Translational cancer genomics and bioinformatics

This research theme focuses on the development and application of computational and genomic approaches for translational cancer research, with particular emphasis on liquid biopsy analysis, next-generation sequencing quality assessment, and mechanisms of therapy resistance.

Combining whole-genome sequencing, targeted sequencing, computational genomics, and functional cancer models, these studies investigated how genomic and epigenetic alterations can be leveraged to improve cancer detection, monitor recurrence, and understand treatment resistance.

A major aspect of this work involved evaluating the reliability and clinical applicability of next-generation sequencing technologies. These projects identified important limitations associated with sequencing sensitivity and mutation detection, while also exploring how tumour-derived circulating DNA and epigenetic cellular states can be used as biomarkers for disease progression and therapeutic response.

Together, these studies highlight the importance of integrating computational analysis, sequencing technologies, and translational cancer biology to improve cancer diagnostics, monitoring, and treatment strategies.

Selected studies

Monitoring circulating tumor DNA by analyzing personalized cancer-specific rearrangements to detect recurrence in gastric cancer

Experimental & Molecular Medicine (2019)
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This study investigated the clinical utility of circulating tumour DNA (ctDNA) for postoperative monitoring of gastric cancer recurrence. Using whole-genome sequencing to identify personalised cancer-specific rearrangements, ctDNA was monitored longitudinally in postoperative blood samples. The study demonstrated that postoperative ctDNA positivity preceded clinical recurrence and could serve as an early biomarker for relapse detection.

False-negative errors in next-generation sequencing contribute substantially to inconsistency of mutation databases

PLoS One (2019)
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This work evaluated inconsistencies between major cancer mutation databases, including GDSC and CCLE, and demonstrated that false-negative errors represent a major source of discordant mutation calls in next-generation sequencing datasets. Comparative analyses revealed substantial limitations associated with highly multiplexed sequencing approaches and highlighted the importance of evaluating sequencing sensitivity in translational genomics applications.

Cancer cells undergoing epigenetic transition show short-term resistance and are transformed into cells with medium-term resistance by drug treatment

Experimental & Molecular Medicine (2020)
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This study explored how transient epigenetic reprogramming states contribute to chemotherapy resistance. Using epigenetically reprogrammed cancer cell models combined with functional assays and single-cell analyses, the work demonstrated that cancer cells undergoing transient epigenetic transitions acquire short-term drug resistance, which can subsequently evolve into more stable resistant cellular states following treatment exposure.

Highlights

• Circulating tumour DNA enables early detection of postoperative cancer recurrence.
• Whole-genome sequencing can identify personalised structural rearrangements for ctDNA monitoring.
• False-negative mutation calls significantly contribute to inconsistencies across cancer genomics databases.
• Sequencing sensitivity remains a critical challenge in translational oncology and precision medicine.
• Epigenetic cellular transitions contribute to transient and persistent chemotherapy resistance.
• Computational genomics approaches improve understanding of tumour evolution and therapeutic response.

My contribution

I contributed to the computational and bioinformatic analysis of whole-genome and targeted sequencing datasets, including structural variant identification, mutation profiling, sequencing quality assessment, and translational genomics analyses. My work also involved analysing genomic biomarkers associated with cancer recurrence and investigating epigenetic mechanisms underlying therapy resistance using integrative computational approaches.