Novel Prognostic Markers in Triple-Negative Breast Cancer Discovered by MALDI-Mass Spectrometry Imaging

Molecular analysis of the extracellular microenvironment: from form to function

The extracellular matrix (ECM) proteome represents an important component of the tissue microenvironment that controls chemical flux and induces cell signaling through encoded structure. The analysis of the ECM represents an analytical challenge through high levels of post-translational modifications, protease-resistant structures, and crosslinked, insoluble proteins. This review provides a comprehensive overview of the analytical challenges involved in addressing the complexities of spatially profiling the extracellular matrix proteome. A synopsis of the process of synthesizing the ECM structure, detailing inherent chemical complexity, is included to present the scope of the analytical challenge. Current chromatographic and spatial techniques addressing these challenges are detailed. Capabilities for multimodal multiplexing with cellular populations are discussed with a perspective on developing a holistic view of disease processes that includes both the cellular and extracellular microenvironment.

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.

MALDI MS Imaging of Chickpea Seeds (Cicer arietinum) and Crab's Eye Vine (Abrus precatorius) after Tryptic Digestion Allows Spatially Resolved Identification of Plant Proteins

Matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) imaging following in situ enzymatic digestion is a versatile analytical method for the untargeted investigation of protein distributions, which has rarely been used for plants so far. The present study describes a workflow for in situ tryptic digestion of plant seed tissue for MALDI MS imaging. Substantial modifications to the sample preparation procedure for mammalian tissues were necessary to cater to the specific properties of plant materials. For the first time, distributions of tryptic peptides were successfully visualized in plant tissue using MS imaging with accurate mass detection. Sixteen proteins were visualized and identified in chickpea seeds showing different distribution patterns, e.g., in the cotyledons, radicle, or testa. All tryptic peptides were detected with a mass resolution higher than 60,000 as well as a mass accuracy better than 1.5 ppm root-mean-square error and were matched to results from complementary liquid chromatography-MS/MS (LC-MS/MS) data. The developed method was also applied to crab's eye vine seeds for targeted MS imaging of the toxic protein abrin, showing the presence of abrin-a in all compartments. Abrin (59 kDa), as well as the majority of proteins visualized in chickpeas, was larger than 50 kDa and would thus not be readily accessible by top-down MS imaging. Since antibodies for plant proteins are often not readily available, in situ digestion MS imaging provides unique information, as it makes the distribution and identification of larger proteins in plant tissues accessible in an untargeted manner. This opens up new possibilities in the field of plant science as well as to assess the nutritional quality and/or safety of crops.

Dynamic tumor microenvironment, molecular heterogeneity, and distinct immunologic portrait of triple-negative breast cancer: an impact on classification and treatment approaches

Heterogeneity of the tumor microenvironment (TME) and the lack of a definite targetable receptor in triple-negative breast cancer (TNBC) has carved a niche for this cancer as a particularly therapeutically challenging form of breast cancer. However, recent advances in high-throughput genomic analysis have provided new insights into the unique microenvironment and defining characteristics of various subsets of TNBC. This improved understanding has contributed to the development of novel therapeutic strategies including targeted therapies such as PARP inhibitors and CDK inhibitors. Moreover, the recent FDA approval of the immune checkpoint inhibitor against programmed cell death protein 1 (PD-1), pembrolizumab and atezolizumab, holds the promise of improving the quality of life and increasing the overall survival of TNBC patients. This recent approval is one of the many therapeutically novel strategies that are currently being exploited in clinical trials toward eventual contribution to the oncologist's toolbox against TNBC. In this review, we comprehensively discuss TNBC's distinct TME and its immunophenotype. Furthermore, we highlight the histological and molecular classification of this cancer. More importantly, we describe how these characteristics and classifications contribute to the current standards of care and how they steer the development of newer and more targeted therapies toward achieving peak therapeutic goals in the treatment of TNBC.

