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Youngwoo Cho,1 Theodore Karrison,2 Matthew M Jack,3 Anish R Choksi,3,4 Randall W Knoebel,3,4 Kiang-Teck J Yeo,1,5 Samuel L Volchenboum,6 Russell Z Szmulewitz,1,7 Everett E Vokes,7 Mark J Ratain,1,3,7 Peter H O’Donnell1,3,7 1Committee on Clinical Pharmacology and Pharmacogenomics, University of Chicago, Chicago, IL, USA; 2Biostatistics, Department of Health Service, University of Chicago, Chicago, IL, USA; 3Center for Personalized Therapeutics, University of Chicago, Chicago, IL, USA; 4Department of Pharmacy, University of Chicago Medicine, Chicago, IL, USA; 5Department of Pathology, University of Chicago Medical Center and Biological Sciences, Chicago, IL, USA; 6Department of Pediatrics, University of Chicago, Chicago, IL, USA; 7Section of Hematology/Oncology, Department of Medicine, University of Chicago Medical Center and Biological Sciences, Chicago, IL, USACorrespondence: Peter H O’Donnell, Email podonnel@bsd.uchicago.eduBackground: Cancer patients frequently suffer from pain, often managed with opioids. However, undertreated pain remains a significant concern. Opioid effectiveness varies due to genetic differences in how individuals metabolize some of these medications. While prior research suggests promise in tailoring opioid prescriptions based on CYP2D6 genetic makeup, its application in cancer pain management remains limited. This study investigates the potential benefits of preemptive CYP2D6 genotyping for cancer patients initiating opioid therapy, focusing on codeine, tramadol, and hydrocodone, whose efficacy is demonstrably impacted by CYP2D6 variations.Methods: This is a randomized, prospective study to evaluate the effects of preemptive pharmacogenomic (PGx) testing on opioid dosing decisions/selections and composite pain score in oncology patients. Patients with metastatic solid tumors for whom near-future opioid therapy is anticipated will be randomized to PGx and control arms, stratified by the presence or absence of bony metastases and history of opioid use. In the PGx arm, patients will be preemptively tested using a panel of pharmacogenomic genetic variants, and providers will receive opioid dosing guidance via an electronic medical record-embedded clinical decision support tool. In the control arm, pain prescribing will occur per standard of care without genotype information.Planned Outcome: The primary study outcome will be composite pain intensity during the first 45 days after an index opioid prescription for codeine, tramadol, or hydrocodone. Safety will be assessed by comparing opioid-related adverse event rates between the two study arms. Secondary outcomes will include rates of hospitalization/emergency room visits, cumulative morphine equivalents received, and type of first opioid prescribed.Keywords: pharmacogenomics, CYP2D6, codeine, tramadol, hydrocodone, genetic testing, pain score

Monogenic diabetes mellitus (MDM) is a group of relatively rare disorders caused by pathogenic variants in key genes that result in hyperglycemia. Lack of identified cases, along with absent data standards, and limited collaboration across institutions have hindered research progress. To address this, the UChicago Monogenic Diabetes Registry (UCMDMR) and UChicago Data for the Common Good (D4CG) created a national consortium of MDM research institutions called the PREcision DIabetes ConsorTium (PREDICT). Following the D4CG model, PREDICT has successfully established a multicenter MDM data commons. PREDICT has created a consensus data dictionary that will be utilized to address critical gaps in understanding of these rare types of diabetes. This approach may be useful for other rare conditions that would benefit from access to harmonized pooled data., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: MSU is involved in a research collaboration between Novo Nordisk and the Broad Institute.

@PedsDataCommons discusses automated approaches for data extraction from electronic health records.

ChatGPT and other artificial intelligence (AI) systems have captivated the attention of healthcare providers and researchers for their potential to improve care processes and outcomes. While these technologies hold promise to automate processes, increase efficiency, and reduce cognitive burden, their use also carries risks. In this commentary, we review basic concepts of AI, outline some of the capabilities and limitations of currently available tools, discuss current and future applications in pediatric hematology/oncology, and provide an evaluation and implementation framework that can be used by pediatric hematologist/oncologists considering the use of AI in clinical practice., (© 2024 Wiley Periodicals LLC.)

Data commons have proven to be an indispensable avenue for advancing pediatric cancer research by serving as unified information technology platforms that, when coupled with data standards, facilitate data sharing. The Pediatric Cancer Data Commons, the flagship project of Data for the Common Good (D4CG), collaborates with disease-based consortia to facilitate development of clinical data standards, harmonization and pooling of clinical data from disparate sources, establishment of governance structure, and sharing of clinical data. In the interest of international collaboration, researchers developed the Hodgkin Lymphoma Data Collaboration and forged a relationship with the Pediatric Cancer Data Commons to establish a data commons for pediatric Hodgkin lymphoma. Herein, we describe the progress made in the formation of Hodgkin Lymphoma Data Collaboration and foundational goals to advance pediatric Hodgkin lymphoma research., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Purpose: Although the International Neuroblastoma Risk Group Data Commons (INRGdc) has enabled seminal large cohort studies, the research is limited by the lack of real-world, electronic health record (EHR) treatment data. To address this limitation, we evaluated the feasibility of extracting treatment data directly from EHRs using the REDCap Clinical Data Interoperability Services (CDIS) module for future submission to the INRGdc., Methods: Patients enrolled on the Children's Oncology Group neuroblastoma biology study ANBL00B1 (ClinicalTrials.gov identifier: NCT00904241) who received care at the University of Chicago (UChicago) or the Vanderbilt University Medical Center (VUMC) after the go-live dates for the Fast Healthcare Interoperability Resources (FHIR)-compliant EHRs were identified. Antineoplastic drug orders were extracted using the CDIS module. To validate the CDIS output, antineoplastic agents extracted through FHIR were compared with those queried through EHR relational databases (UChicago's Clinical Research Data Warehouse and VUMC's Epic Clarity database) and manual chart review., Results: The analytic cohort consisted of 41 patients at UChicago and 32 VUMC patients. Antineoplastic drug orders were identified in the extracted EHR records of 39 (95.1%) UChicago patients and 26 (81.3%) VUMC patients. Manual chart review confirmed that patients with missing (n = 8) or discontinued (n = 1) orders in the CDIS output did not receive antineoplastic agents during the timeframe of the study. More than 99% of the antineoplastic drug orders in the EHR relational databases were identified in the corresponding CDIS output., Conclusion: Our results demonstrate the feasibility of extracting EHR treatment data with high fidelity using HL7-FHIR via REDCap CDIS for future submission to the INRGdc.

Objective: The Pediatric Cancer Data Commons (PCDC)-a project of Data for the Common Good-houses clinical pediatric oncology data and utilizes the open-source Gen3 platform. To meet the needs of end users, the PCDC development team expanded the out-of-box functionality and developed additional custom features that should be useful to any group developing similar data commons., Materials and Methods: Modifications of the PCDC data portal software were implemented to facilitate desired functionality., Results: Newly developed functionality includes updates to authorization methods, expansion of filtering capabilities, and addition of data analysis functions., Discussion: We describe the process by which custom functionalities were developed. Features are open source and available to be implemented and adapted to suit needs of data portals that utilize the Gen3 platform., Conclusion: Data portals are indispensable tools for facilitating data sharing. Open-source infrastructure facilitates a modular and collaborative approach for meeting needs of end users and stakeholders., Competing Interests: K.D.W. receives subaward funding relating to work on the PCDC. L.G., B.F., and S.L.V. are employed by University of Chicago and contribute to the PCDC in the course of their employment. M.F. and R.L.G. are employed by University of Chicago and contribute to the Gen3 platform in the course of their employment., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

The Pediatric Cancer Data Commons (PCDC) comprises an international community whose ironclad commitment to data sharing is combatting pediatric cancer in an unprecedented way. The byproduct of their data sharing efforts is a gold-standard consensus data model covering many types of pediatric cancer. This article describes an effort to utilize SSSOM, an emerging specification for semantically-rich data mappings, to provide a "hub and spoke" model of mappings from several common data models (CDMs) to the PCDC data model. This provides important contributions to the research community, including: 1) a clear view of the current coverage of these CDMs in the domain of pediatric oncology, and 2) a demonstration of creating standardized mappings. These mappings can allow downstream crosswalk for data transformation and enhance data sharing. This can guide those who currently create and maintain brittle ad hoc data mappings in order to utilize the growing volume of viable research data., (©2023 AMIA - All rights reserved.)

