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Cancer variant characterization

See beyond short reads: capture complex genomic variation to inform research and clinical strategy.

Understanding cancer variation from mechanisms to clinical insight

Characterize all types of somatic variants including structural variants and methylation with a single assay.

The complexity of cancer genomes requires methods that can see the full breadth of cancer genomic variation, from SNVs and indels to SVs, CNVs, and differential methylation. Existing methods can typically capture only a part of this variation, requiring multiple assays and technologies to see the whole picture. Long-read sequencing from PacBio allows cancer researchers to characterize the complete spectrum of somatic and germline variation, yielding a clearer view of cancer biology and helping to identify new therapeutic targets and inform clinical decision making.

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Structural variants

Discover more structural variants with long reads while maintaining the accuracy needed for small variant detection.

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Mutation phasing

Long reads mean the ability to resolve and assign variants to haplotypes, meaning you can differentiate between cis and trans mutations to identify compound heterozygosity.

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Methylation

HiFi data includes direct detection of 5mC and 6mA methylation marks to reveal epigenetic silencing linked to treatment resistance in a single assay.

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Repetitive regions

Sequence through complex repetitive regions to identify repeat expansions and microsatellite instability.

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Webinar

Comprehensive variant detection in pediatric leukemia research with accurate long-read whole genome sequencing

Molecular diagnostics in cancer currently requires multiple assays to characterize all types of variants in cancer genomes, increasing cost and turnaround time. In this webinar, discover how clinical researchers used HiFi whole genome sequencing to discover variants in pediatric leukemia that were missed by other methods, as a step towards a single consolidated assay.

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Application note

Workflow for comprehensive somatic variant detection in long-read tumor/normal whole genome sequencing

The rich information provided by HiFi sequencing requires bioinformatic tools optimized for long-read data. In this application note, we outline a recommended workflow for somatic variant calling from long-read tumor/normal WGS data, and provide examples of how HiFi is able to resolve complex events through its ability to simultaneously detect structural variants, phase mutations over long distances, and profile methylation.

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Publication

A detailed map of structural variation in breast cancer

The SK-BR-3 cell line is an important model of HER2+ breast cancer, which has been previously observed to contain many complex rearrangements.

In this paper, researchers apply HiFi whole genome sequencing to this cell line and uncover nearly 20,000 structural variants, the majority of which had been missed by short read sequencing.

These rearrangements included multiple nested duplications and translocations surrounding the HER2 locus that were likely accumulated sequentially, shedding light on the tumor evolution process.

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Figure 1A: Variants found in SK-BR-3 with PacBio long-read sequencing. (A) Circos (Krzywinski et al. 2009) plot showing long-range (larger than 10 kbp or inter-chromosomal) variants found by Sniffles from split-read alignments, with read coverage shown in the outer track.

Fig 2:KMT2A::MLLT10 fusion detected by Severus in a previously genetically undefined acute myeloid leukemia case.

A) Detection of an ∼1.3 Mb segment of 10p12 that is called by Severus (indicated by the blue bars in the haplotagged_severus track of the IGV screen capture) and corresponding long-read sequencing (lrSeq) reads. This segment is inserted into the KMT2Agene on chromosome 11q23. Note: the red and blue coloring of the reads does not denote pair orientation (e.g., +/-) in a lrSeq setting; it is provided here only for visual ease. B) Typical spacing of the KMT2Abreakapart FISH probe utilized for the in-house acute myeloid leukemia (AML) panel. The probes are represented by their respective colors. C) Illustration of the increased spacing of the probes that is expected after the insertion of the ∼1.3 Mb segment of 10p12 (indicated by the yellow bar) into KMT2A. D) Schematic of the orientation of the KMT2A (NM_001197104.2) and MLLT10 (NM_001195626.3) genes including the intronic location of each breakpoint; a functional fusion product is predicted.

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Publication

Successful classification of clinical pediatric leukemia genetic subtypes via structural variant detection using HiFi long-read sequencing

This proof-of-concept study demonstrates that PacBio HiFi long-read sequencing can detect clinically relevant structural variants (SVs) that define genetic subtypes in pediatric leukemia, matching standard diagnostic results in all cases and uncovering additional related SVs missed by routine methods. The results support the potential of long-read sequencing as a comprehensive, single-assay diagnostic tool for pediatric leukemia, streamlining the detection of fusion events and complex rearrangements critical for subtype classification. By improving SV resolution and subtype calling, this work underscores the diagnostic usefulness of high-fidelity long reads in clinical oncology genomics.

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Publication

A telomere-to-telomere map of somatic mutation burden and functional impact in cancer

This study leverages a near-telomere-to-telomere (T2T) diploid genome assembly combined with deep short- and long-read sequencing to chart somatic variation across the entire cancer genome, including regions missing from the standard reference assembly. It reveals that a significant portion of somatic variants reside in previously inaccessible repetitive regions, with satellite repeats and centromere kinetochore domains acting as hotspots for mutation and functional remodeling. The work highlights that copy number changes and epigenetic alterations, rather than individual point mutations, are major drivers in rewiring cancer regulatory programs, establishing a new framework for comprehensive somatic mutation characterization in precision oncology.

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Figure 1.T2T mapping of somatic variants in a melanoma cell line.A. Sequencing schematic for a paired lymphoblastoid and melanoma cell line from the same individual, as well as generation of a donor-specific assembly (DSA) from this individual.

B. Plot of the contiguity of each haploid chromosome in the COLO829BL DSA.

C. Representative images of each chromosome from the COLO829 melanoma karyotype colored by the presumed haplotype of each chromosome and translocation identity based on read coverage along the COLO829BL DSA.

D. (top) Ideogram showing the precise breakpoints mediating the t(1;3)(q12;p21) translocation in COLO829 cells based on the COLO829BL DSA. (middle) Sequence differences in COLO829 cells relative to COLO829BL along reads mapping to intact regions of the loci involved in this translocation, as well as the translocated chromosome. (bottom) Sequence at junction with microhomology base in purple.

E. Same as D, but for the i(4)(p10) chromosome junction.

F. Same as D, but for a t(14;16)(p10;p10) translocation that was identified using the long-read sequencing data. Note that there are no intact regions of these loci in COLO829 cells.

G. CpG methylation data in COLO829 cells relative to the breakpoints involved in creation of the COLO829 i(4)(p10).

H. Same as G, but for the COLO829 t(14;16)(p10;p10) translocation.

I. Schematic showing the genomic events leading to the formation of the COLO829 i(4)(p10).

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Fig. 4.Extrachromosomal DNA (ecDNA) burden, oncogene content, and architecture in osteosarcoma. (A) Relationship between ecDNA size (kb) and copy number. Each point represents a circular amplicon detected across the three ecDNA positive tumors (Ro1759, GP1899, GD2044). The x-axis shows ecDNA size and the y-axis shows estimated copy number. Tier 1 ecDNA (high-confidence circular amplicons) are shown in red and Tier 2 probable ecDNA are shown in blue. The dot size is proportional to the size of ecDNAs. (B) Per-tumor ecDNA burden. Stacked bars indicate the number of ecDNAs detected in each tumor (Ro1759, GP1899, and GD2044), stratified by Tier 1 (red) and Tier 2 probable (blue) elements. (C) Architecture of a representative Tier 1 ecDNA (ec63) from tumor Ro1759. Circular plots depict a 3.07 Mb ecDNA comprising 13 genomic segments (legend, bottom) with a median copy number of ∼4 (maximum 4.8). The outer track shows the reference chromosomal coordinates for the contributing segments. Inner annotation tracks mark gene features overlapped by the ecDNA (CDS, 5’ UTR, 3’ UTR, exons, introns, gene-only regions). Gene labels highlight notable loci carried on this ecDNA, including CTNNB1, TGFBR2, and neighboring genes. Inward colored vertical lines denote segment boundaries and junctions, illustrating the rearranged structure of the circular amplicon.

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Publication

Satellite DNA fragility accompanies complex genome rearrangements and ecDNA oncogene amplification in canine osteosarcomas

Using high-resolution long-read sequencing, this study maps the structural variant landscape of canine osteosarcoma and identifies extensive genome rearrangements enriched near satellite repeat regions marked by focal hypomethylation. The work also discovers multiple extrachromosomal DNA (ecDNA) elements carrying amplified oncogenes, indicating that ecDNA is a prominent mechanism of gene amplification in this aggressive cancer model. These findings provide an integrated genomic and epigenomic view of repeat-driven instability and ecDNA-associated amplification, offering comparative insights relevant to human osteosarcoma biology.

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Publication

Quantifying HLA transcripts by genotype in chimeric mixtures at single-cell resolution

This preprint introduces scrHLA-typing, a novel method that combines single-cell barcoding with long-read sequencing to accurately identify and quantify HLA allele-specific transcript expression in individual cells. Applied to chimeric samples such as post-transplant relapse cases, the assay sensitively resolves donor versus recipient HLA expression patterns across a range of chimerism, revealing allele-specific expression differences that may be clinically actionable. The approach advances HLA profiling at single-cell resolution, addressing longstanding challenges in assessing polymorphic immune loci in complex cellular mixtures.

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Fig. 4.Differential Allele-Specific Expression (ASE) across the HLA haplotype in different groups of cells
a,
HLA gene positions on chromosome 6 with coordinate ‘0’ corresponding to position 29,722,775 bp of the hg38 primary assembly (position of pseudogene HLA-Y was recently described in a ∼60,000 bp indel polymorphism [ref. Alexandrov, N., et al., HLA 102, 599-606 (2023)] between HLA-A and HLA-E). b, Genotyping of ‘AML4’ using scrHLA-typing. c, Mean normalized expression in the example of HLA-DRB1, for the recipient-specific (DRB1*04:01:01) and the shared (DRB1*03:02:01) alleles across the cell categories and timepoints. d, Example using HLA-DRB1, showing mean normalized expression of the recipient-specific allele (DRB1*04:01:01) and the shared allele (DRB1*03:02:01) across cell categories and time points. e, Fold change in the proportion of cells expressing the mismatched allele (under selective pressure) vs. the shared allele, along with statistical significance (Chi-square test), shown across cell categories and time points. f, Proportion of HLA genes exhibiting ‘balanced’ vs. ‘differential’ allele expression among genes with recipient–donor mismatches. ‘Differential’ expression is defined here as a fold change in the proportion expressed of mismatched vs. shared alleles > 2 (either increase or decrease) and a Chi-square p-value < 0.01.

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