The Case for Proteomics and Phospho-Proteomics in Personalized Cancer Medicine

B Cell Lymphocytes as a Potential Source of Breast Carcinoma Marker Candidates

Despite advances in the genomic classification of breast cancer, current clinical tests and treatment decisions are commonly based on protein-level information. Nowadays breast cancer clinical treatment selection is based on the immunohistochemical (IHC) determination of four protein biomarkers: Estrogen Receptor 1 (ESR1), Progesterone Receptor (PGR), Human Epidermal Growth Factor Receptor 2 (HER2), and proliferation marker Ki-67. The prognostic correlation of tumor-infiltrating T cells has been widely studied in breast cancer, but tumor-infiltrating B cells have not received so much attention. We aimed to find a correlation between immunohistochemical results and a proteomic approach in measuring the expression of proteins isolated from B-cell lymphocytes in peripheral blood samples. Shotgun proteomic analysis was chosen for its key advantage over other proteomic methods, which is its comprehensive and untargeted approach to analyzing proteins. This approach facilitates better characterization of disease-associated changes at the protein level. We identified 18 proteins in B cell lymphocytes with a significant fold change of more than 2, which have promising potential to serve as breast cancer biomarkers in the future.

Special Issue "Deployment of Proteomics Approaches in Biomedical Research"

Many angles of personalized medicine, such as diagnostic improvements, systems biology [...].

Neuromuscular disorders in the omics era

Neuromuscular disorders encompass a spectrum of conditions characterized by primary lesions within the peripheral nervous system, which include the anterior horn cell, peripheral nerve, neuromuscular junction, and muscle. In pediatrics, most of these disorders are linked to genetic causes. Despite the considerable progress, the diagnosis of these disorders remains a challenging due to wide clinical presentation, disease heterogeneity and rarity. It is noteworthy that certain neuromuscular disorders, once deemed untreatable, can now be effectively managed through novel therapies. Biomarkers emerge as indispensable tools, serving as objective measures that not only refine diagnostic accuracy but also provide guidance for therapeutic decision-making and the ongoing monitoring of long-term outcomes. Herein a comprehensive review of biomarkers in neuromuscular disorders is provided. We highlight the role of omics-based technologies that further characterize neuromuscular pathophysiology as well as identify potential therapeutic targets to guide treatment strategies.

Incorporating functional genomics into the pathology-supported genetic testing framework implemented in South Africa: A future view of precision medicine for breast carcinomas

A pathology-supported genetic testing (PSGT) framework was established in South Africa to improve access to precision medicine for patients with breast carcinomas. Nevertheless, the frequent identification of variants of uncertain significance (VUSs) with the use of genome-scale next-generation sequencing has created a bottleneck in the return of results to patients. This review highlights the importance of incorporating functional genomics into the PSGT framework as a proposed initiative. Here, we explore various model systems and experimental methods available for conducting functional studies in South Africa to enhance both variant classification and clinical interpretation. We emphasize the distinct advantages of using in vitro, in vivo, and translational ex vivo models to improve the effectiveness of precision oncology. Moreover, we highlight the relevance of methodologies such as protein modelling and structural bioinformatics, multi-omics, metabolic activity assays, flow cytometry, cell migration and invasion assays, tube-formation assays, multiplex assays of variant effect, and database mining and machine learning models. The selection of the appropriate experimental approach largely depends on the molecular mechanism of the gene under investigation and the predicted functional effect of the VUS. However, before making final decisions regarding the pathogenicity of VUSs, it is essential to assess the functional evidence and clinical outcomes under current variant interpretation guidelines. The inclusion of a functional genomics infrastructure within the PSGT framework will significantly advance the reclassification of VUSs and enhance the precision medicine pipeline for patients with breast carcinomas in South Africa.

Targetable lesions and proteomes predict therapy sensitivity through disease evolution in pediatric acute lymphoblastic leukemia

Childhood acute lymphoblastic leukemia (ALL) genomes show that relapses often arise from subclonal outgrowths. However, the impact of clonal evolution on the actionable proteome and response to targeted therapy is not known. Here, we present a comprehensive retrospective analysis of paired ALL diagnosis and relapsed specimen. Targeted next generation sequencing and proteome analysis indicate persistence of actionable genome variants and stable proteomes through disease progression. Paired viably-frozen biopsies show high correlation of drug response to variant-targeted therapies but in vitro selectivity is low. Proteome analysis prioritizes PARP1 as a pan-ALL target candidate needed for survival following cellular stress; diagnostic and relapsed ALL samples demonstrate robust sensitivity to treatment with two PARP1/2 inhibitors. Together, these findings support initiating prospective precision oncology approaches at ALL diagnosis and emphasize the need to incorporate proteome analysis to prospectively determine tumor sensitivities, which are likely to be retained at disease relapse.

Extracellular vesicle-based liquid biopsy biomarkers and their application in precision immuno-oncology

While the field of precision oncology is rapidly expanding and more targeted options are revolutionizing cancer treatment paradigms, therapeutic resistance particularly to immunotherapy remains a pressing challenge. This can be largely attributed to the dynamic tumor-stroma interactions that continuously alter the microenvironment. While to date most advancements have been made through examining the clinical utility of tissue-based biomarkers, their invasive nature and lack of a holistic representation of the evolving disease in a real-time manner could result in suboptimal treatment decisions. Thus, using minimally-invasive approaches to identify biomarkers that predict and monitor treatment response as well as alert to the emergence of recurrences is of a critical need. Currently, research efforts are shifting towards developing liquid biopsy-based biomarkers obtained from patients over the course of disease. Liquid biopsy represents a unique opportunity to monitor intercellular communication within the tumor microenvironment which could occur through the exchange of extracellular vesicles (EVs). EVs are lipid bilayer membrane nanoscale vesicles which transfer a plethora of biomolecules that mediate intercellular crosstalk, shape the tumor microenvironment, and modify drug response. The capture of EVs using innovative approaches, such as microfluidics, magnetic beads, and aptamers, allow their analysis via high throughput multi-omics techniques and facilitate their use for biomarker discovery. Artificial intelligence, using machine and deep learning algorithms, is advancing multi-omics analyses to uncover candidate biomarkers and predictive signatures that are key for translation into clinical trials. With the increasing recognition of the role of EVs in mediating immune evasion and as a valuable biomarker source, these real-time snapshots of cellular communication are promising to become an important tool in the field of precision oncology and spur the recognition of strategies to block resistance to immunotherapy. In this review, we discuss the emerging role of EVs in biomarker research describing current advances in their isolation and analysis techniques as well as their function as mediators in the tumor microenvironment. We also highlight recent lung cancer and melanoma studies that point towards their application as predictive biomarkers for immunotherapy and their potential clinical use in precision immuno-oncology.

