Imaging mass spectrometry--a new and promising method to differentiate Spitz nevi from Spitzoid malignant melanomas

MALDI imaging mass spectrometry differentiates basal cell carcinoma from trichoblastoma and trichoepithelioma: A proof of principle study

BACKGROUND

Basal cell carcinoma (BCC) comprises a large portion of dermatopathology specimens; however, benign mimics such as trichoblastoma/trichoepithelioma (TB/TE) place accurate diagnosis at risk and consequently lead to inappropriate clinical management and overuse of healthcare resources. This study aims to address the challenges of traditional histopathological evaluation by utilizing matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI IMS).

METHODS AND FINDINGS

Formalin-fixed paraffin-embedded BCC and TB/TE tissue blocks were taken from archival tissue. A cohort of 69 BCC and TB/TE specimens were identified, each having three concordant diagnoses given by Dermatopathologists after a blinded analysis. H&E stained sections of each specimen were imaged for pathological analysis and uploaded to a digital annotation software with the following classifications: BCC, TB, TE, BCC stroma, TB stroma, and TE stroma. Mass spectra were collected from unstained serial sections guided by the areas annotated by the Dermatopathologists on the H&E stained serial sections. Before informatics, the data from the cohort were divided randomly into a training set (n = 55) and a validation set (n = 14). Prediction models were developed using a support vector machine (SVM) classification model from the training set data. The platform predicted BCC and TB/TE in model 2 (tumor nests alone) with a sensitivity of 98.9% (95% CI 98.3-99.4%) and specificity of 88.4% (95% CI 78.4-94.5%) at the spectral level in the validation set. Model 1 (stroma alone) had a sensitivity of 46.1% (95% CI 43.0-49.1%) and specificity of 99.2% (95% CI 97.1-99.9%). A combined model 3 (tumor nests and stroma) had a sensitivity of 90.26% (95% CI 89.1%-91.3%) and a specificity of 97.1% (95% CI 94.6% to 98.7%). The limitations of this study included a small sample set, which included easily identifiable cases obtained from a single tissue source.

CONCLUSIONS

Our study proves that BCC and TB/TE exhibit different proteomic profiles that one can use to enable accurate differential diagnosis. While our findings are not yet validated for clinical use, this merits further research to support IMS as an ancillary diagnostic tool for adequately and efficiently identifying the most common cutaneous malignancy in the United States. We recommend that future studies obtain a more extensive set of histologically challenging cases from multiple institutions and adequate clinical follow-up to confirm diagnostic accuracy.

Differential Expression Analysis of Cutaneous Squamous Cell Carcinoma and Basal Cell Carcinoma Proteomic Profiles Sampled with Electroporation-Based Biopsy

Clinical misdiagnosis between cutaneous squamous cell carcinoma (cSCC) and basal cell carcinoma (BCC) affects treatment plans. We report a tissue sampling approach with molecular biopsy using electroporation. This method, coined electroporation-based biopsy (e-biopsy), enables nondestructive nonthermal permeabilization of cells in the skin for vacuum-assisted extraction of biomolecules. We used e-biopsy for ex vivo proteome extraction from 3 locations per patient in 21 cSCC, 20 BCC, and 7 actinic keratosis human skin samples. Using liquid chromatography with tandem mass spectrometry, we identified 5966 proteins observed with nonzero intensity in at least 1 sample. The intrapatient Pearson correlation of 0.888 ± 0.065 for patients with BCC, 0.858 ± 0.077 for patients with cSCC, and 0.876 ± 0.116 for those with solar actinic keratosis indicates high consistency of the e-biopsy sampling. The mass spectra presented significantly different proteome profiles for cSCC, BCC, and solar actinic keratosis, with several hundreds of proteins differentially expressed. Notably, our study showed that proteomes sampled with e-biopsy from cSCC and BCC lesions are different and that proteins of CRNN, SULT1E1, and ITPK1 genes are significantly overexpressed in BCC in comparison with those in cSCC. Our results provide evidence that the e-biopsy approach could potentially be used as a tool to support cutaneous lesions classification with molecular pathology.

Imaging the elemental distribution within human malignant melanomas using Laser-Induced Breakdown Spectroscopy

The diagnosis of malignant melanoma, often an inconspicuous but highly aggressive tumor, is most commonly done by histological examination, while additional diagnostic methods on the level of elements and molecules are constantly being developed. Several studies confirmed differences in the chemical composition of healthy and tumor tissue. Our study presents the potential of the LIBS (Laser-Induced-Breakdown Spectroscopy) technique as a diagnostic tool in malignant melanoma (MM) based on the quantitative changes in elemental composition in cancerous tissue. Our patient group included 17 samples of various types of malignant melanoma and one sample of healthy skin tissue as a control. To achieve a clear perception of results, we have selected two biogenic elements (calcium and magnesium), which showed a dissimilar distribution in cancerous tissue from its healthy surroundings. Moreover, we observed indications of different concentrations of these elements in different subtypes of malignant melanoma, a hypothesis that requires confirmation in a more extensive sample set. The information provided by the LIBS Imaging method could potentially be helpful not only in the diagnostics of tumor tissue but also be beneficial in broadening the knowledge about the tumor itself.

