Proteomic profiling of the cancer cell secretome: informing clinical research

Characterizing the secretome of EGFR mutant lung adenocarcinoma

Background

Lung cancer is the leading cause of cancer related death worldwide, mainly due to the late stage of disease at the time of diagnosis. Non-invasive biomarkers are needed to supplement existing screening methods to enable earlier detection and increased patient survival. This is critical to EGFR-driven lung adenocarcinoma as it commonly occurs in individuals who have never smoked and do not qualify for current screening protocols.

Methods

In this study, we performed mass spectrometry analysis of the secretome of cultured lung cells representing different stages of mutant EGFR driven transformation, from normal to fully malignant. Identified secreted proteins specific to the malignant state were validated using orthogonal methods and their clinical activity assessed in lung adenocarcinoma patient cohorts.

Results

We quantified 1020 secreted proteins, which were compared for differential expression between stages of transformation. We validated differentially expressed proteins at the transcriptional level in clinical tumor specimens, association with patient survival, and absolute concentration to yield three biomarker candidates: MDK, GDF15, and SPINT2. These candidates were validated using ELISA and increased levels were associated with poor patient survival specifically in EGFR mutant lung adenocarcinoma patients.

Conclusions

Our study provides insight into changes in secreted proteins during EGFR driven lung adenocarcinoma transformation that may play a role in the processes that promote tumor progression. The specific candidates identified can harnessed for biomarker use to identify high risk individuals for early detection screening programs and disease management for this molecular subgroup of lung adenocarcinoma patients.

Secretome Screening of BRAFV600E-Mutated Colon Cancer Cells Resistant to Vemurafenib

Patients with metastatic colorectal cancer (mCRC) carrying BRAFV600E mutation have worse response to chemotherapy and poor prognosis. The BRAFV600E inhibitor vemurafenib has shown modest efficacy as monotherapy in BRAF-mutated mCRC due to the development of resistance. The aim of this study was to conduct a comparative proteomics profiling of the secretome from vemurafenib-sensitive vs. -resistant colon cancer cells harboring BRAFV600E mutation in order to identify specific secretory features potentially associated with changes in the resistant cells' phenotype. Towards this aim, we employed two complementary proteomics approaches including two-dimensional gel electrophoresis coupled with MALDI-TOF/TOF mass spectrometry and label-free quantitative LC-MS/MS analysis. Obtained results pointed to aberrant regulation of DNA replication and endoplasmic reticulum stress as the major secretome features associated with chemoresistant phenotype. Accordingly, two proteins implicated in these processes including RPA1 and HSPA5/GRP78 were discussed in more details in the context of biological networks and their importance as potential secretome targets for further functional and clinical evaluation. Expression patterns of RPA1 and HSPA5/GRP78 in tumor tissues from colon cancer patients were also found in additional in silico analyses to be associated with BRAFV600E mutation status, which opens the possibility to extrapolate our findings and their clinical implication to other solid tumors harboring BRAFV600E mutation, such as melanoma.

Deciphering the underlying mechanism of liver diseases through utilization of multicellular hepatic spheroid models

Hepatocellular carcinoma (HCC) is a very common form of cancer worldwide and is often fatal. Although the histopathology of HCC is characterized by metabolic pathophysiology, fibrosis, and cirrhosis, the focus of treatment has been on eliminating HCC. Recently, three-dimensional (3D) multicellular hepatic spheroid (MCHS) models have provided a) new therapeutic strategies for progressive fibrotic liver diseases, such as antifibrotic and anti-inflammatory drugs, b) molecular targets, and c) treatments for metabolic dysregulation. MCHS models provide a potent anti-cancer tool because they can mimic a) tumor complexity and heterogeneity, b) the 3D context of tumor cells, and c) the gradients of physiological parameters that are characteristic of tumors in vivo. However, the information provided by an multicelluar tumor spheroid (MCTS) model must always be considered in the context of tumors in vivo. This mini-review summarizes what is known about tumor HCC heterogeneity and complexity and the advances provided by MCHS models for innovations in drug development to combat liver diseases. [BMB Reports 2023; 56(4): 225-233].

