EpiPROPER

Intrinsic and acquired resistance to therapy is common across cancer types and leads to disease relapse (1). Accumulating evidence suggests that non-genetic mechanisms strongly contribute to therapy failure. Triple negative breast cancer (TNBC) is hypothesized to contain epigenetically-defined, drug-tolerant persister cells (PCs) that are predisposed to survive treatment and fuel disease recurrence (2).

In an International collaborative project (EpiPROPER) with partners from France, Italy, Israel and  Hungary, we will characterize the epigenetic state of prospective PCs in treatment-naïve tumors with a focus on DNA modifications, and uncover molecular changes that drive adaptive behaviors upon treatment. We will use this knowledge to define predictive biomarkers of patient response to treatment and to uncover vulnerabilities of PCs that can be exploited to inhibit disease recurrence.

CRISPR-based molecular barcode readers will be used to identify and isolate prospective PCs from untreated tumors in xenograft models. PC fates will be genetically or chemically perturbed and monitored over time to identify actionable epigenetic targets in PCs, which can be exploited to prevent or to target residual disease.

In our sub-project, we will comprehensively characterize these PCs using a state-of-the-art technology including whole genome methylome sequencing by NEB Next Enzymatic Methyl-seq, scTAM-seq (3,4), amplicon bisulfite sequencing and EPIC arrays. In addition, our collaboration partners will profile single-cell transcriptomes and histone posttranslational modifications via high-throughput droplet scChIP-seq(5), mass-spectrometry based epi-proteomics (6) and CyTOF technology (7). These analyses will define PC epigenetic signatures. The predictive value of these signatures will be tested in patient cohorts with clinical follow up information. By dissecting epigenetic intratumor heterogeneity in TNBC prior to and during therapy, we will identify molecular characteristics that predispose subsets of tumor cells to survive treatment, as well as adaptive changes that fuel disease relapse. Our ability to identify and isolate prospective PCs from treatment-naïve tumors will allow us to define predictive biomarkers that can guide clinicians in the management of patients, with major implications for personalized disease monitoring.