Gene Expression Profiling of Myeloma Cells at Diagnosis Can Predict Response to Therapy with Thalidomide and Dexamethasone Combination.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 508-508
Author(s):  
Shaji Kumar ◽  
Philip R. Greipp ◽  
Jessica Haug ◽  
Michael Kline ◽  
Wee Joo Chng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ribosomal Protein ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Chromosome Region ◽  
Response To Therapy ◽  
M Protein ◽  
Support Vector ◽  
Newly Diagnosed ◽  
Number Of Patients

Abstract Background: Thalidomide and dexamethasone combination has been shown to be an effective therapy for multiple myeloma (MM). In newly diagnosed patients with MM, the combination results in overall response rates of over 70% of patients. However, a considerable number of patients do not respond and will need to proceed to other therapies. The ability to identify patients who are unlikely to respond to particular therapies will allow tailoring of therapeutic approaches and in turn will reduce unnecessary toxicity and morbidity from lack of rapid disease control. With the availability of new technology such as gene expression profiling (GEP), this has become a possibility. We employed this approach to identify genes that can reliably predict lack of response to the thalidomide dexamethasone combination. Methods: Patient samples from the Eastern Co-operative Oncology Group clinical trial (E1A00), that compared thalidomide-dexamethasone combination with dexamethasone, and patient samples from the phase II Mayo Clinic study of thalidomide and dexamethasone, both for newly diagnosed MM, were used for this study. Thirty newly diagnosed patients who were evaluable for response were included in the analysis. GEP was performed using the Affymetrix U133A microarray platform as per manufacturer’s recommendation. The Affymetrix output (CEL files) was imported into Genespring 7.2 (Agilent Technologies) microarray analysis software, normalized across chips using GCRMA followed by per gene normalization to median. For the purposes of this study, responses were defined as reduction in the serum paraprotein (or the urinary M protein in the absence of a serum M protein) of >=50% (PR), 25 to 49% (MR), increase of >=50% (PD) and stable disease for the remaining. Results: Five of the 30 patients had no response to the thalidomide Dexamethasone therapy. Using the class prediction tool available in Genespring (Support Vector Machines), we identified 25 genes that reliable predicted non responders (PD, NC) from the responders (MR, PR, and CR). See table for a list of predictive genes with identified genes. Conclusion: Using a combination of two datasets we have identified a set of genes that can be used to reliably predict lack of response to thalidomide and dexamethasone in patients with newly diagnosed MM. We think that this represents a step towards creation of custom microarrays spotted with genes that are capable of predicting lack of response (or response) for the purpose of tailoring therapy to the patient. Gene Predictive strength hypothetical protein FLJ20719 1.655 Tubulin alpha 6 1.653 SEC3-like 1 (S. cerevisiae) 1.649 Ribosomal protein L18 1.546 Ribosomal protein L4 1.432 Tubulin alpha 6 1.409 cleavage stimulation factor 1.393 Tubulin, alpha, ubiquitous 1.393 CD99 antigen 1.38 hmel2 1.346 Leptin receptor 1.291 Integrin-linked kinaseSMC4 structural maintenance of chromosomes 4-like 1 1.269 Cornichon homolog 1.255 Williams-Beuren syndrome chromosome region 5 1.217 Ubiquitin specific protease 1 1.2 Sarcoma antigen NY-SAR-91 1.2

Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients

Blood ◽  
2010 ◽  
Vol 116 (14) ◽  
pp. 2543-2553 ◽  
Author(s):  
Annemiek Broyl ◽  
Dirk Hose ◽  
Henk Lokhorst ◽  
Yvonne de Knegt ◽  
Justine Peeters ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Plasma Cells ◽  
Medical Science ◽  
Protein Tyrosine Phosphatases ◽  
Molecular Classification ◽  
Newly Diagnosed ◽  
Expression Of Genes

