Iker Sánchez-Navarro

Comparison of gene expression profiling by reverse transcription quantitative PCR between fresh frozen and formalin-fixed, paraffin-embedded breast cancer tissues

Recent reports demonstrate the feasibility of quantifying gene expression by using RNA isolated from blocks of formalin-fixed, paraffin-embedded (FFPE) tumor tissue. The development of molecular tests for clinical use based on archival materials would be of great utility in the search for and validation of important genes or gene expression profiles. In this study, we compared the performance of different normalization strategies in the correlation of quantitative data between fresh frozen (FF) and FFPE samples and analyzed the parameters that characterize such correlation for each gene. Total RNA extracted from FFPE samples presented a shift in raw cycle threshold (Cq) values that can be explained by its extensive degradation. Proper normalization can compensate for the effects of RNA degradation in gene expression measurements. We show that correlation between normalized expression values is better for moderately to highly expressed genes whose expression varies significantly between samples. Nevertheless, some genes had no correlation. These genes should not be included in molecular tests for clinical use based on FFPE samples. Our results could serve as a guide when developing clinical diagnostic tests based on RT-qPCR analyses of FFPE tissues in the coming era of treatment decision-making based on gene expression profiling.

PTRF/Cavin-1 and MIF Proteins Are Identified as Non-Small Cell Lung Cancer Biomarkers by Label-Free Proteomics

With the completion of the human genome sequence, biomedical sciences have entered in the “omics” era, mainly due to high-throughput genomics techniques and the recent application of mass spectrometry to proteomics analyses. However, there is still a time lag between these technological advances and their application in the clinical setting. Our work is designed to build bridges between high-performance proteomics and clinical routine. Protein extracts were obtained from fresh frozen normal lung and non-small cell lung cancer samples. We applied a phosphopeptide enrichment followed by LC-MS/MS. Subsequent label-free quantification and bioinformatics analyses were performed. We assessed protein patterns on these samples, showing dozens of differential markers between normal and tumor tissue. Gene ontology and interactome analyses identified signaling pathways altered on tumor tissue. We have identified two proteins, PTRF/cavin-1 and MIF, which are differentially expressed between normal lung and non-small cell lung cancer. These potential biomarkers were validated using western blot and immunohistochemistry. The application of discovery-based proteomics analyses in clinical samples allowed us to identify new potential biomarkers and therapeutic targets in non-small cell lung cancer.

Aurora kinases as prognostic biomarkers in ovarian carcinoma.

We investigated the expression of Aurora kinases A and B by immunohistochemistry in 68 ovarian carcinomas to analyze their prognostic value. The amplification of AURKA gene by fluorescence in situ hybridization was also assessed. Overall, 58.8% and 85.3% of ovarian carcinomas showed expression of Aurora A and B, respectively. Amplification of AURKA was found in 27.6% of cases examined. Tumors with Aurora A expression showed a lower rate of recurrence than those tumors without Aurora A expression (65% versus 91.7%, P = .019). In the univariate analysis, patients with Aurora A and B expression showed an increased progression-free survival (P = .023 and .06, respectively, log-rank test) and overall survival (P = .03 and .02, respectively, log-rank test). The multivariate analysis adjusted to optimal surgery by Cox proportional hazards regression showed Aurora A expression as an independent prognostic factor for progression-free survival (P = .03) and overall survival (P = .02). In conclusion, Aurora A expression seems to have a prognostic value in ovarian carcinoma.

MALDI profiling of human lung cancer subtypes.

Background Proteomics is expected to play a key role in cancer biomarker discovery. Although it has become feasible to rapidly analyze proteins from crude cell extracts using mass spectrometry, complex sample composition hampers this type of measurement. Therefore, for effective proteome analysis, it becomes critical to enrich samples for the analytes of interest. Despite that one-third of the proteins in eukaryotic cells are thought to be phosphorylated at some point in their life cycle, only a low percentage of intracellular proteins is phosphorylated at a given time. Methodology/Principal Findings In this work, we have applied chromatographic phosphopeptide enrichment techniques to reduce the complexity of human clinical samples. A novel method for high-throughput peptide profiling of human tumor samples, using Parallel IMAC and MALDI-TOF MS, is described. We have applied this methodology to analyze human normal and cancer lung samples in the search for new biomarkers. Using a highly reproducible spectral processing algorithm to produce peptide mass profiles with minimal variability across the samples, lineal discriminant-based and decision tree–based classification models were generated. These models can distinguish normal from tumor samples, as well as differentiate the various non–small cell lung cancer histological subtypes. Conclusions/Significance A novel, optimized sample preparation method and a careful data acquisition strategy is described for high-throughput peptide profiling of small amounts of human normal lung and lung cancer samples. We show that the appropriate combination of peptide expression values is able to discriminate normal lung from non-small cell lung cancer samples and among different histological subtypes. Our study does emphasize the great potential of proteomics in the molecular characterization of cancer.

An 8-gene qRT-PCR-based gene expression score that has prognostic value in early breast cancer

BackgroundGene expression profiling may improve prognostic accuracy in patients with early breast cancer. Our objective was to demonstrate that it is possible to develop a simple molecular signature to predict distant relapse.MethodsWe included 153 patients with stage I-II hormonal receptor-positive breast cancer. RNA was isolated from formalin-fixed paraffin-embedded samples and qRT-PCR amplification of 83 genes was performed with gene expression assays. The genes we analyzed were those included in the 70-Gene Signature, the Recurrence Score and the Two-Gene Index. The association among gene expression, clinical variables and distant metastasis-free survival was analyzed using Cox regression models.ResultsAn 8-gene prognostic score was defined. Distant metastasis-free survival at 5 years was 97% for patients defined as low-risk by the prognostic score versus 60% for patients defined as high-risk. The 8-gene score remained a significant factor in multivariate analysis and its performance was similar to that of two validated gene profiles: the 70-Gene Signature and the Recurrence Score. The validity of the signature was verified in independent cohorts obtained from the GEO database.ConclusionsThis study identifies a simple gene expression score that complements histopathological prognostic factors in breast cancer, and can be determined in paraffin-embedded samples.

Comparison of prognostic gene profiles using qRT-PCR in paraffin samples: a retrospective study in patients with early breast cancer.

Introduction Gene profiling may improve prognostic accuracy in patients with early breast cancer, but this technology is not widely available. We used commercial assays for qRT-PCR to assess the performance of the gene profiles included in the 70-Gene Signature, the Recurrence Score and the Two-Gene Ratio. Methods 153 patients with early breast cancer and a minimum follow-up of 5 years were included. All tumours were positive for hormonal receptors and 38% had positive lymph nodes; 64% of patients received adjuvant chemotherapy. RNA was extracted from formalin-fixed paraffin-embedded (FFPE) specimens using a specific kit. qRT-PCR amplifications were performed with TaqMan Gene Expression Assays products. We applied the three gene-expression-based models to our patient cohort to compare the predictions derived from these gene sets. Results After a median follow-up of 91 months, 22% of patients relapsed. The distant metastasis-free survival (DMFS) at 5 years was calculated for each profile. For the 70-Gene Signature, DMFS was 95% -good prognosis- versus 66% -poor prognosis. In the case of the Recurrence Score, DMFS was 98%, 81% and 69% for low, intermediate and high-risk groups, respectively. Finally, for the Two-Gene Ratio, DMFS was 86% versus 70%. The 70-Gene Signature and the Recurrence Score were highly informative in identifying patients with distant metastasis, even in multivariate analysis. Conclusion Commercially available assays for qRT-PCR can be used to assess the prognostic utility of previously published gene expression profiles in FFPE material from patients with early breast cancer. Our results, with the use of a different platform and with different material, confirm the robustness of the 70-Gene Signature and represent an independent test for the Recurrence Score, using different primer/probe sets.

Angiogenesis-related gene expression profile with independent prognostic value in advanced ovarian carcinoma.

Background Ovarian carcinoma is the most important cause of gynecological cancer-related mortality in Western societies. Despite the improved median overall survival in patients receiving chemotherapy regimens such as paclitaxel and carboplatin combination, relapse still occurs in most advanced diseased patients. Increased angiogenesis is associated with rapid recurrence and decreased survival in ovarian cancer. This study was planned to identify an angiogenesis-related gene expression profile with prognostic value in advanced ovarian carcinoma patients. Methodology/Principal Findings RNAs were collected from formalin-fixed paraffin-embedded samples of 61 patients with III/IV FIGO stage ovarian cancer who underwent surgical cytoreduction and received a carboplatin plus paclitaxel regimen. Expression levels of 82 angiogenesis related genes were measured by quantitative real-time polymerase chain reaction using TaqMan low-density arrays. A 34-gene-profile which was able to predict the overall survival of ovarian carcinoma patients was identified. After a leave-one-out cross validation, the profile distinguished two groups of patients with different outcomes. Median overall survival and progression-free survival for the high risk group was 28.3 and 15.0 months, respectively, and was not reached by patients in the low risk group at the end of follow-up. Moreover, the profile maintained an independent prognostic value in the multivariate analysis. The hazard ratio for death was 2.3 (95% CI, 1.5 to 3.2; p<0.001). Conclusions/Significance It is possible to generate a prognostic model for advanced ovarian carcinoma based on angiogenesis-related genes using formalin-fixed paraffin-embedded samples. The present results are consistent with the increasing weight of angiogenesis genes in the prognosis of ovarian carcinoma.

