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

Abstract

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.

Enhancing metabolite coverage in MALDI-MSI using laser post-ionisation (MALDI-2)

“…15 Metabolite imaging using MSI is also a very promising approach for mapping metabolic changes within tumours where the metabolic environment is signicantly altered, for example, by the Warburg effect. Examples of MALDI-MSI of metabolite distributions in cancer include glioblastoma, 5 thyroid cancer subtypes, 16 pancreatic ductal adenocarcinoma, 17 prostate cancer and breast cancer brain metastasis. 18 MALDI-MSI of metabolites is typically performed in negative ion mode using the matrices 9-aminoacridine (9-AA), diaminonaphthalene (DAN) and N-(1-naphthyl) ethylenediamine dihydrochloride (NEDC).…”

“…15 Metabolite imaging using MSI is also a very promising approach for mapping metabolic changes within tumours where the metabolic environment is significantly altered, for example, by the Warburg effect. Examples of MALDI-MSI of metabolite distributions in cancer include glioblastoma, 5 thyroid cancer subtypes, 16 pancreatic ductal adenocarcinoma, 17 prostate cancer and breast cancer brain metastasis. 18…”

Enhancing Metabolite Coverage in MALDI-MSI using Laser Post-Ionisation (MALDI-2).

“…16 Metabolite imaging using MSI is also a very promising appoach for mapping metabolic changes within tumours where the metabolic environment is significantly altered, for example, by the Warburg effect. Examples of MALDI-MSI of metabolite distributions in cancer include glioblastoma 17 , thyroid cancer subtypes 18 , pancreatic ductul adenocarcinoma 19 and breast cancer brain metastasis. 20 MALDI-MSI of metabolites is typically performed in the negative-ion mode using matrices 9-aminoacridine (9-AA), diaminonaphthalene (DAN) and N- (1-naphthyl) ethylenediamine dihydrochloride (NEDC).…”