Learning and actioning general principles of cancer cell drug sensitivity.

High-throughput screening of drug sensitivity of cancer cell lines (CCLs) holds the potential to unlock anti-tumor therapies. In this study, we leverage such datasets to predict drug response using cell line transcriptomics, focusing on models' interpretability and deployment on patients' data. We use large language models (LLMs) to match drug to mechanisms of action (MOA)-related pathways.

The landscape of cancer-rewired GPCR signaling axes.

We explored the dysregulation of G-protein-coupled receptor (GPCR) ligand systems in cancer transcriptomics datasets to uncover new therapeutics opportunities in oncology. We derived an interaction network of receptors with ligands and their biosynthetic enzymes. Multiple GPCRs are differentially regulated together with their upstream partners across cancer subtypes and are associated to specific transcriptional programs and to patient survival patterns.

EXPANSION: a webserver to explore the functional consequences of protein-coding alternative splice variants in cancer genomics.

Summary: EXPANSION (https://expansion.bioinfolab.sns.

The landscape of cancer rewired GPCR signaling axes.

We explored the dysregulation of GPCR ligand signaling systems in cancer transcriptomics datasets to uncover new therapeutics opportunities in oncology. We derived an interaction network of receptors with ligands and their biosynthetic enzymes, which revealed that multiple GPCRs are differentially regulated together with their upstream partners across cancer subtypes. We showed that biosynthetic pathway enrichment from enzyme expression recapitulated pathway activity signatures from metabolomics datasets, providing valuable surrogate information for GPCRs responding to organic ligands.

Pfeature: A Tool for Computing Wide Range of Protein Features and Building Prediction Models.

In the last three decades, a wide range of protein features have been discovered to annotate a protein. Numerous attempts have been made to integrate these features in a software package/platform so that the user may compute a wide range of features from a single source. To complement the existing methods, we developed a method, Pfeature, for computing a wide range of protein features.

Tool for Predicting, Scanning, and Designing Defensins.

Defensins are host defense peptides present in nearly all living species, which play a crucial role in innate immunity. These peptides provide protection to the host, either by killing microbes directly or indirectly by activating the immune system. In the era of antibiotic resistance, there is a need to develop a fast and accurate method for predicting defensins.

Prognostic biomarkers for predicting papillary thyroid carcinoma patients at high risk using nine genes of apoptotic pathway.

Aberrant expressions of apoptotic genes have been associated with papillary thyroid carcinoma (PTC) in the past, however, their prognostic role and utility as biomarkers remains poorly understood. In this study, we analysed 505 PTC patients by employing Cox-PH regression techniques, prognostic index models and machine learning methods to elucidate the relationship between overall survival (OS) of PTC patients and 165 apoptosis related genes. It was observed that nine genes (ANXA1, TGFBR3, CLU, PSEN1, TNFRSF12A, GPX4, TIMP3, LEF1, BNIP3L) showed significant association with OS of PTC patients.

Universal and cross-cancer prognostic biomarkers for predicting survival risk of cancer patients from expression profile of apoptotic pathway genes.

Numerous cancer-specific prognostic models have been developed in the past, wherein one model is applicable for only one type of cancer. In this study, an attempt has been made to identify universal or multi-cancer prognostic biomarkers and develop models for predicting survival risk across different types of cancer patients. In order to accomplish this, we gauged the prognostic role of mRNA expression of 165 apoptosis-related genes across 33 cancers in the context of patient survival.

Prognostic Biomarker-Based Identification of Drugs for Managing the Treatment of Endometrial Cancer.

Introduction: Uterine corpus endometrial carcinoma (UCEC) causes thousands of deaths per year. To improve the overall survival of patients with UCEC, there is a need to identify prognostic biomarkers and potential drugs.

Objectives: The aim of this study was twofold: the identification of prognostic gene signatures from expression profiles of pattern recognition receptor (PRR) genes and identification of the most effective existing drugs using the prognostic gene signature.

AlgPred 2.0: an improved method for predicting allergenic proteins and mapping of IgE epitopes.

AlgPred 2.0 is a web server developed for predicting allergenic proteins and allergenic regions in a protein. It is an updated version of AlgPred developed in 2006.

A Web-Based Platform on Coronavirus Disease-19 to Maintain Predicted Diagnostic, Drug, and Vaccine Candidates.

A web-based resource CoronaVIR (https://webs.iiitd.edu.

Risk prediction in cutaneous melanoma patients from their clinico-pathological features: superiority of clinical data over gene expression data.

Risk assessment in cutaneous melanoma (CM) patients is one of the major challenges in the effective treatment of CM patients. Traditionally, clinico-pathological features such as Breslow thickness, American Joint Committee on Cancer (AJCC) tumor staging, etc. are utilized for this purpose.

Identification of prognostic biomarkers for major subtypes of non-small-cell lung cancer using genomic and clinical data.

Purpose: Intra-tumor heterogeneity and high mortality among patients with non-small-cell lung carcinoma (NSCLC) emphasize the need to identify reliable prognostic markers unique to each subtype.

Methods: In this study, univariate cox regression and prognostic index (PI)-based approaches were used to develop models for predicting NSCLC patients' subtype-specific survival.

Results: Prognostic analysis of TCGA dataset identified 1334 and 2129 survival-specific genes for LUSC (488 samples) and LUAD (497 samples), respectively.

Computing Skin Cutaneous Melanoma Outcome From the HLA-Alleles and Clinical Characteristics.

Human leukocyte antigen (HLA) are essential components of the immune system that stimulate immune cells to provide protection and defense against cancer. Thousands of HLA alleles have been reported in the literature, but only a specific set of HLA alleles are present in an individual. The capability of the immune system to recognize cancer-associated mutations depends on the presence of a particular set of alleles, which elicit an immune response to fight against cancer.

A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information.

This study describes a method developed for predicting pattern recognition receptors (PRRs), which are an integral part of the immune system. The models developed here were trained and evaluated on the largest possible non-redundant PRRs, obtained from PRRDB 2.0, and non-pattern recognition receptors (Non-PRRs), obtained from Swiss-Prot.

Prediction of risk scores for colorectal cancer patients from the concentration of proteins involved in mitochondrial apoptotic pathway.

One of the major challenges in managing the treatment of colorectal cancer (CRC) patients is to predict risk scores or level of risk for CRC patients. In past, several biomarkers, based on concentration of proteins involved in type-2/intrinsic/mitochondrial apoptotic pathway, have been identified for prognosis of colorectal cancer patients. Recently, a prognostic tool DR_MOMP has been developed that can discriminate high and low risk CRC patients with reasonably high accuracy (Hazard Ratio, HR = 5.