Pilot study demonstrating changes in DNA hydroxymethylation enable detection of multiple cancers in plasma cell-free DNA

Our study employed the detection of 5-hydroxymethyl cytosine (5hmC) profiles on cell free DNA (cfDNA) from the plasma of cancer patients using a novel enrichment technology coupled with sequencing and machine learning based classification method. These classification methods were develoiped to detect the presence of disease in the plasma of cancer and control subjects. Cancer and control patient cfDNA cohorts were accrued from multiple sites consisting of 48 breast, 55 lung, 32 prostate and 53 pancreatic cancer subjects. In addition, a control cohort of 180 subjects (non-cancer) was employed to match cancer patient demographics (age, sex and smoking status) in a case-control study design. Logistic regression methods applied to each cancer case cohort individually, with a balancing non-cancer cohort, were able to classify cancer and control samples with measurably high performance. Measures of predictive performance by using 5-fold cross validation coupled with out-of-fold area under the curve (AUC) measures were established for breast, lung, pancreatic and prostate cancer to be 0.89, 0.84, 0.95 and 0.83 respectively. The genes defining each of these predictive models were enriched for pathways relevant to disease specific etiology, notably in the control of gene regulation in these same pathways. The breast cancer cohort consisted primarily of stage I and II patients, including tumors < 2 cm and these samples exhibited a high cancer probability score. This suggests that the 5hmC derived classification methodology may yield epigenomic detection of early stage disease in plasma. Same observation was made for the pancreatic dataset where >50% of cancers were stage I and II and showed the highest cancer probability score.