Gene expression differences of mammary tumors after treatment
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE81284 | GEO | 2018/05/09
REPOSITORIES: GEO
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Project description:Background: Human breast cancer is a heterogeneous disease consisting of multiple molecular subtypes. Genetically engineered mouse models (GEMMs) are useful resources for studying breast cancers in vivo under genetically controlled and immune competent conditions. Identifying murine models with conserved human tumor features will facilitate etiology determinations, highlight the effects of mutations on pathway activation, and improve preclinical drug validation. Results: Transcriptomic profiles of 27 murine models of mammary carcinoma and normal mammary tissue were determined using gene expression microarrays. Hierarchical clustering analysis identified 17 distinct murine subtypes (classes). Across species analyses using three independent human breast cancer datasets identified eight murine classes that represent specific human breast cancer subtypes. Multiple models were associated with human basal-like tumors including TgC3(1)-Tag, TgWap-Myc, and Trp53-/-. Interestingly, the TgWAPCre-Etv6 model mimicked the HER2-enriched subtype, a group of human tumors without a murine counterpart in previous comparative studies. Gene signature analysis identified hundreds of commonly expressed pathways between linked mouse and human subtypes, highlighting potentially common genetic drivers of tumorigenesis and candidate pathways for therapeutic intervention. Conclusion: This study consolidates murine models of breast carcinoma into the largest comprehensive transcriptomic dataset to date to identify human-mouse disease subtype counterparts. This approach illustrates the value of comparisons between species to identify murine models that faithfully mimic the human condition and indicates that multiple GEMMs are needed to represent the diversity of human breast cancers. These trans-species associations should guide model selection during preclinical study design to ensure appropriate representatives of the human disease subtypes are used. Keywords: breast cancer, comparative genomics, genetically engineered mouse models, and molecular pathway signatures reference x sample
2013-11-04 | E-GEOD-42640 | biostudies-arrayexpress
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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