The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Multiplex plasma protein profiling identifies novel markers to discriminate patients with adenocarcinoma of the lung

Background:The overall prognosis of non-small cell lung cancer (NSCLC) is poor, and currently only patients with localized disease are potentially curable. Therefore, preferably non-invasively determined biomarkers that detect NSCLC patients at early stages of the disease are of high clinical relevance. The aim of this study was to identify and validate novel protein markers in plasma using the highly sensitive DNA-assisted multiplex proximity extension assay (PEA) to discriminate NSCLC from other lung diseases.  Methods:Plasma samples were collected from a total of 343 patients who underwent surgical resection for different lung diseases, including 144 patients with lung adenocarcinoma (LAC),68 patients with non-malignant lung disease, 83 with lung metastasis of colorectal cancers and 48 patients with typical carcinoid. One microliter of plasma was analyzed using PEA, allowing detection and quantification of 92 established cancer related proteins. The concentrations of the plasma proteins were compared between disease groups. Results:The comparison between LAC and benign samples revealed significantly different plasma levels for four proteins; CXL17, CEACAM5, VEGFR2 and ERBB3 (adjusted p-value < 0.05). A multi-parameter classifier was developed to discriminate between samples from LAC patients and from patients with non-malignant lung conditions. With a bootstrap aggregated decision tree algorithm (TreeBagger) a sensitivity of 93% and specificity of 64% was achieved to detect LAC in this risk population.  Conclusion:By applying the highly sensitive PEA, reliable protein profiles could be determined in microliter amounts of plasma. We further identified proteins that demonstrated different plasma concentration in defined disease groups and developed a signature that holds potential to be included in a screening assay for early lung cancer detection.

Gpr116 Receptor Regulates Distinctive Functions in Pneumocytes and Vascular Endothelium.

Despite its known expression in both the vascular endothelium and the lung epithelium, until recently the physiological role of the adhesion receptor Gpr116/ADGRF5 has remained elusive. We generated a new mouse model of constitutive Gpr116 inactivation, with a large genetic deletion encompassing exon 4 to exon 21 of the Gpr116 gene. This model allowed us to confirm recent results defining Gpr116 as necessary regulator of surfactant homeostasis. The loss of Gpr116 provokes an early accumulation of surfactant in the lungs, followed by a massive infiltration of macrophages, and eventually progresses into an emphysema-like pathology. Further analysis of this knockout model revealed cerebral vascular leakage, beginning at around 1.5 months of age. Additionally, endothelial-specific deletion of Gpr116 resulted in a significant increase of the brain vascular leakage. Mice devoid of Gpr116 developed an anatomically normal and largely functional vascular network, surprisingly exhibited an attenuated pathological retinal vascular response in a model of oxygen-induced retinopathy. These data suggest that Gpr116 modulates endothelial properties, a previously unappreciated function despite the pan-vascular expression of this receptor. Our results support the key pulmonary function of Gpr116 and describe a new role in the central nervous system vasculature

Normalized pathological angiogenesis in <i>Gpr116</i>^-/- retinas.

<p>A. Confocal images of post-OIR retinas from <i>Gpr116</i> WT, heterozygous and knockout littermates at P12 (the images shown are representative of 5 mice per genotype). B. Confocal images of post-OIR retinas from <i>Gpr116</i> WT, heterozygous and knockout littermates at P17 (the images shown are representative of 5 mice per genotype). C. Quantification of the avascular area on the post-OIR retinas from <i>Gpr116</i> WT, heterozygous and knockout littermates at P12 (n = 5 mice at least per genotype). D. Quantification of the avascular area on the post-OIR retinas from <i>Gpr116</i> WT, heterozygous and knockout littermates at P17 (n≥7 mice at least per genotype). E. Confocal images of post-OIR tufts (blue arrows) in <i>Gpr116</i> WT, heterozygous and knockout littermates at P17 (the images shown are representative of 5 mice per genotype)</p

Retinal vascular patterning in <i>Gpr116</i>^-/- mice.

<p>A. Vascular network in P4 retinas. Dashed line indicates the limits of the retina (the picture shown is representative of at least 5 mice for each genotype). B. Quantification of the retinal vascular outgrowth at P4 (n = 5 for WT, n = 12 for heterozygotes and n = 6 for knockout). C. Vascular patterning in P7 retinas from <i>Gpr116</i> WT, heterozygous and knockout littermates. Isolectin (red), CD31 (green) and Erg (grey) were used to visualize endothelium, and NG2 (green) and ASMA (red) to detect mural cells (the images shown are representative of 3 mice for each genotype). D. Vascular patterning in P7 retinas from <i>Gpr116</i> ECKO and littermates controls. Isolectin (red) is used to visualize endothelium, and NG2 (green) and smooth muscle actin α (ASMA, blue) to detect mural cells (the images show are representative of 2 mice per genotype). E. Isolectin (red) and FITC-dextran (green) distribution in P21 retinas from <i>Gpr116</i> WT, heterozygous and knockout littermates. CD31 (green) is used to stain the endothelium, and nuclei are stained with Hoechst (blue) (the images shown are representative of 3 mice per genotype). F. Monolayers formed by isolated endothelial cells from <i>Gpr116</i> WT, heterozygous and knockout brain. Endothelial cells (CD31) and nuclei (Hoechst) are indicated in green and blue, respectively (the pictures shown are representative of 3 mice for each genotype)</p

Vascular expression and genetic ablation of the <i>Gpr116</i> gene in mouse.

<p>A. <i>Gpr116</i> mRNA expression in the published organ-specific EC mRNA dataset [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137949#pone.0137949.ref045" target="_blank">45</a>]. B. <i>Gpr116</i> mRNA expression assessed by qRT-PCR in EC from 3-weeks-old and 3-months-old ROSA^mT/mG x Tie2-Cre mice. Results are normalized by brain EC expression. Error bars represent SD. (n = 3 mice per genotype). C. <i>Gpr116</i> mRNA expression in the published brain-specific vascular and EC mRNA dataset [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137949#pone.0137949.ref046" target="_blank">46</a>]. D. Schematic representation of the area targeted by homologous recombination in the <i>Gpr116</i> locus. Dotted lines indicate the regions of homology in between the Gpr116 locus and the cassette. The dark grey arrow indicates the position of WT primers: both are located in the untranslated region of exon 21, but the area recognized by the forward primer is lost in the mutant allele. The light grey arrow represents the knockout primer, specific for the cassette. Critical Gpr116 domains (SEA, IgG, GAIN and transmembraine, TM) are indicated above the corresponding encoding exons. E. Example of genotyping PCR products on genomic DNA (toe) from <i>Gpr116</i> WT, heterozygous and knockout littermates. WT primers amplify a 325-bp fragment in the 3´UTR exon 21 of <i>Gpr116</i> gene representing the wild type allele. The 401 bp band is specific for the mutant allele. F. Example of genotyping PCR products using genomic DNA (toe) from <i>Gpr116</i> WT, heterozygous and knockout littermates. LacZ primers amplify a 210 bp fragment in LacZ gene present in the insert replacing exon 4 to 21. G. <i>Gpr116</i> exon 17–18 mRNA expression assessed by qRT-PCR in <i>Gpr116</i> WT, heterozygous and knockout organs at P4 (n = 3 mice per genotype). H. <i>Gpr116</i> exon 2–3 mRNA expression assessed by qRT-PCR in <i>Gpr116</i> WT, heterozygous and knockout organs at P4 (n = 3 mice per genotype). I. mRNA detection by RNAscope in brain cortical capillary vessels from <i>Gpr116</i> WT (top row), knockout (middle row) and ROSA^mTmG X Tie2-Cre mice (lower row) at 3 weeks. On the left column, note that only the probe signal (red) and the nuclear staining (blue) are visible. On the right column, an endothelial staining (green) is merged to the probe and the nuclear signal: a CD31 antibody staining is on the two upper rows, while Tie-2 Cre mediated GFP is on the lower row. (n = 1 mouse per genotype).</p

Massive accumulation phenotype in lungs of aged <i>Gpr116</i>^-/- mice.

<p>A. Bright field image of the inflated lung from <i>Gpr116</i> WT, heterozygous and knockout littermates. B. Weights of whole lungs over total body weight from <i>Gpr116</i> WT, heterozygous and knockout littermates (n≥5 mice per genotype). C. Bright field images of heart from <i>Gpr116</i> WT, heterozygous and knockout littermates. D. Weights of the heart (left) over total body weight from <i>Gpr116</i> WT, heterozygous and knockout littermates (n≥5 mice per genotype). E. Bright field images of the spleen from <i>Gpr116</i> WT, heterozygous and knockout littermates. F. Weights of the spleen (left) over total body weight from <i>Gpr116</i> WT, heterozygous and knockout littermates (n≥5 mice per genotype). G. BALF collected from <i>Gpr116</i> WT, heterozygous and knockout littermates (The picture shown is representative of 3 mice for each genotype). H. Quantification of saturated phosphatydilcholine in BALF by ELISA (n = 3 mice per genotype). I. Quantification of protein content in BALF by BCA assay (n = 3 mice per genotype). J. Surfactant proteins detection in BALF by western blot. Molecular weights are indicated on the right. (n = 2 mice per genotype). K. Bright field images of the lung, after hematoxylin and eosin staining. The black arrowheads indicate alveolar macrophages (the image is representative of 4 mice for each genotype). L. Electron microscopy view of <i>Gpr116</i> wildtype and knockout lungs (n = 2 mice for each genotype). M. Confocal images of lung sections stained with ADRP (white) and nuclear stain (Hoechst, blue). Note that a red autofluorescent signal appears in knockout lungs. (the image shown is representative of 2 mice for each genotype). N. Confocal images of lung sections stained with nuclear marker Hoechst (blue) to show autofluorescent cells accumulated in the alveolar space, either in the green or red channel (the image is representative of 3 mice for each genotype). O. Autofluorescence emission spectrum of macrophages in the old knockout lung, upon 405 nm excitation (the image is representative of 2 mice). P. Detection of autofluorescent cells from <i>Gpr116</i> knockout lung by FACS (n = 2 mice per genotype)</p

Blood brain barrier breakdown in <i>Gpr116</i>^-/- mice.

<p>A. Whole brain images taken after 1kDa cadaverine perfusion (left) and associated quantification of extravasated cadaverine (right) in aged <i>Gpr116</i> WT, heterozygous and knockout mice (n≥5 mice for each genotype). B. Whole brain images taken 70 kDa tetramethylrhodamine dextran perfusion (left) and quantification of extravasated tracer (right) in <i>Gpr116</i> WT and heterozygous and <i>Gpr116</i> ECKO mice (n = 3 for wild type and ECKO, n = 2 for <i>PDGF-B</i>^{<i>ret/ret</i>}, n = 1 for uninjected control). C. Confocal images of cerebral cortex from aged <i>Gpr116</i> WT, heterozygous and knockout mice. Astrocytes (GFAP) appear in green, endothelial cells (CD31) in red (the images are representative of 4 mice per genotype) and associated quantification of perivascular associated astrocytes in aged <i>Gpr116</i> WT, heterozygous and knockout mice (n = 4 mice for each genotype, 2 sections at least quantified per genotype). D. Whole brain fluorescence images taken after Alexa 555-cadaverine circulation (upper) and quantification of extravasated cadaverine (lower) in 1.5-month-old <i>Gpr116</i> knockout (n = 3 mice per genotype). E. Whole brain fluorescent images taken after cadaverine circulation (upper) and associated quantification of extravasated cadaverine (lower) in 2-months-old <i>Gpr116</i> AEC KO (n = 6 mice per genotype). F. Whole brain fluorescent images taken after cadaverine circulation (upper) and quantification of extravasated cadaverine (lower) in 2-months-old <i>Gpr116</i> ECKO (n = 7 mice per genotype)</p