Diss. (sammanfattning) Göteborg : Göteborgs universitet, 2007. / Härtill 4 uppsatser.

Retroperitoneal liposarcoma is often asymptomatic but sometimes attention is drawn to the neoplasm due to clinical manifestations. These include fever, flu-like symptoms, nausea or vomiting due to pressure or hypoglycemia related to paraneoplastic disease. We present a rare case of a massive retroperitoneal liposarcoma presenting with refractory dry cough. The patient underwent resection of the mass with complete resolution of her cough. Histopathological examination of the mass demonstrated a well-differentiated tumor with myxomatous features. No evidence of metastatic disease to the lungs was observed. This case points to the need for a thorough and careful evaluation of unexplained cough.

Retroperitoneal liposarcoma is often asymptomatic but sometimes attention is drawn to the neoplasm due to clinical manifestations. These include fever, flu-like symptoms, nausea or vomiting due to pressure or hypoglycemia related to paraneoplastic disease. We present a rare case of a massive retroperitoneal liposarcoma presenting with refractory dry cough. The patient underwent resection of the mass with complete resolution of her cough. Histopathological examination of the mass demonstrated a well-differentiated tumor with myxomatous features. No evidence of metastatic disease to the lungs was observed. This case points to the need for a thorough and careful evaluation of unexplained cough.

Apolipoprotein C-I (apoC-I) plays an important role in the metabolism of plasma triglyceride levels and cholesterol metabolism. Little is known about the regulation of apoC-I production by human adipocytes. Aim. To investigate the effect of different tissue culture conditions on the synthesis and secretion of apoC-I and apoE in human SW872 liposarcoma cells and to study the effects of apoC-I overexpression in these same cells. Methods. SW872 cells were grown in DMEM/F-12 (3:1, v/v). QPCR was used to quantify mRNA synthesis. ELISAs were used to quantify intracellular and extracellular proteins. Colorimetric reaction kits were used to analyze intracellular cholesterol and triglyceride concentrations. Results . Maturation experiments revealed that after 17 days in culture, SW872 cells contained significantly more cholesterol (100%) and triglyceride (3-fold). Cell maturation was associated with significantly higher levels of apoE mRNA (+200%) but not apoC-I mRNA (-50%). The cells secreted more apoC-I (+110%) and apoE (+340%). Cellular apoC-I increased 620% and apoE increased 1540%. Treatment of cells during maturation with insulin (0, 10 or 1000 nM) significantly reduced the secretion of apoC-I and apoE (-14% and -56%, respectively) and intracellular apoC-I and apoE (-10% and -12%, respectively. Overexpression of apoC-I in SW872 cells resulted in increased cell number (+70%) and decreased lipids per cell (-32% triglyceride, -36% cholesterol) as compared to controls. Conclusion. These results suggest that apoC-I and apoE production is differentially regulated at the transcriptional level in adipocytes and that apoC-I and apoE play a role in the maturation of human adipocytes and may have an important role in mediating or regulating cell lipid accumulation. As well, overexpression of apoC-I in SW872 cells impedes cellular lipid accumulation and stimulates cellular proliferation.

Diss. (sammanfattning) Göteborg : Univ. , 2007. / Härtill 4 uppsatser.

No description available.

