scholarly journals Predicting 5-fluorouracil chemosensitivity of liver metastases from colorectal cancer using primary tumor specimens: Three-gene expression model predicts clinical response

2006 ◽  
Vol 119 (2) ◽  
pp. 406-413 ◽  
Author(s):  
Ryusei Matsuyama ◽  
Shinji Togo ◽  
Daisuke Shimizu ◽  
Nobuyoshi Momiyama ◽  
Takashi Ishikawa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Liver Metastases ◽  
Clinical Response ◽  
Primary Tumor ◽  
Expression Model ◽  
Gene Expression Model

scholarly journals Discordance of KRAS Mutational Status between Primary Tumors and Liver Metastases in Colorectal Cancer: Impact on Long-Term Survival Following Radical Resection

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2148
Author(s):  
Francesco Ardito ◽  
Francesco Razionale ◽  
Lisa Salvatore ◽  
Tonia Cenci ◽  
Maria Vellone ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Liver Metastases ◽  
Primary Tumor ◽  
Kras Mutation ◽  
Colorectal Tumor ◽  
Term Survival ◽  
Primary Tumors ◽  
Mutation Status ◽  
Primary Colorectal Tumor ◽  
Kras Mutation Status

If KRAS mutation status of primary colorectal tumor is representative of corresponding colorectal liver metastases (CRLM) mutational pattern, is controversial. Several studies have reported different rates of KRAS discordance, ranging from 4 to 32%. Aim of this study is to assess the incidence of discordance and its impact on overall survival (OS) in a homogenous group of patients. KRAS mutation status was evaluated in 107 patients resected for both primary colorectal tumor and corresponding CRLM at the same institution, between 2007 and 2018. Discordance rate was 15.9%. Its incidence varied according to the time interval between the two mutation analyses (p = 0.025; Pearson correlation = 0.2) and it was significantly higher during the first 6 months from the time of primary tumor evaluation. On multivariable analysis, type of discordance (wild-type in primary tumor, mutation in CRLM) was the strongest predictor of poor OS (p < 0.001). At multivariable logistic regression analysis, the number of CRLM >3 was an independent risk factor for the risk of KRAS discordance associated with the worst prognosis (OR = 4.600; p = 0.047). Results of our study suggested that, in the era of precision medicine, possibility of KRAS discordance should be taken into account within multidisciplinary management of patients with metastatic colorectal cancer.

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

Primary tumor location impacts survival in colorectal cancer patients after resection of liver metastases

2020 ◽  
Vol 122 (4) ◽  
pp. 745-752
Author(s):  
Katherine Bingmer ◽  
Asya Ofshteyn ◽  
Jonathan T. Bliggenstorfer ◽  
William Kethman ◽  
John B. Ammori ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Liver Metastases ◽  
Cancer Patients ◽  
Primary Tumor ◽  
Tumor Location ◽  
Primary Tumor Location ◽  
Colorectal Cancer Patients

Gene expression profiling of liver metastases from colorectal cancer: differences between synchronous and metachronous lesions

2008 ◽  
Vol 26 (15_suppl) ◽  
pp. 15062-15062
Author(s):  
M. A. Pantaleo ◽  
A. Astolfi ◽  
M. Nannini ◽  
P. Paterini ◽  
G. Piazzi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Liver Metastases ◽  
Gene Expression Profiling ◽  
Expression Profiling
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