breast cancer research has paved the way of personalized oncology with the introduction of hormonal therapy and the measurement of estrogen receptor as the first widely accepted clinical biomarker. The expression of another receptor-HER2/ERBB2/neu-was initially a sign of worse prognosis, but targeted therapy has granted improved outcome for these patients so that today HER2 positive patients have better prognosis than HER2 negative patients. Later, the introduction of multigene assays provided t ...[more]

PMID: 27605190

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Project description:IntroductionGenomic aberrations in the form of subchromosomal DNA copy number changes are a hallmark of epithelial cancers, including breast cancer. The goal of the present study was to analyze such aberrations in breast cancer at high resolution.MethodsWe employed high-resolution array comparative genomic hybridization with 4,134 bacterial artificial chromosomes that cover the genome at 0.9 megabase resolution to analyze 47 primary breast tumors and 18 breast cancer cell lines.ResultsCommon amplicons included 8q24.3 (amplified in 79% of tumors, with 5/47 exhibiting high level amplification), 1q32.1 and 16p13.3 (amplified in 66% and 57% of tumors, respectively). Moreover, we found several positive correlations between specific amplicons from different chromosomes, suggesting the existence of cooperating genetic loci. Queried by gene, the most frequently amplified kinase was PTK2 (79% of tumors), whereas the most frequently lost kinase was PTK2B (hemizygous loss in 34% of tumors). Amplification of ERBB2 as measured by comparative genomic hybridization (CGH) correlated closely with ERBB2 DNA and RNA levels measured by quantitative PCR as well as with ERBB2 protein levels. The overall frequency of recurrent losses was lower, with no region lost in more than 50% of tumors; the most frequently lost tumor suppressor gene was RB1 (hemizygous loss in 26% of tumors). Finally, we find that specific copy number changes in cell lines closely mimicked those in primary tumors, with an overall Pearson correlation coefficient of 0.843 for gains and 0.734 for losses.ConclusionHigh resolution CGH analysis of breast cancer reveals several regions where DNA copy number is commonly gained or lost, that non-random correlations between specific amplicons exist, and that specific genetic alterations are maintained in breast cancer cell lines despite repeat passage in tissue culture. These observations suggest that genes within these regions are critical to the malignant phenotype and may thus serve as future therapeutic targets.

Project description:BackgroundAbnormal DNA methylation is well established for breast cancer and contributes to its progression by silencing tumor suppressor genes. DNA methylation profiling platforms might provide an alternative approach to expression microarrays for accurate breast tumor subtyping. We sought to determine whether the distinction of the inflammatory breast cancer (IBC) phenotype from the non-IBC phenotype by transcriptomics could be sustained by methylomics.Methodology/principal findingsWe performed methylation profiling on a cohort of IBC (N?=?19) and non-IBC (N?=?43) samples using the Illumina Infinium Methylation Assay. These results were correlated with gene expression profiles. Methylation values allowed separation of breast tumor samples into high and low methylation groups. This separation was significantly related to DNMT3B mRNA levels. The high methylation group was enriched for breast tumor samples from patients with distant metastasis and poor prognosis, as predicted by the 70-gene prognostic signature. Furthermore, this tumor group tended to be enriched for IBC samples (54% vs. 24%) and samples with a high genomic grade index (67% vs. 38%). A set of 16 CpG loci (14 genes) correctly classified 97% of samples into the low or high methylation group. Differentially methylated genes appeared to be mainly related to focal adhesion, cytokine-cytokine receptor interactions, Wnt signaling pathway, chemokine signaling pathways and metabolic processes. Comparison of IBC with non-IBC led to the identification of only four differentially methylated genes (TJP3, MOGAT2, NTSR2 and AGT). A significant correlation between methylation values and gene expression was shown for 4,981 of 6,605 (75%) genes.Conclusions/significanceA subset of clinical samples of breast cancer was characterized by high methylation levels, which coincided with increased DNMT3B expression. Furthermore, an association was observed with molecular signatures indicative of poor patient prognosis. The results of the current study also suggest that aberrant DNA methylation is not the main force driving the molecular biology of IBC.

Project description:There is no study that investigates the potential correlation between the heterogeneity obtained from texture analysis of medical images and the heterogeneity observed from histopathological findings. We investigated whether texture analysis of magnetic resonance images correlates with histopathological findings.Seventy-five patients with estrogen receptor positive invasive ductal carcinoma who underwent preoperative breast magnetic resonance imaging (MRI) were included. Tumor entropy and uniformity were determined on T2- and contrast-enhanced T1-weighted subtraction images under different filter levels. Two pathologists evaluated the detailed histopathological findings of the tumors including tumor cellularity, dominant stroma type, central scar, histologic grade, extensive intraductal component (EIC), and lymphovascular invasion. Entropy and uniformity values on both T2- and contrast-enhanced T1-weighted subtraction images were compared with detailed pathological findings.In a multivariate analysis, entropy significantly increased only on unfiltered T2-weighted images (P?=?0.013). Tumor cellularity and predominant stroma did not affect the uniformity or entropy on both T2- and contrast-enhanced T1-weighted subtraction images. High histologic grades showed increased uniformity and decreased entropy on contrast-enhanced T1-weighted subtraction images, whereas the opposite tendency was observed on T2-weighted images. Invasive ductal carcinoma with an EIC or lymphovascular invasion only affected the contrast-enhanced T1-weighted subtraction images, through increased uniformity and decreased entropy. The best uniformity results were recorded on T2- and contrast-enhanced T1-weighted subtraction images at a filter level of 0.5. Entropy showed the best results at a filter level of 0.5 on contrast-enhanced T1-weighted subtraction images. However, on T2-weighted images, an ideal model was achieved on unfiltered images.MRI texture analysis correlated with pathological tumor heterogeneity.

Dataset Information

Improving Pathological Assessment of Breast Cancer by Employing Array-Based Transcriptome Analysis.

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Improving Pathological Assessment of Breast Cancer by Employing Array-Based Transcriptome Analysis.

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