AMLdb: a comprehensive multi-omics platform to identify biomarkers and drug targets for acute myeloid leukemia.

Acute myeloid leukemia (AML) is one of the leading leukemic malignancies in adults. The heterogeneity of the disease makes the diagnosis and treatment extremely difficult. With the advent of next-generation sequencing (NGS) technologies, exploration at the molecular level for the identification of biomarkers and drug targets has been the focus for the researchers to come up with novel therapies for better prognosis and survival outcomes of AML patients. However, the huge amount of data from NGS platforms requires a comprehensive AML platform to streamline literature mining efforts and save time. To facilitate this, we developed AMLdb, an interactive multi-omics platform that allows users to query, visualize, retrieve, and analyse AML related multi-omics data. AMLdb contains 86 datasets for gene expression profiles, 15 datasets for methylation profiles, CRISPR-Cas9 knockout screens of 26 AML cell lines, sensitivity of 26 AML cell lines to 288 drugs, mutations in 41 unique genes in 23 AML cell lines, and information on 41 experimentally validated biomarkers. In this study, we have reported five genes, i.e. CBFB, ENO1, IMPDH2, SEPHS2, and MYH9 identified via our analysis using AMLdb. ENO1 is uniquely identified gene which requires further investigation as a novel potential target while other reported genes have been previously confirmed as targets through experimental studies. Top of form we believe that these findings utilizing AMLdb can make it an invaluable resource to accelerate the development of effective therapies for AML and assisting the research community in advancing their understanding of AML pathogenesis. AMLdb is freely available at https://project.iith.ac.in/cgntlab/amldb.

Support Vector Machine-Based Prediction Models for Drug Repurposing and Designing Novel Drugs for Colorectal Cancer.

Colorectal cancer (CRC) has witnessed a concerning increase in incidence and poses a significant therapeutic challenge due to its poor prognosis. There is a pressing demand to identify novel drug therapies to combat CRC. In this study, we addressed this need by utilizing the pharmacological profiles of anticancer drugs from the Genomics of Drug Sensitivity in Cancer (GDSC) database and developed QSAR models using the Support Vector Machine (SVM) algorithm for prediction of alternative and promiscuous anticancer compounds for CRC treatment. Our QSAR models demonstrated their robustness by achieving a high correlation of determination (R2) after 10-fold cross-validation. For 12 CRC cell lines, R2 ranged from 0.609 to 0.827. The highest performance was achieved for SW1417 and GP5d cell lines with R2 values of 0.827 and 0.786, respectively. Further, we listed the most common chemical descriptors in the drug profiles of the CRC cell lines and we also further reported the correlation of these descriptors with drug activity. The KRFP314 fingerprint was the predominantly occurring descriptor, with the KRFPC314 fingerprint following closely in prevalence within the drug profiles of the CRC cell lines. Beyond predictive modeling, we also confirmed the applicability of our developed QSAR models via in silico methods by conducting descriptor-drug analyses and recapitulating drug-to-oncogene relationships. We also identified two potential anti-CRC FDA-approved drugs, viomycin and diamorphine, using QSAR models. To ensure the easy accessibility and utility of our research findings, we have incorporated these models into a user-friendly prediction Web server named "ColoRecPred", available at https://project.iith.ac.in/cgntlab/colorecpred. We anticipate that this Web server can be used for screening of chemical libraries to identify potential anti-CRC drugs.

AagingBase: a comprehensive database of anti-aging peptides.

The process of aging is an intrinsic and inevitable aspect of life that impacts every living organism. As biotechnological advancements continue to shape our understanding of medicine, peptide therapeutics have emerged as a promising strategy for anti-aging interventions. This is primarily due to their favorable attributes, such as low immunogenicity and cost-effective production. Peptide-based treatments have garnered widespread acceptance and interest in aging research, particularly in the context of age-related therapies. To effectively develop anti-aging treatments, a comprehensive understanding of the physicochemical characteristics of anti-aging peptides is essential. Factors such as amino acid composition, instability index, hydrophobic areas and other relevant properties significantly determine their efficacy as potential therapeutic agents. Consequently, the creation of 'AagingBase', a comprehensive database for anti-aging peptides, aims to facilitate research on aging by leveraging the potential of peptide therapies. AagingBase houses experimentally validated 282 anti-aging peptides collected from 54 research articles and 236 patents. Employing state-of-the-art computational techniques, the acquired sequences have undergone rigorous physicochemical calculations. Furthermore, AagingBase presents users with various informative analyses highlighting atomic compositions, secondary structure fractions, tertiary structure, amino acid compositions and frequencies. The database also offers advanced search and filtering options and similarity search, thereby aiding researchers in understanding their biological functions. Hence, the database enables efficient identification and prioritization of potential peptide candidates in geriatric medicine and holds immense potential for advancing geriatric medicine research and innovations. AagingBase can be accessed without any restriction. Database URL: https://project.iith.ac.in/cgntlab/aagingbase/.

