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Revealing Targeted Therapy for Human Cancer by Gene Module Maps

ABSTRACT: A major goal of cancer research is to match specific therapies to molecular targets in cancer. Genome-scale expression profiling has identified new subtypes of cancer based on consistent patterns of variation in gene expression, leading to improved prognostic predictions. However, how these new genetic subtypes of cancers should be treated is unknown. Here we show that a gene module map can guide the prospective identification of targeted therapies for genetic subtypes of cancer. By visualizing genome-scale gene expression in cancer as combinations of activated and deactivated functional modules, gene module maps can reveal specific functional pathways associated with each subtype that might be susceptible to targeted therapies. We show that in human breast cancers, activation of a poor-prognosis wound signature is strongly associated with induction of both a mitochondria gene module and a proteasome gene module. We found that 3-bromopyruvic acid, which inhibits glycolysis, selectively killed breast cells expressing the mitochondria and wound signatures. In addition, inhibition of proteasome activity by bortezomib, a drug approved for human use in multiple myeloma, abrogated wound signature expression and selectively killed breast cells expressing the wound signature. Thus, gene module maps may enable rapid translation of complex genomic signatures in human disease to targeted therapeutic strategies. Cell Line: Transduced construct Keywords: cell_type_comparison_design

ORGANISM(S): Homo sapiens

PROVIDER: GSE8957 | GEO | 2008/01/28

SECONDARY ACCESSION(S): PRJNA102395

REPOSITORIES: GEO

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Project description:Epithelial ovarian cancer is morphologically and clinically heterogeneous. Transcriptional profiling has revealed molecular subtypes (referred to as M-bM-^@M-^\C-signaturesM-bM-^@M-^]) that correlate to biological as well as clinical features. We aimed to determine gene expression differences between malignant, benign and borderline serous ovarian tumors, and to investigate similarities to the intrinsic molecular subtypes of breast cancer. Global gene expression profiling was performed using Illumina's HT12 Bead Arrays and applied to 59 fresh-frozen ovarian tumors. SAM analysis revealed enrichment of cell cycel processes among the malignant tumors, in line with malignant tumors being highly proliferative. The borderline tumors were split between the malignant and benign tumor clusters, indicating that borderline tumors have both malignant and benign features. Furthermore, nearest centroid classification was performed applying previously published gene profiles for the ovarian cancer C-signatures and the intrinsic breast cancer subtypes, respectively, and showed significant correlations between the malignant serous tumors and the highly aggressive C1, C2 and C4 ovarian cancer signatures, and the basal-like breast cancer subtype. The benign and borderline serous tumors together were significantly correlated to the normal-like breast cancer subtype and the ovarian cancer C3 signature. The borderline tumors, on the other hand, correlated significantly to the Luminal A breast cancer subtype. These findings remained when analyzed in a large, independent dataset. The data in this study link the transcriptional profiles of serous ovarian cancer to the intrinsic molecular subtypes of breast cancer, in line with the shared clinical and molecular features between high-grade serous ovarian cancer and basal-like breast cancer, including an aggressive phenotype, frequent TP53 mutations and a high degree of genomic instability, and suggest that biomarkers and targeted therapies may overlap between these subsets of ovarian and breast cancers. Finally, the link between benign and borderline ovarian cancer and luminal breast cancer may indicate endocrine responsiveness in a subset of ovarian cancers. Total RNA obtained from serous ovarian adenocarcinomas, adenomas and borderline tumors. Gene expression profiling using Illumina's HT12 v4 bead arrays. Application of ovarian cancer molecular subtypes and intrinsic breast cancer subtypes using nearest centroid classification. KRAS and BRAF mutation analyses in the malignant and borderline tumors.

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Revealing Targeted Therapy for Human Cancer by Gene Module Maps

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