Background: Racial/ethnic survival disparities in neuroblastoma were first reported more than a decade ago. We sought to investigate if these disparities have persisted with current era therapy., Methods: Two patient cohorts were identified in the International Neuroblastoma Risk Group Data Commons (INRGdc) (Cohort 1: diagnosed 2001-2009, n=4359; Cohort 2: diagnosed 2010-2019, n=4891). Chi-squared tests were used to assess the relationship between race/ethnicity and clinical and biologic features. Survival was estimated by the Kaplan-Meier method. Cox proportional hazards regression analyses were performed to investigate the association between racial/ethnic groups and prognostic markers., Results: Significantly higher 5-year event-free survival (EFS) and overall survival (OS) were observed for Cohort 2 compared to Cohort 1 (P<0.001 and P<0.001, respectively). Compared to White patients, Black patients in both cohorts had a higher proportion of high-risk disease (Cohort 1: P<0.001; Cohort 2: P<0.001) and worse EFS (Cohort 1: P<0.001; Cohort 2 P<0.001) and OS (Cohort 1: P<0.001; Cohort 2: P<0.001). In Cohort 1, Native Americans also had a higher proportion of high-risk disease (P=0.03) and inferior EFS/OS. No significant survival disparities were observed for low- or intermediate-risk patients in either cohort or high-risk patients in Cohort 1. Hispanic patients with high-risk disease in Cohort 2 had significantly inferior OS (P=0.047). Significantly worse OS, but not EFS, (P=0.006 and P=0.02, respectively) was also observed among Black and Hispanic patients assigned to receive post-Consolidation dinutuximab on clinical trials (n=885)., Conclusion: Racial/ethnic survival disparities have persisted over time and were observed among high-risk patients assigned to receive post-Consolidation dinutuximab., Competing Interests: Conflict of Interest/Financial Disclosures: Mark A. Applebaum: consultancy fees from Illumina Radiopharmaceuticals. Arlene Naranjo: serves on a DSMC for Novartis. Sam L. Volchenboum: Founder of Litmus Health, Inc. Tara O. Henderson: research funding from Seattle Genetics. Susan L. Cohn: stock ownership in Pfizer, Merck, and Lilly; served on advisory boards for Y-mAbs Therapeutics and US World Meds. Ami V. Desai: stock ownership in Pfizer and Viatris; consultancy/advisory board fees from Ology Medical Education, YMabs Therapeutics, Glaxo Smith-Kline; travel/accommodation expenses from YMabs Therapeutics. The remaining authors made no disclosures.

Data-driven basic, translational, and clinical research has resulted in improved outcomes for children, adolescents, and young adults (AYAs) with pediatric cancers. However, challenges in sharing data between institutions, particularly in research, prevent addressing substantial unmet needs in children and AYA patients diagnosed with certain pediatric cancers. Systematically collecting and sharing data from every child and AYA can enable greater understanding of pediatric cancers, improve survivorship, and accelerate development of new and more effective therapies. To accomplish this goal, the Childhood Cancer Data Initiative (CCDI) was launched in 2019 at the National Cancer Institute. CCDI is a collaborative community endeavor supported by a 10-year, $50-million (in US dollars) annual federal investment. CCDI aims to learn from every patient diagnosed with a pediatric cancer by designing and building a data ecosystem that facilitates data collection, sharing, and analysis for researchers, clinicians, and patients across the cancer community. For example, CCDI's Molecular Characterization Initiative provides comprehensive clinical molecular characterization for children and AYAs with newly diagnosed cancers. Through these efforts, the CCDI strives to provide clinical benefit to patients and improvements in diagnosis and care through data-focused research support and to build expandable, sustainable data resources and workflows to advance research well past the planned 10 years of the initiative. Importantly, if CCDI demonstrates the success of this model for pediatric cancers, similar approaches can be applied to adults, transforming both clinical research and treatment to improve outcomes for all patients with cancer.

Purpose: Matching patients to clinical trials is cumbersome and costly. Attempts have been made to automate the matching process; however, most have used a trial-centric approach, which focuses on a single trial. In this study, we developed a patient-centric matching tool that matches patient-specific demographic and clinical information with free-text clinical trial inclusion and exclusion criteria extracted using natural language processing to return a list of relevant clinical trials ordered by the patient's likelihood of eligibility., Materials and Methods: Records from pediatric leukemia clinical trials were downloaded from ClinicalTrials.gov. Regular expressions were used to discretize and extract individual trial criteria. A multilabel support vector machine (SVM) was trained to classify sentence embeddings of criteria into relevant clinical categories. Labeled criteria were parsed using regular expressions to extract numbers, comparators, and relationships. In the validation phase, a patient-trial match score was generated for each trial and returned in the form of a ranked list for each patient., Results: In total, 5,251 discretized criteria were extracted from 216 protocols. The most frequent criterion was previous chemotherapy/biologics (17%). The multilabel SVM demonstrated a pooled accuracy of 75%. The text processing pipeline was able to automatically extract 68% of eligibility criteria rules, as compared with 80% in a manual version of the tool. Automated matching was accomplished in approximately 4 seconds, as compared with several hours using manual derivation., Conclusion: To our knowledge, this project represents the first open-source attempt to generate a patient-centric clinical trial matching tool. The tool demonstrated acceptable performance when compared with a manual version, and it has potential to save time and money when matching patients to trials.

In this article, we will discuss the genesis, evolution, and progress of the INternational Soft Tissue SaRcoma ConsorTium (INSTRuCT), which aims to foster international research and collaboration focused on pediatric soft tissue sarcoma. We will begin by highlighting the current state of clinical research for pediatric soft tissue sarcomas, including rhabdomyosarcoma and non-rhabdomyosarcoma soft tissue sarcoma. We will then explore challenges and research priorities, describe the development of INSTRuCT, and discuss how the consortium aims to address key research priorities., (© 2022 The Authors. Pediatric Blood & Cancer published by Wiley Periodicals LLC.)

Germ cell tumours (GCTs) are a heterogeneous group of rare neoplasms that present in different anatomical sites and across a wide spectrum of patient ages from birth through to adulthood. Once these strata are applied, cohort numbers become modest, hindering inferences regarding management and therapeutic advances. Moreover, patients with GCTs are treated by different medical professionals including paediatric oncologists, neuro-oncologists, medical oncologists, neurosurgeons, gynaecological oncologists, surgeons, and urologists. Silos of care have thus formed, further hampering knowledge dissemination between specialists. Dedicated biobank specimen collection is therefore critical to foster continuous growth in our understanding of similarities and differences by age, gender, and site, particularly for rare cancers such as GCTs. Here, the Malignant Germ Cell International Consortium provides a framework to create a sustainable, global research infrastructure that facilitates acquisition of tissue and liquid biopsies together with matched clinical data sets that reflect the diversity of GCTs. Such an effort would create an invaluable repository of clinical and biological data which can underpin international collaborations that span professional boundaries, translate into clinical practice, and ultimately impact patient outcomes., (© 2022. The Author(s).)

To evaluate the clinical impact of molecular tumor profiling (MTP) with targeted sequencing panel tests, pediatric patients with extracranial solid tumors were enrolled in a prospective observational cohort study at 12 institutions. In the 345-patient analytical population, median age at diagnosis was 12 years (range 0-27.5); 298 patients (86%) had 1 or more alterations with potential for impact on care. Genomic alterations with diagnostic, prognostic or therapeutic significance were present in 61, 16 and 65% of patients, respectively. After return of the results, impact on care included 17 patients with a clarified diagnostic classification and 240 patients with an MTP result that could be used to select molecularly targeted therapy matched to identified alterations (MTT). Of the 29 patients who received MTT, 24% had an objective response or experienced durable clinical benefit; all but 1 of these patients received targeted therapy matched to a gene fusion. Of the diagnostic variants identified in 209 patients, 77% were gene fusions. MTP with targeted panel tests that includes fusion detection has a substantial clinical impact for young patients with solid tumors., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Purpose: The global pediatric oncology clinical research landscape, particularly in Central and South America, Africa, and Asia, which bear the highest burden of global childhood cancer cases, is less characterized in the literature. Review of how existing pediatric cancer clinical trial groups internationally have been formed and how their research goals have been pursued is critical for building global collaborative research and data-sharing efforts, in line with the WHO Global Initiative for Childhood Cancer., Methods: A narrative literature review of collaborative groups performing pediatric cancer clinical research in each continent was conducted. An inventory of research groups was assembled and reviewed by current pediatric cancer regional and continental leaders. Each group was narratively described with identification of common structural and research themes among consortia., Results: There is wide variability in the structure, history, and goals of pediatric cancer clinical trial collaborative groups internationally. Several continental regions have longstanding endogenously-formed clinical trial groups that have developed and published numerous adapted treatment regimens to improve outcomes, whereas other regions have consortia focused on developing foundational database registry infrastructure supported by large multinational organizations or twinning relationships., Conclusion: There cannot be a one-size-fits-all approach to increasing collaboration between international pediatric cancer clinical trial groups, as this requires a nuanced understanding of local stakeholders and resources necessary to form partnerships. Needs assessments, performed either by local consortia or in conjunction with international partners, have generated productive clinical trial infrastructure. To achieve the goals of the Global Initiative for Childhood Cancer, global partnerships must be sufficiently granular to account for the distinct needs of each collaborating group and should incorporate grassroots approaches, robust twinning relationships, and implementation science., Competing Interests: Pamela KearnsConsulting or Advisory Role: Bristol Myers Squibb, Bayer, AstraZenecaResearch Funding: Pfizer (Inst), Bayer (Inst) A. Lindsay FrazierStock and Other Ownership Interests: Decibel TherapeuticsConsulting or Advisory Role: Decibel Therapeutics Samuel L. VolchenboumStock and Other Ownership Interests: Litmus HealthConsulting or Advisory Role: AccordantTravel, Accommodations, Expenses: Sanford HealthNo other potential conflicts of interest were reported.

