Machine Learning Predicts Therapeutic Response in Gastric Cancer

Artificial Intelligence
Credit: iStock/© agsandrew

Scientists at the Mayo Clinic Cancer Center in Florida say a recent study is validating the use of genomic sequencing to predict the likelihood that patients with gastric cancer will derive benefit from chemotherapy or from immunotherapy. Their paper (“Development and validation of a prognostic and predictive 32-gene signature for gastric cancer”) is published in Nature Communications.

“Gastric cancer is among the leading causes of cancer-related death, worldwide,” says Tae Hyun Hwang, PhD, the Florida Department of Health cancer chair at Mayo Clinic Cancer Center in Florida. Hwang points out that most patients with gastric cancer are treated with chemotherapy, and sometimes immunotherapy, as part of their treatment plan. However, not all patients derive benefit from these therapies. “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,” adds Hwang.

To build this model, Hwang and his team developed and implemented a machine learning algorithm that integrated genetic data from more than 5,000 patients. Then the team developed a molecular signature consisting of 32 genes that could be used to guide patient care decisions.

“Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking,” write the investigators.

“In this retrospective analysis, we use our machine learning algorithm NTriPath to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identify four molecular subtypes that are prognostic for survival. We then built a support vector machine with linear kernel to generate a risk score that is prognostic for five-year overall survival and validate the risk score using three independent datasets. We also find that the molecular subtypes predict response to adjuvant 5-fluorouracil and platinum therapy after gastrectomy and to immune checkpoint inhibitors in patients with metastatic or recurrent disease.

“In sum, we show that the 32-gene signature is a promising prognostic and predictive biomarker to guide the clinical care of gastric cancer patients and should be validated using large patient cohorts in a prospective manner.”

“We were pleased that our 32-gene signature provided not only prognostic information, but also predicted patient benefit from chemotherapy and immunotherapy,” says 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.”

Hwang explains that the 32-gene molecular signature still needs prospective validation, but he believes it eventually will be able to identify patients who are likely to respond to chemotherapy and immunotherapy. “Similarly, we would also be able to identify patients who are unlikely to benefit from chemotherapy and immunotherapy, thereby sparing them the potential side effects of these therapies,” he adds.

The team is working to develop new assays based on the expression level of a single, or several, genes to make biomarkers more accessible and easily deployed in the clinical setting.

“We are working on artificial intelligence algorithms that utilize diagnostic histopathology images to identify patients most likely to derive benefit from immunotherapy,” reports Hwang. “We are also studying the molecular mechanisms of immunotherapy resistance made available by the machine learning and artificial intelligence approaches that we have developed in our lab.”

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