Biology subjects

Patkar, S.

Publications and source records attributed to Patkar, S..

6 recordsLinked to original sources

bioRxiv2026

Occurrence of Biased mTOR Signaling in Hepatocellular Carcinoma

BackgroundmTOR signaling promotes cell growth and anabolic processes in all eukaryotes. Hyperactivation of mTOR signaling is associated with various cancers along with hepatocellular carcinoma (HCC). HCC is a highly lethal malignancy with multiple aetiologies such as viral infection, alcohol abuse, and metabolic dysfunction. Therapeutic options for HCC remain limited due to an incomplete understanding of oncogenic drivers and poorly characterized mechanisms of disease progression. MethodsVarious regimens of DEN and CCl4 carcinogen dosage were investigated on C57BL/6J mice to induce HCC. The histological analysis for fibrosis and serum markers for liver function were performed. Molecular analyses of oncogenic drivers were performed in the HCC tissues obtained from mice and HCC patients. The impact of inhibition of mTOR signaling was assessed on HCC progression. ResultsWe established a rapid DEN+CCl4 induced (DCI) HCC model in C57BL/6 mice to study disease progression longitudinally. The molecular analysis revealed upregulation of MAPK and downregulation of mTORC1-S6K-S6 signaling in HCC. However, other branches of mTOR such as mTORC1-ULK1, mTORC1-4EBP1, and mTORC2-PKC were upregulated due to the increased expression. Similar observations were found in tissues derived from HCC patients. Furthermore, inhibition of mTORC1 alone by Rapamycin did not reduce HCC progression but Torin 1 mediated inhibition of both mTORC1 and mTORC2 significantly reduced HCC progression. ConclusionsWe propose this biased mTOR signaling modulates mTOR activity towards specific downstream processes that are crucial for cancer cell growth and targeting both the mTOR complexes has better therapeutic potential in HCC. Impact and ImplicationsThis study provides a rapid pre-clinical model for understanding HCC progression and to explore various intervention strategies. The study reports a novel phenomenon of biased mTOR signaling where deregulation of downstream substrate levels modulates the mTOR activity towards the specific branches, the master regulator of cell growth and metabolism. Furthermore, the study suggests that the clinical investigations exploring the rapalogs against HCC should be cautiously considered depending on the aetiology and signaling status of HCC. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/727188v2_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@ef3714org.highwire.dtl.DTLVardef@1091b25org.highwire.dtl.DTLVardef@d52f2forg.highwire.dtl.DTLVardef@cc158_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIDEN and CCl4 treatment generated a well-established HCC within 4 months. C_LIO_LILiver fibrosis and serum markers correlated with HCC progression. C_LIO_LIUpregulation of mTOR pathway substrates create biased mTOR signaling. C_LIO_LIDual inhibition of mTORC1 and mTORC2 reduced HCC progression significantly. C_LI

cancer biology↗

bioRxiv2026

OncoBERT: Context-Aware Modeling of Somatic Mutations for Precision Oncology

Somatic mutation profiling is central to cancer diagnosis and treatment selection. However, most studies focus on individual actionable mutations, overlooking the broader mutational context that shapes tumor evolution and treatment response. Here, we introduce OncoBERT, a language model that learns contextual representations of somatic mutations from large-scale clinical sequencing data spanning >210,000 patients, 113 cancer types and 20 institutions. OncoBERT uncovers robust patient-specific mutational subtypes across diverse cohorts and targeted sequencing panels, revealing clinically meaningful mutation patterns that are associated with differential response to chemotherapy, targeted therapies, and immunotherapy. Importantly, integrating OncoBERTs contextual representations with clinically approved biomarkers of immunotherapy response, such as tumor mutational burden (TMB) and microsatellite instability (MSI), significantly improved prediction of clinical benefit. By further incorporating matched tumor transcriptomic profiles, we linked OncoBERT-defined mutational subtypes to distinct cancer hallmark programs and tumor microenvironment states. Together, OncoBERT provides a scalable framework for deciphering somatic mutational landscapes, enabling improved patient stratification and advancing precision oncology.

genomics↗

bioRxiv2025

Towards interpretable molecular and spatial analysis of the tumor microenvironment from digital histopathology images with HistoTME-v2

