Phosphosignature Predicts Dasatinib Response in Non-small Cell Lung Cancer

Targeted drugs are less toxic than traditional chemotherapeutic therapies; however, the proportion of patients that benefit from these drugs is often smaller. A marker that confidently predicts patient response to a specific therapy would allow an individual therapy selection most likely to benefit the patient. Here, we used quantitative mass spectrometry to globally profile the basal phosphoproteome of a panel of non-small cell lung cancer cell lines. The effect of the kinase inhibitor dasatinib on cellular growth was tested against the same panel. From the phosphoproteome profiles, we identified 58 phosphorylation sites, which consistently differ between sensitive and resistant cell lines. Many of the corresponding proteins are involved in cell adhesion and cytoskeleton organization. We showed that a signature of only 12 phosphorylation sites is sufficient to accurately predict dasatinib sensitivity. Four of the phosphorylation sites belong to integrin β4, a protein that mediates cell-matrix or cell-cell adhesion. The signature was validated in cross-validation and label switch experiments and in six independently profiled breast cancer cell lines. The study supports that the phosphorylation of integrin β4, as well as eight further proteins comprising the signature, are candidate biomarkers for predicting response to dasatinib in solid tumors. Furthermore, our results show that identifying predictive phosphorylation signatures from global, quantitative phosphoproteomic data is possible and can open a new path to discovering molecular markers for response prediction.     

Biomarkers of human cutaneous squamous cell carcinoma from tissues and cell lines identified by DNA microarrays and qRT-PCR

Squamous cell carcinoma (SCC) is the second most common form of skin cancer in Caucasians. Here we report on the identification of biomarkers of human cutaneous SCC cell lines in vitro and tissue samples in vivo using DermArray and PharmArray DNA microarrays, consisting of ca. 7400 unique human cDNAs. Differentially expressed genes were identified in two facial skin SCC cell lines (SCC 12 and SCC 13) compared to normal keratinocytes, and three cutaneous SCC tissue samples compared to normal skin. Quantitative validations of up- and down-regulated biomarkers were performed by qRT-PCR on 23 biomarker genes for the cell lines and 20 biomarker genes for the tumor tissues. In addition, three oral SCC cell lines were also included in the qRT-PCR validations for comparison, and the biomarker profiles were highly similar between the cutaneous and the oral SCC cell lines for all 23 biomarkers examined. The expression profiles for a variety of non-cutaneous SCC types, such as head-and-neck, oral, and lung, have been previously published. This report is the first to describe biomarkers for cutaneous SCC in two contexts, in vitro and in vivo. Although there was minimal overlap between the two different contexts using DNA microarrays, five genes were found common to both the cell lines and tissues, namely fibronectin 1, annexin A5, glyceraldehyde 3-phosphate dehydrogenase, zinc-finger protein 254, and huntingtin-associated protein interacting protein. Some of our previously published biomarkers of normal keratinocytes were down-regulated in SCC, suggestive of the dedifferentiated status of the transformed cells. While recent reports have identified some of the same genes as SCC biomarkers, for instance in head-and-neck cancer, thereby validating our approach, we have identified some novel biomarkers for cutaneous disease. These biomarker lists may be useful in molecular diagnostics of non-melanoma skin cancer, and a subset of the biomarkers might serve as suitable targets for drug discovery efforts of therapies for SCC.     

Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the Generalized Singular Value Decomposition

Background

One of the major goals in gene and protein expression profiling of cancer is to identify biomarkers and build classification models for prediction of disease prognosis or treatment response. Many traditional statistical methods, based on microarray gene expression data alone and individual genes' discriminatory power, often fail to identify biologically meaningful biomarkers thus resulting in poor prediction performance across data sets. Nonetheless, the variables in multivariable classifiers should synergistically interact to produce more effective classifiers than individual biomarkers.

Results

We developed an integrated approach, namely network-constrained support vector machine (netSVM), for cancer biomarker identification with an improved prediction performance. The netSVM approach is specifically designed for network biomarker identification by integrating gene expression data and protein-protein interaction data. We first evaluated the effectiveness of netSVM using simulation studies, demonstrating its improved performance over state-of-the-art network-based methods and gene-based methods for network biomarker identification. We then applied the netSVM approach to two breast cancer data sets to identify prognostic signatures for prediction of breast cancer metastasis. The experimental results show that: (1) network biomarkers identified by netSVM are highly enriched in biological pathways associated with cancer progression; (2) prediction performance is much improved when tested across different data sets. Specifically, many genes related to apoptosis, cell cycle, and cell proliferation, which are hallmark signatures of breast cancer metastasis, were identified by the netSVM approach. More importantly, several novel hub genes, biologically important with many interactions in PPI network but often showing little change in expression as compared with their downstream genes, were also identified as network biomarkers; the genes were enriched in signaling pathways such as TGF-beta signaling pathway, MAPK signaling pathway, and JAK-STAT signaling pathway. These signaling pathways may provide new insight to the underlying mechanism of breast cancer metastasis.

Conclusions

We have developed a network-based approach for cancer biomarker identification, netSVM, resulting in an improved prediction performance with network biomarkers. We have applied the netSVM approach to breast cancer gene expression data to predict metastasis in patients. Network biomarkers identified by netSVM reveal potential signaling pathways associated with breast cancer metastasis, and help improve the prediction performance across independent data sets.     

Src Drives Growth of Antiestrogen Resistant Breast Cancer Cell Lines and Is a Marker for Reduced Benefit of Tamoxifen Treatment

The underlying mechanisms leading to antiestrogen resistance in estrogen-receptor α (ER)-positive breast cancer is still poorly understood. The aim of this study was therefore to identify biomarkers and novel treatments for antiestrogen resistant breast cancer. We performed a kinase inhibitor screen on antiestrogen responsive T47D breast cancer cells and T47D-derived tamoxifen and fulvestrant resistant cell lines. We found that dasatinib, a broad-spectrum kinase inhibitor, inhibited growth of the antiestrogen resistant cells compared to parental T47D cells. Furthermore western blot analysis showed increased expression and phosphorylation of Src in the resistant cells and that dasatinib inhibited phosphorylation of Src and also signaling via Akt and Erk in all cell lines. Immunoprecipitation revealed Src: ER complexes only in the parental T47D cells. In fulvestrant resistant cells, Src formed complexes with the Human Epidermal growth factor Receptor (HER)1 and HER2. Neither HER receptors nor ER were co-precipitated with Src in the tamoxifen resistant cell lines. Compared to treatment with dasatinib alone, combined treatment with dasatinib and fulvestrant had a stronger inhibitory effect on tamoxifen resistant cell growth, whereas dasatinib in combination with tamoxifen had no additive inhibitory effect on fulvestrant resistant growth. When performing immunohistochemical staining on 268 primary tumors from breast cancer patients who had received tamoxifen as first line endocrine treatment, we found that membrane expression of Src in the tumor cells was significant associated with reduced disease-free and overall survival. In conclusion, Src was identified as target for treatment of antiestrogen resistant T47D breast cancer cells. For tamoxifen resistant T47D cells, combined treatment with dasatinib and fulvestrant was superior to treatment with dasatinib alone. Src located at the membrane has potential as a new biomarker for reduced benefit of tamoxifen.     

Transforming Growth Factor β Signaling Overcomes Dasatinib Resistance in Lung Cancer

Lung cancer is the second most common cancer and the leading cause of cancer-related deaths. Despite recent advances in the development of targeted therapies, patients with advanced disease remain incurable, mostly because metastatic non-small cell lung carcinomas (NSCLC) eventually become resistant to tyrosine kinase inhibitors (TKIs). Kinase inhibitors have the potential for target promiscuity because the kinase super family is the largest family of druggable genes that binds to a common substrate (ATP). As a result, TKIs often developed for a specific purpose have been found to act on other targets. Drug affinity chromatography has been used to show that dasatinib interacts with the TGFβ type I receptor (TβR-I), a serine-threonine kinase. To determine the potential biological relevance of this association, we studied the combined effects of dasatinib and TGFβ on lung cancer cell lines. We found that dasatinib treatment alone had very little effect; however, when NSCLC cell lines were treated with a combination of TGFβ and dasatinib, apoptosis was induced. Combined TGFβ-1 + dasatinib treatment had no effect on the activity of Smad2 or other non-canonical TGFβ intracellular mediators. Interestingly, combined TGFβ and dasatinib treatment resulted in a transient increase in p-Smad3 (seen after 3 hours). In addition, when NSCLC cells were treated with this combination, the pro-apoptotic protein BIM was up-regulated. Knockdown of the expression of Smad3 using Smad3 siRNA also resulted in a decrease in BIM protein, suggesting that TGFβ-1 + dasatinib-induced apoptosis is mediated by Smad3 regulation of BIM. Dasatinib is only effective in killing EGFR mutant cells, which is shown in only 10% of NSCLCs. Therefore, the observation that wild-type EGFR lung cancers can be manipulated to render them sensitive to killing by dasatinib could have important implications for devising innovative and potentially more efficacious treatment strategies for this disease.     

