A four-gene signature from NCI-60 cell line for survival prediction in non-small cell lung cancer

ANKRD49 promotes the metastasis of NSCLC via activating JNK-ATF2/c-Jun-MMP-2/9 axis

BACKGROUND

Ankyrin repeat domain 49 (ANKRD49) has been found to be highly expressed in multiple cancer including lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC). However, the function of ANKRD49 in the pathogenesis of NSCLC still remains elusive. Previously, ANKRD49 has been demonstrated to promote the invasion and metastasis of A549 cells, a LUAD cell line, via activating the p38-ATF-2-MMP2/MMP9 pathways. Considering the heterogeneity of tumor cells, the function and mechanism of ANKRD49 in NSCLC need more NSCLC-originated cells to clarify.

METHODS

Real-time qPCR was employed to test ANKRD49 expression levels in nine pairs of fresh NSCLC tissues and the corresponding adjacent normal tissues. The function of ANKRD49 was investigated using overexpression and RNA interference assays in lung adenocarcinoma cell line (NCI-H1299) and lung squamous carcinoma cell line (NCI-H1703) through gelatin zymography, cell counting kit-8, colony formation, wound healing, migration and invasion assays mmunoprecipitation was performed to in vitro. Immunoprecipitation was performed to test the interaction of c-Jun and ATF2. Chromatin immunoprecipitation was conducted to assess the transcriptional regulation of ATF2/c-Jun on MMP-2/9. Moreover, the tumorigenicity of ANKRD49 was evaluated in nude mice models and the involved signal molecular was also measured by immunohistochemical method.

RESULTS

We found that the levels of ANKRD49 in cancerous tissues were higher than those in adjacent normal tissues. in vitro assay showed that ANKRD49 promoted the migration and invasion of NCI-H1299 and NCI-H1703 cells via enhancing the levels of MMP-2 and MMP-9. Furthermore, ANKRD49 elevated phosphorylation of JNK and then activated c-Jun and ATF2 which interact in nucleus to promote the binding of ATF2:c-Jun with the promoter MMP-2 or MMP-9. In vivo assay showed that ANKRD49 promoted lung metastasis of injected-NSCLC cells and the high metastatic rate was positively correlated with the high expression of ANKRD49, MMP-2, MMP-9, p-JNK, p-c-Jun and p-ATF2.

CONCLUSION

The present study indicated that ANKRD49 accelerated the invasion and metastasis of NSCLC cells via JNK-mediated transcription activation of c-Jun and ATF2 which regulated the expression of MMP-2/MMP-9. The molecular mechanisms of ANKRD49's function is different from those found in A549 cells. The current study is a supplement and improvement to the previous research.

A Microarray Study on the Expression of ANKRD49 in Lung Squamous Cell Carcinoma and Its Clinicopathologic Significance

Lung squamous cell carcinoma (LUSC) is associated with poor clinical outcomes and identifying novel biomarkers that are involved in the progression of LUSC is important for prognosis and targeted treatment. Herein, ankyrin repeat domain 49 (ANKRD49) protein in LUSC versus paired noncancerous lung tissues was tested and its clinical significance was evaluated through χ 2 test, log-rank test, and Cox proportional hazards model. The results showed the ANKRD49 protein in LUSC was elevated and correlated with the tumor-node-metastasis stage, lymph node metastasis, distal metastasis, and differentiation. Patients with higher ANKRD49 had lower overall survival rate and higher ANKRD49 expression in lung tissues may be used as an independent prognostic marker for LUSC patients.

ANKRD49 promotes the invasion and metastasis of lung adenocarcinoma via a P38/ATF-2 signalling pathway

Lung adenocarcinoma (LUAD) is the most challenging neoplasm to treat in clinical practice. Ankyrin repeat domain 49 protein (ANKRD49) is highly expressed in several carcinomas; however, its pattern of expression and role in LUAD are not known. Tissue microarrays, immunohistochemistry, χ² test, Spearman correlation analysis, Kaplan–Meier, log‐rank test, and Cox's proportional hazard model were used to analyse the clinical cases. The effect of ANKRD49 on the LUAD was investigated using CCK‐8, clonal formation, would healing, transwell assays, and nude mice experiment. Expressions of ANKRD49 and its associated downstream protein molecules were verified by real‐time PCR, Western blot, immunohistochemistry, and/or immunofluorescence analyses. ANKRD49 expression was highly elevated in LUAD. The survival rate and Cox's modelling analysis indicated that there may be an independent prognostic indicator for LUAD patients. We also found that ANKRD49 promoted the invasion and migration in both in in vitro and in vivo assays, through upregulating matrix metalloproteinase (MMP)‐2 and MMP‐9 activities via the P38/ATF‐2 signalling pathway Our findings suggest that ANKRD49 is a latent biomarker for evaluating LUAD prognosis and promotes the metastasis of A549 cells via upregulation of MMP‐2 and MMP‐9 in a P38/ATF‐2 pathway‐dependent manner.

A survey and evaluation of Web-based tools/databases for variant analysis of TCGA data

The Cancer Genome Atlas (TCGA) is a publicly funded project that aims to catalog and discover major cancer-causing genomic alterations with the goal of creating a comprehensive ‘atlas’ of cancer genomic profiles. The availability of this genome-wide information provides an unprecedented opportunity to expand our knowledge of tumourigenesis. Computational analytics and mining are frequently used as effective tools for exploring this byzantine series of biological and biomedical data. However, some of the more advanced computational tools are often difficult to understand or use, thereby limiting their application by scientists who do not have a strong computational background. Hence, it is of great importance to build user-friendly interfaces that allow both computational scientists and life scientists without a computational background to gain greater biological and medical insights. To that end, this survey was designed to systematically present available Web-based tools and facilitate the use TCGA data for cancer research.

