J Transl Med. 2025 Aug 27;23(1):964. doi: 10.1186/s12967-025-06993-3.

ABSTRACT

BACKGROUND: Circulating tumour DNA (ctDNA) in liquid biopsies has emerged as a powerful biomarker in cancer patients. Its relative abundance in cell-free DNA serves as a proxy for the overall tumour burden. Here we present GeneBits, a method for cancer therapy monitoring and relapse detection. GeneBits employs tumour-informed enrichment panels targeting 20-100 somatic single-nucleotide variants (SNVs) in plasma-derived DNA, combined with ultra-deep sequencing and unique molecular barcoding. In conjunction with the newly developed computational method umiVar, GeneBits enables accurate detection of molecular residual disease and early relapse identification.

RESULTS: To assess the performance of GeneBits and umiVar, we conducted benchmarking experiments using three different commercial cell-free DNA reference standards. These standards were tested with targeted next-generation sequencing (NGS) workflows from both IDT and Twist, allowing us to evaluate the consistency and accuracy of our approach across different oligo-enrichment strategies. GeneBits achieved comparable depth of coverage across all target sites, demonstrating robust performance independent of the enrichment kit used. For duplex reads with ≥ 4x UMI-family size, umiVar achieved exceptionally low error rates, ranging from 7.4×10^-7 to 7.5×10^-5. Even when including mixed consensus reads (duplex & simplex), error rates remained low, between 6.1×10^-6 and 9×10^-5. Furthermore, umiVar enabled variant detection at a limit of detection as low as 0.0017%, with no false positive calls in mutation-free reference samples. In a reanalysed melanoma cohort, variant allele frequency kinetics closely mirrored imaging results, confirming the clinical relevance of our method.

CONCLUSION: GeneBits and umiVar enable highly accurate therapy and relapse monitoring in plasma as well as identification of molecular residual disease within four weeks of tumour surgery or biopsy. By leveraging small, tumour-informed sequencing panels, GeneBits provides a targeted, cost-effective, and scalable approach for ctDNA-based cancer monitoring. The benchmarking experiments using multiple commercial cell-free DNA reference standards confirmed the high sensitivity and specificity of GeneBits and umiVar, making them valuable tools for precision oncology. UmiVar is available at https://github.com/imgag/umiVar .

PMID:40866952 | PMC:PMC12382282 | DOI:10.1186/s12967-025-06993-3

Cell Rep. 2025 Aug 21;44(9):116191. doi: 10.1016/j.celrep.2025.116191. Online ahead of print.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease, and new therapeutic strategies are urgently needed. Here, we conduct an integrative, genome-scale examination of genetic dependencies and cell surface targets using CRISPR-Cas screening and multi-omic data, including single-nucleus and spatial transcriptomic data from patient tumors. We systematically identify clinically tractable and biomarker-linked PDAC dependencies, including CDS2 as a synthetic lethal target in cancer cells expressing signatures of epithelial-to-mesenchymal transition. We examine biomarkers and co-dependencies of the KRAS oncogene, defining gene expression signatures of sensitivity and resistance associated with response to pharmacological inhibition of KRAS. mRNA and protein profiling reveal cell surface protein-encoding genes with robust expression in patient tumors and minimal expression in non-malignant tissues. Furthermore, we define intratumoral and interpatient heterogeneity of target gene expression and identify orthogonal targets that suggest combinatorial strategies. Collectively, this work identifies multiple targets that may inform therapeutic strategies for patients with PDAC.

PMID:40848256 | DOI:10.1016/j.celrep.2025.116191