334 - Genomic Signature with Next-Generation Sequencing for Oligometastatic Disease in Breast Cancer
Presenter(s)
P. J. Young1, R. D’Agostino2, A. R. Choi1, J. Hunting3, S. P. Ormond3, Y. Wang4, E. H. Douglas3, K. C. Ansley3, C. K. Cramer1, W. Li5, C. T. Whitlow6, F. Xing7, D. R. Soto-Pantoja7, J. Ruiz3, 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 School of Medicine,, Winston-Salem, NC, 3Department of Cancer Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, 4Department of Molecular and Cellular Bioscience, Wake Forest University School of Medicine, Winston-Salem, NC, 5Department of Pathology, Wake Forest University School of Medicine, Winston-Salem, NC, 6Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 7Department of Cancer Biology, Wake Forest University School of Medicine, Winston-Salem, NC
Purpose/Objective(s):
Biomarkers for oligometastases may be identified by examining the association between genomic data and radiographic evidence of oligometastatic disease.Materials/Methods:
Patients with breast cancer and metastatic disease who underwent next-generation sequencing (NGS) were identified in our departmental database. All NGS was performed with FoundationOne. NGS was performed on a tissue sample at time of diagnosis of metastasis. Oligometastatic disease was defined as patients having =5 metastases without diffuse involvement of a single organ. Widespread disease was any spread beyond oligometastatic. Genes were screened using Fisher exact tests (p<0.1) to identify mutations associated with either oligometastatic or widespread disease. A score of +1 was assigned for mutations associated with oligometastatic disease, and -1 was assigned for mutations associated with widespread disease. Scores were summed to create a risk score for the likelihood of oligometastatic disease, with this risk score modeled to examine its ability to predict oligometastatic disease vs widespread disease. For oligometastatic patients, a competing risk analysis was done to determine the association of the risk score to the cumulative incidence of oligometastatic progression accounting for competing risks of widespread progression of extracranial disease or death.Results: 269 patients with metastatic breast cancer were found, of which 119 (44%) had oligometastatic disease. Among the standard panel of 324 tested genes, 15 genes (FAT1, JAK1, LRP1B, MED12, BAP1, GATA6, GPR124, KLHL6, MLL, NF2, NRAS, PIK3C2G, PIK3CB, PPP2R1A, SYK) were identified having an association with oligometastatic disease (p < 0.1). 8 genes (PIK3CA, CTCF, EP300, ERBB2, ESR1, IGF1R, PDGFRA, PMS2) were associated with not having oligometastatic disease (p < 0.1). Among these genes, MLL and MED12 were the best predictors of oligometastatic disease, with all participants with these genes having oligometastatic disease (p=0.007). 52 (19%) patients had a positive oligometastatic score, 92 (34%) had a neutral score, and 122 (45%) had a negative score. A competing risk analysis was performed assessing the 3-level oligometastatic risk score and its association with likelihood of oligometastatic progression. Wald Chi-square test for positive, neutral, and negative risk scores demonstrated an incidence of oligometastatic progression of 73%, 50% and 41%, respectively (p=0.001).
Conclusion:
NGS can classify patients according to their risk of having oligometastatic disease and this risk score was able to predict patients with oligometastatic progression with a high level of statistical significance. While local therapies often fail the broader oligometastatic breast cancer population, the proposed genomic risk stratification system enables a shift toward precision intervention, identifying those most likely to derive benefit from local therapies.