Validation of ovarian cancer gene expression signatures for survival and subtype in formalin fixed paraffin embedded tissues

Bevacizumab May Differentially Improve Prognosis of Advanced Ovarian Cancer Patients with Low Expression of VEGF-A165b, an Antiangiogenic VEGF-A Splice Variant

PURPOSE

The identification of a robust IHC marker to predict the response to antiangiogenic bevacizumab in ovarian cancer is of high clinical interest. VEGF-A, the molecular target of bevacizumab, is expressed as multiple isoforms with pro- or antiangiogenic properties, of which VEGF-A165b is the most dominant antiangiogenic isoform. The balance of VEGF-A isoforms is closely related to the angiogenic capacity of a tumor and may define its vulnerability to antiangiogenic therapy. We investigated whether the expression of VEGF-A165b could be related to the effect of bevacizumab in advanced ovarian cancer patients.

EXPERIMENTAL DESIGN

Formalin-fixed paraffin-embedded tissues from 413 patients of the ICON7 multicenter phase III trial, treated with standard platinum-based chemotherapy with or without bevacizumab, were probed for VEGF-A165b expression by IHC.

RESULTS

In patients with low VEGF-A165b expression, the addition of bevacizumab to standard platinum-based chemotherapy significantly improved progression-free (HR: 0.727; 95% CI, 0.538-0.984; P = 0.039) and overall survival (HR: 0.662; 95% CI, 0.458-0.958; P = 0.029). Multivariate analysis showed that the addition of bevacizumab in low VEGF-A165b-expressing patients conferred significant improvements in progression-free survival (HR: 0.610; 95% CI, 0.446-0.834; P = 0.002) and overall survival (HR: 0.527; 95% CI, 0.359-0.775; P = 0.001), independently from established risk factors.

CONCLUSIONS

We demonstrate for the first time that bevacizumab may differentially improve the prognosis of advanced ovarian cancer patients with low expression of VEGF-A165b, an antiangiogenic VEGF-A splice variant. We envision that this novel biomarker could be implemented into routine diagnostics and may have direct clinical implications for guiding bevacizumab-related treatment decisions in advanced ovarian cancer patients.

Brief introduction of medical database and data mining technology in big data era

Data mining technology can search for potentially valuable knowledge from a large amount of data, mainly divided into data preparation and data mining, and expression and analysis of results. It is a mature information processing technology and applies database technology. Database technology is a software science that researches manages, and applies databases. The data in the database are processed and analyzed by studying the underlying theory and implementation methods of the structure, storage, design, management, and application of the database. We have introduced several databases and data mining techniques to help a wide range of clinical researchers better understand and apply database technology.

A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer

The objective of this research was to develop a robust gene expression-based prognostic signature and scoring system for predicting overall survival (OS) of patients with high-grade serous ovarian cancer (HGSOC). Transcriptomic data of HGSOC patients were obtained from six independent studies in the NCBI GEO database. Genes significantly deregulated and associated with OS in HGSOCs were selected using GEO2R and Kaplan–Meier analysis with log-rank testing, respectively. Enrichment analysis for biological processes and pathways was performed using Gene Ontology analysis. A resampling/cross-validation method with Cox regression analysis was used to identify a novel gene expression-based signature associated with OS, and a prognostic scoring system was developed and further validated in nine independent HGSOC datasets. We first identified 488 significantly deregulated genes in HGSOC patients, of which 232 were found to be significantly associated with their OS. These genes were significantly enriched for cell cycle division, epithelial cell differentiation, p53 signaling pathway, vasculature development, and other processes. A novel 11-gene prognostic signature was identified and a prognostic scoring system was developed, which robustly predicted OS in HGSOC patients in 100 sampling test sets. The scoring system was further validated successfully in nine additional HGSOC public datasets. In conclusion, our integrative bioinformatics study combining transcriptomic and clinical data established an 11-gene prognostic signature for robust and reproducible prediction of OS in HGSOC patients. This signature could be of clinical value for guiding therapeutic selection and individualized treatment.

