Background

The extracellular matrix (ECM) has a key role in facilitating the progression of ovarian cancer and we have shown recently that the secreted ECM protein TGFBI modulates the response of ovarian cancer to paclitaxel-induced cell death.

Results

We have determined TGFBI signaling from the extracellular environment is preferential for the cell surface αvß3 integrin heterodimer, in contrast to periostin, a TGFBI paralogue, which signals primarily via a ß1 integrin-mediated pathway. We demonstrate that suppression of ß1 integrin expression, in ß3 integrin-expressing ovarian cancer cells, increases adhesion to rTGFBI. In addition, Syndecan-1 and −4 expression is dispensable for adhesion to rTGFBI and loss of Syndecan-1 cooperates with the loss of ß1 integrin to further enhance adhesion to rTGFBI. The RGD motif present in the carboxy-terminus of TGFBI is necessary, but not sufficient, for SKOV3 cell adhesion and is dispensable for adhesion of ovarian cancer cells lacking ß3 integrin expression. In contrast to TGFBI, the carboxy-terminus of periostin, lacking a RGD motif, is unable to support adhesion of ovarian cancer cells. Suppression of ß3 integrin in SKOV3 cells increases resistance to paclitaxel-induced cell death while suppression of ß1 integrin has no effect. Furthermore, suppression of TGFBI expression stimulates a paclitaxel resistant phenotype while suppression of fibronectin expression, which primarily signals through a ß1 integrin-mediated pathway, increases paclitaxel sensitivity.

Conclusions

Therefore, different ECM components use distinct signaling mechanisms in ovarian cancer cells and in particular, TGFBI preferentially interacts through a ß3 integrin receptor mediated mechanism to regulate the response of cells to paclitaxel-induced cell death.

Resistance to chemotherapy in ovarian cancer is poorly understood. Evolutionary models of cancer predict that, following treatment, resistance emerges either due to outgrowth of an intrinsically resistant sub-clone, or evolves in residual disease under the selective pressure of treatment. To investigate genetic evolution in high-grade serous (HGS) ovarian cancers we first analysed cell line series derived from three cases of HGS carcinoma before and after platinum resistance had developed (PEO1, PEO4 and PEO6, PEA1 and PEA2, and PEO14 and PEO23). Analysis with 24-colour fluorescence in situ hybridisation and SNP array comparative genomic hybridisation (CGH) showed mutually exclusive endoreduplication and loss of heterozygosity events in clones present at different timepoints in the same individual. This implies that platinum sensitive and resistant disease was not linearly related but shared a common ancestor at an early stage of tumour development. Array CGH analysis of six paired pre- and post-neoadjuvant treatment HGS samples from the CTCR-OV01 clinical study did not show extensive copy number differences, suggesting that one clone was strongly dominant at presentation. These data show that cisplatin resistance in HGS carcinoma develops from pre-existing minor clones but that enrichment for these clones is not apparent during short-term chemotherapy treatment.

Patient survival in small cell lung cancer (SCLC) is limited by acquired chemoresistance. Here we report the use of a biologically relevant model to identify novel candidate genes mediating in vivo acquired resistance to etoposide. Candidate genes derived from a cDNA microarray analysis were cloned and transiently overexpressed to evaluate their potential functional roles. We identified two promising genes in the DNA repair enzyme DNA Polymerase β and in the neuroendocrine transcription factor NKX2.2. Specific inhibition of DNA Polymerase β reduced the numbers of cells surviving treatment with etoposide and increased the amount of DNA damage in cells. Conversely, stable overexpression of NKX2.2 increased cell survival in response to etoposide in SCLC cell lines. Consistent with these findings, we found that an absence of nuclear staining for NKX2.2 in SCLC primary tumors was an independent predictor of improved outcomes in chemotherapy-treated patients. Taken together, our findings justify future prospective studies to confirm the roles of these molecules in mediating chemotherapy resistance in SCLC.

