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.

Intra-Tumoral Heterogeneity In Multiple Myeloma Gleaned From Gene Expression Profiling Analysis Of Plasma Cells and Bone Marrow Biopsies: Comparison Of Random Bone Marrow Samples and CT-Guided Fine Needle Aspirations Of MRI-Defined Focal Lesions

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 3123-3123
Author(s):  
Bart Barlogie ◽  
Emily Hansen ◽  
Sarah Waheed ◽  
Jameel Muzaffar ◽  
Monica Grazziutti ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Bone Marrow ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Plasma Cells ◽  
P Value ◽  
Focal Lesions ◽  
Molecular Subgroup

Abstract Intra-tumoral heterogeneity (ITH) is increasingly viewed as the Achilles heel of treatment failure in malignant disease including multiple myeloma (MM). Most MM patients harbor focal lesions (FL) that are recognized on MRI long before bone destruction is detectable by conventional X-ray examination. Serial MRI examinations show that eventually 60% of patients will achieve resolution of FL (MRI-CR). However, this will lag behind the onset of a clinical CR by 18 to 24 months, thus attesting to the biological differences between FL and diffuse MM growth patterns. Consequently, we performed concurrent gene expression profiling (GEP) analyses of plasma cells (PC) from both random bone marrow (RBM) via iliac crest and FL. Our primary aims were to first compare the molecular profiles of FL vs. RBM, second to determine if ITH existed (as defined molecular subgroup and risk), and finally to investigate if the bone marrow micro-environment (ME) contained a biologically interesting signature. A total of 176 patients were available for this study with a breakdown of: TT3 (n=23), TT4 for low-risk (n=131) and TT5 for high-risk MM (n=22). Regarding the molecular analyses of PCs, GEP-based risk (GEP-70, GEP-5) and molecular subgroup correspondence were examined for commonalties and differences between RBM and FL. A “filtering” approach for ME genes was also developed and bone marrow biopsy (BMBx) GEP data derived from this method is under analysis. PC risk correspondence between FL and RBM was 86% for GEP70 and 88% for the GEP5 model. Additionally, 82% had a molecular subgroup concordance, however, they did differ among subgroups (p=0.020) by Fisher's Exact Test. A lower concordance was noted in the CD2, LB, and PR subgroups (67%, 69%, 73%, respectively). GEP70 and GEP5 risk concordance between RBM and FL samples by molecular subgroup was also examined. The overall correlation coefficients were 0.619 (GEP70) and 0.597 (GEP5). The best correspondence was noted for CD1, MF and PR subgroups especially for the GEP5 model. HY, LB and MS showed intermediate correlations, while CD2 fared worst with values of only 0.322 for GEP70 and 0.267 for GEP5 model. Figure 1 portrays these data in more detail for the GEP70 and GEP5 models. Good correlations were noted between RBM and FL based risk scores in case of molecular subgroup concordance (left panels) in both GEP5 and GEP70 risk models, whereas considerable scatter existed in case of subgroup discordance (right panels). The clinical implications in TT4 regarding RBM and FL derived risk and molecular subgroup information, viewed in the context of standard prognostic baseline variables are portrayed in Table 1. High B2M levels at both cut-points imparted inferior OS and PFS as did low hemoglobin. Although present in 42% of patients, cytogenetic abnormalities (CA) did not affect outcomes. FL-based GEP5-defined high-risk designation conferred poor OS and PFS. B2M>5.5mg/L and FL-derived GEP5 high-risk MM, pertaining to 29% and 11% of patients, survived the multivariate model for both OS and PFS. Next, in examining PC-GEP differences among RBM and FL sites, 199 gene probes were identified with a false discovery rate (FDR) of 1x10-6. Additionally, 55 of the 199 belong to four molecular networks of inter related genes associated with: lipid metabolism, cellular movement, growth and proliferation, and cell-to-cell interactions. Multivariate analysis identified the GEP5 high risk designation of focal lesion PCs to be significantly prognostic with a HR=3.73 (p=0.023).Table 1Cox regression analysis of variables linked to overall and progression-free survival in TT4.Overall SurvivalProgression-Free SurvivalVariablen/N (%)HR (95% CI)P-valueHR (95% CI)P-valueMultivariateB2M > 5.5 mg/L38/130 (29%)3.71 (1.49, 9.22)0.0053.84 (1.58, 9.31)0.003FL GEP5 High Risk14/130 (11%)3.68 (1.19, 11.41)0.0243.73 (1.20, 11.62)0.023HR- Hazard Ratio, 95% CI- 95% Confidence Interval, P-value from Wald Chi-Square Test in Cox RegressionNS2- Multivariate results not statistically significant at 0.05 level. All univariate p-values reported regardless of significance.Multivariate model uses stepwise selection with entry level 0.1 and variable remains if meets the 0.05 level.A multivariate p-value greater than 0.05 indicates variable forced into model with significant variables chosen using stepwise selection. Disclosures: No relevant conflicts of interest to declare.

scholarly journals New risk model is able to identify patients with a low risk of progression in systemic sclerosis

RMD Open ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e001524
Author(s):  
Nina Marijn van Leeuwen ◽  
Marc Maurits ◽  
Sophie Liem ◽  
Jacopo Ciaffi ◽  
Nina Ajmone Marsan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systemic Sclerosis ◽  
High Risk ◽  
Disease Progression ◽  
Risk Model ◽  
Immunosuppressive Drugs ◽  
Low Risk ◽  
Expert Assessment ◽  
Risk Of Progression

ObjectivesTo develop a prediction model to guide annual assessment of systemic sclerosis (SSc) patients tailored in accordance to disease activity.MethodsA machine learning approach was used to develop a model that can identify patients without disease progression. SSc patients included in the prospective Leiden SSc cohort and fulfilling the ACR/EULAR 2013 criteria were included. Disease progression was defined as progression in ≥1 organ system, and/or start of immunosuppression or death. Using elastic-net-regularisation, and including 90 independent clinical variables (100% complete), we trained the model on 75% and validated it on 25% of the patients, optimising on negative predictive value (NPV) to minimise the likelihood of missing progression. Probability cutoffs were identified for low and high risk for disease progression by expert assessment.ResultsOf the 492 SSc patients (follow-up range: 2–10 years), disease progression during follow-up was observed in 52% (median time 4.9 years). Performance of the model in the test set showed an AUC-ROC of 0.66. Probability score cutoffs were defined: low risk for disease progression (<0.197, NPV:1.0; 29% of patients), intermediate risk (0.197–0.223, NPV:0.82; 27%) and high risk (>0.223, NPV:0.78; 44%). The relevant variables for the model were: previous use of cyclophosphamide or corticosteroids, start with immunosuppressive drugs, previous gastrointestinal progression, previous cardiovascular event, pulmonary arterial hypertension, modified Rodnan Skin Score, creatine kinase and diffusing capacity for carbon monoxide.ConclusionOur machine-learning-assisted model for progression enabled us to classify 29% of SSc patients as ‘low risk’. In this group, annual assessment programmes could be less extensive than indicated by international guidelines.

Resolving driver events in MLL-r negative high-risk infant ALL.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 10030-10030
Author(s):  
Jennifer Seelisch ◽  
Matthew Zatzman ◽  
Federico Comitani ◽  
Fabio Fuligni ◽  
Ledia Brunga ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
High Risk ◽  
B Cell ◽  
Gene Expression Profiling ◽  
Rna Sequencing ◽  
Clinical Features ◽  
Expression Profiling ◽  
High Risk Infant ◽  
Childhood All

10030 Background: Infant acute lymphoblastic leukemia (ALL) is the only subtype of childhood ALL whose outcome has not improved over the past two decades. The most important prognosticator is the presence of rearrangements in the Mixed Lineage Leukemia gene (MLL-r), however, many patients present with high-risk clinical features but without MLL-r. We recently identified two cases of infant ALL with high-risk clinical features resembling MLL-r, but were negative for MLL-r by conventional diagnostics. RNA sequencing revealed a partial tandem duplication in MLL (MLL-PTD). We thus aimed to determine if MLL-PTD, other MLL abnormalities, or other genetic or transcriptomic features were driving this subset of high-risk infant ALL without MLL-r. Methods: We obtained 19 banked patient samples from the Children’s Oncology Group (COG) infant ALL trial (AALL0631) from MLL wildtype patients as determined by FISH and cytogenetics. Utilizing deep RNA-sequencing, we manually inspected the MLL gene for MLL-PTD, while also performing automated fusion detection and gene expression profiling in search of defining features of these tumors. Results: 3 additional MLL-PTDs were identified, all in patients with infant T-cell ALL, whereas both index cases were in patients with infant B-cell ALL. Gene expression profiling analysis revealed that all five MLL-PTD infants clustered together. Eight infants (7 with B-cell ALL) were found to have Ph-like expression. Five of these 8 infants were also found to have an IKZF1/JAK2 expression profile; one of these five had a PAX5-JAK2 fusion detected. Two infants (including the one noted above) had novel PAX5 fusions, known drivers of B-cell leukemia. Additional detected fusions included TCF3-PBX1 and TCF4-ZNF384. Conclusions: MLL-PTDs were found in both B- and T-cell infant ALL. Though Ph-like ALL has been described in adolescents and young adults, we found a substantial frequency of Ph-like expression among MLL-WT infants. Further characterization of these infants is ongoing. If replicated in other infant cohorts, these two findings may help explain the poor prognosis of MLL-WT ALL when compared to children with standard risk ALL, and offer the possibility of targeted therapy for select infants.

scholarly journals Three Years Follow-Up Study Showed Prenatal Methamphetamine Exposure May Cause Chronic Abnormalities In Transcriptomic Pattern

2021 ◽  
Author(s):  
Arvin Haghighatfard ◽  
Soha Seifollahi ◽  
Pegah Rajabi ◽  
Niloofar Rahmani ◽  
Rojin Ghannadzadeh
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Rna Extraction ◽  
Childbearing Age ◽  
Methamphetamine Use ◽  
Methamphetamine Use Disorder

Abstract Background: The high rate of methamphetamine use disorder among young adults and women of childbearing age makes it imperative to clarify the long-term effects of Methamphetamine exposure on the offspring. Behavioral and cognitive problems had been reported in children with parental Methamphetamine exposure (PME). The present study aimed to assess the acute and chronic effects of PME in molecular regulations and gene expression profiles of children during their first years of life.Methods: All subjects were recruited before birth, and sampling was conducted from the first ten days of birth, twelve months, twenty months, and thirty-six months of age. Finally, 2658 children with PME and 3573 normal children had been finished the follow-up. RNA extraction was operated from blood samples and gene expression profiling was conducted by using the Affymetrix GeneChip Human Genome U133 plus 2.0 Array Platform. Gene expression data were confirmed by Real-time PCR. Results: Gene expression profiling during thirty-six months showed several constant mRNA level alterations in children with PME compared with normal. These genes are involved in several gene ontologies and pathways involved with the immune system, neuronal functions, and bioenergetic metabolism. It seems that Methamphetamine use disorder before and during the pregnancy period may affect the expression profile of children, and these changes could remain years after birth. Affected genes have some similarities with the gene expression patterns of addiction, psychiatric disorders, neurodevelopmental disabilities, and immune deficiencies. Conclusion: Findings may shed light on the molecular effects of prenatal methamphetamine exposure and may lead to new psychological and somatic caring protocols for these children based on their potential abnormalities.

Gene expression profiling of bone marrow mesenchymal stem cells from Osteogenesis Imperfecta patients during osteoblast differentiation

2017 ◽  
Vol 60 (6) ◽  
pp. 326-334 ◽  
Author(s):  
Carla Martins Kaneto ◽  
Patrícia S. Pereira Lima ◽  
Karen Lima Prata ◽  
Jane Lima dos Santos ◽  
João Monteiro de Pina Neto ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Bone Marrow ◽  
Mesenchymal Stem Cells ◽  
Osteogenesis Imperfecta ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Osteoblast Differentiation ◽  
Marrow Mesenchymal Stem Cells

scholarly journals Erratum: Dose-dense and less dose-intense total therapy 5 for gene expression profiling-defined high-risk multiple myeloma

2016 ◽  
Vol 6 (9) ◽  
pp. e471-e471 ◽  
Author(s):  
Y Jethava ◽  
A Mitchell ◽  
M Zangari ◽  
S Waheed ◽  
C Schinke ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Total Therapy ◽  
Dose Dense

scholarly journals Impact of gene expression profiling-based risk stratification in patients with myeloma receiving initial therapy with lenalidomide and dexamethasone

Blood ◽  
2011 ◽  
Vol 118 (16) ◽  
pp. 4359-4362 ◽  
Author(s):  
Shaji K. Kumar ◽  
Hajime Uno ◽  
Susanna J. Jacobus ◽  
Scott A. Van Wier ◽  
Greg J. Ahmann ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Risk Stratification ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Chromosomal Abnormalities ◽  
Initial Therapy ◽  
High Risk Group ◽  
C Statistic ◽  
Risk Patients

Abstract Detection of specific chromosomal abnormalities by FISH and metaphase cytogenetics allows risk stratification in multiple myeloma; however, gene expression profiling (GEP) based signatures may enable more specific risk categorization. We examined the utility of 2 GEP-based risk stratification systems among patients undergoing initial therapy with lenalidomide in the context of a phase 3 trial. Among 45 patients studied at baseline, 7 (16%) and 10 (22%), respectively, were high-risk using the GEP70 and GEP15 signatures. The median overall survival for the GEP70 high-risk group was 19 months versus not reached for the rest (hazard ratio = 14.1). Although the medians were not reached, the GEP15 also predicted a poor outcome among the high-risk patients. The C-statistic for the GEP70, GEP15, and FISH based risk stratification systems was 0.74, 0.7, and 0.7, respectively. Here we demonstrate the prognostic value for GEP risk stratification in a group of patients primarily treated with novel agents. This trial was registered at www.clinicaltrials.gov as #NCT00098475.

scholarly journals Differential gene expression profiling of human bone marrow-derived mesenchymal stem cells during adipogenic development

BMC Genomics ◽  
2011 ◽  
Vol 12 (1) ◽  
pp. 461 ◽  
Author(s):  
Adriane Menssen ◽  
Thomas Häupl ◽  
Michael Sittinger ◽  
Bruno Delorme ◽  
Pierre Charbord ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Bone Marrow ◽  
Mesenchymal Stem Cells ◽  
Gene Expression Profiling ◽  
Differential Gene Expression ◽  
Expression Profiling ◽  
Human Bone ◽  
Human Bone Marrow ◽  
Differential Gene

Clonotypic Bone Marrow-Derived Endothelial Progenitor Cells in Multiple Myeloma.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 3497-3497
Author(s):  
Marc J. Braunstein ◽  
Daniel R. Carrasco ◽  
David Kahn ◽  
Kumar Sukhdeo ◽  
Alexei Protopopov ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Bone Marrow ◽  
High Resolution ◽  
Gene Expression Profiling ◽  
Progenitor Cells ◽  
Tumor Cells ◽  
Endothelial Progenitor Cells ◽  
Expression Profiling ◽  
Gains And Losses

Abstract In multiple myeloma (MM), bone marrow-derived endothelial progenitor cells (EPCs) contribute to tumor neoangiogenesis and their levels covary with tumor mass and prognosis. Recent X-chromosome inactivation studies in female patients showed that, similar to tumor cells, EPCs are clonally restricted in MM. Genomic profiling of MM using high-resolution array comparative genomic hybridization (aCGH) has been previously utilized to mine the genome and find clinical correlates in MM patients. In this study, clonotypic aspects of bone marrow-derived EPCs and MM cells were investigated using aCGH and expression profiling analysis. Confluent EPCs were outgrown from bone marrow aspirates by adherence to laminin. EPCs were >98% vWF/CD133/KDR+ and <1% CD38+. The laminin-nonadherent bone marrow fraction enriched for tumor cells was >50% CD38+. For aCGH and for gene expression profiling, genomic DNA and total RNA from EPCs and MM cells were hybridized to human oligonucleotide arrays (Agilent Technologies) and human cDNA microarrays (Affymetrix), respectively. High resolution aCGH with segmentation analysis showed that EPCs and MM cells in one of ten cases share identical patterns of chromosomal gains and losses, while another 5 cases shared multiple focal copy number alterations (CNAs) including gains and losses. The genomes of EPCs and MM cells additionally displayed exclusive CNAs, but these were far fewer in EPCs than in MM cells. In 3 patients, EPCs harbored a common 0.6Mb deletion at 1q21 not shared by MM cells. Pertinent genes in this region that could affect proliferation and tumor suppression include N2N, NBPF10, and TXNIP. Validation studies of aCGH findings by other methods are ongoing. Gene expression profiling showed decreased expression of 1q21 region genes (e.g., calgranulin C and lamin A/C). A genome-wide comparison of patients’ MM cells and EPCs, which is focused on their shared genetic characteristics, will be presented.

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