Accurate detection of acute sleep deprivation using a metabolomic biomarker—A machine learning approach

Sleep deprivation enhances risk for serious injury and fatality on the roads and in workplaces. To facilitate future management of these risks through advanced detection, we developed and validated a metabolomic biomarker of sleep deprivation in healthy, young participants, across three experiments. Bi-hourly plasma samples from 2 × 40-hour extended wake protocols (for train/test models) and 1 × 40-hour protocol with an 8-hour overnight sleep interval were analyzed by untargeted liquid chromatography–mass spectrometry. Using a knowledge-based machine learning approach, five consistently important variables were used to build predictive models. Sleep deprivation (24 to 38 hours awake) was predicted accurately in classification models [versus well-rested (0 to 16 hours)] (accuracy = 94.7%/AUC 99.2%, 79.3%/AUC 89.1%) and to a lesser extent in regression (R2 = 86.1 and 47.8%) models for within- and between-participant models, respectively. Metabolites were identified for replicability/future deployment. This approach for detecting acute sleep deprivation offers potential to reduce accidents through “fitness for duty” or “post-accident analysis” assessments

Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer

Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities. We evaluated sources of variability in sTIL assessment by pathologists in three previous sTIL ring studies. We identify common challenges and evaluate impact of discrepancies on outcome estimates in early TNBC using a newly-developed prognostic tool. Discordant sTIL assessment is driven by heterogeneity in lymphocyte distribution. Additional factors include: technical slide-related issues; scoring outside the tumor boundary; tumors with minimal assessable stroma; including lymphocytes associated with other structures; and including other inflammatory cells. Small variations in sTIL assessment modestly alter risk estimation in early TNBC but have the potential to affect treatment selection if cutpoints are employed. Scoring and averaging multiple areas, as well as use of reference images, improve consistency of sTIL evaluation. Moreover, to assist in avoiding the pitfalls identified in this analysis, we developed an educational resource available at www.tilsinbreastcancer.org/pitfalls.Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities. We evaluated sources of variability in sTIL assessment by pathologists in three previous sTIL ring studies. We identify common challenges and evaluate impact of discrepancies on outcome estimates in early TNBC using a newly-developed prognostic tool. Discordant sTIL assessment is driven by heterogeneity in lymphocyte distribution. Additional factors include: technical slide-related issues; scoring outside the tumor boundary; tumors with minimal assessable stroma; including lymphocytes associated with other structures; and including other inflammatory cells. Small variations in sTIL assessment modestly alter risk estimation in early TNBC but have the potential to affect treatment selection if cutpoints are employed. Scoring and averaging multiple areas, as well as use of reference images, improve consistency of sTIL evaluation. Moreover, to assist in avoiding the pitfalls identified in this analysis, we developed an educational resource available at www.tilsinbreastcancer.org/pitfalls.Peer reviewe

Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group.

Funder: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)Funder: National Center for Research Resources under award number 1 C06 RR12463-01, VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service, the DOD Prostate Cancer Idea Development Award (W81XWH-15-1-0558), the DOD Lung Cancer Investigator-Initiated Translational Research Award (W81XWH-18-1-0440), the DOD Peer Reviewed Cancer Research Program (W81XWH-16-1-0329), the Ohio Third Frontier Technology Validation Fund, the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and the Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University.Funder: Susan G Komen Foundation (CCR CCR18547966) and a Young Investigator Grant from the Breast Cancer Alliance.Funder: The Canadian Cancer SocietyFunder: Breast Cancer Research Foundation (BCRF), Grant No. 17-194Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring

Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer

Abstract: Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities. We evaluated sources of variability in sTIL assessment by pathologists in three previous sTIL ring studies. We identify common challenges and evaluate impact of discrepancies on outcome estimates in early TNBC using a newly-developed prognostic tool. Discordant sTIL assessment is driven by heterogeneity in lymphocyte distribution. Additional factors include: technical slide-related issues; scoring outside the tumor boundary; tumors with minimal assessable stroma; including lymphocytes associated with other structures; and including other inflammatory cells. Small variations in sTIL assessment modestly alter risk estimation in early TNBC but have the potential to affect treatment selection if cutpoints are employed. Scoring and averaging multiple areas, as well as use of reference images, improve consistency of sTIL evaluation. Moreover, to assist in avoiding the pitfalls identified in this analysis, we developed an educational resource available at www.tilsinbreastcancer.org/pitfalls

Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group

Funder: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)Funder: National Center for Research Resources under award number 1 C06 RR12463-01, VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service, the DOD Prostate Cancer Idea Development Award (W81XWH-15-1-0558), the DOD Lung Cancer Investigator-Initiated Translational Research Award (W81XWH-18-1-0440), the DOD Peer Reviewed Cancer Research Program (W81XWH-16-1-0329), the Ohio Third Frontier Technology Validation Fund, the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and the Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University.Funder: Susan G Komen Foundation (CCR CCR18547966) and a Young Investigator Grant from the Breast Cancer Alliance.Funder: The Canadian Cancer SocietyFunder: Breast Cancer Research Foundation (BCRF), Grant No. 17-194Abstract: Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Funder: Breast Cancer Research Foundation (BCRF); doi: https://doi.org/10.13039/100001006Abstract: Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting

Contaminant exposure affects gene expression markers in the cysteine metabolism of Chironomus tepperi