MyeloDB: a multi-omics resource for multiple myeloma.

Multiple myeloma (MM) is a common type of blood cancer affecting plasma cells originating from the lymphoid B-cell lineage. It accounts for about 10% of all hematological malignancies and can cause significant end-organ damage. The emergence of genomic technologies such as next-generation sequencing and gene expression analysis has opened new possibilities for early detection of multiple myeloma and identification of personalized treatment options. However, there remain significant challenges to overcome in MM research, including integrating multi-omics data, achieving a comprehensive understanding of the disease, and developing targeted therapies and biomarkers. The extensive data generated by these technologies presents another challenge for data analysis and interpretation. To bridge this gap, we have developed a multi-omics open-access database called MyeloDB. It includes gene expression profiling, high-throughput CRISPR-Cas9 screens, drug sensitivity resources profile, and biomarkers. MyeloDB contains 47 expression profiles, 3 methylation profiles comprising a total of 5630 patient samples and 25 biomarkers which were reported in previous studies. In addition to this, MyeloDB can provide significant insight of gene mutations in MM on drug sensitivity. Furthermore, users can download the datasets and conduct their own analyses. Utilizing this database, we have identified five novel genes, i.e., CBFB, MANF, MBNL1, SEPHS2, and UFM1 as potential drug targets for MM. We hope MyeloDB will serve as a comprehensive platform for researchers and foster novel discoveries in MM. MyeloDB Database URL: https://project.iith.ac.in/cgntlab/myelodb/ .

Deep learning-based classifier of diffuse large B-cell lymphoma cell-of-origin with clinical outcome.

Diffuse large B-cell lymphoma (DLBCL) is an aggressive form of non-Hodgkin lymphoma with poor response to R-CHOP therapy due to remarkable heterogeneity. Based on gene expression, DLBCL cases were divided into two subtypes, i.e. ABC and GCB, where ABC subtype is associated with poor outcomes. Due to its association with clinical outcome, this classification, also known as cell-of-origin (COO), is an efficient way to predict the response to R-CHOP therapy. Previous COO classification methods have some shortcomings, e.g. limited number of samples in the training dataset. These shortcomings challenge the robustness of methods and make it difficult to implicate these methods at clinical level. To overcome the shortcomings of previous methods, we developed a deep learning-based classifier model on a cohort of 381 DLBCL patients using expression data of 20 genes. We implemented multilayer perceptron (MLP) to train deep learning-based classifier, named MLP-COO. MLP-COO achieved accuracy of 99.70% and 94.70% on training and testing datasets, respectively, with 10-fold cross-validation. We also assessed its performance on an independent dataset of 294 DLBCL patients. On independent dataset, we achieved an accuracy of 95.90% with MCC of 0.917. To show its broader applicability, we used this classifier to predict the clinical outcome using survival data from two large cohorts of DLBCL patients. In survival analysis, MLP-COO recapitulates the survival probabilities of DLBCL patients based on their COO in both cohorts. We anticipate that MLP-COO model developed in this study will benefit in the accurate COO prediction of DLBCL patients and their clinical outcomes.

Real-world serological responses to extended-interval and heterologous COVID-19 mRNA vaccination in frail, older people (UNCoVER): an interim report from a prospective observational cohort study.

