Abstract 2130: Development of a biomarker-driven patient selection strategy for AsiDNA treatment

Abstract

Abstract Purpose: Accurate evaluation and prediction of response to anti-cancer treatment remain a great challenge. Stratification biomarkers are of great value to identify responders or non-responders to a specific drug, or even to distinguish between early and delayed responses. In this study, we identified a gene signature to predict AsiDNA treatment efficacy in patients. Experimental design: The first step of this study consisted on the analysis of gene expression profile in a set of 12 Breast cancer cell lines. Genes retrieved from correlation analysis between sensitivity to AsiDNA and gene expression were used to create a gene signature predicated on the upregulation of positively correlated genes and the down regulation of negatively correlated genes (the AsiDNA sensitivity signature - AsiSS) using the UCSC Xena platform for data visualization. The AsiSS was used to sort the TCGA Breast Cancer (BRCA) patient cohort, DNA repair gene expression and cell lines from the CCLE (1100 cell lines) into rank orders, in order to retrieve a gene set highly predictive of response to AsiDNA. Results: A list of genes the most strongly correlated with survival to AsiDNA was compiled. 39 genes showed expression profiles positively correlated with survival to AsiDNA treatment (Spearman r > 0.67, P<0.005) and 35 genes showed a strong negative correlation with survival (Spearman r > -0.63, P<0.005). Based on these correlated genes, an AsiSS was created. In the BRCA cohort the top 25% AsiDNA sensitivity scoring patients were identified, and DNA repair gene expression levels of these patients were rank ordered and sorted according to AsiSS. The top 7 genes with significantly lower expression in the 25% top AsiDNA sensitive patient group were retrieved (XRCC2, MRE11A, POLQ, BRCA2, NBN, FANCA, RAD54B; 10-14 < p value < 10-9). To validate the predictive value of these genes, cell lines from the CCLE were rank ordered according to the AsiSS, and response to AsiDNA of 20 cell lines (10 predicted sensitive; 10 predicted resistant) as well as the expression level of the top 7 genes were analyzed. Importantly, cells predicted resistant show significantly higher survival (mean survival = 80%) compared to cells predicted sensitive (mean survival = 40%; p < 0.01). Among the 7 retrieved genes, gene expression analysis revealed a correlation between sensitivity to AsiDNA and the expression of BRCA2 (Pearson r: 0.75), NBN (r: 0.62), MRE11A (r: 0.6), RAD54B (r: 0.54), FANCA (r: 0.6) and XRCC2 (r: 0.55) genes (0.001< p <0.05). This set of genes could be used to stratify patients for future clinical trials involving AsiDNA. Conclusion: Overall, our results highlight the interest of retrospective analysis based on patient databases to retrieve gene biomarkers predictive of drug outcome. As AsiDNA is being currently tested in a clinical trial, a potential exist for a rapid validation of our gene set in the aim to develop a Biomarker-driven patient selection strategy for AsiDNA treatment. Citation Format: Wael Jdey, Donogh O'Brien, Richard Tripelon, Séverine Rochas, Marie Dutreix, Françoise Bono. Development of a biomarker-driven patient selection strategy for AsiDNA treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2130.