microRNA biomarkers in body fluids of prostate cancer patients.

The abundance of miRNAs - small non-coding RNAs involved in posttranscriptional regulation of gene expression - in tissues and body fluids of cancer patients hold great promise to identify specific biomarkers, which may be useful for early diagnosis as well as to predict the clinical outcome and treatment response. For the extraction and quantification of miRNAs from cells and tissues, present technologies for transcriptome analyses like microarrays, quantitative real-time PCR or next generation sequencing can be applied. However, the analyses of miRNAs in body fluids like serum or urine is still a challenge with respect to the nucleic acid recovery from very limited sources of biomaterial, normalization strategies and validation using independent technologies. The presence of specific miRNA patterns in body fluids like serum of cancer patients suggests a promising role of these molecules as surrogate markers. However, the majority of miRNA studies were addressed in relatively small patient cohorts limiting the validity and the clinical application of potential miRNA biomarkers or signatures. We reflect the critical steps to translate miRNA biomarker into clinical routine diagnostics and present future aspects for the fast, robust and standardized quantification of miRNAs in body fluids.

Peroxiredoxins 3 and 4 are overexpressed in prostate cancer tissue and affect the proliferation of prostate cancer cells in vitro.

The present study aimed to investigate the proteome profiling of surgically treated prostate cancers. Hereto, 2D-DIGE and mass spectrometry were performed for protein identification, and data validation for peroxiredoxin 3 and 4 (PRDX3 and PRDX4) was accomplished by reverse phase protein arrays (RPPA). The Formal Concept Analysis (FCA) method was applied to assess whether the TMPRSS2-ERG gene fusion could influence the degree of overexpression of PRDX3 and PRDX4 in prostate cancer. Lastly, we performed an in vitro functional characterization of both PRDX3 and PRDX4 using the classical human prostate cancer cell lines DU145 and LNCaP. Reverse phase protein arrays verified that the overexpression of both PRDX3 and PRDX4 in tumor samples is negatively correlated with the presence of the TMPRSS2-ERG gene fusion. Functional characterization of PRDX3 and PRDX4 activity in PCa cell lines suggests a role of these members of the peroxiredoxin family in the pathophysiology of this tumor entity.

Graph based fusion of miRNA and mRNA expression data improves clinical outcome prediction in prostate cancer.

BACKGROUND: One of the main goals in cancer studies including high-throughput microRNA (miRNA) and mRNA data is to find and assess prognostic signatures capable of predicting clinical outcome. Both mRNA and miRNA expression changes in cancer diseases are described to reflect clinical characteristics like staging and prognosis. Furthermore, miRNA abundance can directly affect target transcripts and translation in tumor cells. Prediction models are trained to identify either mRNA or miRNA signatures for patient stratification. With the increasing number of microarray studies collecting mRNA and miRNA from the same patient cohort there is a need for statistical methods to integrate or fuse both kinds of data into one prediction model in order to find a combined signature that improves the prediction. RESULTS: Here, we propose a new method to fuse miRNA and mRNA data into one prediction model. Since miRNAs are known regulators of mRNAs we used the correlations between them as well as the target prediction information to build a bipartite graph representing the relations between miRNAs and mRNAs. This graph was used to guide the feature selection in order to improve the prediction. The method is illustrated on a prostate cancer data set comprising 98 patient samples with miRNA and mRNA expression data. The biochemical relapse was used as clinical endpoint. It could be shown that the bipartite graph in combination with both data sets could improve prediction performance as well as the stability of the feature selection. CONCLUSIONS: Fusion of mRNA and miRNA expression data into one prediction model improves clinical outcome prediction in terms of prediction error and stable feature selection. The R source code of the proposed method is available in the supplement.

Circulating miRNAs are correlated with tumor progression in prostate cancer.

Circulating miRNAs have recently been indicated as practicable and promising biomarkers for noninvasive diagnosis in various tumor entities. However, cell-free miRNAs have not been found to correlate with clinicopathological variables in epithelial carcinomas. To learn more about the potential clinical relevance of circulating miRNAs in prostate cancer, we screened 667 miRNAs in serum samples from patients with metastatic (n = 7) and localized prostate cancer (n = 14). Various miRNAs were highly abundant in the sera of patients with metastatic disease, and five upregulated miRNAs (miRNA-375, miRNA-9*, miRNA-141, miRNA-200b and miRNA-516a-3p) were selected for further validation. In the first validation study (n = 45), selected miRNAs were analyzed in a prospectively collected serum set taken from different prostate cancer risk groups. Most of the selected miRNAs were significantly correlated with adverse risk factors when different clinicopathological variables were analyzed. Circulating miRNA-375 and miRNA-141 turned out to be the most pronounced markers for high-risk tumors. Their levels also correlated with high Gleason score or lymph-node positive status in a second independent validation study (n = 71). In addition, the expression levels of miRNA-375 and miRNA-141 were monitored in 72 prostate tissue samples (36 tumor vs. 36 benign). Both miRNAs were highly expressed in all samples and significantly upregulated in the tumors compared to normal tissues. Overall, our observations suggest that miRNA-375 and miRNA-141 expression is enhanced in prostate cancer specimens and their release into the blood is further associated with advanced cancer disease.

Loss of aquaporin-4 expression and putative function in non-small cell lung cancer.

BACKGROUND: Aquaporins (AQPs) have been recognized to promote tumor progression, invasion, and metastasis and are therefore recognized as promising targets for novel anti-cancer therapies. Potentially relevant AQPs in distinct cancer entities can be determined by a comprehensive expression analysis of the 13 human AQPs. METHODS: We analyzed the presence of all AQP transcripts in 576 different normal lung and non-small cell lung cancer (NSCLC) samples using microarray data and validated our findings by qRT-PCR and immunohistochemistry. RESULTS: Variable expression of several AQPs (AQP1, -3, -4, and -5) was found in NSCLC and normal lung tissues. Furthermore, we identified remarkable differences between NSCLC subtypes in regard to AQP1, -3 and -4 expression. Higher transcript and protein levels of AQP4 in well-differentiated lung adenocarcinomas suggested an association with a more favourable prognosis. Beyond water transport, data mining of co-expressed genes indicated an involvement of AQP4 in cell-cell signalling, cellular movement and lipid metabolism, and underlined the association of AQP4 to important physiological functions in benign lung tissue. CONCLUSIONS: Our findings accentuate the need to identify functional differences and redundancies of active AQPs in normal and tumor cells in order to assess their value as promising drug targets.

TMPRSS2-ERG -specific transcriptional modulation is associated with prostate cancer biomarkers and TGF-ß signaling.

BACKGROUND: TMPRSS2-ERG gene fusions occur in about 50% of all prostate cancer cases and represent promising markers for molecular subtyping. Although TMPRSS2-ERG fusion seems to be a critical event in prostate cancer, the precise functional role in cancer development and progression is still unclear. METHODS: We studied large-scale gene expression profiles in 47 prostate tumor tissue samples and in 48 normal prostate tissue samples taken from the non-suspect area of clinical low-risk tumors using Affymetrix GeneChip Exon 1.0 ST microarrays. RESULTS: Comparison of gene expression levels among TMPRSS2-ERG fusion-positive and negative tumors as well as benign samples demonstrated a distinct transcriptional program induced by the gene fusion event. Well-known biomarkers for prostate cancer detection like CRISP3 were found to be associated with the gene fusion status. WNT and TGF-ß/BMP signaling pathways were significantly associated with genes upregulated in TMPRSS2-ERG fusion-positive tumors. CONCLUSIONS: The TMPRSS2-ERG gene fusion results in the modulation of transcriptional patterns and cellular pathways with potential consequences for prostate cancer progression. Well-known biomarkers for prostate cancer detection were found to be associated with the gene fusion. Our results suggest that the fusion status should be considered in retrospective and future studies to assess biomarkers for prostate cancer detection, progression and targeted therapy.

Serum microRNAs as non-invasive biomarkers for cancer.

Human serum and other body fluids are rich resources for the identification of novel biomarkers, which can be measured in routine clinical diagnosis. microRNAs are small non-coding RNA molecules, which have an important function in regulating RNA stability and gene expression. The deregulation of microRNAs has been linked to cancer development and tumor progression. Recently, it has been reported that serum and other body fluids contain sufficiently stable microRNA signatures. Thus, the profiles of circulating microRNAs have been explored in a variety of studies aiming at the identification of novel non-invasive biomarkers. In this review, we discuss recent findings indicating that circulating microRNAs are useful as non-invasive biomarkers for different tumor types. Additionally, we summarize the knowledge about the mechanism of microRNA release and the putative functional roles of circulating microRNAs. Although several challenges remain to be addressed, circulating microRNAs have the potential to be useful for the diagnosis and prognosis of cancer diseases.

