Cerhan, J. R. | bioRxiv Science

bioRxiv2026

Functional Evaluation of TRPC6 Missense Variants in Cancer Patients via Molecular Docking Analysis Compared with Patch Clamp Electrophysiology

Gain-of-function mutations in the transient receptor potential 6 (TRPC6) channel have recently been recognized as risk factors for both doxorubicin (DOX)-induced cardiomyopathy. Functional evaluation of TRPC6 missense variants is therefore important for cancer patients undergoing anthracycline treatment. However, traditional electrophysiological methods are labor-intensive and time-consuming. In this study, we compared the functional responses of TRPC6 missense variants to 1-oleoyl-2-acetyl-sn-glycerol (OAG), a TRPC6 agonist, using molecular docking and patch clamp recording techniques. For the wild-type (WT) TRPC6 structure (PDB ID: 6UZ8), OAG exhibited a binding energy of -4.49 kcal/mol and a dissociation constant (Kd) of 0.511 mM. Twenty TRPC6 missense variants were identified from cancer patients in the Mayo Clinic database. Of these, fifteen variants had resolvable structures, nine of which displayed increased Kd values and six decreased Kd values compared to WT in molecular docking analysis. Patch clamp recordings revealed that TRPC6 WT and mutant channels were inactive at baseline but were activated upon 50 M OAG stimulation, except two loss-of-function variants. Moreover, a 24-h treatment with 0.5 M DOX significantly enhanced OAG-induced channel activation. All three variants identified in patients with heart failure demonstrated gain-of-function properties in both electrophysiological measurements and in-silico predictions. Importantly, the results obtained from molecular docking and patch clamp recordings were strongly correlated, showing an 82% concordance, higher than the predictions from AlphaMissense. These findings indicate that our computational analysis provides a rapid and reliable method for predicting the functional impact of TRPC6 missense variants, which may aid clinical decision-making in cancer patients receiving chemotherapy.

physiology↗

bioRxiv2025

SensitiveCancerGPT: Leveraging Generative Large Language Model on Structured Omics Data to Optimize Drug Sensitivity Prediction

ObjectiveThe fast accumulation of vast pharmacogenomics data of cancer cell lines provide unprecedented opportunities for drug sensitivity prediction (DSP), a crucial prerequisite for the advancement of precision oncology. Recently, Generative Large Language Models (LLM) have demonstrated performance and generalization prowess across diverse tasks in the field of natural language processing (NLP). However, the structured format of the pharmacogenomics data poses challenge for the utility of LLM in DSP. Therefore, the objective of this study is multi-fold: to adapt prompt engineering for structured pharmacogenomics data toward optimizing LLMs DSP performance, to evaluate LLMs generalization in real-world DSP scenarios, and to compare LLMs DSP performance against that of state-of-the-science baselines. MethodsWe systematically investigated the capability of the Generative Pre-trained Transformer (GPT) as a DSP model on four publicly available benchmark pharmacogenomics datasets, which are stratified by five cancer tissue types of cell lines and encompass both oncology and non-oncology drugs. Essentially, the predictive landscape of GPT is assessed for effectiveness on the DSP task via four learning paradigms: zero-shot learning, few-shot learning, fine-tuning and clustering pretrained embeddings. To facilitate GPT in seamlessly processing the structured pharmacogenomics data, domain-specific novel prompt engineering is employed by implementing three prompt templates (i.e., Instruction, Instruction-Prefix, Cloze) and integrating pharmacogenomics-related features into the prompt. We validated GPTs performance in diverse real-world DSP scenarios: cross-tissue generalization, blind tests, and analyses of drug-pathway associations and top sensitive/resistant cell lines. Furthermore, we conducted a comparative evaluation of GPT against multiple Transformer-based pretrained models and existing DSP baselines. ResultsExtensive experiments on the pharmacogenomics datasets across the five tissue cohorts demonstrate that fine-tuning GPT yields the best DSP performance (28% F1 increase, p-value= 0.0003) followed by clustering pretrained GPT embeddings (26% F1 increase, p-value= 0.0005), outperforming GPT in-context learning (i.e., few-shot). However, GPT in the zero-shot setting had a big F1 gap, resulting in the worst performance. Within the scope of prompt engineering, performance enhancement was achieved by directly instructing GPT about the DSP task and resorting to a concise context format (i.e., instruction-prefix), leading to F1 performance gain of 22% (p-value=0.02); while incorporation of drug-cell line prompt context derived from genomics and/or molecular features further boosted F1 score by 2%. Compared to state-of-the-science DSP baselines, GPT significantly asserted superior mean F1 performance (16% gain, p-value<0.05) on the GDSC dataset. In the crosstissue analysis, GPT showcased comparable generalizability to the within-tissue performances on the GDSC and PRISM datasets, while statistically significant F1 performance improvements on the CCLE (8%, p-value=0.001) and DrugComb (19%, p-value=0.009) datasets. Evaluation on the challenging blind tests suggests GPTs competitiveness on the CCLE and DrugComb datasets compared to random splitting. Furthermore, analyses of the drug-pathway associations and log probabilities provided valuable insights that align with previous DSP findings. ConclusionThe diverse experiment setups and in-depth analysis underscore the importance of generative LLM, such as GPT, as a viable in silico approach to guide precision oncology. Availabilityhttps://github.com/bioIKEA/SensitiveCancerGPT

bioinformatics↗