Requirement for MUC5AC in KRAS-dependent lung carcinogenesis

With more than 150,000 deaths per year in the US alone, lung cancer has the highest number of deaths for any cancer. These poor outcomes reflect a lack of treatment for the most common form of lung cancer, non-small cell lung carcinoma (NSCLC). Lung adenocarcinoma (ADC) is the most prevalent subtype of NSCLC, with the main oncogenic drivers being KRAS and epidermal growth factor receptor (EGFR). Whereas EGFR blockade has led to some success in lung ADC, effective KRAS inhibition is lacking. KRAS-mutant ADCs are characterized by high levels of gel-forming mucin expression, with the highest mucin levels corresponding to worse prognoses. Despite these well-recognized associations, little is known about roles for individual gel-forming mucins in ADC development causatively. We hypothesized that MUC5AC/Muc5ac, a mucin gene known to be commonly expressed in NSCLC, is crucial in KRAS/Kras-driven lung ADC. We found that MUC5AC was a significant determinant of poor prognosis, especially in patients with KRAS-mutant tumors. In addition, by using mice with lung ADC induced chemically with urethane or transgenically by mutant-Kras expression, we observed significantly reduced tumor development in animals lacking Muc5ac compared with controls. Collectively, these results provide strong support for MUC5AC as a potential therapeutic target for lung ADC, a disease with few effective treatments

Rare copy number variants in over 100,000 European ancestry subjects reveal multiple disease associations

Copy number variants (CNVs) are suggested to have a widespread impact on the human genome and phenotypes. To understand the role of CNVs across human diseases, we examine the CNV genomic landscape of 100,028 unrelated individuals of European ancestry, using SNP and CGH array datasets. We observe an average CNV burden of ~650 kb, identifying a total of 11,314 deletion, 5625 duplication, and 2746 homozygous deletion CNV regions (CNVRs). In all, 13.7% are unreported, 58.6% overlap with at least one gene, and 32.8% interrupt coding exons. These CNVRs are significantly more likely to overlap OMIM genes (2.94-fold), GWAS loci (1.52-fold), and non-coding RNAs (1.44-fold), compared with random distribution (P < 1 × 10-3). We uncover CNV associations with four major disease categories, including autoimmune, cardio-metabolic, oncologic, and neurological/psychiatric diseases, and identify several drug-repurposing opportunities. Our results demonstrate robust frequency definition for large-scale rare variant association studies, identify CNVs associated with major disease categories, and illustrate the pleiotropic impact of CNVs in human disease

Design and Implementation of the International Genetics and Translational Research in Transplantation Network

Background. Genetic association studies of transplantation outcomes have been hampered by small samples and highly complex multifactorial phenotypes, hindering investigations of the genetic architecture of a range of comorbidities which significantly impact graft and recipient life expectancy. We describe here the rationale and design of the International Genetics & Translational Research in Transplantation Network. The network comprises 22 studies to date, including 16 494 transplant recipients and 11 669 donors, of whom more than 5000 are of non-European ancestry, all of whom have existing genomewide genotype data sets. Methods. We describe the rich genetic and phenotypic information available in this consortium comprising heart, kidney, liver, and lung transplant cohorts. Results. We demonstrate significant power in International Genetics & Translational Research in Transplantation Network to detect main effect association signals across regions such as the MHC region as well as genomewide for transplant outcomes that span all solid organs, such as graft survival, acute rejection, new onset of diabetes after transplantation, and for delayed graft function in kidney only. Conclusions. This consortium is designed and statistically powered to deliver pioneering insights into the genetic architecture of transplant-related outcomes across a range of different solid-organ transplant studies. The study design allows a spectrum of analyses to be performed including recipient-only analyses, donor-recipient HLA mismatches with focus on loss-of-function variants and nonsynonymous single nucleotide polymorphisms

Design and implementation of the international genetics and translational research in transplantation network

Background. Genetic association studies of transplantation outcomes have been hampered by small samples and highly complex multifactorial phenotypes, hindering investigations of the genetic architecture of a range of comorbidities which significantly impact graft and recipient life expectancy. We describe here the rationale and design of the International Genetics & Translational Research in Transplantation Network. The network comprises 22 studies to date, including 16 494 transplant recipients and 11 669 donors, of whom more than 5000 are of non-European ancestry, all of whom have existing genomewide genotype data sets. Methods. We describe the rich genetic and phenotypic information available in this consortium comprising heart, kidney, liver, and lung transplant cohorts. Results. We demonstrate significant power in International Genetics & Translational Research in Transplantation Network to detect main effect association signals across regions such as the MHC region as well as genomewide for transplant outcomes that span all solid organs, such as graft survival, acute rejection, new onset of diabetes after transplantation, and for delayed graft function in kidney only. Conclusions. This consortium is designed and statistically powered to deliver pioneering insights into the genetic architecture of transplant-related outcomes across a range of different solid-organ transplant studies. The study design allows a spectrum of analyses to be performed including recipient-only analyses, donor-recipient HLA mismatches with focus on loss-of-function variants and nonsynonymous single nucleotide polymorphisms