Sharma Sudhanshu, Singh Rajesh, Kant Shiva, Mishra Manoj K
Cancer Research Center, Department of Biological Sciences, Alabama State University, Montgomery, AL, 36104, USA.
Microbiology, Biochemistry, and Immunology, Cancer Health Equity Institute, Morehouse School of Medicine, Atlanta, GA, USA.
Comput Biol Med. 2025 Jul;193:110432. doi: 10.1016/j.compbiomed.2025.110432. Epub 2025 May 26.
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.
胰腺导管腺癌（PDAC）仍然是最致命的恶性肿瘤之一，尽管有当前的治疗方法，其五年生存率仍低于10%。侵袭性的肿瘤生物学特性、限制药物递送和免疫细胞浸润的促结缔组织增生性基质以及对细胞凋亡的深度抗性使得其治疗更加复杂。在这里，我们描述了一个多层系统生物学和药物发现流程，该流程整合了大量基因组学、单细胞空间转录组学、蛋白质组学、竞争性内源RNA（ceRNA）网络分析以及深度学习驱动的定量构效关系（QSAR）建模。通过实施这个流程，我们预测TNFRSF10A编码TRAILR1死亡受体，是PDAC的一个潜在治疗靶点。突变和表达分析也证实TNFRSF10A是PDAC中的一个推定靶点。PDAC微环境中的癌细胞表现出异常升高的TNFRSF10A表达。免疫排除的肿瘤微环境和促生存信号传导与这种升高的表达相关。使用基于先进Transformer的深度学习方法SELFormer，结合基于QSAR分析的虚拟筛选，我们鉴定出了以前未被探索的FDA批准药物和天然化合物，即替西罗莫司、麦角胺和卡匹西利，它们具有潜在的TRAILR1调节作用。在分子动力学模拟中，这些重新利用的候选药物在300纳秒内对TNFRSF10A表现出最高的结合亲和力。它们显示出有利的结合能（MM-PBSA）、最小的RMSD漂移、主成分分析和溶剂可及表面积。我们提出TNFRSF10A是一个在治疗上重要的PDAC易损靶点，其由空间分辨的表达模式和动态分子建模所塑造。这项研究使用了人工智能实现的化学建模、高通量筛选和多组学方法的新颖整合，来揭示并从药理学上靶向一种众所周知的耐药癌症中特定于癌症微环境的弱点。