© 2015 Dr. Katherine Joanna JeppeIn ecotoxicology, organisms are often exposed to contaminated water or sediment to investigate the toxicity of these aspects of the environment. The results of these exposures are intended to predict responses in the ecosystem. However, these tests often employ a single and sometimes tolerant organism and have been criticized for lacking environmental relevance. The aim of this thesis is to improve toxicity tests that use the Australian species Chironomus tepperi by developing sensitive and rapid gene expression biomarkers of metal exposure. The pathway chosen for the development of these biomarkers is cysteine metabolism. Cysteine is central to several stress responses and is only produced by transsulfuration of methionine. Hence, it potentially offers informative, specific and consistent biomarkers of metal exposure. Nine genes involved in the cysteine metabolism were identified in C. tepperi, using databases of sequences in related dipteran species. Initially, the expression of these genes was assessed under 24 hour water exposures to different metals. It was found that all of the genes considered respond to metal exposure and that the metals tested induce different responses in gene expression profiles. These results provided the basis for further testing. A pulse water exposure was then investigated to establish if these biomarkers responded to both past and present pollution. Gene expression responses correlated with glutathione-S-transferase (GST) activity after the 24 hour exposure and with metallothionein (MT) concentration after the 96 hour depuration. These results highlighted the need to know exposure time and duration to accurately assess gene expression responses. Expression of these genes was then investigated in sediment exposures in the laboratory and the field. Gene expression and metabolomic profiles in laboratory-bred C. tepperi were investigated after copper exposure in sediment of a field-based microcosm. These responses were then compared to indigenous macroinvertebrate community responses and population responses of Potamopyrgus antipodarum and Physa acuta. This experiment demonstrated that gene expression and metabolomic responses were sensitive and correlative markers of copper exposure and that these markers respond at concentrations that caused a decrease in sensitive macroinvertebrate abundances. Finally, a 5 day toxicity test was performed to assess expression profiles of cysteine metabolism genes in C. tepperi after exposure to a simple metal-pesticide mixture as well as the first exposure to field sediments. Expression changes were then compared to whole organism endpoints traditionally used in these tests. This chapter established the usefulness of gene expression in a standard toxicity test, and reinforced the sensitivity of this technique while it also highlighted the complex nature of mixture toxicity. This research is an initial step toward the development of gene expression biomarkers of cysteine metabolism in C. tepperi to be incorporated into sediment toxicity testing. Within limitations, gene expression in cysteine metabolism can provide a powerful addition to a multiple lines-of-evidence approach, particularly if used in conjunction with other cellular biomarkers such as metabolomics, MT concentration or GST activity. This thesis therefore contributes baseline scientific information that is critical if this biomarker is to be useful for toxicity testing

Potentially toxic concentrations of synthetic pyrethroids associated with low density residential land use

Trace organic compounds associated with human activity are now ubiquitous in the environment. As the population becomes more urbanised and the use of pesticides and person care products continues to increase, urban waterways are likely to receive higher loads of trace organic contaminants with unknown ecological consequences. To establish the extent of trace organic contamination in urban runoff, concentrations of emerging chemicals of concern were determined in sediments from 99 urban wetlands in and around Melbourne, Australia between February and April, 2015. As a preliminary estimation of potential risks to aquatic biota, we compared measured concentrations with thresholds for acute and chronic toxicity, and modelled toxic units as a function of demographic and land use trends. The synthetic pyrethroid insecticide bifenthrin was common and widespread, and frequently occurred at concentrations likely to cause toxicity to aquatic life. Personal care products DEET and triclosan were common and widely distributed, while the herbicides diuron and prometryn, and the fungicides pyrimethanil and trifloxystrobin occurred less frequently. Toxic unit modelling using random forests found complex and unexpected associations between urban land uses and trace organic concentrations. Synthetic pyrethroid insecticides were identified as emerging compounds of concern, particularly bifenthrin. In contrast with previous surveys, the highest bifenthrin concentrations were associated with lower housing and population density, implicating low-density residential land use in bifenthrin contamination. We discuss the implications for pesticide regulation and urban wetland management in a global context

Genes involved in cysteine metabolism of Chironomus tepperi are regulated differently by copper and by cadmium

Freshwater invertebrates are often exposed to metal contamination, and changes in gene expression patterns can help understand mechanisms underlying toxicity and act as pollutant-specific biomarkers. In this study the expressions of genes involved in cysteine metabolism are characterized in the midge Chironomus tepperi during exposures to sublethal concentrations of cadmium and copper. These metals altered gene expression of the cysteine metabolism differently. Both metals decreased S-adenosylhomocysteine hydrolase expression and did not change the expression of S-adenosylmethionine synthetase. Cadmium exposure likely increased cystathionine production by up-regulating cystathionine-β-synthase (CβS) expression, while maintaining control level cysteine production via cystathionine-γ-lyase (CγL) expression. Conversely, copper down-regulated CβS expression and up-regulated CγL expression, which in turn could diminish cystathionine to favor cysteine production. Both metals up-regulated glutathione related expression (γ-glutamylcysteine synthase and glutathione synthetase). Only cadmium up-regulated metallothionein expression and glutathione S-transferase d1 expression was up-regulated only by copper exposure. These different transcription responses of genes involved in cysteine metabolism in C. tepperi point to metal-specific detoxification pathways and suggest that the transsulfuration pathway could provide biomarkers for identifying specific metals.6 page(s