Cell-free DNA fragmentation profiling for therapeutic response monitoring in metastatic colorectal cancer.

person Zachary L. Skidmore Delfi Diagnostics, Inc., Baltimore, MD info_outline Zachary L. Skidmore, Iris van 't Erve, Keith Lumbard, Bahar Alipanahi, Laurel Keefer, Lorenzo Rinaldi, Jacob Carey, Jennifer Tom, Cornelis J. A. Punt, Nicholas C. Dracopoli, Gerrit A. Meijer, Robert B. Scharpf, Victor E. Velculescu, Remond Fijneman, Alessandro Leal

Authors person Zachary L. Skidmore Delfi Diagnostics, Inc., Baltimore, MD info_outline Zachary L. Skidmore, Iris van 't Erve, Keith Lumbard, Bahar Alipanahi, Laurel Keefer, Lorenzo Rinaldi, Jacob Carey, Jennifer Tom, Cornelis J. A. Punt, Nicholas C. Dracopoli, Gerrit A. Meijer, Robert B. Scharpf, Victor E. Velculescu, Remond Fijneman, Alessandro Leal Organizations Delfi Diagnostics, Inc., Baltimore, MD, Stanford, Stanford, CA, Delfi Diagnostics, Inc., Palo Alto, CA, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands, Delfi Diagnostics, Baltimore, MD, Netherlands Cancer Institute (Netherlands), Amsterdam, Netherlands, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, The Johns Hopkins University School of Medicine, Baltimore, MD, Netherlands Cancer Institute, Amsterdam, Netherlands Abstract Disclosures Research Funding Pharmaceutical/Biotech Company Delfi Diagnostics Background: Currently available circulating cell-free DNA (cfDNA) assays require deep-targeted sequencing to detect cancer-specific mutations at low mutant allele frequency (MAF) levels in the blood. Recently, we developed a tumor-agnostic, mutation-independent approach that utilizes low-coverage whole genome sequencing called DELFI (DNA evaluation of fragments for early interception) Tumor Fraction (DELFI-TF), a model designed to predict plasma tumor fractions based on genome-wide fragmentation-related features. Here, we report the results of DELFI-TF applied to a prospective cohort of patients with metastatic colorectal cancer (mCRC). Methods: Overall, 692 longitudinal plasma samples collected from 153 initially treatment-naive mCRC patients participating in the phase III CAIRO5 study (NCT02162563) were sequenced at low coverage and used for training and cross-validation. In patients with tumor-tissue-proven RAS/BRAF mutations, the tumor fractions were quantified as the cfDNA MAF of the RAS/BRAF variant measured by droplet digital PCR (ddPCR). Using fragment-sequencing statistics, a Bayesian regression model was trained against the MAF of the tumor-specific driver RAS/BRAF variant in all longitudinal timepoints to generate DELFI-TF scores. Changes in longitudinal DELFI-TF scores during first-line therapy (DELFI-TF slopes) were examined to predict treatment response and survival outcomes. Results: The DELFI-TF scores were strongly correlated with RAS/BRAF cfDNA MAF measured by ddPCR ( Pearson , r = 0.85, p < 0.001). Baseline DELFI-TF correlated with dimensions of liver metastases reported in CT scans ( Pearson , r = 0.49, p < 0.001) as well as clinical response, with pre-treatment levels significantly lower in patients with a later-confirmed partial or complete response ( Wilcoxon , p < 0.05). Patients with low or negative DELFI-TF slopes presented with longer progression-free survival in the overall study population (13.4 months vs 10.4 months, HR = 2.03, 95% CI 1.25 to 3.32, Log-rank p < 0.01) and in patients who experienced durable clinical benefit (16.7 months vs 13.3 months, HR = 2.24, 95% CI 1.1 to 4.55, Log-rank p = 0.023). Patients with low or negative DELFI-TF slopes also experienced significantly longer overall survival (59.4 months vs 29.1 months, HR = 3.05, 95% CI 1.58-5.90, Log-rank p < 0.001). Tissue-informed focal and arm-level copy number changes were detected 4-12 weeks after liver metastases resection. Most patients detected as having a molecular relapse were diagnosed earlier than clinical recurrences identified by conventional imaging. Conclusions: DELFI-TF demonstrates the ability to use cfDNA fragmentomes to estimate cfDNA tumor burden with performance comparable to standard approaches for treatment response monitoring and clinical outcome prediction.

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