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Artificial intelligence (AI) has shown potential for facilitating the detection and classification of tumors. In patients with non-small cell lung cancer, distinguishing between the most common subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SqCC), is crucial for the development of an effective treatment plan. This task, however, may still present challenges in clinical routine. We propose a two-modality, AI-based classification algorithm to detect and subtype tumor areas, which combines information from matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) data and digital microscopy whole slide images (WSIs) of lung tissue sections. The method consists of first detecting areas with high tumor cell content by performing a segmentation of the hematoxylin and eosin-stained (H&E-stained) WSIs, and subsequently classifying the tumor areas based on the corresponding MALDI MSI data. We trained the algorithm on six tissue microarrays (TMAs) with tumor samples from N = 232 patients and used 14 additional whole sections for validation and model selection. Classification accuracy was evaluated on a test dataset with another 16 whole sections. The algorithm accurately detected and classified tumor areas, yielding a test accuracy of 94.7% on spectrum level, and correctly classified 15 of 16 test sections. When an additional quality control criterion was introduced, a 100% test accuracy was achieved on sections that passed the quality control (14 of 16). The presented method provides a step further towards the inclusion of AI and MALDI MSI data into clinical routine and has the potential to reduce the pathologist’s work load. A careful analysis of the results revealed specific challenges to be considered when training neural networks on data from lung cancer tissue.

Many studies have demonstrated that tissue phenotyping (tissue typing) based on mass spectrometric imaging data is possible; however, comprehensive studies assessing variation and classifier transferability are largely lacking. This study evaluated the generalization of tissue classification based on Matrix Assisted Laser Desorption/Ionization (MALDI) mass spectrometric imaging (MSI) across measurements performed at different sites. Sections of a tissue microarray (TMA) consisting of different formalin-fixed and paraffin-embedded (FFPE) human tissue samples from different tumor entities (leiomyoma, seminoma, mantle cell lymphoma, melanoma, breast cancer, and squamous cell carcinoma of the lung) were prepared and measured by MALDI-MSI at different sites using a standard protocol (SOP). Technical variation was deliberately introduced on two separate measurements via a different sample preparation protocol and a MALDI Time of Flight mass spectrometer that was not tuned to optimal performance. Using standard data preprocessing, a classification accuracy of 91.4% per pixel was achieved for intrasite classifications. When applying a leave-one-site-out cross-validation strategy, accuracy per pixel over sites was 78.6% for the SOP-compliant data sets and as low as 36.1% for the mistuned instrument data set. Data preprocessing designed to remove technical variation while retaining biological information substantially increased classification accuracy for all data sets with SOP-compliant data sets improved to 94.3%. In particular, classification accuracy of the mistuned instrument data set improved to 81.3% and from 67.0% to 87.8% per pixel for the non-SOP-compliant data set. We demonstrate that MALDI-MSI-based tissue classification is possible across sites when applying histological annotation and an optimized data preprocessing pipeline to improve generalization of classifications over technical variation and increasing overall robustness.

Amplification of viral ribonucleic acid by real-time reverse transcriptase polymerase chain reaction is the gold standard to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, alternative reliable, fast, and cost-effective methods for the detection of SARS-CoV-2 are still needed. In this chapter, the mass spectrometry-based detection of amplified polymerase chain reaction products of SARS-CoV-2 genes from oral or nasopharyngeal swabs is described. The respective SARS-CoV-2 test has previously been shown to meet standard quality criteria and was therefore approved by the authorities in Europe and the USA.

Subtyping of the most common non-small cell lung cancer (NSCLC) tumor types adenocarcinoma (ADC) and squamous cell carcinoma (SqCC) is still a challenge in the clinical routine and a correct diagnosis is crucial for an adequate therapy selection. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) has shown potential for NSCLC subtyping but is subject to strong technical variability and has only been applied to tissue samples assembled in tissue microarrays (TMAs). To our knowledge, a successful transfer of a classifier from TMAs to whole sections, which are generated in the standard clinical routine, has not been presented in the literature as of yet. We introduce a classification algorithm using extensive preprocessing and a classifier (either a neural network or a linear discriminant analysis (LDA)) to robustly classify whole sections of ADC and SqCC lung tissue. The classifiers were trained on TMAs and validated and tested on whole sections. Vital for a successful application on whole sections is the extensive preprocessing and the use of whole sections for hyperparameter selection. The classification system with the neural network/LDA results in 99.0%/98.3% test accuracy on spectra level and 100.0%/100.0% test accuracy on whole section level, respectively, and, therefore, provides a powerful tool to support the pathologist's decision making process. The presented method is a step further towards a clinical application of MALDI MSI and artificial intelligence for subtyping of NSCLC tissue sections.

Sample processing of formalin-fixed specimens constitutes a major challenge in molecular profiling efforts. Pre-analytical factors such as fixative temperature, dehydration, and embedding media affect downstream analysis, generating data dependent on technical processing rather than disease state. In this study, we investigated two different sample processing methods, including the use of the cytospin sample preparation and automated sample processing apparatuses for proteomic analysis of multiple myeloma (MM) cell lines using imaging mass spectrometry (IMS). In addition, two sample-embedding instruments using different reagents and processing times were considered. Three MM cell lines fixed in 4% paraformaldehyde were either directly centrifuged onto glass slides using cytospin preparation techniques or processed to create paraffin-embedded specimens with an automatic tissue processor, and further cut onto glass slides for IMS analysis. The number of peaks obtained from paraffin-embedded samples was comparable between the two different sample processing instruments. Interestingly, spectra profiles showed enhanced ion yield in cytospin compared to paraffin-embedded samples along with high reproducibility compared to the sample replicate.

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.

The discrimination of malignant melanoma from benign nevi may be difficult in some cases. For this reason, immunohistological and molecular techniques are included in the differential diagnostic toolbox for these lesions. These methods are time consuming when applied subsequently and, in some cases, no definitive diagnosis can be made. We studied both lesions by imaging mass spectrometry (IMS) in a large cohort (n = 203) to determine a different proteomic profile between cutaneous melanomas and melanocytic nevi. Sample preparation and instrument setting were tested to obtain optimal results in term of data quality and reproducibility. A proteomic signature was found by linear discriminant analysis to discern malignant melanoma from benign nevus (n = 113) with an overall accuracy of &gt, 98%. The prediction model was tested in an independent set (n = 90) reaching an overall accuracy of 93% in classifying melanoma from nevi. Statistical analysis of the IMS data revealed mass-to-charge ratio (m/z) peaks which varied significantly (Area under the receiver operating characteristic curve &gt, 0.7) between the two tissue types. To our knowledge, this is the largest IMS study of cutaneous melanoma and nevi performed up to now. Our findings clearly show that discrimination of melanocytic nevi from melanoma is possible by IMS.

BACKGROUND: Targeted genetic profiling of tissue samples is paramount to detect druggable genetic aberrations in patients with non-squamous non-small cell lung cancer (NSCLC). Accurate upfront estimation of tumor cell content (TCC) is a crucial pre-analytical step for reliable testing and to avoid false-negative results. As of now, TCC is usually estimated on hematoxylin-eosin (H&E) stained tissue sections by a pathologist, a methodology that may be prone to substantial intra- and interobserver variability. Here we the investigate suitability of digital pathology for TCC estimation in a clinical setting by evaluating the concordance between semi-automatic and conventional TCC quantification. METHODS: TCC was analyzed in 120 H&E and thyroid transcription factor 1 (TTF-1) stained high-resolution images by 19 participants with different levels of pathological expertise as well as by applying two semi-automatic digital pathology image analysis tools (HALO and QuPath). RESULTS: Agreement of TCC estimations [intra-class correlation coefficients (ICC)] between the two software tools (H&E: 0.87; TTF-1: 0.93) was higher compared to that between conventional observers (0.48; 0.47). Digital TCC estimations were in good agreement with the average of human TCC estimations (0.78; 0.96). Conventional TCC estimators tended to overestimate TCC, especially in H&E stainings, in tumors with solid patterns and in tumors with an actual TCC close to 50%. CONCLUSIONS: Our results determine factors that influence TCC estimation. Computer-assisted analysis can improve the accuracy of TCC estimates prior to molecular diagnostic workflows. In addition, we provide a free web application to support self-training and quality improvement initiatives at other institutions.

Simple Summary Diagnostic subtyping of non-small cell lung cancer is paramount for therapy stratification. Our study shows that the subtyping into pulmonary adenocarcinoma and pulmonary squamous cell carcinoma by mass spectrometry imaging is rapid and accurate using limited tissue material. Abstract Subtyping of non-small cell lung cancer (NSCLC) is paramount for therapy stratification. In this study, we analyzed the largest NSCLC cohort by mass spectrometry imaging (MSI) to date. We sought to test different classification algorithms and to validate results obtained in smaller patient cohorts. Tissue microarrays (TMAs) from including adenocarcinoma (ADC, n = 499) and squamous cell carcinoma (SqCC, n = 440), were analyzed. Linear discriminant analysis, support vector machine, and random forest (RF) were applied using samples randomly assigned for training (66%) and validation (33%). The m/z species most relevant for the classification were identified by on-tissue tandem mass spectrometry and validated by immunohistochemistry (IHC). Measurements from multiple TMAs were comparable using standardized protocols. RF yielded the best classification results. The classification accuracy decreased after including less than six of the most relevant m/z species. The sensitivity and specificity of MSI in the validation cohort were 92.9% and 89.3%, comparable to IHC. The most important protein for the discrimination of both tumors was cytokeratin 5. We investigated the largest NSCLC cohort by MSI to date and found that the classification of NSCLC into ADC and SqCC is possible with high accuracy using a limited set of m/z species.

Background: SRY-related HMG-box 10 (SOX-10) is commonly expressed in triple negative breast cancer (TNBC). However, data on the biological significance of SOX-10 expression is limited. Therefore, we investigated immunhistological SOX-10 expression in TNBC and correlated the results with genetic alterations and clinical data. Methods: A tissue microarray including 113 TNBC cases was stained by SOX-10. Immunohistological data of AR, BCL2, CD117, p53 and Vimentin was available from a previous study. Semiconductor-based panel sequencing data including commonly altered breast cancer genes was also available from a previous investigation. SOX-10 expression was correlated with clinicopathological, immunohistochemical and genetic data. Results: SOX-10 was significantly associated with CD117 and Vimentin, but not with AR expression. An association of SOX-10 with BCL2, EGFR or p53 staining was not observed. SOX-10-positive tumors harbored more often TP53 mutations but less frequent mutations of PIK3CA or alterations of the PIK3K pathway. SOX-10 expression had no prognostic impact either on disease-free, distant disease-free, or overall survival. Conclusions: While there might be a value of SOX-10 as a differential diagnostic marker to identify metastases of TNBC, its biological role remains to be investigated.

Blockade of immune checkpoint pathways such as the programmed cell death protein 1 pathway (PD-1/PD-L1) is an emerging approach in the treatment of solid tumors. In malignant melanoma, the efficiacy of antibodies against PD-L1 has been shown to be associated with PD-L1 protein expression. To evaluate whether this approach may be of use in the rare cases of primary melanoma of the vulva, we have evaluated a series of 13 cases for PD-L1 expression as well as additional molecular alterations of KIT, NRAS, KRAS, and BRAF. PD-L1 expression was detected in 69% of cases and was not associated with any other molecular alteration, tumor stage or morphology. In conclusion, targeting PD-L1 by selective antibodies may be of benefit in the treatment of these uncommon tumors.

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 diagnosis. The applicability and accuracy of this method, however, heavily depends on the quality of the acquired data, and in particular mass misalignment in axial time-of-flight (TOF) MSI continues to be a serious issue. We present a mass alignment and recalibration method that is specifically designed to operate on MALDI peptide imaging data. The proposed method exploits statistical properties of the characteristic chemical noise background observed in peptide imaging experiments. By comparing these properties to a theoretical peptide mass model, the effective mass shift of each spectrum is estimated and corrected. The method was evaluated on a cohort of 31 FFPE tissue samples, pursuing a statistical validation approach to estimate both the reduction of relative misalignment, as well as the increase in absolute mass accuracy. Our results suggest that a relative mass precision of approximately 5 ppm and an absolute accuracy of approximately 20 ppm are achievable using our method.

