Dataset Information

Modeling sporadic loss of heterozygosity in mice by using mosaic analysis with double markers (MADM).

ABSTRACT: The initiation and progression of many human cancers involve either somatic activation of protooncogenes or inactivation of tumor-suppressor genes (TSGs) in sporadic cells. Although sporadic gain-of-function of protooncogenes has been successfully modeled in mice [e.g., Johnson L, Mercer K, Greenbaum D, Bronson RT, Crowley D, Tuveson DA, Jacks T (2001) Nature 410:1111-1116], generating a similar degree of sparseness of TSG loss-of-function remains a challenge. Here, we use mosaic analysis with double markers (MADM) to achieve TSG inactivation and concurrent labeling in sporadic somatic cells of mice, closely mimicking loss of heterozygosity as occurs in human cancers. As proof of principle, we studied the consequence of sporadic loss of p27kip1, a cyclin-dependent kinase inhibitor. MADM-mediated loss of p27kip1 results in mutant cell expansion markedly greater than that observed in conventional p27kip1 knockouts. Moreover, the direct comparison of WT and mutant cells at single-cell resolution afforded by MADM reveals that p27kip1 regulates organ size in vivo by cell-autonomous control of cell cycle exit timing. These studies establish MADM as a high-resolution method for modeling sporadic loss of heterozygosity in mice, providing insights into TSG function.

SUBMITTER: Muzumdar MD

PROVIDER: S-EPMC1810340 | biostudies-literature | 2007 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Modeling sporadic loss of heterozygosity in mice by using mosaic analysis with double markers (MADM).

Muzumdar Mandar Deepak MD Luo Liqun L Zong Hui H

Proceedings of the National Academy of Sciences of the United States of America 20070305 11

The initiation and progression of many human cancers involve either somatic activation of protooncogenes or inactivation of tumor-suppressor genes (TSGs) in sporadic cells. Although sporadic gain-of-function of protooncogenes has been successfully modeled in mice [e.g., Johnson L, Mercer K, Greenbaum D, Bronson RT, Crowley D, Tuveson DA, Jacks T (2001) Nature 410:1111-1116], generating a similar degree of sparseness of TSG loss-of-function remains a challenge. Here, we use mosaic analysis with d ...[more]

PMID: 17360552

Similar Datasets

Project description:BackgroundLoss of heterozygosity (LOH) is an important marker for one of the 'two-hits' required for tumor suppressor gene inactivation. Traditional methods for mapping LOH regions require the comparison of both tumor and patient-matched normal DNA samples. However, for many archival samples, patient-matched normal DNA is not available leading to the under-utilization of this important resource in LOH studies. Here we describe a new method for LOH analysis that relies on the genome-wide comparison of heterozygosity of single nucleotide polymorphisms (SNPs) between cohorts of cases and un-matched healthy control samples. Regions of LOH are defined by consistent decreases in heterozygosity across a genetic region in the case cohort compared to the control cohort.MethodsDNA was collected from 20 Follicular Lymphoma (FL) tumor samples, 20 Diffuse Large B-cell Lymphoma (DLBCL) tumor samples, neoplastic B-cells of 10 B-cell Chronic Lymphocytic Leukemia (B-CLL) patients and Buccal cell samples matched to 4 of these B-CLL patients. The cohort heterozygosity comparison method was developed and validated using LOH derived in a small cohort of B-CLL by traditional comparisons of tumor and normal DNA samples, and compared to the only alternative method for LOH analysis without patient matched controls. LOH candidate regions were then generated for enlarged cohorts of B-CLL, FL and DLBCL samples using our cohort heterozygosity comparison method in order to evaluate potential LOH candidate regions in these non-Hodgkin's lymphoma tumor subtypes.ResultsUsing a small cohort of B-CLL samples with patient-matched normal DNA we have validated the utility of this method and shown that it displays more accuracy and sensitivity in detecting LOH candidate regions compared to the only alternative method, the Hidden Markov Model (HMM) method. Subsequently, using B-CLL, FL and DLBCL tumor samples we have utilised cohort heterozygosity comparisons to localise LOH candidate regions in these subtypes of non-Hodgkin's lymphoma. Detected LOH regions included both previously described regions of LOH as well as novel genomic candidate regions.ConclusionsWe have proven the efficacy of the use of cohort heterozygosity comparisons for genome-wide mapping of LOH and shown it to be in many ways superior to the HMM method. Additionally, the use of this method to analyse SNP microarray data from 3 common forms of non-Hodgkin's lymphoma yielded interesting tumor suppressor gene candidates, including the ETV3 gene that was highlighted in both B-CLL and FL.

