bioRxiv Science⌕ Search

Biology subjects

Demuth, C.

Publications and source records attributed to Demuth, C..

2 recordsLinked to original sources

Error-corrected deep targeted sequencing of circulating cell-free DNA from colorectal cancer patients for sensitive detection of circulating tumor DNA

IntroductionCirculating tumor DNA (ctDNA) is a promising biomarker, reflecting the presence of tumor cells. Sequencing-based detection of ctDNA at low tumor fractions is challenging due to the crude error rate of sequencing. To mitigate this challenge, we developed UMIseq, a fixed-panel deep-targeted sequencing approach, which is universally applicable to all colorectal cancer (CRC) patients. MethodsUMIseq features UMI-mediated error correction, exclusion of mutations related to clonal hematopoiesis, a panel of normals for error modeling, and signal integration from single-nucleotide variations, insertions, deletions, and phased mutations. UMIseq was trained and independently validated on pre-operative (pre-OP) plasma from CRC patients (n=364) and healthy individuals (n=61). ResultsUMIseq displayed an area under the curve surpassing 0.95 for allele frequencies (AF) down to 0.05%. In the training cohort, the pre-OP detection rate reached 80% at 95% specificity, while in the validation cohort, it was 70%. UMIseq enabled the detection of AFs down to 0.004%. To assess the potential for detection of residual disease, 26 post-operative plasma samples from stage III CRC patients were analyzed. Detection of ctDNA was associated with recurrence (p =0.08). ConclusionUMIseq demonstrated robust performance with high sensitivity and specificity, enabling the detection of ctDNA at low allele frequencies.

cancer biology↗

DREAMS: Deep Read-level Error Model for Sequencing data applied to low-frequency variant calling and circulating tumor DNA detection

Circulating tumor DNA detection using Next-Generation Sequencing (NGS) data of plasma DNA is promising for cancer identification and characterization. However, the tumor signal in the blood is often low and difficult to distinguish from errors. We present DREAMS (Deep Read-level Modelling of Sequencing-errors) for estimating error rates of individual read positions. Using DREAMS, we developed statistical methods for variant calling (DREAMS-vc) and cancer detection (DREAMS-cc). For evaluation, we generated deep targeted NGS data of matching tumor and plasma DNA from 85 colorectal cancer patients. The DREAMS approach performed better than state-of-the-art methods for variant calling and cancer detection.

bioinformatics↗