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.

Project description:BackgroundPreclinical studies have shown synergistic antitumour activity by inhibition of insulin-like growth factor-1 receptor (IGF-1R) and mTOR. The expression of IGF-1R seems to be crucial for this effect. We investigated the safety and efficacy of the combination of the IGF-1R antibody cixutumumab and the mTOR inhibitor temsirolimus in patients with chemotherapy-refractory bone and soft-tissue sarcomas according to IGF-1R expression by immunohistochemistry.MethodsWe undertook a multicentre, open-label, phase 2 study in 19 cancer centres in the USA. Patients aged at least 16 years with a histologically confirmed diagnosis of bone or soft-tissue sarcoma were allocated on the basis of IGF-1R expression by immunohistochemistry to one of three treatment groups: IGF-1R-positive soft-tissue sarcoma (group A), IGF-1R-positive bone sarcomas (group B), or IGF-1R-negative bone and soft-tissue sarcoma (group C). Patients received weekly treatment with cixutumumab (6 mg/kg, intravenous) and temsirolimus (25 mg, intravenous flat dose) in 6-week cycles. A Simon optimal two-stage design was used for every arm. The primary endpoint was progression-free survival (PFS) at 12 weeks by intention-to-treat analysis in the first 54 patients assigned to every treatment arm. Although patients still remain on treatment, this trial has completed enrolment and this represents the final analysis. This study is registered with ClinicalTrials.gov, number NCT01016015.FindingsBetween Nov 18, 2009, and April 11, 2012, 388 patients were screened for IGF-1R expression and 54 were assigned to each arm. 17 of 54 patients in the IGF-1R-positive soft-tissue sarcoma group (31%; one-sided 95% CI lower bound 21%; two-sided 90% CI 21-43), 19 of 54 in IGF-1R-positive bone sarcoma group (35%; one-sided 95% CI lower bound 24%; two-sided 90% CI 24-47), and 21 of 54 in the IGF-1R-negative group (39%, one-sided 95% CI lower bound 28%; two-sided 90% CI 28-51) were progression free at 12 weeks. On April 6, 2011, the protocol was amended to include three additional patients in the IGF-1R-positive soft-tissue sarcoma group (total of 57 patients) and nine more in the IGF-1R-negative group (total of 63 patients). There were 2546 adverse events reported during the study, 214 (8%) of which were grade 3-4. The most common grade 3-4 toxicities in the 174 treated patients were anaemia in 16 (9%) patients, hyperglycaemia in 18 (10%), hypophosphataemia in 16 (9%), lymphopenia in 25 (14%), oral mucositis in 19 (11%), and thrombocytopenia in 19 (11%).InterpretationThe combination of cixutumumab and temsirolimus shows clinical activity in patients with sarcoma and forms a basis for future trials. However, IGF-1R expression by immunohistochemistry is not predictive of clinical outcome after treatment with this combination.FundingNational Cancer Institute and CycleforSurvival Fund, Memorial Sloan-Kettering Cancer Center.

Dataset Information

Phase II study of cixutumumab in combination with temsirolimus in pediatric patients and young adults with recurrent or refractory sarcoma: a report from the Children's Oncology Group.

Publications

Phase II study of cixutumumab in combination with temsirolimus in pediatric patients and young adults with recurrent or refractory sarcoma: a report from the Children's Oncology Group.

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