Identification of Novel Cluster Groups in High-Risk Pediatric B-Precursor Acute Lymphoblastic Leukemia (HR-ALL) by Gene Expression Profiling: Correlation with Clinical and Outcome Variables. a Children’s Oncology Group (COG) Study.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 2256-2256
Author(s):  
Richard C. Harvey ◽  
I-Ming Chen ◽  
Kerem Ar ◽  
Stephen P. Hunger ◽  
Mignon Loh ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Therapeutic Targets ◽  
Risk Criteria ◽  
Cluster Group ◽  
Genetic Abnormalities ◽  
New Therapeutic Targets

Abstract Children with HR-ALL are traditionally defined by NCI Risk Criteria (age and white blood cell count) and comprise a highly heterogeneous group of ALL cases that have not been well characterized. In an effort to shed light on the genetic diversity of HR-ALL and identify new therapeutic targets in this resistant form of disease, we previously analyzed 207 patients enrolled on COG HR-ALL Trial P9906. These analyses revealed discrete gene expression profiles (Affymetrix U133 Plus2) that distinguished 8 distinct cluster groups. Two of these groups clustered patients with known underlying genetic abnormalities (E2A-PBX1/t(1;19) or MLL rearrangements) while the remaining 6 clusters were novel and the underlying genetic lesions remain to be identified. Two of the novel clusters were associated with extremely good (94.7% 4 year RFS) or very poor outcomes (20.9% 4 year RFS). In order to validate these findings and increase the size of the study cohort to enhance statistical power, we performed gene expression profiling in an additional 283 children with HR-ALL enrolled on COG Trial AALL0232. Patients enrolled on AALL0232 met NCI high risk criteria similar to those enrolled on P9906 but additionally included some HR-ALL patients with BCR-ABL or TEL-AML1 translocations, hypodiploidy, and favorable trisomies of chromosomes 4 and 10. As shown in the table below, the cluster designation and relative composition were very similar between the two cohorts. Two notable exceptions were the decreased number of Group 1 (MLL rearranged) members and the significantly elevated number of TEL-AML1 positive patients on AALL0232. In addition to identifying again the 6 novel clusters initially identified in the P9906 cohort and the TEL-AML1+ patients, AALL0232 contained another novel cluster group with a distinct gene signature. Expression profiles from P9906 and AALL0232 were merged to yield a cohort of 490 HR-ALL patients. When the same clustering methods were applied to the merged cohort, a similar set of clusters were identified with virtually identical group membership despite being independent experimental cohorts and clinical trials. The merged cohort permitted a more rigorous definition of gene signatures distinguishing each cluster, identifying the top 50 rank order genes associated with each cluster, and a more rigorous statistical analysis of the association of cluster group with outcome and clinical variables. Although the outcome data for the AALL0232 samples is not yet mature, the high degree of correlation of this cohort with certain P9906 clusters permits us to make predictions about outcome that will allow for validation with longer follow up. Furthermore, this larger cohort allows us to continue to identify potential new therapeutic targets and underlying genetic abnormalities in HR-ALL. Group P9906(207) AALL0232(283) 1 21 (10.1%) 10 (3.5%) 2 23 (11.1%) 23 (8.1%) 2A 11 (5.3%) 13 (4.6%) 4 13 (6.3%) 14 (4.9%) 5 11 (5.3%) 16 (5.7%) 6 21 (10.1%) 18 (6.4%) 8 24 (11.6%) 26 (9.2%) TEL-AML1 3 (1.4%) 55 (19.4%)

Use of Genomic Technologies to Identify Novel Genetic Abnormalities and Therapeutic Targets In Acute Lymphoblastic Leukemia.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. sci-8-sci-8 ◽  
Author(s):  
Cheryl L. Willman ◽  
Huining Kang ◽  
Richard C. Harvey ◽  
I-Ming Chen ◽  
Charles Mullighan ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Therapeutic Targets ◽  
Term Survival ◽  
Genomic Technologies ◽  
Genetic Abnormalities ◽  
Current Therapies ◽  
Molecular Classifier