Liver metastasis in colorectal cancer is common and the primary treatment is chemotherapy. To date, there is no routinely used test in clinical practice to predict the effectiveness of conventional chemotherapy. Therefore, biomarkers with predictive value for conventional chemotherapy would be of considerable benefit in treatment planning. We analysed three proteins [excision repair cross-complementing 1 (ERCC1), ribonucleoside-diphosphate reductase 1 (RRM1) and class III β-tubulin (TUBB3)] in colorectal cancer liver metastasis. We used tissue microarray slides with 101 liver metastasis samples, stained for ERCC1, RRM1 and TUBB3 and established scoring systems (fitted for tissue microarray) for each protein. In statistical analysis, we compared the expression of ERCC1, RRM1 and TUBB3 to mismatch proteins (MLH1, MSH2, MSH6 and PMS2), p53 and to apoptosis repressor protein (ARC). Statistically significant correlations were found between ERCC1, TUBB3 and MLH1, MSH2 and RRM1 and MSH2, MSH6. Noteworthy, our analysis revealed a strong significant correlation between cytoplasmic ARC expression and RRM1, TUBB3 (p=0.000 and p=0.001, respectively), implying an additional role of TUBB3 and RRM1 not only in therapy resistance, but also in the apoptotic machinery. Our data strengthens the importance of ERCC1, TUBB3 and RRM1 in the prediction of chemotherapy effectiveness and suggest new functional connections in DNA repair, microtubule network and apoptotic signaling (i.e. ARC protein). In conclusion, we showed the importance and need of predictive biomarkers in metastasized colorectal cancer and pointed out the relevance not only of single predictive markers but also of their interactions with other known and newly explored relations between different signaling pathways.

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) shows a high potential for applications in histopathological diagnosis, and in particular for supporting tumor typing and subtyping. The development of such applications requires the extraction of spectral fingerprints that are relevant for the given tissue and the identification of biomarkers associated with these spectral patterns. We propose a novel data analysis method based on the extraction of characteristic spectral patterns (CSPs) that allow automated generation of classification models for spectral data. Formalin-fixed paraffin embedded (FFPE) tissue samples from N=445 patients assembled on 12 tissue microarrays were analyzed. The method was applied to discriminate primary lung and pancreatic cancer, as well as adenocarcinoma and squamous cell carcinoma of the lung. A classification accuracy of 100% and 82.8%, resp., could be achieved on core level, assessed by cross-validation. The method outperformed the more conventional classification method based on the extraction of individual m/z values in the first application, while achieving a comparable accuracy in the second. LC-MS/MS peptide identification demonstrated that the spectral features present in selected CSPs correspond to peptides relevant for the respective classification. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Cystic echinococcosis (CE) is a pandemic infectious disease caused by the tapeworm Echinococcus granulosus that forms cysts in different organs such as lungs and liver. Imaging examination and serological tests have some drawbacks such as low sensitivity. In this study, we used an up-to-date workflow of laser microdissection-based microproteomics and matrix-assisted laser desorption/ionization time-of-flight imaging mass spectrometry in order to depict the proteomic pattern of CE in the liver. This investigation revealed specific markers of a parasitic cyst in liver. This proteomic pattern could facilitate diagnosis of CE in the future.

We retrospectively investigated concordance of EndoPredict (EPclin) with urokinase plasminogen activator and plasminogen activator inhibitor-1 (uPA/PAI-1) in 72 breast cancer patients and compared the results with grading, molecular subtype and chemotherapy recommendation. Compared to uPA/PAI-1, EPclin proved to be more conservative concerning correlation with clinicopathological parameters and was significantly associated with the recommendation of adjuvant chemotherapy.

PURPOSE To define proteomic differences between pancreatic ductal adenocarcinoma (pDAC) and pancreatic neuroendocrine tumor (pNET) by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI). EXPERIMENTAL DESIGN Ninety-three pDAC and 126 pNET individual tissues are assembled in tissue microarrays and analyzed by MALDI MSI. The cohort is separated in a training (52 pDAC and 83 pNET) and validation set (41 pDAC and 43 pNET). Subsequently, a linear discriminant analysis (LDA) model based on 46 peptide ions is performed on the training set and evaluated on the validation cohort. Additionally, two liver metastases and a whole slide of pDAC are analyzed by the same LDA algorithm. RESULTS Classification of pDAC and pNET by the LDA model is correct in 95% (39/41) and 100% (43/43) of patients in the validation cohort, respectively. The two liver metastases and the whole slide of pDAC are also correctly classified in agreement with the histopathological diagnosis. CONCLUSION AND CLINICAL RELEVANCE In the present study, a large dataset of pDAC and pNET by MALDI MSI is investigated, a class prediction model that allowed separation of both entities with high accuracy is developed, and differential peptide peaks with potential diagnostic, prognostic, and predictive values are highlighted.

