Cross-species analysis of genetically engineered mouse models of MAPK-driven colorectal cancer identifies hallmarks of the human disease

Effective treatment options for advanced colorectal cancer (CRC) are limited, survival rates are poor and this disease continues to be a leading cause of cancer-related deaths worldwide. Despite being a highly heterogeneous disease, a large subset of individuals with sporadic CRC typically harbor relatively few established ‘driver’ lesions. Here, we describe a collection of genetically engineered mouse models (GEMMs) of sporadic CRC that combine lesions frequently altered in human patients, including well-characterized tumor suppressors and activators of MAPK signaling. Primary tumors from these models were profiled, and individual GEMM tumors segregated into groups based on their genotypes. Unique allelic and genotypic expression signatures were generated from these GEMMs and applied to clinically annotated human CRC patient samples. We provide evidence that a Kras signature derived from these GEMMs is capable of distinguishing human tumors harboring KRAS mutation, and tracks with poor prognosis in two independent human patient cohorts. Furthermore, the analysis of a panel of human CRC cell lines suggests that high expression of the GEMM Kras signature correlates with sensitivity to targeted pathway inhibitors. Together, these findings implicate GEMMs as powerful preclinical tools with the capacity to recapitulate relevant human disease biology, and support the use of genetic signatures generated in these models to facilitate future drug discovery and validation efforts

Statistical method on nonrandom clustering with application to somatic mutations in cancer

<p>Abstract</p> <p>Background</p> <p>Human cancer is caused by the accumulation of tumor-specific mutations in oncogenes and tumor suppressors that confer a selective growth advantage to cells. As a consequence of genomic instability and high levels of proliferation, many passenger mutations that do not contribute to the cancer phenotype arise alongside mutations that drive oncogenesis. While several approaches have been developed to separate driver mutations from passengers, few approaches can specifically identify activating driver mutations in oncogenes, which are more amenable for pharmacological intervention.</p> <p>Results</p> <p>We propose a new statistical method for detecting activating mutations in cancer by identifying nonrandom clusters of amino acid mutations in protein sequences. A probability model is derived using order statistics assuming that the location of amino acid mutations on a protein follows a uniform distribution. Our statistical measure is the differences between pair-wise order statistics, which is equivalent to the size of an amino acid mutation cluster, and the probabilities are derived from exact and approximate distributions of the statistical measure. Using data in the Catalog of Somatic Mutations in Cancer (COSMIC) database, we have demonstrated that our method detects well-known clusters of activating mutations in KRAS, BRAF, PI3K, and <it>β</it>-catenin. The method can also identify new cancer targets as well as gain-of-function mutations in tumor suppressors.</p> <p>Conclusions</p> <p>Our proposed method is useful to discover activating driver mutations in cancer by identifying nonrandom clusters of somatic amino acid mutations in protein sequences.</p

Dominância fiscal : uma investigação empírica sobre o caso brasileiro no período de 2003 a 2014

A estabilização econômica dos anos de 1990 e a adoção do tripé econômico, a partir de 1999, marcam o fim de um capítulo delicado da história brasileira; a partir de então, era necessária a existência de certa sintonia de políticas monetária e fiscal para a manutenção do controle dos diversos indicadores econômicos. Contudo, com essa reciprocidade na política econômica, são incitadas discussões sobre a orientação do governo na hora de definir suas prioridades nesse campo: as variáveis fiscais são priorizadas e, por conseguinte, determinadas, forçando as monetárias a se ajustarem – ou o contrário? A resposta para esse questionamento leva à discussão sobre a dominância fiscal. Assim, esse trabalho visa verificar empiricamente, usando das modelagens econométricas VAR e estudo de eventos, se há dominância fiscal ou monetária na economia brasileira e se a eficácia da política monetária mudou na transição do governo Lula para o governo Dilma. O resultado foi inconclusivo para o governo Lula e indicou dominância fiscal no governo Dilma. Ainda verificou-se não haver modificação na eficácia da política monetária.Economic stabilization, in the 1990s, and utilization of an economic tripod, after 1999, represents the end of a delicate chapter in Brazilian history. Ever since, it was necessary the existence of a certain agreement between monetary and fiscal politic, in order to maintain under control a variety of economic indicators. However, this reciprocity (in economic politic) starts discussions about the real government orientations when it comes to define its priority on this subject: are the fiscal variables priorized, and then, determined, forcing monetary variables to adjust themselves, or the opposite? The answer to these questions emerge from the fiscal dominance discussion. This paper intends to empiric verify, using econometric modeling VAR and event study, if there is fiscal dominance or monetary in Brazilian economy and whether the effectiveness of monetary politic has changed in the transition from Lula's government to the Dilma government. The result was inconclusive for the Lula government and indicated fiscal dominance in the Dilma government. There was still no change in the efficiency of the monetary politic.CAPE

Whole Exome Sequencing of Rapid Autopsy Tumors and Xenograft Models Reveals Possible Driver Mutations Underlying Tumor Progression.

Pancreatic Ductal Adenocarcinoma (PDAC) is a highly lethal malignancy due to its propensity to invade and rapidly metastasize and remains very difficult to manage clinically. One major hindrance towards a better understanding of PDAC is the lack of molecular data sets and models representative of end stage disease. Moreover, it remains unclear how molecularly similar patient-derived xenograft (PDX) models are to the primary tumor from which they were derived. To identify potential molecular drivers in metastatic pancreatic cancer progression, we obtained matched primary tumor, metastases and normal (peripheral blood) samples under a rapid autopsy program and performed whole exome sequencing (WES) on tumor as well as normal samples. PDX models were also generated, sequenced and compared to tumors. Across the matched data sets generated for three patients, there were on average approximately 160 single-nucleotide mutations in each sample. The majority of mutations in each patient were shared among the primary and metastatic samples and, importantly, were largely retained in the xenograft models. Based on the mutation prevalence in the primary and metastatic sites, we proposed possible clonal evolution patterns marked by functional mutations affecting cancer genes such as KRAS, TP53 and SMAD4 that may play an important role in tumor initiation, progression and metastasis. These results add to our understanding of pancreatic tumor biology, and demonstrate that PDX models derived from advanced or end-stage likely closely approximate the genetics of the disease in the clinic and thus represent a biologically and clinically relevant pre-clinical platform that may enable the development of effective targeted therapies for PDAC