How Real-World Data Can Impact NSCLC Treatment

A number of abstracts at the 2019 American Society of Clinical Oncology (ASCO) Annual Meeting, held May 31 to June 4, 2019, in Chicago, Illinois, featured studies that focused on using real-world data to advance research and cancer care in non—small cell lung cancer (NSCLC).

“Now, what do we mean by real-world data? We mean data relating to patient health status or the delivery of healthcare routinely collected from a variety of sources including electronic health records, claims data, and more,” explained Sumithra Mandrekar, PhD, of the Mayo Clinic during an oral presentation of the abstracts. "But, that’s different from real-world evidence, which is the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from the analysis of real-world data."

The first presenter, R. Donald Harvey, PharmD, BCOP, FCCP, FHOPA, of the Winship Cancer Institute, discussed the results of a real-world study that surveyed the impact of broadening clinical trial criteria for patients with advanced NSCLC (aNSCLC).

He explained that the study utilized the guidelines put out by groups like ASCO and Friends of Cancer Research for broadening eligibility criteria. The goal was to make the trial population more representative of what is seen in the real world and make the results more generalizable, as well as accelerate trial accrual.

The study found that using expanded criteria would enable nearly twice as many patients with aNSCLC to qualify and consider trial participation, and it would also likely result in trial participants who are more reflective of a broader patient population.

However, Mandrekar offered a note of hesitation when expanding trial criteria: “Sometimes expanding trial criteria actually leaves you with a lack of randomization. Randomization is critical for the success of a trial.”

Interestingly, another study looked at a predictive model for determining 1-year survival in NSCLC based on electronic health records (EHRs) and tumor sequencing data available at the Department of Veterans Affairs (VA).

The cohort characteristics identified 365 patients who were older, predominantly male, and had a high rate of prior or current smokers. The study also found that a large number of patients in the cohort were classified as having stage IV NSCLC.

The genomic features of the predictive model defined binary features that reflected the presence or absence of variation in 96 genes that were included in both the EHR and tumor sequencing data, as well as the total number of these genes that were present without variation.

“We were able to build an accurate predictive model of 1-year survival in patients with NSCLC at the VA, which integrates real-world clinical and genomic data,” said Nathanael Fillmore, PhD, of the VA Boston Healthcare System. "This provides a good foundation to move forward in being able to offer support for clinical decision making for VA clinicians. However, the model does not yet include certain features including weight loss and treatment details."

Another study presented on utilizing big data to advance personalized therapies. Robert Doebele, MD, PhD, of the University of Colorado, began his presentation by first asking the question, “Is big data always best?” He offered an answer to his own question, explaining that sometimes, using big data or big clinical trials can allow researchers to miss smaller, yet significant, findings.

He presented data from a study that enrolled 1692 patients, and then broke that study out to just 9 patients with NSCLC. In a graph of all patients, all responses to the drug seemed to fall along the same curve. However, in the smaller cohort of patients, the data showed that this group actually had a “phenomenal response to the EGFR mutation,” he said.

In keeping with the theme of ASCO’s meeting this year: “Caring for every patient, learning from every patient,” Doebele closed by saying that “Models derived from large cohorts need robust data and, ultimately, need to be independently validated. Small data has ongoing merit and value for new discoveries, and with NSCLC especially, it can’t be thought of as a single disease.”