About this Research Topic
Advances in next generation sequencing (NGS) platforms have enabled rapid genomic, transcriptomic, and epigenomic profiling for clinical applications, from risk assessment, diagnosis, and prognosis, to biomarker discovery and choice of drug therapy. In general, NGS-based diagnostic testing has successfully demonstrated the clinical utility landscape of genomic information, including evaluating rare disorders, identifying therapeutic targets for tailored cancer therapies, and incorporating epigenetic changes as valuable biomarker for cancer detection. In clinical setting, however, these sequencing applications pose unique bioinformatics challenges, impeding the translation of personal genomic data into interpretable information.
First, biopsy samples are often limited in quantity and quality. For cancer genome sequencing, the challenge is more evident in cases where a matched control DNA sample is not available, which complicates the filtering of germline variants, and in other instances where there is remarkable intratumor heterogeneity as a result of clonal evolution. In addition, as a major sample source, formalin-fixed paraffin-embedded (FFPE) tissues are often suffered from low DNA quality. These suboptimal conditions likely lead to low limits of detection on more clinically and therapeutically relevant subclonal events. Second, liquid biopsy represents an ideal alternative or complement to surgical biopsy in clinic genomic assays. It allows non-invasive and real-time monitoring of tumor dynamics, therapeutic response and early cancer detection. For example, cell-free circulating tumor DNA (ctDNA) analysis with liquid biopsy has recently implemented in a number of clinical applications for detecting point mutations and epigenetic patterns in plasma. ctDNA is also being assessed in detecting chromosomal rearrangements and copy number aberrations. Despite the promise in broader clinical applications, there are elevated false positives when sequencing such highly fragmented ctDNA in blood with low-abundance, especially in detecting low allele frequency variants Thus, the efficacy of ctDNA application highly relies on the optimized bioinformatics pipelines to differentiate tumor-specific aberrations with high sensitivity.
Finally, the prognostic relevance of CHIP is increasingly recognized for its association with a higher risk of hematologic neoplasm or cardiovascular disease. Clonal hematopoiesis of indeterminate potential, or CHIP, represents somatic mutations with low variant allele frequency in blood or bone marrow cells. The detection of CHIP requires a rigorous variant calling parameter setting, which in turn may result in compromised specificity. Together, in these scenarios, more sensitive and robust bioinformatics algorithms in combination with big data analysis are highly needed to better address the challenges. Joint effort in this area is expected to fill in the knowledge gap between data analytical team and clinical practice.
This Research Topic welcomes manuscripts that present original research, commentaries, perspectives, and reviews. The scope includes clinical genome sequencing applications, novel bioinformatics methodologies, as well as pipeline development for refining genomic, transcriptomic and epigenomic findings.
First, biopsy samples are often limited in quantity and quality. For cancer genome sequencing, the challenge is more evident in cases where a matched control DNA sample is not available, which complicates the filtering of germline variants, and in other instances where there is remarkable intratumor heterogeneity as a result of clonal evolution. In addition, as a major sample source, formalin-fixed paraffin-embedded (FFPE) tissues are often suffered from low DNA quality. These suboptimal conditions likely lead to low limits of detection on more clinically and therapeutically relevant subclonal events. Second, liquid biopsy represents an ideal alternative or complement to surgical biopsy in clinic genomic assays. It allows non-invasive and real-time monitoring of tumor dynamics, therapeutic response and early cancer detection. For example, cell-free circulating tumor DNA (ctDNA) analysis with liquid biopsy has recently implemented in a number of clinical applications for detecting point mutations and epigenetic patterns in plasma. ctDNA is also being assessed in detecting chromosomal rearrangements and copy number aberrations. Despite the promise in broader clinical applications, there are elevated false positives when sequencing such highly fragmented ctDNA in blood with low-abundance, especially in detecting low allele frequency variants Thus, the efficacy of ctDNA application highly relies on the optimized bioinformatics pipelines to differentiate tumor-specific aberrations with high sensitivity.
Finally, the prognostic relevance of CHIP is increasingly recognized for its association with a higher risk of hematologic neoplasm or cardiovascular disease. Clonal hematopoiesis of indeterminate potential, or CHIP, represents somatic mutations with low variant allele frequency in blood or bone marrow cells. The detection of CHIP requires a rigorous variant calling parameter setting, which in turn may result in compromised specificity. Together, in these scenarios, more sensitive and robust bioinformatics algorithms in combination with big data analysis are highly needed to better address the challenges. Joint effort in this area is expected to fill in the knowledge gap between data analytical team and clinical practice.
This Research Topic welcomes manuscripts that present original research, commentaries, perspectives, and reviews. The scope includes clinical genome sequencing applications, novel bioinformatics methodologies, as well as pipeline development for refining genomic, transcriptomic and epigenomic findings.
Keywords: Clinical genome sequencing; variant detection; copy number variation; structural variant; variant annotation; big data analytics; early epigenetic biomarkers
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