Foresight in clinical proteomics: current status, ethical considerations, and future perspectives

With the advent of robust and high-throughput mass spectrometric technologies and bioinformatics tools to analyze large data sets, proteomics has penetrated broadly into basic and translational life sciences research. More than 95% of FDA-approved drugs currently target proteins, and most diagnostic tests are protein-based. The introduction of proteomics to the clinic, for instance to guide patient stratification and treatment, is already ongoing. Importantly, ethical challenges come with this success, which must also be adequately addressed by the proteomics and medical communities. Consortium members of the H2020 European Union-funded proteomics initiative: European Proteomics Infrastructure Consortium-providing access (EPIC-XS) met at the Core Technologies for Life Sciences (CTLS) conference to discuss the emerging role and implementation of proteomics in the clinic. The discussion, involving leaders in the field, focused on the current status, related challenges, and future efforts required to make proteomics a more mainstream technology for translational and clinical research. Here we report on that discussion and provide an expert update concerning the feasibility of clinical proteomics, the ethical implications of generating and analyzing large-scale proteomics clinical data, and recommendations to ensure both ethical and effective implementation in real-world applications.

Harnessing the value of TCTP in breast cancer treatment resistance: an opportunity for personalized therapy

Early identification of breast cancer (BC) patients at a high risk of progression may aid in therapeutic and prognostic aims. This is especially true for metastatic disease, which is responsible for most cancer-related deaths. Growing evidence indicates that the translationally controlled tumor protein (TCTP) may be a clinically relevant marker for identifying poorly differentiated aggressive BC tumors. TCTP is an intriguing protein with pleiotropic functions, which is involved in multiple signaling pathways. TCTP may also be involved in stress response, cell growth and proliferation-related processes, underlying its potential role in the initiation of metastatic growth. Thus, TCTP marks specific cancer cell sub-populations with pronounced stress adaptation, stem-like and immune-evasive properties. Therefore, we have shown that in vivo phospho-TCTP levels correlate with the response of BC cells to anti-HER2 agents. In this review, we discuss the clinical relevance of TCTP for personalized therapy, specific TCTP-targeting strategies, and currently available therapeutic agents. We propose TCTP as an actionable clinically relevant target that could potentially improve patient outcomes.

Oncogenic Proteomics Approaches for Translational Research and HIV-Associated Malignancy Mechanisms

Recent advances in the field of proteomics have allowed extensive insights into the molecular regulations of the cell proteome. Specifically, this allows researchers to dissect a multitude of signaling arrays while targeting for the discovery of novel protein signatures. These approaches based on data mining are becoming increasingly powerful for identifying both potential disease mechanisms as well as indicators for disease progression and overall survival predictive and prognostic molecular markers for cancer. Furthermore, mass spectrometry (MS) integrations satisfy the ongoing demand for in-depth biomarker validation. For the purpose of this review, we will highlight the current developments based on MS sensitivity, to place quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data for future applications in cancer precision medicine. We will also discuss malignancies associated with oncogenic viruses such as Acquire Immunodeficiency Syndrome (AIDS) and suggest novel mechanisms behind this phenomenon. Human Immunodeficiency Virus type-1 (HIV-1) proteins are known to be oncogenic per se, to induce oxidative and endoplasmic reticulum stresses, and to be released from the infected or expressing cells. HIV-1 proteins can act alone or in collaboration with other known oncoproteins, which cause the bulk of malignancies in people living with HIV-1 on ART.

Functional impact of protein-RNA variation in clinical cancer analyses

Comprehensive molecular characterization of tumors aims to uncover cancer vulnerabilities, drug resistance mechanisms and biomarkers. Identification of cancer drivers was suggested as the basis for patient-tailored therapy, and transcriptomic analyses were proposed to reveal the phenotypic outcome of cancer mutations. With the maturation of the proteomic field, studies of protein-RNA discrepancies suggested that RNA analyses are insufficient to predict cellular functions. In this manuscript we discuss the importance of direct mRNA-protein comparisons in clinical cancer studies. We make use of the large amount of data generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which includes protein and mRNA expression analyses from the exact same samples. Analysis of protein-RNA correlations showed marked differences among cancer types, and highlighted the protein-RNA similarities and discrepancies among functional pathways and drug targets. Additionally, unsupervised clustering of the data based on protein or RNA showed substantial differences in tumor classification and the cellular processes that differentiate between clusters. These analyses show the difficulty to predict protein levels from mRNAs, and the critical role of protein analyses for phenotypic tumor characterization.

Mass spectrometry-based phosphoproteomics in clinical applications

Protein phosphorylation is an essential post-translational modification that regulates many aspects of cellular physiology, and dysregulation of pivotal phosphorylation events is often responsible for disease onset and progression. Clinical analysis on disease-relevant phosphoproteins, while quite challenging, provides unique information for precision medicine and targeted therapy. Among various approaches, mass spectrometry (MS)-centered characterization features discovery-driven, high-throughput and in-depth identification of phosphorylation events. This review highlights advances in sample preparation and instrument in MS-based phosphoproteomics and recent clinical applications. We emphasize the preeminent data-independent acquisition method in MS as one of the most promising future directions and biofluid-derived extracellular vesicles as an intriguing source of the phosphoproteome for liquid biopsy.

Towards Precision Prognostication and Personalized Therapeutics through Proteomics

Next-generation proteomics has allowed the implementation of biomedical proteome research to uncover disease-affected protein expression profiles [...]

Oncogenic ERRB2 signals through the AP-1 transcription factor to control mesenchymal-like properties of oesophageal adenocarcinoma

Oesophageal adenocarcinoma (OAC) is a deadly disease with poor survival statistics and few targeted therapies available. One of the most common molecular aberrations in OAC is amplification or activation of the gene encoding the receptor tyrosine kinase ERBB2, and ERBB2 is targeted in the clinic for this subset of patients. However, the downstream consequences of these ERBB2 activating events are not well understood. Here we used a combination of phosphoproteomics, open chromatin profiling and transcriptome analysis on cell line models and patient-derived datasets to interrogate the molecular pathways operating downstream from ERBB2. Integrated analysis of these data sets converge on a model where dysregulated ERBB2 signalling is mediated at the transcriptional level by the transcription factor AP-1. AP-1 in turn controls cell behaviour by acting on cohorts of genes that regulate cell migration and adhesion, features often associated with EMT. Our study therefore provides a valuable resource for the cancer cell signalling community and reveals novel molecular determinants underlying the dysregulated behaviour of OAC cells.

Panomics reveals patient individuality as the major driver of colorectal cancer progression

BACKGROUND

Colorectal cancer (CRC) is one of the most prevalent cancers, with over one million new cases per year. Overall, prognosis of CRC largely depends on the disease stage and metastatic status. As precision oncology for patients with CRC continues to improve, this study aimed to integrate genomic, transcriptomic, and proteomic analyses to identify significant differences in expression during CRC progression using a unique set of paired patient samples while considering tumour heterogeneity.

METHODS

We analysed fresh-frozen tissue samples prepared under strict cryogenic conditions of matched healthy colon mucosa, colorectal carcinoma, and liver metastasis from the same patients. Somatic mutations of known cancer-related genes were analysed using Illumina's TruSeq Amplicon Cancer Panel; the transcriptome was assessed comprehensively using Clariom D microarrays. The global proteome was evaluated by liquid chromatography-coupled mass spectrometry (LC‒MS/MS) and validated by two-dimensional difference in-gel electrophoresis. Subsequent unsupervised principal component clustering, statistical comparisons, and gene set enrichment analyses were calculated based on differential expression results.

RESULTS

Although panomics revealed low RNA and protein expression of CA1, CLCA1, MATN2, AHCYL2, and FCGBP in malignant tissues compared to healthy colon mucosa, no differentially expressed RNA or protein targets were detected between tumour and metastatic tissues. Subsequent intra-patient comparisons revealed highly specific expression differences (e.g., SRSF3, OLFM4, and CEACAM5) associated with patient-specific transcriptomes and proteomes.

CONCLUSION

Our research results highlight the importance of inter- and intra-tumour heterogeneity as well as individual, patient-paired evaluations for clinical studies. In addition to changes among groups reflecting CRC progression, we identified significant expression differences between normal colon mucosa, primary tumour, and liver metastasis samples from individuals, which might accelerate implementation of precision oncology in the future.

Phosphoproteomics After Guanidinium Thiocyanate Extraction of Tissue Biopsies

Proteogenomic analysis is emerging as an advantageous tool to assist personalized therapy decisions in clinical health care and integrates complementary information from the genome, transcriptome, and (phospho)proteome. A prerequisite for such analysis is a workflow for the simultaneous isolation of DNA, RNA, and protein from a single sample that does not compromise the different biological molecules and their examination. Focusing on the phosphoproteomic aspect of this workflow, we here provide detailed information on our protocol, which is based on commonly used acid guanidinium thiocyanate-phenol-chloroform (AGPC) extraction with RNA-Bee. We describe the necessary steps for biopsy collection, cryoprocessing, and protein extraction. We further share our practice on protein digestion and cleanup of small samples (200 μg protein) and describe settings for automated IMAC-based phosphopeptide enrichment with the AssayMAP Bravo platform.

Engineering SH2 Domains with Tailored Specificities and Affinities

The Src Homology 2 (SH2) domain is an emerging biotechnology with applications in basic science, drug discovery, and even diagnostics. The SH2 domains rapid uptake into different areas of research is a direct result of the wealth of information generated on its biochemical, biological, and biophysical role in mammalian cell biology. Functionally, the SH2 domain binds and recognizes specific phosphotyrosine (pTyr) residues in the cell to mediate protein-protein interactions (PPIs) that govern signal transduction networks. These signal transduction networks are responsible for relaying growth and stress state signals to the cell's nucleus, ultimately effecting a change in cell biology. Protein engineers have been able to increase the affinity of SH2 domains for pTyr while also tailoring the domains' specificity to unique amino acid sequences flanking the pTyr residue. In this way, it has been possible to develop unique SH2 variants for use in affinity-purification coupled to mass spectrometry (AP-MS) experiments, microscopy, or even synthetic biology. This chapter outlines methods to tailor the affinity and specificity of virtually any human SH2 domain using a combination of rational engineering and phage-display approaches.

Translational proteomics and phosphoproteomics: Tissue to extracellular vesicles

We are currently experiencing a rapidly developing era in terms of translational and clinical medical sciences. The relatively mature state of nucleic acid examination has significantly improved our understanding of disease mechanism and therapeutic potential of personalized treatment, but misses a large portion of phenotypic disease information. Proteins, in particular phosphorylation events that regulates many cellular functions, could provide real-time information for disease onset, progression and treatment efficacy. The technical advances in liquid chromatography and mass spectrometry have realized large-scale and unbiased proteome and phosphoproteome analyses with disease relevant samples such as tissues. However, tissue biopsy still has multiple shortcomings, such as invasiveness of sample collection, potential health risk for patients, difficulty in protein preservation and extreme heterogeneity. Recently, extracellular vesicles (EVs) have offered a great promise as a unique source of protein biomarkers for non-invasive liquid biopsy. Membranous EVs provide stable preservation of internal proteins and especially labile phosphoproteins, which is essential for effective routine biomarker detection. To aid efficient EV proteomic and phosphoproteomic analyses, recent developments showcase clinically-friendly EV techniques, facilitating diagnostic and therapeutic applications. Ultimately, we envision that with streamlined sample preparation from tissues and EVs proteomics and phosphoproteomics analysis will become routine in clinical settings.

Mass Spectrometric-Based Proteomics for Biomarker Discovery in Osteosarcoma: Current Status and Future Direction

Due to a lack of novel therapies and biomarkers, the clinical outcomes of osteosarcoma patients have not significantly improved for decades. The advancement of mass spectrometry (MS), peptide quantification, and downstream pathway analysis enables the investigation of protein profiles across a wide range of input materials, from cell culture to long-term archived clinical specimens. This can provide insight into osteosarcoma biology and identify candidate biomarkers for diagnosis, prognosis, and stratification of chemotherapy response. In this review, we provide an overview of proteomics studies of osteosarcoma, indicate potential biomarkers that might be promising therapeutic targets, and discuss the challenges and opportunities of mass spectrometric-based proteomics in future osteosarcoma research.