Bevacizumab May Differentially Improve Ovarian Cancer Outcome in Patients with Proliferative and Mesenchymal Molecular Subtypes

Purpose: Recent progress in understanding the molecular biology of epithelial ovarian cancer has not yet translated into individualized treatment for these women or improvements in their disease outcome. Gene expression has been utilized to identify distinct molecular subtypes, but there have been no reports investigating whether or not molecular subtyping is predictive of response to bevacizumab in ovarian cancer.Experimental Design: DASL gene expression arrays were performed on FFPE tissue from patients enrolled on the ICON7 trial. Patients were stratified into four TCGA molecular subtypes. Associations between molecular subtype and the efficacy of randomly assigned therapy with bevacizumab were assessed.Results: Molecular subtypes were assigned as follows: 122 immunoreactive (34%), 96 proliferative (27%), 73 differentiated (20%), and 68 mesenchymal (19%). In univariate analysis patients with tumors of proliferative subtype obtained the greatest benefit from bevacizumab with a median PFS improvement of 10.1 months [HR, 0.55 (95% CI, 0.34-0.90), P = 0.016]. For the mesenchymal subtype, bevacizumab conferred a nonsignificant improvement in PFS of 8.2 months [HR 0.78 (95% CI, 0.44-1.40), P = 0.41]. Bevacizumab conferred modest improvements in PFS for patients with immunoreactive subtype (3.8 months; P = 0.08) or differentiated subtype (3.7 months; P = 0.61). Multivariate analysis demonstrated significant PFS improvement in proliferative subtype patients only [HR, 0.45 (95% CI, 0.27-0.74), P = 0.0015].Conclusions: Ovarian carcinoma molecular subtypes with the poorest survival (proliferative and mesenchymal) derive a comparably greater benefit from treatment that includes bevacizumab. Validation of our findings in an independent cohort could enable the use of bevacizumab for those patients most likely to benefit, thereby reducing side effects and healthcare cost. Clin Cancer Res; 23(14); 3794-801. ©2017 AACR.

Ovarian cancer: Novel molecular aspects for clinical assessment

Ovarian cancer is a very heterogeneous tumor which has been traditionally characterized according to the different histological subtypes and differentiation degree. In recent years, innovative molecular screening biotechnologies have allowed to identify further subtypes of this cancer based on gene expression profiles, mutational features, and epigenetic factors. These novel classification systems emphasizing the molecular signatures within the broad spectrum of ovarian cancer have not only allowed a more precise prognostic prediction, but also proper therapeutic strategies for specific subgroups of patients. The bulk of available scientific data and the high refinement of molecular classifications of ovarian cancers can today address the research towards innovative drugs with the adoption of targeted therapies tailored for single molecular profiles leading to a better prediction of therapeutic response. Here, we summarize the current state of knowledge on the molecular bases of ovarian cancer, from the description of its molecular subtypes derived from wide high-throughput analyses to the latest discoveries of the ovarian cancer stem cells. The latest personalized treatment options are also presented with recent advances in using PARP inhibitors, anti-angiogenic, anti-folate receptor and anti-cancer stem cells treatment approaches.

Bevacizumab May Differentially Improve Ovarian Cancer Outcome in Patients with Proliferative and Mesenchymal Molecular Subtypes

Purpose: Recent progress in understanding the molecular biology of epithelial ovarian cancer has not yet translated into individualized treatment for these women or improvements in their disease outcome. Gene expression has been utilized to identify distinct molecular subtypes, but there have been no reports investigating whether or not molecular subtyping is predictive of response to bevacizumab in ovarian cancer.Experimental Design: DASL gene expression arrays were performed on FFPE tissue from patients enrolled on the ICON7 trial. Patients were stratified into four TCGA molecular subtypes. Associations between molecular subtype and the efficacy of randomly assigned therapy with bevacizumab were assessed.Results: Molecular subtypes were assigned as follows: 122 immunoreactive (34%), 96 proliferative (27%), 73 differentiated (20%), and 68 mesenchymal (19%). In univariate analysis patients with tumors of proliferative subtype obtained the greatest benefit from bevacizumab with a median PFS improvement of 10.1 months [HR, 0.55 (95% CI, 0.34-0.90), P = 0.016]. For the mesenchymal subtype, bevacizumab conferred a nonsignificant improvement in PFS of 8.2 months [HR 0.78 (95% CI, 0.44-1.40), P = 0.41]. Bevacizumab conferred modest improvements in PFS for patients with immunoreactive subtype (3.8 months; P = 0.08) or differentiated subtype (3.7 months; P = 0.61). Multivariate analysis demonstrated significant PFS improvement in proliferative subtype patients only [HR, 0.45 (95% CI, 0.27-0.74), P = 0.0015].Conclusions: Ovarian carcinoma molecular subtypes with the poorest survival (proliferative and mesenchymal) derive a comparably greater benefit from treatment that includes bevacizumab. Validation of our findings in an independent cohort could enable the use of bevacizumab for those patients most likely to benefit, thereby reducing side effects and healthcare cost. Clin Cancer Res; 23(14); 3794-801. ©2017 AACR.