We have developed a self-inactivating PiggyBac transposon system for tamoxifen inducible insertional mutagenesis from a stably integrated chromosomal donor. This system, which we have named ‘Slingshot’, utilizes a transposon carrying elements for both gain- and loss-of-function screens in vitro. We show that the Slingshot transposon can be efficiently mobilized from a range of chromosomal loci with high inducibility and low background generating insertions that are randomly dispersed throughout the genome. Furthermore, we show that once the Slingshot transposon has been mobilized it is not remobilized producing stable clonal integrants in all daughter cells. To illustrate the efficacy of Slingshot as a screening tool we set out to identify mediators of resistance to puromycin and the chemotherapeutic drug vincristine by performing genetrap screens in mouse embryonic stem cells. From these genome-wide screens we identified multiple independent insertions in the multidrug resistance transporter genes Abcb1a/b and Abcg2 conferring resistance to drug treatment. Importantly, we also show that the Slingshot transposon system is functional in other mammalian cell lines such as human HEK293, OVCAR-3 and PE01 cells suggesting that it may be used in a range of cell culture systems. Slingshot represents a flexible and potent system for genome-wide transposon-mediated mutagenesis with many potential applications.

Background

It has recently emerged that common epithelial cancers such as breast cancers have fusion genes like those in leukaemias. In a representative breast cancer cell line, ZR-75-30, we searched for fusion genes, by analysing genome rearrangements.

Results

We first analysed rearrangements of the ZR-75-30 genome, to around 10kb resolution, by molecular cytogenetic approaches, combining array painting and array CGH. We then compared this map with genomic junctions determined by paired-end sequencing. Most of the breakpoints found by array painting and array CGH were identified in the paired end sequencing—55% of the unamplified breakpoints and 97% of the amplified breakpoints (as these are represented by more sequence reads). From this analysis we identified 9 expressed fusion genes: APPBP2-PHF20L1, BCAS3-HOXB9, COL14A1-SKAP1, TAOK1-PCGF2, TIAM1-NRIP1, TIMM23-ARHGAP32, TRPS1-LASP1, USP32-CCDC49 and ZMYM4-OPRD1. We also determined the genomic junctions of a further three expressed fusion genes that had been described by others, BCAS3-ERBB2, DDX5-DEPDC6/DEPTOR and PLEC1-ENPP2. Of this total of 12 expressed fusion genes, 9 were in the coamplification. Due to the sensitivity of the technologies used, we estimate these 12 fusion genes to be around two-thirds of the true total. Many of the fusions seem likely to be driver mutations. For example, PHF20L1, BCAS3, TAOK1, PCGF2, and TRPS1 are fused in other breast cancers. HOXB9 and PHF20L1 are members of gene families that are fused in other neoplasms. Several of the other genes are relevant to cancer—in addition to ERBB2, SKAP1 is an adaptor for Src, DEPTOR regulates the mTOR pathway and NRIP1 is an estrogen-receptor coregulator.

Conclusions

This is the first structural analysis of a breast cancer genome that combines classical molecular cytogenetic approaches with sequencing. Paired-end sequencing was able to detect almost all breakpoints, where there was adequate read depth. It supports the view that gene breakage and gene fusion are important classes of mutation in breast cancer, with a typical breast cancer expressing many fusion genes.

The elucidation of breast cancer subgroups and their molecular drivers requires integrated views of the genome and transcriptome from representative numbers of patients. We present an integrated analysis of copy number and gene expression in a discovery and validation set of 997 and 995 primary breast tumours, respectively, with long-term clinical follow-up. Inherited variants (copy number variants and single nucleotide polymorphisms) and acquired somatic copy number aberrations (CNAs) were associated with expression in ~40% of genes, with the landscape dominated by cis- and trans-acting CNAs. By delineating expression outlier genes driven in cis by CNAs, we identified putative cancer genes, including deletions in PPP2R2A, MTAP and MAP2K4. Unsupervised analysis of paired DNA–RNA profiles revealed novel subgroups with distinct clinical outcomes, which reproduced in the validation cohort. These include a high-risk, oestrogen-receptor-positive 11q13/14 cis-acting subgroup and a favourable prognosis subgroup devoid of CNAs. Trans-acting aberration hotspots were found to modulate subgroup-specific gene networks, including a TCR deletion-mediated adaptive immune response in the ‘CNA-devoid’ subgroup and a basal-specific chromosome 5 deletion-associated mitotic network. Our results provide a novel molecular stratification of the breast cancer population, derived from the impact of somatic CNAs on the transcriptome.

Background

In molecular profiling studies of cancer patients, experimental and clinical data are combined in order to understand the clinical heterogeneity of the disease: clinical information for each subject needs to be linked to tumour samples, macromolecules extracted, and experimental results. This may involve the integration of clinical data sets from several different sources: these data sets may employ different data definitions and some may be incomplete.

Methods

In this work we employ semantic web techniques developed within the CancerGrid project, in particular the use of metadata elements and logic-based inference to annotate heterogeneous clinical information, integrate and query it.

Results

We show how this integration can be achieved automatically, following the declaration of appropriate metadata elements for each clinical data set; we demonstrate the practicality of this approach through application to experimental results and clinical data from five hospitals in the UK and Canada, undertaken as part of the METABRIC project (Molecular Taxonomy of Breast Cancer International Consortium).

Conclusion

We describe a metadata approach for managing similarities and differences in clinical datasets in a standardized way that uses Common Data Elements (CDEs). We apply and evaluate the approach by integrating the five different clinical datasets of METABRIC.

Background

A somatic mutation in the FOXL2 gene is reported to be present in almost all (97%; 86/89) morphologically defined, adult-type, granulosa-cell tumors (A-GCTs). This FOXL2 c.402C>G mutation changes a highly conserved cysteine residue to a tryptophan (p.C134W). It was also found in a minority of other ovarian malignant stromal tumors, but not in benign ovarian stromal tumors or unrelated ovarian tumors or breast cancers.

Methodology/Principal Findings

Herein we studied other cancers and cell lines for the presence of this mutation. We screened DNA from 752 tumors of epithelial and mesenchymal origin and 28 ovarian cancer cell lines and 52 other cancer cell lines of varied origin. We found the FOXL2 c.402C>G mutation in an unreported A-GCT case and the A-GCT-derived cell line KGN. All other tumors and cell lines analyzed were mutation negative.

Conclusions/Significance

In addition to proving that the KGN cell line is a useful model to study A-GCTs, these data show that the c.402C>G mutation in FOXL2 is not commonly found in a wide variety of other cancers and therefore it is likely pathognomonic for A-GCTs and closely related tumors.

The widespread introduction of high throughput RNA interference screening technology has revealed tumour drug sensitivity pathways to common cytotoxics such as paclitaxel, doxorubicin and 5-fluorouracil, targeted agents such as trastuzumab and inhibitors of AKT and Poly(ADP-ribose) polymerase (PARP) as well as endocrine therapies such as tamoxifen. Given the limited power of microarray signatures to predict therapeutic response in associative studies of small clinical trial cohorts, the use of functional genomic data combined with expression or sequence analysis of genes and microRNAs implicated in drug response in human tumours may provide a more robust method to guide adjuvant treatment strategies in breast cancer that are transferable across different expression platforms and patient cohorts.

We describe an optimized microarray method for identifying genome-wide CpG island methylation called microarray-based methylation assessment of single samples (MMASS) which directly compares methylated to unmethylated sequences within a single sample. To improve previous methods we used bioinformatic analysis to predict an optimized combination of methylation-sensitive enzymes that had the highest utility for CpG-island probes and different methods to produce unmethylated representations of test DNA for more sensitive detection of differential methylation by hybridization. Subtraction or methylation-dependent digestion with McrBC was used with optimized (MMASS-v2) or previously described (MMASS-v1, MMASS-sub) methylation-sensitive enzyme combinations and compared with a published McrBC method. Comparison was performed using DNA from the cell line HCT116. We show that the distribution of methylation microarray data is inherently skewed and requires exogenous spiked controls for normalization and that analysis of digestion of methylated and unmethylated control sequences together with linear fit models of replicate data showed superior statistical power for the MMASS-v2 method. Comparison with previous methylation data for HCT116 and validation of CpG islands from PXMP4, SFRP2, DCC, RARB and TSEN2 confirmed the accuracy of MMASS-v2 results. The MMASS-v2 method offers improved sensitivity and statistical power for high-throughput microarray identification of differential methylation.