Chemoresistant Cancer Cell Lines Are Characterized by Migratory, Amino Acid Metabolism, Protein Catabolism and IFN1 Signalling Perturbations

Simple Summary

While chemoresistance remains a major barrier to improving the outcomes for patients with ovarian cancer, the molecular features, and associated biological functions, which underpin chemoresistance in ovarian cancer remain poorly understood. In this study we aimed to provide insight into the proteins and metabolites, and their associated biological pathways, which play a role in conferring chemoresistance to ovarian cancer. Through mass spectrometry analysis comparing the proteome and metabolome of chemosensitive vs chemoresistant ovarian cancer cell lines we revealed numerous perturbations in signalling and metabolic pathways in chemoresistant cells. Further comparison to primary cells taken from patients with chemoresistant or chemosensitive disease identified a shared dysregulation in cytokine and type 1 interferon signalling. Our research sets the foundation for a deeper understanding of the proteomic and metabolomic features of chemoresistance and identifies type 1 interferon signalling as a common feature of chemoresistance.

Abstract

Chemoresistance remains the major barrier to effective ovarian cancer treatment. The molecular features and associated biological functions of this phenotype remain poorly understood. We developed carboplatin-resistant cell line models using OVCAR5 and CaOV3 cell lines with the aim of identifying chemoresistance-specific molecular features. Chemotaxis and CAM invasion assays revealed enhanced migratory and invasive potential in OVCAR5-resistant, compared to parental cell lines. Mass spectrometry analysis was used to analyse the metabolome and proteome of these cell lines, and was able to separate these populations based on their molecular features. It revealed signalling and metabolic perturbations in the chemoresistant cell lines. A comparison with the proteome of patient-derived primary ovarian cancer cells grown in culture showed a shared dysregulation of cytokine and type 1 interferon signalling, potentially revealing a common molecular feature of chemoresistance. A comprehensive analysis of a larger patient cohort, including advanced in vitro and in vivo models, promises to assist with better understanding the molecular mechanisms of chemoresistance and the associated enhancement of migration and invasion.

A knowledge graph to interpret clinical proteomics data

Implementing precision medicine hinges on the integration of omics data, such as proteomics, into the clinical decision-making process, but the quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across multiple biomedical databases and publications, pose a challenge to data integration. Here we present the Clinical Knowledge Graph (CKG), an open-source platform currently comprising close to 20 million nodes and 220 million relationships that represent relevant experimental data, public databases and literature. The graph structure provides a flexible data model that is easily extendable to new nodes and relationships as new databases become available. The CKG incorporates statistical and machine learning algorithms that accelerate the analysis and interpretation of typical proteomics workflows. Using a set of proof-of-concept biomarker studies, we show how the CKG might augment and enrich proteomics data and help inform clinical decision-making.

Application of Proteomics in the Discovery of Radiosensitive Cancer Biomarkers

Radiation therapy remains an important component of cancer treatment. Gene-encoded proteins were the actual executors of cellular functions. Proteomic was a novel technology that can systematically analysis protein composition and measure their levels of change, this was a high throughput method, and were the import tools in the post genomic era. In recent years, rapid progress of proteomic have been made in the study of cancer mechanism, diagnosis, and treatment. This article elaborates current advances and future directions of proteomics in the discovery of radiosensitive cancer biomarkers.

Phosphoproteomic profiling of T cell acute lymphoblastic leukemia reveals targetable kinases and combination treatment strategies

Protein kinase inhibitors are amongst the most successful cancer treatments, but targetable kinases activated by genomic abnormalities are rare in T cell acute lymphoblastic leukemia. Nevertheless, kinases can be activated in the absence of genetic defects. Thus, phosphoproteomics can provide information on pathway activation and signaling networks that offer opportunities for targeted therapy. Here, we describe a mass spectrometry-based global phosphoproteomic profiling of 11 T cell acute lymphoblastic leukemia cell lines to identify targetable kinases. We report a comprehensive dataset consisting of 21,000 phosphosites on 4,896 phosphoproteins, including 217 kinases. We identify active Src-family kinases signaling as well as active cyclin-dependent kinases. We validate putative targets for therapy ex vivo and identify potential combination treatments, such as the inhibition of the INSR/IGF-1R axis to increase the sensitivity to dasatinib treatment. Ex vivo validation of selected drug combinations using patient-derived xenografts provides a proof-of-concept for phosphoproteomics-guided design of personalized treatments.

Proteomic analysis of archival breast cancer clinical specimens identifies biological subtypes with distinct survival outcomes

Despite advances in genomic classification of breast cancer, current clinical tests and treatment decisions are commonly based on protein level information. Formalin-fixed paraffin-embedded (FFPE) tissue specimens with extended clinical outcomes are widely available. Here, we perform comprehensive proteomic profiling of 300 FFPE breast cancer surgical specimens, 75 of each PAM50 subtype, from patients diagnosed in 2008-2013 (n = 178) and 1986-1992 (n = 122) with linked clinical outcomes. These two cohorts are analyzed separately, and we quantify 4214 proteins across all 300 samples. Within the aggressive PAM50-classified basal-like cases, proteomic profiling reveals two groups with one having characteristic immune hot expression features and highly favorable survival. Her2-Enriched cases separate into heterogeneous groups differing by extracellular matrix, lipid metabolism, and immune-response features. Within 88 triple-negative breast cancers, four proteomic clusters display features of basal-immune hot, basal-immune cold, mesenchymal, and luminal with disparate survival outcomes. Our proteomic analysis characterizes the heterogeneity of breast cancer in a clinically-applicable manner, identifies potential biomarkers and therapeutic targets, and provides a resource for clinical breast cancer classification.

Experimental Nuclear Medicine Meets Tumor Biology

Personalized treatment of cancer patients demands specific and validated biomarkers for tumor diagnosis and therapy. The development and validation of such require translational preclinical models that recapitulate human diseases as accurately as possible. Moreover, there is a need for convergence of different (pre)clinical disciplines that openly share their knowledge and methodologies. This review sheds light on the differential perception of biomarkers and gives an overview of currently used models in tracer development and approaches for biomarker discovery.

Evaluation of Differential Peptide Loading on Tandem Mass Tag-Based Proteomic and Phosphoproteomic Data Quality

Global and phosphoproteome profiling has demonstrated great utility for the analysis of clinical specimens. One barrier to the broad clinical application of proteomic profiling is the large amount of biological material required, particularly for phosphoproteomics─currently on the order of 25 mg wet tissue weight. For hematopoietic cancers such as acute myeloid leukemia (AML), the sample requirement is ≥10 million peripheral blood mononuclear cells (PBMCs). Across large study cohorts, this requirement will exceed what is obtainable for many individual patients/time points. For this reason, we were interested in the impact of differential peptide loading across multiplex channels on proteomic data quality. To achieve this, we tested a range of channel loading amounts (approximately the material obtainable from 5E5, 1E6, 2.5E6, 5E6, and 1E7 AML patient cells) to assess proteome coverage, quantification precision, and peptide/phosphopeptide detection in experiments utilizing isobaric tandem mass tag (TMT) labeling. As expected, fewer missing values were observed in TMT channels with higher peptide loading amounts compared to lower loadings. Moreover, channels with a lower loading have greater quantitative variability than channels with higher loadings. A statistical analysis showed that decreased loading amounts result in an increase in the type I error rate. We then examined the impact of differential loading on the detection of known differences between distinct AML cell lines. Similar patterns of increased data missingness and higher quantitative variability were observed as loading was decreased resulting in fewer statistical differences; however, we found good agreement in features identified as differential, demonstrating the value of this approach.

Chemically labeled ThUBD permits rapid and super-sensitive imaging of polyubiquitination signals

The ThUBD-HRP probe and the consequential developed TUF-WB+ method can detect polyubiquitination signal through one-step incubation with hypersensitivity, unbiased detection and a shorter operation time compared with the antibody method.

Cancer catecholamine conundrum

Exercise, psychosocial stress, and drugs such as adrenergic agonists and antagonists increase the concentrations of catecholamines and/or alter adrenergic signaling. Intriguingly, exercise studies universally suggest that catecholamines are cancer-inhibiting whereas cancer stress studies typically report the opposite, whereas β-blocker studies show variable effects. Here, we term variable effects of catecholamines in cancer the cancer catecholamine conundrum. Variable effects of catecholamines can potentially be explained by variable expression of nine adrenergic receptor isoforms and by other factors including catecholamine effects on cancer versus immune or endothelial cells. Future studies on catecholamines and cancer should seek to understand the mechanisms that explain variable effects of catecholamines in cancer to utilize beneficial or block detrimental effects of catecholamines in cancer patients.

Prospects and challenges of cancer systems medicine: from genes to disease networks

It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.

Reciprocal priming between receptor tyrosine kinases at recycling endosomes orchestrates cellular signalling outputs

Integration of signalling downstream of individual receptor tyrosine kinases (RTKs) is crucial to fine-tune cellular homeostasis during development and in pathological conditions, including breast cancer. However, how signalling integration is regulated and whether the endocytic fate of single receptors controls such signalling integration remains poorly elucidated. Combining quantitative phosphoproteomics and targeted assays, we generated a detailed picture of recycling-dependent fibroblast growth factor (FGF) signalling in breast cancer cells, with a focus on distinct FGF receptors (FGFRs). We discovered reciprocal priming between FGFRs and epidermal growth factor (EGF) receptor (EGFR) that is coordinated at recycling endosomes. FGFR recycling ligands induce EGFR phosphorylation on threonine 693. This phosphorylation event alters both FGFR and EGFR trafficking and primes FGFR-mediated proliferation but not cell invasion. In turn, FGFR signalling primes EGF-mediated outputs via EGFR threonine 693 phosphorylation. This reciprocal priming between distinct families of RTKs from recycling endosomes exemplifies a novel signalling integration hub where recycling endosomes orchestrate cellular behaviour. Therefore, targeting reciprocal priming over individual receptors may improve personalized therapies in breast and other cancers.

Integrated in silico MS-based phosphoproteomics and network enrichment analysis of RASopathy proteins

Background

RASopathies are a group of syndromes showing clinical overlap caused by mutations in genes affecting the RAS-MAPK pathway. Consequent disruption on cellular signaling leads and is driven by phosphoproteome remodeling. However, we still lack a comprehensive picture of the different key players and altered downstream effectors.

Methods

An in silico interactome of RASopathy proteins was generated using pathway enrichment analysis/STRING tool, including identification of main hub proteins. We also integrated phosphoproteomic and immunoblotting studies using previous published information on RASopathy proteins and their neighbors in the context of RASopathy syndromes. Data from Phosphosite database (www.phosphosite.org) was collected in order to obtain the potential phosphosites subjected to regulation in the 27 causative RASopathy proteins. We compiled a dataset of dysregulated phosphosites in RASopathies, searched for commonalities between syndromes in harmonized data, and analyzed the role of phosphorylation in the syndromes by the identification of key players between the causative RASopathy proteins and the associated interactome.

Results

In this study, we provide a curated data set of 27 causative RASopathy genes, identify up to 511 protein–protein associations using pathway enrichment analysis/STRING tool, and identify 12 nodes as main hub proteins. We found that a large group of proteins contain tyrosine residues and their biological processes include but are not limited to the nervous system. Harmonizing published RASopathy phosphoproteomic and immunoblotting studies we identified a total of 147 phosphosites with increased phosphorylation, whereas 47 have reduced phosphorylation. The PKB signaling pathway is the most represented among the dysregulated phosphoproteins within the RASopathy proteins and their neighbors, followed by phosphoproteins implicated in the regulation of cell proliferation and the MAPK pathway.

Conclusions

This work illustrates the complex network underlying the RASopathies and the potential of phosphoproteomics for dissecting the molecular mechanisms in these syndromes. A combined study of associated genes, their interactome and phosphorylation events in RASopathies, elucidates key players and mechanisms to direct future research, diagnosis and therapeutic windows.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13023-021-01934-x.

Comparison of two multiplexed technologies for profiling >1,000 serum proteins that may associate with tumor burden

Background: In this pilot study, we perform a preliminary comparison of two targeted multiplex proteomics technologies for discerning serum protein concentration changes that may correlate to tumor burden in ovarian cancer (OC) patients. Methods: Using the proximity extension assay (PEA) and Quantibody® Kiloplex Array (QKA), we measured >1,000 proteins in the pre-surgical and post-surgical serum from nine OC patients (N=18 samples). We expect that proteins that have decreased significantly in the post-surgical serum concentration may correlate to tumor burden in each patient. Duplicate sera from two healthy individuals were used as controls (N=4 samples). We employed in-house ELISAs to measure five proteins with large serum concentration changes in pre- and post-surgical sera, from four of the original nine patients and the two original controls. Results: Both platforms showed a weak correlation with clinical cancer antigen 125 (CA125) data. The two multiplexed platforms showed a significant correlation with each other for >400 overlapping proteins. PEA uncovered 15 proteins, while QKA revealed 11 proteins, with more than a two-fold post-surgical decrease in at least six of the nine patients. Validation using single enzyme-linked immunosorbent assays (ELISAs) showed at least a two-fold post-surgical decrease in serum concentration of the same patients, as indicated by the two multiplex assays. Conclusion: Both methods identified proteins that had significantly decreased in post-surgical serum concentration, as well as recognizing proteins that had been implicated in OC patients. Our findings from a limited sample size suggest that novel targeted proteomics platforms are promising tools for identifying candidate serological tumor-related proteins.  However further studies are essential for the improvement of accuracy and avoidance of false results.

Proteomic analysis in diffuse large B-cell lymphoma identifies dysregulated tumor microenvironment proteins in non-GCB/ABC subtype patients

The complexity of the activated B-cell like (ABC) diffuse large B-cell lymphoma (DLBCL) subtype is probably not only explained by genetic alterations and methods to measure global protein expression could bring new knowledge regarding the pathophysiology. We used quantitative proteomics to analyze the global protein expression of formalin-fixed paraffin-embedded (FFPE) tumor tissues from 202 DLBCL patients. We identified 6430 proteins and 498 were significantly regulated between the germinal center B-cell like (GCB) and non-GCB groups. A number of proteins previously not described to be upregulated in non-GCB or ABC DLBCL was found, e.g. CD64, CD85A, guanylate-binding protein 1 (GBP1), interferon-induced proteins with tetratricopeptide repeat (IFIT)2, and mixed lineage kinase domain-like protein (MLKL) and immunohistochemical staining showed higher expression of GBP1 and MLKL. A cluster analysis revealed that the most prominent cluster contained proteins involved in the tumor microenvironment and regulation of the immune system. Our data suggest that the therapeutic focus should be expanded toward the tumor microenvironment in non-GCB/ABC subtype patients.

Towards Precision Dermatology: Emerging Role of Proteomic Analysis of the Skin

BACKGROUND

The skin is the largest organ in the human body and serves as a multilayered protective shield from the environment as well as a sensor and thermal regulator. However, despite its importance, many details about skin structure and function at the molecular level remain incompletely understood. Recent advances in liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomics have enabled the quantification and characterization of the proteomes of a number of clinical samples, including normal and diseased skin.

SUMMARY

Here, we review the current state of the art in proteomic analysis of the skin. We provide a brief overview of the technique and skin sample collection methodologies as well as a number of recent examples to illustrate the utility of this strategy for advancing a broader understanding of the pathology of diseases as well as new therapeutic options.

KEY MESSAGES

Proteomic studies of healthy skin and skin diseases can identify potential molecular biomarkers for improved diagnosis and patient stratification as well as potential targets for drug development. Collectively, efforts such as the Human Skinatlas offer improved opportunities for enhancing clinical practice and patient outcomes.

Current Methods of Post-Translational Modification Analysis and Their Applications in Blood Cancers

Post-translational modifications (PTMs) add a layer of complexity to the proteome through the addition of biochemical moieties to specific residues of proteins, altering their structure, function and/or localization. Mass spectrometry (MS)-based techniques are at the forefront of PTM analysis due to their ability to detect large numbers of modified proteins with a high level of sensitivity and specificity. The low stoichiometry of modified peptides means fractionation and enrichment techniques are often performed prior to MS to improve detection yields. Immuno-based techniques remain popular, with improvements in the quality of commercially available modification-specific antibodies facilitating the detection of modified proteins with high affinity. PTM-focused studies on blood cancers have provided information on altered cellular processes, including cell signaling, apoptosis and transcriptional regulation, that contribute to the malignant phenotype. Furthermore, the mechanism of action of many blood cancer therapies, such as kinase inhibitors, involves inhibiting or modulating protein modifications. Continued optimization of protocols and techniques for PTM analysis in blood cancer will undoubtedly lead to novel insights into mechanisms of malignant transformation, proliferation, and survival, in addition to the identification of novel biomarkers and therapeutic targets. This review discusses techniques used for PTM analysis and their applications in blood cancer research.

Elucidation of host-virus surfaceome interactions using spatial proteotyping

The cellular surfaceome and its residing extracellularly exposed proteins are involved in a multitude of molecular signaling processes across the viral infection cycle. Successful viral propagation, including viral entry, immune evasion, virion release and viral spread rely on dynamic molecular interactions with the surfaceome. Decoding of these viral-host surfaceome interactions using advanced technologies enabled the discovery of fundamental new functional insights into cellular and viral biology. In this review, we highlight recently developed experimental strategies, with a focus on spatial proteotyping technologies, aiding in the rational design of theranostic strategies to combat viral infections.

THE ROLE OF PHOSPHORYLATION IN ATRIAL FIBRILLATION: A FOCUS ON MASS SPECTROMETRY APPROACHES

Atrial fibrillation (AF) is the most common arrhythmia worldwide. It is associated with significant increases in morbidity in the form of stroke and heart failure, and a doubling in all-cause mortality. The pathophysiology of AF is incompletely understood, and this has contributed to a lack of effective treatments and disease-modifying therapies. An important cellular process that may explain how risk factors give rise to AF includes post-translational modification (PTM) of proteins. As the most commonly occurring PTM, protein phosphorylation is especially relevant. Although many methods exist for studying protein phosphorylation, a common and highly resolute technique is mass spectrometry (MS). This review will discuss recent evidence surrounding the role of protein phosphorylation in the pathogenesis of AF. MS-based technology to study phosphorylation and uses of MS in other areas of medicine such as oncology will also be presented. Based on these data, future goals and experiments will be outlined that utilize MS technology to better understand the role of phosphorylation in AF and elucidate its role in AF pathophysiology. This may ultimately allow for the development of more effective AF therapies.

