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Biomedical subjects

Jill Mesirov

Publications and source records attributed to Jill Mesirov.

3 recordsLinked to original sources

An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis.

Using advanced gene targeting methods, generating mouse models of cancer that accurately reproduce the genetic alterations present in human tumors is now relatively straightforward. The challenge is to determine to what extent such models faithfully mimic human disease with respect to the underlying molecular mechanisms that accompany tumor progression. Here we describe a method for comparing mouse models of cancer with human tumors using gene-expression profiling. We applied this method to the analysis of a model of Kras2-mediated lung cancer and found a good relationship to human lung adenocarcinoma, thereby validating the model. Furthermore, we found that whereas a gene-expression signature of KRAS2 activation was not identifiable when analyzing human tumors with known KRAS2 mutation status alone, integrating mouse and human data uncovered a gene-expression signature of KRAS2 mutation in human lung cancer. We confirmed the importance of this signature by gene-expression analysis of short hairpin RNA-mediated inhibition of oncogenic Kras2. These experiments identified both a pattern of gene expression indicative of KRAS2 mutation and potential effectors of oncogenic KRAS2 activity in human cancer. This approach provides a strategy for using genomic analysis of animal models to probe human disease.

Adenocarcinoma↗

Genome duplication in the teleost fish Tetraodon nigroviridis reveals the early vertebrate proto-karyotype.

Tetraodon nigroviridis is a freshwater puffer fish with the smallest known vertebrate genome. Here, we report a draft genome sequence with long-range linkage and substantial anchoring to the 21 Tetraodon chromosomes. Genome analysis provides a greatly improved fish gene catalogue, including identifying key genes previously thought to be absent in fish. Comparison with other vertebrates and a urochordate indicates that fish proteins have diverged markedly faster than their mammalian homologues. Comparison with the human genome suggests approximately 900 previously unannotated human genes. Analysis of the Tetraodon and human genomes shows that whole-genome duplication occurred in the teleost fish lineage, subsequent to its divergence from mammals. The analysis also makes it possible to infer the basic structure of the ancestral bony vertebrate genome, which was composed of 12 chromosomes, and to reconstruct much of the evolutionary history of ancient and recent chromosome rearrangements leading to the modern human karyotype.

Animals↗

Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning.

Diffuse large B-cell lymphoma (DLBCL), the most common lymphoid malignancy in adults, is curable in less than 50% of patients. Prognostic models based on pre-treatment characteristics, such as the International Prognostic Index (IPI), are currently used to predict outcome in DLBCL. However, clinical outcome models identify neither the molecular basis of clinical heterogeneity, nor specific therapeutic targets. We analyzed the expression of 6,817 genes in diagnostic tumor specimens from DLBCL patients who received cyclophosphamide, adriamycin, vincristine and prednisone (CHOP)-based chemotherapy, and applied a supervised learning prediction method to identify cured versus fatal or refractory disease. The algorithm classified two categories of patients with very different five-year overall survival rates (70% versus 12%). The model also effectively delineated patients within specific IPI risk categories who were likely to be cured or to die of their disease. Genes implicated in DLBCL outcome included some that regulate responses to B-cell-receptor signaling, critical serine/threonine phosphorylation pathways and apoptosis. Our data indicate that supervised learning classification techniques can predict outcome in DLBCL and identify rational targets for intervention.

Antineoplastic Combined Chemotherapy Protocols↗