Project description:BackgroundHybridization, genetic mixture of distinct populations, gives rise to myriad recombinant genotypes. Characterizing the genomic composition of hybrids is critical for studies of hybrid zone dynamics, inheritance of traits, and consequences of hybridization for evolution and conservation. Hybrid genomes are often summarized either by an estimate of the proportion of alleles coming from each ancestral population or classification into discrete categories like F1, F2, backcross, or merely "hybrid" vs. "pure". In most cases, it is not realistic to classify individuals into the restricted set of classes produced in the first two generations of admixture. However, the continuous ancestry index misses an important dimension of the genotype. Joint consideration of ancestry together with interclass heterozygosity (proportion of loci with alleles from both ancestral populations) captures all of the information in the discrete classification without the unrealistic assumption that only two generations of admixture have transpired.MethodsI describe a maximum likelihood method for joint estimation of ancestry and interclass heterozygosity. I present two worked examples illustrating the value of the approach for describing variation among hybrid populations and evaluating the validity of the assumption underlying discrete classification.ResultsNaively classifying natural hybrids into the standard six line cross categories can be misleading, and false classification can be a serious problem for datasets with few molecular markers. My analysis underscores previous work showing that many (50 or more) ancestry informative markers are needed to avoid erroneous classification.ConclusionAlthough classification of hybrids might often be misleading, valuable inferences can be obtained by focusing directly on distributions of ancestry and heterozygosity. Estimating and visualizing the joint distribution of ancestry and interclass heterozygosity is an effective way to compare the genetic structure of hybrid populations and these estimates can be used in classic quantitative genetic methods for assessing additive, dominant, and epistatic genetic effects on hybrid phenotypes and fitness. The methods are implemented in a freely available package "HIest" for the R statistical software ( http://cran.r-project.org/web/packages/HIest/index.html).

Project description:Cancer results from molecular mutations occurring in specific cell types, thus determining the cell-of-origin is critical for effective cancer treatment. Inferring such information from terminal tumors can be misleading because malignant tumor cells tend to acquire aberrant properties. Animal models are widely used because one can initiate mutations in specific cell types. However, cell-of-mutation that harbors initial molecular changes may not directly transform, rather merely passes along mutations to its progeny cell lineage, which then serves as cell-of-origin and finally transforms into malignancy. Such a problem is exemplified in the glioma field: glioma can be induced either by mutating tumor suppressor genes in neural stem cells (NSCs) or by over-expressing oncogenes in restricted progenitor cells such as oligodendrocyte precursor cells (OPCs). However, for the NSC glioma model, it remains unknown whether NSCs directly transform or simply pass along mutations to OPC lineage for malignancy. Here we use a genetic system termed Mosaic Analysis with Double Markers (MADM) to study the earliest stage of gliomagenesis from mutated NSCs. At an in vivo single-cell resolution, we unbiasedly analyzed tumorigenic potential of NSCs and all cell lineages derived from them. At pathologically undetectable pre-transforming phases, we found no significant aberrant growth of mutant NSCs and their derivatives except for the OPC lineage. We then tracked the tumor progression from earliest stage and confirmed the expansion of OPCs lead to gliomagenesis. Consistently, transcriptome profiling reveals that terminal-stage tumor cells display salient OPC features and resemble human proneural subtype of glioblastoma multiform (GBM). Most importantly, introducing the same mutations directly into OPCs was sufficient for malignant transformation and these tumor cells are indistinguishable in their gene expression profiles from those in which the first mutations were introduced into NSCs. Our findings strongly implicate OPCs as the transforming cell type for glioma even when initial mutations could occur in NSCs, and highlight the importance of analyzing early phases of tumorigenesis to distinguish cell-of-origin from cell-of-mutation.

Dataset Information

Modeling sporadic loss of heterozygosity in mice by using mosaic analysis with double markers (MADM).

Publications

Modeling sporadic loss of heterozygosity in mice by using mosaic analysis with double markers (MADM).

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets