Abstract 2861: Independent validation of a prognostic gene-signature based risk score in formalin-fixed paraffin-embedded melanomas

2014 ◽  
Author(s):  
Georg Brunner ◽  
Achim Heinecke ◽  
Ludwig Suter ◽  
Norbert Blödorn-Schlicht ◽  
Hans-Joachim Schulze ◽  
...  
Keyword(s):  
Risk Score ◽  
Gene Signature ◽  
Independent Validation ◽  
Prognostic Gene ◽  
Prognostic Gene Signature ◽  
Formalin Fixed Paraffin ◽  
Formalin Fixed Paraffin Embedded ◽  
Formalin Fixed

Development and Independent Validation of a Prognostic Assay for Stage II Colon Cancer Using Formalin-Fixed Paraffin-Embedded Tissue

2011 ◽  
Vol 29 (35) ◽  
pp. 4620-4626 ◽  
Author(s):  
Richard D. Kennedy ◽  
Max Bylesjo ◽  
Peter Kerr ◽  
Timothy Davison ◽  
Julie M. Black ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Gene Signature ◽  
Stage Ii ◽  
Independent Validation ◽  
Formalin Fixed Paraffin ◽  
Formalin Fixed Paraffin Embedded ◽  
Risk Patients ◽  
Stage Ii Colon Cancer ◽  
Formalin Fixed

PurposeCurrent prognostic factors are poor at identifying patients at risk of disease recurrence after surgery for stage II colon cancer. Here we describe a DNA microarray–based prognostic assay using clinically relevant formalin-fixed paraffin-embedded (FFPE) samples.Patients and MethodsA gene signature was developed from a balanced set of 73 patients with recurrent disease (high risk) and 142 patients with no recurrence (low risk) within 5 years of surgery.ResultsThe 634–probe set signature identified high-risk patients with a hazard ratio (HR) of 2.62 (P < .001) during cross validation of the training set. In an independent validation set of 144 samples, the signature identified high-risk patients with an HR of 2.53 (P < .001) for recurrence and an HR of 2.21 (P = .0084) for cancer-related death. Additionally, the signature was shown to perform independently from known prognostic factors (P < .001).ConclusionThis gene signature represents a novel prognostic biomarker for patients with stage II colon cancer that can be applied to FFPE tumor samples.

Analytical validation of a melanoma diagnostic gene signature using formalin-fixed paraffin-embedded melanocytic lesions

2015 ◽  
Vol 9 (5) ◽  
pp. 407-416 ◽  
Author(s):  
M Bryan Warf ◽  
Darl D Flake ◽  
Doug Adams ◽  
Alexander Gutin ◽  
Kathryn A Kolquist ◽  
...  
Keyword(s):  
Gene Signature ◽  
Analytical Validation ◽  
Melanocytic Lesions ◽  
Formalin Fixed Paraffin ◽  
Formalin Fixed Paraffin Embedded ◽  
Formalin Fixed

scholarly journals Independent Validation of EarlyR Gene Signature in BIG 1-98: A Randomized, Double-Blind, Phase III Trial Comparing Letrozole and Tamoxifen as Adjuvant Endocrine Therapy for Postmenopausal Women With Hormone Receptor–Positive, Early Breast Cancer

2019 ◽  
Vol 3 (4) ◽  
Author(s):  
Steven A Buechler ◽  
Kathryn P Gray ◽  
Yesim Gökmen-Polar ◽  
Scooter Willis ◽  
Beat Thürlimann ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Confidence Interval ◽  
Gene Signature ◽  
Distant Recurrence ◽  
Free Interval ◽  
Formalin Fixed Paraffin ◽  
Formalin Fixed Paraffin Embedded ◽  
Risk Patients ◽  
Formalin Fixed

Abstract Background EarlyR gene signature in estrogen receptor–positive (ER+) breast cancer is computed from the expression values of ESPL1, SPAG5, MKI67, PLK1, and PGR. EarlyR has been validated in multiple cohorts profiled using microarrays. This study sought to verify the prognostic features of EarlyR in a case-cohort sample from BIG 1–98, a randomized clinical trial of ER+ postmenopausal breast cancer patients treated with adjuvant endocrine therapy (letrozole or tamoxifen). Methods Expression of EarlyR gene signature was estimated by Illumina cDNA-mediated Annealing, Selection, and Ligation assay of RNA from formalin-fixed, paraffin-embedded primary breast cancer tissues in a case-cohort subset of ER+ women (N = 1174; 216 cases of recurrence within 8 years) from BIG 1–98. EarlyR score and prespecified risk strata (≤25 = low, 26–75 = intermediate, &gt;75 = high) were “blindly” computed. Analysis endpoints included distant recurrence–free interval and breast cancer–free interval at 8 years after randomization. Hazard ratios (HRs) and test statistics were estimated with weighted analysis methods. Results The distribution of the EarlyR risk groups was 67% low, 19% intermediate, and 14% high risk in this ER+ cohort. EarlyR was prognostic for distant recurrence–free interval; EarlyR high-risk patients had statistically increased risk of distant recurrence within 8 years (HR = 1.73, 95% confidence interval = 1.14 to 2.64) compared with EarlyR low-risk patients. EarlyR was also prognostic of breast cancer–free interval (HR = 1.74, 95% confidence interval = 1.21 to 2.62). Conclusions This study confirmed the prognostic significance of EarlyR using RNA from formalin-fixed, paraffin-embedded tissues from a case-cohort sample of BIG 1–98. EarlyR identifies a set of high-risk patients with relatively poor prognosis who may be considered for additional treatment. Further studies will focus on analyzing the predictive value of EarlyR signature.

scholarly journals Construction and Validation of a Prognostic Gene-Based Model for Overall Survival Prediction in Hepatocellular Carcinoma Using an Integrated Statistical and Bioinformatic Approach

2021 ◽  
Vol 22 (4) ◽  
pp. 1632
Author(s):  
Eskezeia Yihunie Dessie ◽  
Siang-Jyun Tu ◽  
Hui-Shan Chiang ◽  
Jeffrey J.P. Tsai ◽  
Ya-Sian Chang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Risk Score ◽  
Prognostic Model ◽  
Gene Signature ◽  
Adaptive Lasso ◽  
Functional Enrichment ◽  
Survival Prediction ◽  
Prognostic Gene ◽  
Prognostic Gene Signature

Hepatocellular carcinoma (HCC) is one of the most common lethal cancers worldwide and is often related to late diagnosis and poor survival outcome. More evidence is demonstrating that gene-based prognostic models can be used to predict high-risk HCC patients. Therefore, our study aimed to construct a novel prognostic model for predicting the prognosis of HCC patients. We used multivariate Cox regression model with three hybrid penalties approach including least absolute shrinkage and selection operator (Lasso), adaptive lasso and elastic net algorithms for informative prognostic-related genes selection. Then, the best subset regression was used to identify the best prognostic gene signature. The prognostic gene-based risk score was constructed using the Cox coefficient of the prognostic gene signature. The model was evaluated by Kaplan–Meier (KM) and receiver operating characteristic curve (ROC) analyses. A novel four-gene signature associated with prognosis was identified and the risk score was constructed based on the four-gene signature. The risk score efficiently distinguished the patients into a high-risk group with poor prognosis. The time-dependent ROC analysis revealed that the risk model had a good performance with an area under the curve (AUC) of 0.780, 0.732, 0.733 in 1-, 2- and 3-year prognosis prediction in The Cancer Genome Atlas (TCGA) dataset. Moreover, the risk score revealed a high diagnostic performance to classify HCC from normal samples. The prognosis and diagnosis prediction performances of risk scores were verified in external validation datasets. Functional enrichment analysis of the four-gene signature and its co-expressed genes involved in the metabolic and cell cycle pathways was constructed. Overall, we developed a novel-gene-based prognostic model to predict high-risk HCC patients and we hope that our findings can provide promising insight to explore the role of the four-gene signature in HCC patients and aid risk classification.

scholarly journals Transcriptome analysis reveals the prognostic and immune infiltration characteristics of glycolysis and hypoxia in head and neck squamous cell carcinoma

2021 ◽  
Author(s):  
Jun Liu ◽  
Jianjun Lu ◽  
Wenli Li
Keyword(s):  
Squamous Cell Carcinoma ◽  
Risk Score ◽  
Predictive Ability ◽  
Gene Signature ◽  
Risk Scores ◽  
Combinatorial Screening ◽  
Immune Infiltration ◽  
Cancer Hallmarks ◽  
Prognostic Gene ◽  
Prognostic Gene Signature

Abstract Background This study aims to construct a new prognostic gene signature based on cancer hallmarks for patients with Head and neck squamous cell carcinoma (HNSCC). Method The transcriptome profiling data and hallmark gene sets in the Molecular Signatures Database was used to explore the cancer hallmarks most relevant to the prognosis of HNSCC patients. Differential gene expression analysis, weighted gene co-expression network analysis, univariate COX regression analysis, random forest algorithm and multiple combinatorial screening were used to construct the prognostic gene signature. The predictive ability of gene signature was verified in the TCGA HNSCC cohort as the training set and the GEO HNSCC cohorts (GSE41613 and GSE42743) as the validation sets, respectively. Moreover, the correlations between risk scores and immune infiltration patterns, as well as risk scores and genomic changes were explored. Results A total of 3391 differentially expressed genes in HNSCC were screened. Glycolysis and hypoxia were screened as the main risk factors for OS in HNSCC. Using univariate Cox analysis, 97 prognostic candidates were identified (P<0.05). Top 10 important genes were then screened out by random forest. Using multiple combinatorial screening, a combination with less genes and more significant P value was used to construct the prognostic gene signature (RNF144A, STC1, P4HA1, FMNL3, ANO1, BASP1, MME, PLEKHG2 and DKK1). Kaplan-Meier analysis showed that patients with higher risk scores had worse overall survival (p <0.001). The ROC curve showed that the risk score had a good predictive efficiency (AUC> 0.66). Subsequently, the predictive ability of the risk score was verified in the validation sets. Moreover, the two-factor survival analysis combining the cancer hallmarks and risk scores suggested that HNSCC patients with the high hypoxia or glycolysis & high risk-score showed the worst prognosis. Besides, a nomogram based on the nine-gene signature was established for clinical practice. Furthermore, the risk score was significantly related to tumor immune infiltration profiles and genome changes. Conclusion This nine-gene signature associated with glycolysis and hypoxia can not only be used for prognosis prediction and risk stratification, but also may be a potential therapeutic target for patients with HNSCC.

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