1Developing a blood-based test for diagnosis, prognosis, and monitoring of cholangiocarcinoma

Background and Aims

There are currently no efficient tools for the diagnosis, prognosis, and monitoring of cholangiocarcinoma (CCA), even in high-risk populations such as patients with Primary Sclerosing Cholangitis (PSC). These tumors are rare, often diagnosed at a late stage with a poor survival rate. Current surveillance methods for early detection of CCA in PSC, using magnetic resonance imaging (MRI) and CA19-9 biomarker testing, have limited accuracy. Cancer-associated DNA methylation is changing during the cancer development. We hypothesize that such alterations are detectable in the peripheral blood of people a with CCA even at early disease stages and represent a promising non-invasive cancer detection method implementable clinically. In this study, we aimed to develop and validate a blood-derived DNA methylation-based early detection test of CCA.

Methods

DNA was purified from the whole blood of 394 individuals including CCA, gallbladder cancer, PSC with and without CCA and healthy donors. Initially, a pilot cohort (n = 109) was analyzed using Illumina’s Infinum EPIC I array (850 K DNA methylation sites). Using statistical and machine learning methods, we identified differential DNA methylation sites associated with CCA as compared to PSC and healthy individuals.

Subsequently, and thanks to funds from the Cholangiocarcinoma Foundation Fellowship program, we confirmed our findings in a larger cohort (n = 285), analyzed using Illumina’s Infinum EPIC II array (900 K sites). This validation cohort included external samples from Norway (n = 40) and longitudinal samples (n = 25) from people living with PSC with a follow-up of 5 to 13 years, with and without BTC development. Then, the validated methylation sites were used to build a machine learning model for accurate diagnosis of CCA. The efficacy of the test was assessed using area under the curve, sensitivity, specificity, root squared error, and precision recall.

Results

Genome-wide DNA methylation profiling of CCA revealed significant differentially methylated sites and regions related to CCA in PSC and non-PSC individuals. Around 2500 CCA-associated methylation sites were discovered on the pilot cohort then validated. First, a CCA early detection test in PSC and non-PSC individuals was developed using 100 DNA methylation sites. This non-invasive test offered greater accuracy (AUC = 0.97) than current surveillance methods and can be routinely repeated in high-risk individuals. Second, using enrichment analysis, we identified genes (e.g., GNAS), transcription factors, and pathways (e.g., MAPK, Ras and Rap1 signaling pathways) involved in CCA, that might have implications for disease monitoring.

Conclusion

Overall, this study shows that DNA methylation profiling of peripheral blood provides a reliable, non-invasive approach for both detecting CCA and monitoring disease progression, offering a promising tool for clinical implementation.