Abstract To identify molecularly defined subgroups in multiple myeloma, gene expression profiling was performed on purified CD138+ plasma cells of 320 newly diagnosed myeloma patients included in the Dutch-Belgian/German HOVON-65/GMMG-HD4 trial. Hierarchical clustering identified 10 subgroups; 6 corresponded to clusters described in the University of Arkansas for Medical Science (UAMS) classification, CD-1 (n = 13, 4.1%), CD-2 (n = 34, 1.6%), MF (n = 32, 1.0%), MS (n = 33, 1.3%), proliferation-associated genes (n = 15, 4.7%), and hyperdiploid (n = 77, 24.1%). Moreover, the UAMS low percentage of bone disease cluster was identified as a subcluster of the MF cluster (n = 15, 4.7%). One subgroup (n = 39, 12.2%) showed a myeloid signature. Three novel subgroups were defined, including a subgroup of 37 patients (11.6%) characterized by high expression of genes involved in the nuclear factor kappa light-chain-enhancer of activated B cells pathway, which include TNFAIP3 and CD40. Another subgroup of 22 patients (6.9%) was characterized by distinct overexpression of cancer testis antigens without overexpression of proliferation genes. The third novel cluster of 9 patients (2.8%) showed up-regulation of protein tyrosine phosphatases PRL-3 and PTPRZ1 as well as SOCS3. To conclude, in addition to 7 clusters described in the UAMS classification, we identified 3 novel subsets of multiple myeloma that may represent unique diagnostic entities.

scholarly journals Effectiveness of Gene Expression Profiling for Response Prediction of Rectal Adenocarcinomas to Preoperative Chemoradiotherapy

2005 ◽  
Vol 23 (9) ◽  
pp. 1826-1838 ◽  
Author(s):  
B. Michael Ghadimi ◽  
Marian Grade ◽  
Michael J. Difilippantonio ◽  
Sudhir Varma ◽  
Richard Simon ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response Prediction ◽  
Preoperative Chemoradiotherapy ◽  
Response To Therapy ◽  
Phase Iii ◽  
Correct Prediction

Purpose There is a wide spectrum of tumor responsiveness of rectal adenocarcinomas to preoperative chemoradiotherapy ranging from complete response to complete resistance. This study aimed to investigate whether parallel gene expression profiling of the primary tumor can contribute to stratification of patients into groups of responders or nonresponders. Patients and Methods Pretherapeutic biopsies from 30 locally advanced rectal carcinomas were analyzed for gene expression signatures using microarrays. All patients were participants of a phase III clinical trial (CAO/ARO/AIO-94, German Rectal Cancer Trial) and were randomized to receive a preoperative combined-modality therapy including fluorouracil and radiation. Class comparison was used to identify a set of genes that were differentially expressed between responders and nonresponders as measured by T level downsizing and histopathologic tumor regression grading. Results In an initial set of 23 patients, responders and nonresponders showed significantly different expression levels for 54 genes (P < .001). The ability to predict response to therapy using gene expression profiles was rigorously evaluated using leave-one-out cross-validation. Tumor behavior was correctly predicted in 83% of patients (P = .02). Sensitivity (correct prediction of response) was 78%, and specificity (correct prediction of nonresponse) was 86%, with a positive and negative predictive value of 78% and 86%, respectively. Conclusion Our results suggest that pretherapeutic gene expression profiling may assist in response prediction of rectal adenocarcinomas to preoperative chemoradiotherapy. The implementation of gene expression profiles for treatment stratification and clinical management of cancer patients requires validation in large, independent studies, which are now warranted.

Gene Expression Profiling of Paired Pre- and Post-Prednisolone (PRED) BM Samples from Childhood ALL Identifies Robust Signatures for PRED Response and Eventual Outcome.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 222-222 ◽  
Author(s):  
Yi Lu ◽  
Huiqing Liu ◽  
Ying Xu ◽  
Pei Lin Koh ◽  
Ariffin Hany ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Response To Therapy ◽  
Test Set ◽  
Childhood All ◽  
Blast Count ◽  
Highly Correlated ◽  
Eventual Outcome

Abstract Early response to therapy is the most important prognostic factor for childhood ALL. CCG investigators have shown that Day-7 and Day-14 BM blast counts were prognostically important although there is great inter-observer variability. BFM group have shown that day 8 prednisolone (PRED) response is highly predictive of the treatment outcome. While gene expression profiling (GEP) of diagnostic marrow can discern a pattern of PRED sensitivity as determined by in vitro MTT assay, the accuracy was low at only 70%. We hypothesized that changes in global GEP after therapy have a higher likelihood to predict response as the signatures of sensitivity and resistance may be unmasked during the therapy. We prospectively studied the changes in GEP using Affymetrix HG-U133A or Plus 2 chips on paired BM samples before and after 7-day course of PRED and one dose IT MTX in 58 patients with newly diagnosed or relapsed ALL. Unsupervised hierarchical clustering revealed that pre- and post- PRED samples in the patients still tended to cluster together, indicating that expression profiles of molecular subgroups were still most important. To remove intrinsic influence of molecular subtypes and identify potential signatures independent of genetic abnormalities, we subtracted Day-0 GEP from its paired Day-8 profile and retained probe sets with significant changes (≥ 10-fold). To avoid the ambiguity of variation in BM blast counting at Day-8, we divided the samples into a stringently reproducible group where “Good” PRED response was defined as that Day-8 blast count in PBL < 109/L and BM lymphoblasts ≤ 30% (n=16). “Poor” response was when Day 8 PBL ≥ 109/L (n=11). This stringently reproducible group (n=27) formed the training group to help define a distinct signature while the rest (n=31 pairs) were used as a blinded test set. 54 and 19 discriminating genes were identified by 2 independent statistical methods respectively, and an integrated predictor model was constructed based on shortlisted entries. This model predicted the PRED response with 100% accuracy for the training set using the leave-one-out cross validation but was less accurate in predicting the BM blast count in blinded test set. But intriguingly, in the blinded test set, this model predicted correctly 19 out of 21 reliable “Good” PRED responses are in CCR (91%), while among 8 predicted as “Poor” responses, only 2 are in CCR (25%). This suggests that as gene expression profiling as early as day 8 of PRED could discern the beginning of leukaemia cell death even before morphological changes are discernable and is highly correlated to eventual outcome. In conclusion, we have shown that analyses on the relative changes of gene expression profile can identify real genetic signatures indicating the sensitivity to PRED administration which is highly correlated with outcome.

Metaphase Cytogenetics Adds to Gene Expression Profiling in the Identification of High Risk Multiple Myeloma.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 114-114
Author(s):  
Guido Tricot ◽  
Fenghuang Zhan ◽  
Bart Barlogie ◽  
Yongsheng Huang ◽  
Jeffrey Sawyer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Prognostic Significance ◽  
Staging System ◽  
Treatment Modalities ◽  
Prognostic Impact ◽  
Newly Diagnosed ◽  
Prognostic Information

Abstract The International Staging System (ISS), based on B2-microglobulin and albumin levels at the time of diagnosis, has now generally been adopted as a new prognostic classification system for multiple myeloma (MM). While readily and widely applicable, ISS does not account for genetic disease features, such as metaphase (CA) and interphase fluorescence in situ hybridization (FISH) cytogenetic abnormalities, which when examined in the context of standard prognostic variables, confer higher hazards of relapse and disease-related death. Recently, gene expression profiling (GEP) uncovered the major prognostic significance for outcome of high expression of CKS1B, a gene mapping to an amplicon at chromosome 1q21. We have performed a comprehensive study of CA, FISH, GEP and ISS staging in 351 newly diagnosed MM patients, treated uniformly on Total Therapy 2. We have analyzed outcome based on a combination of high CKS1B by GEP and CA. GEP-based t(11;14) was prognostically favorable, irrespective of expression of CKS1B and, therefore, was removed from the group of patients with high CKS1B expression. After this adjustment, with the combination of both CA and high CKS1B (approximately 10% of all patients) conferred a very poor outcome with only 24% and 40% of such patients being event-free and/surviving at 3 years, compared with 72% and 84% for the others (p values : &lt;.0001). Such patients fared poorly, irrespective of their ISS stage. Similar prognostic information could be gained by combining CA with FISH-defined amplification of 1q21 and t(11;14). Because of their major prognostic impact, all newly diagnosed patients should be tested for these genetic markers. Novel treatment modalities are justified in the small subgroup of such poor prognosis patients, since they derive only a minor benefit from advances in MM therapy. CKS1B Q4 + CA (with no CCND1) vs. Others CKS1B Q4 + CA (with no CCND1) vs. Others

Gene-expression profiling of systemic anaplastic large-cell lymphoma reveals differences based on ALK status and two distinct morphologic ALK+ subtypes

Blood ◽  
2006 ◽  
Vol 109 (5) ◽  
pp. 2156-2164 ◽  
Author(s):  
Laurence Lamant ◽  
Aurélien de Reyniès ◽  
Marie-Michèle Duplantier ◽  
David S. Rickman ◽  
Frédérique Sabourdy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Anaplastic Large Cell Lymphoma ◽  
Expression Profiling ◽  
Cell Lymphoma ◽  
Expression Profiles ◽  
Large Cell ◽  
Molecular Signature ◽  
Number Of Patients ◽  
Large Cell Lymphoma

