In:
Molecular Cancer Therapeutics, American Association for Cancer Research (AACR), Vol. 8, No. 12_Supplement ( 2009-12-10), p. PR-5-PR-5
Abstract:
Effective chemotherapeutic treatment of cancer is stymied by a lack of understanding of tumorigenic pathway defects in individual tumors. Combining large scale measurement technologies with appropriate informatics and statistical approaches is a promising approach to detect such aberrations in signaling pathways and provide rationale(s) for driving the choice of targeted chemotherapies. We have attempted to address these by generating a high resolution understanding of pathway defects in 30 solid tumor cell lines, which were established directly from patient-derived xenografts representing 13 histologies. We simultaneously interrogated ∼100 unique protein signaling events using reverse phase protein arrays (RPPA), along with profiling the same cell lines on Affymetrix expression and Agilent CGH platforms. An in silico validation of the RPPA data was conducted by a) clustering analysis that revealed lineage-independent pathway perturbations, b) combining it with baseline genomic and expression data that elucidated and confirmed several underlying genomic defect(s) associated with protein-level changes, c) studying similarity of protein-protein correlations and KEGG cancer pathways that showed high concordance between the two. We developed signaling cascade maps that estimate tumorigenic pathway activation level for individual samples, which can then be used to propose an appropriate therapeutic intervention. Using proliferation assay data from several known targeted agents, we confirmed our observations and ascertained patterns of protein expression that predict for chemosensitivity. In summary, we propose that the methods described here represent a novel approach to analysis of gene/pathway changes associated with tumorigenesis and will be useful in building models of cancer cell protein circuitry applicable to the development of both drugs and patient stratification biomarkers. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):PR-5.
Type of Medium:
Online Resource
ISSN:
1535-7163
,
1538-8514
DOI:
10.1158/1535-7163.TARG-09-PR-5
Language:
English
Publisher:
American Association for Cancer Research (AACR)
Publication Date:
2009
detail.hit.zdb_id:
2062135-8
SSG:
12
