David Liu

This study identifies a therapeutic target, the entrapment complex, that clears toxic protein accumulation in distinct proteinopathies by disrupting a cargo receptor complex.

This study shows that immunogenic tumor mass dormancy, not cell-level quiescence, persists in residual melanoma lesions after immunotherapy, with active tumor cells and immune interactions.

This study identifies ZC3H18 mutations that stabilize endogenous retroviral RNA, linking ERV RNA accumulation to oncogenesis in zebrafish and human cell lines.

The paper expands P-NET, a deep learning framework that uses biological pathways for genomic and transcriptomic predictions, showing competitive performance and interpretability.

This paper introduces a proteogenomic framework combining genetic perturbations and proteomics to map phospho-signaling pathways in primary human T cells, validated with inflammatory stimuli.

The paper identifies melanoma differentiation and immune exhaustion as predictors of tumor progression and therapy response in in-transit melanoma patients through multiomic profiling.

This study shows that PTEN loss in cancer cells activates intrinsic interferon signaling, leading to immune resistance and vulnerability to CDK12 inhibition.

This paper shows YAP1 promotes therapy resistance in melanoma by altering chromatin structure and immune evasion programs, and suggests TEAD inhibitors could overcome resistance and boost immune response.

The paper identifies a mature dendritic cell subtype associated with improved immune response and prognosis in melanoma patients treated with anti-PD-1 checkpoint inhibitors.

This study develops a zebrafish model of mucosal melanoma by activating an oncogene and inactivating tumor suppressors, revealing that internal melanocytes give rise to tumors with distinct transcriptional and chromatin features.

This study shows that targeting CD38 improves T cell function and restores response to immunotherapy in melanoma models.

The paper maps spatial patterns of tumor cell dedifferentiation and plasticity in melanoma by analyzing RNA and protein expression across 300 histological domains.

The paper introduces BEANIE, a nonparametric method to improve differential enrichment analysis in single-cell transcriptomics by accounting for sample-specific biology and robustness of signatures across cohorts.

This study identifies distinct metastatic phenotypes in cutaneous melanoma using clinico-genomic data and machine learning, linking specific mutations and clinical factors to organ-specific metastasis.

The paper identifies HIF-2α as a therapeutic target for immune-infiltrated melanomas through stratified analysis of clinical and molecular data.

SpatialCells automates the analysis of spatially resolved multiplexed single-cell data to characterize tumor microenvironments, enabling efficient feature extraction and downstream machine learning applications.

This paper develops OncoNPC, a machine learning classifier for CUP patients using tumor sequencing data, showing improved survival outcomes with treatment aligned to predicted cancer types.

This paper reports a case where platinum chemotherapy exposure was linked to the development of aggressive thyroid cancer through mutational signatures and genomic analysis.

This paper introduces a biologically informed deep learning model for prostate cancer classification and identifies molecular drivers of treatment resistance.

This study identifies transcriptional states in mCRPC that mediate treatment resistance, including AR splicing heterogeneity, TGF-β signaling, and immune infiltration patterns.

This study identifies novel cancer driver genes by analyzing mutation nucleotide contexts in whole-exome sequencing data from 12,004 tumors.