Area of research
Cancer Research · Genetics
Research interest
Research interests include Cancer Genomics and Diagnostics, Genomic variations and chromosomal abnormalities, Cancer Cells and Metastasis, and Gene expression and cancer classification.
Chemotherapy reshapes cellular clonal landscape via persister programs in breast cancer xenografts
Re-epithelialization of cancer cells increases autophagy and DNA damage: Implications for breast cancer dormancy and relapse
A large‐scale retrospective study in metastatic breast cancer patients using circulating tumour <scp>DNA</scp> and machine learning to predict treatment outcome and progression‐free survival
Fitness and transcriptional plasticity of human breast cancer single-cell-derived clones
Cancer Research in the Age of Spatial Omics: Lessons from IMAXT
Inducible re-epithelialization of cancer cells increases autophagy and DNA damage: implications for breast cancer dormancy
Interventionally-guided representation learning for robust and interpretable AI models in cancer medicine
The tumor microenvironment of 14,837 breast cancers is associated with clinical outcome independently of genomic subtypes
The molecular cartography of malignant and benign sebaceous tumours
Modeling Drug Responses and Evolutionary Dynamics Using Patient-Derived Xenografts Reveals Precision Medicine Strategies for Triple-Negative Breast Cancer
Development and validation of a reliable DNA copy-number-based machine learning algorithm (CopyClust) for breast cancer integrative cluster classification
Detecting Homologous Recombination Deficiency for Breast Cancer Through Integrative Analysis of Genomic Data
Vitamin B5 supports MYC oncogenic metabolism and tumor progression in breast cancer
Comparison of tumor‐informed and tumor‐naïve sequencing assays for ctDNA detection in breast cancer
A large-scale retrospective study in metastatic breast cancer patients using circulating tumor DNA and machine learning to predict treatment outcome and progression-free survival
Modelling drug responses and evolutionary dynamics using triple negative breast cancer patient-derived xenografts
Development and validation of a reliable DNA copy-number-based machine learning algorithm ( <i>CopyClust</i> ) for breast cancer integrative cluster classification
Single-cell genomic variation induced by mutational processes in cancer
Nucleoporin-93 reveals a common feature of aggressive breast cancers: robust nucleocytoplasmic transport of transcription factors
Genomic analysis of early-stage lung cancer reveals a role for TP53 mutations in distant metastasis
Multi-omic machine learning predictor of breast cancer therapy response
Expansion sequencing: Spatially precise in situ transcriptomics in intact biological systems
Three-dimensional imaging mass cytometry for highly multiplexed molecular and cellular mapping of tissues and the tumor microenvironment
Clonal fitness inferred from time-series modelling of single-cell cancer genomes
DNA methylation landscapes of 1538 breast cancers reveal a replication-linked clock, epigenomic instability and cis-regulation
Landscapes of cellular phenotypic diversity in breast cancer xenografts and their impact on drug response
FGFR1 amplification or overexpression and hormonal resistance in luminal breast cancer: rationale for a triple blockade of ER, CDK4/6, and FGFR1
Deciphering the signaling network of breast cancer improves drug sensitivity prediction
Germline <scp> <i>APOBEC3B</i> </scp> deletion increases somatic hypermutation in Asian breast cancer that is associated with Her2 subtype, <scp> <i>PIK3CA</i> </scp> mutations and immune activation
Positive correlation between transcriptomic stemness and PI3K/AKT/mTOR signaling scores in breast cancer, and a counterintuitive relationship with PIK3CA genotype