Abstract With the progressive intensification of chemotherapy, the majority of children with ALL now achieve long-term survival. In parallel, a number of molecular subtypes of ALL have been identified that are associated with treatment outcomes, which are either excellent (TEL-AML1 or trisomy of chromosomes 4, 10, and 17), intermediate (MLL rearrangements or E2a-PBX), or very poor (BCR-ABL or hypodiploidy). Yet, the underlying genetic abnormalities in the majority of children with ALL, such as those with “high-risk” disease who remain resistant to current therapies, remain to be discovered. Supported by the NCI SPECS and TARGET Initiatives, the Children’s Oncology Group (COG), and The Leukemia & Lymphoma Society, we are using comprehensive genomic technologies (i.e., expression profiling, genome-wide analyses of DNA copy number abnormalities [CNAs] and germline polymorphisms, and direct gene sequencing) to develop molecular classifiers for outcome prediction that can be used to discover novel underlying genetic abnormalities and therapeutic targets in ALL. Our work has focused on a cohort of 220 children with “high-risk” ALL registered to COG Trial 9906. Using supervised learning methods on gene expression profiles, molecular classifiers predictive of relapse-free survival (RFS) and minimal residual disease (MRD) at end-induction have been developed. A 38-gene molecular risk classifier predictive of RFS (MRC-RFS) can distinguish two groups of high-risk ALL patients with different relapse risks: low (4 yr RFS: 81%, n=109) vs. high (4 yr RFS: 50%, n=98) (P< 0.0001). In multivariate analysis, the best predictor combines MRC-RFS and end-induction flow MRD, classifying children into low- (87% RFS), intermediate- (62% RFS), or high-risk (29% RFS) groups (P<0.0001). A 21-gene molecular classifier predictive of MRD can effectively substitute for end-induction MRD, yielding a combined classifier that similarly distinguishes three risk groups at pre-treatment (low: 82% RFS; intermediate: 63% RFS; and high: 45% RFS) (P< 0.0001). This combined molecular classifier was further validated on an independent cohort of 84 children with high-risk ALL registered to COG Trial 1961 (P = 0.006). Using unsupervised clustering methods, 8 distinct cluster groups based on gene expression were identified, 6 of which were entirely novel. Two of the novel clusters were associated with strikingly different outcomes (95% 4-year RFS vs 20% 4-year EFS). Novel underlying genetic abnormalities and genes that may represent novel therapeutic targets have been identified in each of these clusters. Interestingly, children of Hispanic ethnicity were disproportionately represented in the poorest outcome clusters. CNAs were revealed in genes regulating B lymphoid development in 50.2% of cases (PAX5 in 30.7% and IKZF1 in 24.9%). In addition, recurring CNAs were detected in a number of other genes known to play roles in transformation, including CDKN2A/B, RB1, BTG1, IL3RA, NRAS, KRAS, NR3C2, and ERG. CNAs in IKZF1, EBF, and BTLA were strongly associated with the poorest outcome clusters defined by gene expression profiling. Deletion of IKZF1 was particularly associated with negative outcome (p=0.002). These ongoing studies demonstrate that molecular classifiers can be used to distinguish distinct prognostic groups within high-risk ALL, significantly improving risk classification schemes and the ability to prospective identify children who will respond to or fail current therapies. These classifiers are now being integrated into the design of COG clinical trials. The discovery of novel cluster groups and underlying genetic abnormalities is being exploited to develop new therapeutic targets for this disease.

Expression Profiling Identifies Novel Genetic Subgroups with Distinct Clinical Features and Outcome in High-Risk Pediatric Precursor B Acute Lymphoblastic Leukemia (B-ALL). A Children’s Oncology Group Study.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 1430-1430
Author(s):  
Richard C. Harvey ◽  
George S. Davidson ◽  
Xuefei Wang ◽  
Kevin K. Dobbin ◽  
Edward J. Bedrick ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Therapeutic Targets ◽  
Term Survival ◽  
Genetic Abnormalities ◽  
Genetic Features ◽  
Highly Expressed Genes ◽  
Novel Therapeutic

Abstract While 80% of children with B-ALL achieve long term survival, a significant number still relapse. In contrast to low and intermediate risk ALL, the biologic and genetic features of high risk disease have not been well characterized. COG P9906, testing an augmented BFM regimen, enrolled 271 B-ALL patients from 2000–2003 who were predicted to have poor outcomes based on prior trials. To study the heterogeneity of high risk B-ALL and to identify novel therapeutic targets, we obtained gene expression profiles (Affymetrix HG-U133Plus2) from the pre-treatment samples of 207/271 children on COG P9906. This cohort had a mean age of 10.9 yrs, a male predominance (66%), and a 4 year event free survival (EFS) of 61%. Patients with favorable (TEL-AML/trisomy 4+10) or very unfavorable (BCR-ABL/hypodiploidy) genetic features were excluded. While the cohort contained 20 cases with MLL rearrangements and 23 with E2A-PBX, the remainder had no known recurring genetic abnormalities. Using two unsupervised clustering methods (VxInsight, Hierarchical Clustering), an established method to identify outlier genes (COPA), and a novel method we developed to find genes tightly associated with recurring genetic abnormalities (ROSE), we identified 7 distinct cluster groups. Two of these clusters contained the MLL and E2A-PBX cases, while a third had cases with expression profiles similar to E2A-PBX but lacked the fusion transcript. Four clusters were completely novel and two of these were further distinguished by unique clinical and outcome features (see Fig.). One novel cluster (MC5) had strikingly favorable EFS (94.7% at 4 yrs.) with older children (mean age 14.1 yr) who were predominantly male (4:1). Highly expressed genes distinguishing this cluster included BRDG1, CABLES1, CENTG2, CHST2, MCAM and PTPRM. This cluster is very similar to the novel cluster previously described by Yeoh et al. (Cancer Cell, 1:133, 2002). A second novel cluster (MC4) had the most unfavorable outcome, with a 4 year EFS of only 21.0%. MC4 cases were notable for the highest mean WBC (196.7K/μL) and a preponderance of Hispanic children (p=0.002), known to have poorer responses to established therapies. Highly expressed genes in MC4 which may represent novel diagnostic and therapeutic targets include CRLF2, GPR110, IFITM1, MUC4 and TNFSF4. Many of these genes have been associated with an adverse response in solid tumors. As part of the NCI TARGET Project, in collaboration with St. Jude Children’s Research Hospital, genome wide copy number changes are being integrated with expression data to identify the initiating genetic lesions in these novel clusters. Gene expression profiling is highly useful in resolving the genetic heterogeneity of high risk ALL and in identifying novel therapeutic targets for this resistant disease. Figure Figure

Proteomic Profiling of Multiple Myeloma: Correlation of Protein and Gene Expression Data.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1705-1705
Author(s):  
Ricky D Edmondson ◽  
Sheeno P Thyparambil ◽  
Veronica MacLeod ◽  
Bart Barlogie ◽  
John D. Shaughnessy
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Plasma Cells ◽  
Expression Profiles ◽  
Single Agent ◽  
Clinical Samples ◽  
Proteomics Data

Abstract Although melphalan-based autologous stem cell transplantation has improved prognosis for patients diagnosed with Multiple Myeloma, survival varies from a few months to more than 15 years with an individual’s risk not accurately predicted with standard prognostic variables. Correlating genome-wide mRNA expression profiles in purified myeloma cells with outcome, we recently showed that that the differential expression of 70 genes could identify patients at high risk for early disease related death [1]. The utility of a high throughput proteomics platform in the analysis of clinical samples has great potential but as of yet none have been firmly established. Herein, we describe the use of such a platform and its utility in stratifying patients with Multiple Myeloma in terms of high and low risk disease. Preliminary analysis indicates that the proteomics data can separate the patients into risk groups, although the proteins responsible for the assignment are not identical to the 70 genes identified in the gene expression profiling experiments. In addition to the proteomic analysis of plasma cells enriched using anti-CD138 immunomagnetic beads from mononuclear cell fractions of bone marrow aspirates from newly diagnosed myeloma patients; we have performed (in triplicate) LCMS profiling on plasma cells from 30 patients isolated prior to and 48 hours after a single test-dose application of bortezomib at 1.0mg/m2. An aliquot of 100,000 plasma cells was enzymatically digested with trypsin and a fraction (~5,000 cells) analyzed using our proteomics platform (an Eksigent nanoHPLC coupled to a ThermoElectron LTQ-Orbitrap with data analyzed using the Elucidator software package from Rosetta Biosoftware). The correlation of the proteomic profiles to gene expression profiles and clinical parameters will be presented. The analysis of proteins that were observed to change (p&lt;0.01) in abundance after the single agent dose of the proteasome inhibitor bortezomib yielded an unanticipated finding; the abundance of 30 proteins associated with the proteasome were observed to increase in a subset of patients. The majority of the patients with the increased levels of proteasome related proteins are predicted by GEP to have high risk disease. The proteomic data will be discussed in terms of its utility in the identification of activated pathways as well as in the development of a prognostic indicator as was achieved using gene expression profiling.