Discovering cooperative biomarkers for heterogeneous complex disease diagnoses

Biomarkers with high reproducibility and accurate prediction performance can contribute to comprehending the underlying pathogenesis of related complex diseases and further facilitate disease diagnosis and therapy. Techniques integrating gene expression profiles and biological networks for the identification of network-based disease biomarkers are receiving increasing interest. The biomarkers for heterogeneous diseases often exhibit strong cooperative effects, which implies that a set of genes may achieve more accurate outcome prediction than any single gene. In this study, we evaluated various biomarker identification methods that consider gene cooperative effects implicitly or explicitly, and proposed the gene cooperation network to explicitly model the cooperative effects of gene combinations. The gene cooperation network-enhanced method, named as MarkRank, achieves superior performance compared with traditional biomarker identification methods in both simulation studies and real data sets. The biomarkers identified by MarkRank not only have a better prediction accuracy but also have stronger topological relationships in the biological network and exhibit high specificity associated with the related diseases. Furthermore, the top genes identified by MarkRank involve crucial biological processes of related diseases and give a good prioritization for known disease genes. In conclusion, MarkRank suggests that explicit modeling of gene cooperative effects can greatly improve biomarker identification for complex diseases, especially for diseases with high heterogeneity.

High expression of the ANKRD49 protein is associated with progression and poor prognosis of gastric cancer

BACKGROUND

Gastric cancer is one of the most common malignant tumours. Identifying novel genes that govern the development of gastric cancer will help to elucidate its molecular mechanisms and find novel biomarkers.

METHODS

Expression of the ANKRD49 protein was assessed by immunohistochemical analysis of tissue microarrays containing 92 sets of human gastric cancer specimens with adjacent non-cancerous tissue. Associations between ANKRD49 levels and clinicopathological characteristics of the patient were investigated. The correlation between ANKRD49 expression and patient survival was analysed by the Kaplan-Meier method.

RESULTS

The results revealed that the expression level of the ANKRD49 protein in gastric cancer was significantly upregulated and correlated with the tumour size, tumour-node-metastasis (TNM) stage, histological grade, depth of invasion, vessel invasion, lymph node metastasis and distant metastasis. The mean survival time of patients with low expression levels of ANKRD49 was significantly longer than that of patients with high expression levels of ANKRD49. Multivariate Cox regression analysis demonstrated that the ANKRD49 protein expression level was an independent prognostic indicator for the survival rate of patients with gastric cancer.

CONCLUSION

The results of the present study highlighted an important role of the ANKRD49 protein in the progression of gastric cancer. The ANKRD49 protein could act as a potential biomarker for prognosis evaluation of gastric cancer and may be used as a molecular target for gastric cancer treatment.

The PHD1 oxygen sensor in health and disease

The hypoxia-inducible factor (HIF) co-ordinates the adaptive transcriptional response to hypoxia in metazoan cells. The hypoxic sensitivity of HIF is conferred by a family of oxygen-sensing enzymes termed HIF hydroxylases. This family consists of three prolyl hydroxylases (PHD1-3) and a single asparagine hydroxylase termed factor inhibiting HIF (FIH). It has recently become clear that HIF hydroxylases are functionally non-redundant and have discrete but overlapping physiological roles. Furthermore, altered abundance or activity of these enzymes is associated with a number of pathologies. Pharmacological HIF-hydroxylase inhibitors have recently proven to be both tolerated and therapeutically effective in patients. In this review, we focus on the physiology, pathophysiology and therapeutic potential of the PHD1 isoform, which has recently been implicated in diseases including inflammatory bowel disease, ischaemia and cancer.

Proteomic profiling of NCI-60 extracellular vesicles uncovers common protein cargo and cancer type-specific biomarkers

Packed with biological information, extracellular vesicles (EVs) offer exciting promise for biomarker discovery and applications in therapeutics and non-invasive diagnostics. Currently, our understanding of EV contents is confined by the limited cells from which vesicles have been characterized utilizing the same enrichment method. Using sixty cell lines from the National Cancer Institute (NCI-60), here we provide the largest proteomic profile of EVs in a single study, identifying 6,071 proteins with 213 common to all isolates. Proteins included established EV markers, and vesicular trafficking proteins such as Rab GTPases and tetraspanins. Differentially-expressed proteins offer potential for cancer diagnosis and prognosis. Network analysis of vesicle quantity and proteomes identified EV components associated with vesicle secretion, including CD81, CD63, syntenin-1, VAMP3, Rab GTPases, and integrins. Integration of vesicle proteomes with whole-cell molecular profiles revealed similarities, suggesting EVs provide a reliable reflection of their progenitor cell content, and are therefore excellent indicators of disease.

Expression of functional neuronal receptor latrophilin 1 in human acute myeloid leukaemia cells

Acute myeloid leukaemia (AML) is a blood cancer affecting cells of myeloid lineage. It is characterised by rapid growth of malignant leukocytes that accumulate in the bone marrow and suppress normal haematopoiesis. This systemic disease remains a serious medical burden worldwide. Characterisation of protein antigens specifically expressed by malignant cells, but not by healthy leukocytes, is vital for the diagnostics and targeted treatment of AML. Here we report, for the first time, that the neuronal receptor latrophilin-1 is expressed in human monocytic leukaemia cell lines and in primary human AML cells. However, it is absent in healthy leukocytes. Latrophilin-1 is functional in leukaemia cells tested, and its biosynthesis is controlled through the mammalian target of rapamycin (mTOR), a master regulator of myeloid cell translational pathways. Our findings demonstrate that latrophilin-1 could be considered as a novel biomarker of human AML, which offers potential new avenues for AML diagnosis and treatment.

Consensus of gene expression phenotypes and prognostic risk predictors in primary lung adenocarcinoma

Transcriptional profiling of lung adenocarcinomas has identified numerous gene expression phenotype (GEP) and risk prediction (RP) signatures associated with patient outcome. However, classification agreement between signatures, underlying transcriptional programs, and independent signature validation are less studied. We classified 2395 transcriptional adenocarcinoma profiles, assembled from 17 public cohorts, using 11 GEP and seven RP signatures, finding that 16 signatures were associated with patient survival in the total cohort and in multiple individual cohorts. For significant signatures, total cohort hazard ratios were ~2 in univariate analyses (mean=1.95, range=1.4-2.6). Strong classification agreement between signatures was observed, especially for predicted low-risk patients by adenocarcinoma-derived signatures. Expression of proliferation-related genes correlated strongly with GEP subtype classifications and RP scores, driving the gene signature association with prognosis. A three-group consensus definition of samples across 10 GEP classifiers demonstrated aggregation of samples with specific smoking patterns, gender, and EGFR/KRAS mutations, while survival differences were only significant when patients were divided into low- or high-risk. In summary, our study demonstrates a consensus between GEPs and RPs in lung adenocarcinoma through a common underlying transcriptional program. This consensus generalizes reported problems with current signatures in a clinical context, stressing development of new adenocarcinoma-specific single sample predictors for clinical use.

