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Background Mindfulness has emerged as an important correlate of well-being in various clinical populations. The present study evaluated the psychometric properties of the 20-item short form of the Five Facet Mindfulness Questionnaire (FFMQ-SF) in the Chinese context. Methods The study sample was 127 Chinese colorectal cancer patients who completed the FFMQ-SF and validated physical and mental health measures. Factorial validity of the FFMQ-SF was assessed using Bayesian structural equation modeling (BSEM) via informative priors on cross-loadings and residual covariances. Linear regression analysis examined its convergent validity with the health measures on imputed datasets. Results The five-factor BSEM model with approximate zero cross-loadings and one residual covariance provided an adequate model fit (PPP = 0.07, RMSEA = 0.06, CFI = 0.95). Satisfactory reliability (ω = 0.77–0.85) was found in four of the five facets (except nonjudging). Acting with awareness predicted lower levels of perceived stress, negative affect, anxiety, depression, and illness symptoms (β = − 0.37 to − 0.42) and better quality of life (β = 0.29–0.32). Observing, nonjudging, and nonreacting did not show any significant associations ( p  > .05) with health measures. Acting with awareness was not significantly correlated ( r  < 0.15) with the other four facets. Conclusion The present findings provide partial support for the psychometric properties of the FFMQ-SF in colorectal cancer patients. The nonjudging facet showed questionable validity and reliability in the present sample. Further studies with larger sample sizes are needed to elucidate the viability of FFMQ-SF as a measure of mindfulness facets in cancer patients.

Primitive cancers have so-called hot-spot mutations in DICER1 that alter the function of DICER1, an enzyme that processes microRNA. Some of these cancers harbor a loss-of-function mutation in the other DICER1 allele, suggesting a new mutational mechanism of oncogenesis. Sex cord–stromal tumors and germ-cell tumors account for less than 10% of ovarian cancers. 1 Unlike epithelial ovarian cancers, both sex cord–stromal tumors and germ-cell tumors can also occur in the testicle; testicular germ-cell tumors are the most common cancer in boys and men of European descent between the ages of 15 and 34 years. 2 , 3 Other than a pathognomonic somatic mutation in FOXL2 in adult granulosa-cell tumors, 4 – 6 little is known about the pathogenesis of ovarian sex cord–stromal tumors and germ-cell tumors. Recently, germline mutations in the microRNA processing gene DICER1 have been reported in probands with pleuropulmonary blastoma or . . .

The hierarchical Bayesian model identifies and quantifies clonal populations in tumors from deep-sequenced somatic mutations. We introduce PyClone, a statistical model for inference of clonal population structures in cancers. PyClone is a Bayesian clustering method for grouping sets of deeply sequenced somatic mutations into putative clonal clusters while estimating their cellular prevalences and accounting for allelic imbalances introduced by segmental copy-number changes and normal-cell contamination. Single-cell sequencing validation demonstrates PyClone's accuracy.

Deep-genome and single-cell sequencing analyses of patient-derived breast cancer xenografts reveal extensive, dynamic and reproducible changes in intra-tumoral mutational clonal composition on engraftment and serial propagation. Clonal evolution in xenograft tumours Xenograft transplantation of primary human cancer cells into mice provides valuable models in which to study mechanisms underlying tumorigenesis, drug response and resistance. This study demonstrates that clonal evolution resembling that seen in human tumours also occurs on engraftment and during subsequent passaging of breast tumours in immunodeficient mice. In addition, similar clonal expansion patterns emerge in independent grafts of the same starting tumour population, indicating that genomic aberrations can be reproducible determinants of evolutionary trajectories. These findings suggest that patient-derived xenografts may be useful for studying patient-specific tumour characteristics such as the response to drugs tailored to specific genomic alterations. Human cancers, including breast cancers, comprise clones differing in mutation content. Clones evolve dynamically in space and time following principles of Darwinian evolution 1 , 2 , underpinning important emergent features such as drug resistance and metastasis 3 , 4 , 5 , 6 , 7 . Human breast cancer xenoengraftment is used as a means of capturing and studying tumour biology, and breast tumour xenografts are generally assumed to be reasonable models of the originating tumours 8 , 9 , 10 . However, the consequences and reproducibility of engraftment and propagation on the genomic clonal architecture of tumours have not been systematically examined at single-cell resolution. Here we show, using deep-genome and single-cell sequencing methods, the clonal dynamics of initial engraftment and subsequent serial propagation of primary and metastatic human breast cancers in immunodeficient mice. In all 15 cases examined, clonal selection on engraftment was observed in both primary and metastatic breast tumours, varying in degree from extreme selective engraftment of minor (<5% of starting population) clones to moderate, polyclonal engraftment. Furthermore, ongoing clonal dynamics during serial passaging is a feature of tumours experiencing modest initial selection. Through single-cell sequencing, we show that major mutation clusters estimated from tumour population sequencing relate predictably to the most abundant clonal genotypes, even in clonally complex and rapidly evolving cases. Finally, we show that similar clonal expansion patterns can emerge in independent grafts of the same starting tumour population, indicating that genomic aberrations can be reproducible determinants of evolutionary trajectories. Our results show that measurement of genomically defined clonal population dynamics will be highly informative for functional studies using patient-derived breast cancer xenoengraftment.