The present and future of gene profiling in breast cancer.

Gene signatures can provide prognostic and predictive information to help in the treatment of early-stage breast cancer. Although many of these signatures have been described, only a few have been properly validated. MammaPrint and OncoType offer prognostic information and identify low-risk patients who do not benefit from adjuvant chemotherapy. With regard to prediction of response, molecular subtypes of breast cancer differ in their sensitivity to chemotherapy, although further studies are needed in this field. Cost, small sample size, and the need to use central laboratories are common limitations to the widespread use of these tools.

Abstract 392: Predictive value of angiogenesis related genes in advanced ovarian carcinoma

Proceedings: AACR 101st Annual Meeting 2010‐‐ Apr 17‐21, 2010; Washington, DC Management of advanced ovarian cancer (AOC) involves surgery in order to achieve surgical cytoreduction followed by chemotherapy. Combination platinum (C) - paclitaxel (P) chemotherapy has become a standard first line treatment for the advanced-stage disease. Outcome is significantly improved with this regimen and 40 to 50% achieve complete clinical remission. Classical parameters such as age at diagnosis, extent of disease residual disease after surgery, and the histopathological features of the tumor are imperfect predictors of response. Angiogenesis plays a major rol in ovarian carcinogenesis and antiangiogenic compounds such as bevacizumab proved efficacy in AOC in early phase trials. The aim of this study is to build a profile able to predict clinical response to multimodal first line therapy. Materials and Methods: 61 patients with III/IV FIGO stage ovarian cancer who underwent surgical cytoreduction and received a C plus P regimen were included. RNAs were collected from formalin-fixed paraffin-embedded AOC samples. Expression levels of 82 angiogenesis related genes were measured using quantitative real time polymerase chain reaction. Clinical response was evaluated using CT after the completion of multimodal therapy. Statistical analysis was performed using a regression method to generate multiple models based on the significant genes. The accuracy of the models was evaluated using Receiver Operating Characteristic (ROC) curves. The Akaike Information Criterion based selection was used to find the most accurate one. Results: The median age at diagnosis was 53 years (range, 21 to 82 years). All patients had advanced disease (FIGO stages III/IV). Most of them had FIGO stage III (51, 83.6%), grade 3 tumors (35, 57.4%), and serous histology (42, 68.9%). 52 patients (85.2%) achieved an initial response (complete response or partial response by RECIST criteria) to this therapy. It was found an independent model able to predict any degree of response to therapy comprising 8 genes with an Area Under the Curve (AUC) of 0.955 (p<0.001). Leave-one-out cross validation was applied to avoid overfitting of the model, obtaining a corrected AUC of 0.880, 95%IC: 0,776-0,985. Conclusions: It is possible to generate a predictive model of clinical response for ovarian cancer based on angiogenesis related genes using formalin-fixed paraffin-embedded samples. The present results are consistent with the increasing weight of several angiogenesis genes in prognosis of ovarian cancer. Although these results should be validated prospectively in larger series of ovarian cancer patients, this model could identify those patients that would achieve any degree of response to standard treatment. So it could be used to tailor therapy in those patients with no response at all to treatment. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 392.

High-Throughput Phosphoproteomics from Formalin-Fixed, Paraffin-Embedded Tissues

Liquid chromatography coupled with tandem mass spectrometry–based high‐throughput proteomics allows detection and characterization of thousands of peptides and their post‐translational modifications in a single sample. Protein phosphorylation affects most eukaryotic cellular processes, and its deregulation is considered a hallmark of cancer and other diseases. High‐throughput phosphoproteomics may enable monitoring of altered signaling pathways as a means of stratifying tumors and facilitating the discovery of new drugs. Unfortunately, the development of molecular tests for clinical use is constrained by the limited availability of fresh frozen, clinically annotated samples, and protocols that allow the use of human archival formalin‐fixed, paraffin‐embedded samples are required. The protocols in this article describe a global procedure for evaluating hundreds of protein phosphorylation sites in protein extracts obtained from formalin‐fixed, paraffin‐embedded tissues. Curr. Protoc. Chem. Biol. 4:161‐175