Search

Supplemental Figure 3 shows the distributions in plasma from controls and PDAC patients by stage of disease and standardized copy numbers of all MDMs other than HOXA1 and PRKCB.

Supplemental Table 1 Baseline characteristics of patients in Primary and Metastatic Tissue MDM Concordance Study

Supplementary Tables and Figure Legends

Supplemental Figure 1: The average level of all MDMs assayed from DNA extracted from primary and metastatic CRC tissues was not significantly different in Primary and Metastatic Tissue MDM Concordance Study. (A) Average level of all MDMs in metachronous metastatic CRC and the corresponding primary CRC as well as in synchronous metastatic CRC and the corresponding primary CRC; (B) The difference of average level of all MDMs in paired primary and metachronous metastatic CRC as well as in paired primary and synchronous metastatic CRC. MDM: methylated DNA marker; CRC: colorectal cancer.

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.

Supplemental Figure 2: The importance of individual MDMs and CEA in contribution to the performance of random forest model in classification of early CRC from healthy control in MDM Plasma CRC Early Primary Detection Study. MDM: methylated DNA marker; CEA: carcinoembryonic antigen; CRC: colorectal cancer.

Supplemental Figure 4: Carcinoembryonic antigen (CEA) levels in nanograms per milliliter assayed from 10 microliters serum in the Early Detection experiment are 1.5 times higher than values assayed from 10 microliters of plasma in the Surveillance experiment. Blue reference line marks median values in serum for normal control patients and orange line marks median serum value for stage IV colorectal cancer cases. CEA values in the surveillance samples were re-scaled to allow application of the model derived in the Early Detection analysis to the Surveillance data set.

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.

Purpose: Gastric adenocarcinoma is the third most common cause of cancer mortality worldwide. Accurate and affordable noninvasive detection methods have potential value for screening and surveillance. Herein, we identify novel methylated DNA markers (MDM) for gastric adenocarcinoma, validate their discrimination for gastric adenocarcinoma in tissues from geographically separate cohorts, explore marker acquisition through the oncogenic cascade, and describe distributions of candidate MDMs in plasma from gastric adenocarcinoma cases and normal controls.Experimental Design: Following discovery by unbiased whole-methylome sequencing, candidate MDMs were validated by blinded methylation-specific PCR in archival case–control tissues from U.S. and South Korean patients. Top MDMs were then assayed by an analytically sensitive method (quantitative real-time allele-specific target and signal amplification) in a blinded pilot study on archival plasma from gastric adenocarcinoma cases and normal controls.Results: Whole-methylome discovery yielded novel and highly discriminant candidate MDMs. In tissue, a panel of candidate MDMs detected gastric adenocarcinoma in 92% to 100% of U.S. and South Korean cohorts at 100% specificity. Levels of most MDMs increased progressively from normal mucosa through metaplasia, adenoma, and gastric adenocarcinoma with variation in points of greatest marker acquisition. In plasma, a 3-marker panel (ELMO1, ZNF569, C13orf18) detected 86% (95% CI, 71–95) of gastric adenocarcinomas at 95% specificity.Conclusions: Novel MDMs appear to accurately discriminate gastric adenocarcinoma from normal controls in both tissue and plasma. The point of aberrant methylation during oncogenesis varies by MDM, which may have relevance to marker selection in clinical applications. Further exploration of these MDMs for gastric adenocarcinoma screening and surveillance is warranted. Clin Cancer Res; 24(22); 5724–34. ©2018 AACR.

Supplemental Figure 1: Quantitative allele-specific real-time target and signal amplification (QuARTS) assay.

Supplemental Figure 2. Box plots of methylated DNA marker levels in various types of cancers were made from publically available hybridization array data from The Cancer Genome Atlas.

Purpose:The burden of esophageal cancer continues to rise, and noninvasive screening tools are needed. Methylated DNA markers (MDM) assayed from plasma show promise in detection of other cancers. For esophageal cancer detection, we aimed to discover and validate MDMs in tissue, and determine their feasibility when assayed from plasma.Experimental Design:Whole-methylome sequencing was performed on DNA extracted from 37 tissues (28 EC; 9 normal esophagus) and 8 buffy coat samples. Top MDMs were validated by methylation specific PCR on tissue from 76 EC (41 adeno, 35 squamous cell) and 17 normal esophagus. Quantitative allele-specific real-time target and signal amplification was used to assay MDMs in plasma from 183 patients (85 EC, 98 controls). Recursive partitioning (rPART) identified MDM combinations predictive of esophageal cancer. Validation was performed in silico by bootstrapping.Results:From discovery, 23 candidate MDMs were selected for independent tissue validation; median area under the receiver operating curve (AUC) for individual MDMs was 0.93. Among 12 MDMs advanced to plasma testing, rPART modeling selected a 5 MDM panel (FER1L4, ZNF671, ST8SIA1, TBX15, ARHGEF4) which achieved an AUC of 0.93 (95% CI, 0.89–0.96) on best-fit and 0.81 (95% CI, 0.75–0.88) on cross-validation. At 91% specificity, the panel detected 74% of esophageal cancer overall, and 43%, 64%, 77%, and 92% of stages I, II, III, and IV, respectively. Discrimination was not affected by age, sex, smoking, or body mass index.Conclusions:Novel MDMs assayed from plasma detect esophageal cancer with moderate accuracy. Further optimization and clinical testing are warranted.

