Pan-cancer deconvolution of tumour composition using DNA methylation

The nature and extent of immune cell infiltration into solid tumours are key determinants of therapeutic response. Here, using a DNA methylation-based approach to tumour cell fraction deconvolution, we report the integrated analysis of tumour composition and genomics across a wide spectrum of solid cancers. Initially studying head and neck squamous cell carcinoma, we identify two distinct tumour subgroups: ‘immune hot’ and ‘immune cold’, which display differing prognosis, mutation burden, cytokine signalling, cytolytic activity and oncogenic driver events. We demonstrate the existence of such tumour subgroups pan-cancer, link clonal-neoantigen burden to cytotoxic T-lymphocyte infiltration, and show that transcriptional signatures of hot tumours are selectively engaged in immunotherapy responders. We also find that treatment-naive hot tumours are markedly enriched for known immune-resistance genomic alterations, potentially explaining the heterogeneity of immunotherapy response and prognosis seen within this group. Finally, we define a catalogue of mediators of active antitumour immunity, deriving candidate biomarkers and potential targets for precision immunotherapy

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation

Higher prevalence of left ventricular hypertrophy in two Māori cohorts: findings from the Hauora Manawa/Community Heart Study

Objectives: Cardiovascular disease (CVD) is the leading cause of mortality in New Zealand with a disproportionate burden of disease in the Māori population. The Hauora Manawa Project investigated the prevalence of cardiovascular risk factors and CVD in randomly selected Māori and non-Māori participants. This paper reports the prevalence of structural changes in the heart. Methods: A total of 252 rural Māori, 243 urban Māori; and 256 urban non-Māori underwent echocardiography to assess cardiac structure and function. Multivariable logistic regression was used to determine variables associated with heart size. Results: Left ventricular (LV) mass measurements were largest in the rural Māori cohort (183.5,sd 61.4), intermediate in the urban Māori cohort (169.7,sd 57.1) and smallest in the non Māori cohort (152.6,sd 46.7; p<0.001). Similar patterns were observed for other measurements and indexation had no impact. One-third (32.3%) met the gender-based ASE criteria for LV hypertrophy (LVH) with higher prevalence in both Maori cohorts (highest in the rural cohort). There were three significant predictors of LVH: rural Māori (p=0.0001); age (p<0.0001); and gender (p=0.0048). Conclusion: Structural and functional heart abnormalities are more prevalent in Māori compared to non-Māori, and especially rural Māori. Early identification should lead to better management, ultimately improving life expectancy and quality of lif

Identification of C7orf11 (TTDN1) Gene Mutations and Genetic Heterogeneity in Nonphotosensitive Trichothiodystrophy

We have identified C7orf11, which localizes to the nucleus and is expressed in fetal hair follicles, as the first disease gene for nonphotosensitive trichothiodystrophy (TTD). C7orf11 maps to chromosome 7p14, and the disease locus has been designated “TTDN1” (TTDnonphotosensitive 1). Mutations were found in patients with Amish brittle-hair syndrome and in other nonphotosensititive TTD cases with mental retardation and decreased fertility but not in patients with Sabinas syndrome or Pollitt syndrome. Therefore, genetic heterogeneity in nonphotosensitive TTD is a feature similar to that observed in photosensitive TTD, which is caused by mutations in transcription factor II H (TFIIH) subunit genes. Comparative immunofluorescence analysis, however, suggests that C7orf11 does not influence TFIIH directly. Given the absence of cutaneous photosensitivity in the patients with C7orf11 mutations, together with the protein’s nuclear localization, C7orf11 may be involved in transcription but not DNA repair