Maria Pernemalm

The role of the tumor-microenvironment in lung cancer-metastasis and its relationship to potential therapeutic targets.

Non-small cell lung cancer (NSCLC) accounts for >80% of lung cancer cases and currently has an overall five-year survival rate of only 15%. Patients presenting with advanced stage NSCLC die within 18-months of diagnosis. Metastatic spread accounts for >70% of these deaths. Thus elucidation of the mechanistic basis of NSCLC-metastasis has potential to impact on patient quality of life and survival. Research on NSCLC metastasis has recently expanded to include non-cancer cell components of tumors-the stromal cellular compartment and extra-cellular matrix components comprising the tumor-microenvironment. Metastasis (from initial primary tumor growth through angiogenesis, intravasation, survival in the bloodstream, extravasation and metastatic growth) is an inefficient process and few released cancer cells complete the entire process. Micro-environmental interactions assist each of these steps and discovery of the mechanisms by which tumor cells co-operate with the micro-environment are uncovering key molecules providing either biomarkers or potential drug targets. The major sites of NSCLC metastasis are brain, bone, adrenal gland and the liver. The mechanistic basis of this tissue-tropism is beginning to be elucidated offering the potential to target stromal components of these tissues thus targeting therapy to the tissues affected. This review covers the principal steps involved in tumor metastasis. The role of cell-cell interactions, ECM remodeling and autocrine/paracrine signaling interactions between tumor cells and the surrounding stroma is discussed. The mechanistic basis of lung cancer metastasis to specific organs is also described. The signaling mechanisms outlined have potential to act as future drug targets minimizing lung cancer metastatic spread and morbidity.

Affinity prefractionation for MS-based plasma proteomics.

The plasma proteome has proven to be one of the most challenging proteomes to profile using currently available proteomics technologies. A plethora of methodologies have been used to profile human plasma in order to discover potential biomarkers for disease and for therapy optimization. Affinity‐based prefractionation coupled to MS has been shown to be one of the most successful ways to dig deeper into the plasma proteome. Depletion of high abundant plasma proteins is becoming an initial method of choice in any plasma profiling project. However, several other affinity‐based enrichment methods have been published in recent years. Here we review both protein and peptide affinity prefractionation methods coupled with MS‐based proteomics. Analysis of the proportion of cellular and extracellular annotated proteins of publicly available MS plasma proteomics data is performed to estimate the analytical depth of various prefractionation methods.

Network enrichment analysis: extension of gene-set enrichment analysis to gene networks

BackgroundGene-set enrichment analyses (GEA or GSEA) are commonly used for biological characterization of an experimental gene-set. This is done by finding known functional categories, such as pathways or Gene Ontology terms, that are over-represented in the experimental set; the assessment is based on an overlap statistic. Rich biological information in terms of gene interaction network is now widely available, but this topological information is not used by GEA, so there is a need for methods that exploit this type of information in high-throughput data analysis.ResultsWe developed a method of network enrichment analysis (NEA) that extends the overlap statistic in GEA to network links between genes in the experimental set and those in the functional categories. For the crucial step in statistical inference, we developed a fast network randomization algorithm in order to obtain the distribution of any network statistic under the null hypothesis of no association between an experimental gene-set and a functional category. We illustrate the NEA method using gene and protein expression data from a lung cancer study.ConclusionsThe results indicate that the NEA method is more powerful than the traditional GEA, primarily because the relationships between gene sets were more strongly captured by network connectivity rather than by simple overlaps.

Tumor expression of S100A6 correlates with survival of patients with stage I non-small-cell lung cancer

