Multi-institutional Collaborative Effort Research Articles

An ocean practices maturity model: from good to best practices

Ocean practices, intended as a wide spectrum of methodologies supporting ocean-related research, operations, and applications, are constantly developed and improved to enable informed decision-making. Practices start from the idea of an individual or a group and often evolve towards what can be called good or best practices. This bottom-up approach may in principle result in different paths for the evolution of each practice, and ultimately generate situations where it is not clear to a new user how to compare two practices aiming at the same objective, and determine which one is best. Also, although a best practice is supposed to be the result of a multi-institutional collaborative effort based on the principles of evidence, repeatability and comparability, a set of individual requirements is not yet defined in literature for a practice to be considered a good, better, and ultimately a best practice. This paper proposes a method for addressing those questions and presents a new maturity model for ocean practices, built upon existing maturity models for systems and software, developed and adopted in the last decades. The model provides attributes for assessing both the maturity of the practice description and its implementation. It also provides a framework for analyzing gaps and suggesting actions for practice evolution. The model has been tested against a series of widely adopted practices and the results are reported and discussed. This work facilitates a common approach for developing and assessing practices, from which greater interoperability and trust can be achieved.

Clinical implications of Wnt pathway genetic alterations in men with advanced prostate cancer.

Aberrant Wnt signaling has been implicated in prostate cancer tumorigenesis and metastasis in preclinical models but the impact of genetic alterations in Wnt signaling genes in men with advanced prostate cancer is unknown. We utilized the Prostate Cancer Precision Medicine Multi-Institutional Collaborative Effort (PROMISE) clinical-genomic database for this retrospective analysis. Patients with activating mutations in CTNNB1 or RSPO2 or inactivating mutations in APC, RNF43, or ZNRF3 were defined as Wnt-altered, while those lacking such alterations were defined as Wnt non-altered. We compared patient characteristics and clinical outcomes as well as co-occurring genetic alterations according to Wnt alteration status. Of the 1498 patients included, 193 (12.9%) were Wnt-altered. These men had a statistically significant 2-fold increased prevalence of liver and lung metastases as compared with Wnt non-altered patients at the time of initial diagnosis, (4.66% v 2.15% ; 6.22% v 3.07%), first metastatic disease diagnosis (10.88% v 5.29%; 13.99% v 6.21%), and CRPC development (11.40% v 6.36%; 12.95% v 5.29%). Wnt alterations were associated with more co-occurring alterations in RB1 (10.4% v 6.2%), AR (38.9% vs 25.7%), SPOP (13.5% vs 4.1%), FOXA1 (6.7% vs 2.8%), and PIK3CA (10.9% vs 5.1%). We found no significant differences in overall survival or other clinical outcomes from initial diagnosis, first metastatic disease, diagnosis of CRPC, or with AR inhibition for mCRPC between the Wnt groups. Wnt-altered patients with prostate cancer have a higher prevalence of visceral metastases and are enriched in RB1, AR, SPOP, FOXA1, and PIK3CA alterations. Despite these associations, Wnt alterations were not associated with worse survival or treatment outcomes in men with advanced prostate cancer.

Repeat Next-Generation Sequencing Testing on Progression in Men With Metastatic Prostate Cancer Can Identify New Actionable Alterations.

