Society: AGA

Background
Colorectal cancer (CRC) remains a leading cause of cancer related mortality worldwide. We utilized cell-free DNA (cfDNA) methylation, fragmentation characteristics of selected cancer-related biomarker regions, tumor-derived signal deduction and a machine learning algorithm to refine a blood test for the early detection of CRC and advanced adenomas (AA). The aim of the study was to assess the diagnostic accuracy of the test for CRC.
Methods
This was a prospective, international (Spain, Ukraine, Germany and USA [part of NCT04792684 study] population), observational cohort study. Plasma samples from 997 patients were collected either prior to a scheduled screening colonoscopy or prior to colonic surgery for primary CRC. cfDNA samples from 170 early stage (I-II), 128 late-stage (III-IV) CRC patients (mean age 66 [44-84], female 48%, distal cancers 60%), 149 AA patients (63 high grade dysplasia; 84 low grade, > 1cm) and 550 age, gender and country of origin matched colonoscopy-checked controls were included. 155 of the control patients had a negative colonoscopy finding (cNEG), 337 had benign findings of diverticulosis, hemorrhoids, previously undiagnosed gastrointestinal diseases and/or hyperplastic polyps (BEN), 58 had non-advanced adenomas (NAA). Samples were analyzed utilizing hybrid-capture based sequencing methodology. Panel of targeted biomarkers was previously identified through tissue- and plasma-based discovery and verification workflow. Individual cfDNA fragments belonging to each biomarker region were scored for cancer-specific methylation and fragmentation signals. Finally, calculated scores were used in prediction model building and testing for establishing panel accuracy.
Results
Prediction model utilizing a panel of methylation and fragmentation scores originating from biomarkers belonging to relevant cancer development and progression related pathways, correctly classified 93% (276/298) of CRC patients and 54% (81/149) AA patients. Sensitivity per cancer stage ranged from 85% (48/56) for stage I, 94% (107/114) stage II, 94% (90/96) stage III and 97% (31/32) stage IV. Fragmentation signals contributed most to early-stage cancers (I-II), while methylation signals were more significant for late stage (III-IV) detection. High grade dysplasia AA sensitivity was 52% (33/63), while low grade >1cm AA sensitivity was 57% (48/84). Specificity of the model was 92% (504/550), with 83% (48/58) NAA, 93% (312/337) BEN and 93% (144/155) cNEG patients correctly identified. Lesion location, gender, age, BMI and country of origin were not significantly (p> 0.05) 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 (91% sensitivity) and AA (54% sensitivity) at 92% specificity).

Introduction: In 2016, the USPSTF recommended aspirin for primary prevention of colorectal cancer. In 2022, they reversed course citing harms among older adults and uncertainty of the preventive mode of action. Thus, understanding the mechanisms which underlie aspirin’s effects in the gastrointestinal tract, including interactions with metabolic pathways associated with CRC risk factors, will augment the development of precision prevention strategies

Methods: We measured aspirin’s influence on host and gut microbe metabolism from the ASPIRED (NCT02394769) randomized clinical trial (RCT) of daily low and standard-dose aspirin intervention for 2-3 months. We integrated untargeted plasma metabolomic profiling (Metabolon Global HD4 platform), producing 966 annotated metabolites, with paired fecal whole shotgun metagenomic sequencing. We applied a mixed-effects modeling approach to participants with paired pre- and post-treatment multi-omic data (placebo, n=57; 81 mg/d, n=57; 325 mg/d, n=50), adjusting for inter-individual variability, and known confounders such as age, sex, BMI, and technical factors such as batch and read depth.

