Biology subjects

Lord, R. V.

Publications and source records attributed to Lord, R. V..

3 recordsLinked to original sources

bioRxiv2026

Transcriptomic Changes and Biomarkers in Barrett's Metaplasia, Dysplasia and Cancer

Esophageal adenocarcinoma (EAC) has a poor five-year survival rate and one of the fastest-rising incidences of any cancer. The presence of dysplasia in Barrett's esophagus (BE) is the main risk factor for EAC development and guides clinical management. Unfortunately, the current histopathological diagnosis of dysplasia is unreliable, with poor inter-observer agreement, highlighting the need for novel biomarkers that can improve diagnostic accuracy. Here, we performed transcriptome profiling across the full spectrum of BE-related neoplasia in 85 samples to delineate gene expression alterations in progressively worse disease stages and identify biomarkers that could complement histopathology to improve the detection of dysplasia and EAC in endoscopic biopsy specimens. Differential gene expression and pathway analyses revealed that the most extensive transcriptional changes occurred during the transition from normal squamous (NSq) to non-dysplastic BE (NDBE), consistent with metaplastic transformation. Compared to NDBE, dysplasia was characterized by enhanced cellular growth and proliferation; upregulation of immune processes and oncogenic signaling pathways were present in EAC. Using machine learning approaches, we identified a novel five-gene panel suitable for a potential RNAseq-based diagnostic test (SLC11A1, IL36A, LUCAT1, MIR215, RNU6-954P) and performed an initial validation of this signature in an additional 51 samples. We also identified several potential novel immunohistochemical markers that may warrant further evaluation, including TREM1, CXCL5, OSM, and motilin. In summary, by delineating transcriptional changes across the full disease spectrum, this study identifies several candidate biomarkers for improving current diagnostic methods for Barrett's dysplasia and EAC.

cancer biology↗

bioRxiv2024

Federated deep learning enables cancer subtyping by proteomics

Artificial intelligence applications in biomedicine face major challenges from data privacy requirements. To address this issue for clinically annotated tissue proteomic data, we developed a Federated Deep Learning (FDL) approach (ProCanFDL), training local models on simulated sites containing data from a pan-cancer cohort (n=1,260) and 29 cohorts held behind private firewalls (n=6,265), representing 19,930 replicate data-independent acquisition mass spectrometry (DIA-MS) runs. Local parameter updates were aggregated to build the global model, achieving a 43% performance gain on the hold-out test set (n=625) in 14 cancer subtyping tasks compared to local models, and matching centralized model performance. The approachs generalizability was demonstrated by retraining the global model with data from two external DIA-MS cohorts (n=55) and eight acquired by tandem mass tag (TMT) proteomics (n=832). ProCanFDL presents a solution for internationally collaborative machine learning initiatives using proteomic data, e.g., for discovering predictive biomarkers or treatment targets, while maintaining data privacy. Statement of SignificanceA federated deep learning approach applied to human proteomic data, acquired using two distinct proteomic technologies from 40 tumor cohorts from eight countries, enabled accurate cancer histopathological subtyping while preserving data privacy. This approach will enable privacy-compliant development of large-scale proteomic AI models, including foundation models, across institutions globally.

cancer biology↗

bioRxiv2018

Serum glycoprotein biomarker validation for esophageal adenocarcinoma and application to Barrett’s surveillance

BACKGROUND & AIMSEsophageal adenocarcinoma (EAC) is thought to develop from asymptomatic Barretts esophagus (BE) with a low annual rate of conversion. Current endoscopy surveillance for BE patients is probably not cost-effective. Previously, we discovered serum glycoprotein biomarker candidates which could discriminate BE patients from EAC. Here, we aimed to validate candidate serum glycoprotein biomarkers in independent cohorts, and to develop a biomarker panel for BE surveillance.\n\nMETHODSSerum glycoprotein biomarker candidates were measured in 301 serum samples collected from Australia (4 states) and USA (1 clinic) using lectin magnetic bead array (LeMBA) coupled multiple reaction monitoring mass spectrometry (MRM-MS). The area under receiver operating characteristic curve was calculated as a measure of discrimination, and multivariate recursive partitioning was used to formulate a multi-marker panel for BE surveillance.\n\nRESULTSDifferent glycoforms of complement C9 (C9), gelsolin (GSN), serum paraoxonase/arylesterase 1 (PON1) and serum paraoxonase/lactonase 3 (PON3) were validated as diagnostic glycoprotein biomarker candidates for EAC across both cohorts. A panel of 10 serum glycoproteins accurately discriminated BE patients not requiring intervention [BE+/-low grade dysplasia] from those requiring intervention [BE with high grade dysplasia (BE-HGD) or EAC]. Tissue expression of C9 was found to be induced in BE, dysplastic BE and EAC. In longitudinal samples from subjects that have progressed towards EAC, levels of serum C9 glycoforms were increased with disease progression.\n\nCONCLUSIONSFurther prospective clinical validation of the confirmed biomarker candidates in a large cohort is warranted. A first-line BE surveillance blood test may be developed based on these findings.\n\nAbbreviations

biochemistry↗