A transcriptomic signature to predict adjuvant gemcitabine sensitivity in pancreatic adenocarcinoma.

BACKGROUND: Chemotherapy is the only systemic treatment approved for pancreatic ductal adenocarcinoma (PDAC), with a selection of regimens based on patients' performance status and expected efficacy. The establishment of a potent stratification associated with chemotherapeutic efficacy could potentially improve prognosis by tailoring treatments. PATIENTS AND METHODS: Concomitant chemosensitivity and genome-wide RNA profiles were carried out on preclinical models (primary cell cultures and patient-derived xenografts) derived from patients with PDAC included in the PaCaOmics program (NCT01692873). The RNA-based stratification was tested in a monocentric cohort and validated in a multicentric cohort, both retrospectively collected from resected PDAC samples (67 and 368 patients, respectively). Forty-three (65%) and 203 (55%) patients received adjuvant gemcitabine in the monocentric and the multicentric cohorts, respectively. The relationships between predicted gemcitabine sensitivity and patients' overall survival (OS) and disease-free survival were investigated. RESULTS: The GemPred RNA signature was derived from preclinical models, defining gemcitabine sensitive PDAC as GemPred+. Among the patients who received gemcitabine in the test and validation cohorts, the GemPred+ patients had a higher OS than GemPred- (P = 0.046 and P = 0.00216). In both cohorts, the GemPred stratification was not associated with OS among patients who did not receive gemcitabine. Among gemcitabine-treated patients, GemPred+ patients had significantly higher OS than the GemPred-: 91.3 months [95% confidence interval (CI): 61.2-not reached] versus 33 months (95% CI: 24-35.2); hazard ratio 0.403 (95% CI: 0.221-0.735, P = 0.00216). The interaction test for gemcitabine and GemPred+ stratification was significant (P = 0.0245). Multivariate analysis in the gemcitabine-treated population retained an independent predictive value. CONCLUSION: The RNA-based GemPred stratification predicts the benefit of adjuvant gemcitabine in PDAC patients.

Comprehensive molecular classification of localized prostate adenocarcinoma reveals a tumour subtype predictive of non-aggressive disease.

Background: Management of localized prostate cancer (PCa) is a major clinical challenge since most of these cancers would not evolve but a majority of patients will still undergo a life-changing radical surgery. Molecular studies have shown that PCa can be classified according to their genomic alterations but none of the published PCa molecular classifications could identify a subtype corresponding to non-evolutive tumours. Materials and methods: Multi-omics molecular profiling was carried out on post-radical prostatectomy material from a cohort of 130 patients with localized PCa. We used unsupervised classification techniques to build a comprehensive classification of prostate tumours based on three molecular levels: DNA copy number, DNA methylation, and mRNA expression. Merged data from our cohort and The Cancer Genome Atlas cohort were used to characterize the resulting tumour subtypes. We measured subtype-associated risks of biochemical relapse using Cox regression models and survival data from five cohorts including the two aforementioned. Results: We describe three PCa molecular subtypes associated with specific molecular characteristics and different clinical outcomes. Particularly, one subtype was strongly associated with the absence of biochemical recurrence. We validated this finding on 746 samples from 5 distinct cohorts (P = 3.41 × 10-8, N = 746 tumour samples), and showed that our subtyping approach outperformed the most popular prognostic molecular signatures to accurately identify a subset of patients with a non-evolutive disease. We provide a set of 36 transcriptomic biomarkers to robustly identify this subtype of non-evolutive cases whose prevalence was estimated to 22% of all localized PCa tumours. Conclusion: At least 20% of patients with localized PCa can be accurately predicted to have a non-evolutive disease on the basis of their molecular subtype. Those patients should not undergo immediate surgery and rather be placed under active surveillance.

Exon-array profiling unlocks clinically and biologically relevant gene signatures from formalin-fixed paraffin-embedded tumour samples.

BACKGROUND: Degradation and chemical modification of RNA in formalin-fixed paraffin-embedded (FFPE) samples hamper their use in expression profiling studies. This study aimed to show that useful information can be obtained by Exon-array profiling archival FFPE tumour samples. METHODS: Nineteen cervical squamous cell carcinoma (SCC) and 9 adenocarcinoma (AC) FFPE samples (10-16-year-old) were profiled using Affymetrix Exon arrays. The gene signature derived was tested on a fresh-frozen non-small cell lung cancer (NSCLC) series. Exploration of biological networks involved gene set enrichment analysis (GSEA). Differential gene expression was confirmed using Quantigene, a multiplex bead-based alternative to qRT-PCR. RESULTS: In all, 1062 genes were higher in SCC vs AC, and 155 genes higher in AC. The 1217-gene signature correctly separated 58 NSCLC into SCC and AC. A gene network centered on hepatic nuclear factor and GATA6 was identified in AC, suggesting a role in glandular cell differentiation of the cervix. Quantigene analysis of the top 26 differentially expressed genes correctly partitioned cervix samples as SCC or AC. CONCLUSION: FFPE samples can be profiled using Exon arrays to derive gene expression signatures that are sufficiently robust to be applied to independent data sets, identify novel biology and design assays for independent platform validation.