Multimodal MALDI imaging mass spectrometry for improved diagnosis of melanoma

Imaging mass spectrometry (IMS) provides promising avenues to augment histopathological investigation with rich spatio-molecular information. We have previously developed a classification model to differentiate melanoma from nevi lesions based on IMS protein data, a task that is challenging solely by histopathologic evaluation. Most IMS-focused studies collect microscopy in tandem with IMS data, but this microscopy data is generally omitted in downstream data analysis. Microscopy, nevertheless, forms the basis for traditional histopathology and thus contains invaluable morphological information. In this work, we developed a multimodal classification pipeline that uses deep learning, in the form of a pre-trained artificial neural network, to extract the meaningful morphological features from histopathological images, and combine it with the IMS data. To test whether this deep learning-based classification strategy can improve on our previous results in classification of melanocytic neoplasia, we utilized MALDI IMS data with collected serial H&E stained sections for 331 patients, and compared this multimodal classification pipeline to classifiers using either exclusively microscopy or IMS data. The multimodal pipeline achieved the best performance, with ROC-AUCs of 0.968 vs. 0.938 vs. 0.931 for the multimodal, unimodal microscopy and unimodal IMS pipelines respectively. Due to the use of a pre-trained network to perform the morphological feature extraction, this pipeline does not require any training on large amounts of microscopy data. As such, this framework can be readily applied to improve classification performance in other experimental settings where microscopy data is acquired in tandem with IMS experiments.

Advances in multimodal mass spectrometry for single-cell analysis and imaging enhancement

Multimodal mass spectrometry (MMS) incorporates an imaging modality with probe-based mass spectrometry (MS) to enable precise, targeted data acquisition and provide additional biological and chemical data not available by MS alone. Two categories of MMS are covered; in the first, an imaging modality guides the MS probe to target individual cells and to reduce acquisition time by automatically defining regions of interest. In the second category, imaging and MS data are coupled in the data analysis pipeline to increase the effective spatial resolution using a higher resolution imaging method, correct for tissue deformation, and incorporate fine morphological features in an MS imaging dataset. Recent methodological and computational developments are covered along with their application to single-cell and imaging analyses.

Lipid fingerprint-based histology accurately classifies nevus, primary melanoma, and metastatic melanoma samples

Probably, the most important factor for the survival of a melanoma patient is early detection and precise diagnosis. Although in most cases these tasks are readily carried out by pathologists and dermatologists, there are still difficult cases in which no consensus among experts is achieved. To deal with such cases, new methodologies are required. Following this motivation, we explore here the use of lipid imaging mass spectrometry as a complementary tool for the aid in the diagnosis. Thus, 53 samples (15 nevus, 24 primary melanomas, and 14 metastasis) were explored with the aid of a mass spectrometer, using negative polarity. The rich lipid fingerprint obtained from the samples allowed us to set up an artificial intelligence-based classification model that achieved 100% of specificity and precision both in training and validation data sets. A deeper analysis of the image data shows that the technique reports important information on the tumor microenvironment that may give invaluable insights in the prognosis of the lesion, with the correct interpretation.

Diagnosing Cutaneous Melanocytic Tumors in the Molecular Era: Updates and Review of Literature

Over the past decade, molecular and genomic discoveries have experienced unprecedented growth, fundamentally reshaping our comprehension of melanocytic tumors. This review comprises three main sections. The first part gives an overview of the current genomic landscape of cutaneous melanocytic tumors. The second part provides an update on the associated molecular tests and immunohistochemical stains that are helpful for diagnostic purposes. The third section briefly outlines the diverse molecular pathways now utilized for the classification of cutaneous melanomas. The primary goal of this review is to provide a succinct overview of the molecular pathways involved in melanocytic tumors and demonstrate their practical integration into the realm of diagnostic aids. As the molecular and genomic knowledge base continues to expand, this review hopes to serve as a valuable resource for healthcare professionals, offering insight into the evolving molecular landscape of cutaneous melanocytic tumors and its implications for patient care.

Untangling Alzheimer's disease with spatial multi-omics: a brief review

Alzheimer's disease (AD) is the most common form of neurological dementia, specified by extracellular β-amyloid plaque deposition, neurofibrillary tangles, and cognitive impairment. AD-associated pathologies like cerebral amyloid angiopathy (CAA) are also affiliated with cognitive impairment and have overlapping molecular drivers, including amyloid buildup. Discerning the complexity of these neurological disorders remains a significant challenge, and the spatiomolecular relationships between pathogenic features of AD and AD-associated pathologies remain poorly understood. This review highlights recent developments in spatial omics, including profiling and molecular imaging methods, and how they are applied to AD. These emerging technologies aim to characterize the relationship between how specific cell types and tissue features are organized in combination with mapping molecular distributions to provide a systems biology view of the tissue microenvironment around these neuropathologies. As spatial omics methods achieve greater resolution and improved molecular coverage, they are enabling deeper characterization of the molecular drivers of AD, leading to new possibilities for the prediction, diagnosis, and mitigation of this debilitating disease.