Adaptation of a RAS pathway activation signature from FF to FFPE tissues in colorectal cancer

BACKGROUND

The KRAS gene is mutated in about 40 % of colorectal cancer (CRC) cases, which has been clinically validated as a predictive mutational marker of intrinsic resistance to anti-EGFR inhibitor (EGFRi) therapy. Since nearly 60 % of patients with a wild type KRAS fail to respond to EGFRi combination therapies, there is a need to develop more reliable molecular signatures to better predict response. Here we address the challenge of adapting a gene expression signature predictive of RAS pathway activation, created using fresh frozen (FF) tissues, for use with more widely available formalin fixed paraffin-embedded (FFPE) tissues.

METHODS

In this study, we evaluated the translation of an 18-gene RAS pathway signature score from FF to FFPE in 54 CRC cases, using a head-to-head comparison of five technology platforms. FFPE-based technologies included the Affymetrix GeneChip (Affy), NanoString nCounter™ (NanoS), Illumina whole genome RNASeq (RNA-Acc), Illumina targeted RNASeq (t-RNA), and Illumina stranded Total RNA-rRNA-depletion (rRNA).

RESULTS

Using Affy_FF as the "gold" standard, initial analysis of the 18-gene RAS scores on all 54 samples shows varying pairwise Spearman correlations, with (1) Affy_FFPE (r = 0.233, p = 0.090); (2) NanoS_FFPE (r = 0.608, p < 0.0001); (3) RNA-Acc_FFPE (r = 0.175, p = 0.21); (4) t-RNA_FFPE (r = -0.237, p = 0.085); (5) and t-RNA (r = -0.012, p = 0.93). These results suggest that only NanoString has successful FF to FFPE translation. The subsequent removal of identified "problematic" samples (n = 15) and genes (n = 2) further improves the correlations of Affy_FF with three of the five technologies: Affy_FFPE (r = 0.672, p < 0.0001); NanoS_FFPE (r = 0.738, p < 0.0001); and RNA-Acc_FFPE (r = 0.483, p = 0.002).

CONCLUSIONS

Of the five technology platforms tested, NanoString technology provides a more faithful translation of the RAS pathway gene expression signature from FF to FFPE than the Affymetrix GeneChip and multiple RNASeq technologies. Moreover, NanoString was the most forgiving technology in the analysis of samples with presumably poor RNA quality. Using this approach, the RAS signature score may now be reasonably applied to FFPE clinical samples.

Integrated genomic profiling identifies microRNA-92a regulation of IQGAP2 in locally advanced rectal cancer

Locally advanced rectal cancer (LARC) is treated with chemoradiation prior to surgical excision, leaving residual tumors altered or completely absent. Integrating layers of genomic profiling might identify regulatory pathways relevant to rectal tumorigenesis and inform therapeutic decisions and further research. We utilized formalin-fixed, paraffin-embedded pre-treatment LARC biopsies (n=138) and compared copy number, mRNA, and miRNA expression with matched normal rectal mucosa. An integrative model was used to predict regulatory interactions to explain gene expression changes. These predictions were evaluated in vitro using multiple colorectal cancer cell lines. The Cancer Genome Atlas (TCGA) was also used as an external cohort to validate our genomic profiling and predictions. We found differentially expressed mRNAs and miRNAs that characterize LARC. Our integrative model predicted the upregulation of miR-92a, miR-182, and miR-221 expression to be associated with downregulation of their target genes after adjusting for the effect of copy number alterations. Cell line studies using miR-92a mimics and inhibitors demonstrate that miR-92a expression regulates IQGAP2 expression. We show that endogenous miR-92a expression is inversely associated with endogenous KLF4 expression in multiple cell lines, and that this relationship is also present in rectal cancers of TCGA. Our integrative model predicted regulators of gene expression change in LARC using pre-treatment FFPE tissues. Our methodology implicated multiple regulatory interactions, some of which are corroborated by independent lines of study, while others indicate new opportunities for investigation.