Sohrab Shah, David Huntsman and colleagues report the genomic analysis of 133 ovarian cancers spanning different subtypes. They identify seven subgroups using point mutation and structural variation signatures and use these genomic features to stratify ovarian cancers both between and within histotypes. We studied the whole-genome point mutation and structural variation patterns of 133 tumors (59 high-grade serous (HGSC), 35 clear cell (CCOC), 29 endometrioid (ENOC), and 10 adult granulosa cell (GCT)) as a substrate for class discovery in ovarian cancer. Ab initio clustering of integrated point mutation and structural variation signatures identified seven subgroups both between and within histotypes. Prevalence of foldback inversions identified a prognostically significant HGSC group associated with inferior survival. This finding was recapitulated in two independent cohorts ( n = 576 cases), transcending BRCA1 and BRCA2 mutation and gene expression features of HGSC. CCOC cancers grouped according to APOBEC deamination (26%) and age-related mutational signatures (40%). ENOCs were divided by cases with microsatellite instability (28%), with a distinct mismatch-repair mutation signature. Taken together, our work establishes the potency of the somatic genome, reflective of diverse DNA repair deficiencies, to stratify ovarian cancers into distinct biological strata within the major histotypes.

Sohrab Shah, Samuel Aparicio and colleagues analyze whole genomes and single cells from ovarian cancers in the peritoneal cavity to establish patterns of disease spread. They determine the clonal relationships between multiple tumor sites and characterize the migratory potential of genomically diverse clones. We performed phylogenetic analysis of high-grade serous ovarian cancers (68 samples from seven patients), identifying constituent clones and quantifying their relative abundances at multiple intraperitoneal sites. Through whole-genome and single-nucleus sequencing, we identified evolutionary features including mutation loss, convergence of the structural genome and temporal activation of mutational processes that patterned clonal progression. We then determined the precise clonal mixtures comprising each tumor sample. The majority of sites were clonally pure or composed of clones from a single phylogenetic clade. However, each patient contained at least one site composed of polyphyletic clones. Five patients exhibited monoclonal and unidirectional seeding from the ovary to intraperitoneal sites, and two patients demonstrated polyclonal spread and reseeding. Our findings indicate that at least two distinct modes of intraperitoneal spread operate in clonal dissemination and highlight the distribution of migratory potential over clonal populations comprising high-grade serous ovarian cancers.

The Positive and Negative Syndrome Scale (PANSS) is widely used for clinical assessment of symptoms in schizophrenia. Instead of the traditional pyramidal model, recent literature supports the pentagonal model for the dimensionality of the PANSS. The present study aimed to validate the consensus five-factor model of the PANSS and evaluate its convergent validity. Participants were 146 Chinese chronic schizophrenic patients who completed diagnostic interviews and cognitive assessments. Exploratory structural equation modeling (ESEM) was performed to investigate the dimensionality of the PANSS. Covariates (age, sex, and education level) and concurrent outcomes (perceived stress, memory, daily living functions, and motor deficits) were added in the ESEM model. The results supported the consensus 5-factor underlying structure, which comprised 20 items categorized into positive, negative, excitement, depression, and cognitive factors with acceptable reliability (α=.69–.85) and strong factor loadings (λ=.41–.93). The five factors, especially the cognitive factor, showed evident convergent validity with the covariates and concurrent outcomes. The results support the consensus five-factor structure of the PANSS as a robust measure of symptoms in schizophrenia. Future studies could explore the clinical and practical utility of the consensus five-factor model.

The open-source Single Cell Genotyper software addresses common artifacts in single-cell sequencing data in order to robustly infer clonal genotypes, enabling the study of tumor heterogeneity and evolution. Single-cell DNA sequencing has great potential to reveal the clonal genotypes and population structure of human cancers. However, single-cell data suffer from missing values and biased allelic counts as well as false genotype measurements owing to the sequencing of multiple cells. We describe the Single Cell Genotyper ( https://bitbucket.org/aroth85/scg ), an open-source software based on a statistical model coupled with a mean-field variational inference method, which can be used to address these problems and robustly infer clonal genotypes.

IntroductionMothers of children with intellectual disability (ID) are often distressed because of intensive workloads and difficulties in communicating with their children. Given the interdependence between the psychosocial well-being of such dyads, interventions that promote parent–child relationships and mutual communication would be beneficial. Arts provide alternative avenues for expression and offer an imaginative and playful environment for discovering new communication strategies. Given the lack of studies on arts-based dyadic interventions, this study aims to examine the effectiveness of dyadic expressive arts-based intervention (EXAT) in improving the psychosocial outcomes of children with ID and their mothers and the mother–child relationships.Methods and analysisThis study will adopt a mixed-methods randomised controlled trial design, wherein 154 dyads of children with ID and their mothers will be randomised into either the dyadic EXAT group or the treatment-as-usual waitlist control group. Quantitative data will be collected at four time points: baseline (T0), postintervention (T1), 3-month postintervention (T2) and 6-month postintervention (T3). Qualitative data will be collected from a subset of 30 mothers in the intervention group at T1 and T3 to document their experiences and perceived changes after the intervention. Mixed-effects models and path analysis will be adopted to analyse the quantitative data, whereas thematic analysis will be applied to the qualitative data. Both sets of data will be triangulated for an integrated view of the effectiveness and mechanism of the intervention.Ethics and disseminationEthical approval has been obtained from the Human Research Ethics Committee of the University of Hong Kong (Ref. no.: EA200329). Written consent forms will be obtained from all recruited participants (mothers, children with ID and teachers/social workers) before data collection. The study findings will be disseminated in international conferences and peer-reviewed academic journals.Trial registration numberNCT05214859.

Objectives Colorectal cancer survivors are at risks of emotional distress and dysregulated diurnal cortisol rhythms. Dispositional self-compassion has been linked with better psychological adjustment and greater positive affect. This study evaluated the associations between self-compassion and the diurnal cortisol pattern, and the role of positive affect and emotional distress in mediating this association, in cancer patients. Methods This longitudinal study recruited 127 Chinese colorectal cancer survivors, who completed assessments for self-compassion, positive affect, emotional distress, and naturalistic salivary cortisol at baseline. The participants completed follow-up assessments for affect and emotional distress after 2 months (time 2) and the diurnal cortisol pattern after 8 months (time 3). Bootstrapped mediation analysis analyzed the direct and indirect effects of self-compassion on the diurnal cortisol pattern via positive affect and emotional distress. Results A structural equation model with latent factors of self-compassion, self-criticism, and emotional distress provided an adequate fit to the data. The direct effects of self-compassion and self-criticism on the diurnal cortisol pattern were not significant ( p  = 0.11–0.50). Positive affect, but not emotional distress, at time 2 significantly predicted steeper diurnal cortisol slopes at time 3 (β = − 0.22, SE = 0.08, p  < 0.01). Self-compassion and self-criticism showed significant negative and positive indirect effects on time 3 diurnal cortisol slopes via time 2 positive affect, respectively. Conclusions Our findings support indirect linkages between self-compassion and steeper diurnal cortisol slopes via positive affect. Positive affect may mediate temporal relationships between self-compassion and neuroendocrine functioning in colorectal cancer survivors.