Skin cancer risk varies substantially across the body, yet how this relates to the mutations found in normal skin is unknown. Here we mapped mutant clones in skin from high and low risk sites. The density of mutations varied by location. The prevalence of NOTCH1 and FAT1 mutations in forearm, trunk and leg skin was similar to that in keratinocyte cancers. Most mutations were caused by ultraviolet (UV) light, but mutational signature analysis suggested differences in DNA repair processes between sites. 11 mutant genes were under positive selection, with TP53 preferentially selected in the head and FAT1 in the leg. Fine scale mapping revealed 10% of clones had copy number alterations. Analysis of hair follicles showed mutations in the upper follicle resembled adjacent skin, but the lower follicle was sparsely mutated. Normal skin is dense patchwork of mutant clones arising from competitive selection that varies by location. Statement of significance Mapping mutant clones across the body reveals normal skin is a dense patchwork of mutant cells. The variation in cancer risk between sites substantially exceeds that in mutant clone density. More generally, mutant genes cannot be assigned as cancer drivers until their prevalence in normal tissue is known.

B cell specification requires the establishment of accessible transcriptional enhancers and promoters by lineage-specific transcription factors. Loughran et al. show that Mbd3/NuRD chromatin remodeling restricts the accessibility of these regions and therefore controls B versus T cell lineage fate by preventing B cell programming transcription factors from prematurely enacting lineage commitment.

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Assessing the severity of atopic dermatitis (AD, or eczema) traditionally relies on a face-to-face assessment by healthcare professionals, and may suffer from inter- and intra-rater variability. With the expanding role of telemedicine, several machine learning algorithms have been proposed to automatically assess AD severity from digital images. Those algorithms usually detect and then delineate (“segment”) AD lesions before assessing lesional severity, and are trained using the data of AD areas detected by healthcare professionals. To evaluate the reliability of such data, we estimated the inter-rater reliability of AD segmentation in digital images.Four dermatologists independently segmented AD lesions in 80 digital images collected in a published clinical trial. We estimated the inter-rater reliability of the AD segmentation using the intra-class correlation coefficients (ICCs) at the pixel-level and the area-levels for different resolutions of the images. The average ICC was 0.45 (SE = 0.04) corresponding to a “poor” agreement between raters, while the degree of agreement for AD segmentation varied from image to image.The AD segmentation in digital images is highly rater-dependent even between dermatologists. Such limitations need to be taken into consideration when the AD segmentation data are used to train machine learning algorithms that assess eczema severity.

BackgroundCell lines provide a powerful model to study cancer and here we describe a new spontaneously immortalised epithelial ovarian cancer cell line (NUOC-1) derived from the ascites collected at a time of primary debulking surgery for a mixed endometrioid / clear cell / High Grade Serous (HGS) histology.ResultsThis spontaneously immortalised cell line was found to maintain morphology and epithelial markers throughout long-term culture. NUOC-1 cells grow as an adherent monolayer with a doubling time of 58 hours. The cells are TP53 wildtype, positive for PTEN, HER2 and HER3 expression but negative for oestrogen, progesterone and androgen receptor expression. NUOC-1 cells are competent in homologous recombination and non-homologous end joining, but base excision repair defective. Karyotype analysis demonstrated a complex tetraploid karyotype. SNP array analysis of parent and derived subpopulations (NUOC-1-A1 and NUOC-1-A2) cells demonstrated heterogeneous cell populations with numerous copy number alterations and a pro-amplification phenotype. The characteristics of this new cell line lends it to be an excellent model for investigation of a number of the identified targets.Materials and MethodsThe cell line has been characterised for growth, drug sensitivity, expression of common ovarian markers and mutations, clonogenic potential and ability to form xenografts in SCID mice. Copy number changes and clonal evolution were assessed by SNP arrays.