Conventional chemotherapeutics exploit cancer hallmark of active cell cycling, primarily targeting mitotic cells. Consequently, the mitotic index (MI), representing the proportion of cells in mitosis, serves as both a prognostic biomarker for cancer progression and a predictive marker for chemo-responsiveness. In this study, we developed a transcriptome signature to predict the chemotherapeutic responsiveness based on the Active Mitosis Signature Enrichment Score (AMSES), a computational metric previously established to estimate the active mitosis using multi-omics data from The Cancer Genome Atlas (TCGA) lung cancer cohorts, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) patients. Leveraging advanced machine learning techniques, we enhanced the predictive power of AMSES and developed AMSES for chemo-responsiveness, termed A4CR. Comparative analysis revealed a strong correlation between A4CR and the MI of 69 cases from separated non-small cell lung cancer (NSCLC) cohort. The utility of A4CR as a therapeutic biomarker was validated through in silico analysis of public datasets, encompassing transcriptomic profiles of cancer cell lines (CCLs) and their corresponding multiple drug response data as well as clinicogenomic data from TCGA. These findings highlight the potential of integrating gene signatures with machine learning and large-scale datasets to advance precision oncology and improve therapeutic decision-making for cancer patients.

The authors have declared no competing interest.

This work was supported by the ABC-based Regenerative BioTherapeutics (ABC project) grant (RS-2024-00432867) and ERC grant (RS-2023-00218543) funded by the Korea government (the Ministry of Health & Welfare)

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