Integrative In Silico Identification of TP53-Associated Drug Repurposing Candidates in Lung Adenocarcinoma

Background/Aim: Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer and is characterized by high genetic heterogeneity and poor prognosis. TP53 is the most frequently mutated gene in LUAD and plays a critical role in tumor initiation, progression, and therapeutic resistance. The present study aimed to prioritize TP53-associated drug repurposing candidates in LUAD using an integrative in silico approach. Materials and Methods: A total of 1309 TP53-associated compounds were retrieved from the Gene2Drug database. Drug sensitivity profiles of lung adenocarcinoma cell lines were evaluated using PRISM Repurposing Public 22Q2 viability data obtained from the DepMap platform. Candidate compounds were ranked according to Gene2Drug significance scores (p < 1 × 10−3), and compounds with concordant sensitivity patterns in PRISM data were prioritized. Results: LUAD cell lines showed the strongest sensitivity to atropine (p = 6.83 × 10−5). Additionally, LUAD cell lines displayed consistent sensitivity signals for dropropizine (p = 8.47 × 10−3), terazosin (p = 1.11 × 10−3), morantel (p = 9.05 × 10−3), netilmicin (p = 8.37 × 10−3), altretamine (p = 9.82 × 10−3), and perphenazine (p = 9.58 × 10−3). These findings indicate that several non-oncology drugs exhibit distinct and reproducible sensitivity profiles in LUAD cell lines. Conclusions: Based on TP53-associated drug sensitivity profiles, this in silico analysis identifies atropine among the prioritized candidates, showing the strongest TP53-associated sensitivity signal in LUAD cell lines. Although experimental validation is required, the integration of independent computational datasets provides a robust framework for candidate prioritization and our findings provide a rationale for further preclinical investigation of atropine and related compounds in LUAD.