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      Five gene probes carry most of the discriminatory power of the 70-gene risk model in multiple myeloma

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          Abstract

          The prognostic value of gene expression profiling (GEP) in multiple myeloma (MM) has been reported by several groups. 1, 2, 3, 4 We have previously published a 70-gene classifier (GEP70) that identifies patients with high risk for short progression-free survival (PFS) and overall survival (OS). 1 The GEP70 model was developed from data on patients enrolled in Total Therapy 2 (TT2). 1 Its discriminatory power has been validated in several published data sets in the transplant, non-transplant and relapse settings (reviewed in Johnson et al. 5 ). We applied the GEP70 model to 56 previously treated patients with available baseline GEP information who were enrolled in Total Therapy 6 (TT6), a tandem transplant trial the details of which are provided in Supplementary Methods. The gene expression profiles have been deposited at the NCBI GEO data repository (http://www.ncbi.nlm.nih.gov/geo/) under GEO accession number GSE57317. Sample procurement and processing for GEP, as well as calculations of the GEP70 risk score, have been reported previously. 1 The estimated 1-year survival was 62% for the high-risk group and 97% for the low-risk group by GEP70 (Supplementary Figure S1A, P<0.0001). To investigate whether this striking difference in outcomes was driven by a few genes, all 70 probe sets of the GEP70 risk model were ranked by their P-values, based on univariate Cox regression analysis for OS in TT6 (Supplementary Table S1). The five probe sets with the smallest P-values (ENO1, FABP5, TRIP13, TAGLN2 and RFC4) were combined to create a continuous score, using methodology similar to that used to develop the GEP70 model. 1 Because each of the five probe sets had a positive association with short OS in TT6, the GEP5 score was simply the mean of log2 transformed expression levels of the five probe sets. An optimal cutoff for the new risk score (hereafter referred to as GEP5) was then established with the running log-rank test, so that patients with scores higher than the cutoff were deemed to have high-risk MM and others to have low-risk (Figure 1a), with an estimated OS at 1 year of 60% and 95%, respectively (1-year PFS 50% and 91%, respectively). All five genes identified in this study were previously reported to be involved in cell proliferation and have been associated with development and survival in different cancers. ENO1 encodes alpha-enolase. Initiation of translation at an alternative translation start site results in a shorter isoform that produces MYC binding protein 1, which acts as a transcriptional repressor and possibly as a tumor suppressor. 6 Overexpression of FABP5, a member of the family of fatty acid-binding proteins, was associated with poor survival in triple-negative breast cancer and with resistance to all-trans retinoic acid in a preclinical model of pancreatic ductal adenocarcinoma. 7,8 TRIP13 encodes a hormone-dependent transcription factor that interacts with the ligand-binding domain of thyroid hormone receptors and may play a role in early-stage non-small-cell lung cancer. 9 Association of TAGLN2 overexpression and short survival, metastasis and disease progression has been shown for several cancers. 10,11 RFC4 encodes the 37-kDa subunit of the replication factor C protein complex, which, together with the proliferating cell nuclear antigen, is required for DNA elongation. 12 Because the number of patients treated on TT6 was relatively small and follow-up short (median follow-up 26.5 months), a larger data set of 275 uniformly treated patients on TT3a with a longer follow-up was then used to investigate the new GEP5 score's applicability to previously untreated myeloma. We validated the new GEP5 cutoff for patients enrolled in TT3b (n=166). 13 Gene expression data for TT3a and TT3b have previously been published and are deposited in the ArrayExpress archive (http://www.ebi.ac.uk/arrayexpress) under the accession number E-TABM-1138. A new optimal cutoff for the GEP5 model of 10.68 was identified from TT3a using the running log-rank statistics, which identified significant differences in OS and PFS for the groups with high- and low-risk disease. Importantly, these differences are comparable to those obtained by the GEP70 risk model with its established cutoff 1 (Figure 1b and Supplementary Figure S1B). In the validation cohort (TT3b), risk distinction using GEP5 was very similar to GEP70 (Figure 1c and Supplementary Figure S1C) and both were comparable to results in the TT3a training set. We also applied GEP5 to a publicly available external data set of previously untreated patients (HOVON65/GMMG-HD4, n=288) 4 as a second validation set, where GEP5 also differentiated between a high-risk and a low-risk population with significantly different survival (Figure 1d). In order to address the question whether the five probe sets in the GEP5 were truly the best choice, we randomly selected 10 000 quintuplets from all the probe sets within the 70 gene model to create 10 000 continuous scores using the same methodology as for the GEP5 score. Among the 10 000 random scores tested, only 40 performed better in TT6. Of these 40 only 1 performed better in the TT3 test set and none was superior to GEP5 in the TT3b validation set (Supplementary Figure S2 and Supplementary Table S2). We also examined randomly selected continuous scores in TT6 with probe sets ranging between 1 and 10. Of a total of 42 485 models considered, only 1236 had a smaller P-value than GEP5 in TT6. Among those 1236 scores, 68 had a smaller P-value when tested in the TT3a test set and none performed better than GEP5 in the TT3b validation set (Supplementary Figure S3 and Supplementary Table S3). Although some of these random scores showed a better correlation with survival in single data sets, none were consistently better than the GEP5 score across different data sets. The GEP5 always ranked among the top 2% of all scores in all data sets analyzed (data not shown). On multivariate stepwise analysis, the GEP5-defined high-risk designation was selected as the most adverse variable linked to inferior PFS, with an estimated hazard ratio of 3.44 (95% CI: 2.02–5.86), whereas the GEP70 model was selected for OS (Supplementary Table S4). Table 1 summarizes the univariate survival analysis of the GEP5 and GEP70 models. Cross-tabulation of GEP70 and GEP5 risk (low vs high) for TT3A, and TT3B showed an agreement rate between the two models of 0.89, and 0.87, respectively (Supplementary Table S5). GEP70 and GEP5 currently require the use of microarray technology that interrogates the expression levels of more than 47 000 transcripts and variants simultaneously. To assess whether a more targeted approach, only measuring the expression of a small number of genes, could reliably predict risk in MM, we analyzed 48 RNA samples of previously untreated patients on TT3a and TT3b with available GEP data using the nanoString nCounter, with a code set consisting of all five genes (ENO1, FABP5, TAGLN3, TRIP13 and RFC4) of the GEP5 signature and the housekeeping genes RPL27, RPL30, RPS13, RPS29 and SRP14 (code set sequences are provided in Supplementary Table S6). Technical and biological normalization were performed using the nSolver software provided by nanoString. The correlation between microarray and nanoString-based gene expression for all five genes was between r=0.64 and r=0.87. Using the normalized nanoString data, we computed a nanoString-based GEP5 score (nsGEP5) applying the same methodology as for the microarray-based GEP5. nsGEP5 and GEP5 correlated very well with r=0.852 (Supplementary Figure S4A). The receiver operator curve revealed an area under the curve of 0.897, suggesting that GEP5 high/low risk can be predicted using nsGEP5 (Supplementary Figure S4B). In summary, high-risk myeloma remains one of the greatest therapeutic challenges. The striking difference in survival of previously treated patients among GEP70 low- and high-risk groups motivated our search for fewer responsible genes. We indeed identified a set of five genes that are highly predictive of survival in multiple independent data sets. The nsGEP5 based on targeted evaluation of the expression levels of these five genes using the nanoString technology showed a very good correlation with GEP5 (based on microarray data). This new technology could reduce cost and sample requirements and has the great potential of making gene expression-driven risk assessment available to a broader patient population. However, the nsGEP5 will have to be evaluated in an independent homogeneous set of clinical samples before it can be utilized in the routine clinical setting. Recently a large-scale proteomics experiment involving 85 patients with MM identified ENO1, FABP5 and TAGLN2 among a set of 24 proteins that are associated with short OS. 14 This set of 85 patients included 47 who were enrolled in TT3b. The correlation of expression at both mRNA (via our GEP analyses) and protein levels supports the biological relevance of the genes included in the GEP5 model. Work is in progress to identify agents that can effectively target these prognostic genes.

