Johan Vallon-Christersson

BioArray Software Environment (BASE): a platform for comprehensive management and analysis of microarray data.

The microarray technique requires the organization and analysis of vast amounts of data. These data include information about the samples hybridized, the hybridization images and their extracted data matrices, and information about the physical array, the features and reporter molecules. We present a web-based customizable bioinformatics solution called BioArray Software Environment (BASE) for the management and analysis of all areas of microarray experimentation. All software necessary to run a local server is freely available.

Recurrent gross mutations of the PTEN tumor suppressor gene in breast cancers with deficient DSB repair

Basal-like breast cancer (BBC) is a subtype of breast cancer with poor prognosis. Inherited mutations of BRCA1, a cancer susceptibility gene involved in double-strand DNA break (DSB) repair, lead to breast cancers that are nearly always of the BBC subtype; however, the precise molecular lesions and oncogenic consequences of BRCA1 dysfunction are poorly understood. Here we show that heterozygous inactivation of the tumor suppressor gene Pten leads to the formation of basal-like mammary tumors in mice, and that loss of PTEN expression is significantly associated with the BBC subtype in human sporadic and BRCA1-associated hereditary breast cancers. In addition, we identify frequent gross PTEN mutations, involving intragenic chromosome breaks, inversions, deletions and micro copy number aberrations, specifically in BRCA1-deficient tumors. These data provide an example of a specific and recurrent oncogenic consequence of BRCA1-dependent dysfunction in DNA repair and provide insight into the pathogenesis of BBC with therapeutic implications. These findings also argue that obtaining an accurate census of genes mutated in cancer will require a systematic examination for gross gene rearrangements, particularly in tumors with deficient DSB repair.

Molecular subtypes of breast cancer are associated with characteristic DNA methylation patterns

IntroductionFive different molecular subtypes of breast cancer have been identified through gene expression profiling. Each subtype has a characteristic expression pattern suggested to partly depend on cellular origin. We aimed to investigate whether the molecular subtypes also display distinct methylation profiles.MethodsWe analysed methylation status of 807 cancer-related genes in 189 fresh frozen primary breast tumours and four normal breast tissue samples using an array-based methylation assay.ResultsUnsupervised analysis revealed three groups of breast cancer with characteristic methylation patterns. The three groups were associated with the luminal A, luminal B and basal-like molecular subtypes of breast cancer, respectively, whereas cancers of the HER2-enriched and normal-like subtypes were distributed among the three groups. The methylation frequencies were significantly different between subtypes, with luminal B and basal-like tumours being most and least frequently methylated, respectively. Moreover, targets of the polycomb repressor complex in breast cancer and embryonic stem cells were more methylated in luminal B tumours than in other tumours. BRCA2-mutated tumours had a particularly high degree of methylation. Finally, by utilizing gene expression data, we observed that a large fraction of genes reported as having subtype-specific expression patterns might be regulated through methylation.ConclusionsWe have found that breast cancers of the basal-like, luminal A and luminal B molecular subtypes harbour specific methylation profiles. Our results suggest that methylation may play an important role in the development of breast cancers.

High-resolution genomic profiles of breast cancer cell lines assessed by tiling BAC array comparative genomic hybridization.

A BAC‐array platform for comparative genomic hybridization was constructed from a library of 32,433 clones providing complete genome coverage, and evaluated by screening for DNA copy number changes in 10 breast cancer cell lines (BT474, MCF7, HCC1937, SK‐BR‐3, L56Br‐C1, ZR‐75‐1, JIMT1, MDA‐MB‐231, MDA‐MB‐361, and HCC2218) and one cell line derived from fibrocystic disease of the breast (MCF10A). These were also characterized by gene expression analysis and found to represent all five recently described breast cancer subtypes using the “intrinsic gene set” and centroid correlation. Three cell lines, HCC1937 and L56BrC1 derived from BRCA1 mutation carriers and MDA‐MB‐231, were of basal‐like subtype and characterized by a high frequency of low‐level gains and losses of typical pattern, including limited deletions on 5q. Four estrogen receptor positive cell lines were of luminal A subtype and characterized by a different pattern of aberrations and high‐level amplifications, including ERBB2 and other 17q amplicons in BT474 and MDA‐MB‐361. SK‐BR‐3 cells, characterized by a complex genome including ERBB2 amplification, massive high‐level amplifications on 8q and a homozygous deletion of CDH1 at 16q22, had an expression signature closest to luminal B subtype. The effects of gene amplifications were verified by gene expression analysis to distinguish targeted genes from silent amplicon passengers. JIMT1, derived from an ERBB2 amplified trastuzumab resistant tumor, was of the ERBB2 subtype. Homozygous deletions included other known targets such as PTEN (HCC1937) and CDKN2A (MDA‐MB‐231, MCF10A), but also new candidate suppressor genes such as FUSSEL18 (HCC1937) and WDR11 (L56Br‐C1) as well as regions without known genes. The tiling BAC‐arrays constitute a powerful tool for high‐resolution genomic profiling suitable for cancer research and clinical diagnostics.