Brain Metastasis Prediction by Transcriptomic Profiling in Triple-Negative Breast Cancer

BACKGROUND

Triple-negative breast cancer (TNBC) lacks expression of steroid hormone receptors (estrogen receptor α and progesterone) and epidermal growth factor receptor type 2. This phenotype shows high metastatic potential, with particular predilection to lungs and brain. Determination of TNBC transcriptomic profiles associated with high risk of brain metastasis (BM) might identify patients requiring alternative, more aggressive, or specific preventive and therapeutic approaches.

PATIENTS AND METHODS

Using a cDNA-mediated annealing, selection, extension, and ligation assay, we investigated expression of 29,369 gene transcripts in primary TNBC tumor samples from 119 patients-71 in discovery cohort A and 48 in independent cohort B-that included best discriminating genes. Expression of mRNA was correlated with the occurrence of symptomatic BM.

RESULTS

In cohort A, the difference at the noncorrected P < .005 was found for 64 transcripts (P = .23 for global test), but none showed significant difference at a preset level of false-discovery rate of < 10%. Of the 30 transcripts with the largest differences between patients with and without BM in cohort A, none was significantly associated with BM in cohort B.

CONCLUSION

Analysis based on the primary tumor gene transcripts alone is unlikely to predict BM development in advanced TNBC. Despite its negative findings, the study adds to the knowledge on the biology of TNBC and paves the way for future projects using more advanced molecular assays.

Gene Coexpression Analyses Differentiate Networks Associated with Diverse Cancers Harboring TP53 Missense or Null Mutations

In a variety of solid cancers, missense mutations in the well-established TP53 tumor suppressor gene may lead to the presence of a partially-functioning protein molecule, whereas mutations affecting the protein encoding reading frame, often referred to as null mutations, result in the absence of p53 protein. Both types of mutations have been observed in the same cancer type. As the resulting tumor biology may be quite different between these two groups, we used RNA-sequencing data from The Cancer Genome Atlas (TCGA) from four different cancers with poor prognosis, namely ovarian, breast, lung and skin cancers, to compare the patterns of coexpression of genes in tumors grouped according to their TP53 missense or null mutation status. We used Weighted Gene Coexpression Network analysis (WGCNA) and a new test statistic built on differences between groups in the measures of gene connectivity. For each cancer, our analysis identified a set of genes showing differential coexpression patterns between the TP53 missense- and null mutation-carrying groups that was robust to the choice of the tuning parameter in WGCNA. After comparing these sets of genes across the four cancers, one gene (KIR3DL2) consistently showed differential coexpression patterns between the null and missense groups. KIR3DL2 is known to play an important role in regulating the immune response, which is consistent with our observation that this gene's strongly-correlated partners implicated many immune-related pathways. Examining mutation-type-related changes in correlations between sets of genes may provide new insight into tumor biology.

Genetic and molecular changes in ovarian cancer

Epithelial ovarian cancer represents the most lethal gynecological malignancy in the developed world, and can be divided into five main histological subtypes: high grade serous, endometrioid, clear cell, mucinous and low grade serous. These subtypes represent distinct disease entities, both clinically and at the molecular level. Molecular analysis has revealed significant genetic heterogeneity in ovarian cancer, particularly within the high grade serous subtype. As such, this subtype has been the focus of much research effort to date, revealing molecular subgroups at both the genomic and transcriptomic level that have clinical implications. However, stratification of ovarian cancer patients based on the underlying biology of their disease remains in its infancy. Here, we summarize the molecular changes that characterize the five main ovarian cancer subtypes, highlight potential opportunities for targeted therapeutic intervention and outline priorities for future research.

