Algorithm Matches Pediatric Patients with Cancer to Precision Medicines
Utilizing data from the INFORM registry, researchers were able to identify key biologic targets in pediatric patients with cancer utilizing an algorithm that matched them to targeted treatments with promising results.
BY Conor Killmurray
PUBLISHED May 29, 2020
Pediatric patients with cancer who relapse often have a poor prognosis, but a new algorithm has helped researchers identify molecular targets in these children and use that information to match them with targeted therapies, extending the time until disease progression by three months in some groups. The findings were presented during the virtual scientific program of the 2020 American Society of Clinical Oncology (ASCO) Annual Meeting.
The study looked at the multi-center and multi-national INFORM registry that collects clinical and molecular data from frozen tumor tissue donated by pediatric patients with treatment-resistant, relapsed or progressive cancer, with the intention of developing precision medicine approaches and testing their efficacy in this population. The registry has enrolled over 1,300 patients, and 526, with a median age of 12 and hailing from eight countries and 72 study sites, were included in the analysis of the effort to match children with targeted therapies. The study provided some with opportunities to be treated with targeted therapies that are currently only approved for adult use.
“For pediatric patients, if the cancer has relapsed, the prognosis is poor and there are few new innovative treatments,” said lead author Dr. Cornelis van Tilburg, a pediatric oncologist at Hopp Children’s Cancer Center Heidelberg, in Germany, in a press release. “Compare this to adult oncology, where there are many new trials, many new biomarkers and many new drugs. Pediatric oncology is really lagging behind when it comes to precision medicine and the development of new drugs.”
The study aimed to identify the highest-priority targets that could be found in these patients in order to match them with the best treatments. Targets included alterations in genes and in the way they are expressed that could lead to the development of cancer. The highest-priority alterations included ALK, BRAF and NRAS mutations and MET and NTRK fusions.
A total of 149 patients received a targeted treatment based on markers identified in the study. Twenty of those patients had an alteration of the highest priority, and in that group, researchers found a median time until disease progression of 204.5 days compared with 114 days for the other 506 patients. There were no significant differences in the length of survival.
Of the patients studied, 8% had a very high-priority level target, 14.8% had a high target, 20.3% had a moderate target, 23.6% had an intermediate target, 14.4% had borderline targets, 2.5% had a low-priority target, 1% had a very low-priority target and 15.4% of patients had no actionable target.
“Physicians used our algorithm for clinical decision making,” van Tilburg explained in a pre-recorded press briefing. Among study participants, “40 children were identified with cancer-predisposition syndromes, and their families were offered genetic counseling.” Half of those children had not previously been identified as having such a syndrome. Van Tilburg also explained that, in about 8% of patients with brain tumors, study leaders were able to help refine the original diagnosis of the patients based on data they collected. Seventeen of the children with an identified biologic target were treated in clinical trials.
Researchers concluded that implementing this model in a real-world setting is not only feasible but helps patients find treatments that are already approved or being tested in clinical trials.
“This registry has opened up the genomic landscape in pediatric oncology,” said van Tilburg. “It provides a unique source of information to help match new drugs or drug ideas with suitable biomarkers in certain pediatric patient populations.”
Researchers are now planning to analyze the data in the registry using the algorithm they developed and, based on these results, will launch a series of phase 1 and 2 trials exploring treatments for pediatric cancers driven by biomarkers identified in the program.