Biomarker-Informed Treatments for Gastric Cancer Patients Improve Outcomes

Colon cancer, light micrograph

Use of a highly personalized approach to treatment including specific monoclonal antibodies tailored to a patient’s biomarker profile improves outcomes for patients with metastatic gastroesophageal cancers, shows research from the University of Chicago.

The phase II trial split patients into eight groups depending on the biomarker profile of their individual tumors. An algorithm was used to assign patients to the best combination of monoclonal antibodies to treat their tumor profile.

Using this treatment approach improved 1-year survival from 50% to 66% and increased median survival time from less than 12 to 15.7 months.

A big problem with treating advanced cancers is that tumors are highly variable and show a wide range of different biomarker profiles meaning that some treatments work better than others. This makes developing new treatments difficult, as the standard ‘one treatment fits all’ randomized-controlled trial method of developing new drugs does not account for this problem.

To try and address the need for more personalized therapies for advanced cancer patients, Daniel Catenacci, M.D., director of the gastrointestinal oncology program and associate professor of medicine at the University of Chicago Medical School, set up a new kind of trial called the Personalized ANtibodies for Gastro-Esophageal Adenocarcinoma, or PANGEA study, with his colleagues.

“The genetic drivers between patients are often quite different, and it can be hard to run a classical clinical trial because only a fraction of patients has that specific genetic profile. On top of that, differences in the composition of a patient’s primary tumor compared to metastatic tumors adds further treatment complications.”

As described in the journal Cancer Discovery, Catenacci and team recruited 68 patients with newly diagnosed advanced, metastatic gastroesophageal cancer to take part in the trial. The participant’s tumors were biopsied on admission to the study and depending on their biomarker profile were assigned to one of eight treatment groups with monoclonal antibodies such as nivolumab, trastuzumab and ramucirumab. The patients also received “optimally sequenced” chemotherapy.

It is common for tumors to develop resistance to therapies so the trial allowed for up to two re-biopsies and treatment group changes depending on how the patient’s cancer developed.  This happened in almost half the patients.

“Rather than looking at one genetic event at a time with one drug, we’d use a predefined algorithm to test multiple therapies for different genetic drivers at once, and using the same algorithm, adjust therapeutic approaches if a patient’s cancer became resistant to their original treatment,” explained Catenacci.

This kind of trial is unusual and was set up as a pilot to assess whether it was feasible to carry out. Overall, 45 of the 68 patients enrolled were still alive 1 year after beginning the trial, which exceeded the primary endpoint of survival of more than 50% of patients at 1 year.

The success of this trial shows this kind of approach can work, even if it is complex to organize. The PANGEA team now want to expand this approach to include more patients and ideally set up a group of randomized controls to help validate their findings further.

“At first there was only chemo for HER2-negative tumors in the first-line setting, but now other targeted therapies are coming out, including anti-PD1, anti-FGFR2 and anti-claudin,” Catenacci comments.

“How does a physician decide which treatment to prescribe when a patient might be eligible for multiple options given known overlap in a tumor of the predictive biomarkers for each of these therapies? This study shows that using an algorithm such as ours could help with that prioritization to direct optimal care, and may potentially lead to better outcomes for patients.”

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