Angelo Gámez-Pozo

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.

DICER1, DROSHA and miRNAs in patients with non-small cell lung cancer: implications for outcomes and histologic classification.

The clinical and functional significance of RNA-interference machinery in lung cancer is poorly understood. Besides, microRNAs (miRNA) have the potential to serve both as biomarkers and therapeutic agents, by personalizing diagnosis and therapy. In this study, we investigated whether the expression levels of DICER1 and DROSHA, components of the RNA-interference machinery, can predict survival, and whether the miRNA expression profiles can differentiate histologic subtypes in non-small cell lung cancer (NSCLC). Levels of DICER1, DROSHA and five different miRNAs were measured in NSCLC specimens (N = 115) by qRT-PCR assay and correlated with clinical outcomes. Low expression of DROSHA was associated with an increased median survival (154.2 versus 39.8 months, P = 0.016). Also, high DROSHA expression was associated with decreased median survival in the following subgroups: adenocarcinoma (P = 0.011), grade III tumors (P = 0.038) and low-stage patients (P = 0.014). In multivariate analyses, we found two independent predictors of reduced disease-specific survival: high DROSHA expression [hazards ratio = 2.24; P = 0.04] and advanced tumor stage (hazards ratio = 1.29, P = 0.02). In general, the overall tumor miRNA expression was downregulated in our cohort compared with normal tissues. Expression levels of hsa-let-7a (P = 0.005) and miR-16 (P = 0.003) miRNA were significantly higher in squamous cell carcinoma than in adenocarcinoma samples. This study supports the value of the expression profiling of the components of the miRNA-processing machinery in the prognosis of NSCLC patients, especially DROSHA expression levels. In addition, differential expression of miRNAs, such as hsa-let-7a and miR-16 may be helpful tools in the histologic subclassification of NSCLC.

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.

Shotgun proteomics of archival triple‐negative breast cancer samples

Triple‐negative breast cancer (TNBC) accounts for 15–20% of all breast cancers, and has a worse prognosis compared with hormone receptor‐positive disease. Its unfavorable outcome and the lack of hormonal receptors determine the use of adjuvant chemotherapy as part of the standard treatment for these tumors, although several studies have documented that the current standard combination chemotherapy is suboptimal. Therefore, a new functional taxonomy of breast cancer and new targets for therapeutic development are urgently needed.

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.

Combined Label-Free Quantitative Proteomics and microRNA Expression Analysis of Breast Cancer Unravel Molecular Differences with Clinical Implications

Better knowledge of the biology of breast cancer has allowed the use of new targeted therapies, leading to improved outcome. High-throughput technologies allow deepening into the molecular architecture of breast cancer, integrating different levels of information, which is important if it helps in making clinical decisions. microRNA (miRNA) and protein expression profiles were obtained from 71 estrogen receptor-positive (ER(+)) and 25 triple-negative breast cancer (TNBC) samples. RNA and proteins obtained from formalin-fixed, paraffin-embedded tumors were analyzed by RT-qPCR and LC/MS-MS, respectively. We applied probabilistic graphical models representing complex biologic systems as networks, confirming that ER(+) and TNBC subtypes are distinct biologic entities. The integration of miRNA and protein expression data unravels molecular processes that can be related to differences in the genesis and clinical evolution of these types of breast cancer. Our results confirm that TNBC has a unique metabolic profile that may be exploited for therapeutic intervention.

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.

The Long-HER study: clinical and molecular analysis of patients with HER2+ advanced breast cancer who become long-term survivors with trastuzumab-based therapy.

Background Trastuzumab improves survival outcomes in patients with HER2+ metastatic breast cancer. The Long-Her study was designed to identify clinical and molecular markers that could differentiate long-term survivors from patients having early progression after trastuzumab treatment. Methods Data were collected from women with HER2-positive metastatic breast cancer treated with trastuzumab that experienced a response or stable disease during at least 3 years. Patients having a progression in the first year of therapy with trastuzumab were used as a control. Genes related with trastuzumab resistance were identified and investigated for network and gene functional interrelation. Models predicting poor response to trastuzumab were constructed and evaluated. Finally, a mutational status analysis of selected genes was performed in HER2 positive breast cancer samples. Results 103 patients were registered in the Long-HER study, of whom 71 had obtained a durable complete response. Median age was 58 years. Metastatic disease was diagnosed after a median of 24.7 months since primary diagnosis. Metastases were present in the liver (25%), lungs (25%), bones (23%) and soft tissues (23%), with 20% of patients having multiple locations of metastases. Median duration of response was 55 months. The molecular analysis included 35 patients from the group with complete response and 18 patients in a control poor-response group. Absence of trastuzumab as part of adjuvant therapy was the only clinical factor associated with long-term survival. Gene ontology analysis demonstrated that PI3K pathway was associated with poor response to trastuzumab-based therapy: tumours in the control group usually had four or five alterations in this pathway, whereas tumours in the Long-HER group had two alterations at most. Conclusions Trastuzumab may provide a substantial long-term survival benefit in a selected group of patients. Whole genome expression analysis comparing long-term survivors vs. a control group predicted early progression after trastuzumab-based therapy. Multiple alterations in genes related to the PI3K-mTOR pathway seem to be required to confer resistance to this therapy.