Lung cancer diagnosis from proteomic analysis of preinvasive lesions

Adverse clinical outcomes and immunosuppressive microenvironment of RHO-GTPase activation pattern in hepatocellular carcinoma

BACKGROUND

Emerging evidence suggests that Rho GTPases play a crucial role in tumorigenesis and metastasis, but their involvement in the tumor microenvironment (TME) and prognosis of hepatocellular carcinoma (HCC) is not well understood.

METHODS

We aim to develop a tumor prognosis prediction system called the Rho GTPases-related gene score (RGPRG score) using Rho GTPase signaling genes and further bioinformatic analyses.

RESULTS

Our work found that HCC patients with a high RGPRG score had significantly worse survival and increased immunosuppressive cell fractions compared to those with a low RGPRG score. Single-cell cohort analysis revealed an immune-active TME in patients with a low RGPRG score, with strengthened communication from T/NK cells to other cells through MIF signaling networks. Targeting these alterations in TME, the patients with high RGPRG score have worse immunotherapeutic outcomes and decreased survival time in the immunotherapy cohort. Moreover, the RGPRG score was found to be correlated with survival in 27 other cancers. In vitro experiments confirmed that knockdown of the key Rho GTPase-signaling biomarker SFN significantly inhibited HCC cell proliferation, invasion, and migration.

CONCLUSIONS

This study provides new insight into the TME features and clinical use of Rho GTPase gene pattern at the bulk-seq and single-cell level, which may contribute to guiding personalized treatment and improving clinical outcome in HCC.

Validation of a Proteomic Signature of Lung Cancer Risk from Bronchial Specimens of Risk-Stratified Individuals

A major challenge in lung cancer prevention and cure hinges on identifying the at-risk population that ultimately develops lung cancer. Previously, we reported proteomic alterations in the cytologically normal bronchial epithelial cells collected from the bronchial brushings of individuals at risk for lung cancer. The purpose of this study is to validate, in an independent cohort, a selected list of 55 candidate proteins associated with risk for lung cancer with sensitive targeted proteomics using selected reaction monitoring (SRM). Bronchial brushings collected from individuals at low and high risk for developing lung cancer as well as patients with lung cancer, from both a subset of the original cohort (batch 1: n = 10 per group) and an independent cohort of 149 individuals (batch 2: low risk (n = 32), high risk (n = 34), and lung cancer (n = 83)), were analyzed using multiplexed SRM assays. ALDH3A1 and AKR1B10 were found to be consistently overexpressed in the high-risk group in both batch 1 and batch 2 brushing specimens as well as in the biopsies of batch 1. Validation of highly discriminatory proteins and metabolic enzymes by SRM in a larger independent cohort supported their use to identify patients at high risk for developing lung cancer.

Multimodal Lung Cancer Subtyping Using Deep Learning Neural Networks on Whole Slide Tissue Images and MALDI MSI

Artificial intelligence (AI) has shown potential for facilitating the detection and classification of tumors. In patients with non-small cell lung cancer, distinguishing between the most common subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SqCC), is crucial for the development of an effective treatment plan. This task, however, may still present challenges in clinical routine. We propose a two-modality, AI-based classification algorithm to detect and subtype tumor areas, which combines information from matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) data and digital microscopy whole slide images (WSIs) of lung tissue sections. The method consists of first detecting areas with high tumor cell content by performing a segmentation of the hematoxylin and eosin-stained (H&E-stained) WSIs, and subsequently classifying the tumor areas based on the corresponding MALDI MSI data. We trained the algorithm on six tissue microarrays (TMAs) with tumor samples from N = 232 patients and used 14 additional whole sections for validation and model selection. Classification accuracy was evaluated on a test dataset with another 16 whole sections. The algorithm accurately detected and classified tumor areas, yielding a test accuracy of 94.7% on spectrum level, and correctly classified 15 of 16 test sections. When an additional quality control criterion was introduced, a 100% test accuracy was achieved on sections that passed the quality control (14 of 16). The presented method provides a step further towards the inclusion of AI and MALDI MSI data into clinical routine and has the potential to reduce the pathologist's work load. A careful analysis of the results revealed specific challenges to be considered when training neural networks on data from lung cancer tissue.

Assay Performance of a Label-Free, Solution-Phase CYFRA 21-1 Determination

CYFRA 21.1, a cytokeratin fragment of epithelial origin, has long been a valuable blood-based biomarker. As with most biomarkers, the clinical diagnostic value of CYFRA 21.1 is dependent on the quantitative performance of the assay. Looking toward translation, it is shown here that a free-solution assay (FSA) coupled with a compensated interferometric reader (CIR) can be used to provide excellent analytical performance in quantifying CYFRA 21.1 in patient serum samples. This report focuses on the analytical performance of the high-sensitivity (hs)-CYFRA 21.1 assay in the context of quantifying the biomarker in two indeterminate pulmonary nodule (IPN) patient cohorts totaling 179 patients. Each of the ten assay calibrations consisted of 6 concentrations, each run as 7 replicates (e.g., 10 × 6 × 7 data points) and were performed on two different instruments by two different operators. Coefficients of variation (CVs) for the hs-CYFRA 21.1 analytical figures of merit, limit of quantification (LOQ) of ca. 60 pg/mL, B _max, initial slope, probe-target binding affinity, and reproducibility of quantifying an unknown were found to range from 2.5 to 8.3%. Our results demonstrate the excellent performance of our FSA-CIR hs-CYFRA 21-1 assay and a proof of concept for potentially redefining the performance characteristics of this existing important candidate biomarker.

The impact of the lung EDRN-CVC on Phase 1, 2, & 3 biomarker validation studies

The Early Detection Research Network’s (EDRN) purpose is to discover, develop and validate biomarkers and imaging methods to detect early-stage cancers or at-risk individuals. The EDRN is composed of sites that fall into four categories: Biomarker Developmental Laboratories (BDL), Biomarker Reference Laboratories (BRL), Clinical Validation Centers (CVC) and Data Management and Coordinating Centers. Each component has a crucial role to play within the mission of the EDRN. The primary role of the CVCs is to support biomarker developers through validation trials on promising biomarkers discovered by both EDRN and non-EDRN investigators. The second round of funding for the EDRN Lung CVC at Vanderbilt University Medical Center (VUMC) was funded in October 2016 and we intended to accomplish the three missions of the CVCs: To conduct innovative research on the validation of candidate biomarkers for early cancer detection and risk assessment of lung cancer in an observational study; to compare biomarker performance; and to serve as a resource center for collaborative research within the Network and partner with established EDRN BDLs and BRLs, new laboratories and industry partners. This report outlines the impact of the VUMC EDRN Lung CVC and describes the role in promoting and validating biological and imaging biomarkers.

Reduced Hemoglobin Signal and Improved Detection of Endogenous Proteins in Blood-Rich Tissues for MALDI Mass Spectrometry Imaging

Mass spectrometry imaging provides a powerful approach for the direct analysis and spatial visualization of molecules in tissue sections. Using matrix-assisted laser desorption/ionization mass spectrometry, intact protein imaging has been widely investigated for biomarker analysis and diagnosis in a variety of tissue types and diseases. However, blood-rich or highly vascular tissues present a challenge in molecular imaging due to the high ionization efficiency of hemoglobin, which leads to ion suppression of endogenous proteins. Here, we describe a protocol to selectively reduce hemoglobin signal in blood-rich tissues that can easily be integrated into mass spectrometry imaging workflows.

Mass spectrometry based proteome profiling of the exhaled breath condensate for lung cancer biomarkers search

INTRODUCTION

Lung cancer remains the most prevalent cause of cancer mortality worldwide mainly due to insufficient availability of early screening methods for wide-scale application. Exhaled breath condensate (EBC) is currently considered as one of the promising targets for early screening and is particularly attractive due to its absolutely noninvasive collection and possibility for long-term frozen storage. EBC proteome analysis can provide valuable information about the (patho)physiological changes in the respiratory system and may help to identify in time a high risk of lung cancer. Mass spectrometry (MS) profiling of EBC proteome seems to have no alternative in obtaining the most extensive data and characteristic marker panels for screening.

AREAS COVERED

This special report summarizes the data of several proteomic studies of EBC in normal and lung cancer (from 2012 to 2021, PubMed), focuses on the possible reasons for the significant discrepancy in the results, and discusses some aspects for special attention in further studies.

EXPERT OPINION

The significant discrepancy in the results of various studies primarily highlights the need to create standardized protocols for the collection and preparation of EBC for proteomic analysis. The application of quantitative and targeted LC-MS/MS based approaches seems to be the most promising in further EBC proteomic studies.

De Novo Lipogenesis Alters the Phospholipidome of Esophageal Adenocarcinoma

The incidence of esophageal adenocarcinoma is rising, survival remains poor, and new tools to improve early diagnosis and precise treatment are needed. Cancer phospholipidomes quantified with mass spectrometry imaging (MSI) can support objective diagnosis in minutes using a routine frozen tissue section. However, whether MSI can objectively identify primary esophageal adenocarcinoma is currently unknown and represents a significant challenge, as this microenvironment is complex with phenotypically similar tissue-types. Here, we used desorption electrospray ionization-MSI (DESI-MSI) and bespoke chemometrics to assess the phospholipidomes of esophageal adenocarcinoma and relevant control tissues. Multivariate models derived from phospholipid profiles of 117 patients were highly discriminant for esophageal adenocarcinoma both in discovery (AUC = 0.97) and validation cohorts (AUC = 1). Among many other changes, esophageal adenocarcinoma samples were markedly enriched for polyunsaturated phosphatidylglycerols with longer acyl chains, with stepwise enrichment in premalignant tissues. Expression of fatty acid and glycerophospholipid synthesis genes was significantly upregulated, and characteristics of fatty acid acyls matched glycerophospholipid acyls. Mechanistically, silencing the carbon switch ACLY in esophageal adenocarcinoma cells shortened glycerophospholipid chains, linking de novo lipogenesis to the phospholipidome. Thus, DESI-MSI can objectively identify invasive esophageal adenocarcinoma from a number of premalignant tissues and unveils mechanisms of phospholipidomic reprogramming. SIGNIFICANCE: These results call for accelerated diagnosis studies using DESI-MSI in the upper gastrointestinal endoscopy suite, as well as functional studies to determine how polyunsaturated phosphatidylglycerols contribute to esophageal carcinogenesis.

Clinical Application of Mass Spectrometry-Based Proteomics in Lung Cancer Early Diagnosis

The current knowledge on proteomic biomarker analysis for the early diagnosis of lung cancer is summarized, underlining the diversity among the results and the current interest in translating research results into clinical practice. A MEDLINE/PubMed literature search to retrieve all the papers published in the last 10 years is performed. Proteomics studies on lung cancer have gathered evidence on the potential role of biomarkers in early diagnosis. Although promising, none of them have proved to be sufficiently reliable to achieve validation. Future research should evolve toward a multipanel analysis of proteins, considering the possibility that individual biomarkers might not be specific enough to diagnose lung cancer, but could be related to oncological conditions.

Exploring potential biomarkers for lung adenocarcinoma using LC-MS/MS metabolomics

Background

The average 5-year survival rate of lung adenocarcinoma patients is only 15% to 17%, which is primarily due to late-stage diagnosis and a lack of specific prognostic evaluations that can recommend effective therapies. Additionally, there is no clinically recognized biomarker that is effective for early-stage diagnosis.

Methods

Tissue samples from 10 lung adenocarcinoma patients (both tumor and non-tumor tissues) and 10 benign lung tumor samples were collected. The significantly differentially represented metabolites from the three groups were analyzed by liquid chromatography and tandem mass spectrometry.

Results

Pathway analysis indicated that central carbon metabolism was the top altered pathway in lung adenocarcinoma, while protein digestion and absorption, and central carbon metabolism were the top altered pathways in benign lung tumors. Receiver operating characteristic curve analysis revealed that adenosine 3′-monophosphate, creatine, glycerol, and 14 other differential metabolites were potential sensitive and specific biomarkers for the diagnosis and prognosis of lung adenocarcinoma.

Conclusion

Our findings suggest that the metabolomics approach may be a useful method to detect potential biomarkers in lung adenocarcinoma patients.

Next Generation Histology-Directed Imaging Mass Spectrometry Driven by Autofluorescence Microscopy

Histology-directed imaging mass spectrometry (IMS) is a spatially targeted IMS acquisition method informed by expert annotation that provides rapid molecular characterization of select tissue structures. The expert annotations are usually determined on digital whole slide images of histological stains where the staining preparation is incompatible with optimal IMS preparation, necessitating serial sections: one for annotation, one for IMS. Registration is then used to align staining annotations onto the IMS tissue section. Herein, we report a next-generation histology-directed platform implementing IMS-compatible autofluorescence (AF) microscopy taken prior to any staining or IMS. The platform enables two histology-directed workflows, one that improves the registration process between two separate tissue sections using automated, computational monomodal AF-to-AF microscopy image registration, and a registration-free approach that utilizes AF directly to identify ROIs and acquire IMS on the same section. The registration approach is fully automated and delivers state of the art accuracy in histology-directed workflows for transfer of annotations (∼3-10 μm based on 4 organs from 2 species) while the direct AF approach is registration-free, allowing targeting of the finest structures visible by AF microscopy. We demonstrate the platform in biologically relevant case studies of liver stage malaria and human kidney disease with spatially targeted acquisition of sparsely distributed (composing less than one tenth of 1% of the tissue section area) malaria infected mouse hepatocytes and glomeruli in the human kidney case study.

Applications of MALDI-TOF mass spectrometry in clinical proteomics

INTRODUCTION

The development of precision medicine requires advanced technologies to address the multifactorial disease stratification and to support personalized treatments. Among omics techniques, proteomics based on Mass Spectrometry (MS) is becoming increasingly relevant in clinical practice allowing a phenotypic characterization of the dynamic functional status of the organism. From this perspective, Matrix Assisted Laser Desorption Ionization Time of Flight (MALDI-TOF) MS is a suitable platform for providing a high-throughput support to clinics. Areas covered: This review aims to provide an updated overview of MALDI-TOF MS applications in clinical proteomics. The most relevant features of this analysis have been discussed, highlighting both pre-analytical and analytical factors that are crucial in proteomics studies. Particular emphasis is placed on biofluids proteomics for biomarkers discovery and on recent progresses in clinical microbiology, drug monitoring, and minimal residual disease (MRD). Expert commentary: Despite some analytical limitations, the latest technological advances together with the easiness of use, the low time and low cost consuming and the high throughput are making MALDI-TOF MS instruments very attractive for the clinical practice. These features offer a significant potential for the routine of the clinical laboratory and ultimately for personalized medicine.

MALDI MS imaging investigation of the host response to visceral leishmaniasis

Mass spectrometry imaging (MSI) of animal tissues has become an important tool for in situ molecular analyses and biomarker studies in several clinical areas, but there are few applications in parasitological studies. Leishmaniasis is a neglected tropical disease, and experimental mouse models have been essential to evaluate pathological and immunological processes and to develop diagnostic methods. Herein we have employed MALDI MSI to examine peptides and low molecular weight proteins (2 to 20 kDa) differentially expressed in the liver during visceral leishmaniasis in mice models. We analyzed liver sections of Balb/c mice infected with Leishmania infantum using the SCiLS Lab software for statistical analysis, which facilitated data interpretation and thus highlighted several key proteins and/or peptides. We proposed a decision tree classification for visceral leishmaniasis with distinct phases of the disease, which are named here as healthy, acute infection and chronic infection. Among others, the ion of m/z 4963 was the most important to identify acute infection and was tentatively identified as Thymosin β4. This peptide was previously established as a recovery factor in the human liver and might participate in the response of mice to Leishmania infection. This preliminary investigation shows the potential of MALDI MSI to complement classical compound selective imaging techniques and to explore new features not yet recognized by these approaches.

Biophysical technologies for understanding circulating tumor cell biology and metastasis

An understanding of cancer evolution in lung cancer with its associated resistance to therapy can only be achieved with repeated sampling and analysis of the cancer. Given the high risks and costs associated with repeat physical biopsy, alternative technologies must be applied. Several modalities exist for analysis and re-analysis of cancer biology. Among them are the CellSearch platform, the CTC chip, and the high-definition CTC platform. While the former is primarily able to provide prognosticating information in the form of CTC enumeration, the latter two have the advantage of serving as a platform to study tumor biology. Techniques for analysis of single cell genomics, as well as protein expression on a single cell basis provide scientists with the capacity to understand cancer cell populations as a collection of individual cells, rather than as an average of all cells. A multimodal combination of circulating tumor DNAs (ctDNAs), CTCs, proteomics, and CTC-derived xenografts (CDXs) to create computational models useful in diagnosis, prognostication, and predictiveness to treatment is likely the future of tailoring individualized cancer care.

Detecting the Presence and Progression of Premalignant Lung Lesions via Airway Gene Expression

Purpose: Lung cancer is the leading cause of cancer-related death in the United States. The molecular events preceding the onset of disease are poorly understood, and no effective tools exist to identify smokers with premalignant lesions (PMLs) that will progress to invasive cancer. Prior work identified molecular alterations in the smoke-exposed airway field of injury associated with lung cancer. Here, we focus on an earlier stage in the disease process leveraging the airway field of injury to study PMLs and its utility in lung cancer chemoprevention.Experimental Design: Bronchial epithelial cells from normal appearing bronchial mucosa were profiled by mRNA-Seq from subjects with (n = 50) and without (n = 25) PMLs. Using surrogate variable and gene set enrichment analysis, we identified genes, pathways, and lung cancer-related gene sets differentially expressed between subjects with and without PMLs. A computational pipeline was developed to build and test a chemoprevention-relevant biomarker.Results: We identified 280 genes in the airway field associated with the presence of PMLs. Among the upregulated genes, oxidative phosphorylation was strongly enriched, and IHC and bioenergetics studies confirmed pathway findings in PMLs. The relationship between PMLs and squamous cell carcinomas (SCC) was also confirmed using published lung cancer datasets. The biomarker performed well predicting the presence of PMLs (AUC = 0.92, n = 17), and changes in the biomarker score associated with progression/stability versus regression of PMLs (AUC = 0.75, n = 51).Conclusions: Transcriptomic alterations in the airway field of smokers with PMLs reflect metabolic and early lung SCC alterations and may be leveraged to stratify smokers at high risk for PML progression and monitor outcome in chemoprevention trials. Clin Cancer Res; 23(17); 5091-100. ©2017 AACR.

Clinical applications of MALDI imaging technologies in cancer and neurodegenerative diseases

Matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) imaging mass spectrometry (IMS) enables localization of analytes of interest along with histology. More specifically, MALDI-IMS identifies the distributions of proteins, peptides, small molecules, lipids, and drugs and their metabolites in tissues, with high spatial resolution. This unique capacity to directly analyze tissue samples without the need for lengthy sample preparation reduces technical variability and renders MALDI-IMS ideal for the identification of potential diagnostic and prognostic biomarkers and disease gradation. MALDI-IMS has evolved rapidly over the last decade and has been successfully used in both medical and basic research by scientists worldwide. In this review, we explore the clinical applications of MALDI-IMS, focusing on the major cancer types and neurodegenerative diseases. In particular, we re-emphasize the diagnostic potential of IMS and the challenges that must be confronted when conducting MALDI-IMS in clinical settings. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Applications of Mass Spectrometry Imaging to Cancer

Pathologists play an essential role in the diagnosis and prognosis of benign and cancerous tumors. Clinicians provide tissue samples, for example, from a biopsy, which are then processed and thin sections are placed onto glass slides, followed by staining of the tissue with visible dyes. Upon processing and microscopic examination, a pathology report is provided, which relies on the pathologist's interpretation of the phenotypical presentation of the tissue. Targeted analysis of single proteins provide further insight and together with clinical data these results influence clinical decision making. Recent developments in mass spectrometry facilitate the collection of molecular information about such tissue specimens. These relatively new techniques generate label-free mass spectra across tissue sections providing nonbiased, nontargeted molecular information. At each pixel with spatial coordinates (x/y) a mass spectrum is acquired. The acquired mass spectrums can be visualized as intensity maps displaying the distribution of single m/z values of interest. Based on the sample preparation, proteins, peptides, lipids, small molecules, or glycans can be analyzed. The generated intensity maps/images allow new insights into tumor tissues. The technique has the ability to detect and characterize tumor cells and their environment in a spatial context and combined with histological staining, can be used to aid pathologists and clinicians in the diagnosis and management of cancer. Moreover, such data may help classify patients to aid therapy decisions and predict outcomes. The novel complementary mass spectrometry-based methods described in this chapter will contribute to the transformation of pathology services around the world.

