HOMEPRODUCTSCOMPANYCONTACTFAQResearchDictionaryPharmaSign Up FREE or Login

Predicting treatment outcome in classical Hodgkin lymphoma: genomic advances.

Abstract
Classical Hodgkin lymphoma is considered a highly curable disease; however, 20% of patients cannot be cured with standard first-line chemotherapy and have a dismal outcome. Current clinical parameters do not allow accurate risk stratification, and personalized therapies are lacking. In fact, Hodgkin lymphoma (HL) is often over- or undertreated because of this lack of accurate risk stratification. In recent years, the early detection of chemoresistance by fluorodeoxyglucose positron emission tomography has become the most important prognostic tool in the management of HL. However, to date, no prognostic scores or molecular markers are available for the early identification of patients at very high risk of failure of induction therapy. In the last decade, many important advances have been made in understanding the biology of HL. In particular, the development of new molecular profiling technologies, such as SNP arrays, comparative genomic hybridization, and gene-expression profiling, have allowed the identification of new prognostic factors that may be useful for risk stratification and predicting response to chemotherapy. In this review, we focus on the prognostic tools and biomarkers that are available for newly diagnosed HL, and we highlight recent advances in the genomic characterization of classical HL and potential targets for therapy.
AuthorsEnrico Derenzini, Anas Younes
JournalGenome medicine (Genome Med) Vol. 3 Issue 4 Pg. 26 (Apr 28 2011) ISSN: 1756-994X [Electronic] England
PMID21542892 (Publication Type: Journal Article)

Join CureHunter, for free Research Interface BASIC access!

Take advantage of free CureHunter research engine access to explore the best drug and treatment options for any disease. Find out why thousands of doctors, pharma researchers and patient activists around the world use CureHunter every day.
Realize the full power of the drug-disease research graph!


Choose Username:
Email:
Password:
Verify Password:
Enter Code Shown: