Sub-Classification of Hyperdiploid Myeloma Using Global Gene Expression Profiling and SNP-Based Mapping Arrays.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 3390-3390
Author(s):  
Brian A. Walker ◽  
Paola E. Leone ◽  
Matthew W. Jenner ◽  
David C. Johnson ◽  
David Gonzalez ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hierarchical Clustering ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Poor Response ◽  
Response To Therapy ◽  
Genomic Change ◽  
Group 5 ◽  
Mapping Arrays ◽  
Group 1

Abstract The translocation/cyclin classification system in myeloma does not neatly define subgroups of hyperdiploidy (HRD) and we sought a more definitive sub-classification. Using 131 pre-treatment samples (49 HRD with no split IgH locus by FISH) we defined subgroups using both supervised and unsupervised hierarchical clustering of gene expression profiles. RNA was purified from CD138+ cells, amplified using a 2-cycle IVT and hybridised onto U133 Plus 2 GeneChips. On 30 of the 49 HRD samples we also performed 500K SNP mapping arrays to define the true extent of the genomic change in HRD. The most common trisomic chromosomes were 15 (97%), 9 (86%), 19 (80%), 5 (77%), 11 (74%), 3 (64%), 21 (54%) and 7 (54%). There was no association between HRD and any of the major genetic abnormalities (1p, 1q, 6q, 8p, 13, 16q and 17p) compared to the non-HRD (NHRD) group. Many interstitial deletions were seen in all HRD samples, on both odd and even numbered chromosomes. However, using gene mapping alone it was not possible to globally sub-classify HRD myeloma. We compared NHRD and HRD sample gene expression profiles, removing differences between t(4;14) and t(11;14) cases in the NHRD group. This analysis showed that HRD samples segregate into 2 groups; one with a pattern distinct to NHRD samples and another containing genes that are up-regulated in both HRD and NHRD samples. In this analysis 176 genes were up-regulated in the HRD samples and were predominantly located on the trisomic chromosomes, especially 19, 11, 9 and 5. These genes showed a predominant upregulation of HGF and TRAIL, and down-regulation of TRAIL-R2 compared to NHRD samples. Unsupervised hierarchical clustering split the HRD samples into 5 distinct groups suggesting that there are distinct pathological entities. Group 1 overexpressed 90 genes including BCL2, CCNL1 (cyclin L1) and CDK6, consistent with a proliferation signature. Group 2 overexpressed interferon inducible genes including IFI6, IFI27, IFIT1 as well as TRAIL. Group 3 upregulated genes included IL8, MMP9 and TIMP2. Group 4 upregulated transcripts include neurexophilin 3. Group 5 was less well defined but contained transcripts for CCND2, WNT5A and CXCR4. To define clinically relevant subgroups the HRD samples were clustered comparing response or no response to induction chemotherapy. Analysis showed that Group 1 cases cluster together and were either non or minimal responders. This is consistent with the Group 1 cases over-expressing cell-cycle and proliferation related genes. Group 5 clustered together and were either complete or partial responders, and had a low expression of the genes over expressed by Group 1. The non-responder group overexpressed 58 genes and include MMSET-like 1 (in a region on 8p paralogous to 4p containing FGFR1), DVL3 (dishevelled homolog 3) and CCNL1. 23 genes were over expressed in the complete response group including caspase 1 and manic fringe homolog. The unsupervised HRD cluster and the supervised response cluster shared 10 genes, including CCNL1 and ASS. We have used both genetic and expression data to further define the HRD sub-group in terms of gene expression signatures and response to therapy and have identified 5 groups, of which Group 1 has a proliferation signature and poor response to induction therapy.

scholarly journals Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Arika Fukushima ◽  
Masahiro Sugimoto ◽  
Satoru Hiwa ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response To Therapy ◽  
Hcv Infection ◽  
Entire Treatment Period ◽  
Time Points ◽  
Time Course Data ◽  
Conventional Methods

Abstract Background Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.

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.

scholarly journals New Short Term Prediction Method for Chemical Carcinogenicity by Hepatic Transcript Profiling following 28-Day Toxicity Tests in Rats

2011 ◽  
Vol 10 ◽  
pp. CIN.S7789 ◽  
Author(s):  
Hiroshi Matsumoto ◽  
Yoshikuni Yakabe ◽  
Fumiyo Saito ◽  
Koichi Saito ◽  
Kayo Sumida ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Prediction Method ◽  
Predictive Score ◽  
Toxicity Tests ◽  
Support Vector ◽  
Total System ◽  
Prediction Formula ◽  
Group 1

We have previously shown the hepatic gene expression profiles of carcinogens in 28-day toxicity tests were clustered into three major groups (Group-1 to 3). Here, we developed a new prediction method for Group-1 carcinogens which consist mainly of genotoxic rat hepatocarcinogens. The prediction formula was generated by a support vector machine using 5 selected genes as the predictive genes and predictive score was introduced to judge carcinogenicity. It correctly predicted the carcinogenicity of all 17 Group-1 chemicals and 22 of 24 non-carcinogens regardless of genotoxicity. In the dose-response study, the prediction score was altered from negative to positive as the dose increased, indicating that the characteristic gene expression profile emerged over a range of carcinogen-specific doses. We conclude that the prediction formula can quantitatively predict the carcinogenicity of Group-1 carcinogens. The same method may be applied to other groups of carcinogens to build a total system for prediction of carcinogenicity.

Gene Expression Profiling of CD34+ Cells in Patients with Myelodysplastic Syndromes

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 3642-3642
Author(s):  
Andrea Pellagatti ◽  
Mario Cazzola ◽  
Aristoteles Giagounidis ◽  
Janet Perry ◽  
Luca Malcovati ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Hierarchical Clustering ◽  
Myelodysplastic Syndromes ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Good Separation ◽  
Trisomy 8 ◽  
Cd34 Cells

Abstract The myelodysplastic syndromes (MDS) are a heterogeneous group of hematopoietic malignancies, characterized by blood cytopenias, ineffective hematopoiesis and a hypercellular bone marrow. We have investigated the gene expression profiles of a large group of patients with MDS in order to better understand the molecular pathogenesis of this disorder. The CD34+ cells obtained from 154 MDS patients and 17 healthy individuals were analyzed using Affymetrix U133 Plus2.0 arrays. 38 genes were up-regulated by >2-fold in at least 77 MDS patients, and pathway analysis using these genes showed that the interferon signalling pathway was significantly deregulated (p=0.0006). Indeed IFIT1, the most up-regulated gene (up-regulated in 110 of 154 MDS patients), is an interferon-stimulated gene (ISG). Other ISGs, which mediate growth inhibitory effects of interferon, such as IFITM1, IFI44L and IFIT3, were markedly up-regulated in the majority of MDS patients. Up-regulation of ISGs is a major feature of MDS and may be responsible for some of the hematological characteristics of this disorder, such as peripheral blood cytopenias. We investigated differences in gene expression that could distinguish MDS patients according to their FAB subtype classification (48 patients with RA, 44 patients with RARS and 62 patients with RAEB). Hierarchical clustering performed using the 773 significantly differentially expressed probe sets identified showed that MDS patients with RARS constitute the most homogeneous group, while MDS patients with RA and RAEB show more overlap. RARS gene expression profile was characterized by up-regulation of mitochondrial-related genes and by down-regulation of ABCB7, a gene mutated in the rare inherited X-linked sideroblastic anemia with ataxia (XLSA/A). Moreover, a good separation between the 20 patients with RARS and the 20 patients with RCMD-RS was obtained by hierarchical clustering using the 86 significantly differentially expressed genes between these two WHO subgroups. One of the most significant genes was MFN1, which is essential for mitochondrial fusion and maintenance of mitochondrial morphology. The association of distinct gene expression profiles with specific cytogenetic groups was also determined, and we were able to separate by hierarchical clustering MDS patients with del(5q), patients with −7/del(7q) and patients with trisomy 8. The expression profile of patients with the del(5q) was characterized by down-regulation of genes mapping to chromosome 5q. Genes differentially expressed in patients with −7/del(7q) include LOX and UBE2H, while genes differentially expressed in patients with trisomy 8 include HRSP12 and TPM4. These findings suggest distinct molecular pathogenetic pathways for MDS patients with del(5q), −7/del(7q) and trisomy 8. In order to identify differences in gene expression associated with MDS disease progression, we compared the 48 patients with early MDS (RA) and the 35 patients with advanced MDS (RAEB2). Hierarchical clustering performed using 1081 significantly differentially expressed probe sets resulted in a good separation between MDS patients with RA and patients with RAEB2. LEF1, a regulator of neutrophilic granulopoiesis, was the most significant differentially expressed gene with higher expression levels in patients with RA and decreasing in patients with RAEB2. Other genes showing higher expression levels in patients with RA, decreasing in patients with RAEB2, include CASC5, a cancer susceptibility candidate gene, and RBBP8, a gene that plays a role in DNA-damage-induced cell cycle checkpoint control. Several genes mapping to the cell cycle pathway were significantly deregulated between early and advanced MDS. This study provides new important insights into the pathophysiology of MDS and represents a first step towards determining pathway signatures in MDS as a guide to targeted therapies.