Distinct Genomic Profiles in Hereditary Breast Tumors Identified by Array-Based Comparative Genomic Hybridization

Mutations in BRCA1 and BRCA2 account for a significant proportion of hereditary breast cancers. Earlier studies have shown that inherited and sporadic tumors progress along different somatic genetic pathways and that global gene expression profiles distinguish between these groups. To determine whether genomic profiles similarly discriminate among BRCA1, BRCA2, and sporadic tumors, we established DNA copy number profiles using comparative genomic hybridization to BAC-clone microarrays providing <1 Mb resolution. Tumor DNA was obtained from BRCA1 (n = 14) and BRCA2 (n = 12) mutation carriers, as well as sporadic cases (n = 26). Overall, BRCA1 tumors had a higher frequency of copy number alterations than sporadic breast cancers (P = 0.00078). In particular, frequent losses on 4p, 4q, and 5q in BRCA1 tumors and frequent gains on 7p and 17q24 in BRCA2 tumors distinguish these from sporadic tumors. Distinct amplicons at 3q27.1-q27.3 were identified in BRCA1 tumors and at 17q23.3-q24.2 in BRCA2 tumors. A homozygous deletion on 5q12.1 was found in a BRCA1 tumor. Using a set of 169 BAC clones that detect significantly (P < 0.001) different frequencies of copy number changes in inherited and sporadic tumors, these could be discriminated into separate groups using hierarchical clustering. By comparing DNA copy number and RNA expression for genes in these regions, several candidate genes affected by up- or down-regulation were identified. Moreover, using support vector machines, we correctly classified BRCA1 and BRCA2 tumors (P < 0.0000004 and 0.00005, respectively). Further validation may prove this tumor classifier to be useful for selecting familial breast cancer cases for further mutation screening, particularly, as these data can be obtained using archival tissue.

Genomic subtypes of breast cancer identified by array-comparative genomic hybridization display distinct molecular and clinical characteristics

IntroductionBreast cancer is a profoundly heterogeneous disease with respect to biologic and clinical behavior. Gene-expression profiling has been used to dissect this complexity and to stratify tumors into intrinsic gene-expression subtypes, associated with distinct biology, patient outcome, and genomic alterations. Additionally, breast tumors occurring in individuals with germline BRCA1 or BRCA2 mutations typically fall into distinct subtypes.MethodsWe applied global DNA copy number and gene-expression profiling in 359 breast tumors. All tumors were classified according to intrinsic gene-expression subtypes and included cases from genetically predisposed women. The Genomic Identification of Significant Targets in Cancer (GISTIC) algorithm was used to identify significant DNA copy-number aberrations and genomic subgroups of breast cancer.ResultsWe identified 31 genomic regions that were highly amplified in > 1% of the 359 breast tumors. Several amplicons were found to co-occur, the 8p12 and 11q13.3 regions being the most frequent combination besides amplicons on the same chromosomal arm. Unsupervised hierarchical clustering with 133 significant GISTIC regions revealed six genomic subtypes, termed 17q12, basal-complex, luminal-simple, luminal-complex, amplifier, and mixed subtypes. Four of them had striking similarity to intrinsic gene-expression subtypes and showed associations to conventional tumor biomarkers and clinical outcome. However, luminal A-classified tumors were distributed in two main genomic subtypes, luminal-simple and luminal-complex, the former group having a better prognosis, whereas the latter group included also luminal B and the majority of BRCA2-mutated tumors. The basal-complex subtype displayed extensive genomic homogeneity and harbored the majority of BRCA1-mutated tumors. The 17q12 subtype comprised mostly HER2-amplified and HER2-enriched subtype tumors and had the worst prognosis. The amplifier and mixed subtypes contained tumors from all gene-expression subtypes, the former being enriched for 8p12-amplified cases, whereas the mixed subtype included many tumors with predominantly DNA copy-number losses and poor prognosis.ConclusionsGlobal DNA copy-number analysis integrated with gene-expression data can be used to dissect the complexity of breast cancer. This revealed six genomic subtypes with different clinical behavior and a striking concordance to the intrinsic subtypes. These genomic subtypes may prove useful for understanding the mechanisms of tumor development and for prognostic and treatment prediction purposes.

