Deviance-based approach to detect cancer fragments in plasma using methylated sequencing targets

Music City Center 

Background: Differentially methylated regions (DMRs) that distinguish cancer patients from non-cancer controls have been identified in tissue. Detection of these cancer-specific DMRs in plasma is challenging due to low bioavailability, thus prompting investigation into identifying DNA fragments with a high likelihood of originating from tumor. Methods: We fit a generalized additive model (GAM) to the percent of methylated fragments in non-cancer controls to estimate an expected methylation profile for 432 DMRs. A centered and scaled deviance score based on the fitted model is calculated for each DMR and used to compare 144 cancer plasma samples representing 8 cancer subtypes versus 71 controls. Results: Of 432 DMRs tested, 49 had p-values < 0.005. Combining all DMRs within a random forest model achieved an out-of-bag prediction AUC of 0.74 for discriminating cases from controls. Conclusion: Future evaluations with training and test sets consisting of >5000 DMRs is underway with the expectation of improving the prediction accuracy for cancer detection and cancer sub-type in plasma. This modeling approach may enhance multicancer detection efforts in cancer screening paradigms.

methylation



deviance

generalized additive models

prediction 

Section on Statistics in Genomics and Genetics