Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention

Background: Feature selection (FS) process is essential in the medical area as it reduces the effort and time needed for physicians to measure unnecessary features. Choosing useful variables is a difficult task with the presence of censoring which is the unique characteristic in survival analysis. Most survival FS methods depend on Cox's proportional hazard model; however, machine learning techniques (MLT) are preferred but not commonly used due to censoring. Techniques that have been proposed to adopt MLT to perform FS with survival data cannot be used with the high level of censoring. The researcher's previous publications proposed a technique to deal with the high level of censoring. It also used existing FS techniques to reduce dataset dimension. However, in this paper a new FS technique was proposed and combined with feature transformation and the proposed uncensoring approaches to select a reduced set of features and produce a stable predictive model. Methods: In this paper, a FS technique based on artificial neural network (ANN) MLT is proposed to deal with highly censored Endovascular Aortic Repair (EVAR). Survival data EVAR datasets were collected during 2004 to 2010 from two vascular centers in order to produce a final stable model. They contain almost 91% of censored patients. The proposed approach used a wrapper FS method with ANN to select a reduced subset of features that predict the risk of EVAR re-intervention after 5 years to patients from two different centers located in the United Kingdom, to allow it to be potentially applied to cross-centers predictions. The proposed model is compared with the two popular FS techniques; Akaike and Bayesian information criteria (AIC, BIC) that are used with Cox's model. Results: The final model outperforms other methods in distinguishing the high and low risk groups; as they both have concordance index and estimated AUC better than the Cox's model based on AIC, BIC, Lasso, and SCAD approaches. These models have p-values lower than 0.05, meaning that patients with different risk groups can be separated significantly and those who would need re-intervention can be correctly predicted. Conclusion: The proposed approach will save time and effort made by physicians to collect unnecessary variables. The final reduced model was able to predict the long-term risk of aortic complications after EVAR. This predictive model can help clinicians decide patients' future observation plan

Techniques for valuing adaptive capacity in flood risk management

Flood and coastal erosion risk management has always faced the challenge of decision making in the face of multiple uncertainties relating to the climate, the economy and society. Traditionally, this has been addressed by adopting a precautionary approach that seeks to protect against a reasonable worst case. However, a managed adaptive approach can offer advantages. The benefits include improved resilience to negative changes, enabling opportunities from positive changes and greater cost effectiveness. The absence of clear methods and tools to value adaptive approaches has been recognised as an obstacle to wider adoption. This paper proposes a staged approach to building in adaptive capacity, which systematically analyses uncertainties, identifies opportunities to incorporate adaptability and appraises benefits through the analysis of decision trees. A case study is presented. The methodology is set in the context of appraisal processes used in England by the Environment Agency, based on cost–benefit analysis guidance issued by HM Treasury. The approach is transferable to other situations worldwide, where decision making is based on quantified assessment of costs and benefits. The work should help decision makers fully appraise the benefits of building in adaptive capacity and make the economic and technical case for adaptive flood risk management in an uncertain environment

Determining the contribution of IL33 and IL1RL1 polymorphisms to clinical and immunological features of asthma

Rationale: IL33 (9p24.1) and the IL33 receptor (IL1RL, 2q12) have been reproducibly identified as asthma susceptibility genes. However, the variants driving genetic associations are not yet fully defined. Using a population based birth cohort of 1059 children (Manchester Asthma and Allergy Study-(MAAS)) and 2536 adults with asthma (Genetics of Asthma Severity and Phenotypes- (GASP)) cohort we aimed to define genetic variants associated with clinical and immunological features of asthma. Methods: MAAS samples were genotyped using the Illumina 610 Quad array and imputed using 1000G reference panel. GASP samples were genotyped using two custom designed Affymetrix arrays (UK BiLEVE/UK Biobank array). Datasets were quality controlled for gender mismatches, outliers and relatedness. Data was generated for the IL33/IL1RL1 regions consisting of the genes and surrounding regions (chr9:5715785−6757983 & chr2:102427961−103468497) on the following traits: asthma diagnosis (MAAS), atopy, FEV1 (GASP) and FEV1/FVC (MAAS and GASP) as well as total blood eosinophil counts and serum total IgE levels (GASP). Variables for blood eosinophils and total IgE were log10 transformed. Analysis was carried out in PLINK using linear or logistic regression modelling including appropriate covariates for each trait. Results: In the MAAS cohort, we replicated the association of the IL33 locus with asthma diagnosis, identifying potentially two independent novel signals in that locus (rs10975398; P=1.70E-05; B= -1.519; MAF=0.32 and rs2890697; P=1.10E-04; B= -1.573; MAF=0.43). This association survived a Bonferroni correction for multiple testing. Although not surviving correction, an association was also identified for atopy in the IL1RL1 locus for MAAS (P=1.08E-04; MAF=0.48). In GASP we identified modest associations not in known LD with published loci (P-value range: 5.00E-02 – 7.60E-04) for FEV1, FEV1/FVC, atopy, blood eosinophils and total IgE in both the IL33 and IL1RL1 loci. Multiple SNPs presented nominal association (P<0.01) with more than one trait such as atopy & total IgE, providing supporting evidence for association. Conclusion: We replicated the association of IL33 region SNPs with asthma diagnosis in MAAS, highlighting the role of this locus in childhood asthma. Although trait association signals did not survive correction for multiple testing, nominal association across multiple phenotypes in GASP provides suggestive evidence of the role of the IL33/IL1RL1 genetic polymorphisms in determining clinical and immunological features of asthma

Abdominal aortic aneurysm is associated with a variant in low-density lipoprotein receptor-related protein 1

Abdominal aortic aneurysm (AAA) is a common cause of morbidity and mortality and has a significant heritability. We carried out a genome-wide association discovery study of 1866 patients with AAA and 5435 controls and replication of promising signals (lead SNP with a p value &lt; 1 × 10-5) in 2871 additional cases and 32,687 controls and performed further follow-up in 1491 AAA and 11,060 controls. In the discovery study, nine loci demonstrated association with AAA (p &lt; 1 × 10-5). In the replication sample, the lead SNP at one of these loci, rs1466535, located within intron 1 of low-density-lipoprotein receptor-related protein 1 (LRP1) demonstrated significant association (p = 0.0042). We confirmed the association of rs1466535 and AAA in our follow-up study (p = 0.035). In a combined analysis (6228 AAA and 49182 controls), rs1466535 had a consistent effect size and direction in all sample sets (combined p = 4.52 × 10-10, odds ratio 1.15 [1.10-1.21]). No associations were seen for either rs1466535 or the 12q13.3 locus in independent association studies of coronary artery disease, blood pressure, diabetes, or hyperlipidaemia, suggesting that this locus is specific to AAA. Gene-expression studies demonstrated a trend toward increased LRP1 expression for the rs1466535 CC genotype in arterial tissues; there was a significant (p = 0.029) 1.19-fold (1.04-1.36) increase in LRP1 expression in CC homozygotes compared to TT homozygotes in aortic adventitia. Functional studies demonstrated that rs1466535 might alter a SREBP-1 binding site and influence enhancer activity at the locus. In conclusion, this study has identified a biologically plausible genetic variant associated specifically with AAA, and we suggest that this variant has a possible functional role in LRP1 expression

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Does an apple a day keep the aneurysm away?

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