Han Liang

This paper shows that combining IRE with anti-PD-1 induces abscopal effects in PDAC by triggering immune activation and systemic antitumor responses.

The paper identifies two resistance mechanisms in gastric cancer to chemo-immunotherapy, involving CEACAM5/6+ tumor cells and IL-1β+ macrophages, and suggests them as biomarkers and therapeutic targets.

This study identifies a co-evolved tumor-immune interaction involving KRAS signaling and LILRBhigh macrophages that drives PDAC recurrence and presents a therapeutic strategy targeting this axis.

This study identifies NRF2 pathway activation and SPP1⁺TREM2⁺ macrophages as drivers of chemoradiotherapy resistance in ESCC through multi-omics analysis of clinical trial data.

This study introduces AUC-PR as a better metric for evaluating in silico perturbation models' ability to predict differentially expressed genes, showing it outperforms R2 in capturing biologically relevant outcomes.

This study shows that ATRA causes ADAR1 degradation and enhances PD-1 blockade to improve antitumor immunity and survival in pancreatic cancer.

The paper profiles spatial transcriptomic and translatomic data of dopaminergic neurons in young and old mice, identifying markers of aging and disease-related pathways, and highlights CASR as a novel regulator of dopaminergic calcium handling.

This study evaluates the impact of HRD status on clinical outcomes in Chinese EOC patients undergoing first-line chemotherapy and PARPi maintenance therapy, showing improved PFS and OS in HRD-positive patients.

The paper identifies a convergent pattern of amino acid usage in cancers that mirrors reversed tissue development, using ECPA as a biomarker with high accuracy in liver cancer detection.

The paper compiles protein response data from cancer cell lines to drug perturbations, showing improved drug sensitivity prediction and building a protein-drug connectivity map with an accessible data portal.

The paper develops TransPRECISE, a Bayesian network framework, to analyze pan-cancer patient and cell line interactomes, identifying pathway activities and drug sensitivities across multiple cancer types.

The paper identifies gene expression correlation patterns (AEPs) associated with cancer prognosis across 22 cancer types using transcriptomic data from 5,001 patients.

This study characterizes mitochondrial genomes in 2,700 cancer patients across 37 types using whole-genome sequencing and RNA-seq, revealing mutational patterns, truncating mutations, and nuclear transfer of mitochondrial DNA.

The paper analyzes immuno-genomic data across 31 cancer types to identify immune escape mechanisms and immuno-editing histories.