Epigenomic Blood-Based Early Detection of Pancreatic Cancer Employing Cell-Free DNA

Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, there are no population-wide screening programmes for PDAC. Thus, early disease detection, although urgently needed, remains elusive. Individuals in certain high-risk groups are, however, offered screening or surveillance. Here we explore advances in understanding high-risk groups for PDAC and efforts to implement biomarker-driven detection of PDAC in these groups. We review current approaches to early detection biomarker development and the use of artificial intelligence as applied to electronic health records (EHRs) and social media. Finally, we address the cost-effectiveness of applying biomarker strategies for early detection of PDAC.

Lead time trajectory of blood-based protein biomarkers for detection of pancreatic cancer based on repeat testing

In the current study, we assessed whether repeated measurements of a panel of protein biomarkers with relevance to pancreatic ductal adenocarcinoma (PDAC) improves lead time performance for earlier detection over a single timepoint measurement. Specifically, CA125, CEA, LRG1, REG3A, THBS2, TIMP1, TNRFSF1A as well as CA19-9 were assayed in serially collected pre-diagnostic plasma from 242 PDAC cases and 242 age- and sex-matched non-case control participants in the PLCO cohort. We compared performance estimates of a parametric empirical Bayes (PEB) algorithm, which incorporates participant biomarker history, to that of a single-threshold (ST) method. We demonstrated improvements in AUC estimates (2-13 %) for all biomarkers when considering the PEB approach compared to ST. For CA19-9, the PEBCA19-9 yielded an AUC of 0.88 when at least one repeat measurement was within 3 years of clinical diagnosis. At a specificity of 98.5 %, the PEBCA19-9 identified 15 of the 41 PDAC cases and signaled positive at an average lead-time of 1.09 years whereas the ST approach captured 11 of the 41 PDAC cases with an average positive signal at 0.48 years. Among CA19-9 low individuals, a PEB algorithm based on repeat measurements of TIMP1 yielded an additional 14 % sensitivity at 98.5 % specificity. An adaptive algorithm that considers repeated CA19-9 measurements improves sensitivity and lead-time detection of PDAC compared to a single-threshold method. Additional protein biomarkers may improve sensitivity for earlier detection of PDAC among cases with low CA19-9.

Long non-coding RNAs: Emerging regulators of invasion and metastasis in pancreatic cancer

BACKGROUND

The invasion and metastasis of pancreatic cancer (PC) are key factors contributing to disease progression and poor prognosis. This process is primarily driven by EMT, which has been the focus of recent studies highlighting the role of long non-coding RNAs (lncRNAs) as crucial regulators of EMT. However, the mechanisms by which lncRNAs influence invasive metastasis are multifaceted, extending beyond EMT regulation alone.

AIM OF REVIEW

This review primarily aims to characterize lncRNAs affecting invasion and metastasis in pancreatic cancer. We summarize the regulatory roles of lncRNAs across multiple molecular pathways and highlight their translational potential, considering the implications for clinical applications in diagnostics and therapeutics.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The review focuses on three principal scientific themes. First, we primarily summarize lncRNAs orchestrate various signaling pathways, such as TGF-β/Smad, Wnt/β-catenin, and Notch, to regulate molecular changes associated with EMT, thereby enhancing cellular motility and invasivenes. Second, we summarize the effects of lncRNAs on autophagy and ferroptosis and discuss the role of exosomal lncRNAs in the tumor microenvironment to regulate the behavior of neighboring cells and promote cancer cell invasion. Third, we emphasize the effects of RNA modifications (such as m6A and m5C methylation) on stabilizing lncRNAs and enhancing their capacity to mediate invasive metastasis in PC. Lastly, we discuss the translational potential of these findings, emphasizing the inherent challenges in using lncRNAs as clinical biomarkers and therapeutic targets, while proposing prospective research strategies.

Epigenetic modifications of cfDNA in liquid biopsy for the cancer care continuum

This review provides a comprehensive overview of the latest advancements in the clinical utility of liquid biopsy, with a particular focus on epigenetic approaches aimed at overcoming challenges in cancer diagnosis and treatment. It begins by elucidating key epigenetic terms, including methylomics, fragmentomics, and nucleosomics. The review progresses to discuss methods for analyzing circulating cell-free DNA (cfDNA) and highlights recent studies showcasing the clinical relevance of epigenetic modifications in areas such as diagnosis, drug treatment response, minimal residual disease (MRD) detection, and prognosis prediction. While acknowledging hurdles like the complexity of interpreting epigenetic data and the absence of standardization, the review charts a path forward. It advocates for the integration of multi-omic data through machine learning algorithms to refine predictive models and stresses the importance of collaboration among clinicians, researchers, and data scientists. Such cooperative efforts are essential to fully leverage the potential of epigenetic features in clinical practice.

Leveraging epigenetic alterations in pancreatic ductal adenocarcinoma for clinical applications

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy characterized by late detection and poor prognosis. Recent research highlights the pivotal role of epigenetic alterations in driving PDAC development and progression. These changes, in conjunction with genetic mutations, contribute to the intricate molecular landscape of the disease. Specific modifications in DNA methylation, histone marks, and non-coding RNAs are emerging as robust predictors of disease progression and patient survival, offering the potential for more precise prognostic tools compared to conventional clinical staging. Moreover, the detection of epigenetic alterations in blood and other non-invasive samples holds promise for earlier diagnosis and improved management of PDAC. This review comprehensively summarises current epigenetic research in PDAC and identifies persisting challenges. These include the complex nature of epigenetic profiles, tumour heterogeneity, limited access to early-stage samples, and the need for highly sensitive liquid biopsy technologies. Addressing these challenges requires the standardisation of methodologies, integration of multi-omics data, and leveraging advanced computational tools such as machine learning and artificial intelligence. While resource-intensive, these efforts are essential for unravelling the functional consequences of epigenetic changes and translating this knowledge into clinical applications. By overcoming these hurdles, epigenetic research has the potential to revolutionise the management of PDAC and improve patient outcomes.

Risk factors for pancreatic cancer in individuals with intraductal papillary mucinous neoplasms and no high-risk stigmata during up to 5 years of surveillance: a prospective longitudinal cohort study

BACKGROUND

Cyst size, its growth rate, and diameter of the main pancreatic duct (MPD) are all associated with pancreatic carcinoma prevalence in intraductal papillary mucinous neoplasms (IPMNs).

OBJECTIVE

To examine the above factors in relation to future risk of incident pancreatic carcinoma in individuals with IPMNs harbouring no high-risk stigmata.

DESIGN

In a prospective longitudinal cohort, we analysed 2549 patients with IPMNs. A multivariable cause-specific Cox proportional hazards regression model was built to estimate HRs for incident pancreatic carcinoma.

RESULTS

IPMN size at baseline and its annual growth rate over 2 years of follow-up were associated with incident pancreatic carcinoma (ptrend<0.001). The multivariable cause-specific HR per 10 mm increase in IPMN size was 1.28 (95% CI 1.10 to 1.50). The annual growth rates of 1.5-2.4 mm/year and ≥2.5 mm/year over 2 years were associated with multivariable cause-specific HRs of 1.91 (95% CI 0.78 to 4.67) and 4.52 (95% CI 2.28 to 8.98), respectively (vs <1.5 mm/year). Neither IPMN size at 5 years nor its maximum growth rate during 5 years was associated with incident pancreatic carcinoma (ptrend>0.07). MPD diameter at 5 years was associated with incident pancreatic carcinoma (multivariable cause-specific HR per 2 mm increase, 2.12; 95% CI 1.72 to 2.63). A predictive nomogram was generated for calculating the risk of incident pancreatic carcinoma.

CONCLUSION

IPMN size and its growth rate predict future pancreatic carcinoma risk only during first 5 years of follow-up. MPD diameter at 5 years may identify patients who still harbour a high risk for pancreatic carcinoma.

Liquid Biopsy in Pancreatic Ductal Adenocarcinoma: A Review of Methods and Applications

Pancreatic ductal adenocarcinoma (PDAC) remains a malignancy with one of the highest mortality rates. One limitation in the diagnosis and treatment of PDAC is the lack of an early and universal biomarker. Extensive research performed recently to develop new assays which could fit this role is available. In this review, we will discuss the current landscape of liquid biopsy in patients with PDAC. Specifically, we will review the various methods of liquid biopsy, focusing on circulating tumor DNA (ctDNA) and exosomes and future opportunities for improvement using artificial intelligence or machine learning to analyze results from a multi-omic approach. We will also consider applications which have been evaluated, including the utility of liquid biopsy for screening and staging patients at diagnosis as well as before and after surgery. We will also examine the potential for liquid biopsy to monitor patient treatment response in the setting of clinical trial development.

Analytical Validation of an Early Detection Pancreatic Cancer Test Using 5-Hydroxymethylation Signatures

Early detection of pancreatic cancer has been shown to improve patient survival rates. However, effective early detection tools to detect pancreatic cancer do not currently exist. The Avantect Pancreatic Cancer Test, leveraging the 5-hydroxymethylation [5-hydroxymethylcytosine (5hmC)] signatures in cell-free DNA, was developed and analytically validated to address this unmet need. We report a comprehensive analytical validation study encompassing precision, sample stability, limit of detection, interfering substance studies, and a comparison with an alternative method. The assay performance on an independent case-control patient cohort was previously reported with a sensitivity for early-stage (stage I/II) pancreatic cancer of 68.3% (95% CI, 51.9%-81.9%) and an overall specificity of 96.9% (95% CI, 96.1%-97.7%). Precision studies showed a cancer classification of 100% concordance in biological replicates. The sample stability studies revealed stable assay performance for up to 7 days after blood collection. The limit of detection studies revealed equal results between early- and late-stage cancer samples, emphasizing strong early-stage performance characteristics. Comparisons of concordance of the Avantect assay with the enzymatic methyl sequencing (EM-Seq) method, which measures both methylation (5-methylcytosine) and 5hmC, were >95% for all samples tested. The Avantect Pancreatic Cancer Test showed strong analytical validation in multiple validation studies required for laboratory-developed test accreditation. The comparison of 5hmC versus EM-Seq further validated the 5hmC approach as a robust and reproducible assay.

5-Hydroxymethylated Biomarkers in Cell-Free DNA Predict Occult Colorectal Cancer up to 36 Months Before Diagnosis in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial

PURPOSE

Using the prostate, lung, colorectal, and ovarian (PLCO) Cancer Screening Trial samples, we identified cell-free DNA (cfDNA) candidate biomarkers bearing the epigenetic mark 5-hydroxymethylcytosine (5hmC) that detected occult colorectal cancer (CRC) up to 36 months before clinical diagnosis.

MATERIALS AND METHODS

We performed the 5hmC-seal assay and sequencing on ≤8 ng cfDNA extracted from PLCO study participant plasma samples, including n = 201 cases (diagnosed with CRC within 36 months of blood collection) and n = 401 controls (no cancer diagnosis on follow-up). We conducted association studies and machine learning modeling to analyze the genome-wide 5hmC profiles within training and validation groups that were randomly selected at a 2:1 ratio.

RESULTS

We successfully obtained 5hmC profiles from these decades-old samples. A weighted Cox model of 32 5hmC-modified gene bodies showed a predictive detection value for CRC as early as 36 months before overt tumor diagnosis (training set AUC, 77.1% [95% CI, 72.2 to 81.9] and validation set AUC, 72.8% [95% CI, 65.8 to 79.7]). Notably, the 5hmC-based predictive model showed comparable performance regardless of sex and race/ethnicity, and significantly outperformed risk factors such as age and obesity (assessed as BMI). Finally, when splitting cases at median weighted prediction scores, Kaplan-Meier analyses showed significant risk stratification for CRC occurrence in both the training set (hazard ratio, [HR], 3.3 [95% CI, 2.6 to 5.8]) and validation set (HR, 3.1 [95% CI, 1.8 to 5.8]).

CONCLUSION

Candidate 5hmC biomarkers and a scoring algorithm have the potential to predict CRC occurrence despite the absence of clinical symptoms and effective predictors. Developing a minimally invasive clinical assay that detects 5hmC-modified biomarkers holds promise for improving early CRC detection and ultimately patient outcomes.

Plasma cell-free DNA hydroxymethylation profiling reveals anti-PD-1 treatment response and resistance biology in non-small cell lung cancer

BACKGROUND

Treatment with immune checkpoint inhibitors (ICIs) targeting programmed death-1 (PD-1) can yield durable antitumor responses, yet not all patients respond to ICIs. Current approaches to select patients who may benefit from anti-PD-1 treatment are insufficient. 5-hydroxymethylation (5hmC) analysis of plasma-derived cell-free DNA (cfDNA) presents a novel non-invasive approach for identification of therapy response biomarkers which can tackle challenges associated with tumor biopsies such as tumor heterogeneity and serial sample collection.

METHODS

151 blood samples were collected from 31 patients with non-small cell lung cancer (NSCLC) before therapy started and at multiple time points while on therapy. Blood samples were processed to obtain plasma-derived cfDNA, followed by enrichment of 5hmC-containing cfDNA fragments through biotinylation via a two-step chemistry and binding to streptavidin coated beads. 5hmC-enriched cfDNA and whole genome libraries were prepared in parallel and sequenced to obtain whole hydroxymethylome and whole genome plasma profiles, respectively.

RESULTS

Comparison of on-treatment time point to matched pretreatment samples from same patients revealed that anti-PD-1 treatment induced distinct changes in plasma cfDNA 5hmC profiles of responding patients, as judged by Response evaluation criteria in solid tumors, relative to non-responders. In responders, 5hmC accumulated over genes involved in immune activation such as inteferon (IFN)-γ and IFN-α response, inflammatory response and tumor necrosis factor (TNF)-α signaling, whereas in non-responders 5hmC increased over epithelial to mesenchymal transition genes. Molecular response to anti-PD-1 treatment, as measured by 5hmC changes in plasma cfDNA profiles were observed early on, starting with the first cycle of treatment. Comparison of pretreatment plasma samples revealed that anti-PD-1 treatment response and resistance associated genes can be captured by 5hmC profiling of plasma-derived cfDNA. Furthermore, 5hmC profiling of pretreatment plasma samples was able to distinguish responders from non-responders using T cell-inflamed gene expression profile, which was previously identified by tissue RNA analysis.

CONCLUSIONS

These results demonstrate that 5hmC profiling can identify response and resistance associated biological pathways in plasma-derived cfDNA, offering a novel approach for non-invasive prediction and monitoring of immunotherapy response in NSCLC.

Diagnostic ability of deep learning in detection of pancreatic tumour

Pancreatic cancer is associated with higher mortality rates due to insufficient diagnosis techniques, often diagnosed at an advanced stage when effective treatment is no longer possible. Therefore, automated systems that can detect cancer early are crucial to improve diagnosis and treatment outcomes. In the medical field, several algorithms have been put into use. Valid and interpretable data are essential for effective diagnosis and therapy. There is much room for cutting-edge computer systems to develop. The main objective of this research is to predict pancreatic cancer early using deep learning and metaheuristic techniques. This research aims to create a deep learning and metaheuristic techniques-based system to predict pancreatic cancer early by analyzing medical imaging data, mainly CT scans, and identifying vital features and cancerous growths in the pancreas using Convolutional Neural Network (CNN) and YOLO model-based CNN (YCNN) models. Once diagnosed, the disease cannot be effectively treated, and its progression is unpredictable. That's why there's been a push in recent years to implement fully automated systems that can sense cancer at a prior stage and improve diagnosis and treatment. The paper aims to evaluate the effectiveness of the novel YCNN approach compared to other modern methods in predicting pancreatic cancer. To predict the vital features from the CT scan and the proportion of cancer feasts in the pancreas using the threshold parameters booked as markers. This paper employs a deep learning approach called a Convolutional Neural network (CNN) model to predict pancreatic cancer images. In addition, we use the YOLO model-based CNN (YCNN) to aid in the categorization process. Both biomarkers and CT image dataset is used for testing. The YCNN method was shown to perform well by a cent percent of accuracy compared to other modern techniques in a thorough review of comparative findings.