Blood-based lung cancer biomarkers identified through proteomic discovery in cancer tissues,cell lines and conditioned medium

Background

Support for early detection of lung cancer has emerged from the National Lung Screening Trial (NLST), in which low-dose computed tomography (LDCT) screening reduced lung cancer mortality by 20 % relative to chest x-ray. The US Preventive Services Task Force (USPSTF) recently recommended annual screening for the high-risk population, concluding that the benefits (life years gained) outweighed harms (false positive findings, abortive biopsy/surgery, radiation exposure). In making their recommendation, the USPSTF noted that the moderate net benefit of screening was dependent on the resolution of most false-positive results without invasive procedures. Circulating biomarkers may serve as a valuable adjunctive tool to imaging.

Results

We developed a broad-based proteomics discovery program, integrating liquid chromatography/mass spectrometry (LC/MS) analyses of freshly resected lung tumor specimens (n = 13), lung cancer cell lines (n = 17), and conditioned media collected from tumor cell lines (n = 7). To enrich for biomarkers likely to be found at elevated levels in the peripheral circulation of lung cancer patients, proteins were prioritized based on predicted subcellular localization (secreted, cell-membrane associated) and differential expression in disease samples. 179 candidate biomarkers were identified. Several markers selected for further validation showed elevated levels in serum collected from subjects with stage I NSCLC (n = 94), relative to healthy smoker controls (n = 189). An 8-marker model was developed (TFPI, MDK, OPN, MMP2, TIMP1, CEA, CYFRA 21–1, SCC) which accurately distinguished subjects with lung cancer (n = 50) from high risk smokers (n = 50) in an independent validation study (AUC = 0.775).

Conclusions

Integrating biomarker discovery from multiple sample types (fresh tissue, cell lines and conditioned medium) has resulted in a diverse repertoire of candidate biomarkers. This unique collection of biomarkers may have clinical utility in lung cancer detection and diagnoses.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-015-9090-9) contains supplementary material, which is available to authorized users.     

PEDF,a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation

Biomarkers predict World Trade Center-Lung Injury (WTC-LI); however, there remains unaddressed multicollinearity in our serum cytokines, chemokines, and high-throughput platform datasets used to phenotype WTC-disease. To address this concern, we used automated, machine-learning, high-dimensional data pruning, and validated identified biomarkers. The parent cohort consisted of male, never-smoking firefighters with WTC-LI (FEV_{1, %Pred}< lower limit of normal (LLN); n = 100) and controls (n = 127) and had their biomarkers assessed. Cases and controls (n = 15/group) underwent untargeted metabolomics, then feature selection performed on metabolites, cytokines, chemokines, and clinical data. Cytokines, chemokines, and clinical biomarkers were validated in the non-overlapping parent-cohort via binary logistic regression with 5-fold cross validation. Random forests of metabolites (n = 580), clinical biomarkers (n = 5), and previously assayed cytokines, chemokines (n = 106) identified that the top 5% of biomarkers important to class separation included pigment epithelium-derived factor (PEDF), macrophage derived chemokine (MDC), systolic blood pressure, macrophage inflammatory protein-4 (MIP-4), growth-regulated oncogene protein (GRO), monocyte chemoattractant protein-1 (MCP-1), apolipoprotein-AII (Apo-AII), cell membrane metabolites (sphingolipids, phospholipids), and branched-chain amino acids. Validated models via confounder-adjusted (age on 9/11, BMI, exposure, and pre-9/11 FEV_{1, %Pred}) binary logistic regression had AUC_ROC [0.90(0.84–0.96)]. Decreased PEDF and MIP-4, and increased Apo-AII were associated with increased odds of WTC-LI. Increased GRO, MCP-1, and simultaneously decreased MDC were associated with decreased odds of WTC-LI. In conclusion, automated data pruning identified novel WTC-LI biomarkers; performance was validated in an independent cohort. One biomarker—PEDF, an antiangiogenic agent—is a novel, predictive biomarker of particulate-matter-related lung disease. Other biomarkers—GRO, MCP-1, MDC, MIP-4—reveal immune cell involvement in WTC-LI pathogenesis. Findings of our automated biomarker identification warrant further investigation into these potential pharmacotherapy targets.     

