1053 - Optimizing Blood-Based Genomic Characterization of Oligometastatic Non-Small Cell Lung Cancer Patients
Presenter(s)
A. R. Choi1, C. M. Lanier1, S. E. Glynn1, P. J. Young1, R. D'Agostino Jr2, M. Farris1, M. Abdulhaleem3, Y. Wang4, J. Ruiz5, T. Lycan5, W. Petty5, C. K. Cramer1, S. B. Tatter6, A. Laxton6, J. White6, W. Li7, J. Su8, C. T. Whitlow9, F. Xing10, and M. D. Chan1; 1Department of Radiation Oncology, Wake Forest University School of Medicine, Winston-Salem, NC, 2Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston -Salem, NC, 3Division of Hematology/Oncology, West Virginia University Cancer Institute, Morgantown, WV, 4Department of Molecular and Cellular Bioscience, Wake Forest University School of Medicine, Winston-Salem, NC, 5Department of Internal Medicine, Section of Hematology and Oncology, Wake Forest University School of Medicine, Winston-Salem, NC, 6Department of Neurosurgery, Wake Forest University School of Medicine, Winston-Salem, NC, 7Department of Pathology, Wake Forest University School of Medicine, Winston-Salem, NC, 8Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, 9Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC, 10Department of Cancer Biology, Wake Forest University School of Medicine, Winston-Salem, NC
Purpose/Objective(s): Oligometastatic non-small cell lung cancer (NSCLC) represents a distinct clinical entity for which ablative local therapies may significantly improve outcomes. We previously identified a six-gene, liquid biopsy-based signature predictive of extracranial oligometastatic disease and oligoprogression using two independent datasets of NSCLC patients. Herein, we performed a combined analysis so as to develop a more comprehensive genomic signature better profiling the true oligometastatic phenotype.
Materials/Methods: We merged two institutional patient databases: an initial cohort of NSCLC patients with brain metastases and an independent validation cohort of NSCLC patients without brain metastases. Comprehensive genomic data based on circulating tumor DNA (Guardant 360) were analyzed via Fisher’s exact tests to screen for a set of gene mutations moderately associated (p<0.1) with an oligometastatic extracranial disease state (
=5 metastases without diffuse single-organ involvement). A risk score was calculated based on the presence of these mutations. A competing risk analysis for the cumulative incidence of oligoprogression was performed, using the competing risks of widespread progression and death. Cox regression was employed to assess the association between oligometastatic risk score and oligoprogression. Mantel Haenszel Chi-Square testing was used to assess the relationship between the risk score and overall survival (OS).Results: 355 patients were eligible for analysis. An expanded oligometastatic signature comprising 18 unique genes was identified. Patients with low-risk (negative-value) scores based on this signature exhibited a 96.0% likelihood of oligometastatic disease, compared to 63.3% and 46.6% for those with neutral- or high-risk scores (p<0.0001). Likelihood of oligoprogression was significantly higher with low-risk scores; Cox regression analysis revealed a hazard ratio of 6.11 (95% CI: 2.32-16.1) compared to high-risk scores and a hazard ratio of 2.08 (95% CI: 1.28-3.37) compared to neutral-risk scores (p=0.0007). Moreover, low-risk scores were associated with higher 1-year OS (84.0%) compared to neutral- (73.7%) or high-risk scores (68.1%) (p=0.02).
Conclusion: This enhanced 18-gene profile significantly predicted for a state of oligometastatic extracranial disease, oligoprogression, and 1-year overall survival. With further prospective validation, this biomarker may have the potential to identify ideal candidates for local therapies such as SBRT, helping to integrate genomic risk stratification into the management of oligometastatic NSCLC.