Biology subjects

Tagore, S.

Publications and source records attributed to Tagore, S..

12 recordsLinked to original sources

bioRxiv2026

Spatial polarization of endothelial ICAM-1 governs T-cell exclusion in melanoma

An immunosuppressive tumor microenvironment limits therapeutic efficacy and worsens prognosis in melanoma. Beyond T-cell abundance and function, effective tumor control also depends on whether T cells can access malignant cells within the tumor. Although emerging evidence supports that tumor vasculature facilitates immune evasion, the vascular mechanisms that govern intratumoral T-cell positioning remain poorly defined. Using RNA sequencing of endothelial cells isolated from tumor cores versus peripheries in a mouse melanoma model, we identified intercellular adhesion molecule 1 (ICAM-1) as a candidate regulator of T-cell localization. During tumor growth, T cells shifted from a balanced core-margin distribution to marked exclusion from the core, most prominently in T cell-inflamed tumors. This spatial redistribution --less evident in other immune subsets--coincided with high expression of lymphocyte function-associated antigen-1 (LFA-1) on T cells. In parallel, endothelial ICAM-1 became enriched at the tumor periphery, where vascular integrity was compromised, as evidenced by increased vascular leakage and reduced pericyte coverage. Functionally, ICAM-1 blockade restored intratumoral T-cell infiltration, enhanced effector activity, and significantly delayed the growth of immunogenic tumors. Moreover, ICAM-1 inhibition sensitized an immune-refractory tumor to anti-PD-1 checkpoint blockade. Together, these findings identify endothelial ICAM-1 as a vascular determinant of intratumoral T-cell positioning and highlight the ICAM-1/LFA-1 axis as a modifiable checkpoint to reverse T-cell retention at the tumor periphery, thereby enhancing antitumor immunity and immunotherapy efficacy.

cancer biology↗

bioRxiv2025

Chromosomal instability shapes the tumor microenvironment of esophageal adenocarcinoma via a cGAS-chemokine-myeloid axis

Chromosomal instability (CIN), a characteristic feature of esophageal adenocarcinoma (EAC), drives tumor aggressiveness and therapy resistance, presenting an intractable problem in cancer treatment. CIN leads to constitutive stimulation of the innate immune cGAS-STING pathway, which has been typically linked to anti-tumor immunity. However, despite the high CIN burden in EAC, the cGAS- STING pathway remains largely intact. To address this paradox, we developed novel esophageal cancer models, including a CIN-isogenic model, discovering myeloid-attracting chemokines - with the chemokine CXCL8 (IL-8) as a prominent hit - as conserved CIN-driven targets in EAC. Using high-resolution multiplexed immunofluorescence microscopy, we quantified the extent of ongoing cGAS-activating CIN in human EAC tumors by measuring cGAS-positive micronuclei in tumor cells, validated by orthogonal whole-genome sequencing-based CIN metrics. By coupling in situ CIN assessment with single-nucleus RNA sequencing and multiplex immunophenotypic profiling, we found tumor cell-intrinsic innate immune activation and intratumoral myeloid cell inflammation as phenotypic consequences of CIN in EAC. Additionally, we identified increased tumor cell-intrinsic CXCL8 expression in CINhigh EAC, accounting for the inflammatory tumor microenvironment. Using a novel signature of CIN, termed CINMN, which captures ongoing CIN-associated gene expression, we confirm poor patient outcomes in CINhigh tumors with signs of aberrantly rewired cGAS-STING pathway signaling. Together, our findings help explain the counterintuitive maintenance and expression of cGAS-STING pathway components in aggressive, CINhigh tumors and emphasize the need to understand the contribution of CIN to the shaping of a pro-tumor immune landscape. Therapeutic strategies aimed at disrupting the cGAS-driven inflammation axis may be instrumental in improving patient outcomes in this aggressive cancer.

cancer biology↗

bioRxiv2025

The carnosinase dipeptidase CNDP1 is a novel metabolic vulnerability in brain metastasis

Brain metastatic cells undergo metabolic adaptations, such as increased reliance on oxidative phosphorylation. Integrating proteomic and transcriptomic profiling of patient samples revealed a consistent upregulation of the carnosine dipeptidase-1 (CNDP1) in brain versus extracranial metastases. Carnosine is an abundant metabolite in brain and muscle, known to exert anti-proliferative effects on cancer cells. Here, we demonstrate that CNDP1 inhibition suppresses both the establishment and maintenance of melanoma brain metastasis while its ectopic expression is sufficient to confer brain metastatic potential to poorly metastatic cells. CNDP1 suppression results in activation of the Integrated Stress Response via Heme-Regulated Inhibitor Kinase and reprogrammed translation towards preferential expression of mitochondrial and survival transcripts. We further show that CNDP1 upregulation supports mitochondrial activity by limiting the levels of its substrate carnosine, a copper ionophore, thus protecting metastatic cells from carnosine-induced copper toxicity. Our studies reveal a novel metabolic adaptation during brain metastasis, which can be leveraged for therapeutic purposes.

cancer biology↗

bioRxiv2024

Echidna: A Bayesian framework for quantifying gene dosage effect impacting phenotypic plasticity

Phenotypic plasticity, the ability of cells to adapt their behavior in response to genetic or environmental changes, is a fundamental biological process that drives cellular diversity in both normal and pathological contexts, including in tumor evolution. While chromosomal instability and somatic copy number alterations (CNAs) are known to influence cellular states, it remains difficult to separate genetic from cell non-autonomous mechanisms that govern transcriptional variability. Here, we present Echidna, a Bayesian hierarchical model that integrates single-cell RNA sequencing (scRNA-seq) and bulk whole-genome sequencing (WGS) data to quantify the impact of CNAs on gene expression dynamics. By jointly inferring clone-specific CNA profiles and uncovering clonal dependencies, Echidna bridges genomic and transcriptomic landscapes within and across multiple time points, enabling the decoupling of gene dosage effects from cell-extrinsic factors on phenotypic plasticity. Applying Echidna to patient tumor specimens, we demonstrate its superior performance in clonal reconstruction and derive insights into resistance mechanisms.

bioinformatics↗

bioRxiv2023

Systematic Pan-cancer Functional Inference and Validation of Hyper, Hypo and Neomorphic Mutations

While the functional effects of many recurrent cancer mutations have been characterized, the TCGA repository comprises more than 10M non-recurrent events, whose function is unknown. We propose that the context specific activity of transcription factor (TF) proteins--as measured by expression of their transcriptional targets--provides a sensitive and accurate reporter assay to assess the functional role of oncoprotein mutations. Analysis of differentially active TFs in samples harboring mutations of unknown significance--compared to established gain (GOF/hypermorph) or loss (LOF/hypomorph) of function--helped functionally characterize 577,866 individual mutational events across TCGA cohorts, including identification of mutations that are either neomorphic (gain of novel function) or phenocopy other mutations (mutational mimicry). Validation using mutation knock-in assays confirmed 15 out of 15 predicted gain and loss of function mutations and 15 of 20 predicted neomorphic mutations. This could help determine targeted therapy in patients with mutations of unknown significance in established oncoproteins.

systems biology↗

bioRxiv2022

tRNA Methylation Resolves Codon Usage Bias at the Limit of Cell Viability

The codon usage of each genome is closely correlated with the abundance of tRNA isoacceptors. How codon-usage bias is resolved by tRNA post-transcriptional modifications is largely unknown. Here we demonstrate that the N1-methylation of guanosine at position 37 (m1G37) on the 3-side of the anticodon, while not directly responsible for reading of codons, is a neutralizer that resolves differential decoding of proline codons. A genome-wide suppressor screen of a non-viable E. coli strain, m1G37-lacking, identifies proS suppressor mutations, indicating a coupling of methylation with tRNA prolyl-aminoacylation that sets the limit of cell viability. Using these suppressors, where prolyl-aminoacylation is decoupled from tRNA methylation, we show that m1G37 neutralizes differential translation of proline codons by the major isoacceptor. Lacking of m1G37 inactivates this neutralization and exposes the need for a minor isoacceptor for cell viability. This work has medical implications for bacterial genomes that exclusively use the major isoacceptor for survival.

microbiology↗

bioRxiv2022

KINOMO: A non-negative matrix factorization framework for recovering intra- and inter-tumoral heterogeneity from single-cell RNA-seq data

Single-cell RNA-sequencing (scRNA-seq) is a powerful technology to uncover cellular heterogeneity in tumor ecosystems. Due to differences in underlying gene load, direct comparison between patient samples is challenging, and this is further complicated by the sparsity of data matrices in scRNA-seq. Here, we present a factorization method called KINOMO (Kernel dIfferentiability correlation-based NOn-negative Matrix factorization algorithm using Kullback-Leibler divergence loss Optimization). This tool uses quadratic approximation approach for error correction and an iterative multiplicative approach, which improves the quality assessment of NMF-identified factorization, while mitigating biases introduced by inter-patient genomic variability. We benchmarked this new approach against nine different methods across 15 scRNA-seq experiments and find that KINOMO outperforms prior methods when evaluated with an adjusted Rand index (ARI), ranging 0.82-0.91 compared to 0.68-0.77. Thus, KINOMO provides an improved approach for determining coherent transcriptional programs (and meta-programs) from scRNA-seq data of cancer tissues, enabling comparison of patients with variable genomic backgrounds. ClassificationPhysical Sciences (Applied Mathematics; Biophysics and Computational Biology), Biological Sciences (Applied Biological Sciences; Biophysics and Computational Biology; Medical Sciences; Systems Biology.). Significance StatementIdentification of shared or distinct cell programs in single-cell RNA-seq data of patient cancer cells is challenging due to underlying variability of gene load which determines transcriptional output. We developed an analytical approach to define transcriptional variability more accurately across patients and therefore enable comparison of program expression despite inherent genetic heterogeneity. Thus, this method overcomes challenges not adequately addressed by other methods broadly used for the analysis of single-cell genomics data.

systems biology↗

bioRxiv2022

Mutated Tumor Suppressors Follow Oncogenes Profile by the Gene Hypermethylation of Partners in the Protein Interaction Networks

As a result of current advances in the analysis of patient sequencing data, many tumors have been characterized in a personalized manner. Such data can also be used to characterize genes that act as either oncogenes or tumor suppressors. These include "defective" tumor suppressor genes which may function as driver oncogenes that play a key role in cancer proliferation due to various genetic alterations, specifically, chromosomal translocations. In this study, we considered protein networks, mutations, methylation data and cancer fusions to classify tumor suppressors that may convert into oncogenes. Moreover, we developed a novel network-based parameter called the preferential attachment score to categorize genes as oncogenes and/or tumor suppressors. Such classification was achieved using a naive Bayes computation approach. We used an ABC-MCMC method for selecting features for training our classification algorithm. We then performed a survey of tumor suppressors and oncogenes from the perspective of somatic mutations and network properties for 691 TCGA cases. For comparative purposes, we chose currently well-established methods, such as MutSigCV, OncodriveCLUST, Oncodrive-FM, 20/20+, ActiveDriver, MuSiC, TUSON, OncodriveFML, and found that our algorithm outperformed these other tolls, with 93.3% efficiency. Based on 691 TCGA cohorts, we found that tumor suppressors presented the highest mutation frequency in most tumor types, relative to oncogenes. Using protein-protein interaction data, we found that essential proteins, tumor suppressors and oncogenes had higher degrees of connectivity and betweenness centrality, relative to normal proteins. Similarly, tumor suppressors and oncogenes had lower clustering coefficients, as well as shortest path distances (FDR < 0.05). Finally, most mutated tumor suppressors integrate hyper-methylated partners in the protein interaction networks of 3091 fusions, following the patterns of oncogenes (43%). Thus, these results further characterize cancer oncogenes and tumor suppressors in the context of deep analysis of cancer network alterations. AvailabilitySource scripts are available at https://github.com/somnathtagore/NBC and the resource is available at http://ontum.md.biu.ac.il/index.html

systems biology↗

bioRxiv2022

Improving patient survival by direct targeting of chimeric protein-protein interaction networks

Protein fusions produced by the "slippage" of two genes or by chromosomal translocations are essential diagnostic biomarkers of cancer. Fusions produce novel protein-protein interactions, which eliminate other interactions by changing protein domains in such fusions. The impact of these changes disseminates along protein-protein interaction networks, thereby altering cancer-promoting activity and creating cancer phenotypes. Currently, for most patients, the determination of appropriate drugs is totally empirical. As such, a personalized therapy approach based on unique patient genomic markers is needed. In this study, we considered 672 aliquot IDs containing 3091 fusions from The Cancer Genome Atlas (TCGA), accounting for 25 cancer sub-types assigned as leukemias, lymphomas, sarcomas, melanoma, glioblastoma and carcinomas. Protein-protein interaction maps showed distinct patterns according to cancer sub-type, reflected as different phenotypic traits. We induced site-directed percolations, i.e., critical transitions, by selective knockouts of genes encoding proteins in a given interaction network so as to identify breakdown points. The number of genes that needed to be knocked out in a site-directed manner before inducing a breakdown ranged from 1-7 based on whether the fusion protein was part of a network hub. Quantitatively, in leukemia, lymphoma, melanoma and glioblastoma, breakdown was achieved in 218 fusion networks when only higher degree hubs were percolated, and in 280 networks when a combination of higher and lower degree hubs was targeted, with an FDR < 0.05. These were subsequently addressed in survival studies. We found that patient survival may be improved by considering breakdown points as drug targets.

systems biology↗

bioRxiv2022

Multi-modal single-cell and whole-genome sequencing of minute, frozen specimens to propel clinical applications

Single-cell genomics are enabling technologies, but their broad clinical application remains challenging. We report an easily adaptable approach for single-cell transcriptome and T cell receptor (TCR)-sequencing, and matched whole-genome sequencing from tiny, frozen clinical specimens. We achieve similar quality and biological outputs while reducing artifactual signals compared to data from matched fresh tissue samples. Profiling sequentially collected melanoma samples from the KEYNOTE-001 trial, we resolve cellular, genomic, and clonotype dynamics that encapsulate molecular patterns of tumor evolution during anti-PD-1 therapy. To demonstrate applicability to banked biospecimens of rare diseases, we generate a large uveal melanoma liver metastasis single-cell and matched WGS atlas, which revealed niche-specific impairment of clonal T cell expansion. This study provides a foundational framework for propelling single-cell genomics to the clinical arena.

genomics↗

bioRxiv2020

Pancreatic Ductal Adenocarcinoma Comprises Coexisting Regulatory States with both Common and Distinct Dependencies

Despite extensive efforts, reproducible assessment of pancreatic ductal adenocarcinoma (PDA) heterogeneity and plasticity at the single cell level remains elusive. Systematic, network-based analysis of regulatory protein activity in single cells identified three PDA Developmental Lineages (PDLs), coexisting in virtually all tumors, whose transcriptional states are mechanistically driven by aberrant activation of Master Regulator (MR) proteins associated with gastrointestinal lineages (GLS state), morphogen and EMT pathways (MOS state), and acinar-to-ductal metaplasia (ALS state), respectively. Each PDL is further subdivided into sub-states characterized by low vs. high MAPK pathway activity. This taxonomy was remarkably conserved across multiple cohorts, cell lines, and PDX models, and harmonized with bulk profile analyses. Cross-state plasticity and MR essentiality was confirmed by barcode-based lineage tracing and CRISPR/Cas9 assays, respectively, while MR ectopic expression induced PDL transdifferentiation. Together these data provide a mechanistic foundation for PDA heterogeneity and a roadmap for targeting PDA cellular subtypes.

cancer biology↗

bioRxiv2018

A Comprehensive Approach Characterizing Fusion Proteins and Their Interactions Using Biomedical Literature

Todays increase in scientific literature requires the efficient methods of data mining for improving the extraction of the useful information from texts. In this manuscript, we used a data and text mining method to identify fusions and their protein-protein interactions from published biomedical text. The extracted fusion proteins and their protein-protein interactions are used as a training set for a Naive Bayes classifier that is further used for final identification of testing dataset, consisting of 1817 fusions. Our method has a literature corpus, text and annotation mappers; keywords, rule bases, negative tokens, and pattern extractor; synonym tagger, normalization, regular expression mapper; and Naive Bayes classifier. We classified 1817 unique fusion proteins and their corresponding 2908 protein-protein interactions for 18 cancer types. Therefore, it can be used for screening literature for identifying mentions unique cases of fusions that can be further used for downstream analysis. It is available at http://protfus.md.biu.ac.il/.

bioinformatics↗