Evaluation of Omics-Based Strategies for the Management of Advanced Lung Cancer

Author:

Salgia Ravi1,Mambetsariev Isa1,Pharaon Rebecca1,Fricke Jeremy1,Baroz Angel Ray1,Hozo Iztok2,Chen Chen3,Koczywas Marianna1,Massarelli Erminia1,Reckamp Karen14,Djulbegovic Benjamin5

Affiliation:

1. Department of Medical Oncology and Therapeutics Research, City of Hope, Duarte, CA

2. Department of Mathematics, Indiana University Northwest, Gary, IN

3. Applied AI and Data Science, City of Hope, Duarte, CA

4. Division of Medical Oncology, Cedars-Sinai Medical Center, Los Angeles, CA

5. Department of Hematology and Hematopoietic Cell Transplantation, City of Hope, Duarte, CA

Abstract

PURPOSE: Omic-informed therapy is being used more frequently for patients with non–small-cell lung cancer (NSCLC) being treated on the basis of evidence-based decision-making. However, there is a lack of a standardized framework to evaluate those decisions and understand the association between omics-based management strategies and survival among patients. Therefore, we compared outcomes between patients with lung adenocarcinoma who received omics-driven targeted therapy versus patients who received standard therapeutic options. PATIENTS AND METHODS: This was a retrospective study of patients with advanced NSCLC adenocarcinoma (N = 798) at City of Hope who received genomic sequencing at the behest of their treating oncologists. A thoracic oncology registry was used as a clinicogenomic database to track patient outcomes. RESULTS: Of 798 individuals with advanced NSCLC (median age, 65 years [range, 22-99 years]; 60% white; 50% with a history of smoking), 662 patients (83%) had molecular testing and 439 (55%) received targeted therapy on the basis of the omic-data. A fast-and-frugal decision tree (FFT) model was developed to evaluate the impact of omics-based strategy on decision-making, progression-free survival (PFS), and overall survival (OS). We calculated that the overall positive predictive value of the entire FFT strategy for predicting decisions regarding the use of tyrosine kinase inhibitor–based targeted therapy was 88% and the negative predictive value was 96%. In an adjusted Cox regression analysis, there was a significant correlation with survival benefit with the FFT omics-driven therapeutic strategy for both PFS (hazard ratio [HR], 0.56; 95% CI, 0.42 to 0.74; P < .001) and OS (HR, 0.51; 95% CI, 0.36 to 0.71; P < .001) as compared with standard therapeutic options. CONCLUSION: Among patients with advanced NSCLC who received care in the academic oncology setting, omics-driven therapy decisions directly informed treatment in patients and was correlated with better OS and PFS.

Publisher

American Society of Clinical Oncology (ASCO)

Subject

Oncology(nursing),Health Policy,Oncology

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