Recent mass spectrometry-based proteomics for biomarker discovery in lung cancer,COPD, and asthma

Introduction: Lung cancer and related diseases have been one of the most common causes of deaths worldwide. Genomic-based biomarkers may hardly reflect the underlying dynamic molecular mechanism of functional protein interactions, which is the center of a disease. Recent developments in mass spectrometry (MS) have made it possible to analyze disease-relevant proteins expressed in clinical specimens by proteomic challenges.

Areas covered: To understand the molecular mechanisms of lung cancer and its subtypes, chronic obstructive pulmonary disease (COPD), asthma and others, great efforts have been taken to identify numerous relevant proteins by MS-based clinical proteomic approaches. Since lung cancer is a multifactorial disease that is biologically associated with asthma and COPD among various lung diseases, this study focused on proteomic studies on biomarker discovery using various clinical specimens for lung cancer, COPD, and asthma.

Expert commentary: MS-based exploratory proteomics utilizing clinical specimens, which can incorporate both experimental and bioinformatic analysis of protein-protein interaction and also can adopt proteogenomic approaches, makes it possible to reveal molecular networks that are relevant to a disease subgroup and that could differentiate between drug responders and non-responders, good and poor prognoses, drug resistance, and so on.     

Proteomics advances for precision therapy in ovarian cancer

ABSTRACT

Introduction: Due to the relatively low mutation rate and high frequency of copy number variation, finding actionable genetic drivers of high-grade serous carcinoma (HGSC) is a challenging task. Furthermore, emerging studies show that genetic alterations are frequently poorly represented at the protein level adding a layer of complexity. With improvements in large-scale proteomic technologies, proteomics studies have the potential to provide robust analysis of the pathways driving high HGSC behavior.

Areas covered: This review summarizes recent large-scale proteomics findings across adequately sized ovarian cancer sample sets. Key words combined with ‘ovarian cancer’ including ‘proteomics’, ‘proteogenomic’, ‘reverse-phase protein array’, ‘mass spectrometry’, and ‘adaptive response’, were used to search PubMed.

Expert opinion: Proteomics analysis of HGSC as well as their adaptive responses to therapy can uncover new therapeutic liabilities, which can reduce the emergence of drug resistance and potentially improve patient outcomes. There is a pressing need to better understand how the genomic and epigenomic heterogeneity intrinsic to ovarian cancer is reflected at the protein level and how this information could be used to improve patient outcomes.     

Understanding extracellular vesicle diversity – current status

ABSTRACT

Introduction: Extracellular vesicles (EVs) represent an important mode of intercellular communication. There is now a growing awareness that predominant EV subtypes; exosomes from endosomal origin, and shed microvesicles from plasma membrane budding, can be further stratified into distinct subtypes, however specific approaches in their isolation and markers that allow them to be discriminated are lacking.

Areas covered: Knowledge about these distinct EV subpopulations is important including the regulation of composition, release, targeting/localization, uptake, and function. This review discusses the mechanisms of distinct EV biogenesis and release, defining select EV classes (and subpopulations), which will be crucial for development of EV-based functions and clinical applications. We review the dynamics of cargo sorting leading to the mechanisms of EV heterogeneity, their mechanisms of formation, intracellular trafficking pathways, and provide an uptake about biochemical/functional differences. With advances in purification strategies and proteomic-based quantitation, allows significant benefit in accurately describing differences in EV protein cargo composition and modification.

Expert commentary: The advent of quantitative mass spectrometry-based proteomics, in conjunction with advances in molecular cell biology, and EV purification strategies, has contributed significantly to our improved characterization and understanding of the molecular composition and functionality of these distinct EV subpopulations.     