Imaging Mass Spectrometry for the Classification of Melanoma Based on BRAF/NRAS Mutational Status

Mutations of the oncogenes v-raf murine sarcoma viral oncogene homolog B1 (BRAF) and neuroblastoma RAS viral oncogene homolog (NRAS) are the most frequent genetic alterations in melanoma and are mutually exclusive. BRAF V600 mutations are predictive for response to the two BRAF inhibitors vemurafenib and dabrafenib and the mitogen-activated protein kinase kinase (MEK) inhibitor trametinib. However, inter- and intra-tumoral heterogeneity and the development of acquired resistance to BRAF inhibitors have important clinical implications. Here, we investigated and compared the molecular profile of BRAF and NRAS mutated and wildtype melanoma patients' tissue samples using imaging mass spectrometry-based proteomic technology, to identify specific molecular signatures associated with the respective tumors. SCiLSLab and R-statistical software were used to classify peptide profiles using linear discriminant analysis and support vector machine models optimized with two internal cross-validation methods (leave-one-out, k-fold). Classification models showed molecular differences between BRAF and NRAS mutated melanoma, and identification of both was possible with an accuracy of 87-89% and 76-79%, depending on the respective classification method applied. In addition, differential expression of some predictive proteins, such as histones or glyceraldehyde-3-phosphate-dehydrogenase, correlated with BRAF or NRAS mutation status. Overall, these findings provide a new molecular method to classify melanoma patients carrying BRAF and NRAS mutations and help provide a broader view of the molecular characteristics of these patients that may help understand the signaling pathways and interactions involving the altered genes.

MASS SPECTROMETRY TECHNOLOGIES TO ADVANCE CARE FOR CANCER PATIENTS IN CLINICAL AND INTRAOPERATIVE USE

Developments in mass spectrometry technologies have driven a widespread interest and expanded their use in cancer-related research and clinical applications. In this review, we highlight the developments in mass spectrometry methods and instrumentation applied to direct tissue analysis that have been tailored at enhancing performance in clinical research as well as facilitating translation and implementation of mass spectrometry in clinical settings, with a focus on cancer-related studies. Notable studies demonstrating the capabilities of direct mass spectrometry analysis in biomarker discovery, cancer diagnosis and prognosis, tissue analysis during oncologic surgeries, and other clinically relevant problems that have the potential to substantially advance cancer patient care are discussed. Key challenges that need to be addressed before routine clinical implementation including regulatory efforts are also discussed. Overall, the studies highlighted in this review demonstrate the transformative potential of mass spectrometry technologies to advance clinical research and care for cancer patients. © 2020 Wiley Periodicals, Inc. Mass Spec Rev.

Imaging Mass Spectrometry-Based Proteomic Analysis to Differentiate Melanocytic Nevi and Malignant Melanoma

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 >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 > 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.

Diagnosis of melanoma by imaging mass spectrometry: Development and validation of a melanoma prediction model

BACKGROUND

The definitive diagnosis of melanocytic neoplasia using solely histopathologic evaluation can be challenging. Novel techniques that objectively confirm diagnoses are needed. This study details the development and validation of a melanoma prediction model from spatially resolved multivariate protein expression profiles generated by imaging mass spectrometry (IMS).

METHODS

Three board-certified dermatopathologists blindly evaluated 333 samples. Samples with triply concordant diagnoses were included in this study, divided into a training set (n = 241) and a test set (n = 92). Both the training and test sets included various representative subclasses of unambiguous nevi and melanomas. A prediction model was developed from the training set using a linear support vector machine classification model.

RESULTS

We validated the prediction model on the independent test set of 92 specimens (75 classified correctly, 2 misclassified, and 15 indeterminate). IMS detects melanoma with a sensitivity of 97.6% and a specificity of 96.4% when evaluating each unique spot. IMS predicts melanoma at the sample level with a sensitivity of 97.3% and a specificity of 97.5%. Indeterminate results were excluded from sensitivity and specificity calculations.

CONCLUSION

This study provides evidence that IMS-based proteomics results are highly concordant to diagnostic results obtained by careful histopathologic evaluation from a panel of expert dermatopathologists.

Without Missing a Beat: Absence of Cilia Informs the Diagnosis of Histopathologically Challenging Spitzoid Melanocytic Neoplasms

The diagnosis of Spitzoid melanocytic lesions remains a formidable challenge in dermatopathology. Numerous ancillary tests have evolved to improve accurate classification of histopathologically ambiguous lesions. A study by Lang et al. (2020b) demonstrates loss of primary cilia in Spitzoid melanoma compared with Spitz nevi and positions ciliation index as an informative ancillary test for Spitzoid lesions.

Ciliation Index Is a Useful Diagnostic Tool in Challenging Spitzoid Melanocytic Neoplasms

The loss of primary cilia on melanocytes is a useful biomarker for the distinction of melanoma from conventional melanocytic nevi. It is unknown whether ciliation status is beneficial for diagnosing spitzoid tumors-a subclass of melanomas that present inherently ambiguous histology and are challenging to classify. We evaluated the Ciliation Index (CI) in 68 cases of spitzoid tumors ranging from Spitz nevi and atypical Spitz tumors to spitzoid melanoma. We found a significant decrease in CI within the spitzoid melanoma group when compared with either the Spitz nevi or atypical Spitz tumors groups. In addition, we used a machine-learning-based algorithm to determine the value of CI when considered in combination with other histopathologic and molecular features commonly used for diagnosis. We found that a low CI was consistently ranked as a top predictive feature in the diagnosis of malignancy. Predictive models trained on only the top four predictive features (CI, asymmetry, hyperchromatism, and cytologic atypia) outperformed standard histologic assessment in an independent validation cohort of 56 additional cases. The results provide an alternative approach to evaluate diagnostically challenging melanocytic lesions, and further support the use of CI as an ancillary diagnostic test.

Histopathologic, immunophenotypic, and proteomics characteristics of low-grade phyllodes tumor and fibroadenoma: more similarities than differences

Distinguishing low-grade phyllodes tumor from fibroadenoma is practically challenging due to their overlapping histologic features. However, the final interpretation is essential to surgeons, who base their management on the final pathology report. Patients who receive a diagnosis of fibroadenoma might not undergo any additional intervention while lumpectomy with wide margins is the standard of care for phyllodes tumor, which can have significant cosmetic consequences. We studied the clinical, immunophenotypic, and proteomics profiles of 31 histologically confirmed low-grade phyllodes tumor and 30 fibroadenomas. Matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry (IMS) and immunohistochemistry for Ki-67, p53, β-catenin, and E-cadherin were performed on all cases. After the mass spectra for all 31 cases of low-grade phyllodes tumor and 30 cases of fibroadenoma were collected, an average peak value for all cases was generated. There was no significant difference in the overall mass spectra pattern in any of the peaks identified. There was also overlap in the percentage of cells staining positive for Ki-67, p53, β-catenin, and E-cadherin. The two groups of patients showed no statistically significant difference in age, tumor size, or disease-free survival. Neither group developed malignant transformation, distant metastases, or disease-related mortality. We have demonstrated low-grade phyllodes tumor and fibroadenoma to show significant overlapping clinical and proteomics features.

Imaging Mass Spectrometry Is an Accurate Tool in Differentiating Clear Cell Renal Cell Carcinoma and Chromophobe Renal Cell Carcinoma: A Proof-of-concept Study

Clear cell renal cell carcinoma (ccRCC) and chromophobe renal cell carcinoma (chRCC) are relatively common tumors that can have significant risk for mortality. Treatment and prognostication in renal cell carcinoma (RCC) are dependent upon correct histologic typing. ccRCC and chRCC are generally straightforward to diagnose based on histomorphology alone. However, high-grade ccRCC and chRCC can sometimes resemble each other morphologically, particularly in small biopsies. Multiple immunostains and/or colloidal iron stain are sometimes required to differentiate the two. Imaging mass spectrometry (IMS) allows simultaneous spatial mapping of thousands of biomarkers, using formalin-fixed paraffin-embedded tissue sections. In this study, we evaluate the ability of IMS to differentiate between World Health Organization/International Society for Urological Pathology grade 3 ccRCC and chRCC. IMS spectra from a training set of 14 ccRCC and 13 chRCC were evaluated via support vector machine algorithm with a linear kernel for machine learning, building a classification model. The classification model was applied to a separate validation set of 6 ccRCC and 6 chRCC, with 19 to 20, 150-μm diameter tumor foci in each case sampled by IMS. Most evaluated tumor foci were classified correctly as ccRCC versus chRCC (99% accuracy, kappa=0.98), demonstrating that IMS is an accurate tool in differentiating high-grade ccRCC and chRCC.

[The role of new molecular tests in the diagnosis of melanoma in a setting of congenital nævus in an infant]

INTRODUCTION

Congenital and infantile melanomas are extremely rare. We report a case of a child presenting at birth with a giant congenital nevus complicated by melanoma and on long-term follow-up with exploration using new immunohistochemistry and molecular biology tools.

OBSERVATION

A new-born girl presented at birth with a large congenital cervico-mandibular tumour with para-pharyngeal extension and underlying osteolysis. At 7 months, histology and immunohistochemistry of the operative specimen revealed nodules with atypical features (mitotic figures, necrosis and positive expression of KI67 and P53 in approximatively 50 % of the melanocytic nuclei). A diagnosis was made of infantile melanoma associated with congenital nevi. Repeated surgery and monitoring (clinical and imaging) were performed. At the age of 7 years, as there was no evidence of metastatic lesions, further analyses were performed on the initial operative specimen. Investigation of transcription factor expression using immunohistochemistry, comparative genomic hybridization and histology-guided mass spectrometry, although suspect, did not in itself support a diagnosis of melanoma. Finally, at the age of 7 years, hepatic and pulmonary metastases were reported. Despite combined immunotherapy with ipilimumab and nivolumab, the child died 5 months later.

CONCLUSION

This case illustrates the complexity of diagnosis of infantile melanoma and the risk of metastatic involvement long after the initial diagnosis. Diagnosis may be difficult and necessitates expert advice and the application of several recent methods to reach a conclusion and initiate appropriate treatment.

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high-dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data-driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry-based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1-47, 2019.

The 2018 World Health Organization Classification of Cutaneous, Mucosal, and Uveal Melanoma: Detailed Analysis of 9 Distinct Subtypes Defined by Their Evolutionary Pathway

CONTEXT.—

There have been major advances in the understanding of melanoma since the last revision of the World Health Organization (WHO) classification in 2006.

OBJECTIVE.—

To discuss development of the 9 distinct types of melanoma and distinguishing them by their epidemiology, clinical and histologic morphology, and genomic characteristics. Each melanoma subtype is placed at the end of an evolutionary pathway that is rooted in its respective precursor, wherever appropriate and feasible, based on currently known data. Each precursor has a variable risk of progression culminating in its fully evolved, invasive melanoma.

DATA SOURCES.—

This review is based on the "Melanocytic Tumours" section of the 4th edition of the WHO Classification of Skin Tumours, published in 2018.

CONCLUSIONS.—

Melanomas were divided into those etiologically related to sun exposure and those that are not, as determined by their mutational signatures, anatomic site, and epidemiology. Melanomas on the sun-exposed skin were further divided by the histopathologic degree of cumulative solar damage (CSD) of the surrounding skin, into low and high CSD, on the basis of degree of associated solar elastosis. Low-CSD melanomas include superficial spreading melanomas and high-CSD melanomas incorporate lentigo maligna and desmoplastic melanomas. The "nonsolar" category includes acral melanomas, some melanomas in congenital nevi, melanomas in blue nevi, Spitz melanomas, mucosal melanomas, and uveal melanomas. The general term melanocytoma is proposed to encompass "intermediate" tumors that have an increased (though still low) probability of disease progression to melanoma.