Translational biomarker discovery in clinical metabolomics: an introductory tutorial

Metabolomics is increasingly being applied towards the identification of biomarkers for disease diagnosis, prognosis and risk prediction. Unfortunately among the many published metabolomic studies focusing on biomarker discovery, there is very little consistency and relatively little rigor in how researchers select, assess or report their candidate biomarkers. In particular, few studies report any measure of sensitivity, specificity, or provide receiver operator characteristic (ROC) curves with associated confidence intervals. Even fewer studies explicitly describe or release the biomarker model used to generate their ROC curves. This is surprising given that for biomarker studies in most other biomedical fields, ROC curve analysis is generally considered the standard method for performance assessment. Because the ultimate goal of biomarker discovery is the translation of those biomarkers to clinical practice, it is clear that the metabolomics community needs to start “speaking the same language” in terms of biomarker analysis and reporting-especially if it wants to see metabolite markers being routinely used in the clinic. In this tutorial, we will first introduce the concept of ROC curves and describe their use in single biomarker analysis for clinical chemistry. This includes the construction of ROC curves, understanding the meaning of area under ROC curves (AUC) and partial AUC, as well as the calculation of confidence intervals. The second part of the tutorial focuses on biomarker analyses within the context of metabolomics. This section describes different statistical and machine learning strategies that can be used to create multi-metabolite biomarker models and explains how these models can be assessed using ROC curves. In the third part of the tutorial we discuss common issues and potential pitfalls associated with different analysis methods and provide readers with a list of nine recommendations for biomarker analysis and reporting. To help readers test, visualize and explore the concepts presented in this tutorial, we also introduce a web-based tool called ROCCET (ROC Curve Explorer & Tester, http://www.roccet.ca). ROCCET was originally developed as a teaching aid but it can also serve as a training and testing resource to assist metabolomics researchers build biomarker models and conduct a range of common ROC curve analyses for biomarker studies.     

Aberrant Promoter Methylation of Caveolin-1 Is Associated with Favorable Response to Taxane-Platinum Combination Chemotherapy in Advanced NSCLC

Purpose

Aberrant promoter DNA methylation can serve as a predictive biomarker for improved clinical responses to certain chemotherapeutics. One of the major advantages of methylation biomarkers is the ease of detection and clinical application. In order to identify methylation biomarkers predictive of a response to a taxane-platinum based chemotherapy regimen in advanced NSCLC we performed an unbiased methylation analysis of 1,536 CpG dinucleotides in cancer-associated gene loci and correlated results with clinical outcomes.

Methods

We studied a cohort of 49 patients (median age 62 years) with advanced NSCLC treated at the Atlanta VAMC between 1999 and 2010. Methylation analysis was done on the Illumina GoldenGate Cancer panel 1 methylation microarray platform. Methylation data were correlated with clinical response and adjusted for false discovery rates.

Results

Cav1 methylation emerged as a powerful predictor for achieving disease stabilization following platinum taxane based chemotherapy (p = 1.21E-05, FDR significance  = 0.018176). In Cox regression analysis after multivariate adjustment for age, performance status, gender, histology and the use of bevacizumab, CAV1 methylation was significantly associated with improved overall survival (HR 0.18 (95%CI: 0.03–0.94)). Silencing of CAV1 expression in lung cancer cell lines(A549, EKVX)by shRNA led to alterations in taxane retention.

Conclusions

CAV1 methylation is a predictor of disease stabilization and improved overall survival following chemotherapy with a taxane-platinum combination regimen in advanced NSCLC. CAV1 methylation may predict improved outcomes for other chemotherapeutic agents which are subject to cellular clearance mediated by caveolae.     

Prognostic Significance of Systemic Inflammation-Based Lymphocyte- Monocyte Ratio in Patients with Lung Cancer: Based on a Large Cohort Study

Increasing evidence indicates cancer-related inflammatory biomarkers show great promise for predicting the outcome of cancer patients. The lymphocyte- monocyte ratio (LMR) was demonstrated to be independent prognostic factor mainly in hematologic tumor. The aim of the present study was to investigate the prognostic value of LMR in operable lung cancer. We retrospectively enrolled a large cohort of patients with primary lung cancer who underwent complete resection at our institution from 2006 to 2011. Inflammatory biomarkers including lymphocyte count and monocyte count were collected from routinely performed preoperative blood tests and the LMR was calculated. Survival analyses were calculated for overall survival (OS) and disease-free survival (DFS). A total of 1453 patients were enrolled in the study. The LMR was significantly associated with OS and DFS in multivariate analyses of the whole cohort (HR = 1.522, 95% CI: 1.275–1.816 for OS, and HR = 1.338, 95% CI: 1.152–1.556 for DFS). Univariate subgroup analyses disclosed that the prognostic value was limited to patients with non-small-cell lung cancer (NSCLC) (HR: 1.824, 95% CI: 1.520–2.190), in contrast to patients with small cell lung cancer (HR: 1.718, 95% CI: 0.946–3.122). Multivariate analyses demonstrated that LMR was still an independent prognostic factor in NSCLC. LMR can be considered as a useful independent prognostic marker in patients with NSCLC after complete resection. This will provide a reliable and convenient biomarker to stratify high risk of death in patients with operable NSCLC.     

Learning Biomarker Models for Progression Estimation of Alzheimer’s Disease

Being able to estimate a patient’s progress in the course of Alzheimer’s disease and predicting future progression based on a number of observed biomarker values is of great interest for patients, clinicians and researchers alike. In this work, an approach for disease progress estimation is presented. Based on a set of subjects that convert to a more severe disease stage during the study, models that describe typical trajectories of biomarker values in the course of disease are learned using quantile regression. A novel probabilistic method is then derived to estimate the current disease progress as well as the rate of progression of an individual by fitting acquired biomarkers to the models. A particular strength of the method is its ability to naturally handle missing data. This means, it is applicable even if individual biomarker measurements are missing for a subject without requiring a retraining of the model. The functionality of the presented method is demonstrated using synthetic and—employing cognitive scores and image-based biomarkers—real data from the ADNI study. Further, three possible applications for progress estimation are demonstrated to underline the versatility of the approach: classification, construction of a spatio-temporal disease progression atlas and prediction of future disease progression.     

Global Phosphotyrosine Proteomics Identifies PKCδ as a Marker of Responsiveness to Src Inhibition in Colorectal Cancer

Sensitive and specific biomarkers of protein kinase inhibition can be leveraged to accelerate drug development studies in oncology by associating early molecular responses with target inhibition. In this study, we utilized unbiased shotgun phosphotyrosine (pY) proteomics to discover novel biomarkers of response to dasatinib, a small molecule Src-selective inhibitor, in preclinical models of colorectal cancer (CRC). We performed unbiased mass spectrometry shotgun pY proteomics to reveal the pY proteome of cultured HCT-116 colonic carcinoma cells, and then extended this analysis to HCT-116 xenograft tumors to identify pY biomarkers of dasatinib-responsiveness in vivo. Major dasatinib-responsive pY sites in xenograft tumors included sites on delta-type protein kinase C (PKCδ), CUB-domain-containing protein 1 (CDCP1), Type-II SH2-domain-containing inositol 5-phosphatase (SHIP2), and receptor protein-tyrosine phosphatase alpha (RPTPα). The pY313 site PKCδ was further supported as a relevant biomarker of dasatinib-mediated Src inhibition in HCT-116 xenografts by immunohistochemistry and immunoblotting with a phosphospecific antibody. Reduction of PKCδ pY313 was further correlated with dasatinib-mediated inhibition of Src and diminished growth as spheroids of a panel of human CRC cell lines. These studies reveal PKCδ pY313 as a promising readout of Src inhibition in CRC and potentially other solid tumors and may reflect responsiveness to dasatinib in a subset of colorectal cancers.     

Cell-specific aptamer probes for membrane protein elucidation in cancer cells

Disease biomarkers play critical roles in the management of various pathological conditions of diseases. This involves diagnosing diseases, predicting disease progression and monitoring the efficacy of treatment modalities. While efforts to identify specific disease biomarkers using a variety of technologies has increased the number of biomarkers or augmented information about them, the effective use of disease-specific biomarkers is still scarce. Here, we report that a high expression of protein tyrosine kinase 7 (PTK7), a transmembrane receptor protein tyrosine kinase-like molecule, was discovered in a series of leukemia cell lines using whole cell aptamer selection. With the implementation of a two-step strategy (aptamer selection and biomarker discovery), combined with mass spectrometry, PTK7 was ultimately identified as a potential biomarker for T-cell acute lymphoblastic leukemia (T-ALL). Specifically, the aptamers for T-ALL cells were selected using the cell-SELEX process, without any prior knowledge of the cell biomarker population, conjugated with magnetic beads and then used to capture and purify their binding targets on the leukemia cell surface. This demonstrates that a panel of molecular aptamers can be easily generated for a specific type of diseased cells. It further demonstrates that this two-step strategy, that is, first selecting cancer cell-specific aptamers and then identifying their binding target proteins, has major clinical implications in that the technique promises to substantially improve the overall effectiveness of biomarker discovery. Specifically, our strategy will enable efficient discovery of new malignancy-related biomarkers, facilitate the development of diagnostic tools and therapeutic approaches to cancer, and markedly improve our understanding of cancer biology.     

Dasatinib Modulates Invasive and Migratory Properties of Canine Osteosarcoma and has Therapeutic Potential in Affected Dogs

BACKGROUND: This investigation sought to elucidate the relationship between hepatocyte growth factor (HGF)–induced metastatic behavior and the tyrosine kinase inhibitors (TKIs) crizotinib and dasatinib in canine osteosarcoma (OS). Preliminary evidence of an apparent clinical benefit from adjuvant therapy with dasatinib in four dogs is described. METHODS: The inhibitors were assessed for their ability to block phosphorylation of MET; reduce HGF-induced production of matrix metalloproteinase (MMP); and prevent invasion, migration, and cell viability in canine OS cell lines. Oral dasatinib (0.75 mg/kg) was tested as an adjuvant therapy in four dogs with OS. RESULTS: Constitutive phosphorylation of MET was detected in two cell lines, and this was unaffected by 20-nM incubation with either dasatinib or crizotinib. Incubation of cell lines with HGF (MET ligand) increased cell migration and invasion in both cell lines and increased MMP-9 activity in one. Dasatinib suppressed OS cell viability and HGF-induced invasion and migration, whereas crizotinib reduced migration and MMP-9 production but did not inhibit invasion or viability. CONCLUSIONS: Invasion, migration, and viability of canine OS cell lines are increased by exogenous HGF. HGF induces secretion of different forms of MMP in different cell lines. The HGF-driven increase in viability and metastatic behaviors we observed are more uniformly inhibited by dasatinib. These observations suggest a potential clinical benefit of adjuvant dasatinib treatment for dogs with OS.     

Coherent pipeline for biomarker discovery using mass spectrometry and bioinformatics

Background  

Robust biomarkers are needed to improve microbial identification and diagnostics. Proteomics methods based on mass spectrometry can be used for the discovery of novel biomarkers through their high sensitivity and specificity. However, there has been a lack of a coherent pipeline connecting biomarker discovery with established approaches for evaluation and validation. We propose such a pipeline that uses in silico methods for refined biomarker discovery and confirmation.     

The cancer cell secretome: A good source for discovering biomarkers?

Cancer is a leading cause of death. Early detection is usually associated with better clinical outcomes. Recent advances in genomics and proteomics raised hopes that new biomarkers for diagnosis, prognosis or monitoring therapeutic response will soon be discovered. Proteins secreted by cancer cells, referred also as “the cancer cell secretome”, is a promising source for biomarker discovery. In this review we will summarize recent advances in cancer cell secretome analysis, focusing on the five most fatal cancers (lung, breast, prostate, colorectal, and pancreatic). For each cancer type we will describe the proteomic approaches utilized for the identification of novel biomarkers. Despite progress, identification of markers that are superior to those currently used has proven to be a difficult task and very few, if any, newly discovered biomarker has entered the clinic the last 10 years.     

