Although the pathologic correlate of ARDS is diffuse alveolar damage (DAD), studies have demonstrated only moderate agreement between the clinical diagnosis of ARDS and DAD at autopsy . Members of the panel were not in complete agreement that DAD is the sole pathologic correlate of ARDS, and some considered pneumonia and non-cardiogenic edema as compatible with ARDS when clinical criteria are met. Because of this, and since making a pathological diagnosis of ARDS using lung biopsy may be associated with increased risk of complications, the committee did not include this in the definition. At the present time, lung biopsy may be considered in patients with persistent ARDS of unknown etiology to rule out an underlying etiology that may respond to a specific treatment [41, 42].
According to Greenwood, Grubb asked him \"Do you just make this stuff up as you go, or do you really have a huge campaign world\"; Greenwood answered \"yes\" to both questions. TSR felt that the Forgotten Realms would be a more open-ended setting than its epic fantasy counterpart Dragonlance, and chose the Realms as a ready-made campaign setting upon deciding to publish AD&D 2nd edition. Greenwood agreed to work on the project and began working to get Forgotten Realms officially published. He sent TSR a few dozen cardboard boxes stuffed with pencil notes and maps, and sold all rights to the setting for a token fee. He noted that TSR altered his original conception of the Realms being a place that could be accessed from Earth, as \"[c]oncerns over possible lawsuits (kids getting hurt while trying to 'find a gate') led TSR to de-emphasize this meaning.\"
Brian Silliman, for SYFY Wire, described the Forgotten Realms as \"a classic fantasy backdrop\" and highlighted that \"at one time in our history, our world and this one were connected, but over time this magical realm was, well, forgotten. It is an ideal place for any D&D adventure, inspiring limitless possibilities for any smirking dungeon master\".
This heterogeneity arises from the stochastic nature of Darwinian evolution. There are three preconditions for Darwinian evolution: characteristics must vary within a population; this variation must be heritable from parent to offspring; and there must be competition for survival within the population. In the context of somatic cells, heritable variation arises from mutations acquired stochastically throughout life, notwithstanding additional contributions from germline and epigenetic variation. A subset of these mutations alter the cellular phenotype, and a small subset of those variants confer an advantage on clones during the competition to escape the tight physiological controls wired into somatic cells. Mutations that provide a selective advantage to the clone are termed driver mutations, as opposed to selectively neutral passenger mutations.
The expansion of whole-genome sequencing studies from individual ICGC and TCGA working groups presented the opportunity to undertake a meta-analysis of genomic features across tumour types. To achieve this, the PCAWG Consortium was established. A Technical Working Group implemented the informatics analyses by aggregating the raw sequencing data from different working groups that studied individual tumour types, aligning the sequences to the human genome and delivering a set of high-quality somatic mutation calls for downstream analysis (Extended Data Fig. 1). Given the recent meta-analysis of exome data from the TCGA Pan-Cancer Atlas23,24,25, scientific working groups concentrated their efforts on analyses best-informed by whole-genome sequencing data.
The uniformly generated, high-quality set of variant calls across more than 2,500 donors provided the springboard for a series of scientific working groups to explore the biology of cancer. A comprehensive suite of companion papers that describe the analyses and discoveries across these thematic areas is copublished with this paper4,5,6,7,8,9,10,11,12,13,14,15,16,17,18 (Extended Data Table 3).
Although more than 90% of PCAWG cases had identified drivers, we found none in 181 tumours (Extended Data Fig. 4a). Reasons for missing drivers have not yet been systematically evaluated in a pan-cancer cohort, and could arise from either technical or biological causes.
a, Individual estimates of the percentage of tumour-in-normal contamination across patients with no driver mutations in PCAWG (n = 181). No data were available for myelodysplastic syndromes and acute myeloid leukaemia. Points represent estimates for individual patients, and the coloured areas are estimated density distributions (violin plots). Abbreviations of the tumour types are defined in Extended Data Table 1. b, Average detection sensitivity by tumour type for tumours without known drivers (n = 181). Each dot represents a given sample and is the average sensitivity of detecting clonal substitutions across the genome, taking into account purity and ploidy. Coloured areas are estimated density distributions, shown for cohorts with at least five cases. c, Detection sensitivity for TERT promoter hotspots in tumour types in which TERT is frequently mutated. Coloured areas are estimated density distributions. d, Significant copy-number losses identified by two-sided hypothesis testing using GISTIC2.0, corrected for multiple-hypothesis testing. Numbers in parentheses indicate the number of genes in significant regions when analysing medulloblastomas without known drivers (n = 42). Significant regions with known cancer-associated genes are labelled with the representative cancer-associated gene. e, Aneuploidy in chromophobe renal cell carcinomas and pancreatic neuroendocrine tumours without known drivers. Patients are ordered on the y axis by tumour type and then by presence of whole-genome duplication (bottom) or not (top).
