Convergent Genetic Aberrations in Murine and Human T Lineage Acute Lymphoblastic Leukemias
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA542723 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR9050890_1.fastq.gz | Fastqsanger.gz | |||
| SRR9050890_2.fastq.gz | Fastqsanger.gz | |||
| SRR9050900_1.fastq.gz | Fastqsanger.gz | |||
| SRR9050900_2.fastq.gz | Fastqsanger.gz | |||
| SRR9050891_1.fastq.gz | Fastqsanger.gz |
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Project description:The lack of predictive preclinical models is a fundamental barrier to translating knowledge about the molecular pathogenesis of cancer into improved therapies. Insertional mutagenesis (IM) in mice is a robust strategy for generating malignancies that recapitulate the extensive inter- and intra-tumoral genetic heterogeneity found in advanced human cancers. While the central role of "driver" viral insertions in IM models that aberrantly increase the expression of proto-oncogenes or disrupt tumor suppressors has been appreciated for many years, the contributions of cooperating somatic mutations and large chromosomal alterations to tumorigenesis are largely unknown. Integrated genomic studies of T lineage acute lymphoblastic leukemias (T-ALLs) generated by IM in wild-type (WT) and Kras mutant mice reveal frequent point mutations and other recurrent non-insertional genetic alterations that also occur in human T-ALL. These somatic mutations are sensitive and specific markers for defining clonal dynamics and identifying candidate resistance mechanisms in leukemias that relapse after an initial therapeutic response. Primary cancers initiated by IM and resistant clones that emerge during in vivo treatment close key gaps in existing preclinical models, and are robust platforms for investigating the efficacy of new therapies and for elucidating how drug exposure shapes tumor evolution and patterns of resistance.
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Project description:Gene expression analyses, were performed on 121 consecutive childhood leukemias (87 B-lineage acute lymphoblastic leukemias (ALLs), 11 T-cell ALLs, and 23 acute myeloid leukemias; AMLs), investigated during an 8-year period at a single center. The supervised learning algorithm k-nearest neighbor (k-NN) was utilized to build gene expression predictors that could classify the ALLs/AMLs according to clinically important subtypes with high accuracy. Validation experiments in an independent data set verified the high prediction accuracies of our classifiers. B-lineage ALLs with uncharacteristic cytogenetic aberrations or with a normal karyotype displayed heterogeneous gene expression profiles, resulting in low prediction accuracies. Minimal residual disease status (MRD) in T-cell ALLs with a high (>0.1%) MRD at day 29 could be classified with 100% accuracy already at the time of diagnosis. In pediatric leukemias with uncharacteristic cytogenetic aberrations or a normal karyotype, unsupervised analysis identified two novel subgroups: one consisting mainly of cases remaining in complete remission (CR) and one containing a few patients in CR and all but one of the patients who relapsed. This study of a consecutive series of childhood leukemias confirm and further extend previous reports demonstrating that global gene expression profiling provides a valuable tool for genetic and clinical classification of childhood leukemias. Keywords: supervised classification, Pediatric leukemia, AML, B-ALL, T-ALL, Normal bone marrow
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