Jaime Wehr

Purpose: Circulating tumor DNA (ctDNA) analyses are informative as an early indicator of immunotherapy response in advanced non–small cell lung cancer (NSCLC); however, the clinical value of ctDNA molecular response (mR) requires further validation. Experimental Design: As part of a prospective clinical protocol (NCT05995821), we conducted targeted error-correction sequencing of ctDNA (n = 328) and matched white blood cell (WBC) DNA (n = 109) from 109 patients with metastatic NSCLC who received anti–PD-(L)1 either as monotherapy or in combination. Following cellular origin resolution of 2,818 variants, landmark mR was defined as undetectable ctDNA within 3 to 9 weeks of treatment initiation. Results: Pre-treatment ctDNA burden, but not blood tumor mutation burden, predicted survival. Implementing a tumor-naïve WBC DNA-informed approach increased the number of evaluable cases without compromising the overall accuracy of landmark ctDNA mR. A direct comparison of single-timepoint on-therapy ctDNA assessment with ctDNA dynamics from baseline to the 3- to 9-week interval, along with an analysis of heterogeneity in mR within the 3- to 9-week window, showed that undetectable ctDNA at the landmark timepoint can effectively predict survival outcomes. A significant enrichment in landmark ctDNA mR was noted among patients with progression-free survival (PFS) ≥6 months on immunotherapy (P = 2.5e−05) or chemoimmunotherapy (P = 0.02). Patients in the landmark mR group had longer PFS (P = 1.6e−06) and overall survival (P = 2.5e−05) than those with molecular progression. Conclusions: Landmark ctDNA mR provides a real-time, accurate approach for monitoring immunotherapy clinical outcomes. Although not currently validated for regulatory use, these findings demonstrate the potential validity of ctDNA as an early endpoint of immunotherapy response.

Supplementary Table S9. Putative uncommon on-target drivers of EGFR TKI resistance.

Supplementary Table S4. Description of pCGP-informed clinical management.

Figure S2. Actionable alterations identified on all instances of first pCGP.

Supplementary Table S1: Training cohort description with plasmaCHORD feature values. Supplementary Table S2: Description of the serial cohort. Supplementary Table S3: Validation cohort description with plasamCHORD feature values. Supplementary Table S4: Description of plasmaCHORD features. Supplementary Table S5: Correlation of cfDNA fragmentomic feature statistics between baseline and follow-up time points in the serial cohort. Supplementary Table S6: Possible CH-origin variants identified on retrospective analysis of 45 patients reviewed at the JH-MTB.

Supplementary Table S1. Context of first pCGP assays.

Purpose:Targeted next-generation sequencing (NGS) of cell-free DNA (cfDNA) enables comprehensive molecular profiling and can guide the selection of genotype-targeted therapies. However, the detection of variants derived from clonal hematopoiesis (CH) is a significant confounder in liquid biopsies.Experimental Design:Using a training cohort of 426 variants identified in cfDNA NGS from 225 patients with stage I to IV solid tumors, we developed plasma Clonal Hematopoiesis ORigin Detection (plasmaCHORD), a machine learning model that includes fragment-, variant-, and patient-level features to distinguish between tumor and CH origin for each variant detected by liquid biopsies. Model performance was assessed by comparison with the reference origin for each plasma variant determined from matched white blood cell and tumor NGS. Following the locking of the model parameters, we applied plasmaCHORD to an independent validation cohort of 1,418 plasma variants detected in 114 patients with metastatic cancers, as well as to cfDNA NGS from patients enrolled in a prospective clinical trial (NCT05585684).Results:plasmaCHORD predicted tumor origin versus CH origin in the training set with high accuracy (AUC = 0.94). In the independent validation cohort, the locked model maintained similar overall accuracy (AUC = 0.9) and demonstrated significant improvement in accuracy for clinically significant genes. When applied to clinically challenging cases in the context of a precision oncology clinical trial, plasmaCHORD precisely determined variant origin, preventing mismatches with genotype-targeted therapies.Conclusions:plasmaCHORD, a multifeature machine learning model, can significantly enhance the ability to identify bona fide tumor variants in routine plasma-only NGS, addressing a critical need for implementing liquid biopsy–guided therapy by minimizing misinterpretation caused by CH.

Classification schema for determining reference origin of plasma variants using matched WBC and/or tumor tissue sequencing. For each variant detected in plasma, reference origin was determined by comparison with matched WBC sequencing and tumor sequencing (if available). If patient had both WBC and tumor sequencing, then plasma variants that were detected with at least 3 read families in WBC and not detected in tumor were considered WBC-derived. Variants were considered tumor-derived if they were detected with VAF >10% in tumor, or if the variant was detected by IGV in the tumor and was detected in 1 or fewer read families in WBC sequencing. If patient had only WBC sequencing, then plasma variants that were also identified in WBC sequencing with at least 3 read families were called CH-derived. Plasma variants that were not identified in matched WBC sequencing (0 read families) where there was adequate coverage in the WBC sequencing at that locus were called tumor-derived. Variants that did not meet any of these criteria were determined to be unknown origin and were excluded from the remaining analysis.

Supplementary Table S10. Drivers of EGFR TKI resistance.