Prediction of anti-angiogenesis escape

Many clinical trials have demonstrated the benefit of anti-angiogenesis therapy in the treatment of gynecologic cancer. However, these benefits have often been in terms of progression-free rather than overall survival and in some cases, the magnitude of benefit demonstrated in the pivotal phase 3 trials has been disappointing when compared with the percentage of patients who responded in earlier phase 2 trials. Two potential explanations for this are the current inability to stratify patients according to chance of benefit and the development of resistance mechanisms within the tumor. In this article, we review the prediction of response and the proposed resistance and escape mechanisms involved in anti-angiogenesis therapy, including the up-regulation of alternative proangiogenic pathways, vascular co-option, and resistance to hypoxia. These insights may offer a personalized strategy for anti-angiogenesis therapy and help us to consider the best selection of other therapies that should be combined with anti-angiogenesis therapy to improve the outcome of patients with gynecologic cancer.

A Method to Correlate mRNA Expression Datasets Obtained from Fresh Frozen and Formalin-Fixed, Paraffin-Embedded Tissue Samples: A Matter of Thresholds

Background

Gene expression profiling of tumors is a successful tool for the discovery of new cancer biomarkers and potential targets for the development of new therapeutic strategies. Reliable profiling is preferably performed on fresh frozen (FF) tissues in which the quality of nucleic acids is better preserved than in formalin-fixed paraffin-embedded (FFPE) material. However, since snap-freezing of biopsy materials is often not part of daily routine in pathology laboratories, one may have to rely on archival FFPE material. Procedures to retrieve the RNAs from FFPE materials have been developed and therefore, datasets obtained from FFPE and FF materials need to be made compatible to ensure reliable comparisons are possible.

Aim

To develop an efficient method to compare gene expression profiles obtained from FFPE and FF samples using the same platform.

Methods

Twenty-six FFPE-FF sample pairs of the same tumors representing various cancer types, and two FFPE-FF sample pairs of breast cancer cell lines, were included. Total RNA was extracted and gene expression profiling was carried out using Illumina’s Whole-Genome cDNA-mediated Annealing, Selection, extension and Ligation (WG-DASL) V3 arrays, enabling the simultaneous detection of 24,526 mRNA transcripts. A sample exclusion criterion was created based on the expression of 11 stably expressed reference genes. Pearson correlation at the probe level was calculated for paired FFPE-FF, and three cut-off values were chosen. Spearman correlation coefficients between the matched FFPE and FF samples were calculated for three probe lists with varying levels of significance and compared to the correlation based on all measured probes. Unsupervised hierarchical cluster analysis was performed to verify performance of the included probe lists to compare matched FPPE-FF samples.

Results

Twenty-seven FFPE-FF pairs passed the sample exclusion criterion. From the profiles of 27 FFPE and FF matched samples, the best correlating probes were identified for various levels of significance (Pearson P<0.01, n = 1,432; P<0.05, n = 2,530; and P<0.10, n = 3,351 probes). Unsupervised hierarchical clustering of the 27 pairs using the resulting probes yielded 25, 21, and 19 correctly clustered pairs, respectively, compared to 1 pair when all probes were used.

Conclusion

The proposed method enables comparison of gene expression profiles of FFPE and/or FF origin measured on the same platform.

Identification of Gene Expression Pattern Related to Breast Cancer Survival Using Integrated TCGA Datasets and Genomic Tools

Several large-scale human cancer genomics projects such as TCGA offered huge genomic and clinical data for researchers to obtain meaningful genomics alterations which intervene in the development and metastasis of the tumor. A web-based TCGA data analysis platform called TCGA4U was developed in this study. TCGA4U provides a visualization solution for this study to illustrate the relationship of these genomics alternations with clinical data. A whole genome screening of the survival related gene expression patterns in breast cancer was studied. The gene list that impacts the breast cancer patient survival was divided into two patterns. Gene list of each of these patterns was separately analyzed on DAVID. The result showed that mitochondrial ribosomes play a more crucial role in the cancer development. We also reported that breast cancer patients with low HSPA2 expression level had shorter overall survival time. This is widely different to findings of HSPA2 expression pattern in other cancer types. TCGA4U provided a new perspective for the TCGA datasets. We believe it can inspire more biomedical researchers to study and explain the genomic alterations in cancer development and discover more targeted therapies to help more cancer patients.

