Biology subjects

Bhalla, S.

Publications and source records attributed to Bhalla, S..

14 recordsLinked to original sources

bioRxiv2026

Systematic Meta-Analysis of Published Transcriptomic Prognostic Signatures and Development of a Robust Multi-Cohort Prognostic Classifier for Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) exhibits pronounced molecular heterogeneity, yet the majority of published transcriptomic prognostic signatures suffer from limited reproducibility and have not achieved clinical translation. We systematically benchmarked 62 published TNBC prognostic signatures across 6 independent cohorts (n=1,357) using a unified analytical framework spanning multiple scoring algorithms, survival endpoints, and threshold strategies. While 17 signatures demonstrated consistent univariate prognostic associations, only 4 remained independently prognostic after adjustment for clinicopathological variables, and none achieved robustness across all analytical conditions-underscoring the fragility of existing classifiers. Leveraging genes with concordant survival associations across all 6 discovery cohorts, we identified a reproducible 15-gene directionally concordant gene set (DCGS) signature and distilled it into MetaSig-EFS, a 13-gene prognostic model optimized using a cohort-aware DeepSurv framework. MetaSig-EFS demonstrated robust cross-cohort generalizability, achieving validation concordance indices of 0.89 in GSE19615 and 0.69 in JBordet, with corresponding 3- and 5-year time-dependent AUROCs of 0.89 and 0.96 in GSE19615 and 0.80 and 0.71 in JBordet, respectively, while retaining independent prognostic value across established TNBC molecular sub-typing systems. Leveraging the TAHOE-100M transcriptomic perturbation atlas, we systematically prioritized candidate therapeutics through large-scale drug repurposing, identifying Paclitaxel as the top-ranked compound, followed by Venetoclax and Tucatinib. Together, these findings provide biologically informed and clinically actionable therapeutic hypotheses that extend the translational utility of our validated prognostic framework for TNBC risk stratification.

cancer biology↗

bioRxiv2026

Distinct clonal dynamics and interactions within the microenvironment near tumor stroma interfaces in rare histologic variants of bladder cancer

Rare histologic variants of bladder carcinoma, such as squamous cell and neuroendocrine carcinoma, generally have a worse prognosis compared to pure urothelial carcinoma (PUC), but the underlying molecular determinants are not well understood. We developed a novel computational genomics framework to characterize the dynamics of tumor-stroma-immune interactions at tumor borders and interiors from spatial transcriptomics data. We profiled bladder carcinoma samples of different histological variants on the Visium platform. Differences in clonal phylogeny and spatial heterogeneity of major subclones between the samples suggested disparate clonal spatiotemporal dynamics and interaction with stromal and immune compartments - which was notably prominent at the tumor-stroma interface. Our framework captured immune heterogeneity in the tumor microenvironment, including variations in the presence and architecture of tertiary lymphoid structures. Our analyses further indicated that there are substantial histology-specific differences in cell type composition, clonal spatial heterogeneity, inter-cellular signaling, and cellular processes. These variations collectively suggest divergent mechanisms of microenvironment remodeling across bladder cancer histologies. Cell-free DNA profiling from liquid biopsy captured tumor and microenvironment signatures from tumor boundaries and interiors, potentially allowing for tracking clonal dominance non-invasively. Our method tracks the trajectory of neoplastic disease in bladder cancer samples while identifying aggressive features.

genomics↗

bioRxiv2026

Scalable high-fidelity human vascularized cortical assembloids recapitulate neurovascular co-development and cell specialization

Human cortical development involves the coupling of neurogenesis and cerebrovascular growth. However, interactions between neural and vascular cells are largely missing in most brain organoids, which are crucial models for studying neurodevelopment and disease. Here, we establish vascularized cortical assembloids (vCAs) by fusing mesoderm-derived vascular organoids (VOs) with cortical organoids (COs). vCAs self-assemble lumenized networks of endothelial cells, pericytes, and perivascular fibroblasts that acquire blood-brain barrier (BBB) specialization and arteriovenous specification in vitro. Single-cell RNA-sequencing and immunofluorescence imaging revealed that vascularization improves neuroepithelial architecture, reduces hypoxia and apoptosis, expands cortical progenitor pools, and enhances neuronal maturation and connectivity compared with COs. Moreover, atlas-level integration with human neurodevelopmental tissue and cross-protocol benchmarking demonstrate superior transcriptional concordance, especially for vascular cell and glial populations, relative to existing approaches. This scalable and reproducible platform improves fidelity and throughput for modeling human neurovascular co-development and enables systematic studies across brain regions and diseases using engineered or patient-derived iPSCs.

developmental biology↗

bioRxiv2025

Macrophage Cholesterol Homeostasis Underpins the role of Sertraline as an Adjunctive Agent in Tuberculosis Therapy.

Cellular metabolism plays a deterministic role in the macrophage responses to intracellular pathogens such as Mycobacterium tuberculosis (Mtb) and infection control. Here, we demonstrate that the significant rewiring of the macrophage cholesterol metabolism by sertraline (SRT), an FDA-approved antidepressant, effectively enhances bacterial control. SRT, by virtue of its potent cationic amphiphilicity leads to the accumulation of lysosomal cholesterol in macrophages and activating the transcription factor SREBP2 and enhancing cholesterol biosynthesis. By specific gene silencing and biochemical inhibition assays, we further demonstrate that this metabolic reprogramming promotes lysosomal membrane permeabilization, mitochondrial reactive oxygen species (ROS) generation, heightened IL-1{beta} release, collectively enhancing macrophage bactericidal activity. Our results thus highlight a previously underappreciated link between cholesterol homeostasis and inflammasome activation, which contributes to improved Mtb clearance in the presence of adjunctive sertraline therapy. Statement of significanceTuberculosis remains a leading cause of global mortality, and treatment success is limited due to a long treatment regimen. This study identifies macrophage cholesterol homeostasis as a central pathway to enhance anti-tubercular therapy. We show that Sertraline, an FDA-approved antidepressant, a cationic amphiphilic drug (CAD), reprograms cholesterol trafficking through NPC1 inhibition. This drives the consequential processing of the master regulator SREBP2, accompanied by lysosomal membrane permeabilization. Collectively, these lead to mitochondrial ROS production and activation of NLRP3 signalling, boosting inflammasome-mediated bacterial clearance. By establishing cholesterol sequestration as the mechanistic basis of host-directed activity of sertraline and distinguishing it from ineffective CADs, our work provides a mechanistic framework for developing cholesterol-modulating host-directed adjuncts to improve TB treatment outcomes. Graphical abstract: SRT induced lysosomal cholesterol accumulation results in enhanced bacterial control via inflammasome activation O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/665474v3_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1019b4dorg.highwire.dtl.DTLVardef@7b0ae8org.highwire.dtl.DTLVardef@9297eorg.highwire.dtl.DTLVardef@1832bef_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Alternative Promoters Drive Transcriptomic Reprogramming and Prognostic Stratification in TNBC

Transcriptional regulation frequently involves alternative promoters, yet the distinct regulatory mechanisms governing alternative versus reference promoters remain poorly understood in Triple-Negative Breast Cancer (TNBC), a high-risk breast tumor subtype. It is worth emphasizing that, despite the availability of extensive short-read sequencing data, the impact of alternative promoter usage on TNBC transcriptome dynamics and patient survival remains underexplored. The current study leverages RNA sequencing data from the publicly available TNBC tumor samples (360) and adjacent normal samples (88) to identify TNBC-specific and subtype-specific active alternative promoters (AAPs). To further validate these findings, we integrated H3K4me3 ChIP-seq data, confirming key promoter switching events. We found that HDAC9, RPS14, and EPN1 exhibit tumor-specific AAP expression, while AKAP9 and SEC31A show basal subtype-specific promoter activity in TNBC. Beyond their transcriptional impact, we also explored the prognostic significance of AAPs. The alternative promoters of HUWE1 and FTX were recognized as independent survival predictors in TNBC. Notably, multivariate analysis demonstrated that prognostic AAPs remained significant in predicting relapse-free survival (RFS) even after adjusting for copy number alterations (CNA) and mRNA-based subtypes. Our AAP-based prognostic model achieved C-index of 0.73 in training and 0.72 in validation, with AUROC of 0.72 and integrated Brier score of 0.09 in validation dataset. Our findings indicate that AAP activity serves as a crucial prognostic marker beyond traditional clinical parameters, enhancing patient stratification and risk assessment in TNBC. Understanding promoter switching events may further unveil novel therapeutic targets, paving the way for precision oncology strategies in highly aggressive breast tumors.

genomics↗

bioRxiv2024

Differences in systemic immune parameters in individuals with lung cancer according to race

IntroductionRacial and ethnic disparities in the presentation and outcomes of lung cancer are widely known. To evaluate potential factors contributing to these observations, we measured systemic immune parameters in Black and White patients with lung cancer. MethodsPatients scheduled to receive cancer immunotherapy were enrolled in a multi-institutional prospective biospecimen collection registry. Clinical and demographic information were obtained from electronic medical records. Pre-treatment peripheral blood samples were collected and analyzed for cytokines using a multiplex panel and for immune cell populations using mass cytometry. Differences between Black and White patients were determined and corrected for multiple comparisons. ResultsA total of 187 patients with non-small cell lung cancer (Black, 19; White, 168) were included in the analysis. There were no significant differences in baseline characteristics between Black and White patients. Compared to White patients, Black patients had significantly lower levels of CCL23 and CCL27, and significantly higher levels of CCL8, CXCL1, CCL26, CCL25, CCL1, IL-1 b, CXCL16, and IFN-{gamma} (all P<0.05, FDR<0.1). Black patients also exhibited greater populations of non-classical CD16+ monocytes, NKT-like cells, CD4+ cells, CD38+ monocytes, and CD57+ gamma delta T cells (all P<0.05). ConclusionsBlack and White patients with lung cancer exhibit several differences in immune parameters, with Black patients exhibiting greater levels of numerous pro-inflammatory cytokines and cell populations. The etiology and clinical significance of these differences warrant further evaluation.

cancer biology↗

bioRxiv2022

Propagated circulating tumor cells uncovers the rople of NFκB and COP1 in metastasis

Circulating tumor cells (CTCs), a population of cancer cells that represents the seeds of metastatic nodules, are a promising model system for studying metastasis. However, expansion of patient-derived CTCs ex vivo is challenging and dependent on the collection of high numbers of CTCs, which are ultra-rare. Here, we report the development of a combined CTC and CTC-derived xenograft (CDX) platform for expanding and studying patient-derived CTCs from metastatic colon, lung, and pancreatic cancers. Propagated CTCs yielded a highly aggressive population of cells that could be used to routinely and robustly establish primary tumors and metastatic lesions in CDXs. Differential gene analysis of the resultant CTC models emphasized a role for NF-kB signaling as a pan-cancer signaling pathway involved in metastasis. Furthermore, metastatic CTCs were identified through a prospective 5-gene signature (BCAR1, COL1A1, IGSF3, RRAD, and TFPI2). Whole-exome sequencing of CDX models and metastases further identified mutations in constitutive photomorphogenesis protein 1 (COP1) as a potential driver of metastasis. These findings illustrate the utility of the combined patient-derived CTC model and provide a glimpse of the promise of CTCs in identifying drivers of cancer metastasis.

cancer biology↗

bioRxiv2020

Patient Similarity Network of Newly Diagnosed Multiple Myeloma Identifies Patient Sub-groups with Distinct Genetic Features and Clinical Implications

The remarkable genetic heterogeneity of Multiple Myeloma (MM) poses a significant challenge for proper prognostication and clinical management of patients. Accurate dissection of the genetic and molecular landscape of the disease and the robust identification of homogeneous classes of patients are essential steps to reliable risk stratification and the development of novel precision medicine strategies. Here we introduce MM-PSN, the first multi-omics Patient Similarity Network of newly diagnosed MM. MM-PSN has enabled the identification of three broad patient groups and twelve distinct sub-groups defined by five data types generated from genomic and transcriptomic patient profiling of 655 patients. The MM-PSN classification uncovered novel associations between distinct MM hallmarks with significant prognostic implications and allowed further refinement of risk stratification. Our analysis revealed that gain of 1q is the most important single lesion conferring high risk of relapse, and its association with an MMSET translocation is the most significant determinant of poor outcome. We developed a classifier and validated these results in an independent dataset of 559 pts. Our findings suggest that gain of 1q should be incorporated in routine staging systems and risk assessment tools. The MM-PSN classifier is available as a free resource to allow for an easy implementation in most clinical settings.

cancer biology↗

bioRxiv2019

Computing Skin Cutaneous Melanoma Outcome from the HLA-alleles and Clinical Characteristics

