Transcriptome signatures of wastewater effluent exposure in larval zebrafish vary with seasonal mixture composition in an effluent-dominated stream

Wastewater treatment plant (WWTP) effluent-dominated streams provide critical habitat for aquatic and terrestrial organisms but also continually expose them to complex mixtures of pharmaceuticals that can potentially impair growth, behavior, and reproduction. Currently, few biomarkers are available that relate to pharmaceutical-specific mechanisms of action. In the experiment reported in this paper, zebrafish (Danio rerio) embryos at two developmental stages were exposed to water samples from three sampling sites (0.1 km upstream of the outfall, at the effluent outfall, and 0.1 km below the outfall) during base-flow conditions from two months (January and May) of a temperate-region effluent-dominated stream containing a complex mixture of pharmaceuticals and other contaminants of emerging concern. RNA-sequencing identified potential biological impacts and biomarkers of WWTP effluent exposure that extend past traditional markers of endocrine disruption. Transcriptomics revealed changes to a wide range of biological functions and pathways including cardiac, neurological, visual, metabolic, and signaling pathways. These transcriptomic changes varied by developmental stage and displayed sensitivity to variable chemical composition and concentration of effluent, thus indicating a need for stage-specific biomarkers. Some transcripts are known to be associated with genes related to pharmaceuticals that were present in the collected samples. Although traditional biomarkers of endocrine disruption were not enriched in either month, a high estrogenicity signal was detected upstream in May and implicates the presence of unidentified chemical inputs not captured by the targeted chemical analysis. This work reveals associations between bioeffects of exposure, stage of development, and the composition of chemical mixtures in effluent-dominated surface water. The work underscores the importance of measuring effects beyond the endocrine system when assessing the impact of bioactive chemicals in WWTP effluent and identifies a need for non-targeted chemical analysis when bioeffects are not explained by the targeted analysis

Adding new experimental arms to randomised clinical trials: Impact on error rates.

BACKGROUND: Experimental treatments pass through various stages of development. If a treatment passes through early-phase experiments, the investigators may want to assess it in a late-phase randomised controlled trial. An efficient way to do this is adding it as a new research arm to an ongoing trial while the existing research arms continue, a so-called multi-arm platform trial. The familywise type I error rate is often a key quantity of interest in any multi-arm platform trial. We set out to clarify how it should be calculated when new arms are added to a trial some time after it has started. METHODS: We show how the familywise type I error rate, any-pair and all-pairs powers can be calculated when a new arm is added to a platform trial. We extend the Dunnett probability and derive analytical formulae for the correlation between the test statistics of the existing pairwise comparison and that of the newly added arm. We also verify our analytical derivation via simulations. RESULTS: Our results indicate that the familywise type I error rate depends on the shared control arm information (i.e. individuals in continuous and binary outcomes and primary outcome events in time-to-event outcomes) from the common control arm patients and the allocation ratio. The familywise type I error rate is driven more by the number of pairwise comparisons and the corresponding (pairwise) type I error rates than by the timing of the addition of the new arms. The familywise type I error rate can be estimated using Šidák's correction if the correlation between the test statistics of pairwise comparisons is less than 0.30. CONCLUSIONS: The findings we present in this article can be used to design trials with pre-planned deferred arms or to add new pairwise comparisons within an ongoing platform trial where control of the pairwise error rate or familywise type I error rate (for a subset of pairwise comparisons) is required

Comprehensive pharmacogenetic profiling of the epidermal growth factor receptor pathway for biomarkers of response to, and toxicity from, cetuximab

Background Somatic mutations in the epidermal growth factor receptor (EGFR) intracellular signalling pathways predict non-response to cetuximab in the treatment of advanced colorectal cancer (aCRC). We hypothesized that common germline variants within these pathways may also play similar roles. Methods We analysed 54 potentially functional, common, inherited EGFR pathway variants in 815 aCRC patients treated with oxaliplatin-fluoropyrimidine chemotherapy +cetuximab. Primary endpoints were response and skin rash (SR). We had >85% power to detect ORs=1.6 for variants with minor allele frequencies >20%. Results We identified five potential biomarkers for response and four for SR, although none remained significant after correction for multiple testing. Our initial data supported a role for Ser313Pro in PIK3R2 in modulating response to cetuximab - in patients with KRAS wild type CRCs, 36.4% of patients with one allele encoding proline responded, as compared to 71.2% of patients homozygous for alleles encoding serine (OR 0.23, 95% CI 0.09-0.56, P=0.0014) and this association was predictive for cetuximab (Pinteraction=0.017); however, independent replication failed to validate this association. No previously proposed predictive biomarkers were validated. Conclusions Our study highlights the need to validate potential pharmacogenetic biomarkers. We did not find strong evidence for common germline biomarkers of cetuximab response and toxicity

