Therese Sørlie

Molecular portraits of human breast tumours

Human breast tumours are diverse in their natural history and in their responsiveness to treatments. Variation in transcriptional programs accounts for much of the biological diversity of human cells and tumours. In each cell, signal transduction and regulatory systems transduce information from the cells identity to its environmental status, thereby controlling the level of expression of every gene in the genome. Here we have characterized variation in gene expression patterns in a set of 65 surgical specimens of human breast tumours from 42 different individuals, using complementary DNA microarrays representing 8,102 human genes. These patterns provided a distinctive molecular portrait of each tumour. Twenty of the tumours were sampled twice, before and after a 16-week course of doxorubicin chemotherapy, and two tumours were paired with a lymph node metastasis from the same patient. Gene expression patterns in two tumour samples from the same individual were almost always more similar to each other than either was to any other sample. Sets of co-expressed genes were identified for which variation in messenger RNA levels could be related to specific features of physiological variation. The tumours could be classified into subtypes distinguished by pervasive differences in their gene expression patterns.

Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications

The purpose of this study was to classify breast carcinomas based on variations in gene expression patterns derived from cDNA microarrays and to correlate tumor characteristics to clinical outcome. A total of 85 cDNA microarray experiments representing 78 cancers, three fibroadenomas, and four normal breast tissues were analyzed by hierarchical clustering. As reported previously, the cancers could be classified into a basal epithelial-like group, an ERBB2-overexpressing group and a normal breast-like group based on variations in gene expression. A novel finding was that the previously characterized luminal epithelial/estrogen receptor-positive group could be divided into at least two subgroups, each with a distinctive expression profile. These subtypes proved to be reasonably robust by clustering using two different gene sets: first, a set of 456 cDNA clones previously selected to reflect intrinsic properties of the tumors and, second, a gene set that highly correlated with patient outcome. Survival analyses on a subcohort of patients with locally advanced breast cancer uniformly treated in a prospective study showed significantly different outcomes for the patients belonging to the various groups, including a poor prognosis for the basal-like subtype and a significant difference in outcome for the two estrogen receptor-positive groups.

Repeated observation of breast tumor subtypes in independent gene expression data sets

Characteristic patterns of gene expression measured by DNA microarrays have been used to classify tumors into clinically relevant subgroups. In this study, we have refined the previously defined subtypes of breast tumors that could be distinguished by their distinct patterns of gene expression. A total of 115 malignant breast tumors were analyzed by hierarchical clustering based on patterns of expression of 534 “intrinsic” genes and shown to subdivide into one basal-like, one ERBB2-overexpressing, two luminal-like, and one normal breast tissue-like subgroup. The genes used for classification were selected based on their similar expression levels between pairs of consecutive samples taken from the same tumor separated by 15 weeks of neoadjuvant treatment. Similar cluster analyses of two published, independent data sets representing different patient cohorts from different laboratories, uncovered some of the same breast cancer subtypes. In the one data set that included information on time to development of distant metastasis, subtypes were associated with significant differences in this clinical feature. By including a group of tumors from BRCA1 carriers in the analysis, we found that this genotype predisposes to the basal tumor subtype. Our results strongly support the idea that many of these breast tumor subtypes represent biologically distinct disease entities.

Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors

Genomic DNA copy number alterations are key genetic events in the development and progression of human cancers. Here we report a genome-wide microarray comparative genomic hybridization (array CGH) analysis of DNA copy number variation in a series of primary human breast tumors. We have profiled DNA copy number alteration across 6,691 mapped human genes, in 44 predominantly advanced, primary breast tumors and 10 breast cancer cell lines. While the overall patterns of DNA amplification and deletion corroborate previous cytogenetic studies, the high-resolution (gene-by-gene) mapping of amplicon boundaries and the quantitative analysis of amplicon shape provide significant improvement in the localization of candidate oncogenes. Parallel microarray measurements of mRNA levels reveal the remarkable degree to which variation in gene copy number contributes to variation in gene expression in tumor cells. Specifically, we find that 62% of highly amplified genes show moderately or highly elevated expression, that DNA copy number influences gene expression across a wide range of DNA copy number alterations (deletion, low-, mid- and high-level amplification), that on average, a 2-fold change in DNA copy number is associated with a corresponding 1.5-fold change in mRNA levels, and that overall, at least 12% of all the variation in gene expression among the breast tumors is directly attributable to underlying variation in gene copy number. These findings provide evidence that widespread DNA copy number alteration can lead directly to global deregulation of gene expression, which may contribute to the development or progression of cancer.

Genome-wide analysis of DNA copy number variation in breast cancer using DNA microarrays

Gene amplifications and deletions frequently have pathogenetic roles in cancer. 30,000 radiation-hybrid mapped cDNAs provide a genomic resource to map these lesions with high resolution. We developed a cDNA microarray-based comparative genomic hybridisation method for analysing DNA copy number changes across thousands of genes simultaneously. Using this procedure, we could reliably detect DNA copy number alterations of twofold or less. In breast cancer cell lines, we have mapped regions of DNA copy number variation at high resolution, revealing previously unrecognised genomic amplifications and deletions, and new complexities of amplicon structure. Recurrent regions of DNA amplification, which may harbour novel oncogenes, were readily identified. Alterations of DNA copy number and gene expression could be compared and correlated in parallel analyses. We have now collected genome-wide DNA copy number information on a set of 9 breast cancer cell lines and over 35 primary breast tumours. For the breast tumours, DNA copy number information is being compared and correlated with data already collected on p53 status, microarray gene expression profiles, and treatment response and clinical outcome. The results of this analysis will be presented.

Distinct Patterns of DNA Copy Number Alteration Are Associated with Different Clinicopathological Features and Gene-Expression Subtypes of Breast Cancer

Breast cancer is a leading cause of cancer‐death among women, where the clinicopathological features of tumors are used to prognosticate and guide therapy. DNA copy number alterations (CNAs), which occur frequently in breast cancer and define key pathogenetic events, are also potentially useful prognostic or predictive factors. Here, we report a genome‐wide array‐based comparative genomic hybridization (array CGH) survey of CNAs in 89 breast tumors from a patient cohort with locally advanced disease. Statistical analysis links distinct cytoband loci harboring CNAs to specific clinicopathological parameters, including tumor grade, estrogen receptor status, presence of TP53 mutation, and overall survival. Notably, distinct spectra of CNAs also underlie the different subtypes of breast cancer recently defined by expression‐profiling, implying these subtypes develop along distinct genetic pathways. In addition, higher numbers of gains/losses are associated with the “basal‐like” tumor subtype, while high‐level DNA amplification is more frequent in “luminal‐B” subtype tumors, suggesting also that distinct mechanisms of genomic instability might underlie their pathogenesis. The identified CNAs may provide a basis for improved patient prognostication, as well as a starting point to define important genes to further our understanding of the pathobiology of breast cancer. This article contains Supplementary Material available at http://www.interscience.wiley.com/jpages/1045‐2257/suppmat.

Extracellular matrix signature identifies breast cancer subgroups with different clinical outcome

Prediction of the clinical outcome of breast cancer is multi‐faceted and challenging. There is growing evidence that the complexity of the tumour micro‐environment, consisting of several cell types and a complex mixture of proteins, plays an important role in development, progression, and response to therapy. In the current study, we investigated whether invasive breast tumours can be classified on the basis of the expression of extracellular matrix (ECM) components and whether such classification is representative of different clinical outcomes. We first examined the matrix composition of 28 primary breast carcinomas by morphology and gene expression profiling using 22K oligonucleotide Agilent microarrays. Hierarchical clustering of the gene expression profile of 278 ECM‐related genes derived from the literature divided the tumours into four main groups (ECM1–4). A set of selected differentially expressed genes was validated by immunohistochemistry. The robustness of the ECM classification was confirmed by studying the four ECM groups in a previously published gene expression data set of 114 early‐stage primary breast carcinomas profiled using cDNA arrays. Univariate survival analysis showed significant differences in clinical outcome among the various ECM subclasses. One set of tumours, designated ECM4, had a favourable outcome and was defined by the overexpression of a set of protease inhibitors belonging to the serpin family, while tumours with an ECM1 signature had a poorer prognosis and showed high expression of integrins and metallopeptidases, and low expression of several laminin chains. Furthermore, we identified three surrogate markers of ECM1 tumours: MARCO, PUNC, and SPARC, whose expression levels were associated with breast cancer survival and risk of recurrence. Our findings suggest that primary breast tumours can be classified based upon ECM composition and that this classification provides relevant information on the biology of breast carcinomas, further supporting the hypothesis that clinical outcome is strongly related to stromal characteristics. Copyright

