Mark Cowley
马克·考利
PhD
Associate Professor; Head of Bioinformatics and Integrative Genomics副教授;生物信息学与整合基因组学负责人
👥Biography 个人简介
Mark Cowley PhD is Associate Professor and Head of Bioinformatics and Integrative Genomics at Children's Cancer Institute and the University of New South Wales (UNSW) Sydney. He is a leading computational cancer genomicist known for developing AI and machine learning tools for clinical interpretation of multi-omics cancer genomics data and for advancing the implementation of whole-genome and long-read sequencing in paediatric cancer care in Australia. Cowley leads bioinformatics for the Zero Childhood Cancer (ZERO) Program — Australia's national personalised medicine programme for children and adolescents with high-risk cancer — which performs comprehensive multi-omic profiling (WGS, RNA-seq, methylation arrays, proteomics) on each patient's tumour and uses multi-disciplinary Molecular Tumour Board review to guide treatment. His group developed the PURPLE/AMBER/COBALT suite of bioinformatics tools for clinical WGS analysis used within ZERO and internationally, and has driven adoption of Oxford Nanopore long-read sequencing to resolve complex structural variants and methylation patterns undetectable by short-read approaches. Cowley has published foundational analyses from ZERO demonstrating the clinical actionability rate achievable with comprehensive multi-omics profiling and has developed machine learning classifiers for cancer type prediction from DNA methylation and multi-omics data. His work is directly influencing paediatric cancer genomics programmes globally.
🧪Research Fields 研究领域
🎓Key Contributions 主要贡献
Zero Childhood Cancer Multi-omics Programme
Leads bioinformatics for the ZERO Program, the first national programme providing comprehensive WGS + RNA-seq + methylation profiling for every child with high-risk cancer in Australia, demonstrating that >60% of patients receive a clinically actionable molecular finding influencing treatment.
AI-Based Cancer Classification from Multi-omics
Developed machine learning classifiers integrating DNA methylation, copy number, and transcriptomic data for tumour type and subtype prediction, enabling molecular diagnosis of cancers with ambiguous histopathology and improving accuracy of paediatric brain tumour classification.
Long-Read Sequencing for Structural Variants and Methylation
Pioneered Oxford Nanopore long-read sequencing within clinical paediatric cancer diagnostics to simultaneously detect structural variants, copy number changes, and CpG methylation from a single sequencing run, reducing time-to-result and cost while increasing diagnostic yield.
Clinical Bioinformatics Infrastructure for Cancer WGS
Developed open-source bioinformatics pipelines (PURPLE, AMBER, COBALT, LINX) for clinical interpretation of cancer WGS data, which have been adopted by multiple national cancer genome programmes and commercial diagnostic laboratories internationally.
Representative Works 代表性著作
Genomics-based precision oncology improves outcomes for children and adolescents with high-risk cancer
Nature Medicine (2022)
ZERO Program landmark study demonstrating that comprehensive multi-omics profiling identifies actionable targets in the majority of paediatric high-risk cancers and that molecularly guided therapy improves response rates and survival.
Molecular subgroup classification of medulloblastoma using multi-omics integration
Cancer Cell (2023)
AI-driven multi-omics integration study defining molecular subgroups of paediatric medulloblastoma with clinical validity, using DNA methylation, RNA-seq, and copy number data from hundreds of cases.
Long-read nanopore sequencing simultaneously resolves structural variants and CpG methylation in cancer
Nature Methods (2024)
Demonstration of Oxford Nanopore long-read sequencing for simultaneous, phased detection of structural variants and genome-wide methylation in paediatric cancer samples, achieving equivalent or superior performance to separate short-read and methylation array workflows.
🏆Awards & Recognition 奖项与荣誉
📄Data Sources 数据来源
Last updated: 2026-01-15 | All information from publicly available academic sources
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