Project description:PurposeThe combination of irinotecan, temozolomide, dintuximab, and granulocyte-macrophage colony-stimulating factor (I/T/DIN/GM-CSF) demonstrated activity in patients with relapsed/refractory neuroblastoma in the randomized Children's Oncology Group ANBL1221 trial. To more accurately assess response rate and toxicity, an expanded cohort was nonrandomly assigned to I/T/DIN/GM-CSF.Patients and methodsPatients were eligible at first relapse or first designation of refractory disease. Oral T and intravenous (IV) irinotecan were administered on days 1 to 5 of 21-day cycles. DIN was administered IV (days 2-5), and GM-CSF was administered subcutaneously (days 6-12). The primary end point was objective response, analyzed on an intent-to-treat basis per the International Neuroblastoma Response Criteria.ResultsSeventeen eligible patients were randomly assigned to I/T/DIN/GM-CSF (February 2013 to March 2015); 36 additional patients were nonrandomly assigned to I/T/DIN/GM-CSF (August 2016 to May 2017). Objective (complete or partial) responses were observed in nine (52.9%) of 17 randomly assigned patients (95% CI, 29.2% to 76.7%) and 13 (36.1%) of 36 expansion patients (95% CI, 20.4% to 51.8%). Objective responses were seen in 22 (41.5%) of 53 patients overall (95% CI, 28.2% to 54.8%); stable disease was also observed in 22 of 53. One-year progression-free and overall survival for all patients receiving I/T/DIN/GM-CSF were 67.9% ± 6.4% (95% CI, 55.4% to 80.5%) and 84.9% ± 4.9% (95% CI, 75.3% to 94.6%), respectively. Two patients did not receive protocol therapy and were evaluable for response but not toxicity. Common grade ≥ 3 toxicities were fever/infection (18 [35.3%] of 51), neutropenia (17 [33.3%] of 51), pain (15 [29.4%] of 51), and diarrhea (10 [19.6%] of 51). One patient met protocol-defined criteria for unacceptable toxicity (grade 4 hypoxia). Higher DIN trough levels were associated with response.ConclusionI/T/DIN/GM-CSF has significant antitumor activity in patients with relapsed/refractory neuroblastoma. Study of chemoimmunotherapy in the frontline setting is indicated, as is further evaluation of predictive biomarkers.

Project description:BackgroundWhether social determinants of health are associated with survival in the context of pediatric oncology-targeted immunotherapy trials is not known. We examined the association between poverty and event-free survival (EFS) and overall survival (OS) for children with high-risk neuroblastoma treated in targeted immunotherapy trials.MethodsWe conducted a retrospective cohort study of 371 children with high-risk neuroblastoma treated with GD2-targeted immunotherapy in the Children's Oncology Group trial ANBL0032 or ANBL0931 at a Pediatric Health Information System center from 2005 to 2014. Neighborhood poverty exposure was characterized a priori as living in a zip code with a median household income within the lowest quartile for the cohort. Household poverty exposure was characterized a priori as sole coverage by public insurance. Post hoc analyses examined the joint effect of neighborhood and household poverty using a common reference. All statistical tests were 2-sided.ResultsIn multivariable Cox regressions adjusted for disease and treatment factors, household poverty-exposed children experienced statistically significantly inferior EFS (hazard ratio [HR] = 1.90, 95% confidence interval [CI] = 1.28 to 2.82, P = .001) and OS (HR = 2.79, 95% CI = 1.63 to 4.79, P < .001) compared with unexposed children. Neighborhood poverty was not independently associated with EFS or OS. In post hoc analyses exploring the joint effect of neighborhood and household poverty, children with dual-poverty exposure (neighborhood poverty and household poverty) experienced statistically significantly inferior EFS (HR = 2.21, 95% CI = 1.48 to 3.30, P < .001) and OS (HR = 3.70, 95% CI = 2.08 to 6.59, P < .001) compared with the unexposed group.ConclusionsPoverty is independently associated with increased risk of relapse and death among neuroblastoma patients treated with targeted immunotherapy. Incorporation of social and environmental factors in future trials as health-care delivery intervention targets may increase the benefit of targeted therapies.

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.

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. Unsupervised clustering and supervised risk classification analyses of 207 diagnostic samples and associated clinical covariate data. See the Summary for greater details. The data were analyzed using Microarray Suite version 5.0 (MAS 5.0) in the Affymetrix Gene Chip Operating Software Version 1.4. Probe masking was used (see 9906_TT207_Affymetrix_probe_mask.msk, linked below as a supplementary file). Otherwise all Affymetrix default parameter settings were used. Global scaling as the normalization method, with the default target intensity of 500, was used.

Dataset Information

Neuroblastoma Patients' KIR and KIR-Ligand Genotypes Influence Clinical Outcome for Dinutuximab-based Immunotherapy: A Report from the Children's Oncology Group.

Publications

Neuroblastoma Patients' KIR and KIR-Ligand Genotypes Influence Clinical Outcome for Dinutuximab-based Immunotherapy: A Report from the Children's Oncology Group.

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