Applications of Omics Technologies for Three-Dimensional In Vitro Disease Models

Omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, multiomics, and integrated modalities, have greatly contributed to our understanding of various diseases by enabling researchers to probe the molecular wiring of cellular systems in a high-throughput and precise manner. With the development of tissue-engineered three-dimensional (3D) in vitro disease models, such as organoids and spheroids, there is potential of integrating omics technologies with 3D disease models to elucidate the complex links between genotype and phenotype. These 3D disease models have been used to model cancer, infectious disease, toxicity, neurological disorders, and others. In this review, we provide an overview of omics technologies, highlight current and emerging studies, discuss the associated experimental design considerations, barriers and challenges of omics technologies, and provide an outlook on the future applications of omics technologies with 3D models. Overall, this review aims to provide a valuable resource for tissue engineers seeking to leverage omics technologies for diving deeper into biological discovery. Impact statement With the emergence of three-dimensional (3D) in vitro disease models, tissue engineers are increasingly interested to investigate these systems to address biological questions related to disease mechanism, drug target discovery, therapy resistance, and more. Omics technologies are a powerful and high-throughput approach, but their application for 3D disease models is not maximally utilized. This review illustrates the achievements and potential of using omics technologies to leverage the full potential of 3D in vitro disease models. This will improve the quality of such models, advance our understanding of disease, and contribute to therapy development.

Bioinformatics workflows for clinical applications in precision oncology

High-throughput molecular profiling of tumors is a fundamental aspect of precision oncology, enabling the identification of genomic alterations that can be targeted therapeutically. In this context, a patient is matched to a specific drug or therapy based on the tumor's underlying genetic driver events rather than the histologic classification. This approach requires extensive bioinformatics methodology and workflows, including raw sequencing data processing and quality control, variant calling and annotation, integration of different molecular data types, visualization and finally reporting the data to physicians, cancer researchers and pharmacologists in a format that is readily interpretable for clinical decision making. This review comprises a broad overview of these bioinformatics aspects and discusses the multiple analytical, technical and interpretational challenges that remain to efficiently translate molecular findings into personalized treatment recommendations.

Proteomics in Influenza Research: The Emerging Role of Posttranslational Modifications

Influenza viruses continue evolving and have the ability to cause a global pandemic, so it is very important to elucidate its pathogenesis and find new treatment methods. In recent years, proteomics has made important contributions to describing the dynamic interaction between influenza viruses and their hosts, especially in posttranslational regulation of a variety of key biological processes. Protein posttranslational modifications (PTMs) increase the diversity of functionality of the organismal proteome and affect almost all aspects of pathogen biology, primarily by regulating the structure, function, and localization of the modified proteins. Considerable technical achievements in mass spectrometry-based proteomics have been made in a large number of proteome-wide surveys of PTMs in many different organisms. Herein we specifically focus on the proteomic studies regarding a variety of PTMs that occur in both the influenza viruses, mainly influenza A viruses (IAVs), and their hosts, including phosphorylation, ubiquitination and ubiquitin-like modification, glycosylation, methylation, acetylation, and some types of acylation. Integration of these data sets provides a unique scenery of the global regulation and interplay of different PTMs during the interaction between IAVs and their hosts. Various techniques used to globally profiling these PTMs, mostly MS-based approaches, are discussed regarding their increasing roles in mechanical regulation of interaction between influenza viruses and their hosts.

Mass spectrometry-based proteomics analyses of post-translational modifications and proteoforms in human pituitary adenomas

Pituitary adenoma (PA) is a common intracranial neoplasm, which affects the hypothalamus-pituitary-target organ axis systems, and is hazardous to human health. Post-translational modifications (PTMs), including phosphorylation, ubiquitination, nitration, and sumoylation, are vitally important in the PA pathogenesis. The large-scale analysis of PTMs could provide a global view of molecular mechanisms for PA. Proteoforms, which are used to define various protein structural and functional forms originated from the same gene, are the future direction of proteomics research. The global studies of different proteoforms and PTMs of hypophyseal hormones such as growth hormone (GH) and prolactin (PRL) and the proportion change of different GH proteoforms or PRL proteoforms in human pituitary tissue could provide new insights into the clinical value of pituitary hormones in PAs. Multiple quantitative proteomics methods, including mass spectrometry (MS)-based label-free and stable isotope-labeled strategies in combination with different PTM-peptide enrichment methods such as TiO₂ enrichment of tryptic phosphopeptides and antibody enrichment of other PTM-peptides increase the feasibility for researchers to study PA proteomes. This article reviews the research status of PTMs and proteoforms in PAs, including the enrichment method, technical limitation, quantitative proteomics strategies, and the future perspectives, to achieve the goals of in-depth understanding its molecular pathogenesis, and discovering effective biomarkers and clinical therapeutic targets for predictive, preventive, and personalized treatment of PA patients.

Mass Spectrometry-Based Omics for the Characterization of Triple-Negative Breast Cancer Bio-Signature

Triple-negative breast cancer (TNBC) represents an unmet medical need due to a high rate of metastatic occurrence and poor overall survival, pathology aggressiveness, heterogeneous clinical behavior and limited cytotoxic chemotherapy options available because of the absence of targetable receptors. The current standard of care in TNBC is represented by chemotherapy and surgery associated with low overall survival and high relapse rates. Hopes of overcoming current limited and unspecific approaches of TNBC therapy lie in studying the metabolic rewiring of these types of breast cancer, thus understanding the mechanisms involved in the occurrence and progression of the disease. Due to its heterogeneity, a clinically relevant sub-classification of this type of breast cancer based on biomarker panels is greatly needed in order to guide treatment decisions. Mass spectrometry-based omics may provide very useful tools to address the current needs of targetable biomarker discovery and validation. The present review aims to provide a comprehensive view of the current clinical diagnosis and therapy of TNBC highlighting the need for a new approach. Therefore, this paper offers a detailed mass spectrometry-based snapshot of TNBC metabolic adjustment, emphasizing a complex network of variables governing the diverse and aggressive clinical behavior of TNBC.

Accelerating protein biomarker discovery and translation from proteomics research for clinical utility

Discovery proteomics research has made significant progress in the past several years; however, the number of protein biomarkers deployed in clinical practice remains rather limited. There are several scientific and procedural gaps between discovery proteomics research and clinical implementation, which have contributed to poor biomarker validity and few clinical applications. The complexity and low throughput of proteomics approaches have added additional barriers for biomarker assay translation to clinical applications. Recently, targeted proteomics have become a powerful tool to bridge the biomarker discovery to clinical validation. In this perspective, we discuss the challenges and strategies in proteomics research from a clinical perspective, and propose several recommendations for discovery proteomics research to accelerate protein biomarker discovery and translation for future clinical applications.

