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

Comparative Quantitative Proteomic Analysis of Melanoma Subtypes, Nevus-Associated Melanoma, and Corresponding Nevi

A substantial part of cutaneous malignant melanomas develops from benign nevi. However, the precise molecular events driving the transformation from benign to malignant melanoma are not well-understood. We used laser microdissection and mass spectrometry to analyze the proteomes of melanoma subtypes, including superficial spreading melanomas (n = 17), nodular melanomas (n = 17), and acral melanomas (n = 15). Furthermore, we compared the proteomes of nevi cells with those of melanoma cells within the same specimens (nevus-associated melanoma (n = 14)). In total, we quantified 7935 proteins. Despite the genomic and clinical differences of the melanoma subtypes, our analysis revealed relatively similar proteomes, except for the upregulation of proteins involved in immune activation in nodular melanomas versus acral melanomas. Examining nevus-associated melanoma versus nevi, we found 1725 differentially expressed proteins (false discovery rate < 0.05). Among these proteins were 140 that overlapped with cancer hallmarks, tumor suppressors, and regulators of metabolism and cell cycle. Pathway analysis indicated aberrant activation of the phosphoinositide 3-kinase-protein kinase B-mTOR pathways and the Hippo-YAP pathway. Using a classifier, we identified six proteins capable of distinguishing melanoma from nevi samples. Our study represents a comprehensive comparative analysis of the proteome in melanoma subtypes and associated nevi, offering insights into the biological behavior of these distinct entities.

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.

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.

Mapping the single cell spatial immune landscapes of the melanoma microenvironment

Melanoma is a highly immunogenic malignancy with an elevated mutational burden, diffuse lymphocytic infiltration, and one of the highest response rates to immune checkpoint inhibitors (ICIs). However, over half of all late-stage patients treated with ICIs will either not respond or develop progressive disease. Spatial imaging technologies are being increasingly used to study the melanoma tumor microenvironment (TME). The goal of such studies is to understand the complex interplay between the stroma, melanoma cells, and immune cell-types as well as their association with treatment response. Investigators seeking a better understanding of the role of cell location within the TME and the importance of spatial expression of biomarkers are increasingly turning to highly multiplexed imaging approaches to more accurately measure immune infiltration as well as to quantify receptor-ligand interactions (such as PD-1 and PD-L1) and cell-cell contacts. CyTOF-IMC (Cytometry by Time of Flight - Imaging Mass Cytometry) has enabled high-dimensional profiling of melanomas, allowing researchers to identify complex cellular subpopulations and immune cell interactions with unprecedented resolution. Other spatial imaging technologies, such as multiplexed immunofluorescence and spatial transcriptomics, have revealed distinct patterns of immune cell infiltration, highlighting the importance of spatial relationships, and their impact in modulating immunotherapy responses. Overall, spatial imaging technologies are just beginning to transform our understanding of melanoma biology, providing new avenues for biomarker discovery and therapeutic development. These technologies hold great promise for advancing personalized medicine to improve patient outcomes in melanoma and other solid malignancies.

Next-generation pathology practices with mass spectrometry imaging

Mass spectrometry imaging (MSI) is a powerful technique that reveals the spatial distribution of various molecules in biological samples, and it is widely used in pathology-related research. In this review, we summarize common MSI techniques, including matrix-assisted laser desorption/ionization and desorption electrospray ionization MSI, and their applications in pathological research, including disease diagnosis, microbiology, and drug discovery. We also describe the improvements of MSI, focusing on the accumulation of imaging data sets, expansion of chemical coverage, and identification of biological significant molecules, that have prompted the evolution of MSI to meet the requirements of pathology practices. Overall, this review details the applications and improvements of MSI techniques, demonstrating the potential of integrating MSI techniques into next-generation pathology practices.

Molecular Classifiers in Skin Cancers: Challenges and Promises

Skin cancers are common and heterogenous malignancies affecting up to two in three Australians before age 70. Despite recent developments in diagnosis and therapeutic strategies, the mortality rate and costs associated with managing patients with skin cancers remain high. The lack of well-defined clinical and histopathological features makes their diagnosis and classification difficult in some cases and the prognostication difficult in most skin cancers. Recent advancements in large-scale "omics" studies, including genomics, transcriptomics, proteomics, metabolomics and imaging-omics, have provided invaluable information about the molecular and visual landscape of skin cancers. On many occasions, it has refined tumor classification and has improved prognostication and therapeutic stratification, leading to improved patient outcomes. Therefore, this paper reviews the recent advancements in omics approaches and appraises their limitations and potential for better classification and stratification of skin cancers.

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.

Genomic and proteomic findings in early melanoma and opportunities for early diagnosis

Overdiagnosis of early melanoma is a significant problem. Due to subtle unique and overlapping clinical and histological criteria between pigmented lesions and the risk of mortality from melanoma, some benign pigmented lesions are diagnosed as melanoma. Although histopathology is the gold standard to diagnose melanoma, there is a demand to find alternatives that are more accurate and cost-effective. In the current "omics" era, there is gaining interest in biomarkers to help diagnose melanoma early and to further understand the mechanisms driving tumor progression. Genomic investigations have attempted to differentiate malignant melanoma from benign pigmented lesions. However, genetic biomarkers of early melanoma diagnosis have not yet proven their value in the clinical setting. Protein biomarkers may be more promising since they directly influence tissue phenotype, a result of by-products of genomic mutations, posttranslational modifications and environmental factors. Uncovering relevant protein biomarkers could increase confidence in their use as diagnostic signatures. Currently, proteomic investigations of melanoma progression from pigmented lesions are limited. Studies have previously characterised the melanoma proteome from cultured cell lines and clinical samples such as serum and tissue. This has been useful in understanding how melanoma progresses into metastasis and development of resistance to adjuvant therapies. Currently, most studies focus on metastatic melanoma to find potential drug therapy targets, prognostic factors and markers of resistance. This paper reviews recent advancements in the genomics and proteomic fields and reports potential avenues, which could help identify and differentiate melanoma from benign pigmented lesions and prevent the progression of melanoma.

Negative Ion-Mode N-Glycan Mass Spectrometry Imaging by MALDI-2-TOF-MS

Matrix-assisted laser desorption/ionization mass spectrometry imaging with laser-induced postionization (MALDI-2-MSI) has proven a powerful tool for the in situ analysis of N-linked glycosylation, or N-glycans, directly from clinical tissue samples. Here we describe a sample preparation protocol for the analysis of N-glycans from formalin-fixed, paraffin-embedded tissue sections.

Global trends of research on depression in breast cancer: A bibliometric study based on VOSviewer

Background

Depression is common psychiatric morbidity in breast cancer survivors, seriously affecting patients’ quality of life and mental health. A growing body of research has investigated depression in breast cancer. However, no visual bibliometric analysis was conducted in this field. This study aimed to visualize the literature to identify hotspots and frontiers in research on breast cancer and depression.

Methods

The publications related to depression in breast cancer were retrieved in the Web of Science Core Collection between 1 January 2002 and 17 March 2022. VOSviewer was used to identify co-occurrences and collaborations among countries, institutions, and keywords. CiteSpace was used to detect keyword bursts.

Results

A total of 7,350 articles and reviews related to depression in breast cancer were identified. From 2002 to 2022, the United States and the People’s Republic of China were the most productive countries in this field. The University of California, Los Angeles, and the University of Toronto were the most productive institutions in this field. The Journal of Psycho-oncology, followed by Supportive Care in Cancer and the Journal of Clinical Oncology, had the most publications on depression in breast cancer. Of the top 10 journals, seven were from the United States, two were from England, and one was from Germany. Five research hotspots of depression in breast cancer were identified by co-word analysis. Research on post-traumatic growth, spiritual interventions, cognitive-behavioral therapy, physical activity, and symptom cluster management of depression in breast cancer was relatively mature in the core hotspots. Burst detection of keywords on depression in breast cancer showed the latest hotspots, such as mental health, cancer survivor mortality, and activity.

Conclusion

The research on depression in breast cancer is growing. Attention should be paid to the latest hotspots, such as mental health, cancer survivor, mortality, exercise, and physical activity.

Multi-Class Cancer Subtyping in Salivary Gland Carcinomas with MALDI Imaging and Deep Learning

Salivary gland carcinomas (SGC) are a heterogeneous group of tumors. The prognosis varies strongly according to its type, and even the distinction between benign and malign tumor is challenging. Adenoid cystic carcinoma (AdCy) is one subgroup of SGCs that is prone to late metastasis. This makes accurate tumor subtyping an important task. Matrix-assisted laser desorption/ionization (MALDI) imaging is a label-free technique capable of providing spatially resolved information about the abundance of biomolecules according to their mass-to-charge ratio. We analyzed tissue micro arrays (TMAs) of 25 patients (including six different SGC subtypes and a healthy control group of six patients) with high mass resolution MALDI imaging using a 12-Tesla magnetic resonance mass spectrometer. The high mass resolution allowed us to accurately detect single masses, with strong contributions to each class prediction. To address the added complexity created by the high mass resolution and multiple classes, we propose a deep-learning model. We showed that our deep-learning model provides a per-class classification accuracy of greater than 80% with little preprocessing. Based on this classification, we employed methods of explainable artificial intelligence (AI) to gain further insights into the spectrometric features of AdCys.

Multicenter Evaluation of Tissue Classification by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

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.