Assessment of enrollment characteristics for Children’s Oncology Group (COG) upfront therapeutic clinical trials 2004-2015.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 6564-6564
Author(s):  
Kelly Faulk ◽  
Amy Anderson-Mellies ◽  
Myles Cockburn ◽  
Adam Green
Keyword(s):  
Clinical Trials ◽  
Pediatric Cancer ◽  
Cancer Survival ◽  
Comprehensive Evaluation ◽  
Hematologic Malignancies ◽  
Cns Tumors ◽  
Oncology Group ◽  
Eligibility Criteria ◽  
Younger Patients ◽  
Enrollment Rates

6564 Background: Improvements in pediatric cancer survival are attributed to cooperative clinical trials. Under representation of specific demographic groups has been described in adult and pediatric cancer and poses a threat to the generalizability of trial results. A comprehensive evaluation of data provided by the Children’s Oncology Group (COG) of upfront trial enrollment for US patients 0 to 29 years old between 2004 and 2015 was performed to assess for disparities in participation. Methods: Estimates of cancer cases were calculated using the Surveillance, Epidemiology, and End Results registry and the US Census and compared to observed COG cases. Percent enrollment and Standardized Ratios of enrollment were calculated across various demographic, disease, and socioeconomic groups. The COG website was utilized to quantify available upfront trials during the study period and assess age eligibility criteria. Results: 21.3% of estimated US cancer patients age 0 to 19 years enrolled on COG trials. Younger patients were consistently more represented across disease types and race/ethnicities. Patients with hematologic malignancies were more represented compared to solid and central nervous system (CNS) tumors. Conclusions: COG clinical trial enrollment rates are declining, which may be due to challenges in pediatric drug development, difficulty designing feasible trials for highly curable diseases, and issues in ensuring trial availability for the heterogeneous group of solid and CNS tumors. Though racial/ethnic groups and county-level socioeconomic factors were proportionally represented, under representation of the adolescent/young adult (AYA) population and younger patients with solid and CNS tumors remain significant concerns. Targeted enrollment efforts should focus on the identified subgroups and further research should evaluate AYA enrollment across all available trials to provide continued treatment advances for all patients.

scholarly journals Racial Disparities in Cancer Survival Among Randomized Clinical Trials Patients of the Southwest Oncology Group

2009 ◽  
Vol 101 (14) ◽  
pp. 984-992 ◽  
Author(s):  
Kathy S. Albain ◽  
Joseph M. Unger ◽  
John J. Crowley ◽  
Charles A. Coltman ◽  
Dawn L. Hershman
Keyword(s):  
Clinical Trials ◽  
Racial Disparities ◽  
Randomized Clinical Trials ◽  
Cancer Survival ◽  
Oncology Group ◽  
Southwest Oncology Group

Algorithmic matching of genomic profiles to precision cancer medicine clinical trials at DFCI.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 6620-6620
Author(s):  
James Lindsay ◽  
Catherine Del Vecchio Fitz ◽  
Zachary Zwiesler ◽  
Priti Kumari ◽  
Khanh Tu Do ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Precision Medicine ◽  
Open Source ◽  
Patient Specific ◽  
Cancer Type ◽  
Eligibility Criteria ◽  
Enrollment Rates ◽  
And Gender ◽  
Computational Platform

6620 Background: Genomic profiling and access to precision medicine clinical trials are now standard at leading cancer institutes and many community practices. Interpreting patient-specific genomic information and tracking the complex criteria for precision medicine trials requires specialized computational tools, especially for multi-institutional basket studies such as NCI-MATCH and TAPUR. Methods: To address this challenge we have developed an open source computational platform for patient-specific clinical trial matching at Dana-Farber Cancer Institute (DFCI) called MatchMiner, which aides in both patient recruitment to precision medicine trials, as well as decision support for oncologists. Trial matches are computed based on genomic criteria, including mutations, CNAs, and SVs, as well as clinical and demographic information, including cancer type, age, and gender. A formal standard called clinical trial markup language (CTML) to encode complex clinical trial eligibility criteria has also been created. Results: MatchMiner is now available at DFCI. Currently 123 precision medicine clinical trials have been transformed into CTML and 13,000 patient records are available, with over 88% of current patients having at least 1 match (average 2.6). A total of 103 genes are specified as criteria for at least 1 trial. KRAS, TP53, PTEN, PIK3CA and BRAF are the genes driving the most number of matches. General usage statistics and trial enrollment rates are currently being monitored to determine the system effectiveness. As this is an open source initiative, the software is also now publically available at https://github.com/dfci/matchminer. Conclusions: We have developed an open source computational platform that enables patient-specific matching and recruitment to precision medicine clinical trials at DFCI. We are actively seeking collaborators and plan to make CTML a multi-institution standard for encoding complex clinical trial eligibility in a computable form.

scholarly journals Trial Design Challenges and Approaches for Precision Oncology in Rare Tumors: Experiences of the Children’s Oncology Group

2019 ◽  
pp. 1-13
Author(s):  
Lindsay A. Renfro ◽  
Lingyun Ji ◽  
Jin Piao ◽  
Arzu Onar-Thomas ◽  
John A. Kairalla ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Risk Stratification ◽  
Pediatric Cancer ◽  
Trial Design ◽  
Intensive Therapy ◽  
The United States ◽  
Precision Oncology ◽  
Cancer Type ◽  
Oncology Group ◽  
Operating Characteristics

In the United States, cancer remains the leading cause of disease-related death in children. Although survival from any pediatric cancer has improved dramatically during past decades, a number of cancers continue to yield dismal prognoses, which has motivated the continued study of novel therapeutic strategies. Furthermore, even patients cured of pediatric cancer often experience severe adverse effects of treatment and other long-term health implications, such as cardiotoxicity or loss of fertility. For these patients, improved risk stratification to identify those who could safely receive alternate or less-intensive therapy without affecting prognosis is a key objective. Fortunately, pediatric cancers are rare overall, but even among patients with the same narrow cancer type, there is often broad heterogeneity in terms of prognosis, molecular features or pathology, current treatment strategies, and scientific objectives. As a result, the design of clinical trials in the pediatric cancer setting is challenged by a number of practical issues that must be addressed to ensure trial feasibility for this vulnerable group of patients. In this review, we discuss some of the unique trial design considerations often encountered in any rare tumor setting through the lens of our experiences as faculty statisticians for the Children’s Oncology Group, the largest organization in the world dedicated exclusively to pediatric cancer research and clinical trials. These topics include risk stratification within individual trials, relaxation of trial operating characteristics and parameters, use of historical controls, and address of noninferiority-type objectives in small cohorts. We review each in terms of practical motivation, present challenges, and potential solutions described in the literature and implemented in selected example trials from the Children’s Oncology Group.

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