De novo discovery of phenotypic intratumour heterogeneity using imaging mass spectrometry

Advancing rare cancer research by MALDI mass spectrometry imaging: Applications, challenges, and future perspectives in sarcoma

MALDI mass spectrometry imaging (MALDI imaging) uniquely advances cancer research, by measuring spatial distribution of endogenous and exogenous molecules directly from tissue sections. These molecular maps provide valuable insights into basic and translational cancer research, including tumor biology, tumor microenvironment, biomarker identification, drug treatment, and patient stratification. Despite its advantages, MALDI imaging is underutilized in studying rare cancers. Sarcomas, a group of malignant mesenchymal tumors, pose unique challenges in medical research due to their complex heterogeneity and low incidence, resulting in understudied subtypes with suboptimal management and outcomes. In this review, we explore the applicability of MALDI imaging in sarcoma research, showcasing its value in understanding this highly heterogeneous and challenging rare cancer. We summarize all MALDI imaging studies in sarcoma to date, highlight their impact on key research fields, including molecular signatures, cancer heterogeneity, and drug studies. We address specific challenges encountered when employing MALDI imaging for sarcomas, and propose solutions, such as using formalin-fixed paraffin-embedded tissues, and multiplexed experiments, and considerations for multi-site studies and digital data sharing practices. Through this review, we aim to spark collaboration between MALDI imaging researchers and clinical colleagues, to deploy the unique capabilities of MALDI imaging in the context of sarcoma.

Metabolic heterogeneity affects trastuzumab response and survival in HER2-positive advanced gastric cancer

BACKGROUND

Trastuzumab is the only first-line treatment targeted against the human epidermal growth factor receptor 2 (HER2) approved for patients with HER2-positive advanced gastric cancer. The impact of metabolic heterogeneity on trastuzumab treatment efficacy remains unclear.

METHODS

Spatial metabolomics via high mass resolution imaging mass spectrometry was performed in pretherapeutic biopsies of patients with HER2-positive advanced gastric cancer in a prospective multicentre observational study. The mass spectra, representing the metabolic heterogeneity within tumour areas, were grouped by K-means clustering algorithm. Simpson's diversity index was applied to compare the metabolic heterogeneity level of individual patients.

RESULTS

Clustering analysis revealed metabolic heterogeneity in HER2-positive gastric cancer patients and uncovered nine tumour subpopulations. High metabolic heterogeneity was shown as a factor indicating sensitivity to trastuzumab (p = 0.008) and favourable prognosis at trend level. Two of the nine tumour subpopulations associated with favourable prognosis and trastuzumab sensitivity, and one subpopulation associated with poor prognosis and trastuzumab resistance.

CONCLUSIONS

This work revealed that tumour metabolic heterogeneity associated with prognosis and trastuzumab response based on tissue metabolomics of HER2-positive gastric cancer. Tumour metabolic subpopulations may provide an association with trastuzumab therapy efficacy.

CLINICAL TRIAL REGISTRATION

The patient cohort was conducted from a multicentre observational study (VARIANZ;NCT02305043).

Low-melting point agarose as embedding medium for MALDI mass spectrometry imaging and laser-capture microdissection-based proteomics

The combination of MALDI mass spectrometry imaging, laser-capture microdissection, and quantitative proteomics allows the identification and characterization of molecularly distinct tissue compartments. Such workflows are typically performed using consecutive tissue sections, and so reliable sectioning and mounting of high-quality tissue sections is a prerequisite of such investigations. Embedding media facilitate the sectioning process but can introduce contaminants which may adversely affect either the mass spectrometry imaging or proteomics analyses. Seven low-temperature embedding media were tested in terms of embedding temperature and cutting performance. The two media that provided the best results (5% gelatin and 2% low-melting point agarose) were compared with non-embedded tissue by both MALDI mass spectrometry imaging of lipids and laser-capture microdissection followed by bottom-up proteomics. Two out of the seven tested media (5% gelatin and 2% low-melting point agarose) provided the best performances on terms of mechanical properties. These media allowed for low-temperature embedding and for the collection of high-quality consecutive sections. Comparisons with non-embedded tissues revealed that both embedding media had no discernable effect on proteomics analysis; 5% gelatin showed a light ion suppression effect in the MALDI mass spectrometry imaging experiments, 2% agarose performed similarly to the non-embedded tissue. 2% low-melting point agarose is proposed for tissue embedding in experiments involving MALDI mass spectrometry imaging of lipids and laser-capture microdissection, proteomics of consecutive tissue sections.

Metabolic heterogeneity in adrenocortical carcinoma impacts patient outcomes

Spatially resolved metabolomics enables the investigation of tumoral metabolites in situ. Inter- and intratumor heterogeneity are key factors associated with patient outcomes. Adrenocortical carcinoma (ACC) is an exceedingly rare tumor associated with poor survival. Its clinical prognosis is highly variable, but the contributions of tumor metabolic heterogeneity have not been investigated thus far to our knowledge. An in-depth understanding of tumor heterogeneity requires molecular feature-based identification of tumor subpopulations associated with tumor aggressiveness. Here, using spatial metabolomics by high-mass resolution MALDI Fourier transform ion cyclotron resonance mass spectrometry imaging, we assessed metabolic heterogeneity by de novo discovery of metabolic subpopulations and Simpson's diversity index. After identification of tumor subpopulations in 72 patients with ACC, we additionally performed a comparison with 25 tissue sections of normal adrenal cortex to identify their common and unique metabolic subpopulations. We observed variability of ACC tumor heterogeneity and correlation of high metabolic heterogeneity with worse clinical outcome. Moreover, we identified tumor subpopulations that served as independent prognostic factors and, furthermore, discovered 4 associated anticancer drug action pathways. Our research may facilitate comprehensive understanding of the biological implications of tumor subpopulations in ACC and showed that metabolic heterogeneity might impact chemotherapy.

Systematic Review: Urine Biomarker Discovery for Inflammatory Bowel Disease Diagnosis

Inflammatory bowel diseases (IBDs) are chronic, heterogeneous, and inflammatory conditions mainly affecting the gastrointestinal tract. Currently, endoscopy is the gold standard test for assessing mucosal activity and healing in clinical practice; however, it is a costly, time-consuming, invasive, and uncomfortable procedure for the patients. Therefore, there is an urgent need for sensitive, specific, fast and non-invasive biomarkers for the diagnosis of IBD in medical research. Urine is an excellent biofluid for discovering biomarkers because it is non-invasive to sample. In this review, we aimed to summarize proteomics and metabolomics studies performed in both animal models of IBD and humans that identify urinary biomarkers for IBD diagnosis. Future large-scale multi-omics studies should be conducted in collaboration with clinicians, researchers, and industry to make progress toward the development of sensitive and specific diagnostic biomarkers, thereby making personalized medicine possible.

