A workshop on the societal applications of satellite data for ocean health and fisheries was co-hosted by NASA and World Resources Institute (WRI) with assistance from NOAA in November, 2017, garnering 152 registrants from multiple agencies from around the world, non-governmental organizations, and private businesses included research, education, resource management, policy, national security, public health and the fishing industry (Ward and Schollaert Uz, 2018). The meeting was live-streamed to enable remote participation, thus about 80 people participated in person with the rest online. The interactive program brought together multiple perspectives and diverse viewpoints (i.e., fishing businesses, resource managers, lawmakers). The format fostered dialogue between data providers, users, and potential users who could benefit from using satellite products. Four panels focused around the themes of ocean health in the context of climate change, fisheries and ecosystem health, links between fisheries and human security, and resources and tools for accessing ocean satellite data products. Agency managers and panelists shared science and products developed from publicly available ocean satellite data. A keynote presentation described how Global Fishing Watch monitors fishing activity from space to improve understanding, regulation, and protection. Notes taken during the workshop led to an extensive accounting of all presentations and discussions (Ward and Schollaert Uz, 2018).

Nearby NASA Goddard Space Flight Center, the Chesapeake Bay is the largest estuary in North America (**Figure 3**) and benefits the growing population of the region through its ecosystem services, fishing, and recreation. According to the Maryland Department of Natural Resources (DNR), the primary commercially harvested species in the Bay are oysters, blue crabs, and striped bass. Studies indicate the health of the Bay has seen some improvement in recent years, yet threats to its health persist (e.g., warming, oxygen minimum zones, pollution nutrient run-off). Increasing human presence in such a densely populated coastal region requires constant vigilance by agencies managing water quality, especially with a growing oyster aquaculture industry. In April, 2018 we began monthly meetings between NASA and NOAA satellite data providers with local resource managers around the Chesapeake Bay who routinely monitor shellfish beds and are interested in how remote sensing could help them improve the efficiency of their in situ sampling. In addition to meeting monthly, we held a day-long interagency workshop in August, 2018 attended by Chesapeake Bay science and applications stakeholders with the overall theme of monitoring water quality from space. The 95 participants included federal, state, and local agencies, non-governmental organizations, and universities. Future hyperspectral capabilities by PACE were highlighted, as they will provide the opportunity to measure additional indicators of ecological health and water quality. Workshop discussions focused on environmental variables needed by managers and whether and how remote sensing could address those needs through current or future capabilities, including water quality impacts to human health, shellfish poisoning, and quantifying the economic value of information needed to support decisions by resource managers and fishers (Schollaert Uz et al., 2018).

Discussions with these diverse international and regional groups drew increased attention to broader societal issues that could be informed by ocean color data and also identified applied research themes that need to be explored for development. Continuous engagement with these diverse groups informs NASA about their current challenges that satellite data could potentially address and keeps these groups informed about the status of PACE in its design cycle and the way its information will augment the international satellite fleet.

## CURRENT USES OF REMOTE SENSING DATA FOR FISHERIES AND AQUACULTURE

Many examples of ocean color use toward marine food security have been previously described by efforts such as the Societal Applications in Fisheries and Aquaculture using Remotely Sensed Imagery (SAFARI) initiative (IOCCG, 2009). Here we provide updates on uses of remote sensing technologies through several examples, highlighting a few case studies shared during our workshops in which PACE data could help improve operations.

Oceanic management and conservation tools such as Coral Reef Watch and Ocean Health Index plus newer dynamic ecosystem models (e.g., Dynamic Seascapes within the Marine Biodiversity Observation Network) (Strong et al., 2006; Lewison et al., 2015; Hughes et al., 2017) rely on a combination of environmental and biological observations. These products merge in situ data, satellite sensors and big data techniques to quantify the effects of human impacts and natural variability on the marine ecosystem into useable indicators. The need for multiple data types is driven by the scientific understanding that there are multiple stressors acting on the marine ecosystem at the same time, which complicates the interpretation of trends in individual environmental variables.

Fisheries forecasting and management rely on multiple data streams to account for the dynamic nature of the aquatic environment. EcoCast, a fisheries sustainability tool, uses in situ and satellite data for environmental and species-specific information to maximize productive catch while minimizing bycatch (Hazen et al., 2018). This tool is one implementation of a new dynamic approach in fisheries management that accounts for

FIGURE 3 | Landsat 8 image of the Chesapeake Bay, tributaries, estuaries, sediment plumes and proximity to NASA Goddard. Measurements from space are used to guide in situ sampling by water quality resource managers, especially around oyster beds within the growing aquaculture industry. Credit: NASA/Michael Taylor and Ginger Butcher.

spatial and temporal variations in oceanic processes, as opposed to the traditional static approach with catch limits defined over a fixed area. From a commercial perspective, fisheries forecasting also utilizes a combination of remotely sensed physical (microwave SST) and biological (ocean color) indicators.

Aside from the technical challenges of fisheries management, regulatory agencies are tasked with the enforcement of lawful fishing. Global Fishing Watch tracks real-time commercial fishing activity to combat illegal, unreported and unregulated fishing and seafood fraud. This tool utilizes Automatic Identification System (AIS) data, a collison avoidance system for precise vessel location tracking. Combined with Sentinel-1 satellite radar data, Global Fishing Watch has been able to identify non-broadcasting vessels that attempt to go undetected in illegal fishing activities. In the year after its launch in 2016, this tool acquired over 5,000 core users from 189 countries, engaging a broad, international audience.

## Harmful Algal Blooms

NOAA routinely monitors harmful algal blooms in coastal U.S. waters and the Great Lakes based on species' associations with environmental conditions (e.g., salinity fronts) and measured optical properties (Stumpf et al., 2003; Hu et al., 2004; Stumpf and Tomlinson, 2005). MERIS data is used to distinguish cyanobacteria blooms in U.S. inland waters (Wynne et al., 2008). During our Ocean Health and Fisheries workshop, a shellfish aquaculture farmer who has been operating in coastal ocean waters off Santa Barbara, California since 2002, Bernard Friedman described his business as intermittently plagued by harmful algal blooms. He provides ground-truth data to scientists developing the California-Harmful Algal Risk Mapping model to more precisely predict toxic algae events several days in advance (Anderson et al., 2016; Schollaert Uz, 2018), but the model's current resolution of 3 km2 is too coarse to be reliable for him. Applying synoptic-scale satellite data to his 0.1 km2 farm will require refining models in combination with additional observations, just as farmers on land use local weather forecasts from sophisticated models that assimilate many sources of observations.

## Turbidity

For aquaculture planning, water clarity data products from MODIS at 250 m and Landsat-8 at 30 m are used in coastal waters (e.g., Snyder et al., 2017). The Shellfish Monitoring unit of the Maryland Department of the Environment (MDE) routinely patrols the Chesapeake Bay looking for signs of failing septic systems. MDE collects and analyzes water samples near oyster beds and closes the beds to harvesting whenever fecal coliform levels exceed a safe threshold. Scouting for failing septic systems is labor-intensive and time-consuming. DNR and MDE use available satellite products, e.g., water clarity (**Figure 4**), but see an opportunity to increase the efficiency of field sampling through a combination of more advanced remote sensing and other assets (e.g., models, autonomous observations).

## Economic Valuation of Ecosystem Services

Economic benefits of improved water quality on commercial fisheries in the Chesapeake Bay have been evaluated by incorporating the effect of water quality changes on production and growth of the stock and producer and consumer surplus. Benefit assessments are complicated by environmental, regulatory, and business factors, such as new firms entering a commercial fishery once water quality improves and stocks grow. An estimate of the benefit of improved water quality in the Chesapeake Bay to striped bass sport fishing alone was valued at \$15M in 1996 dollars (Morgan and Owens, 2001; Moore and Griffiths, 2017). Efforts to specifically address the benefit of incorporating Earth observations into decisions is currently being undertaken by the NASA-funded Consortium for the Valuation of Applications Benefits Linked with Earth Science (VALUABLES). This consortium has begun conducting rigorous, quantitative studies about how people use improved information to make decisions and quantify how these decisions improve socioeconomically meaningful outcomes such as lives saved or resources conserved. Within the next few years, they plan to evaluate the use of satellite data applied to water quality decisions, hopefully including PACE data.

## ASPIRATIONAL USES OF SATELLITE DATA AND BARRIERS TO USE

In this section, we present survey results from the two workshops described in Section "End User Engagement Activities Around Fisheries and Aquaculture." Links to the surveys are provided below, along with links to other material about each workshop. The post-workshop surveys were completed by 45 respondents, with an additional 37 responses collected during the second workshop around specific water quality needs of resource managers around the Chesapeake Bay. Common survey results are graphed in **Figure 5**. Respondents expressed an overall need for satellite data to support decision making in fisheries management and water quality generally (**Figure 5A**), with smaller percentages of responses for more specific aims such as management of a specific water body or species (under "other management"). Attendees expressed a need for both physical and biological remotely sensed parameters, such as sea surface temperature and salinity, along with ocean color products such as chlorophyll-a concentration and water clarity parameters. Attendees at the Ocean Health workshop uniquely indicated sea surface height as a data need, while Chesapeake Bay workshop attendees require bottom characteristics, submerged aquatic vegetation, precipitation, and terrestrial products for their work. The most desired derived products are harmful algal bloom detection and water safety indices, such as potability and specific bacterial indices (**Figure 5B**).

The main barriers to using satellite and Earth Observation products in their work by most attendees (77%) is a lack of data processing and analysis skills, data access issues and the need for training (**Figure 5C**). Filling this need is an on-going challenge

for data providers and end users alike. IOCCG (2018) discusses a new efforts in Earth Observation data delivery, shifting from the old model of downloading and processing data locally to a new model of moving the processing software to the data source. This is motivated by the ever-increasing volume of data available and the challenges it presents to users for data processing. For example, with Platform as a Service cloud computing services, the provider hosts the data, hardware and/or software on its own infrastructure (e.g., Google Earth Engine, international Open Data Cube, ESA's Thematic Exploitation Platform and Copernicus Data and Information Access Service, DIAS). Cloud computing services will be even more important for PACE data,

as the volume of its hyperspectral data will greatly exceed those of existing polar-orbiting multi-spectral ocean color satellites.

Spectral, spatial, frequency, and latency needs vary widely by community. Attendees primarily seek high spatial resolution for their applications, with 58% stating their ideal spatial scale for data products is finer than 100 m, and a further 21% indicating multiple scales of measurement are important depending on the application. The need for data at <10 m resolution was unique to responses from the Chesapeake Bay workshop, which include users who require this fine scale for land and/or aquatic products. Temporally, 54% indicated their ideal scale for data products is monthly to seasonal, while 35% seek weekly or

more frequent temporal resolution. Needs for near real-time data were expressed by the commercial sector and fisheries management applications.

## NEXT STEPS

Trade-offs in coverage, resolution, or frequency present barriers for practical use and have limited the local application of existing resources. With global coverage, polar-orbiting satellites capture slow-moving, mesoscale and larger biological features as often as daily, but cannot resolve transient, features on the order of 1km or less. Ocean ecosystems move and change - more similar to weather systems in the atmosphere than vegetation on land. Higher resolution Landsat 8 and Sentinel-2a&b data (10–30 m) are now used to intermittently monitor inland and coastal water quality albeit with a 5–16 days repeat cycle (Franz et al., 2015; Claverie et al., 2018; Keith et al., 2018). PACE will provide higher spectral resolution more frequently with which to distinguish phytoplankton communities (**Figure 6**), but still not as frequently as a geostationary platform such as GOCI that can capture diurnal changes (Ryu et al., 2012). Biogeochemicalecosystem models integrated with radiative transfer models yield the absorption and scattering of light by optical constituents and spectrally resolved irradiance and been verified against in situ observations and satellite-derived products (e.g., Dutkiewicz et al., 2015). Models that further combine optical satellite data with fisheries demographic information have recently been developed to predict the location of an endangered species in order to guide resource manager in protection efforts (Breece et al., 2018). Future models that integrate the benefits of each satellite platform (e.g., high spectral resolution from PACE, high temporal frequency from geostationary, and higher spatial resolution from commercial or international sensors) with in situ observations at depth will enable the routine derivation of locally actionable three-dimensional information that a single satellite alone cannot provide.

Assessing the needs of the fisheries and aquaculture community around the Chesapeake Bay through collaboration and regular meetings with resource managers informs applied science priorities for PACE. Many variables that factor into resource manager decisions cannot currently be retrieved from satellite measurements (e.g., toxins, fecal coliform, pH, O2). We need a multi-faceted monitoring system that exploits ecological associations with satellite-derived products. Aquatic features move and change faster than polar-orbiting satellites can observe. Clouds and the intervening atmosphere obstruct a satellite view of the water at visible and infrared wavelengths. Despite challenges, a synoptic view of key variables (e.g., SST, Chl-a, salinity) improves the efficiency of in situ sampling

around aquaculture sites. Sustained satellite measurements also provide a long-term average from which anomalies may be detected. Through our regional effort in the Chesapeake Bay, we collaborate with MDE, NOAA, UMD and others to sample the optical, biological, physical, and chemical properties of water flowing into the Bay from land with a goal of finding unique hyperspectral signatures associated with various water quality indicators. Any progress to apply space-based assets through this and other such campaigns will assist NASA to prioritize PACE satellite data products and data distribution in support of fisheries and aquaculture. No single observing system will meet all needs by resource managers and others, but assimilation could exploit the strengths of each asset (i.e., high spectral resolution by PACE, high spatial resolution by harmonized Landsat/Sentinel, high temporal resolution by GOES). We are planning future interagency workshops around integrating satellite and in situ observations with modeling efforts to increase the utility of satellite data products.

The NASA Applied Sciences Program works together with NASA satellite missions prior to launch to engage user communities, solicit data and accuracy requirements for their operations and prepare them for the upcoming mission (Brown and Escobar, 2014). Now referred to as the Early Adopter program, this was discussed at both workshops as one way to engage end users early in the mission development process and have them test their applications with synthetic data. The European Space Agency conducted user needs assessments to make engineering choices prior to the Copernicus mission. By the time a NASA mission becomes official, it has historically been too late to make substantial hardware trade-offs but taking into account input from Early Adopters is still feasible for setting software priorities. As a cost-capped mission, the PACE Project did not receive funding to formally establish an Early Adopter program until recently. Initial efforts to recruit future early adopters have begun and the PACE Mission expects to begin executing a dedicated effort in 2020 to foster a community of practice specifically around the data that will be collected by OCI and the two multi-angle polarimeters and work directly with those implementing these observing systems.

## PROFESSIONAL REVIEW OF THE PACE USER COMMUNITY

One of the main objectives of the PACE Applied Science Program is to engage potential data users during the early stages of mission development to ensure the PACE product suite and delivery mechanisms meet the most urgent demands of users and have the broadest impact. As part of these outreach activities, we have recently designed a professional review of Earth science data end users and experts who have been using, or are interested in using, observations relevant to PACE data products to address a broad range of applied science questions, from food and water security to fisheries, water quality assessments, air quality monitoring and improved pollution forecasts (PACE Mission Survey, 2019). Conducted via an online questionnaire of 17 questions, the professional review will be used to characterize the PACE mission user community in terms of its composition, activities, remote sensing needs, and research interests. The review questions are structured around three themes: user information, data use and requirements, and data access and outreach. We used experiences from satellite data user community evaluations conducted previously for other NASA missions, including SMAP (Soil Moisture Active Passive) and ICESat-2 (Ice, Cloud and land Elevation Satellite-2) (Brown and Escobar, 2014). The main goal of the PACE survey is to solicit data product, data accuracy and access needs, and apply results to plan outreach and applications before PACE launches in order to tailor them to user needs and enhance the societal value of the mission.

The target audience for the professional review is users from a wide range of sectors from around the world, including nongovernment organizations, non-profit organizations, private industry, universities, local, state and federal agencies. The subject community includes over 1,000 members, representing multiple disciplines including ocean biogeochemistry, oceanography, ecological forecasting, hazards, water resources and quality, terrestrial resources, weather, climate, air quality and human health. To characterize the community in terms of its composition, the survey includes questions on experience and training, and whether the responder is involved in policy making

that involves hazard mitigation, increasing public awareness of emergencies, planning for environmental contingencies such as air quality, water quality, and marine resources, monitoring of environmental parameters, or conducts environmental research.

The need to bridge the gap between the increasing amount of satellite data available and end user requirements for data products, tools, and user training, has been widely highlighted by the international Earth Observations community (World Meteorological Organization [WMO], 2017). The Earth Science Division Directive on Project Applications Program presents guidance to all future directed NASA missions, emphasizing the need to characterize potential satellite data application uses and user needs during the early stages of a satellite mission development to increase the relevance and usefulness of their data products (i.e., latency, direct downloads, spatial resolution, data format, repeat frequency). Information from community surveys and professional reviews can help improve understanding on the use of satellite data for environmental applications by users globally, and to identify current gaps in data products, limitations in data access, and areas for improvement (Brown and Escobar, 2014). For example, a professional review conducted for the Famine EarlyWarning System Network (FEWS NET) created by the United States Agency for International Development (USAID) to improve their emergency response capabilities in Africa, had a significant influence on FEWS NET operations, including increased investment in higher resolution and better quality rainfall and vegetation data (Ross et al., 2009).

## CONCLUSION

The recent Decadal Survey serves as a guiding document for NASA programs of Earth observations from space, placing a new emphasis on use-inspired science, whereby end-user needs explicitly inspire research scientists with the hope of improved outcomes for those applications. This is complementary to the curiosity-inspired science that has historically guided work in this field, in which societal benefits are considered after the scientific research has been completed. In response to this new direction, NASA is initiating direct interaction with the satellite data user community. The early efforts described here have begun to engage a community that could use future PACE data in support of fisheries and aquaculture. Through these activities, we are increasing access to satellite products that can give resource managers, lawmakers, fishing and aquaculture businesses the best available remotely sensed information. This ensures that satellite data providers and program managers understand the impact of various choices they make about PACE (e.g., data processing, latency, product development, and applied research efforts) and it will prepare future users of PACE data and engage them around the potential strengths, limitations, and uncertainties. Data gaps and barriers and a need for more training and dialogue have been identified, particularly around preparing the operational community to assimilate OCI data into early warning systems and dynamic ecosystem models.

Technical limitations aside, there are also practical limits to the satellite data products that PACE will be able to provide. The needs of end-users are as diverse as the data requirements and delivery mechanisms to address those needs. While the role of NASA is to explore new frontiers and foster innovative uses of its satellite data for societal benefit, massaging the data into just the right product and format for each user is not. Likewise, NOAA provides operational products, but has to balance the government's interest in providing timely, open access to global environmental data to promote and protect the safety and security of the American public while not competing with private businesses. Other organizations have developed the ability to take NASA and NOAA satellite data products and get them into the hands of new users. For example, in its Aquaduct water risk platform WRI provides satellite products to the business and investment community via Bloomberg's mapping tool where it is accessed by more than 400K users around the world. Regularly exchanging ideas with diverse end-user groups informs the PACE Mission about potential downstream uses of its data while preparing user communities for the advancements that are coming so that they can align resources to be ready to take full advantage of the data as soon as it flows. Engaging stakeholders on these and future follow-up activities with NOAA and other federal and state agencies and businesses (e.g., fishing, tourism, big data), we will be able to increase the return on investment of PACE data by providing useful satellite products that give managers, fishers, and aquaculture businesses the best available information. Synergy with operational partners and end-users of satellite data products is essential to realizing the full potential of this and future NASA missions. Interagency collaboration on stakeholder engagement through working groups, workshops, and applied research projects will help us understand how current and future satellite data can be optimized in new ways to solve societal problems around marine food security.

## NOTES

Workshop agendas and presentations are available at the following sites: NASA/WRI Ocean Health and Fisheries workshop, 2017, Washington, DC: https://www.wri.org/events/ 2017/11/nasa-wri-ocean-health-and-fisheries-applications-work shop. Post-workshop-Survey: https://www.surveymonkey. com/r/MF2VSHJ. Interagency Chesapeake Bay workshop, 2018, Greenbelt, MD: https://science.gsfc.nasa.gov/610/ applied-sciences/chesapeake\_bay\_workshop.html. Postworkshop Survey: https://docs.google.com/forms/d/1y4hA7JAeDrKwZbjKUkgEtwakyxrcR0v-\_oVhBeil7E/viewform?edit\_ requested=true.

## AUTHOR CONTRIBUTIONS

SS led the workshops, surveys, and working group, and wrote and revised the manuscript. GK analyzed the survey data and helped to revise the manuscript. AM presented at the workshops, participated in the working group, and provided project content to the manuscript. PW presented at the workshop, attended the working group, and edited the manuscript. MT attended the working group and provided content on the PACE Mission Applications survey.

## FUNDING

This study was supported by the NASA funding at NASA Goddard through the PACE Project and Applied Sciences Ecological Forecasting Program.

## ACKNOWLEDGMENTS

feart-07-00283 November 2, 2019 Time: 10:48 # 12

Workshop planning and hosting assistance from Woody Turner of the NASA Applied Sciences Program on Ecological

## REFERENCES

Forecasting, Cara Wilson of NOAA, and Janet Ranganathan of WRI, respectively, as well as meaningful discussions with all panelists and participants of both workshops are gratefully recognized. Motivation to form the interagency Chesapeake Bay working group began with Mark Trice of Maryland DNR and has sustained through collaboration with Rusty McKay of MDE, Shelly Tomlinson and Mike Ondrusek of NOAA, Rossana Del Vecchio of UMD, and many others. NASA Goddard Applied Sciences team members Brendan McAndrew and Jamie Dulaney assist with the monthly meetings and workshops. Encouragement from Jim Irons, Earth Sciences Division Director at NASA Goddard, fosters these activities. Finally, we are grateful to Jim Yoder, Professor Emeritus at URI, and the reviewers for helpful comments on earlier versions of this manuscript.

Sea due to spread of hypoxia. Nat. Commun. 5:4862. doi: 10.1038/ncomms 5862

IOCCG, (2014). Phytoplankton Functional Types from Space. Dartmouth: IOCCG.

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Schollaert Uz, Kim, Mannino, Werdell and Tzortziou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.


*Article* **Integration of Urinary EN2 Protein & Cell-Free RNA Data in the Development of a Multivariable Risk Model for the Detection of Prostate Cancer Prior to Biopsy**

**Shea P. Connell 1 , Robert Mills 2 , Hardev Pandha 3 , Richard Morgan 4 , Colin S. Cooper 1 , Jeremy Clark 1 , Daniel S. Brewer 1,5,\* and The Movember GAP1 Urine Biomarker Consortium 1,†**

**Simple Summary:** Prostate cancer is a disease responsible for a large proportion of all male cancer deaths but there is a high chance that a patient will die with the disease rather than from. Therefore, there is a desperate need for improvements in diagnosing and predicting outcomes for prostate cancer patients to minimise overdiagnosis and overtreatment whilst appropriately treating men with aggressive disease, especially if this can be done without taking an invasive biopsy. In this work we develop a test that predicts whether a patient has prostate cancer and how aggressive the disease is from a urine sample. This model combines the measurement of a protein-marker called EN2 and the levels of 10 genes measured in urine and proves that integration of information from multiple, non-invasive biomarker sources has the potential to greatly improve how patients with a clinical suspicion of prostate cancer are risk-assessed prior to an invasive biopsy.

**Abstract:** The objective is to develop a multivariable risk model for the non-invasive detection of prostate cancer prior to biopsy by integrating information from clinically available parameters, Engrailed-2 (EN2) whole-urine protein levels and data from urinary cell-free RNA. Post-digital-rectal examination urine samples collected as part of the Movember Global Action Plan 1 study which has been analysed for both cell-free-RNA and EN2 protein levels were chosen to be integrated with clinical parameters (*n* = 207). A previously described robust feature selection framework incorporating bootstrap resampling and permutation was applied to the data to generate an optimal feature set for use in Random Forest models for prediction. The fully integrated model was named ExoGrail, and the out-of-bag predictions were used to evaluate the diagnostic potential of the risk model. ExoGrail risk (range 0–1) was able to determine the outcome of an initial trans-rectal ultrasound guided (TRUS) biopsy more accurately than clinical standards of care, predicting the presence of any cancer with an area under the receiver operator curve (AUC) = 0.89 (95% confidence interval(CI): 0.85–0.94), and discriminating more aggressive Gleason ≥ 3 + 4 disease returning an AUC = 0.84 (95% CI: 0.78–0.89). The likelihood of more aggressive disease being detected significantly increased as ExoGrail risk score increased (Odds Ratio (OR) = 2.21 per 0.1 ExoGrail increase, 95% CI: 1.91–2.59). Decision curve analysis of the net benefit of ExoGrail showed the potential to reduce the numbers of unnecessary biopsies by 35% when compared to current standards of care. Integration of information from multiple, non-invasive biomarker sources has the potential to greatly improve how patients with a clinical suspicion of prostate cancer are risk-assessed prior to an invasive biopsy.

Pandha, H.; Morgan, R.; Cooper, C.S.; Clark, J.; Brewer, D.S.; Integration of Urinary EN2 Protein & Cell-Free RNA Data in the Development of a Multivariable Risk Model for the Detection of Prostate Cancer Prior to Biopsy. *Cancers* **2021**, *13*, 2102. https:// doi.org/10.3390/ cancers13092102

**Citation:** Connell, S.P.; Mills, R.;

Academic Editor: Jonas Cicenas

Received: 15 February 2021 Accepted: 14 April 2021 Published: 27 April 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Keywords:** prostate cancer; biomarker; urine; machine learning; TRIPOD; liquid biopsy

#### **1. Introduction**

Prostate cancer is responsible for 13% of all male cancer deaths in the UK, yet this is contrasted by 10-year survival rates approaching 84% [1]. This dichotomy has led to uncertainty for clinicians in how best to diagnose and predict the outcome for prostate cancer patients to minimise overdiagnosis and overtreatment whilst appropriately treating men with aggressive disease [2]. More accurate discrimination of disease state in biopsy naïve men would mark a significant development compared to current standards and impact large numbers of patients suspected of harbouring prostate cancer. The development of such a pre-biopsy screening test would provide a convenient checkpoint along the clinical pathway for patients to exit without the need for further invasive and stressful follow-up.

Under current guidelines patients are selected for further clinical investigations for prostate cancer if they have an elevated prostate specific antigen (PSA) (≥4 ng/mL) and/or an adverse finding on digital rectal examination (DRE) or lower urinary tract symptoms, whilst other factors such as age and ethnicity are also considered alongside patient preference [3–5]. More recently multiparametric MRI (mpMRI) has been used as a triage tool to reduce negative biopsy rates since its validation in the PROMIS clinical trial [6]. However, as it has gained more widespread adoption, mpMRI has shown a higher rate of inter-operator and inter-machine variability than reported in controlled clinical trials; up to 28% of clinically significant disease is missed in practice [5,7–9]. Coupled with the relative expense, time and expertise required to undertake an mpMRI meeting the current clinical guidelines, there is a need to improve on current clinical practices.

Biomarkers utilising tissue samples taken at the time of diagnosis for the detection of aggressive or significant prostate cancer requiring clinical attention are relatively plentiful [10–13]. Many of these markers are good tests, whether that be for discerning the most aggressive disease [11,14], or for predicting disease-free survival following radical prostatectomy [15]. However, requiring tissue means a biopsy must already have been performed, making these tests incompatible with reducing the rates of unnecessary biopsy that come at considerable economic, psychological and societal cost to patients and healthcare systems alike [2,16,17].

As a secretory organ directly interacting with the male urinary tract, the prostate is well-placed as a candidate for non-invasive liquid biopsy from urine samples [18]. Single- or few-biomarker panels such as Engrailed-2 (EN2) protein expression [19], the SelectMDx [20] and ExoDx Prostate (IntelliScore) [21] tests have published promising results for the noninvasive detection of significant disease (Gleason score (Gs) ≥ 7). However, they are in various stages of clinical validation and none are currently implemented in the UK healthcare system [5]. Most urinary biomarkers developed to date for the prediction of biopsy outcome are unimodal; considering a singular fraction of urine (such as the cellpellet or cell-free fractions) or biological aspect of cancer to appraise disease status. Whilst these tests have shown promising clinical use and accuracy, for the majority it has not yet been explored whether extra predictive value could be derived by integrating multiple streams of information from other sources.

