Evidence map›Paper›PMID 40866952›Full record

ArticleJournal of translational medicine2025

GeneBits: ultra-sensitive tumour-informed ctDNA monitoring of treatment response and relapse in cancer patients.

Julian Broche, Olga Kelemen, Aishwarya Sekar, Leon Schütz, Francesc Muyas, Andrea Forschner, Christopher Schroeder, Stephan Ossowski

Abstract read
In one paragraph

Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Julian Broche *Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany.
Olga Kelemen *Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany.
Aishwarya SekarInstitute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany.
Leon SchützInstitute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany.
Francesc MuyasInstitute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany.
Andrea ForschnerDepartment of Dermatology, University Hospital Tübingen, Tübingen, Germany.
Christopher SchroederInstitute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany. christopher.schroeder@med.uni-tuebingen.de.
Stephan OssowskiInstitute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany. stephan.ossowski@med.uni-tuebingen.de.ORCID 0000-0002-7416-9568

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCirculating 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.

resultsTo 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

conclusionGeneBits 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 .

Indexed as

Circulating Tumor DNANeoplasm Recurrence, LocalNeoplasmsHigh-Throughput Nucleotide SequencingHumansLiquid BiopsyPolymorphism, Single NucleotideRecurrenceTreatment OutcomeCirculating Tumor DNACell-free DNACirculating tumour DNALiquid biopsyMolecular residual diseaseNext-Generation-SequencingRelapse detectionTreatment monitoringUltra-deep sequencingUnique molecular barcode

Identifiers

PMID40866952
PMCPMC12382282

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.