MALDI Imaging, a Powerful Multiplex Approach to Decipher Intratumoral Heterogeneity: Combined Hepato-Cholangiocarcinomas as Proof of Concept

Combined hepato-cholangiocarcinomas (cHCC-CCA) belong to the spectrum of primary liver carcinomas, which include hepatocellular carcinomas (HCC) and intrahepatic cholangiocarcinomas (iCCA) at both ends of the spectrum. Mainly due to the high intratumor heterogeneity of cHCC-CCA, its diagnosis and pathological description remain challenging. Taking advantage of in situ non-targeted molecular mapping provided by MALDI (Matrix Assisted Laser Desorption Ionization) imaging, we sought to develop a multiscale and multiparametric morphological approach, integrating molecular and conventional pathological analysis. MALDI imaging was applied to five representative cases of resected cHCC-CCA. Principal component analysis and segmentations with MALDI imaging techniques identified areas related to either iCCA or HCC and also hidden tumor areas not visible microscopically. In addition, the overlap between MALDI segmentation and immunostaining provided a comprehensive description of cHCC-CCA tumor heterogeneity by identifying transitional and micro-metastatic areas. Moreover, a list of peptides derived from in silico digestion was obtained for each immunohistochemical marker and was matched within the peptide peak list acquired by MALDI. Comparison of immunostaining images with ions from in silico digestion revealed an accurate identification of iCCA and HCC areas. Our study provides further evidence on the performance of MALDI imaging in exploring intratumor heterogeneity and offering virtual multiplex immunostaining through a single acquisition.

Lipidomic Profiling of Colorectal Lesions for Real-Time Tissue Recognition and Risk-Stratification Using Rapid Evaporative Ionization Mass Spectrometry

OBJECTIVE

Rapid evaporative ionization mass spectrometry (REIMS) is a metabolomic technique analyzing tissue metabolites, which can be applied intraoperatively in real-time. The objective of this study was to profile the lipid composition of colorectal tissues using REIMS, assessing its accuracy for real-time tissue recognition and risk-stratification.

SUMMARY BACKGROUND DATA

Metabolic dysregulation is a hallmark feature of carcinogenesis; however, it remains unknown if this can be leveraged for real-time clinical applications in colorectal disease.

METHODS

Patients undergoing colorectal resection were included, with carcinoma, adenoma and paired-normal mucosa sampled. Ex vivo analysis with REIMS was conducted using monopolar diathermy, with the aerosol aspirated into a Xevo G2S QToF mass spectrometer. Negatively charged ions over 600 to 1000 m/z were used for univariate and multivariate functions including linear discriminant analysis.

RESULTS

A total of 161 patients were included, generating 1013 spectra. Unique lipidomic profiles exist for each tissue type, with REIMS differentiating samples of carcinoma, adenoma, and normal mucosa with 93.1% accuracy and 96.1% negative predictive value for carcinoma. Neoplasia (carcinoma or adenoma) could be predicted with 96.0% accuracy and 91.8% negative predictive value. Adenomas can be risk-stratified by grade of dysplasia with 93.5% accuracy, but not histological subtype. The structure of 61 lipid metabolites was identified, revealing that during colorectal carcinogenesis there is progressive increase in relative abundance of phosphatidylglycerols, sphingomyelins, and mono-unsaturated fatty acid-containing phospholipids.

CONCLUSIONS

The colorectal lipidome can be sampled by REIMS and leveraged for accurate real-time tissue recognition, in addition to riskstratification of colorectal adenomas. Unique lipidomic features associated with carcinogenesis are described.

High-multiplex tissue imaging in routine pathology-are we there yet?

High-multiplex tissue imaging (HMTI) approaches comprise several novel immunohistological methods that enable in-depth, spatial single-cell analysis. Over recent years, studies in tumor biology, infectious diseases, and autoimmune conditions have demonstrated the information gain accessible when mapping complex tissues with HMTI. Tumor biology has been a focus of innovative multiparametric approaches, as the tumor microenvironment (TME) contains great informative value for accurate diagnosis and targeted therapeutic approaches: unraveling the cellular composition and structural organization of the TME using sophisticated computational tools for spatial analysis has produced histopathologic biomarkers for outcomes in breast cancer, predictors of positive immunotherapy response in melanoma, and histological subgroups of colorectal carcinoma. Integration of HMTI technologies into existing clinical workflows such as molecular tumor boards will contribute to improve patient outcomes through personalized treatments tailored to the specific heterogeneous pathological fingerprint of cancer, autoimmunity, or infection. Here, we review the advantages and limitations of existing HMTI technologies and outline how spatial single-cell data can improve our understanding of pathological disease mechanisms and determinants of treatment success. We provide an overview of the analytic processing and interpretation and discuss how HMTI can improve future routine clinical diagnostic and therapeutic processes.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

Applications of MALDI-MS/MS-Based Proteomics in Biomedical Research

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) is one of the most widely used techniques in proteomics to achieve structural identification and characterization of proteins and peptides, including their variety of proteoforms due to post-translational modifications (PTMs) or protein–protein interactions (PPIs). MALDI-MS and MALDI tandem mass spectrometry (MS/MS) have been developed as analytical techniques to study small and large molecules, offering picomole to femtomole sensitivity and enabling the direct analysis of biological samples, such as biofluids, solid tissues, tissue/cell homogenates, and cell culture lysates, with a minimized procedure of sample preparation. In the last decades, structural identification of peptides and proteins achieved by MALDI-MS/MS helped researchers and clinicians to decipher molecular function, biological process, cellular component, and related pathways of the gene products as well as their involvement in pathogenesis of diseases. In this review, we highlight the applications of MALDI ionization source and tandem approaches for MS for analyzing biomedical relevant peptides and proteins. Furthermore, one of the most relevant applications of MALDI-MS/MS is to provide “molecular pictures”, which offer in situ information about molecular weight proteins without labeling of potential targets. Histology-directed MALDI-mass spectrometry imaging (MSI) uses MALDI-ToF/ToF or other MALDI tandem mass spectrometers for accurate sequence analysis of peptide biomarkers and biological active compounds directly in tissues, to assure complementary and essential spatial data compared with those obtained by LC-ESI-MS/MS technique.

Prognostic Value of Molecular Intratumor Heterogeneity in Primary Oral Cancer and Its Lymph Node Metastases Assessed by Mass Spectrometry Imaging

Different aspects of intra-tumor heterogeneity (ITH), which are associated with the development of cancer and its response to treatment, have postulated prognostic value. Here we searched for potential association between phenotypic ITH analyzed by mass spectrometry imaging (MSI) and prognosis of head and neck cancer. The study involved tissue specimens resected from 77 patients with locally advanced oral squamous cell carcinoma, including 37 patients where matched samples of primary tumor and synchronous lymph node metastases were analyzed. A 3-year follow-up was available for all patients which enabled their separation into two groups: with no evidence of disease (NED, n = 41) and with progressive disease (PD, n = 36). After on-tissue trypsin digestion, peptide maps of all cancer regions were segmented using an unsupervised approach to reveal their intrinsic heterogeneity. We found that intra-tumor similarity of spectra was higher in the PD group and diversity of clusters identified during image segmentation was higher in the NED group, which indicated a higher level of ITH in patients with more favorable outcomes. Signature of molecular components that correlated with long-term outcomes could be associated with proteins involved in the immune functions. Furthermore, a positive correlation between ITH and histopathological lymphocytic host response was observed. Hence, we proposed that a higher level of ITH revealed by MSI in cancers with a better prognosis could reflect the presence of heterotypic components of tumor microenvironment such as infiltrating immune cells enhancing the response to the treatment.

