The Proneural Molecular Signature Is Enriched in Oligodendrogliomas and Predicts Improved Survival among Diffuse Gliomas

The Cancer Genome Atlas Project (TCGA) has produced an extensive collection of ‘-omic’ data on glioblastoma (GBM), resulting in several key insights on expression signatures. Despite the richness of TCGA GBM data, the absence of lower grade gliomas in this data set prevents analysis genes related to progression and the uncovering of predictive signatures. A complementary dataset exists in the form of the NCI Repository for Molecular Brain Neoplasia Data (Rembrandt), which contains molecular and clinical data for diffuse gliomas across the full spectrum of histologic class and grade. Here we present an investigation of the significance of the TCGA consortium''s expression classification when applied to Rembrandt gliomas. We demonstrate that the proneural signature predicts improved clinical outcome among 176 Rembrandt gliomas that includes all histologies and grades, including GBMs (log rank test p = 1.16e-6), but also among 75 grade II and grade III samples (p = 2.65e-4). This gene expression signature was enriched in tumors with oligodendroglioma histology and also predicted improved survival in this tumor type (n = 43, p = 1.25e-4). Thus, expression signatures identified in the TCGA analysis of GBMs also have intrinsic prognostic value for lower grade oligodendrogliomas, and likely represent important differences in tumor biology with implications for treatment and therapy. Integrated DNA and RNA analysis of low-grade and high-grade proneural gliomas identified increased expression and gene amplification of several genes including GLIS3, TGFB2, TNC, AURKA, and VEGFA in proneural GBMs, with corresponding loss of DLL3 and HEY2. Pathway analysis highlights the importance of the Notch and Hedgehog pathways in the proneural subtype. This demonstrates that the expression signatures identified in the TCGA analysis of GBMs also have intrinsic prognostic value for low-grade oligodendrogliomas, and likely represent important differences in tumor biology with implications for treatment and therapy.     

Molecular Signatures Associated with HCV-Induced Hepatocellular Carcinoma and Liver Metastasis

Hepatocellular carcinomas (HCCs) are a heterogeneous group of tumors that differ in risk factors and genetic alterations. In Italy, particularly Southern Italy, chronic hepatitis C virus (HCV) infection represents the main cause of HCC. Using high-density oligoarrays, we identified consistent differences in gene-expression between HCC and normal liver tissue. Expression patterns in HCC were also readily distinguishable from those associated with liver metastases. To characterize molecular events relevant to hepatocarcinogenesis and identify biomarkers for early HCC detection, gene expression profiling of 71 liver biopsies from HCV-related primary HCC and corresponding HCV-positive non-HCC hepatic tissue, as well as gastrointestinal liver metastases paired with the apparently normal peri-tumoral liver tissue, were compared to 6 liver biopsies from healthy individuals. Characteristic gene signatures were identified when normal tissue was compared with HCV-related primary HCC, corresponding HCV-positive non-HCC as well as gastrointestinal liver metastases. Pathway analysis classified the cellular and biological functions of the genes differentially expressed as related to regulation of gene expression and post-translational modification in HCV-related primary HCC; cellular Growth and Proliferation, and Cell-To-Cell Signaling and Interaction in HCV-related non HCC samples; Cellular Growth and Proliferation and Cell Cycle in metastasis. Also characteristic gene signatures were identified of HCV-HCC progression for early HCC diagnosis.

Conclusions

A diagnostic molecular signature complementing conventional pathologic assessment was identified.     

Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures

Background

Current prognostic gene signatures for breast cancer mainly reflect proliferation status and have limited value in triple-negative (TNBC) cancers. The identification of prognostic signatures from TNBC cohorts was limited in the past due to small sample sizes.

Methodology/Principal Findings

We assembled all currently publically available TNBC gene expression datasets generated on Affymetrix gene chips. Inter-laboratory variation was minimized by filtering methods for both samples and genes. Supervised analysis was performed to identify prognostic signatures from 394 cases which were subsequently tested on an independent validation cohort (n = 261 cases).