Predictive biochemical-markers for the development of brain metastases from lung cancer: Clinical evidence and future directions

BackgroundBrain metastases are a common complication of patients with lung cancer and lung cancer is one of the most common causes of brain metastases. The occurrence of brain metastases is associated with poor prognosis and high morbidity, even after intensive multimodal therapy. Therefore, identifying lung cancer patients with who are at high risk of developing brain metastases and applying effect intervention is important to reduce or delay the incidence of brain metastases. Biochemical-markers may meet an unmet need for following patients’ mechanisms of brain metastases.MethodsData for this review were identified by searches of Pubmed and Cochrane databases, and references from relevant articles using the search terms “lung cancer” and “brain metastasis”. Meeting abstracts, unpublished reports and review articles were not considered.ResultsClinical results for pathological and circulating markers including cancer molecular subtypes, miRNA, single nucleotide polymorphisms, and other markers are presented. However, these biochemical-markers are not yet established surrogate assessments for prediction of brain metastases.ConclusionsBiochemical-markers reported allowed physicians to identify which patients with lung cancer are at high risk for brain metastases. Prospective randomized clinical studies are needed to further assess the utility of these biochemical-markers.     

Measurement of the Girth of Coniferous Trees at Braemar in 1894

ABSTRACT

Background: Plant communities are usually characterised by species composition and abundance, but also underlie a multitude of complex interactions that we have only recently started unveiling. Yet, we are still far from understanding ecological and evolutionary processes shaping the network-level organisation of plant diversity, and to what extent these processes are specific to certain spatial scales or environments.

Aims: Understanding the systemic mechanisms of plant–plant network assembly and their consequences for diversity patterns.

Methods: We review recent methods and results of plant–plant networks.

Results: We synthetize how plant–plant networks can help us to: (a) assess how competition and facilitation may balance each other through the network; (b) analyse the role of plant–plant interactions beyond pairwise competition in structuring plant communities, and (c) forecast the ecological implications of complex species dependencies. We discuss pros and cons, assumptions and limitations of different approaches used for inferring plant–plant networks.

Conclusions: We propose novel opportunities for advancing plant ecology by using ecological networks that encompass different ecological levels and spatio-temporal scales, and incorporate more biological information. Embracing networks of interactions among plants can shed new light on mechanisms driving evolution and ecosystem functioning, helping us to mitigate diversity loss.     

Protein biomarkers for subtyping breast cancer and implications for future research

Introduction: Breast cancer subtypes are currently defined by a combination of morphologic, genomic, and proteomic characteristics. These subtypes provide a molecular portrait of the tumor that aids diagnosis, prognosis, and treatment escalation/de-escalation options. Gene expression signatures describing intrinsic breast cancer subtypes for predicting risk of recurrence have been rapidly adopted in the clinic. Despite the use of subtype classifications, many patients develop drug resistance, breast cancer recurrence, or therapy failure.

Areas covered: This review provides a summary of immunohistochemistry, reverse phase protein array, mass spectrometry, and integrative studies that are revealing differences in biological functions within and between breast cancer subtypes. We conclude with a discussion of rigor and reproducibility for proteomic-based biomarker discovery.

Expert commentary: Innovations in proteomics, including implementation of assay guidelines and standards, are facilitating refinement of breast cancer subtypes. Proteomic and phosphoproteomic information distinguish biologically functional subtypes, are predictive of recurrence, and indicate likelihood of drug resistance. Actionable, activated signal transduction pathways can now be quantified and characterized. Proteomic biomarker validation in large, well-designed studies should become a public health priority to capitalize on the wealth of information gleaned from the proteome.     

Recent insights into human bronchial proteomics – how are we progressing and what is next?

Introduction: The human respiratory system is highly prone to diseases and complications. Many lung diseases, including lung cancer (LC), tuberculosis (TB), and chronic obstructive pulmonary disease (COPD) have been among the most common causes of death worldwide. Cystic fibrosis (CF), the most common genetic disease in Caucasians, has adverse impacts on the lungs. Bronchial proteomics plays a significant role in understanding the underlying mechanisms and pathogenicity of lung diseases and provides insights for biomarker and therapeutic target discoveries.

Areas covered: We overview the recent achievements and discoveries in human bronchial proteomics by outlining how some of the different proteomic techniques/strategies are developed and applied in LC, TB, COPD, and CF. Also, the future roles of bronchial proteomics in predictive proteomics and precision medicine are discussed.

Expert commentary: Much progress has been made in bronchial proteomics. Owing to the advances in proteomics, we now have better ability to isolate proteins from desired cellular compartments, greater protein separation methods, more powerful protein detection technologies, and more sophisticated bioinformatic techniques. These all contributed to our further understanding of lung diseases and for biomarker and therapeutic target discoveries.     

