Area of research
Cancer Research · Pathology and Forensic Medicine
Research interest
Functional genomics of breast cancer and its biological and clinical implications. His laboratory redefined the molecular taxonomy of breast cancer. He also co-lead seminal studies that define the clonal heterogeneity of triple negative breast cancers and the patterns of whole-genome ER binding in primary tumours. His group led the studies that established ctDNA as a monitoring biomarker in breast cancer and as a liquid biopsy to unravel therapy resistance. More recently his laboratory has developed and pioneered the use of patient-derived tumour explants as a model system for breast cancer.
Enhanced prediction of breast cancer patient response to chemotherapy by integrating deconvolved expression patterns of immune, stromal and tumor cells.
Evaluating progesterone receptor agonist megestrol plus letrozole for women with early-stage estrogen-receptor-positive breast cancer: the window-of-opportunity, randomized, phase 2b, PIONEER trial.
PI3K Inhibition in Combination with Tamoxifen in Patients with Metastatic HR+/HER2- Breast Cancer: Clinical and Circulating Tumor DNA Results.
Clinical Validity of Repeated Circulating Tumor Cell Enumeration as an Early Treatment Monitoring Tool for Metastatic Breast Cancer in the PREDICT Global Pooled Analysis
Clinical Validity of Repeated Circulating Tumor Cell Enumeration as an Early Treatment Monitoring Tool for Metastatic Breast Cancer in the PREDICT Global Pooled Analysis.
Quantifying the tumour vasculature environment from CD-31 immunohistochemistry images of breast cancer using deep learning based semantic segmentation
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
A large-scale retrospective study in metastatic breast cancer patients using circulating tumour DNA and machine learning to predict treatment outcome and progression-free survival.
Modeling Drug Responses and Evolutionary Dynamics Using Patient-Derived Xenografts Reveals Precision Medicine Strategies for Triple-Negative Breast Cancer.
Fitness and transcriptional plasticity of human breast cancer single-cell-derived clones
Cancer Research in the Age of Spatial Omics: Lessons from IMAXT
Re-epithelialization of cancer cells increases autophagy and DNA damage: Implications for breast cancer dormancy and relapse.
Quantifying the tumour vasculature environment from CD-31 immunohistochemistry images of breast cancer using deep learning based semantic segmentation.
Supplementary Legends from Modeling Drug Responses and Evolutionary Dynamics Using Patient-Derived Xenografts Reveals Precision Medicine Strategies for Triple-Negative Breast Cancer
Detecting homologous recombination deficiency for breast cancer through integrative analysis of genomic data.
Figure 3 from Modeling Drug Responses and Evolutionary Dynamics Using Patient-Derived Xenografts Reveals Precision Medicine Strategies for Triple-Negative Breast Cancer
Figure 1 from Modeling Drug Responses and Evolutionary Dynamics Using Patient-Derived Xenografts Reveals Precision Medicine Strategies for Triple-Negative Breast Cancer
Supplementary Figure from eQTL Set–Based Association Analysis Identifies Novel Susceptibility Loci for Barrett Esophagus and Esophageal Adenocarcinoma
Supplementary Table S6 from Clinical Validity of Repeated Circulating Tumor Cell Enumeration as an Early Treatment Monitoring Tool for Metastatic Breast Cancer in the PREDICT Global Pooled Analysis
Data from Clinical Validity of Repeated Circulating Tumor Cell Enumeration as an Early Treatment Monitoring Tool for Metastatic Breast Cancer in the PREDICT Global Pooled Analysis
Supplementary Figure S3 from Clinical Validity of Repeated Circulating Tumor Cell Enumeration as an Early Treatment Monitoring Tool for Metastatic Breast Cancer in the PREDICT Global Pooled Analysis
Supplementary Figures 1-20 from Modeling Drug Responses and Evolutionary Dynamics Using Patient-Derived Xenografts Reveals Precision Medicine Strategies for Triple-Negative Breast Cancer
Supplementary Table S8 from Clinical Validity of Repeated Circulating Tumor Cell Enumeration as an Early Treatment Monitoring Tool for Metastatic Breast Cancer in the PREDICT Global Pooled Analysis
Supplementary Table S9 from Clinical Validity of Repeated Circulating Tumor Cell Enumeration as an Early Treatment Monitoring Tool for Metastatic Breast Cancer in the PREDICT Global Pooled Analysis
Supplementary Figure S1 from Clinical Validity of Repeated Circulating Tumor Cell Enumeration as an Early Treatment Monitoring Tool for Metastatic Breast Cancer in the PREDICT Global Pooled Analysis
Figure 4 from Modeling Drug Responses and Evolutionary Dynamics Using Patient-Derived Xenografts Reveals Precision Medicine Strategies for Triple-Negative Breast Cancer
Data from Modeling Drug Responses and Evolutionary Dynamics Using Patient-Derived Xenografts Reveals Precision Medicine Strategies for Triple-Negative Breast Cancer
Figure 5 from Modeling Drug Responses and Evolutionary Dynamics Using Patient-Derived Xenografts Reveals Precision Medicine Strategies for Triple-Negative Breast Cancer
Supplementary Table S5 from Clinical Validity of Repeated Circulating Tumor Cell Enumeration as an Early Treatment Monitoring Tool for Metastatic Breast Cancer in the PREDICT Global Pooled Analysis