A 25-signal proteomic signature and outcome for patients with resected non-small-cell lung cancer

Correlation between small-cell lung cancer serum protein/peptides determined by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and chemotherapy efficacy

BACKGROUND

Currently, no effective measures are available to predict the curative efficacy of small-cell lung cancer (SCLC) chemotherapy. We expect to develop a method for effectively predicting the SCLC chemotherapy efficacy and prognosis in clinical practice in order to offer more pertinent therapeutic protocols for individual patients.

METHODS

We adopted matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and ClinPro Tools system to detect serum samples from 154 SCLC patients with different curative efficacy of standard chemotherapy and analyze the different peptides/proteins of SCLC patients to discover predictive tumor markers related to chemotherapy efficacy. Ten peptide/protein peaks were significantly different in the two groups.

RESULTS

A genetic algorithm model consisting of four peptides/proteins was developed from the training group to separate patients with different chemotherapy efficacies. Among them, three peptides/proteins (m/z 3323.35, 6649.03 and 6451.08) showed high expression in the disease progression group, whereas the peptide/protein at m/z 4283.18 was highly expressed in the disease response group. The classifier exhibited an accuracy of 91.4% (53/58) in the validation group. The survival analysis showed that the median progression-free survival (PFS) of 30 SCLC patients in disease response group was 9.0 months; in 28 cases in disease progression group, the median PFS was 3.0 months, a statistically significant difference (χ2 = 46.98, P < 0.001). The median overall survival (OS) of the two groups was 13.0 months and 7.0 months, a statistically significant difference (χ2 = 40.64, P < 0.001).

CONCLUSIONS

These peptides/proteins may be used as potential biological markers for prediction of the curative efficacy and prognosis for SCLC patients treated with standard regimen chemotherapy.

Diagnostic, Predictive, and Prognostic Biomarkers in Non-Small Cell Lung Cancer (NSCLC) Management

Lung cancer is the leading cause of cancer-related deaths worldwide. Despite growing efforts for its early detection by screening populations at risk, the majority of lung cancer patients are still diagnosed in an advanced stage. The management of lung cancer has dramatically improved in the last decade and is no longer based on the "one-fits-all" paradigm or the general histological classification of non-small cell versus small cell lung cancer. Emerging options of targeted therapies and immunotherapies have shifted the management of lung cancer to a more personalized treatment approach, significantly influencing the clinical course and outcome of the disease. Molecular biomarkers have emerged as valuable tools in the prognosis and prediction of therapy response. In this review, we discuss the relevant biomarkers used in the clinical management of lung tumors, from diagnosis to prognosis. We also discuss promising new biomarkers, focusing on non-small cell lung cancer as the most abundant type of lung cancer.

Implementation of MALDI Mass Spectrometry Imaging in Cancer Proteomics Research: Applications and Challenges

Studying the proteome–the entire set of proteins in cells, tissues, organs and body fluids—is of great relevance in cancer research, as differential forms of proteins are expressed in response to specific intrinsic and extrinsic signals. Discovering protein signatures/pathways responsible for cancer transformation may lead to a better understanding of tumor biology and to a more effective diagnosis, prognosis, recurrence and response to therapy. Moreover, proteins can act as a biomarker or potential drug targets. Hence, it is of major importance to implement proteomic, particularly mass spectrometric, approaches in cancer research, to provide new crucial insights into tumor biology. Recently, mass spectrometry imaging (MSI) approaches were implemented in cancer research, to provide individual molecular characteristics of each individual tumor while retaining molecular spatial distribution, essential in the context of personalized disease management and medicine.

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.

Quantitative MRI Biomarkers of Stereotactic Radiotherapy Outcome in Brain Metastasis

About 20-40% of cancer patients develop brain metastases, causing significant morbidity and mortality. Stereotactic radiation treatment is an established option that delivers high dose radiation to the target while sparing the surrounding normal tissue. However, up to 20% of metastatic brain tumours progress despite stereotactic treatment, and it can take months before it is evident on follow-up imaging. An early predictor of radiation therapy outcome in terms of tumour local failure (LF) is crucial, and can facilitate treatment adjustments or allow for early salvage treatment. In this study, an MR-based radiomics framework was proposed to derive and investigate quantitative MRI (qMRI) biomarkers for the outcome of LF in brain metastasis patients treated with hypo-fractionated stereotactic radiation therapy (SRT). The qMRI biomarkers were constructed through a multi-step feature extraction/reduction/selection framework using the conventional MR imaging data acquired from 100 patients (133 lesions), and were applied in conjunction with machine learning techniques for outcome prediction and risk assessment. The results indicated that the majority of the features in the optimal qMRI biomarkers characterize the heterogeneity in the surrounding regions of tumour including edema and tumour/lesion margins. The optimal qMRI biomarker consisted of five features that predict the outcome of LF with an area under the curve (AUC) of 0.79, and a cross-validated sensitivity and specificity of 81% and 79%, respectively. The Kaplan-Meier analyses showed a statistically significant difference in local control (p-value < 0.0001) and overall survival (p = 0.01). Findings from this study are a step towards using qMRI for early prediction of local failure in brain metastasis patients treated with SRT. This may facilitate early adjustments in treatment, such as surgical resection or salvage radiation, that can potentially improve treatment outcomes. Investigations on larger cohorts of patients are, however, required for further validation of the technique.

Single Cell Proteomics for Molecular Targets in Lung Cancer: High-Dimensional Data Acquisition and Analysis

In the proteomic and genomic era, lung cancer researchers are increasingly under challenge with traditional protein analyzing tools. High output, multiplexed analytical procedures are in demand for disclosing the post-translational modification, molecular interactions and signaling pathways of proteins precisely, specifically, dynamically and systematically, as well as for identifying novel proteins and their functions. This could be better realized by single-cell proteomic methods than conventional proteomic methods. Using single-cell proteomic tools including flow cytometry, mass cytometry, microfluidics and chip technologies, chemical cytometry, single-cell western blotting, the quantity and functions of proteins are analyzed simultaneously. Aside from deciphering disease mechanisms, single-cell proteomic techniques facilitate the identification and screening of biomarkers, molecular targets and promising compounds as well. This review summarized single-cell proteomic tools and their use in lung cancer.

PARP1 Is Up-Regulated in Non-small Cell Lung Cancer Tissues in the Presence of the Cyanobacterial Toxin Microcystin

Non-small cell lung cancer (NSCLC) is the major form of lung cancer, with adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) being its major subtypes. Smoking alone cannot completely explain the lung cancer etiology. We hypothesize that altered lung microbiome and chronic inflammatory insults in lung tissues contribute to carcinogenesis. Here we explore the microbiome composition of LUAD samples, compared to LUSC and normal samples. Extraction of microbiome DNA in formalin-fixed, paraffin-embedded (FFPE) lung tumor and normal adjacent tissues was meticulously performed. The 16S rRNA product from extracted microbiota was subjected to microbiome amplicon sequencing. To assess the contribution of the host genome, CD36 expression levels were analyzed then integrated with altered NSCLC subtype-specific microbe sequence data. Surprisingly phylum Cyanobacteria was consistently observed in LUAD samples. Across the NSCLC subtypes, differential abundance across four phyla (Proteobacteria, Bacteroidetes, Actinobacteria, and Firmicutes) was identified based on the univariate analysis (p-value < 6.4e-4 to 3.2e-2). In silico metagenomic and pathway analyses show that presence of microcystin correlates with reduced CD36 and increased PARP1 levels. This was confirmed in microcystin challenged NSCLC (A427) cell lines and Cyanobacteria positive LUAD tissues. Controlling the influx of Cyanobacteria-like particles or microcystin and the inhibition of PARP1 can provide a potential targeted therapy and prevention of inflammation-associated lung carcinogenesis.

Signaling protein signature predicts clinical outcome of non-small-cell lung cancer

BACKGROUND

Non-small-cell lung cancer (NSCLC) is characterized by abnormalities of numerous signaling proteins that play pivotal roles in cancer development and progression. Many of these proteins have been reported to be correlated with clinical outcomes of NSCLC. However, none of them could provide adequate accuracy of prognosis prediction in clinical application.

METHODS

A total of 384 resected NSCLC specimens from two hospitals in Beijing (BJ) and Chongqing (CQ) were collected. Using immunohistochemistry (IHC) staining on stored formalin-fixed paraffin-embedded (FFPE) surgical samples, we examined the expression levels of 75 critical proteins on BJ samples. Random forest algorithm (RFA) and support vector machines (SVM) computation were applied to identify protein signatures on 2/3 randomly assigned BJ samples. The identified signatures were tested on the remaining BJ samples, and were further validated with CQ independent cohort.

RESULTS

A 6-protein signature for adenocarcinoma (ADC) and a 5-protein signature for squamous cell carcinoma (SCC) were identified from training sets and tested in testing sets. In independent validation with CQ cohort, patients can also be divided into high- and low-risk groups with significantly different median overall survivals by Kaplan-Meier analysis, both in ADC (31 months vs. 87 months, HR 2.81; P <  0.001) and SCC patients (27 months vs. not reached, HR 9.97; P <  0.001). Cox regression analysis showed that both signatures are independent prognostic indicators and outperformed TNM staging (ADC: adjusted HR 3.07 vs. 2.43, SCC: adjusted HR 7.84 vs. 2.24). Particularly, we found that only the ADC patients in high-risk group significantly benefited from adjuvant chemotherapy (P = 0.018).

CONCLUSIONS

Both ADC and SCC protein signatures could effectively stratify the prognosis of NSCLC patients, and may support patient selection for adjuvant chemotherapy.

Relapse-related long non-coding RNA signature to improve prognosis prediction of lung adenocarcinoma

Increasing evidence has highlighted the important roles of dysregulated long non-coding RNA (lncRNA) expression in tumorigenesis, tumor progression and metastasis. However, lncRNA expression patterns and their prognostic value for tumor relapse in lung adenocarcinoma (LUAD) patients have not been systematically elucidated. In this study, we evaluated lncRNA expression profiles by repurposing the publicly available microarray expression profiles from a large cohort of LUAD patients and identified specific lncRNA signature closely associated with tumor relapse in LUAD from significantly altered lncRNAs using the weighted voting algorithm and cross-validation strategy, which was able to discriminate between relapsed and non-relapsed LUAD patients with sensitivity of 90.9% and specificity of 81.8%. From the discovery dataset, we developed a risk score model represented by the nine relapse-related lncRNAs for prognosis prediction, which classified patients into high-risk and low-risk subgroups with significantly different recurrence-free survival (HR=45.728, 95% CI=6.241-335.1; p=1.69e-04). The prognostic value of this relapse-related lncRNA signature was confirmed in the testing dataset and other two independent datasets. Multivariable Cox regression analysis and stratified analysis showed that the relapse-related lncRNA signature was independent of other clinical variables. Integrative in silico functional analysis suggested that these nine relapse-related lncRNAs revealed biological relevance to disease relapse, such as cell cycle, DNA repair and damage and cell death. Our study demonstrated that the relapse-related lncRNA signature may not only help to identify LUAD patients at high risk of relapse benefiting from adjuvant therapy but also could provide novel insights into the understanding of molecular mechanism of recurrent disease.

Oncogenic kinase fusions: an evolving arena with innovative clinical opportunities

Cancer biology relies on intrinsic and extrinsic deregulated pathways, involving a plethora of intra-cellular and extra-cellular components. Tyrosine kinases are frequently deregulated genes, whose aberrant expression is often caused by major cytogenetic events (e.g. chromosomal translocations). The resulting tyrosine kinase fusions (TKFs) prompt the activation of oncogenic pathways, determining the biological and clinical features of the associated tumors. First reported half a century ago, oncogenic TKFs are now found in a large series of hematologic and solid tumors. The molecular basis of TKFs has been thoroughly investigated and tailored therapies against recurrent TKFs have recently been developed. This review illustrates the biology of oncogenic TKFs and their role in solid as well as hematological malignancies. We also address the therapeutic implications of TKFs and the many open issues concerning their clinical impact.

Discovery and validation of immune-associated long non-coding RNA biomarkers associated with clinically molecular subtype and prognosis in diffuse large B cell lymphoma

Background

Diffuse large B-cell lymphoma (DLBCL) is an aggressive and complex disease characterized by wide clinical, phenotypic and molecular heterogeneities. The expression pattern and clinical implication of long non-coding RNAs (lncRNAs) between germinal center B-cell-like (GCB) and activated B-cell-like (ABC) subtypes in DLBCL remain unclear. This study aims to determine whether lncRNA can serve as predictive biomarkers for subtype classification and prognosis in DLBCL.

Methods

Genome-wide comparative analysis of lncRNA expression profiles were performed in a large number of DLBCL patients from Gene Expression Omnibus (GEO), including GSE31312 cohort (N = 426), GSE10846 (N = 350) cohort and GSE4475 cohort (N = 129). Novel lncRNA biomarkers associated with clinically molecular subtype and prognosis were identified in the discovery cohort using differential expression analyses and weighted voting algorithm. The predictive value of the lncRNA signature was then assessed in two independent cohorts. The functional implication of lncRNA signature was also analyzed by integrative analysis of lncRNA and mRNA.

