Selecting Patients Most Likely to Respond to Therapy

By Kenneth Anderson, MD

2008-07-01

Dr. Anderson indicated no relevant conflicts of interest.

Mateo G, Montalbán MA, Vidriales MB, et al. Prognostic value of immunophenotyping multiple myeloma: a study by the GEM and the PETHEMA cooperative study groups on patients uniformly treated with high-dose therapy. J Clin Oncol. 2008.[Epub ahead of print]

Multiple myeloma (MM) is a heterogeneous disease with a broad range of biological and clinical features.1,2 Multiple studies have therefore attempted to identify genetic and biologic markers, as well as clinical characteristics to define subgroups of patients. For example, prognostic factors such as beta 2 microglobulin and albumin form the basis of the International Staging System,3 which is predictive of disease course. Conventional cytogenetics can identify patients with adverse outcome to conventional low- and high-dose therapies (i.e., 4;14 translocation or deletion of chromosome 13).4 More recent studies have used microarray profiling5 and array comparative genomic hybridization6 to form the basis for defining mRNA-based and DNA-based prognostic subgroups of myeloma. Importantly, prognostic factors must be defined in a particular clinical context. For example, in patients treated with novel therapies such as bortezomib, chromosome 13 deletion and 4;14 translocation are no longer of adverse prognostic import.7 Moreover, recent studies have utilized microarray profiling to define patient populations with gene signatures predictive of response to specific therapies (i.e., bortezomib).8

Mateo and colleagues have recently carried out a prospective study of 685 newly diagnosed patients with myeloma treated uniformly with six alternating cycles of vincristine, BCNU, melphalan, cytoxan, and prednisone (VBMCP) alternating with vincristine, BCNU, adriamycin, and prednisone (VBAP) therapy, followed by melphalan 200mg/m2 and autologous stem cell transplantation. The median progression-free survival (PFS) and overall survival (OS) were 37 and 67 months, respectively. In order to delineate patient subgroups with differential outcome, CD138 positive bone marrow plasma cells were purified and immunophenotyped prospectively as well. Importantly, CD19+CD28+CD117-phenotype on CD138+BM plasma cells predicted poor outcome, with significantly shorter PFS and OS. An immunophenotype-based staging system, defined on the basis of CD28 and CD117 expression on tumor cells, identified poor-, intermediate-, and good-risk patient subgroups with significantly different PFS and OS. In addition to its clinical utility, understanding the biologic significance of the observed antigen-expression profiles conferring this differential outcome may both delineate mechanisms of sensitivity versus resistance to therapy and also yield insights into MM pathogenesis. Moreover, correlation of these phenotype profiles with microarray profiling and aCGH analysis may identify, and yield, new insights into MM pathogenesis to be exploited in novel, single agent or combination targeted therapeutics.

Several novel therapies are now available for myeloma, and the challenge is to use them most effectively. These investigators are to be congratulated on their major effort of prospectively purifying patient tumor cells for phenotypic analysis and correlation with clinical outcome. Although VBMCP/VBAP is no longer utilized and incorporation of novel therapies such as bortezomib into the initial therapy pre-transplant improves response post high-dose melphalan and autologous stem cell transplantation,9 this study by Mateo and coworkers is an example for future efforts of how correlative science can inform the selection of patients most likely to benefit from novel therapeutics.

 

References

  1. Kuehl WM, Bergsagel PL. Multiple myeloma: evolving genetic events and host interactions. Nat Rev Cancer. 2002;2:175-87.

  2. Hideshima T, Mitsiades C, Tonon G, et al. Understanding multiple myeloma pathogenesis in the bone marrow to identify new therapeutic targets. Nat Rev Cancer. 2007;7:585-98.

  3. Greipp PR, San Miguel J, Durie BG, et al. International staging system for multiple myeloma. J Clin Oncol. 2005;23:3412-20.

  4. Greipp PR. Prognosis in myeloma. Mayo Clin Proc. 1994;69:895-902.

  5. Bergsagel PL, Kuehl WM, Zhan F, et al. Cyclin D dysregulation: an early and unifying pathogenic event in multiple myeloma. Blood. 2005 Jul 1;106:296-303.

  6. Carrasco DR, Tonon G, Huang Y, et al. High-resolution genomic profiles define distinct clinico-pathogenetic subgroups of multiple myeloma patients. Cancer Cell. 2006;9:313-25.

  7. Jagannath S, Richardson PG, Sonneveld P, et al. Bortezomib appears to overcome the poor prognosis conferred by chromosome 13 deletion in phase 2 and 3 trials. Leukemia. 2007;21:151-7.

  8. Mulligan G, Mitsiades C, Bryant B, et al. Gene expression profiling and correlation with outcome in clinical trials of the proteasome inhibitor bortezomib. Blood. 2007;109:3177-88.

  9. Harousseau JL, Mathiot C, Attal M, et al. VELCADE/dexamethasone versus VAD as induction treatment prior to autologous stem cell transplantation in newly diagnosed multiple myeloma: updated results of the IFM 2005/01 trial. Blood (Annual Meeting Abstract). 2007;110:139a.

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