Exploiting the noise: improving biomarkers with ensembles of data analysis methodologies.

BackgroundThe advent of personalized medicine requires robust, reproducible biomarkers that indicate which treatment will maximize therapeutic benefit while minimizing side effects and costs. Numerous molecular signatures have been developed over the past decade to fill this need, but their validation and up-take into clinical settings has been poor. Here, we investigate the technical reasons underlying reported failures in biomarker validation for non-small cell lung cancer (NSCLC).MethodsWe evaluated two published prognostic multi-gene biomarkers for NSCLC in an independent 442-patient dataset. We then systematically assessed how technical factors influenced validation success.ResultsBoth biomarkers validated successfully (biomarker #1: hazard ratio (HR) 1.63, 95% confidence interval (CI) 1.21 to 2.19, P = 0.001; biomarker #2: HR 1.42, 95% CI 1.03 to 1.96, P = 0.030). Further, despite being underpowered for stage-specific analyses, both biomarkers successfully stratified stage II patients and biomarker #1 also stratified stage IB patients. We then systematically evaluated reasons for reported validation failures and find they can be directly attributed to technical challenges in data analysis. By examining 24 separate pre-processing techniques we show that minor alterations in pre-processing can change a successful prognostic biomarker (HR 1.85, 95% CI 1.37 to 2.50, P < 0.001) into one indistinguishable from random chance (HR 1.15, 95% CI 0.86 to 1.54, P = 0.348). Finally, we develop a new method, based on ensembles of analysis methodologies, to exploit this technical variability to improve biomarker robustness and to provide an independent confidence metric.ConclusionsBiomarkers comprise a fundamental component of personalized medicine. We first validated two NSCLC prognostic biomarkers in an independent patient cohort. Power analyses demonstrate that even this large, 442-patient cohort is under-powered for stage-specific analyses. We then use these results to discover an unexpected sensitivity of validation to subtle data analysis decisions. Finally, we develop a novel algorithmic approach to exploit this sensitivity to improve biomarker robustness

Biomarker profiles of acute heart failure patients with a mid-range ejection fraction

OBJECTIVES: In this study, the authors used biomarker profiles to characterize differences between patients with acute heart failure with a midrange ejection fraction (HFmrEF) and compare them with patients with a reduced (heart failure with a reduced ejection fraction [HFrEF]) and preserved (heart failure with a preserved ejection fraction [HFpEF]) ejection fraction. BACKGROUND: Limited data are available on biomarker profiles in acute HFmrEF. METHODS: A panel of 37 biomarkers from different pathophysiological domains (e.g., myocardial stretch, inflammation, angiogenesis, oxidative stress, hematopoiesis) were measured at admission and after 24 h in 843 acute heart failure patients from the PROTECT trial. HFpEF was defined as left ventricular ejection fraction (LVEF) of ≥50% (n = 108), HFrEF as LVEF of <40% (n = 607), and HFmrEF as LVEF of 40% to 49% (n = 128). RESULTS: Hemoglobin and brain natriuretic peptide levels (300 pg/ml [HFpEF]; 397 pg/ml [HFmrEF]; 521 pg/ml [HFrEF]; ptrend <0.001) showed an upward trend with decreasing LVEF. Network analysis showed that in HFrEF interactions between biomarkers were mostly related to cardiac stretch, whereas in HFpEF, biomarker interactions were mostly related to inflammation. In HFmrEF, biomarker interactions were both related to inflammation and cardiac stretch. In HFpEF and HFmrEF (but not in HFrEF), remodeling markers at admission and changes in levels of inflammatory markers across the first 24 h were predictive for all-cause mortality and rehospitalization at 60 days (pinteraction <0.05). CONCLUSIONS: Biomarker profiles in patients with acute HFrEF were mainly related to cardiac stretch and in HFpEF related to inflammation. Patients with HFmrEF showed an intermediate biomarker profile with biomarker interactions between both cardiac stretch and inflammation markers. (PROTECT-1: A Study of the Selective A1 Adenosine Receptor Antagonist KW-3902 for Patients Hospitalized With Acute HF and Volume Overload to Assess Treatment Effect on Congestion and Renal Function; NCT00328692)