COPD GWAS variant at 19q13.2 in relation with DNA methylation and gene expression

Chronic obstructive pulmonary disease (COPD) is among the major health burdens in adults. While cigarette smoking is the leading risk factor, a growing number of genetic variations have been discovered to influence disease susceptibility. Epigenetic modifications may mediate the response of the genome to smoking and regulate gene expression. Chromosome 19q13.2 region is associated with both smoking and COPD, yet its functional role is unclear. Our study aimed to determine whether rs7937 (RAB4B, EGLN2), a top genetic variant in 19q13.2 region identified in genome-wide association studies of COPD, is associated with differential DNA methylation in blood (N = 1490) and gene expression in blood (N = 721) and lungs (N = 1087). We combined genetic and epigenetic data from the Rotterdam Study (RS) to perform the epigenome-wide association analysis of rs7937. Further, we used genetic and transcriptomic data from blood (RS) and from lung tissue (Lung expression quantitative trait loci mapping study), to perform the transcriptome-wide association study of rs7937. Rs7937 was significantly (FDR < 0.05) and consistently associated with differential DNA methylation in blood at 4 CpG sites in cis, independent of smoking. One methylation site (cg11298343-EGLN2) was also associated with COPD (P = 0.001). Additionally, rs7937 was associated with gene expression levels in blood in cis (EGLN2), 42% mediated through cg11298343, and in lung tissue, in cis and trans (NUMBL, EGLN2, DNMT3A, LOC101929709 and PAK2). Our results suggest that changes of DNA methylation and gene expression may be intermediate steps between genetic variants and COPD, but further causal studies in lung tissue should confirm this hypothesis.

A general mechanistic model enables predictions of the biological effectiveness of different qualities of radiation

Predicting the responses of biological systems to ionising radiation is extremely challenging, particularly when comparing X-rays and heavy charged particles, due to the uncertainty in their Relative Biological Effectiveness (RBE). Here we assess the power of a novel mechanistic model of DNA damage repair to predict the sensitivity of cells to X-ray, proton or carbon ion exposures in vitro against over 800 published experiments. By specifying the phenotypic characteristics of cells, the model was able to effectively stratify X-ray radiosensitivity (R² = 0.74) without the use of any cell-specific fitting parameters. This model was extended to charged particle exposures by integrating Monte Carlo calculated dose distributions, and successfully fit to cellular proton radiosensitivity using a single dose-related parameter (R² = 0.66). Using these parameters, the model was also shown to be predictive of carbon ion RBE (R² = 0.77). This model can effectively predict cellular sensitivity to a range of radiations, and has the potential to support developments of personalised radiotherapy independent of radiation type.

Up-regulation of ANKDR49, a poor prognostic factor, regulates cell proliferation of gliomas

The Ankyrin repeat domain 49 (ANKRD49) is an evolutionarily conserved protein, which is related to mediate protein–protein interaction. However, the function of ANKRD49 in human glioma remains elusive. Mining through The Cancer Genome Atlas (TCGA) database, we found that the expression of ANKRD49 was increased in glioma tissues and that high expression of ANKRD49 was strongly associated with high disease grade and poor overall survival. To investigate the role of ANKRD49 in malignant glioma, lentivirus expressing shRNA targetting ANKRD49 was constructed in U251 and U87 malignant glioma cells. We demonstrated that ANKRD49 knockdown reduced the proliferation rate of U251 and U87 cells. Further mechanism analysis indicated that depletion of ANKRD49 led to the cell-cycle arrest and induced apoptosis in U251 and U87 cells. ANKRD49 knockdown also changed the expression of key effectors that are involved in stress response, cell cycle, and apoptosis, including p-HSP27 (heat shock protein 27), p-Smad2 (SMAD family member 2), p-p53, p-p38, p-MAPK (mitogen-activated protein kinase), p-SAPK/JNK (stress-activated protein kinase/c-jun n-terminal kinase), cleveagated Caspase-7, p-Chk1 (checkpoint kinase 1), and p-eIF2a (eukaryotic translation initiation factor 2a). Taken together, our findings implicate that ANKRD49 promotes the proliferation of human malignant glioma cells. ANKRD49 maybe an attractive target for malignant glioma therapy.

Defining the biological basis of radiomic phenotypes in lung cancer

Medical imaging can visualize characteristics of human cancer noninvasively. Radiomics is an emerging field that translates these medical images into quantitative data to enable phenotypic profiling of tumors. While radiomics has been associated with several clinical endpoints, the complex relationships of radiomics, clinical factors, and tumor biology are largely unknown. To this end, we analyzed two independent cohorts of respectively 262 North American and 89 European patients with lung cancer, and consistently identified previously undescribed associations between radiomic imaging features, molecular pathways, and clinical factors. In particular, we found a relationship between imaging features, immune response, inflammation, and survival, which was further validated by immunohistochemical staining. Moreover, a number of imaging features showed predictive value for specific pathways; for example, intra-tumor heterogeneity features predicted activity of RNA polymerase transcription (AUC = 0.62, p=0.03) and intensity dispersion was predictive of the autodegration pathway of a ubiquitin ligase (AUC = 0.69, p<10-4). Finally, we observed that prognostic biomarkers performed highest when combining radiomic, genetic, and clinical information (CI = 0.73, p<10-9) indicating complementary value of these data. In conclusion, we demonstrate that radiomic approaches permit noninvasive assessment of both molecular and clinical characteristics of tumors, and therefore have the potential to advance clinical decision-making by systematically analyzing standard-of-care medical images.

