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Purpose:We recently reported the development of a cell-free DNA (cfDNA) targeted methylation (TM)-based sequencing approach for a multi-cancer early detection (MCED) test that includes cancer signal origin prediction. Here, we evaluated the prognostic significance of cancer detection by the MCED test using longitudinal follow-up data.Experimental Design:As part of a Circulating Cell-free Genome Atlas (CCGA) substudy, plasma cfDNA samples were sequenced using a TM approach, and machine learning classifiers predicted cancer status and cancer signal origin. Overall survival (OS) of cancer participants in the first 3 years of follow-up was evaluated in relation to cancer detection by the MCED test and clinical characteristics.Results:Cancers not detected by the MCED test had significantly better OS (P < 0.0001) than cancers detected, even after accounting for other covariates, including clinical stage and method of clinical diagnosis (i.e., standard-of-care screening or clinical presentation with signs/symptoms). Additionally, cancers not detected by the MCED test had better OS than was expected when data were adjusted for age, stage, and cancer type from the Surveillance, Epidemiology, and End Results (SEER) program. In cancers with current screening options, the MCED test also differentiated more aggressive cancers from less aggressive cancers (P < 0.0001).Conclusions:Cancer detection by the MCED test was prognostic beyond clinical stage and method of diagnosis. Cancers not detected by the MCED test had better prognosis than cancers detected and SEER-based expected survival. Cancer detection and prognosis may be linked by the underlying biological factor of tumor fraction in cfDNA.

Supplementary Table 1 from Prognostic Significance of Blood-Based Multi-cancer Detection in Plasma Cell-Free DNA

Supplementary Table 3 from Prognostic Significance of Blood-Based Multi-cancer Detection in Plasma Cell-Free DNA

Supplementary Table 2 from Prognostic Significance of Blood-Based Multi-cancer Detection in Plasma Cell-Free DNA

Supplementary Figures from Prognostic Significance of Blood-Based Multi-cancer Detection in Plasma Cell-Free DNA

Introduction: Confounding signals originating from white blood cells (WBCs) have been reported as a potential source of false positives for blood-based early cancer detection and disease monitoring. Additionally, the level of aberrant signal originating from clonal expansion (CE) in precursor conditions, such as monoclonal B-cell lymphocytosis (MBL) and monoclonal gammopathy of undetermined significance (MGUS), is not well understood. To characterize CE in a potential screening population, we profiled samples from non-cancer (NC) participants and compared them to samples from participants with hematological precursor and neoplastic conditions (HPNCs). Methods: Using LymphoTrack IGH FR1 Assay (Invivoscribe, Inc), we sequenced DNA derived from WBCs from a subset of enrollees in the Circulating Cancer Genome Atlas study (NCT02889978) comprising NC participants, balanced for age and gender, and participants diagnosed with an HPNC (chronic lymphocytic leukemia [CLL], multiple myeloma [MM], MBL, or MGUS). Additional samples were titrated and processed to determine the limit of quantification (LoQ). Results: Using 12 titration samples, we determined the LoQ of detecting an unknown clone against a polyclonal background to be 0.1% of the total reads. The threshold used to determine evidence of CE was 2.5% of the total reads. We sequenced samples from 112 individuals, 67 of whom were NC participants (35 male of median [range] age 67 [33-85] years; 32 female of 60 [30-85] years). A large fraction of NC samples (10/67; 15%) showed evidence of CE: 9/10 (90%) were monoclonal with clonal percent total reads (PTR) ranging from 2.7% to 80.4% (median: 6.0%), and 7/9 had a mutation rate >2%, indicating somatic hypermutation (SHM). One sample exhibited oligoclonality with 2 clones (6.6% and 3.2%), and both had SHM. Most NC samples (57/67; 85%) lacked evidence of CE: median top clone PTR of 0.10% (mean: 0.18%; SD: 0.24%) and median Simpson clonality of 0.0021 (mean: 0.0030; SD: 0.0030). A positive correlation between the top clone PTR and age was also observed (⍴=0.41 all participants, ⍴=0.43 excluding 80.4% PTR). All CLL samples (9/11 monoclonal; 2/11 oligoclonal) and 3/4 MBL samples showed evidence of CE. Consistent with the observation that a limited number of plasma cells are expected to be in circulation, only 3/10 MM samples and 7/20 MGUS samples showed evidence of CE; 1 MM and 3 MGUS samples had a top clone PTR of >40%. Conclusion: A large fraction of NC samples (15%) had evidence of expanded lymphoid clones with SHM. The PTR of the top clone underscores the higher frequency of CE associated with increased age in asymptomatic participants. Clonal expansion of blood cell lineages can also carry genetic or epigenetic alterations that are similar to those associated with non-hematologic cancers. Thus, differentiating aberrant signals originating from various hematologic compartments is key for accurate early cancer detection. Citation Format: Jing Xiang, Qinwen Liu, Rita Shaknovich, Oliver Venn. Clonal B-cell expansion and the potential challenges to blood-based early cancer detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 779.

Introduction: Minimal or measurable residual disease (MRD) is an emerging independent predictor of progression-free and overall survival in several hematologic diseases. Despite many different methods used in clinical trials, few assays have clinical utility as a surrogate endpoint in a limited number of hematologic malignancies. MRD assays for lymphomas are mostly performed in research settings due to lack of harmonized methods, heterogeneity of diseases, and need for a primary tumor sample. We explored the feasibility of a pan hematologic malignancy classifier (“pan heme classifier”) based on GRAIL’s methylation platform as a potential tumor-agnostic plasma-based MRD assay for hematologic malignancies. Methods: First, 428 plasma samples from various hematologic malignancies including diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), mantle cell lymphoma (MCL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), and multiple myeloma (MM) were blindly tested retrospectively using the pan heme classifier. The majority of samples (375/428; 88%) were from relapsed/refractory (R/R) disease. Cancer signal detection and signal origin prediction were explored at various detection thresholds. Next, sensitivity of the pan heme classifier was evaluated using 74 (22 and 14 unique patients in CLL and MCL, respectively) post-treatment samples with orthogonal methods (flow cytometry in CLL and clonoSEQ in MCL). Finally, dilution experiments were carried out in 4 DLBCL and 4 CLL patient samples. Results: Of 428 samples, 408 (95%) passed quality control. At a prespecified detection threshold, we observed high (91%) cancer detection rate with high cancer signal origin accuracies: 100% in CLL, >98% in MM, >95% in non-Hodgkin lymphoma (DLBCL, FL, MCL), and 87% in AML samples. As expected, targeting lower detection thresholds resulted in increased cancer-positive calls. Cancer was reproducibly detected in 48 of 54 (89%) cases where paired samples were taken prior to treatment (screening and cycle 1, day 1), demonstrating high biologic reproducibility. Analysis of posttreatment CLL and MCL samples with cell-based orthogonal MRD assays suggests the current assay limit of detection (LOD) is ~10-3-10-4 MVAF (a methylation-based tumor fraction estimate). These data suggest that cancer signal score is strongly correlated to the observed MVAF. Furthermore, serially diluted DLBCL and CLL patient plasma samples were spiked into healthy volunteer plasma samples, suggesting an LOD of 10-4 MVAF. Conclusions: These data demonstrate that the pan heme classifier can identify cancer signal from R/R patients across multiple hematologic malignancies. We are currently working to further optimize the assay’s specificity and sensitivity. These results support the development of a blood-based tumor agnostic methylated ctDNA MRD assay with potential utility in several hematologic indications. Citation Format: Veerendra Munugalavadla, Gary de Jesus, Aleksandra Markovets, Qinwen Liu, Oliver Venn, Rafael White, Giulia Fabbri, Paul Labrousse, Dan Stetson, Brian Dougherty, Darren Hodgson, Jill Walker, Anas Younes, Daniel Auclair. Utility of ctDNA-based targeted methylation MRD assay for hematological malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3369.

Current imaging-based cancer screening approaches provide useful but limited prognostic information. Complementary to existing screening tests, cell-free DNA-based multicancer early detection (MCED) tests account for cancer biology [manifested through circulating tumor allele fraction (cTAF)], which could inform prognosis and help assess the cancer's clinical significance. This review discusses the factors affecting circulating tumor DNA (ctDNA) levels and cTAF, and their correlation with the cancer's clinical significance. Furthermore, it discusses the influence of cTAF on MCED test performance, which could help inform prognosis. Clinically significant cancers show higher ctDNA levels quantified by cTAF than indolent phenotype cancers within each stage. This is because more frequent mitosis and cell death combined with increased trafficking of cell-free DNA into circulation leads to greater vascularization and depth of tumor invasion. cTAF has been correlated with biomarkers for cancer aggressiveness and overall survival; cancers with lower cTAF had better survival when compared with cancers as determined by the higher cTAF and Surveillance, Epidemiology, and End Results-based survival for that cancer type at each stage. MCED-detected cancers in case-control studies had comparable survival to Surveillance, Epidemiology, and End Results-based survival at each stage. Because many MCED tests use ctDNA as an analyte, cTAF could provide a common metric to compare performance. The prognostic value of cTAF may allow MCED tests to preferentially detect clinically significant cancers at early stages when outcomes are favorable and this may avoid overdiagnosis.

Background Early cancer detection could identify tumors at a time when outcomes are superior and treatment is less morbid. This prospective case-control sub-study (from NCT02889978 and NCT03085888) assessed the performance of targeted methylation analysis of circulating cell-free DNA (cfDNA) to detect and localize multiple cancer types across all stages at high specificity. Participants and methods The 6689 participants [2482 cancer (>50 cancer types), 4207 non-cancer] were divided into training and validation sets. Plasma cfDNA underwent bisulfite sequencing targeting a panel of >100 000 informative methylation regions. A classifier was developed and validated for cancer detection and tissue of origin (TOO) localization. Results Performance was consistent in training and validation sets. In validation, specificity was 99.3% [95% confidence interval (CI): 98.3% to 99.8%; 0.7% false-positive rate (FPR)]. Stage I–III sensitivity was 67.3% (CI: 60.7% to 73.3%) in a pre-specified set of 12 cancer types (anus, bladder, colon/rectum, esophagus, head and neck, liver/bile-duct, lung, lymphoma, ovary, pancreas, plasma cell neoplasm, stomach), which account for ∼63% of US cancer deaths annually, and was 43.9% (CI: 39.4% to 48.5%) in all cancer types. Detection increased with increasing stage: in the pre-specified cancer types sensitivity was 39% (CI: 27% to 52%) in stage I, 69% (CI: 56% to 80%) in stage II, 83% (CI: 75% to 90%) in stage III, and 92% (CI: 86% to 96%) in stage IV. In all cancer types sensitivity was 18% (CI: 13% to 25%) in stage I, 43% (CI: 35% to 51%) in stage II, 81% (CI: 73% to 87%) in stage III, and 93% (CI: 87% to 96%) in stage IV. TOO was predicted in 96% of samples with cancer-like signal; of those, the TOO localization was accurate in 93%. Conclusions cfDNA sequencing leveraging informative methylation patterns detected more than 50 cancer types across stages. Considering the potential value of early detection in deadly malignancies, further evaluation of this test is justified in prospective population-level studies.