Results

Seventeen of the 156 differentially expressed lncRNAs between GCB and ABC subtypes were identified as candidate biomarkers and integrated into form a lncRNA-based signature (termed SubSigLnc-17) which was able to discriminate between GCB and ABC subtypes with AUC of 0.974, specificity of 89.6% and sensitivity of 92.5%. Furthermore, subgroups of patients characterized by the SubSigLnc-17 demonstrated significantly different clinical outcome. The reproducible predictive power of SubSigLnc-17 in subtype classification and prognosis was successfully validated in the internal validation cohort and another two independent patient cohorts. Integrative analysis of lncRNA-mRNA suggested that these candidate lncRNA biomarkers were mainly related to immune-associated processes, such as T cell activation, leukocyte activation, lymphocyte activation and Chemokine signaling pathway.

Conclusions

Our study uncovered differentiated lncRNA expression pattern between GCB and ABC DLBCL and identified a 17-lncRNA signature for subtype classification and prognosis prediction. With further prospective validation, our study will improve the understanding of underlying molecular heterogeneities in DLBCL and provide candidate lncRNA biomarkers in DLBCL classification and prognosis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12943-017-0580-4) contains supplementary material, which is available to authorized users.

Clinical proteomics in cancer: Where we are

Proteomics has emerged as a promising field in the post-genomic era. Notwithstanding the great advances provided by gene expression analysis in cancer, the lack of a correlation between gene expression and protein levels has highlighted the need for a proteomic focus on cancer. Although the increasing knowledge regarding cancer biology, a reliable marker to improve diagnosis, prognosis and treatment for cancer patients is not a reality at present. In this review, we address the main considerations regarding proteomics-based studies and their clinical applications on cancer research, highlighting some considerations related to strengths and limitations of proteomics-based studies and its application to clinical practice.

Biomarkers in early-stage non-small-cell lung cancer: current concepts and future directions

Advances in molecular biology and bioinformatics have resulted in the identification of a number of potential biomarkers that could be relevant in the management of patients with non-small-cell lung cancer (NSCLC). Although there is an increasing amount of literature related to these biomarkers, major issues need to be resolved including validity and reproducibility of results. Additionally, in order to interpret the existing literature accurately, a clear distinction must be made between the prognostic and predictive value of biomarkers. The practical applicability of biomarker discovery for patients with lung cancer includes the identification of patients with early-stage NSCLC who are most likely to benefit from adjuvant therapy. Information gleaned from biomarkers has the potential to help in evaluating the role of targeted therapies including immunotherapy in the neoadjuvant and adjuvant setting. The role of gene signatures and the use of newer platforms such as RNA, methylation, and protein signatures is being explored in patients with early-stage NSCLC. This review focuses on the applications of biomarker discovery in patients with early-stage NSCLC.

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

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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.

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.

Heparin co-factor II enhances cell motility and promotes metastasis in non-small cell lung cancer

Using the Serial Analysis of Gene Expression (SAGE) database from the Cancer Genome Anatomy Project, we identified heparin co-factor II (HCII), which is over-expressed in non-small cell lung cancer (NSCLC). Here, we investigated the clinical significance of HCII and provided molecular evidence to support the suggestion that HCII could enhance cancer metastasis in NSCLC. We found that high HCII expression in tumour tissue was associated with increased cancer recurrence and shorter overall survival times in 75 clinically operable NSCLC patients. High pretreatment plasma concentration of HCII was associated with reduced overall survival in 57 consecutive NSCLC patients. We over-expressed and knocked down HCII expression in lung cancer cell lines and confirmed that HCII could promote cell motility, invasion ability and filopodium dynamics in NSCLC cells in vitro and increased metastatic colonization in an in vivo mouse model. Exogenous treatment of HCII promoted cancer cell migration, and this promigratory effect of HCII was independent of thrombin. We further showed that HCII could up-regulate cancer cell migration through the activation of PI3K, which acts upstream of Rac1 and Cdc42, and this effect could be blocked by heparin. We suggest that HCII is a novel metastasis enhancer and may be used as a prognostic predictor for heparin treatment in NSCLC.

CLIC4, ERp29, and Smac/DIABLO derived from metastatic cancer stem-like cells stratify prognostic risks of colorectal cancer

PURPOSE

Cancer stem-like cells have been well accepted to be involved in recurrence and metastasis of cancers, but the prognostic potential of biomarkers integrating with metastasis and cancer stem-like cells for colorectal cancer is unclear.

EXPERIMENTAL DESIGN

We identified three proteins, CLIC4, ERp29, and Smac/DIABLO, from metastatic cancer stem-like cells of colorectal cancer and verified the proteins' role in metastatic behaviors. The proteins were detected by IHC in colorectal cancer tumors and matched colonic mucosa from patients with colorectal cancer who underwent radical surgery in the training cohort. The associations between proteins expression levels and five-year disease-specific survival (DSS) were evaluated to predict the survival probability in the training cohort of 421 cases and the validation cohort of 228 cases.

RESULTS

A three-protein panel including CLIC4, ERp29, and Smac/DIABLO, which was generated from multivariate analysis by excluding clinicopathologic characteristics from the training cohort, distinguished patients with colorectal cancer into very low-, low-, middle-, and high-risk groups with significant differences in five-year DSS probability (88.6%, 63.3%, 30.4%, 11.4%; P < 0.001). The panel is independent from tumor-node-metastasis staging system and histologic grading to predict prognosis, and also enables classification of validation cohort into four risk stratifications (five-year DSS probability is 98.2%, 80.2%, 25.6%, and 2.7%; P < 0.001).

CONCLUSIONS

CLIC4, ERp29, and Smac/DIABLO integrated into a novel panel based on cancer stem-like cells in association with metastasis stratify the prognostic risks of colorectal cancer. Prediction of risks with molecular markers will benefit clinicians to make decisions of individual management with postoperative colorectal cancer patients.

