» Articles » PMID: 20371465

Why Most Gene Expression Signatures of Tumors Have Not Been Useful in the Clinic

Overview
Journal Sci Transl Med
Date 2010 Apr 8
PMID 20371465
Citations 62
Authors
Affiliations
Soon will be listed here.
Abstract

Omics technologies are expected to enhance our understanding of a variety of diseases and to open the door to patient-specific personalized medicine. Despite the extensive literature on the use of gene expression arrays to predict prognosis in cancer patients, poor progress has been made in the translation of gene expression signatures for use in the clinics. Breast cancer provides a ripe arena for an analysis of why such signatures have failed to fulfill their promise.

Citing Articles

Identification of a central network hub of key prognostic genes based on correlation between transcriptomics and survival in patients with metastatic solid tumors.

Lazar V, Raymond E, Magidi S, Bresson C, Wunder F, Berindan-Neagoe I Ther Adv Med Oncol. 2024; 16:17588359241289200.

PMID: 39429467 PMC: 11487509. DOI: 10.1177/17588359241289200.


Advancing precision prognostication in neuro-oncology: Machine learning models for data-driven personalized survival predictions in IDH-wildtype glioblastoma.

Karabacak M, Jagtiani P, Di L, Shah A, Komotar R, Margetis K Neurooncol Adv. 2024; 6(1):vdae096.

PMID: 38983675 PMC: 11232516. DOI: 10.1093/noajnl/vdae096.


Identifying high-risk multiple myeloma patients: A novel approach using a clonal gene signature.

Li J, Wang C, Cheng C Int J Cancer. 2024; 155(9):1684-1695.

PMID: 38874435 PMC: 11537842. DOI: 10.1002/ijc.35057.


DNA-based molecular classifiers for the profiling of gene expression signatures.

Zhang L, Liu Q, Guo Y, Tian L, Chen K, Bai D J Nanobiotechnology. 2024; 22(1):189.

PMID: 38632615 PMC: 11025223. DOI: 10.1186/s12951-024-02445-0.


Re-evaluation of publicly available gene-expression databases using machine-learning yields a maximum prognostic power in breast cancer.

Tschodu D, Lippoldt J, Gottheil P, Wegscheider A, Kas J, Niendorf A Sci Rep. 2023; 13(1):16402.

PMID: 37798300 PMC: 10556090. DOI: 10.1038/s41598-023-41090-9.