DNA methylation is a fundamental epigenetic mark that governs chromatin organization, cell identity, and gene expression. Here we describe a human methylome atlas, based on deep whole-genome bisulfite sequencing allowing fragment-level analysis across thousands of unique markers for 39 cell types sorted from 207 healthy tissue samples.Replicates of the same cell-type are >99.5% identical, demonstrating robustness of cell identity programs to genetic variation and environmental perturbation. Unsupervised clustering of the atlas recapitulates key elements of tissue ontogeny, and identifies methylation patterns retained since gastrulation. Loci uniquely unmethylated in an individual cell type often reside in transcriptional enhancers and contain DNA binding sites for tissue-specific transcriptional regulators. Uniquely hyper-methylated loci are rare and are enriched for CpG islands, polycomb targets, and CTCF binding sites, suggesting a novel role in shaping cell type-specific chromatin looping. The atlas provides an essential resource for interpretation of disease-associated genetic variants, and a wealth of potential tissue-specific biomarkers for use in liquid biopsies.Summary paragraphDNA methylation, a fundamental epigenetic mark, governs chromatin organization and gene expression1, thus defining the molecular identity of cells and providing a window into developmental processes with wide-ranging physiologic and clinical ramifications. Current DNA methylation datasets have limitations, typically including only a fraction of methylation sites, many from cell lines that underwent massive changes in culture or from tissues containing unspecified mixtures of cells2–6.We present a human methylome atlas based on deep whole-genome bisulfite sequencing of 39 sorted, primary cell types and use this dataset to address fundamental questions in developmental biology, physiology and pathology. Biological replicates are >99.5% identical, demonstrating unappreciated robustness to genetic variation and environmental perturbations. Clustering recapitulates key elements of tissue ontogeny, identifying methylation patterns retained since gastrulation. Loci uniquely unmethylated in individual cell types identify novel transcriptional enhancers and are enriched for tissue-specific transcription factors binding motifs. In contrast, loci uniquely hyper-methylated in specific cell types are rare, enriched for CpG islands and polycomb targets, and overlap CTCF binding sites, suggesting a novel role in shaping cell-type-specific chromatin looping. Finally, the atlas facilitates fragment-level deconvolution of tissue and plasma methylomes across thousands of cell-type specific regions to quantify their individual components at unprecedented resolution.The human cell-type-specific methylation atlas provides an essential resource for studying gene regulation by defining cell-type-specific distal enhancers and regulators of 3D organization, for identifying pathological changes in DNA methylation, and for the interpretation of methylation-based liquid biopsies.A deep methylation atlas of 39 human cell types, sorted from healthy samplesMethylomes record developmental history of cellsThousands of novel cell type-specific methylation markersHypo-methylation uncovers cell type-specific regulatory map of distal enhancersHyper-methylation across CTCF sitesCell type-specific biomarkers facilitate fragment-level deconvolution of tissues and cfDNA

DNA methylation is a fundamental epigenetic mark that governs gene expression and chromatin organization, thus providing a window into cellular identity and developmental processes1. Current datasets typically include only a fraction of methylation sites and are often based either on cell lines that underwent massive changes in culture or on tissues containing unspecified mixtures of cells2–5. Here we describe a human methylome atlas, based on deep whole-genome bisulfite sequencing, allowing fragment-level analysis across thousands of unique markers for 39 cell types sorted from 205 healthy tissue samples. Replicates of the same cell type are more than 99.5% identical, demonstrating the robustness of cell identity programmes to environmental perturbation. Unsupervised clustering of the atlas recapitulates key elements of tissue ontogeny and identifies methylation patterns retained since embryonic development. Loci uniquely unmethylated in an individual cell type often reside in transcriptional enhancers and contain DNA binding sites for tissue-specific transcriptional regulators. Uniquely hypermethylated loci are rare and are enriched for CpG islands, Polycomb targets and CTCF binding sites, suggesting a new role in shaping cell-type-specific chromatin looping. The atlas provides an essential resource for study of gene regulation and disease-associated genetic variants, and a wealth of potential tissue-specific biomarkers for use in liquid biopsies.

In the Circulating Cell-free Genome Atlas (NCT02889978) substudy 1, we evaluate several approaches for a circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED) test by defining clinical limit of detection (LOD) based on circulating tumor allele fraction (cTAF), enabling performance comparisons. Among 10 machine-learning classifiers trained on the same samples and independently validated, when evaluated at 98% specificity, those using whole-genome (WG) methylation, single nucleotide variants with paired white blood cell background removal, and combined scores from classifiers evaluated in this study show the highest cancer signal detection sensitivities. Compared with clinical stage and tumor type, cTAF is a more significant predictor of classifier performance and may more closely reflect tumor biology. Clinical LODs mirror relative sensitivities for all approaches. The WG methylation feature best predicts cancer signal origin. WG methylation is the most promising technology for MCED and informs development of a targeted methylation MCED test.

3072 Background: The Circulating Cell-free Genome Atlas study (CCGA; NCT02889978) previously demonstrated that a blood-based multi-cancer early detection (MCED) test utilizing cell-free DNA (cfDNA) sequencing in combination with machine learning could detect cancer signals across multiple cancer types and predict cancer signal origin. Cancer classes were defined within the CCGA study for sensitivity reporting. Separately, cancer types defined by the American Joint Committee on Cancer (AJCC) criteria, which outline unique staging requirements and reflect a distinct combination of anatomic site, histology and other biologic features, were assigned to each cancer participant using the same source data for primary site of origin and histologic type. Here, we report CCGA ‘cancer class’ designation and AJCC ‘cancer type’ assignment within the third and final CCGA3 validation substudy to better characterize the diversity of tumors across which a cancer signal could be detected with the MCED test that is nearing clinical availability. Methods: CCGA is a prospective, multicenter, case-control, observational study with longitudinal follow-up (overall population N = 15,254). Plasma cfDNA from evaluable samples was analyzed using a targeted methylation bisulfite sequencing assay and a machine learning approach, and test performance, including sensitivity, was assessed. For sensitivity reporting, CCGA cancer classes were assigned to cancer participants using a combination of the type of primary cancer reported by the site and tumor characteristics abstracted from the site pathology reports by GRAIL pathologists. Each cancer participant also was separately assigned an AJCC cancer type based on the same source data using AJCC staging manual (8th edition) classifications. Results: A total of 4077 participants comprised the independent validation set with confirmed status (cancer: n = 2823; non-cancer: n = 1254 with non-cancer status confirmed at year-one follow-up). Sensitivity was reported for 24 cancer classes (sample sizes ranged from 10 to 524 participants), as well as an “other” cancer class (59 participants). According to AJCC classification, the MCED test was found to detect cancer signals across 50+ AJCC cancer types, including some types not present in the training set; some cancer types had limited representation. Conclusions: This MCED test that is nearing clinical availability and was evaluated in the third CCGA substudy detected cancer signals across 50+ AJCC cancer types. Reporting CCGA cancer classes and AJCC cancer types demonstrates the ability of the MCED test to detect cancer signals across a set of diverse cancer types representing a wide range of biologic characteristics, including cancer types that the classifier has not been trained on, and supports its use on a population-wide scale. Clinical trial information: NCT02889978.

Introduction: Hematologic (heme) malignancies and their precursor conditions are highly prevalent. They are also diverse in biology, clinical presentation, and outcomes, underlining the importance of differentiating them. Previously, we demonstrated that a blood-based targeted methylation assay detected multiple cancer types across stages. Here, we examined test performance on various heme cancers, identifying specific methylation signatures. Methods: From the second substudy (training set) of the Circulating Cell-free Genome Atlas (CCGA) study (NCT02889978), we evaluated 325 participants from 17 different heme disease subtypes and 3,211 non-cancer controls enrolled without a cancer diagnosis. A cross-validated mutual information-based algorithm was used to identify features that discriminated heme subtypes. The resulting feature distribution was visualized using uniform manifold approximation and projection (UMAP) dimensionality reduction on held-out data. In cross validation with feature selection, we then trained a multinomial classifier to distinguish from among the major heme cancers and non-cancer and correlated the model's class probabilities to positions in feature space. Results: Dimensionality reduction and visualization of input features demonstrated that heme malignancies separated into five major clusters reflecting developmental lineages and disease ontogeny: myeloid, circulating lymphomas, hodgkin lymphomas, non-hodgkin lymphomas, and plasma cell neoplasm. The position of samples within each heme cluster correlated with the cancer signal strength. At 99.4% specificity [95% CI: 99.1, 99.7], heme cancer detection was 74.5% [69.4, 79.1] overall, 67.7% [41.1, 87.8] for myeloid, 77.9% [66.3, 86.9] for circulating lymphomas, 90.7% [73.2, 98.4] for hodgkin lymphomas, 68.6% [60.4, 76.1] for other non-hodgkin lymphomas, and 78.8% [67.0, 87.9] for plasma cell neoplasms. Of 18 non-cancer participants who were classified as having heme cancers, 4 were predicted as myeloid, 6 as circulating lymphoid, and 8 as other non-hodgkin lymphoid neoplasms ( Conclusion: Methylation features of cfDNA in patients with heme malignancies delineated five major clusters that reflected disease ontogeny and heme lineage. Lineage-specific signals followed a gradient suggestive of variation in disease-related methylation or tumor DNA shedding. These findings contribute to the understanding of biological signals that arise from various heme conditions. Since in general, most cfDNA arises from blood lineages, this knowledge will guide further efforts towards removing interfering biological signals from cfDNA-based cancer detection assays and achieving even more sensitive detection of multiple cancer types. Citation Format: Qinwen Liu, Rita Shaknovich, Xiaoji Chen, Zhao Dong, M. C. Maher, Samuel Gross, Alexander P. Fields, Jan Schellenberger, Kathryn N. Kurtzman, Eric T. Fung, Anne-Renee Hartman, Earl Hubbell, Arash Jamshidi, Alexander M. Aravanis, Oliver Venn. cfDNA methylation profiling distinguishes lineage-specific hematologic malignancies [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 139.

Accurate tissue of origin (TOO) prediction is crucial for effective clinical follow-up in early cancer detection from blood. In the second substudy of the Circulating Cell-free Genome Atlas (CCGA; NCT02889978), we trained logistic regression classifiers under cross-validation to detect and localize cancer. Input features were methylation states from a targeted cfDNA assay of 2023 participants. TOO classification accuracy was 89% across 20 pre-specified prediction classes. We subsequently sought to understand the causes of the remaining TOO errors. 45% of the errors fell into clusters reflecting similarities in developmental biology, histology, or oncological drivers. Here, we analyzed tissues that may be affected by HPV-driven cancers; these accounted for 21% of TOO errors. The original classifier, which used only human epigenetic states as input, demonstrated cross-scoring between likely HPV-driven cancers of the anus (N = 14) and cervix (N = 11), as well as confirmed HPV-positive head & neck (H&N) cancers (37/62). We also observed HPV-associated vulva (N = 9) and penis (N = 1) cancers, which were not directly trained as TOO classes, were assigned high H&N scores. To test the hypothesis of HPV-driven TOO confusion, we assessed HPV cfDNA and HPV-driven methylation in human peripheral blood cfDNA, and used a specialist classifier restricted to HPV-associated cancers to resolve errors. We corroborated putative HPV-positive participants using targeted sequencing of HPV16 and HPV18 cfDNA fragments, and showed that the number of unique HPV-derived fragments in a sample matched with expected cancer localizations, HPV subtypes, and HPV status. Consistent with the literature, we found little evidence of HPV viremia in non-cancer participants despite the high prevalence of transient HPV infections in the US population. At 99.8% specificity, a cross-validated cutoff on the number of HPV cfDNA fragments in a sample achieved sensitivities of 78.6% (11/14), 36.3% (4/11), 66.6% (6/9), 100% (1/1), and 81.0% (30/37), for anus, cervix, vulva, penis, and confirmed HPV-positive H&N cancers, respectively. These sensitivities were similar to those achieved by the epigenetic classifier. Finally, we trained a cross-validated specialist classifier using the same features as the TOO classifier, but restricted to HPV-driven cancers. This improved TOO accuracy for detected anal cancers from 11% (1/9) to 100% (9/9), with little effect on other classes. These data support that HPV presence may explain observed cross-scoring patterns between H&N, cervix, and anus TOO prediction classes, which were driven by recurrent epigenomic changes in participants with HPV-positive cancers as detected by this assay. This suggests that modelling axes of shared biology across cancer types can be useful for accurate cfDNA TOO classification, which is critical to direct diagnostic work-up of diverse cancer types in a multi-cancer early detection test. Citation Format: Robert Calef, Oliver Venn, M. Cyrus Maher, John F. Beausang, Earl Hubbell, Aman Patel, Alexander P. Fields, Joerg Bredno, Arash Jamshidi, Alexander M. Aravanis. HPV-driven cancers show distinct methylation signatures in cell-free DNA (cfDNA) [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2114.

3052 Background: Cell-free DNA (cfDNA) tumor fraction (TF), the proportion of tumor molecules in a cfDNA sample, is a direct measurement of signal for cfDNA cancer applications. The Circulating Cell-free Genome Atlas study (CCGA; NCT02889978) is a prospective, multi-center, observational, case-control study designed to support development of a methylation-based, multi-cancer detection test in which a classifier is trained to distinguish cancer from non-cancer. Here we leveraged CCGA data to examine the relationship between cfDNA containing tumor DNA methylation patterns, TF, and cancer classification performance. Methods: The CCGA classifier was trained on whole-genome bisulfite sequencing (WGBS) and targeted methylation (TM) sequencing data to detect cancer versus non-cancer. 822 samples had biopsy WGBS performed; of those, 231 also had cfDNA targeted methylation (TM) and cfDNA whole-genome sequencing (WGS). Biopsy WGBS identified somatic single nucleotide variants (SNV) and methylation variants (MV; defined as methylation patterns in sequenced DNA fragments observed commonly in biopsy but rarely [ < 1/10,000] in the cfDNA of non-cancer controls [n = 898]). Observed tumor fragment counts (SNV in WGS; MV in TM), were modeled as a Poisson process with rate dependent on TF. TF and classifier limits of detection (LOD) were each assessed using Bayesian logistic regression. Results: Across biopsy samples, a median of 2,635 MV was distributed across the genome, with a median of 86.8% shared with ≥1 participant, and a median of 69.3% targeted by the TM assay. TF LOD from MV was 0.00050 (95% credible interval [CI]: 0.00041 - 0.00061); MV and SNV estimates were concordant (Spearman’s Rho: 0.820). MV TF estimates explained classifier performance (Spearman’s Rho: 0.856) and allowed determination of the classifier LOD (0.00082 [95% CI: 0.00057 - 0.00115]). Conclusions: These data demonstrate the existence of methylation patterns in tumor-derived cfDNA fragments that are rarely found in individuals without cancer; their abundance directly measured TF, and was a major factor influencing classification performance. Finally, the low classifier LOD (~0.1%) motivates further clinical development of a methylation-based assay for cancer detection. Clinical trial information: NCT02889978 .

Background The Circulating Cell-free Genome Atlas (CCGA; NCT02889978) study is a prospective, multi-center, observational, case-control study with longitudinal follow-up to support the development of a plasma cfDNA-based multi-cancer early detection assay. We previously reported that the fraction of tumor-derived cfDNA fragments in plasma (estimated tumor fraction [TF]) is associated with detection of multiple cancer types using a prototype whole-genome bisulfite sequencing (WGBS) assay in a pre-specified substudy. In addition, detectability of breast cancer varied by hormone-receptor (HR) status: HR negative (HR-) breast cancer had a higher detection rate than the overall breast cancer cohort. Here, we performed a planned in-depth analysis of the breast cancer cohort to understand the main clinical and biological determinants of detectability using our WGBS assay. Methods Blood samples were prospectively collected from participants (pts) with newly-diagnosed, untreated breast cancer. In a pre-specified CCGA substudy, 511 (31.4%) of 1,628 pts had a clinicopathologic diagnosis of breast cancer (any stage, excluding stage 0) and a WGBS result. Pre-specified biological and clinical factors were assessed for correlation with TF: clinical stage, pathologic N stage, lesion size (by imaging), T stage, histologic grade, hormone receptor (HR) status, and proliferative rate (Ki-67 by IHC [MIB-1 clone]) via univariate and multivariate analyses. Ki-67 was obtained from pathology reports provided by enrollment sites and also assessed centrally (Mayo Clinic [Rochester, MN]). Stage-specific differences in detection rates between HR positive (HR+) and HR- breast cancers due to biological differences such as histologic grade and/or proliferative rate was also assessed. Results Of 511 pts, 94 (18.4%; 48.9% HR- and 12.3% in HR+) had WGBS-detected cancer. Higher TF was significantly associated with cancer detection (p Conclusions Using a prototype methylation-based plasma cfDNA cancer detection assay, TF was found to be associated with detection, and with clinical and biological features of breast cancer. Features routinely used to assess clinical aggressiveness (eg, HR- status and histologic grade) also demonstrated stage-specific associations with TF. Given the known relationship between TF and detection, these data suggest that clinical and biological cancer features may also provide insight into the variability of cancer detection using plasma-based cfDNA tests. Citation Format: Minetta C. Liu, Jodi M. Carter, Daniel W. Visscher, Karla Kopp, Rita Shaknovich, Xiaoji Chen, Kathryn N. Kurtzman, Shilpen Patel, Jacqueline D. Brooks, Carlo R. Cosenza, Jafi A. Lipson, Donald A. Richards, Fergus J. Couch, Zhao Dong, Hai Liu, Oliver Venn, Joerg Bredno, Eric T. Fung, Anne-Renee Hartman. Blood-based cancer detection in plasma cell-free DNA (cfDNA): Evaluating clinical and pathologic tumor characteristics in participants with breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P5-01-01.

243 Background: Circulating Cell-free Genome Atlas (CCGA; NCT02889978) is a multi-center, case-control, observational study with longitudinal follow-up to develop a cfDNA assay in which classifiers were trained on whole-genome bisulfite sequencing (WGBS) and targeted methylation (TM) sequencing data for detection of multiple cancer types. Previously, we showed that the fraction of ctDNA fragments (TF) was a stronger predictor of cancer detection than clinical stage and an equivalent predictor for survival. Given that CRC tumors can be described via surface area (TSA) and microscopic tumor extent (microinvasion), CRC was used as a model to examine the biophysical determinants of TF. Methods: Detection of multiple cancers with WGBS at 98% and TM at > 99% specificity, and methods for determining TF, were previously reported. A model to predict the presence of detectable cfDNA fragments for CRC adenocarcinomas of stages I, II, and III included TSA and microinvasion beyond the subserosa. Predictors were combined assuming a linear increase of cfDNA shedding with tumor size, with scaling factors depending on microinvasion. Model parameters were determined for 27 participants (7, 11, 9 for stages I, II, III, resp.) with WGBS and applied to 40 participants (12, 15, 13 for I, II, III, resp.) with TM assay and information on tumor size and microinvasion. Results: CRC detection at stages I/II/III was 33/46, 61/73, 57/74% for WGBS/TM. TF predicted detection with AUC = 97.6. The model predicted TF as TSA multiplied by 3.81*10−6 / mm2 for tumors that invaded beyond the subserosa (p < 0.001). This was 4.4x higher than estimates for tumors below the subserosa. The model trained on the WGBS assay predicted CRC detection in the TM assay with an AUC of 0.844. Conclusions: This model used TSA (number of tumor cells) and microinvasion (bloodstream access) to predict the fraction of CRC ctDNA fragments in blood without needing to account for stage. Tumors not penetrating the subserosa had low ctDNA shedding that likely limited detection. These findings may generalize to other cancer types, providing principles to predict ctDNA shedding and thus cancer detectability based on microinvasion and surface area. Clinical trial information: NCT02889978.

Background Our previous discovery work identified whole-genome bisulfite sequencing as outperforming whole-genome and targeted sequencing approaches for multi-cancer detection. We developed a targeted methylation assay for multi-cancer detection and tissue of origin (TOO) localization. Methods Participants were from Circulating Cell-free Genome Atlas (CCGA; NCT02889978) and STRIVE (NCT03085888), both prospective, multi-center, observational studies with longitudinal follow-up. cfDNA from 3,583 evaluable samples (1,530 cancer, 2,053 non-cancer) spanned >20 tumor types of all stages; a prespecified subset of these comprised the "multi-cancer" group: anorectal, hormone-receptor (HR)-negative breast, colorectal, esophageal, gallbladder, gastric, head and neck, hepatobiliary, lung, lymphoid leukemia, lymphoma, multiple myeloma, ovarian, and pancreatic (937 cancer [all stages]). A cross-validated targeted methylation test evaluated cfDNA for predictability of cancer presence and TOO; precision defined as the fraction of correct calls. Breast and lung cancer subtypes were also assessed. All analyses targeted 99.4% specificity (0.6% false-positive rate). Results Participants with and without cancer were similar in age. Specificity was set to 99.4%. For the prespecified multi-cancer group, overall sensitivity was 76% (73-78): stage I 32% (25-40), stage II 76% (69-82), stage III 85% (80-89), and stage IV 93% (89-95). Among all samples, overall sensitivity was 55% (52-57%): stage I 19% (15-23%), stage II 43% (38-48%), stage III 78% (73-82%), and stage IV 90% (86-93%). Overall TOO precision for the multi-cancer group and all samples was 89%, and was similar across stages. Squamous-cell and small-cell lung cancer had higher sensitivity than adenocarcinoma: 84% (75-91) vs 83% (69-92) vs 58% (49-67); HR-negative had higher sensitivity than HR-positive breast cancer: 66% (53-77) vs 20% (15-27). Conclusions This targeted methylation assay detected cancer signal across >20 cancer types with a single, fixed, low false positive rate and highly accurate TOO localization. These data support the feasibility of a single test that can screen for multiple cancers. Clinical trial identification NCT02889978, NCT03085888. Editorial acknowledgement Sarah Prins, PhD (GRAIL, Inc.), and Megan P. Hall, PhD (GRAIL, Inc.). Legal entity responsible for the study GRAIL, Inc. Funding GRAIL, Inc. Disclosure G.R. Oxnard: Advisory / Consultancy, Officer / Board of Directors: Inivata; Honoraria (self): Guardant Health; Honoraria (self): Sysmex; Honoraria (self): Bio-Rad; Advisory / Consultancy: DropWorks; Advisory / Consultancy: AstraZeneca; Advisory / Consultancy: GRAIL, Inc.. E.A. Klein: Advisory / Consultancy: GRAIL, Inc.; Advisory / Consultancy: Genomic Health; Advisory / Consultancy: GenomeDx Biosciences. M.V. Seiden: Shareholder / Stockholder / Stock options, Full / Part-time employment: McKesson. E. Hubbell: Shareholder / Stockholder / Stock options, Full / Part-time employment: GRAIL, Inc. O. Venn: Shareholder / Stockholder / Stock options, Full / Part-time employment: GRAIL, Inc. A. Jamshidi: Shareholder / Stockholder / Stock options, Full / Part-time employment: GRAIL, Inc.; Shareholder / Stockholder / Stock options: Illumina. N. Zhang: Shareholder / Stockholder / Stock options, Full / Part-time employment: GRAIL, Inc. J.F. Beausang: Shareholder / Stockholder / Stock options, Full / Part-time employment: GRAIL, Inc. S. Gross: Shareholder / Stockholder / Stock options, Full / Part-time employment: GRAIL, Inc. K.N. Kurtzman: Shareholder / Stockholder / Stock options, Full / Part-time employment: GRAIL, Inc.; Shareholder / Stockholder / Stock options: Illumina. E.T. Fung: Shareholder / Stockholder / Stock options, Full / Part-time employment: GRAIL, Inc. B. Allen: Shareholder / Stockholder / Stock options, Full / Part-time employment: GRAIL, Inc. A.P. Fields: Shareholder / Stockholder / Stock options, Full / Part-time employment: GRAIL, Inc. H. Liu: Shareholder / Stockholder / Stock options, Full / Part-time employment: GRAIL, Inc. A.M. Aravanis: Shareholder / Stockholder / Stock options, Full / Part-time employment: GRAIL, Inc. A. Hartman: Shareholder / Stockholder / Stock options, Full / Part-time employment: GRAIL, Inc. M.C. Liu: Honoraria (institution): GRAIL, Inc. All other authors have declared no conflicts of interest.

Previous studies on transplanted tissue or single cancers indicated that cfDNA variant fragment lengths reflect their respective source. The Circulating Cell-free Genome Atlas (NCT02889978) study provides an opportunity to examine cfDNA variant fragment lengths across tumor types and describe the nature of cfDNA variants derived from different sources. Blood samples (N=1406) were evaluated from participants with (n=845) and without (n=561) cancer; cancer samples included 339 breast, 118 lung, 69 prostate, 45 colorectal, 27 uterine, 26 pancreas, 26 renal, 24 esophageal, 22 lymphoma, 19 head/neck, and 17 ovarian (113 remaining samples represented cancers with ≤15 samples each). cfDNA and genomic DNA from white blood cells (WBC) were subjected to a high-intensity targeted panel (507 genes, 60000X) with error-corrected sequencing; 533 samples also had matched tumor biopsy tissue subjected to whole-genome sequencing (30X). Somatic single-nucleotide variants (SNVs; that passed noise filters) were identified and classified using the sequencing results into one of four categories: tumor biopsy-matched (TBM; present in cfDNA and biopsy), WBC-matched (WM; present in cfDNA and WBC), non-matched (NM; low probability [P A total of 21604 SNVs were identified. The proportion of SNVs from each category were: 4% TBM, 68% WM, 19% NM, and 8% AMB. The number of samples (non-mutually exclusive) that had each SNV category were 152 TBM, 1338 WM, 499 NM, and 761 AMB. Across categories, the median (SD) length of fragments containing the reference allele was 167 (16.3). Median (SD) fragment lengths of TBM, WM, NM, and AMB were 156 (22.2), 169 (14.8), 158 (20.8), and 165 (17.8), respectively. AMB and WM median SNV fragment lengths were similar to that of the reference allele, suggesting that fragment length shifts are minimal in SNVs derived from clonal hematopoiesis (CH). Fragment lengths of TBM and NM SNVs were similar; further, most NM SNVs came from cfDNA samples in the cancer cohort, suggesting that NM SNVs may be tumor-derived. As expected in a population with a median (SD) age of 61 (12.2), most SNVs occurred in the WM category. The prediction model distinguished TBM from WM SNVs with an AUC of 0.87. However, at a specificity of 98% (to match filtering based on WBC sequencing), false-negative rates were 35% (TBM) and 52% (NM). Together, these data suggest that source prediction based on fragment length alone is less robust than source assignment using individual-matched WBC sequencing, highlighting the importance of accounting for CH-derived SNVs when using targeted cfDNA-based approaches for cancer detection. Citation Format: Earl Hubbell, Tara Maddala, Oliver Venn, Eric Scott, Susan Tang, Archana Shenoy, Alex Aravanis. Cell-free DNA (cfDNA) fragment length patterns of tumor- and blood-derived variants in participants with and without cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3372.

5574 Background: A noninvasive cell-free DNA (cfDNA)-based cancer detection assay offers the hope of a blood test that might reduce morbidity and mortality of cancers, particularly those without recommended screening tests (eg, some gynecologic cancers). CCGA (NCT02889978) is a prospective, multi-center, longitudinal, case-control study evaluating models for discriminating cancer versus non-cancer. Here, we report F/U of control participants (pts) who demonstrated a cancer-signal in CCGA. Methods: Clinically evaluable samples (N = 2508) from pts enrolled without a cancer diagnosis (dx; NC) and treatment-naive pts with newly diagnosed cancer (C) were divided into training (n = 1564; 580 NC, 984 C) and test (n = 944; 368 NC, 576 C) sets. Classification performance (cancer/non-cancer) was assessed via 3 prototype assays: whole-genome bisulfite (WGBS), whole-genome (WGS), and targeted (507 gene) sequencing. Notable outlier NC pts were identified with cancer-like scores in either ≥2 assay classification results or by the presence of known cancer drivers with ≥1 assay classification result suggesting cancer. All pts are currently in F/U in accordance with study protocol (to date: 80% with > 10 mo and 15% with > 22 mo F/U). Results: Among training and test sets, 8 ( < 1%) NC pts were identified with a cancer-like signal. To-date, 2 have been diagnosed with a gynecologic malignancy: 1 stage IIIc clear cell endometrial carcinoma and 1 stage IIIc ovarian cancer, 3 and 2 months (mo) post-enrollment [PE], respectively. Among C pts in the study, sensitivity (at 98% specificity; WGBS) in these cancer types was: uterine/endometrial: 11% (n = 27 train) and 22% (n = 9 test); ovarian: 82% (n = 17) and 71% (n = 7). In addition, a third NC pt was diagnosed with a stage IV lung cancer 15 mo PE. Conclusions: This cfDNA-based assay detected a cancer-like signal that anticipated a clinical presentation of cancer in undiagnosed pts as early as 15 months prior to the actual dx. High specificity ( > 99%) requires accounting for undiagnosed cancers in study design and analysis. Together, these data suggest that this prototype assay may have high performance detecting a variety of gynecological and other cancers. Clinical trial information: NCT02889978.

Recombination: The birds and the yeast Apes and mice have a specific gene, PRDM9 , that is associated with genomic regions with high rates of recombination, called hotspots. In species with PRDM9 , hotspots move rapidly within the genome, varying among populations and closely related species (see the Perspective by Lichten). To investigate recombination hotspots in species lacking PRDM9 , Singhal et al. examined bird genomes, which lack a PRDM9 gene. They looked closely at the genomes of finch species and found that recombination was localized to the promoter regions of genes and highly conserved over millions of years. Similarly, Lam and Keeney examined recombination localization within yeast, which also lacks PRDM9 . They found a similar more-or-less fixed pattern of hotspots. Thus, recombination in species lacking a PRDM9 gene shows similar patterns of hotspot localization and evolution. Science , this issue p. 913 , p. 928 ; see also p. 932

CCGA [NCT02889978] is the largest study of cfDNA-based early cancer detection; the first CCGA learnings from multiple cfDNA assays are reported here. This prospective, multi-center, observational study has enrolled 10,012 of 15,000 demographically-balanced participants at 141 sites. Blood was collected from participants with newly diagnosed therapy-naive cancer (C, case) and participants without a diagnosis of cancer (noncancer [NC], control) as defined at enrollment. This preplanned substudy included 878 cases, 580 controls, and 169 assay controls (n=1627) across 20 tumor types and all clinical stages. All samples were analyzed by: 1) Paired cfDNA and white blood cell (WBC)-targeted sequencing (60,000X, 507 gene panel); a joint caller removed WBC-derived somatic variants and residual technical noise; 2) Paired cfDNA and WBC whole-genome sequencing (WGS; 35X); a novel machine learning algorithm generated cancer-related signal scores; joint analysis identified shared events; and 3) cfDNA whole-genome bisulfite sequencing (WGBS; 34X); normalized scores were generated using abnormally methylated fragments. In the targeted assay, non-tumor WBC-matched cfDNA somatic variants (SNVs/indels) accounted for 76% of all variants in NC and 65% in C. Consistent with somatic mosaicism (i.e., clonal hematopoiesis), WBC-matched variants increased with age; several were non-canonical loss-of-function mutations not previously reported. After WBC variant removal, canonical driver somatic variants were highly specific to C (e.g., in EGFR and PIK3CA, 0 NC had variants vs 11 and 30, respectively, of C). Similarly, of 8 NC with somatic copy number alterations (SCNAs) detected with WGS, 4 were derived from WBCs. WGBS data revealed informative hyper- and hypo-fragment level CpGs (1:2 ratio); a subset was used to calculate methylation scores. A consistent “cancer-like” signal was observed in 99% specificity for invasive cancer, and support the promise of cfDNA assay for early cancer detection. Additional data will be presented on detected plasma:tissue variant concordance and on multi-assay modeling. Citation Format: Alexander A. Aravanis, Geoffrey R. Oxnard, Tara Maddala, Earl Hubbell, Oliver Venn, Arash Jamshidi, Ling Shen, Hamed Amini, John A. Beausang, Craig Betts, Daniel Civello, Konstantin Davydov, Saniya Fazullina, Darya Filippova, Sante Gnerre, Samuel Gross, Chenlu Hou, Roger Jiang, Byoungsok Jung, Kathryn Kurtzman, Collin Melton, Shivani Nautiyal, Jonathan Newman, Joshua Newman, Cosmos Nicolaou, Richard Rava, Onur Sakarya, Ravi Vijaya Satya, Seyedmehdi Shojaee, Kristan Steffen, Anton Valouev, Hui Xu, Jeanne Yue, Nan Zhang, Jose Baselga, Rosanna Lapham, Daron G. Davis, David Smith, Donald Richards, Michael V. Seiden, Charles Swanton, Timothy J. Yeatman, Robert Tibshirani, Christina Curtis, Sylvia K. Plevritis, Richard Williams, Eric Klein, Anne-Renee Hartman, Minetta C. Liu. Development of plasma cell-free DNA (cfDNA) assays for early cancer detection: first insights from the Circulating Cell-Free Genome Atlas Study (CCGA) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-343.

LBA8501 Background: Plasma cfDNA genomic analysis is used widely for the care of advanced lung cancer, but its suitability for early stage lung cancer detection is not well established. CCGA (NCT02889978) is a prospective, multi-center, observational study launched for the development of a noninvasive assay for cancer detection. Methods: Blood was prospectively collected (N = 1627) from 749 controls (no cancer diagnosis) and 878 participants (pts) with newly-diagnosed untreated cancer in this preplanned substudy, including 127 pts with lung cancer. Three prototype sequencing assays were performed: paired cfDNA and white blood cell (WBC) targeted sequencing (507 genes, 60,000X) for single nucleotide variants/indels; paired cfDNA and WBC whole genome sequencing (WGS) for copy number variation (30X); and cfDNA whole genome bisulfite sequencing (WGBS) for methylation (30X). For each assay, a classification model using 10-fold cross-validation was developed for all pts with cancer, then evaluated in the pts with lung cancer; sensitivity was estimated at 95% specificity. Results: We evaluated pts with lung cancer (127) and a subset of controls (580) with similar ages (mean±SD yrs: 67±9, 60±13), 85% and 43% were ever-smokers, and 46% and 22% were men, respectively. Of 3055 nonsynonymous mutations detected across 122 evaluable pts with lung cancer, > 50% were detected in WBC consistent with clonal hematopoiesis (CH). Accounting for CH, sensitivity in 63 stage I-IIIA pts evaluable across all 3 assays was 48% (35-61, targeted), 54% (41-67, WGS), and 56% (43-68, WGBS); in 54 stage IIIB-IV pts it was 85% (73-93, targeted), 91% (80-97, WGS), and 93% (82-98, WGBS) . Similar sensitivities were observed across histological subtypes (adenocarcinoma, squamous cell, small cell). Comparison to tumor WGS and multi-assay classification will be reported. Conclusions: Early stage lung cancers are detectable in cfDNA using a genome-wide sequencing approach. For lung cancer detection using targeted assays, CH must be accounted for to minimize false positives. Assay optimization is ongoing to allow further clinical development in the intended use population. Clinical trial information: NCT02889978.

12003Background: CHIP is defined by the presence of age-dependent acquired mutations in hematopoietic progenitor cells and has been reported to occur in up to 30% of individuals 60-70 years of age....

536Background: New breast cancer screening approaches are needed to detect clinically aggressive subtypes that may not be detected by mammography or are detected late in unscreened populations. CCG...

11526 Background: A large fraction of cfDNA fragments are derived from hematopoietic sources. Somatic alterations in cfDNA can be tumor-derived but also could represent somatic changes associated with clonal hematopoiesis. We performed deep sequencing of both plasma cfDNA and matched white blood cell (WBC) genomic DNA (gDNA) to determine the contribution of clonal hematopoiesis to the variants observed in cfDNA. Four cohorts were investigated: metastatic breast (BC), non-small cell lung (NSCLC), castration-resistant prostate cancer (CRPC), and non-cancer participants (pts). Methods: Metastatic cancer pts with de novo or progressive disease were prospectively enrolled. Non-cancer pts were blood bank donors. Plasma cfDNA and matched WBC gDNA were sequenced using a targeted 508-gene panel (2 Mb) to > 60,000X raw depth. Variant calling used a novel pipeline that employed molecular barcoding for error suppression followed by de novo assembly and graph-based variant calling. Results: Of 151 metastatic cancer pts (48 BC, 49 NSCLC, 54 CRPC), median age was 64 (30-87) with 53% female and 33% treatment naive. Of 47 non-cancer pts, median age was 61 (20-78) with 51% female. Analysis of cfDNA identified 1072 variants (AF > 0.1%, > 2 mutant reads, passing bioinformatic quality filters) which were also detected in WBC gDNA as non-germline ( < 35% allele frequency [AF]) non-synonymous variants. For these cfDNA variants, AF ranged from 0.1-14.4% and correlated with AF in WBC gDNA (r2 = 0.47, p < 0.001). Mutated genes were consistent with clonal hematopoiesis, with the most frequently mutated genes being DNMT3A, TET2, PPM1D, and TP53 (215, 77, 45, and 36 variants, respectively). For both cancer and non-cancer pts (age > 45), median number of overlapping variants was 5 per pt (range 0-22). The number of WBC gDNA and cfDNA variants per individual was positively associated with age (p < 0.001) in both cancer and non-cancer pts (interaction p = 0.08). Conclusions: Somatic cfDNA variants are frequently derived from clonal hematopoiesis and increase with age. Accurate assessment of somatic alterations in cfDNA should account for this phenomenon to distinguish between tumor-derived and WBC-derived variants.