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 256CONSTRUCTION OF A BOVINE CDNA ARRAY TO MONITOR GENE EXPRESSION PROFILES IN BOVINE PREIMPLANTATION EMBRYOS

2004 ◽  
Vol 16 (2) ◽  
pp. 248
Author(s):  
C. Wrenzycki ◽  
T. Brambrink ◽  
D. Herrmann ◽  
J.W. Carnwath ◽  
H. Niemann
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Cdna Array ◽  
Preimplantation Embryos ◽  
Average Insert Size ◽  
Rt Pcr ◽  
Public Data

Array technology is a widely used tool for gene expression profiling, providing the possibility to monitor expression levels of an unlimited number of genes in various biological systems including preimplantation embryos. The objective of the present study was to develop and validate a bovine cDNA array and to compare expression profiles of embryos derived from different origins. A bovine blastocyst cDNA library was generated. Poly(A+)RNA was extracted from in vitro-produced embryos using a Dynabead mRNA purification kit. First-strand synthesis was performed with SacIT21 primer followed by randomly primed second-strand synthesis with a DOP primer mix (Roche) and a global PCR with 35 cycles using SacIT21 and DOP primers. Complementary DNA fragments from 300 to 1500bp were extracted from the gel and normalized via reassoziation and hydroxyapatite chromatography. Resulting cDNAs were digested with SacI and XhoI, ligated into a pBKs vector, and transfected into competent bacteria (Stratagene). After blue/white colony selection, plasmids were extracted and the inserts were subjected to PCR using vector specific primers. Average insert size was determined by size idenfication on agarose gels stained with ethidium bromide. After purification via precipitation and denaturation, 192 cDNA probes were double-spotted onto a nylon membrane and bound to the membrane by UV cross linking. Amplified RNA (aRNA) probes from pools of three or single blastocysts were generated as described recently (Brambrink et al., 2002 BioTechniques, 33, 3–9) and hybridized to the membranes. Expression profiles of in vitro-produced blastocysts cultured in either SOF plus BSA or TCM plus serum were compared with those of diploid parthenogenetic ones generated by chemical activation. Thirty-three probes have been sequenced and, after comparison with public data bases, 26 were identified as cDNAs or genes. Twelve out of 192 (6%) seem to be differentially expressed within the three groups;; 7/12 (58%) were down-regulated, 3/12 (25%) were up-regulated in SOF-derived embryos, and 2/12 (20%) were up-regulated in parthenogenetic blastocysts compared to their in vitro-generated counterparts. Three of these genes involved in calcium signaling (calmodulin, calreticulin) and regulation of actin cytoskeleton (destrin) were validated by semi-quantitative RT-PCR (Wrenzycki et al., 2001 Biol. Reprod. 65, 309–317) employing poly(A+) RNA from a single blastocyst as starting material. No differences were detected in the relative abundance of the analysed gene transcripts within the different groups. These findings were confirmed employing the aRNA used for hybridization in RT-PCR and showed a good representativity of the selected transcripts. Results indicate that it is possible to construct a homologous cDNA array which could be used for gene expression profiling of bovine preimplantation embryos. Supported by the Deutsche Forschungsgemeinschaft (DFG Ni 256/18-1).

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.

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.

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