Anita Grigoriadis

Expression Profiling of Purified Normal Human Luminal and Myoepithelial Breast Cells Identification of Novel Prognostic Markers for Breast Cancer

The normal duct-lobular system of the breast is lined by two epithelial cell types, inner luminal secretory cells and outer contractile myoepithelial cells. We have generated comprehensive expression profiles of the two normal cell types, using immunomagnetic cell separation and gene expression microarray analysis. The cell-type specificity was confirmed at the protein level by immunohistochemistry in normal breast tissue. New prognostic markers for survival were identified when the luminal- and myoepithelial-specific molecules were evaluated on breast tumor tissue microarrays. Nuclear expression of luminal epithelial marker galectin 3 correlated with a shorter overall survival in these patients, and the expression of SPARC (osteonectin), a myoepithelial marker, was an independent marker of poor prognosis in breast cancers as a whole. These data provide a framework for the interpretation of breast cancer molecular profiling experiments, the identification of potential new diagnostic markers, and development of novel indicators of prognosis.

Tiling Path Genomic Profiling of Grade 3 Invasive Ductal Breast Cancers

Purpose: To characterize the molecular genetic profiles of grade 3 invasive ductal carcinomas of no special type using high-resolution microarray-based comparative genomic hybridization (aCGH) and to identify recurrent amplicons harboring putative therapeutic targets associated with luminal, HER-2, and basal-like tumor phenotypes. Experimental Design: Ninety-five grade 3 invasive ductal carcinomas of no special type were classified into luminal, HER-2, and basal-like subgroups using a previously validated immunohistochemical panel. Tumor samples were microdissected and subjected to aCGH using a tiling path 32K BAC array platform. Selected regions of recurrent amplification were validated by means of in situ hybridization. Expression of genes pertaining to selected amplicons was investigated using quantitative real-time PCR and gene silencing was done using previously validated short hairpin RNA constructs. Results: We show that basal-like and HER-2 tumors are characterized by “sawtooth” and “firestorm” genetic patterns, respectively, whereas luminal cancers were more heterogeneous. Apart from confirming known amplifications associated with basal-like (1q21, 10p, and 12p), luminal (8p12, 11q13, and 11q14), and HER-2 (17q12) cancers, we identified previously unreported recurrent amplifications associated with each molecular subgroup: 19q12 in basal-like, 1q32.1 in luminal, and 14q12 in HER-2 cancers. PPM1D gene amplification (17q23.2) was found in 20% and 8% of HER-2 and luminal cancers, respectively. Silencing of PPM1D by short hairpin RNA resulted in selective loss of viability in tumor cell lines harboring the 17q23.2 amplification. Conclusions: Our results show the power of aCGH analysis in unraveling the genetic profiles of specific subgroups of cancer and for the identification of novel therapeutic targets.

Array CGH profiling of favourable histology Wilms tumours reveals novel gains and losses associated with relapse

Despite the excellent survival of Wilms tumour patients treated with multimodality therapy, approximately 15% will suffer from tumour relapse, where response rates are markedly reduced. We have carried out microarray‐based comparative genomic hybridisation on a series of 76 Wilms tumour samples, enriched for cases which recurred, to identify changes in DNA copy number associated with clinical outcome. Using 1Mb‐spaced genome‐wide BAC arrays, the most significantly different genomic changes between favourable histology tumours that did (n = 37), and did not (n = 39), subsequently relapse were gains on 1q, and novel deletions at 12q24 and 18q21. Further relapse‐associated loci included losses at 1q32.1, 2q36.3‐2q37.1, and gain at 13q31. 1q gains correlated strongly with loss of 1p and/or 16q. In 3 of 11 cases with concurrent 1p−/1q+, a breakpoint was identified at 1p13. Multiple low‐level sub‐megabase gains along the length of 1q were identified using chromosome 1 tiling‐path arrays. One such recurrent region at 1q22‐q23.1 included candidate genes RAB25, NES, CRABP2, HDGF and NTRK1, which were screened for mRNA expression using quantitative RT‐PCR. These data provide a high‐resolution catalogue of genomic copy number changes in relapsing favourable histology Wilms tumours. Copyright

