Weight changes and lifestyle behaviors in women after breast cancer diagnosis: a cross-sectional study

Background: Weight gain rather than weight loss often occurs after breast cancer diagnosis despite breast cancer survivors frequently reported making healthful lifestyle changes. This study describes the prevalence and magnitude of changes in weight before and after breast cancer diagnosis and examines lifestyle behaviors of breast cancer survivors with stable weight, weight gain or weight loss. Methods. Respondents were 368 women with breast cancer characterized by stages I, II and III. All were recruited from hospitals or breast cancer support groups and had completed conventional treatment. Current weight and height were measured while weight at cancer diagnosis and 1 year before diagnosis were self-reported. Weight change was calculated as the difference between current weight and weight a year preceding breast cancer diagnosis. A 24-hour diet recall and Global Physical Activity Questionnaire assessed dietary intake and physical activity, respectively. Differences in lifestyle behaviors among weight change groups were examined using Analysis of Covariance (ANCOVA). Results: Mean weight change from a year preceding diagnosis to study entry was 2.73 kg (95% CI: 1.90-3.55). Most women (63.3%) experienced weight gain rather than weight loss (36.7%) with a higher percentage (47.8%) having at least 5% weight gain (47.8%) rather than weight loss (22%), respectively. Compared to other weight change groups, women in >10% weight gain group had the lowest fruit and vegetable servings (1.58 servings/day; 95% CI: 1.36-1.82) and highest servings of dairy products (0.41 servings/day; 95% CI: 0.30-0.52). Conclusions: Weight gain was evident in this sample of women after breast cancer diagnosis. Information on magnitude of weight change after breast cancer diagnosis and lifestyle behaviors of breast cancer survivors with varying degrees of weight change could facilitate the development and targeting of effective intervention strategies to achieve healthy weight and optimal health for better survival

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