Kataegis events were found in 60.5% of all cancers, with particularly high abundance in lung squamous cell carcinoma, bladder cancer, acral melanoma and sarcomas (Fig. 4a, b). Typically, kataegis comprises C > N mutations in a TpC context, which are probably caused by APOBEC activity49,50,51, although a T > N conversion in a TpT or CpT process (the affected T is highlighted in bold) attributed to error-prone polymerases has recently been described57. The APOBEC signature accounted for 81.7% of kataegis events and correlated positively with APOBEC3B expression levels, somatic SV burden and age at diagnosis (Supplementary Fig. 5). Furthermore, 5.7% of kataegis events involved the T > N error-prone polymerase signature and 2.3% of events, most notably in sarcomas, showed cytidine deamination in an alternative GpC or CpC context.
Samples with extreme kataegis burden (more than 30 foci) comprise four types of focal hypermutation (Extended Data Fig. 6): (1) off-target somatic hypermutation and foci of T > N at CpTpT, found in B cell non-Hodgkin lymphoma and oesophageal adenocarcinomas, respectively; (2) APOBEC kataegis associated with complex rearrangements, notably found in sarcoma and melanoma; (3) rearrangement-independent APOBEC kataegis on the lagging strand and in early-replicating regions, mainly found in bladder and head and neck cancer; and (4) a mix of the last two types. Kataegis only occasionally led to driver mutations (Supplementary Table 5).
We consolidated histopathology descriptions of the tumour samples, using the ICD-0-3 tumour site controlled vocabulary89. Overall, the PCAWG dataset comprises 38 distinct tumour types (Extended Data Table 1 and Supplementary Table 1). Although the most common tumour types are included in the dataset, their distribution does not match the relative population incidences, largely owing to differences among contributing ICGC/TCGA groups in the numbers of sequenced samples.
We performed call-set benchmarking, merging, variant genotyping and statistical haplotype-block phasing91 (Supplementary Methods 3.4). Using this strategy, we identified 80.1 million germline single-nucleotide polymorphisms, 5.9 million germline indels, 1.8 million multi-allelic short (99% for the phased merged call-set, and sensitivity estimates ranged from 92% to 98%.
There is ample evidence that transient increases of metabolic or immune mediator levels are benign physiological responses to biochemical challenges, such as the rise of systemic glucose or cytokines following meals. However, chronic elevations of such mediators, even when modest in amplitude, are usually detrimental to cellular functions . In the case of glucose, the term glucose toxicity was coined to describe this phenomenon . Prolonged conditions of elevated glucose concentrations cause dysfunction of numerous cell types in the body, including beta cells, neurons, and the endothelium, via several pathways, including increased oxidative stress and activation of the sorbitol pathway [31,32,33]. As described below, there seems to be a similar detrimental outcome of long-term elevated insulin concentrations on cellular functions, a corresponding term would be insulin toxicity.
These mechanistic insights lend support to the view that the association of hyperinsulinemia with several detrimental health outcomes is of causal nature. Outcomes include obesity, endothelial dysfunction, hypertension, myocardial infarction, and decreased lifespan. We did not discuss the possible contribution of hyperinsulinemia to cancer development or to the deterioration of cognitive functions. Final proof of a causal relationship between hyperinsulinemia and disease risk cannot be obtained by randomized controlled trials, due to problems with masking the type of intervention, long-term compliance, and because of ethical concerns. Alternatively, Mendelian randomization studies are suitable tools to test for causality in humans, and such studies have found hyperinsulinemia to increase the risk of obesity [27, 28] and cardiovascular disease [127, 128]. 153554b96e