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Recent research advances include a novel method for predicting gastric cancer risk and two novel treatment approaches.
Researchers at Cleveland Clinic developed a machine learning model that uses data from electronic health records to find patients most at risk for gastric cancer. The model, which incorporated data from 11,000 patients — including 567 with gastric cancer — predicted gastric cancer risk with surprising accuracy, they reported at the 2024 American College of Gastroenterology Annual Scientific Meeting. They believe using such a tool routinely could improve survival rates by catching more incidences of gastric cancer early, when the disease is easiest to treat.
In clinical trials, the multi-kinase inhibitor Stivarga (regorafenib) produced an improvement in overall survival in patients with refractory advanced gastric cancer. The drug is currently on the market to treat colorectal cancer, gastrointestinal stromal tumors and hepatocellular carcinoma. Investigators believe the results show the drug improves quality of life and delays disease progression, and that it could prove effective for treating gastric cancer patients in combination with chemotherapy and immunotherapy.
Patients with HER2-positive gastric cancer can become resistant to therapies targeting HER2. A novel drug currently in clinical trials, pelcitoclax, is designed to overcome that resistance by binding to two tumor targets and inducing the death of cancer cells. In a recently published preclinical study, combining the drug with chemotherapy was effective against HER2-positive tumors.
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