Biology subjects

Cibulskis, C.

Publications and source records attributed to Cibulskis, C..

3 recordsLinked to original sources

bioRxiv2026

Clustered structural variant hotspots enable oncogenic addiction and plasticity in osteosarcoma

The genomic landscape of osteosarcoma, the most common bone cancer worldwide, is among the most structurally complex of all human malignancies. The identification of recurrent and functionally consequential patterns has thus remained a challenge. Across 236 whole-genome sequencing osteosarcoma samples, we uncovered five genomic hotspots of clustered structural variation collectively altered in 58% of tumors. Four were associated with amplification of oncogenes ( MYC , CCND3 , CCNE1 , CDK4) , while the fifth mapped largely upstream of TP53 . Hotspot events showed coordinated patterns of co-occurrence and mutual exclusivity with each other and with tumor suppressor alterations, suggesting genomic context-specific selection. We found localized transcriptional dysregulation at hotspot event loci, and single cells harboring these events converged on a neural crest-like program, linking these structural alterations to a less differentiated cell state. These events were also detectable non-invasively through liquid biopsies and displayed ongoing structural evolution throughout disease progression, a finding with potential clinical utility. Our results provide novel insight into how complex rearrangements shape oncogenesis in osteosarcoma, with broader relevance to other cancers characterized by complex genomes.

cancer biology↗

bioRxiv2025

Benchmarking of duplex sequencing approaches to reveal somatic mutation landscapes

Detecting somatic mutations in normal tissues is challenging due to sequencing errors and the low allele fractions of post-zygotic variants. Duplex sequencing greatly reduces errors and can detect mutations at any allele fraction, but systematic, cross-platform comparisons are lacking. We present a comprehensive benchmarking of six duplex sequencing technologies used by the SMaHT Network: CODEC, CompDuplex-seq, HiDEF-seq, NanoSeq, ppmSeq, and VISTA-seq. We evaluated their performance using cord blood DNA, a tumor-normal cell line mixture, and homogenates from six human tissues. Each method shows distinct profiles in genomic footprint, sensitivity, and cost. Despite differences in library construction and sequencing platforms, estimates of mutation rates and mutational signatures are highly concordant. Integration with ultra-deep whole-genome sequencing shows that duplex approaches sensitively capture mutations and signatures beyond embryonic or clonally expanded variants. These results provide a foundation for selecting duplex methods and interpreting their data, enabling scalable single-molecule analyses of somatic mutation landscapes. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/692823v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@123b221org.highwire.dtl.DTLVardef@83ba0aorg.highwire.dtl.DTLVardef@2b0ab0org.highwire.dtl.DTLVardef@1cae35e_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2025

Comprehensive benchmarking of somatic single-nucleotide variant and indel detection at ultra-low allele fractions using short- and long-read data

Mosaic mutations in normal tissues occur at low variant allele fractions (VAFs), complicating detection. To benchmark strategies, the SMaHT Network created a cell-line mixture (1:49) and produced ultra-deep whole-genome sequencing using short and long reads (five centers, 180-500x each). We assembled a reference of 44,008 mosaic SNVs and 2,059 Indels, cross-validation between platforms to expose limits of short-read analysis. We also partitioned the genome by mappability to examine the impact of genomic context, added a negative reference set, and accounted for culture-derived mutations. When seven institutions applied eleven algorithms to mixture data, call sets were largely discordant across tools and replicates, partly reflecting stochastic presence of low-VAF mutations in biological replicants. For >2% VAF SNVs, sensitivity and precision approached [~]80% at [≥]300x, with little gain from additional sequencing. This work provides a comprehensive framework for reliable detection of low-VAF mutations in non-cancer tissues and a valuable resource for the community.

bioinformatics↗