Hilde Johnsen

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.

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.

Specific P53 mutations are associated with de novo resistance to doxorubicin in breast cancer patients

The mechanisms causing resistance to chemotherapeutic drugs in cancer patients are poorly understood. Recent evidence suggests that different forms of chemotherapy may exert their cytotoxic effects by inducing apoptosis1. The tumor suppressor gene P53 has a pivotal role inducing apoptosis in response to cellular damage. In vitro investigations have shown intact p53 to play a critical role executing cell death in response to treatment with cytotoxic drugs like 5–fluorouracil, etoposide and doxorubicin2. Recently, mutations in the P53 gene were found to confer resistance to anthracyclines in a mouse sarcoma tumor model3, and overexpression of the p53 protein (which, in most cases, is due to a mutated gene) was found to be associated with lack of response to cisplatin–based chemotherapy in non–small cell lung cancer4. Previous studies have shown mutations in the P53 gene or overexpression of the p53 protein to predict a poor prognosis5–7, but also a beneficial effect of adjuvant radiotherapy8 or chemotherapy9 in breast cancer. In this study we present data linking specific mutations in the P53 gene to primary resistance to doxorubicin therapy and early relapse in breast cancer patients.

Distinct molecular mechanisms underlying clinically relevant subtypes of breast cancer: gene expression analyses across three different platforms

BackgroundGene expression profiling has been used to define molecular phenotypes of complex diseases such as breast cancer. The luminal A and basal-like subtypes have been repeatedly identified and validated as the two main subtypes out of a total of five molecular subtypes of breast cancer. These two are associated with distinctly different gene expression patterns and more importantly, a significant difference in clinical outcome. To further validate and more thoroughly characterize these two subtypes at the molecular level in tumors at an early stage, we report a gene expression profiling study using three different DNA microarray platforms.ResultsExpression data from 20 tumor biopsies of early stage breast carcinomas were generated on three different DNA microarray platforms; Applied Biosystems Human Genome Survey Microarrays, Stanford cDNA Microarrays and Agilents Whole Human Genome Oligo Microarrays, and the resulting gene expression patterns were analyzed. Both unsupervised and supervised analyses identified the different clinically relevant subtypes of breast tumours, and the results were consistent across all three platforms. Gene classification and biological pathway analyses of the genes differentially expressed between the two main subtypes revealed different molecular mechanisms descriptive of the two expression-based subtypes: Signature genes of the luminal A subtype were over-represented by genes involved in fatty acid metabolism and steroid hormone-mediated signaling pathways, in particular estrogen receptor signaling, while signature genes of the basal-like subtype were over-represented by genes involved in cell proliferation and differentiation, p21-mediated pathway, and G1-S checkpoint of cell cycle-signaling pathways. A minimal set of 54 genes that best discriminated the two subtypes was identified using the combined data sets generated from the three different array platforms. These predictor genes were further verified by TaqMan® Gene Expression assays.ConclusionWe have identified and validated the two main previously defined clinically relevant subtypes, luminal A and basal-like, in a small set of early stage breast carcinomas. Signature genes characterizing these two subtypes revealed that distinct molecular mechanisms might have been pre-programmed at an early stage in different subtypes of the disease. Our results provide further evidence that these breast tumor subtypes represent biologically distinct disease entities and may require different therapeutic strategies. Finally, validated by multiple gene expression platforms, including quantitative PCR, the set of 54 predictor genes identified in this study may define potential prognostic molecular markers for breast cancer.

miRNA-mRNA Integrated Analysis Reveals Roles for miRNAs in Primary Breast Tumors

Introduction Few studies have performed expression profiling of both miRNA and mRNA from the same primary breast carcinomas. In this study we present and analyze data derived from expression profiling of 799 miRNAs in 101 primary human breast tumors, along with genome-wide mRNA profiles and extensive clinical information. Methods We investigate the relationship between these molecular components, in terms of their correlation with each other and with clinical characteristics. We use a systems biology approach to examine the correlative relationship between miRNA and mRNAs using statistical enrichment methods. Results We identify statistical significant differential expression of miRNAs between molecular intrinsic subtypes, and between samples with different levels of proliferation. Specifically, we point to miRNAs significantly associated with TP53 and ER status. We also show that several cellular processes, such as proliferation, cell adhesion and immune response, are strongly associated with certain miRNAs. We validate the role of miRNAs in regulating proliferation using high-throughput lysate-microarrays on cell lines and point to potential drivers of this process. Conclusion This study provides a comprehensive dataset as well as methods and system-level results that jointly form a basis for further work on understanding the role of miRNA in primary breast cancer.

P53 polymorphism and risk of cervical cancer

Storey and co-workers recently presented results indicating that the allele encoding arginine in the codon-72 polymorphism of the p53 gene represents a significant risk factor in the development of cancers associated with human papilloma virus (HPV). The form of the p53 protein carrying an arginine residue at this position was found to be significantly more susceptible to degradation by the HPV E6 protein than by the proline form. Genotype analysis of 30 cervical tumours and 12 skin carcinomas revealed that the homozygous Arg/Arg genotype was overrepresented compared with 41 controls. We have now analysed this polymorphism in leukocyte DNA from a larger sample of cancer patients and controls but have found no significant overrepresentation of this genotype.

p53‐repressed miRNAs are involved with E2F in a feed‐forward loop promoting proliferation

Normal cell growth is governed by a complicated biological system, featuring multiple levels of control, often deregulated in cancers. The role of microRNAs (miRNAs) in the control of gene expression is now increasingly appreciated, yet their involvement in controlling cell proliferation is still not well understood. Here we investigated the mammalian cell proliferation control network consisting of transcriptional regulators, E2F and p53, their targets and a family of 15 miRNAs. Indicative of their significance, expression of these miRNAs is downregulated in senescent cells and in breast cancers harboring wild‐type p53. These miRNAs are repressed by p53 in an E2F1‐mediated manner. Furthermore, we show that these miRNAs silence antiproliferative genes, which themselves are E2F1 targets. Thus, miRNAs and transcriptional regulators appear to cooperate in the framework of a multi‐gene transcriptional and post‐transcriptional feed‐forward loop. Finally, we show that, similarly to p53 inactivation, overexpression of representative miRNAs promotes proliferation and delays senescence, manifesting the detrimental phenotypic consequence of perturbations in this circuit. Taken together, these findings position miRNAs as novel key players in the mammalian cellular proliferation network.

Deregulation of MYCN, LIN28B and LET7 in a molecular subtype of aggressive high-grade serous ovarian cancers

Molecular subtypes of serous ovarian cancer have been recently described. Using data from independent datasets including over 900 primary tumour samples, we show that deregulation of the Let-7 pathway is specifically associated with the C5 molecular subtype of serous ovarian cancer. DNA copy number and gene expression of HMGA2, alleles of Let-7, LIN28, LIN28B, MYC, MYCN, DICER1, and RNASEN were measured using microarray and quantitative reverse transcriptase PCR. Immunohistochemistry was performed on 127 samples using tissue microarrays and anti-HMGA2 antibodies. Fluorescence in situ hybridisation of bacterial artificial chromosomes hybridized to 239 ovarian tumours was used to measure translocation at the LIN28B locus. Short interfering RNA knockdown in ovarian cell lines was used to test the functionality of associations observed. Four molecular subtypes (C1, C2, C4, C5) of high-grade serous ovarian cancers were robustly represented in each dataset and showed similar pattern of patient survival. We found highly specific activation of a pathway involving MYCN, LIN28B, Let-7 and HMGA2 in the C5 molecular subtype defined by MYCN amplification and over-expression, over-expression of MYCN targets including the Let-7 repressor LIN28B, loss of Let-7 expression and HMGA2 amplification and over-expression. DICER1, a known Let-7 target, and RNASEN were over-expressed in C5 tumours. We saw no evidence of translocation at the LIN28B locus in C5 tumours. The reported interaction between LIN28B and Let-7 was recapitulated by siRNA knockdown in ovarian cancer cell lines. Our results associate deregulation of MYCN and downstream targets, including Let-7 and oncofetal genes, with serous ovarian cancer. We define for the first time how elements of an oncogenic pathway, involving multiple genes that contribute to stem cell renewal, is specifically altered in a molecular subtype of serous ovarian cancer. By defining the drivers of a molecular subtype of serous ovarian cancers we provide a novel strategy for targeted therapeutic intervention.