e13510 Background: Comprehensive genomic profiling (CGP) has played key roles in cancer precision medicine through optimization of therapeutic interventions based on genomic alterations in cancer cells. However, the probability of discovering mutation-based treatments through CGP remains low. To enhance the effectiveness and efficiency of cancer precision medicine, it is crucial to identify patients who are likely to benefit from CGP tests. This study aims to identify characteristics of patients in which mutation-based treatments are discovered by CGP tests. Methods: We retrospectively analyzed data from 60,655 patients who underwent CGP tests and were registered in the Center for Cancer Genomics and Advanced Therapeutics (C-CAT), the national data center for cancer CGP in Japan. The C-CAT database covers 99.7% of cancer patients who have undergone CGP tests in Japan. Major cancer types included 10,182 cases of bowel cancer, 8,691 pancreas cancer, 5,062 biliary tract cancer, and 3,777 breast cancer. We developed an eXtreme Gradient Boosting (XGBoost) model using machine learning, and used clinical information as input to predict whether one or more Japanese Pharmaceuticals and Medical Devices Agency (PMDA)-approved drugs are discovered through CGP tests or not. Shapley Additive Explanations (SHAP) was employed to extract significant features that contribute to the model prediction. Results: The prediction model achieved an area under the receiver operating characteristic curve of 0.826 for the overall cancer population. Positive SHAP values were observed for patients with breast (mean SHAP in breast cancer patients: 1.38), lung (1.08), bowel (0.75), and pancreas cancers (0.35), while negative SHAP values were associated with head and neck (-1.75), cervical cancers (-1.54), and brain (-1.33). Positive SHAP values were also associated with presence of liver or lymph node metastasis (0.23, 0.08), shorter intervals between diagnosis and CGP testing or specimen collection and CGP testing, and advanced age. Similar trends were observed in cancer-type-specific prediction models, which also identified their own unique features. In the adolescent and young adult (AYA) age group, primary brain tumors were strongly associated with negative SHAP values (-1.37). Conclusions: Our machine learning-based analysis of nationwide CGP data identified features that predict cases in which mutation-based treatments are discovered by CGP tests, both in the overall cancer population and within specific cancer types and the AYA age group. Expedited CGP testing is recommended for patients who match the identified profile to facilitate early targeted therapy interventions.
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