Establishment of the epithelial-specific transcriptome of normal and malignant human breast cells based on MPSS and array expression data

IntroductionDiverse microarray and sequencing technologies have been widely used to characterise the molecular changes in malignant epithelial cells in breast cancers. Such gene expression studies to identify markers and targets in tumour cells are, however, compromised by the cellular heterogeneity of solid breast tumours and by the lack of appropriate counterparts representing normal breast epithelial cells.MethodsMalignant neoplastic epithelial cells from primary breast cancers and luminal and myoepithelial cells isolated from normal human breast tissue were isolated by immunomagnetic separation methods. Pools of RNA from highly enriched preparations of these cell types were subjected to expression profiling using massively parallel signature sequencing (MPSS) and four different genome wide microarray platforms. Functional related transcripts of the differential tumour epithelial transcriptome were used for gene set enrichment analysis to identify enrichment of luminal and myoepithelial type genes. Clinical pathological validation of a small number of genes was performed on tissue microarrays.ResultsMPSS identified 6,553 differentially expressed genes between the pool of normal luminal cells and that of primary tumours substantially enriched for epithelial cells, of which 98% were represented and 60% were confirmed by microarray profiling. Significant expression level changes between these two samples detected only by microarray technology were shown by 4,149 transcripts, resulting in a combined differential tumour epithelial transcriptome of 8,051 genes. Microarray gene signatures identified a comprehensive list of 907 and 955 transcripts whose expression differed between luminal epithelial cells and myoepithelial cells, respectively. Functional annotation and gene set enrichment analysis highlighted a group of genes related to skeletal development that were associated with the myoepithelial/basal cells and upregulated in the tumour sample. One of the most highly overexpressed genes in this category, that encoding periostin, was analysed immunohistochemically on breast cancer tissue microarrays and its expression in neoplastic cells correlated with poor outcome in a cohort of poor prognosis estrogen receptor-positive tumours.ConclusionUsing highly enriched cell populations in combination with multiplatform gene expression profiling studies, a comprehensive analysis of molecular changes between the normal and malignant breast tissue was established. This study provides a basis for the identification of novel and potentially important targets for diagnosis, prognosis and therapy in breast cancer.

Integrated Functional, Gene Expression and Genomic Analysis for the Identification of Cancer Targets

The majority of new drug approvals for cancer are based on existing therapeutic targets. One approach to the identification of novel targets is to perform high-throughput RNA interference (RNAi) cellular viability screens. We describe a novel approach combining RNAi screening in multiple cell lines with gene expression and genomic profiling to identify novel cancer targets. We performed parallel RNAi screens in multiple cancer cell lines to identify genes that are essential for viability in some cell lines but not others, suggesting that these genes constitute key drivers of cellular survival in specific cancer cells. This approach was verified by the identification of PIK3CA, silencing of which was selectively lethal to the MCF7 cell line, which harbours an activating oncogenic PIK3CA mutation. We combined our functional RNAi approach with gene expression and genomic analysis, allowing the identification of several novel kinases, including WEE1, that are essential for viability only in cell lines that have an elevated level of expression of this kinase. Furthermore, we identified a subset of breast tumours that highly express WEE1 suggesting that WEE1 could be a novel therapeutic target in breast cancer. In conclusion, this strategy represents a novel and effective strategy for the identification of functionally important therapeutic targets in cancer.

Conditional deletion of the Lkb1 gene in the mouse mammary gland induces tumour formation

Heterozygous germline mutations in the LKB1 (STK11) gene cause Peutz‐Jeghers syndrome (PJS), an autosomal dominant disorder characterized by hamartomatous polyposis of the gastrointestinal tract and an increased risk of colorectal, breast, and other cancers. To model the role of LKB1 mutation in mammary tumourigenesis, we have used a conditional gene targeting strategy to generate a mouse in which exons encoding the kinase domain of Lkb1 were deleted specifically in the mammary gland. Mammary gland tumours developed in these mice with a latency of 46–85 weeks and occurred in the thoracic or inguinal glands. These tumours were grade 2 invasive ductal carcinomas or solid papillary carcinomas with histological features similar to those described in breast cancers arising in patients with PJS. This mouse model of Lkb1 deficiency provides a potentially useful tool to investigate the role of Lkb1 in tumourigenesis and to guide the development of therapeutic approaches. Copyright

