Dataset Information

Factors influencing survival after recurrence in osteosarcoma: A report from the Children's Oncology Group.

ABSTRACT: BACKGROUND:Despite drastic improvement in overall survival for pediatric patients with cancer, those with osteosarcoma have stable rates of survival since the 1980s. This project evaluates the effect of several variables on survival after first recurrence in patients with osteosarcoma. METHODS:Data from three prospective North American cooperative group trials for newly diagnosed osteosarcoma are included: INT-0133, POG-9754, and AOST0121. The analytic population for this study is all enrolled patients with first event-free survival (EFS) event of relapse. The primary outcome measure for this retrospective analysis was survival after recurrence (SAR). RESULTS:The analytic population consisted of N = 431 patients. SAR was statistically significantly associated with age at enrollment (<10 years, P = 0.027), presence of metastatic disease at diagnosis (localized, P < 0.0001), site of relapse (combination lung + bone, unfavorable, P = 0.005), and time to first relapse (2+ years, favorable, P < 0.0001) in multivariate analysis. Ethnicity, primary site of tumor, race, and sex were not significantly related to SAR. CONCLUSIONS:Prolonged SAR in patients with relapsed osteosarcoma is associated with age, extent of disease at diagnosis, site of and time to relapse. Adolescent and young adult patients with osteosarcoma have shorter SAR than younger patients, consistent with studies showing decreased overall survival in this group. Although patients with primary metastatic disease have shorter SAR, there is a subset of patients who relapse greater than 2 years from initial diagnosis that will become survivors. Histological response was significantly associated with time to relapse, but was not predictive of SAR.

SUBMITTER: Spraker-Perlman HL

PROVIDER: S-EPMC6249072 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Factors influencing survival after recurrence in osteosarcoma: A report from the Children's Oncology Group.

Spraker-Perlman Holly L HL Barkauskas Donald A DA Krailo Mark D MD Meyers Paul A PA Schwartz Cindy L CL Doski John J Gorlick Richard R Janeway Katherine A KA Isakoff Michael S MS

Pediatric blood & cancer 20180925 1

<h4>Background</h4>Despite drastic improvement in overall survival for pediatric patients with cancer, those with osteosarcoma have stable rates of survival since the 1980s. This project evaluates the effect of several variables on survival after first recurrence in patients with osteosarcoma.<h4>Methods</h4>Data from three prospective North American cooperative group trials for newly diagnosed osteosarcoma are included: INT-0133, POG-9754, and AOST0121. The analytic population for this study is ...[more]

PMID: 30255612

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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

Factors influencing survival after recurrence in osteosarcoma: A report from the Children's Oncology Group.

Publications

Factors influencing survival after recurrence in osteosarcoma: A report from the Children's Oncology Group.

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