University of Cambridge

About: University of Cambridge is a education organization based out in Cambridge, United Kingdom. It is known for research contribution in the topics: Population & Galaxy. The organization has 118293 authors who have published 282289 publications receiving 14497093 citations. The organization is also known as: Cambridge University & Cambridge.

The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes

Abstract: The genomic landscape of breast cancer is complex, and inter- and intra-tumour heterogeneity are important challenges in treating the disease. In this study, we sequence 173 genes in 2,433 primary breast tumours that have copy number aberration (CNA), gene expression and long-term clinical follow-up data. We identify 40 mutation-driver (Mut-driver) genes, and determine associations between mutations, driver CNA profiles, clinical-pathological parameters and survival. We assess the clonal states of Mut-driver mutations, and estimate levels of intra-tumour heterogeneity using mutant-allele fractions. Associations between PIK3CA mutations and reduced survival are identified in three subgroups of ER-positive cancer (defined by amplification of 17q23, 11q13–14 or 8q24). High levels of intra-tumour heterogeneity are in general associated with a worse outcome, but highly aggressive tumours with 11q13–14 amplification have low levels of intra-tumour heterogeneity. These results emphasize the importance of genome-based stratification of breast cancer, and have important implications for designing therapeutic strategies.

Genome-wide association studies: theoretical and practical concerns

Abstract: To fully understand the allelic variation that underlies common diseases, complete genome sequencing for many individuals with and without disease is required. This is still not technically feasible. However, recently it has become possible to carry out partial surveys of the genome by genotyping large numbers of common SNPs in genome-wide association studies. Here, we outline the main factors - including models of the allelic architecture of common diseases, sample size, map density and sample-collection biases - that need to be taken into account in order to optimize the cost efficiency of identifying genuine disease-susceptibility loci.

The 1993 IGTI Scholar Lecture: Loss Mechanisms in Turbomachines

Abstract: The origins and effects of loss in turbomachines are discussed with the emphasis on trying to understand the physical origins of loss rather than on reviewing the available prediction methods. Loss is defined in terms of entropy increase and the relationship of this to the more familiar loss coefficients is derived and discussed. The sources of entropy are, in general: viscous effects in boundary layers, viscous effects in mixing processes, shock waves, and heat transfer across temperature differences. These are first discussed in general and then the results are applied to turbomachinery flows. Understanding of the loss due to heat transfer requires some discussion of cycle thermodynamics

Continuous deformations in random networks

Abstract: We examine some current ideas concerning the differences between covalent random networks with high and low average coordination. These ideas can be made rigorous by considering the number of continuous deformations (i.e. zero frequency modes) allowed within the network. In the transition from one kind of network to another, rigidity percolates through the system. This leads to a picture in which random networks with low average coordination (polymeric glasses) have large floppy or spongy regions with a few rigid inclusions. On the other hand in random networks with high average coordination (amorphous solids) the rigid regions have percolated to form a rigid solid with a few floppy or spongy inclusions.