Innovative methodology for the identification of soluble biomarkers in fresh tissues

Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, there are no population-wide screening programmes for PDAC. Thus, early disease detection, although urgently needed, remains elusive. Individuals in certain high-risk groups are, however, offered screening or surveillance. Here we explore advances in understanding high-risk groups for PDAC and efforts to implement biomarker-driven detection of PDAC in these groups. We review current approaches to early detection biomarker development and the use of artificial intelligence as applied to electronic health records (EHRs) and social media. Finally, we address the cost-effectiveness of applying biomarker strategies for early detection of PDAC.

Stage-Specific Plasma Metabolomic Profiles in Colorectal Cancer

Background/Objectives: The objective of this study was to investigate the metabolomic profiles of patients with colorectal cancer (CRC) across various stages of the disease. Methods: The plasma samples were obtained from 255 subjects, including patients with CRC in stages I-IV, polyps, and controls. We employed capillary electrophoresis time-of-flight mass spectrometry and liquid chromatography triple quadrupole mass spectrometry to analyze hydrophilic metabolites comprehensively. The data were randomly divided into two groups, and consistent differences observed in both groups were analyzed. Results: Acetylated polyamines, such as N1-acetylspermine and N1, N12-diacetylspermine, consistently showed elevated concentrations in stage IV compared to stages I-III. Non-acetylated polyamines, including spermine and spermidine, exhibited increasing trends from polyp to stage IV. Other metabolites, such as histidine and o-acetylcarnitine, showed decreasing trends across stages. While acetylated polyamines have been reported as CRC detection markers, our findings suggest that they also possess diagnostic potential for distinguishing stage IV from other stages. Conclusions: This study showed stage-specific changes in metabolic profiles, including polyamines, of colorectal cancer.

Integrated meta-analysis of colorectal cancer public proteomic datasets for biomarker discovery and validation

The cancer biomarker field has been an object of thorough investigation in the last decades. Despite this, colorectal cancer (CRC) heterogeneity makes it challenging to identify and validate effective prognostic biomarkers for patient classification according to outcome and treatment response. Although a massive amount of proteomics data has been deposited in public data repositories, this rich source of information is vastly underused. Here, we attempted to reuse public proteomics datasets with two main objectives: i) to generate hypotheses (detection of biomarkers) for their posterior/downstream validation, and (ii) to validate, using an orthogonal approach, a previously described biomarker panel. Twelve CRC public proteomics datasets (mostly from the PRIDE database) were re-analysed and integrated to create a landscape of protein expression. Samples from both solid and liquid biopsies were included in the reanalysis. Integrating this data with survival annotation data, we have validated in silico a six-gene signature for CRC classification at the protein level, and identified five new blood-detectable biomarkers (CD14, PPIA, MRC2, PRDX1, and TXNDC5) associated with CRC prognosis. The prognostic value of these blood-derived proteins was confirmed using additional public datasets, supporting their potential clinical value. As a conclusion, this proof-of-the-concept study demonstrates the value of re-using public proteomics datasets as the basis to create a useful resource for biomarker discovery and validation. The protein expression data has been made available in the public resource Expression Atlas.

Analysis of polar primary metabolites in biological samples using targeted metabolomics and LC-MS

Primary metabolites are molecules of essential biochemical reactions that define the biological phenotype. All primary metabolites cannot be measured in a single analysis. In this protocol, we outline the multiplexed and quantitative measurement of 106 metabolites that cover the central part of primary metabolism. The protocol includes several sample preparation techniques and one liquid chromatography-mass spectrometry method. Then, we describe the steps of the bioinformatic data analysis to better understand the metabolic perturbations that may occur in a biological system. For complete details on the use and execution of this protocol, please refer to: Costanza et al.,1 Blomme et al.,2 Blomme et al.,3 Guillon et al.,4 Stuani et al.5.

Multiplexed targeted analysis of polyunsaturated fatty acids and oxylipins using liquid chromatography-tandem mass spectrometry

Polyunsaturated fatty acids (PUFAs) and their oxidized products (oxylipins) are important mediators in intra- and extra-cellular signaling. We describe here the simultaneous quantification of 163 PUFAs and oxylipins using liquid chromatography-mass spectrometry (LC-MS). The protocol details steps for PUFA purification from various biological materials, the conditions for LC-MS analysis, as well as quantitative approaches for data evaluation. We provide an example of PUFA quantification in animal tissue along with the bioinformatic protocol, enabling efficient inter-sample comparison and statistical analysis. For complete details on the use and execution of this protocol, please refer to Vila et al.,1 Costanza et al.,2 Blomme et al.,3 and Blomme et al.4.

Inflammation-related citrullination of matrisome proteins in human cancer

INTRODUCTION

Protein arginine deiminases (PADs) are intracellular enzymes that may, especially in pathological conditions, also citrullinate extracellular substrates, including matrisome proteins such as structural proteins in extracellular matrix (ECM). PADs are abundantly expressed in human cancer cells. Citrullination of matrisome proteins has been reported in colon cancer but the phenomenon has never been systematically studied.

METHODS

To gain a broader view of citrullination of matrisome proteins in cancer, we analyzed cancer proteomics data sets in 3 public databases for citrullinated matrisome proteins. In addition, we used three-dimensional cell cocultures of fibroblasts and cancer cells and analyzed citrullination of ECM.

RESULTS AND DISCUSSION

Our new analysis indicate that citrullination of ECM occurs in human cancer, and there is a significant variation between tumors. Most frequently citrullinated proteins included fibrinogen and fibronectin, which are typically citrullinated in rheumatoid inflammation. We also detected correlation between immune cell marker proteins, matrix metalloproteinases and ECM citrullination, which suggests that in cancer, citrullination of matrisome proteins is predominantly an inflammation-related phenomenon. This was further supported by our analysis of three-dimensional spheroid co-cultures of nine human cancer cell lines and fibroblasts by mass spectrometry, which gave no evidence that cancer cells or fibroblasts could citrullinate matrisome proteins in tumor stroma. It also appears that in the spheroid cultures, matrisome proteins are protected from citrullination.

High-frequency electrical stimulation attenuates neuronal release of inflammatory mediators and ameliorates neuropathic pain

BACKGROUND

Neuroinflammation is an important driver of acute and chronic pain states. Therefore, targeting molecular mediators of neuroinflammation may present an opportunity for developing novel pain therapies. In preclinical models of neuroinflammatory pain, calcitonin gene-related peptide (CGRP), substance P and high mobility group box 1 protein (HMGB1) are molecules synthesized and released by sensory neurons which activate inflammation and pain. High-frequency electrical nerve stimulation (HFES) has achieved clinical success as an analgesic modality, but the underlying mechanism is unknown. Here, we reasoned that HFES inhibits neuroinflammatory mediator release by sensory neurons to reduce pain.

METHODS

Utilizing in vitro and in vivo assays, we assessed the modulating effects of HFES on neuroinflammatory mediator release by activated sensory neurons. Dorsal root ganglia (DRG) neurons harvested from wildtype or transgenic mice expressing channelrhodopsin-2 (ChR2) were cultured on micro-electrode arrays, and effect of HFES on optogenetic- or capsaicin-induced neuroinflammatory mediator release was determined. Additionally, the effects of HFES on local neuroinflammatory mediator release and hyperalgesia was assessed in vivo using optogenetic paw stimulation and the neuropathic pain model of chronic constriction injury (CCI) of the sciatic nerve.

RESULTS

Light- or capsaicin-evoked neuroinflammatory mediator release from cultured transgenic DRG sensory neurons was significantly reduced by concurrent HFES (10 kHz). In agreement with these findings, elevated levels of neuroinflammatory mediators were detected in the affected paw following optogenetic stimulation or CCI and were significantly attenuated using HFES (20.6 kHz for 10 min) delivered once daily for 3 days.

CONCLUSION

These studies reveal a previously unidentified mechanism for the pain-modulating effect of HFES in the setting of acute and chronic nerve injury. The results support the mechanistic insight that HFES may reset sensory neurons into a less pro-inflammatory state via inhibiting the release of neuroinflammatory mediators resulting in reduced inflammation and pain.

