The clinical significance of activated p-AKT expression in peripheral T-cell lymphoma.

BACKGROUND: The oncogenic PI3K/serine-threonine kinase (PI3K/AKT) pathway is a downstream pathway of B-cell receptor (BCR) signaling pathway and plays a crucial role in the pathogenesis of B-cell lymphoma. However, there have been preclinical data showing PI3K/AKT pathway activation in T-cell lymphoma, with in different mechanisms from those in B-cell lymphoma. In this study, we investigated the impact of p-AKT expression on clinical outcomes of peripheral T-cell lymphoma (PTCL). MATERIALS AND METHODS: We analyzed 63 patients with PTCL [PTCL-not otherwise specified (NOS), angioimmunoblastic T-cell lymphoma (AITL), anaplastic large cell lymphoma (ALCL) or extranodal natural kiler T-cell lymphoma (NKTCL)]. To define the clinical implications of p-AKT expression in PTCL, we calculated arbitrary units (AUs) by multiplying the intensity and the proportion of p-AKT expression. RESULTS: Based on a cutoff value of the upper limit of the third quartile (Q3) of the AU, 12 patients were classified into the high p-AKT group, while the remaining 51 patients were classified into the low p-AKT group. The overall response rate to frontline chemotherapy was significantly lower in the high p-AKT group than in the low p-AKT group (20.0% vs. 71.1%, p=0.004). The high p-AKT group showed substantially worse overall survival (OS) (median OS=2.3 vs. 25.2 months, p<0.001) and progression-free survival (PFS) (median PFS=1.6 vs. 8.8 months, p<0.001) compared with the low p-AKT group. Multivariate analysis showed that high p-AKT expression remained a significant independent poor prognostic factor for OS (hazard ratio (HR)=7.0; 95% confidence interval (CI)=3.0-16.6; p<0.001) and PFS (HR=6.8; 95% CI=3.0-15.2; p<0.001). CONCLUSION: PTCL patients with high p-AKT expression showed aggressive clinical courses with significantly worse OS and PFS and a poor chemotherapy response rate. We suggest that targeting the PI3K/AKT pathway may be a promising therapeutic strategy for PTCL.

Integrated copy number and gene expression profiling analysis of Epstein-Barr virus-positive diffuse large B-cell lymphoma.

Viral oncogenes and host immunosenescence have been suggested as causes of Epstein-Barr virus-positive diffuse large B-cell lymphoma (EBV + DLBCL) of the elderly. To investigate the molecular genetic basis of immune evasion and tumor outgrowth, we analyzed copy number alterations (CNAs) and gene expression profiles in EBV + DLBCL samples compared with EBV - DLBCL. There were relatively few genomic alterations in EBV + DLBCL compared with those detected in EBV-negative DLBCL. The most frequent CNAs (>30%) in EBV + DLBCLs were gains at 1q23.2-23.3, 1q23.3, 1q32.1, 5p15.3, 8q22.3, 8q24.1-24.2, and 9p24.1; losses at 6q27, 7q11.2, and 7q36.2-36.3 were also recurrent. A gene expression profile analysis identified the host immune response as a key molecular signature in EBV + DLBCL. Antiviral response genes, proinflammatory cytokines, and chemokines associated with the innate immune response were overexpressed, indicating the presence of a virusinduced inflammatory microenvironment. Genes associated with the B-cell receptor signaling pathway were downregulated. An integrated analysis indicated that SLAMF1 and PDL2 were key targets of the gains detected at 1q23.2-23.3 and 9p24.1. The chromosomal gain at 9p24.1 was associated with poor overall survival. Taken together, our results led to the identification of recurrent copy number alterations and distinct gene expression associated with the host immune response in EBV + DLBCL. We suggest that the upregulation of PDL2 on 9p24.1 promotes immune evasion and is associated with poor prognosis in EBV + DLBCL.

Loss of E-cadherin activates EGFR-MEK/ERK signaling, which promotes invasion via the ZEB1/MMP2 axis in non-small cell lung cancer.

Loss of E-cadherin, a hallmark of epithelial-mesenchymal transition (EMT), can significantly affect metastatic dissemination. However, the molecular mechanism of EMT-associated metastatic dissemination by loss of E-cadherin still remains unclear in non-small cell lung cancers (NSCLCs). In the present study, we show that the knockdown of E-cadherin was sufficient to convert A549 NSCLC cells into mesenchymal type with the concurrent up-regulation of typical EMT inducers such as ZEB1 and TWIST1. Interestingly, the EMT-induced cells by E-cadherin depletion facilitate invasion in a matrix metalloproteinase-2 (MMP2)-dependent manner with aberrant activation of EGFR signaling. We demonstrated that the elevated invasiveness was a result of the activated EGFR-MEK/ERK signaling, which in turn leads to ZEB1 dependent MMP2 induction. These results suggest that the EGFR-MEK/ERK/ZEB1/MMP2 axis is responsible for promoted invasion in EMT-induced NSCLCs. Consistently, ERK activation and loss of E-cadherin were both observed in the disseminating cancer cells at the invasive tumor fronts in NSCLS cancer tissues. Thereby, these data suggest that the EGFR-MEK/ERK signaling would be a promising molecular target to control aberrant MMP2 expression and consequent invasion in the EMT-induced NSCSLs.

ERCC1 as a biomarker for bladder cancer patients likely to benefit from adjuvant chemotherapy.

BACKGROUND: The role of adjuvant chemotherapy and the value of molecular biomarkers in bladder cancer have not been determined. We aimed to assess the predictive and prognostic values of excision repair cross-complementation 1 (ERCC1) in identifying appropriate patients who may potentially benefit from adjuvant chemotherapy for bladder cancer. METHODS: A retrospective analysis was performed on 93 patients with completely resected transitional cell carcinoma of the bladder. ERCC1 expression was assessed by immunohistochemistry. ERCC1 expression was analyzed in 57 patients treated with adjuvant gemcitabine plus cisplatin chemotherapy and 36 who were not treated. RESULTS: Among 93 patients, ERCC1 expression was positive in 54 (58.1%) and negative in 39 (41.9%). ERCC1 positivity was significantly associated with longer survival (adjusted hazard ratio for death, 0.12, 95% confidence interval [CI] 0.014-0.99; P = 0.049) in the group without adjuvant chemotherapy while ERCC1 positivity was associated with shorter survival among patients who have received adjuvant chemotherapy (adjusted hazard ratio for death, 2.64; 95% CI 1.01-6.85; P = 0.047). Therefore, clinical benefit from adjuvant chemotherapy was associated with ERCC1 negativity as measured by overall survival (test for interaction, P = 0.034) and by disease-free survival (test for interaction, P = 0.20). CONCLUSIONS: Among patients with completely resected transitional cell carcinoma of the bladder, those with ERCC1-negative tumors seemed to benefit more from adjuvant gemcitabine plus cisplatin chemotherapy than those with ERCC1-positive tumors. Future prospective, randomized studies are warranted to confirm our findings.

Prediction of postoperative recurrence-free survival in non-small cell lung cancer by using an internationally validated gene expression model.

PURPOSE: This study was performed to discover prognostic genomic markers associated with postoperative outcome of stage I to III non-small cell lung cancer (NSCLC) that are reproducible between geographically distant and demographically distinct patient populations. EXPERIMENTAL DESIGN: American patients (n = 27) were stratified on the basis of recurrence and microarray profiling of their tumors was performed to derive a training set of 44 genes. A larger Korean patient validation cohort (n = 138) was also stratified by recurrence and screened for these genes. Four reproducible genes were identified and used to construct genomic and clinicogenomic Cox models for both cohorts. RESULTS: Four genomic markers, DBN1 (drebrin 1), CACNB3 (calcium channel beta 3), FLAD1 (PP591; flavin adenine dinucleotide synthetase), and CCND2 (cyclin D2), exhibited highly significant differential expression in recurrent tumors in the training set (P < 0.001). In the validation set, DBN1, FLAD1 (PP591), and CACNB3 were significant by Cox univariate analysis (P ≤ 0.035), whereas only DBN1 was significant by multivariate analysis. Genomic and clinicogenomic models for recurrence-free survival (RFS) were equally effective for risk stratification of stage I to II or I to III patients (all models P < 0.0001). For stage I to II or I to III patients, 5-year RFS of the low- and high-risk patients was approximately 70% versus 30% for both models. The genomic model for overall survival of stage I to III patients was improved by addition of pT and pN stage (P < 0.0013 vs. 0.010). CONCLUSION: A 4-gene prognostic model incorporating the multivariate marker DBN1 exhibits potential clinical utility for risk stratification of stage I to III NSCLC patients.

Predicting recurrence using the clinical factors of patients with non-small cell lung cancer after curative resection.

We present a recurrence prediction model using multiple clinical parameters in patients surgically treated for non-small cell lung cancer. Among 1,578 lung cancer patients who underwent complete resection, we compared the early-recurrence group with the 3-yr non-recurrence group for evaluating those factors that influence early recurrence within one year after surgery. Adenocarcinoma and squamous cell carcinoma were analyzed independently. We used multiple logistic regression analysis to identify the independent clinical predictors of recurrence and Cox's proportional hazard regression method to develop a clinical prediction model. We randomly divided our patients into the training and test subsets. The pathologic stages, tumor cell type, differentiation of tumor, neoadjuvant therapy and age were significant factors on the multivariable analysis. We constructed the model for the training set with adenocarcinoma (n=236) and squamous cell carcinoma (n=305), and we applied it to the test set with adenocarcinoma (n=110) and squamous cell carcinoma (n=154). It was predictive for the in adenocarcinoma (P<0.001) and the squamous cell carcinoma (P=0.037), respectively. Our results showed that our recurrence prediction model based on the clinical parameters could significantly predict the individual patients who were at high risk or low risk for recurrence.

Stage III thymic epithelial neoplasms are not homogeneous with regard to clinical, pathological, and prognostic features.

INTRODUCTION: The main therapeutic approach to a Masaoka stage III thymic epithelial neoplasm (TEN) is surgical resection, and the 5-year survival rate is approximately 60%. According to the Masaoka staging system, invasion of neighboring organs is classified as stage III disease, regardless of the number of organs involved or the size of the tumor. We retrospectively analyzed the prognostic significance associated with the extent of disease in patients with Masaoka stage III TENs. METHODS: From 1995 to 2006, 241 patients were identified with thymomas. Among these patients, 59 were diagnosed with Masaoka stage III disease. The patients with a stage III TEN were advised to have extended thymectomy with en bloc resection of the invaded structures as the initial treatment. The prognostic significance of the size, organs invaded, and other factors were analyzed. RESULTS: The overall survival rates for the stage III patients were 83% and 64%, and the recurrence-free survival rates were 56% and 51%, at 5 and 8 years, respectively. Patients with a low-grade World Health Organization classification (p = 0.0202) or a complete resection (p < 0.0001) had a better overall survival. In addition, patients with tumors less than 6.5 cm (p = 0.0311) or with pericardium invasion (p = 0.0299) had a better recurrence-free survival. The patients with limited disease had a better prognosis for a recurrence-free survival than did patients with extensive disease (p = 0.0007). CONCLUSIONS: Heterogeneous prognostic subgroups based on tumor size and organs invaded were identified in patients with Masaoka stage III TENs. Therapeutic plans, based on these subgroups, will potentially improve patient management and treatment outcomes.

Prediction of recurrence-free survival in postoperative non-small cell lung cancer patients by using an integrated model of clinical information and gene expression.

PURPOSE: One of the main challenges of lung cancer research is identifying patients at high risk for recurrence after surgical resection. Simple, accurate, and reproducible methods of evaluating individual risks of recurrence are needed. EXPERIMENTAL DESIGN: Based on a combined analysis of time-to-recurrence data, censoring information, and microarray data from a set of 138 patients, we selected statistically significant genes thought to be predictive of disease recurrence. The number of genes was further reduced by eliminating those whose expression levels were not reproducible by real-time quantitative PCR. Within these variables, a recurrence prediction model was constructed using Cox proportional hazard regression and validated via two independent cohorts (n = 56 and n = 59). RESULTS: After performing a log-rank test of the microarray data and successively selecting genes based on real-time quantitative PCR analysis, the most significant 18 genes had P values of <0.05. After subsequent stepwise variable selection based on gene expression information and clinical variables, the recurrence prediction model consisted of six genes (CALB1, MMP7, SLC1A7, GSTA1, CCL19, and IFI44). Two pathologic variables, pStage and cellular differentiation, were developed. Validation by two independent cohorts confirmed that the proposed model is significantly accurate (P = 0.0314 and 0.0305, respectively). The predicted median recurrence-free survival times for each patient correlated well with the actual data. CONCLUSIONS: We have developed an accurate, technically simple, and reproducible method for predicting individual recurrence risks. This model would potentially be useful in developing customized strategies for managing lung cancer.

Clinical validity of the lung cancer biomarkers identified by bioinformatics analysis of public expression data.

Identification of molecular markers often leads to important clinical applications such as early diagnosis, prognosis, and drug targeting. Lung cancer, the leading cause of cancer-related deaths, still lacks reliable molecular markers. We have combined the bioinformatics analysis of the public gene expression data and clinical validation to identify biomarker genes for non-small-cell lung cancer. The serial analysis of gene expression and the expressed sequence tag data were meta-analyzed to produce a list of the differentially expressed genes in lung cancer. Through careful inspection of the predicted genes, we selected 20 genes for experimental validation using semiquantitative reverse transcriptase-PCR. The microdissected clinical specimens used in the study consisted of three groups: lung tissues from benign diseases and the paired (cancer and pathologic normal) tissues from non-small-cell lung cancer patients. After extensive statistical analyses, seven genes (CBLC, CYP24A1, ALDH3A1, AKR1B10, S100P, PLUNC, and LOC147166) were identified as potential diagnostic markers. Quantitative real-time PCR was carried out to additionally assess the value of the seven identified genes leading to the confirmation of at least two genes (CBLC and CYP24A1) as highly probable novel biomarkers. The gene properties of the identified markers, especially their relationship to lung cancer and cell signaling pathway regulation, further suggest their potential value as drug targets as well.