Since initial development, the SelectMDx model has been updated to include clinically available parameters of serum PSA, patient age and DRE alongside urinary *HOXC6* and *DLX1* mRNA, adding significant predictive ability for patients with a PSA < 10 ng/mL [22]. We have also recently shown the benefit of such a holistic approach, presenting the development of the multivariable ExoMeth risk prediction model integrating clinical parameters, hypermethylation within the urinary cell pellet and urinary cell-free RNA expression data that displayed improved clinical utility over any single mode [23].

EN2 is a homeodomain-containing transcription factor that has an essential function in early development, which in mammals includes the delineation of the midbrain/hindbrain border [24]. For a transcription factor it has a number of unusual properties, including the ability to be secreted from cells and taken up by others [25]. Indeed, a recent study indicated that prostate cancer cells can secrete EN2 protein through vesicles which are then taken up by other non-EN2 expressing cells, where it can directly influence the transcription of target genes [25].

This secretory behaviour of EN2 makes it a potential biomarker for prostate cancer, and indeed EN2 protein can be detected in the urine of men with prostate tumours [19]. The original and subsequent studies have generally supported a diagnostic role for urinary EN2, including a relationship between urinary EN2 concentration and tumour volume [19,26]. More recently, a lateral flow-based test for EN2 has been described that could potentially allow point-of-care testing [27].

In this study, we report the utility of a predictive model produced by the integration of clinically available parameters, urinary EN2 protein levels and targeted cell-free RNA transcriptomics. The data were collected within the Movember Global Action Plan 1 (GAP1) study that explored a range of biomarkers in urine for PCa diagnosis and prognosis. The clinical utility of this model is determined by the ability to predict the presence of Gs ≥ 7 and Gs ≥ 4 + 3 disease on biopsy, both critical distinctions in clinical settings, where patients with Gs ≥ 7 are recommended radical therapy [5], whilst patients with Gs 4 + 3 have significantly worse outcomes than Gs 3 + 4 patients [28]. Aware that most cancer biomarkers and predictive models fail to reach clinical adoption, we have adhered to the guidelines for the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) whilst developing the models and results presented here [29].

#### **2. Materials and Methods**

#### *2.1. Patient Population and Characteristics*

The full Movember GAP1 urine cohort comprises 1257 first-catch post-DRE urine samples collected between 2009 and 2015 from urology clinics at multiple sites, as described in Connell et al. (2019). As a diverse range of techniques was applied to samples from this cohort and restricted amounts of urine, the number of experiments that could be performed on any one sample was limited. Samples within the Movember cohort that were quantified for both EN2 levels by ELISA and cell-free-RNA (cf-RNA) expression by NanoString (Seattle, WA, USA) were eligible for selection for model development in the current study (*n* = 218).

Exclusion criteria for model development included a recent prostate biopsy or transurethral resection of the prostate (<6 weeks) and metastatic disease (confirmed by a positive bone-scan or PSA > 100 ng/mL), resulting in a cohort of 207 samples, deemed the ExoGrail cohort (Table 1). All samples analysed in the ExoGrail cohort were collected from the Norfolk and Norwich University Hospital (NNUH, Norwich, UK). Sample collections and processing were ethically approved by the East of England REC.

#### *2.2. Sample Processing and Analysis*

Urine samples were processed according to the Movember GAP1 standard operating procedure (Supplementary Methods). In brief, within 30 min of collection, urine was centrifuged (1200× *g* 10 min, 6 ◦C) to remove cellular material. Supernatant extracellular vesicles were harvested by microfiltration and cell-free mRNA extracted (RNeasy micro kit, #74004, Qiagen, Hilden, Germany) on the same day that they were provided by the patient. RNA was amplified as cDNA with an Ovation PicoSL WTA system V2 (Nugen, Redwood City, CA, USA, #3312-48). Urinary EN2 protein concentration was quantified by ELISA from whole urine using a monoclonal anti-mouse EN2 antibody, as described by Morgan et al. (2011) [19]. Cell-free mRNA was quantified from urinary extracellular vesicles using NanoString technology, with 167 gene-probes (Table S1), as described in Connell et al. (2019), with the modification that NanoString data were normalised according to NanoString guidelines using NanoString internal positive controls, and log2 transformed. Clinical variables serum PSA, age at sample collection, DRE finding, and urine volume collected were considered.

**Table 1.** Characteristics of the ExoGrail development cohort, stratified according to a record of cancer or not, either on an initial biopsy or for a no cancer finding if a biopsy was not performed.

#### *2.3. Statistical Analysis*

All analyses, model construction and data preparation were undertaken in R version 3.5.3 [30], and unless otherwise stated, utilised base R and default parameters. All data and code required to reproduce these analyses can be found at the UEA Cancer Genetic GitHub repository [31].

#### *2.4. Feature Selection*

In total, 172 variables were available for prediction (cf-RNA (*n* = 167), clinical variables (*n* = 4) and urinary EN2 (*n* = 1); for full list see Table S1), making feature selection a key task for minimising model overfitting and increasing the robustness of trained models. To avoid dataset-specific features being positively selected [32], we implemented a robust feature selection workflow utilising the Boruta algorithm [33] and bootstrap resampling. Boruta is a random forest-based algorithm that iteratively compares feature importance against random predictors, deemed "shadow features." Features that perform significantly worse compared to the maximally performing shadow feature at each permutation, (*p* ≤ 0.01, calculated by Z-score difference in mean accuracy decrease) are consecutively dropped until only confirmed, stable features remain.

Boruta was applied on 1000 datasets generated by resampling with replacement. Features were only positively selected for model construction when confirmed as stable features in ≥90% of resampled Boruta runs.

#### *2.5. Comparator Models*

To evaluate potential clinical utility, additional models were trained as comparators using subsets of the available variables across the patient population: a clinical standard of care (SoC) model was trained by incorporating age, PSA, T-staging and clinician DRE impression; a model using only the values from the EN2 ELISA (EN2, *n* = 1); and a model only using NanoString gene-probe information (NanoString, *n* = 167). The fully integrated ExoGrail model was trained by incorporating information from all of the above variables (*n* = 177). Each set of variables for comparator models were independently selected via the bootstrapped Boruta feature selection process described above to select the most optimal subset of variables possible for each predictive model.

#### *2.6. Model Construction*

All models were trained via the random forest algorithm [34], using the *randomForest* package [35] with default parameters except for resampling without replacement and 401 trees being grown per model. Risk scores from trained models are presented as the out-of-bag predictions; the aggregated outputs from decision trees within the forest where the sample in question has not been included within the resampled dataset [34]. Bootstrap resamples were identical for feature selection and model training for all models and used the same seed for random number generator.

Models were trained on a modified continuous label, based on biopsy outcome and constructed as follows: samples were scored on a continuous scale (range: 0–1) according to the dominant Gleason pattern: where 0 represented no evidence of cancer, Gleason scores 6 & 3 + 4 were assigned to 0.5 and Gleason scores ≥ 4 + 3 are set to 1. Following this categorisation, the score is treated as a continuous variable by the Random Forest algorithm described above. This process was designed to recognise that two patients with the same TRUS-biopsy Gleason score will not share the exact same proportions of tumour pattern, or overall disease burden. This scale was solely used for model training and was not represented in any endpoint measurements, or for determining the predictive ability and clinical utility.

#### *2.7. Statistical Evaluation of Models*

Area Under the Receiver-Operator Characteristic curve (AUC) metrics were produced using the *pROC* package [36], with confidence intervals calculated via 1000 stratified bootstrap resamples. Density plots of model risk scores, and all other plots were created using the *ggplot2* package [37]. Partial dependency plots were calculated using the *pdp* package [38]. Cumming estimation plots and calculations were produced using the *dabestr* package [39] and 1000 bootstrap resamples were used to visualise robust effect size estimates of model predictions.

Decision curve analysis (DCA) [40] examined the potential net benefit of using PURsignatures in the clinic. Standardised net benefit (sNB) was calculated with the *rmda* package [41] and presented throughout our decision curve analyses as it is a more directly interpretable metric compared to net benefit [42]. In order to ensure DCA was representative of a more general population, the prevalence of Gleason scores within the ExoGrail cohort were adjusted via bootstrap resampling to match those observed in a population of 219,439 men that were in the control arm of the Cluster Randomised Trial of PSA Testing for Prostate Cancer (CAP) Trial [43], as described in Connell et al. (2019). Briefly, of the biopsied men within this CAP cohort, 23.6% were Gs 6, 8.7% Gs 7 and 7.1% Gs ≥ 8, with 60.6% of biopsies showing no evidence of cancer. These ratios were used to perform stratified bootstrap sampling with a replacement of the Movember cohort to produce a "new" dataset of 197 samples with risk scores from each comparator model. sNB was then calculated for this resampled dataset, and the process repeated for a total of 1000 resamples with replacement. The mean sNB for each risk score and the "treat-all" options over all of the iterations were used to produce the presented figures to account for variance in

resampling. Net reduction in biopsies, based on the adoption of models versus the default treatment option of undertaking biopsy in all men with PSA ≥ 4 ng/mL was calculated as:

$$\text{Biopsy}\_{\text{NetReduction}} = (\text{NB}\_{\text{Model}} - \text{NB}\_{\text{All}}) \times \frac{1 - \text{Threshold}}{\text{Threshold}} \tag{1}$$

where the decision threshold (*Threshold*) is determined by accepted patient/clinician risk [40]. For example, a clinician may accept up to a 25% perceived risk of cancer before recommending biopsy to a patient, equating to a decision threshold of 0.25.

#### **3. Results**

#### *3.1. The ExoGrail Development Cohort*

Urinary EN2 protein and cf-RNA data were available for 207 patients within the Movember GAP1 cohort, with all samples originating from the NNUH to form the ExoGrail development cohort (Table 1). The proportion of Gleason ≥ 7 disease in the ExoGrail cohort was 48%, whilst 25 patients were deemed to have no evidence of cancer (NEC, PSA < 4 ng/mL), and did not receive a biopsy.

#### *3.2. Feature Selection and Model Development*

Using the robust feature selection framework, four models were produced in total: a standard of care (SoC) model incorporating only clinically available parameters (age and PSA), a model using urinary EN2 protein levels as the sole predictor variable (Engrailed), a model using only cf-RNA information (ExoRNA, 11 gene-probes) and the integrated model, named ExoGrail that incorporated variables from all three sources (12 variables) (Table 2). The ExoGrail model is a multivariable risk prediction model incorporating clinical parameters, urinary EN2 protein levels and cf-RNA expression information. When the resampling strategy was applied for feature reduction using Boruta, 12 variables were selected for the ExoGrail model. Each of the retained variables were positively selected in every resample and notably included information from clinical and cf-RNA variables, as well as urinary EN2 (Figure 1; full resample-derived Boruta variable importance for the SoC, Engrailed and ExoRNA comparator models can be seen in Figures S1–S3, respectively).

**Table 2.** Features positively selected for each model by bootstrap resampling and the Boruta algorithm. Features are selected for each model by being confirmed as important for predicting biopsy outcome, categorised as a modified ordinal variable (see Methods) by Boruta in ≥90% of bootstrap resamples.

 *NKAIN1 - PCA3 PCA3 PPFIA2 PPFIA2 RPLP2 -*  - *SLC12A1 TMEM45B TMEM45B TMPRSS2/ERG* fusion *TMPRSS2/ERG* fusion

**Figure 1.** Analysis of variables available for the training of the ExoGrail model through the application of the Boruta algorithm via bootstrap resampling. 1000 resamples with replacement of the available data were made, with the normalised permutation importance of each variable recorded at each iteration, along with the decision of Boruta within that resample. Fill colour shows the proportion of resamples that a feature was positively retained by Boruta. Those features selected in ≥90% of resamples were selected for fitting predictive models. Variables rejected in all of the 1000 **Figure 1.** Analysis of variables available for the training of the ExoGrail model through the application of the Boruta algorithm via bootstrap resampling. 1000 resamples with replacement of the available data were made, with the normalised permutation importance of each variable recorded at each iteration, along with the decision of Boruta within that resample. Fill colour shows the proportion of resamples that a feature was positively retained by Boruta. Those features selected in ≥90% of resamples were selected for fitting predictive models. Variables rejected in all of the 1000 resamples are not shown here but are fully detailed in Table S1.

resamples are not shown here but are fully detailed in Table S1.

In the SoC comparator model, only PSA and age were selected as important predictors. Urinary EN2 levels were confirmed as important in the independent Engrailed model as the sole variable, and also within the ExoGrail model (Table 2). For the cf-RNA model, ExoRNA, 11 transcripts were selected, notably including both variants of the ERG geneprobe and *TMPRSS2/ERG* fusion gene-probe. ExoGrail incorporated an additional cf-RNA transctript, *SLC12A1*, which was not previously selected in the ExoRNA comparator model. When this was examined by partial dependency plots, an additive interaction effect was observed between quantified levels of urinary EN2 and counts of SLC12A1 on the predicted ExoGrail risk signature output (Figure S4).

+ 3.

and any cancer (Table S2).

#### *3.3. ExoGrail Predictive Ability 3.3. ExoGrail Predictive Ability*

the predicted ExoGrail risk signature output (Figure S4).

*Cancers* **2021**, *13*, x FOR PEER REVIEW 8 of 16

As ExoGrail Risk Score (range 0–1) increased, the likelihood of high-grade disease detection on TRUS-biopsy was significantly greater (Proportional odds ratio = 2.21 per 0.1 ExoGrail increase, 95% CI: 1.91–2.59; ordinal logistic regression, Figure 2). The median ExoGrail risk score for metastatic patients was 0.76 (*n* = 11). These patients were excluded from model training and can be considered as a positive control for model calibration. As ExoGrail Risk Score (range 0–1) increased, the likelihood of high-grade disease detection on TRUS-biopsy was significantly greater (Proportional odds ratio = 2.21 per 0.1 ExoGrail increase, 95% CI: 1.91–2.59; ordinal logistic regression, Figure 2). The median ExoGrail risk score for metastatic patients was 0.76 (*n* = 11). These patients were excluded from model training and can be considered as a positive control for model calibration.

In the SoC comparator model, only PSA and age were selected as important predictors. Urinary EN2 levels were confirmed as important in the independent Engrailed model as the sole variable, and also within the ExoGrail model (Table 2). For the cf-RNA model, ExoRNA, 11 transcripts were selected, notably including both variants of the ERG gene-probe and *TMPRSS2/ERG* fusion gene-probe. ExoGrail incorporated an additional cf-RNA transctript, *SLC12A1*, which was not previously selected in the ExoRNA compar-

**Figure 2.** Representation of the ExoGrail risk score for each patient within a waterfall plot, where each coloured bar represents an individual's biopsy outcome (fill colour) and predicted ExoGrail risk score (bar height). Green—No evidence of cancer, Blue—Gs 6, Orange—Gs 3 + 4, Red—Gs ≥ 4 **Figure 2.** Representation of the ExoGrail risk score for each patient within a waterfall plot, where each coloured bar represents an individual's biopsy outcome (fill colour) and predicted ExoGrail risk score (bar height). Green—No evidence of cancer, Blue—Gs 6, Orange—Gs 3 + 4, Red—Gs ≥ 4 + 3.

ExoGrail was superior to all other models for the detection of Gleason ≥ 3 + 4 (AUC = 0.90 (95% CI: 0.86–0.94), *p* < 0.001, bootstrap test with 1000 resamples) and for any cancer (AUC = 0.89 (95% CI: 0.85–0.94), *p* < 0.001, bootstrap test with 1000 resamples) (Table 3). When Gleason ≥ 4 + 3 was considered, ExoGrail returned an AUC = 0.84 (95% CI: 0.78– 0.89), outperforming the SoC and cf-RNA models (*p* < 0.001, bootstrap test with 1000 resamples), whilst the Engrailed model displayed similar performance by AUC metrics (Table 3). A model consisting of the combination of EN2 and PSA showed a similar ability in the detection of Gleason ≥ 4 + 3 compared to ExoGrail (AUCs of 0.85 compared to 0.84), whilst ExoGrail showed a small improvement in the detection of Gleason ≥ 3 + 4 disease ExoGrail was superior to all other models for the detection of Gleason ≥ 3 + 4 (AUC = 0.90 (95% CI: 0.86–0.94), *p* < 0.001, bootstrap test with 1000 resamples) and for any cancer (AUC = 0.89 (95% CI: 0.85–0.94), *p* < 0.001, bootstrap test with 1000 resamples) (Table 3). When Gleason ≥ 4 + 3 was considered, ExoGrail returned an AUC = 0.84 (95% CI: 0.78–0.89), outperforming the SoC and cf-RNA models (*p* < 0.001, bootstrap test with 1000 resamples), whilst the Engrailed model displayed similar performance by AUC metrics (Table 3). A model consisting of the combination of EN2 and PSA showed a similar ability in the detection of Gleason ≥ 4 + 3 compared to ExoGrail (AUCs of 0.85 compared to 0.84), whilst ExoGrail showed a small improvement in the detection of Gleason ≥ 3 + 4 disease and any cancer (Table S2).

**Table 3.** Area under the receiver operator curve (AUC) of all trained models for detecting outcomes of an initial biopsy for varying clinically significant thresholds. Numbers within brackets detail 95% confidence intervals of the AUC, calculated from 1000 stratified bootstrap resamples. Input variables for each model are detailed in Table 1.

As revealed by the distributions of risk scores and AUC, ExoGrail achieved clearer discrimination of disease status Gleason ≥ 3 + 4 disease from other biopsy outcomes when compared to any of the other models (ExoGrail all comparisons *p* < 0.01 bootstrap test, 1000 resamples, Figure 3).

**Figure 3.** Risk score distributions of the four trained models, calculated as the out-of-bag predictions and represented as density plots. Area under the receiver operator curves (AUCs) for each model's predictive ability for clinically relevant outcomes are detailed underneath each panel. Each random forest model was fit using different input variables; (**A**) SoC clinical risk model, including Age and serum prostate specific antigen (PSA), (**B**) Engrailed model, (**C**) ExoRNA model and (**D**) ExoGrail model, combining predictors from all three modes of analysis. The full list of variables in each model is available in Table 1. Fill colour shows the risk score distribution of patients with respect to biopsy outcome: No evidence of cancer (Green), Gleason 6 (Blue), Gleason 3 + 4 (Orange), Gleason ≥ 4 + 3 (Red).

