scholarly journals Whole-transcriptome analysis in acute lymphoblastic leukemia: a report from the DFCI ALL Consortium Protocol 16-001

2021 ◽  
Author(s):  
Thai Hoa Tran ◽  
Sylvie Langlois ◽  
Caroline Meloche ◽  
Maxime Caron ◽  
Pascal St-Onge ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mapk Pathway ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Gene Expression Profiles ◽  
Molecular Classification ◽  
Genetic Alterations ◽  
Rna Seq ◽  
Childhood All ◽  
Next Generation Sequencing Technology

The molecular hallmark of childhood ALL is characterized by recurrent, prognostic genetic alterations, many of which are cryptic by conventional cytogenetics. RNA-seq is a powerful next-generation sequencing technology with the ability to simultaneously identify cryptic gene rearrangements, sequence mutations and gene expression profiles in a single assay. We examined the feasibility and utility of incorporating RNA-seq into a prospective multi-center phase 3 clinical trial for children with newly-diagnosed ALL. Patients enrolled on the DFCI ALL Consortium Protocol 16-001 who consented to optional studies and had available material underwent RNA-seq. RNA-seq was performed in 173 ALL patients. RNA-seq identified at least one alteration in 157 (91%) patients. Fusion detection was 100% concordant with results obtained from conventional cytogenetic analyses. An additional 56 gene fusions were identified by RNA-seq, many of which confer prognostic or therapeutic significance. Gene expression profiling enabled further molecular classification into the following B-ALL subgroups: High hyperdiploid (n=36), ETV6-RUNX1/-like (n=31), TCF3-PBX1 (n=7), KMT2A-rearranged (n=5), iAMP21 (n=1), hypodiploid (n=1), BCR-ABL1/-like (n=16), DUX4-rearranged (n=11), PAX5 alterations (n=11), PAX5 P80R (n=1), ZNF384-rearranged (n=4), NUTM1-rearranged (n=1), MEF2D-rearranged (n=1) and Others (n=10). RNA-seq identified 141 nonsynonymous mutations in 93 (54%) patients; the most frequent were RAS-MAPK pathway mutations. Among 79 patients with both low-density array and RNA-seq data for the Ph-like gene signature prediction, results were concordant in 74 (94%) patients. In conclusion, RNA-seq identified several clinically-relevant genetic alterations not detected by conventional methods, supporting the integration of this technology in frontline pediatric ALL trials.

Comparative Analysis of Gene Expression Profiles between Diagnosis and Relapse of Childhood Acute Lymphoblastic Leukemia.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 2809-2809
Author(s):  
Tomasz Szczepanski ◽  
Dick de Ridder ◽  
Vincent H.J. van der Velden ◽  
Tom Schonewille ◽  
Elisabeth van Wering ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukemia ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Clonal Evolution ◽  
Gene Expression Profiles ◽  
Detailed Comparison ◽  
Cell Precursor ◽  
Childhood All

Abstract Approximately 30% of childhood acute lymphoblastic leukemia (ALL) patients relapse, which is the most frequent adverse event in this otherwise curable disease. The mechanisms of relapse are largely unknown. Earlier studies indicated that some relapses might originate from subclones with many different biological features compared to the original ALL clones at diagnosis. Therefore, we aimed at detailed comparison of gene expression profiles between diagnosis and relapse of childhood ALL. The study group consisted of 41 children, 27 diagnosed with B-cell precursor ALL (BCP-ALL) and 14 with T-cell precursor ALL (T-ALL). All samples obtained at diagnosis and relapse were subjected to purification using CliniMACS system and enriched to more than 95% of blasts in each sample. RNA isolation and gene expression profiling were performed according to standard procedures using Affymetrix HG-U133+2 set GeneChip arrays (Affymetrix). The samples were also screened at the RNA level for the most common genetic aberrations occurring in ALL such as t(9;22), t(4;11), t(12;21) and TAL1 deletion. The studies at the DNA level involved detailed comparison of immunoglobulin (Ig) and T-cell receptor (TCR) gene rearrangements between diagnosis and relapse to assess clonal evolution. GeneChip array data were quantile normalized and background was removed using robust multichip analysis. Significance Analysis of Microarrays (SAM) and t-test were applied to find differentially expressed probe sets between diagnosis and relapse using both the paired and unpaired criterion. The p values < 0.05 were considered significant. The paired SAM analysis revealed 388 significantly differentially expressed (SDE) probe sets for BCP-ALL and 10 SDE probe sets for T-ALL. The differences in expression levels were relatively low, generally not exceeding two-fold. SDE gene sets revealed in our study were mainly different from previously published data, which is most probably due to more stringent purification procedures. Using Ingenuity Systems the SDE genes in BCP-ALL could be significantly linked to several networks involved in cell cycle, DNA replication, recombination, and repair, cellular assembly and organization, cellular growth, proliferation and cancer. There were no significant differences in gene expression profiling in smaller immunophenotypic and cytogenetic ALL subgroups as well as in relation to remission duration (early vs. late relapse). Several SDE genes were found when comparing the ALL with stable Ig/TCR configuration and with some clonal evolution (22 probes for T-ALL and 8 probes for BCP-ALL). In conclusion, discrete differences of gene expression profiles between diagnosis and relapse of childhood ALL indicate heterogeneous origin of relapse. Many relapses represent the simple outgrowth of the original clone, while in other cases many different (leukemia-related) relapse mechanisms might be involved.

High Interleukin-15 Expression Characterizes Childhood Acute Lymphoblastic Leukemia With Involvement of the CNS

2007 ◽  
Vol 25 (30) ◽  
pp. 4813-4820 ◽  
Author(s):  
Gunnar Cario ◽  
Shai Izraeli ◽  
Anja Teichert ◽  
Peter Rhein ◽  
Julia Skokowa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukemia ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Gene Expression Profiles ◽  
Childhood Acute Lymphoblastic Leukemia ◽  
Leukemic Cells ◽  
Cns Involvement ◽  
Childhood All ◽  
Interleukin 15

Purpose Applying current diagnostic methods, overt CNS involvement is a rare event in childhood acute lymphoblastic leukemia (ALL). In contrast, CNS-directed therapy is essential for all patients with ALL because without it, the majority of patients eventually will experience relapse. To approach this discrepancy and to explore potential distinct biologic properties of leukemic cells that migrate into the CNS, we compared gene expression profiles of childhood ALL patients with initial CNS involvement with the profiles of CNS-negative patients. Patients and Methods We evaluated leukemic gene expression profiles from the bone marrow of 17 CNS-positive patients and 26 CNS-negative patients who were frequency matched for risk factors associated with CNS involvement. Results were confirmed by real-time quantitative polymerase chain reaction analysis and validated using independent patient samples. Results Interleukin-15 (IL-15) expression was consistently upregulated in leukemic cells of CNS-positive patients compared with CNS-negative patients. In multivariate analysis, IL-15 expression levels greater than the median were associated with CNS involvement compared with expression equal to or less than the median (odds ratio [OR] = 10.70; 95% CI, 2.95 to 38.81). Diagnostic likelihood ratios for CNS positivity were 0.09 (95% CI, 0.01 to 0.65) for the first and 6.93 (95% CI, 2.55 to 18.83) for the fourth IL-15 expression quartiles. In patients who were CNS negative at diagnosis, IL-15 levels greater than the median were associated with subsequent CNS relapse compared with expression equal to or less than the median (OR = 13.80; 95% CI, 3.38 to 56.31). Conclusion Quantification of leukemic IL-15 expression at diagnosis predicts CNS status and could be a new tool to further tailor CNS-directed therapy in childhood ALL.

scholarly journals A Robust Gene selection Method for Microarray-based Cancer Classification

2010 ◽  
Vol 9 ◽  
pp. CIN.S3794 ◽  
Author(s):  
Xiaosheng Wang ◽  
Osamu Gotoh
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Selection ◽  
Information Gain ◽  
Expression Profiles ◽  
Feature Selection Method ◽  
Gene Expression Profiles ◽  
Molecular Classification ◽  
Selection Method ◽  
Chi Square