Secreted protease PRSS35 suppresses hepatocellular carcinoma by disabling CXCL2-mediated neutrophil extracellular traps

Hepatocytes function largely through the secretion of proteins that regulate cell proliferation, metabolism, and intercellular communications. During the progression of hepatocellular carcinoma (HCC), the hepatocyte secretome changes dynamically as both a consequence and a causative factor in tumorigenesis, although the full scope of secreted protein function in this process remains unclear. Here, we show that the secreted pseudo serine protease PRSS35 functions as a tumor suppressor in HCC. Mechanistically, we demonstrate that active PRSS35 is processed via cleavage by proprotein convertases. Active PRSS35 then suppresses protein levels of CXCL2 through targeted cleavage of tandem lysine (KK) recognition motif. Consequently, CXCL2 degradation attenuates neutrophil recruitment to tumors and formation of neutrophil extracellular traps, ultimately suppressing HCC progression. These findings expand our understanding of the hepatocyte secretome's role in cancer development while providing a basis for the clinical translation of PRRS35 as a therapeutic target or diagnostic biomarker.

Proteomics of Human Biological Fluids for Biomarker Discoveries: Technical Advances and Recent Applications

INTRODUCTION

Biological fluids are routine samples for diagnostic testing and monitoring. Blood samples are typically measured because of their moderate collection invasiveness and high information content on health and disease. Several body fluids, such as cerebrospinal fluid (CSF), are also studied and suited to specific pathologies. Over the last two decades proteomics has quested to identify protein biomarkers but with limited success. Recent technologies and refined pipelines have accelerated the profiling of human biological fluids.

AREAS COVERED

We review proteomic technologies for the identification of biomarkers. Those are based on antibodies/aptamers arrays or mass spectrometry (MS), but new ones are emerging. Advances in scalability and throughput have allowed to better design studies and cope with the limited sample size that had until now prevailed due to technological constraints. With these enablers, plasma/serum, CSF, saliva, tears, urine, and milk proteomes have been further profiled; we provide a non-exhaustive picture of some recent highlights (mainly covering literature from last five years in the Scopus database) using MS-based proteomics.

EXPERT OPINION

While proteomics has been in the shadow of genomics for years, proteomic tools and methodologies have reached a certain maturity. They are better suited to discover innovative and robust biofluid biomarkers.

Proteomic profiling of extracellular vesicles and particles reveals the cellular response to cisplatin in NSCLC

BACKGROUND

Cisplatin-based chemotherapy is a therapeutic strategy against non-small cell lung cancer (NSCLC). However, cancers relapse after chemotherapy due to a dormant state of residual cancer cells. Extracellular vesicles and particles (EVPs) are active carriers of proteins and nucleic acid. Here, we aimed to study the molecular alterations and proteomic characteristics of EPV in dormant and reactivated cancer cells induced by cisplatin.

METHODS

We used a short-term single dose of cisplatin to induce the dormant and reactivated cell status. We examined the gene expressional profiling and proteomic profiling of EVPs from dormant and reactivated cancer cells by RNA-sequencing and LC-MS/MS.

RESULTS

We found substantial changes in gene expression and protein level in EVP. The genes with higher expression in dormant cancer cells were lipid transporter- and lipid metabolic-related genes. A total of 111 EVP proteins were upregulated in dormant cancer cells compared to those in control cells. Fifty differential expressed proteins (DEPs) were identified in EVPs from reactivated cancer cells compared to those in dormant cancer cells. Among the DEPs, we found that apolipoproteins such as APOA1 and APOE were significantly increased in dormant cancer cell-derived EVPs. Integration of EVP proteomes with transcriptional profiles of cancer cells revealed that the proteomic profiling of EVP derived from cancer cells can reflect the cellular status of cancer cells, which showed an activated lipid metabolism in dormant state.

CONCLUSION

Lipoproteins enriched in EVPs reflect the activated lipid metabolism in dormant cancer cells and may provide potential biomarkers or therapeutic targets for cisplatin-based therapy.