Supplemental Table 2. Intraclass correlation coefficients (with 95% confidence intervals) between primary colorectal cancer and metastatic disease, either synchronous or metachronous indicate concordance of methylated DNA marker levels in total DNA extracted from these tissues

Supplemental Figure 5: No difference in carcinoembryonic antigen (CEA) values in nanograms per milliliter (ng/mL) was seen in a comparison of available clinical serum versus experimental plasma samples from colorectal cancer patients with no evidence of disease (NED) or with recurrent & metastatic disease. Clinical serum measurements of CEA were not available for controls.

Supplemental Table 1: Discrimination for gastric adenocarcinoma (GAC) from normal gastric mucosa by the top 22 methylated DNA marker candidates in Discovery and Technical Validation Sets. Supplemental Table 2: Function of Genes Containing Differentially Methylated Regions Identified as Targets for Candidate Markers Supplemental Table 3: Covariate Effects on Tissue Levels of Methylated DNA Marker Candidates (Data shown represent p-values).

Supplemental Figure 3: The distribution of MDMs in MDM Plasma CRC Early Primary Detection Study (i.e., training data) and in MDM Plasma CRC Surveillance Study (i.e., Recurrence data). There are reference lines relative to the healthy controls (blue line) and stage IV cancers (orange line). These reference lines are helpful in assessing drifts between patient populations.

Supplemental Table 3 Performance of MDMs in differentiating recurrent CRC from healthy controls in MDM Plasma CRC Surveillance Study. MDM: methylated DNA marker; CRC: colorectal cancer; AUC: area under the receiver operating characteristic curve; CI: confidence interval; NED: no radiographic evidence of disease.

Background and Aims We previously identified a 5 methylated DNA marker (MDM) panel for the detection of nonendoscopic Barrett’s esophagus (BE). In this study, we aimed to recalibrate the performance of the 5 MDM panel using a simplified assay in a training cohort, validate the panel in an independent test cohort, and explore the accuracy of an MDM panel with only 3 markers. Methods Participants were recruited from 3 medical centers. The sponge on a string device (EsophaCap; CapNostics, Concord, NC, USA) was swallowed and withdrawn, followed by endoscopy, in BE cases and control subjects. A 5 MDM panel was blindly assayed using a simplified assay. Random forest modeling analysis was performed, in silico cross-validated in the training set, and then locked down, before test set analysis. Results The training set had 199 patients: 110 BE cases and 89 control subjects, and the test set had 89 patients: 60 BE cases and 29 control subjects. Sensitivity of the 5 MDM panel for BE diagnosis was 93% at 90% specificity in the training set and 93% at 93% specificity in the test set. Areas under the receiver operating characteristic curves were .96 and .97 in the training and test sets, respectively. Model accuracy was not influenced by age, sex, or smoking history. Multiple 3 MDM panels achieved similar accuracy. Conclusions A 5 MDM panel for BE is highly accurate in training and test sets in a blinded multisite case-control analysis using a simplified assay. This panel may be reduced to only 3 MDMs in the future. (Clinical trial registration number: NCT 02560623.)

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.

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.

Background: Emerging colorectal cancer trends demonstrate increased incidence and mortality in younger populations, prompting consideration of average-risk colorectal cancer screening initiation at age 45 versus 50 years. However, screening test performance characteristics in adults 45–49 years have been minimally described. To inform the biologic rationale for multi-target stool DNA (mt-sDNA) screening in younger patients, we analyzed and compared tissue levels of methylation (BMP3, NDRG4) and mutation (KRAS) markers included in the FDA-approved, mt-sDNA assay (Cologuard; Exact Sciences Corporation). Methods: Within 40–44, 45–49, and 50–64 year age groups, archived colorectal tissue specimens were identified for 211 sporadic colorectal cancer cases, 123 advanced precancerous lesions (APLs; adenomas >1 cm, high-grade dysplasia, ≥25% villous morphology, or sessile serrated polyp; 45–49 and 50–64 age groups only), and 204 histologically normal controls. Following DNA extraction, KRAS, BMP3, and NDRG4 were quantified using QuARTS assays, relative to ACTB (reference gene). Results: None of the molecular marker concentrations were significantly associated with age (P > 0.05 for all comparisons), with the exception of NDRG4 concentration in APL samples (higher in older vs. younger cases; P = 0.008). However, NDRG4 levels were also statistically higher in APL case versus normal control samples in both the 45–49 (P < 0.0001) and 50–64 (P < 0.0001) year age groups. Conclusions: Overall, these findings support the potential for earlier onset of average-risk colorectal cancer screening with the mt-sDNA assay. Impact: These novel data address an identified knowledge gap and strengthen the biologic basis for earlier-onset, average-risk screening with the mt-sDNA assay.

Background: A multi-analyte blood test has the potential to maximize performance for early detection across different cancer stages and types. Improvements in early-stage cancer detection might be achieved using multi-component tests with high sensitivities and specificities. We recently performed a large feasibility study to assess the performance of 4 biomarkers (aneuploidy, methylation, mutation, and protein) for the detection of cancers from up to 15 organ sites. Specifically, a training and validation set was tested for 3 biomarkers (aneuploidy, methylation, and protein) and the performance was subsequently confirmed in an independent testing set. Methods: We have now further improved the performance of a 4-marker cancer detection blood test by fine-tuning the respective marker calling models and thresholds, exploring prostate-specific antigen (PSA) for prostate cancer detection, and developing an overarching Machine Learning (ML) cancer classifier. To improve the mutation detection, we tested (in triplicate) 200 plasma and buffy samples from young, non-cancer subjects and mutant DNA from cell lines to develop an ML-based mutation calling algorithm. This caller was validated on 186 samples and tested on an independent set of 1388 cancer and non-cancer samples. The calling of cancer-associated DNA methylation events was refined by performing training, validation, and testing across different studies. We also explored models for methylation detection based solely on distribution of methylation signal observed in non-cancer samples. Free and total PSA were investigated as markers for prostate cancer detection by including clinically relevant Gleason scores in the development of the protein-based cancer calling algorithm. Results: In the previous analysis the combination of mutation, aneuploidy, methylation, and protein biomarkers resulted in an overall sensitivity of 61.0% (95% CI: 56.9%-65.0) at a specificity of 98.2% (95% CI: 97.1 – 99.4%). We will present the added performance benefit of ML-based mutation variant calling. PSA derived features were evaluated with the goal of increasing the detectability of high-grade prostate cancers while minimizing the detection of indolent cancers. Lastly, we compared the Boolean logic-based 4-biomarker combination algorithm used in the previous analysis with an ML-based cancer classifier. The results of the modeling, applied to the testing set, will be shared. Conclusions: In summary, improvements in cancer detection performance may be achieved by optimizing each biomarker calling algorithm as well as overarching cancer classifier. When combining these improvements, we believe that a single blood test will provide robust sensitivity for the detection of several cancer types, particularly for earlier-stage disease in real world settings. Citation Format: Vladimir Gianullin, Leonardo Hagmann, Kevin Arvai, Amira Djebbari, Christopher L. Nobles, Larson Hogstrom, Mael Manesse, Vuna Fa, Fanglei Zhuang, Xi Chen, Viatcheslav E. Katerov, Jorge Garces, Hatim T. Allawi, Abigail McElhinny, Frank Diehl, Gustavo C Cerqueira. Improved sensitivity of a multi-analyte early detection test based on mutation, methylation, aneuploidy, and protein biomarkers. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P041.

Background: A multi-analyte blood test has the potential to maximize performance for early detection across different cancer stages and types. Improvements in early-stage cancer detection might be achieved using multi-component tests with high sensitivities and specificities. We recently performed a large feasibility study to assess the performance of 4 biomarkers (aneuploidy, methylation, mutation, and protein) for the detection of cancers from up to 15 organ sites. Specifically, a training and validation set was tested for 3 biomarkers (aneuploidy, methylation, and protein) and the performance was subsequently confirmed in an independent testing set. Methods: We have now further improved the performance of a 4-marker cancer detection blood test by fine-tuning the respective marker calling models and thresholds, exploring prostate-specific antigen (PSA) for prostate cancer detection, and developing an overarching Machine Learning (ML) cancer classifier. To improve the mutation detection, we tested (in triplicate) 200 plasma and buffy samples from young, non-cancer subjects and mutant DNA from cell lines to develop an ML-based mutation calling algorithm. This caller was validated on 186 samples and tested on an independent set of 1388 cancer and non-cancer samples. The calling of cancer-associated DNA methylation events was refined by performing training, validation, and testing across different studies. We also explored models for methylation detection based solely on distribution of methylation signal observed in non-cancer samples. Free and total PSA were investigated as markers for prostate cancer detection by including clinically relevant Gleason scores in the development of the protein-based cancer calling algorithm. Results: In the previous analysis the combination of mutation, aneuploidy, methylation, and protein biomarkers resulted in an overall sensitivity of 61.0% (95% CI: 56.9%-65.0) at a specificity of 98.2% (95% CI: 97.1 – 99.4%). We will present the added performance benefit of ML-based mutation variant calling. PSA derived features were evaluated with the goal of increasing the detectability of high-grade prostate cancers while minimizing the detection of indolent cancers. Lastly, we compared the Boolean logic-based 4-biomarker combination algorithm used in the previous analysis with an ML-based cancer classifier. The results of the modeling, applied to the testing set, will be shared. Conclusions: In summary, improvements in cancer detection performance may be achieved by optimizing each biomarker calling algorithm as well as overarching cancer classifier. When combining these improvements, we believe that a single blood test will provide robust sensitivity for the detection of several cancer types, particularly for earlier-stage disease in real world settings. Citation Format: Vladimir Gianullin, Leonardo Hagmann, Kevin Arvai, Amira Djebbari, Christopher L. Nobles, Larson Hogstrom, Mael Manesse, Vuna Fa, Fanglei Zhuang, Xi Chen, Viatcheslav E. Katerov, Jorge Garces, Hatim T. Allawi, Abigail McElhinny, Frank Diehl, Gustavo C Cerqueira. Improved sensitivity of a multi-analyte early detection test based on mutation, methylation, aneuploidy, and protein biomarkers. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr IA023.

