Why Whole Genome Sequencing Is Transforming Hematologic Cancer Research

Hematologic malignancies are among the most genomically complex cancers studied today. Researchers investigating leukemias, lymphomas, and myeloid neoplasms often need to assess multiple classes of genomic alterations simultaneously, including small variants, indels, structural rearrangements, copy number alterations, and other key biomarkers. Yet these biomarkers are typically distributed across multiple testing modalities, requiring researchers to combine results from several assays to build a complete genomic picture.

As genomic classification systems evolve and biomarker discovery accelerates, research laboratories are increasingly turning to whole-genome sequencing (WGS) to:

• Identify novel biomarkers and genomic signatures
• Characterize biobanks and longitudinal research cohorts at scale

WGS consolidates detection of diverse genomic alteration types into a single assay while providing a more complete view of disease biology. As adoption grows in hematologic cancer research, laboratories need high-throughput sequencing and robust analysis software capable of transforming complex genomic data into meaningful insights.

To help researchers operationalize this transition, Illumina offers the DRAGEN Heme WGS T/O application, a preconfigured, push-button analysis solution purpose-built for the unique complexities of research for hematologic cancers. 

Beyond targeted questions: Enabling broader heme discovery

For decades, genomic characterization of hematologic cancers has relied on a collection of specialized tests, each designed to answer a specific question. Cytogenetics provides chromosome-level information, FISH evaluates targeted rearrangements, RT-PCR detects specific fusions, and targeted NGS panels assess selected mutations. Additional assays may be required to evaluate copy number changes and other disease-defining events. While effective, many of today's most important hematologic malignancy research questions extend beyond the capabilities of these approaches, which can leave relevant genomic events undetected. For instance: 

• Conventional karyotyping has extremely low resolution (<5–10 Mb), often missing cryptic and mid-size structural events that fall below its detection threshold. Cell culture is also subject to a 10–20% failure rate, meaning some samples yield no result at all.
• Targeted NGS panels are designed to interrogate selected genes and regions, but by definition cannot detect genome-wide structural variants, novel rearrangements, or alterations outside their panel boundaries.
• No single technology captures all major variant classes, including SVs, indels, CNAs, fusions, and LOH, from a single sample. As a result, relevant findings can be missed in a multi-technology workflow.

Researchers searching for novel biomarkers need access to genomic regions beyond established panel content. Investigators revisiting historical clinical trial cohorts want to evaluate biomarkers that were unknown when samples were originally collected. Likewise, biobanks are increasingly characterized using approaches that maximize future utility rather than addressing a single research question.

These needs require comprehensive genomic data that can be reanalyzed as new biomarkers, disease classifications, and genomic signatures emerge. Whole-genome sequencing is uniquely positioned to support this objective by capturing the entire genomic landscape in a single experiment.

A single assay for comprehensive genomic characterization

Whole-genome sequencing interrogates the entire genome in a single experiment, allowing researchers to evaluate multiple variant classes from one sequencing run and dataset, including:

• Single nucleotide variants (SNVs)
• Insertions and deletions (indels)
• Copy number alterations
• Loss of heterozygosity (LOH)
• Structural variants
• Gene fusions
• FLT3-ITD
• Complex chromosomal rearrangements and karyotype-level events
• Internal tandem duplications and other complex rearrangements

Because all variant classes are evaluated from the same assay, WGS can reduce reliance on multiple testing workflows while providing more comprehensive genomic characterization. This breadth is particularly valuable for biomarker discovery and translational research, where novel findings may exist outside established hotspots or panel boundaries.

For archived clinical trial cohorts and biobanks, WGS data can also be revisited as new biomarkers and genomic signatures emerge, without redesigning assays or generating additional sequencing data.

Why Dedicated Analysis Software Matters as Much as Sequencing

Generating whole-genome data is only part of the challenge. Hematologic malignancies often exhibit variable tumor burden, clonal heterogeneity, and subclonal populations, requiring sensitive methods capable of detecting diverse variant classes across the genome.

Historically, laboratories have addressed this complexity by assembling custom bioinformatics pipelines using multiple algorithms and resource files. While flexible, these workflows often require substantial development, and maintenance effort.

The DRAGEN WGS Heme Tumor-Only application is a preconfigured secondary analysis workflow designed specifically for hematologic malignancy research. 

Key capabilities include:

• Comprehensive variant detection from a single workflow: Simultaneously identifies small variants, copy number alterations, structural variants, loss of heterozygosity, FLT3 internal tandem duplications, gene fusions, and DUX4 rearrangements.
• Accelerated time to insights: Secondary analysis can be completed in approximately 1–2 hours per sample at 100× coverage*, enabling rapid conversion of sequencing data into genomic findings.
• Reduced implementation burden: Preconfigured resources, optimized parameters, and automated workflows reduce the effort required to build and maintain custom pipelines.
• Flexible deployment options: Supports FASTQ, BAM, and run-folder inputs and can be deployed on local DRAGEN servers or cloud-based environments to fit existing laboratory infrastructure.
• Designed for scalability: Analyze individual samples or large cohorts using a single workflow, supporting biomarker discovery, retrospective clinical trial analyses, and biobank characterization.
• Built for future research questions: Standardized outputs enable datasets to be revisited as new biomarkers, genomic signatures, and disease classifications emerge.

Looking Ahead

The future of hematologic malignancy research will be shaped by increasingly refined classification systems, deeper insights into disease biology, and the discovery of new genomic biomarkers. As genomic classification systems continue to evolve, researchers increasingly need comprehensive datasets that can be reanalyzed as new biomarkers, genomic signatures, and disease subtypes emerge. Whole-genome sequencing is uniquely positioned to support this shift by providing a complete view of the genomic landscape from a single assay.

Advances in sequencing and bioinformatics are making comprehensive characterization of hematologic malignancies increasingly practical. The DRAGEN WGS Heme Tumor-Only Application helps researchers move from raw sequencing data to genomic insights in hours, supporting biomarker discovery, cohort characterization, and the next generation of hematologic cancer research.

Get Started Today

Ready to learn more? Visit the DRAGEN WGS Heme application page to explore capabilities, performance data, and getting started resources. 

For Research Use Only. Not for use in diagnostic procedures.

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