BACKGROUND: The use of COVID-19 vaccines has been prioritised to protect the most vulnerable-notably, older people. Because of fluctuations in vaccine availability, strategies such as delayed second dose and heterologous prime-boost have been used. However, the effectiveness of these strategies in frail, older people are unknown. We aimed to assess the antigenicity of mRNA-based COVID-19 vaccines in frail, older people in a real-world setting, with a rationed interval dosing of 16 weeks between the prime and boost doses. METHODS: This prospective observational cohort study was done across 12 long-term care facilities of the Montréal Centre-Sud - Integrated University Health and Social Services Centre in Montréal, Québec, Canada. Under a rationing strategy mandated by the provincial government, adults aged 65 years and older residing in long-term care facilities in Québec, Canada, with or without previously documented SARS-CoV-2 infection, were administered homologous or heterologous mRNA vaccines, with an extended 16-week interval between doses. All older residents in participating long-term care facilities who received two vaccine doses were eligible for inclusion in this study. Participants were enrolled from Dec 31, 2020, to Feb 16, 2021, and data were collected up to June 9, 2021. Clinical data and blood samples were serially collected from participants at the following timepoints: at baseline, before the first dose; 4 weeks after the first dose; 6-10 weeks after the first dose; 16 weeks after the first dose, up to 2 days before administration of the second dose; and 4 weeks after the second dose. Sera were tested for SARS-CoV-2-specific IgG antibodies (to the trimeric spike protein, the receptor-binding domain [RBD] of the spike protein, and the nucleocapsid protein) by automated chemiluminescent ELISA. Two cohorts were used in this study: a discovery cohort, for which blood samples were collected before administration of the first vaccine dose and longitudinally thereafter; and a confirmatory cohort, for which blood samples were only collected from 4 weeks after the prime dose. Analyses were done in the discovery cohort, with validation in the confirmatory cohort, when applicable. FINDINGS: The total study sample consisted of 185 participants. 65 participants received two doses of mRNA-1273 (Spikevax; Moderna), 36 received two doses of BNT162b2 (Comirnaty; Pfizer-BioNTech), and 84 received mRNA-1273 followed by BNT162b2. In the discovery cohort, after a significant increase in anti-RBD and anti-spike IgG concentrations 4 weeks after the prime dose (from 4·86 log binding antibody units [BAU]/mL to 8·53 log BAU/mL for anti-RBD IgG and from 5·21 log BAU/mL to 8·05 log BAU/mL for anti-spike IgG), there was a significant decline in anti-RBD and anti-spike IgG concentrations until the boost dose (7·10 log BAU/mL for anti-RBD IgG and 7·60 log BAU/mL for anti-spike IgG), followed by an increase 4 weeks later for both vaccines (9·58 log BAU/mL for anti-RBD IgG and 9·23 log BAU/mL for anti-spike IgG). SARS-CoV-2-naive individuals showed lower antibody responses than previously infected individuals at all timepoints tested up to 16 weeks after the prime dose, but achieved similar antibody responses to previously infected participants by 4 weeks after the second dose. Individuals primed with the BNT162b2 vaccine showed a larger decrease in mean anti-RBD and anti-spike IgG concentrations with a 16-week interval between doses (from 8·12 log BAU/mL to 4·25 log BAU/mL for anti-RBD IgG responses and from 8·18 log BAU/mL to 6·66 log BAU/mL for anti-spike IgG responses) than did those who received the mRNA-1273 vaccine (two doses of mRNA-1273: from 8·06 log BAU/mL to 7·49 log BAU/mL for anti-RBD IgG responses and from 6·82 log BAU/mL to 7·56 log BAU/mL for anti-spike IgG responses; mRNA-1273 followed by BNT162b2: from 8·83 log BAU/mL to 7·95 log BAU/mL for anti-RBD IgG responses and from 8·50 log BAU/mL to 7·97 log BAU/mL for anti-spike IgG responses). No differences in antibody responses 4 weeks after the second dose were noted between the two vaccines, in either homologous or heterologous combinations. INTERPRETATION: Interim results of this ongoing longitudinal study show that among frail, older people, previous SARS-CoV-2 infection and the type of mRNA vaccine influenced antibody responses when used with a 16-week interval between doses. In these cohorts of frail, older individuals with a similar age and comorbidity distribution, we found that serological responses were similar and clinically equivalent between the discovery and confirmatory cohorts. Homologous and heterologous use of mRNA vaccines was not associated with significant differences in antibody responses 4 weeks following the second dose, supporting their interchangeability. FUNDING: Public Health Agency of Canada, Vaccine Surveillance Reference Group; and the COVID-19 Immunity Task Force. TRANSLATION: For the French translation of the abstract see Supplementary Materials section.