Paladin, overexpressed in colon cancer, is required for actin polymerisation and liver metastasis dissemination

Introduction

Colorectal cancer remains a public health issue and most colon cancer patients succumb to the development of metastases. Using a specific protocol of pressure-assisted interstitial fluid extrusion to recover soluble biomarkers, we identified paladin as a potential colon cancer liver metastases biomarker.

Methods

Using shRNA gene knockdown, we explored the biological function of paladin in colon cancer cells and investigated the phospho-proteome within colon cancer cells. We successively applied in vitro migration assays, in vivo metastasis models and co-immunoprecipitation experiments.

Results

We discovered that paladin is required for colon cancer cell migration and metastasis, and that paladin depletion altered the phospho-proteome within colon cancer cells. Data are available via ProteomeXchange with identifier PXD030803. Thanks to immunoprecipitation experiments, we demonstrated that paladin, was interacting with SSH1, a phosphatase involved in colon cancer metastasis. Finally, we showed that paladin depletion in cancer cells results in a less dynamic actin cytoskeleton.

Conclusions

Paladin is an undervalued protein in oncology. This study highlights for the first time that, paladin is participating in actin cytoskeleton remodelling and is required for efficient cancer cell migration.

New Proteins Contributing to Immune Cell Infiltration and Pannus Formation of Synovial Membrane from Arthritis Diseases

An inflamed synovial membrane plays a major role in joint destruction and is characterized by immune cells infiltration and fibroblast proliferation. This proteomic study considers the inflammatory process at the molecular level by analyzing synovial biopsies presenting a histological inflammatory continuum throughout different arthritis joint diseases. Knee synovial biopsies were obtained from osteoarthritis (OA; n = 9), chronic pyrophosphate arthropathy (CPPA; n = 7) or rheumatoid arthritis (RA; n = 8) patients. The histological inflammatory score was determined using a semi-quantitative scale based on synovial hyperplasia, lymphocytes, plasmocytes, neutrophils and macrophages infiltration. Proteomic analysis was performed by liquid chromatography-mass spectrometry (LC-MS/MS). Differentially expressed proteins were confirmed by immunohistochemistry. Out of the 1871 proteins identified and quantified by LC-MS/MS, 10 proteins (LAP3, MANF, LCP1, CTSZ, PTPRC, DNAJB11, EML4, SCARA5, EIF3K, C1orf123) were differentially expressed in the synovial membrane of at least one of the three disease groups (RA, OA and CPPA). Significant increased expression of the seven first proteins was detected in RA and correlated to the histological inflammatory score. Proteomics is therefore a powerful tool that provides a molecular pattern to the classical histology usually applied for synovitis characterization. Except for LCP1, CTSZ and PTPRC, all proteins have never been described in human synovitis.

Extracellular HMGB1 blockade inhibits tumor growth through profoundly remodeling immune microenvironment and enhances checkpoint inhibitor-based immunotherapy

BACKGROUND

High-mobility group box 1 (HMGB1) is a multifunctional redox-sensitive protein involved in various intracellular (eg, chromatin remodeling, transcription, autophagy) and extracellular (inflammation, autoimmunity) processes. Regarding its role in cancer development/progression, paradoxical results exist in the literature and it is still unclear whether HMGB1 mainly acts as an oncogene or a tumor suppressor.

METHODS

HMGB1 expression was first assessed in tissue specimens (n=359) of invasive breast, lung and cervical cancer and the two distinct staining patterns detected (nuclear vs cytoplasmic) were correlated to the secretion profile of malignant cells, patient outcomes and the presence of infiltrating immune cells within tumor microenvironment. Using several orthotopic, syngeneic mouse models of basal-like breast (4T1, 67NR and EpRas) or non-small cell lung (TC-1) cancer, the efficacy of several HMGB1 inhibitors alone and in combination with immune checkpoint blockade antibodies (anti-PD-1/PD-L1) was then investigated. Isolated from retrieved tumors, 14 immune cell (sub)populations as well as the activation status of antigen-presenting cells were extensively analyzed in each condition. Finally, the redox state of HMGB1 in tumor-extruded fluids and the influence of different forms (oxidized, reduced or disulfide) on both dendritic cell (DC) and plasmacytoid DC (pDC) activation were determined.

