DNA methylation is a key determinant of response to targeted and immune checkpoint therapies in metastatic renal cell carcinoma

Abstract The response to targeted therapies and immune checkpoint inhibitors for patients suffering from metastatic clear cell renal cell carcinoma (ccRCC) is heterogeneous and currently not predictable in clinic. We conducted a meta-analysis of 700 ccRCCs profiled by DNA methylation to compare the ability ofseveral published probe signatures to cluster patients into hyper- and hypomethylation subtypes. To gain further insights into the biological relevance of these subtypes, we characterized them using matched RNA sequencing profiles. We studied differences affecting biological pathways, transcriptional regulators, tumor microenvironment composition, and the expression levels of enzymes known to control cytosine DNA methylation. Then, to facilitate the clinical translation of these findings, we developed predictive models capable of inferring ccRCC methylation subtypes from specific gene expression markers. Finally, we evaluated the association between the predicted DNA methylation subtypes and patient responses to ccRCC therapies. We showed that the hyper-methylated tumors exhibited a worse prognosis, a higher fraction of cycling tumor cells and a lower activity of homeobox transcription factors. To translate the use of DNA methylation information into a clinical setting, we developed a simple model accurately predicting the ccRCC methylation subtypes (AUC-ROCs of 0.91) from two gene expression ratios (IGF2BP3/PCCA, TNNT1/TMEM88). In addition, these methylation subtypes were significantly associated with the therapeutic outcome of patients to anti-PD-1, mTOR inhibitor or tyrosine kinase inhibitor therapies. Overall, our framework for predicting the ccRCC DNA methylation subtypes from targeted gene expression data is easy to translate in clinic and contributes to better personalization of ccRCC therapies. Code repository This repository contains data and code in order to reproduce the entire data analysis from the article entitled "DNA methylation is a key determinant of response to targeted and immune checkpoint therapies in metastatic renal cell carcinoma" (bioRxiv 2024.11.05.622095; doi: https://doi.org/10.1101/2024.11.05.622095). CodeNotebook.zip This archive contains all the Jupyter notebooks needed to reproduce data analyses. SingularityImage.sif To seamlessly reproduce the entire workflow of Jupyter notebooks without dependency issues, you can use the Singularity image. To open a Jupyter Lab session and execute the notebooks from the CodeNotebook directory (uncompressed) mounted inside the Singularity image, please run this command: singularity exec --bind /pathTo/CodeNotebook/:/home/pathInside/mnt SingularityImage.sif jupyter lab --no-browser --NotebookApp.iopub_data_rate_limit=1.0e10 Please, note that due to dependencies between the notebook results, you should follow the numbering to run each notebook. So, run the "5.a.*", then the "5.b.*", and so on. For any issues and questions, please contact Florian Jeanneret and Christophe Battail.

Jeanneret, F., Schoch, S., Ballester, P., Symeonides, S. N., Laird, A., Axelson, H., PFLIEGER, D., & Battail, C. (2025). DNA methylation is a key determinant of response to targeted and immune checkpoint therapies in metastatic renal cell carcinoma. Zenodo. https://doi.org/10.5281/zenodo.15052574