仅基于血浆对CHIP、低VAF胚系变异和体细胞变异的分类可在无配对正常样本的情况下实现准确的肿瘤分数估算

Background: Clonal hematopoiesis (CHIP) and germline variants commonly appear in cell-free DNA (cfDNA) and confound tumor genotyping. We analyzed historical paired plasma and Peripheral Blood Mononuclear Cell (PBMC)/buffy coat samples to (i) quantify CHIP prevalence and variant allele frequency (VAF) distributions, (ii) characterize low-VAF germline signals attributable to copy number variation (CNV) or alignment artifacts, (iii) identify tumor-derived somatic variants by integrating fragmentomics, CNV context, and longitudinal VAF dynamics, and (iv) benchmark plasma-only tumor-fraction (TF) estimation. Data were generated with the PredicineCARE and PredicineATLAS assays. Methods: We retrospectively profiled ~1,000 plasma samples spanning prostate, breast, colorectal, lung, pancreatic, and other solid tumors with matched PBMC/buffy coat specimens. UMI-aware pipelines called single nucleotide variants/insertions/deletions/CNVs. Variants were annotated for CHIP drivers (e.g., DNMT3A, TET2, ASXL1, PPM1D, TP53, SF3B1/SRSF2/U2AF1, JAK2 ), population AF, hotspots, and an in-house knowledge base. Low-VAF germline events were adjudicated using a genome-wide germline SNP skeleton to explain deviations from the ~50% heterozygous expectation under local CNV. We trained and locked a plasma-only classifier (CHIP/germline/somatic) and estimated mutation-derived TF from tumor-assigned variants, benchmarking both against matched-normal labels. Longitudinal analyses (baseline + follow-up, months apart) assessed VAF dynamics and further improved the tumor fraction detection sensitivity. Results: CHIP was prevalent and dominated by DNMT3A/TET2/ASXL1 , with a minority of high-VAF clones; VAF distributions were summarized with and without TF normalization. Low-VAF germline signals were frequent but often CNV-driven, resolved by the SNP-skeleton model. On single-timepoint plasma, >95% of CHIP variants with VAF > 2% were correctly classified. Incorporating longitudinal VAF dynamics further differentiated low-VAF CHIP when TF changed >2-fold between draws, while tumor-derived variants tracked response/progression. The plasma-only TF showed excellent concordance with matched-normal TF (concordance correlation coefficient > 0.95). Conclusions: CHIP is common and often low-VAF; a non-trivial fraction of apparent low-VAF germline findings reflect copy-number-induced VAF shifts. A plasma-only strategy that integrates CNV-aware germline modeling, CHIP gene context, fragmentomics, and longitudinal VAF dynamics closely reproduces matched-normal truth, enabling accurate somatic calling and tumor-fraction estimation without matched PBMC/buffy coat.