Genetic Signature Predicts Patients’ Response to Gastric Cancer Therapy

Globally, stomach cancer is the fifth-leading type of cancer and the third-leading cause of death from cancer, making up 7% of cases and 9% of deaths. Outcomes are often poor, with a less than 10% five-year survival rate in the Western world for advanced cases. This is largely because most people with the condition present with advanced disease. On the other hand, if stomach cancer is treated early, it can be cured. Treatments may include some combination of surgery, chemotherapy, radiation therapy, and targeted therapy. For certain subtypes of gastric cancer, cancer immunotherapy is an option as well.

Biomarkers that reliably predict gastric cancer patient response to chemotherapy and immune checkpoint inhibition are lacking. For this reason, investigators at the Mayo Clinic (Jacksonville, FL, USA) developed and implemented a machine learning algorithm (NTriPath) that integrated genetic data from more than 5,000 patients. NtriPath (Network regularized non-negative TRI matrix factorization for PATHway identification) is a method to integrate somatic mutations with biological prior knowledge (e.g., protein-protein interaction networks, pathway database) to detect cancer-type specific altered pathways by somatic mutations across cancers.

The data generated by NtriPath enabled the development of a molecular signature consisting of 32 genes that could be used to guide patient care decisions. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, the investigators identified four molecular subtypes that were prognostic for survival. They then built a support vector machine with linear kernel to generate a risk score that was prognostic for five-year overall survival and validated the risk score using three independent datasets. They also found that the molecular subtypes predicted response to adjuvant 5-fluorouracil and platinum therapy after gastrectomy and to immune checkpoint inhibitors in patients with metastatic or recurrent disease.

"Gastric cancer is among the leading causes of cancer-related death, worldwide," said senior author Dr. Tae Hyun Hwang, the Florida Department of Health cancer chair at the Mayo Clinic Cancer Center. "We sought to use genomic sequencing to build a model that predicts the likelihood that a patient will derive benefit from chemotherapy or from immunotherapy."

"We were pleased that our 32-gene signature provided not only prognostic information, but also predicted patient benefit from chemotherapy and immunotherapy," said Dr. Hwang. "In particular, we were surprised that the 32-gene signature we identified was able to predict a patient's response to immunotherapy because identifying reliable biomarkers for immunotherapy response in patients with gastric cancer has been a challenge for the field."

The study was published in the February 9, 2022, online edition of the journal Nature Communications.

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