Background
Treatment with immune checkpoint inhibitors (ICIs) targeting programmed death-1 (PD-1) can yield durable antitumor responses, yet not all patients respond to ICIs. Current approaches to select patients who may benefit from anti-PD-1 treatment are insufficient. 5-hydroxymethylation (5hmC) analysis of plasma-derived cell-free DNA (cfDNA) presents a novel non-invasive approach for identification of therapy response biomarkers which can tackle challenges associated with tumor biopsies such as tumor heterogeneity and serial sample collection.
Methods
151 blood samples were collected from 31 patients with non-small cell lung cancer (NSCLC) before therapy started and at multiple time points while on therapy. Blood samples were processed to obtain plasma-derived cfDNA, followed by enrichment of 5hmC-containing cfDNA fragments through biotinylation via a two-step chemistry and binding to streptavidin coated beads. 5hmC-enriched cfDNA and whole genome libraries were prepared in parallel and sequenced to obtain whole hydroxymethylome and whole genome plasma profiles, respectively.
Results
Comparison of on-treatment time point to matched pretreatment samples from same patients revealed that anti-PD-1 treatment induced distinct changes in plasma cfDNA 5hmC profiles of responding patients, as judged by Response evaluation criteria in solid tumors, relative to non-responders. In responders, 5hmC accumulated over genes involved in immune activation such as inteferon (IFN)- and IFN-α response, inflammatory response and tumor necrosis factor (TNF)-α signaling, whereas in non-responders 5hmC increased over epithelial to mesenchymal transition genes. Molecular response to anti-PD-1 treatment, as measured by 5hmC changes in plasma cfDNA profiles were observed early on, starting with the first cycle of treatment. Comparison of pretreatment plasma samples revealed that anti-PD-1 treatment response and resistance associated genes can be captured by 5hmC profiling of plasma-derived cfDNA. Furthermore, 5hmC profiling of pretreatment plasma samples was able to distinguish responders from non-responders using T cell-inflamed gene expression profile, which was previously identified by tissue RNA analysis.
Conclusions
These results demonstrate that 5hmC profiling can identify response and resistance associated biological pathways in plasma-derived cfDNA, offering a novel approach for non-invasive prediction and monitoring of immunotherapy response in NSCLC.