Classification of Inflammatory Bowel Disease from Formalin-Fixed, Paraffin-Embedded Tissue Biopsies via Imaging Mass Spectrometry

Challenges in IBD Research 2024: Precision Medicine

Precision medicine is part of 5 focus areas of the Challenges in IBD Research 2024 research document, which also includes preclinical human IBD mechanisms, environmental triggers, novel technologies, and pragmatic clinical research. Building on Challenges in IBD Research 2019, the current Challenges aims to provide a comprehensive overview of current gaps in inflammatory bowel diseases (IBDs) research and deliver actionable approaches to address them with a focus on how these gaps can lead to advancements in interception, remission, and restoration for these diseases. The document is the result of multidisciplinary input from scientists, clinicians, patients, and funders, and represents a valuable resource for patient-centric research prioritization. In particular, the precision medicine section is focused on the main research gaps in elucidating how to bring the best care to the individual patient in IBD. Research gaps were identified in biomarker discovery and validation for predicting disease progression and choosing the most appropriate treatment for each patient. Other gaps were identified in making the best use of existing patient biosamples and clinical data, developing new technologies to analyze large datasets, and overcoming regulatory and payer hurdles to enable clinical use of biomarkers. To address these gaps, the Workgroup suggests focusing on thoroughly validating existing candidate biomarkers, using best-in-class data generation and analysis tools, and establishing cross-disciplinary teams to tackle regulatory hurdles as early as possible. Altogether, the precision medicine group recognizes the importance of bringing basic scientific biomarker discovery and translating it into the clinic to help improve the lives of IBD patients.

Mucosal expression of PI3, ANXA1, and VDR discriminates Crohn's disease from ulcerative colitis

Differential diagnosis of inflammatory bowel disease (IBD) to Crohn's disease (CD) or ulcerative colitis (UC) is crucial for treatment decision making. With the aim of generating a clinically applicable molecular-based tool to classify IBD patients, we assessed whole transcriptome analysis on endoscopy samples. A total of 408 patient samples were included covering both internal and external samples cohorts. Whole transcriptome analysis was performed on an internal cohort of FFPE IBD samples (CD, n = 16 and UC, n = 17). The 100 most significantly differentially expressed genes (DEG) were tested in two external cohorts. Ten of the DEG were further processed by functional enrichment analysis from which seven were found to show consistent significant performance in discriminating CD from UC: PI3, ANXA1, VDR, MTCL1, SH3PXD2A-AS1, CLCF1, and CD180. Differential expression of PI3, ANXA1, and VDR was reproduced by RT-qPCR, which was performed on an independent sample cohort of 97 patient samples (CD, n = 44 and UC, n = 53). Gene expression levels of the three-gene profile, resulted in an area under the curve of 0.84 (P = 0.02) in discriminating CD from UC, and therefore appear as an attractive molecular-based diagnostic tool for clinicians to distinguish CD from UC.

Are We Ready to Reclassify Crohn's Disease Using Molecular Classification?

Crohn's disease (CD) is a type of inflammatory bowel disease. The number of IBD cases worldwide was estimated to be 4.9 million in 2019. CD exhibits heterogeneity in clinical presentation, anatomical involvement, disease behaviour, clinical course and response to treatment. The classical description of CD involves transmural inflammation with skip lesions anywhere along the entire gastrointestinal tract. The complexity and heterogeneity of Crohn's disease is not currently reflected in the conventional classification system. Though the knowledge of Crohn's pathophysiology remains far from understood, the established complex interplay of the omics-genomics, transcriptomics, proteomics, epigenomics, metagenomics, metabolomics, lipidomics and immunophenomics-provides numerous targets for potential molecular markers of disease. Advancing technology has enabled identification of small molecules within these omics, which can be extrapolated to differentiate types of Crohn's disease. The multi-omic future of Crohn's disease is promising, with potential for advancements in understanding of its pathogenesis and implementation of personalised medicine.

Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks

Despite numerous diagnostic and therapeutic advances, pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate, and is the fourth leading cause of cancer death in developing countries. Besides its increasing prevalence, pancreatic malignancies are characterized by poor prognosis. Omics technologies have potential relevance for PDAC assessment but are time-intensive and relatively cost-intensive and limited by tissue heterogeneity. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can obtain spatially distinct peptide-signatures and enables tumor classification within a feasible time with relatively low cost. While MALDI-MSI data sets are inherently large, machine learning methods have the potential to greatly decrease processing time. We present a pilot study investigating the potential of MALDI-MSI in combination with neural networks, for classification of pancreatic ductal adenocarcinoma. Neural-network models were trained to distinguish between pancreatic ductal adenocarcinoma and other pancreatic cancer types. The proposed methods are able to correctly classify the PDAC types with an accuracy of up to 86% and a sensitivity of 82%. This study demonstrates that machine learning tools are able to identify different pancreatic carcinoma from complex MALDI data, enabling fast prediction of large data sets. Our results encourage a more frequent use of MALDI-MSI and machine learning in histopathological studies in the future.

AGPAT1 as a Novel Colonic Biomarker for Discriminating Between Ulcerative Colitis With and Without Primary Sclerosing Cholangitis

INTRODUCTION

Ulcerative colitis (UC) associated with primary sclerosing cholangitis (PSC-UC) is considered a unique inflammatory bowel disease (IBD) entity. PSC diagnosis in an IBD individual entails a significantly higher risk of gastrointestinal cancer; however, biomarkers for identifying patients with UC at risk for PSC are lacking. We, therefore, performed a thorough PSC-UC biomarker study, starting from archived colonic tissue.

METHODS

Proteins were extracted out of formalin-fixed paraffin-embedded proximal colon samples from PSC-UC (n = 9), UC (n = 7), and healthy controls (n = 7). Patients with IBD were in clinical and histological remission, and all patients with UC had a history of pancolitis. Samples were processed by the multienzyme digestion FASP and subsequently analyzed by liquid chromatography-tandem mass spectrometry. Candidate proteins were replicated in an independent cohort (n: PSC-UC = 16 and UC = 21) and further validated by immunohistochemistry.

RESULTS

In the discovery step, 7,279 unique proteins were detected. The top 5 most differentiating proteins (PSC-UC vs UC) based on linear regression analysis were selected for replication. Of these, 1-acetylglycerol-3-phosphate O-acyltransferase 1 (AGPAT1) was verified as higher in PSC-UC than UC (P = 0.009) in the replication cohort. A difference on the group level was also confirmed by immunohistochemistry, showing more intense AGPAT1 staining in patients with PSC-UC compared with UC.

DISCUSSION

We present AGPAT1 as a potential colonic biomarker for differentiating PSC-UC from UC. Our findings have possible implication for future PSC-IBD diagnostics and surveillance.

Use of MALDI Mass Spectrometry Imaging to Identify Proteomic Signatures in Aortic Aneurysms after Endovascular Repair

Endovascular repair (EVAR) has become the standard procedure in treating thoracic (TAA) or abdominal aortic aneurysms (AAA). Not entirely free of complications, a persisting perfusion of the aneurysm after EVAR, called Endoleak (EL), leads to reintervention and risk of secondary rupture. How the aortic wall responds to the implantation of a stentgraft and EL is mostly uncertain. We present a pilot study to identify peptide signatures and gain new insights in pathophysiological alterations of the aortic wall after EVAR using matrix-assisted laser desorption or ionization mass spectrometry imaging (MALDI-MSI). In course of or accompanying an open aortic repair, tissue sections from 15 patients (TAA = 5, AAA = 5, EVAR = 5) were collected. Regions of interest (tunica media and tunica adventitia) were defined and univariate (receiver operating characteristic analysis) statistical analysis for subgroup comparison was used. This proof-of-concept study demonstrates that MALDI-MSI is feasible to identify discriminatory peptide signatures separating TAA, AAA and EVAR. Decreased intensity distributions for actin, tropomyosin, and troponin after EVAR suggest impaired contractility in vascular smooth muscle cells. Furthermore, inability to provide energy caused by impaired respiratory chain function and continuous degradation of extracellular matrix components (collagen) might support aortic wall destabilization. In case of EL after EVAR, this mechanism may result in a weakened aortic wall with lacking ability to react on reinstating pulsatile blood flow.

Cross-Normalization of MALDI Mass Spectrometry Imaging Data Improves Site-to-Site Reproducibility

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is an established tool for the investigation of formalin-fixed paraffin-embedded (FFPE) tissue samples and shows a high potential for applications in clinical research and histopathological tissue classification. However, the applicability of this method to serial clinical and pharmacological studies is often hampered by inevitable technical variation and limited reproducibility. We present a novel spectral cross-normalization algorithm that differs from the existing normalization methods in two aspects: (a) it is based on estimating the full statistical distribution of spectral intensities and (b) it involves applying a non-linear, mass-dependent intensity transformation to align this distribution with a reference distribution. This method is combined with a model-driven resampling step that is specifically designed for data from MALDI imaging of tryptic peptides. This method was performed on two sets of tissue samples: a single human teratoma sample and a collection of five tissue microarrays (TMAs) of breast and ovarian tumor tissue samples (N = 241 patients). The MALDI MSI data was acquired in two labs using multiple protocols, allowing us to investigate different inter-lab and cross-protocol scenarios, thus covering a wide range of technical variations. Our results suggest that the proposed cross-normalization significantly reduces such batch effects not only in inter-sample and inter-lab comparisons but also in cross-protocol scenarios. This demonstrates the feasibility of cross-normalization and joint data analysis even under conditions where preparation and acquisition protocols themselves are subject to variation.

Discovery of Spatial Peptide Signatures for Neuroblastoma Risk Assessment by MALDI Mass Spectrometry Imaging

Simple Summary

The childhood tumor, neuroblastoma, has a broad clinical presentation. Risk assessment at diagnosis is particularly difficult in molecularly heterogeneous high-risk cases. Here we investigate the potential of imaging mass spectrometry to directly detect intratumor heterogeneity on the protein level in tissue sections. We show that this approach can produce discriminatory peptide signatures separating high- from low- and intermediate-risk tumors, identify 8 proteins aassociated with these signatures and validate two marker proteins using tissue immunostaining that have promise for further basic and translational research in neuroblastoma. We provide proof-of-concept that mass spectrometry-based technology could assist early risk assessment in neuroblastoma and provide insights into peptide signature-based detection of intratumor heterogeneity.

Abstract

Risk classification plays a crucial role in clinical management and therapy decisions in children with neuroblastoma. Risk assessment is currently based on patient criteria and molecular factors in single tumor biopsies at diagnosis. Growing evidence of extensive neuroblastoma intratumor heterogeneity drives the need for novel diagnostics to assess molecular profiles more comprehensively in spatial resolution to better predict risk for tumor progression and therapy resistance. We present a pilot study investigating the feasibility and potential of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to identify spatial peptide heterogeneity in neuroblastoma tissues of divergent current risk classification: high versus low/intermediate risk. Univariate (receiver operating characteristic analysis) and multivariate (segmentation, principal component analysis) statistical strategies identified spatially discriminative risk-associated MALDI-based peptide signatures. The AHNAK nucleoprotein and collapsin response mediator protein 1 (CRMP1) were identified as proteins associated with these peptide signatures, and their differential expression in the neuroblastomas of divergent risk was immunohistochemically validated. This proof-of-concept study demonstrates that MALDI-MSI combined with univariate and multivariate analysis strategies can identify spatially discriminative risk-associated peptide signatures in neuroblastoma tissues. These results suggest a promising new analytical strategy improving risk classification and providing new biological insights into neuroblastoma intratumor heterogeneity.