Gliome database: a comprehensive web-based tool to access and analyze glia secretome data

Glial cells are phenotypically heterogeneous non-neuronal components of the central and peripheral nervous systems. These cells are endowed with diverse functions and molecular machineries to detect and regulate neuronal or their own activities by various secreted mediators, such as proteinaceous factors. In particular, glia-secreted proteins form a basis of a complex network of glia-neuron or glia-glia interactions in health and diseases. In recent years, the analysis and profiling of glial secretomes have raised new expectations for the diagnosis and treatment of neurological disorders due to the vital role of glia in numerous physiological or pathological processes of the nervous system. However, there is no online database of glia-secreted proteins available to facilitate glial research. Here, we developed a user-friendly 'Gliome' database (available at www.gliome.org), a web-based tool to access and analyze glia-secreted proteins. The database provides a vast collection of information on 3293 proteins that are released from glia of multiple species and have been reported to have differential functions under diverse experimental conditions. It contains a web-based interface with the following four key features regarding glia-secreted proteins: (i) fundamental information, such as signal peptide, SecretomeP value, functions and Gene Ontology category; (ii) differential expression patterns under distinct experimental conditions; (iii) disease association; and (iv) interacting proteins. In conclusion, the Gliome database is a comprehensive web-based tool to access and analyze glia-secretome data obtained from diverse experimental settings, whereby it may facilitate the integration of bioinformatics into glial research.

Genetic predisposition and prediction protocol for epithelial neoplasms in disease-free individuals: A systematic review

Background

Epithelial neoplasm is an important global health-care problem, with high morbidity and mortality rates. Early diagnosis and appropriate treatment are essential for increased life survival. Prediction of occurrence of malignancy in a disease-free individual by any means will be a great breakthrough for healthy living.

Aims and Objectives

The aims and objectives were to predict the genetic predisposition and propose a prediction protocol for epithelial malignancy of various systems in our body, in a disease-free individual.

Methods

We have searched databases both manually and electronically, published in English language in Cochrane group, Google search, MEDLINE and PubMed from 2000 to 2019. We have included all the published, peer-reviewed, narrative reviews; randomized controlled trials; case-control studies; and cohort studies and excluded the abstract-only articles and duplicates. Specific words such as "etiological factors," "pathology and mutations," "signs and symptoms," "genetics and IHC marker," and "treatment outcome" were used for the search. A total of 1032 citations were taken, and only 141 citations met the inclusion criteria and were analyzed.

Results

After analyzing various articles, the etiological factors, clinical signs and symptoms, genes and the pathology involved and the commonly used blood and tissue markers were analyzed. A basic investigation strategy using immunohistochemistry markers was established.

Conclusion

The set of proposed biomarkers should be studied in future to predict genetic predisposition in disease-free individuals.

Pattern-recognition-based Sensor Arrays for Cell Characterization: From Materials and Data Analyses to Biomedical Applications

To capture a broader scope of complex biological phenomena, alternatives to conventional sensing based on specificity for cell detection and characterization are needed. Pattern-recognition-based sensing is an analytical method designed to mimic mammalian sensory systems for analyte identification based on the pattern recognition of multivariate data, which are generated using an array of multiple probes that cross-reactively interact with analytes. This sensing approach is significantly different from conventional specific cell sensing based on highly specific probes, including antibodies against biomarkers. Encouraged by the advantages of this technique, such as the simplicity, rapidity, and tunability of the systems without requiring a priori knowledge of biomarkers, numerous sensor arrays have been developed over the past decade and used in a variety of cell sensing applications; these include disease diagnosis, drug discovery, and fundamental research. This review summarizes recent progress in pattern-recognition-based cell sensing, with a particular focus on guidelines for designing materials and arrays, techniques for analyzing response patterns, and applications of sensor systems that are focused primarily for the biomedical field.

An Integrated Meta-Analysis of Secretome and Proteome Identify Potential Biomarkers of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is extremely aggressive, has an unfavorable prognosis, and there are no biomarkers for early detection of the disease or identification of individuals at high risk for morbidity or mortality. The cellular and molecular complexity of PDAC leads to inconsistences in clinical validations of many proteins that have been evaluated as prognostic biomarkers of the disease. The tumor secretome, a potential source of biomarkers in PDAC, plays a crucial role in cell proliferation and metastasis, as well as in resistance to treatments, which together contribute to a worse clinical outcome. The massive amount of proteomic data from pancreatic cancer that has been generated from previous studies can be integrated and explored to uncover secreted proteins relevant to the diagnosis and prognosis of the disease. The present study aimed to perform an integrated meta-analysis of PDAC proteome and secretome public data to identify potential biomarkers of the disease. Our meta-analysis combined mass spectrometry data obtained from two systematic reviews of the pancreatic cancer literature, which independently selected 20 studies of the secretome and 35 of the proteome. Next, we predicted the secreted proteins using seven in silico tools or databases, which identified 39 secreted proteins shared between the secretome and proteome data. Notably, the expression of 31 genes of these secretome-related proteins was upregulated in PDAC samples from The Cancer Genome Atlas (TCGA) when compared to control samples from TCGA and The Genotype-Tissue Expression (GTEx). The prognostic value of these 39 secreted proteins in predicting survival outcome was confirmed using gene expression data from four PDAC datasets (validation set). The gene expression of these secreted proteins was able to distinguish high- and low-survival patients in nine additional tumor types from TCGA, demonstrating that deregulation of these secreted proteins may also contribute to the prognosis in multiple cancers types. Finally, we compared the prognostic value of the identified secreted proteins in PDAC biomarkers studies from the literature. This analysis revealed that our gene signature performed equally well or better than the signatures from these previous studies. In conclusion, our integrated meta-analysis of PDAC proteome and secretome identified 39 secreted proteins as potential biomarkers, and the tumor gene expression profile of these proteins in patients with PDAC is associated with worse overall survival.

