Citations

Abstract Motivation Testing SNP–SNP interactions is considered as a key for overcoming bottlenecks of genetic association studies. However, related statistical methods for testing SNP–SNP interactions are underdeveloped. Results We propose the SNP Interaction Pattern Identifier (SIPI), which tests 45 biologically meaningful interaction patterns for a binary outcome. SIPI takes non-hierarchical models, inheritance modes and mode coding direction into consideration. The simulation results show that SIPI has higher power than MDR (Multifactor Dimensionality Reduction), AA_Full, Geno_Full (full interaction model with additive or genotypic mode) and SNPassoc in detecting interactions. Applying SIPI to the prostate cancer PRACTICAL consortium data with approximately 21 000 patients, the four SNP pairs in EGFR-EGFR, EGFR-MMP16 and EGFR-CSF1 were found to be associated with prostate cancer aggressiveness with the exact or similar pattern in the discovery and validation sets. A similar match for external validation of SNP–SNP interaction studies is suggested. We demonstrated that SIPI not only searches for more meaningful interaction patterns but can also overcome the unstable nature of interaction patterns. Availability and Implementation The SIPI software is freely available at http://publichealth.lsuhsc.edu/LinSoftware/. Supplementary information Supplementary data are available at Bioinformatics online.

Coffee consumption has been shown in some studies to be associated with lower risk of prostate cancer. However, it is unclear if this association is causal or due to confounding or reverse causality. We conducted a Mendelian randomisation analysis to investigate the causal effects of coffee consumption on prostate cancer risk and progression. We used two genetic variants robustly associated with caffeine intake (rs4410790 and rs2472297) as proxies for coffee consumption in a sample of 46,687 men of European ancestry from 25 studies in the PRACTICAL consortium. Associations between genetic variants and prostate cancer case status, stage and grade were assessed by logistic regression and with all‐cause and prostate cancer‐specific mortality using Cox proportional hazards regression. There was no clear evidence that a genetic risk score combining rs4410790 and rs2472297 was associated with prostate cancer risk (OR per additional coffee increasing allele: 1.01, 95% CI: 0.98,1.03) or having high‐grade compared to low‐grade disease (OR: 1.01, 95% CI: 0.97,1.04). There was some evidence that the genetic risk score was associated with higher odds of having nonlocalised compared to localised stage disease (OR: 1.03, 95% CI: 1.01, 1.06). Amongst men with prostate cancer, there was no clear association between the genetic risk score and all‐cause mortality (HR: 1.00, 95% CI: 0.97,1.04) or prostate cancer‐specific mortality (HR: 1.03, 95% CI: 0.98,1.08). These results, which should have less bias from confounding than observational estimates, are not consistent with a substantial effect of coffee consumption on reducing prostate cancer incidence or progression.

Prostate cancer is the most common cancer in men in developed countries, and is a target for risk reduction strategies. The effects of alcohol consumption on prostate cancer incidence and survival remain unclear, potentially due to methodological limitations of observational studies. In this study, we investigated the associations of genetic variants in alcohol‐metabolising genes with prostate cancer incidence and survival. We analysed data from 23,868 men with prostate cancer and 23,051 controls from 25 studies within the international PRACTICAL Consortium. Study‐specific associations of 68 single nucleotide polymorphisms (SNPs) in 8 alcohol‐metabolising genes (Alcohol Dehydrogenases (ADHs) and Aldehyde Dehydrogenases (ALDHs)) with prostate cancer diagnosis and prostate cancer‐specific mortality, by grade, were assessed using logistic and Cox regression models, respectively. The data across the 25 studies were meta‐analysed using fixed‐effect and random‐effects models. We found little evidence that variants in alcohol metabolising genes were associated with prostate cancer diagnosis. Four variants in two genes exceeded the multiple testing threshold for associations with prostate cancer mortality in fixed‐effect meta‐analyses. SNPs within ALDH1A2 associated with prostate cancer mortality were rs1441817 (fixed effects hazard ratio, HRfixed = 0.78; 95% confidence interval (95%CI):0.66,0.91; p values = 0.002); rs12910509, HRfixed = 0.76; 95%CI:0.64,0.91; p values = 0.003); and rs8041922 (HRfixed = 0.76; 95%CI:0.64,0.91; p values = 0.002). These SNPs were in linkage disequilibrium with each other. In ALDH1B1, rs10973794 (HRfixed = 1.43; 95%CI:1.14,1.79; p values = 0.002) was associated with prostate cancer mortality in men with low‐grade prostate cancer. These results suggest that alcohol consumption is unlikely to affect prostate cancer incidence, but it may influence disease progression.

