Key result
A 1,000-CpG methylation signature classifies cancer types with ~95% accuracy.
Why the study?
Cancers of unknown primary often result in empirical chemotherapy and poor outcomes, prompting the development of a prediction model using CpG methylation signatures to enhance diagnostic accuracy and tumor classification.
Observational (n=7,507)
Yes
Effect estimate: AUC 0.998
A CpG-based methylation signature using ridge regression provides highly accurate multi-class cancer classification, potentially improving the diagnosis of cancers of unknown primary.
Background: Cancers of unknown primary (CUP) are metastatic malignancies where the primary site cannot be identified, often resulting in empirical chemotherapy and poor outcomes. DNA methylation profiling has emerged as a promising tool for improving tumor identification and classification. By leveraging unique methylation signatures, this approach can enhance diagnostic accuracy and guide personalized treatment strategies. Objective: To develop and validate a prediction model for cancer type classification based on a focused set of CpG sites. Methods: Methylation data (Infinium HumanMethylation450) from 7,476 patients across 21 cancer types were obtained from TCGA and other public datasets. Data were divided into training and test cohorts. A hybrid feature selection approach combining Shapley values and gradient boosting was applied to identify CpG regions, and model performance was evaluated on the test cohort. Louvain clustering based on selected CpG profiles was used to explore tumor phenotypes and assess associations between heterogeneity and prediction performance. Independent validation was performed using Infinium MethylationEPIC v2.0 data from 31 cases representing 17 cancer types from our institution. Results: A total of 1,000 CpG regions were selected. Ridge regression achieved the best performance among tested models, with classification accuracy (CA) of 95.4%, AUC of 0.998, F1 score of 0.953, and Matthews correlation coefficient (MCC) of 0.951 averaged across classes in the training cohort. Performance on the test cohort was 94.7% CA, 0.998 AUC, 0.945 F1, and 0.943 MCC, while independent validation yielded 87.1% CA, 0.9993 AUC, 0.847 F1, and 0.867 MCC. Unsupervised analysis revealed 20 distinct Louvain clusters, highlighting heterogeneity across cancer types. Although heterogeneity and purity correlated with MCC, regression analysis did not confirm an independent predictive effect after adjusting for confounders. Conclusion: A CpG-based methylation signature combined with ridge regression enables highly accurate multi-class cancer classification and demonstrates strong generalizability across platforms. These findings support the clinical utility of methylation profiling for CUP diagnosis and personalized treatment planning. Citation Format: Marco A. De Velasco, Kazuko Sakai, Daiki Nakatsu, Seiichiro Mitani, Shuji Minamoto, Takahiro Haeno, Hidetoshi Hayashi, Kazuto Nishio. Leveraging CpG methylation signatures for robust multi-class cancer classification across platforms [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3869.
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Velasco et al. (2026) conducted an observational in Cancers of unknown primary (CUP) (n=7,507). CpG-based methylation signature with ridge regression was evaluated on Cancer type classification accuracy (AUC 0.998). A 1,000-CpG methylation signature using ridge regression accurately classified cancer types, achieving 94.7% accuracy in the test cohort and 87.1% (AUC 0.9993) in an independent validation cohort.
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