Comparing gene expression data from formalin-fixed, paraffin embedded tissues and qPCR with that from snap-frozen tissue and microarrays for modeling outcomes of patients with ovarian carcinoma

Background

Previously, we have used clinical and gene expression data from The Cancer Genome Atlas (TCGA) to model a pathway-based index predicting outcomes in ovarian carcinoma. This data were obtained from snap-frozen tissue measured with the Affymetrix U133 platform. In the current study, we correlate the data used to model with data derived from TaqMan qPCR both snap frozen and paraffin embedded (FFPE) samples.

Methods

To compare the effect of preservation methods on gene expression measured by qPCR, we assessed 18 patient and tumor sample matched snap-frozen and FFPE ovarian carcinoma samples. To compare gene measurement technologies, we correlated qPCR data from 10 patients with tumor sample matched snap-frozen ovarian carcinoma samples with the microarray data from TCGA. We normalized results to the average expression of three housekeeping genes. We scaled and centered the data for comparison to the Affymetrix output.

Results

For the 18 specimens, gene expression data obtained from snap-frozen tissue correlated highly with that from FFPE samples in our TaqMan assay (r > 0.82). For the 10 duplicate TCGA specimens, the reported microarray data correlated well (r = 0.6) with our qPCR data, and ranges of expression along pathways were similar.

Conclusions

Gene expression data obtained by qPCR from FFPE serous ovarian carcinoma samples can be used to assess in the pathway-based predictive model. The normalization procedures described control variations in expression, and the range calculated along a specific pathway can be interpreted for a patient’s risk profile.

Electronic supplementary material

The online version of this article (doi:10.1186/s12907-015-0017-1) contains supplementary material, which is available to authorized users.

Ovarian cancer: diagnostic, biological and prognostic aspects

Ovarian cancer remains the most lethal gynecologic malignancy, owing to late detection, intrinsic and acquired chemoresistance and remarkable heterogeneity. Despite optimization of surgical and chemotherapy protocols and initiation of clinical trials incorporating targeted therapy, only modest gains have been achieved in prolonging survival in this cancer. This review provides an update of recent developments in our understanding of the etiology, origin, diagnosis, progression and treatment of this malignancy, with emphasis on clinically relevant genetic classification approaches. In the authors' opinion, focused effort directed at understanding the molecular make-up of recurrent and metastatic ovarian cancer, while keeping in mind the unique molecular character of each of its histological types, is central to our effort to improve patient outcome in this cancer.

Revealing the Molecular Portrait of Triple Negative Breast Tumors in an Understudied Population through Omics Analysis of Formalin-Fixed and Paraffin-Embedded Tissues

Triple negative breast cancer (TNBC), defined by the lack of expression of the estrogen receptor, progesterone receptor and human epidermal receptor 2, is an aggressive form of breast cancer that is more prevalent in certain populations, in particular in low- and middle-income regions. The detailed molecular features of TNBC in these regions remain unexplored as samples are mostly accessible as formalin-fixed paraffin embedded (FFPE) archived tissues, a challenging material for advanced genomic and transcriptomic studies. Using dedicated reagents and analysis pipelines, we performed whole exome sequencing and miRNA and mRNA profiling of 12 FFPE tumor tissues collected from pathological archives in Mexico. Sequencing analyses of the tumor tissues and their blood pairs identified TP53 and RB1 genes as the most frequently mutated genes, with a somatic mutation load of 1.7 mutations/exome Mb on average. Transcriptional analyses revealed an overexpression of growth-promoting signals (EGFR, PDGFR, VEGF, PIK3CA, FOXM1), a repression of cell cycle control pathways (TP53, RB1), a deregulation of DNA-repair pathways, and alterations in epigenetic modifiers through miRNA:mRNA network de-regulation. The molecular programs identified were typical of those described in basal-like tumors in other populations. This work demonstrates the feasibility of using archived clinical samples for advanced integrated genomics analyses. It thus opens up opportunities for investigating molecular features of tumors from regions where only FFPE tissues are available, allowing retrospective studies on the search for treatment strategies or on the exploration of the geographic diversity of breast cancer.

