33 Dynamic monitoring of response to immune checkpoint blockade through deep-learning empowered ultra-sensitive liquid biopsy in melanoma. (10th December 2020) - Widman, Adam; Khamnei, Cole; Bass, Jake; Liao, Will; Shah, Minita; Robine, Nicolas; Wolchok, Jedd; Callahan, Margaret; Landau, Dan
Abstract : Background: Clearance of circulating tumor DNA (ctDNA) following checkpoint blockade (CB) can precede radiographic response, 1 2 though current state of the art ctDNA detection via targeted panels faces limited sensitivity in low burden disease (figure 1 ). We previously showed that whole genome sequencing (WGS) of plasma can overcome low input of ctDNA to dynamically track low volume malignancy using matched tumor tissue. 3 We therefore sought to evaluate ctDNA for tracking early response to checkpoint blockade (CB) in melanoma, and developed a novel classifier that allows us to track disease without matched tumor tissue for expanded applicability in immunotherapy. Methods: To identify ctDNA sparsely diluted in noncancerous plasma cell free DNA (cfDNA), we developed Phoenix, a deep-learning classifier that uses genomic and epigenomic features to distinguish single nucleotide variants (SNVs) in melanoma from sequencing noise. We evaluated Phoenix on a retrospective cohort of serially sampled plasma from patients with advanced cutaneous melanoma on CB (nivolumab alone or with ipilimumab). Plasma was collected at 0, 3, 6 and 12 weeks after first dose of immunotherapy. ctDNA dynamics were compared to radiographic imaging results at 12 weeks. Results: We trained Phoenix on tumor-confirmed SNVs in plasma from a single patient with high tumor mutational burden (TMB) melanoma and cfDNA from age-matched patients without known cancer. Overall ctDNA signal-to-noise enrichment ranged from 100 - 260x in validation patients (n=2) with bulky disease. Phoenix learned key features of melanoma ctDNA including the UV mutational signature and short fragment size (figure 2 ), and sensitively tracked persistent low burden disease seen on imaging (figure 3 ). To validate these findings, we expanded our cohort (n= 15) of serially tracked tumors. In our preliminary analysis of 12 patients, Phoenix detected pretreatment ctDNA in 92% of patients at a specificity of 97% (figure 4 ), compared with only 17% with the benchmark in the field (iChorCNA, a plasma-based WGS liquid biopsy tool; table 1 ). Phoenix detected a decrease in ctDNA 3 weeks after initiation of CB in 80% of patients (figure 5 ) with an objective response on imaging. No change in ctDNA was seen in patients who did not respond to treatment. Conclusions: Phoenix successfully identified pretreatment melanoma ctDNA without matched tumor tissue and identified response to CB as early as 3 weeks after treatment. Our ongoing studies aim to optimize this technology for early identification of CB response in clinical practice. Acknowledgements: Thanks to support from the Conquer Cancer Foundation Ethics Approval: Use of human data in this study was approved by Memorial Sloan Kettering's IRB, Assurance Number FWA0000499 References: Zhang Q, Luo J, et al. Prognostic and predictive impact of circulating tumor DNA in patients with advanced cancers treated with immune checkpoint blockade. Cancer Discov 2020 pp: CD-20-0047. doi:10.1158/2159-8290.CD-20-0047 Bratman SV, Yang SYC., Iafolla MAJ, et al. Personalized circulating tumor DNA analysis as a predictive biomarker in solid tumor patients treated with pembrolizumab. Nat Cancer (2020). https://doi.org/10.1038/s43018-020-0096-53. Zviran A, Schulman RC, Shah M, et al. Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring. Nat Med 2020;26(7):1114–1124. doi:10.1038/s41591-020-0915-3 Adalsteinsson VA, Ha G, Freeman SS, et al. Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. Nat Commun 2017;8(1):1324. Published 2017 Nov 6. doi:10.1038/s41467-017-00965-y