Riders on the Storm: Hurricane Risk and Coastal Insurance and Mitigation Decisions

This paper utilizes cross-sectional, household-level, survey data combined with data on subjective risk perceptions and experimentally derived risk preferences to analyze the decision to insure against hurricane losses. Our sample encompasses 670 individuals in five states of the United States Gulf Coast Region (Texas, Louisiana, Mississippi, Alabama, and Florida). This study represents one of the few papers to examine wind insurance empirically and the only study to examine flood insurance, wind insurance, and mitigation behavior contemporaneously. Because these decisions are closely related, we employ a mixed-process regression, which allows for correlated error terms across a random-effects bivariate probit model (flood/wind insurance) and a Poisson Log-Normal count model (mitigation). Results indicate positive and statistically significant correlations between the error terms of the insurance and mitigation models but no significant correlation between the error terms of the two insurance models, conditioned on the covariates. We find evidence that risk perceptions and other household factors have some influence on storm risk management, but the strongest effects tend to be related to mandatory insurance requirements associated with location in high-hazard areas

Differential expression of prognostic proteomic markers in primary tumour, venous tumour thrombus and metastatic renal cell cancer tissue and correlation with patient outcome.

Renal cell carcinoma (RCC) is the most deadly of urological malignancies. Metastatic disease affects one third of patients at diagnosis with a further third developing metastatic disease after extirpative surgery. Heterogeneity in the clinical course ensures predicting metastasis is notoriously difficult, despite the routine use of prognostic clinico-pathological parameters in risk stratification. With greater understanding of pathways involved in disease pathogenesis, a number of biomarkers have been shown to have prognostic significance, including Ki67, p53, vascular endothelial growth factor receptor 1 (VEGFR1) and ligand D (VEGFD), SNAIL and SLUG. Previous pathway analysis has been from study of the primary tumour, with little attention to the metastatic tumours which are the focus of targeted molecular therapies. As such, in this study a tissue microarray from 177 patients with primary renal tumour, renal vein tumour thrombus and/or RCC metastasis has been created and used with Automated Quantitative Analysis (AQUA) of immunofluorescence to study the prognostic significance of these markers in locally advanced and metastatic disease. Furthermore, this has allowed assessment of differential protein expression between the primary tumours, renal vein tumour thrombi and metastases. The results demonstrate that clinico-pathological parameters remain the most significant predictors of cancer specific survival; however, high VEGFR1 or VEGFD can predict poor cancer specific survival on univariate analysis for locally advanced and metastatic disease. There was significantly greater expression of Ki67, p53, VEGFR1, SLUG and SNAIL in the metastases compared with the primary tumours and renal vein tumour thrombi. With the exception of p53, these differences in protein expression have not been shown previously in RCC. This confirms the importance of proliferation, angiogenesis and epithelial to mesenchymal transition in the pathogenesis and metastasis of RCC. Importantly, this work highlights the need for further pathway analysis of metastatic tumours for overcoming drug resistance and developing new therapies

A new method for determining the sensitivity of X-ray imaging observations and the X-ray number counts

We present a new method for determining the sensitivity of X-ray imaging observations, which correctly accounts for the observational biases that affect the probability of detecting a source of a given X-ray flux, without the need to perform a large number of time consuming simulations. We use this new technique to estimate the X-ray source counts in different spectral bands (0.5-2, 0.5-10, 2-10 and 5-10keV) by combining deep pencil-beam and shallow wide-area Chandra observations. The sample has a total of 6295 unique sources over an area of $\rm 11.8deg^2$ and is the largest used to date to determine the X-ray number counts. We determine, for the first time, the break flux in the 5-10 keV band, in the case of a double power-law source count distribution. We also find an upturn in the 0.5-2keV counts at fluxes below about 6e-17erg/s/cm2. We show that this can be explained by the emergence of normal star-forming galaxies which dominate the X-ray population at faint fluxes. The fraction of the diffuse X-ray background resolved into point sources at different spectral bands is also estimated. It is argued that a single population of Compton thick AGN cannot be responsible for the entire unresolved X-ray background in the energy range 2-10keV.Comment: Accepted for publication in MNRAS. Data products available at http://astro.imperial.ac.uk/research/xray

The Chandra survey of the COSMOS field II: source detection and photometry

The Chandra COSMOS Survey (C-COSMOS) is a large, 1.8 Ms, Chandra program, that covers the central contiguous ~0.92 deg^2 of the COSMOS field. C-COSMOS is the result of a complex tiling, with every position being observed in up to six overlapping pointings (four overlapping pointings in most of the central ~0.45 deg^2 area with the best exposure, and two overlapping pointings in most of the surrounding area, covering an additional ~0.47 deg^2). Therefore, the full exploitation of the C-COSMOS data requires a dedicated and accurate analysis focused on three main issues: 1) maximizing the sensitivity when the PSF changes strongly among different observations of the same source (from ~1 arcsec up to ~10 arcsec half power radius); 2) resolving close pairs; and 3) obtaining the best source localization and count rate. We present here our treatment of four key analysis items: source detection, localization, photometry, and survey sensitivity. Our final procedure consists of a two step procedure: (1) a wavelet detection algorithm, to find source candidates, (2) a maximum likelihood Point Spread Function fitting algorithm to evaluate the source count rates and the probability that each source candidate is a fluctuation of the background. We discuss the main characteristics of this procedure, that was the result of detailed comparisons between different detection algorithms and photometry tools, calibrated with extensive and dedicated simulations.Comment: Accepted for publication in The Astrophysical Journal Supplement Serie

5-hydroxymethylcytosine profiling as an indicator of cellular state

A Laird is supported by the Medical Research Council Scottish Clinical Pharmacology and Pathology Programme, The Royal College of Surgeons of Edinburgh Robertson’s Trust and The Melville Trust for the Care and Cure of Cancer. J Thomson is supported by the MARCAR project. Work in RR Meehan’s laboratory is supported by the Medical Research Council, the BBSRC and by the Innovative Medicine Initiative Joint Undertaking (IMI JU) under grant agreement number 115001 (MARCAR project).DNA methylation is widely studied in the context of cancer. However, the rediscovery of 5-hydroxymethylation of DNA adds a new layer of complexity to understanding the epigenetic basis of development and disease, including carcinogenesis. There have been significant advances in techniques for the detection of 5-hydroxymethylcytosine and, with this, greater insight into the distribution, regulation and function of this mark, which are reviewed here. Better understanding of the associated pathways involved in regulation of, and by, 5-hydroxymethylcytosine may give promise to new therapeutic targets. We discuss evidence to support the view of 5-hydroxymethylcytosine as a unique and dynamic mark of cellular state. These 5-hydroxymethylcytosine profiles may offer optimism for the development of diagnostic, prognostic and predictive biomarkers.Publisher PDFPeer reviewe

Overcoming intratumoural heterogeneity for reproducible molecular risk stratification: a case study in advanced kidney cancer.

BACKGROUND: Metastatic clear cell renal cell cancer (mccRCC) portends a poor prognosis and urgently requires better clinical tools for prognostication as well as for prediction of response to treatment. Considerable investment in molecular risk stratification has sought to overcome the performance ceiling encountered by methods restricted to traditional clinical parameters. However, replication of results has proven challenging, and intratumoural heterogeneity (ITH) may confound attempts at tissue-based stratification. METHODS: We investigated the influence of confounding ITH on the performance of a novel molecular prognostic model, enabled by pathologist-guided multiregion sampling (n = 183) of geographically separated mccRCC cohorts from the SuMR trial (development, n = 22) and the SCOTRRCC study (validation, n = 22). Tumour protein levels quantified by reverse phase protein array (RPPA) were investigated alongside clinical variables. Regularised wrapper selection identified features for Cox multivariate analysis with overall survival as the primary endpoint. RESULTS: The optimal subset of variables in the final stratification model consisted of N-cadherin, EPCAM, Age, mTOR (NEAT). Risk groups from NEAT had a markedly different prognosis in the validation cohort (log-rank p = 7.62 × 10(-7); hazard ratio (HR) 37.9, 95% confidence interval 4.1-353.8) and 2-year survival rates (accuracy = 82%, Matthews correlation coefficient = 0.62). Comparisons with established clinico-pathological scores suggest favourable performance for NEAT (Net reclassification improvement 7.1% vs International Metastatic Database Consortium score, 25.4% vs Memorial Sloan Kettering Cancer Center score). Limitations include the relatively small cohorts and associated wide confidence intervals on predictive performance. Our multiregion sampling approach enabled investigation of NEAT validation when limiting the number of samples analysed per tumour, which significantly degraded performance. Indeed, sample selection could change risk group assignment for 64% of patients, and prognostication with one sample per patient performed only slightly better than random expectation (median logHR = 0.109). Low grade tissue was associated with 3.5-fold greater variation in predicted risk than high grade (p = 0.044). CONCLUSIONS: This case study in mccRCC quantitatively demonstrates the critical importance of tumour sampling for the success of molecular biomarker studies research where ITH is a factor. The NEAT model shows promise for mccRCC prognostication and warrants follow-up in larger cohorts. Our work evidences actionable parameters to guide sample collection (tumour coverage, size, grade) to inform the development of reproducible molecular risk stratification methods.We acknowledge financial support from the Royal Society of Edinburgh Scottish Government Fellowship co-funded by Marie Curie Actions (IMO), Carnegie Trust (50115; IMO, DJH, GDS), IGMM DTF (IMO, GDS), Medical Research Council (MC_UU_12018/25; IMO), Chief Scientist Office Scotland (ETM37; GDS, DJH), Cancer Research UK (Experimental Medicine Centre; TP, DJH), Renal Cancer Research Fund (GDS), Kidney Cancer Scotland (GDS), MRC Clinical Training Fellowship (AL), RCSEd Robertson Trust (AL) and Melville Trust (AL)