There are limited data available on the real-world patterns of molecular testing in men with advanced prostate cancer. We thus sought to evaluate next-generation sequencing (NGS) testing in the United States, focused on single versus serial NGS testing, the different disease states of testing (hormone-sensitive v castration-resistant, metastatic vs nonmetastatic), tissue versus plasma circulating tumor DNA (ctDNA) assays, and how often actionable data were found on each NGS test. The Prostate Cancer Precision Medicine Multi-Institutional Collaborative Effort clinical-genomic database was used for this retrospective analysis, including 1,597 patients across 15 institutions. Actionable NGS data were defined as including somatic alterations in homologous recombination repair genes, mismatch repair deficiency, microsatellite instability (MSI-high), or a high tumor mutational burden ≥10 mut/MB. Serial NGS testing (two or more NGS tests with specimens collected more than 60 days apart) was performed in 9% (n = 144) of patients with a median of 182 days in between test results. For the second NGS test and beyond, 82.1% (225 of 274) of tests were from ctDNA assays and 76.1% (217 of 285) were collected in the metastatic castration-resistant setting. New actionable data were found on 11.1% (16 of 144) of second NGS tests, with 3.5% (5 of 144) of tests detecting a new BRCA2 alteration or MSI-high. A targeted therapy (poly (ADP-ribose) polymerase inhibitor or immunotherapy) was given after an actionable result on the second NGS test in 31.3% (5 of 16) of patients. Repeat somatic NGS testing in men with prostate cancer is infrequently performed in practice and can identify new actionable alterations not present with initial testing, suggesting the utility of repeat molecular profiling with tissue or blood of men with metastatic castration-resistant prostate cancer to guide therapy choices.

An Internal Medicine Learning Collaborative Facilitating a Virtual Continuing Medical Education Program in Guyana and the Wider Caribbean During the COVID-19 Pandemic.

To collaborate and share medical knowledge between US and Caribbean physicians during the COVID-19 pandemic via a free online continuing medical education (CME) series. Method: This was a multi-institution collaborative effort between the Southern Regional Area Health Education Center and Cape Fear Valley Medical Center, both located in North Carolina, USA, and its Caribbean partners, the Guyana Medical Council and Ministry of Health, and the University of the West Indies Medical Alumni Association, Jamaica. The lecture series ran from July 2021 to October 2022. The Zoom (Zoom Video Communications Inc., San Jose, CA, USA) meeting platform was used for the monthly lectures on the fourth Thursday between 7 and 8 p.m. Eastern Standard Time (EST). Results: Analysis of program data from July 2021 through October 2022 (excluding December 2021) found 1,105 unique individuals engaged in the 15 continuing education sessions. The series had a cumulative total of 2,411 participants, with a mean session participation of 161 and a range of 94 to 299 participants per lecture. An outcome survey assessing the reasons for attendance identified that the most significant factors in their participation in the series were: a) the quality of educational content (83.21%), b) the ease of access and Zoom platform (81.76%), and c) the lectures being offered at no cost (61.31%), and 80.84% gained new medical knowledge leading to practice changes. Conclusion: The Internal Medicine Learning Collaborative (IMLC) model can be easily replicated by following the steps outlined. It overcomes barriers such as travel and quarantine restrictions and is cost-effective to initiate and maintain. It allows physicians with access to resources and specialty training in the United States to share medical knowledge with colleagues in the developing world where such access may be limited, thus promoting health care and continuing education activity in their respective regions using freely available technologies.

FedFSA: Hybrid and federated framework for functional status ascertainment across institutions

IntroductionPatients' functional status assesses their independence in performing activities of daily living, including basic ADLs (bADL), and more complex instrumental activities (iADL). Existing studies have discovered that patients’ functional status is a strong predictor of health outcomes, particularly in older adults. Depite their usefulness, much of the functional status information is stored in electronic health records (EHRs) in either semi-structured or free text formats. This indicates the pressing need to leverage computational approaches such as natural language processing (NLP) to accelerate the curation of functional status information. In this study, we introduced FedFSA, a hybrid and federated NLP framework designed to extract functional status information from EHRs across multiple healthcare institutions. MethodsFedFSA consists of four major components: 1) individual sites (clients) with their private local data, 2) a rule-based information extraction (IE) framework for ADL extraction, 3) a BERT model for functional status impairment classification, and 4) a concept normalizer. The framework was implemented using the OHNLP Backbone for rule-based IE and open-source Flower and PyTorch library for federated BERT components. For gold standard data generation, we carried out corpus annotation to identify functional status-related expressions based on ICF definitions. Four healthcare institutions were included in the study. To assess FedFSA, we evaluated the performance of category- and institution-specific ADL extraction across different experimental designs. ResultsADL extraction performance ranges from an F1-score of 0.907 to 0.986 for bADL and 0.825 to 0.951 for iADL across the four healthcare sites. The performance for ADL extraction with impairment ranges from an F1-score of 0.722 to 0.954 for bADL and 0.674 to 0.813 for iADL across four healthcare sites. For category-specific ADL extraction, laundry and transferring yielded relatively high performance, while dressing, medication, bathing, and continence achieved moderate-high performance. Conversely, food preparation and toileting showed low performance. ConclusionNLP performance varied across ADL categories and healthcare sites. Federated learning using a FedFSA framework performed higher than non-federated learning for impaired ADL extraction at all healthcare sites. Our study demonstrated the potential of the federated learning framework in functional status extraction and impairment classification in EHRs, exemplifying the importance of a large-scale, multi-institutional collaborative development effort.

Clinical implications of AR alterations in advanced prostate cancer: a multi-institutional collaboration.

AR gene alterations can develop in response to pressure of testosterone suppression and androgen receptor targeting agents (ARTA). Despite this, the relevance of these gene alterations in the context of ARTA treatment and clinical outcomes remains unclear. Patients with castration-resistant prostate cancer (CRPC) who had undergone genomic testing and received ARTA treatment were identified in the Prostate Cancer Precision Medicine Multi-Institutional Collaborative Effort (PROMISE) database. Patients were stratified according to the timing of genomic testing relative to the first ARTA treatment (pre-/post-ARTA). Clinical outcomes such as time to progression, PSA response, and overall survival were compared based on alteration types. In total, 540 CRPC patients who received ARTA and had tissue-based (n = 321) and/or blood-based (n = 244) genomic sequencing were identified. Median age was 62 years (range 39-90) at the time of the diagnosis. Majority were White (72.2%) and had metastatic disease (92.6%) at the time of the first ARTA treatment. Pre-ARTA genomic testing was available in 24.8% of the patients, and AR mutations and amplifications were observed in 8.2% and 13.1% of the patients, respectively. Further, time to progression was longer in patients with AR amplifications (25.7 months) compared to those without an AR alteration (9.6 months; p = 0.03). In the post-ARTA group (n = 406), AR mutations and AR amplifications were observed in 18.5% and 35.7% of the patients, respectively. The most common mutation in post-ARTA group was L702H (9.9%). In this real-world clinicogenomics database-driven study we explored the development of AR alterations and their association with ARTA treatment outcomes. Our study showed that AR amplifications are associated with longer time to progression on first ARTA treatment. Further prospective studies are needed to optimize therapeutic strategies for patients with AR alterations.

The impact of SPOP gene alterations in men with metastatic prostate cancer: Results from the Prostate Cancer Precision Medicine Multi-Institutional Collaborative Effort (PROMISE) consortium.

225 Background: Inactivating mutations in the SPOP gene, encoding speckled-type poxvirus and zinc-finger protein, are common among men with localized and metastatic prostate cancer, occurring at a frequency of 6-15%. Previous studies have suggested the presence of an inactivating mutation in SPOP results in increased sensitivity to androgen deprivation therapy (ADT). In this multi-institutional clinical-genomic database, we evaluated the impact of SPOP alterations on survival outcomes in men with metastatic prostate cancer. Methods: Retrospective data from the PROMISE Consortium were utilized for this analysis. Eligible patients had metastatic prostate cancer and had undergone standard of care next-generation sequencing (NGS). Patients with inactivating mutations in SPOP were defined as SPOP mutated, where. as those lacking such alterations were defined as SPOP wild-type. The primary endpoint was overall survival defined as the time from diagnosis of metastatic prostate cancer to death from any cause, censored at the date of last follow-up. Secondary endpoints included time from metastatic disease to castration resistance and time from castration resistance to death. Results: Of the 2097 patients with available NGS testing, 5.5% (n=115) had SPOP alterations. The median age at diagnosis was 63 years, 427 were Black, and 83 were Hispanic. At last assessment, 66% had bone metastases, 7% lung metastases, and 5% liver metastases. The most frequent co-occurring alterations in the SPOP-mutated group were: TP53 (30.4%), APC (25.2%), and AR (21.7%). 1832 patients were included in the survival analysis [n=96 (5.2%) with and n=1736 (94.8%) without SPOP alterations]. Median overall survival was numerically longer, though not statistically significant, in the SPOP-mutated compared SPOP-wild-type group (75.9 versus 59.5 months, p=0.12). In patients with metastatic disease, median time to castration resistance was 14.6 months in the SPOP-mutated group versus 12.2 months in the SPOP-wild-type group (p=0.30). Median time from metastatic castration resistant to death was 45.0 months in the SPOP-mutated group and 40.2 months in the SPOP-wild type group. Conclusions: Our hypothesis generating data support that SPOP-mutated prostate cancer likely represents a unique molecular subtype of prostate cancer that may confer prolonged survival. Future studies of novel androgen-receptor targeting treatments should be tested in this molecular population.