Cardiovascular Risk Factors Associated With Venous Thromboembolism

Importance It is uncertain to what extent established cardiovascular risk factors are associated with venous thromboembolism (VTE). Objective To estimate associations between major cardiovascular risk factors and VTE, i.e., deep-vein thrombosis (DVT) and pulmonary embolism (PE). Design Analysis of individual-participant data from the Emerging Risk Factors Collaboration (ERFC; 731,728 participants; 75 cohorts; latest date of follow-up 2015), and UK Biobank (UKBB; 421,537 participants; latest date of follow-up 2016). Setting Approximately population-based prospective cohort studies. Participants Individuals without cardiovascular disease at baseline. Exposures A panel of several established cardiovascular risk factors. Main Outcomes and Measures Hazard ratios (HRs) per 1-SD higher risk factor levels (or presence/absence). Incident fatal outcomes in ERFC (n=1041 VTE, n=25,131 CHD) and incident fatal/non-fatal outcomes in UKBB (n=2321 VTE, n=3385 CHD). HRs were adjusted for age, sex, smoking status, diabetes mellitus, and body-mass index. Results Adjusted HRs for VTE were: 2.67 (2.45-2.91) in ERFC and 1.81 (1.71-1.92) in UKBB per decade older age; 1.38 (1.20-1.58) in ERFC and 1.23 (1.08-1.40) in UKBB with smoking; 1.43 (1.35-1.50) in ERFC and 1.37 (1.32-1.41) in UKBB per 1-SD higher body-mass index; and 0.75 (0.61-0.93) in ERFC and 0.82 (0.71-0.94) in UKBB with current alcohol consumption. For the preceding factors, there were similar HRs for pulmonary embolism versus deep vein thrombosis in UKBB (except adiposity was more strongly associated with PE; P<0.01), and similar HRs for unprovoked versus provoked VTE. Apart from adiposity, these risk factors were less strongly associated with VTE than coronary heart disease. We noted inconsistent associations with diabetes and blood pressure for VTEs across ERFC and UKBB, and had limited ability to study lipid and inflammation markers. Conclusions and Relevance Older age, smoking, adiposity, and lower alcohol consumption were consistently associated with higher VTE risk.A study website (http://www.phpc.cam.ac.uk/ceu/erfc/list-of-studies/) includes a list that investigators have provided of funding agencies that have supported individual cohorts in the ERFC contributing to the present consortium. This research has been conducted using the UK Biobank resource (application 26865)

Image-based consensus molecular subtype classification (imCMS) of colorectal cancer using deep learning

Objective Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. Design Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. Results Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. Conclusion This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows

Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning

OBJECTIVE Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. DESIGN Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. RESULTS Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. CONCLUSION This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows

Adjuvant Sorafenib for Renal Cell Carcinoma at Intermediate or High Risk of Relapse: Results From the SORCE Randomized Phase III Intergroup Trial.

PURPOSE: SORCE is an international, randomized, double-blind, three-arm trial of sorafenib after surgical excision of primary renal cell carcinoma (RCC) found to be at intermediate or high risk of recurrence. PATIENTS AND METHODS: We randomly assigned participants (2:3:3) to 3 years of placebo (arm A), 1 year of sorafenib followed by 2 years of placebo (arm B), or 3 years of sorafenib (arm C). The initial sorafenib dose was 400 mg twice per day orally, amended to 400 mg daily. The primary outcome analysis, which was revised as a result of external results, was investigator-reported disease-free survival (DFS) comparing 3 years of sorafenib versus placebo. RESULTS: Between July 2007 and April 2013, we randomly assigned 1,711 participants (430, 642, and 639 participants in arms A, B, and C, respectively). Median age was 58 years, 71% of patients were men, 84% had clear cell histology, 53% were at intermediate risk of recurrence, and 47% were at high risk of recurrence. We observed no differences in DFS or overall survival in all randomly assigned patients, patients with high risk of recurrence, or patients with clear cell RCC only. Median DFS was not reached for 3 years of sorafenib or for placebo (hazard ratio, 1.01; 95% CI, 0.83 to 1.23; P = .95). We observed nonproportional hazards; the restricted mean survival time (RMST) was 6.81 years for 3 years of sorafenib and 6.82 years for placebo (RMST difference, 0.01 year; 95% CI, -0.49 to 0.48 year; P = .99). Despite offering treatment adaptations, more than half of participants stopped treatment by 12 months. Grade 3 hand-foot skin reaction was reported in 24% of participants on sorafenib. CONCLUSION: Sorafenib should not be used as adjuvant therapy for RCC. Active surveillance remains the standard of care for patients at intermediate or high risk of recurrence after nephrectomy and is the appropriate control of our current international adjuvant RCC trial, RAMPART.CRU