Identification of new microRNAs in paired normal and tumor breast tissue suggests a dual role for the ERBB2/Her2 gene.

To comprehensively characterize microRNA (miRNA) expression in breast cancer, we performed the first extensive next-generation sequencing expression analysis of this disease. We sequenced small RNA from tumors with paired samples of normal and tumor-adjacent breast tissue. Our results indicate that tumor identity is achieved mainly by variation in the expression levels of a common set of miRNAs rather than by tissue-specific expression. We also report 361 new, well-supported miRNA precursors. Nearly two-thirds of these new genes were detected in other human tissues and 49% of the miRNAs were found associated with Ago2 in MCF7 cells. Ten percent of the new miRNAs are located in regions with high-level genomic amplifications in breast cancer. A new miRNA is encoded within the ERBB2/Her2 gene and amplification of this gene leads to overexpression of the new miRNA, indicating that this potent oncogene and important clinical marker may have two different biological functions. In summary, our work substantially expands the number of known miRNAs and highlights the complexity of small RNA expression in breast cancer.

Identification of Subtypes in Human Epidermal Growth Factor Receptor 2–Positive Breast Cancer Reveals a Gene Signature Prognostic of Outcome

PURPOSE Human epidermal growth factor receptor 2 (HER2) gene amplification or protein overexpression (HER2 positivity) defines a clinically challenging subgroup of patients with breast cancer (BC) with variable prognosis and response to therapy. We aimed to investigate the heterogeneous biologic appearance and clinical behavior of HER2-positive tumors using molecular profiling. PATIENTS AND METHODS Hierarchical clustering of gene expression data from 58 HER2-amplified tumors of various stage, histologic grade, and estrogen receptor (ER) status was used to construct a HER2-derived prognostic predictor that was further evaluated in several large independent BC data sets. RESULTS Unsupervised analysis identified three subtypes of HER2-positive tumors with mixed stage, histologic 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-positive BC across multiple independent BC data sets and identify a sizable HER2-positive group with long disease-free survival and low mortality. Significant correlation to prognosis was also observed in basal-like, ER-negative, lymph node-positive, and high-grade tumors, irrespective of HER2 status. The predictor included genes associated with immune response, tumor invasion, and metastasis. CONCLUSION The HER2-derived prognostic predictor provides further insight into the heterogeneous biology of HER2-positive tumors and may become useful for improved selection of patients who need additional treatment with new drugs targeting the HER2 pathway.

The non-coding RNA of the multidrug resistance-linked vault particle encodes multiple regulatory small RNAs.

Vault particles are conserved organelles implicated in multidrug resistance and intracellular transport. They contain three different proteins and non-coding vault RNAs (vRNAs). Here we show that human vRNAs produce several small RNAs (svRNAs) by mechanisms different from those in the canonical microRNA (miRNA) pathway. At least one of these svRNAs, svRNAb, associates with Argonaute proteins to guide sequence-specific cleavage and regulate gene expression similarly to miRNAs. We demonstrate that svRNAb downregulates CYP3A4, a key enzyme in drug metabolism. Our findings expand the repertoire of small regulatory RNAs and assign, for the first time, a function to vRNAs that may help explain the association between vault particles and drug resistance.

Segmentation-based detection of allelic imbalance and loss-of-heterozygosity in cancer cells using whole genome SNP arrays

We present a strategy for detection of loss-of-heterozygosity and allelic imbalance in cancer cells from whole genome single nucleotide polymorphism genotyping data. Using a dilution series of a tumor cell line mixed with its paired normal cell line and data generated on Affymetrix and Illumina platforms, including paired tumor-normal samples and tumors characterized by fluorescent in situ hybridization, we demonstrate a high sensitivity and specificity of the strategy for detecting both minute and gross allelic imbalances in heterogeneous tumor samples.