Originally published as JCO Early Release 10.1200/JCO.2008.19.1916 on July 27 2009

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Gene Expression Profiles of Tumor Biology Provide a Novel Approach to Prognosis and May Guide the Selection of Therapeutic Targets in Multiple Myeloma

Ariel Anguiano, Sascha A. Tuchman, Chaitanya Acharya, Kelly Salter, Cristina Gasparetto, Fenghuang Zhan, Madhav Dhodapkar, Joseph Nevins, Bart Barlogie, John D. Shaughnessy, Jr, Anil Potti

From the Institute for Genome Sciences and Policy, Duke University; Department of Medicine, Duke University Medical Center, Durham; Regional Cancer Care, US Oncology Network, Chapel Hill, NC; Myeloma Institute for Research and Therapy, University of Arkansas for Medical Sciences, Little Rock, AR; and Section of Hematology, Yale University, New Haven, CT.

Corresponding author: Anil Potti, MD, Box 3382, 101 Science Dr, Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708; e-mail: anil.potti{at}duke.edu.

Purpose Monoclonal gammopathy of undetermined significance (MGUS) and multiple myeloma (MM) comprise heterogeneous disorders with incompletely understood molecular defects and variable clinical features. We performed gene expression profiling (GEP) with microarray data to better dissect the molecular phenotypes, sensitivity to particular chemotherapeutic agents, and prognoses of these diseases.

Methods Using gene expression and clinical data from 877 patients ranging from normal plasma cells (NPC) to relapsed MM (RMM), we applied gene expression signatures reflecting deregulation of oncogenic pathways and tumor microenvironment to highlight molecular changes that occur as NPCs transition to MM, create a high-risk MGUS gene signature, and subgroup International Staging System (ISS) stages into more prognostically accurate clusters of patients. Lastly, we used gene signatures to predict sensitivity to conventional cytotoxic chemotherapies among identified clusters of patients.

Results Myc upregulation and increasing chromosomal instability (CIN) characterized the evolution from NPC to RMM (P < .0001 for both). Studies of MGUS revealed that some samples shared biologic features with RMM, which comprised the basis for a high-risk MGUS signature. Regarding MM, we subclassified ISS stages into clusters based on shared features of tumor biology. These clusters differentiated themselves based on predictions for prognosis and chemotherapy sensitivity (eg, in ISS stage I, one cluster was characterized by increased CIN, cyclophosphamide resistance, and a poor prognosis).

Conclusion GEP provides insight into the molecular defects underlying plasma cell dyscrasias that may explain their clinical heterogeneity. GEP also may also refine current prognostic and therapeutic models for MGUS and MM.

A.A. and S.A.T. contributed equally to the work.

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

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