Human Leukocyte Antigen (HLA) is an essential component of the immune system which stimulates immune cells to provide protection and defense against cancer. More than thousands of HLA alleles have been reported in the literature; but, only a specific set of HLA alleles expressed in an individual. Recognition of cancer-associated mutations by the immune system depends on the presence of a particular set of alleles, that elicit an immune response to fight against cancer. It indicates that the occurrence of specific HLA alleles also affects the outcome of the cancer patients. In the current study, prediction models have been developed using 415 skin cutaneous melanoma (SKCM) patients for predicting the overall survival of patients from their HLA-alleles. It has been observed that, the presence of certain superalleles in the patients, is responsible for improved overall survival which were referred as favourable superalleles like HLA-B*55 (HR=0.15, 95% CI 0.034 to 0.67), HLA-A*01 (HR=0.5, 95% CI 0.3 to 0.8). In contrast, presence of certain superalleles in the patients is responsible for their poor survival, those superalleles were referred as unfavourable superalleles such as HLA-B*50 (HR=2.76, 95% CI 1.284 to 5.941), HLA-DRB1*12 (HR=3.44, 95% CI 1.64 to 7.2). We developed prediction models using 14 HLA-superalleles and five clinical characteristics for predicting high-risk SKCM patients and achieve HR=4.52 (95% CI 3.088-6.609) with p-value = 8.01E-15. Lastly, we provide a web-based service to community for predicting the risk in SKCM patients (https://webs.iiitd.edu.in/raghava/skcmhrp/)

bioinformatics↗

bioRxiv2019

GPSRdocker: A Docker-based Resource for Genomics, Proteomics and Systems biology

BackgroundIn past number of web-based resources has been developed in the field of Bioinformatics. These resources are heavily used by scientific community to provide solution for challenges faced by experimental researchers particularly in the field of biomedical sciences. There are number of challenges in utilizing full potential of these services that includes internet speed, limits on computing power, and security of data. In order to enhance utilities of these web-based assets, we developed a docker-based container that integrates large number resources available in literature.\n\nResultsThis paper describes GPSRdocker a docker-based container developed for providing wide-range of computational tools in the field of bioinformatics particularly in genomics, proteomics and system biology. Majority of tools integrated in GPSRdocker are based on web services developed at Raghavas group in last two decades. Broadly, these tools can be categorized in three categories; i) general scripts, ii) supporting software and iii) major standalone software. In order to facilitate students or developers working in the field of bioinformatics, we developed general scripts in Perl and Python. These general-purpose scripts serve as building block for any bioinformatics tools like computing features/descriptors of a protein. Supporting software packages includes SCIKIT, WEKA, SVMlight, and PSI-BLAST; these software packages allow one to develop/implement bioinformatics software. Major Standalone software is core of this container which allows predicting function/class of biomolecules. These tools can be classified broadly in following categories; protein annotation, epitope-based vaccines, prediction of interaction and drug discovery.\n\nConclusionA docker-based container has been developed which can be easily run on any operating system as well as it can be directly ported on cloud. Scripts can be run to build pipelines for addressing problems at system level like prediction of vaccine candidate for a pathogen. GPSRdocker including manual is available free for academic use from https://webs.iiitd.edu.in/gpsrdocker.

bioinformatics↗

bioRxiv2018

Expression based biomarkers and models to classify early and late stage samples of Papillary Thyroid Carcinoma

In this study, we describe the key transcripts and machine learning models developed for classifying the early and late stage samples of Papillary Thyroid Cancer (PTC), using transcripts expression data from The Cancer Genome Atlas (TCGA). First, we rank all the transcripts on the basis of area under receiver operating characteristic curve, (AUROC) value to discriminate the early and late stage, based on an expression threshold. With the expression of a single transcript DCN, we can classify the stage samples with a 68.5% accuracy and AUROC of 0.66. Then we implemented various combination of multiple gene panels, selected using various gold standard feature selection techniques. The model based on the expression of 36 multiple transcripts (protein coding and non-coding) selected using SVC-L1 achieves the maximum accuracy of 74.51% with AUROC of 0.75 on independent validation dataset with balanced sensitivity and specificity. Further, these signatures also performed well on external microarray data obtained from GEO, predicting nearly 70% (12 samples out of 17 samples) early stage samples correctly. Further, multiclass model, classifying the normal, early and late stage samples achieves the accuracy of 75.43% with AUROC of 0.80 on independent validation dataset. With correlation analysis, we found that transcripts with maximum change in correlation of their expression in both the stages are significantly enriched in neuroactive ligand receptor interaction pathway. We also propose a panel of five protein coding transcripts, which on the basis of their expression, can segregate cancer and normal samples with 97.32% accuracy and AUROC of 0.99 on independent validation dataset. All the models and dataset used in this study are available from the web server CancerTSP (http://webs.iiitd.edu.in/raghava/cancertsp/).

bioinformatics↗

bioRxiv2018

Prediction and analysis of skin cancer progression using genomics profiles of patients

Metastatic state of the Skin Cutaneous Melanoma (SKCM) has led to high mortality rate worldwide. Previously, various studies have revealed the association of the metastatic melanoma with the diminished survival rate in comparison to primary tumors. Thus, prediction of melanoma at primary tumor state is crucial to employ optimal therapeutic strategy for prolonged survival of patients. The RNA, miRNA and methylation data of The Cancer Genome Atlas (TCGA) cohort of SKCM is comprehensively analysed to recognize key genomic features that can categorize various states of metastatic tumors from primary tumors with high precision. Subsequently, various prediction models were developed using filtered genomic features implementing various machine learning techniques to classify these primary tumors from metastatic tumors. The SVC model (with class weight and RBF kernel) developed using 17 mRNA features achieved maximum MCC 0.73 with sensitivity, specificity and accuracy 89.19%, 90.48% and 89.47% respectively on independent validation dataset. Our study reveals that gene expression based features performs better than features obtained from miRNA profiling and epigenomic profiling. Our analysis shows that the expression of genes C7, MMP3, KRT14, KRT17, MASP1, and miRNA hsa-mir-205 and hsa-mir-203a are among the key genomic features that may substantially contribute to the oncogenesis of melanoma even on the basis of simple expression threshold. The major prediction models and analysis modules to predict metastatic and primary tumor samples of SKCM are available from a webserver, CancerSPP (http://webs.iiitd.edu.in/raghava/cancerspp/).

bioinformatics↗

bioRxiv2018

A web bench for analysis and prediction of oncological status from proteomics data of urine samples

Urine-based cancer biomarkers offer numerous advantages over the other biomarkers and play a crucial role in cancer management. In this study, an attempt has been made to develop proteomics-based prediction models to discriminate patients of oncological disorders related to urinary tract and healthy controls from their urine samples. The dataset used in this study was obtained from human urinary peptide database that contains urine proteomics data of 1525 oncological and 1503 healthy controls with the spectral intensity of 5605 peptides. First, we identified peptide spectra using various feature selection techniques, which display different intensity and occurrence in oncological samples and healthy controls. Based on selected 173 peptide-based biomarkers, we developed models for predicting oncological samples and achieved maximum accuracy of 91.94% with 0.84 MCC. Prediction models were also developed based on spectral intensities with known peptide sequences. We also quantitated the amount of protein in a sample based on intensities of its fragments/peptides and developed prediction models based on protein expression. It was observed that certain proteins and their peptides such as fragments of collagen protein are more abundant in oncological samples. Based on this study, we also developed a web bench, CancerUBM, for mining proteomics data, which is freely available at http://webs.iiitd.edu.in/raghava/cancerubm/.

bioinformatics↗

bioRxiv2018

Classification of early and late stage Liver Hepatocellular Carcinoma patients from their genomics and epigenomics profiles.

BackgroundLiver Hepatocellular Carcinoma (LIHC) is the second major cancer worldwide, responsible for millions of premature deaths every year. Prediction of clinical staging is vital to implement optimal therapeutic strategy and prognostic prediction in cancer patients. However, to date, no method has been developed for predicting stage of LIHC from genomic profile of samples.\n\nResultsIn current study, in silico models have been developed for classifying LIHC patients in early and late stage using RNA expression and DNA methylation data. The Cancer Genome Atlas (TCGA) dataset contains 173 early and 177 late stage samples of LIHC, was extensively analysed to identify differentially expressed RNA transcripts and methylated CpG sites that can discriminate early and late stages of LIHC samples with high precision. Naive Bayes model developed using 51 features that combine 21 CpG methylation sites and 30 RNA transcripts achieved maximum MCC 0.58 with accuracy 78.87% on validation dataset. Further, we also analysed genomics and epigenomics profiles of normal and LIHC samples and developed model to classify LIHC samples with AUROC 0.99. In addition, multiclass models developed for classifying samples in normal, early and late stage of cancer and achieved accuracy of 76.54% and AUROC of 0.86.\n\nConclusionOur study reveals stage prediction of LIHC samples with high accuracy based on genomics and epigenomics profiling is a challenging task in comparison to classification of LIHC and normal samples. Comprehensive analysis, differentially expressed RNA transcripts, methylated CpG sites in LIHC samples and prediction models are available from CancerLSP (http://webs.iiitd.edu.in/raghava/cancerlsp/).

bioinformatics↗