Integrated molecular characterisation of endometrioid ovarian carcinoma identifies opportunities for stratification

Endometrioid ovarian carcinoma (EnOC) is an under-investigated ovarian cancer type. Recent studies have described disease subtypes defined by genomics and hormone receptor expression patterns; here, we determine the relationship between these subtyping layers to define the molecular landscape of EnOC with high granularity and identify therapeutic vulnerabilities in high-risk cases. Whole exome sequencing data were integrated with progesterone and oestrogen receptor (PR and ER) expression-defined subtypes in 90 EnOC cases following robust pathological assessment, revealing dominant clinical and molecular features in the resulting integrated subtypes. We demonstrate significant correlation between subtyping approaches: PR-high (PR + /ER + , PR + /ER−) cases were predominantly CTNNB1-mutant (73.2% vs 18.4%, P < 0.001), while PR-low (PR−/ER + , PR−/ER−) cases displayed higher TP53 mutation frequency (38.8% vs 7.3%, P = 0.001), greater genomic complexity (P = 0.007) and more frequent copy number alterations (P = 0.001). PR-high EnOC patients experience favourable disease-specific survival independent of clinicopathological and genomic features (HR = 0.16, 95% CI 0.04–0.71). TP53 mutation further delineates the outcome of patients with PR-low tumours (HR = 2.56, 95% CI 1.14–5.75). A simple, routinely applicable, classification algorithm utilising immunohistochemistry for PR and p53 recapitulated these subtypes and their survival profiles. The genomic profile of high-risk EnOC subtypes suggests that inhibitors of the MAPK and PI3K-AKT pathways, alongside PARP inhibitors, represent promising candidate agents for improving patient survival. Patients with PR-low TP53-mutant EnOC have the greatest unmet clinical need, while PR-high tumours—which are typically CTNNB1-mutant and TP53 wild-type—experience excellent survival and may represent candidates for trials investigating de-escalation of adjuvant chemotherapy to agents such as endocrine therapy

Quantifying single nucleotide variant detection sensitivity in exome sequencing

BACKGROUND: The targeted capture and sequencing of genomic regions has rapidly demonstrated its utility in genetic studies. Inherent in this technology is considerable heterogeneity of target coverage and this is expected to systematically impact our sensitivity to detect genuine polymorphisms. To fully interpret the polymorphisms identified in a genetic study it is often essential to both detect polymorphisms and to understand where and with what probability real polymorphisms may have been missed. RESULTS: Using down-sampling of 30 deeply sequenced exomes and a set of gold-standard single nucleotide variant (SNV) genotype calls for each sample, we developed an empirical model relating the read depth at a polymorphic site to the probability of calling the correct genotype at that site. We find that measured sensitivity in SNV detection is substantially worse than that predicted from the naive expectation of sampling from a binomial. This calibrated model allows us to produce single nucleotide resolution SNV sensitivity estimates which can be merged to give summary sensitivity measures for any arbitrary partition of the target sequences (nucleotide, exon, gene, pathway, exome). These metrics are directly comparable between platforms and can be combined between samples to give “power estimates” for an entire study. We estimate a local read depth of 13X is required to detect the alleles and genotype of a heterozygous SNV 95% of the time, but only 3X for a homozygous SNV. At a mean on-target read depth of 20X, commonly used for rare disease exome sequencing studies, we predict 5–15% of heterozygous and 1–4% of homozygous SNVs in the targeted regions will be missed. CONCLUSIONS: Non-reference alleles in the heterozygote state have a high chance of being missed when commonly applied read coverage thresholds are used despite the widely held assumption that there is good polymorphism detection at these coverage levels. Such alleles are likely to be of functional importance in population based studies of rare diseases, somatic mutations in cancer and explaining the “missing heritability” of quantitative traits

Loss of ALDH18A1 function is associated with a cellular lipid droplet phenotype suggesting a link between autosomal recessive cutis laxa type 3A and Warburg Micro syndrome

Autosomal recessive cutis laxa type 3A is caused by mutations in ALDH18A1, a gene encoding the mitochondrial enzyme Δ(1)-pyrroline-5-carboxylate synthase (P5CS). It is a rare disorder with only six pathogenic mutations and 10 affected individuals from five families previously described in the literature. Here we report the identification of novel compound heterozygous missense mutations in two affected siblings from a Lebanese family by whole-exome sequencing. The mutations alter a conserved C-terminal domain of the encoded protein and reduce protein stability as determined through Western blot analysis of patient fibroblasts. Patient fibroblasts exhibit a lipid droplet phenotype similar to that recently reported in Warburg Micro syndrome, a disorder with similar features but hitherto unrelated cellular etiology

New insights into the classification and nomenclature of cortical GABAergic interneurons.

A systematic classification and accepted nomenclature of neuron types is much needed but is currently lacking. This article describes a possible taxonomical solution for classifying GABAergic interneurons of the cerebral cortex based on a novel, web-based interactive system that allows experts to classify neurons with pre-determined criteria. Using Bayesian analysis and clustering algorithms on the resulting data, we investigated the suitability of several anatomical terms and neuron names for cortical GABAergic interneurons. Moreover, we show that supervised classification models could automatically categorize interneurons in agreement with experts' assignments. These results demonstrate a practical and objective approach to the naming, characterization and classification of neurons based on community consensus

Toll-like receptor orchestrates a tumor suppressor response in non-small cell lung cancer

Targeting early-stage lung cancer is vital to improve survival. However, the mechanisms and components of the early tumor suppressor response in lung cancer are not well understood. In this report, we study the role of Toll-like receptor 2 (TLR2), a regulator of oncogene-induced senescence, which is a key tumor suppressor response in premalignancy. Using human lung cancer samples and genetically engineered mouse models, we show that TLR2 is active early in lung tumorigenesis, where it correlates with improved survival and clinical regression. Mechanistically, TLR2 impairs early lung cancer progression via activation of cell intrinsic cell cycle arrest pathways and the proinflammatory senescence-associated secretory phenotype (SASP). The SASP regulates non-cell autonomous anti-tumor responses, such as immune surveillance of premalignant cells, and we observe impaired myeloid cell recruitment to lung tumors after Tlr2 loss. Last, we show that administration of a TLR2 agonist reduces lung tumor growth, highlighting TLR2 as a possible therapeutic target.F.R.M. is funded by a Wellcome Trust clinical research fellowship through the Edinburgh Clinical Academic Track (ECAT) program (203913/Z/16/Z), a Wellcome Trust-ISSF3 award (IS3-R1.07 20/21), and a Wellcome Trust iTPA award (209710/Z/17/Z). J.C.A. core lab funding was received from Cancer Research UK (C47559/A16243, Training and Career Development Board – Career Development Fellowship), the University of Edinburgh (Chancellor’s Fellowship), and the Ministry of Science and Innovation of the Government of Spain (Proyecto PID2020-117860GB-100 financiado por MCIN/AEI/10.13039/501100011033). S.W. is supported by a Cancer Research UK senior fellowship (A29576). J.C. is supported by a Wellcome Trust clinical lectureship through the ECAT program (203913/Z/16/Z). M.M. is supported by a CRUK Edinburgh Centre Award (C157/A25140). S.V. and J.F.P. are funded by National Institute on Aging (NIA) grants (R01AG 68048-1 and UG3CA 268103-1)