A Hybrid Deep Learning Model for Predicting Molecular Subtypes of Human Breast Cancer Using Multimodal Data

BackgroundThe prediction of breast cancer subtypes plays a key role in the diagnosis and prognosis of breast cancer. In recent years, deep learning (DL) has shown good performance in the intelligent prediction of breast cancer subtypes. However, most of the traditional DL models use single modality data, which can just extract a few features, so it cannot establish a stable relationship between patient characteristics and breast cancer subtypes.DatasetWe used the TCGA-BRCA dataset as a sample set for molecular subtype prediction of breast cancer. It is a public dataset that can be obtained through the following link: https://portal.gdc.cancer.gov/projects/TCGA-BRCAMethodsIn this paper, a Hybrid DL model based on the multimodal data is proposed. We combine the patient's gene modality data with image modality data to construct a multimodal fusion framework. According to the different forms and states, we set up feature extraction networks respectively, and then we fuse the output of the two feature networks based on the idea of weighted linear aggregation. Finally, the fused features are used to predict breast cancer subtypes. In particular, we use the principal component analysis to reduce the dimensionality of high-dimensional data of gene modality and filter the data of image modality. Besides, we also improve the traditional feature extraction network to make it show better performance.ResultsThe results show that compared with the traditional DL model, the Hybrid DL model proposed in this paper is more accurate and efficient in predicting breast cancer subtypes. Our model achieved a prediction accuracy of 88.07% in 10 times of 10-fold cross-validation. We did a separate AUC test for each subtype, and the average AUC value obtained was 0.9427. In terms of subtype prediction accuracy, our model is about 7.45% higher than the previous average.     

Proteomic interrogation of HSP90 and insights for medical research

Introduction: Heat shock protein 90 (HSP90) regulates protein homeostasis in eukaryotes. As a ‘professional interactor’, HSP90 binds to and chaperones many proteins and has both housekeeping and disease-related functions but its regulation remains in part elusive. HSP90 complexes are a target for therapy, notably against cancer, and several inhibitors are currently in clinical trials. Proteomic studies have revealed the vast interaction network of HSP90 and, in doing so, the extent of cellular processes the chaperone takes part in, especially in yeast and human cells. Furthermore, small-molecule inhibitors were used to probe the global impact of its inhibition on the proteome.

Areas covered: We review here recent HSP90-related interactomics and total proteome studies and their relevance for research on cancer, neurodegenerative and pathogen diseases.

Expert commentary: Proteomics experiments are our best chance to identify the context-dependent global proteome of HSP90 and thus uncover and understand its disease-specific biology. However, understanding the complexity of HSP90 will require multiple complementary, quantitative approaches and novel bioinformatics to translate interactions into ordered functional networks and pathways. Developing therapies will necessitate more knowledge on HSP90 complexes and networks with disease relevance and on total proteome changes induced by their perturbation. Most work has been done in cancer, thus a lot remains to be done in the context of other diseases.     

Proteomic analysis of mitochondria: biological and clinical progresses in cancer

Introduction: Mitochondria play important roles in regulating multiple biological processes and signalling pathways in eukaryotic cells, and mitochondrial dysfunction may result in a wide range of serious diseases, including cancer. With improvements in the identification of mitochondrial proteins, mitochondrial proteomics has made great achievements. In particular, this approach has been widely used to compare tumour cells at different stages of malignancy. Therefore, there is an urgent need to identify and characterize the function of mitochondrial proteins in cancer progression and to determine the involved mechanisms.

Areas covered: We provide an overview of recent progress related to mitochondrial proteomics in cancer and the application of comparative mitochondrial proteomics in various biological processes, including apoptosis, necroptosis, autophagy and metastasis, as well as clinical progress in cancer. Proteomics-related reports were found using PubMed and Google Scholar databases.

Expert commentary: Understanding both post-translational modification and post-translational processing is important in the comprehensive characterization of protein function. The application of comparative mitochondrial proteomics to investigate clinical samples and cancer cells will contribute to our understanding of the molecular interplay of mitochondrial proteins in the development of cancer. This approach will mine more biomarkers for diagnosis and prognosis and improve therapeutic outcomes among cancer patients.     

