Finding weak spots of cancer cells with unsupervised learning
Diego Oyarzún and doctoral student Denise Thiel are co-authors of a PNAS paper led by Imperial College scientists that explores ‘weak spots' in cancer cells recovering from chemotherapy.
Diego Oyarzún from ANC and student Denise Thiel co-authored a PNAS paper led by Imperial College that explores ‘weak spots' in cancer cells recovering from chemotherapy. Cancers are notoriously difficult to treat because they can withstand the toxic effects of chemotherapy, making therapies for extremely invasive cancers ineffective. The study looked at the effects of chemotherapy on multiple myeloma cells, an incurable form of bone marrow cancer. The findings reveal that cells recovering from chemotherapy show specific vulnerabilities which could be targeted by a ‘second punch’ treatment.
The Edinburgh team supported the data analysis by combining Gaussian process regression and graph diffusion processes to carry out unsupervised clustering of large, high-dimensional, time series data. The machine learning methods revealed a new vulnerability in cells that can potentially be targeted with a second drug to prevent cells from recovering.
The paper ‘Systems level profiling of chemotherapy-induced stress resolution in cancer cells reveals druggable trade-offs’ by Paula Saavedra-García et al is now available to read from PNAS.