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:ImportanceWhile the Affordable Care Act (ACA) set out to eliminate insurer discrimination based on preexisting conditions, the ACA health exchanges allow insurers to select what markets to enter and afford them great freedom on how they design their physician networks. Strategic market participation and physician network design based on population race, ethnicity, and health characteristics may give rise to a present-day form of redlining within health insurance markets-ie, a systematic underprovision of insurance plans and in-network practitioners within areas that are populated with higher proportions of non-Hispanic Black residents.ObjectiveTo examine if markets with relatively higher non-Hispanic Black populations have systematically fewer insurers and lower network inclusion of physicians residing within these areas.Design, setting, and participantsThis cohort study conducted a regression analysis of the US ACA health insurance exchange marketplace across 34 states with federal exchanges and physicians located within the 500 most populous US cities in 2014. County-level data were sourced from individual market and issuer enrollment databases and county health rankings; census tract data came from a national database of physician networks in 2014 marketplace plans, US Census Bureau data, and the Centers for Disease Control and Prevention's PLACES database. Adjustment was made for a rich set of county (or census tract) controls and state fixed effects to capture broad market and/or policy differences across states. Analyses were performed in June 2021.Main outcomes and measuresThe raw count of insurers within a county and the mean percentage of insurance networks that physicians participate in within each census tract.ResultsA total of 2270 counties were examined within our first analyses. In the counties analyzed, a mean (SD) of 23.0% (3.2%) of the population was aged 18 years or younger, and a mean (SD) of 11.0% (15.8%) of the population had non-Hispanic Black race and ethnicity. For the second analysis, 16 006 to 25 096 census tracts were examined (depending on physician specialty). With adjustment for population size, age, and race and ethnicity, a 1-SD increase in the county non-Hispanic Black population was associated with a 14.1% reduction in the number of insurers (mean [SE] marginal effect size, -2.18 [0.13]; P < .001). Accounting for additional county-level risk selection controls and state fixed effects, a 1-SD increase in the non-Hispanic Black population was associated with a 2.3% reduction in available insurers (marginal effect size, -0.36 [0.17]; P = .04). For practitioners network breadth inclusion, a 1-SD increase in the non-Hispanic Black population was associated with a 15.8% (marginal effect size, -0.32 [0.01]; P < .001) to 24.7% (marginal effect size, -0.14 [0.02]; P < .001) reduction in the physicians' network participation depending on their specialty. Adjusting for additional state fixed effects yielded estimates of 6% (marginal effect size, -0.08 [0.01]; P < .001) to 13.5% (marginal effect size, -0.12 [0.02]; P < .001) reductions in practitioner network participation.Conclusions and relevanceThese findings suggest that strategic decisions by insurers may contribute toward markets with higher racial or ethnic minority populations having systematically fewer participating insurers, as well as a higher prevalence of local physicians not included in coverage networks. These findings call for further examination of potential insurance redlining within the ACA marketplaces.

Dataset Information

Correlation of Insurance, Race, and Ethnicity with Pathologic Risk in a Controlled Retinoblastoma Cohort: A Children's Oncology Group Study.

Purpose

Design

Participants

Main outcome measures

Results

Conclusions

Publications

Correlation of Insurance, Race, and Ethnicity with Pathologic Risk in a Controlled Retinoblastoma Cohort: A Children's Oncology Group Study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets