Blood Signature of Pre-Heart Failure: A Microarrays Study

International audienceBACKGROUND: The preclinical stage of systolic heart failure (HF), known as asymptomatic left ventricular dysfunction (ALVD), is diagnosed only by echocardiography, frequent in the general population and leads to a high risk of developing severe HF. Large scale screening for ALVD is a difficult task and represents a major unmet clinical challenge that requires the determination of ALVD biomarkers. METHODOLOGY/PRINCIPAL FINDINGS: 294 individuals were screened by echocardiography. We identified 9 ALVD cases out of 128 subjects with cardiovascular risk factors. White blood cell gene expression profiling was performed using pangenomic microarrays. Data were analyzed using principal component analysis (PCA) and Significant Analysis of Microarrays (SAM). To build an ALVD classifier model, we used the nearest centroid classification method (NCCM) with the ClaNC software package. Classification performance was determined using the leave-one-out cross-validation method. Blood transcriptome analysis provided a specific molecular signature for ALVD which defined a model based on 7 genes capable of discriminating ALVD cases. Analysis of an ALVD patients validation group demonstrated that these genes are accurate diagnostic predictors for ALVD with 87% accuracy and 100% precision. Furthermore, Receiver Operating Characteristic curves of expression levels confirmed that 6 out of 7 genes discriminate for left ventricular dysfunction classification. CONCLUSIONS/SIGNIFICANCE: These targets could serve to enhance the ability to efficiently detect ALVD by general care practitioners to facilitate preemptive initiation of medical treatment preventing the development of HF

Patient-reported prevalence of metamorphopsia and predictors of vision-related quality of life in vitreomacular traction: a prospective, multi-centre study

© 2018, The Author(s). Objectives: To report the prevalence and severity of metamorphopsia, estimate its impact on vision-related quality of life (VRQoL) and evaluate predictors of VRQoL in patients with vitreomacular traction (VMT). Patients and methods: A prospective, cross-sectional multi-centre study in the United Kingdom of 185 patients with VMT, with or without a full thickness macular hole (FTMH). Self-reported metamorphopsia was determined using the metamorphopsia questionnaire. VRQoL was assessed using the Visual Function Questionnaire (VFQ-25). Physicians recorded clinical and ocular characteristics in both eyes including a physician assessment of metamorphopsia. ANOVA and predicted least-squares means were used to estimate the impact of metamorphopsia on VRQoL. Predictors of VRQoL were assessed using ordinary-least-squares regression adjusting for clinically important variables. Results: The prevalence of self-reported metamorphopsia was 69.7% (95% CI 62.6–76.3%) and was higher in eyes with a concomitant FTMH vs. without FTMH (85.4% vs. 64.2%). Physician assessment of metamorphopsia was 53.0% (95% CI: 45.5–60.3%). Comparing eyes with metamorphopsia vs. without metamorphopsia, the VFQ-25 composite score was lower (82.3 vs. 91.4), and mean VA (LogMAR) was worse (0.44 vs. 0.33). The largest difference in VFQ-25 scores was observed for near activities (metamorphopsia: 75.3, No metamorphopsia: 90.2). The adjusted model showed that metamorphopsia severity and age were significantly associated with lower VFQ-25 scores. Conclusion: Metamorphopsia was highly prevalent in patients with VMT and associated with significantly lower VRQoL. Physician assessment of symptoms underestimated the self-reported presence of metamorphopsia. Metamorphopsia severity acts as a predictor of impaired VRQoL, over and above decrements due to reduced vision

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

A gene expression signature associated with overall survival in patients with hepatocellular carcinoma suggests a new treatment strategy

Despite improvements in the management of liver cancer, the survival rate for patients with hepatocellular carcinoma (HCC) remains dismal. The survival benefit of systemic chemotherapy for the treatment of liver cancer is only marginal. Although the reasons for treatment failure are multifactorial, intrinsic resistance to chemotherapy plays a primary role. Here, we analyzed the expression of 377 multidrug resistance (MDR)-associated genes in two independent cohorts of patients with advanced HCC, with the aim of finding ways to improve survival in this poor-prognosis cancer. Taqman-based quantitative polymerase chain reaction revealed a 45-gene signature that predicts overall survival (OS) in patients with HCC. Using the Connectivity Map Tool, we were able to identify drugs that converted the gene expression profiles of HCC cell lines from ones matching patients with poor OS to profiles associated with good OS. We found three compounds that convert the gene expression profiles of three HCC cell lines to gene expression profiles associated with good OS. These compounds increase histone acetylation, which correlates with the synergistic sensitization of those MDR tumor cells to conventional chemotherapeutic agents, including cisplatin, sorafenib, and 5-fluorouracil. Our results indicate that it is possible to modulate gene expression profiles in HCC cell lines to those associated with better outcome. This approach also increases sensitization of HCC cells toward conventional chemotherapeutic agents. This work suggests new treatment strategies for a disease for which few therapeutic options exist