Precision medicine for lung cancer decision-making: Evaluation of an -omics based FFT approach to personalized medicine.

Abstract

e20713 Background: The prevalence of next-generation sequencing and the availability of a large number of targeted therapies in the clinics, has complicated treatment decision-making in lung cancer. While national guidelines and commercial pathways offer a method to improve the oncologists’ adherence to appropriate testing and treatment modalities available, more effort is required to solidify this as a standard of care model at academic and community sites. A better understanding of the improved durable survival of targeted therapy assignment compared with non-targeted therapy outside of the clinical trial setting is needed to understand the efficacy and accuracy of precision medicine. Methods: We perform an in-depth analysis of a series of lung AD patients (n = 798) with genomic and clinical data in a recently created thoracic patient registry, who were treated at COH between 2009-2018 period. Results: 798 individuals with lung AD were identified in the Thoracic Oncology Registry who were treated or were intended to be treated at COH; 662 (83%) of the patients had genomic testing performed at the request of their treating oncologist and 460 (58%) of whom received a 1st-line targeted therapy decision (including clinical trial assignment based on bio-marker). Oncogenic alterations were detected in 653 (82%) patients with the majority presenting with EGFR (47%), who were mostly treated with erlotinib (78%). 462/653 (70%) patients had an alteration detected with an available FDA approved therapy and 90% (416/462) of the patients were appropriately matched to a targeted therapy based on the oncologist’s decision. Several decision-making algorithms were tested and fast-and-frugal trees (FFTs) proved superior with a positive predictive value (PPV) of 90% and only required two important cues in informing the decision of the type of treatment to give to the patient. Furthermore, a targeted therapy treatment decision showed a significant benefit with a median OS of 38 months as compared to 22 months in the non-targeted therapy decision-making group (p < 0.00001). This was also evident in the PFS analysis where targeted therapy decision-making had a median survival of 9 months as compared with 5 months in the other groups (p < 0.00001). Conclusions: FFTs are a novel tool to test the efficacy of precision medicine in a real-world setting and can provide a more streamlined method for clinical guidance and decision-making. FFTs were able to predict with 90% PPV a precision medicine decision that was correlated with improved PFS (9 vs 5 months) and OS (38 vs 22 months).