Integrating AI/ML and multi-omics approaches to investigate the role of TNFRSF10A/TRAILR1 and its potential targets in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a five-year survival of under 10 % despite current therapies. Aggressive tumor biology, a desmoplastic stroma that limits drug delivery and immune cell infiltration, and profound resistance to apoptosis make it more complex to treat. Here, we describe a multi-layered system biology and drug discovery pipeline that integrates bulk genomics, single-cell spatial transcriptomics, proteomics, competing endogenous RNA (ceRNA) network analysis, and deep learning-driven quantitative structure-activity relationship (QSAR) modeling. By implementing this pipeline, we predicted that TNFRSF10A encodes for the TRAILR1 death receptor as a potential therapeutic target in PDAC. Mutational and expressional analysis also confirmed TNFRSF10A as a putative target in PDAC. Cancer cells within the PDAC microenvironment exhibit aberrantly elevated TNFRSF10A expression. Immune-excluded tumor niches and pro-survival signaling link this elevated expression. Using an advanced transformer-based deep learning approach, SELFormer, combined with QSAR analysis-based virtual screening, we identified previously unexplored FDA-approved drugs and natural compounds, i.e., Temsirolimus, Ergotamine, and capivasertib, with potential TRAILR1 modulatory effects. During molecular dynamics simulations, these repurposed candidates showed the highest binding affinities against TNFRSF10A for 300 ns. These showed favorable binding energies (MM-PBSA), minimal RMSD drift, PCA, and SASA. We propose TNFRSF10A as a therapeutically important PDAC vulnerability nurtured by spatially resolved expression patterns and dynamic molecular modeling. This study has used a novel integration of AI-implemented chemical modeling, high-throughput screening, and a multi-omics approach to unravel and pharmacologically target a cancer compartment-specific weakness in a notoriously drug-resistant cancer.

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Publication Details

Journal
Computers in Biology and Medicine
Published
2025-05-26
DOI
https://doi.org/10.1016/j.compbiomed.2025.110432
Citations
4
Primary Topic
Biomarkers in Disease Mechanisms
Type
article
Field-Weighted Citation Impact
3.19
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article

Integrating AI/ML and multi-omics approaches to investigate the role of TNFRSF10A/TRAILR1 and its potential targets in pancreatic cancer

Sudhanshu Sharma, Manoj K. Mishra, Rajesh Singh, Shiva Kant
4 citations
Computers in Biology and Medicine
Biomarkers in Disease Mechanisms
3.19
article

Integrating AI/ML and multi-omics approaches to investigate the role of TNFRSF10A/TRAILR1 and its potential targets in pancreatic cancer

Sudhanshu Sharma, Manoj K. Mishra, Rajesh Singh, Shiva Kant
article en
4 citations

Abstract

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a five-year survival of under 10 % despite current therapies. Aggressive tumor biology, a desmoplastic stroma that limits drug delivery and immune cell infiltration, and profound resistance to apoptosis make it more complex to treat. Here, we describe a multi-layered system biology and drug discovery pipeline that integrates bulk genomics, single-cell spatial transcriptomics, proteomics, competing endogenous RNA (ceRNA) network analysis, and deep learning-driven quantitative structure-activity relationship (QSAR) modeling. By implementing this pipeline, we predicted that TNFRSF10A encodes for the TRAILR1 death receptor as a potential therapeutic target in PDAC. Mutational and expressional analysis also confirmed TNFRSF10A as a putative target in PDAC. Cancer cells within the PDAC microenvironment exhibit aberrantly elevated TNFRSF10A expression. Immune-excluded tumor niches and pro-survival signaling link this elevated expression. Using an advanced transformer-based deep learning approach, SELFormer, combined with QSAR analysis-based virtual screening, we identified previously unexplored FDA-approved drugs and natural compounds, i.e., Temsirolimus, Ergotamine, and capivasertib, with potential TRAILR1 modulatory effects. During molecular dynamics simulations, these repurposed candidates showed the highest binding affinities against TNFRSF10A for 300 ns. These showed favorable binding energies (MM-PBSA), minimal RMSD drift, PCA, and SASA. We propose TNFRSF10A as a therapeutically important PDAC vulnerability nurtured by spatially resolved expression patterns and dynamic molecular modeling. This study has used a novel integration of AI-implemented chemical modeling, high-throughput screening, and a multi-omics approach to unravel and pharmacologically target a cancer compartment-specific weakness in a notoriously drug-resistant cancer.

Computers in Biology and MedicineVol. 193
Alabama State University (US), Cancer Research Center (US), Morehouse School of Medicine (US), Auburn University at Montgomery (US)
Good health and well-being
Openalex Percentile: Top 8%
Biomarkers in Disease Mechanisms
3.19
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