Clonal hematopoiesis in patients with dyskeratosis congenita

Dyskeratosis congenita (DC) is a rare inherited telomeropathy most frequently caused by mutations in a number of genes all thought to be involved in telomere maintenance. The main causes of mortality in DC are bone marrow failure as well as malignancies including leukemias and solid tumors. The clinical picture including the degree of bone marrow failure is highly variable and factors that contribute to this variability are poorly understood. Based on the recent finding of frequent clonal hematopoiesis in related bone marrow failure syndromes, we hypothesized that somatic mutations may also occur in DC and may contribute at least in part to the variability in blood production. To evaluate for the presence of clonal hematopoiesis in DC, we used a combination of X-inactivation, comparative whole exome sequencing (WES) and single nucleotide polymorphism array (SNP-A) analyses. We found that clonal hematopoiesis in DC is common, as suggested by skewed X-inactivation in 8 out of 9 female patients compared to 3 out of 10 controls, and by the finding of acquired copy neutral loss-of-heterozygosity on SNP-A analysis. In addition, 3 out of 6 independent DC patients were found to have acquired somatic changes in their bone marrow by WES, including a somatic reversion in DKC1, as well as missense mutations in other protein coding genes. Our results indicate that clonal hematopoiesis is a common feature of DC, and suggest that such somatic changes, though commonly expected to indicate malignancy, may lead to improved blood cell production or stem cell survival. Am. J. Hematol. 91:1227-1233, 2016. © 2016 Wiley Periodicals, Inc.

Mechanistic Modelling of DNA Repair and Cellular Survival Following Radiation-Induced DNA Damage

Characterising and predicting the effects of ionising radiation on cells remains challenging, with the lack of robust models of the underlying mechanism of radiation responses providing a significant limitation to the development of personalised radiotherapy. In this paper we present a mechanistic model of cellular response to radiation that incorporates the kinetics of different DNA repair processes, the spatial distribution of double strand breaks and the resulting probability and severity of misrepair. This model enables predictions to be made of a range of key biological endpoints (DNA repair kinetics, chromosome aberration and mutation formation, survival) across a range of cell types based on a set of 11 mechanistic fitting parameters that are common across all cells. Applying this model to cellular survival showed its capacity to stratify the radiosensitivity of cells based on aspects of their phenotype and experimental conditions such as cell cycle phase and plating delay (correlation between modelled and observed Mean Inactivation Doses R(2) > 0.9). By explicitly incorporating underlying mechanistic factors, this model can integrate knowledge from a wide range of biological studies to provide robust predictions and may act as a foundation for future calculations of individualised radiosensitivity.

Integrative analysis of lung development-cancer expression associations reveals the roles of signatures with inverse expression patterns

Recent studies have focused on exploring the associations between organ development and malignant tumors; however, the clinical relevance of the development signatures was inadequately addressed in lung cancer. In this study, we explored the associations between lung development and lung cancer progression by analyzing a total of two development and seven cancer datasets. We identified representative expression patterns (continuously up- and down-regulated) from development and cancer profiles, and inverse pattern associations were observed at both the gene and functional levels. Furthermore, we dissected the biological processes dominating the associations, and found that proliferation and immunity were respectively involved in the two inverse development-cancer expression patterns. Through sub-pathway analysis of the signatures with inverse expression patterns, we finally identified a 13-gene risk signature from the cell cycle sub-pathway, and evaluated its predictive performance for lung cancer patient clinical outcome using independent cohorts. Our findings indicated that the integrative analysis of development and cancer expression patterns provided a framework for identifying effective molecular signatures for clinical utility.

Adhesion GPCRs in Tumorigenesis

Alterations in the homeostasis of several adhesion GPCRs (aGPCRs) have been observed in cancer. The main cellular functions regulated by aGPCRs are cell adhesion, migration, polarity, and guidance, which are all highly relevant to tumor cell biology. Expression of aGPCRs can be induced, increased, decreased, or silenced in the tumor or in stromal cells of the tumor microenvironment, including fibroblasts and endothelial and/or immune cells. For example, ADGRE5 (CD97) and ADGRG1 (GPR56) show increased expression in many cancers, and initial functional studies suggest that both are relevant for tumor cell migration and invasion. aGPCRs can also impact the regulation of angiogenesis by releasing soluble fragments following the cleavage of their extracellular domain (ECD) at the conserved GPCR-proteolytic site (GPS) or other more distal cleavage sites as typical for the ADGRB (BAI) family. Interrogation of in silico cancer databases suggests alterations in other aGPCR members and provides the impetus for further exploration of their potential role in cancer. Integration of knowledge on the expression, regulation, and function of aGPCRs in tumorigenesis is currently spurring the first preclinical studies to examine the potential of aGPCR or the related pathways as therapeutic targets.

The Ankyrin Repeat Domain 49 (ANKRD49) Augments Autophagy of Serum-Starved GC-1 Cells through the NF-κB Pathway

The ankyrin repeat domain 49 (ANKRD49) is an evolutionarily conserved protein highly expressed in testes. However, the function of ANKRD49 in spermatogenesis is unknown. In this study, we found that ANKRD49 resides primarily in nucleus of spermatogonia, spermatocytes and round spermatids. ANKRD49 overexpression augments starvation-induced autophagy in male germ GC-1 cells whereas shRNA knockdown of ANKRD49 attenuates the autophagy. Inhibition of NF-κB pathway by its inhibitors or p65 siRNA prevents the ANKRD49-dependent autophagy augmentation, demonstrating that ANKRD49 enhances autophagy via NF-κB pathway. Our findings suggest that ANKRD49 plays an important role in spermatogenesis via promotion of autophagy-dependent survival.

Unique genomic profile of fibrolamellar hepatocellular carcinoma

BACKGROUND & AIMS

Fibrolamellar hepatocellular carcinoma (FLC) is a rare primary hepatic cancer that develops in children and young adults without cirrhosis. Little is known about its pathogenesis, and it can be treated only with surgery. We performed an integrative genomic analysis of a large series of patients with FLC to identify associated genetic factors.

METHODS

By using 78 clinically annotated FLC samples, we performed whole-transcriptome (n = 58), single-nucleotide polymorphism array (n = 41), and next-generation sequencing (n = 48) analyses; we also assessed the prevalence of the DNAJB1-PRKACA fusion transcript associated with this cancer (n = 73). We performed class discovery using non-negative matrix factorization, and functional annotation using gene-set enrichment analyses, nearest template prediction, ingenuity pathway analyses, and immunohistochemistry. The genomic identification of significant targets in a cancer algorithm was used to identify chromosomal aberrations, MuTect and VarScan2 were used to identify somatic mutations, and the random survival forest was used to determine patient prognoses. Findings were validated in an independent cohort.

