title: Generation of a predictive melphalan resistance index by drug screen of B-cell cancer cell lines.
abstract: BACKGROUND
  Recent reports indicate that in vitro drug screens combined with gene expression profiles (GEP) of cancer cell lines may generate informative signatures predicting the clinical outcome of chemotherapy. In multiple myeloma (MM) a range of new drugs have been introduced and now challenge conventional therapy including high dose melphalan. Consequently, the generation of predictive signatures for response to melphalan may have a clinical impact. The hypothesis is that melphalan screens and GEPs of B-cell cancer cell lines combined with multivariate statistics may provide predictive clinical information.
  
  
  METHODS
  Microarray based GEPs and a melphalan growth inhibition screen of 59 cancer cell lines were downloaded from the National Cancer Institute database. Equivalent data were generated for 18 B-cell cancer cell lines. Linear discriminant analyses (LDA), sparse partial least squares (SPLS) and pairwise comparisons of cell line data were used to build resistance signatures from both cell line panels. A melphalan resistance index was defined and estimated for each MM patient in a publicly available clinical data set and evaluated retrospectively by Cox proportional hazards and Kaplan-Meier survival analysis.
  
  
  RESULTS
  Both cell line panels performed well with respect to internal validation of the SPLS approach but only the B-cell panel was able to predict a significantly higher risk of relapse and death with increasing resistance index in the clinical data sets. The most sensitive and resistant cell lines, MOLP-2 and RPMI-8226 LR5, respectively, had high leverage, which suggests their differentially expressed genes to possess important predictive value.
  
  
  CONCLUSIONS
  The present study presents a melphalan resistance index generated by analysis of a B-cell panel of cancer cell lines. However, the resistance index needs to be functionally validated and correlated to known MM biomarkers in independent data sets in order to better understand the mechanism underlying the preparedness to melphalan resistance.
authors.affiliation: Department of Haematology, Aalborg Hospital Science and Innovation Center, Aarhus University Hospital, Aalborg, Denmark. martin.boegsted@rn.dk
authors.forename: Martin
authors.initials: M
authors.lastname: Boegsted
authors.forename: Johanne M
authors.initials: JM
authors.lastname: Holst
authors.forename: Kirsten
authors.initials: K
authors.lastname: Fogd
authors.forename: Steffen
authors.initials: S
authors.lastname: Falgreen
authors.forename: Suzette
authors.initials: S
authors.lastname: Sørensen
authors.forename: Alexander
authors.initials: A
authors.lastname: Schmitz
authors.forename: Anne
authors.initials: A
authors.lastname: Bukh
authors.forename: Hans E
authors.initials: HE
authors.lastname: Johnsen
authors.forename: Mette
authors.initials: M
authors.lastname: Nyegaard
authors.forename: Karen
authors.initials: K
authors.lastname: Dybkaer
mesh_terms: D000970:Antineoplastic Agents / Q000494:pharmacology; D001402:B-Lymphocytes / Q000187:drug effects*; D045744:Cell Line, Tumor; D019008:Drug Resistance, Neoplasm; D004354:Drug Screening Assays, Antitumor / Q000379:methods*; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D016018:Least-Squares Analysis; D016403:Lymphoma, Large B-Cell, Diffuse / Q000188:drug therapy*; D008558:Melphalan / Q000494:pharmacology*; D009101:Multiple Myeloma / Q000188:drug therapy*; D020411:Oligonucleotide Array Sequence Analysis; D010954:Plasmacytoma / Q000188:drug therapy*
chemical_list: D000970:Antineoplastic Agents; D008558:Melphalan