The application of paired-end next generation sequencing approaches has made it possible to systematically characterize rearrangements of the cancer genome to base-pair level. Utilizing this approach, we report the first detailed analysis of ovarian cancer rearrangements, comparing high-grade serous and clear cell cancers, and these histotypes with other solid cancers. Somatic rearrangements were systematically characterized in eight high-grade serous and five clear cell ovarian cancer genomes and we report here the identification of > 600 somatic rearrangements. Recurrent rearrangements of the transcriptional regulator gene, TSHZ3, were found in three of eight serous cases. Comparison to breast, pancreatic and prostate cancer genomes revealed that a subset of ovarian cancers share a marked tandem duplication phenotype with triple-negative breast cancers. The tandem duplication phenotype was not linked to BRCA1/2 mutation, suggesting that other common mechanisms or carcinogenic exposures are operative. High-grade serous cancers arising in women with germline BRCA1 or BRCA2 mutation showed a high frequency of small chromosomal deletions. These findings indicate that BRCA1/2 germline mutation may contribute to widespread structural change and that other undefined mechanism(s), which are potentially shared with triple-negative breast cancer, promote tandem chromosomal duplications that sculpt the ovarian cancer genome.

This review takes a sceptical view of the impact of breast cancer studies that have used microarrays to identify predictors of clinical outcome. In addition to discussing general pitfalls of microarray experiments, we also critically review the key breast cancer studies to highlight methodological problems in cohort selection, statistical analysis, validation of results and reporting of raw data. We conclude that the optimum use of microarrays in clinical studies requires further optimisation and standardisation of methodology and reporting, together with improvements in clinical study design.

Little consideration has been given to the effect of different segmentation methods on the variability of data derived from microarray images. Previous work has suggested that the significant source of variability from microarray image analysis is from estimation of local background. In this study, we used Analysis of Variance (ANOVA) models to investigate the effect of methods of segmentation on the precision of measurements obtained from replicate microarray experiments. We used four different methods of spot segmentation (adaptive, fixed circle, histogram and GenePix) to analyse a total number of 156 172 spots from 12 microarray experiments. Using a two-way ANOVA model and the coefficient of repeatability, we show that the method of segmentation significantly affects the precision of the microarray data. The histogram method gave the lowest variability across replicate spots compared to other methods, and had the lowest pixel-to-pixel variability within spots. This effect on precision was independent of background subtraction. We show that these findings have direct, practical implications as the variability in precision between the four methods resulted in different numbers of genes being identified as differentially expressed. Segmentation method is an important source of variability in microarray data that directly affects precision and the identification of differentially expressed genes.

Background

Expression microarrays have evolved into a powerful tool with great potential for clinical application and therefore reliability of data is essential. RNA amplification is used when the amount of starting material is scarce, as is frequently the case with clinical samples. Purification steps are critical in RNA amplification and labelling protocols, and there is a lack of sufficient data to validate and optimise the process.

Results

Here the purification steps involved in the protocol for indirect labelling of amplified RNA are evaluated and the experimentally determined best method for each step with respect to yield, purity, size distribution of the transcripts, and dye coupling is used to generate targets tested in replicate hybridisations. DNase treatment of diluted total RNA samples followed by phenol extraction is the optimal way to remove genomic DNA contamination. Purification of double-stranded cDNA is best achieved by phenol extraction followed by isopropanol precipitation at room temperature. Extraction with guanidinium-phenol and Lithium Chloride precipitation are the optimal methods for purification of amplified RNA and labelled aRNA respectively.

Conclusion

This protocol provides targets that generate highly reproducible microarray data with good representation of transcripts across the size spectrum and a coefficient of repeatability significantly better than that reported previously.

Breast cancers differ in response to treatment and may have a divergent clinical course despite having a similar histopathological appearance. New technology using DNA microarrays provides a systematic method to identify key markers for prognosis and treatment response by profiling thousands of genes expressed in a single cancer. Microarray profiling of 38 invasive breast cancers now confirms striking molecular differences between ductal carcinoma specimens and suggests a new classification for oestrogen-receptor negative breast cancer. Future approaches will need to include methods for high-throughput clinical validation and the ability to analyze microscopic samples.