scholarly journals Dissection of Clinical and Gene Expression Signatures of Familial versus Multifactorial Chylomicronemia

2020 ◽  
Vol 4 (6) ◽  
Author(s):  
Karine Tremblay ◽  
Daniel Gaudet ◽  
Etienne Khoury ◽  
Diane Brisson
Keyword(s):  
Gene Expression ◽  
Roc Curve ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Poor Response ◽  
Postprandial Metabolism ◽  
Increased Risk ◽  
Gene Expression Analyses ◽  
Eruptive Xanthomas ◽  
New Treatments

Abstract Familial chylomicronemia syndrome (FCS) is a rare disorder associated with chylomicronemia (CM) and an increased risk of pancreatitis. Most individuals with CM do not have FCS but exhibit multifactorial CM (MCM), which differs from FCS in terms of risk and disease management. This study aimed to investigate clinical and gene expression profiles of FCS and MCM patients. Anthropometrics, clinical, and biochemical variables were analyzed in 57 FCS and 353 MCM patients. Gene expression analyses were performed in a subsample of 19 FCS, 28 MCM, and 15 normolipidemic controls. Receiver operating characteristic (ROC) curve analyses were performed to analyze the capacity of variables to discriminate FCS from MCM. Sustained fasting triglycerides ≥20 mmol/L (&gt;15 mmol/L with eruptive xanthomas), history of pancreatitis, poor response to fibrates, diagnosis of CM at childhood, body mass index &lt;22 kg/m2, and delipidated apolipoprotein B or glycerol levels &lt;0.9 g/L and &lt;0.05 mmol/L, respectively, had an area under the ROC curve ≥0.7. Gene expression analyses identified 142 probes differentially expressed in FCS and 32 in MCM compared with controls. Among them, 13 probes are shared between FCS and MCM; 63 are specific to FCS and 2 to MCM. Most FCS-specific or shared biomarkers are involved in inflammatory, immune, circadian, postprandial metabolism, signaling, docking systems, or receptor-mediated clearance mechanisms. This study reveals differential signatures of FCS and MCM. It opens the door to the identification of key mechanisms of CM expression and potential targets for the development of new treatments.

scholarly journals Tissue-based Gene Expression as Potential Biomarkers for IBD Course

2020 ◽  
Vol 26 (10) ◽  
pp. 1485-1489
Author(s):  
Yael Haberman
Keyword(s):  
Gene Expression ◽  
High Throughput Sequencing ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response To Therapy ◽  
Classification Systems ◽  
Disease Heterogeneity ◽  
Therapeutic Decisions ◽  
Bowel Diseases ◽  
Histopathologic Analysis

Abstract Inflammatory bowel diseases (IBDs) are highly heterogeneous in disease phenotype, behavior, and response to therapy. Diagnostic and therapeutic decisions in IBD are based primarily on clinical and endoscopic severity and histopathologic analysis of intestinal biopsies. With this approach, however, only a minority of patients experience durable remission. This may be due to substantial heterogeneity in disease pathogenicity that is not accounted for by current classification systems. Patients can present with similar clinical and endoscopic severity and receive similar therapy but show divergent response ranging from mucosal/transmural healing to nonresponse. Using mucosal biopsy samples that are already obtained as part of the clinical practice to support the diagnosis and state-of-the-art high throughput sequencing approaches can detect the widest range in host gene expression in the actual lining of the affected gut. These analyses can better dissect disease heterogeneity and guide potential treatment response. Here we review studies that use gut tissue–based gene expression profiles to predict disease outcome in IBD.

Use of gene expression analysis of periampullary carcinomas to identify biliary-like and intestinal-like subgroups of ampullary and duodenal carcinomas.