Tumor Transcriptome Reveals High Expression of IL-8 in Non-Small Cell Lung Cancer Patients with Low Pectoralis Muscle Area and Reduced Survival

Cachexia is a syndrome characterized by an ongoing loss of skeletal muscle mass associated with poor patient prognosis in non-small cell lung cancer (NSCLC). However, prognostic cachexia biomarkers in NSCLC are unknown. Here, we analyzed computed tomography (CT) images and tumor transcriptome data to identify potentially secreted cachexia biomarkers (PSCB) in NSCLC patients with low-muscularity. We integrated radiomics features (pectoralis muscle, sternum, and tenth thoracic (T10) vertebra) from CT of 89 NSCLC patients, which allowed us to identify an index for screening muscularity. Next, a tumor transcriptomic-based secretome analysis from these patients (discovery set) was evaluated to identify potential cachexia biomarkers in patients with low-muscularity. The prognostic value of these biomarkers for predicting recurrence and survival outcome was confirmed using expression data from eight lung cancer datasets (validation set). Finally, C2C12 myoblasts differentiated into myotubes were used to evaluate the ability of the selected biomarker, interleukin (IL)-8, in inducing muscle cell atrophy. We identified 75 over-expressed transcripts in patients with low-muscularity, which included IL-6, CSF3, and IL-8. Also, we identified NCAM1, CNTN1, SCG2, CADM1, IL-8, NPTX1, and APOD as PSCB in the tumor secretome. These PSCB were capable of distinguishing worse and better prognosis (recurrence and survival) in NSCLC patients. IL-8 was confirmed as a predictor of worse prognosis in all validation sets. In vitro assays revealed that IL-8 promoted C2C12 myotube atrophy. Tumors from low-muscularity patients presented a set of upregulated genes encoding for secreted proteins, including pro-inflammatory cytokines that predict worse overall survival in NSCLC. Among these upregulated genes, IL-8 expression in NSCLC tissues was associated with worse prognosis, and the recombinant IL-8 was capable of triggering atrophy in C2C12 myotubes.

[Liquid biopsy in tumor diagnostics : Applications, perspectives, and limitations of the "cancer liquidome"]

The detection of tumor-specific genetic alterations in body fluids as an addition to or even replacement for established tissue-based tumor diagnostics is currently a hot topic in academic research and industry. Progress in methods for nucleic acid analyses together with promising results from clinical studies have raised great expectations for cancer screening, diagnosis, prognosis, and therapy monitoring by means of a minimally invasive blood draw. Individual focused assays have already been introduced into routine diagnostics and represent a valuable option in cases where no tissue samples are available. However, before the use of liquid biopsy outside of clinical studies is enforced and more complex markers (like tumor mutational burden) are analyzed, several practical challenges and principal problems have to be addressed. This review focusses on the detection of free-circulating nucleic acids in blood plasma and critically discusses established and future applications as well as challenges and limitations of this new method.

Subtype-specific secretomic characterization of pulmonary neuroendocrine tumor cells

Pulmonary neuroendocrine (NE) cancer, including small cell lung cancer (SCLC), is a particularly aggressive malignancy. The lineage-specific transcription factors Achaete-scute homolog 1 (ASCL1), NEUROD1 and POU2F3 have been reported to identify the different subtypes of pulmonary NE cancers. Using a large-scale mass spectrometric approach, here we perform quantitative secretome analysis in 13 cell lines that signify the different NE lung cancer subtypes. We quantify 1,626 proteins and identify IGFBP5 as a secreted marker for ASCL1^High SCLC. ASCL1 binds to the E-box elements in IGFBP5 and directly regulates its transcription. Knockdown of ASCL1 decreases IGFBP5 expression, which, in turn, leads to hyperactivation of IGF-1R signaling. Pharmacological co-targeting of ASCL1 and IGF-1R results in markedly synergistic effects in ASCL1^High SCLC in vitro and in mouse models. We expect that this secretome resource will provide the foundation for future mechanistic and biomarker discovery studies, helping to delineate the molecular underpinnings of pulmonary NE tumors.

Secreted proteins present a rich resource of potential cancer biomarkers. Here, the authors use mass spectrometry to analyze secretome remodeling in pulmonary neuroendocrine lung cancer cell lines and validate potential biomarkers and therapeutic targets in vitro and in mouse models.

Metal-Organic Framework Nanoparticle-Based Biomineralization: A New Strategy toward Cancer Treatment

Cancer treatment using functional proteins, DNA/RNA, or complex bio-entities is important in both preclinical and clinical studies. With the help of nano-delivery systems, these biomacromolecules can enrich cancer tissues to match the clinical requirements. Biomineralization via a self-assembly process has been widely applied to provide biomacromolecules exoskeletal-like protection for immune shielding and preservation of bioactivity. Advanced metal-organic framework nanoparticles (MOFs) are excellent supporting matrices due to the low toxicity of polycarboxylic acids and metals, high encapsulation efficiency, and moderate synthetic conditions. In this review, we study MOFs-based biomineralization for cancer treatment and summarize the unique properties of MOF hybrids. We also evaluate the outlook of potential cancer treatment applications for MOFs-based biomineralization. This strategy likely opens new research orientations for cancer theranostics.

Prediction of lymph node metastasis in oral squamous cell carcinoma based on protein profile

OBJECTIVE

Lymph node metastasis leads to high mortality rates of oral squamous cell carcinoma (OSCC). However, it is still controversial to define clinically negative neck (cN0) and positive neck (cN1-3).

METHODS

We retrieved candidate biomarkers identified by proteomic analysis in OSCC from published works of literature. In training stage, immunohistochemistry (IHC) analysis was used to determine the expression of proteins and logistic regression models with stepwise variable selection were used to identify potential factors that might affect lymph node metastasis and life status. Furthermore, the prediction model was validated in validating stage.

RESULTS

We screened eight highly expressed proteins related to lymph node metastasis in OSCC and found that the expression levels of SOD2, BST2, CAD, ITGB6, and PRDX4 were significantly elevated in patients with lymph node metastasis compared to the patients without lymph node metastasis. Furthermore, in training and validating stages, the prediction model base on the combination of CAD, SOD2 expression levels, and histopathologic grade was developed and validated in patients with OSCC.

CONCLUSIONS

Our findings showed that the developed model well predicts the lymph node metastasis and life status in patients with OSCC, independent of TNM stage.

Biomarkers: Delivering on the expectation of molecularly driven, quantitative health

Biomarkers are the pillars of precision medicine and are delivering on expectations of molecular, quantitative health. These features have made clinical decisions more precise and personalized, but require a high bar for validation. Biomarkers have improved health outcomes in a few areas such as cancer, pharmacogenetics, and safety. Burgeoning big data research infrastructure, the internet of things, and increased patient participation will accelerate discovery in the many areas that have not yet realized the full potential of biomarkers for precision health. Here we review themes of biomarker discovery, current implementations of biomarkers for precision health, and future opportunities and challenges for biomarker discovery. Impact statement Precision medicine evolved because of the understanding that human disease is molecularly driven and is highly variable across patients. This understanding has made biomarkers, a diverse class of biological measurements, more relevant for disease diagnosis, monitoring, and selection of treatment strategy. Biomarkers' impact on precision medicine can be seen in cancer, pharmacogenomics, and safety. The successes in these cases suggest many more applications for biomarkers and a greater impact for precision medicine across the spectrum of human disease. The authors assess the status of biomarker-guided medical practice by analyzing themes for biomarker discovery, reviewing the impact of these markers in the clinic, and highlight future and ongoing challenges for biomarker discovery. This work is timely and relevant, as the molecular, quantitative approach of precision medicine is spreading to many disease indications.