PHASED VARIANTS IMPROVE DLBCL MINIMAL RESIDUAL DISEASE DETECTION AT THE END OF THERAPY

Background: Detection of circulating tumor DNA (ctDNA) has prognostic value in diverse tumors, including DLBCL. Despite uses for assessing molecular response to therapy, current methods using immunoglobulin or hybrid-capture sequencing have suboptimal sensitivity, particularly when disease-burden is low. This contributes to a high false negative rate at key milestones such as at the end of therapy (EOT; Kumar A, ASH 2020). We explored the utility of detecting multiple mutations (phased variants, PVs, Fig 1A) on individual cell-free DNA (cfDNA) strands to improve MRD in DLBCL. Methods: We applied Phased Variant Enrichment and Detection Sequencing (PhasED-Seq) to track PVs from 485 specimens from 117 DLBCL patients undergoing first-line therapy. We sequenced cfDNA prior to, during, and after therapy to assess the prognostic value of MRD. We compared the performance of PhasED-Seq to current techniques, including SNV-based CAPP-Seq and duplex sequencing. Results: To establish a detection limit, we compared the background error-profile of PVs and SNVs in healthy cfDNA. PVs demonstrated a lower background profile than SNVs, even when considering duplex molecules (n = 12; 8.0e-7 vs 3.3e-5 and 1.2e-5; P < 0.0001; Fig 1B). We also assessed sensitivity within a ctDNA limiting dilution series from 3 patients, simulating tumor fractions from 0.1% to 0.00005% (1:2,000,000). PhasED-Seq outperformed SNV and duplex based methods for recovery of expected tumor content below 0.01% (P < 0.0001 and P = 0.005 respectively by paired t-test; Fig 1C). We then explored disease detection in clinical samples. We identified SNVs and PVs from pretreatment tumor or plasma and followed these variants in serial cfDNA. Using SNV-based methods, 40% and 59% of patients had undetectable ctDNA after 1 or 2 cycles (n = 82 and 88). However, 24% and 25% of these cases had detectable ctDNA by PhasED-Seq. Importantly, MRD detection by PhasED-Seq was prognostic for event-free survival even in patients with undetectable ctDNA by SNVs. We next explored the utility of PhasED-Seq at the EOT in 19 subjects, 5 of whom experienced eventual disease progression. While only 2/5 cases with progression had detectable disease at EOT using SNVs, PhasED-Seq detected all 5/5 cases (Fig 1D). PhasED-Seq also correctly identified all patients (14/14) without clinical relapse as having no residual disease, including one patient who discontinued therapy after1 cycle due to toxicity, but remains in remission >5 years after this single treatment. This resulted in superior classification of patients for EFS using PVs compared with SNVs (C-statistic: 0.98 vs 0.60, P = 0.02, Fig 1E). Conclusions: Tracking PVs results in significantly lower background rates than SNV-based approaches, enabling detection to parts per million range. PhasED-Seq improves disease detection in DLBCL at the EOT, suggesting it is ideal for use in MRD-driven consolidative approaches. (A) structure of phased variants(PVs). (B) background signal from cell-free DNA sequencing for SNVs with error suppression, duplex SNVs, and PVs across 12 control samples. (C) dilution series comparing SNV-based CAPP-Seq, duplex sequencing, PhasED-Seq. (D) stratification of patients with DLBCL based on ctDNA at the end of therapy using SNV-based methods or PhasED-Seq. (E) Performance metrics (specificity, sensitivitiy, and AUC) for identification of ctDNA residual disease by SNV-based methods and PhasED-Seq at key landmarks in DLBCL, including pretreatment, cycle 2 day 1, cycle 3 day 1, and at the end of therapy EA - previously submitted to ASCO 2021 The research was funded by: National Cancer Institute (R01CA233975 and R01CA188298 to A.A.A. and M.D., K08CA241076 to D.M.K.), the Virginia and D.K. Ludwig Fund for Cancer Research (A.A.A. and M.D.), and the Damon Runyon Cancer Research Foundation (PST#09-16 to D.M.K. and DR-CI#71-14 to A.A.A). Keywords: Liquid biopsy, Minimal residual disease, Aggressive B-cell non-Hodgkin lymphoma Conflicts of interests pertinent to the abstract D. M. Kurtz Consultant or advisory role: Roche, Genentech, Foresight Diagnostics Stock ownership: Foresight Diagnostics J. J. Chabon Employment or leadership position: Foresight Diagnostics Stock ownership: Foresight Diagnostics R.-O. Casasnovas Consultant or advisory role: Roche D. Rossi Research funding: Gilead, Janssen, Roche, AbbVie M. Diehn Consultant or advisory role: Roche, AstraZeneca, Illumina, RefleXion, BioNTech, Foresight Diagnostics Stock ownership: Foresight Diagnostics, CiberMed Research funding: Varian Medical Systems, Illumina A. A. Alizadeh Consultant or advisory role: Foresight Diagnostics, Roche, Genentech, Janssen, Pharmacyclics, Gilead, Celgene, Chugai Stock ownership: FortySeven, CiberMed, Foresight Diagnostics Research funding: Celgene, Pfizer