Genealogy reconstruction is widely used in biology when relationships among entities are studied. Phylogenies, or evolutionary trees, show the differences between species. They are of profound importance because they help to obtain better understandings of evolutionary processes. Pedigrees, or family trees, on the other hand visualize the relatedness between individuals in a population. The reconstruction of pedigrees and the inference of parentage in general is now a cornerstone in molecular ecology. Applications include the direct infer- ence of gene flow, estimation of the effective population size and parameters describing the population’s mating behaviour such as rates of inbreeding. In the first part of this thesis, we construct genealogies of various types of cancer. Histopatho- logical classification of human tumors relies in part on the degree of differentiation of the tumor sample. To date, there is no objective systematic method to categorize tumor subtypes by maturation. We introduce a novel algorithm to rank tumor subtypes according to the dis- similarity of their gene expression from that of stem cells and fully differentiated tissue, and thereby construct a phylogenetic tree of cancer. We validate our methodology with expression data of leukemia and liposarcoma subtypes and then apply it to a broader group of sarcomas and of breast cancer subtypes. This ranking of tumor subtypes resulting from the application of our methodology allows the identification of genes correlated with differentiation and may help to identify novel therapeutic targets. Our algorithm represents the first phylogeny-based tool to analyze the differentiation status of human tumors. In contrast to asexually reproducing cancer cell populations, pedigrees of sexually reproduc- ing populations cannot be represented by phylogenetic trees. Pedigrees are directed acyclic graphs (DAGs) and therefore resemble more phylogenetic networks where reticulate events are indicated by vertices with two incoming arcs. We present a software package for pedigree reconstruction in natural populations using co-dominant genomic markers such as microsatel- lites and single nucleotide polymorphism (SNPs) in the second part of the thesis. If available, the algorithm makes use of prior information such as known relationships (sub-pedigrees) or the age and sex of individuals. Statistical confidence is estimated by Markov chain Monte Carlo (MCMC) sampling. The accuracy of the algorithm is demonstrated for simulated data as well as an empirical data set with known pedigree. The parentage inference is robust even in the presence of genotyping errors. We further demonstrate the accuracy of the algorithm on simulated clonal populations. We show that the joint estimation of parameters of inter- est such as the rate of self-fertilization or clonality is possible with high accuracy even with marker panels of moderate power. Classical methods can only assign a very limited number of statistically significant parentages in this case and would therefore fail. The method is implemented in a fast and easy to use open source software that scales to large datasets with many thousand individuals.

O objetivo deste trabalho foi estudar a expressão protéica de mdm2 e avaliar a sua relação com alguns aspectos anatomopatológicos, visando também identificar fatores prognósticos no que diz respeito à sobrevida livre de recidiva local (SLRL), sobrevida livre de metástase (SLM) e sobrevida global (SG), em pacientes portadores de lipossarcoma primário de extremidades. Vinte e cinco entre 50 pacientes admitidos no Instituto de Ortopedia e Traumatologia do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo – IOT/HC/FMUSP, entre 1968 e 2004, foram eleitos para o estudo. As probabilidades de sobrevida acumuladas foram feitas pela técnica de Kaplan-Meier e as curvas de sobrevida comparadas pelo teste de Log Rank. A validade estatística foi estabelecida para valores de p<0,05. As associações entre os índices positivo ou negativo para o mdm2 com outras variáveis foram feitas utilizando-se o teste exato de Fischer. A expressão imunoistoquímica da proteína mdm2 não foi considerada de valor prognóstico em nenhuma das sobrevidas estudadas (SLRL, SLM ou SG). Os fatores adversos que influenciaram o risco de recidiva local na análise univariada foram: o gênero masculino (p = 0,023), subtipo histológico pleomórfico (p = 0,027) e alto grau histológico (p=0,007). Em relação a SLM a idade inferior a 50 anos (p = 0,040), o gênero masculino (p = 0,040), o subtipo pleomórfico (p < 0,001), o alto grau histológico (p = 0,003) tiveram um pior prognóstico. Os fatores adversos para SG foram: idade inferior a 50 anos (p = 0,040); o gênero masculino (p = 0,040); o subtipo pleomórfico (p < 0,001) e o alto grau histológico (p = 0,003). / The purpose of this was to study the expression by imunohistochemistry of mdm2 and your correlation with anatomopathological selected variables, aiming at identifying prognostic factors concerning to local recurrence free survival (LRFS), metastases free survival (MFS) and overall survival (OS) in patients with liposarcomas primary extremities. This study included 25 patients registred in the Instituto de Ortopedia e Traumatologia do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo - Brazil, from 1968 to 2004. The accumulated survival probabilities were calculated by Kaplan-Meier method and survival curves were compared using the logrank test. Statistical significance was defined as a p value less than 0,05. Associations between expression of mdm2 and other variables were analyzed using Fischer’s exact test. The expression by imunohistochemistry of mdm2 was not significant factor for LRFS, MFS or OS. The adverse factors for LRFS in univariate analysis were male gender (p = 0,023), pleomorfic histologic subtypes (p = 0,027) and high grade tumor (p = 0,007). For MFS age < 50 years (p = 0,040), male gender (p = 0,040), pleomorfic histologic subtypes (p < 0,001) and high grade tumor (p = 0,003) had worse prognostic. Negative prognostic factors for OS were age < 50 years (p = 0,040), male gender (p = 0,040), pleomorfic histologic subtypes (p < 0,001) and high grade tumor (p = 0,003).

O objetivo deste trabalho foi estudar a expressão protéica de mdm2 e avaliar a sua relação com alguns aspectos anatomopatológicos, visando também identificar fatores prognósticos no que diz respeito à sobrevida livre de recidiva local (SLRL), sobrevida livre de metástase (SLM) e sobrevida global (SG), em pacientes portadores de lipossarcoma primário de extremidades. Vinte e cinco entre 50 pacientes admitidos no Instituto de Ortopedia e Traumatologia do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo – IOT/HC/FMUSP, entre 1968 e 2004, foram eleitos para o estudo. As probabilidades de sobrevida acumuladas foram feitas pela técnica de Kaplan-Meier e as curvas de sobrevida comparadas pelo teste de Log Rank. A validade estatística foi estabelecida para valores de p<0,05. As associações entre os índices positivo ou negativo para o mdm2 com outras variáveis foram feitas utilizando-se o teste exato de Fischer. A expressão imunoistoquímica da proteína mdm2 não foi considerada de valor prognóstico em nenhuma das sobrevidas estudadas (SLRL, SLM ou SG). Os fatores adversos que influenciaram o risco de recidiva local na análise univariada foram: o gênero masculino (p = 0,023), subtipo histológico pleomórfico (p = 0,027) e alto grau histológico (p=0,007). Em relação a SLM a idade inferior a 50 anos (p = 0,040), o gênero masculino (p = 0,040), o subtipo pleomórfico (p < 0,001), o alto grau histológico (p = 0,003) tiveram um pior prognóstico. Os fatores adversos para SG foram: idade inferior a 50 anos (p = 0,040); o gênero masculino (p = 0,040); o subtipo pleomórfico (p < 0,001) e o alto grau histológico (p = 0,003). / The purpose of this was to study the expression by imunohistochemistry of mdm2 and your correlation with anatomopathological selected variables, aiming at identifying prognostic factors concerning to local recurrence free survival (LRFS), metastases free survival (MFS) and overall survival (OS) in patients with liposarcomas primary extremities. This study included 25 patients registred in the Instituto de Ortopedia e Traumatologia do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo - Brazil, from 1968 to 2004. The accumulated survival probabilities were calculated by Kaplan-Meier method and survival curves were compared using the logrank test. Statistical significance was defined as a p value less than 0,05. Associations between expression of mdm2 and other variables were analyzed using Fischer’s exact test. The expression by imunohistochemistry of mdm2 was not significant factor for LRFS, MFS or OS. The adverse factors for LRFS in univariate analysis were male gender (p = 0,023), pleomorfic histologic subtypes (p = 0,027) and high grade tumor (p = 0,007). For MFS age < 50 years (p = 0,040), male gender (p = 0,040), pleomorfic histologic subtypes (p < 0,001) and high grade tumor (p = 0,003) had worse prognostic. Negative prognostic factors for OS were age < 50 years (p = 0,040), male gender (p = 0,040), pleomorfic histologic subtypes (p < 0,001) and high grade tumor (p = 0,003).