Results: Aspirin use explained a significant proportion of variance in plasma metabolite composition (PERMANOVA, R²=4.5%, P=0.001), driven by the aspirin-related metabolites: salicylate, salicylurate, salicyluric glucuronide, and gentisate (mixed effects, FDR q<0.05). In addition, aspirin significantly decreased plasma kynurenate from baseline (β=-0.14, q=4.5x10-4), a tryptophan metabolite implicated in oncogenesis. The change in plasma kynurenate was inversely associated with change in plasma salicylate (rho=-0.45, P=2.4x10-9). “Aspirin responders'', those achieving a 33.5% reduction in urinary PGE-M from baseline, a biomarker associated with reduced risk of recurrent adenoma, had a greater reduction in plasma kynurenate than non-responders (Wilcoxon, P=0.004). In contrast, changes in the aspirin-related metabolites above were not dependent on responder status, suggesting a specific modulatory role for kynurenate in CRC risk. Aspirin also led to a diminished capacity for gut microbial tryptophan biosynthesis (mixed effects FDR < 0.25), where plasma salicylate was inversely associated with 2 microbial enzymes in the tryptophan biosynthesis pathway (E.C. 4.1.1.48, rho=-0.17, P=0.03; 5.3.1.24, rho=-0.16, P=0.04), suggesting a novel mediating role for the gut microbiome.

Conclusions: In the ASPIRED RCT, aspirin significantly decreased the circulating tryptophan metabolite, kynurenate, and altered gut microbe functional profiles involved in tryptophan biosynthesis. Our findings suggest kynurenate is a novel biomarker and tryptophan metabolism a novel mechanism by which aspirin may reduce CRC risk.

Background/Purpose: Esophageal adenocarcinoma (EAC), a cancer with a devasting 5-year survival rate of <20%, has been rapidly increasing among Western populations during the past 40 years. Barrett’s esophagus (BE), a metaplastic condition that originates in the distal esophagus, is characterized histologically by the transformation of squamous to columnar epithelium with gastric and intestinal features and is the only known precursor lesion for the development of EAC. The large number of subjects diagnosed with BE, and therefore at risk of developing EAC, underscore the importance of identifying biomarkers of disease progression that would benefit surveillance and potentially early treatment.
Methods: We generated epithelial stem cell organoids and organoid-derived monolayers from tissue samples of normal cardia (n=4), non-dysplastic BE (NDBE) (n=3), BE with low-grade dysplasia (LGD) (n=5), BE with high-grade dysplasia (HGD) (n=3), esophageal adenocarcinoma (EAC) (n=4), and gastric adenocarcinoma (GAC) (n=4). RNA was isolated from the organoids at equivalent number of passages for RNA-Seq and comparative transcriptome analysis.
Results: BE organoid stem cells and organoid-derived differentiated epithelium displayed canonical BE markers, including SOX9 and CDX2, regardless of the presence of dysplasia compared with normal gastric cardia-derived organoids and organoid-derived epithelium. Using RNASeq, we compared epithelial stem cell gene expression in NDBE, BE-LGD, BE-HGD, EAC and GAC with normal cardia (Fig.1). The intestinal transcription factor CDX2 as well as HOXA13, HOXB3, HOXC10, and to a lesser extent, TFF3 were strongly upregulated in both NDBE and dysplastic BE (Fig. 1), confirming a classical BE signature. Interestingly, NDBE and dysplastic BE displayed stronger upregulation of stemness and cancer-associated genes than both EAC and GAC (Fig. 1). Strikingly, cancer testis antigen 83 (CT83), a gene not previously linked to BE and not typically expressed in normal tissue outside testis, showed remarkable specificity for BE epithelial stem cells compared to EAC and GAC, suggesting a potential novel biomarker of BE.
Conclusion: The similar transcriptional patterns among NDBE and dysplastic BE calls into question current surveillance protocols and challenges the dogma of basing the risk of developing EAC primarily on histology. Screening transcriptional patterns, regardless of dysplasia status, could help identify BE more likely to progress to EAC. While this study is cross-sectional, it does provide a framework for future work in which the genes or pathways identified here may be manipulated in organoid models in order to determine whether they are essential in driving BE progression to EAC.

Background: While there is an increased risk of pancreatic ductal adenocarcinoma (PDAC) in patients with acute pancreatitis (AP), that risk is highest ≤ 2 years after AP diagnosis, suggesting that AP could be a sign of subclinical PDAC. It is crucial to identify individuals at high risk for subclinical PDAC–associated AP to facilitate early detection and improve survival.

Specific Aims: To estimate the 2-year cumulative incidence of PDAC diagnosis following AP and develop a prediction model using readily available clinical information to predict the presence of subclinical PDAC at the time of AP diagnosis.

Methods: Using a validated AP case finding algorithm (positive predictive value 89.1%), we identified individuals ≥ 40 years old hospitalized for AP from 1/1/1998 – 9/1/2019 within the US Veterans Health Administration. Candidate predictors included age, sex, BMI, tobacco use, alcohol use, chronic pancreatitis, gallstones, hypertriglyceridemia, diabetes, history of pancreatic cyst, history of cancers, ERCP use, and labs [hemoglobin, albumin, total cholesterol, LDL, HDL, triglycerides, AST, ALT, alkaline phosphatase (ALP), bilirubin, and maximal lipase]. We used least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation to identify a parsimonious logistic regression model for predicting the outcome: PDAC diagnosed within 2 years following the AP diagnosis. We evaluated model discrimination by estimating area under the receiver operating curve (AUC) and model calibration by comparing predicted vs. observed probability of outcome.

Results: Out of 51,613 individuals in our cohort, the mean age was 62.4 years old, 95.7% were male, and 50.3% were White (Table). Eight hundred and one individuals were diagnosed with PDAC within 2 years of AP diagnosis (2-year PDAC incidence: 1.6%), a risk 23 times higher than the SEER general population ages 60-64 years old. Almost half of the PDACs (n=326, 40.7%) were discovered after 3 months. In the final prediction model, history of pancreatic cyst was positively associated with PDAC, while common etiologies of pancreatitis were negative associated [history of gallstones and log(triglycerides)]. Positive laboratory predictors on admission included log(ALP), log(ALT), and log(bilirubin). Negative laboratory predictors included log(ALP) prior to admission and log(AST) on admission. The AUC was 0.70 after 10-fold cross validation for internal validation (Figure) with appropriate calibration.

Conclusions: The risk of PDAC diagnosis is high 2 years following acute pancreatitis. We developed a novel prediction model based on readily available clinical information that can identify among AP patients those at high risk for having subclinical PDAC. If externally validated, this model can be applied to all patients presenting with AP to facilitate early diagnosis of PDAC.

Table. Cohort Information and Final Multivariable Prediction Model for PDAC (0 months - 2 years)

Table. Cohort Information and Final Multivariable Prediction Model for PDAC (0 months - 2 years)

ROC Curve

Background: Over the past three decades, Esophageal Adenocarcinoma (EAC) has been reported a deadly malignancy with a five-year survival rate of less than 20% in the United States and western countries. Tobacco smoking is one of the major significant risk factors for the progression from Barrett’s esophagus, the pre-malignant disease of the esophagus, to EAC. WEE1 kinase is a G2/M checkpoint regulator kinase involved in drug resistance after chemotherapy.

Methods and Results: Using Western blot and Immunofluorescence (IF) staining, we found that smoking induces both WEE1 protein expression and STAT3 phosphorylation and activation in EAC cells. Moreover, our data indicated that smoking causes STAT3 and downstream targets activation dependent on WEE1 expression in EAC. Moreover, functions of WEE1 in EAC other than cell cycle regulation, we performed the test on western blot and immunofluorescence (IF) staining confirmed that WEE1 was overexpressed in both nucleus and cytosol and overexpression of WEE1-LTV increased STAT3 phosphorylation whereas p-CDC2 is a downstream target of WEE1. Using in vitro kinase assay, immunoprecipitation, and proximity ligation assay, our results showed that WEE1 directly binds to and phosphorylates JAK2 in EAC for the first time. Our data also indicated that the smoking-WEE1-JAK2 axis plays a crucial role in EAC cell survival and drug resistance. Using in vitro cell viability assay, short term ATP-Glo and long-term clone formation to sensitize cells on MK1775 alone and in combination with Docetaxel or Oxaliplatin on EAC cells were significantly decreased. Moreover, synergistic effect on WEE1 inhibition with combine drug treatment in Docetaxel or Oxaliplatin of EAC cells showed that MK1775 was synergized the best effect to EAC cells. Using SynergySeq analysis with WEE1 siRNA knockdown RNA sequencing data in EAC cells, it is predicted that WEE1 inhibition has top synergy effect with AZD-1280 or Erlotinib. We further validated that both drug combinations had synergy effects on EAC cell models. We confirmed that WEE1 inhibition resulted in an increased chemosensitivity in EAC cells, in addition to an increased rate of apoptosis in response to p-H2AX expression, and decreased expression level of pro-survival genes of c-MYC and BCL-2 in EAC cells. Our in vivo study using TE-10 smoking machine validated our in vitro findings that smoking induces docetaxel drug resistance in vivo, which could be attenuated by WEE1 inhibition in EAC patient-derived xenograft mouse models.

Conclusions: In conclusion, our findings demonstrated a novel mechanism in which smoking activates the WEE1-JAK2-STAT3 axis in EAC for cancer cell survival and drug resistance in vitro and in vivo. WEE1 inhibition with combination drug treatment could be a powerful therapeutic target for EAC patients.

Introduction
We have previously demonstrated that methylated DNA markers (MDMs) assayed from pancreatic juice (PJ) can detect early pancreatic ductal adenocarcinoma (PDAC). In this prospective multicenter study, we analyzed the diagnostic performance of MDMs in distinguishing PDAC from controls including those with chronic pancreatitis (CP) and intraductal papillary mucinous neoplasms (IPMNs).
Methods
Secretin-stimulated pancreatic juice was prospectively collected by endoscopic duodenal aspirate from January 2018 to August 2022 in 100 biopsy-proven treatment-naïve PDAC cases and 169 controls (normal healthy control: 71, disease controls: 98; CP: 29, IPMN: 69). From 850 µL of buffered PJ, 100 ng of bisulfite-converted DNA was analyzed for 14 MDMs (NDRG4, BMP3, TBX15, C13orf18, PRKCB, CLEC11A, CD1D, ELMO1, IGF2BP1, RYR2, ADCY1, FER1L4, EMX1, LRRC4 and reference gene B3GALT6), by long-probe quantitative amplified signal (LQAS) assay. Random Forest (rFor) was used to train and cross-validate a model for predicting case/control status using all MDMs. Individual MDMs were ranked for their predictive importance within the cross-validated rFor model by randomly permutating MDM levels and estimating the impact on model prediction accuracy. Discrimination accuracy was measured using the area under the receiver operating characteristic curve (AUROC) with corresponding 95% confidence intervals. Logistic regression was used to assess performance of a 3-MDM panel comprising the most discriminant individual MDMs.
Results
Clinical and demographic characteristics are summarized in Table 1. Variable importance ranking indicated that FER1L4, C13orf18, and BMP3 were the most discriminant individual PJ-MDMs for distinguishing cases from normal and disease controls matching our previously published results. Methylated FER1L4 had the highest individual AUROC of 0.80 (0.74-0.86) and the AUROC for the 3-MDM panel (FER1L4, C13orf18, and BMP3) was 0.84 (0.79-0.89). The rFor model using all 14 MDMs had a cross-validated AUC of 0.82 (0.77-0.88) (Figure 1). At an 80% specificity cut-off for the 3-MDM panel, the sensitivity for detecting any stage of PDAC was 73% (63-81%) and 65% (46-80%) for stage I/II PDAC. False positive rates in normal controls, CP, and IPMN patients were 8% (3-17%), 10% (2-27%), and 36% (25-49%), respectively.
Discussion
In a large prospective case-control study, we demonstrate that a PJ-MDM panel can distinguish PDAC from both normal and pancreatic disease controls with reasonable accuracy. Candidate MDMs with the highest variable importance for predicting PDAC in this study are identical to those reported previously providing justification for further assay optimization and clinical test development. Combining pancreatic juice MDMs with blood-based biomarkers may potentially further enhance diagnostic performance and serve as a tool for early detection of PDAC.

<b>Table 1:</b> Clinical and demographic characteristics of the study population

Table 1: Clinical and demographic characteristics of the study population

<b>Figure 1:</b> Receiver operating characteristic (ROC) curves demonstrating the area under ROC curve (AUROC) for the 3 methylated DNA markers (MDMs) with highest prediction importance, individually and in combination, along with the cross-validated AUC for the random Forest model incorporating all 14 MDMs.

Figure 1: Receiver operating characteristic (ROC) curves demonstrating the area under ROC curve (AUROC) for the 3 methylated DNA markers (MDMs) with highest prediction importance, individually and in combination, along with the cross-validated AUC for the random Forest model incorporating all 14 MDMs.

John Kisiel

Mayo Clinic

Appearances