Investigation of risk score distributions found that whilst the SoC model returned respectable AUCs and detection of the higher grade disease (Gleason ≥ 3 + 4), it displayed a relative inability to clearly stratify intermediate disease states. This uncertainty would cause large numbers of patients to be inappropriately selected for further investigation (Figure 3A). For example, to classify 90% of patients with Gleason 7 disease correctly, an SoC risk score of 0.251 would misclassify 64.5% of men with less significant, or no disease. The Engrailed model detailed clearer discrimination, though featured a bimodal distribution of patients without prostate cancer (Figure 3B, green density plot), misidentifying 51.4% of patients with low-grade disease as similar to those with more clinically significant disease (Figure 3B). A similar bimodal distribution was seen for the EN2 plus PSA model (Figure S5). Whilst the AUCs returned for the ExoRNA model were lower, the distribution of risk scores shows that ExoRNA could more accurately discriminate cancer from noncancer than either the SoC or EN2 models, a key clinical step in the triage of patients prior to biopsy (Figure 3C).

Examination of ExoGrail scores displayed similar distributions for NEC patients as the ExoRNA model whilst also being able to more accurately separate different cancer outcomes from biopsy, resulting in fewer misclassifications of patients without cancer if binary detection of 95% of Gleason ≥ 3 + 4 were considered (28% of NEC patients misclassified). The greater discriminatory ability of the ExoGrail model when biopsy outcomes are considered as a binary Gleason ≥ 3 + 4 threshold can also be seen in Figure S6. patients prior to biopsy (Figure 3C). Examination of ExoGrail scores displayed similar distributions for NEC patients as the ExoRNA model whilst also being able to more accurately separate different cancer outcomes from biopsy, resulting in fewer misclassifications of patients without cancer if binary detection of 95% of Gleason ≥ 3 + 4 were considered (28% of NEC patients misclassified). The greater discriminatory ability of the ExoGrail model when biopsy outcomes are considered as a binary Gleason ≥ 3 + 4 threshold can also be seen in Figure S6.

model (Figure S5). Whilst the AUCs returned for the ExoRNA model were lower, the distribution of risk scores shows that ExoRNA could more accurately discriminate cancer from non-cancer than either the SoC or EN2 models, a key clinical step in the triage of

*Cancers* **2021**, *13*, x FOR PEER REVIEW 10 of 16

Comparisons of mean ExoGrail scores between groups were performed with resampling and Cumming estimation plots (1000 bias-corrected and accelerated bootstrap resamples, Figure 4). The mean ExoGrail differences between patients with no evidence of cancer on biopsy were: Gleason 6 = 0.3 (95% CI: 0.22–0.37), Gleason 3 + 4 = 0.48 (95% CI: 0.41–0.53) and Gleason ≥ 4 + 3 = 0.56 (95% CI: 0.51–0.61). Of note, patients with no evidence of cancer had a lower ExoGrail risk score (mean difference = 0.17 (95% CI: 0.11–0.24)) than those with a raised PSA but no findings of cancer on biopsy (Figure 4). Comparisons of mean ExoGrail scores between groups were performed with resampling and Cumming estimation plots (1000 bias-corrected and accelerated bootstrap resamples, Figure 4). The mean ExoGrail differences between patients with no evidence of cancer on biopsy were: Gleason 6 = 0.3 (95% CI: 0.22–0.37), Gleason 3 + 4 = 0.48 (95% CI: 0.41–0.53) and Gleason ≥ 4 + 3 = 0.56 (95% CI: 0.51–0.61). Of note, patients with no evidence of cancer had a lower ExoGrail risk score (mean difference = 0.17 (95% CI: 0.11–0.24)) than those with a raised PSA but no findings of cancer on biopsy (Figure 4).

**Figure 4.** Mean ExoGrail risk score differences between biopsy outcomes, as represented by Cumming estimation plots. Individual patient risk scores (y-axis) are presented as points in the top panel, separated according to Gleason score (x-axis) with gapped vertical lines detailing the mean and standard deviation of each clinical group's ExoGrail risk score. Mean ExoGrail risk score differences relative to the no evidence of cancer (NEC) group are shown in the bottom panel. Mean difference and 95% confidence intervals are shown as a point estimate and vertical bar, respectively, with density plots generated from 1000 bias-corrected and accelerated bootstrap resamples. **Figure 4.** Mean ExoGrail risk score differences between biopsy outcomes, as represented by Cumming estimation plots. Individual patient risk scores (y-axis) are presented as points in the top panel, separated according to Gleason score (x-axis) with gapped vertical lines detailing the mean and standard deviation of each clinical group's ExoGrail risk score. Mean ExoGrail risk score differences relative to the no evidence of cancer (NEC) group are shown in the bottom panel. Mean difference and 95% confidence intervals are shown as a point estimate and vertical bar, respectively, with density plots generated from 1000 bias-corrected and accelerated bootstrap resamples.

Decision curve analyses examined the net benefit of ExoGrail adoption in a population of patients with a clinical suspicion of prostate cancer and a PSA level suitable to trigger biopsy (≥4 ng/mL). The biopsy of men based upon their ExoGrail risk score provided a net benefit over current standards of care across all decision thresholds examined and was the most consistent amongst all comparator models across a range of clinically relevant endpoints for biopsy (Figure 5). Decision curve analyses examined the net benefit of ExoGrail adoption in a population of patients with a clinical suspicion of prostate cancer and a PSA level suitable to trigger biopsy (≥4 ng/mL). The biopsy of men based upon their ExoGrail risk score provided a net benefit over current standards of care across all decision thresholds examined and was the most consistent amongst all comparator models across a range of clinically relevant endpoints for biopsy (Figure 5).

**Figure 5.** Exploration of the standardised net benefit (sNB) by decision curve analysis (DCA) for adopting risk models to aid the decision to undertake an initial biopsy for patients presenting with a serum PSA ≥ 4 ng/mL, where current clinical practice is to biopsy all patients. The accepted patient/clinician risk threshold for accepting biopsy is detailed on the x-axis. Different biopsy outcomes are shown in each of the three panels; (**A**) detection of Gleason ≥ 4 + 3, (**B**) detection of Gleason ≥ 3 + 4, (**C**) any cancer; Blue—biopsy all patients with a PSA >4 ng/mL, Orange—biopsy patients according to the SoC model, Green—biopsy patients based on the Engrailed model, Purple—biopsy patients based on the exoRNA model, Red—biopsy patients based on a the ExoGrail model. To assess the benefit of adopting these risk models in a clinically relevant population, we used data available from the control arm of the The Cluster Randomized Trial of PSA Testing for Prostate Cancer (CAP) study [42] for proportionally resampling the ExoGrail cohort. DCA curves were calculated from 1000 bootstrap resamples of the available data to match the distribution of **Figure 5.** Exploration of the standardised net benefit (sNB) by decision curve analysis (DCA) for adopting risk models to aid the decision to undertake an initial biopsy for patients presenting with a serum PSA ≥ 4 ng/mL, where current clinical practice is to biopsy all patients. The accepted patient/clinician risk threshold for accepting biopsy is detailed on the x-axis. Different biopsy outcomes are shown in each of the three panels; (**A**) detection of Gleason ≥ 4 + 3, (**B**) detection of Gleason ≥ 3 + 4, (**C**) any cancer; Blue—biopsy all patients with a PSA >4 ng/mL, Orange—biopsy patients according to the SoC model, Green—biopsy patients based on the Engrailed model, Purple—biopsy patients based on the exoRNA model, Red—biopsy patients based on a the ExoGrail model. To assess the benefit of adopting these risk models in a clinically relevant population, we used data available from the control arm of the The Cluster Randomized Trial of PSA Testing for Prostate Cancer (CAP) study [42] for proportionally resampling the ExoGrail cohort. DCA curves were calculated from 1000 bootstrap resamples of the available data to match the distribution of disease reported in the CAP trial population. Mean sNB from these resampled DCA results are plotted here. See Methods for full details.

disease reported in the CAP trial population. Mean sNB from these resampled DCA results are plotted here. See Methods for full details. Using the SoC model as the baseline with which to compare the potential for biopsy reduction of each model, we found that ExoGrail could reduce unnecessary biopsy rates by upwards of 40%, depending on accepted patient-clinician risk. For example, if a decision threshold of 0.1 were accepted, representing a perceived risk of 1 in 10 for Gleason ≥ 3 + 4 on biopsy, ExoGrail could result in up to a 35% reduction in unnecessary biopsies of men presenting with a suspicion of prostate cancer, whilst also correctly identifying patients with more aggressive disease. If Gleason ≥ 4 + 3 were considered the threshold of Using the SoC model as the baseline with which to compare the potential for biopsy reduction of each model, we found that ExoGrail could reduce unnecessary biopsy rates by upwards of 40%, depending on accepted patient-clinician risk. For example, if a decision threshold of 0.1 were accepted, representing a perceived risk of 1 in 10 for Gleason ≥ 3 + 4 on biopsy, ExoGrail could result in up to a 35% reduction in unnecessary biopsies of men presenting with a suspicion of prostate cancer, whilst also correctly identifying patients with more aggressive disease. If Gleason ≥ 4 + 3 were considered the threshold of clinical significance, a more conservative decision threshold of 0.05 could save 32% of men from receiving an unnecessary biopsy (Figure 6).

clinical significance, a more conservative decision threshold of 0.05 could save 32% of men

from receiving an unnecessary biopsy (Figure 6).

**Figure 6.** Estimation of biopsy reduction, as calculated by comparing the DCA-calculated net benefit of each risk model to the net benefit of the standard of care (SoC) model. The accepted patient/clinician risk threshold for accepting biopsy is detailed on the x-axis. Different biopsy outcomes are shown in each of the three panels: (**A**) detection of Gleason ≥ 4 + 3, (**B**) detection of Gleason ≥ 3 + 4 and (**C**) any cancer. Coloured lines show differing comparator models; Orange biopsy patients according to the SoC model, Green—biopsy patients based on the Engrailed model, Purple—biopsy patients based on the ExoRNA model, Red**—**biopsy patients based on the ExoGrail model. To assess the benefit of adopting these risk models in a clinically relevant population we used data available from the control arm of the CAP study [42] for proportionally resampling the ExoGrail cohort. DCA curves were calculated from 1000 bootstrap resamples of the available data to match the distribution of disease reported in the CAP trial population. Net benefit averaged over all resamples was used to calculate the potential reductions in biopsy rates here. **Figure 6.** Estimation of biopsy reduction, as calculated by comparing the DCA-calculated net benefit of each risk model to the net benefit of the standard of care (SoC) model. The accepted patient/clinician risk threshold for accepting biopsy is detailed on the x-axis. Different biopsy outcomes are shown in each of the three panels: (**A**) detection of Gleason ≥ 4 + 3, (**B**) detection of Gleason ≥ 3 + 4 and (**C**) any cancer. Coloured lines show differing comparator models; Orange—biopsy patients according to the SoC model, Green—biopsy patients based on the Engrailed model, Purple—biopsy patients based on the ExoRNA model, Red—biopsy patients based on the ExoGrail model. To assess the benefit of adopting these risk models in a clinically relevant population we used data available from the control arm of the CAP study [42] for proportionally resampling the ExoGrail cohort. DCA curves were calculated from 1000 bootstrap resamples of the available data to match the distribution of disease reported in the CAP trial population. Net benefit averaged over all resamples was used to calculate the potential reductions in biopsy rates here. See Methods for full details.

#### **4. Discussion**

See Methods for full details.

**4. Discussion**  Discriminating disease status in patients before a diagnostic biopsy with higher accuracy than current standards could bring about a sizeable change in treatment pathways and reduce the number of men sent forward for ultimately unnecessary biopsy. Given that up to 75% of patients are negative for prostate cancer when presenting with serum PSA levels ≥ 4 ng/mL [5,43,44], a concentration of research efforts has been made to address this problem. To date, several biomarker panels have been successfully developed to non-invasively detect prostate cancer using urine samples, Gleason ≥ 3 + 4 disease with superior accuracy to current clinically implemented methods, including the PUR model developed by ourselves [20,21,45,46]. However, as only a single aspect of urine, assay method or biological process are assessed by these examples, the heterogeneity of prostate cancer may not be entirely accounted for [47], requiring an approach to be taken that pro-Discriminating disease status in patients before a diagnostic biopsy with higher accuracy than current standards could bring about a sizeable change in treatment pathways and reduce the number of men sent forward for ultimately unnecessary biopsy. Given that up to 75% of patients are negative for prostate cancer when presenting with serum PSA levels ≥ 4 ng/mL [5,43,44], a concentration of research efforts has been made to address this problem. To date, several biomarker panels have been successfully developed to non-invasively detect prostate cancer using urine samples, Gleason ≥ 3 + 4 disease with superior accuracy to current clinically implemented methods, including the PUR model developed by ourselves [20,21,45,46]. However, as only a single aspect of urine, assay method or biological process are assessed by these examples, the heterogeneity of prostate cancer may not be entirely accounted for [47], requiring an approach to be taken that provides a more holistic insight into disease status.

vides a more holistic insight into disease status. Recent analyses, including those presented here, have demonstrated the added value of integrating multiple prognostic biomarkers within the process of fitting risk models for determining patient risk upon an initial biopsy [23,48]. Urine clearly contains a wealth of Recent analyses, including those presented here, have demonstrated the added value of integrating multiple prognostic biomarkers within the process of fitting risk models for determining patient risk upon an initial biopsy [23,48]. Urine clearly contains a wealth of useful information concerning the disease status of the prostate through the quantification

useful information concerning the disease status of the prostate through the quantification

of cf-RNA transcripts, circulating and cell-free DNA, hypermethylation of DNA, and protein biomarker levels [19,46,49–52].

Our results show that an improved multivariable risk prediction model can be developed from the careful consideration of information from multiple different urine fractions in men suspected to have prostate cancer. Urinary levels of EN2 protein were quantified by ELISA, whilst the transcript levels of 167 cell-free mRNAs were quantified using NanoString technology. The final model integrating information from those assays with serum PSA levels was deemed ExoGrail. Markers selected for the model include wellknown genes associated with prostate cancer and proven in other diagnostic tests, such as *PCA3* [45], *HOXC6* [20], and the *TMPRSS2/ERG* gene fusion [53]. An interaction between urinary EN2 protein levels and quantified transcripts of *SLC12A1* was observed, further demonstrating the benefit of considering information from multiple biological sources (Figure S4).

ExoGrail was able to accurately predict the presence of significant (Gs ≥ 7) prostate cancer on biopsy with an AUC of 0.89, comparing favourably to other published tests (AUCs for Gs ≥ 7: PUR = 0.77 [46], ExoMeth = 0.89 [23], ExoDX Prostate IntelliScore = 0.77 [21], SelectMDX = 0.78 [20], epiCaPture Gs ≥ 4 + 3 AUC = 0.73 [49]). Furthermore, ExoGrail resulted in accurate predictions even when serum PSA levels alone proved inaccurate; patients with a raised PSA but negative biopsy result possessed ExoGrail scores significantly different from both clinically benign patients and those with low-grade Gleason 6 disease, whilst still able to discriminate between more clinically significant Gleason ≥ 7 cancers (Figure 4). The adoption of ExoGrail into current clinical pathways for reducing unnecessary biopsies was considered, showing the potential for up to 32% of patients to safely forgo an invasive biopsy without incurring excessive risk (Figure 6).

ExoGrail was developed with the explicit goal of being robust to potential overfitting and bias, using strong internal validation methods in bootstrap resampling and out-of-bag predictions. Nonetheless, ExoGrail was developed in a relatively small dataset and so requires external validation in an independent cohort before it can be considered for use as a clinical risk model. To this end, we are currently collecting samples from multiple sites in the UK, EU and Canada using an updated 'At-Home' Collection system [54]. The At-Home collection system enables biomarker analysis to be performed on urine samples provided by patients at home, which they send in the post to a centralised laboratory. This collection and analysis system will sidestep the need for a visit to the clinic and lead to a postal screening system for prostate cancer diagnosis and prognosis. In this study, we will also assess the potential utility of supplementing MP-MRI with ExoGrail, as MP-MRI can misrepresent disease status, even with rigorous controls in place [6]. The NanoString expression analysis system used in the ExoGrail signature is a rapid and cost-effective analysis system that is also used in the FDA-approved Prosigna Pam50 test for breast cancer aggressiveness [55], making ExoGrail well-positioned for implementation for patient benefit.

#### **5. Conclusions**

ExoGrail was able to accurately predict the presence of significant (Gs ≥ 7) prostate cancer on biopsy and showed the potential for an important number of patients to safely forgo an invasive biopsy. If validated in future studies, ExoGrail has the potential to positively impact the clinical experience of patients being investigated for prostate cancer that ultimately have no disease or indolent prostate cancer.

#### **6. Patents**

A patent application has been filed by the authors for the present work and work related to this.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/cancers13092102/s1, Figure S1: Boruta analysis of variables available for the training of the SoC model, Figure S2: Boruta analysis of variables available for the training of the Engrailed model, Figure S3: Boruta analysis of variables available for the training of the ExoRNA model, Figure S4: Partial dependency plots detailing the marginal effects and interactions of SLC12A1 and urinary EN2 on predicted ExoGrail Risk Score, Figure S5: Risk score distributions of the trained models including the EN2 and PSA model, Figure S6: Density plots detailing risk score distributions generated from four trained models. Table S1: List of all features available for selection as input variables for each model prior to bootstrapped Boruta feature selection, Table S2: AUC of all trained models, including a combination of EN2 and PSA, for detecting outcomes of an initial biopsy for varying clinically significant thresholds. Supplementary Methods.

**Author Contributions:** S.P.C. and D.S.B drafted the manuscript and conceived, designed, and performed the statistical analyses. H.P. and R.M. (Richard Morgan) were involved in sample collection and ELISA analyses at their respective institutes. J.C. and R.M. were involved in sample collection and NanoString analyses, as well as the development of clinical methodologies. D.S.B., J.C., R.M. (Robert Mills), H.P., and C.S.C. had joint and equal contributions to senior authorship and were contributors in writing the manuscript. C.S.C., J.C., H.P. and D.S.B. provided the original idea for this study. All authors have read and agreed to the published version of the manuscript. All authors critiqued the manuscript for intellectual content.

**Funding:** This research was funded by Movember Foundation GAP1 Urine Biomarker project, The Masonic Charitable Foundation, The Bob Champion Cancer Trust, the King family, The Andy Ripley Memorial Fund and the Stephen Hargrave Trust.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approval was granted for the collection and processing of samples by the Ethics Committees at the East of England REC.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** All data and code required to reproduce these analyses can be found at https://github.com/UEA-Cancer-Genetics-Lab/ExoGrail\_paper.

**Acknowledgments:** The research presented in this paper was carried out on the High Performance Computing Cluster supported by the Research and Specialist Computing Support service at the University of East Anglia.

**Conflicts of Interest:** A patent application has been filed by the authors for the present work and work related to this. There are no other conflicts of interest to disclose.

#### **References**


**2. Pathophysiology of hyperlipidaemia of pregnancy**

**Pregnancy** is a dynamic state consequent of the fact that normal fetal development needs the availability of essential nutrients such as glucose, free fatty acids(FFAs), long-chain polyunsaturated fatty acids(LCPUFAs), amino acids, minerals, vitamins, to be continuously supplied to the growing fetus despite intermittent maternal food intake[10,25]. The dynamism of the gestational period support fetal growth and development while maintaining maternal homeostasis and preparation for lactation. This is achieved by complex and continuously evolving adjustments in maternal nutrient metabolism occurring throughout gestation.

Many of these maternal adjustments occur in the early stages of pregnancy when the fetus is too small to make considerable metabolic demands of the mother, resulting in the maternal metabolism working from a different baseline compared with the nonpregnant state. This period is called the anabolic phase. In late pregnancy, however, the maternal metabolic processes become more complicated because of the two-way interaction between the mother and the developing fetus. This is caked the catabolic phase.

The changes in nutrient metabolism can be described by several general concepts[8]: (a) adjustments in nutrient metabolism are driven by hormonal changes, fetal demands and maternal nutrient supply; (b) more than one potential adjustment exists for each nutrient; (c) maternal behavioural changes augment physiologic adjustment; and (d) a limit exists in the physiologic capacity to adjust nutrient metabolism to meet pregnancy needs, which when exceeded, fetal growth and development are impaired. Subsequently, metabolic adaptations, during pregnancy are essential [26]: 1, To ensure adequate growth and development of the fetus; 2, to provide the fetus with adequate energy stores and substrates that are needed following birth; 3, and, to provide the mother with sufficient energy stores and substrates to cope with the demands of pregnancy as well as those of labour and lactation. One of the maternal metabolic adjustments during pregnancy includes accumulation of fat depots in maternal tissues[26]. During this anabolic phase, the number of insulin receptors on the adipocytes increases, culminating into increased insulin sensitivity, increase lipoprotein lipase(LPL) activity which hydrolyses circulating triglycerides for tissue uptake, enhanced lipogenesis and marked maternal fat deposition(about 3.5 to 6.0kg) which is used as energy sources for the mother so that glucose is spared for the developing fetus in the catabolic part of the pregnancy[27, 28]. Lean women increase their fat stores more than obese women per kg body weight, likely due to higher insulin sensitivity in them, in early pregnancy, promoting lipid uptake and de novo synthesis.

throughout gestation.

modulators of lipid metabolism in pregnancy.

**2. Pathophysiology of hyperlipidaemia of pregnancy**

and the developing fetus. This is caked the catabolic phase.

promoting lipid uptake and de novo synthesis.

is that Statins, classified by FDA as category X, should be avoided in pregnancy[23, 24]. The use of lipid and lipoprotein ratios in interpreting pregnancy associated hyperlipidaemia may provide a balanced hyperlipidaemia not only in normal pregnancy but also in the other

**Pregnancy** is a dynamic state consequent of the fact that normal fetal development needs the availability of essential nutrients such as glucose, free fatty acids(FFAs), long-chain polyunsaturated fatty acids(LCPUFAs), amino acids, minerals, vitamins, to be continuously supplied to the growing fetus despite intermittent maternal food intake[10,25]. The dynamism of the gestational period support fetal growth and development while maintaining maternal homeostasis and preparation for lactation. This is achieved by complex and continuously evolving adjustments in maternal nutrient metabolism occurring

Many of these maternal adjustments occur in the early stages of pregnancy when the fetus is too small to make considerable metabolic demands of the mother, resulting in the maternal metabolism working from a different baseline compared with the nonpregnant state. This period is called the anabolic phase. In late pregnancy, however, the maternal metabolic processes become more complicated because of the two-way interaction between the mother

The changes in nutrient metabolism can be described by several general concepts[8]: (a) adjustments in nutrient metabolism are driven by hormonal changes, fetal demands and maternal nutrient supply; (b) more than one potential adjustment exists for each nutrient; (c) maternal behavioural changes augment physiologic adjustment; and (d) a limit exists in the physiologic capacity to adjust nutrient metabolism to meet pregnancy needs, which when exceeded, fetal growth and development are impaired. Subsequently, metabolic adaptations, during pregnancy are essential [26]: 1, To ensure adequate growth and development of the fetus; 2, to provide the fetus with adequate energy stores and substrates that are needed following birth; 3, and, to provide the mother with sufficient energy stores and substrates to cope with the demands of pregnancy as well as those of labour and lactation. One of the maternal metabolic adjustments during pregnancy includes accumulation of fat depots in maternal tissues[26]. During this anabolic phase, the number of insulin receptors on the adipocytes increases, culminating into increased insulin sensitivity, increase lipoprotein lipase(LPL) activity which hydrolyses circulating triglycerides for tissue uptake, enhanced lipogenesis and marked maternal fat deposition(about 3.5 to 6.0kg) which is used as energy sources for the mother so that glucose is spared for the developing fetus in the catabolic part of the pregnancy[27, 28]. Lean women increase their fat stores more than obese women per kg body weight, likely due to higher insulin sensitivity in them, in early pregnancy, The important attributes of fat deposits during the anabolic phase in pregnant women are :(1) Hyperphagia, present in pregnant women and increases as gestational time advances. This progressive increase in the availability of exogenous substrates actively contributes to maternal accumulation of fat depots [29]; (2) Promotion of lipogenesis and suppression of lipolysis mediated by progressive increase in insulin and its sensitivity and enhanced by progesterone and cortisol [30]; (3) The proportional increase in adipose tissue lipoprotein lipase (LPL) activity [1,12,31) which hydrolyzes triglycerides(TGs) in form of TG-rich lipoproteins, chylomicron and very-low density lipoprotein(VLDL), which are respectively converted into remnant particles and intermediate-density lipoprotein (IDL). The hydrolytic products, non-esterified fatty acids(NEFA) and glycerol, are partially taken up by subjacent tissues [11, 12, 32, 33); (4) the unique capacity of tissue to utilize intracellularly the glycerol released during lipolysis. Under normal circumstances, the negligible glycerol kinase activity in adipose tissues hampers the utilization of glycerol for glycerol-3-phosphate synthesis and its use for the synthesis of TGs [34,35]. However, an increase in glycerol kinase activity and its subsequent capacity to metabolize glycerol has been found in rodents under condition of hyperinsulinaemia and enhanced fat accumulation, such as occurs in obesity [35, 36]. The lower lipolytic activity together with the augmented capacity of the tissues for the synthesis of glycerol-3-phosphate for uses in TG synthesis from both glucose and intracellular released glycerol results in net intracellular accumulations of TGs. Since all these pathways are stimulated by insulin, it is proposed that the enhanced insulin responsiveness [37] in the presence of an augmented response of the pancreatic β-cells to the insulinotropic stimulus of glucose that has been found in early pregnant women [38] would be the principal driving forces for the net fat depot accumulation at this stage of pregnancy. These ultimately lead to maternal fat accumulations in the anabolic phase of gestation.

The anabolic condition of adipose tissue during early pregnancy switches to a net catabolic state during the last 1/3 of gestation. The signals responsible for this switch from lipid storage to lipid mobilization are not well understood; however, placental hormones that increase with advancing gestation, known to induce maternal insulin resistance, may play a major role. Placental growth hormone, human placental lactogen, leptin, and tumour necrosis factor-α(TNF-α) are placental hormones that induce insulin resistance. The presence of high plasma levels of placental hormones, known to have lipolytic effects, human placental lipase (HPL), an augmented production of catecholamine secondary to maternal hypoglycemia [38], and the insulin-resistant condition present at this stage [39, 40], appear to be responsible for the net breakdown of maternal fat depots, consistently causing increments of plasma nonesterified fatty acids(NEFA) and glycerol levels during the 3rd trimester of gestation. The main destination of these lipolytic products released from maternal adipose tissue is the maternal liver. They are converted in the liver into their respective active forms, acyl-CoA and glycerol-3-phosphate, to become partially reesterified for the synthesis of triglycerides, which are transferred to native VLDL particles and released into the circulation. Acyl-CoA can also be converted throughout the βoxidation pathway to acetyl-CoA for energy production and ketone body synthesis, whereas glycerol may also be used for glucose synthesis. Fetal-placental glucose and amino acids

utilization rates are highest at 22 to 26weeks decreasing near term. In contrast lipid transport is maximal in the 3rd trimester coincident with rapid fetal fat accretion, this spares the mother to utilize glucose during this period. Humans are born with the highest percentage of fat (12 to 15%) compared to any species and 90% deposition occurs in the last 10weeks of gestation, exponentially increasing to 7g/day near term. The preferential use of glycerol released from maternal adipose tissue for gluconeogenesis acquire greater importance during maternal fasting period, when circulating glucose levels are lower than under nonpregnant conditions[34]. Under fed condition in early gestation, plasma ketone body values are even lower in pregnant than in nonpregnant condition [41], indicating an enhanced use of these fuels by maternal tissues as alternative substrate to glucose. However, during fasting period maternal ketogenesis become highly accelerated, as indicated by the exaggerated increase in plasma ketone bodies that occur [41]. This benefit the fetus in two ways: (1) ketone bodies are used by maternal tissues, thus, saving glucose for essential function and delivery to the fetus, (2) placental transfer of ketone bodies is very efficient, attaining the same concentration in fetal plasma as in maternal circulation[42]. In addition, ketone bodies may be used by the fetus as oxidative fuels as well as substrate for brain lipid synthesis [43].

Insulin is well known to inhibits adipose tissue lipolytic activity, hepatic gluconeogenesis and ketogenesis but to increases adipose tissue LPL activity. Thus, it is not surprising that all of these pathways change in the opposite direction which is consistent with insulin resistance occurring in later part of pregnancy. These pathways become even further modified under uncontrolled gestational diabetes mellitus(GDM), where insulin resistance is further enhanced [44].