Taylor, William R.

AbstractExtracellular vesicles (EVs) are of growing interest due to their potential diagnostic, disease surveillance, and therapeutic applications. While several studies have evaluated EV isolation methods in various biofluids, there are few if any data on these techniques when applied to stool. The latter is an ideal biospecimen for studying EVs and colorectal cancer (CRC) because the release of tumour markers by luminal exfoliation into stool occurs earlier than vascular invasion. Since EV release is a conserved mechanism, bacteria in stool contribute to the overall EV population. In this study, we assessed five EV separation methods (ultracentrifugation [UC], precipitation [EQ‐O, EQ‐TC], size exclusion chromatography [SEC], and ultrafiltration [UF]) for total recovery, reproducibility, purity, RNA composition, and protein expression in stool supernatant. CD63, TSG101, and ompA proteins were present in EV fractions from all methods except UC. Human (18s) and bacterial (16s) rRNA was detected in stool EV preparations. Enzymatic treatment prior to extraction is necessary to avoid non‐vesicular RNA contamination. Ultrafiltration had the highest recovery, RNA, and protein yield. After assessing purity further, SEC was the isolation method of choice. These findings serve as the groundwork for future studies that use high throughput omics technologies to investigate the potential of stool‐derived EVs as a source for novel biomarkers for early CRC detection.

AbstractBackground:The role of methylation in pancreatic cancer risk remains unclear. We integrated genome and methylome data to identify CpG sites (CpG) with the genetically predicted methylation to be associated with pancreatic cancer risk. We also studied gene expression to understand the identified associations.Methods:Using genetic data and white blood cell methylation data from 1,595 subjects of European descent, we built genetic models to predict DNA methylation levels. After internal and external validation, we applied prediction models with satisfactory performance to the genetic data of 8,280 pancreatic cancer cases and 6,728 controls of European ancestry to investigate the associations of predicted methylation with pancreatic cancer risk. For associated CpGs, we compared their measured levels in pancreatic tumor versus benign tissue.Results:We identified 45 CpGs at nine loci showing an association with pancreatic cancer risk, including 15 CpGs showing an association independent from identified risk variants. We observed significant correlations between predicted methylation of 16 of the 45 CpGs and predicted expression of eight adjacent genes, of which six genes showed associations with pancreatic cancer risk. Of the 45 CpGs, we were able to compare measured methylation of 16 in pancreatic tumor versus benign pancreatic tissue. Of them, six showed differentiated methylation.Conclusions:We identified methylation biomarker candidates associated with pancreatic cancer using genetic instruments and added additional insights into the role of methylation in regulating gene expression in pancreatic cancer development.Impact:A comprehensive study using genetic instruments identifies 45 CpG sites at nine genomic loci for pancreatic cancer risk.