There have been major advances in our understanding of the cellular and molecular biology of the human malignancies collectively referred to as ovarian cancer. At a recent Helene Harris Memorial Trust meeting, an international group of researchers considered actions that should be taken to improve the outcome for women with ovarian cancer. Nine major recommendations are outlined in this Perspective.

Numerous studies have tested the association between TP53 mutations in ovarian cancer and prognosis but these have been consistently confounded by limitations in study design, methodology, and/or heterogeneity in the sample cohort. High-grade serous (HGS) carcinoma is the most clinically important histological subtype of ovarian cancer. As these tumours may arise from the ovary, Fallopian tube or peritoneum, they are collectively referred to as high-grade pelvic serous carcinoma (HGPSC). To identify the true prevalence of TP53 mutations in HGPSC, we sequenced exons 2–11 and intron–exon boundaries in tumour DNA from 145 patients. HGPSC cases were defined as having histological grade 2 or 3 and FIGO stage III or IV. Surprisingly, pathogenic TP53 mutations were identified in 96.7% (n = 119/123) of HGPSC cases. Molecular and pathological review of mutation-negative cases showed evidence of p53 dysfunction associated with copy number gain of MDM2 or MDM4, or indicated the exclusion of samples as being low-grade serous tumours or carcinoma of uncertain primary site. Overall, p53 dysfunction rate approached 100% of confirmed HGPSCs. No association between TP53 mutation and progression-free or overall survival was found. From this first comprehensive mapping of TP53 mutation rate in a homogeneous group of HGPSC patients, we conclude that mutant TP53 is a driver mutation in the pathogenesis of HGPSC cancers. Because TP53 mutation is almost invariably present in HGPSC, it is not of substantial prognostic or predictive significance. Copyright © 2010 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Background

The accurate and high resolution mapping of DNA copy number aberrations has become an important tool by which to gain insight into the mechanisms of tumourigenesis. There are various commercially available platforms for such studies, but there remains no general consensus as to the optimal platform. There have been several previous platform comparison studies, but they have either described older technologies, used less-complex samples, or have not addressed the issue of the inherent biases in such comparisons. Here we describe a systematic comparison of data from four leading microarray technologies (the Affymetrix Genome-wide SNP 5.0 array, Agilent High-Density CGH Human 244A array, Illumina HumanCNV370-Duo DNA Analysis BeadChip, and the Nimblegen 385 K oligonucleotide array). We compare samples derived from primary breast tumours and their corresponding matched normals, well-established cancer cell lines, and HapMap individuals. By careful consideration and avoidance of potential sources of bias, we aim to provide a fair assessment of platform performance.

Results

By performing a theoretical assessment of the reproducibility, noise, and sensitivity of each platform, notable differences were revealed. Nimblegen exhibited between-replicate array variances an order of magnitude greater than the other three platforms, with Agilent slightly outperforming the others, and a comparison of self-self hybridizations revealed similar patterns. An assessment of the single probe power revealed that Agilent exhibits the highest sensitivity. Additionally, we performed an in-depth visual assessment of the ability of each platform to detect aberrations of varying sizes. As expected, all platforms were able to identify large aberrations in a robust manner. However, some focal amplifications and deletions were only detected in a subset of the platforms.

Conclusion

Although there are substantial differences in the design, density, and number of replicate probes, the comparison indicates a generally high level of concordance between platforms, despite differences in the reproducibility, noise, and sensitivity. In general, Agilent tended to be the best aCGH platform and Affymetrix, the superior SNP-CGH platform, but for specific decisions the results described herein provide a guide for platform selection and study design, and the dataset a resource for more tailored comparisons.

