scholarly journals GENE-EXPRESSION PROFILING PREDICTS DISEASE PROGRESSION IN FOLLICULAR LYMPHOMA

2017 ◽  
Vol 35 ◽  
pp. 113-115 ◽  
Author(s):  
S. Huet ◽  
B. Tesson ◽  
J. Jais ◽  
A.L. Feldman ◽  
L. Magnano ◽  
...  
Keyword(s):  
Gene Expression ◽  
Follicular Lymphoma ◽  
Gene Expression Profiling ◽  
Disease Progression ◽  
Expression Profiling

The 70-Gene MyPRSR prognostic Risk Score Signature Predicts Increased Risk of Progression from MGUS to Multiple Myeloma Requring Treatment

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 3275-3275 ◽  
Author(s):  
Ryan van Laar ◽  
Phillip Farmer ◽  
Richard A Bender ◽  
Aga Zielinski ◽  
Kenton Leigh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bone Marrow ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Disease Progression ◽  
Risk Score ◽  
Expression Profiling ◽  
Medical Sciences ◽  
Risk Of Progression

Abstract Background: The 70-gene prognostic risk score (MyPRS), originally developed by the University of Arkansas for Medical Sciences, is the most validated genomic assay for prediction of event free and overall survival in asymptomatic, newly diagnosed and relapsed multiple myeloma. Gene expression profiling was performed on CD138+ plasma cells obtained from the bone marrow of individuals with the precursor condition, monoclonal gammopathy of undetermined significance (MGUS), who later progressed to MM. Analysis of the 70 gene risk score vs. the probability of progression to MM requiring therapy was performed. Method and Results: Between 2011 and 2015, MyPRS gene expression profiling of 266 individuals who initially presented with MGUS was performed. The mean length of time between MGUS diagnosis and disease progression or last follow-up was 6.9 years (standard deviation = 4.0 years). The mean length of time between MGUS gene expression profiling and either disease progression or date of last follow-up was 4.8 years (standard deviation = 2.9 years). Disease progression was defined as the development of CRAB criteria or bone marrow plasmacytosis exceeding 60%. 28 patients developed MM requiring therapy within two years of their MGUS GEP. Twelve individuals (5%) were classified as high risk using the previously established threshold for AMG (GEP70 >37.2 = high risk). Four high risk patients (33%) progressed to active MM within 2 years. 24/255 (9%) patients who were classified as low risk progressed to MM within the same length of time. A risk score histogram and binary fitted line plot of risk score vs. probability of progression to MM within 2 years were generated. Conclusion: Performing MyPRS gene expression profiling on patients diagnosed with MGUS provides personalized information on the individuals' risk of progression to MM requiring treatment. While the overall rate of progression is low, approximately 5% of individuals are at higher risk and may benefit from increased monitoring. The 70-gene signature appears useful for identifying high risk behavior in MGUS patients thereby allowing early intervention and possible inclusion in clinical trials. MyPRS provides a risk assessment at a single point in time unlike recently reported metrics (ASCO Abs. # 8004) which measure a change in Hgb and M protein over time, along with bone marrow plasmacytosis, in order to determine the risk of progression. Figure 1 Figure 1. Figure 2 Figure 2. Disclosures van Laar: Signal Genetics, Inc.: Employment. Farmer:University of Arkansas: Employment. Bender:Signal Genetics, Inc.: Employment. Zielinski:Signal Genetics, Inc.: Employment. Leigh:Signal Genetics, Inc.: Employment. Brown:Signal Genetics, Inc.: Employment. Barlogie:Mount Sinai Hospital: Employment. Morgan:Univ of AR for Medical Sciences: Employment; Bristol Meyers: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Research Funding; Celgene: Consultancy, Honoraria, Research Funding.

scholarly journals Indolent lymphoma: follicular lymphoma and the microenvironment—insights from gene expression profiling

Hematology ◽  
2014 ◽  
Vol 2014 (1) ◽  
pp. 163-168 ◽  
Author(s):  
Lisa M. Rimsza ◽  
Melba C. Jaramillo
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Follicular Lymphoma ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Response To Therapy ◽  
Future Studies ◽  
Complex Interactions ◽  
Choice Of Therapy

Abstract As shown with gene expression profiling (GEP), the development and progression of follicular lymphoma (FL) involves complex interactions between neoplastic B cells and the surrounding microenvironment. GEP further reveals that the tumor microenvironment may predict survival in patients with FL and influence the response to therapy and the risk of transformation. Here, we briefly review GEP technology and summarize the role of the tumor microenvironment in FL diagnosis, prognosis, and transformation. Genes expressed by infiltrating T cells and macrophages appear to be the most important predictors of survival, clinical behavior, and outcome. These findings provide a basis for future studies into the pathogenesis and pathophysiology of FL and may ultimately provide guidance in the choice of therapy and the identification of potential therapeutic targets.

