Dataset Information

Identified Enrollment Challenges of Adolescent and Young Adult Patients on the Nonchemotherapy Arm of Children's Oncology Group Study ARST1321.

ABSTRACT: ARST1321, a trial of patients with advanced soft tissue sarcoma, was the first National Clinical Trials Network study codeveloped by pediatric and adult consortia with two treatment cohorts. We report on the findings of a survey to identify barriers to enrolling adolescent and young adult patients (15-39 years) onto the nonchemotherapy arm. The survey response rate was 31% with a 70% completion rate. Common identified reasons for low accrual in order of decreasing frequency included insufficient funding, lack of study awareness or interest, competing trials, toxicity concerns, philosophical differences in the therapy backbone, and regulatory and infrastructure barriers. Clinical Trials.gov ID: NCT02180867.

SUBMITTER: Avutu V

PROVIDER: S-EPMC9354033 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Identified Enrollment Challenges of Adolescent and Young Adult Patients on the Nonchemotherapy Arm of Children's Oncology Group Study ARST1321.

Avutu Viswatej V Weiss Aaron R AR Reed Damon R DR Ahmed Safia K SK Allen-Rhoades Wendy A WA Chen Yen-Lin E YE Davis Lara E LE Eaton Bree R BR Hawkins Douglas S DS Indelicato Danny J DJ Patel Shreyaskumar R SR Randall R Lor RL Reinke Denise K DK Riedel Richard F RF Scharschmidt Thomas J TJ Thornton Katherine A KA Wang Dian D Janeway Katherine A KA Kopp Lisa M LM

Journal of adolescent and young adult oncology 20210909 3

ARST1321, a trial of patients with advanced soft tissue sarcoma, was the first National Clinical Trials Network study codeveloped by pediatric and adult consortia with two treatment cohorts. We report on the findings of a survey to identify barriers to enrolling adolescent and young adult patients (15-39 years) onto the nonchemotherapy arm. The survey response rate was 31% with a 70% completion rate. Common identified reasons for low accrual in order of decreasing frequency included insufficient ...[more]

PMID: 34515544

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Project description:PAPER 1:"Identification of novel subgroups of high-risk pediatric precursor B acute lymphoblastic leukemia (B-ALL) by unsupervised microarray analysis: clinical correlates and therapeutic implications. A Children's Oncology Group (COG) study." ABSTRACT We examined gene expression profiles of pre-treatment specimens from 207 patients from the COG P9906 study to identify signatures of children with high risk B-precursor acute lymphoblastic leukemia (ALL) and to determine whether the resulting clusters are associated with either specific clinical features or treatment response characteristics. Four unsupervised clustering methods were utilized to classify patients into similar groups. The different clustering algorithms showed significant overlap in cluster membership. Two clusters contained all cases with either t(1;19)(q23;p13) translocations or MLL rearrangements. The other six clusters were novel and had no recurring chromosomal abnormalities or distinctive clinical features. Members of two of these novel clusters had significant survival differences when compared to the overall 4-year relapse-free survival (RFS) of 61%. These included clusters of patients with either significantly better (94.7%) or worse (21.0%) RFS at 4 years. Children of Hispanic/Latino ethnicity were disproportionately present in the poor outcome cluster. The poor outcome cluster represents a novel biologically distinctive subset of B-precursor ALL that may occur at least as frequently as BCR/ABL. Further molecular characterization of this cluster may lead to the discovery of genomic abnormalities that can be targeted to improve the currently dismal outcome for children with this gene signature. The Sample data have also been used in another study: PAPER 2: "Gene expression classifiers for minimal residual disease and relapse free survival improve outcome prediction and risk classification in children with high risk acute lymphoblastic leukemia. A Children's Oncology Group study". ABSTRACT Background. Nearly 25% of children with B-precursor ALL present with "high-risk" disease (HR-ALL) that is resistant to current therapies. Gene expression profiling may yield molecular classifiers for outcome prediction that can be used to improve risk classification and therapeutic targeting. Methods. Expression profiles were obtained in pre-treatment leukemic samples from 207 uniformly treated children with HR-ALL. Relapse free survival (RFS) was 61% at 4 years and flow cytometric measures of minimal residual disease (MRD) at the end of induction (day 29) were predictive of outcome (P<0.001). Molecular classifiers predictive of RFS and MRD were developed using extensive cross-validation procedures. Results. A 38 gene molecular risk classifier predictive of RFS (MRC-RFS) distinguished two groups in HR-ALL with different relapse risks: low (4 yr RFS: 81%, n=109) vs. high (4 yr RFS: 50%, n=98) (P<0.0001). In multivariate analysis, the best predictor combined MRC-RFS and day 29 flow MRD data, classifying children into low (87% RFS), intermediate (62% RFS), or high risk (29% RFS) groups (P<0.0001). A 21 gene molecular classifier predictive of MRD could effectively substitute for day 29 flow MRD, yielding a combined classifier that similarly distinguished three risk groups at pre-treatment (low: 82% RFS; intermediate: 63% RFS; and high risk: 45% RFS) (P<0.0001). This combined molecular classifier was further validated on an independent cohort of 84 children with HR-ALL (P = 0.006). Conclusions. Molecular classifiers predictive of RFS and MRD can be used to distinguish distinct prognostic groups within HR-ALL, significantly improving risk classification schemes and the ability to prospectively identify children at diagnosis who will respond to or fail current treatment regimens. NOTE: Due to Children's Oncology Group (COG) restrictions, outcome and MRD data cannot be provided as part of the covariate data for this dataset at the present time. If you would like to arrange individual access to this data, please contact COG or the PI of this study, Dr. Cheryl Willman, at the University of New Mexico Cancer Center (cwillman@unm.edu) to arrange a collaboration.

Dataset Information

Identified Enrollment Challenges of Adolescent and Young Adult Patients on the Nonchemotherapy Arm of Children's Oncology Group Study ARST1321.

Publications

Identified Enrollment Challenges of Adolescent and Young Adult Patients on the Nonchemotherapy Arm of Children's Oncology Group Study ARST1321.

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