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Transcriptional changes after overexpression of proliferation drivers in human mammary epithelial cells.

ABSTRACT: Genomics has provided a detailed structural description of the cancer genome. Identifying oncogenic drivers that work primarily through dosage changes is a current challenge. Unrestrained proliferation is a critical hallmark of human cancer. We constructed modular, barcoded libraries of human open reading frames (ORFs) and performed screens for proliferation regulators in multiple cell types. Approximately 10% of genes tested regulate proliferation, many performing in an unexpectedly highly tissue-specific manner. Proliferation drivers in a given cell type showed specific enrichment in SCNAs (somatic copy number changes) from cognate tumors and helped predict aneuploidy patterns in those tumors, implying that tissue type-specific genetic network architectures underlie SCNA selection in different cancers. In vivo screening confirmed these results. We report a substantial contribution to the catalog of SCNA-associated cancer drivers, identifying 147 amplified and 107 deleted genes as potential drivers, and derive new insights about the genetic network architecture of aneuploidy in tumors. KRTAPs are a class of human genes that promote proliferation in mammary epithelial cells (HMEC), but the mechanism is not understood. We performed RNAseq to study transcriptional changes associated with oeverxepression of KRTAPs and other oncogenes in hTERT-immortalized human mammary epithelial cells. GSEA analysis revealed the top enriched pathways upregulated by KRTAP expression are E2F-mediated regulation of DNA replication, G1-S specific transcription, cell cycle, translation and ribosome. KRTAP-induced mRNA changes are most closely related to those due to CCND1 expression, including induction of E2F1 transcription factor.

ORGANISM(S): Homo sapiens

PROVIDER: GSE109326 | GEO | 2018/05/01

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

Project description:Breast cancer is the second leading cause of cancer related death in American women. Patient care is complicated by inherent tumor heterogeneity that can be classified into at least six intrinsic subtypes. While targeted treatments are standard of care for most subtypes, there remains a clinical need for targeted therapies against basal-like tumors that are typically ‘triple negative breast cancers’. As such, the molecular mechanisms underlying basal-like tumors are under intense investigation to identify genetic drivers and possible drug targets of this subtype. Somatic p53 mutations are one of the most common genetic events in basal-like breast tumors. This genetic foundation primes cells to accumulate secondary genetic aberrations, a subset of which is predicted to promote tumorigenesis. To identify additional drivers of basal-like tumors, a comparative study between human and murine tumors was performed utilizing a p53null mammary transplant murine model. The p53null mammary transplant murine model produced a genomically diverse set of tumors, a subset of which we show resemble the human basal-like subtype. Microarray and sequencing technologies were used to interrogate the secondary genetic aberrations of these murine tumors which were then compared to human basal-like tumors to highlight conserved features. Of the ‘omic’ datasets analyzed, DNA copy number variation produced the largest number of conserved candidate driver genes. These candidate gene lists were further filtered using a DNA-RNA Pearson correlation cutoff of 0.5 and a requirement that the gene was deemed essential in at least one human basal-like cell line from a genome-wide RNA-mediated interference screen database. These steps highlighted seven potential driver genes that are at amplified loci in both murine and human basal-like tumors: Atp11a, Col4a2, Cul4a, Lamp1, Met, Pnpla6, and Tubgcp3. Inhibition of Met using Crizotinib caused Met amplified tumors to regress, confirming that this genetic event is a driver in a subset of p53null transplant mammary tumors. This study identifies MET as a driver of basal-like murine tumors, thus identifying a shared potential driver of human basal-like breast cancer. Our results also highlight the importance of comparative genomic studies for discovering drug targets and for providing models to study whether patient populations are likely to respond to selective targeted treatments.

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Transcriptional changes after overexpression of proliferation drivers in human mammary epithelial cells.

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