Project description:Purpose HER2 gene amplification or protein overexpression (HER2+) defines a clinically challenging subgroup of breast cancer with variable prognosis and response to therapy. We aimed to investigate the heterogeneous biological appearance and clinical behavior of HER2+ tumors using molecular profiling. Materials and Methods Hierarchical clustering of gene expression data from 58 HER2-amplified tumors of various stage, histological grade and estrogen receptor (ER) status was used to construct a HER2-derived prognostic predictor that was further evaluated in several large independent breast cancer data sets. Results Unsupervised analysis identified three subtypes of HER2+ tumors with mixed stage, histological grade and ER-status. One subtype had a significantly worse clinical outcome. A prognostic predictor was created based on differentially expressed genes between the subtype with worse outcome and the other subtypes. The predictor was able to define patient groups with better and worse outcome in HER2+ breast cancer across multiple independent breast cancer data sets and identify a sizable HER2+ group with long disease-free survival and low mortality. Significant correlation to prognosis was also observed in basal-like, ER−, lymph node positive or high-grade tumors, irrespective of HER2-status. The predictor included genes associated to immune response, tumor invasion and metastasis. Conclusion The HER2-derived prognostic predictor provides further insight into the heterogeneous biology of HER2+ tumors and may become useful for improved selection of patients that need additional treatment with new drugs targeting the HER2 pathway. Array comparative genomic hybridization (aCGH) identified 58 breast tumors with amplification of HER2 from a larger cohort of approx 500 tumors breast. Global gene expression profiles were obtained using 70-mer oligonucleotide microarrays. Unsupervised hierarchical clustering of the 58 tumors, using Pearson correlation and complete linkage, identified three main clusters. One cluster showed significantly poorer clinical outcome. Significance of microarray (SAM) analysis was performed to identify 158 genes separating the poor outcome cluster compared to the other two clusters. Gene expression centroids, based on the 158 genes, were created for each cluster for validation in independent breast cancer data sets.

Project description:Background: Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. Methods: Tumor tissue from 425 primary breast cancer patients of the Oslo2 study was cut and blended before being divided into fractions for DNA-, RNA- and protein isolation, and metabolomics, allowing for representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a “cluster-of-clusters” approach with consensus clustering. Results: Based on DNA copy number data, tumors were scored into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and using integrative clustering, tumors were classified into ten subtypes. The final consensus clustering incorporating all the aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype where these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significant different outcome, and in another a trend was observed. Conclusions: The herein identified six integrated subtypes confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing the knowledge of the luminal A subtype heterogeneity may add pivotal information guiding therapeutic choices, evidently bringing us closer to more personalized treatment for this largest subgroup of breast cancer.

Project description:Current clinical guidelines suggest that breast cancers with low hormone receptor expression (LowHR) in 1% to 10% of tumor cells should be regarded as hormone receptor positive tumors. However, clinical data shows that patients with such tumors have a worse outcome compared to patients with hormone receptor expression above 10 %. Further, gene expression studies suggest that these tumors have a TNBC-like signature similar to triple negative breast cancers (TNBC). The goal of this study was to use DNA methylation-based classification to clarify the status for this infrequent but important patient subgroup. We performed whole genome DNA methylation profiling on 23 LowHR breast cancer specimens, including 13 samples with HER2 amplification and compared our results with a reference breast cancer cohort from The Cancer Genome Atlas. Unsupervised clustering and dimensionality reduction revealed that breast cancers with low hormone receptor expression that lacked HER2 amplification usually clustered with TNBC reference samples (8/10; “LowHR TNBC-like”). In contrast, most specimens with low hormone receptor expression and HER2 amplification grouped with hormone receptor positive cancers (11/13; “LowHR HRpos-like”). We observed highly similar DNA methylation patterns of LowHR TNBC-like samples and true TNBCs with almost no differential methylation. Furthermore, the Ki67 proliferation index of LowHR TNBC-like samples as well as clinical outcome parameters were more similar to TNBCs and differed from LowHR_HRpos-like cases. We here demonstrate that LowHR breast cancer comprises two molecularly distinct groups that can be separated by DNA methylation profiling. More clinical data is required for a definite classification of these tumors, but our data strongly suggests that LowHR TNBC-like samples are molecularly, histologically and clinically closely related to TNBC, while LowHR HRpos-like specimens are closely related to hormone receptor positive tumors.