Characterization of Hormone Receptor and HER2 Status in Breast Cancer Using Mass Spectrometry Imaging

Immunohistochemical evaluation of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 status stratify the different subtypes of breast cancer and define the treatment course. Triple-negative breast cancer (TNBC), which does not register receptor overexpression, is often associated with worse patient prognosis. Mass spectrometry imaging transcribes the molecular content of tissue specimens without requiring additional tags or preliminary analysis of the samples, being therefore an excellent methodology for an unbiased determination of tissue constituents, in particular tumor markers. In this study, the proteomic content of 1191 human breast cancer samples was characterized by mass spectrometry imaging and the epithelial regions were employed to train and test machine-learning models to characterize the individual receptor status and to classify TNBC. The classification models presented yielded high accuracies for estrogen and progesterone receptors and over 95% accuracy for classification of TNBC. Analysis of the molecular features revealed that vimentin overexpression is associated with TNBC, supported by immunohistochemistry validation, revealing a new potential target for diagnosis and treatment.

Emerging Computational Methods in Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.

Proteomics-Based Identification of Dysregulated Proteins in Breast Cancer

Immunohistochemistry (IHC) is still widely used as a morphology-based assay for in situ analysis of target proteins as specific tumor antigens. However, as a very heterogeneous collection of neoplastic diseases, breast cancer (BC) requires an accurate identification and characterization of larger panels of candidate biomarkers, beyond ER, PR, and HER2 proteins, for diagnosis and personalized treatment, without the limited availability of antibodies that are required to identify specific proteins. Top-down, middle-down, and bottom-up mass spectrometry (MS)-based proteomics approaches complement traditional histopathological tissue analysis to examine expression, modification, and interaction of hundreds to thousands of proteins simultaneously. In this review, we discuss the proteomics-based identification of dysregulated proteins in BC that are essential for the following issues: discovery and validation of new biomarkers by analysis of solid and liquid/non-invasive biopsies, cell lines, organoids and xenograft models; identification of panels of biomarkers for early detection and accurate discrimination between cancer, benign and normal tissues; identification of subtype-specific and stage-specific protein expression profiles in BC grading and measurement of disease progression; characterization of new subtypes of BC; characterization and quantitation of post-translational modifications (PTMs) and aberrant protein-protein interactions (PPI) involved in tumor development; characterization of the global remodeling of BC tissue homeostasis, diagnosis and prognostic information; and deciphering of molecular functions, biological processes and mechanisms through which the dysregulated proteins cause tumor initiation, invasion, and treatment resistance.

Investigation of the effects of downregulation of jumping translocation breakpoint (JTB) protein expression in MCF7 cells for potential use as a biomarker in breast cancer

MCF7 is a commonly used luminal type A non-invasive/poor-invasive human breast cancer cell line that does not usually migrate or invade compared with MDA-MB-231 highly metastatic cells, which emphasize an invasive and migratory behavior. Under special conditions, MCF7 cells might acquire invasive features. The aberration in expression and biological functions of the jumping translocation breackpoint (JTB) protein is associated with malignant transformation of cells, based on mitochondrial dysfunction, inhibition of tumor suppressive function of TGF-β, and involvement in cancer cell cycle. To investigate new putative functions of JTB by cellular proteomics, we analyzed the biological processes and pathways that are associated with the JTB protein downregulation. The results demonstrated that MCF7 cell line developed a more "aggressive" phenotype and behavior. Most of the proteins that were overexpressed in this experiment promoted the actin cytoskeleton reorganization that is involved in growth and metastatic dissemination of cancer cells. Some of these proteins are involved in the epithelial-mesenchymal transition (EMT) process (ACTBL2, TUBA4A, MYH14, CSPG5, PKM, UGDH, HSP90AA2, and MIF), in correlation with the energy metabolism reprogramming (PKM, UGDH), stress-response (HSP10, HSP70A1A, HSP90AA2), and immune and inflammatory response (MIF and ERp57-TAPBP). Almost all upregulated proteins in JTB downregulated condition promote viability, motility, proliferation, invasion, survival into a hostile microenvironment, metabolic reprogramming, and escaping of tumor cells from host immune control, leading to a more invasive phenotype for MCF7 cell line. Due to their downregulated condition, four proteins, such as CREBZF, KMT2B, SELENOS and CACNA1I are also involved in maintenance of the invasive phenotype of cancer cells, promoting cell proliferation, migration, invasion and tumorigenesis. Other downregulated proteins, such as MAZ, PLEKHG2, ENO1, TPI2, TOR2A, and CNNM1, may promote suppression of cancer cell growth, invasion, EMT, tumorigenic abilities, interacting with glucose and lipid metabolism, disrupting nuclear envelope stability, or suppressing apoptosis and developing anti-angiogenetic activities. Therefore, the main biological processes and pathways that may increase the tumorigenic potential of the MCF7 cells in JTB downregulated condition are related to the actin cytoskeleton organization, EMT, mitotic cell cycle, glycolysis and fatty acid metabolism, inflammatory response and macrophage activation, chemotaxis and migration, cellular response to stress condition (oxidative stress and hypoxia), transcription control, histone modification and ion transport.

