Circulating myeloid cells invade the brain during pancreatic cancer, where they accumulate at a unique central nervous system interface and drive anorexia and muscle catabolism.

A method was developed for the metabolomic analysis of small numbers of flow cytometrically isolated cells from rare cell populations such as hematopoietic stem cells and circulating cancer cells.

Tumor-induced myeloid-derived suppressor cells exert systemic suppression of adaptive immunity by limiting L-selectin-directed trafficking of T cells at vascular checkpoints in lymph nodes.

Oxaliplatin-resistant colorectal cancer cells exhibit unregulated death receptor 4 expression with increased receptor palmitoylation and translocation into lipid rafts, increasing their sensitivity to apoptosis via TRAIL.

Application of machine learning to serum miRNA profiles generated through next generation sequencing identifies a biologically relevant miRNA signature which can be deployed as a qPCR test to assist the diagnosis of epithelial ovarian cancer.

Machine learning-assisted experiments reveal CD81 as a novel binding partner of CD44 in driving cancer stemness, tumor cell clustering, and extracellular vesicle integrity in human and mouse breast cancer.

Whole-transcriptomic profiling of circulating small extracellular vesicle RNA facilitates the detection of T1a stage colorectal cancer and precancerous advanced adenoma.

Circulating immune cells reflect intra-tumoural immunity and can predict response to systemic therapy in head and neck cancer.

SPOT-MAS (screening for the presence of tumor by methylation and size) is an affordable and effective multi-cancer early detection test analyzing multiple features of circulating tumor DNA.

Isotope tracing into macromolecules enables functional analysis of metabolism in specific cell populations in tumors and provides insight into metabolic differences between cancer and stromal cells in their endogenous microenvironment.