Pharmacogenetic analyses of 2,183 patients with advanced colorectal cancer; Potential role for common dihydropyrimidine dehydrogenase variants in toxicity to chemotherapy.

BACKGROUND: Inherited genetic variants may influence response to, and side-effects from, chemotherapy. We sought to generate a comprehensive inherited pharmacogenetic profile for oxaliplatin and 5FU/capecitabine therapy in advanced colorectal cancer (aCRC). METHODS: We analysed more than 200 potentially functional, common, inherited variants in genes within the 5FU, capecitabine, oxaliplatin and DNA repair pathways, together with four rare dihydropyrimidine dehydrogenase (DPYD) variants, in 2183 aCRC patients treated with oxaliplatin-fluoropyrimidine chemotherapy with, or without, cetuximab (from MRC COIN and COIN-B trials). Primary end-points were response, any toxicity and peripheral neuropathy. We had >85% power to detect odds ratios (ORs) = 1.3 for variants with minor allele frequencies >20%. RESULTS: Variants in DNA repair genes (Asn279Ser in EXO1 and Arg399Gln in XRCC1) were most associated with response (OR 1.9, 95% confidence interval [CI] 1.2-2.9, P = 0.004, and OR 0.7, 95% CI 0.5-0.9, P = 0.003, respectively). Common variants in DPYD (Cys29Arg and Val732Ile) were most associated with toxicity (OR 0.8, 95% CI 0.7-1.0, P = 0.008, and OR 1.6, 95% CI 1.1-2.1, P = 0.006, respectively). Two rare DPYD variants were associated with increased toxicity (Asp949Val with neutropenia, nausea and vomiting, diarrhoea and infection; IVS14+1G>A with lethargy, diarrhoea, stomatitis, hand-foot syndrome and infection; all ORs > 3). Asp317His in DCLRE1A was most associated with peripheral neuropathy (OR 1.3, 95% CI 1.1-1.6, P = 0.003). No common variant associations remained significant after Bonferroni correction. CONCLUSIONS: DNA repair genes may play a significant role in the pharmacogenetics of aCRC. Our data suggest that both common and rare DPYD variants may be associated with toxicity to fluoropyrimidine-based chemotherapy

Thromboxane biosynthesis in cancer patients and its inhibition by aspirin: a sub-study of the Add-Aspirin trial

BACKGROUND: Pre-clinical models demonstrate that platelet activation is involved in the spread of malignancy. Ongoing clinical trials are assessing whether aspirin, which inhibits platelet activation, can prevent or delay metastases. METHODS: Urinary 11-dehydro-thromboxane B2 (U-TXM), a biomarker of in vivo platelet activation, was measured after radical cancer therapy and correlated with patient demographics, tumour type, recent treatment, and aspirin use (100 mg, 300 mg or placebo daily) using multivariable linear regression models with log-transformed values. RESULTS: In total, 716 patients (breast 260, colorectal 192, gastro-oesophageal 53, prostate 211) median age 61 years, 50% male were studied. Baseline median U-TXM were breast 782; colorectal 1060; gastro-oesophageal 1675 and prostate 826 pg/mg creatinine; higher than healthy individuals (~500 pg/mg creatinine). Higher levels were associated with raised body mass index, inflammatory markers, and in the colorectal and gastro-oesophageal participants compared to breast participants (P < 0.001) independent of other baseline characteristics. Aspirin 100 mg daily decreased U-TXM similarly across all tumour types (median reductions: 77-82%). Aspirin 300 mg daily provided no additional suppression of U-TXM compared with 100 mg. CONCLUSIONS: Persistently increased thromboxane biosynthesis was detected after radical cancer therapy, particularly in colorectal and gastro-oesophageal patients. Thromboxane biosynthesis should be explored further as a biomarker of active malignancy and may identify patients likely to benefit from aspirin