scholarly journals Identification of stage I/IIA melanoma patients at high risk for disease relapse using a clinicopathologic and gene expression model

2020 ◽  
Vol 140 ◽  
pp. 11-18
Author(s):  
Alexander M.M. Eggermont ◽  
Domenico Bellomo ◽  
Suzette M. Arias-Mejias ◽  
Enrica Quattrocchi ◽  
Sindhuja Sominidi-Damodaran ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Stage I ◽  
Disease Relapse ◽  
Expression Model ◽  
Melanoma Patients ◽  
Gene Expression Model

Identification of stage IIA melanoma patients at high risk for disease relapse using a clinicopathologic and gene expression model.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e22088-e22088
Author(s):  
Renske Wever ◽  
Félicia Tjien-Fooh ◽  
Domenico Bellomo ◽  
Enrica Quattrocchi ◽  
Sindhuja Sominidi Damodaran ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Adjuvant Therapy ◽  
Operating Point ◽  
Disease Relapse ◽  
High Risk Patients ◽  
Risk Patients ◽  
Expression Model ◽  
Melanoma Patients ◽  
Gene Expression Model

e22088 Background: In recent years, adjuvant therapy trials in stage III melanoma have been successful and trials have started with the inclusion of stage IIB/C patients. However, stage IIA melanoma patients are currently not eligible for adjuvant therapy, even though a large part of all melanoma related deaths occur in this patient group. Therefore, a strong clinical need has emerged for diagnostic tools that can identify high-risk patients who currently have no access to adjuvant therapy. Here, we sought to assess the ability of a recently introduced clinicopathologic gene expression model (CP-GEP) (Bellomo et al., JCO Precis Oncol. 2020: in press) to select stage IIA patients at high risk for disease relapse, upon design of a stage-specific operating point. Methods: We assessed the prognostic performance of the CP-GEP model in all 141 stage IIA patients from a Mayo Clinic cohort of 837 consecutive melanoma patients who had a sentinel lymph node biopsy (SLNb) performed within 90 days of their diagnosis. The CP-GEP model combines Breslow thickness and patient age, with the expression of 8 genes in the primary tumor. Moreover, it stratifies patients according to their risk of relapse: CP-GEP High Risk or CP-GEP Low Risk, based on an operating point that was specifically developed for stage IIA. This stage-specific operating point was selected to fulfill the following criteria: hazard ratio RFS > 2 with a p-value < 0.05, and risk groups of similar size. The main clinical endpoint was five-year relapse free survival (RFS). Results: The CP-GEP High Risk group corresponds to 45% (63/141) of all stage IIA patients and captures 62% (18/29) of the total relapses in this substage. Moreover, CP-GEP High Risk patients relapse more frequently than CP-GEP Low Risk patients (RFS of 56% versus 78%; HR, 2.23; P < 0.05). The prognosis for stage IIA CP-GEP High Risk patients in our cohort is worse than for stage IIC/IIIA patients with reported RFS ranging from 63% to 77%. Conclusions: The CP-GEP model can be optimized by designing a stage-specific operating point, to identify a subset of stage IIA patients with an increased risk for disease relapse, not very different from IIC/IIIA patients. Therefore, stage IIA CP-GEP High Risk patients may be considered for inclusion in adjuvant trials. Independent validation studies are ongoing for the newly developed operating point. [Table: see text]

Using a clinicopathologic and gene expression model to identify melanoma patients at high risk for disease relapse.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 10068-10068
Author(s):  
Alexander M. Eggermont ◽  
Domenico Bellomo ◽  
Félicia Tjien-Fooh ◽  
Renske Wever ◽  
Enrica Quattrocchi ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Early Stage ◽  
Low Risk ◽  
Stage I ◽  
Diagnostic Tools ◽  
Disease Relapse ◽  
High Risk Patients ◽  
Risk Patients ◽  
Melanoma Patients

10068 Background: The identification of early stage melanoma patients at high risk for relapse is still difficult. Roughly 50% of melanoma deaths occur in patients who were initially diagnosed with nonmetastatic melanoma. Therefore, a strong clinical need has emerged for diagnostic tools that can identify melanoma patients at high risk for relapse. Here, we assessed the performance of a recently developed model (Bellomo et al., JCO Precis Oncol. 2020: in press), combining clinicopathologic and gene expression variables (CP-GEP), in identifying melanoma patients that have a high risk for disease relapse. Methods: We assessed the prognostic performance of the CP-GEP model in a cohort of 837 consecutive melanoma patients from Mayo Clinic who had a sentinel lymph node biopsy (SLNb) performed within 90 days of their diagnosis. The CP-GEP model combines Breslow thickness and patient age, with the expression of 8 genes in the primary tumor, to stratify patients according to their risk of relapse: CP-GEP High Risk or CP-GEP Low Risk. The main clinical endpoint of this study was five-year relapse free survival (RFS). Results: Patients were stratified based on SLNb status and CP-GEP classification. 76% of the patients were SLNb negative and had an RFS of 79% versus 52% for SLNb positive patients; HR, 3.21; P < 0.0001. 60% of the patients were identified as CP-GEP High Risk and had an RFS of 62% versus 87% for CP-GEP Low Risk patients; HR, 4.12; P < 0.0001. Within the SLNb negative group (637 patients of which 65% stage I), 51% of patients were classified as CP-GEP High Risk. Here, RFS was 70% for CP-GEP High Risk patients versus 89% for CP-GEP Low Risk patients; HR, 3.61; P < 0.0001. The prognosis of these CP-GEP High Risk patients is similar to stage IIC/IIIA patients with reported RFS ranging from 63% to 77%. This confirms the heterogeneity in prognosis among patients with stage I/II melanoma disease. Conclusions: The CP-GEP model can be successfully used to stratify patients based on their risk for relapse. In particular, it can be used to identify SLNb negative patients with a high risk for disease relapse who may benefit from therapeutic interventions. Independent validation studies are ongoing to validate the CP-GEP model in various patient populations. [Table: see text]

TWO DIFFERENT APPROACHES FOR GENE EXPRESSION MODEL CONTROL

BIOMAT 2011 ◽  
2012 ◽  
pp. 153-177
Author(s):  
N. A. BARBOSA ◽  
H DÍAZ ◽  
A. RAMIREZ
Keyword(s):  
Gene Expression ◽  
Model Control ◽  
Expression Model ◽  
Gene Expression Model

scholarly journals Evaluation of Genotype-Based Gene Expression Model Performance: A Cross-Framework and Cross-Dataset Study

Genes ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1531
Author(s):  
Vânia Tavares ◽  
Joana Monteiro ◽  
Evangelos Vassos ◽  
Jonathan Coleman ◽  
Diana Prata
Keyword(s):  
Gene Expression ◽  
Frontal Cortex ◽  
Model Performance ◽  
Variation Method ◽  
Expression Model ◽  
The Brain ◽  
Gene Expression Model ◽  
And Training ◽  
Predict Gene Expression ◽  
Fold Cross Validation

Predicting gene expression from genotyped data is valuable for studying inaccessible tissues such as the brain. Herein we present eGenScore, a polygenic/poly-variation method, and compare it with PrediXcan, a method based on regularized linear regression using elastic nets. While both methods have the same purpose of predicting gene expression based on genotype, they carry important methodological differences. We compared the performance of expression quantitative trait loci (eQTL) models to predict gene expression in the frontal cortex, comparing across these frameworks (eGenScore vs. PrediXcan) and training datasets (BrainEAC, which is brain-specific, vs. GTEx, which has data across multiple tissues). In addition to internal five-fold cross-validation, we externally validated the gene expression models using the CommonMind Consortium database. Our results showed that (1) PrediXcan outperforms eGenScore regardless of the training database used; and (2) when using PrediXcan, the performance of the eQTL models in frontal cortex is higher when trained with GTEx than with BrainEAC.

A Multidimensional Gene Expression Model that Accurately Predicts Tumor Response to Pembrolizumab or Nivolumab

2020 ◽  
Vol 106 (5) ◽  
pp. 1132-1133
Author(s):  
D. Adkins ◽  
J. Ley ◽  
N. LaFranzo ◽  
J. Hiken ◽  
I. Schillebeeckx ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Response ◽  
Expression Model ◽  
Gene Expression Model

scholarly journals The ATRA-21 gene-expression model predicts retinoid sensitivity in CEBPA double mutant, t(8;21) and inv(16) AML patients

2019 ◽  
Vol 9 (10) ◽  
Author(s):  
Marco Bolis ◽  
Mineko Terao ◽  
Linda Pattini ◽  
Enrico Garattini ◽  
Maddalena Fratelli
Keyword(s):  
Gene Expression ◽  
Double Mutant ◽  
Expression Model ◽  
Gene Expression Model

Analysis of molecular profiling of renal cell carcinoma: Identification of a 4-microRNA signature as a prognostic value in patients with stage I-II disease.

2013 ◽  
Vol 31 (6_suppl) ◽  
pp. 395-395 ◽  
Author(s):  
Daniel E. Castellano ◽  
Carlos A. Farfán ◽  
Gamez Angelo ◽  
Juan M. Sepúlveda ◽  
Guillermo De Velasco ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
High Risk ◽  
Cell Carcinoma ◽  
Mirna Expression ◽  
Renal Cell ◽  
Recurrence Risk ◽  
Stage I ◽  
Risk Of Relapse ◽  
Mirna Expression Signature

395 Background: Renal-cell carcinoma (RCC) accounts for 3% of all cancers and produces over 12,000 deaths every year in the EE.UU. Currently, there is a need to identify stage I/II RCC patients with high risk of relapse following nephrectomy, given that these patients do not receive adjuvant treatment and ≈ 25% of them relapse. MicroRNAs (miRNAs) are a class of small noncoding RNAs that control gene expression by targeting mRNA and playing an important role as regulators of gene expression during tumorigenesis. Our willing is to define a miRNA expression profile associated with a high risk of relapse in early RCC. Methods: We analyzed 113 pts. with RCC stage I-II of a local data-base who had undergone nephrectomy from 2000 to 2008. RNA was extracted from FFPE samples using RecoverAll (Ambion). RNA samples were hybridized to Human miRNA Microarray Release 14.0, 8x15K (Agilent Technologies. Data were normalized using Quantile Normalization. Only 396 miRNAs with detectable signal in at least 10% of the hybridized samples were considered for further analysis. Identification of miRNAs related with recurrence risk and subsequent developing and validation of miRNA expression-based prediction models of recurrence risk were performed in BRB-ArrayTools v4.2.1. Results: We identified a 4-miRNA expression signature that distinguishes early stage RCC patients with low and high recurrence risk (p value = 0.0013; HR = 4.68 [1.82-12.0]). Distant recurrence free survival rate at five years was 97.4 and 81.2 for the low and high recurrence risk groups respectively. High levels of miR-424 were related with a high recurrence risk (p = 0.023). Conclusions: The TNM staging system lacks accuracy to identify prognostic markers of survival in early RCC. Our results suggest that specific miRNAs are involved in the recurrence of early disease. We have found a miRNA expression signature that identifies patients with high risk of developing distant metastasis using the FFPE sample of primary tumors. High expression levels of miR-424 were related with a high recurrence risk. MiR-424 is a hypoxia induced miRNA whose expression has been related with HIF-1α and HIF-2α stabilization and angiogenesis induction.

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