Proteomics approaches to characterize the immune responses in cancer

Despite the dynamic development of cancer research, annually millions of people die of cancer. The human immune system is the major 'guard' against tumor development. Unfortunately, cancer cells have the ability to evade the immune system and continue to grow. The proper understanding of the intricate immune response in tumorigenesis remains the holy grail of cancer immunology and designing effective immunotherapy. To decode the immune responses in cancer, in recent years, proteomics studies have received considerable attention. Proteomics studies focus on the detection and quantification of proteins, which are the effectors of biological functions, and as such, are proven to reflect the cell state more accurately, in comparison to genomic or transcriptomic studies. In this review, we discuss the proteomics studies applied to characterize the immune responses in cancer and tumor immune microenvironment heterogeneity. Further, we describe emerging single-cell proteomics approaches that have the potential to be applied in cancer immunity studies.

What are we imaging? Software tools and experimental strategies for annotation and identification of small molecules in mass spectrometry imaging

Mass spectrometry imaging (MSI) has become a widespread analytical technique to perform nonlabeled spatial molecular identification. The Achilles' heel of MSI is the annotation and identification of molecular species due to intrinsic limitations of the technique (lack of chromatographic separation and the difficulty to apply tandem MS). Successful strategies to perform annotation and identification combine extra analytical steps, like using orthogonal analytical techniques to identify compounds; with algorithms that integrate the spectral and spatial information. In this review, we discuss different experimental strategies and bioinformatics tools to annotate and identify compounds in MSI experiments. We target strategies and tools for small molecule applications, such as lipidomics and metabolomics. First, we explain how sample preparation and the acquisition process influences annotation and identification, from sample preservation to the use of orthogonal techniques. Then, we review twelve software tools for annotation and identification in MSI. Finally, we offer perspectives on two current needs of the MSI community: the adaptation of guidelines for communicating confidence levels in identifications; and the creation of a standard format to store and exchange annotations and identifications in MSI.

Mass Spectrometry Imaging Spatial Tissue Analysis toward Personalized Medicine

Novel profiling methodologies are redefining the diagnostic capabilities and therapeutic approaches towards more precise and personalized healthcare. Complementary information can be obtained from different omic approaches in combination with the traditional macro- and microscopic analysis of the tissue, providing a more complete assessment of the disease. Mass spectrometry imaging, as a tissue typing approach, provides information on the molecular level directly measured from the tissue. Lipids, metabolites, glycans, and proteins can be used for better understanding imbalances in the DNA to RNA to protein translation, which leads to aberrant cellular behavior. Several studies have explored the capabilities of this technology to be applied to tumor subtyping, patient prognosis, and tissue profiling for intraoperative tissue evaluation. In the future, intercenter studies may provide the needed confirmation on the reproducibility, robustness, and applicability of the developed classification models for tissue characterization to assist in disease management.

Multiplex Tissue Imaging: Spatial Revelations in the Tumor Microenvironment

Simple Summary

Cancer is the leading cause of death worldwide, and the overall aging of the population results in an increased risk of a cancer diagnosis during a person’s lifetime. Diagnosis and treatment at an early stage will typically increase the chances of survival. Tumors can develop therapy resistance, and it is difficult to predict how individual patients will respond to therapy. Most studies that aim to resolve this problem have focused on studying the composition and characteristics of dissociated tumors, while ignoring the role of cell localization and interactions within the tumor microenvironment. In the past decade, technological innovations have enabled multiplex imaging analyses of intact tumors to study localization and interaction parameters, which can be used as biomarkers, or can be correlated with treatment responses and clinical outcomes.

Abstract

The tumor microenvironment is a complex ecosystem containing various cell types, such as immune cells, fibroblasts, and endothelial cells, which interact with the tumor cells. In recent decades, the cancer research field has gained insight into the cellular subtypes that are involved in tumor microenvironment heterogeneity. Moreover, it has become evident that cellular interactions in the tumor microenvironment can either promote or inhibit tumor development, progression, and drug resistance, depending on the context. Multiplex spatial analysis methods have recently been developed; these have offered insight into how cellular crosstalk dynamics and heterogeneity affect cancer prognoses and responses to treatment. Multiplex (imaging) technologies and computational analysis methods allow for the spatial visualization and quantification of cell–cell interactions and properties. These technological advances allow for the discovery of cellular interactions within the tumor microenvironment and provide detailed single-cell information on properties that define cellular behavior. Such analyses give insights into the prognosis and mechanisms of therapy resistance, which is still an urgent problem in the treatment of multiple types of cancer. Here, we provide an overview of multiplex imaging technologies and concepts of downstream analysis methods to investigate cell–cell interactions, how these studies have advanced cancer research, and their potential clinical implications.

Histamine H4 Receptor Expression in Triple-negative Breast Cancer: An Exploratory Study

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype. There are neither universally accepted prognostic markers nor molecular targets related to TNBC. The histamine H4 receptor (H4R) has been characterized in TNBC experimental models, demonstrating its critical role in tumor development and progression. In this study, H4R expression was compared in breast cancer subtypes and correlated with clinical features using The Cancer Genome Atlas data (Pan-Cancer Atlas). The H4R status was further evaluated by immunohistochemistry in 30 TNBC human samples in relation to clinicopathological parameters. Results indicate that H4R was downregulated in basal-like/TNBC compared with luminal A and normal breast-like tumors. The higher expression of H4R was associated with improved progression-free and overall survival outcomes in basal-like/TNBC. H4R immunoreactivity was detected in about 70% of tumors, and its expression was positively correlated with the levels in the histologically normal peritumoral tissue. High H4R expression in peritumoral tissue correlated with reduced number of lymph node involvement and unifocal TNBC, while it was associated with increased patient survival. In conclusion, the H4R might represent a potential prognostic biomarker in TNBC. Further studies in large cohorts are needed to better understand the significance of H4R in breast cancer biology.

Tissue sample preparations for preclinical research determined by molecular imaging mass spectrometry using MALDI

Matrix-assisted laser desorption/ionization - imaging mass spectrometry is an alternative tool, which can be implemented in order to obtain and visualize the "omic" signature of tissue samples. Its application to clinical study enables simultaneous imaging-based morphological observations and mass spectrometry analysis. Application of fully informative material like tissue, allows to obtain the complex and unique profile of analyzed samples. This knowledge leads to diagnose disease, study the mechanism of cancer development, select the potential biomarkers as well as correlating obtained image with prognosis. Nevertheless, it is worth to notice that this method is found to be objective but the result of analysis is mainly influenced by the sample preparation protocol, included collection of biological material, its preservation and processing. However, application of this approach requires a special sample preparation procedure. The main goal of the study is to present the current knowledge on the clinical application of matrix-assisted laser desorption/ionization - imaging mass spectrometry in cancer research, with particular emphasis on the sample preparation step. For this purpose, several protocols based on cryosections and formalin-fixed paraffin embedded tissue were compiled and compared, taking into account the measured metabolites of potential diagnostic importance for a given type of cancer. This article is protected by copyright. All rights reserved.