Quantitative proteomic profiling identifies DPYSL3 as pancreatic ductal adenocarcinoma-associated molecule that regulates cell adhesion and migration by stabilization of focal adhesion complex

Elucidation of how pancreatic cancer cells give rise to distant metastasis is urgently needed in order to provide not only a better understanding of the underlying molecular mechanisms, but also to identify novel targets for greatly improved molecular diagnosis and therapeutic intervention. We employed combined proteomic technologies including mass spectrometry and isobaric tags for relative and absolute quantification peptide tagging to analyze protein profiles of surgically resected human pancreatic ductal adenocarcinoma tissues. We identified a protein, dihydropyrimidinase-like 3, as highly expressed in human pancreatic ductal adenocarcinoma tissues as well as pancreatic cancer cell lines. Characterization of the roles of dihydropyrimidinase-like 3 in relation to cancer cell adhesion and migration in vitro, and metastasis in vivo was performed using a series of functional analyses, including those employing multiple reaction monitoring proteomic analysis. Furthermore, dihydropyrimidinase-like 3 was found to interact with Ezrin, which has important roles in cell adhesion, motility, and invasion, while that interaction promoted stabilization of an adhesion complex consisting of Ezrin, c-Src, focal adhesion kinase, and Talin1. We also found that exogenous expression of dihydropyrimidinase-like 3 induced activating phosphorylation of Ezrin and c-Src, leading to up-regulation of the signaling pathway. Taken together, the present results indicate successful application of combined proteomic approaches to identify a novel key player, dihydropyrimidinase-like 3, in pancreatic ductal adenocarcinoma tumorigenesis, which may serve as an important biomarker and/or drug target to improve therapeutic strategies.

Assessment, origin, and implementation of breath volatile cancer markers

A new non-invasive and potentially inexpensive frontier in the diagnosis of cancer relies on the detection of volatile organic compounds (VOCs) in exhaled breath samples. Breath can be sampled and analyzed in real-time, leading to fascinating and cost-effective clinical diagnostic procedures. Nevertheless, breath analysis is a very young field of research and faces challenges, mainly because the biochemical mechanisms behind the cancer-related VOCs are largely unknown. In this review, we present a list of 115 validated cancer-related VOCs published in the literature during the past decade, and classify them with respect to their "fat-to-blood" and "blood-to-air" partition coefficients. These partition coefficients provide an estimation of the relative concentrations of VOCs in alveolar breath, in blood and in the fat compartments of the human body. Additionally, we try to clarify controversial issues concerning possible experimental malpractice in the field, and propose ways to translate the basic science results as well as the mechanistic understanding to tools (sensors) that could serve as point-of-care diagnostics of cancer. We end this review with a conclusion and a future perspective.

Mass spectrometry-based proteomics: the road to lung cancer biomarker discovery

Lung cancer is the leading cause of cancer death in men and women in Western nations, and is among the deadliest cancers with a 5-year survival rate of 15%. The high mortality caused by lung cancer is attributable to a late-stage diagnosis and the lack of effective treatments. So, it is crucial to identify new biomarkers that could function not only to detect lung cancer at an early stage but also to shed light on the molecular mechanisms that underlie cancer development and serve as the basis for the development of novel therapeutic strategies. Considering that DNA-based biomarkers for lung cancer showed inadequate sensitivity, specificity, and reproducibility, proteomics could represent a better tool for the identification of useful biomarkers and therapeutic targets for this cancer type. Among the proteomics technologies, the most powerful tool is mass spectrometry. In this review, we describe studies that use mass spectrometry-based proteomics technologies to analyze tumor proteins and peptides, which might represent new diagnostic, prognostic, and predictive markers for lung cancer. We focus in particular on those findings that hold promise to impact significantly on the clinical management of this disease.

Biomarkers and molecular testing for early detection, diagnosis, and therapeutic prediction of lung cancer

The search for biomarkers in the management of lung cancer involves the use of multiple platforms to examine changes in gene, protein, and microRNA expression. Multiple studies have been published in an attempt to describe early detection, diagnostic, prognostic, and predictive biomarkers using chiefly tissues and blood elements. Studies are characterized by a lack of commonality of specific biomarkers, and a lack of validated, clinically useful markers. The future of biomarker discovery as a means of tailoring therapy for patients with lung cancer will involve next-generation sequencing along with collaborative efforts to integrate and validate candidate markers.

Genetic susceptibility to lung cancer--light at the end of the tunnel?

Lung cancer is one of the most common and deadliest cancers in the world. The major socio-environmental risk factor involved in the development of lung cancer is cigarette smoking. Additionally, there are multiple genetic factors, which may also play a role in lung cancer risk. Early work focused on the presence of relatively prevalent but low-penetrance alterations in candidate genes leading to increased risk of lung cancer. Development of new technologies such as genomic profiling and genome-wide association studies has been helpful in the detection of new genetic variants likely involved in lung cancer risk. In this review, we discuss the role of multiple genetic variants and review their putative role in the risk of lung cancer. Identifying genetic biomarkers and patterns of genetic risk may be useful in the earlier detection and treatment of lung cancer patients.

Identification of proteins expressed differently among surgically resected stage I lung adenocarcinomas

RATIONALE

Among patients with surgically resected stage I lung adenocarcinoma, some succumb to early recurrence, while others survive for more than 5 years. Few markers to predict prognoses in these patients have been accepted. Recent advances in proteomic methodologies offer a unique chance to identify new candidate biomarkers. The aim of this study is to find differences in protein expression in resected lung cancer tissue of stage I adenocarcinoma from patients with no recurrence for more than 5 years and from those with early recurrence.

METHODS

Lung cancer tissues were obtained from 15 patients with pathologically confirmed stage I adenocarcinoma. The patients were divided into two groups, those with recurrence within 36 months (early recurrence group, n=9) and those that were disease-free for over 5 years (disease free group, n=6). Tissue proteins were separated by a two-dimensional electrophoresis long gel system (30 × 40 cm) with set ranges (3-10 NL) and examined by nano-LC-ESI-MS/MS. Western blot assays were performed to validate these proteins.

RESULTS

Twelve protein spots were up-regulated and 8 were down-regulated in the disease-free group as compared with the recurrence group. Of the 12 up-regulated proteins, haptoglubin, tau-tubulin kinase-2 (TTBK2), thymidine phosphorylase, annexin-1, PIN1, CAPG, and SEC23 were validated by Western blot. Among the 8 down-regulated proteins, serpinB6 and trangelin-2 were validated.

CONCLUSIONS

A total of 9 differentially expressed proteins were successfully extracted, identified, and confirmed from stage I lung adenocarcinoma tissues. The increased or decreased expression of these proteins according to prognosis may be the basis for further studies of proteomics in developing prognostic biomarkers.

In-depth proteomic analysis of nonsmall cell lung cancer to discover molecular targets and candidate biomarkers

Advances in proteomic analysis of human samples are driving critical aspects of biomarker discovery and the identification of molecular pathways involved in disease etiology. Toward that end, in this report we are the first to use a standardized shotgun proteomic analysis method for in-depth tissue protein profiling of the two major subtypes of nonsmall cell lung cancer and normal lung tissues. We identified 3621 proteins from the analysis of pooled human samples of squamous cell carcinoma, adenocarcinoma, and control specimens. In addition to proteins previously shown to be implicated in lung cancer, we have identified new pathways and multiple new differentially expressed proteins of potential interest as therapeutic targets or diagnostic biomarkers, including some that were not identified by transcriptome profiling. Up-regulation of these proteins was confirmed by multiple reaction monitoring mass spectrometry. A subset of these proteins was found to be detectable and differentially present in the peripheral blood of cases and matched controls. Label-free shotgun proteomic analysis allows definition of lung tumor proteomes, identification of biomarker candidates, and potential targets for therapy.

The state of molecular biomarkers for the early detection of lung cancer

Using biomarkers to select the most at-risk population, to detect the disease while measurable and yet not clinically apparent has been the goal of many investigations. Recent advances in molecular strategies and analytic platforms, including genomics, epigenomics, proteomics, and metabolomics, have identified increasing numbers of potential biomarkers in the blood, urine, exhaled breath condensate, bronchial specimens, saliva, and sputum, but none have yet moved to the clinical setting. Therefore, there is a recognized gap between the promise and the product delivery in the cancer biomarker field. In this review, we define clinical contexts where risk and diagnostic biomarkers may have use in the management of lung cancer, identify the most relevant candidate biomarkers of early detection, provide their state of development, and finally discuss critical aspects of study design in molecular biomarkers for early detection of lung cancer.

Seven-signal proteomic signature for detection of operable pancreatic ductal adenocarcinoma and their discrimination from autoimmune pancreatitis

There is urgent need for biomarkers that provide early detection of pancreatic ductal adenocarcinoma (PDAC) as well as discrimination of autoimmune pancreatitis, as current clinical approaches are not suitably accurate for precise diagnosis. We used mass spectrometry to analyze protein profiles of more than 300 plasma specimens obtained from PDAC, noncancerous pancreatic diseases including autoimmune pancreatitis patients and healthy subjects. We obtained 1063 proteomic signals from 160 plasma samples in the training cohort. A proteomic signature consisting of 7 mass spectrometry signals was used for construction of a proteomic model for detection of PDAC patients. Using the test cohort, we confirmed that this proteomic model had discrimination power equal to that observed with the training cohort. The overall sensitivity and specificity for detection of cancer patients were 82.6% and 90.9%, respectively. Notably, 62.5% of the stage I and II cases were detected by our proteomic model. We also found that 100% of autoimmune pancreatitis patients were correctly assigned as noncancerous individuals. In the present paper, we developed a proteomic model that was shown able to detect early-stage PDAC patients. In addition, our model appeared capable of discriminating patients with autoimmune pancreatitis from those with PDAC.

Aberrant expression of proteins involved in signal transduction and DNA repair pathways in lung cancer and their association with clinical parameters

BACKGROUND

Because cell signaling and cell metabolic pathways are executed through proteins, protein signatures in primary tumors are useful for identifying key nodes in signaling networks whose alteration is associated with malignancy and/or clinical outcomes. This study aimed to determine protein signatures in primary lung cancer tissues.

METHODOLOGY/ PRINCIPAL FINDINGS

We analyzed 126 proteins and/or protein phosphorylation sites in case-matched normal and tumor samples from 101 lung cancer patients with reverse-phase protein array (RPPA) assay. The results showed that 18 molecules were significantly different (p<0.05) by at least 30% between normal and tumor tissues. Most of those molecules play roles in cell proliferation, DNA repair, signal transduction and lipid metabolism, or function as cell surface/matrix proteins. We also validated RPPA results by Western blot and/or immunohistochemical analyses for some of those molecules. Statistical analyses showed that Ku80 levels were significantly higher in tumors of nonsmokers than in those of smokers. Cyclin B1 levels were significantly overexpressed in poorly differentiated tumors while Cox2 levels were significantly overexpressed in neuroendocrinal tumors. A high level of Stat5 is associated with favorable survival outcome for patients treated with surgery.

CONCLUSIONS/ SIGNIFICANCE

Our results revealed that some molecules involved in DNA damage/repair, signal transductions, lipid metabolism, and cell proliferation were drastically aberrant in lung cancer tissues, and Stat5 may serve a molecular marker for prognosis of lung cancers.

Adjuvant chemotherapy for resected non-small-cell lung cancer: future perspectives for clinical research

Adjuvant chemotherapy for non-small-cell lung carcinoma (NSCLC) is a debated issue in clinical oncology. Although it is considered a standard for resected stage II-IIIA patients according to the available guidelines, many questions are still open. Among them, it should be acknowledged that the treatment for stage IB disease has shown so far a limited (if sizable) efficacy, the role of modern radiotherapies requires to be evaluated in large prospective randomized trials and the relative impact of age and comorbidities should be weighted to assess the reliability of the trials' evidences in the context of the everyday-practice. In addition, a conclusive evidence of the best partner for cisplatin is currently awaited as well as a deeper investigation of the fading effect of chemotherapy over time. The limited survival benefit since first studies were published and the lack of reliable prognostic and predictive factors beyond pathological stage, strongly call for the identification of bio-molecular markers and classifiers to identify which patients should be treated and which drugs should be used. Given the disappointing results of targeted therapy in this setting have obscured the initial promising perspectives, a biomarker-selection approach may represent the basis of future trials exploring adjuvant treatment for resected NSCLC.

State-of-the-art MS technology applications in lung disease

Two frontline MS technologies, which have recently gained much attention, are discussed within the scope of this review. Besides a brief summary on the contemporary state of lung cancer and chronic obstructive pulmonary disease, the principles of multiple reaction monitoring and matrix assisted laser desorption ionization (MALDI) MS imaging are presented. A comprehensive overview of quantitative mass spectrometry applications is provided, covering multiple reaction monitoring assay developments for analysis of proteins (biomarkers) and low-molecular-weight compounds (drugs) with a special focus on the disease areas of lung cancer and chronic obstructive pulmonary disease. The MALDI-MS imaging applications are discussed similarly, providing references to studies conducted on lung tissues in order to localize drug compounds and protein biomarkers.

A new frontier in personalized cancer therapy: mapping molecular changes

Mutations in the genome of a normal cell can affect the function of its many genes and pathways. These alterations could eventually transform the cell from a normal to a malignant state by allowing an uncontrolled proliferation of the cell and formation of a cancer tumor. Each tumor in an individual patient can have hundreds of mutated genes and perturbed pathways. Cancers clinically presenting as the same type or subtype could potentially be very different at the molecular level and thus behave differently in response to therapy. The challenge is to distinguish the key mutations driving the cancer from the background of mutational noise and find ways to effectively target them. The promise is that such a molecular approach to classifying cancer will lead to better diagnostic, prognostic and personalized treatment strategies. This article provides an overview of advances in the molecular characterization of cancers and their applications in therapy.