Aims Human papilloma virus (HPV) infection is a causative agent for approximately 5% of all new cancer cases in humans. The virus is detected in cervical, anal, vaginal, penile, vulvar and head and neck cancers and has prognostic implications. Thus, test systems are required to detect high-risk but also low-risk HPV subtypes with high specificity and sensitivity in a time-effective and cost-effective manner. In the present study we developed a new mass spectrometry (MS)-based test system for the detection of HPV infections in formalin-fixed paraffin-embedded (FFPE) tissue samples. Methods A high-throughput matrix-assisted laser desorption ionisation time of flight MS-based assay was applied to genotype 19 HPV types in FFPE tissue specimens (n=46). The results from the MS assay were compared with the results obtained from two hybridisation-based test systems: the HPV 3.5 LCD-array kit and the EuroArrayHPV system. Results In 36 out of 46 (78%) tissue samples, a HPV infection could be detected by the MS-based HPV assay. In 16 samples (44%) only one and in 20 samples (56%) two to six HPV subtypes were identified. The overall agreement of all three assays was almost perfect (Cohen9s k value: 0.83). Conclusions The MS-based assay is highly sensitive, reliable as well as cost-effective and represents a suitable technology for the detection of HPV infections in FFPE tissue material.

Proteomic methods are today widely applied to formalin-fixed paraffin-embedded (FFPE) tissue samples for several applications in research, especially in molecular pathology. To date, there is an unmet need for the analysis of small tissue samples, such as for early cancerous lesions. Indeed, no method has yet been proposed for the reproducible processing of small FFPE tissue samples to allow biomarker discovery. In this work, we tested several procedures to process laser microdissected tissue pieces bearing less than 3000 cells. Combined with appropriate settings for liquid chromatography mass spectrometry–mass spectrometry (LC–MS/MS) analysis, a citric acid antigen retrieval (CAAR)-based procedure was established, allowing to identify more than 1400 proteins from a single microdissected breast cancer tissue biopsy. This work demonstrates important considerations concerning the handling and processing of laser microdissected tissue samples of extremely limited size, in the process opening new perspectives in molecular pathology. A proof of the proposed method for biomarker discovery, with respect to these specific handling considerations, is illustrated using the differential proteomic analysis of invasive breast carcinoma of no special type and invasive lobular triple-negative breast cancer tissues. This work will be of utmost importance for early biomarker discovery or in support of matrix-assisted laser desorption/ionization (MALDI) imaging for microproteomics from small regions of interest.

Histopathological diagnoses have been done in the last century based on hematoxylin and eosin staining. These methods were complemented by histochemistry, electron microscopy, immunohistochemistry (IHC), and molecular techniques. Mass spectrometry (MS) methods allow the thorough examination of various biocompounds in extracts and tissue sections. Today, mass spectrometry imaging (MSI), and especially matrix-assisted laser desorption ionization (MALDI) imaging links classical histology and molecular analyses. Direct mapping is a major advantage of the combination of molecular profiling and imaging. MSI can be considered as a cutting edge approach for molecular detection of proteins, peptides, carbohydrates, lipids, and small molecules in tissues. This review covers the detection of various biomolecules in histopathological sections by MSI. Proteomic methods will be introduced into clinical histopathology within the next few years.

Purpose Differential diagnosis of ulcerative colitis (UC) and Crohn's disease (CD) is of utmost importance for the decision making of respective therapeutic treatment strategies but in about 10-15% of cases, a clinical and histopathological assessment does not lead to a definite diagnosis. The aim of the study is to characterize proteomic differences between UC and CD. Experimental design Microproteomics is performed on formalin-fixed paraffin-embedded colonic tissue specimens from 9 UC and 9 CD patients. Protein validation is performed using immunohistochemistry (IHC) (nUC =51, nCD =62, nCTRL =10) followed by digital analysis. Results Microproteomic analyses reveal eight proteins with higher abundance in CD compared to UC including proteins related to neutrophil activity and damage-associated molecular patterns. Moreover, one protein, Aldo-keto reductase family 1 member C3 (AKR1C3), is present in eight out of nine CD and absent in all UC samples. Digital IHC analysis reveal a higher percentage and an increased expression intensity of AKR1C3-positive epithelial cells in CD compared to UC and in controls compared to inflammatory bowel disease (IBD). Conclusion and clinical relevance Overall, the results suggest that microproteomics is an adequate tool to highlight protein patterns in IBD. IHC and digital pathology might support future differential diagnosis of UC and CD.