Background The rarity of mutations in PALB2, CHEK2 and ATM make it difficult to estimate precisely associated cancer risks. Population-based family studies have provided evidence that at least some of these mutations are associated with breast cancer risk as high as those associated with rare BRCA2 mutations. We aimed to estimate the relative risks associated with specific rare variants in PALB2, CHEK2 and ATM via a multicentre case-control study. Methods We genotyped 10 rare mutations using the custom iCOGS array: PALB2 c.1592delT, c.2816T>G and c.3113G>A, CHEK2 c.349A>G, c.538C>T, c.715G>A, c.1036C>T, c.1312G>T, and c.1343T>G and ATM c.7271T>G. We assessed associations with breast cancer risk (42 671 cases and 42 164 controls), as well as prostate (22 301 cases and 22 320 controls) and ovarian (14 542 cases and 23 491 controls) cancer risk, for each variant. Results For European women, strong evidence of association with breast cancer risk was observed for PALB2 c.1592delT OR 3.44 (95% CI 1.39 to 8.52, p=7.1×10−5), PALB2 c.3113G>A OR 4.21 (95% CI 1.84 to 9.60, p=6.9×10−8) and ATM c.7271T>G OR 11.0 (95% CI 1.42 to 85.7, p=0.0012). We also found evidence of association with breast cancer risk for three variants in CHEK2, c.349A>G OR 2.26 (95% CI 1.29 to 3.95), c.1036C>T OR 5.06 (95% CI 1.09 to 23.5) and c.538C>T OR 1.33 (95% CI 1.05 to 1.67) (p≤0.017). Evidence for prostate cancer risk was observed for CHEK2 c.1343T>G OR 3.03 (95% CI 1.53 to 6.03, p=0.0006) for African men and CHEK2 c.1312G>T OR 2.21 (95% CI 1.06 to 4.63, p=0.030) for European men. No evidence of association with ovarian cancer was found for any of these variants. Conclusions This report adds to accumulating evidence that at least some variants in these genes are associated with an increased risk of breast cancer that is clinically important.

Objective: The Lung Injury Prediction Score identifies patients at risk for acute respiratory distress syndrome in the emergency department, but it has not been validated in non-emergency department hospitalized patients. We aimed to evaluate whether Lung Injury Prediction Score identifies non-emergency department hospitalized patients at risk of developing acute respiratory distress syndrome at the time of critical care contact. Design: Retrospective study. Setting: Five academic medical centers. Patients: Nine hundred consecutive patients (≥ 18 yr old) with at least one acute respiratory distress syndrome risk factor at the time of critical care contact. Interventions: None. Measurements and Main Results: Lung Injury Prediction Score was calculated using the worst values within the 12 hours before initial critical care contact. Patients with acute respiratory distress syndrome at the time of initial contact were excluded. Acute respiratory distress syndrome developed in 124 patients (13.7%) a median of 2 days (interquartile range, 2–3) after critical care contact. Hospital mortality was 22% and was significantly higher in acute respiratory distress syndrome than non-acute respiratory distress syndrome patients (48% vs 18%; p < 0.001). Increasing Lung Injury Prediction Score was significantly associated with development of acute respiratory distress syndrome (odds ratio, 1.31; 95% CI, 1.21–1.42) and the composite outcome of acute respiratory distress syndrome or death (odds ratio, 1.26; 95% CI, 1.18–1.34). A Lung Injury Prediction Score greater than or equal to 4 was associated with the development of acute respiratory distress syndrome (odds ratio, 4.17; 95% CI, 2.26–7.72), composite outcome of acute respiratory distress syndrome or death (odds ratio, 2.43; 95% CI, 1.68–3.49), and acute respiratory distress syndrome after accounting for the competing risk of death (hazard ratio, 3.71; 95% CI, 2.05–6.72). For acute respiratory distress syndrome development, the Lung Injury Prediction Score has an area under the receiver operating characteristic curve of 0.70 and a Lung Injury Prediction Score greater than or equal to 4 has 90% sensitivity (misses only 10% of acute respiratory distress syndrome cases), 31% specificity, 17% positive predictive value, and 95% negative predictive value. Conclusions: In a cohort of non-emergency department hospitalized patients, the Lung Injury Prediction Score and Lung Injury Prediction Score greater than or equal to 4 can identify patients at increased risk of acute respiratory distress syndrome and/or death at the time of critical care contact but it does not perform as well as in the original emergency department cohort.