Mass Spectrometry Analysis of Intact Proteins from Crude Samples

Analysis of intact proteins by native mass spectrometry has emerged as a powerful tool for obtaining insight into subunit diversity, post-translational modifications, stoichiometry, structural arrangement, stability, and overall architecture. Typically, such an analysis is performed following protein purification procedures, which are time consuming, costly, and labor intensive. As this technology continues to move forward, advances in sample handling and instrumentation have enabled the investigation of intact proteins in situ and in crude samples, offering rapid analysis and improved conservation of the biological context. This emerging field, which involves various ion source platforms such as matrix-assisted laser desorption ionization (MALDI) and electrospray ionization (ESI) for both spatial imaging and solution-based analysis, is expected to impact many scientific fields, including biotechnology, pharmaceuticals, and clinical sciences. In this Perspective, we discuss the information that can be retrieved by such experiments as well as the current advantages and technical challenges associated with the different sampling strategies. Furthermore, we present future directions of these MS-based methods, including current limitations and efforts that should be made to make these approaches more accessible. Considering the vast progress we have witnessed in recent years, we anticipate that the advent of further innovations enabling minimal handling of MS samples will make this field more robust, user friendly, and widespread.

A streamlined mass spectrometry-based proteomics workflow for large-scale FFPE tissue analysis

Formalin fixation and paraffin-embedding (FFPE) is the most common method to preserve human tissue for clinical diagnosis, and FFPE archives represent an invaluable resource for biomedical research. Proteins in FFPE material are stable over decades but their efficient extraction and streamlined analysis by mass spectrometry (MS)-based proteomics has so far proven challenging. Herein we describe a MS-based proteomic workflow for quantitative profiling of large FFPE tissue cohorts directly from histopathology glass slides. We demonstrate broad applicability of the workflow to clinical pathology specimens and variable sample amounts, including low-input cancer tissue isolated by laser microdissection. Using state-of-the-art data dependent acquisition (DDA) and data independent acquisition (DIA) MS workflows, we consistently quantify a large part of the proteome in 100 min single-run analyses. In an adenoma cohort comprising more than 100 samples, total workup took less than a day. We observed a moderate trend towards lower protein identification in long-term stored samples (>15 years), but clustering into distinct proteomic subtypes was independent of archival time. Our results underscore the great promise of FFPE tissues for patient phenotyping using unbiased proteomics and they prove the feasibility of analyzing large tissue cohorts in a robust, timely, and streamlined manner. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Phosphorylation Ratio Determination in Fresh-Frozen and Formalin-Fixed Paraffin-Embedded Tissue with Targeted Mass Spectrometry

Formalin-fixed paraffin-embedded (FFPE) tissues are routinely prepared and collected for diagnostics in pathology departments. These are, therefore, the most accessible research sources in pathology archives. In this study we investigated whether we can apply a targeted and quantitative parallel reaction monitoring (PRM) method for FFPE tissue samples in a sensitive and reproducible way. The feasibility of this technical approach was demonstrated for normal brain and glioblastoma multiforme tissues. Two methods were used: PRM measurement of a tryptic digest without phosphopeptide enrichment (Direct-PRM) and after Fe-NTA phosphopeptide enrichment (Fe-NTA-PRM). With these two methods, the phosphorylation ratio could be determined for four selected peptide pairs that originate from neuroblast differentiation-associated protein (AHNAK S5448-p), calcium/calmodulin-dependent protein kinase type II subunit delta (CAMK2D T337-p), eukaryotic translation initiation factor 4B (EIF4B S93-p), and epidermal growth factor receptor (EGFR S1166-p). In normal brain FFPE tissues, the Fe-NTA-PRM method enabled the quantification of targeted phosphorylated peptides with high reproducibility (CV < 14%). Our results indicate that formalin fixation does not impede relative quantification of a phospho-site and its phosphorylation ratio in FFPE tissues. The developed workflow combining these methods opens ways to study archival FFPE tissues for phosphorylation ratio determination in proteins.

Spatially resolved analysis of FFPE tissue proteomes by quantitative mass spectrometry

Bottom-up mass spectrometry-based proteomics relies on protein digestion and peptide purification. The application of such methods to broadly available clinical samples such as formalin-fixed and paraffin-embedded (FFPE) tissues requires reversal of chemical crosslinking and the removal of reagents that are incompatible with mass spectrometry. Here, we describe in detail a protocol that combines tissue disruption by ultrasonication, heat-induced antigen retrieval and two alternative methods for efficient detergent removal to enable quantitative proteomic analysis of limited amounts of FFPE material. To show the applicability of our approach, we used hepatocellular carcinoma (HCC) as a model system. By combining the described protocol with laser-capture microdissection, we were able to quantify the intra-tumor heterogeneity of a tumor specimen on the proteome level using a single slide with tissue of 10-µm thickness. We also demonstrate broader applicability to other tissues, including human gallbladder and heart. The procedure described in this protocol can be completed within 8 d.

Limited Environmental Serine and Glycine Confer Brain Metastasis Sensitivity to PHGDH Inhibition

A hallmark of metastasis is the adaptation of tumor cells to new environments. Metabolic constraints imposed by the serine and glycine-limited brain environment restrict metastatic tumor growth. How brain metastases overcome these growth-prohibitive conditions is poorly understood. Here, we demonstrate that 3-phosphoglycerate dehydrogenase (PHGDH), which catalyzes the rate-limiting step of glucose-derived serine synthesis, is a major determinant of brain metastasis in multiple human cancer types and preclinical models. Enhanced serine synthesis proved important for nucleotide production and cell proliferation in highly aggressive brain metastatic cells. In vivo, genetic suppression and pharmacologic inhibition of PHGDH attenuated brain metastasis, but not extracranial tumor growth, and improved overall survival in mice. These results reveal that extracellular amino acid availability determines serine synthesis pathway dependence, and suggest that PHGDH inhibitors may be useful in the treatment of brain metastasis. SIGNIFICANCE: Using proteomics, metabolomics, and multiple brain metastasis models, we demonstrate that the nutrient-limited environment of the brain potentiates brain metastasis susceptibility to serine synthesis inhibition. These findings underscore the importance of studying cancer metabolism in physiologically relevant contexts, and provide a rationale for using PHGDH inhibitors to treat brain metastasis.This article is highlighted in the In This Issue feature, p. 1241.