Glucosylsphingosine Is a Highly Sensitive and Specific Biomarker for Primary Diagnostic and Follow-Up Monitoring in Gaucher Disease in a Non-Jewish, Caucasian Cohort of Gaucher Disease Patients

Gaucher disease (GD) is the most common lysosomal storage disorder (LSD). Based on a deficient β-glucocerebrosidase it leads to an accumulation of glucosylceramide. Standard diagnostic procedures include measurement of enzyme activity, genetic testing as well as analysis of chitotriosidase and CCL18/PARC as biomarkers. Even though chitotriosidase is the most well-established biomarker in GD, it is not specific for GD. Furthermore, it may be false negative in a significant percentage of GD patients due to mutation. Additionally, chitotriosidase reflects the changes in the course of the disease belatedly. This further enhances the need for a reliable biomarker, especially for the monitoring of the disease and the impact of potential treatments.Here, we evaluated the sensitivity and specificity of the previously reported biomarker Glucosylsphingosine with regard to different control groups (healthy control vs. GD carriers vs. other LSDs).Only GD patients displayed elevated levels of Glucosylsphingosine higher than 12 ng/ml whereas the comparison controls groups revealed concentrations below the pathological cut-off, verifying the specificity of Glucosylsphingosine as a biomarker for GD. In addition, we evaluated the biomarker before and during enzyme replacement therapy (ERT) in 19 patients, demonstrating a decrease in Glucosylsphingosine over time with the most pronounced reduction within the first 6 months of ERT. Furthermore, our data reveals a correlation between the medical consequence of specific mutations and Glucosylsphingosine.In summary, Glucosylsphingosine is a very promising, reliable and specific biomarker for GD

Proteomics-on-a-chip for Biomarker discovery

In proteomics research still two-dimensional gel electrophoresis (2D-GE) is currently used for biomarker discovery. We applied free flow electrophoresis (FFE) separation technology combined with biomolecular interaction sensing using Surface Plasmon Resonance (SPR) imaging in an integrated proteomics-on-a-chip device as a proof of concept for biomarker discovery

Disease Knowledge Transfer across Neurodegenerative Diseases

We introduce Disease Knowledge Transfer (DKT), a novel technique for transferring biomarker information between related neurodegenerative diseases. DKT infers robust multimodal biomarker trajectories in rare neurodegenerative diseases even when only limited, unimodal data is available, by transferring information from larger multimodal datasets from common neurodegenerative diseases. DKT is a joint-disease generative model of biomarker progressions, which exploits biomarker relationships that are shared across diseases. Our proposed method allows, for the first time, the estimation of plausible, multimodal biomarker trajectories in Posterior Cortical Atrophy (PCA), a rare neurodegenerative disease where only unimodal MRI data is available. For this we train DKT on a combined dataset containing subjects with two distinct diseases and sizes of data available: 1) a larger, multimodal typical AD (tAD) dataset from the TADPOLE Challenge, and 2) a smaller unimodal Posterior Cortical Atrophy (PCA) dataset from the Dementia Research Centre (DRC), for which only a limited number of Magnetic Resonance Imaging (MRI) scans are available. Although validation is challenging due to lack of data in PCA, we validate DKT on synthetic data and two patient datasets (TADPOLE and PCA cohorts), showing it can estimate the ground truth parameters in the simulation and predict unseen biomarkers on the two patient datasets. While we demonstrated DKT on Alzheimer's variants, we note DKT is generalisable to other forms of related neurodegenerative diseases. Source code for DKT is available online: https://github.com/mrazvan22/dkt.Comment: accepted at MICCAI 2019, 13 pages, 5 figures, 2 table

On the trophic fate of Phaeocystis pouchetii: VII. Sterols and fatty acids reveal sedimentation of Phaocystis-derived organic matter via krill fecal strings