BACKGROUND In a previously published in vitro study based on top-down proteomics we found that the calcium-binding proteins S100A6 and S100A4 were affected by exposure to ionizing radiation in a p53-dependent fashion. Both proteins showed post-translational modification changes, and S100A6 also showed increased expression and translocation in response to irradiation. Aim of the present study was to evaluate the expression of S100A6 and S100A4 in non-small-cell lung cancer (NSCLC). METHODS S100A6 expression on archival tumor cell lysates from 39 patients with radically resected NSCLC was assessed with SELDI-TOF-MS. S100A6 identity was confirmed using a SELDI-based antibody-capture method on lysates from the A549 lung cancer cell line, cell lysates from two freshly prepared NSCLC samples, four plasma samples and one pleural effusion sample. Immunostainings for S100A6, S100A4 and p53 were performed on tissue microarrays containing 103 stage I surgically resected NSCLC cases and 14 normal lung parenchyma specimens. RESULTS The presence of post-translationally modified S100A6 forms was confirmed with SELDI-MS on enriched tumor cell lysates, as well as in plasma and pleural effusion samples. In addition, high S100A6 peak intensity was associated with longer median survival (35 months vs. 18 months for high and low peak intensity, respectively; p=n.s.). The immunohistochemical analysis showed that 25% of tumors were S100A6 positive. S100A6 expression correlated directly with non-squamous histology (p<0.0001) and S100A4 expression (p=0.005), and inversely with p53 expression (p=0.01). S100A6-positive cases showed a trend of longer survival compared with S100A6-negative cases (p=0.07). This difference became significant when the analysis was restricted to p53-negative cases (n=72). In this subgroup of patients, whose tumors likely exhibit a functional p53, S100A6 was an independent prognostic factor of improved survival at multivariate analysis (HR 0.49, 95% CI 0.27-0.81, p=0.017). CONCLUSIONS In this study we have validated on clinical material our previous findings on cell lines in terms of S100A6 expression and post-translational modifications pattern in NSCLC. Moreover, the survival results obtained in p53-negative stage I NSCLC cases support the proposed pro-apoptotic function of S100A6 and suggest the hypothesis of a cross regulation between these two proteins.

Up-regulation, Modification, and Translocation of S100A6 Induced by Exposure to Ionizing Radiation Revealed by Proteomics Profiling

The cellular response to genotoxic stress is a complex cascade of events including altered protein expression, interactions, modifications, and relocalization, leading to cell cycle arrest and DNA repair or to apoptosis. p53 protein has a central role in this process, and p53 status is an important factor in the response of a tumor to genotoxic anticancer therapy. We studied p53-related changes postexposure to ionizing radiation using top-down mass spectrometry. Initially two cell lines were compared, HCT116 p53 wild type (wt) and p53−/−, in a time course study postirradiation. In the p53 wt cell line a striking increase of a 10.2-kDa protein was detected, and this protein was identified with MS/MS analysis as S100A6. Further MS profiling led to detection of two post-translationally modified variants of S100A6, namely glutathionylated and cysteinylated forms. In p53 wt cells, a specific shift from glutathionylated to cysteinylated S100A6 occurred postirradiation. The p53 dependence of this specific change in protein level and modification pattern of S100A6 postirradiation was confirmed in a panel of four lung cancer cell lines (H23, U1810, H69, and A549) with different p53 status and using small interfering RNA against p53. Interestingly the closely related S100 family protein S100A4 showed the same changes in modification pattern post-ionizing radiation in the p53 wt lung cancer cell line, and S100A4 also showed p53-dependent expression. Using confocal microscopy, relocalization of S100A6 from nucleus to cytosol and a colocalization with tropomyosin in stress fibers was detected in A549 cells postirradiation. This relocalization coincided with the change in S100A6 modification pattern. Based on these results, we suggest that S100A6 and S100A4 are regulated via redox modifications in vivo and that these proteins are involved in the cellular response to genotoxic stress.

Use of narrow-range peptide IEF to improve detection of lung adenocarcinoma markers in plasma and pleural effusion.

In this study we applied narrow‐range peptide IEF to plasma or pleural effusion prior to LC/MS/MS. Two methods for narrow‐range IEF were run; IPG strips and free‐flow electrophoresis. Data from this study was compared with cell line data to evaluate the method performance in body fluids. To test the methods potential in quantitative biomarker discovery studies, plasma and pleural effusion from patients with lung adenocarcinoma (n=3) were compared with inflammatory pleuritis (n=3) using iTRAQ quantification. Using narrow‐range IEF on the peptide level we were able to identify and quantify 282 proteins in plasma and 300 proteins in pleural effusion. These body fluid proteomes demonstrated high degree of overlap; however, more proteins significantly differently altered levels related to adenenocarcinoma were found in pleural effusion compared with plasma, suggesting enrichment of lung tissue‐related proteins in pleural effusion. Nine proteins were chosen for initial validation with Western blot, and one protein (NPC2) was chosen for further validation using imunohistochemistry. Overall, the quantitative results from IEF/LC/MS/MS showed good correlation with the results from Western blot and imunohistochemistry, showing the potential of this methodology in quantitative biomarker discovery studies.

Evaluation of three principally different intact protein prefractionation methods for plasma biomarker discovery.

The aim of this study was to evaluate three principally different top-down protein prefractionation methods for plasma: high-abundance protein depletion, size fractionation and peptide ligand affinity beads, focusing in particular on compatibility with downstream analysis, reproducibility and analytical depth. Our data clearly demonstrates the benefit of high-abundance protein depletion. However, MS/MS analysis of the proteins eluted from the high-abundance protein depletion column show that more proteins than aimed for are removed and, in addition, that the depletion efficacy varies between the different high-abundance proteins. Although a smaller number of proteins were identified per fraction using the peptide ligand affinity beads, this technique showed to be both robust and versatile. Size fractionation, as performed in this study, focusing on the low molecular weight proteome using a combination of gel filtration chromatography and molecular weight cutoff filters, showed limitations in the molecular weight cutoff precision leading detection of high molecular weight proteins and, in the case of the cutoff filters, high variability. GeLC-MS/MS analysis of the fractionation methods in combination with pathway analysis demonstrates that increased fractionation primarily leads to high proteome coverage of pathways related to biological functions of plasma, such as acute phase reaction, complement cascade and coagulation. Further, the prefractionation methods in this study induces limited effect on the proportion of tissue proteins detected, thereby highlighting the importance of extensive or targeted downstream fractionation.

Molecular histology of lung cancer: From targets to treatments

Lung cancer is the leading cause of cancer-related death worldwide with a 5-year survival rate of less than 15%, despite significant advances in both diagnostic and therapeutic approaches. Combined genomic and transcriptomic sequencing studies have identified numerous genetic driver mutations that are responsible for the development of lung cancer. In addition, molecular profiling studies identify gene products and their mutations which predict tumour responses to targeted therapies such as protein tyrosine kinase inhibitors and also can offer explanation for drug resistance mechanisms. The profiling of circulating micro-RNAs has also provided an ability to discriminate patients in terms of prognosis/diagnosis and high-throughput DNA sequencing strategies are beginning to elucidate cell signalling pathway mutations associated with oncogenesis, including potential stem cell associated pathways, offering the promise that future therapies may target this sub-population, preventing disease relapse post treatment and improving patient survival. This review provides an assessment of molecular profiling within lung cancer concerning molecular mechanisms, treatment options and disease-progression. Current areas of development within lung cancer profiling are discussed (i.e. profiling of circulating tumour cells) and future challenges for lung cancer treatment addressed such as detection of micro-metastases and cancer stem cells.

Mass spectrometry-based plasma proteomics: state of the art and future outlook

Mass spectrometry-based plasma proteomics is a field where intense research has been performed during the last decade. Being closely linked to biomarker discovery, the field has received a fair amount of criticism, mostly due to the low number of novel biomarkers reaching the clinic. However, plasma proteomics is under gradual development with improvements on fractionation methods, mass spectrometry instrumentation and analytical approaches. These recent developments have contributed to the revival of plasma proteomics. The goal of this review is to summarize these advances, focusing in particular on fractionation methods, both for targeted and global mass spectrometry-based plasma analysis.

Quantitative Proteomics Profiling of Primary Lung Adenocarcinoma Tumors Reveals Functional Perturbations in Tumor Metabolism

In this study, we have analyzed human primary lung adenocarcinoma tumors using global mass spectrometry to elucidate the biological mechanisms behind relapse post surgery. In total, we identified over 3000 proteins with high confidence. Supervised multivariate analysis was used to select 132 proteins separating the prognostic groups. Based on in-depth bioinformatics analysis, we hypothesized that the tumors with poor prognosis had a higher glycolytic activity and HIF activation. By measuring the bioenergetic cellular index of the tumors, we could detect a higher dependency of glycolysis among the tumors with poor prognosis. Further, we could also detect an up-regulation of HIF1α mRNA expression in tumors with early relapse. Finally, we selected three proteins that were upregulated in the poor prognosis group (cathepsin D, ENO1, and VDAC1) to confirm that the proteins indeed originated from the tumor and not from a stromal or inflammatory component. Overall, these findings show how in-depth analysis of clinical material can lead to an increased understanding of the molecular mechanisms behind tumor progression.