The imprinted insulin-like growth factor 2 (IGF2) gene is expressed predominantly from the paternal allele. Loss of imprinting (LOI) associated with hypomethylation at the promoter proximal sequence (DMR0) of the IGF2 gene was proposed as a predisposing constitutive risk biomarker for colorectal cancer. We used pyrosequencing to assess whether IGF2 DMR0 methylation is either present constitutively prior to cancer or whether it is acquired tissue-specifically after the onset of cancer. DNA samples from tumour tissues and matched non-tumour tissues from 22 breast and 42 colorectal cancer patients as well as peripheral blood samples obtained from colorectal cancer patients [SEARCH (n=case 192, controls 96)], breast cancer patients [ABC (n=case 364, controls 96)] and the European Prospective Investigation of Cancer [EPIC-Norfolk (n=breast 228, colorectal 225, controls 895)] were analysed. The EPIC samples were collected 2–5 years prior to diagnosis of breast or colorectal cancer. IGF2 DMR0 methylation levels in tumours were lower than matched non-tumour tissue. Hypomethylation of DMR0 was detected in breast (33%) and colorectal (80%) tumour tissues with a higher frequency than LOI indicating that methylation levels are a better indicator of cancer than LOI. In the EPIC population, the prevalence of IGF2 DMR0 hypomethylation was 9.5% and this correlated with increased age not cancer risk. Thus, IGF2 DMR0 hypomethylation occurs as an acquired tissue-specific somatic event rather than a constitutive innate epimutation. These results indicate that IGF2 DMR0 hypomethylation has diagnostic potential for colon cancer rather than value as a surrogate biomarker for constitutive LOI.

Summary

The extracellular matrix (ECM) can induce chemotherapy resistance via AKT-mediated inhibition of apoptosis. Here, we show that loss of the ECM protein TGFBI (transforming growth factor beta induced) is sufficient to induce specific resistance to paclitaxel and mitotic spindle abnormalities in ovarian cancer cells. Paclitaxel-resistant cells treated with recombinant TGFBI protein show integrin-dependent restoration of paclitaxel sensitivity via FAK- and Rho-dependent stabilization of microtubules. Immunohistochemical staining for TGFBI in paclitaxel-treated ovarian cancers from a prospective clinical trial showed that morphological changes of paclitaxel-induced cytotoxicity were restricted to areas of strong expression of TGFBI. These data show that ECM can mediate taxane sensitivity by modulating microtubule stability.

High resolution array-CGH and expression profiling identifies a novel genomic subtype of ER negative breast cancer, and provides a genome-wide list of common copy number alterations associated with aberrant expression and poor prognosis.

Background

The characterization of copy number alteration patterns in breast cancer requires high-resolution genome-wide profiling of a large panel of tumor specimens. To date, most genome-wide array comparative genomic hybridization studies have used tumor panels of relatively large tumor size and high Nottingham Prognostic Index (NPI) that are not as representative of breast cancer demographics.

Results

We performed an oligo-array-based high-resolution analysis of copy number alterations in 171 primary breast tumors of relatively small size and low NPI, which was therefore more representative of breast cancer demographics. Hierarchical clustering over the common regions of alteration identified a novel subtype of high-grade estrogen receptor (ER)-negative breast cancer, characterized by a low genomic instability index. We were able to validate the existence of this genomic subtype in one external breast cancer cohort. Using matched array expression data we also identified the genomic regions showing the strongest coordinate expression changes ('hotspots'). We show that several of these hotspots are located in the phosphatome, kinome and chromatinome, and harbor members of the 122-breast cancer CAN-list. Furthermore, we identify frequently amplified hotspots on 8q22.3 (EDD1, WDSOF1), 8q24.11-13 (THRAP6, DCC1, SQLE, SPG8) and 11q14.1 (NDUFC2, ALG8, USP35) associated with significantly worse prognosis. Amplification of any of these regions identified 37 samples with significantly worse overall survival (hazard ratio (HR) = 2.3 (1.3-1.4) p = 0.003) and time to distant metastasis (HR = 2.6 (1.4-5.1) p = 0.004) independently of NPI.

Conclusion

We present strong evidence for the existence of a novel subtype of high-grade ER-negative tumors that is characterized by a low genomic instability index. We also provide a genome-wide list of common copy number alteration regions in breast cancer that show strong coordinate aberrant expression, and further identify novel frequently amplified regions that correlate with poor prognosis. Many of the genes associated with these regions represent likely novel oncogenes or tumor suppressors.

