2527 - Prediction of Oligometastatic Disease from Breast Cancer Using Next Generation Sequencing Genomics
Presenter(s)
P. J. Young1, A. R. Choi1, J. Hunting2, S. P. Ormond2, Y. Wang3, E. H. Douglas2, K. C. Ansley2, C. K. Cramer1, W. Li4, C. T. Whitlow5, F. Xing6, D. R. Soto-Pantoja6, J. Ruiz2, and M. D. Chan1; 1Department of Radiation Oncology, Wake Forest University School of Medicine, Winston-Salem, NC, 2Department of Internal Medicine, Section of Hematology and Oncology, Wake Forest University School of Medicine, Winston-Salem, NC, 3Department of Molecular and Cellular Bioscience, Wake Forest University School of Medicine, Winston-Salem, NC, 4Department of Pathology, Wake Forest University School of Medicine, Winston-Salem, NC, 5Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC, 6Department of Cancer Biology, Wake Forest University School of Medicine, Winston-Salem, NC
Materials/Methods: Patients with breast cancer and metastatic disease who underwent next generation sequencing (NGS) were identified from electronic medical records. All NGS in the cohort was performed using FoundationOne. Oligometastatic disease was defined as patients having =5 metastases without diffuse involvement of a single organ. Widespread disease was any spread beyond oligometastatic. Fisher exact test was used to identify mutations discovered on NGS to be statistically associated (p<0.1) with either oligometastatic or widespread disease. A score of +1 was assigned for every mutation present associated with oligometastatic disease, and -1 was assigned for mutations associated with widespread disease. Patient scores were summed to create a risk score to predict oligometastatic disease, and scores were correlated to the likelihood of having oligometastatic disease on imaging. For patients with oligometastatic disease, a competing risk analysis was done to determine the cumulative incidence of oligometastatic progression accounting for the potential competing risks of widespread progression of extracranial disease or death. Cox regression was used to determine association between oligometastatic risk score and oligometastatic progression.
Results: A total of 87 patients with metastatic breast cancer were included in the study, of which 37 (43%) had oligometastatic disease. Among the standard panel of 324 tested genes, 12 genes were identified as showing some association with oligometastatic disease (p < 0.1). Among these genes, two were associated with not having oligometastatic disease (PIK3CA and ZNF703), whereas the other 10 (CDH1, EPHA3, ERBB3, FAM123B, FAT1, JAK1, LRP1B, MAP2K4, MED12, and PTEN) were associated with having oligometastatic disease. Among these 12 genes, MAP2K4 was the best predictor of oligometastatic disease, with all participants with this gene (5/87) also having oligometastatic disease (p=0.01). The competing risk analysis found that a 3-level oligometastatic risk score was significantly associated with the likelihood of oligometastatic progression based on the Wald Chi-square test (p=0.0003). Patients with positive, neutral and negative risk scores for oligometastatic disease had a cumulative 24-month incidence of oligometastatic progression of 100%, 20% and 25%, respectively (p=.008 from competing risk model). Similar findings were seen for the time to widespread progression, where patients with positive, neutral and negative risk scores had a cumulative 24-month incidence of widespread progression of 50%, 7%, and 20%, respectively (p<0.001).
Conclusion: NGS was able to classify patients according to their risk of having oligometastatic disease and this score was able to identify patients with oligometastatic progression.