The tumor microenvironment (TME) is a critical focus for biomarker discovery and therapeutic targeting in cancer. However, widespread clinical adoption of TME profiling is hindered by the high cost and technical complexity of current platforms such as spatial transcriptomics and proteomics. Artificial Intelligence (AI)-based analysis of the TME from routine Hematoxylin & Eosin (H&E)-stained pathology slides presents a promising alternative. Yet, most existing deep learning approaches depend on extensive high-quality single-cell or patch-level annotations, which are labor-intensive and costly to generate. To address these limitations, we previously introduced HistoTME, a weakly supervised deep learning framework that predicts the activity of cell type-specific transcriptomic signatures directly from whole slide H&E images of non-small cell lung cancer. This enables rapid, high throughput analysis of the TME composition from whole slide H&E images (WSI) without the need for segmenting and classifying individual cells. In this work, we present HistoTME-v2, a pan-cancer extension of HistoTME, applied across 25 solid tumor types, substantially broadening the scope of prior efforts. HistoTME-v2 demonstrates high accuracy for predicting cell type-specific transcriptomic signature activity from H&E images, achieving a median Pearson correlation of 0.61 with ground truth measurements in internal cross- validation on The Cancer Genome Atlas (TCGA), encompassing 7,586 WSIs, 6,901 patients, and 24 cancer types, and a median Pearson correlation of 0.53 on external validation datasets spanning 5,657 WSIs, 1,775 patients and 9 cancer types. Furthermore, HistoTME- v2 resolves the spatial distribution of key immune and stromal cell types, exhibiting strong spatial concordance with single-cell measurements derived from multiplex imaging (CODEX, IHC) as well as Visium spatial transcriptomics, spanning 259 WSI, 154 patients, and 7 cancer types. Overall, across both bulk and spatial settings, HistoTME-v2 significantly outperforms baselines, positioning it as a robust, interpretable and cost-efficient tool for TME profiling and advancing the integration of spatial biology into routine pathology workflows.

pathology↗

bioRxiv2023

Deactivation of ligand-receptor interactions enhancing lymphocyte infiltration drives melanoma resistance to Immune Checkpoint Blockade

Immune checkpoint blockade (ICB) is a promising cancer therapy; however, resistance often develops. To learn more about ICB resistance mechanisms, we developed IRIS (Immunotherapy Resistance cell-cell Interaction Scanner), a machine learning model aimed at identifying candidate ligand-receptor interactions (LRI) that are likely to mediate ICB resistance in the tumor microenvironment (TME). We developed and applied IRIS to identify resistance-mediating cell-type-specific ligand-receptor interactions by analyzing deconvolved transcriptomics data of the five largest melanoma ICB therapy cohorts. This analysis identifies a set of specific ligand-receptor pairs that are deactivated as tumors develop resistance, which we refer to as resistance deactivated interactions (RDI). Quite strikingly, the activity of these RDIs in pre-treatment samples offers a markedly stronger predictive signal for ICB therapy response compared to those that are activated as tumors develop resistance. Their predictive accuracy surpasses the state-of-the-art published transcriptomics biomarker signatures across an array of melanoma ICB datasets. Many of these RDIs are involved in chemokine signaling. Indeed, we further validate on an independent large melanoma patient cohort that their activity is associated with CD8+ T cell infiltration and enriched in hot/brisk tumors. Taken together, this study presents a new strongly predictive ICB response biomarker signature, showing that following ICB treatment resistant tumors turn inhibit lymphocyte infiltration by deactivating specific key ligand-receptor interactions.

bioinformatics↗

bioRxiv2021

Deconvolving clinically relevant cellular immune crosstalk from bulk gene expression using CODEFACS and LIRICS

The tumor microenvironment (TME) is a complex mixture of cell types whose interactions affect tumor growth and clinical outcome. To discover such interactions, we developed CODEFACS (COnfident DEconvolution For All Cell Subsets), a tool deconvolving cell-type-specific gene expression in each sample from bulk expression, and LIRICS (LIgand Receptor Interactions between Cell Subsets), a statistical framework prioritizing clinically relevant ligand-receptor interactions between cell types from the deconvolved data. We first demonstrate the superiority of CODEFACS versus the state-of-the-art deconvolution method, CIBERSORTx. Second, analyzing the TCGA, we uncover cell-type-specific interactions of mismatch-repair-deficient tumors that are associated with their higher anti-PD1 response rates, including specific T-cell co-stimulating interactions that enhance immunotherapy response independently of the tumors mutation burden levels. Finally, we identify a subset of ligand-receptor interactions in the melanoma TME that predict patient response to anti-PD1 therapy better than recently published transcriptomics-based methods.

bioinformatics↗

bioRxiv2019

Cancer-type specific aneuploidies hard-wire chromosome-wide gene expression patterns of their tissue of origin

Most carcinomas have characteristic chromosomal aneuploidies specific to the tissue of tumor origin. The reason for this specificity is unknown. As aneuploidies directly affect gene expression, we hypothesized that cancer-type specific aneuploidies, which emerge at early stages of tumor evolution, confer adaptive advantages to the physiological requirements of the tissue of origin. To test this hypothesis, we compared chromosomal aneuploidies reported in the TCGA database to chromosome arm-wide gene expression levels of normal tissues from the GTEx database. We find that cancer-type specific chromosomal aneuploidies mirror differential gene expression levels specific to the respective normal tissues which cannot be explained by copy number alterations of resident cancer driver genes. We propose that cancer-type specific aneuploidies "hard-wire" chromosome arm-wide gene expression levels present in normal tissues, favoring clonal expansion and tumorigenesis. One sentence summaryThe clonal evolution of cancer is initiated by tissue-specific transcriptional requirements

cancer biology↗