Shunqiang Li

This study shows that HER2 and TP53 co-mutant cancers have increased histone acetylation and are sensitive to neratinib plus trastuzumab deruxtecan.

This paper develops chemotherapy-specific predictors for triple-negative breast cancer using patient-derived xenografts and multi-omic data, achieving high AUROC scores for response prediction.

The paper tests neratinib combinations with T-DM1 and vinorelbine in HER2 mutated breast cancer PDX models and organoids, finding synergistic effects that could inform clinical trials.

The paper presents a pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis of tumor regions and xenograft-transplant lymphoproliferative disorder prediction.

The paper develops radiomic signatures from FDG-PET imaging to predict response to neoadjuvant chemotherapy in triple-negative breast cancer using machine learning in a co-clinical trial setting.