Proteomics as a guiding tool for more effective personalized therapy

Ovarian cancer remains the deadliest gynecological malignancy in the Western world and is most often diagnosed at a rarely curable late stage. Examination of protein end points has been employed as an investigative mechanism to guide targeted therapy and to stratify ovarian cancer. Proteomics allows characterization of the proteins and the associated protein and peptide modifications. This has given us insight into the perturbations of signaling pathways within tumor cells and has improved the discovery of new drug targets and possible prognostic indicators of outcome and disease response to therapy. Development of validated assays that survey the genetic and/or proteomic make-up of an individual tumor will add greatly to the histological classification of the tumor and may lead to different treatment approaches tailored to the unique expression pattern of each individual patient. It is anticipated that application of proteomics may bring to reality the clinical adoption of molecular stratification, e.g. not, 'is the gene overexpressed?', but 'is the pathway upregulated?' This will be successful if validated peptide biomarkers are applied for patient selection prospectively and with inclusion of preplanned biological correlates. These events will guide future directions of proteomics as a selector and as a validator and will guide how we integrate proteomics information daily into patient care and into selecting therapy of advanced and recurrent ovarian cancer and other cancers.

The morphological and molecular diagnosis of lung cancer

BACKGROUND

In Germany, lung cancer causes more deaths than any other malignant disease. Its main etiology is smoking, but other risk factors need to be considered as well. The morphological, molecular and biological phenotype is complex and should no longer be just categorized as either small-cell or non-small cell lung cancer.

METHODS

This review article is based on the authors' longstanding involvement in the scientific investigation and diagnostic evaluation of lung cancer, including contributions to the current WHO classification and collaboration in the new interdisciplinary classification of adenocarcinoma. The relevant literature was selectively reviewed.

RESULTS

Lung cancer is morphologically classified into four main subtypes-small-cell carcinoma, squamous-cell carcinoma, adenocarcinoma, and large-cell carcinoma. Genetic and molecular analyses have revealed distinct differences within subtypes; in particular, adenocarcinomas can be further subdivided. Complex techniques of genomic analysis are now available, but clinicopathological data are still the most important determinants of prognosis and are clearly better for this purpose than molecular classification alone. Nonetheless, the assessment of specific molecular markers is becoming increasingly important.

CONCLUSION

The morphological and molecular classification of lung cancer is undergoing a re-evaluation which will lead to more accurate assessment of individual prognoses and to improved prediction of the response to specific treatment regimens.

Proteomic profiling identifies pathways dysregulated in non-small cell lung cancer and an inverse association of AMPK and adhesion pathways with recurrence

INTRODUCTION

The identification of key pathways dysregulated in non-small cell lung cancer (NSCLC) is an important step toward understanding lung pathogenesis and developing new therapeutic approaches.

METHODS

Toward this goal, reverse-phase protein lysate arrays (RPPA) were used to compare signaling pathways between NSCLC tumors and paired normal lung tissue from 46 patients and assess their association with clinical outcome.

RESULTS

After RPPA quantification of 63 proteins and phosphoproteins, tissue pairs were randomized to a training set (n = 25 pairs) and test set (n = 21 pairs). In the training set, 15 protein markers were differentially expressed between tumors and normal lung (p ≤ 0.01), including markers in the PI3K/AKT and p38 MAPK signaling pathways (e.g., p70S6K, S6, p38, and phospho-p38), as well as caveolin-1 and β-catenin. A four-protein signature (p70S6K, cyclin B1, pSrc(Y527), and caveolin-1) independent of histology classified specimens as tumor versus normal with a predicted accuracy of 83%, sensitivity of 67%, and specificity of 100%. The signature was validated in the test set, correctly classifying all normal tissues and 14 of 21 tumor tissues. RPPA results were confirmed by immunohistochemistry for caveolin-1 and p70S6K. In tumors from patients with resected NSCLC, expression of proteins in the energy-sensing AMPK pathway (pLKB1, AMPK, p-Acetyl-CoA, pTSC2), adhesion, EGFR, and Rb signaling pathways was inversely associated with NSCLC recurrence.

CONCLUSIONS

These data provide evidence for dysregulation of several pathways including those involving energy sensing and adhesion that are potentially associated with NSCLC pathogenesis and disease recurrence.

MALDI imaging mass spectrometry of human tissue: method challenges and clinical perspectives

The molecular complexity of biological tissue and the spatial and temporal variation in the biological processes involved in human disease requires new technologies and new approaches to provide insight into disease processes. Imaging mass spectrometry is an effective tool that provides molecular images of tissues in the molecular discovery process. The analysis of human tissue presents special challenges and limitations because the heterogeneity among human tissues and diseases is much greater than that observed in animal models, and discoveries made in animal tissues might not translate well to their human counterparts. In this article, we briefly review the challenges of imaging human tissue using mass spectrometry and suggest approaches to address these issues.

[Long-term survival of personalized surgical treatment of locally advanced non-small cell lung cancer based on molecular staging]

BACKGROUND AND OBJECTIVE

Approximately 35%-40% of patients with newly diagnosed non-small cell Lung cancer have locally advanced disease. The average survival time of these patients only have 6-8 months with chemotherapy. The aim of this study is to explore and summarize the probability of detection of micrometastasis in peripheral blood for molecular staging, and for selection of indication of surgical treatment, and beneficiary of neoadjuvant chemotherapy and postoperative adjuvant therapy in locally advanced lung cancer; to summarize the long-time survival result of personalized surgical treatment of 516 patients with locally advanced non-small cell lung cancer based on molecular staging methods.

METHODS

CK19 mRNA expression of peripheral blood samples was detected in 516 lung cancer patients by RT-PCR before operation for molecular diagnosis of micrometastasis, personalized molecular staging, and for selection of indication of surgical treatment and the beneficiary of neoadjuvant chemotherapy and postoperative adjuvant therapy in patients with locally advanced nonsmall cell lung cancer invaded heart, great vessels or both. The long-term survival result of personalized surgical treatment was retrospectively analyzed in 516 patients with locally advanced non-small cell lung cancer based on molecular staging methods.

RESULTS

There were 322 patients with squamous cell carcinoma and 194 cases with adenocarcinoma in the series of 516 patients with locally advanced lung cancer involved heart, great vessels or both. There were 112 patients with IIIA disease and 404 cases with IIIB disease according to P-TNM staging. There were 97 patients with M-IIIA disease, 278 cases with M-IIIB disease and 141 cases with III disease according to our personalized molecular staging. Of the 516 patients, bronchoplastic procedures and pulmonary artery reconstruction was carried out in 256 cases; lobectomy combined with resection and reconstruction of partial left atrium was performed in 41 cases; Double sleeve lobectomy combined with resection and reconstruction of super vena cava was carried out in 90 cases; Lobectomy combined with resection and reconstruction of diaphragm was performed in 3 cases; Double sleeve lobectomy combined with resection and reconstruction of partial left atrium was performed in 30 cases; Bronchoplastic procedures and pulmonary artery reconstruction combined with reconstruction of aorta sheath was carried out in 10 cases; Right pneumonectomy combined with resection and reconstruction partial left atrium, total right diaphragm with Dacron, and post cava and right liver vein was performed in one case; Lobectomy combined with resection and reconstruction of carina was carried out in 10 cases; Bronchoplastic procedures and pulmonary artery reconstruction combined with resection and reconstruction of carina and superior vane cava, or combined with superior vena cava and left atrium, or with carina and left atrium was performed in 55 cases in this series. Five patients died of operative complications and the operative mortality was 0.97%. CK19 mRNA expression was found in 141 patients. The positive rate of CK19 mRNA expression was 27.3% in peripheral blood samples in the 516 cases. The positive rates of micrometastasis in peripheral blood was significantly related to histological classification, P-TNM staging and N staging of the cancer (P < 0.05), but not to age, sex, smoking status of the patients, and size of primary tumor, and locations of the tumor (P > 0.05). The median survival time was 43.74 months. The 1, 3, 5 and 10 year survival rates of the 516 cases was 89.1%, 39.3%, 19.8% and 10.4%, respectively. The postoperative survival rate was remarkably correlated with micrometastasis in peripheral blood, histological classification of the tumor, size of primary cancer and lymph mode involvement (P < 0.05). The results of multivariable Cox model analysis showed that "personalized molecular P-TNM staging", micrometastasis in peripheral blood, pathological types of the tumor and mediastinal lymph node metastasis of the cancer were the most significant factors for predicting prognosis in the patients with locally advanced nonsmall lung cancer.

CONCLUSIONS

(1) Micrometastasis was existed in peripheral blood of patients with lung cancer, which can not be detected with conventional methods. (2) Detecting of CK19 mRNA expression in peripheral blood in lung cancer patients can be used for diagnosis of micrometastasis of lung cancer and "molecular staging" and "molecular P-TNM staging" for lung cancer patients. It will be helpful for selection of surgical treatment indication, the beneficiary of neoadjuvant chemotherapy and postoperative adjuvant therapy in the patients with locally advanced non-small cell lung cancer. (3) Personalized surgical treatment can significantly improve prognosis and increase curative rate and long-term survival rate of locally advanced nonsmall cell lung cancer based on personalized molecular staging.

Prediction of postoperative recurrence-free survival in non-small cell lung cancer by using an internationally validated gene expression model

PURPOSE

This study was performed to discover prognostic genomic markers associated with postoperative outcome of stage I to III non-small cell lung cancer (NSCLC) that are reproducible between geographically distant and demographically distinct patient populations.

EXPERIMENTAL DESIGN

American patients (n = 27) were stratified on the basis of recurrence and microarray profiling of their tumors was performed to derive a training set of 44 genes. A larger Korean patient validation cohort (n = 138) was also stratified by recurrence and screened for these genes. Four reproducible genes were identified and used to construct genomic and clinicogenomic Cox models for both cohorts.

RESULTS

Four genomic markers, DBN1 (drebrin 1), CACNB3 (calcium channel beta 3), FLAD1 (PP591; flavin adenine dinucleotide synthetase), and CCND2 (cyclin D2), exhibited highly significant differential expression in recurrent tumors in the training set (P < 0.001). In the validation set, DBN1, FLAD1 (PP591), and CACNB3 were significant by Cox univariate analysis (P ≤ 0.035), whereas only DBN1 was significant by multivariate analysis. Genomic and clinicogenomic models for recurrence-free survival (RFS) were equally effective for risk stratification of stage I to II or I to III patients (all models P < 0.0001). For stage I to II or I to III patients, 5-year RFS of the low- and high-risk patients was approximately 70% versus 30% for both models. The genomic model for overall survival of stage I to III patients was improved by addition of pT and pN stage (P < 0.0013 vs. 0.010).

CONCLUSION

A 4-gene prognostic model incorporating the multivariate marker DBN1 exhibits potential clinical utility for risk stratification of stage I to III NSCLC patients.

Novel metastasis-related gene CIM functions in the regulation of multiple cellular stress-response pathways

Various stresses of the tumor microenvironment produced by insufficient nutrients, pH, and oxygen can contribute to the generation of altered metabolic and proliferative states that promote the survival of metastatic cells. Among many cellular stress-response pathways activated under such conditions are the hypoxia-inducible factor (HIF) pathway and the unfolded protein response (UPR), which is elicited as a response to endoplasmic reticulum (ER) stress. In this study, we report the identification of a novel cancer invasion and metastasis-related gene (hereafter referred to as CIM, also called ERLEC1), which influences both of these stress-response pathways to promote metastasis. CIM was identified by comparing the gene expression profile of a highly metastatic human lung cancer cell line with its weakly metastatic parental clone. We showed that CIM is critical for metastatic properties in this system. Proteomic approaches combined with bioinformatic analyses revealed that CIM has multifaceted roles in controlling the response to hypoxia and ER stress. Specifically, CIM sequestered OS-9 from the HIF-1α complex and PHD2, permitting HIF-1α accumulation by preventing its degradation. Ectopic expression of CIM in lung cancer cells increased their tolerance to hypoxia. CIM also modulated UPR through interaction with the key ER stress protein BiP, influencing cell proliferation under ER stress conditions. Our findings shed light on how tolerance to multiple cellular stresses at a metastatic site can be evoked by an integrated mechanism involving CIM, which can function to coordinate those responses in a manner that promotes metastatic cell survival.

The grand challenge to decipher the cancer proteome

The quest to decipher protein alterations in cancer has spanned well over half a century. The vast dynamic range of protein abundance coupled with a plethora of isoforms and disease heterogeneity have been formidable challenges. Progress in cancer proteomics has substantially paralleled technological developments. Advances in analytical techniques and the implementation of strategies to de-complex the proteome into manageable components have allowed proteins across a wide dynamic range to be explored. The massive amounts of data that can currently be collected through proteomics allow the near-complete definition of cancer subproteomes, which reveals the alterations in signalling and developmental pathways. This allows the discovery of predictive biomarkers and the annotation of the cancer genome based on proteomic findings. There remains a considerable need for infrastructure development and the organized collaborative efforts to efficiently mine the cancer proteome.