As part of a joint project on the fate of phytoplankton in Balsfjorden in Northern Norway, we investigated the trophic fate and sedimentation potential of Phaeocystis pouchetii by tracing the transition of biomarker patterns from a phytoplankton bloom to sediment traps and during a gut passage experiment. The phytoplankton biomass during the spring bloom 1996 was dominated by colonial P. pouchetii (ca. 85 %) and four members of the diatom family Thalassiosiraceae (ca. 10%). Particulate organic carbon in sediment traps largely consisted of fecal material from the Arctic krill Thysanoessa sp.. Sterol and fatty acid biomarker patterns in the phytoplankton bloom could be reproduced by combining the individual biomarker patterns of the isolated phytoplankters P. pouchetii and Thalassiosira decipiens in a ratio of ca. 75:25. In a laboratory experiment, Arctic krill (Thysanoessa raschii) fed with similar efficiency on Phaeocystis colonies and the Thalassiosiraceae. During gut passage, the abundance of Thalassiosiraceae biomarkers in fecal strings increased relative to Phaeocystis biomarkers, while biomarkers from krill became dominant. This transition of biomarker patterns due to gut passage in T. raschii closely resembled the biomarker transition from the surface bloom to material in sediment traps at 40-170 m depth, which was mainly composed of krill fecal strings. We conclude that krill grazed efficiently on Phaeocystis colonies in Balsfjorden, and caused sedimentation of Phaeocystis-derived organic matter below the euphotic zone via fecal strings. Hence, both transfer to higher trophic levels and sedimentation of Phaeocystis-derived organic matter can be more effective than commonly believed

Modern spatial sea-ice variability in the central Arctic Ocean and adjacent marginal seas: Reconstruction from biomarker data

Sea ice is a fundamental component of Earth’s climate system, contributing to heat reduction (albedo) and deep-water formation. In order to understand processes controlling the recent dramatic reduction in Arctic sea-ice cover, it is essential to determine spatial and temporal changes in sea-ice occurrence and its natural variability in the present and past. Here, we present biomarker data from surface sediments and related to the modern spatial (seasonal) sea-ice variability in the central Arctic Ocean and adjacent marginal seas (i.e., Bering, Chukchi, Laptev and Kara seas) as well as the Fram Strait/Yermak Plateau area. We determined concentrations of the sea-ice diatom-derived biomarker “IP25″ (highly-branched isoprenoid – HBI – with 25 carbon atom; Belt et al., 2007), phytoplankton-derived biomarkers (brassicasterol and dinosterol) and terrigenous biomarkers (campesterol and Î_-sitosterol) to estimate recent sea-ice conditions in the study area. A combined phytoplankton-IP25 biomarker approach (“PIP25 index”; Müller et al., 2009, 2011) is used to reconstruct the modern sea-ice distribution more quantitatively. In addition, the distribution pattern of HBI-diene/IP25 ratios has been determined to test a proposed relationship between the diene/IP25 ratio and sea-surface temperatures in Arctic marginal ice-zone environments (Fahl and Stein, 2012; Stein et al., 2012). Assessment of sea-ice conditions based on these biomarker data display that a quite stable marginal ice zone exists along the continental shelf/slope of Kara and Laptev seas during summer/early fall. Elevated IP25 as well as brassicasterol and dinosterol values occurring in the central Kara and Laptev seas are related to extended sea-ice-cover and higher primary production (close to ice-edge situation). Further to the north and the central Arctic Ocean, lower IP25 and phytoplankton biomarker concentrations point to a more close sea-ice cover situation

Computational Models for Transplant Biomarker Discovery.

Translational medicine offers a rich promise for improved diagnostics and drug discovery for biomedical research in the field of transplantation, where continued unmet diagnostic and therapeutic needs persist. Current advent of genomics and proteomics profiling called "omics" provides new resources to develop novel biomarkers for clinical routine. Establishing such a marker system heavily depends on appropriate applications of computational algorithms and software, which are basically based on mathematical theories and models. Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful. Here, we review the key advances in theories and mathematical models relevant to transplant biomarker developments. Advantages and limitations inherent inside these models are discussed. The principles of key -computational approaches for selecting efficiently the best subset of biomarkers from high--dimensional omics data are highlighted. Prediction models are also introduced, and the integration of multi-microarray data is also discussed. Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems