Biology subjects

Bhowmick, R.

Publications and source records attributed to Bhowmick, R..

4 recordsLinked to original sources

bioRxiv2025

Potentiation of EGFR mutant lung cancer treatment targeting replication stress.

Background/ObjectivesOncogene-targeted therapies against growth factor receptors are effective treatment options for driver mutation-positive non-small cell lung cancer (NSCLC). The third-generation EGFR-Tyrosine Kinase inhibitor (TKI) osimertinib, is a standard first-line therapy for patients with EGFR-mutated NSCLC cancer. Acquired resistance to osimertinib is a significant problem that limits survival. In addition to that clinical data show approximately 15% of EGFR mutant non-small cell lung cancer patients have innate resistance to Osimertinib. Tumor heterogeneity and multiple resistance mechanisms add to the complexity of EGFR-mutated NSCLC. Tumor cells that acquire resistance, independent of the mechanism, experience replication stress (RS) as proliferation resumes. MethodsWe have employed a series of non-small cell lung cancer models to assess the efficacy of combining osimertinib with NERx-329, targeting RS via inhibiting RPA, and the impact of these treatments on growth and damage signaling pathways. ResultsWe demonstrate that targeting RS with RPAi treatment induces cell death in multiple EGFR-mutant cell lines alone and when combined with osimertinib. Dissection of signaling pathways revealed that RPAi treatment does not block osimertinib inhibition of EGFR signaling. Analysis of DNA damage response (DDR) signaling reveals that NERx329 potentiates the osimertinib-dependent downregulation of Chk1 activity and expression. The Chk1 loss was shown to be dependent on proteasome degradation as proteasome inhibitor MG-132 restores Chk1 activity and expression. ConclusionsFrom these data, we infer that targeting RS via RPAi NERx-329 in combination with osimertinib is an effective strategy and represents a promising drug combination targeted therapy to enhance efficacy and limit the development of resistance. Simple SummaryThis research aims to assess a new mechanism to potentiate osimertinib treatment of EGFR-mutated non-small cell lung cancer. Targeting replication stress with the small molecule Replication proteins A inhibitor (RPAi), NERx-329, induces cancer cell death and enhance the effectiveness of osimertinib. The findings suggest that combining NERx-329 with osimertinib could be a promising strategy to improve treatment outcomes and reduce resistance. Importantly, we demonstrate that neither RPAi or osimertinib alter the anticancer mechanism of each single agent, providing a new approach to increase therapeutic efficacy and delay or eliminate the development of drug resistance.

cancer biology↗

bioRxiv2024

Rapid prediction of thermodynamically destabilizing tyrosine phosphorylations in cancers

Tyrosine phosphorylations are a prominent characteristic of numerous cancers, necessitating the use of computational tools to comprehensively analyze phosphoproteomes and identify potentially (dys)functional phosphorylations. Here we propose a machine learning-based method to predict the thermodynamic stability change resulting from tyrosine phosphorylation. Our approach, based on prediction of phosphomimetic delta-delta-G from structural features, strongly correlates with experimental mutational scanning cDNA proteolysis data (R = 0.71). We predicted the destabilizing effects of all 384,857 tyrosine residues from the Alphafold2 database. We then applied our approach to a pan-cancer phosphoproteomics dataset, comprising over 600 unique tyrosine phosphorylations across 11 cancer subtypes. We predict destabilizing phosphorylations in both oncogenes and tumor suppressors, where the former likely reflects a generalized relief of auto-inhibition or activating conformational change. We find that the number of circuit topological parallel relations with respect to residues contacting the phosphorylated site is greater for autoinhibited oncogenes than for other proteins (Wilcoxon p = 0.03). Utilizing an extreme gradient-boosting machine learning approach, we obtain an AUC of 0.85 for the prediction of autoinhibited phosphorylation states from circuit topological features. The top destabilized proteins from the pan-cancer data are enriched for chemical and oxidative stress pathways. Among metabolic proteins, highly destabilizing phosphorylations tend to occur in more peripheral proteins with lower network centrality measures (Wilcoxon p = 0.005). We predict 58% of recurrent tyrosine cancer phosphorylations to be destabilizing at the 1 kcal/mol threshold. Our approach can enable rapid screening of destabilizing phosphorylations and phosphomimetic mutations.

systems biology↗

bioRxiv2024

Recon8D: A metabolic regulome network from oct-omics and machine learning

To explore multiomic regulation of the metabolome, we used machine learning to predict metabolomic variation across [~]1000 different cancer cell lines with matched omics data from 8 biomolecular classes: genomics (copy-number and mutations), epigenomics (histone post-translational modifications (PTMs) and DNA-methylation), transcriptomics and RNA splice variants, non-coding transcriptomics (miRNA and lncRNA), proteomics, and phosphoproteomics. Overall, the metabolome is tightly associated with the transcriptome, with coding and non-coding RNAs emerging as top predictors. Peripheral metabolites are predictable via levels of corresponding enzymes, while those in central metabolism require combinatorial predictors in signaling and redox pathways, and may not reflect corresponding pathway expression. We reconstruct multiomic interaction subnetworks for highly predictable metabolites, and YAP1 signaling emerged as a top global predictor across 4 omic layers. We prioritize predictive multiomic features for single-cell and spatial metabolomics assays. Top predictors were enriched for synthetic-lethal interactions and synergistic combination therapies that target compensatory metabolic modulators.

systems biology↗

bioRxiv2023

Data-Driven Screening to Infer Metabolic Modulators of the Cancer Epigenome

Metabolites such as acetyl-CoA and citrate play an important moonlighting role by influencing the levels of histone post-translational modifications (PTMs) and regulating gene expression. This cross talk between metabolism and epigenome impacts numerous biological processes including development and tumorigenesis. However, the extent of moonlighting activities of cellular metabolites in modulating the epigenome is unknown. We developed a data-driven screen to discover moonlighting metabolites by constructing a histone PTM-metabolite interaction network using global chromatin profiles, metabolomics, and epigenetic drug sensitivity data from over 600 cell lines. Our ensemble statistical learning approach uncovered metabolites that are predictive of histone PTM levels and epigenetic drug sensitivity. We experimentally validated synergistic and antagonistic interactions between histone deacetylase and demethylase inhibitors with epigenetic metabolites kynurenic acid, pantothenate, and 1-methylnicotinamide. We apply our approach to track metaboloepigenetic interactions during the epithelial-mesenchymal transition. Overall, our data-driven approach unveils a broader range of metaboloepigenetic interactions than anticipated from previous studies, with implications for reversing aberrant epigenetic alterations and enhancing epigenetic therapies through diet.

systems biology↗