Christine M. Lovly

ASCO Guidelines provide recommendations with comprehensive review and analyses of the relevant literature for each recommendation, following the guideline development process as outlined in the ASCO Guidelines Methodology Manual . ASCO Guidelines follow the ASCO Conflict of Interest Policy for Clinical Practice Guidelines . Clinical Practice Guidelines and other guidance (“Guidance”) provided by ASCO is not a comprehensive or definitive guide to treatment options. It is intended for voluntary use by clinicians and should be used in conjunction with independent professional judgment. Guidance may not be applicable to all patients, interventions, diseases, or stages of diseases. Guidance is based on review and analysis of relevant literature and is not intended as a statement of the standard of care. ASCO does not endorse third-party drugs, devices, services, or therapies and assumes no responsibility for any harm arising from or related to the use of this information. See complete disclaimer in Appendix 1 and 2 (online only) for more . PURPOSE: To provide guidance on the use of circulating tumor DNA (ctDNA) testing in patients with solid tumors or lymphoma. METHODS: A systematic review by a multidisciplinary panel with patient representation was conducted. The PubMed database was searched from January 2017 to February 2025. Guideline recommendations were based on consideration of the identified evidence. RESULTS: Fifty-four meta-analyses and 22 study reports were identified, including reports of seven randomized trials. RECOMMENDATIONS: ctDNA testing for tumor genetic alterations may be used in situations where: tumor tissue testing is challenging, not feasible, or tumor biopsy represents unacceptable risks to the patient; tumor tissue testing results may not be available within a clinically actionable time frame to determine appropriate management options; or a drug's regulatory approved indication allows for or requires ctDNA testing. If ctDNA testing results are negative, inconclusive, or inconsistent with the clinical scenario, tissue-based confirmation should be sought. Other than testing for genetic alterations, ctDNA testing may be offered if a specific, evidence-based action can be taken with the results or there is conflict or ambiguity with standard-of-care assessment that may be resolved by ctDNA testing. Fractional, percentage, or concentration-based measures of ctDNA or total cell-free DNA concentration are not recommended as a surrogate measure of disease. The panel recognizes this is a rapidly evolving field and guidelines are anticipated to change, bringing in tumor-type specific recommendations and incorporating more data on molecular residual disease settings. Additional information is available at www.asco.org/molecular-testing-and-biomarkers-guidelines .

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.

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.

Performance of individual features in predicting variant origin in the training cohort. Receiver-operator curves and area-under the curve (AUC) values for individual model features as predictors for CH versus tumor-origin. The top performing individual features are gene heme fraction, length cluster mutant, and length max delta S. However, no individual feature performs as well as the plasmaCHORD model combining all features.

Assessment of fragmentomic features. (A-B) Illustrative examples of fragmentome distribution differences seen between mutant and wild-type reads from CH-origin versus tumor origin variants. (A) Tumor-derived plasma variant TP53 p.S215R with fragment length density left-shifted (shorter) for mutant compared to wild-type fragments (middle), and with the fragment endpoints relative to mutation location closer together and left-shifted in mutant versus wild-type fragments (bottom). (B) CH-origin variant TP53 p.M237I with overlapping fragment length densities (middle) and fragment endpoint locations (bottom) between mutant and wild-type reads. (C) The serial cohort used to assess fragmentomic features over time. Each column represents a variant, with points on the y-axis depicting VAF at baseline (black) and at follow-up (gray) time points. This cohort included patients with colorectal, esophageal, and NSCLC, stage I-III and included both tumor-origin and WBC-origin plasma variants. (D) Pearson’s product moment correlation of fragmentomic feature values at baseline and follow-up timepoints. Correlation of features between baseline and follow-up timepoint was calculated regardless of variant origin (black bars). Features that were correlated over time with p-value <0.05 were subsequently used for development of the machine learning model. Correlation of features between baseline and follow-up timepoint was also calculated for the subset of tumor-derived variants (red bars) and CH-derived variants (blue bars), demonstrating similar correlation of features over time regardless of variant origin.

Pearson correlation for each individual fragmentomic statistic between baseline and follow-up time points as determined using the serial cohort. Definition of summary statistics are provided in Supplementary Table S2. Each statistic is calculated by comparing mutant versus wild-type reads at a given locus. Statistics that were highly correlated between baseline and follow-up time points (regardless of mutation origin) were included as features in the machine learning model.

Description of the independent validation cohort. (A) Distribution of plasma variant allele frequency for tumor-origin and WBC-origin variants in the validation cohort. (B) Distribution of variant origin in the validation cohort was determined in the original publication. IMPACT-BAM-matched as defined in the original publication refers to variants detected in tissue biopsy below the limit for variant calling as required for clinical reporting. Note we combined biopsy-matched and IMPACT-BAM-matched as tumor-derived. In this cohort, reference origin for plasma variants as determined by matched sequencing is 63.8% CH-derived and 36.1% tumor-derived. (C) Distribution of alterations and variant origins in genes with more than 10 variants.

Model performance in the training cohort by number of mutant reads supporting the mutant DNA molecules. Receiver operator curve and AUC values for subsets of training cohort as determined by the number of mutant reads. As the number of mutant reads decreases, the margin of error in the estimated value of the summary statistics comparing distributions between wild-type and mutant reads increases. To evaluate the impact of this on the overall model performance, we determined the model performance in subsets of variants based on number of mutant reads: 3-5 reads inclusive, 6-10 reads, 11-20 reads, 21-100 reads, and >100 reads. For variants with only 3-5 mutant reads, the model performed slightly less well, with AUC = 0.841. For all subsets with >5 mutant reads, the model performed similarly well with AUC 0.89-0.939.

Description of the training cohort. (A) Distribution of cancer types, stage, and patient age in the training cohort. The mean age for patients in this cohort was 65 years old (range 19-87), the most common cancer type was colorectal, and most patients had early stage disease (22 stage I, 101 stage II, 48 stage III, 53 stage IV, and 1 unknown stage). (B) Distribution of WBC only versus WBC and tumor matched sequencing and determined reference variant origin. The proportion of variants with tumor-derived reference origin is slightly higher from samples that had both WBC and tumor sequencing versus WBC sequencing alone. (C) Distribution of variants included in training cohort by gene and reference variant origin. After DNMT3A, TP53 was the second-most commonly mutated gene (98 variants) of which 67 (68%) are tumor-derived and 31 are WBC-derived.