Conclusions/Significance

Using two distinct false discovery rate thresholds, 25% and <3.5%, a larger (n = 264 probesets) and a smaller (n = 26 probesets) prognostic gene sets were identified and used as prognostic predictors. Most of these genes were positively associated with poor prognosis and correlated to metagenes for inflammation and angiogenesis. No correlation to other previously published prognostic signatures (recurrence score, genomic grade index, 70-gene signature, wound response signature, 7-gene immune response module, stroma derived prognostic predictor, and a medullary like signature) was observed. In multivariate analyses in the validation cohort the two signatures showed hazard ratios of 4.03 (95% confidence interval [CI] 1.71–9.48; P = 0.001) and 4.08 (95% CI 1.79–9.28; P = 0.001), respectively. The 10-year event-free survival was 70% for the good risk and 20% for the high risk group. The 26-gene signatures had modest predictive value (AUC = 0.588) to predict response to neoadjuvant chemotherapy, however, the combination of a B-cell metagene with the prognostic signatures increased its response predictive value. We identified a 264-gene prognostic signature for TNBC which is unrelated to previously known prognostic signatures.     

Embryonic Morphogen Nodal Is Associated with Progression and Poor Prognosis of Hepatocellular Carcinoma

Background

Nodal, a TGF-β-related embryonic morphogen, is involved in multiple biologic processes. However, the expression of Nodal in hepatocellular carcinoma (HCC) and its correlation with tumor angiogenesis, epithelial-mesenchymal transition, and prognosis is unclear.

Methods

We used real-time PCR and Western blotting to investigate Nodal expression in 6 HCC cell lines and 1 normal liver cell line, 16 pairs of tumor and corresponding paracarcinomatous tissues from HCC patients. Immunohistochemistry was performed to examine Nodal expression in HCC and corresponding paracarcinomatous tissues from 96 patients. CD34 and Vimentin were only examined in HCC tissues of patients mentioned above. Nodal gene was silenced by shRNA in MHCC97H and HCCLM3 cell lines, and cell migration and invasion were detected. Statistical analyses were applied to evaluate the prognostic value and associations of Nodal expression with clinical parameters.

Results

Nodal expression was detected in HCC cell lines with high metastatic potential alone. Nodal expression is up-regulated in HCC tissues compared with paracarcinomatous and normal liver tissues. Nodal protein was expressed in 70 of the 96 (72.9%) HCC tumors, and was associated with vascular invasion (P = 0.000), status of metastasis (P = 0.004), AFP (P = 0.049), ICGR₁₅ (indocyanine green retention rate at 15 min) (P = 0.010) and tumor size (P = 0.000). High Nodal expression was positively correlated with high MVD (microvessal density) (P = 0.006), but not with Vimentin expression (P = 0.053). Significantly fewer migrated and invaded cells were seen in shRNA group compared with blank group and negative control group (P<0.05). High Nodal expression was found to be an independent factor for predicting overall survival of HCC.

Conclusions

Our study demonstrated that Nodal expression is associated with aggressive characteristics of HCC. Its aberrant expression may be a predictive factor of unfavorable prognosis for HCC after surgery.     

Integrative genomics: liver regeneration and hepatocellular carcinoma

Numerous genome wide profiles of gene expression changes in human hepatocellular carcinoma (HCC), compared to normal liver tissue, have been reported. Hierarchical clustering of these data reveal distinct patterns, which underscore conservation between human disease and mouse models of HCC, as well as suggest specific classification of subtypes within the heterogeneous disease of HCC. Global profiling of gene expression in mouse liver, challenged by partial hepatectomy to regenerate, reveals alterations in gene expression that occur in response to acute injury, inflammation, and re-entry into cell cycle. When we integrated datasets of gene expression changes in mouse models of HCC and those that are altered at specific times of liver regeneration, we saw shared, conserved alterations in gene expression within specific biological pathways, both up-regulated, for example, cell cycle, cell death, and cellular development, or down-regulated, for example, vitamin and mineral metabolism, lipid metabolism, and molecular transport. Additional molecular mechanisms shared by liver regeneration and HCC, as yet undiscovered, may have important implications in tumor development and recurrence. These comparisons may offer a way to judge how liver resection, in the treatment of HCC, introduces challenges to care of the disease. Further, uncovering the pathways conserved in inflammatory response, hypertrophy, proliferation, and architectural remodeling of the liver, which are shared in liver regeneration and HCC, versus those specific to tumor development and progression in HCC, may reveal new biomarkers or potential therapeutic targets in HCC.     

Gene Expression-Based Classification of Non-Small Cell Lung Carcinomas and Survival Prediction

Background

Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy.

Methodology and Principal Findings

A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples. We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of non-small cell lung cancer. Correlation analysis identified 17 genes which showed the best association with post-surgery survival time. This signature was used for stratification of all patients in two risk groups. Kaplan-Meier survival curves show that the two groups display a significant difference in post-surgery survival time (p = 5.6E-6). The performance of the signatures was validated using a patient cohort of similar size (Duke University, n = 96). Compared to previously published prognostic signatures for NSCLC, the 17 gene signature performed well on these two cohorts.

Conclusions

The gene signatures identified are promising tools for histo-pathological classification of non-small cell lung cancer, and may improve the prediction of clinical outcome.     

A Thirteen-Gene Expression Signature Predicts Survival of Patients with Pancreatic Cancer and Identifies New Genes of Interest

Background

Currently, prognostication for pancreatic ductal adenocarcinoma (PDAC) is based upon a coarse clinical staging system. Thus, more accurate prognostic tests are needed for PDAC patients to aid treatment decisions.

Methods and Findings

Affymetrix gene expression profiling was carried out on 15 human PDAC tumors and from the data we identified a 13-gene expression signature (risk score) that correlated with patient survival. The gene expression risk score was then independently validated using published gene expression data and survival data for an additional 101 patients with pancreatic cancer. Patients with high-risk scores had significantly higher risk of death compared to patients with low-risk scores (HR 2.27, p = 0.002). When the 13-gene score was combined with lymph node status the risk-score further discriminated the length of patient survival time (p<0.001). Patients with a high-risk score had poor survival independent of nodal status; however, nodal status increased predictability for survival in patients with a low-risk gene signature score (low-risk N1 vs. low-risk N0: HR = 2.0, p = 0.002). While AJCC stage correlated with patient survival (p = 0.03), the 13-gene score was superior at predicting survival. Of the 13 genes comprising the predictive model, four have been shown to be important in PDAC, six are unreported in PDAC but important in other cancers, and three are unreported in any cancer.

Conclusions

We identified a 13-gene expression signature that predicts survival of PDAC patients and could prove useful for making treatment decisions. This risk score should be evaluated prospectively in clinical trials for prognostication and for predicting response to chemotherapy. Investigation of new genes identified in our model may lead to novel therapeutic targets.     

An Attempt at a Molecular Prediction of Metastasis in Patients with Primary Cutaneous Melanoma

Background

Current prognostic clinical and morphological parameters are insufficient to accurately predict metastasis in individual melanoma patients. Several studies have described gene expression signatures to predict survival or metastasis of primary melanoma patients, however the reproducibility among these studies is disappointingly low.

Methodology/Principal Findings

We followed extended REMARK/Gould Rothberg criteria to identify gene sets predictive for metastasis in patients with primary cutaneous melanoma. For class comparison, gene expression data from 116 patients with clinical stage I/II (no metastasis) and 72 with III/IV primary melanoma (with metastasis) at time of first diagnosis were used. Significance analysis of microarrays identified the top 50 differentially expressed genes. In an independent data set from a second cohort of 28 primary melanoma patients, these genes were analyzed by multivariate Cox regression analysis and leave-one-out cross validation for association with development of metastatic disease. In a multivariate Cox regression analysis, expression of the genes Ena/vasodilator-stimulated phosphoprotein-like (EVL) and CD24 antigen gave the best predictive value (p = 0.001; p = 0.017, respectively). A multivariate Cox proportional hazards model revealed these genes as a potential independent predictor, which may possibly add (both p = 0.01) to the predictive value of the most important morphological indicator, Breslow depth.

Conclusion/Significance

Combination of molecular with morphological information may potentially enable an improved prediction of metastasis in primary melanoma patients. A strength of the gene expression set is the small number of genes, which should allow easy reevaluation in independent data sets and adequately designed clinical trials.     