Purpose: Molecular tumor profiling is becoming a routine part of clinical cancer care, typically involving tumor-only panel testing without matched germline. We hypothesized that integrated germline sequencing could improve clinical interpretation and enhance the identification of germline variants with significant hereditary risks., Materials and Methods: Tumors from pediatric patients with high-risk, extracranial solid malignancies were sequenced with a targeted panel of cancer-associated genes. Later, germline DNA was analyzed for a subset of these genes. We performed a post hoc analysis to identify how an integrated analysis of tumor and germline data would improve clinical interpretation., Results: One hundred sixty participants with both tumor-only and germline sequencing reports were eligible for this analysis. Germline sequencing identified 38 pathogenic or likely pathogenic variants among 35 (22%) patients. Twenty-five (66%) of these were included in the tumor sequencing report. The remaining germline pathogenic or likely pathogenic variants were single-nucleotide variants filtered out of tumor-only analysis because of population frequency or copy-number variation masked by additional copy-number changes in the tumor. In tumor-only sequencing, 308 of 434 (71%) single-nucleotide variants reported were present in the germline, including 31% with suggested clinical utility. Finally, we provide further evidence that the variant allele fraction from tumor-only sequencing is insufficient to differentiate somatic from germline events., Conclusion: A paired approach to analyzing tumor and germline sequencing data would be expected to improve the efficiency and accuracy of distinguishing somatic mutations and germline variants, thereby facilitating the process of variant curation and therapeutic interpretation for somatic reports, as well as the identification of variants associated with germline cancer predisposition., Competing Interests: Alanna J. Church Honoraria: The Jackson Laboratory Consulting or Advisory Role: AlphaSights, The Jackson Laboratory, Bayer Travel, Accommodations, Expenses: Bayer Laura B. Corson Employment: Sema4 Stock and Other Ownership Interests: Alnylam, Inovio Pharmaceuticals, Agios Consulting or Advisory Role: Biomatics Patents, Royalties, Other Intellectual Property: Patent from work done at H3 Biomedicine before 2016 Brennan Decker Employment: Foundation Medicine Stock and Other Ownership Interests: Avidea Technologies, Roche Consulting or Advisory Role: Foundation Medicine, Avidea Technologies Catherine M. Clinton Employment: Helix OpCo LLC Mark A. Applebaum Consulting or Advisory Role: Fennec Pharma Rochelle Bagatell Uncompensated Relationships: Ymabs Therapeutics Inc Steven G. DuBois Consulting or Advisory Role: Bayer, Amgen Research Funding: Merck (Inst), Roche/Genentech (Inst), Lilly (Inst), Curis (Inst), Loxo (Inst), BMS (Inst), Eisai (Inst), Pfizer (Inst), Turning Point Therapeutics (Inst), Bayer (Inst), Salarius Pharmaceuticals (Inst) Travel, Accommodations, Expenses: Roche/Genentech, Salarius Pharmaceuticals Uncompensated Relationships: Ymabs Therapeutics Inc Julia L. Glade-Bender Research Funding: Eisai (Inst), Lilly (Inst), Loxo (Inst), Roche/Genentech (Inst), Bayer (Inst) Patents, Royalties, Other Intellectual Property: Patent on a T lympboblastic lymphoma cell line, CUTLL1 Travel, Accommodations, Expenses: Amgen (Inst) Uncompensated Relationships: SpringWorks Therapeutics, Bristol Myers Squibb, Merck, Eisai Open Payments Link: https://openpaymentsdata.cms.gov/physician/708514 AeRang Kim Research Funding: Esai (Inst); AstraZeneca (Inst); Oncternal (Inst); Ascentage (Inst); BioAlta (Inst). Theodore W. Laetsch Consulting or Advisory Role: Novartis, Bayer, Cellectis, Aptitude Health, Clinical Education Alliance, Deciphera, Jumo Health, Massive Bio, Med Learning Group, Medscape, Physicans' Education Resource, Y-mAbs Therapeutics Research Funding: Pfizer (Inst), Novartis (Inst), Bayer (Inst), AbbVie (Inst), Amgen (Inst), Atara Biotherapeutics (Inst), Bristol Myers Squibb (Inst), Lilly (Inst), Epizyme (Inst), GlaxoSmithKline (Inst), Janssen (Inst), Jubilant Pharmaceuticals (Inst), Novella Clinical (Inst), Servier (Inst), Foundation Medicine (Inst), Merck Sharp & Dohme (Inst) Margaret E. Macy Stock and Other Ownership Interests: Johnson & Johnson, GE Healthcare, Varian Medical Systems Consulting or Advisory Role: Ymabs Therapeutics Inc Research Funding: Bayer (Inst), Ignyta (Inst), Roche (Inst), Lilly (Inst), Merck (Inst), Oncternal Therapeutics Inc (Inst), AbbVie (Inst), Jubilant DraxImage (Inst), Actuate Therapeutics (Inst) Patents, Royalties, Other Intellectual Property: Patent for non-invasive methods of leukemia cell detection with MRI/MRS—Patent No. 8894975 Luke Maese Honoraria: Jazz Pharmaceuticals Consulting or Advisory Role: Jazz Pharmaceuticals Amit J. Sabnis Consulting or Advisory Role: Cardinal Health Joshua D. Schiffman Employment: PEEL Therapeutics Leadership: PEEL Therapeutics Stock and Other Ownership Interests: ItRunsInMyFamily.com, PEEL Therapeutics Honoraria: Affymetrix Consulting or Advisory Role: N-of-One, Fabric Genomics Samuel L. Volchenboum Stock and Other Ownership Interests: Litmus Health Consulting or Advisory Role: Accordant Travel, Accommodations, Expenses: Sanford Health Daniel A. Weiser Consulting or Advisory Role: Illumina Radiopharmaceuticals Jonathan A. Nowak Research Funding: NanoString Technologies, Illumina Katherine A. Janeway Honoraria: Foundation Medicine, Takeda Consulting or Advisory Role: Bayer, Ipsen Travel, Accommodations, Expenses: Bayer Brian D. Crompton Employment: Acceleron Pharma (I) Stock and Other Ownership Interests: Acceleron Pharma (I) Research Funding: Gradalis Junne Kamihara Stock and Other Ownership Interests: PanTher Therapeutics (I), ROME Therapeutics (I), TellBio (I) Honoraria: Pfizer (I), NanoString Technologies (I), Foundation Medicine (I), Ikena Oncology (I) Consulting or Advisory Role: ROME Therapeutics (I), Third Rock Ventures (I), Tekla Capital Management (I) Research Funding: PureTech (I), Ribon Therapeutics (I), ACD Biotechne (I) Patents, Royalties, Other Intellectual Property: Patent on drug delivery device licensed to PanTher Therapeutics (I), Patents on Repeat RNA biomarkers and therapeutics licensed to Rome Therapeutics (I), Patents on Circulating Tumor Cell Biomarkers Licensed to TellBio Inc (I) No other potential conflicts of interest were reported. Alanna J. Church Honoraria: The Jackson Laboratory Consulting or Advisory Role: AlphaSights, The Jackson Laboratory, Bayer Travel, Accommodations, Expenses: Bayer Laura B. Corson Employment: Sema4 Stock and Other Ownership Interests: Alnylam, Inovio Pharmaceuticals, Agios Consulting or Advisory Role: Biomatics Patents, Royalties, Other Intellectual Property: Patent from work done at H3 Biomedicine before 2016 Brennan Decker Employment: Foundation Medicine Stock and Other Ownership Interests: Avidea Technologies, Roche Consulting or Advisory Role: Foundation Medicine, Avidea Technologies Catherine M. Clinton Employment: Helix OpCo LLC Mark A. Applebaum Consulting or Advisory Role: Fennec Pharma Rochelle Bagatell Uncompensated Relationships: Ymabs Therapeutics Inc Steven G. DuBois Consulting or Advisory Role: Bayer, Amgen Research Funding: Merck (Inst), Roche/Genentech (Inst), Lilly (Inst), Curis (Inst), Loxo (Inst), BMS (Inst), Eisai (Inst), Pfizer (Inst), Turning Point Therapeutics (Inst), Bayer (Inst), Salarius Pharmaceuticals (Inst) Travel, Accommodations, Expenses: Roche/Genentech, Salarius Pharmaceuticals Uncompensated Relationships: Ymabs Therapeutics Inc Julia L. Glade-Bender Research Funding: Eisai (Inst), Lilly (Inst), Loxo (Inst), Roche/Genentech (Inst), Bayer (Inst) Patents, Royalties, Other Intellectual Property: Patent on a T lympboblastic lymphoma cell line, CUTLL1 Travel, Accommodations, Expenses: Amgen (Inst) Uncompensated Relationships: SpringWorks Therapeutics, Bristol Myers Squibb, Merck, Eisai Open Payments Link: https://openpaymentsdata.cms.gov/physician/708514 AeRang Kim Research Funding: Esai (Inst); AstraZeneca (Inst); Oncternal (Inst); Ascentage (Inst); BioAlta (Inst). Theodore W. Laetsch Consulting or Advisory Role: Novartis, Bayer, Cellectis, Aptitude Health, Clinical Education Alliance, Deciphera, Jumo Health, Massive Bio, Med Learning Group, Medscape, Physicans' Education Resource, Y-mAbs Therapeutics Research Funding: Pfizer (Inst), Novartis (Inst), Bayer (Inst), AbbVie (Inst), Amgen (Inst), Atara Biotherapeutics (Inst), Bristol Myers Squibb (Inst), Lilly (Inst), Epizyme (Inst), GlaxoSmithKline (Inst), Janssen (Inst), Jubilant Pharmaceuticals (Inst), Novella Clinical (Inst), Servier (Inst), Foundation Medicine (Inst), Merck Sharp & Dohme (Inst) Margaret E. Macy Stock and Other Ownership Interests: Johnson & Johnson, GE Healthcare, Varian Medical Systems Consulting or Advisory Role: Ymabs Therapeutics Inc Research Funding: Bayer (Inst), Ignyta (Inst), Roche (Inst), Lilly (Inst), Merck (Inst), Oncternal Therapeutics Inc (Inst), AbbVie (Inst), Jubilant DraxImage (Inst), Actuate Therapeutics (Inst) Patents, Royalties, Other Intellectual Property: Patent for non-invasive methods of leukemia cell detection with MRI/MRS—Patent No. 8894975 Luke Maese Honoraria: Jazz Pharmaceuticals Consulting or Advisory Role: Jazz Pharmaceuticals Amit J. Sabnis Consulting or Advisory Role: Cardinal Health Joshua D. Schiffman Employment: PEEL Therapeutics Leadership: PEEL Therapeutics Stock and Other Ownership Interests: ItRunsInMyFamily.com, PEEL Therapeutics Honoraria: Affymetrix Consulting or Advisory Role: N-of-One, Fabric Genomics Samuel L. Volchenboum Stock and Other Ownership Interests: Litmus Health Consulting or Advisory Role: Accordant Travel, Accommodations, Expenses: Sanford Health Daniel A. Weiser Consulting or Advisory Role: Illumina Radiopharmaceuticals Jonathan A. Nowak Research Funding: NanoString Technologies, Illumina Katherine A. Janeway Honoraria: Foundation Medicine, Takeda Consulting or Advisory Role: Bayer, Ipsen Travel, Accommodations, Expenses: Bayer Brian D. Crompton Employment: Acceleron Pharma (I) Stock and Other Ownership Interests: Acceleron Pharma (I) Research Funding: Gradalis Junne Kamihara Stock and Other Ownership Interests: PanTher Therapeutics (I), ROME Therapeutics (I), TellBio (I) Honoraria: Pfizer (I), NanoString Technologies (I), Foundation Medicine (I), Ikena Oncology (I) Consulting or Advisory Role: ROME Therapeutics (I), Third Rock Ventures (I), Tekla Capital Management (I) Research Funding: PureTech (I), Ribon Therapeutics (I), ACD Biotechne (I) Patents, Royalties, Other Intellectual Property: Patent on drug delivery device licensed to PanTher Therapeutics (I), Patents on Repeat RNA biomarkers and therapeutics licensed to Rome Therapeutics (I), Patents on Circulating Tumor Cell Biomarkers Licensed to TellBio Inc (I) No other potential conflicts of interest were reported., (© 2021 by American Society of Clinical Oncology.)