Purpose To facilitate the transition of MALDI-MS Imaging (MALDI-MSI) from basic science to clinical application, it is necessary to analyze formalin-fixed paraffin-embedded (FFPE) tissues. The aim is to improve in situ tryptic digestion for MALDI-MSI of FFPE samples and determine if similar results would be reproducible if obtained from different sites. Experimental design FFPE tissues (mouse intestine, human ovarian teratoma, tissue microarray of tumor entities sampled from three different sites) are prepared for MALDI-MSI. Samples are coated with trypsin using an automated sprayer then incubated using deliquescence to maintain a stable humid environment. After digestion, samples are sprayed with CHCA using the same spraying device and analyzed with a rapifleX MALDI Tissuetyper at 50 µm spatial resolution. Data are analyzed using flexImaging, SCiLS, and R. Results Trypsin application and digestion are identified as sources of variation and loss of spatial resolution in the MALDI-MSI of FFPE samples. Using the described workflow, it is possible to discriminate discrete histological features in different tissues and enabled different sites to generate images of similar quality when assessed by spatial segmentation and PCA. Conclusions and clinical relevance Spatial resolution and site-to-site reproducibility can be maintained by adhering to a standardized MALDI-MSI workflow.

Objective Recognition of neuroendocrine differentiation is important for tumor classification and treatment stratification. To detect and confirm neuroendocrine differentiation, a combination of morphology and immunohistochemistry is often required. In this regard, synaptophysin, chromogranin A, and CD56 are established immunohistochemical markers. Insulinoma-associated protein 1 (INSM1) has been suggested as a novel stand-alone marker with the potential to replace the current standard panel. In this study, we compared the sensitivity and specificity of INSM1 and established markers. Materials and methods A cohort of 493 lung tumors including 112 typical, 39 atypical carcinoids, 77 large cell neuroendocrine carcinomas, 144 small cell lung cancers, 30 thoracic paragangliomas, 47 adenocarcinomas, and 44 squamous cell carcinomas were selected and tissue microarrays were constructed. Synaptophysin, chromogranin A, CD56, and INSM1 were stained on all cases and evaluated manually as well as with an analysis software. Positivity was defined as ≥1% stained tumor cells in at least 1 of 2 cores per patient. Results INSM1 was positive in 305 of 402 tumors with expected neuroendocrine differentiation (typical and atypical carcinoids, large cell neuroendocrine carcinomas, small cell lung cancers, and paraganglioma; sensitivity: 76%). INSM1 was negative in all but 1 of 91 analyzed non-neuroendocrine tumors (adenocarcinomas, squamous cell carcinomas; specificity: 99%). All conventional markers, as well as their combination, had a higher sensitivity (97%) and a lower specificity (78%) for neuroendocrine differentiation compared with INSM1. Conclusions Although INSM1 might be a meaningful adjunct in the differential diagnosis of neuroendocrine neoplasias, a general uncritical vote for replacing the traditional markers by INSM1 may not be justified.

Purpose Identification of proteolytic peptides from matrix-assisted laser desorption/ionization (MALDI) imaging remains a challenge. The low fragmentation yields obtained using in situ post source decay impairs identification. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is an alternative to in situ MS/MS, but leads to multiple identification candidates for a given mass. The authors propose to use LC-MS/MS-based biomarker discovery results to reliably identify proteolytic peptides from MALDI imaging. Experimental design The authors defined m/z values of interest for high grade squamous intraepithelial lesion (HSIL) by MALDI imaging. In parallel the authors used data from a biomarker discovery study to correlate m/z from MALDI imaging with masses of peptides identified by LC-MS/MS in HSIL. The authors neglected candidates that were not significantly more abundant in HSIL according to the biomarker discovery investigation. Results The authors assigned identifications to three m/z of interest. The number of possible identifiers for MALDI imaging m/z peaks using LC-MS/MS-based biomarker discovery studies was reduced by about tenfold compared using a single LC-MS/MS experiment. One peptide identification candidate was validated by immunohistochemistry. Conclusion and clinical relevance This concept combines LC-MS/MS-based quantitative proteomics with MALDI imaging and allows reliable peptide identification. Public datasets from LC-MS/MS biomarker discovery experiments will be useful to identify MALDI imaging m/z peaks.