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          Most cited references 14

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          A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1.

          To molecularly define high-risk disease, we performed microarray analysis on tumor cells from 532 newly diagnosed patients with multiple myeloma (MM) treated on 2 separate protocols. Using log-rank tests of expression quartiles, 70 genes, 30% mapping to chromosome 1 (P < .001), were linked to early disease-related death. Importantly, most up-regulated genes mapped to chromosome 1q, and down-regulated genes mapped to chromosome 1p. The ratio of mean expression levels of up-regulated to down-regulated genes defined a high-risk score present in 13% of patients with shorter durations of complete remission, event-free survival, and overall survival (training set: hazard ratio [HR], 5.16; P < .001; test cohort: HR, 4.75; P < .001). The high-risk score also was an independent predictor of outcome endpoints in multivariate analysis (P < .001) that included the International Staging System and high-risk translocations. In a comparison of paired baseline and relapse samples, the high-risk score frequency rose to 76% at relapse and predicted short postrelapse survival (P < .05). Multivariate discriminant analysis revealed that a 17-gene subset could predict outcome as well as the 70-gene model. Our data suggest that altered transcriptional regulation of genes mapping to chromosome 1 may contribute to disease progression, and that expression profiling can be used to identify high-risk disease and guide therapeutic interventions.
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            Prediction of survival in multiple myeloma based on gene expression profiles reveals cell cycle and chromosomal instability signatures in high-risk patients and hyperdiploid signatures in low-risk patients: a study of the Intergroupe Francophone du Myélome.

            Survival of patients with multiple myeloma is highly heterogeneous, from periods of a few weeks to more than 10 years. We used gene expression profiles of myeloma cells obtained at diagnosis to identify broadly applicable prognostic markers. In a training set of 182 patients, we used supervised methods to identify individual genes associated with length of survival. A survival model was built from these genes. The validity of our model was assessed in our test set of 68 patients and in three independent cohorts comprising 853 patients with multiple myeloma. The 15 strongest genes associated with the length of survival were used to calculate a risk score and to stratify patients into low-risk and high-risk groups. The survival-predictor score was significantly associated with survival in both the training and test sets and in the external validation cohorts. The Kaplan-Meier estimates of rates of survival at 3 years were 90.5% (95% CI, 85.6% to 95.3%) and 47.4% (95% CI, 33.5% to 60.1%), respectively, in our patients having a low risk or high risk independently of traditional prognostic factors. High-risk patients constituted a homogeneous biologic entity characterized by the overexpression of genes involved in cell cycle progression and its surveillance, whereas low-risk patients were heterogeneous and displayed hyperdiploid signatures. Gene expression-based survival prediction and molecular features associated with high-risk patients may be useful for developing prognostic markers and may provide basis to treat these patients with new targeted antimitotics.
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              miR-1 as a tumor suppressive microRNA targeting TAGLN2 in head and neck squamous cell carcinoma

              Based on the microRNA (miRNA) expression signatures of hypopharyngeal and esophageal squamous cell carcinoma, we found that miR-1 was significantly down-regulated in cancer cells. In this study, we investigated the functional significance of miR-1 in head and neck squamous cell carcinoma (HNSCC) cells and identified miR-1-regulated novel cancer pathways. Gain-of-function studies using miR-1 revealed significant decreases in HNSCC cell proliferation, invasion, and migration. In addition, the promotion of cell apoptosis and cell cycle arrest was demonstrated following miR-1 transfection of cancer cells. A search for the targets of miR-1 revealed that transgelin 2 (TAGLN2) was directly regulated by miR-1. Silencing of TAGLN2 significantly inhibited cell proliferation and invasion in HNSCC cells. Down-regulation of miR-1 and up-regulation of TAGLN2 were confirmed in HNSCC clinical specimens. Our data indicate that TAGLN2 may have an oncogenic function and may be regulated by miR-1, a tumor suppressive miRNA in HNSCC. The identification of novel miR-1-regulated cancer pathways could provide new insights into potential molecular mechanisms of HNSCC carcinogenesis.
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                Author and article information

                Journal
                Leukemia
                Leukemia
                Leukemia
                Nature Publishing Group
                0887-6924
                1476-5551
                December 2014
                31 July 2014
                05 September 2014
                : 28
                : 12
                : 2410-2413
                Affiliations
                [1 ]Myeloma Institute for Research and Therapy , Little Rock, AR, USA
                [2 ]Cancer Research and Biostatistics , Seattle, WA, USA
                Author notes
                Article
                leu2014232
                10.1038/leu.2014.232
                4274609
                25079174
                Copyright © 2014 Macmillan Publishers Limited

                This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/

                Categories
                Letter to the Editor

                Oncology & Radiotherapy

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