RESULTS

Unsupervised gene expression clustering showed 3 robust molecular classes of tumors: the proliferation class (51% of samples) had altered expression of genes that regulate proliferation and mammalian target of rapamycin signaling activation; the inflammation class (26% of samples) had altered expression of genes that regulate inflammation and cytokine enriched production; and the unannotated class (23% of samples) had a gene expression signature that was not associated previously with liver tumors. Expression of genes that regulate neuroendocrine function, as well as histologic markers of cholangiocytes and hepatocytes, were detected in all 3 classes. FLCs had few copy number variations; the most frequent were focal amplification at 8q24.3 (in 12.5% of samples), and deletions at 19p13 (in 28% of samples) and 22q13.32 (in 25% of samples). The DNAJB1-PRKACA fusion transcript was detected in 79% of samples. FLC samples also contained mutations in cancer-related genes such as BRCA2 (in 4.2% of samples), which are uncommon in liver neoplasms. However, FLCs did not contain mutations most commonly detected in liver cancers. We identified an 8-gene signature that predicted survival of patients with FLC.

CONCLUSIONS

In a genomic analysis of 78 FLC samples, we identified 3 classes based on gene expression profiles. FLCs contain mutations and chromosomal aberrations not previously associated with liver cancer, and almost 80% contain the DNAJB1-PRKACA fusion transcript. By using this information, we identified a gene signature that is associated with patient survival time.

Aberrant glycosylation promotes lung cancer metastasis through adhesion to galectins in the metastatic niche

Metastasis is the leading cause of cancer-associated deaths. Although dissemination of tumor cells likely occurs early in tumorigenesis, the constituents of the microenvironment play essential rate-limiting roles in determining whether these cells will form clinically relevant tumors. Recent studies have uncovered many molecular factors that contribute to the establishment of a protumorigenic metastatic niche. Here, we demonstrate that galectin-3, whose expression has clinical associations with advanced malignancy and poor outcome, contributes to metastatic niche formation by binding to carbohydrates on metastatic cells. We show that galectin-3 is expressed early during tumorigenesis by both CD11b(+)Gr-1(+) and CD11b(+)Ly-6C(hi) leukocytes. Tumors mobilize these myeloid populations through secretion of soluble factors, including IL6. We find that metastatic cancer cells exhibit elevated presentation of the oncofetal galectin-3 carbohydrate ligand, the Thomsen-Friedenreich antigen, on their surfaces as a result of altered C2GnT2 and St6GalNAc4 glycosyltransferase activity that inhibits further glycosylation of this carbohydrate motif and promotes metastasis.

SIGNIFICANCE

Although clinical observations of elevated serum galectin-3 levels and altered glycosylation have been associated with malignancy, we identify novel roles for glycosyltransferases in promoting adhesion to galectins in the metastatic niche. This identification of a cytokine-leukocyte-glycosylation axis in metastasis provides mechanistic explanations for clinical associations between malignancy and aberrant glycosylation.

MicroRNA-133a suppresses multiple oncogenic membrane receptors and cell invasion in non-small cell lung carcinoma

Non-small cell lung cancers (NSCLCs) cause high mortality worldwide, and the cancer progression can be activated by several genetic events causing receptor dysregulation, including mutation or amplification. MicroRNAs are a group of small non-coding RNA molecules that function in gene silencing and have emerged as the fine-tuning regulators during cancer progression. MiR-133a is known as a key regulator in skeletal and cardiac myogenesis, and it acts as a tumor suppressor in various cancers. This study demonstrates that miR-133a expression negatively correlates with cell invasiveness in both transformed normal bronchial epithelial cells and lung cancer cell lines. The oncogenic receptors in lung cancer cells, including insulin-like growth factor 1 receptor (IGF-1R), TGF-beta receptor type-1 (TGFBR1), and epidermal growth factor receptor (EGFR), are direct targets of miR-133a. MiR-133a can inhibit cell invasiveness and cell growth through suppressing the expressions of IGF-1R, TGFBR1 and EGFR, which then influences the downstream signaling in lung cancer cell lines. The cell invasive ability is suppressed in IGF-1R- and TGFBR1-repressed cells and this phenomenon is mediated through AKT signaling in highly invasive cell lines. In addition, by using the in vivo animal model, we find that ectopically-expressing miR-133a dramatically suppresses the metastatic ability of lung cancer cells. Accordingly, patients with NSCLCs who have higher expression levels of miR-133a have longer survival rates compared with those who have lower miR-133a expression levels. In summary, we identified the tumor suppressor role of miR-133a in lung cancer outcome prognosis, and we demonstrated that it targets several membrane receptors, which generally produce an activating signaling network during the progression of lung cancer.

Risk classification of cancer survival using ANN with gene expression data from multiple laboratories

Numerous cancer studies have combined gene expression experiments and clinical survival data to predict the prognosis of patients of specific gene types. However, most results of these studies were data dependent and were not suitable for other data sets. This study performed cross-laboratory validations for the cancer patient data from 4 hospitals. We investigated the feasibility of survival risk predictions using high-throughput gene expression data and clinical data. We analyzed multiple data sets for prognostic applications in lung cancer diagnosis. After building tens of thousands of various ANN architectures using the training data, five survival-time correlated genes were identified from 4 microarray gene expression data sets by examining the correlation between gene signatures and patient survival time. The experimental results showed that gene expression data can be used for valid predictions of cancer patient survival classification with an overall accuracy of 83.0% based on survival time trusted data. The results show the prediction model yielded excellent predictions given that patients in the high-risk group obtained a lower median overall survival compared with low-risk patients (log-rank test P-value<0.00001). This study provides a foundation for further clinical studies and research into other types of cancer. We hope these findings will improve the prognostic methods of cancer patients.

Smoking dysregulates the human airway basal cell transcriptome at COPD risk locus 19q13.2

Genome-wide association studies (GWAS) and candidate gene studies have identified a number of risk loci associated with the smoking-related disease COPD, a disorder that originates in the airway epithelium. Since airway basal cell (BC) stem/progenitor cells exhibit the earliest abnormalities associated with smoking (hyperplasia, squamous metaplasia), we hypothesized that smoker BC have a dysregulated transcriptome, enriched, in part, at known GWAS/candidate gene loci. Massive parallel RNA sequencing was used to compare the transcriptome of BC purified from the airway epithelium of healthy nonsmokers (n = 10) and healthy smokers (n = 7). The chromosomal location of the differentially expressed genes was compared to loci identified by GWAS to confer risk for COPD. Smoker BC have 676 genes differentially expressed compared to nonsmoker BC, dominated by smoking up-regulation. Strikingly, 166 (25%) of these genes are located on chromosome 19, with 13 localized to 19q13.2 (p<10⁻⁴ compared to chance), including 4 genes (NFKBIB, LTBP4, EGLN2 and TGFB1) associated with risk for COPD. These observations provide the first direct connection between known genetic risks for smoking-related lung disease and airway BC, the population of lung cells that undergo the earliest changes associated with smoking.