Purpose: There is a need for an improved understanding of clinical and biologic risk factors in pediatric cancer to improve patient outcomes. Machine learning (ML) represents the application of computational inference from advanced statistical methods that can be applied to increasing amount of data available for study in pediatric oncology. The goal of this systematic review was to systematically characterize the state of ML in pediatric oncology and highlight advances and opportunities in the field., Methods: We conducted a systematic review of the Embase, Scopus, and MEDLINE databases for applications of ML in pediatric oncology. Query results from all three databases were aggregated and duplicate studies were removed., Results: A total of 42 unique articles that examined the applications of ML in pediatric oncology met inclusion criteria for review. We identified 20 studies of CNS tumors, 13 of solid tumors, and nine of leukemia. ML tasks included classification, prediction of treatment response, and dose optimization with a variety of methods being used including neural network, k-nearest neighbor, random forest, naive Bayes, and support vector machines. Strengths of the identified studies included matching or outperforming physician comparators via automated analysis and predicting therapeutic response. Common limitations included significant heterogeneity in reporting standards, clinical applicability, small sample sizes, and missing external validation cohorts., Conclusion: We identified areas where ML can enhance clinical care in ways that may not otherwise be achievable. Although ML promises enormous potential in improving diagnostics, decision making, and monitoring for children with cancer, the field remains in early stages and future work will be aided by standards and guidelines to ensure rigorous methodologic design and maximizing clinical utility., Competing Interests: Samuel L. VolchenboumStock and Other Ownership Interests: Litmus HealthConsulting or Advisory Role: AccordantTravel, Accommodations, Expenses: Sanford Health Anoop MayampurathStock and Other Ownership Interests: Litmus Health Mark A. ApplebaumConsulting or Advisory Role: Fennec PharmaNo other potential conflicts of interest were reported.

Purpose: Metaiodobenzylguanidine (MIBG) scans are a radionucleotide imaging modality that undergo Curie scoring to semiquantitatively assess neuroblastoma burden, which can be used as a marker of therapy response. We hypothesized that a convolutional neural network (CNN) could be developed that uses diagnostic MIBG scans to predict response to induction chemotherapy., Methods: We analyzed MIBG scans housed in the International Neuroblastoma Risk Group Data Commons from patients enrolled in the Children's Oncology Group high-risk neuroblastoma study ANBL12P1. The primary outcome was response to upfront chemotherapy, defined as a Curie score ≤ 2 after four cycles of induction chemotherapy. We derived and validated a CNN using two-dimensional whole-body MIBG scans from diagnosis and evaluated model performance using area under the receiver operating characteristic curve (AUC). We also developed a clinical classification model to predict response on the basis of age, stage, and MYCN amplification., Results: Among 103 patients with high-risk neuroblastoma included in the final cohort, 67 (65%) were responders. Performance in predicting response to upfront chemotherapy was equivalent using the CNN and the clinical model. Class-activation heatmaps verified that the CNN used areas of disease within the MIBG scans to make predictions. Furthermore, integrating predictions using a geometric mean approach improved detection of responders to upfront chemotherapy (geometric mean AUC 0.73 v CNN AUC 0.63, P < .05; v clinical model AUC 0.65, P < .05)., Conclusion: We demonstrate feasibility in using machine learning of diagnostic MIBG scans to predict response to induction chemotherapy for patients with high-risk neuroblastoma. We highlight improvements when clinical risk factors are also integrated, laying the foundation for using a multimodal approach to guiding treatment decisions for patients with high-risk neuroblastoma., Competing Interests: Anoop MayampurathConsulting or Advisory Role: Litmus Health Diana MichaelEmployment: AbbVie Arlene NaranjoConsulting or Advisory Role: Novartis Susan L. CohnStock and Other Ownership Interests: Merck, Stryker (I), Amgen (I), Pfizer (I), AbbVie, Lilly, Sanofi, Pfizer, Accelerated Medical Diagnostics, Novo Nordisk¸ Gilead Sciences, United Health Group, TevaHonoraria: Y-mAbs TherapeuticsResearch Funding: United Therapeutics (Inst), Merck (Inst)Open Payments Link: https://openpaymentsdata.cms.gov/physician/46569/summary Samuel L. VolchenboumStock and Other Ownership Interests: Litmus HealthConsulting or Advisory Role: AccordantTravel, Accommodations, Expenses: Sanford Health Mark A. ApplebaumConsulting or Advisory Role: Fennec PharmaNo other potential conflicts of interest were reported.