Biomarker Profiling by Nuclear Magnetic Resonance Spectroscopy for the Prediction of All-Cause Mortality: An Observational Study of 17,345 Persons

Background

Early identification of ambulatory persons at high short-term risk of death could benefit targeted prevention. To identify biomarkers for all-cause mortality and enhance risk prediction, we conducted high-throughput profiling of blood specimens in two large population-based cohorts.

Methods and Findings

106 candidate biomarkers were quantified by nuclear magnetic resonance spectroscopy of non-fasting plasma samples from a random subset of the Estonian Biobank (n = 9,842; age range 18–103 y; 508 deaths during a median of 5.4 y of follow-up). Biomarkers for all-cause mortality were examined using stepwise proportional hazards models. Significant biomarkers were validated and incremental predictive utility assessed in a population-based cohort from Finland (n = 7,503; 176 deaths during 5 y of follow-up). Four circulating biomarkers predicted the risk of all-cause mortality among participants from the Estonian Biobank after adjusting for conventional risk factors: alpha-1-acid glycoprotein (hazard ratio [HR] 1.67 per 1–standard deviation increment, 95% CI 1.53–1.82, p = 5×10⁻³¹), albumin (HR 0.70, 95% CI 0.65–0.76, p = 2×10⁻¹⁸), very-low-density lipoprotein particle size (HR 0.69, 95% CI 0.62–0.77, p = 3×10⁻¹²), and citrate (HR 1.33, 95% CI 1.21–1.45, p = 5×10⁻¹⁰). All four biomarkers were predictive of cardiovascular mortality, as well as death from cancer and other nonvascular diseases. One in five participants in the Estonian Biobank cohort with a biomarker summary score within the highest percentile died during the first year of follow-up, indicating prominent systemic reflections of frailty. The biomarker associations all replicated in the Finnish validation cohort. Including the four biomarkers in a risk prediction score improved risk assessment for 5-y mortality (increase in C-statistics 0.031, p = 0.01; continuous reclassification improvement 26.3%, p = 0.001).

Conclusions

Biomarker associations with cardiovascular, nonvascular, and cancer mortality suggest novel systemic connectivities across seemingly disparate morbidities. The biomarker profiling improved prediction of the short-term risk of death from all causes above established risk factors. Further investigations are needed to clarify the biological mechanisms and the utility of these biomarkers for guiding screening and prevention. Please see later in the article for the Editors'' Summary     

Identifying Resistance Mechanisms against Five Tyrosine Kinase Inhibitors Targeting the ERBB/RAS Pathway in 45 Cancer Cell Lines

Because of the low overall response rates of 10–47% to targeted cancer therapeutics, there is an increasing need for predictive biomarkers. We aimed to identify genes predicting response to five already approved tyrosine kinase inhibitors. We tested 45 cancer cell lines for sensitivity to sunitinib, erlotinib, lapatinib, sorafenib and gefitinib at the clinically administered doses. A resistance matrix was determined, and gene expression profiles of the subsets of resistant vs. sensitive cell lines were compared. Triplicate gene expression signatures were obtained from the caArray project. Significance analysis of microarrays and rank products were applied for feature selection. Ninety-five genes were also measured by RT-PCR. In case of four sunitinib resistance associated genes, the results were validated in clinical samples by immunohistochemistry. A list of 63 top genes associated with resistance against the five tyrosine kinase inhibitors was identified. Quantitative RT-PCR analysis confirmed 45 of 63 genes identified by microarray analysis. Only two genes (ANXA3 and RAB25) were related to sensitivity against more than three inhibitors. The immunohistochemical analysis of sunitinib-treated metastatic renal cell carcinomas confirmed the correlation between RAB17, LGALS8, and EPCAM and overall survival. In summary, we determined predictive biomarkers for five tyrosine kinase inhibitors, and validated sunitinib resistance biomarkers by immunohistochemistry in an independent patient cohort.