Figure 4 from Inferring Drug–Gene Relationships in Cancer Using Literature-Augmented Large Language Models
Year: 2025
Type: other
Abstract: Drug–target network in liver cancer. A, Drug–target network in liver cancer. The network was constructed by extracting drug–gene pairs associated with liver cancer from Fig. 3A, in which liver cancer was identified as a primary cancer type among multiple possible types generated through GPT-4o inference. The node size and font size are proportional to the degree (i.e., the number of connections). Drugs and genes discussed in the main text are highlighted in bold. B, Kaplan–Meier curves for progression-free survival of patients with HCC, the major type of liver cancer, treated with sorafenib in the IMBrave150 clinical trial, comparing CTNNB1-mutated (CTNNB1-Mut; n = 19) and CTNNB1 wild-type patients (CTNNB1-WT; n = 26). Statistical significance was assessed by the log-rank test. C, Boxplots showing differential sensitivity to sorafenib between CTNNB1-mutated (CTNNB1-Mut; n = 2) and CTNNB1 wild-type liver cancer cell lines (CTNNB1-WT; n = 18). Drug sensitivity data are represented as the log2 fold change in cell counts following treatment, derived from the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) drug screening project. Statistical significance was assessed by the one-tailed Mann–Whitney U test. D, Correlation between CTNNB1 genetic dependency (represented by the Chronos gene-effect score from DepMap) and sorafenib sensitivity (PRISM) in liver cancer cell lines (n = 18). A more negative gene-effect score represents a stronger genetic dependency. The names of cell lines exhibiting strong dependency on CTNNB1 (with gene-effect scores < −1) are labeled.
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4410344010