Abstract Resistance is one the main reason for overall decrease in survival of cancer patient treated with cisplatin in different types of cancer. Cisplatin kills cancer cells by various mechanisms, but mainly through formation of inter- and intra stand crosslinks of DNA. Different types of translesion polymerase including Polymerase kappa (POLK) are involved in repair of DNA lesions. We observed high expression levels of POLK in cisplatin resistant bladder and ovarian cancer cells compared to parental cells. Due to its low proof-reading activity POLK can incorporate 8-oxo-dGTP into DNA. The MTH1 protein (Nudix hydrolase- NUDT1) sanitizes oxidized dNTP pools to prevent incorporation of damaged bases during DNA replication. Recently we have generated MTH1 inhibitors that damage the DNA and induce cancer specific cell death through incorporation of more oxidized dNTPs. We found cisplatin resistant bladder cancer cells (NTUB1/P) were more sensitive to MTH1 inhibitors in comparison to parental NTUB1 cells. As POLK is involved in incorporation of 8-oxo-dGTP into DNA, we hypothesized that high expression levels of POLK in cisplatin resistant cells make them more sensitive to MTH1 inhibitors as more 8-oxo-dGTP would be incorporated into DNA, resulting in more DNA damage and cell death in comparison to parental cells. Indeed, we observes higher induction of cleaved-PARP, γH2AX, cleaved-Caspase 3 and more annexin v positive cells in cisplatin resistant NTUB1/P cells in comparison to parental NTUB1 cells upon treatment with MTH1 inhibitors. MTH1 inhibitor also significantly delays the NTUB1/P xenograft tumor growth in comparison to vehicle treatment in immunosuppressive mice. Knocking down POLK in cisplatin resistant NTUB1/P cells by siRNA resulted in decreased incorporation of 8-oxo-dGTP and sensitivity to MTH1 inhibitors compared to non target control cells. Overexpression of POLK in NTUB1 and NTUB1/P cells results in further sensitization to MTH1 inhibitors. In conclusion elevated levels of POLK in cisplatin resistance cells determines increased sensitivity towards MTH1 inhibitors. Thus MTH1 inhibitors can be a potential promising therapy for the treatment of cisplatin resistant tumors in patients. Citation Format: Kumar Sanjiv, Helge Gad, Sean Rudd, Rachel Hurley, Patric Herr, José Manuel Calderón Montaño, Oliver Mortusewicz, Tobias Koolmeister, Sylvain Jaques, Estefanía Burgos Morón, Andreas Hoglund, Te-Chang Lee, Martin Scobie, Scott Kaufmann, John Weroha, Ulrika Warpman Berglund, Andrea Wahner Hendrickson, Thomas Helleday. Polymerase kappa determines the sensitivity of MTH1 inhibitors to cisplatin-resistant cell. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1260.