Genome-Wide DNA Methylation Profiling in Early Stage I Lung Adenocarcinoma Reveals Predictive Aberrant Methylation in the Promoter Region of the Long Noncoding RNA PLUT: An Exploratory Study.

INTRODUCTION: Surgical procedure is the treatment of choice in early stage I lung adenocarcinoma. However, a considerable number of patients experience recurrence within the first 2 years after complete resection. Suitable prognostic biomarkers that identify patients at high risk of recurrence (who may probably benefit from adjuvant treatment) are still not available. This study aimed at identifying methylation markers for early recurrence that may become important tools for the development of new treatment modalities. METHODS: Genome-wide DNA methylation profiling was performed on 30 stage I lung adenocarcinomas, comparing 14 patients with early metastatic recurrence with 16 patients with a long-term relapse-free survival period using methylated-CpG-immunoprecipitation followed by high-throughput next-generation sequencing. The differentially methylated regions between the two subgroups were validated for their prognostic value in two independent cohorts using the MassCLEAVE assay, a high-resolution quantitative methylation analysis. RESULTS: Unsupervised clustering of patients in the discovery cohort on the basis of differentially methylated regions identified patients with shorter relapse-free survival (hazard ratio: 2.23; 95% confidence interval: 0.66-7.53; p = 0.03). In two validation cohorts, promoter hypermethylation of the long noncoding RNA PLUT was significantly associated with shorter relapse-free survival (hazard ratio: 0.54; 95% confidence interval: 0.31-0.93; p < 0.026) and could be reported as an independent prognostic factor in the multivariate Cox regression analysis. CONCLUSIONS: Promoter hypermethylation of the long noncoding RNA PLUT is predictive in patients with early stage I adenocarcinoma at high risk for early recurrence. Further studies are needed to validate its role in carcinogenesis and its use as a biomarker to facilitate patient selection and risk stratification.

Classification across gene expression microarray studies.

BACKGROUND: The increasing number of gene expression microarray studies represents an important resource in biomedical research. As a result, gene expression based diagnosis has entered clinical practice for patient stratification in breast cancer. However, the integration and combined analysis of microarray studies remains still a challenge. We assessed the potential benefit of data integration on the classification accuracy and systematically evaluated the generalization performance of selected methods on four breast cancer studies comprising almost 1000 independent samples. To this end, we introduced an evaluation framework which aims to establish good statistical practice and a graphical way to monitor differences. The classification goal was to correctly predict estrogen receptor status (negative/positive) and histological grade (low/high) of each tumor sample in an independent study which was not used for the training. For the classification we chose support vector machines (SVM), predictive analysis of microarrays (PAM), random forest (RF) and k-top scoring pairs (kTSP). Guided by considerations relevant for classification across studies we developed a generalization of kTSP which we evaluated in addition. Our derived version (DV) aims to improve the robustness of the intrinsic invariance of kTSP with respect to technologies and preprocessing. RESULTS: For each individual study the generalization error was benchmarked via complete cross-validation and was found to be similar for all classification methods. The misclassification rates were substantially higher in classification across studies, when each single study was used as an independent test set while all remaining studies were combined for the training of the classifier. However, with increasing number of independent microarray studies used in the training, the overall classification performance improved. DV performed better than the average and showed slightly less variance. In particular, the better predictive results of DV in across platform classification indicate higher robustness of the classifier when trained on single channel data and applied to gene expression ratios. CONCLUSIONS: We present a systematic evaluation of strategies for the integration of independent microarray studies in a classification task. Our findings in across studies classification may guide further research aiming on the construction of more robust and reliable methods for stratification and diagnosis in clinical practice.

Differential expression of apoptotic genes PDIA3 and MAP3K5 distinguishes between low- and high-risk prostate cancer.

BACKGROUND: Despite recent progress in the identification of genetic and molecular alterations in prostate cancer, markers associated with tumor progression are scarce. Therefore precise diagnosis of patients and prognosis of the disease remain difficult. This study investigated novel molecular markers discriminating between low and highly aggressive types of prostate cancer. RESULTS: Using 52 microdissected cell populations of low- and high-risk prostate tumors, we identified via global cDNA microarrays analysis almost 1200 genes being differentially expressed among these groups. These genes were analyzed by statistical, pathway and gene enrichment methods. Twenty selected candidate genes were verified by quantitative real time PCR and immunohistochemistry. In concordance with the mRNA levels, two genes MAP3K5 and PDIA3 exposed differential protein expression. Functional characterization of PDIA3 revealed a pro-apoptotic role of this gene in PC3 prostate cancer cells. CONCLUSIONS: Our analyses provide deeper insights into the molecular changes occurring during prostate cancer progression. The genes MAP3K5 and PDIA3 are associated with malignant stages of prostate cancer and therefore provide novel potential biomarkers.

Down-regulation of HLA Class I and NKG2D ligands through a concerted action of MAPK and DNA methyltransferases in colorectal cancer cells.

Most malignant features of cancer cells are triggered by activated oncogenes and the loss of tumor suppressors due to mutation or epigenetic inactivation. It is still unclear, to what extend the escape of emerging cancer cells from recognition and elimination by the immune system is determined by similar mechanisms. We compared the transcriptomes of HCT116 colorectal cancer cells deficient in DNA methyltransferases (DNMTs) and of cells, in which the RAS pathway as the major growth-promoting signaling system is blocked by inhibition of MAPK. We identified the MHC Class I genes HLA-A1/A2 and the ULBP2 gene encoding 1 of the 8 known ligands of the activating NK receptor NKG2D among a cluster of immune genes up-regulated under the conditions of both DNMT-deficiency and MEK-inhibition. Bisulphite sequencing analyses of HCT116 with DNMT deficiency or after MEK-inhibition showed that de-methylation of the ULPB2 promoter correlated with its enhanced surface expression. The HLA-A promoters were not methylated indicating that components of the HLA assembly machinery were also suppressed in DNMT-deficient and MEK-inhibited cells. Increased HLA-A2 surface expression was correlated with enhanced recognition and lysis by A2-specific CTL. On the contrary, elevated ULBP2 expression was not reflected by enhanced recognition and lysis by NK cells. Cosuppression of HLA Class I and NKG2D ligands and genes encoding peptide transporters or proteasomal genes mediates a strong functional link between RAS activation, DNMT activity and disruption of the antigen presenting system controlling immune recognition in colorectal cancer cells.

Effects of infiltrating lymphocytes and estrogen receptor on gene expression and prognosis in breast cancer.

The involvement of the immune system for the course of breast cancer, as evidenced by varying degrees of lymphocyte infiltration (LI) into the tumor is still poorly understood. The aim of this study was to evaluate the prognostic value of LI in breast cancer samples using microarray-based screening for LI-associated genes. Starting from the observation that most published ER gene signatures are heavily influenced by the LI effect, we developed and applied a novel approach to dissect molecular signatures. Further, a meta-analysis encompassing 1,044 hybridizations showed that LI alone is not sufficient to highlight breast cancer patients with different prognosis. However, for ER positive patients, high LI was associated with shorter survival times, whereas for ER negative patients, high LI is significantly associated with longer survival. Annotation of LI, in addition to ER status, is important for breast cancer patient prognosis and may have implications for the future treatment of breast cancer.

Global gene expression analysis reveals specific patterns of cell junctions in non-small cell lung cancer subtypes.

Non-small cell lung cancer (NSCLC) can be classified into the major subtypes adenocarcinoma (AC) and squamous cell carcinoma (SCC). Although explicit molecular, histological and clinical characteristics have been reported for both subtypes, no specific therapy exists so far. However, the characterization of suitable molecular targets holds great promises to develop novel therapies in NSCLC. In the present study, global gene expression profiling of 58 human NSCLC specimens revealed large transcriptomic differences between AC and SCC subtypes: more than 1700 genes were found to be differentially expressed. The assignment of these genes to biological processes pointed to the deregulation of distinct sets of genes coding for cell junctions in both tumor subtypes. We focused on 17 cell adhesion genes and 11 reported marker genes for epithelial-mesenchymal transition (EMT), and investigated their expression in matched tumor-normal specimens by quantitative real-time PCR. The majority of the cell adhesion genes was significantly up-regulated in at least one tumor subtype compared to normal tissue, predominantly desmosomes and gap junctions in SCC, and tight junctions in AC. The higher expression of EMT marker transcripts in tumor specimens suggested a large potential for invasion and migration processes in NSCLC. Our results indicate that AC and SCC in the lung are characterized by the expression of distinct sets of cell adhesion molecules which may represent promising targets for novel specific therapies.

The human let-7a-3 locus contains an epigenetically regulated microRNA gene with oncogenic function.

