Discovery of Spatial Peptide Signatures for Neuroblastoma Risk Assessment by MALDI Mass Spectrometry Imaging

Proteomic insights into paediatric cancer: Unravelling molecular signatures and therapeutic opportunities

Survival rates in some paediatric cancers have improved greatly over recent decades, in part due to the identification of diagnostic, prognostic and predictive molecular signatures, and the development of risk-directed therapies. However, other paediatric cancers have proved difficult to treat, and there is an urgent need to identify novel biomarkers that reveal therapeutic opportunities. The proteome is the total set of expressed proteins present in a cell or tissue at a point in time, and is vastly more dynamic than the genome. Proteomics holds significant promise for cancer research, as proteins are ultimately responsible for cellular phenotype and are the target of most anticancer drugs. Here, we review the discoveries, opportunities and challenges of proteomic analyses in paediatric cancer, with a focus on mass spectrometry (MS)-based approaches. Accelerating incorporation of proteomics into paediatric precision medicine has the potential to improve survival and quality of life for children with cancer.

Improved Detection of Tryptic Peptides from Tissue Sections Using Desorption Electrospray Ionization Mass Spectrometry Imaging

DESI-MSI is an ambient ionization technique used frequently for the detection of lipids, small molecules, and drug targets. Until recently, DESI had only limited use for the detection of proteins and peptides due to the setup and needs around deconvolution of data resulting in a small number of species being detected at lower spatial resolution. There are known differences in the ion species detected using DESI and MALDI for nonpeptide molecules, and here, we identify that this extends to proteomic species. DESI MS images were obtained for tissue sections of mouse and rat brain using a precommercial heated inlet (approximately 450 °C) to the mass spectrometer. Ion mobility separation resolved spectral overlap of peptide ions and significantly improved the detection of multiply charged species. The images acquired were of pixel size 100 μm (rat brain) and 50 μm (mouse brain), respectively. Observed tryptic peptides were filtered against proteomic target lists, generated by LC-MS, enabling tentative protein assignment for each peptide ion image. Precise localizations of peptide ions identified by DESI and MALDI were found to be comparable. Some spatially localized peptides ions were observed in DESI that were not found in the MALDI replicates, typically, multiply charged species with a low mass to charge ratio. This method demonstrates the potential of DESI-MSI to detect large numbers of tryptic peptides from tissue sections with enhanced spatial resolution when compared to previous DESI-MSI studies.

Clinical Peptidomics: Advances in Instrumentation, Analyses, and Applications

Extensive effort has been devoted to the discovery, development, and validation of biomarkers for early disease diagnosis and prognosis as well as rapid evaluation of the response to therapeutic interventions. Genomic and transcriptomic profiling are well-established means to identify disease-associated biomarkers. However, analysis of disease-associated peptidomes can also identify novel peptide biomarkers or signatures that provide sensitive and specific diagnostic and prognostic information for specific malignant, chronic, and infectious diseases. Growing evidence also suggests that peptidomic changes in liquid biopsies may more effectively detect changes in disease pathophysiology than other molecular methods. Knowledge gained from peptide-based diagnostic, therapeutic, and imaging approaches has led to promising new theranostic applications that can increase their bioavailability in target tissues at reduced doses to decrease side effects and improve treatment responses. However, despite major advances, multiple factors can still affect the utility of peptidomic data. This review summarizes several remaining challenges that affect peptide biomarker discovery and their use as diagnostics, with a focus on technological advances that can improve the detection, identification, and monitoring of peptide biomarkers for personalized medicine.

The enhancer landscape predetermines the skeletal regeneration capacity of stromal cells

Multipotent stromal cells are considered attractive sources for cell therapy and tissue engineering. Despite numerous experimental and clinical studies, broad application of stromal cell therapeutics is not yet emerging. A major challenge is the functional diversity of available cell sources. Here, we investigated the regenerative potential of clinically relevant human stromal cells from bone marrow (BMSCs), white adipose tissue, and umbilical cord compared with mature chondrocytes and skin fibroblasts in vitro and in vivo. Although all stromal cell types could express transcription factors related to endochondral ossification, only BMSCs formed cartilage discs in vitro that fully regenerated critical-size femoral defects after transplantation into mice. We identified cell type-specific epigenetic landscapes as the underlying molecular mechanism controlling transcriptional stromal differentiation networks. Binding sites of commonly expressed transcription factors in the enhancer and promoter regions of ossification-related genes, including Runt and bZIP families, were accessible only in BMSCs but not in extraskeletal stromal cells. This suggests an epigenetically predetermined differentiation potential depending on cell origin that allows common transcription factors to trigger distinct organ-specific transcriptional programs, facilitating forward selection of regeneration-competent cell sources. Last, we demonstrate that viable human BMSCs initiated defect healing through the secretion of osteopontin and contributed to transient mineralized bone hard callus formation after transplantation into immunodeficient mice, which was eventually replaced by murine recipient bone during final tissue remodeling.