Mass spectrometry imaging identifies altered hepatic lipid signatures during experimental Leishmania donovani infection

Introduction

Spatial analysis of lipids in inflammatory microenvironments is key to understand the pathogenesis of infectious disease. Granulomatous inflammation is a hallmark of leishmaniasis and changes in host and parasite lipid metabolism have been observed at the bulk tissue level in various infection models. Here, mass spectrometry imaging (MSI) is applied to spatially map hepatic lipid composition following infection with Leishmania donovani, an experimental mouse model of visceral leishmaniasis.

Methods

Livers from naïve and L. donovani-infected C57BL/6 mice were harvested at 14- and 20-days post-infection (n=5 per time point). 12 µm transverse sections were cut and covered with norhamane, prior to lipid analysis using MALDI-MSI. MALDI-MSI was performed in negative mode on a Rapiflex (Bruker Daltonics) at 5 and 50 µm spatial resolution and data-dependent analysis (DDA) on an Orbitrap-Elite (Thermo-Scientific) at 50 µm spatial resolution for structural identification analysis of lipids.

Results

Aberrant lipid abundances were observed in a heterogeneous distribution across infected mouse livers compared to naïve mouse liver. Distinctive localized correlated lipid masses were found in granulomas and surrounding parenchymal tissue. Structural identification revealed 40 different lipids common to naïve and d14/d20 infected mouse livers, whereas 15 identified lipids were only detected in infected mouse livers. For pathology-guided MSI imaging, we deduced lipids from manually annotated granulomatous and parenchyma regions of interests (ROIs), identifying 34 lipids that showed significantly different intensities between parenchyma and granulomas across all infected livers.

Discussion

Our results identify specific lipids that spatially correlate to the major histopathological feature of Leishmania donovani infection in the liver, viz. hepatic granulomas. In addition, we identified a three-fold increase in the number of unique phosphatidylglycerols (PGs) in infected liver tissue and provide direct evidence that arachidonic acid-containing phospholipids are localized with hepatic granulomas. These phospholipids may serve as important precursors for downstream oxylipin generation with consequences for the regulation of the inflammatory cascade. This study provides the first description of the use of MSI to define spatial-temporal lipid changes at local sites of infection induced by Leishmania donovani in mice.

Protein Profiling of Breast Cancer for Treatment Decision-Making

The increasing use of neoadjuvant therapy has resulted in therapeutic decisions being made on the basis of diagnostic needle core biopsy. For many patients, this method might yield the only fragment of tumor available for biomarker analysis, necessitating judicious use. Many multiplex protein analytic methods have been developed that employ fluorescence or other tags to overcome the limitations of immunohistochemistry while still retaining the spatial annotation. Interpretation of the data can be difficult because of the limitations of the human eye. Computational deconvolution of the signals may be necessary for some of these methods to enable identification of cell-specific localization and coexpression of biomarkers. Herein, we present the different methods that are coming of age and their application in cancer research, with a focus on breast cancer. We also discuss the limitations, which include high costs and long turnaround times. The methods are also based on the premise that preanalytical factors will have identical impact on all proteins analyzed. There is a need to establish standards to normalize the data and enable cross-sample comparisons. In spite of these limitations, the multiplex technologies are extremely valuable discovery tools and can provide novel insights into the biology of cancer and mechanisms of drug resistance.

Moving translational mass spectrometry imaging towards transparent and reproducible data analyses: a case study of an urothelial cancer cohort analyzed in the Galaxy framework

Background

Mass spectrometry imaging (MSI) derives spatial molecular distribution maps directly from clinical tissue specimens and thus bears great potential for assisting pathologists with diagnostic decisions or personalized treatments. Unfortunately, progress in translational MSI is often hindered by insufficient quality control and lack of reproducible data analysis. Raw data and analysis scripts are rarely publicly shared. Here, we demonstrate the application of the Galaxy MSI tool set for the reproducible analysis of a urothelial carcinoma dataset.

Methods

Tryptic peptides were imaged in a cohort of 39 formalin-fixed, paraffin-embedded human urothelial cancer tissue cores with a MALDI-TOF/TOF device. The complete data analysis was performed in a fully transparent and reproducible manner on the European Galaxy Server. Annotations of tumor and stroma were performed by a pathologist and transferred to the MSI data to allow for supervised classifications of tumor vs. stroma tissue areas as well as for muscle-infiltrating and non-muscle infiltrating urothelial carcinomas. For putative peptide identifications, m/z features were matched to the MSiMass list.

Results

Rigorous quality control in combination with careful pre-processing enabled reduction of m/z shifts and intensity batch effects. High classification accuracy was found for both, tumor vs. stroma and muscle-infiltrating vs. non-muscle infiltrating urothelial tumors. Some of the most discriminative m/z features for each condition could be assigned a putative identity: stromal tissue was characterized by collagen peptides and tumor tissue by histone peptides. Immunohistochemistry confirmed an increased histone H2A abundance in the tumor compared to the stroma tissues. The muscle-infiltration status was distinguished via MSI by peptides from intermediate filaments such as cytokeratin 7 in non-muscle infiltrating carcinomas and vimentin in muscle-infiltrating urothelial carcinomas, which was confirmed by immunohistochemistry. To make the study fully reproducible and to advocate the criteria of FAIR (findability, accessibility, interoperability, and reusability) research data, we share the raw data, spectra annotations as well as all Galaxy histories and workflows. Data are available via ProteomeXchange with identifier PXD026459 and Galaxy results via https://github.com/foellmelanie/Bladder_MSI_Manuscript_Galaxy_links.

Conclusion

Here, we show that translational MSI data analysis in a fully transparent and reproducible manner is possible and we would like to encourage the community to join our efforts.

massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation

MOTIVATION

Mass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computational challenges in its analysis. The complexity of the data arises from its large size, high-dimensionality and spectral nonlinearity. Preprocessing, including peak picking, has been used to reduce raw data complexity; however, peak picking is sensitive to parameter selection that, perhaps prematurely, shapes the downstream analysis for tissue classification and ensuing biological interpretation.

RESULTS

We propose a deep learning model, massNet, that provides the desired qualities of scalability, nonlinearity and speed in MSI data analysis. This deep learning model was used, without prior preprocessing and peak picking, to classify MSI data from a mouse brain harboring a patient-derived tumor. The massNet architecture established automatically learning of predictive features, and automated methods were incorporated to identify peaks with potential for tumor delineation. The model's performance was assessed using cross-validation, and the results demonstrate higher accuracy and a substantial gain in speed compared to the established classical machine learning method, support vector machine.

AVAILABILITY AND IMPLEMENTATION

https://github.com/wabdelmoula/massNet. The data underlying this article are available in the NIH Common Fund's National Metabolomics Data Repository (NMDR) Metabolomics Workbench under project id (PR001292) with http://dx.doi.org/10.21228/M8Q70T.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging

Simple Summary

Classic histopathological examination of tissues remains the mainstay for cancer diagnosis and staging. However, in some cases histopathologic analysis yields ambiguous results, leading to inconclusive disease classification. We set out to explore the diagnostic potential of mass spectrometry-based imaging for tumour classification based on proteomic fingerprints. Combining mass spectrometry with supervised machine learning, we were able to distinguish colorectal tumor from normal tissue with an overall accuracy of 98%. In addition, this approach was able to predict the presence of lymph node metastasis in primary tumour of endometrial cancer with an overall accuracy of 80%. These results highlight the potential of this technology to determine the optimal treatment for cancer patients to reduce morbidity and improve patients’ outcomes.

Abstract

Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) can determine the spatial distribution of analytes such as protein distributions in a tissue section according to their mass-to-charge ratio. Here, we explored the clinical potential of machine learning (ML) applied to MALDI MSI data for cancer diagnostic classification using tissue microarrays (TMAs) on 302 colorectal (CRC) and 257 endometrial cancer (EC)) patients. ML based on deep neural networks discriminated colorectal tumour from normal tissue with an overall accuracy of 98% in balanced cross-validation (98.2% sensitivity and 98.6% specificity). Moreover, our machine learning approach predicted the presence of lymph node metastasis (LNM) for primary tumours of EC with an accuracy of 80% (90% sensitivity and 69% specificity). Our results demonstrate the capability of MALDI MSI for complementing classic histopathological examination for cancer diagnostic applications.

Molecular tissue profiling by MALDI imaging: recent progress and applications in cancer research

Matrix-assisted laser desorption/ionization (MALDI) imaging is an emergent technology that has been increasingly adopted in cancer research. MALDI imaging is capable of providing global molecular mapping of the abundance and spatial information of biomolecules directly in the tissues without labeling. It enables the characterization of a wide spectrum of analytes, including proteins, peptides, glycans, lipids, drugs, and metabolites and is well suited for both discovery and targeted analysis. An advantage of MALDI imaging is that it maintains tissue integrity, which allows correlation with histological features. It has proven to be a valuable tool for probing tumor heterogeneity and has been increasingly applied to interrogate molecular events associated with cancer. It provides unique insights into both the molecular content and spatial details that are not accessible by other techniques, and it has allowed considerable progress in the field of cancer research. In this review, we first provide an overview of the MALDI imaging workflow and approach. We then highlight some useful applications in various niches of cancer research, followed by a discussion of the challenges, recent developments and future prospect of this technique in the field.

Intra-Tumor Heterogeneity Revealed by Mass Spectrometry Imaging Is Associated with the Prognosis of Breast Cancer

Intra-tumor heterogeneity (ITH) results from the coexistence of genetically distinct cancer cell (sub)populations, their phenotypic plasticity, and the presence of heterotypic components of the tumor microenvironment (TME). Here we addressed the potential association between phenotypic ITH revealed by mass spectrometry imaging (MSI) and the prognosis of breast cancer. Tissue specimens resected from 59 patients treated radically due to the locally advanced HER2-positive invasive ductal carcinoma were included in the study. After the on-tissue trypsin digestion of cellular proteins, peptide maps of all cancer regions (about 380,000 spectra in total) were segmented by an unsupervised approach to reveal their intrinsic heterogeneity. A high degree of similarity between spectra was observed, which indicated the relative homogeneity of cancer regions. However, when the number and diversity of the detected clusters of spectra were analyzed, differences between patient groups were observed. It is noteworthy that a higher degree of heterogeneity was found in tumors from patients who remained disease-free during a 5-year follow-up (n = 38) compared to tumors from patients with progressive disease (distant metastases detected during the follow-up, n = 21). Interestingly, such differences were not observed between patients with a different status of regional lymph nodes, cancer grade, or expression of estrogen receptor at the time of the primary treatment. Subsequently, spectral components with different abundance in cancer regions were detected in patients with different outcomes, and their hypothetical identity was established by assignment to measured masses of tryptic peptides identified in corresponding tissue lysates. Such differentiating components were associated with proteins involved in immune regulation and hemostasis. Further, a positive correlation between the level of tumor-infiltrating lymphocytes and heterogeneity revealed by MSI was observed. We postulate that a higher heterogeneity of tumors with a better prognosis could reflect the presence of heterotypic components including infiltrating immune cells, that facilitated the response to treatment.

Spatial epi-proteomics enabled by histone post-translational modification analysis from low-abundance clinical samples

BACKGROUND

Increasing evidence linking epigenetic mechanisms and different diseases, including cancer, has prompted in the last 15 years the investigation of histone post-translational modifications (PTMs) in clinical samples. Methods allowing the isolation of histones from patient samples followed by the accurate and comprehensive quantification of their PTMs by mass spectrometry (MS) have been developed. However, the applicability of these methods is limited by the requirement for substantial amounts of material.

RESULTS

To address this issue, in this study we streamlined the protein extraction procedure from low-amount clinical samples and tested and implemented different in-gel digestion strategies, obtaining a protocol that allows the MS-based analysis of the most common histone PTMs from laser microdissected tissue areas containing as low as 1000 cells, an amount approximately 500 times lower than what is required by available methods. We then applied this protocol to breast cancer patient laser microdissected tissues in two proof-of-concept experiments, identifying differences in histone marks in heterogeneous regions selected by either morphological evaluation or MALDI MS imaging.

CONCLUSIONS

These results demonstrate that analyzing histone PTMs from very small tissue areas and detecting differences from adjacent tumor regions is technically feasible. Our method opens the way for spatial epi-proteomics, namely the investigation of epigenetic features in the context of tissue and tumor heterogeneity, which will be instrumental for the identification of novel epigenetic biomarkers and aberrant epigenetic mechanisms.

Tissue Proteomic Approaches to Understand the Pathogenesis of Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) has become a global disease encompassing a group of progressive disorders characterized by recurrent chronic inflammation of the gut with variable disease courses and complications. Despite recent advances in the knowledge of IBD pathophysiology, the elucidation of its etiopathology and progression is far from fully understood, requiring complex and multiple approaches. Therefore, limited clinical progress in diagnosis, assessment of disease activity, and optimal therapeutic regimens have been made over the past few decades. This review explores recent advances and challenges in tissue proteomics with an emphasis on biomarker discovery and better understanding of the molecular mechanisms underlying IBD pathogenesis. Future multi-omic studies are required for the comprehensive molecular characterization of disease biology in real time with a future impact on early detection, disease monitoring, and prediction of the clinical outcome.

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.