MicroRNAs (miRNAs) are small noncoding RNAs that repress their target mRNAs by complementary base pairing and induction of the RNA interference pathway. It has been shown that miRNA expression can be regulated by DNA methylation and it has been suggested that altered miRNA gene methylation might contribute to human tumorigenesis. In this study, we show that the human let-7a-3 gene on chromosome 22q13.31 is associated with a CpG island. Let-7a-3 belongs to the archetypal let-7 miRNA gene family and was found to be methylated by the DNA methyltransferases DNMT1 and DNMT3B. The gene was heavily methylated in normal human tissues but hypomethylated in some lung adenocarcinomas. Let-7a-3 hypomethylation facilitated epigenetic reactivation of the gene and elevated expression of let-7a-3 in a human lung cancer cell line resulted in enhanced tumor phenotypes and oncogenic changes in transcription profiles. Our results thus identify let-7a-3 as an epigenetically regulated miRNA gene with oncogenic function and suggest that aberrant miRNA gene methylation might contribute to the human cancer epigenome.

Genomic analysis reveals poor separation of human cardiomyopathies of ischemic and nonischemic etiologies.

Clinically, the differentiation between ischemic (ICM) and nonischemic (NICM) human cardiomyopathies is highly relevant, because ICM and NICM differ with respect to prognosis and certain aspects of pharmacological therapy, despite a common final phenotype characterized by ventricular dilatation and reduced contractility. So far, it is unclear whether microarray-based signatures can be used to infer the etiology of heart failure. Using three different classification algorithms, we independently analyzed one cDNA and two publicly available high-density oligonucleotide microarray studies comprising a total of 279 end-stage human heart failure samples. When classifiers identified in a single study were applied to the remaining studies, misclassification rates >25% for ICM and NICM specimens were noted, indicating poor separation of both etiologies. However, data mining of 458 classifier genes that were concordantly identified in at least two of the three data sets points to different biological processes in ICM vs. NICM. Consistent with the underlying ischemia, cytokine signaling pathways and immediate-early response genes were overrepresented in ICM samples, whereas NICM samples displayed a deregulation of cytoskeletal transcripts, genes encoding for the major histocompatibility complex, and antigen processing and presentation pathways, potentially pointing to immunologic processes in NICM. Overall, our results suggest that ICM and NICM exhibit substantial heterogeneity at the transcriptomic level. Prospective studies are required to test whether etiology-specific gene expression patterns are present at earlier disease stages or in subsets of both etiologies.

Identification of aberrant chromosomal regions from gene expression microarray studies applied to human breast cancer.

MOTIVATION: In cancer, chromosomal imbalances like amplifications and deletions, or changes in epigenetic mechanisms like DNA methylation influence the transcriptional activity. These alterations are often not limited to a single gene but affect several genes of the genomic region and may be relevant for the disease status. For example, the ERBB2 amplicon (17q21) in breast cancer is associated with poor patient prognosis. We present a general, unsupervised method for genome-wide gene expression data to systematically detect tumor patients with chromosomal regions of distinct transcriptional activity. The method aims to find expression patterns of adjacent genes with a consistently decreased or increased level of gene expression in tumor samples. Such patterns have been found to be associated with chromosomal aberrations and clinical parameters like tumor grading and thus can be useful for risk stratification or therapy. RESULTS: Our approach was applied to 12 independent human breast cancer microarray studies comprising 1422 tumor samples. We prioritized chromosomal regions and genes predominantly found across all studies. The result highlighted not only regions which are well known to be amplified like 17q21 and 11q13, but also others like 8q24 (distal to MYC) and 17q24-q25 which may harbor novel putative oncogenes. Since our approach can be applied to any microarray study it may become a valuable tool for the exploration of transcriptional changes in diverse disease types. AVAILABILITY: The R source codes which implement the method and an exemplary analysis are available at http://www.dkfz.de/mga2/people/buness/CTP/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of cellular targets for the human papillomavirus E6 and E7 oncogenes by RNA interference and transcriptome analyses.

Specific types of human papillomaviruses (HPVs) cause cervical cancer, the second most common tumor in women worldwide. Both cellular transformation and the maintenance of the oncogenic phenotype of HPV-positive tumor cells are linked to the expression of the viral E6 and E7 oncogenes. To identify downstream cellular target genes for the viral oncogenes, we silenced endogenous E6 and E7 expression in HPV-positive HeLa cells by RNA interference (RNAi). Subsequently, we assessed changes of the cellular transcriptome by genome-wide microarray analysis. We identified 648 genes, which were either downregulated (360 genes) or upregulated (288 genes), upon inhibition of E6/E7 expression. A large fraction of these genes is involved in tumor-relevant processes, such as apoptosis control, cell cycle regulation, or spindle formation. Others may represent novel cellular targets for the HPV oncogenes, such as a large group of C-MYC-associated genes involved in RNA processing and splicing. Comparison with published microarray data revealed a substantial concordance between the genes repressed by RNAi-mediated E6/E7 silencing in HeLa cells and genes reported to be upregulated in HPV-positive cervical cancer biopsies.

Molecular signatures in IASLC/ATS/ERS classified growth patterns of lung adenocarcinoma.

BACKGROUND: The current classification of human lung adenocarcinoma defines five different histological growth patterns within the group of conventional invasive adenocarcinomas. The five growth patterns are characterised by their typical architecture, but also by variable tumor biological behaviour. AIMS: The aim of this study was to identify specific gene signatures of the five adenocarcinoma growth patterns defined by the joint IASLC/ATS/ERS working group. METHODS: Total RNA from microdissected adenocarcinoma tissue samples of ten lepidic, ten acinar, ten solid, nine papillary, and nine micropapillary tumor portions was isolated and prepared for gene expression analysis. Differential expression of genes was determined using the R package "LIMMA". The overall significance of each signature was assessed via global test. Gene ontology statistics were analysed using GOstat. For immunohistochemical validation, tissue specimens from 20 tumors with solid and 20 tumors with lepidic growth pattern were used. RESULTS: Microarray analyses between the growth patterns resulted in numerous differentially expressed genes between the solid architecture and other patterns. The comparison of transcriptomic activity in the solid and lepidic patterns revealed 705 up- and 110 downregulated non-redundant genes. The pattern-specific protein expression of Inositol-1,4,5-trisphosphate-kinase-A (ITPKA) and angiogenin by immunohistochemistry confirmed the RNA levels. The strongest differences in protein expression between the two patterns were shown for ITPKA (p = 0.02) and angiogenin (p = 0.113). CONCLUSIONS: In this study growth pattern-specific gene signatures in pulmonary adenocarcinoma were identified and distinct transcriptomic differences between lung adenocarcinoma growth patterns were defined. The study provides valuable new information about pulmonary adenocarcinoma and allows a better assessment of the five adenocarcinoma subgroups.

Gene expression in kidney cancer is associated with cytogenetic abnormalities, metastasis formation, and patient survival.

Current diagnosis of renal cancer consists of histopathologic examination of tissue sections and classification into tumor stages and grades of malignancy. Until recently, molecular differences between tumor types were largely unknown. To examine such differences, we did gene expression measurements of 112 renal cell carcinoma and normal kidney samples on renal cell carcinoma-specific cDNA microarrays containing 4,207 genes and expressed sequence tags. The gene expression patterns showed deregulation of complete biological pathways in the tumors. Many of the molecular changes corresponded well to the histopathologic tumor types, and a set of 80 genes was sufficient to classify tumors with a very low error rate. Distinct gene expression signatures were associated with chromosomal abnormalities of tumor cells, metastasis formation, and patient survival. The data highlight the benefit of microarrays to detect novel tumor classes and to identify genes that are associated with patient variables and tumor properties.

Cross-talk between AMPK and EGFR dependent Signaling in Non-Small Cell Lung Cancer.

Lung cancers globally account for 12% of new cancer cases, 85% of these being Non Small Cell Lung Cancer (NSCLC). Therapies like erlotinib target the key player EGFR, which is mutated in about 10% of lung adenocarcinoma. However, drug insensitivity and resistance caused by second mutations in the EGFR or aberrant bypass signaling have evolved as a major challenge in controlling these tumors. Recently, AMPK activation was proposed to sensitize NSCLC cells against erlotinib treatment. However, the underlying mechanism is largely unknown. In this work we aim to unravel the interplay between 20 proteins that were previously associated with EGFR signaling and erlotinib drug sensitivity. The inferred network shows a high level of agreement with protein-protein interactions reported in STRING and HIPPIE databases. It is further experimentally validated with protein measurements. Moreover, predictions derived from our network model fairly agree with somatic mutations and gene expression data from primary lung adenocarcinoma. Altogether our results support the role of AMPK in EGFR signaling and drug sensitivity.