The airway epithelium undergoes metabolic reprogramming in individuals at high risk for lung cancer

The molecular determinants of lung cancer risk remain largely unknown. Airway epithelial cells are prone to assault by risk factors and are considered to be the primary cell type involved in the field of cancerization. To investigate risk-associated changes in the bronchial epithelium proteome that may offer new insights into the molecular pathogenesis of lung cancer, proteins were identified in the airway epithelial cells of bronchial brushing specimens from risk-stratified individuals by shotgun proteomics. Differential expression of selected proteins was validated by parallel reaction monitoring mass spectrometry in an independent set of individual bronchial brushings. We identified 2,869 proteins, of which 312 proteins demonstrated a trend in expression. Pathway analysis revealed enrichment of carbohydrate metabolic enzymes in high-risk individuals. Glucose consumption and lactate production were increased in human bronchial epithelial BEAS2B cells treated with cigarette smoke condensate for 7 months. Increased lipid biosynthetic capacity and net reductive carboxylation were revealed by metabolic flux analyses of [U-¹³C₅] glutamine in this in vitro model, suggesting profound metabolic reprogramming in the airway epithelium of high-risk individuals. These results provide a rationale for the development of potentially new chemopreventive strategies and selection of patients for surveillance programs.

Matrix-assisted laser desorption/ionization imaging mass spectrometry for the spatial location of feline oviductal proteins

With the purpose of identifying factors involved in early stages of embryo development in the domestic cat, matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) was used for the first time to describe the spatial localization of proteins in the oviducts of queens. Oviducts were obtained from two 2 and 4 years old cross-bred queens, divided into three segments, snap-frozen in liquid nitrogen and then stored at -80°C until use. Next, they were sectioned in a cryostat, fixed on ITO (indium tin oxide) conductive glass slides for MALDI-IMS and serial sections were collected on microscope slides for histology. As confirmed by histology, MALDI-IMS was able to show contrasting protein distributions in the oviductal infundibulum, ampulla and isthmus. Mass spectra were characterized by abundant ions of m/z 1,259, 4,939, 4,960 and 10,626, which have been tentatively attributed to keratin, thymosin β10, thymosin β4 and S100, respectively. Keratin and thymosins are involved in the biological response to tissue damage. S100 proteins are calcium-modulated proteins implicated in a variety of cellular activities, including cell differentiation and regulation of cell motility. These results suggest that protein composition differs between segments of the cat oviduct, which corresponds to morphological changes within these sections. Further functional studies could elucidate the effects of these proteins on feline reproductive physiology.

MALDI imaging mass spectrometry - From bench to bedside

Today, pathologists face many challenges in defining the precise morphomolecular diagnosis and in guiding clinicians to the optimal patients' treatment. To achieve this goal, increasingly, classical histomorphological methods have to be supplemented by high throughput molecular assays. Since MALDI imaging mass spectrometry (IMS) enables the assessment of spatial molecular arrangements in tissue sections, it goes far beyond microscopy in providing hundreds of different molecular images from a single scan without the need of target-specific reagents. Thus, this technology has the potential to uncover new markers for diagnostic purposes or markers that correlate with disease severity as well as prognosis and therapeutic response. Additionally, in the future MALDI IMS based classifiers measured with this technology in real time in the diagnostic setting might be applicable in the routine diagnostic setting. In this review, recently published studies that show the usefulness, advantages, and applicability of MALDI IMS in different fields of pathology (diagnosis, prognosis and treatment response) are highlighted. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Rapid Diagnosis of Lung Tumors, a Feasability Study Using Maldi-Tof Mass Spectrometry

OBJECTIVE

Despite recent advances in imaging and core or endoscopic biopsies, a percentage of patients have a major lung resection without diagnosis. We aimed to assess the feasibility of a rapid tissue preparation/analysis to discriminate cancerous from non-cancerous lung tissue.

METHODS

Fresh sample preparations were analyzed with the Microflex LTTM MALDI-TOF analyzer. Each main reference spectra (MSP) was consecutively included in a database. After definitive pathological diagnosis, each MSP was labeled as either cancerous or non-cancerous (normal, inflammatory, infectious nodules). A strategy was constructed based on the number of concordant responses of a mass spectrometry scoring algorithm. A 3-step evaluation included an internal and blind validation of a preliminary database (n = 182 reference spectra from the 100 first patients), followed by validation on a whole cohort database (n = 300 reference spectra from 159 patients). Diagnostic performance indicators were calculated.

RESULTS

127 cancerous and 173 non-cancerous samples (144 peripheral biopsies and 29 inflammatory or infectious lesions) were processed within 30 minutes after biopsy sampling. At the most discriminatory level, the samples were correctly classified with a sensitivity, specificity and global accuracy of 92.1%, 97.1% and 95%, respectively.

CONCLUSIONS

The feasibility of rapid MALDI-TOF analysis, coupled with a very simple lung preparation procedure, appears promising and should be tested in several surgical settings where rapid on-site evaluation of abnormal tissue is required. In the operating room, it appears promising in case of tumors with an uncertain preoperative diagnosis and should be tested as a complementary approach to frozen-biopsy analysis.

Probing amyloid-β pathology in transgenic Alzheimer's disease (tgArcSwe) mice using MALDI imaging mass spectrometry

The pathological mechanisms underlying Alzheimer's disease (AD) are still not understood. The disease pathology is characterized by the accumulation and aggregation of amyloid-β (Aβ) peptides into extracellular plaques, however the factors that promote neurotoxic Aβ aggregation remain elusive. Imaging mass spectrometry (IMS) is a powerful technique to comprehensively elucidate the spatial distribution patterns of lipids, peptides and proteins in biological tissues. In the present study, matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS)-based imaging was used to study Aβ deposition in transgenic mouse brain tissue and to elucidate the plaque-associated chemical microenvironment. The imaging experiments were performed in brain sections of transgenic Alzheimer's disease mice carrying the Arctic and Swedish mutation of amyloid-beta precursor protein (tgArcSwe). Multivariate image analysis was used to interrogate the IMS data for identifying pathologically relevant, anatomical features based on their chemical identity. This include cortical and hippocampal Aβ deposits, whose amyloid peptide content was further verified using immunohistochemistry and laser microdissection followed by MALDI MS analysis. Subsequent statistical analysis on spectral data of regions of interest revealed brain region-specific differences in Aβ peptide aggregation. Moreover, other plaque-associated protein species were identified including macrophage migration inhibitory factor suggesting neuroinflammatory processes and glial cell reactivity to be involved in AD pathology. The presented data further highlight the potential of IMS as a powerful approach in neuropathology. Hanrieder et al. described an imaging mass spectrometry based study on comprehensive spatial profiling of C-terminally truncated Aβ species within individual plaques in tgArcSwe mice. Here, brain region-dependent differences in Aβ truncation and other plaque-associated proteins, such as macrophage migration inhibitory factor, were observed. The data shed further light on plaque-associated molecular mechanisms implicated in Alzheimer's pathogenesis. Cover image for this issue: doi: 10.1111/jnc.13328.

Applying computation biology and "big data" to develop multiplex diagnostics for complex chronic diseases such as osteoarthritis

The data explosion in the last decade is revolutionizing diagnostics research and the healthcare industry, offering both opportunities and challenges. These high-throughput “omics” techniques have generated more scientific data in the last few years than in the entire history of mankind. Here we present a brief summary of how “big data” have influenced early diagnosis of complex diseases. We will also review some of the most commonly used “omics” techniques and their applications in diagnostics. Finally, we will discuss the issues brought by these new techniques when translating laboratory discoveries to clinical practice.

[Research Progress of Biomakers Proteomics-based in Lung Cancer]

蛋白组学技术可以应用于癌症研究来检测差异蛋白质表达以发现癌症生物标志物。肺癌的生物标志物在肺癌早期诊断、指导治疗和预后监测方面起着关键作用。因此，迫切需要确定新的早期诊断和预后指标以开辟新的治疗途径。本文简要介绍了基于蛋白质组学的肺癌生物标志物的最新研究报告。他包括作为诊断、预后和预测性的生物标志物，以及基于最近发表文献的基础上和我们所做的相关工作的总结。

Implementation of proteomics for cancer research: past, present, and future

Cancer is the leading cause of the death, accounts for about 13% of all annual deaths worldwide. Many different fields of science are collaborating together studying cancer to improve our knowledge of this lethal disease, and find better solutions for diagnosis and treatment. Proteomics is one of the most recent and rapidly growing areas in molecular biology that helps understanding cancer from an omics data analysis point of view. The human proteome project was officially initiated in 2008. Proteomics enables the scientists to interrogate a variety of biospecimens for their protein contents and measure the concentrations of these proteins. Current necessary equipment and technologies for cancer proteomics are mass spectrometry, protein microarrays, nanotechnology and bioinformatics. In this paper, we provide a brief review on proteomics and its application in cancer research. After a brief introduction including its definition, we summarize the history of major previous work conducted by researchers, followed by an overview on the role of proteomics in cancer studies. We also provide a list of different utilities in cancer proteomics and investigate their advantages and shortcomings from theoretical and practical angles. Finally, we explore some of the main challenges and conclude the paper with future directions in this field.

MALDI TOF imaging mass spectrometry in clinical pathology: a valuable tool for cancer diagnostics (review)

Matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) imaging mass spectrometry (IMS) is an evolving technique in cancer diagnostics and combines the advantages of mass spectrometry (proteomics), detection of numerous molecules, and spatial resolution in histological tissue sections and cytological preparations. This method allows the detection of proteins, peptides, lipids, carbohydrates or glycoconjugates and small molecules.Formalin-fixed paraffin-embedded tissue can also be investigated by IMS, thus, this method seems to be an ideal tool for cancer diagnostics and biomarker discovery. It may add information to the identification of tumor margins and tumor heterogeneity. The technique allows tumor typing, especially identification of the tumor of origin in metastatic tissue, as well as grading and may provide prognostic information. IMS is a valuable method for the identification of biomarkers and can complement histology, immunohistology and molecular pathology in various fields of histopathological diagnostics, especially with regard to identification and grading of tumors.

Respiratory proteomics: from descriptive studies to personalized medicine

Respiratory diseases are highly prevalent and affect humankind worldwide, causing extensive morbidity and mortality with the environment playing an important role. Given the complex structure of the airways, sophisticated tools are required for early diagnosis; initial symptoms are nonspecific, and the clinical diagnosis is made frequently late. Over the past few years, proteomics has made high technological progress in mass-spectrometry-based protein identification and has allowed us to gain new insights into disease mechanisms and identify potential novel therapeutic targets. This review will highlight the contributions of proteomics toward the understanding of the respiratory proteome listing potential biomarkers and its potential application to the clinic. We also outline the contributions of proteomics to creating a personalized approach in respiratory medicine.

Current clinical application of genomic and proteomic profiling in non-small-cell lung cancer

BACKGROUND

Genomic or proteomic profiling of cancer can be broadly defined as a systematic grouping of cancer based on its genetic or protein makeup. In the management of non-small-cell lung cancer (NSCLC), genomic and proteomic profiling applications have become useful in early disease detection, diagnosis, treatment, and prognostication.

METHODS

We reviewed the recent literature on the applications of genomic and proteomic profiling in NSCLC. Important applications were summarized into those already adopted as standard care and those still under investigation.

RESULTS

For genomic profiling, testing for EGFR mutation and ALK rearrangement has become routine for adenocarcinoma. Multiplex assay and malignancy-risk gene signature are both important applications in development. A test to predict outcome after treatment with an epidermal growth factor rector/tyrosine kinase inhibitor and a screening blood test for lung cancer are being investigated for use in proteomic profiling.

CONCLUSIONS

Genomic profiling is routine in patients with NSCLC, and proteomic profiling shows promise. Additional genomic and proteomic profiling applications may also prove to be useful contributions in the care of these patients.