Abstract Purpose: Screening for high prevalence cancers (breast, colorectum and uterine cervix) reduces mortality; however, most cancer deaths arise in unscreened organs. An ideal solution would accurately detect multiple currently unscreened cancers in a single round of testing. We aimed to assess the feasibility of a targeted assay panel of methylated DNA markers (MDMs) and proteins for detection of highly lethal cancers in a multi-cancer case-control study. Experimental Procedures: Prospectively collected plasmas from 180 cases of 6 cancer types (stage A-D liver (n=36), and TNM stage II-IV esophageal (n=18), lung (n=36), ovarian (n=30), pancreatic (n=30), and stomach (n=30)) and 257 smoking status, age-, and gender-matched asymptomatic controls were tested in blinded fashion. To quantify 26 MDMs previously found to be common among many cancers, multiplex PCR followed by LQAS (Long probe Quantitative Amplified Signal) assay was performed on bisulfite converted DNA extracted from 3 mL of plasma. Additionally, 5 proteins were tested from paired serum aliquots and combined with MDMs for a multi-analyte analysis. Two-thirds of the cases and controls were used to develop prediction algorithms, both logistic regression and random forest models, and the remaining 1/3 were used to validate the models. Results: Using stepwise logistic regression, a combination of 3 proteins (CEA, CA-125, CA19-9) and 5 MDMs (ZNF671, GRIN2D, NDGR4, SHOX2, B3GALT6) resulted in an AUC of 0.95 and an overall sensitivity of 87% for all cancers at 95% specificity. The logistic model on the validation set of the samples resulted in an AUC of 0.96 and a sensitivity of 83% with an observed specificity of 94%. The cancer-specific sensitivities ranged from 78% for lung cancer to 90% for ovarian and pancreatic cancer (Table). Random forest and logistic analyses were in agreement. Conclusion: Targeted assay of select MDMs and proteins show high discrimination for multiple cancers in a case control study, which included early stage patients. Performance with 95% confidence intervals of multi-target assay for multi-cancer detectionSensitivitySpecificityModelOverallBy cancer typeLungEsophagealGastricPancreaticLiverOvarianLogisticTraining set87%(79-92%)79%83%90%95%88%85%95%(90-97%)Test set83%(72-91%)75%100%80%80%75%100%94%(87%-98%)Training + Test186%(80-90%)78%89%87%90%83%90%95%(91-97%)Training + Test275%(68-81%)50%89%83%76%81%80%98%(96-99%)Random forest377%(70-83%)58%78%83%77%81%87%95%(92-97%)195% specificity goal, 5 MDMs and 3 proteins.298% specificity goal, 5 MDMs and 3 proteins.3With 500-fold in silico cross-validation on all samples and all markers (26 MDMs + 5 proteins) Citation Format: Slava Katerov, Abram Vaccaro, Jacquelyn Hennek, Jennifer Carlson, William R. Taylor, Douglas Mahoney, John B. Kisiel, Hatim T. Allawi. Accurate multi-cancer detection using methylated DNA markers and proteins in plasma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 111.

5526 Background: HR-HPV DNA testing, with or without cervical cytology, provides excellent sensitivity for detection of cervical cancer (CC) and its precursors; negative test results indicate that risk of disease is extremely low and enable women to undergo reduced screening with safety. However, management of women who screen positive remains challenging as many will prove to have self-limited HR-HPV infections. DNA methylation is an early event in carcinogenesis that could enhance CC screening specificity. Methods: For discovery, DNA from 70 FFPE CC (36 squamous, 34 adenocarcinoma) tissues that were reviewed microscopically, 18 fresh frozen benign cervicovaginal (BCV) tissues collected at the time of benign hysterectomy, and 18 buffy coats from cancer-free women underwent reduced representation bisulfite sequencing (RRBS) to identify MDMs associated with CC. Candidate MDM selection was based on area under the receiver operating characteristic curve (AUC) discrimination, methylation fold change, and low background methylation among benign controls. Candidate MDMs were re-tested using methylation-specific PCR (MSP) to confirm performance. Blinded biological validation was performed using MSP on DNA extracted from independent FFPE CC (38 squamous, 43 adenocarcinoma) and BCV (40) tissues. The performance of CC MDMs was also tested in DNA extracted from cervical dysplasia (36 adenocarcinoma in situ (AIS), 32 cervical intraepithelial neoplasia (CIN) 2/3, 11 CIN 1) FFPE tissues. Results: From RRBS discovery and technical validation via MSP, 30 candidate MDMs showed marked methylation fold changes (10 to >1000) across both CC histologies compared to BCV tissue from cancer-free women. Each of the 30 MDMs highly discriminated CC from BCV tissue with 9 MDMs having an AUC >0.90 (Table). CC MDMs also highly discriminated AIS from BCV but did not perform well in CIN 2/3 and CIN 1 (Table). Conclusions: Whole methylome sequencing, stringent filtering criteria, and biological validation have yielded outstanding candidate MDMs for CC that highly discriminate CC from BCV, notably with high specificity. Performance in cervical dysplasias varied with higher positivity rates in AIS than in CIN 2/3 and CIN 1. Translation to testing these novel MDMs in lower genital tract biospecimens and the addition of HR-HPV to the CC panel are warranted.[Table: see text]

e15511 Background: Aberrantly methylated DNA marker (MDM) candidates are strongly associated with primary colorectal cancer (CRC) before treatment and detect CRC recurrence with high sensitivity when assayed from plasma. The relationship of these MDMs in association to chemotherapy treatment response is unknown. Methods: In a prospective cohort of patients receiving systemic therapy for advanced CRC, peripheral blood was collected serially during restaging visits. 15 patients were retrospectively identified to have partial response (PR), stable disease (SD) and progressive disease (PD) to treatment (n=5 for each group) based on the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Using paired samples from each patient before and after response assessment, we analyzed 11 MDMs ( GRIN2D, ZNF671, ANKRD13B, QKI, VAV3, JAM3, SFMBT2, CHST2, ZNF568, FER1L4 and CNNM1) to assess correlation with treatment response. Cell-free DNA was extracted and bisulfite treated before MDMs were quantified by target enrichment long-probe quantitative-amplified signal assay and normalized to a methylated sequence of B3GALT6. Continuous variables are summarized as a median with corresponding interquartile ranges (IQR) and comparisons between subgroups were based on the Wilcox Rank Sums test. Results: The median interval between pre- and post-response assessment visits was 69 days (IQR: 63-83 days) and the level of tumor burden at pre-assessment was similar across all response types (Table 1). Patients with PD had higher levels of methylated GRIN2D, ZNF671 and ANKRD13B than those with PR or SD at baseline and may offer additional prognostic value over CEA which was similar in the PR and PD groups before treatment (Table 1). Elevation of pre-assessment MDMs preceded radiographic evidence of disease progression by 82 days (IQR 69-83 days). Conclusions: Three MDMs, GRIN2D, ZNF671 and ANKRD13B, were found to reflect treatment response (PD vs. PR + SD) as shown in the table. Although this pilot study was limited by a small sample size, it demonstrated the feasibility of using plasma-based MDMs in monitoring treatment response to systemic therapy for advanced CRC and should be compared to CEA in a larger study.[Table: see text]

Abstract Purpose: We have previously identified tissue methylated DNA markers (MDMs) associated with pancreatic ductal adenocarcinoma (PDAC). In this case–control study, we aimed to assess the diagnostic performance of plasma MDMs for PDAC. Experimental Design: Thirteen MDMs (GRIN2D, CD1D, ZNF781, FER1L4, RYR2, CLEC11A, AK055957, LRRC4, GH05J042948, HOXA1, PRKCB, SHISA9, and NTRK3) were identified on the basis of selection criteria applied to results of prior tissue experiments and assays were optimized in plasma. Next, 340 plasma samples (170 PDAC cases and 170 controls) were assayed using target enrichment long-probe quantitative amplified signal method. Initially, 120 advanced-stage PDAC cases and 120 healthy controls were used to train a prediction algorithm at 97.5% specificity using random forest modeling. Subsequently, the locked algorithm derived from the training set was applied to an independent blinded test set of 50 early-stage PDAC cases and 50 controls. Finally, data from all 340 patients were combined, and cross-validated. Results: The cross-validated area under the receiver operating characteristic curve (AUC) for the training set was 0.93 (0.89–0.96) for the MDM panel alone, 0.91 (95% confidence interval, 0.87–0.96) for carbohydrate antigen 19-9 (CA19-9) alone, and 0.99 (0.98–1) for the combined MDM-CA19-9 panel. In the test set of early-stage PDAC, the AUC for MDMs alone was 0.84 (0.76–0.92), CA19-9 alone was 0.87 (0.79–0.94), and combined MDM-CA19-9 panel was 0.90 (0.84–0.97) significantly better compared with either MDMs alone or CA19-9 alone (P = 0.0382 and 0.0490, respectively). At a preset specificity of 97.5%, the sensitivity for the combined panel in the test set was 80% (28%–99%) for stage I disease and 82% (68%–92%) for stage II disease. Using the combined datasets, the cross-validated AUC was 0.9 (0.86–0.94) for the MDM panel alone and 0.89 for CA19-9 alone (0.84–0.93) versus 0.97 (0.94–0.99) for the combined MDM-CA19-9 panel (P ≤ 0.0001). Overall, cross-validated sensitivity of MDM-CA19-9 panel was 92% (83%–98%), with an observed specificity of 92% at the preset specificity of 97.5%. Conclusions: Plasma MDMs in combination with CA19-9 detect PDAC with significantly higher accuracy compared with either biomarker individually.

