Atrial arrhythmias following CAR-chimeric antigen receptor T-cell therapy: Incidence, risk factors and biomarker profile.

Recent reports have raised concerns about the association of chimeric antigen receptor T cell (CAR-T) with non-negligible cardiotoxicity, particularly atrial arrhythmias. First, we conducted a pharmacovigilance study to assess the reporting of atrial arrhythmias following CD19-directed CAR-T. Subsequently, to determine the incidence, risk factors and outcomes of atrial arrhythmias post-CAR-T, we compiled a retrospective single-centre cohort of non-Hodgkin lymphoma patients. Only commercial CAR-T products were considered. Atrial arrhythmias were nearly fourfold more likely to be reported after CAR-T therapy compared to all other cancer patients in the FAERS (adjusted ROR = 3.76 [95% CI 2.67-5.29]). Of the 236 patients in our institutional cohort, 23 (10%) developed atrial arrhythmias post-CAR-T, including 12 de novo arrhythmias, with most (83%) requiring medical intervention. Atrial arrhythmias frequently co-occurred with cytokine release syndrome and were associated with higher post-CAR-T infusion peak levels of IL-10, TNF-alpha and LDH, and lower trough levels of fibrinogen. In a multivariable analysis, risk factors for atrial arrhythmia were history of atrial arrhythmia (OR = 6.80 [2.39-19.6]) and using CAR-T product with a CD28-costimulatory domain (OR = 5.17 [1.72-18.6]). Atrial arrhythmias following CD19-CAR-T therapy are prevalent and associated with elevated inflammatory biomarkers, a history of atrial arrhythmia and the use of a CAR-T product with a CD28 costimulatory domain.

Peripheral Circulating Tumor DNA Detection Predicts Poor Outcomes After Liver Resection for Metastatic Colorectal Cancer.

BACKGROUND: Liver resection can be curative for well-selected metastatic colorectal cancer (CRC) patients. Circulating tumor DNA (ctDNA) has shown promise as a biomarker for tumor dynamics and recurrence following CRC resection. This prospective pilot study investigated the use of ctDNA to predict disease outcome in resected CRC patients. METHODS: Between November 2014 and November 2015, 60 patients with CRC were identified and prospectively enrolled. During liver resection, blood was drawn from peripheral (PERIPH), portal (PV), and hepatic (HV) veins, and 3-4 weeks postoperatively from a peripheral vein (POSTOP). Kappa statistics were used to compare mutated (mt) genes in tissue and ctDNA. Disease-specific and disease-free survival (DSS and DFS) were assessed from surgery with Kaplan-Meier and Cox methods. RESULTS: For the 59 eligible patients, the most commonly mutated genes were TP53 (mtTP53: 47.5%) and APC (mtAPC: 50.8%). Substantial to almost-perfect agreement was seen between ctDNA from PERIPH and PV (mtTP53: 89.8%, κ = 0.73, 95% confidence interval [CI] 0.53-0.93; mtAPC: 94.9%, κ = 0.83, 95% CI 0.64-1.00), as well as HV (mtTP53: 91.5%, κ = 0.78, 95% CI 0.60-0.96; mtAPC: 91.5%, κ = 0.73, 95% CI 0.51-0.95). Tumor mutations and PERIPH ctDNA had fair-to-moderate agreement (mtTP53: 72.9%, κ = 0.44, 95% CI 0.23-0.66; mtAPC: 61.0%, κ = 0.23, 95% CI 0.04-0.42). Detection of PERIPH mtTP53 was associated with worse 2-year DSS (mt+ 79% vs. mt- 90%, P = 0.024). CONCLUSIONS: Peripheral blood reflects the perihepatic ctDNA signature. Disagreement between tissue and ctDNA mutations may reflect the true natural history of tumor genes or an assay limitation. Peripheral ctDNA detection before liver resection is associated with worse DSS.

Mapping the pericentric heterochromatin by comparative genomic hybridization analysis and chromosome deletions in Drosophila melanogaster.

