Computational approaches to support comparative analysis of multiparametric tests: Modelling versus Training.

Multiparametric assays for risk stratification are widely used in the management of breast cancer, with applications being developed for a number of other cancer settings. Recent data from multiple sources suggests that different tests may provide different risk estimates at the individual patient level. There is an increasing need for robust methods to support cost effective comparisons of test performance in multiple settings. The derivation of similar risk classifications using genes comprising the following multi-parametric tests Oncotype DX® (Genomic Health.), Prosigna™ (NanoString Technologies, Inc.), MammaPrint® (Agendia Inc.) was performed using different computational approaches. Results were compared to the actual test results. Two widely used approaches were applied, firstly computational "modelling" of test results using published algorithms and secondly a "training" approach which used reference results from the commercially supplied tests. We demonstrate the potential for errors to arise when using a "modelling" approach without reference to real world test results. Simultaneously we show that a "training" approach can provide a highly cost-effective solution to the development of real-world comparisons between different multigene signatures. Comparisons between existing multiparametric tests is challenging, and evidence on discordance between tests in risk stratification presents further dilemmas. We present an approach, modelled in breast cancer, which can provide health care providers and researchers with the potential to perform robust and meaningful comparisons between multigene tests in a cost-effective manner. We demonstrate that whilst viable estimates of gene signatures can be derived from modelling approaches, in our study using a training approach allowed a close approximation to true signature results

Pathway-based subnetworks enable cross-disease biomarker discovery.

Biomarkers lie at the heart of precision medicine. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers usually involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. Here we present SIMMS (Subnetwork Integration for Multi-Modal Signatures): an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We apply SIMMS to multiple data types across five diseases, and in each it reproducibly identifies known and novel subtypes, and makes superior predictions to the best bespoke approaches. To demonstrate its ability on a new dataset, we profile 33 genes/nodes of the PI3K pathway in 1734 FFPE breast tumors and create a four-subnetwork prediction model. This model out-performs a clinically-validated molecular test in an independent cohort of 1742 patients. SIMMS is generic and enables systematic data integration for robust biomarker discovery

The association between maternal 25-hydroxyvitamin D concentration during gestation and early childhood cardio-metabolic outcomes: is there interaction with pre-pregnancy BMI?

Both maternal 25-hydroxyvitamin D (25OHD) status and pre-pregnancy BMI (pBMI) may influence offspring cardio-metabolic outcomes. Lower 25OHD concentrations have been observed in women with both low and high pBMIs, but the combined influence of pBMI and 25OHD on offspring cardio-metabolic outcomes is unknown. Therefore, this study investigated the role of pBMI in the association between maternal 25OHD concentration and cardio-metabolic outcomes in 5-6 year old children. Data were obtained from the ABCD cohort study and 1882 mother-child pairs were included. The offspring outcomes investigated were systolic and diastolic blood pressure, heart rate, BMI, body fat percentage (%BF), waist-to-height ratio, total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides, glucose, C-peptide, and insulin resistance (HOMA2-IR). 62% of the C-peptide samples were below the detection limit and were thus imputed using survival analysis. Models were corrected for maternal and offspring covariates and tested for interaction with pBMI. Interaction with pBMI was observed in the associations with insulin resistance markers: in offspring of overweight mothers (≥25.0 kg/m2), a 10 nmol/L increase in maternal 25OHD was associated with a 0.007(99%CI:-0.01,-0.001) nmol/L decrease in C-peptide and a 0.02(99%CI:-0.03,-0.004) decrease in HOMA2-IR. When only non-imputed data were analyzed, there was a trend for interaction in the relationship but the results lost significance. Interaction with pBMI was not observed for the other outcomes. A 10 nmol/L increase in maternal 25OHD was significantly associated with a 0.13%(99%CI:-0.3,-0.003) decrease in %BF after correction for maternal and child covariates. Thus, intrauterine exposure to both low 25OHD and maternal overweight may be associated with increased insulin resistance in offspring, while exposure to low 25OHD in utero may be associated with increased offspring %BF with no interactive effects from pBMI. Due to the limitations of this study, these results are not conclusive, however the observations of this study pose important research questions for future studies to investigate

An updated end-to-end ecosystem model of the Northern California Current reflecting ecosystem changes due to recent marine heatwaves.

The Northern California Current is a highly productive marine upwelling ecosystem that is economically and ecologically important. It is home to both commercially harvested species and those that are federally listed under the U.S. Endangered Species Act. Recently, there has been a global shift from single-species fisheries management to ecosystem-based fisheries management, which acknowledges that more complex dynamics can reverberate through a food web. Here, we have integrated new research into an end-to-end ecosystem model (i.e., physics to fisheries) using data from long-term ocean surveys, phytoplankton satellite imagery paired with a vertically generalized production model, a recently assembled diet database, fishery catch information, species distribution models, and existing literature. This spatially-explicit model includes 90 living and detrital functional groups ranging from phytoplankton, krill, and forage fish to salmon, seabirds, and marine mammals, and nine fisheries that occur off the coast of Washington, Oregon, and Northern California. This model was updated from previous regional models to account for more recent changes in the Northern California Current (e.g., increases in market squid and some gelatinous zooplankton such as pyrosomes and salps), to expand the previous domain to increase the spatial resolution, to include data from previously unincorporated surveys, and to add improved characterization of endangered species, such as Chinook salmon (Oncorhynchus tshawytscha) and southern resident killer whales (Orcinus orca). Our model is mass-balanced, ecologically plausible, without extinctions, and stable over 150-year simulations. Ammonium and nitrate availability, total primary production rates, and model-derived phytoplankton time series are within realistic ranges. As we move towards holistic ecosystem-based fisheries management, we must continue to openly and collaboratively integrate our disparate datasets and collective knowledge to solve the intricate problems we face. As a tool for future research, we provide the data and code to use our ecosystem model

Subregional heatmaps of functional group spatial distributions.

Each functional group was broken out into the 15 subregions via survey data, fisheries landings data, or species distribution models (see Methods), or distributional assumptions when there was a lack of available information. The color of each subregional cell is a gradient denoting the proportion of biomass (for each functional group) that is within each subregional cell (with red being the highest and pale yellow being the lowest proportions, respectively). The proportion of biomass in each subregion sums to 1 across all subregions. See “SubRegions/” in supplemental data and code to reproduce this plot, and for plots of all functional groups). Adult Chinook, common murre, and sooty shearwater distributions are based off of the juvenile salmon and ocean ecosystem survey (JSOES); hake distributions are from the hake acoustic trawl survey; herring, jack mackerel, and sardine distributions are from the coastal pelagic species (CPS) acoustic trawl survey; and Southern resident killer whale (SRKW) distributions are based off of movement data from satellite-tagged Southern resident killer whales [130]. State outline data comes from US Department of Commerce, Census Bureau, Cartographic Boundary Files.</p

Coastal pelagic fish functional group time series as both ecosystem model output and independent estimates.

Independently derived estimates (blue points; blue lines = locally estimated scatterplot smoothing lines) of relative biomass of sardine, anchovy, jack mackerel, and Pacific chub mackerel via stock assessments [56, 84, 86, 87] are plotted against ecosystem model-derived estimates of matching functional groups (black lines).</p

Seabird and mammal functional group time series as both ecosystem model output and independent estimates.

Independently derived estimates (blue lines and points) of relative biomass via a Juvenile Salmon and Ocean Ecosystem Survey (JSOES; common murre and sooty shearwaters), a humpback whale mark-recapture study (baleen whales [88]), and counts of the well-monitored Southern resident killer whale population [https://www.whaleresearch.com/orca-population] are plotted against ecosystem model-derived estimates of matching functional groups (black lines).</p

Invertebrate functional group time series as both ecosystem model output and independent estimates.

Independently derived estimates (blue points; blue lines = locally estimated scatterplot smoothing lines) of relative biomass via a Juvenile Salmon and Ocean Ecosystem Survey (JSOES; large jellies), fisheries landings (small cephalopod aggregate = market squid), and a pre-season abundance model (Dungeness crabs [83]) are plotted against ecosystem model-derived estimates of matching functional groups (black lines). The ecosystem model is driven entirely by nutrient inputs to the system as determined by the coastal upwelling transport index (CUTI) [42] and trophic relationships, and any resemblance of the two time series is an indication that the ecosystem model is matching independently-observed dynamics.</p