Interactive Estimation of Heterogeneity from Mass Spectrometry Imaging

In this work, we demonstrate a new approach for interactively assessing hyperspectral data spatial structures for heterogeneity using mass spectrometry imaging. This approach is based on the visualization of the cosine distance as the similarity levels between mass spectra of a chosen region and the rest of the image (sample). The applicability of the method is demonstrated on a set of mass spectrometry images of frontal mouse brain slices. Selection of the reference pixel of the mass spectrometric image and a further view of the corresponding cosine distance map helps to prepare supporting vectors for further analysis, select features, and carry out biological interpretation of different tissues in the mass spectrometry context with or without histological annotation. Visual inspection of the similarity maps reveals the spatial distribution of features in tissue samples, which can serve as the molecular histological annotation of a slide.

N-Glycomic Signature of Stage II Colorectal Cancer and Its Association With the Tumor Microenvironment

The choice for adjuvant chemotherapy in stage II colorectal cancer is controversial as many patients are cured by surgery alone and it is difficult to identify patients with high risk of recurrence of the disease. There is a need for better stratification of this group of patients. Mass spectrometry imaging could identify patients at risk. We report here the N-glycosylation signatures of the different cell populations in a group of stage II colorectal cancer tissue samples. The cancer cells, compared with normal epithelial cells, have increased levels of sialylation and high-mannose glycans, as well as decreased levels of fucosylation and highly branched N-glycans. When looking at the interface between cancer and its microenvironment, it seems that the cancer N-glycosylation signature spreads into the surrounding stroma at the invasive front of the tumor. This finding was more outspoken in patients with a worse outcome within this sample group.

In Situ Metabolomics Expands the Spectrum of Renal Tumours Positive on 99mTc-sestamibi Single Photon Emission Computed Tomography/Computed Tomography Examination

Background

Definite noninvasive characterisation of renal tumours positive on ^99mTc-sestamibi single photon emission computed tomography/computed tomography (SPECT/CT) examination including renal oncocytomas (ROs), hybrid oncocytic chromophobe tumours (HOCTs), and chromophobe renal cell carcinoma (chRCC) is currently not feasible.

Objective

To investigate whether combined ^99mTc-sestamibi SPECT/CT and in situ metabolomic profiling can accurately characterise renal tumours exhibiting ^99mTc-sestamibi uptake.

Design, setting, and participants

A tissue microarray analysis of 33 tumour samples from 28 patients was used to investigate whether their in situ metabolomic status correlates with their features on ^99mTc-sestamibi SPECT/CT examination. In order to validate emerging data, an independent cohort comprising 117 tumours was subjected to matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI MSI).

Outcome measurements and statistical analysis

MALDI MSI data analysis and image generation were facilitated by FlexImaging v. 4.2, while k-means analysis by SCiLS Lab software followed by R-package CARRoT analysis was used for assessing the highest predictive power in the differential of RO versus chRCC. Heatmap-based clustering, sparse partial least-squares discriminant analysis, and volcano plots were created with MetaboAnalyst 3.0.

Results and limitations

We identified a discriminatory metabolomic signature for ^99mTc-sestamibi SPECT/CT–positive Birt-Hogg-Dubè–associated HOCTs versus other renal oncocytic tumours. Metabolomic differences were also evident between ^99mTc-sestamibi–positive and ^99mTc-sestamibi–negative chRCCs, prompting additional expert review; two of three ^99mTc-sestamibi–positive chRCCs were reclassified as low-grade oncocytic tumours (LOTs). Differences were identified between distal-derived tumours from those of proximal tubule origin, including differences between ROs and chRCCs.

Conclusions

The current study expands the spectrum of ^99mTc-sestamibi SPECT/CT–positive renal tumours, encompassing ROs, HOCTs, LOTs, and chRCCs, and supports the feasibility of in situ metabolomic profiling in the diagnostics and classification of renal tumours.

Patient summary

For preoperative evaluation of solid renal tumours, ^99mTc-sestamibi single photon emission computed tomography/computed tomography (SPECT/CT) is a novel examination method. To increase diagnostic accuracy, we propose that ^99mTc-sestamibi–positive renal tumours should be biopsied and followed by a combined histometabolomic analysis.

A methodological approach to correlate tumor heterogeneity with drug distribution profile in mass spectrometry imaging data

BACKGROUND

Drug mass spectrometry imaging (MSI) data contain knowledge about drug and several other molecular ions present in a biological sample. However, a proper approach to fully explore the potential of such type of data is still missing. Therefore, a computational pipeline that combines different spatial and non-spatial methods is proposed to link the observed drug distribution profile with tumor heterogeneity in solid tumor. Our data analysis steps include pre-processing of MSI data, cluster analysis, drug local indicators of spatial association (LISA) map, and ions selection.

RESULTS

The number of clusters identified from different tumor tissues. The spatial homogeneity of the individual cluster was measured using a modified version of our drug homogeneity method. The clustered image and drug LISA map were simultaneously analyzed to link identified clusters with observed drug distribution profile. Finally, ions selection was performed using the spatially aware method.

CONCLUSIONS

In this paper, we have shown an approach to correlate the drug distribution with spatial heterogeneity in untargeted MSI data. Our approach is freely available in an R package 'CorrDrugTumorMSI'.

137Cs γ Ray and 28Si Irradiation Induced Murine Hepatocellular Carcinoma Lipid Changes in Liver Assessed by MALDI-MSI Combined with Spatial Shrunken Centroid Clustering Algorithm: A Pilot Study

Characterization of lipids by matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) is of great interest because not only are lipids important structural molecules in both the cell and internal organelle membranes, but they are also important signaling molecules. MALDI-MSI combined with spatial image segmentation has been previously used to identify tumor heterogeneities within tissues with distinct anatomical regions such as the brain. However, there has been no systematic study utilizing MALDI-MSI combined with spatial image segmentation to assess the tumor microenvironment in the liver. Here, we present that image segmentation can be used to evaluate the tumor microenvironment in the liver. In particular, to better understand the molecular mechanisms of irradiation-induced hepatic carcinogenesis, we used MALDI-MSI in the negative ion mode to identify lipid changes 12 months post exposure to low dose ²⁸Si and ¹³⁷Cs γ ray irradiation. We report here the changes in the lipid profiles of male C3H/HeNCrl mice liver tissues after exposure to irradiation and analyzed using the spatial shrunken centroid clustering algorithm. These findings provide valuable information as astronauts will be exposed to high-charge high-energy (HZE) particles and low-energy γ-ray irradiation during deep space travel. Even at low doses, exposure to these irradiations can lead to cancer. Previous studies infer that irradiation of mice with low-dose HZE particles induces oxidative damage and microenvironmental changes that are thought to play roles in the pathophysiology of hepatocellular carcinoma.

