Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

A TP-LPV-LMI Approach to Control of Tumor Growth

By using advanced control techniques to control physiological systems sophisticated control regimes can be realized. There are several challenges need to be solved in these approaches, however. Most of the time, the lack of information of the internal dynamics, the nonlinear behavior of the system to be controlled and the variabilities coming from that simple fact that people are different and their specifics vary in time makes the control design difficult. Nevertheless, the use of appropriate methodologies can facilitate to find solutions to them. In this study, our aim is to introduce different techniques and by combining them we show an effective way for control design with respect to physiological systems. Our solution stands on four pillars: transformation of the formulated model into control oriented model (COM) form; use the COM for linear parameter varying (LPV) kind modeling to handle unfavorable dynamics as linear dependencies; tensor product modeling (TPM) to downsize the computational costs both from modeling and control design viewpoint; and finally, using linear matrix inequalities (LMI) based controller design to satisfy predefined requirements. The occurring TP-LPV-LMI controller is able to enforce a given, nonlinear system to behave as a selected reference system. In this study, the detailed control solution is applied for tumor growth control to maintain the volume of the tumor