Extracellular miRNAs for the Management of Barrett's Esophagus and Esophageal Adenocarcinoma: A Systematic Review

Surface-Enhanced Raman Spectroscopy-Based Detection of Micro-RNA Biomarkers for Biomedical Diagnosis Using a Comparative Study of Interpretable Machine Learning Algorithms

Surface-enhanced Raman spectroscopy (SERS) has wide diagnostic applications due to narrow spectral features that allow multiplex analysis. We have previously developed a multiplexed, SERS-based nanosensor for micro-RNA (miRNA) detection called the inverse molecular sentinel (iMS). Machine learning (ML) algorithms have been increasingly adopted for spectral analysis due to their ability to discover underlying patterns and relationships within large and complex data sets. However, the high dimensionality of SERS data poses a challenge for traditional ML techniques, which can be prone to overfitting and poor generalization. Non-negative matrix factorization (NMF) reduces the dimensionality of SERS data while preserving information content. In this paper, we compared the performance of ML methods including convolutional neural network (CNN), support vector regression, and extreme gradient boosting combined with and without NMF for spectral unmixing of four-way multiplexed SERS spectra from iMS assays used for miRNA detection. CNN achieved high accuracy in spectral unmixing. Incorporating NMF before CNN drastically decreased memory and training demands without sacrificing model performance on SERS spectral unmixing. Additionally, models were interpreted using gradient class activation maps and partial dependency plots to understand predictions. These models were used to analyze clinical SERS data from single-plexed iMS in RNA extracted from 17 endoscopic tissue biopsies. CNN and CNN-NMF, trained on multiplexed data, performed most accurately with RMSElabel = 0.101 and 9.68 × 10-2, respectively. We demonstrated that CNN-based ML shows great promise in spectral unmixing of multiplexed SERS spectra, and the effect of dimensionality reduction on performance and training speed.

Epigenetic Alterations from Barrett's Esophagus to Esophageal Adenocarcinoma

Barrett's esophagus (BE) is a disease entity that is a sequela of chronic gastroesophageal reflux disease that may result in esophageal adenocarcinoma (EAC) due to columnar epithelial dysplasia. The histological degree of dysplasia is the sole biomarker frequently utilized by clinicians. However, the cost of endoscopy and the fact that the degree of dysplasia does not progress in many patients with BE diminish the effectiveness of histological grading as a perfect biomarker. Multiple or more quantitative biomarkers are required by clinicians since early diagnosis is crucial in esophageal adenocancers, which have a high mortality rate. The presence of epigenetic factors in the early stages of this neoplastic transformation holds promise as a predictive biomarker. In this review, current studies on DNA methylations, histone modifications, and noncoding RNAs (miRNAs) that have been discovered during the progression from BE dysplasia to EAC were collated.

Computational pathology aids derivation of microRNA biomarker signals from Cytosponge samples

BACKGROUND

Non-endoscopic cell collection devices combined with biomarkers can detect Barrett's intestinal metaplasia and early oesophageal cancer. However, assays performed on multi-cellular samples lose information about the cell source of the biomarker signal. This cross-sectional study examines whether a bespoke artificial intelligence-based computational pathology tool could ascertain the cellular origin of microRNA biomarkers, to inform interpretation of the disease pathology, and confirm biomarker validity.

METHODS

The microRNA expression profiles of 110 targets were assessed with a custom multiplexed panel in a cohort of 117 individuals with reflux that took a Cytosponge test. A computational pathology tool quantified the amount of columnar epithelium present in pathology slides, and results were correlated with microRNA signals. An independent cohort of 139 Cytosponges, each from an individual patient, was used to validate the findings via qPCR.

FINDINGS

Seventeen microRNAs are upregulated in BE compared to healthy squamous epithelia, of which 13 remain upregulated in dysplasia. A pathway enrichment analysis confirmed association to neoplastic and cell cycle regulation processes. Ten microRNAs positively correlated with columnar epithelium content, with miRNA-192-5p and -194-5p accurately detecting the presence of gastric cells (AUC 0.97 and 0.95). In contrast, miR-196a-5p is confirmed as a specific BE marker.

INTERPRETATION

Computational pathology tools aid accurate cellular attribution of molecular signals. This innovative design with multiplex microRNA coupled with artificial intelligence has led to discovery of a quality control metric suitable for large scale application of the Cytosponge. Similar approaches could aid optimal interpretation of biomarkers for clinical use.

FUNDING

Funded by the NIHR Cambridge Biomedical Research Centre, the Medical Research Council, the Rosetrees and Stoneygate Trusts, and CRUK core grants.

Barrett's esophagus: The pathomorphological and molecular genetic keystones of neoplastic progression

Barrett's esophagus is a widespread chronically progressing disease of heterogeneous nature. A life threatening complication of this condition is neoplastic transformation, which is often overlooked due to lack of standardized approaches in diagnosis, preventative measures and treatment. In this essay, we aim to stratify existing data to show specific associations between neoplastic transformation and the underlying processes which predate cancerous transition. We discuss pathomorphological, genetic, epigenetic, molecular and immunohistochemical methods related to neoplasia detection on the basis of Barrett's esophagus. Our review sheds light on pathways of such neoplastic progression in the distal esophagus, providing valuable insight into progression assessment, preventative targets and treatment modalities. Our results suggest that molecular, genetic and epigenetic alterations in the esophagus arise earlier than cancerous transformation, meaning the discussed targets can help form preventative strategies in at-risk patient groups.