Genealogy reconstruction is widely used in biology when relationships among entities are studied. Phylogenies, or evolutionary trees, show the differences between species. They are of profound importance because they help to obtain better understandings of evolutionary processes. Pedigrees, or family trees, on the other hand visualize the relatedness between individuals in a population. The reconstruction of pedigrees and the inference of parentage in general is now a cornerstone in molecular ecology. Applications include the direct infer- ence of gene flow, estimation of the effective population size and parameters describing the population’s mating behaviour such as rates of inbreeding. In the first part of this thesis, we construct genealogies of various types of cancer. Histopatho- logical classification of human tumors relies in part on the degree of differentiation of the tumor sample. To date, there is no objective systematic method to categorize tumor subtypes by maturation. We introduce a novel algorithm to rank tumor subtypes according to the dis- similarity of their gene expression from that of stem cells and fully differentiated tissue, and thereby construct a phylogenetic tree of cancer. We validate our methodology with expression data of leukemia and liposarcoma subtypes and then apply it to a broader group of sarcomas and of breast cancer subtypes. This ranking of tumor subtypes resulting from the application of our methodology allows the identification of genes correlated with differentiation and may help to identify novel therapeutic targets. Our algorithm represents the first phylogeny-based tool to analyze the differentiation status of human tumors. In contrast to asexually reproducing cancer cell populations, pedigrees of sexually reproduc- ing populations cannot be represented by phylogenetic trees. Pedigrees are directed acyclic graphs (DAGs) and therefore resemble more phylogenetic networks where reticulate events are indicated by vertices with two incoming arcs. We present a software package for pedigree reconstruction in natural populations using co-dominant genomic markers such as microsatel- lites and single nucleotide polymorphism (SNPs) in the second part of the thesis. If available, the algorithm makes use of prior information such as known relationships (sub-pedigrees) or the age and sex of individuals. Statistical confidence is estimated by Markov chain Monte Carlo (MCMC) sampling. The accuracy of the algorithm is demonstrated for simulated data as well as an empirical data set with known pedigree. The parentage inference is robust even in the presence of genotyping errors. We further demonstrate the accuracy of the algorithm on simulated clonal populations. We show that the joint estimation of parameters of inter- est such as the rate of self-fertilization or clonality is possible with high accuracy even with marker panels of moderate power. Classical methods can only assign a very limited number of statistically significant parentages in this case and would therefore fail. The method is implemented in a fast and easy to use open source software that scales to large datasets with many thousand individuals.:Abstract v Acknowledgments vii 1 Introduction 1 2 Cancer Phylogenies 7 2.1 Introduction..................................... 7 2.2 Background..................................... 9 2.2.1 PhylogeneticTrees............................. 9 2.2.2 Microarrays................................. 10 2.3 Methods....................................... 11 2.3.1 Datasetcompilation ............................ 11 2.3.2 Statistical Methods and Analysis..................... 13 2.3.3 Comparison of our methodology to other methods . . . . . . . . . . . 15 2.4 Results........................................ 16 2.4.1 Phylogenetic tree reconstruction method. . . . . . . . . . . . . . . . . 16 2.4.2 Comparison of tree reconstruction methods to other algorithms . . . . 28 2.4.3 Systematic analysis of methods and parameters . . . . . . . . . . . . . 30 2.5 Discussion...................................... 32 3 Wild Pedigrees 35 3.1 Introduction..................................... 35 3.2 The molecular ecologist’s tools of the trade ................... 36 3.2.1 3.2.2 3.2.3 3.2.1 Sibship inference and parental reconstruction . . . . . . . . . . . . . . 37 3.2.2 Parentage and paternity inference .................... 39 3.2.3 Multigenerational pedigree reconstruction . . . . . . . . . . . . . . . . 40 3.3 Background..................................... 40 3.3.1 Pedigrees .................................. 40 3.3.2 Genotypes.................................. 41 3.3.3 Mendelian segregation probability .................... 41 3.3.4 LOD Scores................................. 43 3.3.5 Genotyping Errors ............................. 43 3.3.6 IBD coefficients............................... 45 3.3.7 Bayesian MCMC.............................. 46 3.4 Methods....................................... 47 3.4.1 Likelihood Model.............................. 47 3.4.2 Efficient Likelihood Calculation...................... 49 3.4.3 Maximum Likelihood Pedigree ...................... 51 3.4.4 Full siblings................................. 52 3.4.5 Algorithm.................................. 53 3.4.6 Missing Values ............................... 56 3.4.7 Allelefrequencies.............................. 58 3.4.8 Rates of Self-fertilization.......................... 60 3.4.9 Rates of Clonality ............................. 60 3.5 Results........................................ 61 3.5.1 Real Microsatellite Data.......................... 61 3.5.2 Simulated Human Population....................... 62 3.5.3 SimulatedClonalPlantPopulation.................... 64 3.6 Discussion...................................... 71 4 Conclusions 77 A FRANz 79 A.1 Availability ..................................... 79 A.2 Input files...................................... 79 A.2.1 Maininputfile ............................... 79 A.2.2 Knownrelationships ............................ 80 A.2.3 Allele frequencies.............................. 81 A.2.4 Sampling locations............................. 82 A.3 Output files..................................... 83 A.4 Web 2.0 Interface.................................. 86 List of Figures 87 List of Tables 88 List Abbreviations 90 Bibliography 92 Curriculum Vitae I