Mass Spectrometry Contribution to Pediatric Cancers Research

For over four decades, mass spectrometry-based methods have provided a wealth of information relevant to various challenges in the field of cancers research. These challenges included identification and validation of novel biomarkers for various diseases, in particular for various forms of cancer. These biomarkers serve various objectives including monitoring patient response to the various forms of therapy, differentiating subgroups of the same type of cancer, and providing proteomic data to complement datasets generated by genomic, epigenetic, and transcriptomic methods. The same proteomic data can be used to provide prognostic information and could guide scientists and medics to new and innovative targeted therapies The past decade has seen a rapid emergence of epigenetics as a major contributor to carcinogenesis. This development has given a fresh momentum to MS-based proteomics, which demonstrated to be an unrivalled tool for the analyses of protein post-translational modifications associated with chromatin modifications. In particular, high-resolution mass spectrometry has been recently used for systematic quantification of chromatin modifications. Data generated by this approach are central in the search for new therapies for various forms of cancer and will help in attempts to decipher antitumor drug resistance. To appreciate the contribution of mass spectrometry-based proteomics to biomarkers discovery and to our understanding of mechanisms behind the initiation and progression of various forms of cancer, a number of recent investigations are discussed. These investigations also include results provided by two-dimensional gel electrophoresis combined with mass spectrometry.

Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks

Despite numerous diagnostic and therapeutic advances, pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate, and is the fourth leading cause of cancer death in developing countries. Besides its increasing prevalence, pancreatic malignancies are characterized by poor prognosis. Omics technologies have potential relevance for PDAC assessment but are time-intensive and relatively cost-intensive and limited by tissue heterogeneity. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can obtain spatially distinct peptide-signatures and enables tumor classification within a feasible time with relatively low cost. While MALDI-MSI data sets are inherently large, machine learning methods have the potential to greatly decrease processing time. We present a pilot study investigating the potential of MALDI-MSI in combination with neural networks, for classification of pancreatic ductal adenocarcinoma. Neural-network models were trained to distinguish between pancreatic ductal adenocarcinoma and other pancreatic cancer types. The proposed methods are able to correctly classify the PDAC types with an accuracy of up to 86% and a sensitivity of 82%. This study demonstrates that machine learning tools are able to identify different pancreatic carcinoma from complex MALDI data, enabling fast prediction of large data sets. Our results encourage a more frequent use of MALDI-MSI and machine learning in histopathological studies in the future.

Tumoral heterogeneity in neuroblastoma

Neuroblastoma is a solid, neuroendocrine tumor with divergent clinical behavior ranging from asymptomatic to fatal. The diverse clinical presentations of neuroblastoma are directly linked to the high intra- and inter-tumoral heterogeneity it presents. This heterogeneity is strongly associated with therapeutic resistance and continuous relapses, often leading to fatal outcomes. The development of successful risk assessment and tailored treatment strategies lies in evaluating the extent of heterogeneity via the accurate genetic and epigenetic profiling of distinct cell subpopulations present in the tumor. Recent studies have focused on understanding the molecular mechanisms that drive tumoral heterogeneity in pursuing better therapeutic and diagnostic approaches. This review describes the cellular, genetic, and epigenetic aspects of neuroblastoma heterogeneity. In addition, we summarize the recent findings on three crucial factors that can lead to heterogeneity in solid tumors: the inherent diversity of the progenitor cells, the presence of cancer stem cells, and the influence of the tumor microenvironment.

A 3D Bioprinted in vitro Model of Neuroblastoma Recapitulates Dynamic Tumor-Endothelial Cell Interactions Contributing to Solid Tumor Aggressive Behavior

Neuroblastoma (NB) is the most common extracranial tumor in children resulting in substantial morbidity and mortality. A deeper understanding of the NB tumor microenvironment (TME) remains an area of active research but there is a lack of reliable and biomimetic experimental models. This study utilizes a 3D bioprinting approach, in combination with NB spheroids, to create an in vitro vascular model of NB for exploring the tumor function within an endothelialized microenvironment. A gelatin methacryloyl (gelMA) bioink is used to create multi-channel cubic tumor analogues with high printing fidelity and mechanical tunability. Human-derived NB spheroids and human umbilical vein endothelial cells (HUVECs) are incorporated into the biomanufactured gelMA and cocultured under static versus dynamic conditions, demonstrating high levels of survival and growth. Quantification of NB-EC integration and tumor cell migration suggested an increased aggressive behavior of NB when cultured in bioprinted endothelialized models, when cocultured with HUVECs, and also as a result of dynamic culture. This model also allowed for the assessment of metabolic, cytokine, and gene expression profiles of NB spheroids under varying TME conditions. These results establish a high throughput research enabling platform to study the TME-mediated cellular-molecular mechanisms of tumor growth, aggression, and response to therapy.

Peptide Signatures for Prognostic Markers of Pancreatic Cancer by MALDI Mass Spectrometry Imaging

Despite the overall poor prognosis of pancreatic cancer there is heterogeneity in clinical courses of tumors not assessed by conventional risk stratification. This yields the need of additional markers for proper assessment of prognosis and multimodal clinical management. We provide a proof of concept study evaluating the feasibility of Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to identify specific peptide signatures linked to prognostic parameters of pancreatic cancer. On 18 patients with exocrine pancreatic cancer after tumor resection, MALDI imaging analysis was performed additional to histopathological assessment. Principal component analysis (PCA) was used to explore discrimination of peptide signatures of prognostic histopathological features and receiver operator characteristic (ROC) to identify which specific m/z values are the most discriminative between the prognostic subgroups of patients. Out of 557 aligned m/z values discriminate peptide signatures for the prognostic histopathological features lymphatic vessel invasion (pL, 16 m/z values, eight proteins), nodal metastasis (pN, two m/z values, one protein) and angioinvasion (pV, 4 m/z values, two proteins) were identified. These results yield proof of concept that MALDI-MSI of pancreatic cancer tissue is feasible to identify peptide signatures of prognostic relevance and can augment risk assessment.