RESULTS

Associated with an unfavorable prognosis in human patients, we clearly demonstrated that targeting extracellular HMGB1 elicits a profound remodeling of tumor immune microenvironment for efficient cancer therapy. Indeed, without affecting the global number of (CD45+) immune cells, drastic reductions of monocytic/granulocytic myeloid-derived suppressor cells (MDSC) and regulatory T lymphocytes, a higher M1/M2 ratio of macrophages as well as an increased activation of both DC and pDC were continually observed following HMGB1 inhibition. Moreover, blocking HMGB1 improved the efficacy of anti-PD-1 cancer monoimmunotherapy. We also reported that a significant fraction of HMGB1 encountered within cancer microenvironment (interstitial fluids) is oxidized and, in opposite to its reduced isoform, oxidized HMGB1 acts as a tolerogenic signal in a receptor for advanced glycation endproducts-dependent manner.

CONCLUSION

Collectively, we present evidence that extracellular HMGB1 blockade may complement first-generation cancer immunotherapies by remobilizing antitumor immune response.

Proteins involved in the endoplasmic reticulum stress are modulated in synovitis of osteoarthritis, chronic pyrophosphate arthropathy and rheumatoid arthritis, and correlate with the histological inflammatory score

It is now well recognized that osteoarthritis (OA) synovial membrane presents inflammatory components. The aim of this work is to provide evidence that similar inflammatory mechanisms exist in synovial membrane (n = 24) obtained from three pathologies presenting altogether an inflammatory gradient: OA, chronic pyrophosphate arthropathy (CPPA) and rheumatoid arthritis (RA). Synovial biopsies were first characterized by a histological score based on synovial hyperplasia and infiltration of lymphocytes, plasma cells, polymorphonuclear and macrophages. All biopsies were also analyzed by 2D-nano-UPLC-ESI-Q-Orbitrap for protein identification and quantification. Protein levels were correlated with the histological score. Histological score was in the range of 3 to 8 for OA, 5 to 13 for CPPA and 12 to 17 for RA. Of the 4,336 proteins identified by mass spectrometry, 51 proteins were selected for their strong correlation (p < 0.001) with the histological score of which 11 proteins (DNAJB11, CALR, ERP29, GANAB, HSP90B1, HSPA1A, HSPA5, HYOU1, LMAN1, PDIA4, and TXNDC5) were involved in the endoplasmic reticulum (ER) stress. Protein levels of S100A8 and S100A9 were significantly higher in RA compared to OA (for both) or to CPPA (for S100A8 only) and also significantly correlated with the histological score. Eighteen complement component proteins were identified, but only C1QB and C1QBP were weakly correlated with the histological score. This study highlights the inflammatory gradient existing between OA, CPPA and RA synovitis either at the protein level or at the histological level. Inflamed synovitis was characterized by the overexpression of ER stress proteins.

Normalization Method Utilizing Endogenous Proteins for Quantitative Proteomics

We developed a normalization method utilizing the expression levels of a panel of endogenous proteins as normalization standards (EPNS herein). We tested the validity of the method using two sets of tandem mass tag (TMT)-labeled data and found that this normalization method effectively reduced global intensity bias at the protein level. The coefficient of variation (CV) of the overall median was reduced by 55% and 82% on average, compared to the reduction by 72% and 86% after normalization using the upper quartile. Furthermore, we used differential protein expression analysis and statistical learning to identify biomarkers for colorectal cancer from a CPTAC data set. The expression changes of a panel of proteins, including NUP205, GTPBP4, CNN2, GNL3, and S100A11, all of which highly correlate with colorectal cancer. Applying these five proteins as model features, random forest modeling obtained prediction results with the maximum AUC of 0.9998 using EPNS-normalized data, comparing favorably to the AUC of 0.9739 using the raw data. Thus, the normalization method based on EPNS reduced the global intensity bias and is applicable for quantitative proteomic analysis.