297 Risk stratification of patients with stage I cutaneous melanoma (CM) using 31-gene expression profiling (GEP)

Abstract Detection of specific chromosomal abnormalities by FISH and metaphase cytogenetics allows risk stratification in multiple myeloma; however, gene expression profiling (GEP) based signatures may enable more specific risk categorization. We examined the utility of 2 GEP-based risk stratification systems among patients undergoing initial therapy with lenalidomide in the context of a phase 3 trial. Among 45 patients studied at baseline, 7 (16%) and 10 (22%), respectively, were high-risk using the GEP70 and GEP15 signatures. The median overall survival for the GEP70 high-risk group was 19 months versus not reached for the rest (hazard ratio = 14.1). Although the medians were not reached, the GEP15 also predicted a poor outcome among the high-risk patients. The C-statistic for the GEP70, GEP15, and FISH based risk stratification systems was 0.74, 0.7, and 0.7, respectively. Here we demonstrate the prognostic value for GEP risk stratification in a group of patients primarily treated with novel agents. This trial was registered at www.clinicaltrials.gov as #NCT00098475.

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Metascoring and Gene Expression Profiling in Clinical Routine in Multiple Myeloma,

Blood ◽

10.1182/blood.v118.21.3940.3940 ◽

2011 ◽

Vol 118 (21) ◽

pp. 3940-3940

Author(s):

Tobias Meißner ◽

Anja Seckinger ◽

Thierry Rème ◽

Thomas Hielscher ◽

Thomas Möhler ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Prognostic Factors ◽

Risk Stratification ◽

Gene Expression Profiling ◽

Open Source ◽

Expression Profiling ◽

Target Genes ◽

Molecular Heterogeneity ◽

Clinical Routine

Abstract Abstract 3940 BACKGROUND. Multiple myeloma is characterized by molecular heterogeneity transmitting to survival ranging from several months to over 15 years. Gene expression profiling allows assessment of biological entities, risk, and targets. Its translation into clinical routine alongside conventional prognostic factors has been prevented by lack of appropriated reporting tools and the integration with other prognostic factors into a single risk stratification (metascoring). METHODS. We present here a non-commercial open source software-framework developed in the open source language R (GEP-report) containing a graphic user interphase based on Gtk2. Affymetrix microarray raw-data and a documentation-by-value strategy to directly apply thresholds and grouping-algorithms from a reference cohort of 262 myeloma patients are used. Gene expression-based and conventional prognostic factors are integrated within one risk-stratification (HM-metascore) and the final report is created as a PDF-file. RESULTS. The GEP-report comprises i) quality control, ii) test of sample identity, iii) biological classifications of multiple myeloma, iv) risk stratification, v) assessment of target-genes, and vi) integration of expression-based and clinical risk factors within one metascore. This HM-metascore sums over the weighted factors gene-expression based risk-assessment (UAMS-, IFM-score), proliferation, ISS-stage, t(4;14), and expression of prognostic target-genes (AURKA, IGF1R) for which clinical grade inhibitors exist. It delineates three significantly different groups of 13.1, 72.1 and 14.7% of patients with a 6-year survival of 89.3, 60.6 and 18.6%, respectively. CONCLUSION. GEP-reporting allows prospective assessment of target gene expression and integration of current prognostic factors within one risk stratification (metascoring), being customizable regarding novel parameters or other cancer entities. Disclosures: No relevant conflicts of interest to declare.

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Prospective validation of the prognostic 31‐gene expression profiling test in primary cutaneous melanoma

Cancer Medicine ◽

10.1002/cam4.2128 ◽

2019 ◽

Vol 8 (5) ◽

pp. 2205-2212 ◽

Cited By ~ 12

Author(s):

Jennifer Keller ◽

Theresa L. Schwartz ◽

Jason M. Lizalek ◽

Ea‐sle Chang ◽

Ashaki D. Patel ◽

...

Keyword(s):

Gene Expression ◽

Gene Expression Profiling ◽

Cutaneous Melanoma ◽

Expression Profiling ◽

Primary Cutaneous Melanoma

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Prediction of distant relapse risk in stage I-II non-small cell lung cancer (NSCLC) by gene expression profiling

Journal of Clinical Oncology ◽

10.1200/jco.2007.25.18_suppl.7657 ◽

2007 ◽

Vol 25 (18_suppl) ◽

pp. 7657-7657

Author(s):

J. Jassem ◽

M. Jarzab ◽

J. Niklinski ◽

W. Rzyman ◽

M. Kowalska ◽

...

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Gene Expression Profiling ◽

Predictive Value ◽

Expression Profiling ◽

Gene Selection ◽

Low Risk ◽

Stage I ◽

Clinical Variable ◽

Risk Of Relapse

7657 Background: Our aim was to determine the genes predictive for relapse-free and overall survival in stage I-II NSCLC. Methods: We analyzed the gene expression profiles in lung cancer specimens collected from 70 NSCLC patients (pts) who underwent curative pulmonary resection between 1999 and 2004 in two Polish centers (Gdansk, Bialystok). There were 54 men and 16 women aged 37–77 yrs (median 62.5 yrs), 45 with squamous cell ca, 22 with adenoca and 3 with large cell ca. Eight pts were staged pT1, 59 pT2 and 3 pT3; there were 49 and 21 pN0 and pN1 pts, respectively. 30 pts had a relapse and 32 pts died (median follow-up 36 months). Samples of tumor tissue were collected intraoperatively and snap-frozen, total RNA was isolated and gene expression profiling was carried out by Affymetrix HG-U133 2.0 Plus oligonucleotide microarray. Samples were pre-processed with RMA algorithm, gene selection was carried out by Support Vector Machines and Bayesian Compound Covariate Classifier, using own procedures and BRB-Array software developed by Simon and Peng Lam. Survival time prediction was carried out by method developed by Bair and Tibshirani (PLoS Biology 2004). Results: Based on the microarray gene expression profiling, the relapse could be predicted with 75.0% specificity and 53.3% sensitivity (positive predictive value [PPV] 61.5%, negative predictive value [NPV] 68.2%). The classifier, obtained by cross-validation of 70-sample dataset, consisted of 170 transcripts. Further gene selection was based on the prediction of the relapse-free survival: 1,919 genes, selected by fitting Cox proportional hazard model (p<0.05) and further used to predict survival by 4 principal components, distinguished between pts with high and low risk of relapse (p<0.05, log-rank test). The prediction of death was possible with 66.7% specificity and 57.1% sensitivity (PPV 53.3%, NPV 70%), but the distinction between high- and low-risk pts was significantly weaker than based on lymph node involvement (N0 vs. N1). Thus, for the final selection of genes this clinical variable was incorporated into the model as a covariate. Conclusions: Prediction of the risk of relapse in stage I-II NSCLC based on the gene expression profile is feasible, with NPV of 68.2%. No significant financial relationships to disclose.

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