Pediatric Cancer Data Commons: Federating and Democratizing Data for Childhood Cancer Research

Proteomic insights into paediatric cancer: Unravelling molecular signatures and therapeutic opportunities

Survival rates in some paediatric cancers have improved greatly over recent decades, in part due to the identification of diagnostic, prognostic and predictive molecular signatures, and the development of risk-directed therapies. However, other paediatric cancers have proved difficult to treat, and there is an urgent need to identify novel biomarkers that reveal therapeutic opportunities. The proteome is the total set of expressed proteins present in a cell or tissue at a point in time, and is vastly more dynamic than the genome. Proteomics holds significant promise for cancer research, as proteins are ultimately responsible for cellular phenotype and are the target of most anticancer drugs. Here, we review the discoveries, opportunities and challenges of proteomic analyses in paediatric cancer, with a focus on mass spectrometry (MS)-based approaches. Accelerating incorporation of proteomics into paediatric precision medicine has the potential to improve survival and quality of life for children with cancer.

Accelerating pediatric Hodgkin lymphoma research: the Hodgkin Lymphoma Data Collaboration (NODAL)

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.

An open-source platform for pediatric cancer data exploration: a report from Data for the Common Good

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.

Applications of artificial intelligence in magnetic resonance imaging of primary pediatric cancers: a scoping review and CLAIM score assessment

PURPOSES

To review the uses of AI for magnetic resonance (MR) imaging assessment of primary pediatric cancer and identify common literature topics and knowledge gaps. To assess the adherence of the existing literature to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines.

MATERIALS AND METHODS

A scoping literature search using MEDLINE, EMBASE and Cochrane databases was performed, including studies of > 10 subjects with a mean age of < 21 years. Relevant data were summarized into three categories based on AI application: detection, characterization, treatment and monitoring. Readers independently scored each study using CLAIM guidelines, and inter-rater reproducibility was assessed using intraclass correlation coefficients.

RESULTS

Twenty-one studies were included. The most common AI application for pediatric cancer MR imaging was pediatric tumor diagnosis and detection (13/21 [62%] studies). The most commonly studied tumor was posterior fossa tumors (14 [67%] studies). Knowledge gaps included a lack of research in AI-driven tumor staging (0/21 [0%] studies), imaging genomics (1/21 [5%] studies), and tumor segmentation (2/21 [10%] studies). Adherence to CLAIM guidelines was moderate in primary studies, with an average (range) of 55% (34%-73%) CLAIM items reported. Adherence has improved over time based on publication year.

CONCLUSION

The literature surrounding AI applications of MR imaging in pediatric cancers is limited. The existing literature shows moderate adherence to CLAIM guidelines, suggesting that better adherence is required for future studies.

The Childhood Cancer Data Initiative: Using the Power of Data to Learn From and Improve Outcomes for Every Child and Young Adult With Pediatric Cancer

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.

Automated Matching of Patients to Clinical Trials: A Patient-Centric Natural Language Processing Approach for Pediatric Leukemia

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.

Informatics tools to implement late cardiovascular risk prediction modeling for population management of high-risk childhood cancer survivors

BACKGROUND

Clinical informatics tools to integrate data from multiple sources have the potential to catalyze population health management of childhood cancer survivors at high risk for late heart failure through the implementation of previously validated risk calculators.

METHODS

The Oklahoma cohort (n = 365) harnessed data elements from Passport for Care (PFC), and the Duke cohort (n = 274) employed informatics methods to automatically extract chemotherapy exposures from electronic health record (EHR) data for survivors 18 years old and younger at diagnosis. The Childhood Cancer Survivor Study (CCSS) late cardiovascular risk calculator was implemented, and risk groups for heart failure were compared to the Children's Oncology Group (COG) and the International Guidelines Harmonization Group (IGHG) recommendations. Analysis within the Oklahoma cohort assessed disparities in guideline-adherent care.

RESULTS

The Oklahoma and Duke cohorts both observed good overall concordance between the CCSS and COG risk groups for late heart failure, with weighted kappa statistics of .70 and .75, respectively. Low-risk groups showed excellent concordance (kappa > .9). Moderate and high-risk groups showed moderate concordance (kappa .44-.60). In the Oklahoma cohort, adolescents at diagnosis were significantly less likely to receive guideline-adherent echocardiogram surveillance compared with survivors younger than 13 years old at diagnosis (odds ratio [OD] 0.22; 95% confidence interval [CI]: 0.10-0.49).

CONCLUSIONS

Clinical informatics tools represent a feasible approach to leverage discrete treatment-related data elements from PFC or the EHR to successfully implement previously validated late cardiovascular risk prediction models on a population health level. Concordance of CCSS, COG, and IGHG risk groups using real-world data informs current guidelines and identifies inequities in guideline-adherent care.

Adjuvant chemotherapy does not improve outcome in children with ovarian immature teratoma: A comparative analysis of clinical trial data from the Malignant Germ Cell International Consortium

BACKGROUND/OBJECTIVES

Surgery is the mainstay of therapy for children with ovarian immature teratoma (IT), whereas adults receive adjuvant chemotherapy, except those with stage-I, grade-1 disease. In Brazil, children with metastatic ovarian IT received postoperative chemotherapy. This practice variation allowed evaluation of the value of chemotherapy, by comparison of Brazilian patients with those in the United States and United Kingdom.

DESIGN/METHODS

From the Malignant Germ Cell International Consortium data commons, data on ovarian IT patients from two recently added Brazilian trials (TCG-99/TCG-2008) were compared with data from US/UK (INT-0106/GC-2) trials. Primary outcome measure was event-free (EFS) and overall survival (OS).

RESULTS

Forty-two Brazilian patients were included (stage I: 27, stage II: 4, stage III: 8, stage IV: 3). Twenty-nine patients had surgery alone, whereas 13 patients received postoperative chemotherapy. The EFS and OS for entire cohort was 0.80 (95% CI: 0.64-0.89) and 0.97 (0.84-0.99). There was no difference in relapse risk based on stage, grade, or receipt of chemotherapy. Comparing the Brazilian cohort with 98 patients in US/UK cohort (stage I: 59, stage II: 12, stage III: 27), there was no difference in EFS and OS across all stages, despite 87% of stage II-IV Brazilian patients receiving postoperative chemotherapy compared with only 13% of US/UK patients. The EFS and OS for Brazilian compared with US/UK cohort was stage I: 88% versus 98% (p = .05), stage II-IV EFS: 67% versus 79% (p = .32), stage II-IV OS: 93% versus 97% (p = .44); amongst grade-3 patients, there was no difference in EFS or OS.

CONCLUSION

Addition of postoperative chemotherapy did not improve outcome in children with ovarian IT, even at higher grade or stage, compared with surgery alone.

Establishment, Maintenance, and Performance of the Cooperative Osteosarcoma Study Group (COSS)

INTRODUCTION

Osteosarcoma treatment has benefitted greatly from collaborative research. This paper describes the history and accomplishments of the Cooperative Osteosarcoma Study Group (COSS), mainly dedicated to clinical questions, as well as remaining challenges.

MATERIALS AND METHODS

Narrative review of over four decades of uninterrupted collaboration within the multi-national German-Austrian-Swiss COSS group.

RESULTS

Since its very first prospective osteosarcoma trial starting in 1977, COSS has continuously been able to provide high-level evidence on various tumor- and treatment-related questions. This includes both the cohort of patients enrolled into prospective trials as well as those patients excluded from them for various reasons, followed in a prospective registry. Well over one hundred disease-related publications attest to the group's impact on the field. Despite these accomplishments, challenging problems remain.

DISCUSSION

Collaborative research within a multi-national study group resulted in better definitions of important aspects of the most common bone tumor, osteosarcoma, and its treatments. Important challenges continue to persist.

Data standards in pediatric oncology: Past, present, and future

In this commentary, we highlight the central role that data standards play in facilitating data-driven efforts to advance research in pediatric oncology. We discuss the current state of data standards for pediatric oncology and propose five steps to achieve an improved future state with benefits for clinicians, researchers, and patients.

Cardio oncology: Digital innovations, precision medicine and health equity

The rapid emergence of cardio-oncology has resulted in a rapid growth of cardio-oncology programs, dedicated professional societies sections and committees, and multiple collaborative networks that emerged to amplify the access to care in this new subspecialty. However, most existing data, position statements and guidelines are limited by the lack of availability of large clinical trials to support these recommendations. Furthermore, there are significant challenges regarding proper access to cardio-oncology care and treatment, particularly in marginalized and minority populations. The emergence and evolution of personalized medicine, artificial intelligence (AI), and machine learning in medicine and in cardio-oncology provides an opportunity for a more targeted, personalized approach to cardiovascular complications of cancer treatment. The proper implementation of these new modalities may facilitate a more equitable approach to adequate and universal access to cardio-oncology care, improve health related outcomes, and enable health care systems to eliminate the digital divide. This article reviews and analyzes the current status on these important issues.

Creating a data commons: The INternational Soft Tissue SaRcoma ConsorTium (INSTRuCT)

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.

Mapping Pediatric Oncology Clinical Trial Collaborative Groups on the Global Stage

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.

Local stakeholder engagement is necessary to collaborate with global pediatric cancer trial groups.

Pediatric Acute Myeloid Leukemia—Past, Present, and Future

This review reports about the main steps of development in pediatric acute myeloid leukemia (AML) concerning diagnostics, treatment, risk groups, and outcomes. Finally, a short overview of present and future approaches is given.