Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict
patient outcomes, and inform treatment planning. Here, we review recent applications of ML …

No cell lives in a vacuum, and the molecular interactions between cells define most
phenotypes. Transcriptomics provides rich information to infer cell–cell interactions and …

KRAS inhibitors demonstrate clinical efficacy in pancreatic ductal adenocarcinoma (PDAC);
however, resistance is common. Among patients with KRAS G12C-mutant PDAC treated …

Abstract RNA-sequencing (RNA-seq) has become an increasingly cost-effective technique
for molecular profiling and immune characterization of tumors. In the past decade, many …

Historically, the primary focus of cancer research has been molecular and clinical studies of
a few essential pathways and genes. Recent years have seen the rapid accumulation of …

Low response rate, treatment relapse, and resistance remain key challenges for cancer
treatment with immune checkpoint blockade (ICB). Here we report that loss of specific tumor …

Despite decades of clinical and preclinical studies, the markers and mechanisms of
resistance to cancer immunotherapy, particularly immune checkpoint blockade (ICB), remain …

Drugs that kill tumors through multiple mechanisms have the potential for broad clinical
benefits. Here, we first developed an in silico multiomics approach (BipotentR) to find cancer …

Background The mechanism of decreased T cells infiltrating tumor tissues in hepatocellular
carcinoma is poorly understood. Methods Cells were separated from the single‐cell RNA …

Deciphering cell-type heterogeneity is crucial for systematically understanding tissue
homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient …