Cell-free DNA Methylation Research Articles

DNA methylation patterns between tissue and blood samples in ovarian cancer.

5558 Background: Altered DNA methylation pattern, with functional consequences in the activity of key carcinogenic pathways, has been shown to be involved in the carcinogenesis of ovarian cancer. Tumor DNA is released into the blood via apoptosis or necrosis. Therefore, cell-free DNA (cfDNA) methylation alteration can also be detected in blood, which has shown promising performance in ovarian cancer early detection and prognostication. However, the difference of DNA methylation patterns between the tissue and blood samples of ovarian cancer is still unclear. Methods: PERCEIVE-I study (NCT04903665) is a prospective study aimed at the early detection of gynecological malignancies. Blood and paired tissue samples from this study were collected at the Fudan University Shanghai Cancer Center. Blood samples from age-matched non-cancer controls were also collected. All the blood and paired tissue samples were sequenced by a target methylation panel covering ~490,000 CpG sites. Finally, The ovarian cancer methylation patterns were analyzed based on 11 paired cancer-adjacent tissue samples and 170 blood samples (cancer, n=48; non-cancer control, n=122). Results: In total, 7,225 differentially methylated blocks (DMBs) were identified by comparing cancer and adjacent tissues, with 5,093 hypomethylated and 2,132 hypermethylated. By contrast, there were 5,853 hypomethylated and 8,685 hypermethylated DMBs in cancer blood samples compared with non-cancer controls. Among the 7,225 DMBs in tissues, 3,962 (54.8%) blocks were also observed to be altered in blood samples, including 2,354 hypomethylated blocks and 1,114 hypermethylated blocks. GO analysis revealed that these hypomethylated blocks were mainly associated with functions such as regulation of synaptic membrane and DNA-binding transcription activator activity, while the hypermethylated blocks were mainly enriched in the pattern specification process, transcription factor complex, and the DNA-binding transcription activator activity. The alteration of the above 3,962 DMBs in tissue were highly correlated with the alteration in blood (correlation coefficient of 0.71, 0.50, 0.73 and 0.73 for stage I-IV, respectively). Furthermore, in patients carrying germline BRCA1/2 mutations (n=11), most of the above DMBs were not significantly different from those without germline BRCA1/2 mutations (n=37). Conclusions: In this study, we demonstrated that blood DNA methylation alteration was highly consistent with tissue DNA methylation alteration in ovarian cancer, indicating the sustained release of DNA methylation signals from the primary cancer sites. These results suggest that cfDNA methylation can be used as reliable biomarkers for the early detection of ovarian cancer. Clinical trial information: NCT04903665 .

Global circulating free DNA methylation and fragmentome deconvolution in patients with metastatic renal cell carcinoma treated with immunotherapy (GOLDEN).

4544 Background: Metastatic renal cell carcinoma (mRCC) lacks molecular biomarkers. Cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) is a new, non-invasive approach to the detection of RCC-derived DNA in the circulation. In the GOLDEN study (NCT03702309), we explored the role of cfMeDIP-seq and fragmentomic dynamics as a biomarker, during immune checkpoint inhibition (ICI)-based treatment in mRCC patients (pts). Methods: Blood samples from mRCC pts were collected before and during treatment. Response to treatment was defined by the treating oncologist as: response, any degree of radiological tumor regression; stable disease (SD), no radiological change; or progressive disease (PD), radiological/clinical progression. More than 10ng of plasma cfDNA was subject to cfMeDIP-seq. A prognostic pan-cancer methylation signature previously generated using TCGA tissue methylation arrays was applied to cfMeDIP-seq output and cancer-signal methylation (CSM) score was calculated. An RCC-specific methylation signature, previously generated by identifying differentially methylated regions between RCC pts and controls, was applied ( Nuzzo et al, Nat Med 2020). Genome-wide fragmentation profiles were generated using DELFI. Using medians as cut off, log-rank test was performed to assess overall survival (OS) and progression-free survival (PFS). Results: Thirty-five patients were enrolled and n=33 received systemic treatment. 31/35 pts (89%) had clear cell and 4/35 (11%) non-clear cell histology. Median age was 61 (30-81) years and the M:F ratio was 6:1. Patients’ IMDC prognostic risk score was favorable n=6/35 (17%), intermediate n=23/35 (65%) and poor n=6/35 (17%). 20/33 (61%) patients received ipilimumab/nivolumab, 10/33 (30%) nivolumab and n=3/33 (9%) pembrolizumab/axitinib. There were n=14/33 (42%) responders, n=4/33 (12%) pts with SD and 15/33 (45%) pts with PD. Patients with high pan-cancer CSM score at baseline trended towards worse OS (HR 3.86, p = 0.079). Fragmentomics signature, consisting of increased proportion of short cfDNA fragments, showed a trend for worse PFS (HR = 3.04, p = 0.075) in on-treatment samples. Using the RCC-specific methylation signature, we found that those with an increase in signature score from baseline to on-treatment had better PFS (HR=2.4x10-10, p=0.002, n=10) and a trend towards better OS (HR=0.17, p=0.074, n=10). We hypothesize that RCC tissue-specific cell death early during ICI treatment could underpin this increase in the methylation score. Conclusions: This small study is the first report on the role of cfMeDIPseq-derived methylation and fragmentomic signatures in patients with mRCC undergoing ICI-based treatment. We found several trends and one significant association between these signatures and pt outcomes, which support further validation in larger cohorts. Clinical trial information: NCT03702309 .

Early detection of lung cancer using a panel of circulating cell-free DNA methylation biomarkers.

e20614 Background: Lung cancer remains the leading cause of cancer mortality worldwide. Early detection of lung cancer helps improve treatment and survival. Numerous aberrant DNA methylations have been reported in early-stage lung cancer. Here, we sought to identify novel DNA methylation biomarkers that could potentially be used for noninvasive early diagnosis of lung cancers. Methods: This prospective-specimen collection and retrospective-blinded-evaluation trial enrolled a total of 317 participants (198 tissues and 119 plasmas) comprising healthy controls, patients with lung cancer and benign disease from The First Affiliated Hospital of Soochow University between January 2020 and December 2021. Tissue and plasma samples were subjected to targeted bisulfite sequencing with a lung cancer specific panel targeting 9,307 differential methylation regions (DMRs). DMRs associated with lung cancer were identified by comparing the methylation profiles of tissue samples from patients with lung cancer and benign disease. Markers were selected with minimum redundancy and maximum relevance (mRMR) algorithm. A prediction model for lung cancer diagnosis was built through logistic regression algorithm and validated independently in tissue samples. Furthermore, the performance of this developed model was evaluated in a set of plasma cell-free DNA (cfDNA) samples. Results: We identified 7 DMRs corresponding to 7 differentially methylated genes (DMRs, including HOXB4, HOXA7, HOXD8, ITGA4, ZNF808, PTGER4, and B3GNTL1), highly associated with lung cancer by comparing the methylation profiles of lung cancer and benign nodule tissues. Based on the 7-DMR biomarker panel, we developed a new diagnostic model in tissue samples, termed “7-DMR model”, to distinguish lung cancers from benign diseases, achieving AUCs of 0.966 (95%CI: 0.933-1.000)/0.961 (0.924-1.000), sensitivities of 0.887 (0.824-0.951)/0.922 (0.863-0.980), specificities of 0.938 (0.889-0.986)/1.000 (1.000-1.000), and accuracies of 0.896 (0.835-0.957)/0.938 (0.886-0.991) in the discovery cohort (n = 96) and the independent validation cohort (n = 81), respectively. Furthermore, the 7-DMR model was applied to noninvasive discrimination of lung cancers and non-lung cancers including benign lung diseases and healthy controls in an independent validation cohort of plasma samples (n = 106), yielding an AUC of 0.935 (0.862-1.000), sensitivity of 0.808 (0.733-0.883), specificity of 0.975 (0.945-1.000), and accuracy of 0.934 (0.887-0.981). Conclusions: The 7 novel DMRs could be promising methylation biomarkers that merits further development as a noninvasive test for early detection of lung cancer.

Non-invasive detection of neuroendocrine prostate cancer through targeted cell-free DNA methylation

Non-invasive detection of neuroendocrine prostate cancer through targeted cell-free DNA methylation

Accurate detection of early-stage lung cancer using a panel of circulating cell-free DNA methylation biomarkers

BackgroundLung cancer remains the leading cause of cancer mortality worldwide. Early detection of lung cancer helps improve treatment and survival. Numerous aberrant DNA methylations have been reported in early-stage lung cancer. Here, we sought to identify novel DNA methylation biomarkers that could potentially be used for noninvasive early diagnosis of lung cancers.MethodsThis prospective-specimen collection and retrospective-blinded-evaluation trial enrolled a total of 317 participants (198 tissues and 119 plasmas) comprising healthy controls, patients with lung cancer and benign disease between January 2020 and December 2021. Tissue and plasma samples were subjected to targeted bisulfite sequencing with a lung cancer specific panel targeting 9,307 differential methylation regions (DMRs). DMRs associated with lung cancer were identified by comparing the methylation profiles of tissue samples from patients with lung cancer and benign disease. Markers were selected with minimum redundancy and maximum relevance algorithm. A prediction model for lung cancer diagnosis was built through logistic regression algorithm and validated independently in tissue samples. Furthermore, the performance of this developed model was evaluated in a set of plasma cell-free DNA (cfDNA) samples.ResultsWe identified 7 DMRs corresponding to 7 differentially methylated genes (DMGs) including HOXB4, HOXA7, HOXD8, ITGA4, ZNF808, PTGER4, and B3GNTL1 that were highly associated with lung cancer by comparing the methylation profiles of lung cancer and benign nodule tissue. Based on the 7-DMR biomarker panel, we developed a new diagnostic model in tissue samples, termed “7-DMR model”, to distinguish lung cancers from benign diseases, achieving AUCs of 0.97 (95%CI: 0.93-1.00)/0.96 (0.92-1.00), sensitivities of 0.89 (0.82–0.95)/0.92 (0.86–0.98), specificities of 0.94 (0.89–0.99)/1.00 (1.00–1.00), and accuracies of 0.90 (0.84–0.96)/0.94 (0.89–0.99) in the discovery cohort (n = 96) and the independent validation cohort (n = 81), respectively. Furthermore, the 7-DMR model was applied to noninvasive discrimination of lung cancers and non-lung cancers including benign lung diseases and healthy controls in an independent validation cohort of plasma samples (n = 106), yielding an AUC of 0.94 (0.86-1.00), sensitivity of 0.81 (0.73–0.88), specificity of 0.98 (0.95-1.00), and accuracy of 0.93 (0.89–0.98).ConclusionThe 7 novel DMRs could be promising methylation biomarkers that merits further development as a noninvasive test for early detection of lung cancer.Graphical abstract

Cell-free DNA Methylation as a Predictive Biomarker of Response to Neoadjuvant Chemotherapy for Patients with Muscle-invasive Bladder Cancer in SWOG S1314

BackgroundNeoadjuvant chemotherapy (NAC) is the standard of care in muscle-invasive bladder cancer (MIBC). However, treatment is intense, and the overall benefit is small, necessitating effective biomarkers to identify patients who will benefit most. ObjectiveTo characterize cell-free DNA (cfDNA) methylation in patients receiving NAC in SWOG S1314, a prospective cooperative group trial, and to correlate the methylation signatures with pathologic response at radical cystectomy. Design, setting, and participantsSWOG S1314 is a prospective cooperative group trial for patients with MIBC (cT2-T4aN0M0, ≥5 mm of viable tumor), with a primary objective of evaluating the coexpression extrapolation (COXEN) gene expression signature as a predictor of NAC response, defined as achieving pT0N0 or ≤pT1N0 at radical cystectomy. For the current exploratory analysis, blood samples were collected prospectively from 72 patients in S1314 before and during NAC, and plasma cfDNA methylation was measured using the Infinium MethylationEPIC BeadChip array. InterventionNo additional interventions besides plasma collection. Outcome measurements and statistical analysisDifferential methylation between pathologic responders (≤pT1N0) and nonresponders was analyzed, and a classifier predictive of treatment response was generated using the Random Forest machine learning algorithm. Results and limitationsUsing prechemotherapy plasma cfDNA, we developed a methylation-based response score (mR-score) predictive of pathologic response. Plasma samples collected after the first cycle of NAC yielded mR-scores with similar predictive ability. Furthermore, we used cfDNA methylation data to calculate the circulating bladder DNA fraction, which had a modest but independent predictive ability for treatment response. In a model combining mR-score and circulating bladder DNA fraction, we correctly predicted pathologic response in 79% of patients based on their plasma collected at baseline and after one cycle of chemotherapy. Limitations of this study included a limited sample size and relatively low circulating bladder DNA levels. ConclusionsOur study provides the proof of concept that cfDNA methylation can be used to generate classifiers of NAC response in bladder cancer patients. Patient summaryIn this exploratory analysis of S1314, we demonstrated that cell-free DNA methylation can be profiled to generate biomarker signatures associated with neoadjuvant chemotherapy response. With validation in additional cohorts, this minimally invasive approach may be used to predict chemotherapy response in locally advanced bladder cancer and perhaps also in metastatic disease.

Abstract LB298: Molecular phenotype classification of metastatic prostate cancer by cell-free DNA methylation analysis

Abstract Introduction: Metastatic castration-resistant prostate cancer (mCRPC) is a heterogeneous disease which can be classified into clinically relevant subtypes based on the expression of transcription factors (TF), such as the androgen receptor (AR) and neuroendocrine markers. Neuroendocrine prostate cancer (NEPC), characterized by gain of stem-like and neuroendocrine features and lack of AR expression is a clinically aggressive variant. Due to the absence of adequate biomarkers, NEPC is usually detected at a very advanced stage. There is mounting evidence that molecular subtype changes seen in NEPC are enforced by widespread epigenetic alterations, in particular DNA methylation changes. In this study, we aim to devise a novel DNA methylation-based assay for molecular subtyping and disease monitoring from cell-free DNA (cfDNA). Methods: We analyzed genome wide methylation patterns in 60 prostate cancer patient-derived xenograft (PDX) and 133 mCRPC tumors using array- and sequencing-based assays. We integrated DNA methylation with TF cistrome data to determine the landscape of methylation alterations at key lineage TF binding sites (TFBS). A linear regression model was trained on low-pass Enzymatic Methyl-Seq (EM-seq) cfDNA data derived from PDXs to identify molecular subtype specific DNA methylation changes at these TFBS. The model performance was optimized with in silico admixture experiments. This model was then used to discern tumor molecular phenotypes from cfDNA in three independent cohorts of mCRPC patients using low-pass whole genome bisulfite sequencing and EM-seq. Results: We observed a strong association between TFBS methylation and TF expression. For lineage specific TFs such as AR and ASCL1, we identified core sets of TFBSs whose differential methylation allowed for accurate assay-independent molecular subtype classification in tumor tissues. Applying an optimized quantitative model to mCRPC patients who underwent comprehensive tissue sampling by rapid autopsy we observed perfect subtype prediction from both tissue samples and cfDNA (AUC=1). A similar analytical performance was observed in additional clinical mCRPC cohorts with cfDNA. Conclusions: We show that methylation patterns at TFBSs can determine TF activity and can be used to classify molecular subtypes from both tumor tissue and cfDNA. For prostate cancer, we demonstrate that this approach can accurately detect NEPC by cost-effective low-pass EM-seq. More broadly, this study provides a novel analysis framework for robustly assessing molecular tumor phenotypes in cfDNA with applications in solid and liquid tumor diagnostics. Citation Format: Mohamed Adil, Brian Hanratty, Pallabi Mustafi, Ilsa Coleman, Radhika Patel, Anna-Lisa Doebley, Robert Patton, Eden Cruikshank, Patricia Galipeau, Ruth Dumpit, Martine Roudier, Jin-Yih Low, Navonil De Sarkar, Robert Montgomery, Eva Corey, Colm Morrissey, Peter Nelson, Gavin Ha, Michael Haffner. Molecular phenotype classification of metastatic prostate cancer by cell-free DNA methylation analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB298.

Abstract LB297: Analytical validation of a tissue-free, multi-cancer, post-diagnosis cancer research test that uses cell-free DNA methylation profiling

Abstract Introduction: Cancer detection blood tests have shown clinical utility after cancer diagnosis, but many evaluate only single cancers and require tumor tissue, thereby limiting their utility. We developed a versatile, tissue-free (ie, no tumor tissue required), multi-cancer detection test (“Post-Diagnosis Cancer Research Solution”) based on methylation sequencing of cfDNA from blood. This technology solution can be used to evaluate cfDNA applications in cancer research, including treatment evaluation, recurrence monitoring, and prognostic guidance. We report the analytical validation of this Post-Diagnosis Cancer Research Solution, characterizing sensitivity, specificity, precision, and input range. Methods: cfDNA samples from cancer and non-cancer donors were analyzed. Analytical sensitivity (LoD95, limit of detection with ≥95% probability) was determined as a function of methyl variant allele fraction (MVAF), a measure of circulating tumor allele fraction. A total of 6 ng of cfDNA was used (cancer cfDNA titrated into a background of non-cancer cfDNA). LoD95 was defined as the lowest observed MVAF with ≥95% cancer signal detection across tested replicates or was estimated using probit regression for eligible samples. Analytical specificity was the rate of non-cancer classification among samples from 128 non-cancer donors. Precision (defined as concordance with the expected cancer/non-cancer result) was evaluated for 15 cancer donors tested near sample LoD95 and 8 non-cancer donors, with ≥18 replicates/donor. Reliability of classification was evaluated as a function of cfDNA input mass (0.25-100 ng total cfDNA, 14 cancer and 2 non-cancer donors). Results: A total of 12 different solid cancer types from 22 individuals with cancer were assessed in the LoD95 analysis. Median LoD95 at 6 ng total cfDNA input was 0.023% (10th percentile 0.0037%; 90th percentile 0.04%) MVAF. LoD95 estimates from in silico titration analyses of >200 clinical samples across a subset of the 12 cancer types were consistent with experimental LoD95 values. Analytical specificity was determined to be 98.47% (95% CI: 94.60-99.58%). Median precision across individuals was 100% (10th percentile 87%; 90th percentile 100%). Classification performance was accurate and consistent across a wide range of cfDNA input mass (100% correct cancer/non-cancer classification from 1.5-100 ng). Conclusions: Results demonstrate that this multi-cancer Post-Diagnosis Cancer Research Solution has high analytical sensitivity, specificity, and precision, with reliable performance across a broad cfDNA input range. This technology does not require a tumor sample and provides a cancer signal estimate in terms of MVAF. These features may enable understanding of cfDNA dynamics in a wide variety of cancers for research studies. Citation Format: Mohini Desai, Svetlana Rakhmanova Shchegrov, Shoujie Chai, Yifan Zhou, Tracy Nguyen, Yirang Cho, Collin Melton, Eric Scott, Manami Roychowdhury-Saha, Pei-Yun Chang, Rita Shaknovich, Byoungsok Jung. Analytical validation of a tissue-free, multi-cancer, post-diagnosis cancer research test that uses cell-free DNA methylation profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB297.

Abstract 2307: Targeted cell-free DNA methylation signature in metastatic castration-resistant prostate cancer

Abstract Background: DNA methylation of cytosines in CpG sites serves as an important epigenetic regulator for gene expression. In prostate cancer (PC), differences in DNA methylation regions have been observed between normal and tumor cells. However, acquiring the samples to study these patterns can be challenging through standard biopsies. Therefore, liquid biopsies provide a minimally invasive method which can be utilized to investigate the methylome of PC patients. In this study, we used liquid biopsies from a well-defined group of PC patients to examine cell-free DNA (cfDNA) methylation signatures and determine their clinical significance in metastatic castration-resistant prostate cancer. Methods: Plasma was prospectively collected from a cohort 108 PC patients representing localized PC (n=24), metastatic hormone-sensitive PC (mHSPC) (n=28), and metastatic castration resistant PC (mCRPC) (n=37 pre-, n=19 post- mCRPC treatments). cfDNA from the patient plasma was used for an enzymatic methylation sequencing (EM-seq) that targeted a panel of genes (n=441) implicated in prostate cancer biology. Methylation status at CpG islands of these genes were examined using Twist targeted cfDNA methylation assay. The Bismark program was used for methylation calling. Differentially methylated regions (DMRs) across various patient outcomes were assessed by linking the CpG methylation data from target genes to the associated clinical data. Results: We identified DMRs in 305 out of the 411 target genes in the plasma epigenome when comparing the mCRPC to non-mCRPC states (localized PC and mHSPC) (false discovery rate, FDR < 0.2). From these 305 genes, we found that in patients experiencing only PSA relapse after failure of mHSPC (biochemical mCRPC) versus patients with clinical progression (clinical mCRPC), 261 out of the 305 genes had DMRs (p <0.05). We also compared DMRs before and after 3-months of treatment in the mCRPC state and found that 175 out of the 305 genes showed significant changes after 3-months of mCRPC-specific treatment. Furthermore, from these 175 genes, 24 genes showed significant methylation differences when comparing patients with and without disease progression during mCRPC treatment and 20 genes showed significant differences when comparing patients with neuroendocrine features to those with common adenocarcinoma of PC at the mCRPC state (p<0.05). These differentially methylated genes include RUNX3, FOXA2, HIC1 and MYCN. Conclusions: cfDNA-based liquid biopsies provide a minimally invasive method to investigate the methylome of mCRPC patients. Our observations that cfDNA methylation was correlated with disease progression provide evidence that DMRs across a select panel of genes may serve as predictive biomarker for poor response to therapy and survival. Citation Format: Jodie Wong, Yijun Tian, Amir Bitaraf, Claire Hanson, Matthew Larson, Jennifer Lloyd, Julia Batten, Neeraj Agarwal, Umang Swami, Anna Neibling, Jonathan Tward, Benjamin Maughan, Sumati Gupta, Manish Kohli, Liang Wang. Targeted cell-free DNA methylation signature in metastatic castration-resistant prostate cancer [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 2307.

Cell-free DNA methylation biomarker for the diagnosis of papillary thyroid carcinoma.

Cell-free DNA (cfDNA) is being explored as biomarker for non-invasive diagnosis of cancer. We aimed to establish a cfDNA-based DNA methylation marker panel to differentially diagnose papillary thyroid carcinoma (PTC) from benign thyroid nodule (BTN). 220 PTC- and 188 BTN patients were enrolled. Methylation markers of PTC were identified from patients' tissue and plasma by reduced representation bisulfite sequencing and methylation haplotype analyses. They were combined with PTC markers from literatures and were tested on additional PTC and BTN samples to verify PTC-detecting ability using targeted methylation sequencing. Top markers were developed into ThyMet and were tested in 113 PTC and 88 BTN cases to train and validate a PTC-plasma classifier. Integration of ThyMet and thyroid ultrasonography was explored to improve accuracy. From 859 potential PTC plasma-discriminating markers that include 81 markers identified by us, the top 98 most PTC plasma-discriminating markers were selected for ThyMet. A 6-marker ThyMet classifier for PTC plasma was trained. In validation it achieved an Area Under the Curve (AUC) of 0.828, similar to thyroid ultrasonography (0.833) but at higher specificity (0.722 and 0.625 for ThyMet and ultrasonography, respectively). A combinatorial classifier by them, ThyMet-US, improved AUC to 0.923 (sensitivity=0.957, specificity=0.708). The ThyMet classifier improved the specificity of differentiating PTC from BTN over ultrasonography. The combinatorial ThyMet-US classifier may be effective in preoperative diagnosis of PTC. This work was supported by the grants from National Natural Science Foundation of China (82072956 and 81772850).

Cardiovascular disease biomarkers derived from circulating cell-free DNA methylation.

Acute coronary syndrome (ACS) remains a major cause of worldwide mortality. The syndrome occurs when blood flow to the heart muscle is decreased or blocked, causing muscle tissues to die or malfunction. There are three main types of ACS: Non-ST-elevation myocardial infarction, ST-elevation myocardial infarction, and unstable angina. The treatment depends on the type of ACS, and this is decided by a combination of clinical findings, such as electrocardiogram and plasma biomarkers. Circulating cell-free DNA (ccfDNA) is proposed as an additional marker for ACS since the damaged tissues can release DNA to the bloodstream. We used ccfDNA methylation profiles for differentiating between the ACS types and provided computational tools to repeat similar analysis for other diseases. We leveraged cell type specificity of DNA methylation to deconvolute the ccfDNA cell types of origin and to find methylation-based biomarkers that stratify patients. We identified hundreds of methylation markers associated with ACS types and validated them in an independent cohort. Many such markers were associated with genes involved in cardiovascular conditions and inflammation. ccfDNA methylation showed promise as a non-invasive diagnostic for acute coronary events. These methods are not limited to acute events, and may be used for chronic cardiovascular diseases as well.

Significance of Circulating Cell-Free DNA Biomarkers in HBeAg-Negative Chronic Hepatitis B Virus Infection and Their Changes after Treatment Initiation

Chronic hepatitis B virus (HBV) infection is a common chronic liver disease that is closely associated with increased morbidity and mortality. Circulating cell-free DNA (cf-DNA) and global DNA methylation, expressed as circulating levels of 5-methyl-2'-deoxycytidine, are increasingly used to monitor chronic inflammatory diseases of several etiologies. This study attempts to investigate the serum levels of circulating cf-DNA and 5-methyl-2'-deoxycytidine in HBeAg-negative patients with chronic infection (carriers) and chronic hepatitis B (CHB), as well as their changes after treatment initiation in CHB. Serum samples from a total of 61 HBeAg-negative patients (30 carriers and 31 CHB patients) were included in order to quantify the levels of circulating cf-DNA and 5-methyl-2'-deoxycytidine. In addition, serum samples from 17 CHB patients in complete virological and biochemical remission after initiation of treatment with a nucleos(t)ide analogue were included. Circulating cf-DNA concentration was significantly increased after the initiation of treatment (15 vs. 10 ng/mL, p = 0.022). There was a trend in higher mean levels of circulating 5-methyl-2'-deoxycytidine in carriers compared to CHB patients (211.02 vs. 175.66 ng/mL, p = 0.089), as well as a trend in increasing 5-methyl-2'-deoxycytidine levels after treatment initiation in CHB patients compared to pre-treatment levels (215 vs. 173 ng/mL, p = 0.079). Both circulating levels of cf-DNA and 5-methyl-2'-deoxycytidine might be useful biomarkers in order to monitor liver disease activity and response to antiviral treatment in HBeAg-negative chronic HBV patients, but further studies are essential in order to validate these intriguing findings.

Abstract OT1-23-01: Development of a hoRizontal data intEgration classifier for NOn-invasive early diAgnosis of breasT cancEr: the RENOVATE trial

Abstract Background: The detection of breast lesions through self-examination or during screening tests is a frequent finding. Breast biopsy is required in case of radiologically suspect lesions, bestowing a high burden on both patients and national healthcare system, since only one every four biopsies is breast cancer (BC). To date, the assessment of circulating biomarkers failed to demonstrate clinical utility in the early diagnosis of BC, for its suboptimal accuracy and difficult transferability to clinical practice. The combination of novel cutting-edge methods for the assessment of circulating analytes in an integrated multiomic classifier may overcome such limitations, possibly allowing liquid biopsy to become a novel noninvasive procedure for the differential diagnosis of BC. Design: In the RENOVATE trial (NCT04781062), women with suspect (BI-RADS-4/5) breast lesions ≤ 2 cm (cT1) are asked, before biopsy, to donate ~ 35 mL of blood collected in four dedicated tubes and ~ 50 mL of urine at the Diagnostic Senology Unit of Ospedale Policlinico San Martino (Genoa, IT). Plasma cell-free DNA methylation and copy number alterations are assessed in a cohort of patients diagnosed with early BC and a matched set of patients with histologically proven benign lesions through cell-free methylated DNA immunoprecipitation and high throughput sequencing (cfMeDIPseq), as well as ultra-low pass whole genome sequencing (ULP-WGS). Thanks to the volume and quality of our sample set, other experimental techniques will be tested as well. Results from cfMeDIP-seq and ULP-WGS, possibly in combination with other findings, will be integrated in a unique classifier for the noninvasive differential diagnosis of suspect breast lesions. Eligibility criteria: Patients with radiologically suspect breast lesions ≤ 2 cm (i.e. BIRADS 4/5) are eligible. Patients with previous history of cancer, or diagnosed with autoimmune or active allergic diseases, acute or chronic hepatic, renal, or cardiac diseases, or acute or chronic infectious diseases are excluded from the present trial. Specific aims: The primary aim of the present trial is to develop a noninvasive classifier for the differential diagnosis of suspect breast lesions detected through mammography and/or ultrasound. For such purpose we will assess the performance of plasma cfMeDIPseq, ULP-WGS, and other promising techniques for the differential diagnosis of BC. Such techniques will be integrated in a unique classifier in order to reach the maximum possible accuracy. Statistical methods: Sample size was calculated with a semi-parametric simulation-based approach from beta-distributions of PBMC datasets: assuming to test 20,000 CpG regions, with 300 differentially methylated target CpGs, a target maximal difference in DNA methylation of 0.2 between groups and an FDR of 0.05, 1 – beta ~ 0.90 would be achieved with an overall sample size of 150 samples split in a 1:2 ratio. Target accrual and present accrual: Minimum target accrual is set at 49 patients with BC and 98 patients with benign lesions. To date, we have collected plasma samples from 74 eligible patients with BC and 115 eligible patients with benign lesions. A validation cohort accounting for ~30% of our sample set will be recruited at Istituto Nazionale dei Tumori (Milan, IT). Contact information: For further information, please contact Gabriele Zoppoli at gabriele.zoppoli@unige.it. Citation Format: Francesco Ravera, Martina Dameri, Isabella Lombardo, Mario Stabile, Alberto Tagliafico, Massimo Calabrese, Alberto Ballestrero, Lorenzo Ferrando, Gabriele Zoppoli. Development of a hoRizontal data intEgration classifier for NOn-invasive early diAgnosis of breasT cancEr: the RENOVATE trial [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr OT1-23-01.

Unintrusive multi-cancer detection by circulating cell-free DNA methylation sequencing (THUNDER): development and independent validation studies

Early detection of cancer offers the opportunity to identify candidates when curative treatments are achievable. The THUNDER study (THe UNintrusive Detection of EaRly-stage cancers, NCT04820868) aimed to evaluate the performance of ELSA-seq, a previously described cfDNA methylation-based technology, in the early detection and localization of six types of cancers in the colorectum, esophagus, liver, lung, ovary and pancreas. A customized panel of 161,984 CpG sites was constructed and validated by public and in-house (cancer: n=249; non-cancer: n=288) methylome data, respectively. The cfDNA samples from 1,693 participants (cancer: n=735; non-cancer: n=958) were retrospectively collected to train and validate two multi-cancer detection blood test models (MCDBT-1/2) for different clinical scenarios. The models were validated on a prospective and independent cohort of age-matched 1,010 participants (cancer: n=505; non-cancer: n=505). Simulation using the cancer incidence in China was applied to infer stage-shift and survival benefits to demonstrate the potential utility of the models in the real world. MCDBT-1 yielded a sensitivity of 69.1% (64.8%‒73.3%), a specificity of 98.9% (97.6%‒99.7%) and tissue origin accuracy of 83.2% (78.7%‒87.1%) in the independent validation set. For early stage (I‒III) patients, the sensitivity of MCDBT-1 was 59.8% (54.4%‒65.0%). In the real-world simulation, MCDBT-1 achieved the sensitivity of 70.6% in detecting the six cancers, thus decreasing late-stage incidence by 38.7%‒46.4%, and increasing 5-year survival rate by 33.1%‒40.4%, respectively. In parallel, MCDBT-2 was generated at a slightly low specificity of 95.1% (92.8%-96.9%) but a higher sensitivity of 75.1% (71.9%-79.8%) than MCDBT-1 for populations at relatively high risk of cancers, and also had ideal performance. In this large-scale clinical validation study, MCDBT-1/2 models showed a high sensitivity, specificity, and accuracy of predicted origin in detecting six types of cancers.

Methylated Cell-Free DNA Sequencing (MeD-seq) of LpnPI Digested Fragments to Identify Early Progression in Metastatic Renal Cell Carcinoma Patients on Watchful Waiting

According to the current guidelines, watchful waiting (WW) is a feasible option for patients with good or intermediate prognosis renal-cell carcinoma (RCC). However, some patients rapidly progress during WW, requiring the initiation of treatment. Here, we explore whether we can identify those patients using circulating cell-free DNA (cfDNA) methylation. We first defined a panel of RCC-specific circulating methylation markers by intersecting differentially methylated regions from a publicly available dataset with known RCC methylation markers from the literature. The resulting RCC-specific methylation marker panel of 22 markers was subsequently evaluated for an association with rapid progression by methylated DNA sequencing (MeD-seq) in serum from 10 HBDs and 34 RCC patients with a good or intermediate prognosis starting WW in the IMPACT-RCC study. Patients with an elevated RCC-specific methylation score compared to HBDs had a shorter progression-free survival (PFS, p = 0.018), but not a shorter WW-time (p = 0.15). Cox proportional hazards regression showed that only the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) criteria were significantly associated with WW time (HR 2.01, p = 0.01), whereas only our RCC-specific methylation score (HR 4.45, p = 0.02) was significantly associated with PFS. The results of this study suggest that cfDNA methylation is predictive of PFS but not WW.

Diagnosis of prostate cancer using cell-free DNA methylation profiles from expressed prostatic secretions.

389 Background: Urinary and seminal cell-free DNA (cfDNA) have been recognized as promising biomarkers of prostate cancer. DNA methylation signal in tumor is increasingly used as tumor diagnosis and longitudinal monitoring indicator. However, the clinical utility of cfDNA from expressed prostatic secretions (EPS) remains unknown. Methods: The prospective study includes 50 prostate cancer (PC) patients and equivalent benign prostatic hyperplasia (BPH) patients, where EPS samples were collected after the prostatic massage of each patient. Cell-free DNA from EPS was extracted and treated by PredicineEPIC, a liquid biopsy comprehensive DNA methylation assay. The study developed integrated bioinformatic algorithms to profile the genome-wide epigenomic characteristics and investigate tumor-specific methylation patterns. Results: The initial exploratory cohort included 5 PC and 11 BPH cases. PredicineEPIC whole-genome DNA methylation of EPS identified over 300 differentially methylated regions (DMRs). Prostate cancers were distinguished from the BPH group by an unsupervised clustering method, suggesting that the identified DMRs embody the specific profiles of malignant tumors. Additionally, this study detected genome-wide copy number variation burden (CNB) in parallel. A risk model was built for cancer risk assessment based on methylation and CNB characteristics. Conclusions: This study demonstrated the feasibility of methylation profiling of cfDNA in EPS in prostate cancer. This non-invasive liquid biopsy approach could sensitively navigate prostate cancer from benign prostatic hyperplasia, suggesting future direction of methylation-based liquid biopsy in detecting prostate cancer.

Integrated, Longitudinal Analysis of Cell-free DNA in Uveal Melanoma.

Here, we demonstrate integrated, longitudinal cfDNA sequencing using multi-omic approaches is more effective than unimodal analysis. This approach supports the use of frequent blood testing using comprehensive genomic, fragmentomic, and epigenomic techniques.

Early detection and localization of major gastrointestinal cancers by combining DNA methylation and fragmentomic signatures generated from a single cfDNA targeted sequencing assay.

330 Background: Gastrointestinal (GI) cancers totally account for more than one third of the cancerous deaths, yet there is no cost-effective blood-based assay for the early detection of GI cancers. We sought to develop GutSeer, a noninvasive test based on cell-free DNA (cfDNA) methylation and fragmentation signatures derived from one single targeted DNA methylation sequencing panel, for early detection and localization of five major GI cancers, including colorectal (CC), gastric (GC), liver (LC), esophageal (EC), and pancreatic cancer (PC). Methods: A DNA methylation targeted sequencing panel with 1656 target regions was designed. It was then verified in a large cohort of retrospective cancer and control plasma samples for feature selection and modeling. The participants were randomly divided into a training cohort and a validation cohort in a 1:1 ratio. DNA methylation and fragmentomic features were calculated based on GutSeer sequencing data. An ensemble stacked machine learning approach was built to classify cancer and healthy samples in training cohort and tested in validation cohort. We also constructed a TOO model to predict the tissue of origin of detected cancer samples. Results: To develop GutSeer assay, we have enrolled and tested a total of 1844 retrospective plasma samples (787 healthy, 342 LC, 239 GC, 209 EC, 180 CC, and 87 PC), over half of the cancer samples were diagnosed with early-stage disease (TNM stage I 35.6%; stage II 23.3%; stage III 21.7%; stage IV 12.5%). Cancer- vs-healthy model was built on training cohort and tested in validation cohort, achieving an AUC of 0.94 (sensitivity=77.7%, specificity=96.4%) with methylation features, and 0.95 (sensitivity=77.1%, specificity=95.9%) with fragmentomic features. Combining these features could achieve AUC of 0.963 (sensitivity = 86.2%, specificity = 96.7%). For individual cancer types, the sensitivity was 93.3% (CC), 81.1% (EC), 70.3% (GC), 96.5% (LC) and 86.4% (PC), respectively. For predicted cancer samples, we achieved an 82% top-one (66.7% CC, 87.0% GC/EC, 89.0% LC, 63.2% PC) and 95.2% top-two (86,9% CC, 98.2% GC/EC, 97.6% LC, 89.5% PC) TOO accuracy (ACC, accuracy of predicting the most likely, and the top 2 most likely tissue or organ types where the identified cancer was located, respectively) in validation cohort with TOO model combined all features. Conclusions: Based on a single targeted DNA methylation sequencing assay, GutSeer, which combined cfDNA methylation and fragmentomic signatures, could detect and localize the major five GI cancers with high accuracy but low cost. Although this is a pilot study with limited sample size, GutSeer demonstrated the potential to be further optimized into non-invasive diagnostics for blood-based early screening and diagnosis for GI cancers.

A prospective clinical trial to evaluate the performance of a multi-analyte blood test for early detection of hepatocellular carcinoma among at-risk patients with liver cirrhosis: The CLiMB study.

TPS617 Background: Hepatocellular carcinoma (HCC) is the leading cause of cancer-related death in patients with cirrhosis in the U.S. HCC surveillance is associated with significant improvements in early tumor detection, receipt of curative therapy and overall survival in patients with cirrhosis. However, current surveillance strategies suffer from poor adherence and suboptimal sensitivity. The multi-analyte HelioLiver test is a novel blood test for the early detection of HCC. The test provides a qualitative result based on a multiparametric algorithm that incorporates information from cell-free DNA (cfDNA) methylation markers, protein tumor markers (AFP, AFP-L3%, DCP) and patient demographic characteristics (sex, and age). Here, we describe the protocol for a prospective, multicenter clinical trial designed to investigate the performance of this multi-analyte blood test in cirrhosis patients at risk for HCC. Methods: The Prospective Clinical Trial to Detect Liver Cancer through Quantification of cfDNA Methylation in Blood Samples (CLiMB) is a longitudinal, multicenter, prospective study with a targeted enrollment of 1,600 participants. The main inclusion and exclusion criteria are described in the table. Within the Initial Surveillance Visit (t=0 months), all enrolled subjects will provide blood specimens for the multi-analyte blood test and undergo conventional ultrasound to examine the liver. Every subject will then undergo diagnostic imaging by multiphasic MRI to determine the clinical truth. The results of diagnostic imaging will be scored by a Liver Reporting and Data System (LI-RADS) score and the number and size of any malignant lesions identified will be recorded. After the Initial Surveillance Visit (t=0 months), subjects with an indeterminant HCC finding by MRI (LI-RADS 3) will be recommended to have up to three Follow-Up Visits (t=6 months, t=12 months, and t=18 months) to attempt to resolve the clinical truth for these subjects. The primary endpoints of the trial are to demonstrate the superiority for sensitivity and non-inferiority for specificity of the multi-analyte blood test over ultrasound for the detection of HCC. The data of all diagnostic imaging by MRI will be saved and uploaded for evaluation by a blinded, centralized team of radiologists, which will be used as the basis of the clinical truth for all subjects. All participants are currently enrolled across the U.S. from over 40 clinical sites. Clinical trial information: NCT03694600 . [Table: see text]

Use of methylation and fragmentation signals in the detection of early-stage colorectal cancer.

201 Background: Colorectal cancer (CRC) remains a leading cause of cancer related mortality worldwide. We utilized cell-free DNA (cfDNA) methylation and fragmentation characteristics of selected cancer-related biomarker regions and applied tumor-derived signal deduction and a machine learning algorithm to refine a blood test for the early detection of CRC. Methods: This was a prospective, international (Spain, Ukraine, Germany and USA [part of NCT04792684 study] population), observational cohort study. Plasma samples were collected either prior to a scheduled screening colonoscopy or prior to colonic surgery for primary CRC. 95 cfDNA samples from 48 early stage (I-II), 47 late-stage (III-IV) CRC patients (mean age 65 [48-83], female 45%, distal cancers 51%) and 204 age, gender and country of origin matched colonoscopy-checked controls were analyzed. 79 of the control patients had a negative colonoscopy finding (cNEG), 96 had benign findings of diverticulosis, hemorrhoids and/or hyperplastic polyps (BEN), 29 had non-advanced adenomas (NAA). Samples were analyzed utilizing previously described hybrid-capture based sequencing methodology. Panel of targeted biomarkers was previously identified through tissue- and plasma-based discovery and further narrowed down through cancer-related biological pathways analysis workflow. Individual cfDNA fragments belonging to each biomarker region were scored for cancer-specific signal. Finally, calculated scores were used in prediction model building and testing for establishing panel accuracy for CRC detection. Results: Prediction model utilizing a panel of methylation and fragmentation scores originating from cfDNA biomarkers belonging to relevant cancer development and progression related pathways, such as axonal guidance, ephrin receptor signaling, epithelial-mesenchymal transition and FGF signaling, correctly classified 92% (87/95) of CRC patients. Sensitivity per cancer stage ranged from 91% (21/23) for stage I, 92% (23/25) for stage II, 91% (30/33) for stage III and 93% (13/14) for stage IV. Fragmentation signals contributed most to early-stage cancers (I-II), while methylation signals were more significant for late stage (III-IV) detection. Specificity of the model was 94% (199/204), with 97% (28/29) NAA, 91% (87/96) BEN and 96% (76/79) cNEG patients correctly identified. Lesion location, gender, age and country of origin were not significantly correlated to prediction outcome. Conclusions: Use of methylation and fragmentation characteristics of cancer-related cfDNA regions, combined with a machine-learning algorithm is highly accurate for early-stage (I-II) CRCs (92% sensitivity at 94% specificity). The study is being further expanded on larger cohort for validation of a highly accurate and minimally invasive blood-based CRC screening test.