Overcoming intratumoural heterogeneity for reproducible molecular risk stratification: a case study in advanced kidney cancer

Abstract Background Metastatic clear cell renal cell cancer (mccRCC) portends a poor prognosis and urgently requires better clinical tools for prognostication as well as for prediction of response to treatment. Considerable investment in molecular risk stratification has sought to overcome the performance ceiling encountered by methods restricted to traditional clinical parameters. However, replication of results has proven challenging, and intratumoural heterogeneity (ITH) may confound attempts at tissue-based stratification. Methods We investigated the influence of confounding ITH on the performance of a novel molecular prognostic model, enabled by pathologist-guided multiregion sampling (n = 183) of geographically separated mccRCC cohorts from the SuMR trial (development, n = 22) and the SCOTRRCC study (validation, n = 22). Tumour protein levels quantified by reverse phase protein array (RPPA) were investigated alongside clinical variables. Regularised wrapper selection identified features for Cox multivariate analysis with overall survival as the primary endpoint. Results The optimal subset of variables in the final stratification model consisted of N-cadherin, EPCAM, Age, mTOR (NEAT). Risk groups from NEAT had a markedly different prognosis in the validation cohort (log-rank p = 7.62 × 10−7; hazard ratio (HR) 37.9, 95% confidence interval 4.1–353.8) and 2-year survival rates (accuracy = 82%, Matthews correlation coefficient = 0.62). Comparisons with established clinico-pathological scores suggest favourable performance for NEAT (Net reclassification improvement 7.1% vs International Metastatic Database Consortium score, 25.4% vs Memorial Sloan Kettering Cancer Center score). Limitations include the relatively small cohorts and associated wide confidence intervals on predictive performance. Our multiregion sampling approach enabled investigation of NEAT validation when limiting the number of samples analysed per tumour, which significantly degraded performance. Indeed, sample selection could change risk group assignment for 64% of patients, and prognostication with one sample per patient performed only slightly better than random expectation (median logHR = 0.109). Low grade tissue was associated with 3.5-fold greater variation in predicted risk than high grade (p = 0.044). Conclusions This case study in mccRCC quantitatively demonstrates the critical importance of tumour sampling for the success of molecular biomarker studies research where ITH is a factor. The NEAT model shows promise for mccRCC prognostication and warrants follow-up in larger cohorts. Our work evidences actionable parameters to guide sample collection (tumour coverage, size, grade) to inform the development of reproducible molecular risk stratification methods

The immunopeptidome from a genomic perspective:Establishing the noncanonical landscape of MHC class I–associated peptides