Gene selection is of vital importance in molecular classification of cancer using high-dimensional gene expression data. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust feature selection methods is extremely crucial. We investigated the properties of one feature selection approach proposed in our previous work, which was the generalization of the feature selection method based on the depended degree of attribute in rough sets. We compared the feature selection method with the established methods: the depended degree, chi-square, information gain, Relief-F and symmetric uncertainty, and analyzed its properties through a series of classification experiments. The results revealed that our method was superior to the canonical depended degree of attribute based method in robustness and applicability. Moreover, the method was comparable to the other four commonly used methods. More importantly, the method can exhibit the inherent classification difficulty with respect to different gene expression datasets, indicating the inherent biology of specific cancers.

scholarly journals A tensor decomposition-based integrated analysis applicable to multiple gene expression profiles without sample matching

2021 ◽  
Author(s):  
Taguchi Y-h. ◽  
Turki Turki
Keyword(s):  
Gene Expression ◽  
Learning Strategies ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Tensor Decomposition ◽  
Integrated Analysis ◽  
Rna Seq ◽  
Prior Learning ◽  
Multiple Gene ◽  
Machine Learning Applications

Abstract The integrated analysis of multiple gene expression profiles measured in distinct studies is always problematic. Especially, missing sample matching and missing common labeling between distinct studies prevent the integration of multiple studies in fully data-driven and unsupervised manner. In this study, we propose a strategy enabling the integration of multiple gene expression profiles among multiple independent studies without either labeling or sample matching, using tensor decomposition-based unsupervised feature extraction. As an example, we applied this strategy to Alzheimer’s disease (AD)-related gene expression profiles that lack exact correspondence among samples as well as AD single-cell RNA-seq (scRNA-seq) data. We found that we could select biologically reasonable genes with integrated analysis. Overall, integrated gene expression profiles can function analogously to prior learning and/or transfer learning strategies in other machine learning applications. For scRNA-seq, the proposed approach was able to drastically reduce the required computational memory.

scholarly journals Diversity of Breast Carcinoma: Histological Subtypes and Clinical Relevance

2015 ◽  
Vol 8 ◽  
pp. CPath.S31563 ◽  
Author(s):  
Jaafar Makki
Keyword(s):  
Gene Expression ◽  
Breast Carcinoma ◽  
Clinical Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Classification ◽  
Developed Countries ◽  
Breast Cancers ◽  
Wide Scope ◽  
Histological Subtypes

Mammary carcinoma is the most common malignant tumor in women, and it is the leading cause of mortality, with an incidence of ≥1,000,000 cases occurring worldwide annually. It is one of the most common human neoplasms, accounting for approximately one-quarter of all cancers in females worldwide and 27% of cancers in developed countries with a Western lifestyle. They exhibit a wide scope of morphological features, different immunohistochemical profiles, and unique histopathological subtypes that have specific clinical course and outcome. Breast cancers can be classified into distinct subgroups based on similarities in the gene expression profiles and molecular classification.

The characteristics of mesenteric adipose tissue attached to different intestinal segments and their roles in immune regulation

2022 ◽  
Author(s):  
Haowei Zhang ◽  
Yujin Ding ◽  
Qin Zeng ◽  
Dandan Wang ◽  
Ganglei Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Adipose Tissue ◽  
Immune Regulation ◽  
Expression Profiles ◽  
Critical Role ◽  
Gene Expression Profiles ◽  
Rna Seq ◽  
Mesenteric Adipose Tissue ◽  
Cell Components ◽  
Subsequent Effect

Background: Mesenteric adipose tissue (MAT) plays a critical role in the intestinal physiological ecosystems. Small and large intestines have evidently intrinsic and distinct characteristics. However, whether there exist any mesenteric differences adjacent to the small and large intestines (SMAT and LMAT) has not been properly characterized. We studied the important facets of these differences, such as morphology, gene expression, cell components and immune regulation of MATs, to characterize the mesenteric differences. Methods: The SMAT and LMAT of mice were utilized for comparison of tissue morphology. Paired mesenteric samples were analyzed by RNA-seq to clarify gene expression profiles. MAT partial excision models were constructed to illustrate the immune regulation roles of MATs, and 16S-seq was applied to detect the subsequent effect on microbiota. Results: Our data show that different segments of mesenteries have different morphological structures. SMAT not only has smaller adipocytes but also contains more fat-associated lymphoid clusters than LMAT. The gene expression profile is also discrepant between these two MATs in mice. B-cell markers were abundantly expressed in SMAT, while development-related genes were highly expressed in LMAT. Adipose-derived stem cells of LMAT exhibited higher adipogenic potential and lower proliferation rates than those of SMAT. In addition, SMAT and LMAT play different roles in immune regulation and subsequently affect microbiota components. Finally, our data clarified the described differences between SMAT and LMAT in humans. Conclusions: There were significant differences in cell morphology, gene expression profiles, cell components, biological characteristics, and immune and microbiota regulation roles between regional MATs.

scholarly journals Identification and Validation of Novel Reference Genes in Acute Lymphoblastic Leukemia for Droplet Digital PCR

Genes ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 376 ◽  
Author(s):  
Vanessa Villegas-Ruíz ◽  
Karina Olmos-Valdez ◽  
Kattia Alejandra Castro-López ◽  
Victoria Estefanía Saucedo-Tepanecatl ◽  
Josselen Carina Ramírez-Chiquito ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukemia ◽  
Reference Genes ◽  
Expression Profiles ◽  
Lymphoblastic Leukemia ◽  
Gene Expression Profiles ◽  
Housekeeping Genes ◽  
Digital Pcr ◽  
Droplet Digital Pcr ◽  
Cellular Processes

Droplet digital PCR is the most robust method for absolute nucleic acid quantification. However, RNA is a very versatile molecule and its abundance is tissue-dependent. RNA quantification is dependent on a reference control to estimate the abundance. Additionally, in cancer, many cellular processes are deregulated which consequently affects the gene expression profiles. In this work, we performed microarray data mining of different childhood cancers and healthy controls. We selected four genes that showed no gene expression variations (PSMB6, PGGT1B, UBQLN2 and UQCR2) and four classical reference genes (ACTB, GAPDH, RPL4 and RPS18). Gene expression was validated in 40 acute lymphoblastic leukemia samples by means of droplet digital PCR. We observed that PSMB6, PGGT1B, UBQLN2 and UQCR2 were expressed ~100 times less than ACTB, GAPDH, RPL4 and RPS18. However, we observed excellent correlations among the new reference genes (p < 0.0001). We propose that PSMB6, PGGT1B, UBQLN2 and UQCR2 are housekeeping genes with low expression in childhood cancer.

scholarly journals Comparative Transcriptome Analysis of Different Dendrobium Species Reveals Active Ingredients-Related Genes and Pathways

2020 ◽  
Vol 21 (3) ◽  
pp. 861 ◽  
Author(s):  
Yingdan Yuan ◽  
Bo Zhang ◽  
Xinggang Tang ◽  
Jinchi Zhang ◽  
Jie Lin
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Rna Seq ◽  
Active Ingredients ◽  
Hub Genes ◽  
Transcriptome Database ◽  
Gene Modules ◽  
Functional Investigation ◽  
First Time

Dendrobium is widely used in traditional Chinese medicine, which contains many kinds of active ingredients. In recent years, many Dendrobium transcriptomes have been sequenced. Hence, weighted gene co-expression network analysis (WGCNA) was used with the gene expression profiles of active ingredients to identify the modules and genes that may associate with particular species and tissues. Three kinds of Dendrobium species and three tissues were sampled for RNA-seq to generate a high-quality, full-length transcriptome database. Based on significant changes in gene expression, we constructed co-expression networks and revealed 19 gene modules. Among them, four modules with properties correlating to active ingredients regulation and biosynthesis, and several hub genes were selected for further functional investigation. This is the first time the WGCNA method has been used to analyze Dendrobium transcriptome data. Further excavation of the gene module information will help us to further study the role and significance of key genes, key signaling pathways, and regulatory mechanisms between genes on the occurrence and development of medicinal components of Dendrobium.

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