Biomarker-Directed Therapy in Black and White Men With Metastatic Castration-Resistant Prostate Cancer

Black men have higher incidence and mortality from prostate cancer. Whether precision oncology disparities affect Black men with metastatic castration-resistant prostate cancer (mCRPC) is unknown. To compare precision medicine data and outcomes between Black and White men with mCRPC. This retrospective cohort study used data collected by the Prostate Cancer Precision Medicine Multi-Institutional Collaborative Effort (PROMISE) consortium, a multi-institutional registry with linked clinicogenomic data, from April 2020 to December 2021. Participants included Black and White patients with mCRPC with molecular data. Data were analyzed from December 2021 to May 2023. Database-reported race and ethnicity. The primary outcome was the frequency of actionable molecular data, defined as the presence of mismatch repair deficiency (MMRD) or high microsatellite instability (MSI-H), homologous recombination repair deficiency, or tumor mutational burden of 10 mutations per megabase or greater. Secondary outcomes included the frequency of other alterations, the type and timing of genomic testing performed, and use of targeted therapy. Efficacy outcomes were prostate-specific antigen response rate, site-reported radiographic response, and overall survival. A total of 962 eligible patients with mCRPC were identified, including 204 Black patients (21.2%; median [IQR] age at diagnosis, 61 [55-67] years; 131 patients [64.2%] with Gleason scores 8-10; 92 patients [45.1%] with de novo metastatic disease) and 758 White patients (78.8%; median [IQR] age, 63 [57-69] years; 445 patients [58.7%] with Gleason scores 8-10; 310 patients [40.9%] with de novo metastatic disease). Median (IQR) follow-up from mCRPC was 26.6 (14.2-44.7) months. Blood-based molecular testing was more common in Black men (111 men [48.7%]) than White men (317 men [36.4%]; P < .001). Rates of actionable alterations were similar between groups (65 Black men [32.8%]; 215 White men [29.1%]; P = .35), but MMRD or MSI-H was more common in Black men (18 men [9.1]) than White men (36 men [4.9%]; P = .04). PTEN alterations were less frequent in Black men than White men (31 men [15.7%] vs 194 men [26.3%]; P = .003), as were TMPRSS alterations (14 men [7.1%] vs 155 men [21.0%]; P < .001). No other differences were seen in the 15 most frequently altered genes, including TP53, AR, CDK12, RB1, and PIK3CA. Matched targeted therapy was given less frequently in Black men than White men (22 men [33.5%] vs 115 men [53.5%]; P = .008). There were no differences in response to targeted therapy or survival between the two cohorts. This cohort study of men with mCRPC found higher frequency of MMRD or MSI-H and lower frequency of PTEN and TMPRSS alterations in Black men compared with White men. Although Black men received targeted therapy less frequently than White men, no differences were observed in clinical outcomes.

Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks

Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics.

Standardization of imaging methods for machine learning in neuro-oncology.

Radiomics is a novel technique in which quantitative phenotypes or features are extracted from medical images. Machine learning enables analysis of large quantities of medical imaging data generated by radiomic feature extraction. A growing number of studies based on these methods have developed tools for neuro-oncology applications. Despite the initial promises, many of these imaging tools remain far from clinical implementation. One major limitation hindering the use of these models is their lack of reproducibility when applied across different institutions and clinical settings. In this article, we discuss the importance of standardization of methodology and reporting in our effort to improve reproducibility. Ongoing efforts of standardization for neuro-oncological imaging are reviewed. Challenges related to standardization and potential disadvantages in over-standardization are also described. Ultimately, greater multi-institutional collaborative effort is needed to provide and implement standards for data acquisition and analysis methods to facilitate research results to be interoperable and reliable for integration into different practice environments.

Evaluating the Soft Tissue Sarcoma Paradigm for the Local Management of Extraskeletal Ewing Sarcoma.

We reviewed our experience treating patients with localized extraskeletal Ewing sarcoma (EES) to determine optimal local management strategies for this rare disease. Sixty patients with localized EES treated at our institution between 1994 and 2018 were reviewed. The Kaplan-Meier method was used to estimates disease outcomes. The median follow-up time was 74 months (interquartile range [IQR], 17-121). Half the patients (n = 30) received combined-modality local therapy (CMT) with both surgery and radiation therapy (RT), whereas the other half received single-modality local therapy (SMT) with either surgery or RT. All patients received chemotherapy. The 5-year overall survival was 76%. Twenty-two patients (37%) developed recurrence at a median time of 15 months (IQR, 5-56 months) resulting in 3-year progression-free survival (PFS) of 65%. On univariate analysis, the use of both neoadjuvant and adjuvant chemotherapy was associated with improved 5-year PFS (71% vs. 50%, p = .04) compared with those who received one or the other. Furthermore, 11 patients (18%) developed local recurrences at a median time of 14 months (IQR, 2-19 months), resulting in a 5-year local control (LC) rate of 77%. Use of CMT was not associated with improved LC (83% vs. 72% SMT, p = .41). Also, use of CMT was the only factor associated with poorer disease-specific survival (vs. SMT; hazard ratio, 3.4; p = .047; 95% confidence interval, 1.01-11.4). For patients with EES, CMT was not associated with a decreased rate of local relapse. These data suggest that SMT alone may be sufficient for LC in select patients. A multi-institutional collaborative effort should be considered to validate these findings. Extraskeletal Ewing sarcoma is a rare chemosensitive sarcoma whose clinical course more closely follows Ewing sarcoma of bone rather than that of other soft tissue sarcomas. Based on this study, combined-modality local therapy did not confer a local control advantage compared with single-modality local therapy. Therefore, single-modality local therapy is likely adequate in select patients with favorable disease features, which has the advantage of ensuring prompt administration of systemic therapy. A multi-institutional collaborative effort is warranted to determine which patients may benefit from de-escalated local therapy.

Rethinking the Soft Tissue Sarcoma Paradigm for the Local Management of Extraskeletal Ewing Sarcoma

