Generation of a Predictive Melphalan Resistance Index by Drug Screen of B-Cell Cancer Cell Lines

Abstract

BackgroundRecent 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.Materials and MethodsMicroarray 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.Principal FindingsBoth 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.ConclusionThe 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.

Human B-cell cancer cell lines as a preclinical model for studies of drug effect in diffuse large B-cell lymphoma and multiple myeloma

“…Some data support this concept [16][17][18], but it is not yet implemented into patient care owing to a range of controversies, mostly related to the NCI-60 tissue type, the drug screening end points, and the development of reproducible bioinformatic and statistical methods [19][20][21][22][23]. The application of NCI-60 data to hematopoietic tumors has further been questioned because the experiments included only six hematologic cell lines, with only two derived from MMand DLBCL-like patients [24]. As the use of cell line panels offers many benefits, we have established a lineage-specific human B-cell cancer cell line (HBCCL) panel, as required for data generation and use in preclinical translational applications [24][25][26].…”

The myeloma stem cell concept, revisited: from phenomenology to operational terms

“…During the last decade, MSCNET has performed detailed studies to confirm or refute previous studies [47][48][49][50][51][52][53][54][55][56][57][58][59][60] and established protocols 17,[61][62][63][64][65][66][67][68][69][70] to delineate the phenotypes of subpopulations of cells in randomly selected primary tumor samples and in preclinical disease models. While our investigations did not confirm that pre-PC B cells are myeloma initiating, 50 low/-memory B cells engrafted into human bone grafts, resulting in the repopulation of polyclonal B cells, which supports the hypothesis that memory B cells have the ability to self-renew.…”

“…However, meta-analyses of HMCL responses to known anticancer drugs illustrate how these cells can be used to reveal associations between drug sensitivity and gene-expression profiling in cell lines from individual patients; these gene signatures of resistance have documented prognostic value. 57,58 Single cells, single genes, single clones By the time MM is diagnosed, it consists of millions of myeloma cells carrying genetic abnormalities that initiate malignant proliferation, and other mutations are acquired during disease evolution. Some of these secondary mutations emerge due to selective pressure and act as "drivers"; others may be "passengers" resulting from random mutational exposures or genomic instability during many cell divisions.…”

“…Plasticity in MM is perhaps best illustrated by the subtyping of clinical tumor samples based on B-cell subset-associated gene signatures; tumors previously assigned to PreB-II and memory-cell subtypes of malignant PCs were associated with inferior prognoses. [57][58][59][60] This observation provides a new tool for generating insight into the stages of clonal plasticity associated with oncogenesis and deregulated differentiation. The mechanisms of myelomacell plasticity should be exploited, and their significance for the concept of MMSCs assessed.…”

“…We have taken the first step toward defining gene signatures of drug-specific resistance in pre-clinical models of HMCL. [57][58][59][60] Self-renewal, plasticity, and resistance have been studied in the preclinical model of HMCL, which is considered to be the most advanced and homogenous myeloma tissue available. It is important to recognize that each HMCL reflects the end stage of an individual patient's genetic evolution and selection, but each HMCL also reflects the aggregation of stepwise oncogenic events over time, some of which may have deregulated the hallmarks of MMSCs.…”