Although targeted therapies are prescribed on the basis of a patient's molecular makeup, they do not work every time. And in those instances where they do work, they basically stop working every time. In response, researchers have developed a number of systems whose goal it is to predict which drugs will be effective for an individual patients.

Prophet, Oncohost Ltd.’s artificial intelligence-driven proteomics profiling platform, predicted response to immune checkpoint inhibitor (ICI) therapy in non-small-cell lung carcinoma (NSCLC) patients in a study presented at the European Society for Medical Oncology Virtual Congress 2021. While ICI can generate a significant positive response in some patients with advanced NSCLC, in others the therapy promotes tumor growth, making identification of likely responders prior to treatment critical.

Researchers have retrospectively divided more than 16,000 non-small-cell lung cancer (NSCLC) patients with EGFR mutations into four structure-based subgroups, and looked at how the members of each subgroup fared depending on which EGFR inhibitor they were given.

Monoclonal antibodies are already a pillar of cancer therapy, and cancer makes up the largest indication of FDA-approved antibodies, with almost 40% of the total. At the 2021 European Society of Medical Oncology (ESMO) Congress, Elena Garralda predicted that engineered versions of classical antibodies will soon join them on their pedestal.