The international pediatric oncology community has a long history of research collaboration. In the United States, the 2019 launch of the Children's Cancer Data Initiative puts the focus on developing a rich and robust data ecosystem for pediatric oncology. In this spirit, we present here our experience in constructing the Pediatric Cancer Data Commons (PCDC) to highlight the significance of this effort in fighting pediatric cancer and improving outcomes and to provide essential information to those creating resources in other disease areas. The University of Chicago's PCDC team has worked with the international research community since 2015 to build data commons for children's cancers. We identified six critical features of successful data commons design and implementation: (1) establish the need for a data commons, (2) develop and deploy the technical infrastructure, (3) establish and implement governance, (4) make the data commons platform easy and intuitive for researchers, (5) socialize the data commons and create working knowledge and expertise in the research community, and (6) plan for longevity and sustainability. Data commons are critical to conducting research on large patient cohorts that will ultimately lead to improved outcomes for children with cancer. There is value in connecting high-quality clinical and phenotype data to external sources of data such as genomic, proteomics, and imaging data. Next steps for the PCDC include creating an informed and invested data-sharing culture, developing sustainable methods of data collection and sharing, standardizing genetic biomarker reporting, incorporating radiologic and molecular analysis data, and building models for electronic patient consent. The methods and processes described here can be extended to any clinical area and provide a blueprint for others wishing to develop similar resources., Competing Interests: Suzi BirzConsulting or Advisory Role: Litmus Health Stefanie Hecker-NoltingResearch Funding: Eisai Nathalie GasparConsulting or Advisory Role: Eisai, IpsenTravel, Accommodations, Expenses: Eisai Gianni BisognoConsulting or Advisory Role: Bayer, Roche, iQone healthcareTravel, Accommodations, Expenses: Jazz Pharmaceuticals Dirk ReinhardtConsulting or Advisory Role: Novartis, Bluebird Bio, Hexal, BMS, Jazz Pharmaceuticals, JanssenResearch Funding: Celgene, Roche/Genentech, Novartis, BluebirBioTravel, Accommodations, Expenses: Jazz Pharmaceuticals C. Michel ZwaanConsulting or Advisory Role: Takeda, incyte, Sanofi, Bayer, NovartisResearch Funding: Pfizer, Jazz Pharmaceuticals, Celgene, TakedaTravel, Accommodations, Expenses: Jazz Pharmaceuticals Lindsay FrazierStock and Other Ownership Interests: Decibel TherapeuticsConsulting or Advisory Role: Decibel Therapeutics Katherine JanewayHonoraria: Foundation MedicineConsulting or Advisory Role: Bayer, IpsenTravel, Accommodations, Expenses: Bayer Doug HawkinsResearch Funding: Loxo, Bristol Myers Squibb, Merck Sharp & Dohme, Bayer, Lilly, Eisai, Amgen, Incyte, Jazz Pharmaceuticals, PfizerTravel, Accommodations, Expenses: Bayer, AstraZeneca E. Anders KolbTravel, Accommodations, Expenses: Roche/Genentech Susan CohnStock and Other Ownership Interests: Merck, Stryker (I), Amgen (I), Pfizer (I), AbbVie, Amgen, Lilly, Sanofi, Accelerated Medical Diagnostics, Novo Nordisk, Gilead Sciences, United Health Group, TevaHonoraria: Y-mAbs TherapeuticsResearch Funding: United Therapeutics, Merck Andrew D. J. PearsonConsulting or Advisory Role: Lilly, Takeda, Merck, CelgeneTravel, Accommodations, Expenses: Lilly, Takeda, Merck, Celgene Samuel VolchenboumStock and Other Ownership Interests: Litmus HealthConsulting or Advisory Role: AccordantTravel, Accommodations, Expenses: Sanford HealthNo other potential conflicts of interest were reported.

Background: Applied pharmacogenomics presents opportunities for improving patient care through precision medicine, particularly when paired with appropriate clinical decision support (CDS). However, a lack of patient resources for understanding pharmacogenomic test results may hinder shared decision-making and patient confidence in treatment. We sought to create a patient pharmacogenomics education and results delivery platform complementary to a CDS system to facilitate further research on the relevance of patient education to pharmacogenomics., Methods: We conceptualized a model that extended the data access layer of an existing institutional CDS tool to allow for the pairing of decision supports offered to providers with patient-oriented summaries at the same level of phenotypic specificity. We built a two-part system consisting of a secure portal for patient use and an administrative dashboard for patient summary creation. The system was built in an ASP.NET and AngularJS architecture, and all data was housed in a HIPAA-compliant data center, with PHI secure in transit and at rest., Results: The YourPGx Patient Portal was deployed on the institutional network in June 2019. Fifty-eight unique patient portal summaries have been written so far, which can provide over 4500 results modules to the pilot population of 544 patients. Patient behavior on the portal is being logged for further research., Conclusions: To our knowledge, this is the first automated system designed and deployed to provide detailed, personalized patient pharmacogenomics education complementary to a clinical decision support system. Future work will expand upon this system to allow for telemedicine and patient notification of new or updated results., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Cancer Informatics for Cancer Centers (CI4CC) is a grassroots, nonprofit 501c3 organization intended to provide a focused national forum for engagement of senior cancer informatics leaders, primarily aimed at academic cancer centers anywhere in the world but with a special emphasis on the 70 National Cancer Institute-funded cancer centers. This consortium has regularly held topic-focused biannual face-to-face symposiums. These meetings are a place to review cancer informatics and data science priorities and initiatives, providing a forum for discussion of the strategic and pragmatic issues that we faced at our respective institutions and cancer centers. Here, we provide meeting highlights from the latest CI4CC Symposium, which was delayed from its original April 2020 schedule because of the COVID-19 pandemic and held virtually over three days (September 24, October 1, and October 8) in the fall of 2020. In addition to the content presented, we found that holding this event virtually once a week for 6 hours was a great way to keep the kind of deep engagement that a face-to-face meeting engenders. This is the second such publication of CI4CC Symposium highlights, the first covering the meeting that took place in Napa, California, from October 14-16, 2019. We conclude with some thoughts about using data science to learn from every child with cancer, focusing on emerging activities of the National Cancer Institute's Childhood Cancer Data Initiative., Competing Interests: Anne C. KirchhoffStock and Other Ownership Interests: Medtronic Kara L. DavisHonoraria: NovartisResearch Funding: Jazz Pharmaceuticals Karlyne ReillyConsulting or Advisory Role: Saul Ewing LLCPatents, Royalties, Other Intellectual Property: I hold a patent to a potential therapeutic: Reilly KM, Beutler JA, Turbyville T, Wiemer DF: The Natural Product Schweinfurthin A and Synthetic Schweinfurthin Analogs Specifically Inhibit Nf1-Null Cells and may be Useful as a Therapy for Neurofibromatosis Type 1. US patent 61/174,338, April 19, 2014; International patent PCT/US10/33153. There are no royalties or licensing fees from this patent at the time Richard AplencExpert Testimony: Vorys Samuel L. VolchenboumStock and Other Ownership Interests: Litmus HealthConsulting or Advisory Role: AccordantTravel, Accommodations, Expenses: Sanford Health Bruce J. AronowPatents, Royalties, Other Intellectual Property: Patents issued for some data analysis algorithms related to data mining and discovery approaches to drug repositioning for new clinical indications Olena VaskeEmployment: NantWorksStock and Other Ownership Interests: NantHealth Casey GreeneOther Relationship: Alex's Lemonade Stand Foundation Jack DiGiovannaEmployment: Biogen (I)Stock and Other Ownership Interests: Biogen Sorena NadafThis author is a member of the JCO Clinical Cancer Informatics Editorial Board. Journal policy recused the author from having any role in the peer review of this manuscript. Warren A. KibbeThis author is a member of the JCO Clinical Cancer Informatics Editorial Board. Journal policy recused the author from having any role in the peer review of this manuscript.No other potential conflicts of interest were reported.

Background: Tumor-infiltrating CD8 + T cells and neoantigens are predictors of a favorable prognosis and response to immunotherapy with checkpoint inhibitors in many types of adult cancer, but little is known about their role in pediatric malignancies. Here, we analyzed the prognostic strength of T cell-inflamed gene expression and neoantigen load in high-risk neuroblastoma. We also compared transcriptional programs in T cell-inflamed and non-T cell-inflamed high-risk neuroblastomas to investigate possible mechanisms of immune exclusion., Methods: A defined T cell-inflamed gene expression signature was used to categorize high-risk neuroblastomas in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program (n=123), and the Gabriella Miller Kids First (GMKF) program (n=48) into T cell-inflamed, non-T cell-inflamed, and intermediate groups. Associations between the T cell-inflamed and non-T cell-inflamed group, MYCN amplification, and survival were analyzed by Cox proportional hazards models. Additional survival analysis was conducted after integrating neoantigen load predicted from somatic mutations. Pathways activated in non-T cell-inflamed relative to T cell-inflamed tumors were analyzed using causal network analysis., Results: Patients with T cell-inflamed high-risk tumors showed improved overall survival compared with those with non-T cell-inflamed tumors (p<0.05), independent of MYCN amplification status, in both TARGET and GMKF cohorts. Higher neoantigen load was also associated with better event-free and overall survival (p<0.005) and was independent of the T cell-inflamed signature. Activation of MYCN, ASCL1, SOX11, and KMT2A transcriptional programs was inversely correlated with the T cell-inflamed signature in both cohorts., Conclusions: Our results indicate that tumors from children with high-risk neuroblastoma harboring a strong T cell-inflamed signature have a more favorable clinical outcome, and neoantigen load is a prognosis predictor, independent of T cell inflammation. Strategies to target SOX11 and other signaling pathways associated with non-T cell-inflamed tumors should be pursued as potential immune-potentiating interventions., Competing Interests: Competing interests: RB declares patents: (all provisional) PCT/US15/612657 (Cancer Immunotherapy), PCT/US18/36052 (Microbiome Biomarkers for Anti-PD-1/PD-L1 Responsiveness: Diagnostic, Prognostic and Therapeutic Uses Thereof), PCT/US63/055227 (Methods and Compositions for Treating Autoimmune and Allergic Disorders); JJL declares Data and Safety Monitoring Board: TTC Oncology, Scientific Advisory Board: 7 Hills, Actym, Alphamab Oncology, Array, BeneVir, Mavu, Tempest, Consultancy: Aduro, Astellas, AstraZeneca, Bayer, Bristol-Myers Squibb, Castle, CheckMate, Compugen, EMD Serono, IDEAYA, Immunocore, Janssen, Jounce, Leap, Merck, Mersana, NewLink, Novartis, RefleXion, Spring Bank, Syndax, Tempest, Vividion, WntRx, Research Support: (all to institution for clinical trials unless noted) AbbVie, Array (Scientific Research Agreement; SRA), Boston Biomedical, Bristol-Myers Squibb, Celldex, CheckMate (SRA), Compugen, Corvus, EMD Serono, Evelo (SRA), Delcath, Five Prime, FLX Bio, Genentech, Immunocore, Incyte, Leap, MedImmune, Macrogenics, Novartis, Pharmacyclics, Palleon (SRA), Merck, Tesaro, Xencor, Travel: Array, AstraZeneca, Bayer, BeneVir, Bristol-Myers Squibb, Castle, CheckMate, EMD Serono, IDEAYA, Immunocore, Janssen, Jounce, Merck, Mersana, NewLink, Novartis, RefleXion, Patents: (both provisional) Serial #15/612,657 (Cancer Immunotherapy), PCT/US18/36052 (Microbiome Biomarkers for Anti-PD-1/PD-L1 Responsiveness: Diagnostic, Prognostic and Therapeutic Uses Thereof). SS declares a patent on WNT/β-catenin targeting to enhance anti-tumor immune responses (PCT15/155,099), serves on the SAB on Venn Therapeutics, Tango Therapeutics, Arcus Biosciences and consults for TAKEDA, Replimune, Ribon, Dragonfly and Merck. TFG has received consultancy fees from Merck, Roche-Genentech, Abbvie, Bayer, Jounce, Aduro, Fog Pharma, Adaptimmune, FivePrime, and Sanofi. TFG has received research support from Roche-Genentech, BMS, Merck, Incyte, Seattle Genetics, Celldex, Ono, Evelo, Bayer, Aduro. TFG has intellectual property/licensing agreements with Aduro, Evelo, and BMS. TFG is a co-founder/shareholder of Jounce and Pyxis Oncology. AVD declares Research funding (all to institution for clinical trials): Merck, Roche, Jubilant DraxImage, YMabs, GlaxoSmithKline, Actuate Therapeutics, Lilly; Scientific Advisory Board: Merck; Consultancy: Ology Medical Education; Travel/Accommodations: GlaxoSmithKline; Stock: Pfizer (all outside the submitted work). SLC declares Research funding (to the institution for clinical trials): Merck and United Therapeutics; Stock (personal or immediate family member): United Therapeutics, Merck, Stryker, Amgen, Pfizer, AbbVie, Jazz Pharmaceuticals, Lilly, Sanofi, Varex Imaging, Accelerated Medical Diagnostics, Anthem, Cardinal Health, Novo Nordisk, Regeneron, Zimmer BioMet (all outside the submitted work). The remaining authors declare no competing interests., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Importance: Participants in clinical trials may experience benefits associated with new therapeutic strategies as well as tight adherence to best supportive care practices., Objectives: To investigate whether participation in a clinical trial is associated with improved survival among children with neuroblastoma and investigate potential recruitment bias of patients in clinical trials., Design, Setting, and Participants: This cohort study included pediatric patients with intermediate- or high-risk neuroblastoma in North American studies who were included in the International Neuroblastoma Risk Group Data Commons and who received a diagnosis between January 1, 1991, and March 1, 2020., Exposure: Enrollment in a clinical trial., Main Outcomes and Measures: Event-free survival and overall survival (OS) of patients with intermediate- or high-risk neuroblastoma enrolled in an up-front Children's Oncology Group (COG) clinical trial vs a biology study alone were analyzed using log-rank tests and Cox proportional hazards regression models. The racial/ethnic composition and the demographic characteristics of the patients in both groups were compared., Results: The cohort included 3058 children with intermediate-risk neuroblastoma (1533 boys [50.1%]; mean [SD] age, 10.7 [14.7] months) and 6029 children with high-risk neuroblastoma (3493 boys [57.9%]; mean [SD] age, 45.8 [37.4] months) who were enrolled in a Children's Oncology Group or legacy group neuroblastoma biology study between 1991 and 2020. A total of 1513 patients with intermediate-risk neuroblastoma (49.5%) and 2473 patients with high-risk neuroblastoma (41.0%) were also enrolled in a clinical trial, for a cohort total of 3986 of 9087 children (43.9%) enrolled in a clinical trial. The prevalence of prognostic markers for the clinical trial and non-clinical trial cohorts differed, although representation of patients from racial/ethnic minority groups was similar in both cohorts. Among patients with intermediate-risk neuroblastoma, OS was higher among those who participated in a clinical trial compared with those enrolled only in a biology study (OS, 95% [95% CI, 94%-96%] vs 91% [95% CI, 89%-94%]; P = .01). Among patients with high-risk neuroblastoma, participation in a clinical trial was not associated with OS (OS, 38% [95% CI, 35%-41%] in the clinical trial group vs 41% [95% CI, 38%-44%] in the biology study group; P = .23)., Conclusions and Relevance: Approximately 44% of patients in this large cohort of patients with neuroblastoma were enrolled in up-front clinical trials. Compared with children not enrolled in clinical trials, a higher prevalence of favorable prognostic markers was identified among patients with intermediate-risk neuroblastoma enrolled in clinical trials, and unfavorable features were more prevalent among patients with high-risk neuroblastoma enrolled in clinical trials. No evidence of recruitment bias according to race/ethnicity was observed. Participation in a clinical trial was not associated with OS in this cohort, likely reflecting the common practice of treating nontrial participants with therapeutic and supportive care regimens used in a previous therapeutic trial.

Objective: Using sickle cell disease (SCD) as a model, the objective of this study was to create a comprehensive learning healthcare system to support disease management and research. A multidisciplinary team developed a SCD clinical data dictionary to standardize bedside data entry and inform a scalable environment capable of converting complex electronic healthcare records (EHRs) into knowledge accessible in real time., Materials and Methods: Clinicians expert in SCD care developed a data dictionary to describe important SCD-associated health maintenance and adverse events. The SCD data dictionary was deployed in the EHR using EPIC SmartForms, an efficient bedside data entry tool. Additional data elements were extracted from the EHR database (Clarity) using Pentaho Data Integration and stored in a data analytics database (SQL). A custom application, the Sickle Cell Knowledgebase, was developed to improve data analysis and visualization. Utilization, accuracy, and completeness of data entry were assessed., Results: The SCD Knowledgebase facilitates generation of patient-level and aggregate data visualization, driving the translation of data into knowledge that can impact care. A single patient can be selected to monitor health maintenance, comorbidities, adverse event frequency and severity, and medication dosing/adherence., Conclusions: Disease-specific data dictionaries used at the bedside will ultimately increase the meaningful use of EHR datasets to drive consistent clinical data entry, improve data accuracy, and support analytics that will facilitate quality improvement and research., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

For children with neuroblastoma, the likelihood of cure varies widely according to age at diagnosis, disease stage, and tumor biology. Treatments are tailored for children with this clinically heterogeneous malignancy on the basis of a combination of markers that are predictive of risk of relapse and death. Sequential risk-based, cooperative-group clinical trials conducted during the past 4 decades have led to improved outcome for children with neuroblastoma. Increasingly accurate risk classification and refinements in treatment stratification strategies have been achieved with the more recent discovery of robust genomic and molecular biomarkers. In this review, we discuss the history of neuroblastoma risk classification in North America and Europe and highlight efforts by the International Neuroblastoma Risk Group (INRG) Task Force to develop a consensus approach for pretreatment stratification using seven risk criteria including an image-based staging system-the INRG Staging System. We also update readers on the current Children's Oncology Group risk classifier and outline plans for the development of a revised 2021 Children's Oncology Group classifier that will incorporate INRG Staging System criteria to facilitate harmonization of risk-based frontline treatment strategies conducted around the globe. In addition, we discuss new approaches to establish increasingly robust, future risk classification algorithms that will further refine treatment stratification using machine learning tools and expanded data from electronic health records and the INRG Data Commons.

Germ cell tumors (GCTs) are considered a rare disease but are the most common solid tumors in adolescents and young adults, accounting for 15% of all malignancies in this age group. The rarity of GCTs in some groups, particularly children, has impeded progress in treatment and biologic understanding. The most effective GCT research will result from the interrogation of data sets from historical and prospective trials across institutions. However, inconsistent use of terminology among groups, different sample-labeling rules, and lack of data standards have hampered researchers' efforts in data sharing and across-study validation. To overcome the low interoperability of data and facilitate future clinical trials, we worked with the Malignant Germ Cell International Consortium (MaGIC) and developed a GCT clinical data model as a uniform standard to curate and harmonize GCT data sets. This data model will also be the standard for prospective data collection in future trials. Using the GCT data model, we developed a GCT data commons with data sets from both MaGIC and public domains as an integrated research platform. The commons supports functions, such as data query, management, sharing, visualization, and analysis of the harmonized data, as well as patient cohort discovery. This GCT data commons will facilitate future collaborative research to advance the biologic understanding and treatment of GCTs. Moreover, the framework of the GCT data model and data commons will provide insights for other rare disease research communities into developing similar collaborative research platforms.

Pediatric cancer is a rare disease with a low annual incidence, which presents a significant challenge in being able to collect enough data to fuel clinical discoveries. Big data registry trials hold promise to advance the study of pediatric cancers by allowing for the combination of traditional randomized controlled trials with the power of larger cohort sizes. The emergence of big data resources and data-sharing initiatives are becoming transformative for pediatric cancer diagnosis and treatment. This review discusses the uses of big data in pediatric cancer, existing pediatric cancer registry initiatives and research, the challenges in harmonizing these data to improve accessibility for study, and building pediatric data commons and other important future endeavors., (Copyright © 2020 Elsevier Inc. All rights reserved.)

The importance of genetic ancestry characterization is increasing in genomic implementation efforts, and clinical pharmacogenomic guidelines are being published that include population-specific recommendations. Our aim was to test the ability of focused clinical pharmacogenomic SNP panels to estimate individual genetic ancestry (IGA) and implement population-specific pharmacogenomic clinical decision-support (CDS) tools. Principle components and STRUCTURE were utilized to assess differences in genetic composition and estimate IGA among 1572 individuals from 1000 Genomes, two independent cohorts of Caucasians and African Americans (AAs), plus a real-world validation population of patients undergoing pharmacogenomic genotyping. We found that clinical pharmacogenomic SNP panels accurately estimate IGA compared to genome-wide genotyping and identify AAs with ≥70 African ancestry (sensitivity >82%, specificity >80%, PPV >95%, NPV >47%). We also validated a new AA-specific warfarin dosing algorithm for patients with ≥70% African ancestry and implemented it at our institution as a novel CDS tool. Consideration of IGA to develop an institutional CDS tool was accomplished to enable population-specific pharmacogenomic guidance at the point-of-care. These capabilities were immediately applied for guidance of warfarin dosing in AAs versus Caucasians, but also provide a real-world model that can be extended to other populations and drugs as actionable genomic evidence accumulates.

Purpose: Robust institutional tumor banks depend on continuous sample curation or else subsequent biopsy or resection specimens are overlooked after initial enrollment. Curation automation is hindered by semistructured free-text clinical pathology notes, which complicate data abstraction. Our motivation is to develop a natural language processing method that dynamically identifies existing pathology specimen elements necessary for locating specimens for future use in a manner that can be re-implemented by other institutions., Patients and Methods: Pathology reports from patients with gastroesophageal cancer enrolled in The University of Chicago GI oncology tumor bank were used to train and validate a novel composite natural language processing-based pipeline with a supervised machine learning classification step to separate notes into internal (primary review) and external (consultation) reports; a named-entity recognition step to obtain label (accession number), location, date, and sublabels (block identifiers); and a results proofreading step., Results: We analyzed 188 pathology reports, including 82 internal reports and 106 external consult reports, and successfully extracted named entities grouped as sample information (label, date, location). Our approach identified up to 24 additional unique samples in external consult notes that could have been overlooked. Our classification model obtained 100% accuracy on the basis of 10-fold cross-validation. Precision, recall, and F1 for class-specific named-entity recognition models show strong performance., Conclusion: Through a combination of natural language processing and machine learning, we devised a re-implementable and automated approach that can accurately extract specimen attributes from semistructured pathology notes to dynamically populate a tumor registry.

Introduction: Global data sharing is essential. This is the premise of the Academic Research Organization (ARO) Council, which was initiated in Japan in 2013 and has since been expanding throughout Asia and into Europe and the United States. The volume of data is growing exponentially, providing not only challenges but also the clear opportunity to understand and treat diseases in ways not previously considered. Harnessing the knowledge within the data in a successful way can provide researchers and clinicians with new ideas for therapies while avoiding repeats of failed experiments. This knowledge transfer from research into clinical care is at the heart of a learning health system., Methods: The ARO Council wishes to form a worldwide complementary system for the benefit of all patients and investigators, catalyzing more efficient and innovative medical research processes. Thus, they have organized Global ARO Network Workshops to bring interested parties together, focusing on the aspects necessary to make such a global effort successful. One such workshop was held in Austin, Texas, in November 2017. Representatives from Japan, Taiwan, Singapore, Europe, and the United States reported on their efforts to encourage data sharing and to use research to inform care through learning health systems., Results: This experience report summarizes presentations and discussions at the Global ARO Network Workshop held in November 2017 in Austin, TX, with representatives from Japan, Korea, Singapore, Taiwan, Europe, and the United States. Themes and recommendations to progress their efforts are explored. Standardization and harmonization are at the heart of these discussions to enable data sharing. In addition, the transformation of clinical research processes through disruptive innovation, while ensuring integrity and ethics, will be key to achieving the ARO Council goal to overcome diseases such that people not only live longer but also are healthier and happier as they age., Conclusions: The achievement of global learning health systems will require further exploration, consensus-building, funding aligned with incentives for data sharing, standardization, harmonization, and actions that support global interests for the benefit of patients., Competing Interests: Ida Sim is a co‐founder of Vivli and receives consulting fees from Vivli for technical and strategic consultation. The remaining authors affirm that they have no conflicts of interest to disclose.

As the availability and sophistication of mobile health (mHealth) technology (wearables, mobile technology, and sensors) continues to increase, there is great promise that these tools will be transformative for clinical trials and drug development. This review provides an overview of the current landscape of potential measurement options, including the various types of data collected, methods/tools for collecting them, and a crosswalk of available options. The opportunities and potential drawbacks of mHealth in cancer clinical trials are discussed. Specific concerns related to data accuracy, provenance, and regulatory issues are highlighted, with suggestions for how to address these in future research. Next steps for establishing mHealth methods and tools as legitimate and accepted measures in oncology clinical trials include continuation of regulatory definition by the FDA; establishment of security standards and protocols; refinement and implementation of methods to establish and document data accuracy; and finally, creation of feedback loops wherein regulators receive updates from researchers with better and more timely data, which should decrease trial times and lessen drug development costs. Implementing mHealth technologies into cancer clinical trials has the potential to transform and propel oncology drug development and precision medicine to keep pace with the rapidly increasing developments in genomics and immunology.

Pediatric oncology patients are at high risk of developing clinical deterioration and organ dysfunction during their illness. Heart rate variability (HRV) measured using electrocardiography waveforms is associated with increased organ dysfunction and clinical deterioration in adult and pediatric patients in the intensive care unit (ICU). Here, we explore the feasibility of using photoplethysmography (PPG)-derived integer pulse rate variability (PRVi) to estimate HRV and determine its association with organ dysfunction in pediatric oncology patients in the ward and pediatric ICU. The advantage of using PPG sensor data over electrocardiography is its higher availability in most healthcare settings and in wearable technology. In a cohort of 38 patients, reduced median daily PRVi was significantly associated with increase in two pediatric organ dysfunction scores after adjusting for confounders ( p  < 0.001). PRVi shows promise as a real-time physiologic marker of clinical deterioration using highly-available PPG data, but further research is warranted., Competing Interests: Competing interestsThe authors declare no competing interests.

Next-generation sequencing (NGS) diagnostic assays increasingly are becoming the standard of care in oncology practice. As the scale of an NGS laboratory grows, management of these assays requires organizing large amounts of information, including patient data, laboratory processes, genomic data, as well as variant interpretation and reporting. Although several Laboratory Information Systems and/or Laboratory Information Management Systems are commercially available, they may not meet all of the needs of a given laboratory, in addition to being frequently cost-prohibitive. Herein, we present the System for Informatics in the Molecular Pathology Laboratory (SIMPL), a free and open-source Laboratory Information System/Laboratory Information Management System for academic and nonprofit molecular pathology NGS laboratories, developed at the Genomic and Molecular Pathology Division at the University of Chicago Medicine. SIMPL was designed as a modular end-to-end information system to handle all stages of the NGS laboratory workload from test order to reporting. We describe the features of SIMPL, its clinical validation at University of Chicago Medicine, and its installation and testing within a different academic center laboratory (University of Colorado), and we propose a platform for future community co-development and interlaboratory data sharing., (Copyright © 2018 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.)

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two US academic medical centers with 216,221 adult patients hospitalized for at least 24 h. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting: in-hospital mortality (area under the receiver operator curve [AUROC] across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed traditional, clinically-used predictive models in all cases. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios. In a case study of a particular prediction, we demonstrate that neural networks can be used to identify relevant information from the patient's chart., Competing Interests: Competing interestsThe authors declare no competing interests.

Background: A barrier to the use of genomic information during prescribing is the limited number of software solutions that combine a user-friendly interface with complex medical data. We built and designed an online, secure, electronic custom interface termed the Genomic Prescribing System (GPS)., Methods: Actionable pharmacogenomic (PGx) information was reviewed, collected, and stored in the back-end of GPS to enable creation of customized drug- and variant-specific clinical decision support (CDS) summaries. The database architecture utilized the star schema to store information. Patient raw genomic data underwent transformation via custom-designed algorithms to enable gene and phenotype-level associations. Multiple external data sets (PubMed, The Systematized Nomenclature of Medicine (SNOMED), National Drug File - Reference Terminology (ND-FRT), and a publically-available PGx knowledgebase) were integrated to facilitate the delivery of patient, drug, disease, and genomic information. Institutional security infrastructure was leveraged to securely store patient genomic and clinical data on a HIPAA-compliant server farm., Results: As of May 17, 2016, the GPS back-end housed 257 CDS encompassing 112 genetic variants, 42 genes, and 46 PGx-actionable drugs. The GPS user interface presented patient-specific CDS alongside a recognizable traffic light symbol (green/yellow/red), denoting PGx risk for each genomic result. The number of traffic lights per visit increased with the corresponding increase in the number of available PGx-annotated drugs over time. An integrated drug and disease search functionality, links to primary literature sources, and potential alternative PGx drugs were indicated. The system, which was initially used as stand-alone CDS software within our clinical environment, was then integrated with the institutional electronic medical record for enhanced usability. There have been nearly 2000 logins in 43months since inception, with usage exceeding 56 logins per month and system up-times of 99.99%. For all patient-provider visits encompassing >3years of implementation, unique alert click-through rates corresponded to genomic risk: red lights clicked 100%, yellow lights 79%, green lights 43%., Conclusions: Successful deployment of GPS by combining complex data and recognizable iconography led to a tool that enabled point-of-care genomic delivery with high usability. Continued scalability and incorporation of additional clinical elements to be considered alongside PGx information could expand future impact., (Copyright © 2017. Published by Elsevier Inc.)

To investigate genetic predispositions for MYCN-amplified neuroblastoma, we performed a meta-analysis of three genome-wide association studies totaling 615 MYCN-amplified high-risk neuroblastoma cases and 1869 MYCN-nonamplified non-high-risk neuroblastoma cases as controls using a fixed-effects model with inverse variance weighting. All statistical tests were two-sided. We identified a novel locus at 3p21.31 indexed by the single nucleotide polymorphism (SNP) rs80059929 (odds ratio [OR] = 2.95, 95% confidence interval [CI] = 2.17 to 4.02, Pmeta = 6.47 × 10-12) associated with MYCN-amplified neuroblastoma, which was replicated in 127 MYCN-amplified cases and 254 non-high-risk controls (OR = 2.30, 95% CI = 1.12 to 4.69, Preplication = .02). To confirm this signal is exclusive to MYCN-amplified tumors, we performed a second meta-analysis comparing 728 MYCN-nonamplified high-risk patients to identical controls. rs80059929 was not statistically significant in MYCN-nonamplified high-risk patients (OR = 1.24, 95% CI = 0.90 to 1.71, Pmeta = .19). SNP rs80059929 is within intron 16 in the KIF15 gene. Additionally, the previously reported LMO1 neuroblastoma risk locus was statistically significant only in patients with MYCN-nonamplified high-risk tumors (OR = 0.63, 95% CI = 0.53 to 0.75, Pmeta = 1.51 × 10-8; Pmeta = .95). Our results indicate that common genetic variation predisposes to different neuroblastoma genotypes, including the likelihood of somatic MYCN-amplification., (© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Background: The incidence of second malignant neoplasm (SMN) within the first ten years of diagnosis in high-risk neuroblastoma patients treated with modern, intensive therapy is unknown. Further, the underlying germline genetics that contribute to SMN in these survivors are not known., Methods: The International Neuroblastoma Risk Group (INRG) database of patients diagnosed from 1990 to 2010 was analysed. SMN risk was accessed by cumulative incidence, standardised incidence ratios (SIRs) and absolute excess risk. A candidate gene-based association study evaluated genetic susceptibility to SMN in neuroblastoma survivors., Results: Of the 5987 patients in the INRG database with SMN data enrolled in a clinical trial, 43 (0.72%) developed a SMN. The 10-year cumulative incidence of SMN for high-risk patients was 1.8% (95% confidence interval [CI] 1.0-2.6%) compared with 0.38% (95% CI: 0.22-0.94%) for low-risk patients (P = 0.01). High-risk patients had an almost 18-fold higher incidence of SMN compared to age- and sex-matched controls (SIR = 17.5 (95% CI: 11.4-25.3), absolute excess risk = 27.6). For patients treated on high- and intermediate-risk clinical trials, the SIR of acute myelogenous leukaemia was 106.8 (95% CI: 28.7-273.4) and 127.7 (95%CI: 25.7-373.3), respectively. Variants implicating DNA repair genes XRCC3 (rs861539: P = 0.006; odds ratio: 2.04, 95%CI: 1.19-3.46) and MSH2 (rs17036651: P = 0.009; odds ratio: 0.26, 95% CI: 0.08-0.81) were associated with SMN., Conclusion: The intensive multi-modality treatment strategy currently used to treat high-risk neuroblastoma is associated with a significantly increased risk of secondary acute myelogenous leukaemia. Defining the interactions of treatment exposures and genetic factors that promote the development of SMN is critical for optimising survivorship care., Competing Interests: statement The authors have no conflicts to disclose, (Copyright © 2016 Elsevier Ltd. All rights reserved.)

The falling costs and increasing fidelity of high-throughput biomedical research data have led to a renaissance in cancer surveillance and treatment. Yet, the amount, velocity, and complexity of these data have overcome the capacity of the increasing number of researchers collecting and analyzing this information. By centralizing the data, processing power, and tools, there is a valuable opportunity to share resources and thus increase the efficiency, power, and impact of research. Herein, we describe current data commons and how they operate in the oncology landscape, including an overview of the International Neuroblastoma Risk Group data commons as a paradigm case. We outline the practical steps and considerations in building data commons. Finally, we discuss the unique opportunities and benefits of creating a data commons within the context of pediatric cancer research, highlighting the particular advantages for clinical oncology and suggested next steps.

The presence of the dense hydroxyapatite matrix within human bone limits the applicability of conventional protocols for protein extraction. This has hindered the complete and accurate characterization of the human bone proteome thus far, leaving many bone-related disorders poorly understood. We sought to refine an existing method of protein extraction from mouse bone to extract whole proteins of varying molecular weights from human cranial bone. Whole protein was extracted from human cranial suture by mechanically processing samples using a method that limits protein degradation by minimizing heat introduction to proteins. The presence of whole protein was confirmed by western blotting. Mass spectrometry was used to sequence peptides and identify isolated proteins. The data have been deposited to the ProteomeXchange with identifier PXD003215. Extracted proteins were characterized as both intra- and extracellular and had molecular weights ranging from 9.4 to 629 kDa. High correlation scores among suture protein spectral counts support the reproducibility of the method. Ontology analytics revealed proteins of myriad functions including mediators of metabolic processes and cell organelles. These results demonstrate a reproducible method for isolation of whole protein from human cranial bone, representing a large range of molecular weights, origins and functions., (Copyright © 2016 Elsevier Inc. All rights reserved.)