Approaching sites of action of drugs in clinical pharmacology: New analytical options and their challenges

Clinical pharmacology is an important discipline for drug development aiming to define pharmacokinetics (PK), pharmacodynamics (PD) and optimum exposure to drugs, i.e. the concentration-response relationship and its modulators. For this purpose, information on drug concentrations at the anatomical, cellular and molecular sites of action is particularly valuable. In pharmacological assays, the limited accessibility of target cells in readily available samples (i.e. blood) often hampers mass spectrometry-based monitoring of the absolute quantity of a compound and the determination of its molecular action at the cellular level. Recently, new sample collection methods have been developed for the specific capture of rare circulating cells, especially for the diagnosis of circulating tumour cells. In parallel, new advances and developments in mass spectrometric instrumentation now allow analyses to be scaled down to the cellular level. Together, these developments may permit the monitoring of minute drug quantities and show their effect at the cellular level. In turn, such PK/PD associations on a cellular level would not only enrich our pharmacological knowledge of a given compound but also expand the basis for PK/PD simulations. In this review, we describe novel concepts supporting clinical pharmacology at the anatomical, cellular and molecular sites of action, and highlight the new challenges in mass spectrometry-based monitoring. Moreover, we present methods to tackle these challenges and define future needs.

Molecular signatures of tumor progression in myxoid liposarcoma identified by N-glycan mass spectrometry imaging

Myxoid liposarcoma (MLS) is the second most common subtype of liposarcoma, accounting for ~6% of all sarcomas. MLS is characterized by a pathognomonic FUS-DDIT3, or rarely EWSR1-DDIT3, gene fusion. The presence of ≥5% hypercellular round cell areas is associated with a worse prognosis for the patient and is considered high grade. The prognostic significance of areas with moderately increased cellularity (intermediate) is currently unknown. Here we have applied matrix-assisted laser desorption/ionization mass spectrometry imaging to analyze the spatial distribution of N-linked glycans on an MLS microarray in order to identify molecular markers for tumor progression. Comparison of the N-glycan profiles revealed that increased relative abundances of high-mannose type glycans were associated with tumor progression. Concomitantly, an increase of the average number of mannoses on high-mannose glycans was observed. Although overall levels of complex-type glycans decreased, an increase of tri- and tetra-antennary N-glycans was observed with morphological tumor progression and increased tumor histological grade. The high abundance of tri-antennary N-glycan species was also associated with poor disease-specific survival. These findings mirror recent observations in colorectal cancer, breast cancer, ovarian cancer, and cholangiocarcinoma, and are in line with a general role of high-mannose glycans and higher-antennary complex-type glycans in cancer progression.

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.

Spatial UBE2N protein expression indicates genomic instability in colorectal cancers

BACKGROUND

One major hallmark of colorectal cancers (CRC) is genomic instability with its contribution to tumor heterogeneity and therapy resistance. To facilitate the investigation of intra-sample phenotypes and the de novo identification of tumor sub-populations, imaging mass spectrometry (IMS) provides a powerful technique to elucidate the spatial distribution patterns of peptides and proteins in tissue sections.

METHODS

In the present study, we analyzed an in-house compiled tissue microarray (n = 60) comprising CRCs and control tissues by IMS. After obtaining protein profiles through direct analysis of tissue sections, two validation sets were used for immunohistochemical evaluation.

RESULTS

A total of 28 m/z values in the mass range 800-3500 Da distinguished euploid from aneuploid CRCs (p < 0.001, ROC AUC values < 0.385 or > 0.635). After liquid chromatograph-mass spectrometry identification, UBE2N could be successfully validated by immunohistochemistry in the initial sample cohort (p = 0.0274, ROC AUC = 0.7937) and in an independent sample set of 90 clinical specimens (p = 0.0070, ROC AUC = 0.6957).

CONCLUSIONS

The results showed that FFPE protein expression profiling of surgically resected CRC tissue extracts by MALDI-TOF MS has potential value for improved molecular classification. Particularly, the protein expression of UBE2N was validated in an independent clinical cohort to distinguish euploid from aneuploid CRCs.

Precise co-registration of mass spectrometry imaging, histology, and laser microdissection-based omics

Mass spectrometry imaging (MSI) is an analytical technique for the unlabeled and multiplex imaging of molecules in biological tissue sections. It therefore enables the spatial and molecular annotations of tissues complementary to histology. It has already been shown that MSI can guide subsequent material isolation technologies such as laser microdissection (LMD) to enable a more in-depth molecular characterization of MSI-highlighted tissue regions. However, with MSI now reaching spatial resolutions at the single-cell scale, there is a need for a precise co-registration between MSI and the LMD. As proof-of-principle, MSI of lipids was performed on a breast cancer tissue followed by a segmentation of the data to detect molecularly distinct segments within its tumor areas. After image processing of the segmentation results, the coordinates of the MSI-detected segments were passed to the LMD system by three co-registration steps. The errors of each co-registration step were quantified and the total error was found to be less than 13 μm. With this link established, MSI data can now accurately guide LMD to excise MSI-defined regions of interest for subsequent extract-based analyses. In our example, the excised tissue material was then subjected to ultrasensitive microproteomics in order to determine predominant molecular mechanisms in each of the MSI-highlighted intratumor segments. This work shows how the strengths of MSI, histology, and extract-based omics can be combined to enable a more comprehensive molecular characterization of in situ biological processes.

Colocalization Features for Classification of Tumors Using Desorption Electrospray Ionization Mass Spectrometry Imaging

Supervised modeling of mass spectrometry imaging (MSI) data is a crucial component for the detection of the distinct molecular characteristics of cancerous tissues. Currently, two types of supervised analyses are mainly used on MSI data: pixel-wise segmentation of sample images and whole-sample-based classification. A large number of mass spectra associated with each MSI sample can represent a challenge for designing models that simultaneously preserve the overall molecular content while capturing valuable information contained in the MSI data. Furthermore, intensity-related batch effects can introduce biases in the statistical models. Here we introduce a method based on ion colocalization features that allows the classification of whole tissue specimens using MSI data, which naturally preserves the spatial information associated the with the mass spectra and is less sensitive to possible batch effects. Finally, we propose data visualization strategies for the inspection of the derived networks, which can be used to assess whether the correlation differences are related to coexpression/suppression or disjoint spatial localization patterns and can suggest hypotheses based on the underlying mechanisms associated with the different classes of analyzed samples.

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.

MALDI Imaging for Proteomic Painting of Heterogeneous Tissue Structures

PURPOSE

To present matrix-assisted laser desorption/ionization (MALDI) imaging as a powerful method to highlight various tissue compartments.

EXPERIMENTAL DESIGN

Formalin-fixed paraffin-embedded (FFPE) tissue of a uterine cervix, a pancreas, a duodenum, a teratoma, and a breast cancer tissue microarray (TMA) are analyzed by MALDI imaging and by immunohistochemistry (IHC). Peptide images are visualized and analyzed using FlexImaging and SCiLS Lab software. Different histological compartments are compared by hierarchical cluster analysis.

RESULTS

MALDI imaging highlights tissue compartments comparable to IHC. In cervical tissue, normal epithelium can be discerned from intraepithelial neoplasia. In pancreatic and duodenal tissues, m/z signals from lymph follicles, vessels, duodenal mucosa, normal pancreas, and smooth muscle structures can be visualized. In teratoma, specific m/z signals to discriminate squamous epithelium, sebaceous glands, and soft tissue are detected. Additionally, tumor tissue can be discerned from the surrounding stroma in small tissue cores of TMAs. Proteomic data acquisition of complex tissue compartments in FFPE tissue requires less than 1 h with recent mass spectrometers.

CONCLUSION AND CLINICAL RELEVANCE

The simultaneous characterization of morphological and proteomic features in the same tissue section adds proteomic information for histopathological diagnostics, which relies at present on conventional hematoxylin and eosin staining, histochemical, IHC and molecular methods.

Discrimination of normal oral mucosa from oral cancer by mass spectrometry imaging of proteins and lipids

Identification of biomarkers for molecular classification of cancer and for differentiation between cancerous and normal epithelium remains a vital issue in the field of head and neck cancer. Here we aimed to compare the ability of proteome and lipidome components to discriminate oral cancer from normal mucosa. Tissue specimens including squamous cell cancer and normal epithelium were analyzed by MALDI mass spectrometry imaging. Two molecular domains of tissue components were imaged in serial sections-peptides (resulting from trypsin-processed proteins) and lipids (primarily zwitterionic phospholipids), then regions of interest corresponding to cancer and normal epithelium were compared. Heterogeneity of cancer regions was higher than the heterogeneity of normal epithelium, and the distribution of peptide components was more heterogeneous than the distribution of lipid components. Moreover, there were more peptide components than lipid components that showed significantly different abundance between cancer and normal epithelium (median of the Cohen's effect was 0.49 and 0.31 in case of peptide and lipid components, respectively). Multicomponent cancer classifier was tested (vs. normal epithelium) using tissue specimens from three patients and then validated with a tissue specimen from the fourth patient. Peptide-based signature and lipid-based signature allowed cancer classification with a weighted accuracy of 0.85 and 0.69, respectively. Nevertheless, both classifiers had very high precision (0.98 and 0.94, respectively). We concluded that though molecular differences between cancerous and normal mucosa were higher in the proteome domain than in the analyzed lipidome subdomain, imaging of lipidome components also enabled discrimination of oral cancer and normal epithelium. Therefore, both cancer proteome and lipidome are promising sources of biomarkers of oral malignancies.

Mass spectrometry imaging: How will it affect clinical research in the future?

INTRODUCTION

Mass spectrometry imaging (MSI) is a label free, multiplex imaging technology able to simultaneously record the distributions of 100's to 1000's of species, and which may be configured to study metabolites, lipids, glycans, peptides, and proteins simply by changing the tissue preparation protocol. Areas covered: The capability of MSI to complement established histopathological practice through the identification of biomarkers for differential diagnosis, patient prognosis, and response to therapy; the capability of MSI to annotate tissues on the basis of each pixel's mass spectral signature; the development of reproducible MSI through multicenter studies. Expert commentary: We discuss how MSI can be combined with microsampling/microdissection technologies in order to investigate, with more depth of coverage, the molecular changes uncovered by MSI.

Applications of MALDI-TOF mass spectrometry in clinical proteomics

INTRODUCTION

The development of precision medicine requires advanced technologies to address the multifactorial disease stratification and to support personalized treatments. Among omics techniques, proteomics based on Mass Spectrometry (MS) is becoming increasingly relevant in clinical practice allowing a phenotypic characterization of the dynamic functional status of the organism. From this perspective, Matrix Assisted Laser Desorption Ionization Time of Flight (MALDI-TOF) MS is a suitable platform for providing a high-throughput support to clinics. Areas covered: This review aims to provide an updated overview of MALDI-TOF MS applications in clinical proteomics. The most relevant features of this analysis have been discussed, highlighting both pre-analytical and analytical factors that are crucial in proteomics studies. Particular emphasis is placed on biofluids proteomics for biomarkers discovery and on recent progresses in clinical microbiology, drug monitoring, and minimal residual disease (MRD). Expert commentary: Despite some analytical limitations, the latest technological advances together with the easiness of use, the low time and low cost consuming and the high throughput are making MALDI-TOF MS instruments very attractive for the clinical practice. These features offer a significant potential for the routine of the clinical laboratory and ultimately for personalized medicine.

Multi-region proteome analysis quantifies spatial heterogeneity of prostate tissue biomarkers

Application of pressure cycling technology and Sequential Windowed Acquisition of all THeoretical mass spectrometry allows quantifying the degree of intra-tumor heterogeneity of protein expression in prostate tumors. The data show that protein intra-tumor heterogeneity, if not characterized, may distort protein biomarker suitability in tumor tissues.

It remains unclear to what extent tumor heterogeneity impacts on protein biomarker discovery. Here, we quantified proteome intra-tissue heterogeneity (ITH) based on a multi-region analysis of prostate tissues using pressure cycling technology and Sequential Windowed Acquisition of all THeoretical fragment ion mass spectrometry. We quantified 6,873 proteins and analyzed the ITH of 3,700 proteins. The level of ITH varied depending on proteins and tissue types. Benign tissues exhibited more complex ITH patterns than malignant tissues. Spatial variability of 10 prostate biomarkers was validated by immunohistochemistry in an independent cohort (n = 83) using tissue microarrays. Prostate-specific antigen was preferentially variable in benign prostatic hyperplasia, whereas growth/differentiation factor 15 substantially varied in prostate adenocarcinomas. Furthermore, we found that DNA repair pathways exhibited a high degree of variability in tumorous tissues, which may contribute to the genetic heterogeneity of tumors. This study conceptually adds a new perspective to protein biomarker discovery: it suggests that recent technological progress should be exploited to quantify and account for spatial proteome variation to complement biomarker identification and utilization.

Mass spectrometry in pathology - Vision for a future workflow

Mass spectrometric (MS) techniques are applied in various areas of medical diagnostics. For the detection of microbiological germs and genetic mutations, MS is a method used in routine. Since MS also allows the analysis of proteins and peptides, it seems an ideal candidate to supplement histopatholological diagnostics. Matrix-assisted laser desorption/ionization time-of-flight Imaging MS links molecular analysis of numerous analytes with morphological information about their spatial distribution in cells or tissues. Herein, we review principle MS techniques as well as potential applications in pathology and discuss our vision for a future workflow.

Protein drug target activation homogeneity in the face of intra-tumor heterogeneity: implications for precision medicine

Introduction: Recent studies indicated tumors may be comprised of heterogeneous molecular subtypes and incongruent molecular portraits may emerge if different areas of the tumor are sampled. This study explored the impact of intra-tumoral heterogeneity in terms of activation/phosphorylation of FDA approved drug targets and downstream kinase substrates.

Material and methods: Two independent sets of liver metastases from colorectal cancer were used to evaluate protein kinase-driven signaling networks within different areas using laser capture microdissection and reverse phase protein array.

Results: Unsupervised hierarchical clustering analysis indicated that the signaling architecture and activation of the MAPK and AKT-mTOR pathways were consistently maintained within different regions of the same biopsy. Intra-patient variability of the MAPK and AKT-mTOR pathway were <1.06 fold change, while inter-patients variability reached fold change values of 5.01.

Conclusions: Protein pathway activation mapping of enriched tumor cells obtained from different regions of the same tumor indicated consistency and robustness independent of the region sampled. This suggests a dominant protein pathway network may be activated in a high percentage of the tumor cell population. Given the genomic intra-tumoral variability, our data suggest that protein/phosphoprotein signaling measurements should be integrated with genomic analysis for precision medicine based analysis.

Pre-treatment tumour perfusion parameters and initial RECIST response do not predict long-term survival outcomes for patients with head and neck squamous cell carcinoma treated with induction chemotherapy

Objectives

Previously, we showed that pre-treatment tumour plasma perfusion (F_p) predicts RECIST response to induction chemotherapy (ICT) in locoregionally advanced head and neck squamous cell carcinoma (HNSCC). The aim here was to determine whether the pre-treatment tumour F_p estimate, changes in tumour F_p or RECIST response post 2 cycles of ICT were prognostic for long-term survival outcomes.

Methods

A prospective study enrolled patients with high stage HNSCC treated with docetaxel (T), cisplatin (P) and 5-fluorouracil (F) (ICT) followed by synchronous cisplatin and intensity modulated radiotherapy. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) before and after two cycles of ICT was used to measure F_p and RECIST response.

Results

Forty-two patients were recruited and 37 underwent two scans. The median follow-up was 36 (range 23–49) months. Pre-treatment tumour F_p (stratified by median) was not prognostic for overall survival (p = 0.42), disease specific survival (p = 0.20) and locoregional control (p = 0.64). Neither change in tumour F_p nor RECIST response post two cycles of ICT was prognostic for any outcome (p>0.21).

Conclusion

DCE-MRI parameters do not predict long-term survival outcomes following ICT and RECIST response to ICT may not be an appropriate endpoint to determine early efficacy of a treatment in HNSCC patients.

Molecular Characterization of Gastric Carcinoma: Therapeutic Implications for Biomarkers and Targets

Palliative chemotherapy is the mainstay of treatment of advanced gastric carcinoma (GC). Monoclonal antibodies including trastuzumab, ramucirumab, and pembrolizumab have been shown to provide additional benefits. However, the clinical outcomes are often unpredictable and they can vary widely among patients. Currently, no biomarker is available for predicting treatment response in the individual patient except human epidermal growth factor receptor 2 (HER2) amplification and programmed death-ligand 1 (PD-L1) expression for effectiveness of trastuzumab and pembrolizumab, respectively. Multi-platform molecular analysis of cancer, including GC, may help identify predictive biomarkers to guide selection of therapeutic agents. Molecular classification of GC by The Cancer Genome Atlas Research Network and the Asian Cancer Research Group is expected to identify therapeutic targets and predictive biomarkers. Complementary to molecular characterization of GC is molecular profiling by expression analysis and genomic sequencing of tumor DNA. Initial analysis of patients with gastroesophageal carcinoma demonstrates that the ratio of progression-free survival (PFS) on molecular profile (MP)-based treatment to PFS on treatment prior to molecular profiling exceeds 1.3, suggesting the potential value of MP in guiding selection of individualized therapy. Future strategies aiming to integrate molecular classification and profiling of tumors with therapeutic agents for achieving the goal of personalized treatment of GC are indicated.

High-Resolution Tissue Mass Spectrometry Imaging Reveals a Refined Functional Anatomy of the Human Adult Adrenal Gland

In the adrenal gland, neuroendocrine cells that synthesize catecholamines and epithelial cells that produce steroid hormones are united beneath a common organ capsule to function as a single stress-responsive organ. The functional anatomy of the steroid hormone-producing adrenal cortex and the catecholamine-producing medulla is ill defined at the level of small molecules. Here, we report a comprehensive high-resolution mass spectrometry imaging (MSI) map of the normal human adrenal gland. A large variety of biomolecules was accessible by matrix-assisted laser desorption/ionization-Fourier transform-ion cyclotron resonance MSI, including nucleoside phosphates indicative of oxidative phosphorylation, sterol and steroid metabolites, intermediates of glycolysis and the tricarboxylic acid cycle, lipids, and fatty acids. Statistical clustering analyses yielded a molecularly defined adrenal anatomy of 10 distinct molecular zones including a highly structured corticomedullary interface. By incorporating pathway information, activities of carbohydrate, amino acid, and lipid metabolism as well as endocrine bioactivity were revealed to be highly spatially organized, which could be visualized as different molecularly defined zones. Together, these findings provide a molecular definition of human adult adrenal gland structure beyond classical histological anatomy.

Integrating Analysis of Cellular Heterogeneity in High-Content Dose-Response Studies

Heterogeneity is a complex property of cellular systems and therefore presents challenges to the reliable identification and characterization. Large-scale biology projects may span many months, requiring a systematic approach to quality control to track reproducibility and correct for instrumental variation and assay drift that could mask biological heterogeneity and preclude comparisons of heterogeneity between runs or even between plates. However, presently there is no standard approach to the tracking and analysis of heterogeneity. Previously, we demonstrated the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in a screen and described the use of three heterogeneity indices as a means to characterize, filter, and browse cellular heterogeneity in big data sets (Gough et al., Methods 96:12-26, 2016). In this chapter, we present a detailed method for integrating the analysis of cellular heterogeneity in assay development, validation, screening, and post screen. Importantly, we provide a detailed method for quality control, to normalize cellular data, track heterogeneity over time, and analyze heterogeneity in big data sets, along with software tools to assist in that process. The example screen for this method is from an HCS project, but the approach applies equally to other experimental methods that measure populations of cells.

Recent advances and opportunities in proteomic analyses of tumour heterogeneity

Solid tumour malignancies comprise a highly variable admixture of tumour and non-tumour cellular populations, forming a complex cellular ecosystem and tumour microenvironment. This tumour heterogeneity is not incidental, and is known to correlate with poor patient prognosis for many cancer types. Indeed, non-malignant cell populations, such as vascular endothelial and immune cells, are known to play key roles supporting and, in some cases, driving aggressive tumour biology, and represent targets of emerging therapeutics, such as antiangiogenesis and immune checkpoint inhibitors. The biochemical interplay between these cellular populations and how they contribute to molecular tumour heterogeneity remains enigmatic, particularly from the perspective of the tumour proteome. This review focuses on recent advances in proteomic methods, namely imaging mass spectrometry, single-cell proteomic techniques, and preanalytical sample processing, that are uniquely positioned to enable detailed analysis of discrete cellular populations within tumours to improve our understanding of tumour proteomic heterogeneity. This review further emphasizes the opportunity afforded by the application of these techniques to the analysis of tumour heterogeneity in formalin-fixed paraffin-embedded archival tumour tissues, as these represent an invaluable resource for retrospective analyses that is now routinely accessible, owing to recent technological and methodological advances in tumour tissue proteomics. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.