Transformative Genomic Technologies Shaping the Future of Precision Approaches for Haematological Malignancies

Dear Colleagues,

We are pleased to announce the new topic ‘Transformative Genomic Technologies Shaping the Future of Precision Approaches for Haematological Malignancies’. 

This Special Issue aims to highlight cutting-edge research advancing genomic technologies and computational frameworks in hematologic malignancies. Hematological malignancies are characterized by genomic complexity with clonal heterogeneity, impacting diagnosis, prognosis, measurable residual disease assessment, and therapeutic response. While conventional cytogenetics, fluorescence in situ hybridization, cytomicroarrays, and targeted molecular assays are the foundation of clinical diagnostics, they may not be able to capture the full range of genomic alterations driving hematological malignancies. Despite the adoption of next-generation sequencing (NGS)-based assays, important technical, standardization, and interpretative challenges remain, including detection of complex structural variants, cryptic gene fusions and splicing, low-variant allele frequency mutations, and clinically ambiguous variants. These limitations underscore an ongoing need for integrated, high-resolution genomic approaches coupled with bioinformatics frameworks to enable accurate, reproducible, and clinically actionable molecular profiling across hematologic malignancies.

Recent advances in genomic technologies are reshaping the diagnostic landscape. Targeted sequencing panels continue to provide high-depth, clinically validated detection of recurrent single-nucleotide variants (SNVs), insertions and deletions (INDELs), copy-number variation (CNV), and structural alterations. Whole-exome sequencing (WES) and whole-genome sequencing (WGS) expand this scope, enabling unbiased interrogation of coding and non-coding regions, improved detection of copy number alterations and structural variants, and discovery of novel driver events. Emerging platforms such as long-read sequencing, optical genome mapping (OGM), and Hi-C-based chromatin conformation assays further enhance the detection of large-scale genomic rearrangements, balanced translocations, complex karyotypes, and alterations in three-dimensional genome architecture that may underlie enhancer hijacking and dysregulated gene expression. Complementary RNA sequencing approaches are essential for comprehensive fusion detection, transcript isoform characterization, and gene expression profiling, particularly in leukemias driven by cryptic or transcriptionally defined rearrangements. A comprehensive understanding of malignancies' genomic makeup provides avenues for discovery research to identify key disease-influencing factors as targets for therapeutic approaches.

However, technological advancement introduces substantial analytical complexity. From probe design and library preparation to sequencing chemistry and coverage optimization, technical considerations influence sensitivity and specificity across variant classes. Bioinformatics pipelines must address challenges in alignment accuracy, error modeling, variant calling for SNVs/INDELs/MNVs, structural variant detection, copy number estimation, and fusion identification. Short-read sequencing remains limited in repetitive regions and in resolving long insertions or complex rearrangements, while structural variant callers frequently generate discordant outputs requiring careful verification. Distinguishing true somatic alterations from sequencing artifacts or germline variants further complicates interpretation.

Automation and artificial intelligence (AI) offer promising opportunities to address these challenges. Machine learning approaches may improve variant calling accuracy, artifact suppression, structural variant prioritization, and fusion classification. AI-assisted literature mining and evidence aggregation platforms may streamline variant curation and reduce inter-observer variability. Emerging agentic AI frameworks have the potential to support automated draft reporting, dynamic knowledge base updating, and cross-platform data integration. Nonetheless, rigorous validation, transparency, explainability, and regulatory oversight are essential before widespread clinical implementation.

We invite contributions that explore applications of targeted panels, WES, WGS, OGM, Hi-C-based assays, RNA sequencing, long- or short-read sequencing technologies, and integrated multi-omics approaches to improve molecular characterization, risk stratification, and advances in therapeutic guidance in hematological malignancies.

Topics of interest include, but are not limited to, the following:

• Probe and assay design optimization; 
• Benchmarking of SNV/INDEL/MNV/SV detection pipelines; 
• Mutational signatures;
• Gene fusion detection methodologies;
• Multi-platform genomic integration;
• Variant curation frameworks;
• Automation of laboratory and bioinformatics workflows;
• Artificial intelligence and machine learning applications in genomic diagnostics;
• Identification of emerging novel cancer drivers or therapeutic targets identified through genomic analysis;
• Translational models that bridge genomic discovery with clinical reporting and patient management.

We also welcome methodological studies, consensus frameworks, validation cohorts, and implementation science research aimed at harmonizing genomic diagnostics and accelerating precision therapeutics for hematological malignancies.

Dr. Chung Hoow Kok
Dr. Barbara McClure
Guest Editors

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• multi-omics
• risk stratification
• bioinformatics
• AI/machine learning
• hematological malignancies
• biomarkers
• targeted therapeutics
• genomics
• cancer cell biology