New research from Memorial Sloan Kettering Cancer Center (MSK) shows how a metabolite boosts immune cells against cancer; adds to the understanding of how colorectal cancer spreads; reveals tumor CD19 levels may predict success of CAR T cell therapy; and finds a new targeted drug combination shows promise in early trial for small cell lung cancer.

A recent study demonstrates that integrating multi-algorithm bioinformatics with machine learning models reveals peptide deformylase (PDF) as a novel biomarker for “cold” Colorectal cancer. High expression of PDF correlated with poor prognosis, low immune infiltration and high level of oxidative phosphorylation (OXPHOS) in Colorectal cancer. This may offer a metabolic–immune roadmap for precision immunotherapy of “cold” Colorectal cancer.

Northwestern Medicine scientists have developed an AI tool that rapidly and successfully measures colonoscopy quality by reviewing footage of the procedure.

Scientists from NTU Singapore have developed a new RNA-delivery platform based on liquid peptide microdroplets. In a preclinical study, these patent-pending microdroplets delivered siRNA into human immune cells and colorectal cancer cells, disrupting a “molecular handshake” between them from inside the cells. In mice with a hard-to-treat colorectal cancer model, the combined treatment reduced tumour growth by about 67 per cent, comparable with the gold-standard monoclonal antibody treatment used as the study benchmark. The new drug delivery platform can concentrate large RNA payloads and is being developed as a possible alternative to conventional lipid nanoparticles (LNPs). The researchers estimate that, if successfully scaled, such siRNA-based treatment could be up to 10 times cheaper to manufacture than monocloal antibodies, potentially leading to lower treatment costs.

One of Australia's largest real world studies of younger adults with advanced bowel cancer has identified important differences between cancers diagnosed before and after the age of 50, which could help doctors tailor treatments for younger adults more effectively.

Colorectal polyp detection from endoscopic images is critical for the early diagnosis of colorectal cancer. However, traditional deep learning methods often suffer from limited generalization when deployed across datasets containing different polyp morphologies. This work aimed to investigate whether vision-language foundation models can facilitate zero-shot generalization across multiple polyp datasets without target-domain fine-tuning.