It remains an unsettled debated as to whether the optimal local therapy for patients with extraskeletal Ewing sarcoma (EES) is combined modality surgery and RT (CMT) versus single modality local therapy (SMT) as is done for Ewing sarcoma arising in bone. Given our comparatively large experience managing patients with EES, we sought to evaluate outcomes of this rare disease and better define optimal local management strategies. We conducted a retrospective review of 60 consecutive patients who received treatment for localized EES at our institution between 1994 and 2018. Kaplan-Meier curves were used to estimate oncologic outcomes, and a Cox proportional hazards regression was conducted to identify associated factors. The median follow-up time was 74 months (m) (interquartile range [IQR], 17-121), and the median age was 28 years (IQR, 21-39) with a 2:1 male:female distribution. Most patients had non-extremity tumors (60%: trunk, n = 32 of which 9 were visceral; H&N n = 4) and the median tumor size was 7cm (IQR, 4-11). Local treatment consisted of CMT in 50% of patients (n = 30), whereas the other 50% received SMT (surgery n = 26 or RT n = 4). The median RT doses were: preop 50 Gy (n = 16; IQR, 50-50), postop 56 Gy (n = 14; IQR, 45-60), and definitive 60 Gy (IQR, 52.8-60). All patients received chemo with 18 (30%) receiving only neoadjuvant, 17 (28%) only adjuvant, and 25 (41%) receiving both. The 5 year (y) overall survival was 76%. There were 13 (22%) deaths attributable to disease resulting in a median DSS of 57 m (IQR, 10-97) and a 5y DSS of 77%. Seventeen patients (28%) developed distant metastasis (DM), most commonly to the lung (n = 7) at a median time of 15 m (IQR, 4-42) resulting in a 5 y DM free survival of 70%. Eleven patients (18%) developed local recurrence at a median time of 14 m (IQR 2-19) for a 5-y local control (LC) of 77%. On univariate analysis, only tumor location was associated with worse LC: non-extremity (5y LC 65% vs extremity 95%, P = 0.03); use of CMT was not associated with improved LC (5y LC 83% vs. SMT 72%, P = 0.41). Ultimately, no factors emerged as significantly associated with LC on multivariate analysis. Of the 22 patients that relapsed, the median survival time after relapse was 16 m (IQR 6-56). At least 5 patients (local n = 3; distant n = 2) were ultimately salvaged; all received chemo followed by additional local therapy (surgery and/or RT). Patients with localized EES have favorable outcomes compared to Ewing sarcoma of bone but worse LC compared to that of other STS. In our series, CMT was not associated with a decreased rate of local relapse. These data suggest that SMT alone may be sufficient for LC in select patients, which has the added benefit of decreasing any delays of initiating systemic therapy. A multi-institutional collaborative effort should be considered to validate these findings.

30 Day Survival in Heart Transplant Patients Bridged with Short Term Mechanical Circulatory Support: Are We Failing to Rescue?

UNOS heart allocation policy has changed with patients receiving temporary MCS being prioritized. Failure to rescue (FTR) describes a phenomenon where patients with in-hospital complications demonstrate increased 30 day mortality. We sought to identify 30 day mortality rates in BTT recipients supported with temporary MCS. International Society for Heart and Lung Transplantation (ISHLT) provided de-identified patient data. Between 2005 and 2018, 63,866 heart transplant recipients ≥18 years were identified. Of these, 658 (1.0%) were bridged with VA-ECMO, 1991 (3.1%) with IABP, 11960 (18.7%) with continuous-flow LVAD. Patients who underwent re-operative heart transplantation were excluded. Patients who were pre-operatively supported with VA-ECMO were younger when compared to the IABP or LVAD group. Donors were more likely to be older, female, and with a slightly longer ischemic time when transplanted into VA-ECMO recipients compared to IABP or LVAD recipients (Table 1). Kaplan-Meier analysis demonstrated a post-transplant survival of 81% (124/658 VA-ECMO), 95% (104/1991 IABP), 95% (552/11960 LVAD), at 30 days (p < 0.001). BTT with VA-ECMO is associated with decreased 30-day survival compared to IABP or LVADs. With the prioritization of patients transplanted from temporary MCS, multi-institutional collaborative efforts are necessary to define if FTR versus specific donor or recipient factors contribute to this survival differential.

CHLSOC: the Chilean Soil Organic Carbon database, a multi-institutional collaborative effort

Abstract. A critical aspect of predicting soil organic carbon (SOC) concentrations is the lack of available soil information; where information on soil characteristics is available, it is usually focused on regions of high agricultural interest. To date, in Chile, a large proportion of the SOC data have been collected in areas of intensive agricultural or forestry use; however, vast areas beyond these forms of land use have few or no soil data available. Here we present a new SOC database for the country, which is the result of an unprecedented national effort under the framework of the Global Soil Partnership. This partnership has helped build the largest database of SOC to date in Chile, named the Chilean Soil Organic Carbon database (CHLSOC), comprising 13 612 data points compiled from numerous sources, including unpublished and difficult-to-access data. The database will allow users to fill spatial gaps where no SOC estimates were publicly available previously. Presented values of SOC range from 6×10-5 % to 83.3 %, reflecting the variety of ecosystems that exist in Chile. The database has the potential to inform and test current models that predict SOC stocks and dynamics at larger spatial scales, thus enabling benefits from the richness of geochemical, topographic and climatic variability in Chile. The database is freely available to registered users at https://doi.org/10.17605/OSF.IO/NMYS3 (Pfeiffer et al., 2019b) under the Creative Commons Attribution 4.0 International Public License.

Near haploidization is a genomic hallmark which defines a molecular subgroup of giant cell glioblastoma.

BackgroundGiant cell glioblastoma (gcGBM) is a rare histologic subtype of glioblastoma characterized by numerous bizarre multinucleate giant cells and increased reticulin deposition. Compared with conventional isocitrate dehydrogenase (IDH)-wildtype glioblastomas, gcGBMs typically occur in younger patients and are generally associated with an improved prognosis. Although prior studies of gcGBMs have shown enrichment of genetic events, such as TP53 alterations, no defining aberrations have been identified. The aim of this study was to evaluate the genomic profile of gcGBMs to facilitate more accurate diagnosis and prognostication for this entity.MethodsThrough a multi-institutional collaborative effort, we characterized 10 gcGBMs by chromosome studies, single nucleotide polymorphism microarray analysis, and targeted next-generation sequencing. These tumors were subsequently compared to the genomic and epigenomic profile of glioblastomas described in The Cancer Genome Atlas (TCGA) dataset.ResultsOur analysis identified a specific pattern of genome-wide massive loss of heterozygosity (LOH) driven by near haploidization in a subset of glioblastomas with giant cell histology. We compared the genomic signature of these tumors against that of all glioblastomas in the TCGA dataset (n = 367) and confirmed that our cohort of gcGBMs demonstrated a significantly different genomic profile. Integrated genomic and histologic review of the TCGA cohort identified 3 additional gcGBMs with a near haploid genomic profile.ConclusionsMassive LOH driven by haploidization represents a defining molecular hallmark of a subtype of gcGBM. This unusual mechanism of tumorigenesis provides a diagnostic genomic hallmark to evaluate in future cases, may explain reported differences in survival, and suggests new therapeutic vulnerabilities.

AI Meets Exascale Computing: Advancing Cancer Research With Large-Scale High Performance Computing.

The application of data science in cancer research has been boosted by major advances in three primary areas: (1) Data: diversity, amount, and availability of biomedical data; (2) Advances in Artificial Intelligence (AI) and Machine Learning (ML) algorithms that enable learning from complex, large-scale data; and (3) Advances in computer architectures allowing unprecedented acceleration of simulation and machine learning algorithms. These advances help build in silico ML models that can provide transformative insights from data including: molecular dynamics simulations, next-generation sequencing, omics, imaging, and unstructured clinical text documents. Unique challenges persist, however, in building ML models related to cancer, including: (1) access, sharing, labeling, and integration of multimodal and multi-institutional data across different cancer types; (2) developing AI models for cancer research capable of scaling on next generation high performance computers; and (3) assessing robustness and reliability in the AI models. In this paper, we review the National Cancer Institute (NCI) -Department of Energy (DOE) collaboration, Joint Design of Advanced Computing Solutions for Cancer (JDACS4C), a multi-institution collaborative effort focused on advancing computing and data technologies to accelerate cancer research on three levels: molecular, cellular, and population. This collaboration integrates various types of generated data, pre-exascale compute resources, and advances in ML models to increase understanding of basic cancer biology, identify promising new treatment options, predict outcomes, and eventually prescribe specialized treatments for patients with cancer.

A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States.

Influenza infects an estimated 9-35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multiinstitution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the United States for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of seven targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the United States, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1 wk, 2 wk, and 3 wk ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision making.

Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging

Deep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment outcome, exact electrode placement is required. Moreover, to analyze the relationship between electrode location and clinical results, a precise reconstruction of electrode placement is required, posing specific challenges to the field of neuroimaging. Since 2014 the open source toolbox Lead-DBS is available, which aims at facilitating this process. The tool has since become a popular platform for DBS imaging. With support of a broad community of researchers worldwide, methods have been continuously updated and complemented by new tools for tasks such as multispectral nonlinear registration, structural/functional connectivity analyses, brain shift correction, reconstruction of microelectrode recordings and orientation detection of segmented DBS leads. The rapid development and emergence of these methods in DBS data analysis require us to revisit and revise the pipelines introduced in the original methods publication.Here we demonstrate the updated DBS and connectome pipelines of Lead-DBS using a single patient example with state-of-the-art high-field imaging as well as a retrospective cohort of patients scanned in a typical clinical setting at 1.5T. Imaging data of the 3T example patient is co-registered using five algorithms and nonlinearly warped into template space using ten approaches for comparative purposes. After reconstruction of DBS electrodes (which is possible using three methods and a specific refinement tool), the volume of tissue activated is calculated for two DBS settings using four distinct models and various parameters. Finally, four whole-brain tractography algorithms are applied to the patient‘s preoperative diffusion MRI data and structural as well as functional connectivity between the stimulation volume and other brain areas are estimated using a total of eight approaches and datasets. In addition, we demonstrate impact of selected preprocessing strategies on the retrospective sample of 51 PD patients. We compare the amount of variance in clinical improvement that can be explained by the computer model depending on the preprocessing method of choice.This work represents a multi-institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field.

Replicating an Educational OSCE Project for Skill Assessment of Junior Anesthesiology Residents at Multiple Institutions: A Qualitative Description

Educational research projects are often developed and implemented at a single institution. However, the research project methods and results may not be generalizable and able to be replicated successfully at other institutions. The aim of this study was to investigate the process of replicating an effective educational Objective Structured Clinical Examination (OSCE) event at multiple other institutions. An OSCE event was initially designed and implemented at the primary institution to assess the skill level of junior residents on the performance of basic anesthesia tasks. After the initial implementation, additional institutions were recruited to participate in a replication of this OSCE event at their own institutions. The primary institution provided the OSCE scenarios, assessment tools, rater training, and resident participant instructions. The participating secondary institutions' (n = 4) event managers obtained Institutional Review Board [IRB] approval, developed the event schedule, assigned faculty evaluators, and organized the simulation space at their own medical centers. The events were assessed by the secondary institutions' resident and faculty participants via an anonymous survey regarding the event's content and their perception of its educational value. We replicated a complex educational OSCE event, developed and implemented at 1 institution, at 4 other institutions. Resident participants (n = 60), participating faculty (n = 24), and event directors (n = 4) indicated a high level of appreciation for the OSCE event. Using a structured approach, educational OSCE events can be successfully replicated at multiple institutions. Organization of multi-institutional studies and collaborative efforts is complex. This study illustrates 1 example of how to successfully approach multi-institutional educational projects.

Illustrating and analyzing the processes of multi-institutional collaboration: Lessons learnt at Howard University Hospital.

Multi-institutional collaboration offers a promising approach to the dissemination of resources for capacity building and the improvement of the training of new investigators and residents, especially in areas of novel curricular content. Physicians should keep pace with the rapid growth of curricular content in an era of restricted resources. Such collaborations, in which educational entities work together and share resources and infrastructure, have been employed in health care to improve quality of care, capacity building, disparity reduction, and resident training. This paper examines a federally funded multi-institutional collaboration for the project STRIDE (Seek, Treat, Reach to Identify Pretrial Defendants Enhancement) between Yale University, George Mason University (GMU), and Howard University, a Historically Black University. The STRIDE study collaboration focused on mental health, opioid addiction, and infectious disease/HIV among Africans Americans involved in CJS (Criminal Justice System). We discuss some of the challenges and benefits of collaborative research projects conducted at Historically Black Colleges and University (HBCUs) and highlight the educational opportunities created by such collaborations for residents and other trainees, leading to the development of independent investigators through multi-institutional, structured collaborative research. We identify some unique challenges such as substance use, race, stigma, incarceration among participants, and the cultural and power difference between participating institutions, and thereby address these issues and how it impacted the course of the multi-institutional collaborative effort.