Abstract B018: Development of DNA methylation signature predictive of response to neoadjuvant chemotherapy in osteosarcoma

Abstract Introduction Clinical assessment of response to pre-operative therapy in osteosarcoma and identification of patients who would benefit from additional-line therapy currently relies on the extent of necrosis of the resected tumor. However, this is an imperfect surrogate marker which can be assessed only after multiple rounds of therapy have been administered and the whole tumor has been removed by surgery. Currently, it is not possible to identify patients who will benefit from preoperative therapy before it is applied, and it is not possible to obtain clinically relevant insights into the efficiency of this treatment before surgery. In this study, we seek to address the urgent need for predictive biomarkers for pediatric patients with osteosarcoma.Methods We sought to develop a DNA methylation signature of response to preoperative therapy in osteosarcoma.For this purpose, we analyzed the publicly available DNA methylation data from the NCI TARGET dataset, generated using Illumina 450k arrays. Clinical annotation of response to neoadjuvant therapy was available for 34 OS patients in this dataset. This cohort included 16 patients with good response (> 90% necrosis), and 18 patients with poor response (≤ 90% necrosis). We also generated a new EPICv2 array dataset (Illumina) from an independent set of 40 archival primary untreated osteosarcoma specimens obtained from patients treated at Stanford University and McGill University Health Centre.Results First, we performed unsupervised clustering of 34 primary untreated OS, which showed two distinct clusters: one cluster with 12/16 specimens from patients with good response, and the second cluster with 15/18 specimens from patients with poor response. The clusters were not associated with sex, tumour location or metastatic status. Next, we used ChAMP package for differential methylation analysis between the patients with good and poor response, which identified 45 differentially methylated CpGs that had an average β difference > 0.4 with adjusted p value < 0.05. Next, we performed a machine learning analysis using random forest algorithm, which indicated that a panel of as little as 10 of these 45 differentially methylated CpGs may be useful for prediction of response to neoadjuvant therapy. Currently, we are performing validation of these findings in the independent cohort of 40 patients. Conclusion Our preliminary results show that it is feasible to construct a DNA methylation signature predictive of response to neoadjuvant chemotherapy for pediatric patients with osteosarcoma. Next, we will evaluate the potential clinical utility of this signature by detecting the identified DNA methylation markers in circulating tumor DNA. The ultimate goal of this study is to develop a predictive liquid biopsy assay that will allow for stratification for neoadjuvant chemotherapy for pediatric patients with osteosarcoma. Citation Format: Philippe Jolivet, Livia Garzia, Sungmi Jung, Claudia Kleinman, Brooke Howitt, Nada Jabado, Janusz Rak, Joanna Przybyl. Development of DNA methylation signature predictive of response to neoadjuvant chemotherapy in osteosarcoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr B017.