No cancer is like any other, so it’s no surprise that there’s a lot of variability in how patients respond to cancer drugs. This variability creates a challenge for drug developers: A compound can fail in clinical trials if it does not reverse or at least stall disease progression in a significant percentage of patients, even if it greatly helps some.
Researchers at Memorial Sloan-Kettering Cancer Center in New York examined one such case in a clinical trial of a drug called everolimus. While most patients in the trial were not helped by the drug, one did experience a remarkable recovery. The metastatic bladder cancer of this patient disappeared after a few months on everolimus.
By comparing the genome of this patient’s cancer to his or her healthy genome, the researchers identified a mutation in a gene known to be linked to everolimus’ molecular target. They then looked for mutations in the same gene in 13 other patients involved in the trial. Of the four additional patients with mutations in that gene, three had shown minor positive responses to everolimus, the team reports today in Science.
The authors write that the results show that tumor genome sequencing can identify previously unrecognized biomarkers for drug response in solid tumors. They also suggest that the genomes of tumors from “outlier” patients, who respond to a drug when many others don’t, may provide clues for identifying other patients who could also be helped by the treatment
“Although single patient anecdotes are often dismissed as failing to provide meaningful clinical evidence, this example illustrates the potential for such cases to inform future clinical development of drugs in molecularly defined populations.”
This new data poisoning tool lets artists fight back against generative AI
The tool, called Nightshade, messes up training data in ways that could cause serious damage to image-generating AI models.
Rogue superintelligence and merging with machines: Inside the mind of OpenAI’s chief scientist
An exclusive conversation with Ilya Sutskever on his fears for the future of AI and why they’ve made him change the focus of his life’s work.
Data analytics reveal real business value
Sophisticated analytics tools mine insights from data, optimizing operational processes across the enterprise.
Driving companywide efficiencies with AI
Advanced AI and ML capabilities revolutionize how administrative and operations tasks are done.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.