1. Introduction
Pancreatic ductal adenocarcinoma (PDAC) accounts for more than 90% of all pancreatic malignancies, and has generally a poor prognosis [
1]. With a 5 year overall-survival of less than 8%, PDAC is the fourth most frequent cause of cancer-related deaths worldwide [
2]. Projections indicate that the number of PDAC diagnoses as well as PDAC-related deaths will more than double in the next decade in the United States [
3] and in European countries [
4]. This is due to lifestyle habits such as alcohol and tobacco abuse, which are known to increase the risk of several other cancers and also seem to play a role in the development of PDAC [
4,
5,
6,
7,
8]. Pancreatic ductal adenocarcinoma (PDAC) and ampullary carcinoma (AC) are gastrointestinal cancers with overlapping clinical symptoms [
9]. Although several studies hypothesize that pathogeneses and molecular composition are different, the clinical regime and therapy remain similar [
10]. Moreover, existing studies demonstrate that the 5 year overall-survival-rate of patients with PDAC is lower in comparison with patients with AC [
11]. Pancreatic ductal adenocarcinoma of the pancreas and ampullary carcinoma arise in close proximity to each other [
12]. As a result, differential diagnosis of PDAC remains clinically challenging. Differential diagnosis is hindered by the lack of accurate and reliable diagnostic assays. Identification of molecular signatures for PDAC diagnostics offers the possibility of improving the clinical and patient management. However, common tissue-based proteomic and genomic techniques require large amounts of homogenized tissue material, which does not enable a direct correlation of molecular alterations with tissue histology.
Matrix-assisted-laser-desorption/ionization (MALDI) imaging technology combines common mass spectrometry with histological approaches. This technique is suitable for analyzing molecules (e.g., metabolites, proteins, peptides, lipids and glycans) and their spatial distribution in a single tissue-section in an unsupervised and label-free manner [
13,
14,
15,
16]. MALDI mass spectrometry imaging (MALDI-MSI) enables the high-throughput determination of spatial molecular-signatures in a clinically acceptable period and at relatively low cost, in comparison to other omics technologies. This provides new capabilities to classify different patient subgroups and even supports prediction of disease progression and/or resistance development.
In previous studies, MALDI-MSI was applied to in situ proteomic analysis of preneoplastic lesions in pancreatic cancer in genetically engineered mice [
17]. In the study, intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasm (IPMN) could be discriminated from normal pancreatic tissue and early pancreatic ductal adenocarcinoma. Further studies have shown differences in the chemical structure of phospholipids and their distribution patterns in human pancreatic islets with intra-islet spatial resolution using MALDI-MSI [
18]. Besides the investigation of the underlying mechanism, several proof-of-concept studies demonstrate the potential of MALDI-MSI in combination with machine learning algorithms to identify peptide signatures of prognostic relevance in pancreatic cancer [
19,
20].
In recent years, neural networks gained great popularity in many machine learning tasks such as image and speech recognition [
21], image segmentation [
22], and various classification tasks. Convolutional neural networks especially outperform many classical machine-learning approaches [
23]. Neural networks are able to approximate highly complex decision functions, through their layer structure. Each layer consists of so-called neurons, which themselves contain weights and a nonlinear gating function. During training, the weights are tuned to solve the task at hand, which typically goes hand in hand with intrinsic learning, a meaningful feature-representation of the given data [
24]. In the context of MALDI-MSI, machine learning algorithms have been used less frequently. Most studies make use of statistical methods, such as hierarchical clustering [
25] or linear discriminant analysis (LDA). The high-dimensional nature of MALDI-MSI spectra often prohibits applying machine learning directly to the raw spectrum. Thus, strategies for feature selection or dimension reduction are employed to enable machine learning tools to be used. The most commonly used techniques are peak picking [
26], principal component analysis (PCA), and non-negative matrix factorization (NMF) [
27].
In this proof-of-concept study, we investigate the feasibility of a neural network-based analysis of MALDI-MSI data for accurate classification of pancreatic ductal adenocarcinoma (
Figure 1). We explicitly rely on the inherent feature-selection capability of neural networks, and evaluate the feasibility of feeding the full-scale spectral data to the classification methods in order to reduce the problem-specific modeling overhead as well as human interaction, and allow all available data to be used.
2. Materials and Methods
2.1. Patient and Sample Cohort
Tissue microarrays (TMAs) from formalin-fixed paraffin-embedded tissue from patients diagnosed with exocrine pancreatic cancer, in particular pancreatic ductal adenocarcinoma (PDAC) and ampullary carcinoma (AC) were prepared at the University of British Columbia Research (
Table 1). The use of these samples is covered by ethical approval from the University of British Columbia Research Ethics Board H22-00073. Besides PDAC and AC, the investigated cohort included tissue material of other pancreatic cancer types, which are: acinar cell carcinoma, carcinoma NOS, benign chronic pancreatitis, intraductal papillary-mucinous carcinoma-invasive, intraductal papillary-mucinous carcinoma-noninvasive, mucinous cystic neoplasm-noninvasive, mucinous noncystic carcinoma, neuroendocrine tumor, pseudo-papillary tumor, serous cystadenoma, and signet-ring cell carcinoma.
2.2. MALDI-MSI
Formalin-fixed paraffin-embedded (FFPE) tissue sections (tissue microarrays) were prepared as described before [
28]. Briefly, 6-µm thick tissue-sections were mounted onto conductive glass slides, coated with indium tin oxide (Bruker Daltonik GmbH, Bremen, Germany). Sections were preheated to 80 °C for 15 min, followed by paraffin removal, and heat-induced antigen retrieval. Trypsin solution (20 µg Modified Porcine Trypsin in 800 µL digestion buffer (20 mM ammonium bicarbonate with 0.01% glycerol) was applied by an automated spraying device (HTX TM-Sprayer, HTX Technologies LLC, ERC GmbH, Riemerling, Germany), at 30 °C. The tryptic digest was performed in a humidity chamber for 2 h, at 50 °C. Matrix solution (7 g/L a-cyano-4-hydroxycinnamic acid in 70% acetonitrile and 1% trifluoroacetic acid, at 75 °C) was applied using an HTX TM sprayer. A RapifleX MALDI Tissuetyper with flexImaging 5.1 and flexControl 3.0 software (Bruker Daltonik GmbH, Bremen, Germany) was used in positive-ion reflector mode, detection range of 800–3200
m/z, 500 laser-shots per spot, a sampling rate of 1.25 GS/s (gigasamples per second) and a raster width of 50 µm for MALDI-MSI data acquisition. External calibration was carried out using a peptide calibration standard (Bruker Daltonik GmbH). After matrix removal, TMA sections were stained with hematoxylin and eosin, for histology. Tumor regions were digitally annotated in the QuPath open-source software by a pathologist, and transferred into SCiLS Lab software (Version 2019c Pro, Bruker Daltonik GmbH).
2.3. Processing of MALDI-MSI Data
MALDI-MSI raw data were converted to SCiLS Lab base data .sbd file and SCiLS Lab extended file .slx format using SCiLS Lab software version 2019c Pro (Bruker Daltonik GmbH). Data were set to total ion count without baseline removal. Patient tissue cores were categorized into ductal adenocarcinoma and non-PDAC (AC+ other pancreatic cancer types) attributes, to split the data into independent data sets (different tumor or patient-characteristics) for analysis. For peak detection and alignment, a standard segmentation pipeline (SCiLS Lab software) was used with the following parameters: width = 0.2 Da, maximal interval processing, total ion-count TIC normalization, medium noise reduction and no smoothing (Sigma: 0.75) [
29,
30].
2.4. Univariate Statistical Analyses
Supervised receiver-operating-characteristic (ROC) analyses were applied to identify m/z values, which are discriminative between tumor tissue regions of pancreatic ductal adenocarcinoma (PDAC) and ampullary-carcinoma (AC) tumor tissue regions. Area-under-the-curve values (AUC) close to 0 and 1 indicate that m/z values (peptides) are discriminatory. A comparable number of spectra must be used for the ROC analyses, and 10,000 spectra were randomly selected per group. Finally, m/z values with an AUC > 0.7 or <0.3 and a p-value < 0.001 (Wilcoxon rank-sum test) were selected as discriminative markers.
2.5. Protein Identification by Electrospray Ionization Tandem Mass Spectrometry
In order to identify the proteins corresponding to MALDI-MSI-derived
m/z values (peptide), bottom-up liquid chromatography-based mass spectrometry (LC MS/MS) was performed on adjacent tissue sections, as previously described [
26,
31]. Briefly, tissue deparaffinization, antigen retrieval and tryptic digest were performed, as for the MALDI-MSI analyses. Using 40 µL of 0.1% trifluoroacetic acid, peptides were extracted from the tissue section. The peptide solution was desalted and purified using a ZipTip
® C18, following the manufacturer instructions. Eluates were vacuum concentrated (Eppendorf
® Concentrator 5301, Eppendorf AG, Hamburg Germany) and reconstituted separately in 20 µL 0.1% trifluoroacetic acid. A total of 2 µL eluate were injected into a NanoHPLC (Dionex UltiMate 3000, Thermo Fisher Scientific, Waltham, MA, USA) coupled with an ESI-QTOF mass spectrometer (Impact II™, Bruker Daltonic GmbH, Bremen, Germany). All raw spectra from the MS/MS measurement were converted to Mascot generic files (.mgf) using the ProteinScape software. Mascot search engine (version 2.4, MatrixScience; London, UK) and UniProt database were used to analyze mass spectra. The search was performed with the following set of parameters: (i) taxonomy: human; (ii) proteolytic enzyme: trypsin; (iii) peptide tolerance: 10 ppm; (iv) maximum of accepted missed cleavages: 1; (v) peptide charge: 2+, 3+, 4+; (vi) variable modification: oxidation (M); (vii) MS/MS tolerance: 0.8 Da; and (viii) MOWSE score > 25.
The comparison of MALDI-MSI (
Supplementary Table S1) and LC−MS/MS
m/z values (
Supplementary Table S2) required the identification of more than one peptide (mass differences < 0.3 Da). The peptides with highest MOWSE peptide score and smallest mass differences between MALDI-MSI and nanoLC-MS/MS data were accepted as correctly identified.
2.6. Model Architectures for PDAC Classification
In order to classify PDAC, several neural network-based classifiers were employed. Firstly, a 2-layer residual network with skip connections between each layer, where the input of the proceeding layer was passed unmodified to the subsequent layer as additional input. In the following, this model is denoted as Residual.
Secondly, an encoder-only variant of the Transformer architecture [
32] was implemented. The size of the attention matrices used in this model is
n ×
n, where
n denotes the sequence length. As each spectrum consists of several thousand data points, applying a Transformer to the full-scale sequence is unfeasible; therefore, the sequence length was reduced using a pooling layer with a kernel-size of 4, based on each peak consisting of 3 individual data points, before passing the input to the encoder. We refer to this architecture as Transformer-1/2.
All models were implemented using the PyTorch (version 1.3.1) framework, and the trainable weights were initialized by randomly selecting values from a truncated normal distribution. Experiments were conducted using the Adam optimizer and rectified-linear-units (ReLU) activation functions. Different hyperparameters such as learning rate, batch size and kernel size were tested. The configurations resulting in the overall best performance on the test set for each architecture are shown in
Table 2.
2.7. Dataset Design
The data points were converted to NumPy arrays using the NumPy toolbox and a Python-based development environment. We kept the full mass range and stored the spectral data and corresponding label and coordinate information in HDF5 format. A custom tool was used to manually assign spectra to tissue samples. Patient tissue samples with fewer than 20 spectra were excluded, and spectra were normalized to unit median.
The TMA was randomly split into three subsets, which can be seen in
Supplementary Figure S1. For both classes (Ductal, non-PDAC), around 70% of the data were used for training the machine learning algorithms, further divided into a training dataset (50% of patient samples) and a validation dataset (20%). The remaining 30% were used as a test dataset to evaluate the classification performance and were not used during the training phase.
We applied 3-fold cross-validation to create distinct data sets for training by repeating the random-splitting process three times (Dataset 1–3). The patient- and spectra-distributions in the training, validation, and test datasets varied slightly among different splits, due to the assignment process. The splitting was performed by selecting full patient core-tissue samples randomly without replacement, and assigning them and all associated spectra to one of the three datasets until the desired size was reached (
Table 3,
Supplementary Figure S1).
2.8. Filtering of Noise Spectra
We implemented a filter to remove spectra with little or no relevance, based on an informativeness score. The informativeness of each spectrum was measured in terms of the number of peaks greater than the variance within the spectrum. We considered a spectrum informative whenever the number of such peaks exceeded a predefined threshold. We restricted the range of data points to evaluate the first 60% of data points in a given spectrum, since there were few peaks present in higher Dalton ranges. We provide a visual interpretation of our measure of informativeness in
Supplementary Figure S2.
2.9. Classification
Following the preprocessing steps above, the spectra and (during training) class-label information were directly passed to the neural networks. All network classifiers were trained on batched single spectra from the training data-set, monitoring the performance on the validation data-set. The model with the highest accuracy score on the validation data-set was selected to evaluate the performance on the unseen test-data.
Accuracy, sensitivity, and specificity metrics were computed on the unseen test-data in order to measure the performance for each classifier. This evaluation was performed on an individual spectrum level, counting the number of correctly classified spectra to determine the metric scores. In addition, a majority voting-strategy was employed to assign one class to each patient, based on the classes of all associated spectra in that patient’s sample, and metrics were also evaluated on the patient level.
All metrics were averaged over the three different data splits, described in
Section 2.7. All experiments were performed on a 2 × 6-core Intel Xeon Gold 6128 CPU @ 3.40 GHz with 24 logical cores and 3× GeForce RTX 2080 Ti GPUs with 11 GB of memory each.
4. Discussion
MALDI-MSI combines spatial molecular (mass-spectrometric) analysis with conventional histological tissue-assessment. This technology enables the simultaneous analyzing of the spatial distribution of hundreds of m/z values without prior knowledge (label-free). MALDI-MSI is performed in high-throughput format (less than 5 min/mm2 analysis time) with relatively low consumable cost (less than EUR one hundred per glass-slide). Tissue microarrays can be used to transfer up to 100 samples onto a single slide, again reducing the cost per patient-sample. These advantages make MALDI-MSI promising for identifying biomarker signatures and exploring tumor complexity in a clinically relevant format. In the presented study, we were able to demonstrate that the MALDI-MSI analysis results in biologically relevant m/z values to discriminate AC and PDAC by using univariate statistical analysis in combination with complementary nanoLC-MS/MS. The statistical analysis (receiver-operating-characteristic; ROC) results in 131 discriminative m/z values between pancreatic ductal adenocarcinoma and ampullary adenocarcinoma. Using complementary nanoLC-MS/MS, MALDI-MSI-derived m/z values could be assigned to three discriminative proteins: PLEC, Collagen CO6A3, and AHNK. However, direct identification of proteins, from which the m/z values (peptides, acquired by MALDI-MSI) stem, remain limited to only a few abundant proteins.
Recent studies have shown that high-resolution MSI data combined with microproteomics (high-resolution mass spectrometry) can be a valuable tool for protein assignment with high mass-accuracy and spatial specificity [
44,
45,
46]. As a result, this strategy is a promising candidate for exploring potentially disease-causing protein changes in small patient collectives, but inadequate for large-scale studies because the processing time for both microdissection and mass spectrometry is longer, and the cost is higher.
Moreover, due to tissue heterogeneity, single-protein-marker classification usually does not result in sufficient accuracy for clinical routine. Several previous studies described the fact that MALDI-MSI-derived
m/z value (signatures) in combination with supervised machine learning models enable a robust cancer-tissue classification [
28,
47,
48,
49]. In previous studies, we used MALDI-MSI to distinguish among four different epithelial ovarian-cancer histotypes [
50] to predict a proteomic signature in early-stage ovarian cancer disease, which is a prognostic marker for recurrence [
51], and to classify molecular subtypes of high-grade serous ovarian cancer [
28]
In the present study, we apply this technique in combination with neural-network strategies to expose spatially resolved proteomic-signatures for pancreatic-ductal-adenocarcinoma classification. As data size and the high dimensionality of MALDI-MSI analyses still pose complex computational and memory requirements that hinder highly accurate identification of relevant molecular patterns, we explore the feasibility of MALDI-MSI in combination with neuronal-network-evaluation strategies [
52]. We demonstrate that machine learning tools, in particular neural networks, given high-dimensional MALDI-MSI data are able to identify ductal carcinoma, giving high-dimensional MALDI-MSI data with an accuracy of up to 86%. MALDI-MSI combined with machine learning enables an accurate and quick PDAC prediction of large data sets, with a minimum of data preprocessing.
Fast and robust prediction is needed to enable the integration of MALDI-MSI in the clinical workflow. However, most studies apply a two-step pipeline for data processing [
53], consisting of feature selection and subsequent classification steps. Firstly, features are selected either by hand, using the well-known practice of peak selection [
54], or using a dimension-reduction algorithm such as principal-component analysis or non-negative matrix factorization [
27], before applying an—often linear—classifier or a thresholding approach. This data-processing pipeline is time consuming, and selecting peaks (features) to increase classification performance can result in potentially valuable data being discarded, thus resulting in negative robustness. Therefore, in this work, we make use of the non-processed spectra, and do not apply any type of explicit feature selection. Non-linear spectral diversity has the potential to determine biologically relevant clusters for tissue assessment and clinical phenotypes prediction.
Neural networks can use non-linear mapping to reveal correlations in the spectral data which are not accessible with the established linear methods. In addition, these methods enable the combination of the feature-selection and classification stage in an end-to-end fashion [
49]. Our model architectures are well suited to deal with sequential data such as MALDI-MSI data.
Consequently, we directly apply neural networks to the raw spectrum-data, and utilize the inherent-feature-extraction capabilities, similar to in the work of Behrmann et al. in [
49]. In our previous work, we demonstrated that a convolutional network with skip-connections can differentiate four different subtypes of ovarian cancer when applied to MALDI-MSI data [
50]. Our implementation is based on the original ResNet architecture [
55], which famously increased the performance on the famous ImageNet classification challenge [
56] by a large margin. We also considered the Transformer architecture, which has gained increasing popularity in fields such as language translation or caption generation, and is one of the most effective tools for processing sequential data: MALDI-MSI-derived spectra can be seen as sequential data, as each mass peak is linked to a specific detection-time. As we are interested in classification rather than sequence-to-sequence transformation, we only employ the encoder part of the original Transformer’s encoder-decoder design, with an added classification layer.
The capability of the proposed methods to extract robust features from the given spectral data is limited by the extent of noise. These features are crucial for the subsequent classification steps. The MALDI-MSI acquisition technique, due to its high resolution, results in noisy data. This sensitivity to noise hinders the learning process. The problem of acquisition-related noise is described in [
29], and can be compensated for, but the problem of noise due to structurally non-informative spectral data points remains. In our work, we implemented a filter to manage these data points. The proposed filter aims to reject noisy spectra before the classification step. Applying the filter to each spectrum results in a total of 2183 rejected spectra, due to their low informativeness, and improves classification by 3% across the models.
In total, our proposed method allows us to correctly classify ductal carcinoma with an accuracy of 86% and a sensitivity of 82%. The entire spectral data (full m/z range) can be used without time-consuming feature (m/z) selection. In upcoming studies, larger cohorts will be tested by a trained network to verify these findings. This will allow us to elevate our method from a classification algorithm to a more broadly applicable tool for diagnostic research.