Fred Hutchinson Cancer Research Center

About: Fred Hutchinson Cancer Research Center is a nonprofit organization based out in Cape Town, South Africa. It is known for research contribution in the topics: Population & Transplantation. The organization has 12322 authors who have published 30954 publications receiving 2288772 citations. The organization is also known as: Fred Hutch & The Hutch.

Kaplan—meier, marginal or conditional probability curves in summarizing competing risks failure time data?

Abstract: In the context of competing risks the Kaplan-Meier estimator is often unsuitable for summarizing failure time data. We discuss some alternative descriptive methods including marginal probability and conditional probability estimators. Two-sample test statistics are also presented.

Non–Small Cell Lung Cancer

Abstract: In 2010, approximately 222,520 new cases of lung or bronchial cancer will be diagnosed in the USA, and 157,300 patients are expected to die of this disease [1]. Lung cancer is the leading cause of cancer-related death in both men and women, and non-small cell lung cancer (NSCLC) accounts for about 80 % of these cases. Lung cancer is most often asymptomatic in its early stages; consequently, the disease is usually diagnosed at an advanced stage, when it is much more difficult to treat. One or more genes are believed to be responsible for an inherited increase in risk of developing lung cancer in the general population. Smoking remains one of the main environmental factors associated with the development of lung cancer [2]. Although the development of lung cancer seems to be the result of several sequential molecular abnormalities in individuals at high risk of developing the disease, the genetic mechanisms by which an individual develops lung cancer remain largely unknown. These steps involve abnormalities in the expression of angiogenic factors (e.g., vascular endothelial growth factor, or VEGF and epithelial growth factor receptors, or EGFRs) [3]. The heterogeneity of lung cancer and the diversity of its morphologic appearance and molecular properties make the application of molecular targeted therapies used in other cancers more complex, but such therapies are certainly a goal for the future.

Genetic analyses of diverse populations improves discovery for complex traits

Abstract: Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry1-3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific4-10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations11,12. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States-where minority populations have a disproportionately higher burden of chronic conditions13-the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities.