Detecting variation of clonal hematopoiesis using machine-learning in liquid biopsy.

e15027 Background: Tumor‐derived DNA released to the plasma of cancer patients which could be captured and detected by ultra-deep sequencing. NGS provides the technical probability of somatic mutations detection from circulating cell‐free DNA (cfDNA) in plasma samples, while clonal hematopoiesis (CH) mutations affect the accuracy of liquid biopsy for clinical diagnosis or choice of drug and tracing of minimal residual disease (MRD). CH mutation screening of cfDNA samples still an unsolved issue, herein we propose a method that based on multi-variable tree model which screen the CH mutation effectively. Methods: We sequenced 1992 cfDNA (raw depth 35000x) and matched peripheral blood lymphocyte (PBL, raw depth 2000x) samples by 1123 genes target capture sequencing from cohorts which include 962 non-small cell lung cancers (NSCLC), 631 gastric cancers (GC), 379 other non-hematological cancers and 40 healthy normal control. First, we compared the coherence patterns of cfDNA-PBL between CH genes (ASXL1, DNMT3A, TET2) hotspot mutation and tumor associated mutation (EGFR L858R T790M, KRAS G12D G13D, TP53 R43H R141H). Next, we labeled canonical mutations by: point mutation and indels; top frequent tumor genes (from cbioportal collected TCGA cancers); published frequent CH genes; known hotspots and unknown significance genes. Furthermore, hotspot tumor mutation and hotspot CH mutations and other mutations were labeled. kNN classify all mutation to labeled mutations groups, and decided the next steps tree model’s node thresholds build a classify tree model screen the confident CH and somatic mutations. Finally, we validated 6 TP53 CH mutation from healthy normal control by ddPCR. Results: Previous published imbalance test model: 'fisher test p.value > 0.5 & 0.5 < OR < 1.5' only covered 23.2%, 26.9%, 17.5% top 10 hotspot CH mutations (ASXL1, DNMT3A, TET2) correspondingly, while 0.75 quantiles both g_VAF and t_VAF less than 1% and alter reads count less than 20. In terms of tumor associated mutations only 3/155,6/123,7/108,7/71,323/998,16/189 exceed the imbalance test models threshold, especially L858R with median t_VAF 3.4% and t_alter_count 60. Population frequency distribution of CH and tumor genes mutation were different significantly (median 0.776% and 0.569%; p.value < 1.44e-05, F-test). After assign hotspot and CH mutations classify by kNN, The tree model screened 42.9% and 83.6% CH mutations in cancer and healthy normal samples respectively. TP53 were common both in tumor and CH mutation ambiguously, tree model screened 6 TP53 CH mutation (c.12G > A, R43H, G113D, R70C, c.16C > T, A80T) from normal plasma sample all validated by ddPCR. Conclusions: CH mutations were ubiquitous in plasma sample and abundance pattern different from common tumor mutations significantly. CH mutation could be screened by optimal condition assembled decision trees effectively.