Emerging power of proteomics for delineation of intrinsic tumor subtypes and resistance mechanisms to anti-cancer therapies

Introduction: Despite extreme genetic heterogeneity, tumors often show similar alterations in the expression, stability, and activation of proteins important in oncogenic signaling pathways. Thus, classifying tumor samples according to shared proteomic features may help facilitate the identification of cancer subtypes predictive of therapeutic responses and prognostic for patient outcomes. Meanwhile, understanding mechanisms of intrinsic and acquired resistance to anti-cancer therapies at the protein level may prove crucial to devising reversal strategies.

Areas covered: Herein, we review recent advances in quantitative proteomic technology and their applications in studies to identify intrinsic tumor subtypes of various tumors, to illuminate mechanistic aspects of pharmacological and oncogenic adaptations, and to highlight interaction targets for anti-cancer compounds and cancer-addicted proteins.

Expert commentary: Quantitative proteomic technologies are being successfully employed to classify tumor samples into distinct intrinsic subtypes, to improve existing DNA/RNA based classification methods, and to evaluate the activation status of key signaling pathways.     

Prognostic value of pre-treatment red blood cell distribution width in lung cancer: a meta-analysis

Abstract

Objective: In recent years, increasing studies found that pre-treatment red blood cell distribution width (RDW) could predict clinical outcomes in various cancers. However, the prognostic value of pre-treatment RDW in lung cancer was inconsistent. Therefore, we performed a meta-analysis to determine prognostic value of pre-treatment RDW in lung cancer.

Methods: We performed a search in PubMed, The Cochrane Library, EMBASE (via OVID), Web of Science, CNKI, Wanfang, VIP, SinoMed databases, then we identified all records up to February 15, 2019. Outcomes of interest were overall survival (OS) and disease-free survival (DFS). Hazard ratios (HRs) and corresponding 95% confidence intervals (95% CIs) were calculated to assess the relevance of pre-treatment RDW to OS in lung cancer.

Results: We included ten articles in total. Pooled results revealed that elevated pre-treatment RDW was significantly associated with poor OS (HR?=?1.55, 95% CI: 1.26–1.92, p?<?0.001) and DFS (HR?=?1.53, 95% Cl: 1.15–2.05; p?=?0.004) in lung cancer. Further subgroup analysis manifested that lung cancer patients with elevated pre-treatment RDW had worse prognosis.

Conclusions: A higher value of pre-treatment RDW indicated worse survival of patients with lung cancer. RDW may serve as a reliable and economical marker for prediction of lung cancer prognosis.     

Drug response prediction using graph representation learning and Laplacian feature selection

Background

Essential proteins are indispensable to the development and survival of cells. The identification of essential proteins not only is helpful for the understanding of the minimal requirements for cell survival, but also has practical significance in disease diagnosis, drug design and medical treatment. With the rapidly amassing of protein–protein interaction (PPI) data, computationally identifying essential proteins from protein–protein interaction networks (PINs) becomes more and more popular. Up to now, a number of various approaches for essential protein identification based on PINs have been developed.

Results

In this paper, we propose a new and effective approach called iMEPP to identify essential proteins from PINs by fusing multiple types of biological data and applying the influence maximization mechanism to the PINs. Concretely, we first integrate PPI data, gene expression data and Gene Ontology to construct weighted PINs, to alleviate the impact of high false-positives in the raw PPI data. Then, we define the influence scores of nodes in PINs with both orthological data and PIN topological information. Finally, we develop an influence discount algorithm to identify essential proteins based on the influence maximization mechanism.

Conclusions

We applied our method to identifying essential proteins from saccharomyces cerevisiae PIN. Experiments show that our iMEPP method outperforms the existing methods, which validates its effectiveness and advantage.