Heterochromatin represents a significant portion of eukaryotic genomes and has essential structural and regulatory functions. Its molecular organization is largely unknown due to difficulties in sequencing through and assembling repetitive sequences enriched in the heterochromatin. Here we developed a novel strategy using chromosomal rearrangements and embryonic phenotypes to position unmapped Drosophila melanogaster heterochromatic sequence to specific chromosomal regions. By excluding sequences that can be mapped to the assembled euchromatic arms, we identified sequences that are specific to heterochromatin and used them to design heterochromatin specific probes ("H-probes") for microarray. By comparative genomic hybridization (CGH) analyses of embryos deficient for each chromosome or chromosome arm, we were able to map most of our H-probes to specific chromosome arms. We also positioned sequences mapped to the second and X chromosomes to finer intervals by analyzing smaller deletions with breakpoints in heterochromatin. Using this approach, we were able to map >40% (13.9 Mb) of the previously unmapped heterochromatin sequences assembled by the whole-genome sequencing effort on arm U and arm Uextra to specific locations. We also identified and mapped 110 kb of novel heterochromatic sequences. Subsequent analyses revealed that sequences located within different heterochromatic regions have distinct properties, such as sequence composition, degree of repetitiveness, and level of underreplication in polytenized tissues. Surprisingly, although heterochromatin is generally considered to be transcriptionally silent, we detected region-specific temporal patterns of transcription in heterochromatin during oogenesis and early embryonic development. Our study provides a useful approach to elucidate the molecular organization and function of heterochromatin and reveals region-specific variation of heterochromatin.

A snapshot of microbial communities from the Kutch: one of the largest salt deserts in the World.

Here we present the first report on the taxonomic diversity of the microbial communities of the saline desert of the Great Rann of Kutch, Gujarat, India, using a metagenomic approach. Seven samples, differing in salinity levels and covering different seasons, were analysed to investigate the dynamics of microbial communities in relation to salinity and season. Metagenomic data generated using whole metagenome sequencing revealed that despite its very high salinity (4.11-30.79 %), the saline desert's microbiota had a rich microbial diversity that included all major phyla. Notably, 67 archaeal genera, representing more than 60 % of all known archaeal genera, were present in this ecosystem. A strong positive correlation (0.85) was observed between the presence of the extremely halophilic bacterium Salinibacter and salinity level. Taxonomic and functional comparisons of the saline desert metagenome with those of other publicly available metagenomes (i.e. sea, hypersaline lagoon, solar saltern, brine, hot desert) was carried out. The microbial community of the Kutch was found to be unique yet more similar to the sea biomes followed by hypersaline lagoon.

Development and validation of an automated computational approach to grade immune effector cell-associated hematotoxicity.

Hematologic toxicity frequently complicates chimeric antigen receptor (CAR) T-cell therapy, resulting in significant morbidity and mortality. In an effort to standardize reporting, the European Hematology Association (EHA) and European Society of Blood and Marrow Transplantation (EBMT) devised the immune effector cell-associated hematotoxicity (ICAHT) grading system, distinguishing between early (day 0-30) and late (after day +30) events based on neutropenia depth and duration. However, manual implementation of ICAHT grading criteria is time-consuming and susceptible to subjectivity and error. To address these challenges, we introduce a novel computational approach, utilizing the R programming language, to automate early and late ICAHT grading. Given the complexities of early ICAHT grading, we benchmarked our approach both manually and computationally in two independent cohorts totaling 1251 patients. Our computational approach offers significant implications by streamlining grading processes, reducing manual time and effort, and promoting standardization across varied clinical settings. We provide this tool to the scientific community alongside a comprehensive implementation guide, fostering its widespread adoption and enhancing reporting consistency for ICAHT.

Sugar-rich foods exacerbate antibiotic-induced microbiome injury.

Intestinal microbiota composition is implicated in several diseases; understanding the factors that influence it are key to elucidating host-commensal interactions and to designing microbiome-targeted therapies. We quantified how diet influences microbiome dynamics in hospitalized patients. We recorded 9,419 meals consumed by 173 patients undergoing hematopoietic cell transplantation and profiled the microbiome in 1,009 longitudinally collected stool samples from 158 of them. Caloric intake was correlated with fecal microbiota diversity. Bayesian inference revealed associations between intake of sweets or sugars during antibiotic exposure with microbiome disruption, as assessed by low diversity or expansion of the pathobiont Enterococcus. We validated this observation experimentally, finding that sucrose exacerbated antibiotic-induced Enterococcus expansion in mice. Taken together, our results suggest that avoiding sugar-rich foods during antibiotic treatment may reduce microbiome injury.

Enhanced Feature Selection for Microbiome Data using FLORAL: Scalable Log-ratio Lasso Regression.

Identifying predictive biomarkers of patient outcomes from high-throughput microbiome data is of high interest, while existing computational methods do not satisfactorily account for complex survival endpoints, longitudinal samples, and taxa-specific sequencing biases. We present FLORAL (https://vdblab.github.io/FLORAL/), an open-source computational tool to perform scalable log-ratio lasso regression and microbial feature selection for continuous, binary, time-to-event, and competing risk outcomes, with compatibility of longitudinal microbiome data as time-dependent covariates. The proposed method adapts the augmented Lagrangian algorithm for a zero-sum constraint optimization problem while enabling a two-stage screening process for extended false-positive control. In extensive simulation and real-data analyses, FLORAL achieved consistently better false-positive control compared to other lasso-based approaches, and better sensitivity over popular differential abundance testing methods for datasets with smaller sample size. In a survival analysis in allogeneic hematopoietic-cell transplant, we further demonstrated considerable improvement by FLORAL in microbial feature selection by utilizing longitudinal microbiome data over only using baseline microbiome data.

A Personalized Arrhythmia Monitoring Platform.

Arrhythmia detection is the core of cardiovascular disease diagnosis. Though, there is no such generic solution for detecting the arrhythmias at the moment they occur which is due to the non-stationary nature and inter-patient variations of ECG signals. The feature extraction and classification techniques are significant tools widely used in the automated classification of arrhythmias. This study aims to develop a personalized arrhythmia monitoring platform allowing real-time detection of arrhythmias from the subject's electrocardiogram (ECG) signal for point-of-care usage. A novel method, i.e. discrete orthogonal stockwell transform (DOST) technique for feature extraction is employed to capture the significant time-frequency coefficients to constitute the feature set representing each of the ECG signals. These coefficients or features are classified using artificial bee colony (ABC) optimized twin least-square support vector machine (LSTSVM) for classifying the different categories of ECG signals. The ABC optimizes the dimension of the feature set and the learning parameters of the classifier. The proposed method is prototyped on the commercially available ARM-based embedded platform and validated on the benchmark MIT-BIH arrhythmia database. Further, the prototype is evaluated under two schemes, i.e. class and personalized schemes which reported a higher overall accuracy of 96.29% and 96.08% in the respective schemes than the existing works to the state-of-art CVDs diagnosis.

Development of robust, fast and efficient QRS complex detector: a methodological review.

The basis and reliability for timely diagnosis of cardiovascular diseases depend on the robust and accurate detection of QRS complexes along with the fiducial points in the electrocardiogram (ECG) signal. Despite, the several QRS detection algorithms reported in the literature, the development of an efficient QRS detector remains a challenge in the clinical environment. Therefore, this article summarizes the performance analysis of various QRS detection techniques depending upon three assessment factors which include robustness to noise, computational load, and sensitivity validated on the benchmark MIT-BIH arrhythmia database. Moreover, the limitations of these algorithms are discussed and compared with the standard signal processing algorithms, followed by the future suggestions to develop a reliable and efficient QRS methodology. Further, the suggested method can be implemented on suitable hardware platforms to develop smart health monitoring systems for continuous and long-term ECG assessment for real-time applications.

Automated recognition of cardiac arrhythmias using sparse decomposition over composite dictionary.

BACKGROUND AND OBJECTIVE: Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. Due to an increase in the rate of global mortalities, biopathological signal processing and evaluation are widely used in the ambulatory situations for healthcare applications. For decades, the processing of pathological electrocardiogram (ECG) signals for arrhythmia detection has been thoroughly studied for diagnosis of various cardiovascular diseases. Apart from these studies, efficient diagnosis of ECG signals remains a challenge in the clinical cardiovascular domain due to its non-stationary nature. The classical signal processing methods are widely employed to analyze the ECG signals, but they exhibit certain limitations and hence, are insufficient to achieve higher accuracy. METHODS: This study presents a novel technique for an efficient representation of electrocardiogram (ECG) signals using sparse decomposition using composite dictionary (CD). The dictionary consists of the stockwell, sine and cosine analytical functions. The technique decomposes an input ECG signal into stationary and non-stationary components or atoms. For each of these atoms, five features i.e., permutation entropy, energy, RR-interval, standard deviation and kurtosis are extracted to determine the feature sets representing the heartbeats that are classified into different categories using the multi-class least-square twin support vector machines. The artificial bee colony (ABC) technique is used to determine the optimal classifier parameters. The proposed method is evaluated under category and personalized schemes and its validation is performed on MIT-BIH data. RESULTS: The experimental results reported a higher overall accuracy of 99.21% and 90.08% in category and personalized schemes respectively than the existing techniques reported in the literature. Further a sensitivity, positive predictivity and F-score of 99.21% each in the category based scheme and 90.08% each in the personalized schemes respectively. CONCLUSIONS: The proposed methodology can be utilized in computerized decision support systems to monitor different classes of cardiac arrhythmias with higher accuracy for early detection and treatment of cardiovascular diseases.

A Next-Generation TRK Kinase Inhibitor Overcomes Acquired Resistance to Prior TRK Kinase Inhibition in Patients with TRK Fusion-Positive Solid Tumors.

Larotrectinib, a selective TRK tyrosine kinase inhibitor (TKI), has demonstrated histology-agnostic efficacy in patients with TRK fusion-positive cancers. Although responses to TRK inhibition can be dramatic and durable, duration of response may eventually be limited by acquired resistance. LOXO-195 is a selective TRK TKI designed to overcome acquired resistance mediated by recurrent kinase domain (solvent front and xDFG) mutations identified in multiple patients who have developed resistance to TRK TKIs. Activity against these acquired mutations was confirmed in enzyme and cell-based assays and in vivo tumor models. As clinical proof of concept, the first 2 patients with TRK fusion-positive cancers who developed acquired resistance mutations on larotrectinib were treated with LOXO-195 on a first-in-human basis, utilizing rapid dose titration guided by pharmacokinetic assessments. This approach led to rapid tumor responses and extended the overall duration of disease control achieved with TRK inhibition in both patients.Significance: LOXO-195 abrogated resistance in TRK fusion-positive cancers that acquired kinase domain mutations, a shared liability with all existing TRK TKIs. This establishes a role for sequential treatment by demonstrating continued TRK dependence and validates a paradigm for the accelerated development of next-generation inhibitors against validated oncogenic targets. Cancer Discov; 7(9); 963-72. ©2017 AACR.See related commentary by Parikh and Corcoran, p. 934This article is highlighted in the In This Issue feature, p. 920.

Cardiac arrhythmia beat classification using DOST and PSO tuned SVM.

BACKGROUND AND OBJECTIVE: The increase in the number of deaths due to cardiovascular diseases (CVDs) has gained significant attention from the study of electrocardiogram (ECG) signals. These ECG signals are studied by the experienced cardiologist for accurate and proper diagnosis, but it becomes difficult and time-consuming for long-term recordings. Various signal processing techniques are studied to analyze the ECG signal, but they bear limitations due to the non-stationary behavior of ECG signals. Hence, this study aims to improve the classification accuracy rate and provide an automated diagnostic solution for the detection of cardiac arrhythmias. METHODS: The proposed methodology consists of four stages, i.e. filtering, R-peak detection, feature extraction and classification stages. In this study, Wavelet based approach is used to filter the raw ECG signal, whereas Pan-Tompkins algorithm is used for detecting the R-peak inside the ECG signal. In the feature extraction stage, discrete orthogonal Stockwell transform (DOST) approach is presented for an efficient time-frequency representation (i.e. morphological descriptors) of a time domain signal and retains the absolute phase information to distinguish the various non-stationary behavior ECG signals. Moreover, these morphological descriptors are further reduced in lower dimensional space by using principal component analysis and combined with the dynamic features (i.e based on RR-interval of the ECG signals) of the input signal. This combination of two different kinds of descriptors represents each feature set of an input signal that is utilized for classification into subsequent categories by employing PSO tuned support vector machines (SVM). RESULTS: The proposed methodology is validated on the baseline MIT-BIH arrhythmia database and evaluated under two assessment schemes, yielding an improved overall accuracy of 99.18% for sixteen classes in the category-based and 89.10% for five classes (mapped according to AAMI standard) in the patient-based assessment scheme respectively to the state-of-art diagnosis. The results reported are further compared to the existing methodologies in literature. CONCLUSIONS: The proposed feature representation of cardiac signals based on symmetrical features along with PSO based optimization technique for the SVM classifier reported an improved classification accuracy in both the assessment schemes evaluated on the benchmark MIT-BIH arrhythmia database and hence can be utilized for automated computer-aided diagnosis of cardiac arrhythmia beats.

A new system for comparative functional genomics of Saccharomyces yeasts.

Whole-genome sequencing, particularly in fungi, has progressed at a tremendous rate. More difficult, however, is experimental testing of the inferences about gene function that can be drawn from comparative sequence analysis alone. We present a genome-wide functional characterization of a sequenced but experimentally understudied budding yeast, Saccharomyces bayanus var. uvarum (henceforth referred to as S. bayanus), allowing us to map changes over the 20 million years that separate this organism from S. cerevisiae. We first created a suite of genetic tools to facilitate work in S. bayanus. Next, we measured the gene-expression response of S. bayanus to a diverse set of perturbations optimized using a computational approach to cover a diverse array of functionally relevant biological responses. The resulting data set reveals that gene-expression patterns are largely conserved, but significant changes may exist in regulatory networks such as carbohydrate utilization and meiosis. In addition to regulatory changes, our approach identified gene functions that have diverged. The functions of genes in core pathways are highly conserved, but we observed many changes in which genes are involved in osmotic stress, peroxisome biogenesis, and autophagy. A surprising number of genes specific to S. bayanus respond to oxidative stress, suggesting the organism may have evolved under different selection pressures than S. cerevisiae. This work expands the scope of genome-scale evolutionary studies from sequence-based analysis to rapid experimental characterization and could be adopted for functional mapping in any lineage of interest. Furthermore, our detailed characterization of S. bayanus provides a valuable resource for comparative functional genomics studies in yeast.