Signature Node Research Articles

PD41-08 CAN BLADDER CANCER GENE EXPRESSION SIGNATURES BE USED TO PREDICT LYMPH NODE METASTASIS AT THE TIME OF RADICAL CYSTECTOMY?

INTRODUCTION AND OBJECTIVES: Despite optimal imaging, occult lymph node (LN) metastases are found in 25% of patients with muscle invasive bladder cancer (MIBC) undergoing radical cystectomy (RC). These patients are at high risk for subsequent disease recurrence and death. Better prediction of LN metastases is warranted to improve patient management. METHODS: Transcriptome expression profiles of 199 RC samples were generated using a 1.4 million feature Human Exon microarray. All patients underwent RC and extended pelvic LN dissection (1998-2004) at the University of Southern California. The patient cohort was divided into a discovery set (n1⁄4133) and a validation set (n1⁄466). In the discovery set, features were identified using a Wilcoxon test and modeled into a K-nearest neighbor (KNN) classifier for LN metastases prediction. Two additional gene signatures, the 15 gene cancer recurrence signature (Mitra2014) and 20 gene LN signature (Smith2011) were also modeled in the discovery set for comparison. Area under the curve (AUC) and odds ratios (OR) were used to compare the performance of these signatures in the validation set. RESULTS: The KNN51 model was developed from 133 radical cystectomy patients to predict LN metastases. In the validation set, this model achieved an AUC of 0.82 [0.71 e 0.93] for predicting LN positive patients, significantly outperforming Mitra2014 and Smith2011 which had AUCs of 0.62 [0.47 e 0.76] and 0.46 [0.32 e 0.60], respectively. Only KNN51hadsignificantodds for predictingLNmetastasiswith anORof2.65 [1.68 4.67] for every 10% increase in score (p1⁄4 0.0002). Both Mitra2011 and Smith2011 were not found to have significant odds ratios of 1.21 [0.97 e 1.54, p 1⁄4 0.09] and 1.39 [0.52 e 3.77, p 1⁄4 0.5], respectively. CONCLUSIONS: The integrated expression of 51 genes in primary MIBC was successfully used to predict LN metastases and was superior to previously described gene signatures. If validated in TURBT samples, KNN51 could be used to guide the extent of pelvic LN dissection and to direct high risk patients towards treatment intensification, for example with neoadjuvant chemotherapy.

Four-protein signature accurately predicts lymph node metastasis and survival in oral squamous cell carcinoma

The presence of lymph node (LN) metastasis significantly affects the survival of patients with oral squamous cell carcinoma (OSCC). Successful detection and removal of positive LNs are crucial in the treatment of this disease. Current evaluation methods still have their limitations in detecting the presence of tumor cells in the LNs, where up to a third of clinically diagnosed metastasis-negative (N0) patients actually have metastasis-positive LNs in the neck. We developed a molecular signature in the primary tumor that could predict LN metastasis in OSCC. A total of 211 cores from 55 individuals were included in the study. Eleven proteins were evaluated using immunohistochemical analysis in a tissue microarray. Of the 11 biomarkers evaluated using receiver operating curve analysis, epidermal growth factor receptor (EGFR), v-erb-b2 erythroblastic leukemia viral oncogene homolog 2 (HER-2/neu), laminin, gamma 2 (LAMC2), and ras homolog family member C (RHOC) were found to be significantly associated with the presence of LN metastasis. Unsupervised hierarchical clustering-demonstrated expression patterns of these 4 proteins could be used to differentiate specimens that have positive LN metastasis from those that are negative for LN metastasis. Collectively, EGFR, HER-2/neu, LAMC2, and RHOC have a specificity of 87.5% and a sensitivity of 70%, with a prognostic accuracy of 83.4% for LN metastasis. We also demonstrated that the LN signature could independently predict disease-specific survival (P = .036). The 4-protein LN signature validated in an independent set of samples strongly suggests that it could reliably distinguish patients with LN metastasis from those who were metastasis-free and therefore could be a prognostic tool for the management of patients with OSCC.

A Threshold Signature Scheme Based on TPM

For the traditional threshold signature mechanism does not considers whether the nodes which generate part signature are trusted and the traditional signature strategy doesn’t do well in resisting internal attacks and external attacks and collusion attacks, so this paper presents a new threshold signature based on Trusted Platform Module (TPM), based on TPM the signature node first should finish the trust proof between it an other members who take part in the signature. Using a no-trusted center and the threshold of the signature policy, this strategy can track active attacks of the key management center and can prevent framing the key management center, this strategy takes into account the limited computing power TPM and has parameters of simple, beneficial full using of the limited computing power TPM.

Four-protein signature accurately predicts lymph node metastasis and survival in oral squamous cell carcinoma

Abstract The presence of lymph node metastasis significantly affects the survival of oral squamous cell carcinoma patients and successful detection and removal of positive lymph nodes is crucial in the treatment of this disease. Current evaluation methods still have their limitations in detecting the presence of tumor cells in the lymph nodes, where up to a third of clinically diagnosed metastasis-negative (N0) patients actually have metastasis—positive LNs in the neck. We aimed to develop a molecular signature in the primary tumor that could predict lymph node (LN) metastasis in oral squamous cell carcinoma. The expression levels of 11 proteins was evaluated using immunohistochemical analysis in a tissue microarray (TMA) consisting of 110 specimens from 32 individuals. We used receiver operating characteristic (ROC) curve to identify proteins that could significantly differentiate patients with LN metastasis from those that did not. Unsupervised hierarchical clustering analysis was used to verify the ability of chosen biomarkers in segregating LN positive patients from those with no LN involvement. Kaplan-Meier survival curve and log rank test was utilized to determine the association between LN metastasis and biomarker expression with disease-specific survival. Of the 11 biomarkers, EGFR, HER-2/neu, LAMC2 and RHOC were found to be significantly associated with LN metastasis. Unsupervised hierarchical clustering demonstrated that expression patterns of these 4 proteins could be used to differentiate the positive LN metastasis specimens from specimens that are negative. Collectively, EGFR, HER-2/neu, LAMC2 and RHOC have a specificity of 87.5% and sensitivity of 70% with a prognostic accuracy of 83.4% for LN metastasis. We also demonstrated the LN signature could independently predict disease-specific survival (p = 0.023). In summary, we developed a 4-protein LN signature that could reliably distinguish patients with lymph node metastasis from those who were metastasis free. In addition, this LN signature is also associated with disease-specific survival indicating that would be a useful prognostic tool for the management of oral cancer patients.