S100P Expression Is a Novel Prognostic Factor in Hepatocellular Carcinoma and Predicts Survival in Patients with High Tumor Stage or Early Recurrent Tumors

The calcium-binding protein S100P is expressed in a variety of human cancer cells and is important in cancer cell growth and invasion. Using differential display, we found S100P is overexpressed in human hepatocellular carcinoma (HCC). We examined the expression of 305 unifocal, primary HCC tumors using immunohistochemistry. The S100P protein was expressed in 173 of the 305 (56.7%) HCC tumors. The expression of S100P correlated with female sex (P = 0.0162), high serum α-fetoprotein level (P = 0.0001), high tumor grade (P = 0.0029), high tumor stage (P = 0.0319), the presence of the p53 mutation (P = 0.0032), and the absence of the β-catenin mutation (P = 0.0489). Patients with HCC tumors that expressed S100P were more likely to have early tumor recurrence (ETR) (P = 0.0189) and lower 5-year survival (P = 0.0023). The multivariate analysis confirmed that S100P expression was an independent prognostic factor in HCC. The combinatorial analysis showed an additive unfavorable prognostic interaction between S100P expression and the p53 mutation. In contrast, the β-catenin mutation was associated with better prognosis in both S100P-positive and -negative HCCs. Furthermore, S100P expression was a predictor of survival in HCC patients with high tumor stage or ETR (P = 0.0026 and P = 0.0002, respectively). Our study indicates the expression of the S100P protein is a novel independent predictor for poor prognosis in HCC, and it is also an unfavorable prognostic predictor in HCC patients with high tumor stage or ETR.     

Microarray-based oncogenic pathway profiling in advanced serous papillary ovarian carcinoma

Introduction

The identification of specific targets for treatment of ovarian cancer patients remains a challenge. The objective of this study is the analysis of oncogenic pathways in ovarian cancer and their relation with clinical outcome.

Methodology

A meta-analysis of 6 gene expression datasets was done for oncogenic pathway activation scores: AKT, β-Catenin, BRCA, E2F1, EGFR, ER, HER2, INFα, INFγ, MYC, p53, p63, PI3K, PR, RAS, SRC, STAT3, TNFα, and TGFβ and VEGF-A. Advanced serous papillary tumours from uniformly treated patients were selected (N = 464) to find differences independent from stage-, histology- and treatment biases. Survival and correlations with documented prognostic signatures (wound healing response signature WHR/genomic grade index GGI/invasiveness gene signature IGS) were analysed.

Results

The GGI, WHR, IGS score were unexpectedly increased in chemosensitive versus chemoresistant patients. PR and RAS activation score were associated with survival outcome (p = 0.002;p = 0.004). Increased activations of β-Catenin (p = 0.0009), E2F1 (p = 0.005), PI3K (p = 0.003) and p63 (p = 0.05) were associated with more favourable clinical outcome and were consistently correlated with three prognostic gene signatures.

Conclusions

Oncogenic pathway profiling of advanced serous ovarian tumours revealed that increased β-Catenin, E2F1, p63, PI3K, PR and RAS –pathway activation scores were significantly associated with favourable clinical outcome. WHR, GGI and IGS scores were unexpectedly increased in chemosensitive tumours. Earlier studies have shown that WHR, GGI and IGS are strongly associated with proliferation and that high-proliferative ovarian tumours are more chemosensitive. These findings may indicate opposite confounding of prognostic versus predictive factors when studying biomarkers in epithelial ovarian cancer.     

Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice.     

Single sample expression-anchored mechanisms predict survival in head and neck cancer

Gene expression signatures that are predictive of therapeutic response or prognosis are increasingly useful in clinical care; however, mechanistic (and intuitive) interpretation of expression arrays remains an unmet challenge. Additionally, there is surprisingly little gene overlap among distinct clinically validated expression signatures. These “causality challenges” hinder the adoption of signatures as compared to functionally well-characterized single gene biomarkers. To increase the utility of multi-gene signatures in survival studies, we developed a novel approach to generate “personal mechanism signatures” of molecular pathways and functions from gene expression arrays. FAIME, the Functional Analysis of Individual Microarray Expression, computes mechanism scores using rank-weighted gene expression of an individual sample. By comparing head and neck squamous cell carcinoma (HNSCC) samples with non-tumor control tissues, the precision and recall of deregulated FAIME-derived mechanisms of pathways and molecular functions are comparable to those produced by conventional cohort-wide methods (e.g. GSEA). The overlap of “Oncogenic FAIME Features of HNSCC” (statistically significant and differentially regulated FAIME-derived genesets representing GO functions or KEGG pathways derived from HNSCC tissue) among three distinct HNSCC datasets (pathways:46%, p<0.001) is more significant than the gene overlap (genes:4%). These Oncogenic FAIME Features of HNSCC can accurately discriminate tumors from control tissues in two additional HNSCC datasets (n = 35 and 91, F-accuracy = 100% and 97%, empirical p<0.001, area under the receiver operating characteristic curves = 99% and 92%), and stratify recurrence-free survival in patients from two independent studies (p = 0.0018 and p = 0.032, log-rank). Previous approaches depending on group assignment of individual samples before selecting features or learning a classifier are limited by design to discrete-class prediction. In contrast, FAIME calculates mechanism profiles for individual patients without requiring group assignment in validation sets. FAIME is more amenable for clinical deployment since it translates the gene-level measurements of each given sample into pathways and molecular function profiles that can be applied to analyze continuous phenotypes in clinical outcome studies (e.g. survival time, tumor volume).     

3′-End Sequencing for Expression Quantification (3SEQ) from Archival Tumor Samples

Gene expression microarrays are the most widely used technique for genome-wide expression profiling. However, microarrays do not perform well on formalin fixed paraffin embedded tissue (FFPET). Consequently, microarrays cannot be effectively utilized to perform gene expression profiling on the vast majority of archival tumor samples. To address this limitation of gene expression microarrays, we designed a novel procedure (3′-end sequencing for expression quantification (3SEQ)) for gene expression profiling from FFPET using next-generation sequencing. We performed gene expression profiling by 3SEQ and microarray on both frozen tissue and FFPET from two soft tissue tumors (desmoid type fibromatosis (DTF) and solitary fibrous tumor (SFT)) (total n = 23 samples, which were each profiled by at least one of the four platform-tissue preparation combinations). Analysis of 3SEQ data revealed many genes differentially expressed between the tumor types (FDR<0.01) on both the frozen tissue (∼9.6K genes) and FFPET (∼8.1K genes). Analysis of microarray data from frozen tissue revealed fewer differentially expressed genes (∼4.64K), and analysis of microarray data on FFPET revealed very few (69) differentially expressed genes. Functional gene set analysis of 3SEQ data from both frozen tissue and FFPET identified biological pathways known to be important in DTF and SFT pathogenesis and suggested several additional candidate oncogenic pathways in these tumors. These findings demonstrate that 3SEQ is an effective technique for gene expression profiling from archival tumor samples and may facilitate significant advances in translational cancer research.     

Identification of histone deacetylase 3 as a biomarker for tumor recurrence following liver transplantation in HBV-associated hepatocellular carcinoma

Background

Recent studies have shown that high expression levels of class I histone deacetylases (HDACs) correlate with malignant phenotype and poor prognosis in some human tumors. However, the expression patterns and prognostic role of class I HDAC isoforms in hepatocellular carcinoma (HCC) remain unclear.

Methodology/Principal Findings

The expression patterns and clinical significance of class I HDAC isoforms were assessed by immunohistochemistry in a cohort of 43 hepatitis B virus-associated HCC patients treated with liver transplantation. In addition, the effects of HDAC inhibition on HCC cell behavior were investigated by knockdown of the HDAC isoform with short interfering RNA. Class I HDACs were highly expressed in a subset of HCCs with positivity for HDAC1 in 51.2%, HDAC2 in 48.8%, and HDAC3 in 32.6% of cases. The expression levels of HDAC isoforms were significantly associated with the proliferation index of HCC. Kaplan-Meier curves showed that a high expression level of HDAC2 or HDAC3 implicated significantly reduced recurrence-free survival. Cox proportional hazards model analysis revealed HDAC3 overexpression was an unfavorable independent prognostic factor (P = 0.002; HR 3.907). In vitro, inhibition of HDAC2 and HDAC3, but not HDAC1, suppressed proliferation and the invasiveness of liver cancer cells.

Conclusions/Significance

Our findings demonstrate that HDAC3 plays a significant role in regulating tumor cell proliferation and invasion, and it could be served as a candidate biomarker for predicting the recurrence of hepatitis B virus-associated HCC following liver transplantation and a potential therapeutic target.