Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: EC:2.7.10.2 (focal adhesion kinase)
 44,029 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Hematologic malignancies are characterized by fusion genes of biological/clinical importance. Immortalized cell lines with such aberrations are today widely used to model different aspects of leukemogenesis. Using cDNA microarrays, we determined the gene expression profiles of 40 cell lines as well as of primary leukemias harboring 11q23/MLL rearrangements, t(1;19)[TCF3/PBX1], t(12;21)[ETV6/RUNX1], t(8;21)[RUNX1/CBFA2T1], t(8;14)[IGH@/MYC], t(8;14)[TRA@/MYC], t(9;22)[BCR/ABL1], t(10;11)[PICALM/MLLT10], t(15;17)[PML/RARA], or inv(16)[CBFB/MYH11]. Unsupervised classification revealed that hematopoietic cell lines of diverse origin, but with the same primary genetic changes, segregated together, suggesting that pathogenetically important regulatory networks remain conserved despite numerous passages. Moreover, primary leukemias cosegregated with cell lines carrying identical genetic rearrangements, further supporting that critical regulatory pathways remain intact in hematopoietic cell lines. Transcriptional signatures correlating with clinical subtypes/primary genetic changes were identified and annotated based on their biological/molecular properties and chromosomal localization. Furthermore, the expression profile of tyrosine kinase-encoding genes was investigated, identifying several differentially expressed members, segregating with primary genetic changes, which may be targeted with tyrosine kinase inhibitors. The identified conserved signatures are likely to reflect regulatory networks of importance for the transforming abilities of the primary genetic changes and offer important pathogenetic insights as well as a number of targets for future rational drug design.
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PMID:Gene expression profiling of leukemic cell lines reveals conserved molecular signatures among subtypes with specific genetic aberrations. 1584 27

Background: Advances in treatment and supportive care have significantly improved the overall survival (OS) of pediatric patients with acute lymphoblastic leukemia (ALL). However, there is a large number of these patients who continue to relapse after receiving standard treatment. Accurate identification of patients at high risk of relapse and targeted therapy may significantly improve their prognosis. Therefore, the aim of this study was to identify significant prognostic factors for pediatric ALL and establish a novel nomogram for the prediction of survival. Methods: The ALL clinical data of Phases I and II of the Therapeutic Applicable Research to Generate Effective Treatments (TARGET) project were merged and randomly divided into training and validation groups. The LASSO regression model was used to select the specific factors related to the OS of the training group and generate prognostic nomograms according to the selected characteristics. The predictive accuracy of the nomogram for OS was verified using the concordance index of the training and validation groups, the area under the receiver operating characteristic curve for prognostic diagnosis, and the calibration curve. Results: A total of 1,000 children with ALL were included in the TARGET project. Of those, 489 patients had complete follow-up data for further analysis. The data were randomly divided into the training group (n = 345) and the validation group (n = 144). Seven clinical characteristics, namely age at diagnosis, peripheral white blood cells, bone marrow and CNS site of relapse, ETV6/RUNX1 fusion, TCF3/PBX1, and BCR/ABL1 status, were selected to construct the nomogram. The concordance indices of the training and validation groups were 0.809 (95% confidence interval: 0.766-0.852) and 0.826 (95% confidence interval: 0.767-0.885), respectively. The areas under the receiver operating characteristic curve of the 3-year, 5-year, and 10-year OS in the training group were 0.804, 0.848, and 0.885, respectively, while that of the validation group were 0.777, 0.825, and 0.863, respectively. Moreover, the calibration curves demonstrated a favorable consistency between the predicted and actual survival probabilities. Conclusions: Independent predictors of OS in children with ALL included age at diagnosis, white blood cells, bone marrow site of relapse, CNS site of relapse, ETV6/RUNX1 fusion, TCF3/PBX1, and BCR/ABL1 status. The nomograms developed using these high-risk factors can more simply, accurately, and quantitatively predict the survival of children, and improve treatment and prognosis.
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PMID:A Nomogram for the Prediction of Progression and Overall Survival in Childhood Acute Lymphoblastic Leukemia. 3298 14