Histopathology-guided mass spectrometry differentiates benign nevi from malignant melanoma

PURPOSE

Distinguishing benign nevi from malignant melanoma using current histopathological criteria may be very challenging and is one the most difficult areas in dermatopathology. The goal of this study was to identify proteomic differences, which would more reliably differentiate between benign and malignant melanocytic lesions.

METHODS

We performed histolpathology - guided mass spectrometry (HGMS) profiling analysis on formalin-fixed, paraffin embedded tissue samples to identify differences at the proteomic level between different types of benign nevi and melanomas. A total of 756 cases, of which 357 cases of melanoma and 399 benign nevi, were included in the study. The specimens originated from both biopsies (376 samples) and tissue microarray (TMA) cores (380 samples). After obtaining mass spectra from each sample, classification models were built using a training set of biopsy specimens from 111 nevi and 100 melanomas. The classification algorithm developed on the training data set was validated on an independent set of 288 nevi and 257 melanomas from both biopsies and TMA cores.

RESULTS

In the melanoma cohort, 239/257 (93%) cases classified correctly in the validation set, 3/257 (1.2%) classified incorrectly, and 15/257 (5.8%) classified as indeterminate. In the cohort of nevi, 282/288 (98%) cases classified correctly, 1/288 (0.3%) classified incorrectly, and 5/288 (1.7%) were indeterminate. HGMS showed a sensitivity of 98.76% and specificity of 99.65% in determining benign vs malignant.

CONCLUSION

HGMS proteomic analysis is an objective and reliable test with minimal tissue requirements, which can be a helpful ancillary test in the diagnosis of challenging melanocytic lesions.

MicroRNA Ratios Distinguish Melanomas from Nevi

The use of microRNAs as biomarkers has been proposed for many diseases, including the diagnosis of melanoma. Although hundreds of microRNAs have been identified as differentially expressed in melanomas as compared to benign melanocytic lesions, a limited consensus has been achieved across studies, constraining the effective use of these potentially useful markers. In this study, we applied a machine learning-based pipeline to a dataset consisting of genetic features, clinical features, and next-generation microRNA sequencing from micro-dissected formalin-fixed paraffin embedded melanomas and their adjacent benign precursor nevi. We identified patient age and tumor cellularity as variables that frequently confound the measured expression of potentially diagnostic microRNAs. By employing the ratios of microRNAs that were either enriched or depleted in melanoma compared to the nevi as a normalization strategy, we developed a model that classified all the available published cohorts with an area under the receiver operating characteristic curve of 0.98. External validation on an independent cohort classified lesions with 81% sensitivity and 88% specificity and was uninfluenced by the tumor content of the sample or patient age.

Egg White as a Quality Control in Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI)

The strength of MALDI-MSI is to analyze and visualize spatial intensities of molecular features from an intact tissue. The distribution of the intensities can then be visualized within a single tissue section or compared in between sections, acquired consecutively. This method can be reliably used to reveal physiological structures and has the potential to identify molecular details, which correlate with biological outcomes. MALDI-MSI implementation in clinical laboratories requires the ability to ensure method quality and validation to meet diagnostic expectations. To be able to get consistent qualitative and quantitative results, standardized sample preparation and data acquisition are of highest priority. We have previously shown that the deposition of internal standards onto the tissue section during sample preparation can be used to improve the mass accuracy of monitored m/z features across the sample. Here, we present the use of external and internal controls for the quality check of sample preparation and data acquisition, which is particularly relevant when either many spectra are acquired during a single MALDI-MSI experiment or data from independent experiments are processed together. To monitor detector performance and sample preparation, we use egg white as an external control for peptide and N-glycan MALDI-MSI throughout the experiment. We have also identified endogenous peptides from cytoskeletal proteins, which can be reliably monitored in gynecological tissue samples. Lastly, we summarize our standard quality control workflow designed to produce reliable and comparable MALDI-MSI data from single sections and tissue microarrays (TMAs).

A recommended and verified procedure for in situ tryptic digestion of formalin-fixed paraffin-embedded tissues for analysis by matrix-assisted laser desorption/ionization imaging mass spectrometry

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is a molecular imaging technology uniquely capable of untargeted measurement of proteins, lipids, and metabolites while retaining spatial information about their location in situ. This powerful combination of capabilities has the potential to bring a wealth of knowledge to the field of molecular histology. Translation of this innovative research tool into clinical laboratories requires the development of reliable sample preparation protocols for the analysis of proteins from formalin-fixed paraffin-embedded (FFPE) tissues, the standard preservation process in clinical pathology. Although ideal for stained tissue analysis by microscopy, the FFPE process cross-links, disrupts, or can remove proteins from the tissue, making analysis of the protein content challenging. To date, reported approaches differ widely in process and efficacy. This tutorial presents a strategy derived from systematic testing and optimization of key parameters, for reproducible in situ tryptic digestion of proteins in FFPE tissue and subsequent MALDI IMS analysis. The approach describes a generalized method for FFPE tissues originating from virtually any source.

Site-to-Site Reproducibility and Spatial Resolution in MALDI-MSI of Peptides from Formalin-Fixed Paraffin-Embedded Samples

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.