     

Circulating MicroRNAs as Non-Invasive Biomarkers for Early Detection of Non-Small-Cell Lung Cancer

BackgroundDetection of lung cancer at an early stage by sensitive screening tests could be an important strategy to improving prognosis. Our objective was to identify a panel of circulating microRNAs in plasma that will contribute to early detection of lung cancer.ResultsWe identified a panel of 24 microRNAs with optimum classification performance. The combination of these 24 microRNAs alone could discriminate lung cancer cases from non-cancer controls with an AUC of 0.92 (95% CI: 0.87-0.95). This classification improved to an AUC of 0.94 (95% CI: 0.90-0.97) following addition of sex, age and smoking status to the model. Internal validation of the model suggests that the discriminatory power of the panel will be high when applied to independent samples with a corrected AUC of 0.78 for the 24-miRNA panel alone.ConclusionOur 24-microRNA predictor improves lung cancer prediction beyond that of known risk factors.     

Current status of proteomics of soft tissue sarcomas

Introduction: Proteomics has been used in soft tissue sarcoma (STS) research in the attempts to improve the understanding of the disease background and develop novel clinical applications. Using various proteomics modalities, aberrant regulations of numerous intriguing proteins were identified in STSs, and the possible utilities of identified proteins as biomarkers or therapeutic targets have been explored. STS is an exceptionally diverse group of malignant diseases with highly complex molecular backgrounds and, therefore, an overview of the achievements and prospects of STS proteomics could enhance our knowledge of the possibilities and limitations of cancer proteomics.

Areas covered: This review examines all STSs that have been examined using proteomics modalities, discussing unique aspects, limitations, and possible improvements of individual reports. To contribute to the current progress in cancer treatment development using novel anti-cancer drugs, proteomics plays a central role in linking cutting-edge technologies, application of proteogenomics, patient-derived cancer models, and biobanking system.

Expert commentary: Therefore, proteomic-based STS research will be developed as an interdisciplinary science. STS proteomics will be further developed based on the interaction of oncologists with basic researchers in various fields, aimed at obtaining an enhanced understanding of the biology of the disease and achieving superior clinical outcomes for patients.     

Adult lung stem cells and their contribution to lung tumourigenesis

The isolation and characterization of lung stem and progenitor cells represent an important step towards the understanding of lung repair after injury, lung disease pathogenesis and the identification of the target cells of transformation in lung carcinogenesis. Different approaches using prospective isolation of progenitor cells by flow cytometry or lineage-tracing experiments in mouse models of lung injury have led to the identification of distinct progenitor subpopulations in different morphological regions of the adult lung. Genetically defined mouse models of lung cancer are offering new perspectives on the cells of origin of different subtypes of lung cancer. These mouse models pave the way to further investigate human lung progenitor cells at the origin of lung cancers, as well as to define the nature of the lung cancer stem cells. It will be critical to establish the link between oncogenic driver mutations recently discovered in lung cancers, target cells of transformation and subtypes of lung cancers to enable better stratification of patients for improved therapeutic strategies.     

Proteomic approaches for cancer epigenetics research

Introduction: Epigenetic dysregulation drives or supports numerous human cancers. The chromatin landscape in cancer cells is often marked by abnormal histone post-translational modification (PTM) patterns and by aberrant assembly and recruitment of protein complexes to specific genomic loci. Mass spectrometry-based proteomic analyses can support the discovery and characterization of both phenomena.

Areas covered: We broadly divide this literature into two parts: ‘modification-centric’ analyses that link histone PTMs to cancer biology; and ‘complex-centric’ analyses that examine protein–protein interactions that occur de novo as a result of oncogenic mutations. We also discuss proteomic studies of oncohistones. We highlight relevant examples, discuss limitations, and speculate about forthcoming innovations regarding each application.

Expert commentary: ‘Modification-centric’ analyses have been used to further understanding of cancer’s histone code and to identify associated therapeutic vulnerabilities. ‘Complex-centric’ analyses have likewise revealed insights into mechanisms of oncogenesis and suggested potential therapeutic targets, particularly in MLL-associated leukemia. Proteomic experiments have also supported some of the pioneering studies of oncohistone-mediated tumorigenesis. Additional applications of proteomics that may benefit cancer epigenetics research include middle-down and top-down histone PTM analysis, chromatin reader profiling, and genomic locus-specific protein identification. In the coming years, proteomic approaches will remain powerful ways to interrogate the biology of cancer.