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January 22, 2026Genes2 citationsOpen Access

Optical Genome Mapping Enhances Structural Variant Detection and Refines Risk Stratification in Chronic Lymphocytic Leukemia

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SCSoma Roy ChakrabortyMBMichelle A BickfordNSNarcisa A Smuliac

Key Points

  • This research aims to assess the effectiveness of optical genome mapping in detecting structural variants in chronic lymphocytic leukemia and its impact on risk stratification.
  • Retrospective evaluation of 50 chronic lymphocytic leukemia patients using optical genome mapping.
  • Integration of findings with cytogenomics, targeted next-generation sequencing, and clinical time-to-first-treatment data.
  • Assessment of structural variants and genomic profiles alongside clinical outcomes.
  • Optical genome mapping identified structural variants in 82% of cases, with del(13q) being the most common abnormality.
  • Co-occurrence of adverse genomic lesions was linked to shorter clinical time-to-first-treatment.
  • Integration of optical genome mapping with next-generation sequencing improved genomic risk classification, particularly in complex cases.

Abstract

Background: Optical genome mapping (OGM) detects genome-wide structural variants (SVs), including balanced rearrangements and complex copy-number alterations beyond standard-of-care cytogenomic assays. In chronic lymphocytic leukemia (CLL), cytogenetic and genomic risk stratification is traditionally based on fluorescence in situ hybridization (FISH), karyotyping, targeted next-generation sequencing (NGS), and immunogenetic assessment of immunoglobulin heavy chain variable region (IGHV) somatic hypermutation status, each of which interrogates only a limited aspect of disease biology. Methods: We retrospectively evaluated fifty patients with CLL using OGM and integrated these findings with cytogenomics, targeted NGS, IGHV mutational status, and clinical time-to-first-treatment (TTFT) data. Structural variants were detected using OGM and pathogenic NGS variants were derived from a clinical heme malignancy panel. Clinical outcomes were extracted from the electronic medical record. Results: OGM identified reportable structural variants in 82% (41/50) of cases. The most frequent abnormality was del(13q), observed in 29/50 (58%) and comprising 73% (29/40) of all OGM-detected deletions with pathologic significance. Among these, 12/29 (42%) represented large RB1-spanning deletions, while 17/29 (58%) were focal deletions restricted to the miR15a/miR16-1 minimal region, mapping to the non-coding host gene DLEU2. Co-occurrence of adverse lesions, including deletion 11q/ATM, BIRC3 loss, trisomy 12, and deletion 17p/TP53, were recurrent and strongly associated with shorter TTFT. OGM also uncovered multiple cryptic rearrangements involving chromosomal loci that are not represented in the canonical CLL FISH probe panel, including IGL::CCND1, IGH::BCL2, IGH::BCL11A, IGH::BCL3, and multi-chromosomal copy-number complexity. IGHV data were available in 37/50 (74%) of patients; IGHV-unmutated status frequently co-segregated with OGM-defined high-risk profiles (del(11q), del(17p), trisomy 12 with secondary hits, and complex genomes whereas mutated IGHV predominated in OGM-negative or structurally simple del(13q) cases and aligned with indolent TTFT. Integration of OGM with NGS further improved genomic risk classification, particularly in cases with discordant or inconclusive routine testing. Conclusions: OGM provides a comprehensive, genome-wide view of structural variation in CLL, resolving deletion architecture, identifying cryptic translocations, and defining complex multi-hit genomic profiles that tracked closely with clinical behavior. Combining OGM and NGS analysis refined risk stratification beyond standard FISH panels and supports more precise, individualized management strategies in CLL. Prospective studies are warranted to evaluate the clinical utility of OGM-guided genomic profiling in contemporary treatment paradigms.

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Cite This Study

Chakraborty et al. (2026) studied this question.

synapsesocial.com/papers/6971bea8642b1836717e34b7https://doi.org/10.3390/genes17010106
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