TRON tumor-naïve ctDNA detection
Circulating tumor DNA (ctDNA) detection offers non-invasive monitoring of cancer from blood samples. While tumor-informed approaches are sensitive, their clinical application is limited by cost, tissue availability, and turnaround time. In contrast, tumor-naïve assays are more rapid and broadly applicable but often sacrifice sensitivity. Here, we present DEEPctMUT, a computational pipeline for tumor-naïve ctDNA detection that integrates unique molecular identifiers (UMIs) with three complementary error-polishing strategies: (1) machine learning-based sequencing error suppression, (2) deep learning-based background noise filtering (DeepES), and (3) removal of clonal hematopoietic mutations using matched peripheral blood. We designed an efficient sequencing panel and developed and validated DEEPctMUT using cell lines, spike-in samples, healthy and CRC plasma samples. DEEPctMUT accurately detected mutations down to a 0.03% VAF, outperforming other tumor-naïve methods. In a head-to-head comparison, our pipeline identified pre-surgical CRC cases with 100% accuracy, whereas the Roche Avenio Surveillance Kit achieved only 50% sensitivity. Furthermore, a highly versatile panel-independent model in DEEPctMUT could improve existing Roche Avenio performance without further Avenio specific training. We make the extensive ground truth data and the fully adaptable DEEPctMUT pipeline and models available.  Our findings underscore that advanced error polishing methods can effectively compensate for the absence of matched tumor information, enabling sensitive ctDNA detection.
Circulating tumor DNA (ctDNA) detection offers non-invasive monitoring of cancer from blood samples. While tumor-informed approaches are sensitive, their clinical application is limited by cost, tissue availability, and turnaround time. In contrast, tumor-naïve assays are more rapid and broadly applicable but often sacrifice sensitivity. Here, we present DEEPctMUT, a computational pipeline for tumor-naïve ctDNA detection that integrates unique molecular identifiers (UMIs) with three complementary error-polishing strategies: (1) machine learning-based sequencing error suppression, (2) deep learning-based background noise filtering (DeepES), and (3) removal of clonal hematopoietic mutations using matched peripheral blood. We designed an efficient sequencing panel and developed and validated DEEPctMUT using cell lines, spike-in samples, healthy and CRC plasma samples. DEEPctMUT accurately detected mutations down to a 0.03% VAF, outperforming other tumor-naïve methods. In a head-to-head comparison, our pipeline identified pre-surgical CRC cases with 100% accuracy, whereas the Roche Avenio Surveillance Kit achieved only 50% sensitivity. Furthermore, a highly versatile panel-independent model in DEEPctMUT could improve existing Roche Avenio performance without further Avenio specific training. We make the extensive ground truth data and the fully adaptable DEEPctMUT pipeline and models available.  Our findings underscore that advanced error polishing methods can effectively compensate for the absence of matched tumor information, enabling sensitive ctDNA detection.
TRON - TRANSLATIONAL ONCOLOGY AT THE JOHANNES GUTENBERG-UNIVERSITY MAINZ