scholarly journals Gene expression profiling impacts treatment decision making in newly diagnosed multiple myeloma patients in the prospective PROMMIS trial

eJHaem ◽  
2021 ◽  
Author(s):  
Noa Biran ◽  
Binod Dhakal ◽  
Suzanne Lentzsch ◽  
David Siegel ◽  
Saad Z. Usmani ◽  
...  
Keyword(s):  
Gene Expression ◽  
Decision Making ◽  
Multiple Myeloma ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Treatment Decision ◽  
Newly Diagnosed ◽  
Treatment Decision Making

Access to personalized medicine: Factors influencing the use and value of gene expression profiling in treatment decision making.

2013 ◽  
Vol 31 (31_suppl) ◽  
pp. 10-10
Author(s):  
Yvonne Bombard ◽  
Linda Rozmovits ◽  
Maureen E. Trudeau ◽  
Natasha B. Leighl ◽  
Ken Deal ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Decision Making ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Clinical Utility ◽  
Perceived Value ◽  
Treatment Decision ◽  
Treatment Decision Making ◽  
Comparison Results

10 Background: Genomic information is increasingly used to personalize health care. One example is gene-expression profiling (GEP) tests that estimate recurrence risk to inform chemotherapy decisions in breast cancer treatment. Recently, GEP tests were publicly funded in Ontario. We assessed the clinical utility of GEP tests, exploring the factors facilitating their use and value in treatment decision-making. Methods: As part of a mixed-methods clinical utility study, we conducted interviews with oncologists (n=14), and focus groups and interviews with breast cancer patients (n=28) who underwent GEP, recruited through oncology clinics in Ontario. Data were analyzed using content analysis and constant comparison. Results: Various factors governing access to GEP have given rise to challenges for patients and oncologists. Oncologists are positioned as gatekeepers of GEP, providing access in medically appropriate cases. However, varying perceptions of appropriateness led to perceived inequities in access and negative impacts on the doctor-patient relationship. Media attention facilitated patient awareness of GEP but complicated gatekeeping. Additional administration burden and long waits for results led to increased patient anxiety and delayed treatment. Collectively, these factors inadvertently heightened GEP’s perceived value for patients relative to other prognostic indicators because of barriers to access. Conclusions: This study delineates the factors facilitating and restricting access to GEP, and highlights the roles of the media and organization of services in GEP’s perceived value and utilization. Results identify a need for administrative changes and practice guidelines to support streamlined and standardized utilization of the test.

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.

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 : <.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 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 (> 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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