Use of formalin-fixed paraffin-embedded samples for gene expression studies in breast cancer patients

To obtain gene expression profiles from samples collected in clinical trials, we conducted a pilot study to assess feasibility and estimate sample attrition rates when profiling formalin-fixed, paraffin-embedded specimens. Ten matched fresh-frozen and fixed breast cancer samples were profiled using the Illumina HT-12 and Ref-8 chips, respectively. The profiles obtained with Ref 8, were neither technically nor biologically reliable since they failed to yield the expected separation between estrogen receptor positive and negative samples. With the use of Affymetrix HG-U133 2.0 Plus chips on fixed samples and a quantitative polymerase chain reaction -based sample pre-assessment step, results were satisfactory in terms of biological reliability, despite the low number of present calls (M = 21%±5). Compared with the Illumina DASL WG platform, Affymetrix data showed a wider interquartile range (1.32 vs 0.57, P<2.2 E-16,) and larger fold changes. The Affymetrix chips were used to run a pilot study on 60 fixed breast cancers. By including in the workflow the sample pre-assessment steps, 96% of the samples predicted to give good results (44/46), were in fact rated as satisfactory from the point of view of technical and biological meaningfulness. Our gene expression profiles showed strong agreement with immunohistochemistry data, were able to reproduce breast cancer molecular subtypes, and allowed the validation of an estrogen receptor status classifier derived in frozen samples. The approach is therefore suitable to profile formalin-fixed paraffin-embedded samples collected in clinical trials, provided that quality controls are run both before (sample pre-assessment) and after hybridization on the array.

MicroRNAs as prognostic markers in ovarian cancer

Ovarian cancer (OC) is the most lethal gynecological malignancy among women. Over 70% of women with OC are diagnosed in advanced stages and most of these cases are incurable. Although most patients respond well to primary chemotherapy, tumors become resistant to treatment. Mechanisms of chemoresistance in cancer cells may be associated with mutational events and/or alterations of gene expression through epigenetic events. Although focusing on known genes has already yielded new information, previously unknown non-coding RNAs, such as microRNAs (miRNAs), also lead insight into the biology of chemoresistance. In this review we summarize the current evidence examining the role of miRNAs as biomarkers of response and survival to therapy in OC. Beside their clinical implications, we also discuss important differences between studies that may have limited their use as clinical biomarkers and suggest new approaches.

Prognostic biomarkers in endometrial and ovarian carcinoma

This article reviews the main prognostic and predictive biomarkers of endometrial (EC) and ovarian carcinoma (OC). In EC, prognosis still relies on conventional pathological features such as histological type and grade, as well as myometrial or lymphovascular space invasion. Estrogen receptor, p53, Ki-67, and ploidy analysis are the most promising biomarkers among a long list of molecules that have been proposed. Also, a number of putative predictive biomarkers have been proposed in molecular targeted therapy. In OC, prognosis is predominantly dependent on disease stage at diagnosis and the extent of residual disease at primary operation. Diagnostic markers which aid in establishing histological type in OC are available. However, not a single universally accepted predictive or prognostic marker exists to date. Targeted therapy has been growingly focused at in recent years, in view of the frequent development of chemoresistance at recurrent disease. The present review emphasizes the crucial role of correct pathological classification and stringent selection criteria of the material studied as basis for any evaluation of biological markers. It further emphasizes the promise of targeted therapy in EC and OC, while simultaneously highlighting the difficulties remaining before this can become standard of care.