Project description:Male breast cancer (MBC) is extremely rare and poorly characterized on the molecular level. Using high-resolution genomic data, we aimed to characterize MBC by genomic imbalances and to compare it with female breast cancer (FBC), and further to investigate whether the genomic profiles hold any prognostic infor- mation. Fifty-six fresh frozen MBC tumors were analyzed using high-resolution tiling BAC arrays. Significant regions in common between cases were assessed using Genomic Identification of Significant Targets in Cancer (GISTIC) analysis. A publicly available genomic data set of 359 FBC tumors was used for reference purposes. The data revealed a broad pattern of aberrations, confirming that MBC is a heterogeneous tumor type. Genomic gains were more common in MBC than in FBC and often involved whole chromosome arms, while losses of genomic material were less frequent. The most common aberrations were similar between the genders, but high-level amplifications were more common in FBC. We identified two genomic subgroups among MBCs; male-complex and male-simple. The male-complex subgroup displayed striking similarities with the previously reported luminal-complex FBC sub- group, while the male-simple subgroup seems to represent a new subgroup of breast cancer occurring only in men. There are many similarities between FBC and MBC with respect to genomic imbalances, but there are also distinct differ- ences as revealed by high-resolution genomic profiling. MBC can be divided into two comprehensive genomic subgroups, which may be of prognostic value. The male- simple subgroup appears notably different from any geno- mic subgroup so far defined in FBC. Male breast cancer (MBC) is extremely rare and poorly characterized on the molecular level. Using high-resolution genomic data, we aimed to characterize MBC by genomic imbalances and to compare it with female breast cancer (FBC), and further to investigate whether the genomic profiles hold any prognostic infor- mation. Fifty-six fresh frozen MBC tumors were analyzed using high-resolution tiling BAC arrays. Significant regions in common between cases were assessed using Genomic Identification of Significant Targets in Cancer (GISTIC) analysis. A publicly available genomic data set of 359 FBC tumors was used for reference purposes. The data revealed a broad pattern of aberrations, confirming that MBC is a heterogeneous tumor type. Genomic gains were more common in MBC than in FBC and often involved whole chromosome arms, while losses of genomic material were less frequent. The most common aberrations were similar between the genders, but high-level amplifications were more common in FBC. We identified two genomic subgroups among MBCs; male-complex and male-simple. The male-complex subgroup displayed striking similarities with the previously reported luminal-complex FBC sub- group, while the male-simple subgroup seems to represent a new subgroup of breast cancer occurring only in men. There are many similarities between FBC and MBC with respect to genomic imbalances, but there are also distinct differ- ences as revealed by high-resolution genomic profiling. MBC can be divided into two comprehensive genomic subgroups, which may be of prognostic value. The male- simple subgroup appears notably different from any geno- mic subgroup so far defined in FBC. Tumor cellularity was determined on H&E-stained sections and only tumors with high tumor cell content were included. DNA was extracted from fresh frozen tissue using a modification of a back-extraction protocol from the organic phase of the Qiagen Lipid mini RNA kit (Qiagen, Valencia, CA) as follows: 1 M Tris-buffer containing 4 M Guanidine Thiocyanate and 50 mM Sodium Citrate, followed by glycogen precipitation. DNA quality was assessed with a BioAnalyzer. Sufficient good quality DNA from 56 fresh frozen MBC tumors was available for high-resolution tiling BAC aCGH. BAC arrays, containing about 32,000 BAC clones mapped to the UCSC Human Genome build 17, were produced at the SCIBLU Genomics Resource Center, Lund University, Sweden. The aCGH data were normalized using PopLowess. Circular binary segmentation (CBS) was used for breakpoint analysis with an a of 0.01 and segments containing at least four probes were used in subsequent analyses.

Dataset Information

Transcription profiling of human breast cancers for recovery of biological information under heterogeneous experimental conditions using subgroup standardization.

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