2011 ◽  
Vol 29 (4_suppl) ◽  
pp. 161-161
Author(s):  
M. J. Overman ◽  
J. Zhang ◽  
G. R. Varadhachary ◽  
R. F. Hwang ◽  
M. Kapoor ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hierarchical Clustering ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Kras Mutations ◽  
Histologic Subtype ◽  
Therapeutic Implications ◽  
Periampullary Carcinomas

161 Background: Though adenocarcinomas of the ampulla of Vater are classified as biliary cancers, the epithelium of origin and treatment approach for these rare tumors remains controversial. We compared the gene expression profiles of ampullary carcinomas with that of known periampullary carcinomas. Methods: We analyzed 32 fresh-frozen resected untreated periampullary carcinomas (8 pancreatic, 2 extrahepatic biliary, 8 non-ampullary duodenal, and 14 ampullary) with verified histology and >70% tumor tissue using the Affymetrix U133 Plus 2.0 genome array. Hierarchical clustering of all samples based upon pancreatic and duodenal differentially expressed genes and unsupervised hierarchical clustering was done. Ampullary and duodenal samples were analyzed for histologic subtype (pancreaticobiliary, intestinal, mixed), MSI by PCR, CDX-2 by IHC and KRAS and PI3K mutations by mass spectroscopy-based sequencing (Sequenom). Results: We identified 3 subgroups: pancreatic (8 pancreatic, 1 duodenal), biliary-like (4 duodenal, 7 ampullary, 2 biliary) and intestinal-like (3 duodenal, 7 ampullary). The intestinal-like subgroup had a significantly improved RFS (p=0.03) and OS (p =0.04) compared to the biliary-like subgroup, after stratification by grade and stage. Unsupervised clustering of only ampullary and duodenal samples identified very similar good prognostic (4 duodenal, 5 ampullary) and bad prognostic groups (3 duodenal, 9 ampullary) with 3-year RFS 75% vs. 31%, p =0.05 and 3-year OS 100% vs. 27%, p =0.01. These 2 groups showed no statistically significant differences in adenoma (56% vs 25%), poor differentiation (11% vs 42%), T4 (33% vs 25%), N1 (67% vs 100%), MSI-high (22% vs 0%), KRAS mutations (33% vs 17%), or PI3K mutations (0% vs 17%). CDX-2 expression (100% vs 50%, p=.04) and intestinal histologic subtype (100% vs 0%, p <0.01) were more common in the good prognostic group. Conclusions: Gene expression analysis classifies ampullary carcinomas with duodenal carcinomas and identifies a good prognosis intestinal-like group and a poor prognosis biliary-like group. These findings have therapeutic implications. [Table: see text]

Gene expression profiles of rectal cancers with mutant or wild-type Kras.

2013 ◽  
Vol 31 (4_suppl) ◽  
pp. 414-414 ◽  
Author(s):  
Julio Garcia-Aguilar ◽  
Zhenbin Chen ◽  
Charles Warden ◽  
Karin Avila ◽  
Ning Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Expression Profiles ◽  
Pathologic Complete Response ◽  
Gene Expression Profiles ◽  
Response To Therapy ◽  
Cdna Libraries ◽  
Wild Type ◽  
Aggressive Phenotype ◽  
Rectal Cancers

414 Background: The Kras oncogene is one of the most common mutations in colorectal cancer. Kras mutations are associated with increased tumor aggressiveness, poor response to selected targeted therapies, and reduced patient survival. We have previously shown that rectal cancers carrying a Kras mutation were less likely to achieve a pathologic complete response to radiation compared to tumors with wild type Kras. Our objective was to compare the gene expression profiles of rectal cancers with mutant and wild type Kras to identify genes that could be related to the Kras–dependent aggressive phenotype. Methods: Pretreatment biopsy tissue was collected from 120 patients with stages I, II and III rectal cancer treated in two prospective trials (NCT00335816 and NCT00114231). DNA and total RNA were extracted from microdissected cancer cells. Mutations in codons 12, 13, and 61 of the Kras gene were detected by pCR. 50ng of total RNAs were amplified to generate cDNA libraries using Ovation FFPE WTA System (NuGEN Technologies, Inc., San Carlos, CA). The amplified cDNA was labeled using the Encore Biotin Module, and hybridized to GeneChip Human Genome U133A plus 2.0 arrays (Affymetrix, Inc., Cleveland, OH). Differences in gene expression between mutant and wild type Kras tumors were determined using T-test and Q-bound to correct for multiple testing by performing false discovery rate (FDR) analysis. Results: A total of 44 of 117 (37.6%) were mutant Kras. A total of 379 probes were upregulated and 262 were downregulated in tumors with Kras mutant compared to Kras wild type. Heatmap based on differentially expressed genes showed separation according to Kras mutant status. REG4 expression was increased 3 fold and CXCL5 was reduced 2.4 fold in tumors with mutant Kras compared to wild type Kras. The changes in expression on these genes are concordant with their known involvement in prognosis and response to therapy of colorectal cancer. Conclusions: The search for changes in gene expression in response to Kras activation led to identifying a number of genes associated with the tumor aggressive phenotype.

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