Feasibility of RNA and DNA extraction from fresh pipelle and archival endometrial tissues for use in gene expression and SNP arrays

Identifying molecular markers of endometrial hyperplasia (neoplasia) progression is critical to cancer prevention. To assess RNA and DNA quantity and quality from routinely collected endometrial samples and evaluate the performance of RNA- and DNA-based arrays across endometrial tissue types, we collected fresh frozen (FF) Pipelle, FF curettage, and formalin-fixed paraffin-embedded (FFPE) hysterectomy specimens (benign indications) from eight women. Additionally, neoplastic and uninvolved tissues from 24 FFPE archival hysterectomy specimens with endometrial hyperplasias and carcinomas were assessed. RNA was extracted from 15 of 16 FF and 51 of 51 FFPE samples, with yields >1.2 μg for 13/15 (87%) FF and 50/51 (98%) FFPE samples. Extracted RNA was of high quality; all samples performed successfully on the Illumina whole-genome cDNA-mediated annealing, selection, extension, and ligation (WG-DASL) array and performance did not vary by tissue type. While DNA quantity from FFPE samples was excellent, quality was not sufficient for successful performance on the Affymetrix SNP Array 6.0. In conclusion, FF Pipelle samples, which are minimally invasive, yielded excellent quantity and quality of RNA for gene expression arrays (similar to FF curettage) and should be considered for use in genomic studies. FFPE-derived DNA should be evaluated on new rapidly evolving sequencing platforms.

Molecular signatures of epithelial ovarian cancer: analysis of associations with tumor characteristics and epidemiologic risk factors

BACKGROUND

Six gene expression subtypes of invasive epithelial ovarian cancer were recently defined using microarrays by Tothill and colleagues. The Cancer Genome Atlas (TCGA) project subsequently replicated these subtypes and identified a signature predictive of survival in high-grade serous (HGS) cancers. We previously validated these signatures for use in formalin-fixed paraffin-embedded tissues. The aim of the present study was to determine whether these signatures are associated with specific ovarian cancer risk factors, which would add to the evidence that they reflect the heterogeneous etiology of this disease.

METHODS

We modeled signature-specific tumor characteristics and epidemiologic risk factor relationships using multiple regression and multivariate response multiple regression models in 193 patients from a case-control study of epithelial ovarian cancer.

RESULTS

We observed associations between the Tothill gene expression subtype signatures and both age at diagnosis (P = 0.0008) and race (P = 0.008). Although most established epidemiologic risk factors were not associated with molecular signatures, there was an association between breast feeding (P = 0.024) and first-degree family history of breast or ovarian cancer (P = 0.034) among the 106 HGS cases. Some of the above associations were validated using gene expression microarray data from the TCGA project. Weak associations were seen with age at menarche and duration of oral contraceptive use and the TCGA survival signature.

CONCLUSIONS

These data support the potential for genomic characterization to elucidate the etiologic heterogeneity of epithelial ovarian cancer.

IMPACT

This study suggests that molecular signatures may augment the ability to define etiologic subtypes of epithelial ovarian cancer.

Molecular signatures of epithelial ovarian cancer: analysis of associations with tumor characteristics and epidemiologic risk factors

BACKGROUND

Six gene expression subtypes of invasive epithelial ovarian cancer were recently defined using microarrays by Tothill and colleagues. The Cancer Genome Atlas (TCGA) project subsequently replicated these subtypes and identified a signature predictive of survival in high-grade serous (HGS) cancers. We previously validated these signatures for use in formalin-fixed paraffin-embedded tissues. The aim of the present study was to determine whether these signatures are associated with specific ovarian cancer risk factors, which would add to the evidence that they reflect the heterogeneous etiology of this disease.

METHODS

We modeled signature-specific tumor characteristics and epidemiologic risk factor relationships using multiple regression and multivariate response multiple regression models in 193 patients from a case-control study of epithelial ovarian cancer.

RESULTS

We observed associations between the Tothill gene expression subtype signatures and both age at diagnosis (P = 0.0008) and race (P = 0.008). Although most established epidemiologic risk factors were not associated with molecular signatures, there was an association between breast feeding (P = 0.024) and first-degree family history of breast or ovarian cancer (P = 0.034) among the 106 HGS cases. Some of the above associations were validated using gene expression microarray data from the TCGA project. Weak associations were seen with age at menarche and duration of oral contraceptive use and the TCGA survival signature.

CONCLUSIONS

These data support the potential for genomic characterization to elucidate the etiologic heterogeneity of epithelial ovarian cancer.

IMPACT

This study suggests that molecular signatures may augment the ability to define etiologic subtypes of epithelial ovarian cancer.