Genomic Landscape of Spitzoid Neoplasms Impacting Patient Management

Spitzoid neoplasms are a distinct group of melanocytic proliferations characterized by epithelioid and/ or spindle shaped melanocytes. Intermediate forms that share features of both benign Spitz nevi (SN) and Spitz melanoma, i.e., malignant Spitz tumor (MST) represent a diagnostically and clinically challenging group of melanocytic lesions. A multitude of descriptive diagnostic terms exist for these ambiguous lesions with atypical Spitz tumor (AST) or Spitz tumor of uncertain malignant potential (STUMP) just naming two of them. This diagnostic gray zone creates confusion and high insecurity in clinicians and in patients. Biological behavior and clinical course of this intermediate group still remains largely unknown, often leading to difficulties with uncertainties in clinical management and prognosis. Consequently, a better stratification of Spitzoid neoplasms in benign and malignant forms is required thereby keeping the diagnostic group of AST/STUMP as small as possible. Ancillary diagnostic techniques such as immunohistochemistry, comparative genomic hybridization, fluorescence in situ hybridization, next generation sequencing, micro RNA and mRNA analysis as well as mass spectrometry imaging offer new opportunities for the distinct diagnosis, thereby allowing the best clinical management of Spitzoid neoplasms. This review gives an overview on these additional diagnostic techniques and the recent developments in the field of molecular genetic alterations in Spitzoid neoplasms. We also discuss how the recent findings might facilitate the diagnosis and stratification of atypical Spitzoid neoplasms and how these findings will impact the diagnostic work up as well as patient management. We suggest a stepwise implementation of ancillary diagnostic techniques thereby integrating immunohistochemistry and molecular pathology findings in the diagnosis of challenging ambiguous Spitzoid neoplasms. Finally, we will give an outlook on pending future research objectives in the field of Spitzoid melanocytic lesions.

Mass spectrometry imaging for clinical research - latest developments, applications, and current limitations

Mass spectrometry is being used in many clinical research areas ranging from toxicology to personalized medicine. Of all the mass spectrometry techniques, mass spectrometry imaging (MSI), in particular, has continuously grown towards clinical acceptance. Significant technological and methodological improvements have contributed to enhance the performance of MSI recently, pushing the limits of throughput, spatial resolution, and sensitivity. This has stimulated the spread of MSI usage across various biomedical research areas such as oncology, neurological disorders, cardiology, and rheumatology, just to name a few. After highlighting the latest major developments and applications touching all aspects of translational research (i.e. from early pre-clinical to clinical research), we will discuss the present challenges in translational research performed with MSI: data management and analysis, molecular coverage and identification capabilities, and finally, reproducibility across multiple research centers, which is the largest remaining obstacle in moving MSI towards clinical routine.

Mass Spectrometry Imaging Can Distinguish on a Proteomic Level Between Proliferative Nodules Within a Benign Congenital Nevus and Malignant Melanoma

Histopathological interpretation of proliferative nodules occurring in association with congenital melanocytic nevi can be very challenging due to their similarities with congenital malignant melanoma and malignant melanoma arising in association with congenital nevi. We hereby report a diagnostically challenging case of congenital melanocytic nevus with proliferative nodules and ulcerations, which was originally misdiagnosed as congenital malignant melanoma. Subsequent histopathological examination in consultation by one of the authors (R.L.) and mass spectrometry imaging analysis rendered a diagnosis of congenital melanocytic nevus with proliferative nodules. In this case, mass spectrometry imaging, a novel method capable of distinguishing benign from malignant melanocytic lesions on a proteomic level, was instrumental in making the diagnosis of a benign nevus. We emphasize the importance of this method as an ancillary tool in the diagnosis of difficult melanocytic lesions.

Label-free molecular imaging of the kidney

In this review, we will highlight technologies that enable scientists to study the molecular characteristics of tissues and/or cells without the need for antibodies or other labeling techniques. Specifically, we will focus on matrix-assisted laser desorption/ionization imaging mass spectrometry, infrared spectroscopy, and Raman spectroscopy.

MALDI (matrix assisted laser desorption ionization) Imaging Mass Spectrometry (IMS) of skin: Aspects of sample preparation

MALDI (matrix assisted laser desorption ionization) Imaging Mass Spectrometry (IMS) allows molecular analysis of biological materials making possible the identification and localization of molecules in tissues, and has been applied to address many questions on skin pathophysiology, as well as on studies about drug absorption and metabolism. Sample preparation for MALDI IMS is the most important part of the workflow, comprising specimen collection and preservation, tissue embedding, cryosectioning, washing, and matrix application. These steps must be carefully optimized for specific analytes of interest (lipids, proteins, drugs, etc.), representing a challenge for skin analysis. In this review, critical parameters for MALDI IMS sample preparation of skin samples will be described. In addition, specific applications of MALDI IMS of skin samples will be presented including wound healing, neoplasia, and infection.

Paediatric melanoma

Paediatric melanoma, although rare, is the most common skin cancer in children. Our current knowledge on paediatric melanoma incidence trends is expanding, as several studies have addressed this issue with conflicting results. Known risk factors for paediatric melanoma include family history of melanoma, a previous history of malignancy, large congenital nevi, numerous melanocytic nevi, sunburns, increased UV exposure and a sun-sensitive phenotype. In younger children, melanoma more often presents with atypical features, such as a changing, amelanotic or uniformly coloured, often bleeding lesion, not fulfilling in most cases the conventional ABCDE criteria. The major differential diagnoses are melanocytic nevi, proliferative nodules in congenital nevi and atypical Spitz tumours. Moreover, in the younger age group non-Caucasian children are over-represented, tumours tend to be thicker and lymph nodes are often involved. Despite the frequent diagnosis at an advanced stage, the overall survival is fair in paediatric melanoma. Specific guidelines for management of melanoma in children do not exist, and most often the disease is treated similarly to melanoma in adults.

MALDI imaging mass spectrometry reveals multiple clinically relevant masses in colorectal cancer using large-scale tissue microarrays

For identification of clinically relevant masses to predict status, grade, relapse and prognosis of colorectal cancer, we applied Matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry (IMS) to a tissue micro array containing formalin-fixed and paraffin-embedded tissue samples from 349 patients. Analysis of our MALDI-IMS data revealed 27 different m/z signals associated with epithelial structures. Comparison of these signals showed significant association with status, grade and Ki-67 labeling index. Fifteen out of 27 IMS signals revealed a significant association with survival. For seven signals (m/z 654, 776, 788, 904, 944, 975 and 1013) the absence and for eight signals (m/z 643, 678, 836, 886, 898, 1095, 1459 and 1477) the presence were associated with decreased life expectancy, including five masses (m/z 788, 836, 904, 944 and 1013) that provided prognostic information independently from the established prognosticators pT and pN. Combination of these five masses resulted in a three-step classifier that provided prognostic information superior to univariate analysis. In addition, a total of 19 masses were associated with tumor stage, grade, metastasis and cell proliferation. Our data demonstrate the suitability of combining IMS and large-scale tissue micro arrays to simultaneously identify and validate clinically useful molecular marker. Copyright © 2017 John Wiley & Sons, Ltd.

Clinical applications of MALDI imaging technologies in cancer and neurodegenerative diseases

Matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) imaging mass spectrometry (IMS) enables localization of analytes of interest along with histology. More specifically, MALDI-IMS identifies the distributions of proteins, peptides, small molecules, lipids, and drugs and their metabolites in tissues, with high spatial resolution. This unique capacity to directly analyze tissue samples without the need for lengthy sample preparation reduces technical variability and renders MALDI-IMS ideal for the identification of potential diagnostic and prognostic biomarkers and disease gradation. MALDI-IMS has evolved rapidly over the last decade and has been successfully used in both medical and basic research by scientists worldwide. In this review, we explore the clinical applications of MALDI-IMS, focusing on the major cancer types and neurodegenerative diseases. In particular, we re-emphasize the diagnostic potential of IMS and the challenges that must be confronted when conducting MALDI-IMS in clinical settings. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Mass Spectrometry Imaging for the Investigation of Intratumor Heterogeneity

One of the big clinical challenges in the treatment of cancer is the different behavior of cancer patients under guideline therapy. An important determinant for this phenomenon has been identified as inter- and intratumor heterogeneity. While intertumor heterogeneity refers to the differences in cancer characteristics between patients, intratumor heterogeneity refers to the clonal and nongenetic molecular diversity within a patient. The deciphering of intratumor heterogeneity is recognized as key to the development of novel therapeutics or treatment regimens. The investigation of intratumor heterogeneity is challenging since it requires an untargeted molecular analysis technique that accounts for the spatial and temporal dynamics of the tumor. So far, next-generation sequencing has contributed most to the understanding of clonal evolution within a cancer patient. However, it falls short in accounting for the spatial dimension. Mass spectrometry imaging (MSI) is a powerful tool for the untargeted but spatially resolved molecular analysis of biological tissues such as solid tumors. As it provides multidimensional datasets by the parallel acquisition of hundreds of mass channels, multivariate data analysis methods can be applied for the automated annotation of tissues. Moreover, it integrates the histology of the sample, which enables studying the molecular information in a histopathological context. This chapter will illustrate how MSI in combination with statistical methods and histology has been used for the description and discovery of intratumor heterogeneity in different cancers. This will give evidence that MSI constitutes a unique tool for the investigation of intratumor heterogeneity, and could hence become a key technology in cancer research.

MALDI imaging mass spectrometry - From bench to bedside

Today, pathologists face many challenges in defining the precise morphomolecular diagnosis and in guiding clinicians to the optimal patients' treatment. To achieve this goal, increasingly, classical histomorphological methods have to be supplemented by high throughput molecular assays. Since MALDI imaging mass spectrometry (IMS) enables the assessment of spatial molecular arrangements in tissue sections, it goes far beyond microscopy in providing hundreds of different molecular images from a single scan without the need of target-specific reagents. Thus, this technology has the potential to uncover new markers for diagnostic purposes or markers that correlate with disease severity as well as prognosis and therapeutic response. Additionally, in the future MALDI IMS based classifiers measured with this technology in real time in the diagnostic setting might be applicable in the routine diagnostic setting. In this review, recently published studies that show the usefulness, advantages, and applicability of MALDI IMS in different fields of pathology (diagnosis, prognosis and treatment response) are highlighted. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Imaging mass spectrometry assists in the classification of diagnostically challenging atypical Spitzoid neoplasms

BACKGROUND

Previously, using imaging mass spectrometry (IMS), we discovered proteomic differences between Spitz nevi and Spitzoid melanomas.

OBJECTIVE

We sought to determine whether IMS can assist in the classification of diagnostically challenging atypical Spitzoid neoplasms (ASN), to compare and correlate the IMS and histopathological diagnoses with clinical behavior.

METHODS

We conducted a retrospective collaborative study involving centers from 11 countries and 11 US institutions analyzing 102 ASNs by IMS. Patients were divided into clinical groups 1 to 4 representing best to worst clinical behavior. The association among IMS findings, histopathological diagnoses, and clinical groups was assessed.

RESULTS

There was a strong association between a diagnosis of Spitzoid melanoma by IMS and lesions categorized as clinical groups 2, 3, and 4 (recurrence of disease, metastases, or death) compared with clinical group 1 (no recurrence or metastasis beyond a sentinel node) (P < .0001). Older age and greater tumor thickness were strongly associated with poorer outcome (P = .01).

CONCLUSIONS

IMS diagnosis of ASN better predicted clinical outcome than histopathology. Diagnosis of Spitzoid melanoma by IMS was strongly associated with aggressive clinical behavior. IMS analysis using a proteomic signature may improve the diagnosis and prediction of outcome/risk stratification for patients with ASN.

High-mass-resolution MALDI mass spectrometry imaging of metabolites from formalin-fixed paraffin-embedded tissue

Formalin-fixed and paraffin-embedded (FFPE) tissue specimens are the gold standard for histological examination, and they provide valuable molecular information in tissue-based research. Metabolite assessment from archived tissue samples has not been extensively conducted because of a lack of appropriate protocols and concerns about changes in metabolite content or chemical state due to tissue processing. We present a protocol for the in situ analysis of metabolite content from FFPE samples using a high-mass-resolution matrix-assisted laser desorption/ionization fourier-transform ion cyclotron resonance mass spectrometry imaging (MALDI-FT-ICR-MSI) platform. The method involves FFPE tissue sections that undergo deparaffinization and matrix coating by 9-aminoacridine before MALDI-MSI. Using this platform, we previously detected ∼1,500 m/z species in the mass range m/z 50-1,000 in FFPE samples; the overlap compared with fresh frozen samples is 72% of m/z species, indicating that metabolites are largely conserved in FFPE tissue samples. This protocol can be reproducibly performed on FFPE tissues, including small samples such as tissue microarrays and biopsies. The procedure can be completed in a day, depending on the size of the sample measured and raster size used. Advantages of this approach include easy sample handling, reproducibility, high throughput and the ability to demonstrate molecular spatial distributions in situ. The data acquired with this protocol can be used in research and clinical practice.

MALDI mass spectrometry imaging: A cutting-edge tool for fundamental and clinical histopathology

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.

Imaging mass spectrometry: Molecular microscopy for the new age of biology and medicine

Imaging mass spectrometry provides a powerful tool for monitoring and discovery of molecular processes in the spatial domain in tissues for research and practical applications in both biology and medicine. This technology directly measures molecular compounds in tissues without the use of target-specific reagents such as antibodies, is applicable to a wide variety of analytes, and can provide spatial resolutions below the single cell level. Importantly, it has paradigm shifting capabilities in clinical applications, especially for anatomic pathology.

Protein biomarkers on tissue as imaged via MALDI mass spectrometry: A systematic approach to study the limits of detection

MALDI mass spectrometry imaging (MSI) is emerging as a tool for protein and peptide imaging across tissue sections. Despite extensive study, there does not yet exist a baseline study evaluating the potential capabilities for this technique to detect diverse proteins in tissue sections. In this study, we developed a systematic approach for characterizing MALDI-MSI workflows in terms of limits of detection, coefficients of variation, spatial resolution, and the identification of endogenous tissue proteins. Our goal was to quantify these figures of merit for a number of different proteins and peptides, in order to gain more insight in the feasibility of protein biomarker discovery efforts using this technique. Control proteins and peptides were deposited in serial dilutions on thinly sectioned mouse xenograft tissue. Using our experimental setup, coefficients of variation were <30% on tissue sections and spatial resolution was 200 μm (or greater). Limits of detection for proteins and peptides on tissue were in the micromolar to millimolar range. Protein identification was only possible for proteins present in high abundance in the tissue. These results provide a baseline for the application of MALDI-MSI towards the discovery of new candidate biomarkers and a new benchmarking strategy that can be used for comparing diverse MALDI-MSI workflows.

Pathology interface for the molecular analysis of tissue by mass spectrometry

Background:

Imaging mass spectrometry (IMS) generates molecular images directly from tissue sections to provide better diagnostic insights and expand the capabilities of clinical anatomic pathology. Although IMS technology has matured over recent years, the link between microscopy imaging currently used by pathologists and MS-based molecular imaging has not been established.

Methods:

We adapted the Vanderbilt University Tissue Core workflow for IMS into a web-based system that facilitates remote collaboration. The platform was designed to perform within acceptable web response times for viewing, annotating, and processing high resolution microscopy images.

Results:

We describe a microscopy-driven approach to tissue analysis by IMS.

Conclusion:

The Pathology Interface for Mass Spectrometry is designed to provide clinical access to IMS technology and deliver enhanced diagnostic value.

The Need for Speed in Matrix-Assisted Laser Desorption/Ionization Imaging Mass Spectrometry

Imaging mass spectrometry (IMS) has emerged as a powerful analytical tool enabling the direct molecular mapping of many types of tissue. Specifically, matrix-assisted laser desorption/ ionization (MALDI) represents one of the most broadly applicable IMS technologies. In recent years, advances in solid state laser technology, mass spectrometry instrumentation, computer technology, and experimental methodology have produced IMS systems capable of unprecedented data acquisition speeds (>50 pixels/second). In applications of this technology, throughput is an important consideration when designing an IMS experiment. As IMS becomes more widely adopted, continual improvements in experimental setups will be important to address biologically and clinically relevant time scales.