Quantification of the heterogeneity of prognostic cellular biomarkers in ewing sarcoma using automated image and random survival forest analysis

Driven by genomic somatic variation, tumour tissues are typically heterogeneous, yet unbiased quantitative methods are rarely used to analyse heterogeneity at the protein level. Motivated by this problem, we developed automated image segmentation of images of multiple biomarkers in Ewing sarcoma to generate distributions of biomarkers between and within tumour cells. We further integrate high dimensional data with patient clinical outcomes utilising random survival forest (RSF) machine learning. Using material from cohorts of genetically diagnosed Ewing sarcoma with EWSR1 chromosomal translocations, confocal images of tissue microarrays were segmented with level sets and watershed algorithms. Each cell nucleus and cytoplasm were identified in relation to DAPI and CD99, respectively, and protein biomarkers (e.g. Ki67, pS6, Foxo3a, EGR1, MAPK) localised relative to nuclear and cytoplasmic regions of each cell in order to generate image feature distributions. The image distribution features were analysed with RSF in relation to known overall patient survival from three separate cohorts (185 informative cases). Variation in pre-analytical processing resulted in elimination of a high number of non-informative images that had poor DAPI localisation or biomarker preservation (67 cases, 36%). The distribution of image features for biomarkers in the remaining high quality material (118 cases, 104 features per case) were analysed by RSF with feature selection, and performance assessed using internal cross-validation, rather than a separate validation cohort. A prognostic classifier for Ewing sarcoma with low cross-validation error rates (0.36) was comprised of multiple features, including the Ki67 proliferative marker and a sub-population of cells with low cytoplasmic/nuclear ratio of CD99. Through elimination of bias, the evaluation of high-dimensionality biomarker distribution within cell populations of a tumour using random forest analysis in quality controlled tumour material could be achieved. Such an automated and integrated methodology has potential application in the identification of prognostic classifiers based on tumour cell heterogeneity.

MALDI-ToF mass spectrometry for the rapid diagnosis of cancerous lung nodules

Recently, tissue-based methods for proteomic analysis have been used in clinical research and appear reliable for digestive, brain, lymphomatous, and lung cancers classification. However simple, tissue-based methods that couple signal analysis to tissue imaging are time consuming. To assess the reliability of a method involving rapid tissue preparation and analysis to discriminate cancerous from non-cancerous tissues, we tested 141 lung cancer/non-tumor pairs and 8 unique lung cancer samples among the stored frozen samples of 138 patients operated on during 2012.

Samples were crushed in water, and 1.5 µl was spotted onto a steel target for analysis with the Microflex LT analyzer (Bruker Daltonics). Spectra were analyzed using ClinProTools software. A set of samples was used to generate a random classification model on the basis of a list of discriminant peaks sorted with the k-nearest neighbor genetic algorithm. The rest of the samples (n = 43 cancerous and n = 41 non-tumoral) was used to verify the classification capability and calculate the diagnostic performance indices relative to the histological diagnosis. The analysis found 53 m/z valid peaks, 40 of which were significantly different between cancerous and non-tumoral samples. The selected genetic algorithm model identified 20 potential peaks from the training set and had 98.81% recognition capability and 89.17% positive predictive value. In the blinded set, this method accurately discriminated the two classes with a sensitivity of 86.7% and a specificity of 95.1% for the cancer tissues and a sensitivity of 87.8% and a specificity of 95.3% for the non-tumor tissues. The second model generated to discriminate primary lung cancer from metastases was of lower quality.

The reliability of MALDI-ToF analysis coupled with a very simple lung preparation procedure appears promising and should be tested in the operating room on fresh samples coupled with the pathological examination.

Acyl-coenzyme A-binding protein regulates Beta-oxidation required for growth and survival of non-small cell lung cancer

We identified acyl-coenzyme A-binding protein (ACBP) as part of a proteomic signature predicting the risk of having lung cancer. Because ACBP is known to regulate β-oxidation, which in turn controls cellular proliferation, we hypothesized that ACBP contributes to regulation of cellular proliferation and survival of non-small cell lung cancer (NSCLC) by modulating β-oxidation. We used matrix-assisted laser desorption/ionization-imaging mass spectrometry (MALDI-IMS) and immunohistochemistry (IHC) to confirm the tissue localization of ABCP in pre-invasive and invasive NSCLCs. We correlated ACBP gene expression levels in NSCLCs with clinical outcomes. In loss-of-function studies, we tested the effect of the downregulation of ACBP on cellular proliferation and apoptosis in normal bronchial and NSCLC cell lines. Using tritiated-palmitate ((3)H-palmitate), we measured β-oxidation levels and tested the effect of etomoxir, a β-oxidation inhibitor, on proliferation and apoptosis. MALDI-IMS and IHC analysis confirmed that ACBP is overexpressed in pre-invasive and invasive lung cancers. High ACBP gene expression levels in NSCLCs correlated with worse survival (HR = 1.73). We observed a 40% decrease in β-oxidation and concordant decreases in proliferation and increases in apoptosis in ACBP-depleted NSCLC cells as compared with bronchial airway epithelial cells. Inhibition of β-oxidation by etomoxir in ACBP-overexpressing cells produced dose-dependent decrease in proliferation and increase in apoptosis (P = 0.01 and P < 0.001, respectively). These data suggest a role for ACBP in controlling lung cancer progression by regulating β-oxidation.

Imaging mass spectrometry to discriminate breast from pancreatic cancer metastasis in formalin-fixed paraffin-embedded tissues

Diagnosis of the origin of metastasis is mandatory for adequate therapy. In the past, classification of tumors was based on histology (morphological expression of a complex protein pattern), while supportive immunohistochemical investigation relied only on few "tumor specific" proteins. At present, histopathological diagnosis is based on clinical information, morphology, immunohistochemistry, and may include molecular methods. This process is complex, expensive, requires an experienced pathologist and may be time consuming. Currently, proteomic methods have been introduced in various clinical disciplines. MALDI imaging MS combines detection of numerous proteins with morphological features, and seems to be the ideal tool for objective and fast histopathological tumor classification. To study a special tumor type and to identify predictive patterns that could discriminate metastatic breast from pancreatic carcinoma MALDI imaging MS was applied to multitissue paraffin blocks. A statistical classification model was created using a training set of primary carcinoma biopsies. This model was validated on two testing sets of different breast and pancreatic carcinoma specimens. We could discern breast from pancreatic primary tumors with an overall accuracy of 83.38%, a sensitivity of 85.95% and a specificity of 76.96%. Furthermore, breast and pancreatic liver metastases were tested and classified correctly.

Clinical significance and prognostic value of pentraxin-3 as serologic biomarker for lung cancer

PURPOSES

Lung cancer is prevalent worldwide and improvements in timely and effective diagnosis are need. Pentraxin-3 as a novel serum marker for lung cancer (LC) has not been validated in large cohort studies. The aim of the study was to assess its clinical value in diagnosis and prognosis.