OBJECTIVES: Pancreatic cystic lesions (PCLs) may be precancerous. Those likely to harbor high-grade dysplasia (HGD) or pancreatic cancer (PC) are targets for surgical resection. Current algorithms to predict advanced neoplasia (HGD/PC) in PCLs lack diagnostic accuracy. In pancreatic tissue and cystfluid (CF) from PCLs, we sought to identify and validate novel methylated DNA markers (MDMs) that discriminate HGD/PC from low-grade dysplasia (LGD) or no dysplasia (ND). METHODS: From an unbiased whole-methylome discovery approach using predefined selection criteria followed by multistep validation on case (HGD or PC) and control (ND or LGD) tissues, we identified discriminant MDMs. Top candidate MDMs were then assayed by quantitative methylation-specific polymerase chain reaction on archival CF from surgically resected PCLs. RESULTS: Of 25 discriminant MDMs identified in tissue, 13 were selected for validation in 134 CF samples (21 cases [8 HGD, 13 PC], 113 controls [45 ND, 68 LGD]). A tree-based algorithm using 2 CF-MDMs (TBX15, BMP3) achieved sensitivity and specificity above 90%. Discrimination was significantly better by this CF-MDM panel than by mutant KRAS or carcinoembryonic antigen, with areas under the receiver operating characteristic curve of 0.93 (95% confidence interval: 0.86–0.99), 0.71 (0.57–0.85), and 0.72(0.60–0.84), respectively. Cutoffs for the MDM panel applied to an independent CF validation set (31 cases, 56 controls) yielded similarly high discrimination, areas under the receiver operating characteristic curve 5 0.86 (95% confidence interval: 0.77–0.94, P = 0.2). DISCUSSION: Novel MDMs discovered and validated in tissue accurately identify PCLs harboring HGD/PC. A panel of 2 MDMs assayed in CF yielded results with potential to enhance current risk prediction algorithms. Prospective studies are indicated to optimize and further evaluate CF-MDMs for clinical use.

Purpose: Many cancer deaths could be avoided, and survival could be improved with detection of cancer at an earlier stage. Screening programs for breast, colon, and cervical cancer as well as the NLST trial for lung cancer screening demonstrated significant improvement in survival rates with screening. However, for 2021, the American Cancer Society estimates these screened cancers represent less than ~40% of cancer incidence and deaths. The remainder of cancer deaths occur because of tumors in unscreened organs and, therefore, a multicancer test that detects cancer in these organs can increase survival rates. By focusing on detection of unscreened cancers, this type of multicancer test would be complementary to current screening procedures. Here we describe the validation of a combined panel of methylated DNA markers (MDMs) and proteins for multicancer detection through testing an independent set of case/control samples. Experimental Procedures: In this study, we further evaluate the performance of our previously identified panel of 15 MDMs and 5 proteins for multicancer detection (Allawi et al., 2021 AACR Annual Meeting) by testing 315 controls and 160 cases encompassing 6 cancer types (liver, esophageal, lung, ovarian, pancreatic, and stomach). All samples used in the study were case-control collections with smoking status, age, and gender matching between cases and asymptomatic controls. Testing was performed in blinded fashion using multiplex PCR followed by LQAS (Long probe Quantitative Amplified Signal) assay on bisulfite converted DNA extracted from 3 mL of plasma collected in LB Gard® blood tubes. Protein concentrations were determined from paired serum aliquots and combined with MDMs for a multi-analyte analysis. The subjects were divided into training and validation with equal representation of cancer type, staging, gender, and age. Two thirds of the cases and controls were used to train with a logistic prediction algorithm, and the remaining 1/3 were used to validate the model. Results: Using stepwise logistic regression, a training model of MDMs and protein markers resulted in an area under the receiver operating characteristics curve (AUC) of 0.97 and cancer sensitivity of 89% at 98% specificity. The same model predicted the validation set with an AUC of 0.96 and cancer sensitivity of 85% at a specificity of 95%. Applying the algorithm to the combined training and validation sets resulted in sensitivities and specificities of 88% and 97%, respectively. The sensitivities per cancer type ranged from 73% for pancreatic cancer to 97% for lung cancer. Conclusion and Next Steps: This study demonstrates the performance of our MDMs and protein markers and their importance as components in our multi-omics strategy for multicancer detection. The next steps would be to expand the testing to include additional cancer types and combine with NGS-based methods to improve performance and optimize workflow. Citation Format: Hatim T. Allawi, Slava Katerov, Abram Vaccaro, Harrison L. Fleming, Debra E. Rugowski, Brittany Otto, Justin Heilberger, Jillian Cassel, Jacquelyn Hennek, William Taylor, Graham Lidgard. Validation of a panel of methylated DNA and protein markers for multi-cancer detection in plasma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 631.

Introduction Nonendoscopic Barrett's esophagus (BE) screening may help improve esophageal adenocarcinoma outcomes. We previously demonstrated promising accuracy of methylated DNA markers (MDMs) for the nonendoscopic diagnosis of BE using samples obtained from a capsule sponge-on-string (SOS) device. We aimed to assess the accuracy of these MDMs in an independent cohort using a commercial grade assay. Methods BE cases had ≥ 1 cm of circumferential BE with intestinal metaplasia; controls had no endoscopic evidence of BE. The SOS device was withdrawn 8 minutes after swallowing, followed by endoscopy (the criterion standard). Highest performing MDMs from a previous study were blindly assessed on extracted bisulfite-converted DNA by target enrichment long-probe quantitative amplified signal (TELQAS) assays. Optimal MDM combinations were selected and analyzed using random forest modeling with in silico cross-validation. Results Of 295 patients consented, 268 (91%) swallowed the SOS device; 112 cases and 89 controls met the pre-established inclusion criteria. The median BE length was 6 cm (interquartile range 4-9), and 50% had no dysplasia. The cross-validated sensitivity and specificity of a 5 MDM random forest model were 92% (95% confidence interval 85%-96%) and 94% (95% confidence interval 87%-98%), respectively. Model performance was not affected by age, gender, or smoking history but was influenced by the BE segment length. SOS administration was well tolerated (median [interquartile range] tolerability 2 [0, 4] on 10 scale grading), and 95% preferred SOS over endoscopy. Discussion Using a minimally invasive molecular approach, MDMs assayed from SOS samples show promise as a safe and accurate nonendoscopic test for BE prediction.

Purpose: Gastric adenocarcinoma is the third most common cause of cancer mortality worldwide. Accurate and affordable noninvasive detection methods have potential value for screening and surveillance. Herein, we identify novel methylated DNA markers (MDM) for gastric adenocarcinoma, validate their discrimination for gastric adenocarcinoma in tissues from geographically separate cohorts, explore marker acquisition through the oncogenic cascade, and describe distributions of candidate MDMs in plasma from gastric adenocarcinoma cases and normal controls.Experimental Design: Following discovery by unbiased whole-methylome sequencing, candidate MDMs were validated by blinded methylation-specific PCR in archival case–control tissues from U.S. and South Korean patients. Top MDMs were then assayed by an analytically sensitive method (quantitative real-time allele-specific target and signal amplification) in a blinded pilot study on archival plasma from gastric adenocarcinoma cases and normal controls.Results: Whole-methylome discovery yielded novel and highly discriminant candidate MDMs. In tissue, a panel of candidate MDMs detected gastric adenocarcinoma in 92% to 100% of U.S. and South Korean cohorts at 100% specificity. Levels of most MDMs increased progressively from normal mucosa through metaplasia, adenoma, and gastric adenocarcinoma with variation in points of greatest marker acquisition. In plasma, a 3-marker panel (ELMO1, ZNF569, C13orf18) detected 86% (95% CI, 71–95) of gastric adenocarcinomas at 95% specificity.Conclusions: Novel MDMs appear to accurately discriminate gastric adenocarcinoma from normal controls in both tissue and plasma. The point of aberrant methylation during oncogenesis varies by MDM, which may have relevance to marker selection in clinical applications. Further exploration of these MDMs for gastric adenocarcinoma screening and surveillance is warranted. Clin Cancer Res; 24(22); 5724–34. ©2018 AACR.

Simple Summary Circulating tumor cells are cancer cells that have entered blood or lymphatic vessels wherefrom they might get access to distant body parts and form metastases. The presence of cancer cells in a blood sample can be exploited for non-invasive diagnostic purposes. However, as blood consists of a vast number of healthy red and white blood cells the task of identifying the few potential cancer cells in a sample is a technical challenge. In this study we explore strategies for detecting circulating tumor cells after a pre-enrichment through binding to VAR2CSA protein coupled to magnetic beads. We evaluate the performance of a novel workflow that recognizes and detects the cancer cells based on their gene expression and compare this with the more traditional detection strategy using antibodies for cell staining. The highly sensitive assay presented here could potentially provide a novel strategy for early cancer detection. Abstract Analysis of circulating tumor cells (CTCs) from blood samples provides a non-invasive approach for early cancer detection. However, the rarity of CTCs makes it challenging to establish assays with the required sensitivity and specificity. We combine a highly sensitive CTC capture assay exploiting the cancer cell binding recombinant malaria VAR2CSA protein (rVAR2) with the detection of colon-related mRNA transcripts (USH1C and CKMT1A). Cancer cell transcripts are detected by RT-qPCR using proprietary Target Enrichment Long-probe Quantitative Amplified Signal (TELQAS) technology. We validate each step of the workflow using colorectal cancer (CRC) cell lines spiked into blood and compare this with antibody-based cell detection. USH1C and CKMT1A are expressed in healthy colon tissue and CRC cell lines, while only low-level expression can be detected in healthy white blood cells (WBCs). The qPCR reaction shows a near-perfect amplification efficiency for all primer targets with minimal interference of WBC cDNA. Spike-in of 10 cancer cells in 3 mL blood can be detected and statistically separated from control blood using the RT-qPCR assay after rVAR2 capture (p < 0.01 for both primer targets, Mann-Whitney test). Our results provide a validated workflow for highly sensitive detection of magnetically enriched cancer cells.

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.

Background & Aims Precursors of pancreatic cancer arise in the ductal epithelium; markers exfoliated into pancreatic juice might be used to detect high-grade dysplasia (HGD) and cancer. Specific methylated DNA sequences in pancreatic tissue have been associated with adenocarcinoma. We analyzed these methylated DNA markers (MDMs) in pancreatic juice samples from patients with pancreatic ductal adenocarcinomas (PDACs) or intraductal papillary mucinous neoplasms (IPMNs) with HGD (cases), and assessed their ability to discriminate these patients from individuals without dysplasia or with IPMNs with low-grade dysplasia (controls). Methods We obtained pancreatic juice samples from 38 patients (35 with biopsy-proven PDAC or pancreatic cystic lesions with invasive cancer and 3 with HGD) and 73 controls (32 with normal pancreas and 41 with benign disease), collected endoscopically from the duodenum after secretin administration from February 2015 through November 2016 at 3 medical centers. Samples were analyzed for the presence of 14 MDMs (in the genes NDRG4, BMP3, TBX15, C13orf18, PRKCB, CLEC11A, CD1D, ELMO1, IGF2BP1, RYR2, ADCY1, FER1L4, EMX1, and LRRC4), by quantitative allele-specific real-time target and signal amplification. We performed area under the receiver operating characteristic curve analyses to determine the ability of each marker, and panels of markers, to distinguish patients with HGD and cancer from controls. MDMs were combined to form a panel for detection using recursive partition trees. Results We identified a group of 3 MDMs (at C13orf18, FER1L4, and BMP3) in pancreatic juice that distinguished cases from controls with an area under the receiver operating characteristic value of 0.90 (95% CI, 0.83–0.97). Using a specificity cut-off value of 86%, this group of MDMs distinguished patients with any stage of pancreatic cancer from controls with 83% sensitivity (95% CI, 66%–93%) and identified patients with stage I or II PDAC or IPMN with HGD with 80% sensitivity (95% CI, 56%–95%). Conclusions We identified a group of 3 MDMs in pancreatic juice that identify patients with pancreatic cancer with an area under the receiver operating characteristic value of 0.90, including patients with early stage disease or advanced precancer. These DNA methylation patterns might be included in algorithms for early detection of pancreatic cancer, especially in high-risk cohorts. Further optimization and clinical studies are needed.

Early detection improves hepatocellular carcinoma (HCC) outcomes, but better noninvasive surveillance tools are needed. We aimed to identify and validate methylated DNA markers (MDMs) for HCC detection. Reduced representation bisulfite sequencing was performed on DNA extracted from 18 HCC and 35 control tissues. Candidate MDMs were confirmed by quantitative methylation-specific PCR in DNA from independent tissues (74 HCC, 29 controls). A phase I plasma pilot incorporated quantitative allele-specific real-time target and signal amplification assays on independent plasma-extracted DNA from 21 HCC cases and 30 controls with cirrhosis. A phase II plasma study was then performed in 95 HCC cases, 51 controls with cirrhosis, and 98 healthy controls using target enrichment long-probe quantitative amplified signal (TELQAS) assays. Recursive partitioning identified best MDM combinations. The entire MDM panel was statistically cross-validated by randomly splitting the data 2:1 for training and testing. Random forest (rForest) regression models performed on the training set predicted disease status in the testing set; median areas under the receiver operating characteristics curve (AUCs; and 95% confidence interval [CI]) were reported after 500 iterations. In phase II, a six-marker MDM panel (homeobox A1 [HOXA1], empty spiracles homeobox 1 [EMX1], AK055957, endothelin-converting enzyme 1 [ECE1], phosphofructokinase [PFKP], and C-type lectin domain containing 11A [CLEC11A]) normalized by beta-1,3-galactosyltransferase 6 (B3GALT6) level yielded a best-fit AUC of 0.96 (95% CI, 0.93-0.99) with HCC sensitivity of 95% (88%-98%) at specificity of 92% (86%-96%). The panel detected 3 of 4 (75%) stage 0, 39 of 42 (93%) stage A, 13 of 14 (93%) stage B, 28 of 28 (100%) stage C, and 7 of 7 (100%) stage D HCCs. The AUC value for alpha-fetoprotein (AFP) was 0.80 (0.74-0.87) compared to 0.94 (0.9-0.97) for the cross-validated MDM panel (P < 0.0001). Conclusion: MDMs identified in this study proved to accurately detect HCC by plasma testing. Further optimization and clinical testing of this promising approach are indicated.

Background & Aims Patients with inflammatory bowel diseases (IBDs), including ulcerative colitis and Crohn’s disease, are at increased risk for colorectal cancer (CRC). Analyses of DNA methylation patterns in stool samples have been reported to detect CRC in patients with IBD. We sought to validate these findings in larger cohorts and assess the accuracy of analysis of DNA methylation patterns in stool for detection of CRC and high-grade dysplasia (HGD) normalized to methylation level at ZDHHC1. Methods We obtained buffered, frozen stool samples from a US case–control study and from 2 European surveillance cohorts (referral or population based) of patients with chronic ulcerative colitis (n = 248), Crohn's disease (n = 82), indeterminate colitis (n = 2), or IBD with primary sclerosing cholangitis (n = 38). Stool samples were collected before bowel preparation for colonoscopy or at least 1 week after colonoscopy. Among the study samples, stools from individuals with IBD but without neoplasia were used as controls (n = 291). DNA was isolated from stool, exposed to bisulfite, and then assayed by multiplex quantitative allele-specific real-time target and signal amplification. We analyzed methylation levels of BMP3, NDRG4, VAV3, and SFMBT2 relative to the methylation level of ZDHHC1, and compared these between patients with CRC or HGD and controls. Results Levels of methylation at BMP3 and VAV3, relative to ZDHHC1 methylation, identified patients with CRC and HGD with an area under the curve value of 0.91 (95% CI, 0.77–1.00). Methylation levels at specific promotor regions of these genes identified 11 of the 12 patients with CRC and HGD, with 92% sensitivity (95% CI, 60%–100%) and 90% specificity (95% CI, 86%–93%). The proportion of false-positive results did not differ significantly among the case–control, referral cohort, and population cohort studies (P = .60) when the 90% specificity cut-off from the whole sample set was applied. Conclusions In an analysis of stool samples from 3 independent studies of 332 patients with IBD, we associated levels of methylation at 2 genes (BMP3 and VAV3), relative to level of methylation at ZDHHC1, with detection of CRC and HGD. These methylation patterns identified patients with CRC and HGD with more than 90% specificity, and might be used in CRC surveillance.

BACKGROUND Aberrantly methylated genes represent important markers for cancer diagnosis. We describe a multiplex detection approach to efficiently quantify these markers for clinical applications such as colorectal cancer screening. METHODS Quantitative allele-specific real-time target and signal amplification (QuARTS) combines a polymerase-based target amplification with an invasive cleavage-based signal amplification. The fluorescence signal is detected in a fashion similar to real-time PCR. We measured the dynamic range and analytical sensitivity of multiplex QuARTS reactions with titrated plasmid DNA. We used the QuARTS technology to quantify methylated BMP3, NDRG4, VIM, and TFPI2 genes on 91 DNA samples extracted from colorectal tissues, including 37 cancers, 25 adenomas, and 29 healthy epithelia. The assays were designed in triplex format that incorporated ACTB as a reference gene. Percent methylation was calculated by dividing methylated strands over ACTB strands and multiplying by 100. RESULTS The QuARTS method linearly detected methylated or unmethylated VIM gene down to 10 copies. No cross-reactivity was observed when methylated assays were used to amplify 105 copies of unmethylated gene and vice versa. The multiplex assay detected methylated genes spiked in unmethylated genes at a 0.01% ratio and vice versa. At a diagnostic specificity cutoff of 95%, methylated BMP3, NDRG4, VIM, and TFPI2 detected 84%, 92%, 86%, and 92% of colorectal cancers and 68%, 76%, 76%, and 88% of adenomas, respectively. CONCLUSIONS The QuARTS technology provides a promising approach for quantifying methylated markers. The markers assayed highly discriminated colorectal neoplasia from healthy epithelia.

Liquid biopsy continues to gain traction as a minimally invasive method to monitor biomarkers associated with malignancy and metastasis. A simple blood draw can reveal key biomarkers such as circulating tumor cells (CTCs) and cell-free DNA (cfDNA). However, these analytes are present in low quantities and are prone to rapid degradation and contamination, which presents challenges for the detection of rare CTCs and cfDNA targets. Higher molecular weight genomic DNA from poorly stabilized leukocytes can contaminate plasma, compromising liquid biopsy assay results by increasing the total amount of extracted DNA while diluting rare cfDNA targets. Preservation of CTCs and cfDNA over time is thus essential for the accuracy of downstream tests, and preanalytical variables that impact plasma volume, cfDNA yield and quality, and cell recovery in ex vivo blood must be understood. Here, we identified several preanalytical variables, including type of blood collection tube, storage time and conditions post-phlebotomy, and centrifugation conditions for plasma fractionation. We then carried out a series of studies to better understand the interplay between these preanalytical variables and plasma recovery, cell recovery, and cfDNA preservation in healthy donor blood, contrived samples, and patient samples. We found that choice of blood collection tube, in particular, has a significant impact on preservation of plasma volume, CTCs, cfDNA over time, particularly under real-world stress conditions including temperature excursions, sample shipping, and storage time post-draw. The work presented here demonstrates the importance of assessing preanalytical variables for the preservation of cancer biomarkers in whole blood without compromising sample integrity. This will ultimately help researchers make informed decisions on how best to stabilize their analytes of interest without introducing un-intended variables due to blood tube selection, shipping and storage conditions, and plasma isolation conditions. Citation Format: Anita Pottekat, Hatim T. Allawi, Genna T. Boragine, Michael W. Kaiser, Tamara Sander, Chateen Krueger, Janelle J. Bruinsma, Amber N. Murray. A comprehensive assessment of the impact of preanalytical variables on cell free DNA and circulating tumor cells in blood [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 4598.

RNA accessible sites are the regions in an RNA molecule, which are available for hybridization with complementary DNA or RNA molecules. The identification of these accessible sites is a critical first step in identifying antisense-mediated gene suppression sites, as well as in a variety of other RNA-based analysis methods. Here, we present a rapid, hybridization-based, label-free method of identifying RNA accessible sites with surface plasmon resonance imaging (SPRi) on in situ synthesized oligonucleotide arrays prepared on carbon-on-metal substrates. The accessible sites of three pre-miRNAs, miRNA precursors of ~75 nt in length, were determined by hybridizing the RNA molecules to RNA-specific tiling arrays. An array comprised of all possible 6mer oligonucleotide sequences was also utilized in this work, offering a universal platform capable of studying RNA molecules in a high throughput manner.

We report here on the development and validation of a prototype Invader Plus method for the qualitative detection of herpes simplex virus types 1 and 2 in cerebrospinal fluid (CSF). The method combines PCR and Invader techniques in a single, closed-tube, continuous-reaction format that gives an analytical sensitivity of approximately 10 copies per reaction. The clinical sensitivity and specificity were 100.0% and 98.6%, respectively, when the results of the method were validated against the results obtained with a PCR colorimetric microtiter plate system by use of clinical CSF specimens.

Purpose: Lung cancer is the leading cause of cancer deaths worldwide. Most present symptomatically at late stage with high lethality. Early detection reduces mortality but accurate and readily accessible tools for population screening are limited. By whole methylome sequencing, we have identified novel methylated DNA markers (MDMs) for lung cancer in tissue (Giakoumopoulos et al. ASCO 2016). Using top candidate MDMs in the present study, we now explore their clinical accuracy for lung cancer detection when assayed from plasma. Experimental Procedures: Archival plasmas from two independent study groups were tested in blinded fashion. Lung cancer cases and controls (apparently healthy smokers) for each group were balanced on age and sex (Group 1: 64 cases, 231 controls; Group 2: 23 cases, 80 controls). Using multiplex PCR followed by QuARTS (Quantitative Allele-Specific Real-time Target and Signal amplification) assay, a post-bisulfite quantification of MDMs on DNA extracted from plasma was performed. We selected 31 MDM candidates for initial evaluation in Group 1 (1 ml plasma/patient); top individual MDMs were subsequently tested in Group 2 to identify optimal MDM panels for lung cancer detection (2 ml/patient). Results: From Group 1 analyses, 13 high performance MDMs were selected for further testing (CYP26C1, SOBP, SUCLG2, SHOX2, ZDHHC1, NFIX, FLJ45983, HOXA9, B3GALT6, ZNF781, SP9, BARX1, EMX1) with individual areas under the receiver operator curve (AUCs) ranging from 0.593 to 0.939. Discrimination by individual MDMs was corroborated in Group 2 in which data was analyzed using two methods: a logistic regression fit and a regression partition tree approach. The logistic fit model identified a 4-marker panel (ZNF781, BARX1, EMX1, and SOBP) with an AUC of 0.96 and an overall sensitivity of 91% and 90% specificity. Analysis of the data using a regression partition tree approach identified 4 markers (ZNF781, BARX1, EMX1, and HOXA9) with AUC of 0.93 and an overall sensitivity of 96% and specificity of 94%. For both approaches, B3GALT6 was used as a standardizing marker of total DNA input. Conclusion: A panel of MDMs assayed in plasma achieved high sensitivity and specificity for all types of lung cancer. Further clinical evaluation and validation of this promising panel in larger patient groups are clearly indicated. Citation Format: Hatim T. Allawi, Maria Giakoumopoulos, Evan Flietner, Austin Oliphant, Carla Volkmann, Brian Aizenstein, Tamara Sander, Drew Eckmayer, Ashley Poenitzsch Strong, Melissa Gray, Barry Berger, Tracy Yab, William Taylor, Douglas Mahoney, John B. Kisiel, David E. Midthun, David A. Ahlquist, Graham P. Lidgard. Detection of lung cancer by assay of novel methylated DNA markers in plasma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 712. doi:10.1158/1538-7445.AM2017-712

The three-dimensional solution structure of the self-complementary DNA dodecamer (CGT_GACGT_TACG above GCAT_TGCAG_TGC] which contains the thermodynamically destabilizing [TG_A above AT_T] motif was determined using two-dimensional NMR spectroscopy and simulated annealing protocols. Relaxation matrix analysis methods were used to yield accurate NOE derived distance restraints. Scalar coupling constants for the sugar protons were determined by quantitative simulations of DQF-COSY cross-peaks and used to determine sugar pucker populations. Twenty refined structures starting from random geometries converged to an average pairwise root mean square deviation of 0.49 A. Back calculated NOEs give Rc and Rx factors of 0.38 and 0.088, respectively. The final structure shows that each of the single G@T mismatches form a wobble pair with two hydrogen bonds where the guanine projects into the minor groove and the thymine projects into the major groove. The incorporation of the destabilizing [TG_A above AT_T] motif has little effect on the backbone torsion angles and helical parameters compared to standard B-form duplexes, which may explain why G.T mismatches are among the most commonly observed in DNA. The structure shows that perturbations caused by a G.T mismatch extend only to its neighboring Watson-Crick base pair, thus providing a structural basis for the applicability of the nearest-neighbor model to the thermodynamics of internal G.T mismatches.

Thermodynamics of 23 oligonucleotides with internal single C.T mismatches were obtained by measuring UV absorbance as a function of temperature. Results from these 23 duplexes were combined with three measurements from the literature to derive nearest-neighbor thermodynamic parameters for seven linearly independent trimer sequences with internal C.T mismatches. The data show that the nearest-neighbor model is adequate for predicting thermodynamics of oligonucleotides with internal C.T with average deviations for Delta G degrees37, Delta H degrees, Delta S degrees and T m of 6.4%, 9.9%, 10.6%, and 1.9 degreesC respectively. C.T mismatches destabilize the duplex in all sequence contexts. The thermodynamic contribution of C. T mismatches to duplex stability varies weakly depending on the orientation of the mismatch and its context and ranges from +1.02 kcal/mol for GCG/CTC and CCG/GTC to +1.95 kcal/mol for TCC/ATG.

Thermodynamics of 39 oligonucleotides with internal G.T mismatches dissolved in 1 M NaCl were determined from UV absorbance versus temperature profiles. These data were combined with literature values of six sequences to derive parameters for 10 linearly independent trimer and tetramer sequences with G.T mismatches and Watson-Crick base pairs. The G.T mismatch parameters predict DeltaG degrees 37, DeltaH degrees , DeltaS degrees , and TM with average deviations of 5.1%, 7.5%, 8.0%, and 1.4 degrees C, respectively. These predictions are within the limits of what can be expected for a nearest-neighbor model. The data show that the contribution of a single G.T mismatch to helix stability is context dependent and ranges from +1.05 kcal/mol for AGA/TTT to -1.05 kcal/mol for CGC/GTG. Several tests of the applicability of the nearest-neighbor model to G.T mismatches are described. Analysis of imino proton chemical shifts show that structural perturbations from the G.T mismatches are highly localized. One-dimensional NOE difference spectra demonstrate that G.T mismatches form stable hydrogen-bonded wobble pairs in diverse contexts. Refined nearest-neighbor parameters for Watson-Crick base pairs are also presented.