Regulator of G-protein signalling 2 mRNA is differentially expressed in mammary epithelial subpopulations and over-expressed in the majority of breast cancers

IntroductionTo understand which signalling pathways become deregulated in breast cancer, it is necessary to identify functionally significant gene expression patterns in the stem, progenitor, transit amplifying and differentiated cells of the mammary epithelium. We have previously used the markers 33A10, CD24 and Sca-1 to identify mouse mammary epithelial cell subpopulations. We now investigate the relationship between cells expressing these markers and use gene expression microarray analysis to identify genes differentially expressed in the cell populations.MethodsFreshly isolated primary mouse mammary epithelial cells were separated on the basis of staining with the 33A10 antibody and an α-Sca-1 antibody. The populations identified were profiled using gene expression microarray analysis. Gene expression patterns were confirmed on normal mouse and human mammary epithelial subpopulations and were examined in a panel of breast cancer samples and cell lines.ResultsAnalysis of the separated populations demonstrated that Sca-1- 33A10High stained cells were estrogen receptor α (Esr1)- luminal epithelial cells, whereas Sca-1+ 33A10Low/- stained cells were a mix of nonepithelial cells and Esr1+ epithelial cells. Analysis of the gene expression data identified the gene Rgs2 (regulator of G-protein signalling 2) as being highly expressed in the Sca-1- 33A10Low/- population, which included myoepithelial/basal cells. RGS2 has previously been described as a regulator of angiotensin II receptor signalling. Gene expression analysis by quantitative real-time RT-PCR of cells separated on the basis of CD24 and Sca-1 expression confirmed that Rgs2 was more highly expressed in mouse myoepithelial/basal mammary cells than luminal cells. This expression pattern was conserved in normal human breast cells. Functional analysis demonstrated RGS2 to be a modulator of oxytocin receptor signalling. The potential significance of RGS2 expression in breast cancer was demonstrated by semi-quantitative RT-PCR analysis, data mining and quantitative real-time RT-PCR approaches, which showed that RGS2 was expressed in the majority of solid breast cancers at much higher levels than in normal human mammary cells.ConclusionMolecular analysis of prospectively isolated mammary epithelial cells identified RGS2 as a modulator of oxytocin receptor signalling, which is highly expressed in the myoepithelial cells. The RGS2 gene, but not the oxytocin receptor, was also shown to be over-expressed in the majority of breast cancers, identifying the product of this gene, or the pathway(s) it regulates, as potentially significant therapeutic targets.

Delineation of a 1Mb breakpoint region at 1p13 in Wilms tumors by fine-tiling oligonucleotide array CGH

Wilms tumor karyotypes frequently exhibit recurrent, large‐scale chromosomal imbalances, among the most common of which are concurrent loss of 1p and gain of 1q. We have previously identified a novel breakpoint at 1p13 by 1 Mb‐spaced array CGH, and have now undertaken a fine‐tiling oligonucleotide array approach to map the region accurately in four tumors exhibiting rearrangements at this locus. The use of a 10 bp‐spaced platform revealed that all four tumors in fact harbored different breakpoints, which targeted intragenic sequences in PHTF1, DCLRE1B, and NRAS, and an intergenic region immediately downstream of TRIM33. All four genes and breakpoints were within the 1.78 Mb intervals identified by the genome‐wide BAC arrays. The precise breakpoint interval was in each case mapped to a 200–1,200 bp region and was confirmed for one case to lie within intron 3 of DCLRE1B by quantitative PCR. Analysis of local genome architecture revealed no convincing conservation of repetitive sequences or specific translocation/recombination‐associated elements within the breakpoint regions. This study highlights the power of fine‐tiling oligonucleotide arrays to delineate breakpoint regions identified by genome‐wide screens.

Additional file 6: Table S3. of Gene expression modules in primary breast cancers as risk factors for organotropic patterns of first metastatic spread: a case control study

Distribution of gene module scores for cases and controls in each case-control series, and estimated ORs using conditional logistic and logistic regression models. (XLSX 47 kb)