G.B., D.B., K.W., A.P., R.F., T.R.H., S.K., and J.A.A. received support from Fundacja na rzecz Nauki Polskiej (FNP) (grant ID: MAB/3/2017). D.R.G. received support from Genome Canada & Genome BC (grant ID: 264PRO). D.J.H. received support from NuCana plc (grant ID: SMD0-ZIUN05). H.A. received support from Swedish Cancer Foundation (grant ID: 211709). H.G. received support from United Kingdom Research and Innovation (UKRI) (grant ID: EP/S02431X/1). C.P. received support from Fundação para a Ciência e a Tecnologia (FCT) through LASIGE Research Unit (grant ID: UIDB/00408/2020 and UIDP/00408/2020). A.L. F.M.Z., C.P., A.R., A.P., and J.A.A. received support from European Union’s Horizon 2020 research and innovation programme (grant ID: 101017453). C.B. received support from Agence Nationale de la Recherche (ANR) through GRAL LabEX (grant ID: ANR-10-LABX-49-01) and CBH-EUR-GS 32 (grant ID: ANR-17-EURE0003). S.N.S. received support from Cancer Research UK (CRUK) and the Chief Scientist's Office of Scotland (CSO): Experimental Cancer Medicine Centre (ECMC) (grant ID: ECMCQQR-2022/100017). A.L. received support from Chief Scientist's Office of Scotland (CSO) NRS Career Researcher Fellowship. R.O.N. received support from CRUK Cambridge Centre Thoracic Cancer Programme (grant ID: CTRQQR-2021\100012).Tumor antigens can emerge through multiple mechanisms, including translation of non-coding genomic regions. This non-canonical category of antigens has recently gained attention; however, our understanding of how they recur within and between cancer types is still in its infancy. Therefore, we developed a proteogenomic pipeline based on deep learning de novo mass spectrometry to enable the discovery of non-canonical MHC-associated peptides (ncMAPs) from non-coding regions. Considering that the emergence of tumor antigens can also involve post-translational modifications, we included an open search component in our pipeline. Leveraging the wealth of mass spectrometry-based immunopeptidomics, we analyzed 26 MHC class I immunopeptidomic studies of 9 different cancer types. We validated the de novo identified ncMAPs, along with the most abundant post-translational modifications, using spectral matching and controlled their false discovery rate (FDR) to 1%. Interestingly, the non-canonical presentation appeared to be 5 times enriched for the A03 HLA supertype, with a projected population coverage of 54.85%. Here, we reveal an atlas of 8,601 ncMAPs with varying levels of cancer selectivity and suggest 17 cancer-selective ncMAPs as attractive targets according to a stringent cutoff. In summary, the combination of the open-source pipeline and the atlas of ncMAPs reported herein could facilitate the identification and screening of ncMAPs as targeting agents for T-cell therapies or vaccine development.Publisher PDFPeer reviewe

Epigenetic sampling effects: nephrectomy modifies the clear cell renal cell cancer methylome.

PURPOSE: Currently, it is unclear to what extent sampling procedures affect the epigenome. Here, this phenomenon was evaluated by studying the impact of artery ligation on DNA methylation in clear cell renal cancer. METHODS: DNA methylation profiles between vascularised tumour biopsy samples and devascularised nephrectomy samples from two individuals were compared. The relevance of significantly altered methylation profiles was validated in an independent clinical trial cohort. RESULTS: We found that six genes were differentially methylated in the test samples, of which four were linked to ischaemia or hypoxia (REXO1L1, TLR4, hsa-mir-1299, ANKRD2). Three of these genes were also found to be significantly differentially methylated in the validation cohort, indicating that the observed effects are genuine. CONCLUSION: Tissue ischaemia during normal surgical removal of tumour can cause epigenetic changes. Based on these results, we conclude that the impact of sampling procedures in clinical epigenetic studies should be considered and discussed, particularly after inducing hypoxia/ischaemia, which occurs in most oncological surgery procedures through which tissues are collected for translational research.This work was supported by the Chief Scientist Office, Scotland (ETM37; GDS, DJH), Cancer Research UK (Experimental Cancer Medicine Centre; TP, London, DJH, Edinburgh), Medical Research Council (AL, DJH), Royal College of Surgeons of Edinburgh Robertson Trust (AL), Melville Trust AL), Renal Cancer Research Fund (GDS), Kidney Cancer Scotland (GDS) and an educational grant from Pfizer (TP). CVN and TDM were funded by Ghent University Multidisciplinary Research Partnership 'Bioinformatics: from nucleotides to networks'

High-throughput analysis of the mutagenic and cytotoxic properties of DNA lesions by next-generation sequencing

Human cells are constantly exposed to environmental and endogenous agents which can induce damage to DNA. Understanding the implications of these DNA modifications in the etiology of human diseases requires the examination about how these DNA lesions block DNA replication and induce mutations in cells. All previously reported shuttle vector-based methods for investigating the cytotoxic and mutagenic properties of DNA lesions in cells have low-throughput, where plasmids containing individual lesions are transfected into cells one lesion at a time and the products from the replication of individual lesions are analyzed separately. The advent of next-generation sequencing (NGS) technology has facilitated investigators to design scientific approaches that were previously not technically feasible or affordable. In this study, we developed a new method employing NGS, together with shuttle vector technology, to have a multiplexed and quantitative assessment of how DNA lesions perturb the efficiency and accuracy of DNA replication in cells. By using this method, we examined the replication of four carboxymethylated DNA lesions and two oxidatively induced bulky DNA lesions including (5′S) diastereomers of 8,5′-cyclo-2′-deoxyguanosine (cyclo-dG) and 8,5′-cyclo-2′-deoxyadenosine (cyclo-dA) in five different strains of Escherichia coli cells. We further validated the results obtained from NGS using previously established methods. Taken together, the newly developed method provided a high-throughput and readily affordable method for assessing quantitatively how DNA lesions compromise the efficiency and fidelity of DNA replication in cells