Meta-Emotions in Daily Life: Associations with Emotional Awareness and Depression

Meta-emotions are emotions that occur in response to other emotions (e.g., guilt about anger). Although preliminary evidence indicates that depression is associated with a greater likelihood of meta-emotions, much remains unknown about meta-emotions, including how regularly they are experienced and whether emotional awareness constructs (including attention to and clarity of emotion) influence their occurrence. In the present study, we aim to establish norms for meta-emotions in everyday life, determine whether increased emotional awareness is associated with a greater likelihood of meta-emotions, and examine whether negative emotions about negative emotions (negative-negative meta-emotional experiences) are associated with depressive severity. We recruited an adult community sample (n=79) to complete seven days of experience sampling. At each survey, they indicated current attention to emotion, clarity of emotion, and whether and what kind of meta-emotional experience they were having. Experiences were categorized as negative-negative, negative-positive, positive-positive or negative-negative. Approximately 53% of participants reported at least one meta-emotional experience. Meta-emotional experiences were reported about twice a week; negative-negative experiences were most frequent. Although attention to and clarity of emotion each individually predicted the likelihood of meta-emotional experiences, only attention to emotion contributed unique variance. Using multi-level modeling, we found that higher self-reported depressive severity was associated with the likelihood of meta-emotional experiences and specifically with negative-negative experiences. Findings indicate that most adults experience meta-emotions, especially during moments of high attention to emotion, and that negative-negative experiences are associated with depressive severity. These findings suggest that treatments for depression would benefit from emphasizing acceptance of negative emotions

The repertoire of mutational signatures in human cancer.

Somatic mutations in cancer genomes are caused by multiple mutational processes, each of which generates a characteristic mutational signature1. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium2 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we characterized mutational signatures using 84,729,690 somatic mutations from 4,645 whole-genome and 19,184 exome sequences that encompass most types of cancer. We identified 49 single-base-substitution, 11 doublet-base-substitution, 4 clustered-base-substitution and 17 small insertion-and-deletion signatures. The substantial size of our dataset, compared with previous analyses3-15, enabled the discovery of new signatures, the separation of overlapping signatures and the decomposition of signatures into components that may represent associated-but distinct-DNA damage, repair and/or replication mechanisms. By estimating the contribution of each signature to the mutational catalogues of individual cancer genomes, we revealed associations of signatures to exogenous or endogenous exposures, as well as to defective DNA-maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes that contribute to the development of human cancer

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

Mutational processes contributing to the development of multiple myeloma.

To gain insight into multiple myeloma (MM) tumorigenesis, we analyzed the mutational signatures in 874 whole-exome and 850 whole-genome data from the CoMMpass Study. We identified that coding and non-coding regions are differentially dominated by distinct single-nucleotide variant (SNV) mutational signatures, as well as five de novo structural rearrangement signatures. Mutational signatures reflective of different principle mutational processes-aging, defective DNA repair, and apolipoprotein B editing complex (APOBEC)/activation-induced deaminase activity-characterize MM. These mutational signatures show evidence of subgroup specificity-APOBEC-attributed signatures associated with MAF translocation t(14;16) and t(14;20) MM; potentially DNA repair deficiency with t(11;14) and t(4;14); and aging with hyperdiploidy. Mutational signatures beyond that associated with APOBEC are independent of established prognostic markers and appear to have relevance to predicting high-risk MM

Cancer LncRNA Census reveals evidence for deep functional conservation of long noncoding RNAs in tumorigenesis.

Long non-coding RNAs (lncRNAs) are a growing focus of cancer genomics studies, creating the need for a resource of lncRNAs with validated cancer roles. Furthermore, it remains debated whether mutated lncRNAs can drive tumorigenesis, and whether such functions could be conserved during evolution. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we introduce the Cancer LncRNA Census (CLC), a compilation of 122 GENCODE lncRNAs with causal roles in cancer phenotypes. In contrast to existing databases, CLC requires strong functional or genetic evidence. CLC genes are enriched amongst driver genes predicted from somatic mutations, and display characteristic genomic features. Strikingly, CLC genes are enriched for driver mutations from unbiased, genome-wide transposon-mutagenesis screens in mice. We identified 10 tumour-causing mutations in orthologues of 8 lncRNAs, including LINC-PINT and NEAT1, but not MALAT1. Thus CLC represents a dataset of high-confidence cancer lncRNAs. Mutagenesis maps are a novel means for identifying deeply-conserved roles of lncRNAs in tumorigenesis

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.

The discovery of drivers of cancer has traditionally focused on protein-coding genes1-4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available

Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing

Chromothripsis is a mutational phenomenon characterized by massive, clustered genomic rearrangements that occurs in cancer and other diseases. Recent studies in selected cancer types have suggested that chromothripsis may be more common than initially inferred from low-resolution copy-number data. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we analyze patterns of chromothripsis across 2,658 tumors from 38 cancer types using whole-genome sequencing data. We find that chromothripsis events are pervasive across cancers, with a frequency of more than 50% in several cancer types. Whereas canonical chromothripsis profiles display oscillations between two copy-number states, a considerable fraction of events involve multiple chromosomes and additional structural alterations. In addition to non-homologous end joining, we detect signatures of replication-associated processes and templated insertions. Chromothripsis contributes to oncogene amplification and to inactivation of genes such as mismatch-repair-related genes. These findings show that chromothripsis is a major process that drives genome evolution in human cancer

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Fearful Versus Dismissive Beliefs about Emotion: Divergent Pathways to Non-Acceptance of Emotion

High non-acceptance of emotion, or the rejection of one’s own emotional experience as bad or unacceptable, is consistently associated with depressive pathology, including elevated depressive symptoms and past and current major depressive (MDD) diagnoses. To progress toward a fuller understanding of non-acceptance and depressive pathology, it is important to identify other associated constructs that could theoretically contribute to this association. Indirect evidence suggests that negative beliefs about emotion—that is, stable underlying negative beliefs about the meaning, value, or consequences of one’s emotions—could be one such factor, as could negative emotion intensity and emotional clarity (or the degree to which one can identify, distinguish, and describe one\u27s emotions). In the present research, we tested the hypotheses that beliefs about emotions (1) could be best represented by a two-factor model; (2) would have indirect positive associations with non-acceptance of emotion through high negative emotion intensity and low emotional clarity; and (3) would have indirect positive associations with depressive symptoms through high non-acceptance of emotion. Further, we expected that these three indirect associations would be moderated by age, such that the associations would weaken as age increased. Finally, we tested whether mean levels and dynamic associations between variables varied as by depression status. In Study 1, participants included 410 adults (Mage = 44.1, SD = 15.6) recruited from the community who completed self-report measures of negative beliefs about emotions, non-acceptance, negative emotion intensity, emotional clarity, and depressive symptomatology. In Study 2, we used an intensive longitudinal design, in which a subset of 215 participants (Mage = 44.3, SD = 16.1) from Study 1 reported five times a day for two weeks on their emotional experiences. These participants were clinically interviewed and met diagnostic criteria for one of three groups: current depressed (n = 48), remitted depressed (n = 80), and healthy control (n = 87). In Study 1, we found that a single-factor, not a two-factor, hierarchical model was the best fit to the beliefs about emotions data. In both studies, we found support for a positive indirect effect of beliefs about emotions on non-acceptance of emotion through negative emotion intensity, but not through emotional clarity. In Study 1, but not Study 2, we found support for a positive indirect effect of beliefs about emotions on depressive symptoms through non-acceptance of emotion. As expected, we found in Study 2 that mean levels of negative beliefs about emotions, non-acceptance of emotion, and negative emotion intensity varied across diagnostic groups, but the strengths of pathways did not vary, suggesting that elevated levels of these emotional characteristics are just as maladaptive in healthy controls as they are in individuals with a current or past history of depression. The present study is the first to illuminate the association between beliefs about emotions and non-acceptance of emotion in community and clinical samples. Our findings also build on clinical theory to suggest that intensity of emotion mediates this link, such that high negative emotion helps explain the relation between high negative beliefs about emotion and high non-acceptance of emotion. Our findings from Study 1 also implicate non-acceptance of emotion as a mediator of the association between beliefs about emotions and depressive symptoms; however, given that Study 2 did not confirm these findings, future (ideally longitudinal) research is needed to further examine these associations