Abstract Purpose: We aimed to assess the concordance of colorectal cancer–associated methylated DNA markers (MDM) in primary and metastatic colorectal cancer for feasibility in detection of distantly recurrent/metastatic colorectal cancer in plasma. Experimental Design: A panel of previously discovered colorectal cancer–associated MDMs was selected. MDMs from primary and paired metastatic colorectal cancer tissue were assayed with quantitative methylation-specific PCR. Plasma MDMs were measured blindly by target enrichment long-probe quantitative-amplified signal assays. Random forest modeling was used to derive a prediction algorithm of MDMs in archival plasma samples from primary colorectal cancer cases. This algorithm was validated in prospectively collected plasma samples from recurrent colorectal cancer cases. The accuracy of the algorithm was summarized as sensitivity, specificity, and area under the curve (AUC). Results: Of the 14 selected MDMs, the concordance between primary and metastatic tissue was considered moderate or higher for 12 MDMs (86%). At a preset specificity of 95% (91%–98%), a panel of 13 MDMs, in plasma from 97 colorectal cancer cases and 200 controls, detected stage IV colorectal cancer with 100% (80%–100%) sensitivity and all stages of colorectal cancer with an AUC of 0.91 (0.87–0.95), significantly higher than carcinoembryonic antigen [AUC, 0.72 (0.65–0.79)]. This panel, in plasma from 40 cases and 60 healthy controls, detected recurrent/metastatic colorectal cancer with 90% (76%–97%) sensitivity, 90% (79%–96%) specificity, and an AUC of 0.96 (0.92–1.00). The panel was positive in 0.30 (0.19–0.43) of 60 patients with no evidence of disease in post-operative patients with colorectal cancer. Conclusions: Plasma assay of novel colorectal cancer–associated MDMs can reliably detect both primary colorectal cancer and distantly recurrent colorectal cancer with promising accuracy.

e22024 Background: Disease surveillance in high risk melanoma patients using PET/CT is recommended every 6 months, an algorithm that is extremely expensive and often difficult to access. Biomarkers capable of detecting recurrence and low volume metastasis have application in tools to improve the cost effectiveness of surveillance. Towards this long term goal, our aim was to discover melanoma-specific methylated DNA markers (MDMs). Methods: Candidate MDMs were identified by reduced representation bisulfite sequencing of selected age- and sex-balanced snap frozen metastatic melanoma, benign control skin and white blood cells. Sequences were filtered for highest methylation difference, lowest control methylation background ( < 1%), and highest discrimination by variance inflated logistic regression. Using formalin-fixed tissues, the top 32 MDMs were further evaluated in metastatic melanoma from 35 patients, primary melanoma from 26 patients, and 73 control samples (47 benign precursor lesions or normal skin, and 26 healthy buffy coat samples to assess for background DNA in circulation). Candidate markers were blindly assayed by quantitative methylation specific PCR. After normalization by within sample ACTB level, random forest modeling with cross-validation identified marker combinations with greatest predictive accuracy. Results: On receiver operator characteristics analyses of individual marker candidates, areas under the curve (AUCs) ranged from 0.43 to 0.97. Median AUC was 0.835. At 100% specificity, a cross-validated panel of five MDMs (see Table), yielded a sensitivity of 33/35 cases (94.3%; 95% CI, 86.6-100%) for metastatic melanoma and 22/26 cases (84.6%; 95% CI, 70.7-98.5%) for primary melanoma. Furthermore, none of the 5 markers were detected in normal buffy coat samples. Conclusions: A panel of five novel MDMs assayed on tissue and undetectable in normal buffy coat achieves very high discrimination between melanoma and benign control tissues. This panel can be assessed in blood or other bodily fluids for application in melanoma surveillance. [Table: see text]

6072 Background: Effective screening tests for ovarian cancer (OC) are lacking; most cases present at advanced stage and portend poor prognosis. DNA methylation is an early event in carcinogenesis and can be detected in blood plasma samples from cancer patients. In DNA extracted from tissues, we first discovered, then validated discriminant methylated DNA marker (MDM) candidates for OC and subsequently tested independent plasma from women with and without OC. Methods: For discovery, DNA from 67 frozen tissues (18 high grade serous (HGS), 18 endometrioid, 15 clear cell (CC), 6 mucinous OCs; 10 benign fallopian tube epithelium (FT); and 19 buffy coats from cancer-free women underwent reduced representation bisulfite sequencing (RRBS) to identify MDMs associated with OC. Candidate MDM selection was based on receiver operating characteristic (ROC) discrimination, methylation fold change, and low background methylation among controls. Blinded biological validation was performed using methylated specific PCR on DNA extracted from independent FFPE tissues from OCs (36 HGS, 22 endometrioid, 21 CC, and 14 mucinous) and 29 FT. Top performing MDMs in tissue were tested using long-probe quantitative amplified signal assays in independent pre-treatment plasma samples from women newly-diagnosed with OC and population-sampled healthy women. A random forest modeling analysis was performed to generate predictive probability of disease; results were 500-fold in silico cross-validated. Results: After RRBS discovery and biological validation, 33 MDMs showed marked methylation fold changes (10 to > 1000) across all OC histologies vs FT. The top 11 MDMs ( GPRIN1, CDO1, SRC, SIM2, AGRN, FAIM2, CELF2, DSCR6, GYPC, CAPN2, BCAT1) were tested on plasma from 91 women with OC (76 (84%) HGS) and 91 without OC; the cross-validated 11-MDM panel highly discriminated OC from controls (96% (95%CI 89-99%) specificity; 79% (69-87%) sensitivity, and AUC 0.91 (0.86 - 0.96)). Among HGS, the panel correctly identified 83%, including 5/6 stage I/II, and the majority of other subtypes (Table). Conclusions: Whole methylome sequencing, stringent filtering criteria, and biological validation yielded outstanding candidate MDMs for OC that performed with promisingly high sensitivity and specificity in plasma. Larger plasma-based OC MDM testing studies, with larger numbers of non-HGS histologies are warranted. [Table: see text]

e16705 Background: The prevalence of pancreatic neuroendocrine tumors (PNETs) has increased in the last decade. Despite being clinically asymptomatic PNETs can be biologically aggressive. There is currently no reliable non-invasive diagnostic biomarker for PNETs. In this study we aimed to identify and validate methylated DNA marker (MDM) candidates that differentiate PNET from normal pancreas. Methods: For discovery, reduced representation bisulfite sequencing (RRBS) was performed on DNA extracted from frozen normal pancreas (n = 13) and PNET (n = 51) tissues. Area under the receiver operator characteristic curve (AUC), fold-change, and p-value criteria selected candidates MDMs for blinded validation in independent FFPE tissues from primary PNET (n = 67; solid = 50, cystic = 17), normal pancreas controls (n = 24), and normal buffy coat (n = 36) using methylation specific PCR. MDM distributions in primary PNETs were compared to primary lung (n = 36) and small bowel (n = 36) NETs and metastatic PNET tissue (n = 25). The discrimination accuracy of candidate markers was summarized as the AUC with corresponding 95% confidence intervals (CI). Results: From the RRBS discovery, 31 candidate MDMs were selected for validation. Four MDMs ( SRRM3, HCN2, SPTBN4 and TMC6) achieved individual AUCs ≥0.95 in the validation set (Table). These MDMs were similarly discriminant in metastatic PNET tissue and in primary lung and small bowel NETs. Three out of these 4 MDMs perfectly differentiated PNET tissue from buffy coat with AUC of 1 and may be ideally suited for further development of a blood-based assay. Conclusions: We identified and validated novel MDMs in tissue that discriminate PNETs from controls with normal pancreas and buffy coat with high accuracy. These MDMs also differentiated metastatic PNETs from normal pancreas tissue. Further exploration of these candidate tissue MDMs in plasma can potentially guide diagnosis and management of PNETs. Funding: P30DK084567. [Table: see text]