Applications of spatially resolved omics in the field of endocrine tumors

Endocrine tumors derive from endocrine cells with high heterogeneity in function, structure and embryology, and are characteristic of a marked diversity and tissue heterogeneity. There are still challenges in analyzing the molecular alternations within the heterogeneous microenvironment for endocrine tumors. Recently, several proteomic, lipidomic and metabolomic platforms have been applied to the analysis of endocrine tumors to explore the cellular and molecular mechanisms of tumor genesis, progression and metastasis. In this review, we provide a comprehensive overview of spatially resolved proteomics, lipidomics and metabolomics guided by mass spectrometry imaging and spatially resolved microproteomics directed by microextraction and tandem mass spectrometry. In this regard, we will discuss different mass spectrometry imaging techniques, including secondary ion mass spectrometry, matrix-assisted laser desorption/ionization and desorption electrospray ionization. Additionally, we will highlight microextraction approaches such as laser capture microdissection and liquid microjunction extraction. With these methods, proteins can be extracted precisely from specific regions of the endocrine tumor. Finally, we compare applications of proteomic, lipidomic and metabolomic platforms in the field of endocrine tumors and outline their potentials in elucidating cellular and molecular processes involved in endocrine tumors.

Proteomics and its applications in breast cancer

Breast cancer is an individually unique, multi-faceted and chameleonic disease, an eternal challenge for the new era of high-integrated precision diagnostic and personalized oncomedicine. Besides traditional single-omics fields (such as genomics, epigenomics, transcriptomics and metabolomics) and multi-omics contributions (proteogenomics, proteotranscriptomics or reproductomics), several new "-omics" approaches and exciting proteomics subfields are contributing to basic and advanced understanding of these "multiple diseases termed breast cancer": phenomics/cellomics, connectomics and interactomics, secretomics, matrisomics, exosomics, angiomics, chaperomics and epichaperomics, phosphoproteomics, ubiquitinomics, metalloproteomics, terminomics, degradomics and metadegradomics, adhesomics, stressomics, microbiomics, immunomics, salivaomics, materiomics and other biomics. Throughout the extremely complex neoplastic process, a Breast Cancer Cell Continuum Concept (BCCCC) has been modeled in this review as a spatio-temporal and holistic approach, as long as the breast cancer represents a complex cascade comprising successively integrated populations of heterogeneous tumor and cancer-associated cells, that reflect the carcinoma's progression from a "driving mutation" and formation of the breast primary tumor, toward the distant secondary tumors in different tissues and organs, via circulating tumor cell populations. This BCCCC is widely sustained by a Breast Cancer Proteomic Continuum Concept (BCPCC), where each phenotype of neoplastic and tumor-associated cells is characterized by a changing and adaptive proteomic profile detected in solid and liquid minimal invasive biopsies by complex proteomics approaches. Such a profile is created, beginning with the proteomic landscape of different neoplastic cell populations and cancer-associated cells, followed by subsequent analysis of protein biomarkers involved in epithelial-mesenchymal transition and intravasation, circulating tumor cell proteomics, and, finally, by protein biomarkers that highlight the extravasation and distant metastatic invasion. Proteomics technologies are producing important data in breast cancer diagnostic, prognostic, and predictive biomarkers discovery and validation, are detecting genetic aberrations at the proteome level, describing functional and regulatory pathways and emphasizing specific protein and peptide profiles in human tissues, biological fluids, cell lines and animal models. Also, proteomics can identify different breast cancer subtypes and specific protein and proteoform expression, can assess the efficacy of cancer therapies at cellular and tissular level and can even identify new therapeutic target proteins in clinical studies.

Distinctive Properties of Endothelial Cells from Tumor and Normal Tissue in Human Breast Cancer

Tumor microenvironments shape aggressiveness and are largely maintained by the conditions of angiogenesis formation. Thus, endothelial cells’ (ECs) biological reactions are crucial to understand and control the design of efficient therapies. In this work, we used models of ECs to represent a breast cancer tumor site as well as the same, healthy tissue. Cells characterization was performed at the transcriptome and protein expression levels, and the cells functional biological responses (angiogenesis and permeability) were assessed. We showed that the expression of proteins specific to ECs (ACE+, VWF+), their differentiation (CD31+, CD 133+, CD105+, CD34-), their adhesion properties (ICAM-1+, VCAM-1+, CD62-L+), and their barrier formation (ZO-1+) were all downregulated in tumor-derived ECs. NGS-based differential transcriptome analysis confirmed CD31-lowered expression and pointed to the increase of Ephrin-B2 and SNCAIP, indicative of dedifferentiation. Functional assays confirmed these differences; angiogenesis was impaired while permeability increased in tumor-derived ECs, as further validated by the distinctly enhanced VEGF production in response to hypoxia, reflecting the tumor conditions. This work showed that endothelial cells differed highly significantly, both phenotypically and functionally, in the tumor site as compared to the normal corresponding tissue, thus influencing the tumor microenvironment.

The Roles of DNA Demethylases in Triple-Negative Breast Cancer

Triple-negative breast cancers (TNBCs) are very heterogenous, molecularly diverse, and are characterized by a high propensity to relapse or metastasize. Clinically, TNBC remains a diagnosis of exclusion by the lack of hormone receptors (Estrogen Receptor (ER) and Progesterone Receptor (PR)) as well as the absence of overexpression and/or amplification of HER2. DNA methylation plays an important role in breast cancer carcinogenesis and TNBCs have a distinct DNA methylation profile characterized by marked hypomethylation and lower gains of methylations compared to all other subtypes. DNA methylation is regulated by the balance of DNA methylases (DNMTs) and DNA demethylases (TETs). Here, we review the roles of TETs as context-dependent tumor-suppressor genes and/or oncogenes in solid tumors, and we discuss the current understandings of the oncogenic role of TET1 and its therapeutic implications in TNBCs.

Mass spectrometry-based chemical mapping and profiling toward molecular understanding of diseases in precision medicine

Precision medicine has been strongly promoted in recent years. It is used in clinical management for classifying diseases at the molecular level and for selecting the most appropriate drugs or treatments to maximize efficacy and minimize adverse effects. In precision medicine, an in-depth molecular understanding of diseases is of great importance. Therefore, in the last few years, much attention has been given to translating data generated at the molecular level into clinically relevant information. However, current developments in this field lack orderly implementation. For example, high-quality chemical research is not well integrated into clinical practice, especially in the early phase, leading to a lack of understanding in the clinic of the chemistry underlying diseases. In recent years, mass spectrometry (MS) has enabled significant innovations and advances in chemical research. As reported, this technique has shown promise in chemical mapping and profiling for answering "what", "where", "how many" and "whose" chemicals underlie the clinical phenotypes, which are assessed by biochemical profiling, MS imaging, molecular targeting and probing, biomarker grading disease classification, etc. These features can potentially enhance the precision of disease diagnosis, monitoring and treatment and thus further transform medicine. For instance, comprehensive MS-based biochemical profiling of ovarian tumors was performed, and the results revealed a number of molecular insights into the pathways and processes that drive ovarian cancer biology and the ways that these pathways are altered in correspondence with clinical phenotypes. Another study demonstrated that quantitative biomarker mapping can be predictive of responses to immunotherapy and of survival in the supposedly homogeneous group of breast cancer patients, allowing for stratification of patients. In this context, our article attempts to provide an overview of MS-based chemical mapping and profiling, and a perspective on their clinical utility to improve the molecular understanding of diseases for advancing precision medicine.

Alternative splicing acts as an independent prognosticator in ovarian carcinoma

Alternative splicing (AS) events associated with oncogenic processes present anomalous perturbations in many cancers, including ovarian carcinoma. There are no reliable features to predict survival outcomes for ovarian cancer patients. In this study, comprehensive profiling of AS events was conducted by integrating AS data and clinical information of ovarian serous cystadenocarcinoma (OV). Survival-related AS events were identified by Univariate Cox regression analysis. Then, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were used to construct the prognostic signatures within each AS type. Furthermore, we established a splicing-related network to reveal the potential regulatory mechanisms between splicing factors and candidate AS events. A total of 730 AS events were identified as survival-associated splicing events, and the final prognostic signature based on all seven types of AS events could serve as an independent prognostic indicator and had powerful efficiency in distinguishing patient outcomes. In addition, survival-related AS events might be involved in tumor-related pathways including base excision repair and pyrimidine metabolism pathways, and some splicing factors might be correlated with prognosis-related AS events, including SPEN, SF3B5, RNPC3, LUC7L3, SRSF11 and PRPF38B. Our study constructs an independent prognostic signature for predicting ovarian cancer patients’ survival outcome and contributes to elucidating the underlying mechanism of AS in tumor development.

Implementation of Mass Spectrometry Imaging in Pathology: Advances and Challenges

Mass spectrometry imaging (MSI) combines the excellence in molecular characterization of mass spectrometry with microscopic imaging capabilities of hematoxylin- and eosin-stained samples, enabling the precise location of several analytes in the tissue. Especially in the field of pathology, MSI may have an impactful role in tumor diagnosis, biomarker identification, prognostic prediction, and characterization of tumor margins during tumor resection procedures. This article discusses the recent developments in the field that are paving the way for this technology to become accepted as an analytical tool in the clinical setting, its current limitations, and future directions.

The matrix in cancer

The extracellular matrix is a fundamental, core component of all tissues and organs, and is essential for the existence of multicellular organisms. From the earliest stages of organism development until death, it regulates and fine-tunes every cellular process in the body. In cancer, the extracellular matrix is altered at the biochemical, biomechanical, architectural and topographical levels, and recent years have seen an exponential increase in the study and recognition of the importance of the matrix in solid tumours. Coupled with the advancement of new technologies to study various elements of the matrix and cell-matrix interactions, we are also beginning to see the deployment of matrix-centric, stromal targeting cancer therapies. This Review touches on many of the facets of matrix biology in solid cancers, including breast, pancreatic and lung cancer, with the aim of highlighting some of the emerging interactions of the matrix and influences that the matrix has on tumour onset, progression and metastatic dissemination, before summarizing the ongoing work in the field aimed at developing therapies to co-target the matrix in cancer and cancer metastasis.

INFORM: INFrared-based ORganizational Measurements of tumor and its microenvironment to predict patient survival

The structure and organization of a tumor and its microenvironment are often associated with cancer outcomes due to spatially varying molecular composition and signaling. A persistent challenge is to use this physical and chemical spatial organization to understand cancer progression. Here, we present a high-definition infrared imaging-based organizational measurement framework (INFORM) that leverages intrinsic chemical contrast of tissue to label unique components of the tumor and its microenvironment. Using objective and automated computational methods, further, we determine organization characteristics important for prediction. We show that the tumor spatial organization assessed with this framework is predictive of overall survival in colon cancer that adds to capability from clinical variables such as stage and grade, approximately doubling the risk of death in high-risk individuals. Our results open an all-digital avenue for measuring and studying the association between tumor spatial organization and disease progression.

Uncovering Tumor-Stroma Inter-relationships Using MALDI Mass Spectrometry Imaging

Tumorigenesis involves a complex interplay between genetically modified cancer cells and their adjacent normal tissue, the stroma. We used an established breast cancer mouse model to investigate this inter-relationship. Conditional activation of Rho-associated protein kinase (ROCK) in a model of mammary tumorigenesis enhances tumor growth and progression by educating the stroma and enhancing the production and remodeling of the extracellular matrix. We used peptide matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to quantify the proteomic changes occurring within tumors and their stroma in their regular spatial context. Peptides were ranked according to their ability to discriminate between the two groups, using a receiver operating characteristic tool. Peptides were identified by liquid chromatography tandem mass spectrometry, and protein expression was validated by quantitative immunofluorescence using an independent set of tumor samples. We have identified and validated four key proteins upregulated in ROCK-activated mammary tumors relative to those expressing kinase-dead ROCK, namely, collagen I, α-SMA, Rab14, and tubulin-β4. Rab14 and tubulin-β4 are expressed within tumor cells, whereas collagen I is localized within the stroma. α-SMA is predominantly localized within the stroma but is also expressed at higher levels in the epithelia of ROCK-activated tumors. High expression of COL1A, the gene encoding the pro-α 1 chain of collagen, correlates with cancer progression in two human breast cancer genomic data sets, and high expression of COL1A and ACTA2 (the gene encoding α-SMA) are associated with a low survival probability (COLIA, p = 0.00013; ACTA2, p = 0.0076) in estrogen receptor-negative breast cancer patients. To investigate whether ROCK-activated tumor cells cause stromal cancer-associated fibroblasts (CAFs) to upregulate expression of collagen I and α-SMA, we treated CAFs with medium conditioned by primary mammary tumor cells in which ROCK had been activated. This led to abundant production of both proteins in CAFs, clearly highlighting the inter-relationship between tumor cells and CAFs and identifying CAFs as the potential source of high levels of collagen 1 and α-SMA and associated enhancement of tissue stiffness. Our research emphasizes the capacity of MALDI-MSI to quantitatively assess tumor-stroma inter-relationships and to identify potential prognostic factors for cancer progression in human patients, using sophisticated mouse cancer models.

Implementation of MALDI Mass Spectrometry Imaging in Cancer Proteomics Research: Applications and Challenges

Studying the proteome–the entire set of proteins in cells, tissues, organs and body fluids—is of great relevance in cancer research, as differential forms of proteins are expressed in response to specific intrinsic and extrinsic signals. Discovering protein signatures/pathways responsible for cancer transformation may lead to a better understanding of tumor biology and to a more effective diagnosis, prognosis, recurrence and response to therapy. Moreover, proteins can act as a biomarker or potential drug targets. Hence, it is of major importance to implement proteomic, particularly mass spectrometric, approaches in cancer research, to provide new crucial insights into tumor biology. Recently, mass spectrometry imaging (MSI) approaches were implemented in cancer research, to provide individual molecular characteristics of each individual tumor while retaining molecular spatial distribution, essential in the context of personalized disease management and medicine.

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.

Applications of Mass Spectrometry for Clinical Diagnostics: The Influence of Turnaround Time

This critical review discusses how the need for reduced clinical turnaround times has influenced chemical instrumentation. We focus on the development of modern mass spectrometry (MS) and its application in clinical diagnosis. With increased functionality that takes advantage of novel front-end modifications and computational capabilities, MS can now be used for non-traditional clinical analyses, including applications in clinical microbiology for bacteria differentiation and in surgical operation rooms. We summarize here recent developments in the field that have enabled such capabilities, which include miniaturization for point-of-care testing, direct complex mixture analysis via ambient ionization, chemical imaging and profiling, and systems integration.

Pathway activity profiling of growth factor receptor network and stemness pathways differentiates metaplastic breast cancer histological subtypes

BACKGROUND

Gene expression profiling of rare cancers has proven challenging due to limited access to patient materials and requirement of intact, non-degraded RNA for next-generation sequencing. We customized a gene expression panel compatible with degraded RNA from formalin-fixed, paraffin-embedded (FFPE) patient cancer samples and investigated its utility in pathway activity profiling in patients with metaplastic breast cancer (MpBC).

METHODS

Activity of various biological pathways was profiled in samples from nineteen patients with MpBC and 8 patients with invasive ductal carcinoma with triple negative breast cancer (TNBC) phenotype using a custom gene expression-based assay of 345 genes.

RESULTS

MpBC samples of mesenchymal (chondroid and/or osteoid) histology demonstrated increased SNAI1 and BCL2L11 pathway activity compared to samples with non-mesenchymal histology. Additionally, late cornified envelope and keratinization genes were downregulated in MpBC compared to TNBC, and epithelial-to-mesenchymal transition (EMT) and collagen genes were upregulated in MpBC. Patients with high activity of an invasiveness gene expression signature, as well as high expression of the mesenchymal marker and extracellular matrix glycoprotein gene SPARC, experienced worse outcomes than those with low invasiveness activity and low SPARC expression.

CONCLUSIONS

This study demonstrates the utility of gene expression profiling of metaplastic breast cancer FFPE samples with a custom counts-based assay. Gene expression patterns identified by this assay suggest that, although often histologically triple negative, patients with MpBC have distinct pathway activation compared to patients with invasive ductal TNBC. Incorporation of targeted therapies may lead to improved outcome for MpBC patients, especially in those patients expressing increased activity of invasiveness pathways.