Predictive advantage of a cell type classification for pulmonary adenocarcinoma coupled with data for p53, K-ras and EGFR alterations

We analyzed relationships between histological subtypes of pulmonary adenocarcinomas and three gene alterations (p53, K-ras, and epidermal growth factor receptor gene), or thyroid transcription factor-1 (TTF-1) expression, and also studied prognoses by the subtypes, with or without combined multiple gene mutation status. Our purpose was to clearly determine pathogenesis, along with the best predictive value for biology and therapy-related traits. A total of 223 consecutively resected pulmonary adenocarcinomas were sub-classified using either the World Health Organization (WHO) or our five-cell type (FCT) classification system (hobnail, columnar/cuboidal, mixed, polygonal/oval, and goblet cell types). DNAs extracted from frozen samples of the adenocarcinomas were examined for gene alterations, and TTF-1 expressions were determined using immunohistochemistry. Next, relationships among the various data and clinicopathological factors were analyzed. The most striking result was: while almost 70% of adenocarcinomas were sub-classified as a mixed subtype by WHO, the FCT classified many of them as other cell subtypes. The FCT closely reflected differences in etiological factors, cellular lineages, and frequencies of gene mutations; and whether the data from combined gene mutations were used or not, differences among the cell types in postoperative survivals appeared. In contrast, subtypes of WHO did not show any association with the gene alteration or prognosis, and the FCT more suitably indicated sensitivity to gefitinib therapy than did WHO. The FCT combined with multiple gene mutation status appears to be useful in indicating pathogenesis and predicting the biological nature of pulmonary adenocarcinomas, and it could facilitate development of new therapies for each subtype.

Lung cancer proteomics, clinical and technological considerations

The overall survival of lung cancer patients is disappointingly low. This is due to several factors, including the lack of an effective screening strategy to detect tumors at a potentially curable early stage, a marked resistance of lung cancer cells to drug treatment and a still superficial knowledge about the multifactorial cellular networks that are activated or suppressed during cancer progression. Furthermore, the armamentarium of clinicians and researchers in the field does not yet include reliable biomarkers to predict tumor response to treatment and foresee the natural history of the disease. In the present situation, a potential breakthrough is presented by proteomics technologies with the potential to discover relevant biomarkers which can be accurately quantified in multiplexed assays. Proteomics field can also contribute greatly in the understanding of mechanisms in tumor progression and treatment response. In this review we will describe the work that is being done in the field of lung cancer proteomics, focusing on clinically relevant questions that need to be addressed and on the possible applications of novel technologies.

A novel method for sample preparation of fresh lung cancer tissue for proteomics analysis by tumor cell enrichment and removal of blood contaminants

BACKGROUND

In-depth proteomics analyses of tumors are frequently biased by the presence of blood components and stromal contamination, which leads to large experimental variation and decreases the proteome coverage. We have established a reproducible method to prepare freshly collected lung tumors for proteomics analysis, aiming at tumor cell enrichment and reduction of plasma protein contamination. We obtained enriched tumor-cell suspensions (ETS) from six lung cancer cases (two adenocarcinomas, two squamous-cell carcinomas, two large-cell carcinomas) and from two normal lung samples. The cell content of resulting ETS was evaluated with immunocytological stainings and compared with the histologic pattern of the original specimens. By means of a quantitative mass spectrometry-based method we evaluated the reproducibility of the sample preparation protocol and we assessed the proteome coverage by comparing lysates from ETS samples with the direct lysate of corresponding fresh-frozen samples.

RESULTS

Cytological analyses on cytospin specimens showed that the percentage of tumoral cells in the ETS samples ranged from 20% to 70%. In the normal lung samples the percentage of epithelial cells was less then 10%. The reproducibility of the sample preparation protocol was very good, with coefficient of variation at the peptide level and at the protein level of 13% and 7%, respectively. Proteomics analysis led to the identification of a significantly higher number of proteins in the ETS samples than in the FF samples (244 vs 109, respectively). Albumin and hemoglobin were among the top 5 most abundant proteins identified in the FF samples, showing a high contamination with blood and plasma proteins, whereas ubiquitin and the mitochondrial ATP synthase 5A1 where among the top 5 most abundant proteins in the ETS samples.

CONCLUSION

The method is feasible and reproducible. We could obtain a fair enrichment of cells but the major benefit of the method was an effective removal of contaminants from red blood cells and plasma proteins resulting in larger proteome coverage compared to the direct lysis of frozen samples. This sample preparation method may be successfully implemented for the discovery of lung cancer biomarkers on tissue samples using mass spectrometry-based proteomics.

High-throughput molecular analysis in lung cancer: insights into biology and potential clinical applications

During the last decade, high-throughput technologies including genomic, epigenomic, transcriptomic and proteomic have been applied to further our understanding of the molecular pathogenesis of this heterogeneous disease, and to develop strategies that aim to improve the management of patients with lung cancer. Ultimately, these approaches should lead to sensitive, specific and noninvasive methods for early diagnosis, and facilitate the prediction of response to therapy and outcome, as well as the identification of potential novel therapeutic targets. Genomic studies were the first to move this field forward by providing novel insights into the molecular biology of lung cancer and by generating candidate biomarkers of disease progression. Lung carcinogenesis is driven by genetic and epigenetic alterations that cause aberrant gene function; however, the challenge remains to pinpoint the key regulatory control mechanisms and to distinguish driver from passenger alterations that may have a small but additive effect on cancer development. Epigenetic regulation by DNA methylation and histone modifications modulate chromatin structure and, in turn, either activate or silence gene expression. Proteomic approaches critically complement these molecular studies, as the phenotype of a cancer cell is determined by proteins and cannot be predicted by genomics or transcriptomics alone. The present article focuses on the technological platforms available and some proposed clinical applications. We illustrate herein how the "-omics" have revolutionised our approach to lung cancer biology and hold promise for personalised management of lung cancer.

Mass spectrometry-based proteomic profiling of lung cancer

In an effort to further our understanding of lung cancer biology and to identify new candidate biomarkers to be used in the management of lung cancer, we need to probe these tissues and biological fluids with tools that address the biology of lung cancer directly at the protein level. Proteins are responsible of the function and phenotype of cells. Cancer cells express proteins that distinguish them from normal cells. Proteomics is defined as the study of the proteome, the complete set of proteins produced by a species, using the technologies of large-scale protein separation and identification. As a result, new technologies are being developed to allow the rapid and systematic analysis of thousands of proteins. The analytical advantages of mass spectrometry (MS), including sensitivity and high-throughput, promise to make it a mainstay of novel biomarker discovery to differentiate cancer from normal cells and to predict individuals likely to develop or recur with lung cancer. In this review, we summarize the progress made in clinical proteomics as it applies to the management of lung cancer. We will focus our discussion on how MS approaches may advance the areas of early detection, response to therapy, and prognostic evaluation.