BACKGROUND In matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) standardized sample preparation is important to obtain reliable results. Herein, the impact of section thickness in formalin-fixed paraffin embedded (FFPE) tissue microarrays (TMA) on spectral intensities is investigated. PATIENTS AND METHODS TMAs consisting of ten different tissues represented by duplicates of ten patients (n = 200 cores) are cut at 1, 3, and 5 μm. MSI analysis is performed and mean intensities of all evaluable cores are extracted. Measurements are merged and mean m/z intensities are compared. RESULTS Visual inspection of spectral intensities between 1, 3, and 5 μm reveals generally higher intensities in thinner tissue sections. Specifically, higher intensities are observed in the vast majority of peaks (98.6%, p

Purpose To develop a mass spectrometry imaging (MSI) based workflow for extracting m/z values related to putative protein biomarkers and using these for reliable tumor classification. Experimental design Given a list of putative breast and ovarian cancer biomarker proteins, a set of related m/z values are extracted from heterogeneous MSI datasets derived from formalin-fixed paraffin-embedded tissue material. Based on these features, a linear discriminant analysis classification model is trained to discriminate the two tumor types. Results It is shown that the discriminative power of classification models based on the extracted features is increased compared to the automatic training approach, especially when classifiers are applied to spectral data acquired under different conditions (instrument, preparation, laboratory). Conclusions and clinical relevance Robust classification models not confounded by technical variation between MSI measurements are obtained. This supports the assumption that the classification of the respective tumor types is based on biological rather than technical differences, and that the selected features are related to the proteomic profiles of the tumor types under consideration.

Objective Tissue slides analyzed by MS imaging (MSI) are stained by H&E (Haematoxylin and Eosin) to identify regions of interest. As it can be difficult to identify specific cells of interest by H&E alone, data analysis may be impaired. Immunohistochemistry (IHC) can highlight cells of interest but single or combined IHC on tissue sections analyzed by MSI have not been performed. Methods We performed MSI on bone marrow biopsies from patients with multiple myeloma and stained different antibodies (CD38, CD138, MUM1, kappa- and lambda). A combination of CK5/6/TTF1 and Napsin-A/p40 is stained after MSI on adenocarcinoma and squamous cell carcinoma of the lung. Staining intensities of p40 after MSI and on a serial section are quantified on a tissue microarray (n = 44) by digital analysis. Results Digital evaluation reveals weaker staining intensities after MSI as compared to serial sections. Staining quality and quantity after MSI enables to identify cells of interest. On the tissue microarray, one out of 44 tissue specimens shows no staining of p40 after MSI, but weak nuclear staining on a serial section. Conclusion We demonstrated that single and double IHC staining is feasible on tissue sections previously analyzed by MSI, with decreased staining intensities.

Purpose Matrix assisted laser desorption/ionization time-of-flight mass spectrometry imaging (MALDI-MSI) is a powerful tool to analyze the spatial distribution of peptides in tissues. Digital PCR (dPCR) is a method to reliably detect genetic mutations. Biopsy material is often limited due to minimally invasive techniques, but information on diagnosis, prognosis, and prediction is required for subsequent clinical decision making. Thus, saving tissue material during diagnostic workup is highly warranted for best patient care. The possibility to combine proteomic analysis by MALDI-MSI and mutational analysis by dPCR from the same tissue section is evaluated. Experimental design Ten 0.5 × 0.5 cm formalin-fixed paraffin embedded tissue samples of pulmonary adenocarcinomas with known EGFR or KRAS mutations are analyzed by MALDI-MSI. Subsequently, DNA is extracted from the analyzed tissue material and tested for the respective driver mutation by dPCR. Results Detection of driver gene mutations after MALDI MSI analysis is successful in all analyzed samples. Determined mutant allele frequencies are in good agreement with values assessed from untreated serial tissue sections with a mean absolute deviation of 0.16. Conclusion and clinical relevance It has been demonstrated that MALDI-MSI can be combined with genetic analysis, like dPCR. Workflows enabling the subsequent analysis of proteomic and genetic markers are particularly promising for the analysis of limited sample material such as biopsy specimen.

