Integrative Multi-Model Analysis Identifies a Core Gene Signature Associated with WEE1 Inhibitor Resistance, the Resistance-Associated Tumor Microenvironment, and Therapeutic Vulnerability in Ovarian Cancer
Chuntian Lei, Jiaqiang Xiong, Yurou Chen, Jiahui Zhao, Ruinan Song, Yusong Xue, Hayisha Ayihen, Feng Li, Wei Zhang, Yuan Yuan, Rourou XiaoBackground:
Ovarian cancer is the most lethal gynecological malignancy, and acquired resistance to chemotherapy and targeted therapy is a central cause of treatment failure. The WEE1 inhibitor AZD1775 (adavosertib) has shown clinical activity in ovarian cancer, particularly in tumors with TP53 dysfunction, but acquired resistance frequently emerges, and its molecular basis remains incompletely understood. This study aims to identify a conserved, cross-species gene signature associated with the AZD1775-resistant state, to characterize its relationship with the Tumor Immune Microenviron-ment (TIME) and patient prognosis, and to nominate candidate drugs for combination testing.
Methods:
Transcriptomic data from a self-established murine AZD1775-resistance model (ID8 vs ID8R; RNA-seq, GSE313926) and from publicly available human AZD1775-resistant ovarian cancer cells (ES-2 vs ES-2R clones; GSE142124) were integrated. Differentially Expressed Genes (DEGs) were intersected across models, Weighted Gene Co-Expression Network Analysis (WGCNA) was applied to the ID8/ID8R data, and a Protein–Protein Interaction (PPI) network was used to prioritize connected candidates. In the TCGA Ovarian Cancer (TCGA-OV) cohort, ESTIMATE and five immune deconvo-lution algorithms (CIBERSORT, EPIC, TIMER, MCP-counter, and quanTIseq) were used to estimate stromal/immune admixture. The prognostic associations were assessed in Kaplan-Meier Plotter. Candi-date drugs were prioritized from CellMiner using a pre-specified effect-size threshold, and drug combi-nations with AZD1775 were tested in ID8R cells by CCK-8 with Chou–Talalay analysis.
Results:
A nine-gene signature (VCAN, RNASEL, PROS1, TNFRSF9, PXDN, KCNH1, KLF11, ATF3, and CDK15) was consistently upregulated across resistant models and within the resistance-associated WGCNA module. ESTIMATE and deconvolution analyses associated the signature with stromal/CAF and endothelial compartments, with weaker, method-dependent associations with immune-cell fractions. The nine-gene score was associated with overall survival, with heterogeneous direction for individual genes (VCAN, ATF3, CDK15, KLF11, and PXDN adverse; TNFRSF9, KCNH1, RNASEL protective). CellMiner drug sensitivity prediction and in vitro validation revealed that cell lines with high expression of the core gene set were more sensitive to Bleomycin, Lenvatinib, and Dasatinib; combinations with AZD1775 produced additive-to-synergistic effects under the standard Chou–Talalay grading (CI < 0.9) in ID8R. These agents are nominated as candidate combination partners for experimental testing.
Discussion:
Cross-species integration of a murine isogenic resistance pair with independent human re-sistant clones identifies a nine-gene panel associated with the AZD1775-resistant state and with re-sistance-coupled stromal–myeloid remodeling, adverse prognosis and differential drug sensitivity in ovarian cancer. Because the panel was derived by association and several of its genes are predominantly expressed by stromal, myeloid, and activated immune cells rather than malignant cells, it should be regarded as a cross-compartment, resistance-associated marker rather than a set of causal drivers; func-tional perturbation and validation in independent resistant models are required. Nevertheless, the panel may help stratify patients for WEE1-inhibitor therapy and prioritize candidate combination partners for experimental testing.
Conclusion:
Cross-species integration nominates a nine-gene signature associated with the AZD1775-resistant state and resistance-coupled stromal–myeloid remodeling in ovarian cancer. The signature is a candidate marker of resistance-associated states and of drug response.