Project description:Breast cancer is the most prevalent cancer in women, and most cases are believed to have a sporadic, rather than heritable basis. Therefore, a major challenge in cancer research is to determine the underlying genomic alterations leading to carcinogenesis and malignancy, and then use this information for personalized therapies. Genomic studies of human cancers that aim to identify causative mutations are complicated by the prevalence of passenger mutations, genetic heterogeneity, and the diversity of breast cancer etiologies and tumor subtypes. Mouse cancer models are powerful for untangling the genomic basis of cancers because genetic and phenotypic variation can be eliminated or controlled. To identify genes contributing to mammary tumorigenesis, we exploited the C3H-Mcm4Chaos3/Chaos3 (“Chaos3”) mouse model that, by virtue of bearing a defective DNA replicative helicase subunit that causes elevated genomic instability (GIN), sustains somatic alterations ultimately causing mammary adenocarcinomas. Genomic analysis of Chaos3 mammary tumors revealed recurrent copy number alterations (CNAs) of specific genomic regions, most notably deletion of the Neurofibromin 1 (Nf1) tumor suppressor gene in all cases. NF1, a negative regulator of RAS, is traditionally recognized for its role in driving the development of neurofibromas in the context of the human disease Neurofibromitosis but not breast cancer. We observed elevated RAS activation and increased sensitivity of both Chaos3 and human Nf1-mutated breast cancer lines to MAPK and/or PI3K/AKT pathway inhibitors. We also found striking overlap between Chaos3 CNAs and human breast cancer CNA data curated in public genomic databases, including Nf1 deletion. Together, our results indicate that spontaneous NF1 loss can drive breast cancer and suggests a potential therapeutic strategy in that subset of patients. reference x sample
Project description:Breast cancer is the most prevalent cancer in women 1, and most cases are believed to have a sporadic, rather than heritable basis 2. To identify breast cancer driver genes, we exploited the C3H-Mcm4Chaos3/Chaos3 (“Chaos3”) mouse model that, by virtue of bearing a defective DNA replicative helicase subunit that causes elevated genomic instability (GIN), sustains somatic alterations ultimately causing mammary adenocarcinomas 6. Array Comparative Genomic Hybridization (aCGH) analysis of Chaos3 mammary tumors revealed recurrent copy number alterations (CNAs), most notably deletion of the Neurofibromin 1 (Nf1) tumor suppressor gene in all cases. NF1, a negative regulator of RAS, is traditionally recognized for its role in driving the development of neurofibromas in the context of the human disease Neurofibromatosis Type 1, but not breast cancer. Genomic DNA from tumor and reference samples were hybridized to NimbleGen 3x720K mouse CGH arrays. Two reference samples were used independently. CNAs were visualized using Nimblegen, IGV, and KCsmart software 32. Select genes were validated via qPCR. Critical regions within each Chaos3 CNA were identified as the region with the greatest overlap across multiple Chaos3 tumors. Recurring Copy Number Variations (CNVs) for 12 Chaos3 tumors and 2 MMTV-Neu mammary tumors analyzed by aCGH are indicated. Samples analyzed are primary tumors except where indicated.
Project description:Breast cancer is the most prevalent cancer in women, and most cases are believed to have a sporadic, rather than heritable basis. Therefore, a major challenge in cancer research is to determine the underlying genomic alterations leading to carcinogenesis and malignancy, and then use this information for personalized therapies. Genomic studies of human cancers that aim to identify causative mutations are complicated by the prevalence of passenger mutations, genetic heterogeneity, and the diversity of breast cancer etiologies and tumor subtypes. Mouse cancer models are powerful for untangling the genomic basis of cancers because genetic and phenotypic variation can be eliminated or controlled. To identify genes contributing to mammary tumorigenesis, we exploited the C3H-Mcm4Chaos3/Chaos3 (“Chaos3”) mouse model that, by virtue of bearing a defective DNA replicative helicase subunit that causes elevated genomic instability (GIN), sustains somatic alterations ultimately causing mammary adenocarcinomas. Genomic analysis of Chaos3 mammary tumors revealed recurrent copy number alterations (CNAs) of specific genomic regions, most notably deletion of the Neurofibromin 1 (Nf1) tumor suppressor gene in all cases. NF1, a negative regulator of RAS, is traditionally recognized for its role in driving the development of neurofibromas in the context of the human disease Neurofibromitosis but not breast cancer. We observed elevated RAS activation and increased sensitivity of both Chaos3 and human Nf1-mutated breast cancer lines to MAPK and/or PI3K/AKT pathway inhibitors. We also found striking overlap between Chaos3 CNAs and human breast cancer CNA data curated in public genomic databases, including Nf1 deletion. Together, our results indicate that spontaneous NF1 loss can drive breast cancer and suggests a potential therapeutic strategy in that subset of patients.
Project description:Breast cancer is the most prevalent cancer in women 1, and most cases are believed to have a sporadic, rather than heritable basis 2. To identify breast cancer driver genes, we exploited the C3H-Mcm4Chaos3/Chaos3 (“Chaos3”) mouse model that, by virtue of bearing a defective DNA replicative helicase subunit that causes elevated genomic instability (GIN), sustains somatic alterations ultimately causing mammary adenocarcinomas 6. Array Comparative Genomic Hybridization (aCGH) analysis of Chaos3 mammary tumors revealed recurrent copy number alterations (CNAs), most notably deletion of the Neurofibromin 1 (Nf1) tumor suppressor gene in all cases. NF1, a negative regulator of RAS, is traditionally recognized for its role in driving the development of neurofibromas in the context of the human disease Neurofibromatosis Type 1, but not breast cancer. Genomic DNA from tumor and reference samples were hybridized to NimbleGen 3x720K mouse CGH arrays. Two reference samples were used independently. CNAs were visualized using Nimblegen, IGV, and KCsmart software 32. Select genes were validated via qPCR. Critical regions within each Chaos3 CNA were identified as the region with the greatest overlap across multiple Chaos3 tumors.
Project description:We investigated the CNAs in a four stage tumorigenesis model. This model included copy number analyses in non-transgenic NMRI mice (normal) and in transgenic SVT/t mice: non-malignant hyperplastic mammary glands and breast cancers, as well as breast cancer derived cell lines. We focused our research on copy number analyses to compare the genomic alterations that occur during tumorigenesis. We addressed the question, whether common predisposed chromosomal breakpoints could be seen to promote malignant transformation. We can report a characteristic increase of copy number alterations from normal to tumor stage in our model. Furthermore, we have identified chromosomal segments and found characteristic fragmentations.
Project description:We investigated the CNAs in a four stage tumorigenesis model. This model included copy number analyses in non-transgenic NMRI mice (normal) and in transgenic SVT/t mice: non-malignant hyperplastic mammary glands and breast cancers, as well as breast cancer derived cell lines. We focused our research on copy number analyses to compare the genomic alterations that occur during tumorigenesis. We addressed the question, whether common predisposed chromosomal breakpoints could be seen to promote malignant transformation. We can report a characteristic increase of copy number alterations from normal to tumor stage in our model. Furthermore, we have identified chromosomal segments and found characteristic fragmentations. Affymetrix SNP array analysis was performed with Mouse Diversity Genotyping Arrays (Affymetrix). DNA was extracted from frozen biopsies of mammary tumor samples of six mice and two cell lines. Normalization and allele summarization were performed with the BRLMM-P algorithm provided and copy number analysis was performed for the each sample using the average signal intensity of both normal samples as the reference for copy number inference.