Paced left ventricular QRS width and ECG parameters predict outcomes after cardiac resynchronization therapy: PROSPECT-ECG substudy.

International audienceBACKGROUND: For patients with symptomatic New York Heart Association class III or IV, ejection fraction ≤ 35%, and QRS ≥ 130 ms, cardiac resynchronization therapy (CRT) has become an established treatment option. However, use of these implant criteria fails to result in clinical or echocardiographic improvement in 30% to 45% of CRT patients. METHODS AND RESULTS: The Predictors of Response to CRT (PROSPECT)-ECG is a substudy of the prospective observational PROSPECT trial. ECGs collected before, during, and after CRT implantation were analyzed. Primary outcomes were improvement in clinical composite score (CCS) and reduction of left ventricular end systolic volume (LVESV) of >15% after 6 months. Age, sex, cause of cardiomyopathy, myocardial infarction location, right ventricular function, mitral regurgitation, preimplantation QRS width, preimplantation PR interval, preimplantation right ventricular-paced QRS width, preimplantation axis categories, LV-paced QRS width, postimplantation axis categories, difference between biventricular (Bi-V) pacing and preimplantation QRS width, and QRS bundle branch morphological features were analyzed univariably in logistic regression models to predict outcomes. All significant predictors (α=0.1), age, and sex were used for multivariable analyses. Cardiomyopathy cause interaction and subanalyses were also performed. In multivariable analyses, only QRS left bundle branch morphological features predicted both CCS (odds ratio [OR]=2.46, P=0.02) and LVESV (OR=2.89, P=0.048) response. The difference between Bi-V and preimplantation QRS width predicted CCS improvement (OR=0.89, P=0.04). LV-paced QRS width predicted LVESV reduction (OR=0.86, P=0.01). Specifically, an LV-paced QRS width of ≤ 200 ms was predictive of nonischemic LVESV reduction (OR=5.12, P=0.01). CONCLUSIONS: Baseline left bundle branch QRS morphological features, LV-paced QRS width, and the difference between Bi-V and preimplantation QRS width can predict positive outcomes after CRT and may represent a novel intraprocedural method to optimize coronary sinus lead placement. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00253357

Taking the First Steps towards a Standard for Reporting on Phylogenies: Minimum Information about a Phylogenetic Analysis (MIAPA)

In the eight years since phylogenomics was introduced as the intersection of genomics and phylogenetics, the field has provided fundamental insights into gene function, genome history and organismal relationships. The utility of phylogenomics is growing with the increase in the number and diversity of taxa for which whole genome and large transcriptome sequence sets are being generated. We assert that the synergy between genomic and phylogenetic perspectives in comparative biology would be enhanced by the development and refinement of minimal reporting standards for phylogenetic analyses. Encouraged by the development of the Minimum Information About a Microarray Experiment (MIAME) standard, we propose a similar roadmap for the development of a Minimal Information About a Phylogenetic Analysis (MIAPA) standard. Key in the successful development and implementation of such a standard will be broad participation by developers of phylogenetic analysis software, phylogenetic database developers, practitioners of phylogenomics, and journal editors. This paper is part of the special issue of OMICS on data standards.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63208/1/omi.2006.10.231.pd

Drug Target Prediction Using Context-Specific Metabolic Models Reconstructed from rFASTCORMICS.

Metabolic modeling is a powerful computational tool to analyze metabolism. It has not only been used to identify metabolic rewiring strategies in cancer but also to predict drug targets and candidate drugs for repurposing. Here, we will elaborate on the reconstruction of context-specific metabolic models of cancer using rFASTCORMICS and the subsequent prediction of drugs for repurposing using our drug prediction workflow

Machine learning-based prediction of frailty in elderly people : Data from the Berlin Aging Study II (BASE-II)

Frailty is a geriatric medical condition that is highly associated with age and age-related diseases. The multidimensional consequences of frailty are heavily impacting the quality of life, and will inevitably increase the burden on healthcare systems in the future. Most importantly, the lack of a universal standard to describe, diagnose, or let alone treat frailty, is further complicating the situation in the long-term. Nowadays, more and more frailty assessment tools are being developed on a regional and institutional basis, which is continuing to drive the heterogeneity in the characterization of frailty further apart. Gaining better insights into the underlying causes and pathophysiology of frailty, and how it is developing in patients is, therefore, required to establish strong and accurately tailored response schemes for frail patients, where currently only symptoms are treated. Thus, in this study, we deployed machine learning-based classification and optimization techniques to predict frailty in the Berlin Aging Study II (BASE-II, N=1512, frail=484) and revealed some of the most informative biomedical information to characterize frailty, including new potential biomarkers. Frailty in BASE-II was measured by the Fried et al. 5-item frailty index, composed of the clinical variables grip strength, weight loss, exhaustion, physical activity, and gait. The level of frailty in BASE-II was adapted for binary classification purposes by merging the pre-frail and frail levels as frail. A configurable in-house pipeline was developed for pre-processing the clinical data, predicting the target disease, and determining the most informative subgroup of clinical measurements with regards to frailty. The best prediction power was yielded with resampling and dimensionality reduction techniques using the F-beta-2 score, and was further increased by adding one item of the Fried et al. frailty index. We suggest that a combination of the easy-to-obtain biomedical information on frailty risk factors together with one Fried et al. phenotype information provided by i.e. smart wearable devices (gait, grip strength, . . . ) could significantly improve the frailty prediction power

Improving Machine Learning-based Prediction of Frailty in Elderly People with Digital Wearables : Data from the Berlin Aging Study II (BASE-II)

Frailty is a geriatric medical condition that is highly associated with age and age-related diseases. The multidimensional consequences of frailty are heavily impacting the quality of life, and will inevitably increase the burden on healthcare systems in the future. Most importantly, the lack of a universal standard to describe, diagnose, or let alone treat frailty, is further complicating the situation in the long-term. Nowadays, more and more frailty assessment tools are being developed on a regional and institutional basis, which is continuing to drive the heterogeneity in the characterization of frailty further apart. Gaining better insights into the underlying causes and pathophysiology of frailty, and how it is developing in patients is, therefore, required to establish strong and accurately tailored response schemes for frail patients, where currently only symptoms are treated. Thus, in this study, we deployed machine learning-based classification and optimization techniques to predict frailty in elderly people aged 65 or above from the Berlin Aging Study II (BASE-II, n=1512, frail=484) and revealed some of the most informative biomedical information to characterize frailty, including new potential biomarkers. Frailty in BASE-II was measured by the Fried et al. 5-item frailty index, composed of the clinical variables grip strength, weight loss, exhaustion, physical activity, and gait. The level of frailty in BASE-II was adapted for binary classification purposes by merging the pre-frail and frail levels as frail. A configurable in-house pipeline was developed for pre-processing the clinical data and predicting the target disease by deploying Support Vector Machines Classification. The most informative and essential subgroup of clinical measurements with regards to frailty was investigated by re-optimizing an initially full data-driven model by sequentially leaving out one subgroup. The best prediction power was yielded with resampling and dimensionality reduction techniques using the F-beta-2 score, and was further improved by adding one item of the Fried et al. frailty index. Furthermore, differences between the gender in the data set led to the investigation of gender-specific model configurations, followed by re-optimizations. As a result, we were able to specifically increase the predictive power in gender-specific groups, and will simultaneously emphasize on the differences between the most informative clinical biomarkers as well as the most essential subgroups for mixed and gender-specific BASE-II. The results herein suggest that a combination of the detected easy-to-obtain biomedical information on frailty risk factors together with one Fried et al. phenotype information provided by i.e., smart wearable devices (gait, grip strength, …) could significantly improve the frailty prediction power in mixed and gender-specific clinical cohort data

Paced Left Ventricular QRS Width and ECG Parameters Predict Outcomes After Cardiac Resynchronization Therapy

International audienceBACKGROUND: For patients with symptomatic New York Heart Association class III or IV, ejection fraction ≤ 35%, and QRS ≥ 130 ms, cardiac resynchronization therapy (CRT) has become an established treatment option. However, use of these implant criteria fails to result in clinical or echocardiographic improvement in 30% to 45% of CRT patients. METHODS AND RESULTS: The Predictors of Response to CRT (PROSPECT)-ECG is a substudy of the prospective observational PROSPECT trial. ECGs collected before, during, and after CRT implantation were analyzed. Primary outcomes were improvement in clinical composite score (CCS) and reduction of left ventricular end systolic volume (LVESV) of >15% after 6 months. Age, sex, cause of cardiomyopathy, myocardial infarction location, right ventricular function, mitral regurgitation, preimplantation QRS width, preimplantation PR interval, preimplantation right ventricular-paced QRS width, preimplantation axis categories, LV-paced QRS width, postimplantation axis categories, difference between biventricular (Bi-V) pacing and preimplantation QRS width, and QRS bundle branch morphological features were analyzed univariably in logistic regression models to predict outcomes. All significant predictors (α=0.1), age, and sex were used for multivariable analyses. Cardiomyopathy cause interaction and subanalyses were also performed. In multivariable analyses, only QRS left bundle branch morphological features predicted both CCS (odds ratio [OR]=2.46, P=0.02) and LVESV (OR=2.89, P=0.048) response. The difference between Bi-V and preimplantation QRS width predicted CCS improvement (OR=0.89, P=0.04). LV-paced QRS width predicted LVESV reduction (OR=0.86, P=0.01). Specifically, an LV-paced QRS width of ≤ 200 ms was predictive of nonischemic LVESV reduction (OR=5.12, P=0.01). CONCLUSIONS: Baseline left bundle branch QRS morphological features, LV-paced QRS width, and the difference between Bi-V and preimplantation QRS width can predict positive outcomes after CRT and may represent a novel intraprocedural method to optimize coronary sinus lead placement. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00253357

Distinct molecular profiles and immunotherapy treatment outcomes of V600E and V600K BRAF-mutant melanoma

Purpose: BRAF V600E and V600K melanomas have distinct clinicopathologic features, and V600K appear to be less responsive to BRAFi±MEKi. We investigated mechanisms for this and explored whether genotype affects response to immunotherapy. Experimental Design: Pretreatment formalin-fixed paraffin-embedded tumors from patients treated with BRAFi±MEKi underwent gene expression profiling and DNA sequencing. Molecular results were validated using The Cancer Genome Atlas (TCGA) data. An independent cohort of V600E/K patients treated with anti–PD-1 immunotherapy was examined. Results: Baseline tissue and clinical outcome with BRAFi±MEKi were studied in 93 patients (78 V600E, 15 V600K). V600K patients had numerically less tumor regression (median, −31% vs. −52%, P = 0.154) and shorter progression-free survival (PFS; median, 5.7 vs. 7.1 months, P = 0.15) compared with V600E. V600K melanomas had lower expression of the ERK pathway feedback regulator dual-specificity phosphatase 6, confirmed with TCGA data (116 V600E, 17 V600K). Pathway analysis showed V600K had lower expression of ERK and higher expression of PI3K-AKT genes than V600E. Higher mutational load was observed in V600K, with a higher proportion of mutations in PIK3R1 and tumor-suppressor genes. In patients treated with anti–PD-1, V600K (n = 19) had superior outcomes than V600E (n = 84), including response rate (53% vs. 29%, P = 0.059), PFS (median, 19 vs. 2.7 months, P = 0.049), and overall survival (20.4 vs. 11.7 months, P = 0.081). Conclusions: BRAF V600K melanomas appear to benefit less from BRAFi±MEKi than V600E, potentially due to less reliance on ERK pathway activation and greater use of alternative pathways. In contrast, these melanomas have higher mutational load and respond better to immunotherapy.J.S. Wilmott is supported by an NHMRC Research Fellowship. J.L. McQuade is supported by an ASCO/CCF Career Development Award, a Melanoma SPORE Developmental Research Program Award, and an NIH T32 Training Grant CA009666. R.A. Scolyer is supported by an NHMRC Practitioner Fellowship. J.Y.H. Yang is supported by NHMRC CDF and ARC Discovery Project grant (DP170100654). G.V. Long is supported by an NHMRC Practitioner Fellowship and the University of Sydney Medical Foundation. A.M. Menzies is supported by a Cancer Institute NSW Fellowship. This work was supported by a Pfizer Australia grant (WS2345795 to A.M. Menzies), and a Cancer Council NSW grant (RG17-04 to J. Holst, J.S. Wilmott, and A.M. Menzies). This research was also supported by an Australian National Health and Medical Research Council program grant