The Number of CD25+ Tumor-Infiltrating Cells May Predict Clinical Response to Rituximab in Follicular Lymphoma Patients.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 748-748 ◽  
Author(s):  
Christopher Y. Park ◽  
Sean P. Bohen ◽  
Ronald Levy ◽  
Roger A. Warnke ◽  
Yasodha Natkunam
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Lymph Node ◽  
Follicular Lymphoma ◽  
Gene Expression Profiling ◽  
Clinical Response ◽  
Expression Profiling ◽  
Complete Response ◽  
Infiltrating Cells ◽  
Clinical Responses

Abstract Introduction: Rituximab, a chimeric IgG1 monoclonal antibody directed at the B-cell antigen CD20, has become a mainstay in the treatment of follicular lymphoma. Unfortunately, not all patients respond to therapy, and therefore the identification of factors that accurately predict response has become an active area of investigation. Recently, gene expression profiling analysis of whole lymph nodes from follicular lymphoma patients resulted in the identification of numerous genes that are differentially expressed in responders versus nonresponders (Bohen, et al. PNAS 100: 1926–1930, 2003). However, as the gene expression profiling technique still requires clinical validation and is also a cost- and time-intensive method, we sought to identify immunohistologic factors that may act as predictors of clinical response to Rituximab. As the gene profiling data suggested a relationship between the tumor immune and clinical responses, we reasoned that characterizing the different immune cell populations in lymph node biopsies by immunohistochemistry might similarly provide prognostic information for Rituximab response. Methods/Materials: Diagnostic lymph node biopsies from 31 patients diagnosed with follicular lymphoma and for whom the clinical response to Rituximab was known were included in the study. These included lymph nodes from 20 of the 24 patients included in the previously described gene expression profiling study. 12 non-responders, 10 partial responders, and 9 complete responders were evaluated, representing 11 grade 1, 17 grade 2, and 3 grade 3 follicular lymphomas. Numerous immunohistochemical stains were performed, including markers used to diagnose follicular lymphoma (CD20, CD10, bcl-2, Ki-67), as well as additional markers to evaluate tumor-infiltrating T-cells (CD3, CD4, CD8, CD25, TIA-1), follicular dendritic cell networks (CD21, CD23), and monocytes/macrophages (CD163). For each marker, the number, distribution, intensity and variability of staining were scored. Results: Utilizing multiple logistic regression analyses, we found that increased numbers of intrafollicular CD25+ cells showed a trend towards being associated with partial or complete response (p=0.064); however, the number of intrafollicular CD25+ cells was less reliable in distinguishing partial (p=0.11) from complete responses (p=0.12). The number of intrafollicular CD3+ T-cells was also positively correlated with complete response (p=0.0467), but did not reliably distinguish between partial responders or nonresponders. None of the other markers evaluated showed a statistically significant association with response. Conclusions: Our data suggest that the clinical response to Rituximab is positively associated with increased numbers of CD25+ tumor-infiltrating cells. This result runs counter to the previously published gene expression profiling data that suggested a negative association between T-cells and clinical response. As our initial sample size was limited, we are presently evaluating additional biopsies and immunohistochemical markers to validate and refine our observations. Nevertheless, these initial observations provide evidence that an immunohistochemical assay may eventually prove useful in predicting clinical responses to Rituximab therapy in follicular lymphoma patients.

scholarly journals Clinical quantitation of immune signature in follicular lymphoma by RT-PCR–based gene expression profiling

Blood ◽  
2008 ◽  
Vol 111 (9) ◽  
pp. 4764-4770 ◽  
Author(s):  
Richard J. Byers ◽  
Ebrahim Sakhinia ◽  
Preethi Joseph ◽  
Caroline Glennie ◽  
Judith A. Hoyland ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Response ◽  
Follicular Lymphoma ◽  
Gene Expression Profiling ◽  
Real Time ◽  
Expression Profiling ◽  
Immune Response Gene ◽  
Routine Practice ◽  
Gene Signatures ◽  
Survival Interval

Abstract Microarray gene expression profiling studies have demonstrated immune response gene signatures that appear predictive of outcome in follicular lymphoma (FL). However, measurement of these marker genes in routine practice remains difficult. We have therefore investigated the immune response in FL using real-time polymerase chain reaction (PCR) to measure expression levels of 35 candidate Indicator genes, selected from microarray studies, to polyA cDNAs prepared from 60 archived human frozen lymph nodes, in parallel with immunohistochemical analysis for CD3, CD4, CD7, CD8, CD10, CD20, CD21, and CD68. High levels of CCR1, a marker of monocyte activation, were associated with a shorter survival interval, and high levels of CD3 with better survival, while immunohistochemistry demonstrated association of high numbers of CD68+ macrophages with a shorter survival interval and of high numbers of CD7+ T cells with a longer survival interval. The results confirm the role of the host immune response in outcome in FL and identify CCR1 as a prognostic indicator and marker of an immune switch between macrophages and a T cell–dominant response. They demonstrate the utility of polyA DNA and real-time PCR for measurement of gene signatures and the applicability of using this type of “molecular block” in clinical practice.

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