Triple-negative breast cancer: Present challenges and new perspectives

Triple‐negative breast cancers (TNBC), characterized by absence of estrogen receptor (ER), progesterone receptor (PR) and lack of overexpression of human epidermal growth factor receptor 2 (HER2), are typically associated with poor prognosis, due to aggressive tumor phenotype(s), only partial response to chemotherapy and present lack of clinically established targeted therapies. Advances in the design of individualized strategies for treatment of TNBC patients require further elucidation, by combined ‘omics’ approaches, of the molecular mechanisms underlying TNBC phenotypic heterogeneity, and the still poorly understood association of TNBC with BRCA1 mutations. An overview is here presented on TNBC profiling in terms of expression signatures, within the functional genomic breast tumor classification, and ongoing efforts toward identification of new therapy targets and bioimaging markers. Due to the complexity of aberrant molecular patterns involved in expression, pathological progression and biological/clinical heterogeneity, the search for novel TNBC biomarkers and therapy targets requires collection of multi‐dimensional data sets, use of robust multivariate data analysis techniques and development of innovative systems biology approaches.

Cell-Type-Specific Responses to Chemotherapeutics in Breast Cancer

Recent microarray studies have identified distinct subtypes of breast tumors that arise from different cell types and that show statistically significant differences in patient outcome. To gain insight into these differences, we identified in vitro and in vivo changes in gene expression induced by chemotherapeutics. We treated two cell lines derived from basal epithelium (immortalized human mammary epithelial cells) and two lines derived from luminal epithelium (MCF-7 and ZR-75–1) with chemotherapeutics used in the treatment of breast cancer and assayed for changes in gene expression using DNA microarrays. Treatment doses for doxorubicin and 5-fluorouracil were selected to cause comparable cytotoxicity across all four cell lines. The dominant expression response in each of the cell lines was a general stress response; however, distinct expression patterns were observed. Both cell types induced DNA damage-response genes such as p21waf1, but the response in the luminal cells showed higher fold changes and included more p53-regulated genes. Luminal cell lines repressed a large number of cell cycle-regulated genes and other genes involved in cellular proliferation, whereas the basal cell lines did not. Instead, the basal cell lines repressed genes that were involved in differentiation. These in vitro responses were compared with expression responses in breast tumors sampled before and after treatment with doxorubicin or 5-fluorouracil/mitomycin C. The in vivo data corroborated the cell-type-specific responses to chemotherapeutics observed in vitro, including the induction of p21waf1. Similarities between in vivo and in vitro responses help to identify important response mechanisms to chemotherapeutics.

Genomic architecture characterizes tumor progression paths and fate in breast cancer patients

This study demonstrates the relation among structural genomic alterations, molecular subtype, and clinical behavior and shows that an objective score of genomic complexity can provide independent prognostic information in breast cancer. Form and Malfunction Breast cancer is an iniquitous disease with a panoply of predisposing genetic and environmental causes, the details of which have yet to be fully understood. One of every four women will be diagnosed with breast cancer, hence the early and accurate identification of specific tumor features that may affect overall survival is imperative in achieving an optimal prognosis. A widely appreciated taxonomy in the breast cancer field has enabled the molecular discernment of five pathological subtypes; however, as research dives deeper into the chromosomal underpinnings of the disease, new classifiers are needed to augment what is known with key structural details to create a more vivid tumor landscape. Now, Russnes and colleagues have generated new algorithms that can estimate the specific genomic region as well as the architectural type of rearrangement—gains or losses of chromosome arms. A cohort of breast tumors was scored using this method, and all tumors with complex rearrangements had more whole chromosome arms affected than those without complex rearrangement. Moreover, there was an overlapping correlation with the molecular subtyping features of the tumors, and the score could confer prognostic power. Distinct molecular subtypes of breast carcinomas have been identified, but translation into clinical use has been limited. We have developed two platform-independent algorithms to explore genomic architectural distortion using array comparative genomic hybridization data to measure (i) whole-arm gains and losses [whole-arm aberration index (WAAI)] and (ii) complex rearrangements [complex arm aberration index (CAAI)]. By applying CAAI and WAAI to data from 595 breast cancer patients, we were able to separate the cases into eight subgroups with different distributions of genomic distortion. Within each subgroup data from expression analyses, sequencing and ploidy indicated that progression occurs along separate paths into more complex genotypes. Histological grade had prognostic impact only in the luminal-related groups, whereas the complexity identified by CAAI had an overall independent prognostic power. This study emphasizes the relation among structural genomic alterations, molecular subtype, and clinical behavior and shows that objective score of genomic complexity (CAAI) is an independent prognostic marker in breast cancer.