Abstract With the use of microarray gene-expression profiling, we analyzed a homogeneous series of 32 patients with systemic anaplastic large-cell lymphoma (ALCL) and 5 ALCL cell lines. Unsupervised analysis classified ALCL in 2 clusters, corresponding essentially to morphologic subgroups (ie, common type vs small cell and “mixed” variants) and clinical variables. Patients with a morphologic variant of ALCL had advanced-stage disease. This group included a significant number of patients who experienced early relapse. Supervised analysis showed that ALK+ALCL and ALK− ALCL have different gene-expression profiles, further confirming that they are different entities. Among the most significantly differentially expressed genes between ALK+ and ALK− samples, we found BCL6, PTPN12, CEBPB, and SERPINA1 genes to be overexpressed in ALK+ ALCL. This result was confirmed at the protein level for BCL-6, C/EBPβ and serpinA1 through tissue microarrays. The molecular signature of ALK− ALCL included overexpression of CCR7, CNTFR, IL22, and IL21 genes but did not provide any obvious clues to the molecular mechanism underlying this tumor subtype. Once confirmed on a larger number of patients, the results of the present study could be used for clinical and therapeutic management of patients at the time of diagnosis.

Gene Expression Profiling of Myeloma Cells To Predict Response to Primary Therapy with Thalidomide-Dexamethasone for Newly Diagnosed Multilple Myeloma.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 1494-1494
Author(s):  
Abderrahman Abdelkefi ◽  
John de Vos ◽  
Said Assou ◽  
Tarek Ben Othman ◽  
Jean-Francois Rossi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Gene Expression Profiling ◽  
Treatment Strategy ◽  
Expression Profiling ◽  
Plasma Cells ◽  
Gene List ◽  
Microarray Platform ◽  
Primary Therapy ◽  
Newly Diagnosed

Abstract Background: Thalidomide which represents an effective treatment strategy for relapsed/refractory multiple myeloma, actually represents a standard of care also for newly diagnosed multiple myeloma patients. Methods: In the present study, we adopted a gene expression profiling (GEP) strategy in an attempt to predict response (&gt; 50% reduction in serum M protein) to primary therapy with thalidomide-dexamethasone for newly diagnosed multiple myeloma. Plasma cells (CD138+) were purified from bone marrow aspirates from 17 patients at diagnosis, before initiation of treatment with thalidomide-dexamethasone. GEP was performed using the Affymetrix U133 Plus_2 microarray platform. The Affymetrix output (CEL files) was imported into Genespring 7.3 (Agilent technologies) microarray analysis software, where data files were normalized across chips using GCRMA and to the 50th percentile, followed by per gene normalization to median. Criteria of response were those established by Bladè et al. Results: After sufficient follow-up, responders (n=9) and nonresponders (n=8) were identified, and gene expression differences in baselines samples were examined. Of the 11000 genes surveyed, Wilcoxon rank sum test identified 149 genes that distinguished response from non response. A multivariate step-wise discriminant analysis (MSDA) revealed that 14 of the 149 genes could be used in a response predictor model (see table). Of interest, the gene list encompasses WXSC1, known to be involved in the chromosomal translocation t(4;14) (p16.3;q32.3) in multiple myeloma. Conclusion: These results could be the first step to adopt microfluidic cards, in an attempt to select at diagnosis patients who will respond favourably to a particular treatment strategy. List of 14 genes able to predict response to primary therapy with thalidomide-dexamethasone for newly diagnosed multiple myeloma. Gene ID Gene Name Chromosomal location 212771_at C10orf38 10p13 229874_x_at LOC400741 1p36.13 219690_at U2AF1L4 19q13.12 202207_at ARL7 2q37.1 243819_at GNG2 14q21 203753_at TCF4 18q21.1 235400_at FCRLM1 1q23.3 211474_s_at SERPINB6 6p25 226785_at ATP11C Xq27.1 215440_s_at BEXL1 Xq22.1–q22.3 209054_s_at WXSC1 4p16.3 227168_at FLJ25967 22p12.1 213355_at ST3GAL6 3q12.1 223218_s_at NFKBIZ 3p12–q12

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