Project description:Purpose: In 2010, a Children's Oncology Group (COG) phase III randomized trial for patients with high-risk neuroblastoma (ANBL0032) demonstrated improved event-free survival (EFS) and overall survival (OS) following treatment with an immunotherapy regimen of dinutuximab, GM-CSF, IL2, and isotretinoin compared with treatment with isotretinoin alone. Dinutuximab, a chimeric anti-GD2 monoclonal antibody, acts in part via natural killer (NK) cells. Killer immunoglobulin-like receptors (KIR) on NK cells and their interactions with KIR-ligands can influence NK cell function. We investigated whether KIR/KIR-ligand genotypes were associated with EFS or OS in this trial.Experimental Design: We genotyped patients from COG study ANBL0032 and evaluated the effect of KIR/KIR-ligand genotypes on clinical outcomes. Cox regression models and log-rank tests were used to evaluate associations of EFS and OS with KIR/KIR-ligand genotypes.Results: In this trial, patients with the "all KIR-ligands present" genotype as well as patients with inhibitory KIR2DL2 with its ligand (HLA-C1) together with inhibitory KIR3DL1 with its ligand (HLA-Bw4) were associated with improved outcome if they received immunotherapy. In contrast, for patients with the complementary KIR/KIR-ligand genotypes, clinical outcome was not significantly different for patients who received immunotherapy versus those receiving isotretinoin alone.Conclusions: These data show that administration of immunotherapy is associated with improved outcome for neuroblastoma patients with certain KIR/KIR-ligand genotypes, although this was not seen for patients with other KIR/KIR-ligand genotypes. Further investigation of KIR/KIR-ligand genotypes may clarify their role in cancer immunotherapy and may enable KIR/KIR-ligand genotyping to be used prospectively for identifying patients likely to benefit from certain cancer immunotherapy regimens. Clin Cancer Res; 24(1); 189-96. ©2017 AACRSee related commentary by Cheung and Hsu, p. 3.

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: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

KIR/KIR-ligand genotypes and clinical outcomes following chemoimmunotherapy in patients with relapsed or refractory neuroblastoma: a report from the Children's Oncology Group.

Background

Methods

Results

Conclusions

Trial registration number

Publications

KIR/KIR-ligand genotypes and clinical outcomes following chemoimmunotherapy in patients with relapsed or refractory neuroblastoma: a report from the Children's Oncology Group.

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