METHODS

We analyzed serum PTX-3 levels in a total of 1,605 patients with LC, benign lung diseases and healthy controls, as well as 493 non- lung cancer patients including 12 different types of cancers. Preoperative and postoperative data were further assessed in patients undergoing LC resection. The diagnostic performance of PTX-3 for LC and early-stage LC was assessed using receiver operating characteristics (ROC) by comparing with serum carcinoembryonic antigen (CEA), cytokeratin 19 fragments (CYFRA 21-1).

RESULTS

Levels of PTX-3 in serum were significantly higher in patients with LC than all controls. ROC curves showed the optimum diagnostic cutoff was 8.03ng/mL (AUC 0.823, [95%CI 0.789-0.856], sensitivity 72.8%, and specificity 77.3% in the test cohort; 0.802, [95%CI 0.762-0.843], sensitivity 69.7%, and specificity 76.4% in the validate cohort). Similar diagnostic performance of PTX-3 was observed for early-stage LC. PTX-3 decreased following surgical resection of LC and increased with tumor recurrence. Significantly elevated PTX-3 levels were also seen in patients with non-lung cancers.

CONCLUSIONS

The present data revealed that PTX-3 was significantly increased in both tissue and serum samples in LC patients. PTX-3 is a valuable biomarker for LC and improved identification of patients with LC and early-stage LC from those with non-malignant lung diseases.

Research status and funding trends of lung cancer biomarkers

Lung cancer is one of malignant tumors with the highest morbidity and mortality in the world. At present, research of early diagnosis, treatment, prognosis, and metastasis associated biomarkers is most active. This article reviewed the research status of lung cancer biomarkers and analyzed the funding situation in the field of lung cancer markers in recent 10 years in China and abroad, to provide a reference for the future basic and clinical translational research of lung cancer biomarkers.

Deeper understanding of biological tissue: quantitative correlation of MALDI-TOF and Raman imaging

In order to achieve a comprehensive description of biological tissue, spectral information about proteins, lipids, nucleic acids, and other biochemical components need to be obtained concurrently. Different analytical techniques may be combined to record complementary information of the same sample. Established techniques, which can be utilized to elucidate the biochemistry of tissue samples are, for instance, MALDI-TOF-MS and Raman microscopic imaging. With this contribution, we combine these two techniques for the first time. The combination of both techniques allows the utilization and interpretation of complementary information (i.e., the information about the protein composition derived from the Raman spectra with data of the lipids analyzed by the MALDI-TOF measurements). Furthermore, we demonstrate how spectral information from MALDI-TOF experiments can be utilized to interpret Raman spectra.

Epidemiology of lung cancer: Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines

BACKGROUND

Ever since a lung cancer epidemic emerged in the mid-1900 s, the epidemiology of lung cancer has been intensively investigated to characterize its causes and patterns of occurrence. This report summarizes the key findings of this research.

METHODS

A detailed literature search provided the basis for a narrative review, identifying and summarizing key reports on population patterns and factors that affect lung cancer risk.

RESULTS

Established environmental risk factors for lung cancer include smoking cigarettes and other tobacco products and exposure to secondhand tobacco smoke, occupational lung carcinogens, radiation, and indoor and outdoor air pollution. Cigarette smoking is the predominant cause of lung cancer and the leading worldwide cause of cancer death. Smoking prevalence in developing nations has increased, starting new lung cancer epidemics in these nations. A positive family history and acquired lung disease are examples of host factors that are clinically useful risk indicators. Risk prediction models based on lung cancer risk factors have been developed, but further refinement is needed to provide clinically useful risk stratification. Promising biomarkers of lung cancer risk and early detection have been identified, but none are ready for broad clinical application.

CONCLUSIONS

Almost all lung cancer deaths are caused by cigarette smoking, underscoring the need for ongoing efforts at tobacco control throughout the world. Further research is needed into the reasons underlying lung cancer disparities, the causes of lung cancer in never smokers, the potential role of HIV in lung carcinogenesis, and the development of biomarkers.

Molecular biomarkers for future screening of lung cancer

The Landmark National Lung Screening Trial established the potential for low dose CT screening (LDCT) to reduce lung cancer-specific mortality in high-risk patients as defined by smoking history and age. However, the prevalence of lung cancer in asymptomatic smokers selected based on the NLST criteria is low. Recent advances have facilitated biomarker discovery for early diagnosis of lung cancer through the analysis of surrogate tissues, including airway epithelium, sputum, exhaled breath, and blood. Although a number of candidate diagnostic biomarkers have been described, none have been validated for use in the clinical setting. The NLST ACRIN biomarker repository is a valuable resource of annotated biological specimens that were collected during the NLST trial, which has the potential to facilitate validation of candidate biomarkers for early diagnosis identified in discovery trials. It will be important to perform retrospective and prospective analysis of biomarkers to screen for lung cancer. The review below summarizes some of our understanding of biomarkers in screening.

Detection and minimally invasive treatment of early squamous lung cancer

Non-small cell lung cancer (NSCLC) is the most common cause of cancer deaths worldwide. The majority of patents presenting with NSCLC have advanced disease, which precludes curative treatment. Early detection and treatment might result in the identification of more patients with early central lung cancer and improve survival. In addition, the study of early lung cancer improves understanding of lung carcinogenesis and might also reveal new treatment targets for advanced lung cancer. Bronchoscopic investigation of the central airways can reveal both early central lung cancer in situ (stage 0) and other preinvasive lesions such as dysplasia. In the current review we discuss the detection of early squamous lung cancer, the natural history of preinvasive lesions and whether biomarkers can be used to predict progression to cancer. Finally we will review the staging and management of preinvasive lung cancer lesions and the different therapeutic modalities that are available.

Lung cancer chemoprevention: current status and future prospects

Lung cancer is the leading cause of cancer death worldwide, making it an attractive disease for chemoprevention. Although avoidance of tobacco use and smoking cessation will have the greatest impact on lung cancer development, chemoprevention could prove to be very effective, particularly in former smokers. Chemoprevention is the use of agents to reverse or inhibit carcinogenesis and has been successfully applied to other common malignancies. Despite prior studies in lung cancer chemoprevention failing to identify effective agents, we now have the ability to identify high-risk populations, and our understanding of lung tumour and premalignant biology continues to advance. There are distinct histological lesions that can be reproducibly graded as precursors of non-small-cell lung cancer and similar precursor lesions exist for adenocarcinoma. These premalignant lesions are being targeted by chemopreventive agents in current trials and will continue to be studied in the future. In addition, biomarkers that predict risk and response to targeted agents are being investigated and validated. In this Review, we discuss the principles of chemoprevention, data from preclinical models, completed clinical trials and observational studies, and describe new treatments for novel targeted pathways and future chemopreventive efforts.

Localized in situ hydrogel-mediated protein digestion and extraction technique for on-tissue analysis

A simultaneous on-tissue proteolytic digestion and extraction method is described for the in situ analysis of proteins from spatially distinct areas of a tissue section. The digestion occurs on-tissue within a hydrogel network, and peptides extracted from this gel are identified with liquid chromatography tandem MS (LC-MS/MS). The hydrogels are compatible with solubility agents (e.g., chaotropes and detergents) known to improve enzymatic digestion of proteins. Additionally, digestions and extractions are compatible with imaging mass spectrometry (IMS) experiments. As an example application, an initial IMS experiment was conducted to profile lipid species using a traveling wave ion mobility mass spectrometer. On-tissue MS/MS was also performed on the same tissue section to identify lipid ions that showed spatial differences. Subsequently, the section underwent an on-tissue hydrogel digestion to reveal 96 proteins that colocalized to the rat brain cerebellum. Hematoxylin and eosin (H & E) staining was then performed to provide additional histological information about the tissue structure. This technology provides a versatile workflow that can be used to correlate multiple complementary analytical approaches in the analysis of a single tissue section.

Influence of delays in diagnosis and treatment on survival in small cell lung cancer patients

The purpose of this study was to evaluate the influence on survival of delays in the diagnosis and treatment in an unselected population of small cell lung (SCLC) patients. Demographic and disease data of 3,479 SCLC patients were registered in the National Tuberculosis and Lung Diseases Research Institute in Warsaw, Poland during 1995-1998. In 50 % of patients, treatment started within 78 days from the appearance of first symptom(s). The median delay was 30 days (mean 47 days) and the median referral delay to a specialist was 19 days (mean 36 days). Half of SCLC patients were diagnosed during 34 days (mean 55 days). The mean time elapse from the diagnosis to the onset of therapy was 30 days (median 6 days). The multivariate analysis revealed that male gender-HR (hazard ratio = 1.2), ECOG Performance Status of 2 (HR = 1.5) and 3 + 4 (HR = 2.4), and clinical stage III (HR = 1.3) and IV (HR = 1.9) of the disease were independent negative predictors of survival. The patients treated with surgery and combined modality treatment had a better prognosis than those treated with chemoradiotherapy (HR = 1.6), chemotherapy (HR = 2.5), symptomatically (HR = 4.0), or those who refused therapy (HR = 3.9). The delay in the diagnosis and treatment had no effect on survival. Interestingly, patients who were diagnosed faster (below 42 days) actually had a worse prognosis than those diagnosed later. We conclude that a prolonged workup of SCLC patients and an extended time for treatment onset have a positive influence on survival, which may likely have to do with the determination of disease stage and more targeted treatment.

Identification of an Acinetobacter baumannii zinc acquisition system that facilitates resistance to calprotectin-mediated zinc sequestration

Acinetobacter baumannii is an important nosocomial pathogen that accounts for up to 20 percent of infections in intensive care units worldwide. Furthermore, A. baumannii strains have emerged that are resistant to all available antimicrobials. These facts highlight the dire need for new therapeutic strategies to combat this growing public health threat. Given the critical role for transition metals at the pathogen-host interface, interrogating the role for these metals in A. baumannii physiology and pathogenesis could elucidate novel therapeutic strategies. Toward this end, the role for calprotectin- (CP)-mediated chelation of manganese (Mn) and zinc (Zn) in defense against A. baumannii was investigated. These experiments revealed that CP inhibits A. baumannii growth in vitro through chelation of Mn and Zn. Consistent with these in vitro data, Imaging Mass Spectrometry revealed that CP accompanies neutrophil recruitment to the lung and accumulates at foci of infection in a murine model of A. baumannii pneumonia. CP contributes to host survival and control of bacterial replication in the lung and limits dissemination to secondary sites. Using CP as a probe identified an A. baumannii Zn acquisition system that contributes to Zn uptake, enabling this organism to resist CP-mediated metal chelation, which enhances pathogenesis. Moreover, evidence is provided that Zn uptake across the outer membrane is an energy-dependent process in A. baumannii. Finally, it is shown that Zn limitation reverses carbapenem resistance in multidrug resistant A. baumannii underscoring the clinical relevance of these findings. Taken together, these data establish Zn acquisition systems as viable therapeutic targets to combat multidrug resistant A. baumannii infections.

Acinetobacter baumannii is a bacterium responsible for an increasing number of infections in the hospital setting. These infections are particularly challenging because most strains of A. baumannii are resistant to commonly used antibiotics. Unfortunately, there is relatively little known about this organism and how it causes disease, making it difficult to identify new drug targets. In order to address this problem we examined the role for nutrient manganese (Mn) and zinc (Zn) in A. baumannii infections. We have determined that the host protein, calprotectin (CP), contributes to defense against A. baumannii pneumonia through chelation of nutrient Mn and Zn. Moreover, employing purified calprotectin as a probe allowed us to identify a Zn acquisition system in A. baumannii that is required for efficient Zn uptake in vitro and full pathogenesis in vivo. Finally, we demonstrate that inhibiting Zn acquisition can reverse antibiotic resistance mechanisms that rely on Zn-dependent enzymes. Taken together, these results demonstrate the importance of Zn acquisition to A. baumannii pathogenesis and antibiotic resistance, establishing Zn acquisition as a potential target for therapeutic development.

Potential molecular approaches for the early diagnosis of lung cancer (review)

Lung cancer is the leading cause of mortality from cancer among men and women worldwide. More individuals die each year of lung cancer than of colon, breast and prostate cancer combined. Despite new diagnostic techniques, the overall 5-year survival rate remains at approximately 15% and the majority of patients still present with advanced disease. Therefore, lung cancer is the most lethal cancer at present. Diagnosing and treating cancer at its early stages, ideally during the precancerous stages, could increase the 5-year survival rate by 3-4‑fold, with the possibility of cure. To date, no screening method has been shown to decrease the disease-specific mortality rate. This review describes issues related to early lung cancer screening and their rationale, the management of primary cancers detected by screening and the different approaches that have been tested for cancer screening; these include imaging techniques, bronchoscopies and molecular screening, such as analysis of epigenomics using different noninvasive or invasive sources, such as blood, sputum, bronchoscopic samples and exhaled breath.

Imaging mass spectrometry in biomarker discovery and validation

Biomarker discovery and validation involves the consideration of many issues and challenges in order to be effectively used for translation from bench to bedside. Imaging mass spectrometry (IMS) is a new technology to assess spatial molecular arrangements in tissue sections, going far beyond microscopy in providing hundreds of different molecular images from a single scan without the need of target-specific reagents. The possibility to correlate distribution maps of multiple analytes with histological and clinical features makes it an ideal tool to discover diagnostic and prognostic markers of diseases. Some recently published studies that show the usefulness and advantages of this technology in the field of cancer research are highlighted.