Using cell-free DNA to predict response to immunotherapy in non-small cell lung cancer pre- and post-treatment

e21198 Background: Despite revolutionizing cancer therapy, immune checkpoint inhibitors (ICI) still do not benefit a significant proportion of patients. The risks associated with ICI-related adverse events, mixed performance of PD-L1 staining in predicting treatment response and its high cost, present a clinical need for more precise methods to define disease states in the context of ICI treatment. ICI response is thought to depend on the phenotype of the tumor and the associated immune response, especially functional state of CD8+ T cells in the tumor microenvironment (TME). Methods: Here we show that the CD8+ T cell signatures from plasma cell-free DNA (cfDNA) could be used to predict response of patients to anti-PD-1 therapy, using samples collected before and shortly after start of treatment. Blood samples were drawn just before the first dose, 1 day to 1 week before the start of treatment and 45-60 days since start of treatment. The treatment duration varied depending on response. Response was evaluated by CT scans every 8-12 weeks. 11 of the samples are from patients with no or minor response ( < 6 months of treatment), and 12 are from patients with prolonged benefit of the medication ( > 1 year of treatment). Exhausted CD8+ T cells, one of the targets of immunotherapy treatment are epigenetically regulated. So, we utilized naïve and PD-1high CD8+ T cell ATAC-seq data to define the repertoire of accessible chromatin specific to these cell types. We found an enrichment of short cfDNA fragments at a significant fraction of these sites, enabling us to define responder-specific and non-responder-specific accessible PD-1high regions from cfDNA. Results: We used the enrichment of short- versus long-cfDNA fragments (reflecting transcription factor-nucleosome dynamics in tissue of origin of cfDNA) as a scoring function, and repeated-cross-validation as a classifier model to identify differentially enriched features between responders and non-responders. Our model accurately predicts the two classes of response both pre-treatment (mean test AUC = 0.86; SD = 0.14). Conclusions: Notably, in addition to generating an accurate classifier, our analysis enabled us to identify predominant transcription factor motifs from predictive ATAC-seq peaks that characterize both response and lack of response, paving the way for further understanding the mechanistic basis of patient-specific response to ICI. Our results suggest the possibility of personalized prediction of treatment response that is independent of specific tumor genotype.