Background

p53 is commonly inactivated by mutations in the DNA-binding domain in a wide range of cancers. As mutant p53 often influences response to therapy, effective and rapid methods to scan for mutations in TP53 are likely to be of clinical value. We therefore evaluated the use of high resolution melting (HRM) as a rapid mutation scanning tool for TP53 in tumour samples.

Methods

We designed PCR amplicons for HRM mutation scanning of TP53 exons 5 to 8 and tested them with DNA from cell lines hemizygous or homozygous for known mutations. We assessed the sensitivity of each PCR amplicon using dilutions of cell line DNA in normal wild-type DNA. We then performed a blinded assessment on ovarian tumour DNA samples that had been previously sequenced for mutations in TP53 to assess the sensitivity and positive predictive value of the HRM technique. We also performed HRM analysis on breast tumour DNA samples with unknown TP53 mutation status.

Results

One cell line mutation was not readily observed when exon 5 was amplified. As exon 5 contained multiple melting domains, we divided the exon into two amplicons for further screening. Sequence changes were also introduced into some of the primers to improve the melting characteristics of the amplicon. Aberrant HRM curves indicative of TP53 mutations were observed for each of the samples in the ovarian tumour DNA panel. Comparison of the HRM results with the sequencing results revealed that each mutation was detected by HRM in the correct exon. For the breast tumour panel, we detected seven aberrant melt profiles by HRM and subsequent sequencing confirmed the presence of these and no other mutations in the predicted exons.

Conclusion

HRM is an effective technique for simple and rapid scanning of TP53 mutations that can markedly reduce the amount of sequencing required in mutational studies of TP53.

A consensus prognostic classifier for estrogen receptor positive breast tumors has been developed and shown to be valid in nearly 900 samples across different microarray platforms.

Background

A consensus prognostic gene expression classifier is still elusive in heterogeneous diseases such as breast cancer.

Results

Here we perform a combined analysis of three major breast cancer microarray data sets to hone in on a universally valid prognostic molecular classifier in estrogen receptor (ER) positive tumors. Using a recently developed robust measure of prognostic separation, we further validate the prognostic classifier in three external independent cohorts, confirming the validity of our molecular classifier in a total of 877 ER positive samples. Furthermore, we find that molecular classifiers may not outperform classical prognostic indices but that they can be used in hybrid molecular-pathological classification schemes to improve prognostic separation.

Conclusion

The prognostic molecular classifier presented here is the first to be valid in over 877 ER positive breast cancer samples and across three different microarray platforms. Larger multi-institutional studies will be needed to fully determine the added prognostic value of molecular classifiers when combined with standard prognostic factors.

Background

Post-translational modification of histones resulting in chromatin remodelling plays a key role in the regulation of gene expression. Here we report characteristic patterns of expression of 12 members of 3 classes of chromatin modifier genes in 6 different cancer types: histone acetyltransferases (HATs)- EP300, CREBBP, and PCAF; histone deacetylases (HDACs)- HDAC1, HDAC2, HDAC4, HDAC5, HDAC7A, and SIRT1; and histone methyltransferases (HMTs)- SUV39H1and SUV39H2. Expression of each gene in 225 samples (135 primary tumours, 47 cancer cell lines, and 43 normal tissues) was analysedby QRT-PCR, normalized with 8 housekeeping genes, and given as a ratio by comparison with a universal reference RNA.

Results

This involved a total of 13,000 PCR assays allowing for rigorous analysis by fitting a linear regression model to the data. Mutation analysis of HDAC1, HDAC2, SUV39H1, and SUV39H2 revealed only two out of 181 cancer samples (both cell lines) with significant coding-sequence alterations. Supervised analysis and Independent Component Analysis showed that expression of many of these genes was able to discriminate tumour samples from their normal counterparts. Clustering based on the normalized expression ratios of the 12 genes also showed that most samples were grouped according to tissue type. Using a linear discriminant classifier and internal cross-validation revealed that with as few as 5 of the 12 genes, SIRT1, CREBBP, HDAC7A, HDAC5 and PCAF, most samples were correctly assigned.

Conclusion

The expression patterns of HATs, HDACs, and HMTs suggest these genes are important in neoplastic transformation and have characteristic patterns of expression depending on tissue of origin, with implications for potential clinical application.