Amyloidosis is a heterogeneous group of protein misfolding diseases characterized by deposition of amyloid proteins. The kidney is frequently affected, especially by immunoglobulin light chain (AL) and serum amyloid A (SAA) amyloidosis as the most common subgroups. Current diagnosis relies on histopathological examination, Congo red staining, or electron microscopy. Subtyping is done by immunohistochemistry; however, commercially available antibodies lack specificity. The purpose of this study was to identify and map amyloid proteins in formalin-fixed paraffin-embedded tissue sections using matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis in an integrated workflow. Renal amyloidosis and non-amyloidosis biopsies were processed for histological and MS analysis. Mass spectra corresponding to the congophilic areas were directly linked to the histological and MS images for correlation studies. Peptides for SAA and AL were detected by MALDI IMS associated to Congo red-positive areas. Sequence determination of amyloid peptides by LC-MS/MS analysis provided protein distribution and identification. Serum amyloid P component, apolipoprotein E, and vitronectin proteins were identified in both AA and AL amyloidosis, showing a strong correlation with Congo red-positive regions. Our findings highlight the utility of MALDI IMS as a new method to type amyloidosis in histopathological routine material and characterize amyloid-associated proteins that may provide insights into the pathogenetic process of amyloid formation.

Matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) imaging mass spectrometry (IMS) is an evolving technique in cancer diagnostics and combines the advantages of mass spectrometry (proteomics), detection of numerous molecules, and spatial resolution in histological tissue sections and cytological preparations. This method allows the detection of proteins, peptides, lipids, carbohydrates or glycoconjugates and small molecules.Formalin-fixed paraffin-embedded tissue can also be investigated by IMS, thus, this method seems to be an ideal tool for cancer diagnostics and biomarker discovery. It may add information to the identification of tumor margins and tumor heterogeneity. The technique allows tumor typing, especially identification of the tumor of origin in metastatic tissue, as well as grading and may provide prognostic information. IMS is a valuable method for the identification of biomarkers and can complement histology, immunohistology and molecular pathology in various fields of histopathological diagnostics, especially with regard to identification and grading of tumors.

Motivation: Tumor classification using Imaging Mass Spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to fully process the data. Deep learning offers an approach to learn feature extraction and classification combined in a single model. Commonly these steps are handled separately in IMS data analysis, hence deep learning offers an alternative strategy worthwhile to explore. Results: Methodologically, we propose an adapted architecture based on deep convolutional networks to handle the characteristics of mass spectrometry data, as well as a strategy to interpret the learned model in the spectral domain based on a sensitivity analysis. The proposed methods are evaluated on two challenging tumor classification tasks and compared to a baseline approach. Competitiveness of the proposed methods are shown on both tasks by studying the performance via cross-validation. Moreover, the learned models are analyzed by the proposed sensitivity analysis revealing biologically plausible effects as well as confounding factors of the considered task. Thus, this study may serve as a starting point for further development of deep learning approaches in IMS classification tasks., Comment: 10 pages, 5 figures

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) technology creates a link between the molecular assessment of numerous molecules and the morphological information about their special distribution. The application of MALDI IMS on formalin-fixed paraffin-embedded (FFPE) tissue microarrays (TMAs) is suitable for large-scale discovery analyses. Data acquired from FFPE TMA cancer samples in current research are very promising, and applications for routine diagnostics are under development. With the current rapid advances in both technology and applications, MALDI IMS technology is expected to enter into routine diagnostics soon. This chapter is intended to be comprehensive with respect to all aspects and considerations for the application of MALDI IMS on FFPE cancer TMAs with in-depth notes on technical aspects.

The accurate diagnosis of periprosthetic joint infection (PJI) relies on clinical investigation, laboratory parameters, radiological methods, sterile joint aspiration for synovial fluid leucocyte count and microbiological analysis and tissue sampling for histopathology. Due to the limits in specificity and sensitivity of these methods, molecular techniques and new biomarkers were introduced into the diagnostic procedure. Histological examination is related to the amount of neutrophils in the periprosthetic tissue in frozen sections and formalin-fixed paraffin embedded material (FFPE). However, the threshold of neutrophils per defined area of tissue among various studies is very inconsistent.We have applied matrix-assisted laser desorption ionisation time-of-flight imaging mass spectrometry (MALDI IMS) to a total of 32 periprosthetic tissue samples of patients with PJI to detect peptides associated with areas of neutrophil infiltration.Specific peaks associated with a high amount of neutrophils were detected. Of these m/z peaks, four could be assigned to predictive neutrophil molecules. These peptides include annexin A1, calgizzarin (S100A11), calgranulin C (S100A12) and histone H2A. By MALDI IMS, these peptides could be shown to be co-localised with the infiltration of neutrophils in the immediate vicinity of the periprosthetic interface, whereas more distant areas did not show neutrophil invasion or infection-related peptides.MALDI IMS is a new method allowing identification of neutrophil peptides in periprosthetic tissues and may be a surrogate for counting neutrophils as an objective parameter for PJI.