PROGgene: gene expression based survival analysis web application for multiple cancers

Background

Identification of prognostic mRNA biomarkers has been done for various cancer types. The data that are published from such studies are archived in public repositories. There are hundreds of such datasets available for multiple cancer types in public repositories. Wealth of such data can be utilized to study prognostic implications of mRNA in different cancers as well as in different populations or subtypes of same cancer.

Description

We have created a web application that can be used for studying prognostic implications of mRNA biomarkers in a variety of cancers. We have compiled data from public repositories such as GEO, EBI Array Express and The Cancer Genome Atlas for creating this tool. With 64 patient series from 18 cancer types in our database, this tool provides the most comprehensive resource available for survival analysis to date. The tool is called PROGgene and it is available at http://www.compbio.iupui.edu/proggene.

Conclusions

We present this tool as a hypothesis generation tool for researchers to identify potential prognostic mRNA biomarkers to follow up with further research. For this reason, we have kept the web application very simple and straightforward. We believe this tool will be useful in accelerating biomarker discovery in cancer and quickly providing results that may indicate disease-specific prognostic value of specific biomarkers.

MicroRNA profiling in chemoresistant and chemosensitive acute myeloid leukemia

MicroRNA (miRNA) deregulation is associated with progression and treatment outcome in various types of cancers. To identify miRNAs related to therapeutic response, we applied an miRNA microarray followed by PCR verification of 33 available diagnostic bone marrow core biopsies from 33 acute myeloid leukemia patients including 15 chemoresistant and 18 chemosensitive patients. We found 3 significantly upregulated miRNAs, miR-363, miR-532-5p and miR-342-3p, related to therapeutic response (q < 0.05). Further validation of miR-532-5p and miR-363 expression by quantitative RT-PCR confirmed microarray analysis results. Genes targeted by miR-363 include RGS17 and HIPK3, both reported to be associated with drug response.

Genome-wide analysis of three-way interplay among gene expression, cancer cell invasion and anti-cancer compound sensitivity

BACKGROUND

Chemosensitivity and tumor metastasis are two primary issues in cancer management. Cancer cells often exhibit a wide range of sensitivity to anti-cancer compounds. To gain insight on the genetic mechanism of drug sensitivity, one powerful approach is to employ the panel of 60 human cancer cell lines developed by the National Cancer Institute (NCI). Cancer cells also show a broad range of invasion ability. However, a genome-wide portrait on the contributing molecular factors to invasion heterogeneity is lacking.

METHODS

Our lab performed an invasion assay on the NCI-60 panel. We identified invasion-associated (IA) genes by correlating our invasion profiling data with the Affymetrix gene expression data on NCI-60. We then employed the recently released chemosensitivity data of 99 anti-cancer drugs of known mechanism to investigate the gene-drug correlation, focusing on the IA genes. Afterwards, we collected data from four independent drug-testing experiments to validate our findings on compound response prediction. Finally, we obtained published clinical and molecular data from two recent adjuvant chemotherapy cohorts, one on lung cancer and one on breast cancer, to test the performance of our gene signature for patient outcome prediction.

RESULTS

First, we found 633 IA genes from the invasion-gene expression correlation study. Then, for each of the 99 drugs, we obtained a subset of IA genes whose expression levels correlated with drug-sensitivity profiles. We identified a set of eight genes (EGFR, ITGA3, MYLK, RAI14, AHNAK, GLS, IL32 and NNMT) showing significant gene-drug correlation with paclitaxel, docetaxel, erlotinib, everolimus and dasatinib. This eight-gene signature (derived from NCI-60) for chemosensitivity prediction was validated by a total of 107 independent drug tests on 78 tumor cell lines, most of which were outside of the NCI-60 panel. The eight-gene signature predicted relapse-free survival for the lung and breast cancer patients (log-rank P = 0.0263; 0.00021). Multivariate Cox regression yielded a hazard ratio of our signature of 5.33 (95% CI = 1.76 to 16.1) and 1.81 (95% CI = 1.19 to 2.76) respectively. The eight-gene signature features the cancer hallmark epidermal growth factor receptor (EGFR) and genes involved in cell adhesion, migration, invasion, tumor growth and progression.

CONCLUSIONS

Our study sheds light on the intricate three-way interplay among gene expression, invasion and compound-sensitivity. We report the finding of a unique signature that predicts chemotherapy survival for both lung and breast cancer. Augmenting the NCI-60 model with in vitro characterization of important phenotype-like invasion potential is a cost-effective approach to power the genomic chemosensitivity analysis.

Update on recent prognostic markers in adenocarcinoma

SUMMARY Biomarkers have evolved from individual markers to gene signatures validated in multi-institutional studies and independent datasets. Multiple gene signatures have been associated with survival, as well as defining outcome following specific adjuvant chemotherapy. The heterogeneity of lung cancer has necessitated the incorporation of multiple genes in mRNA signatures. The use of plasma miRNA profiles in the diagnosis or prognosis of early-stage lung carcinoma also appears promising. However, challenges remain, with questions regarding reproducibility, standardized techniques, cost and differences between gene signatures. Before they can be widely adopted, molecular profiles must be validated in large, prospective trials evaluating the specific patient population for which the profiles are to be utilized.

Statistical aspects of omics data analysis using the random compound covariate

Background

Dealing with high dimensional markers, such as gene expression data obtained using microarray chip technology or genomics studies, is a key challenge because the numbers of features greatly exceeds the number of biological samples. After selecting biologically relevant genes, how to summarize the expression of selected genes and then further build predicted model is an important issue in medical applications. One intuitive method of addressing this challenge assigns different weights to different features, subsequently combining this information into a single score, named the compound covariate. Investigators commonly employ this score to assess whether an association exists between the compound covariate and clinical outcomes adjusted for baseline covariates. However, we found that some clinical papers concerned with such analysis report bias p-values based on flawed compound covariate in their training data set.

Results

We correct this flaw in the analysis and we also propose treating the compound score as a random covariate, to achieve more appropriate results and significantly improve study power for survival outcomes. With this proposed method, we thoroughly assess the performance of two commonly used estimated gene weights through simulation studies. When the sample size is 100, and censoring rates are 50%, 30%, and 10%, power is increased by 10.6%, 3.5%, and 0.4%, respectively, by treating the compound score as a random covariate rather than a fixed covariate. Finally, we assess our proposed method using two publicly available microarray data sets.

Conclusion

In this article, we correct this flaw in the analysis and the propose method, treating the compound score as a random covariate, can achieve more appropriate results and improve study power for survival outcomes.

An insertion/deletion polymorphism within RERT-lncRNA modulates hepatocellular carcinoma risk

The Prolyl hydroxylase 1 (EGLN2) is known to affect tumorigenesis by regulating the degradation of hypoxia-inducible factor. Polymorphisms in EGLN2 may facilitate cancer cell survival under hypoxic conditions and directly associate with cancer susceptibility. Here, we examined the contribution of a 4-bp insertion/deletion polymorphism (rs10680577) within the distal promoter of EGLN2 to the risk of hepatocelluar carcinoma (HCC) in Chinese populations. The contribution of rs10680577 to HCC risk was investigated in 623 HCC cases and 1,242 controls and replicated in an independent case-control study consisting of 444 HCC cases and 450 controls. Logistic regression analysis showed that the deletion allele of rs10680577 was significantly associated with increased risk for HCC occurrence in both case-control studies [OR = 1.40; 95% confidence interval (CI) = 1.18-1.66, P < 0.0001; OR = 1.49; 95% CI = 1.18-1.88, P = 0.0007]. Such positive association was more pronounced in current smokers (OR = 3.49, 95% CI = 2.24-5.45) than nonsmokers (OR = 1.24, 95% CI = 1.03-1.50; heterogeneity P = 0.0002). Genotype-phenotype correlation studies showed that the deletion allele was significantly correlated with higher expression of both EGLN2 and RERT-lncRNA [a long noncoding RNA whose sequence overlaps with Ras-related GTP-binding protein 4b (RAB4B) and EGLN2)] in vivo and in vitro. Furthermore, RERT-lncRNA expression was also significantly correlated with EGLN2 expression in vivo, consistent with in vitro gain-of-function study that showed overexpressing RERT-lncRNA upregulated EGLN2. Finally, in silico prediction suggested that the insertion allele could disrupt the structure of RERT-lncRNA. Taken together, our findings provided strong evidence for the hypothesis that rs10680577 contributes to hepatocarcinogenesis, possibly by affecting RERT-lncRNA structure and subsequently EGLN2 expression, making it a promising biomarker for early diagnosis of HCC.

Biomarkers and transcriptome profiling of lung cancer

Lung cancer is the leading cause of cancer-related deaths worldwide. High-throughput technologies such as microarrays provide an opportunity to explore biomarkers for cancer prevention, prognosis and treatment guidance. Recent studies have revealed many biomarkers with the potential for clinical application. However, major limitations still exist. Although useful data on cancer genomics has accumulated rapidly, there has also been a simultaneous tendency for amplification of the complex relationships among the enormous number of variables that need to be considered. Disentangling these complex gene-gene interactions requires new approaches to data analysis to reveal information that has been obscured by traditional methods. Here, we review the current findings on biomarker identification in lung cancer, address their limitations and discuss some future directions for improvements in this area of research.

Reconstruction of nuclear receptor network reveals that NR2E3 is a novel upstream regulator of ESR1 in breast cancer

ESR1 is one of the most important transcription factors and therapeutic targets in breast cancer. By applying systems-level re-analysis of publicly available gene expression data, we uncovered a potential regulator of ESR1. We demonstrated that orphan nuclear receptor NR2E3 regulates ESR1 via direct binding to the ESR1 promoter with concomitant recruitment of PIAS3 to the promoter in breast cancer cells, and is essential for physiological cellular activity of ESR1 in estrogen receptor (ER)-positive breast cancer cells. Moreover, expression of NR2E3 was significantly associated with recurrence-free survival and a favourable response to tamoxifen treatment in women with ER-positive breast cancer. Our results provide mechanistic insights on the regulation of ESR1 by NR2E3 and the clinical relevance of NR2E3 in breast cancer.

Protein kinase Cα promotes cell migration through a PDZ-dependent interaction with its novel substrate discs large homolog 1 (DLG1)

Protein scaffolds maintain precision in kinase signaling by coordinating kinases with components of specific signaling pathways. Such spatial segregation is particularly important in allowing specificity of signaling mediated by the 10-member family of protein kinase C (PKC) isozymes. Here we identified a novel interaction between PKCα and the Discs large homolog (DLG) family of scaffolds that is mediated by a class I C-terminal PDZ (PSD-95, disheveled, and ZO1) ligand unique to this PKC isozyme. Specifically, use of a proteomic array containing 96 purified PDZ domains identified the third PDZ domains of DLG1/SAP97 and DLG4/PSD95 as interaction partners for the PDZ binding motif of PKCα. Co-immunoprecipitation experiments verified that PKCα and DLG1 interact in cells by a mechanism dependent on an intact PDZ ligand. Functional assays revealed that the interaction of PKCα with DLG1 promotes wound healing; scratch assays using cells depleted of PKCα and/or DLG1 have impaired cellular migration that is no longer sensitive to PKC inhibition, and the ability of exogenous PKCα to rescue cellular migration is dependent on the presence of its PDZ ligand. Furthermore, we identified Thr-656 as a novel phosphorylation site in the SH3-Hook region of DLG1 that acts as a marker for PKCα activity at this scaffold. Increased phosphorylation of Thr-656 is correlated with increased invasiveness in non-small cell lung cancer lines from the NCI-60, consistent with this phosphorylation site serving as a marker of PKCα-mediated invasion. Taken together, these data establish the requirement of scaffolding to DLG1 for PKCα to promote cellular migration.

Overexpression of the lung cancer-prognostic miR-146b microRNAs has a minimal and negative effect on the malignant phenotype of A549 lung cancer cells

Introduction

Expression levels of miR-146b-5p and -3p microRNAs in human non-small cell lung cancer (NSCLC) are associated with recurrence of the disease after surgery. To understand this, the effect of miR-146b overexpression was studied in A549 human lung cancer cells.

Methods

A549 cells, engineered with lentiviruses to overexpress the human pre-miR-146b precursor microRNA, were examined for proliferation, colony formation on plastic surface and in soft agar, migration and invasiveness in cell culture and in vivo in mice, chemosensitivity to cisplatin and doxorubicin, and global gene expression. miR-146b expressions were assessed in microdissected stroma and epithelia of human NSCLC tumors. Association of miR-146b-5p and -3p expression in early stage NSCLC with recurrence was analyzed.

Principal Findings

A549 pre-miR-146b-overexpressors had 3–8-fold higher levels of both miR-146b microRNAs than control cells. Overexpression did not alter cellular proliferation, chemosensitivity, migration, or invasiveness; affected only 0.3% of the mRNA transcriptome; and, reduced the ability to form colonies in vitro by 25%. In human NSCLC tumors, expression of both miR-146b microRNAs was 7–10-fold higher in stroma than in cancerous epithelia, and higher miR-146b-5p but lower -3p levels were predictive of recurrence.

Conclusions

Only a minimal effect of pre-miR-146b overexpression on the malignant phenotype was seen in A549 cells. This could be because of opposing effects of miR-146b-5p and -3p overexpression as suggested by the conflicting recurrence-predictive values of the two microRNAs, or because miR-146b expression changes in non-cancerous stroma and not cancerous epithelia of tumors are responsible for the prognostic value of miR-146b.

Population differences in microRNA expression and biological implications

Population differences observed for complex traits may be attributed to the combined effect of socioeconomic, environmental, genetic and epigenetic factors. To better understand population differences in complex traits, genome-wide genetic and gene expression differences among ethnic populations have been studied. Here we set out to evaluate population differences in small non-coding RNAs through an evaluation of microRNA (miRNA) baseline expression in HapMap lymphoblastoid cell lines (LCLs) derived from 53 CEU (Utah residents with northern and western European ancestry) and 54 YRI (African from Ibadan, Nigeria). Using the Exiqon miRCURYTM LNA arrays, we found that 16% of all miRNAs evaluated in our study differ significantly between these 2 ethnic groups (pBonferroni corrected< 0.05). Furthermore, we explored the potential biological function of these observed differentially expressed miRNAs by comprehensively examining their effect on the transcriptome and their relationship with cellular sensitivity drug phenotypes. After multiple testing adjustment (false discovery rate (FDR)< 0.1), we found that 55% and 88% of the differentially expressed miRNAs were significantly and inversely correlated with an mRNA expression phenotype in the CEU and YRI samples, respectively. Interestingly, a substantial proportion (64%) of these miRNAs correlated with cellular sensitivity to chemotherapeutic agents (FDR< 0.05). Lastly, upon performing a genome-wide association study between SNPs and miRNA expression, we identified a large number of SNPs exhibiting different allele frequencies that affect the expression of these differentially expressed miRNAs, suggesting the role of genetic variants in mediating the observed population differences.

Global microRNA analysis of the NCI-60 cancer cell panel

MicroRNAs (miRNA) are a group of short noncoding RNAs that regulate gene expression at the posttranscriptional level. They are involved in many biological processes, including development, differentiation, apoptosis, and carcinogenesis. Because miRNAs may play a role in the initiation and progression of cancer, they comprise a novel class of promising diagnostic and prognostic molecular markers and potential drug targets. By applying an LNA-enhanced microarray platform, we studied the expression profiles of 955 miRNAs in the NCI-60 cancer cell lines and identified tissue- and cell-type-specific miRNA patterns by unsupervised hierarchical clustering and statistical analysis. A comparison of our data to three previously published miRNA expression studies on the NCI-60 panel showed a remarkably high correlation between the different technical platforms. In addition, the current work contributes expression data for 369 miRNAs that have not previously been profiled. Finally, by matching drug sensitivity data for the NCI-60 cells to their miRNA expression profiles, we found numerous drug-miRNAs pairs, for which the miRNA expression and drug sensitivity profiles were highly correlated and thus represent potential candidates for further investigation of drug resistance and sensitivity mechanisms.

Prognostic and predictive gene signature for adjuvant chemotherapy in resected non-small-cell lung cancer

PURPOSE

The JBR.10 trial demonstrated benefit from adjuvant cisplatin/vinorelbine (ACT) in early-stage non-small-cell lung cancer (NSCLC). We hypothesized that expression profiling may identify stage-independent subgroups who might benefit from ACT.

PATIENTS AND METHODS

Gene expression profiling was conducted on mRNA from 133 frozen JBR.10 tumor samples (62 observation [OBS], 71 ACT). The minimum gene set that was selected for the greatest separation of good and poor prognosis patient subgroups in OBS patients was identified. The prognostic value of this gene signature was tested in four independent published microarray data sets and by quantitative reverse-transcriptase polymerase chain reaction (RT-qPCR).

RESULTS

A 15-gene signature separated OBS patients into high-risk and low-risk subgroups with significantly different survival (hazard ratio [HR], 15.02; 95% CI, 5.12 to 44.04; P < .001; stage I HR, 13.31; P < .001; stage II HR, 13.47; P < .001). The prognostic effect was verified in the same 62 OBS patients where gene expression was assessed by qPCR. Furthermore, it was validated consistently in four separate microarray data sets (total 356 stage IB to II patients without adjuvant treatment) and additional JBR.10 OBS patients by qPCR (n = 19). The signature was also predictive of improved survival after ACT in JBR.10 high-risk patients (HR, 0.33; 95% CI, 0.17 to 0.63; P = .0005), but not in low-risk patients (HR, 3.67; 95% CI, 1.22 to 11.06; P = .0133; interaction P < .001). Significant interaction between risk groups and ACT was verified by qPCR.

CONCLUSION

This 15-gene expression signature is an independent prognostic marker in early-stage, completely resected NSCLC, and to our knowledge, is the first signature that has demonstrated the potential to select patients with stage IB to II NSCLC most likely to benefit from adjuvant chemotherapy with cisplatin/vinorelbine.