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Systematic computation with functional gene-sets among leukemic and hematopoietic stem cells reveals a favorable prognostic signature for acute myeloid leukemia.

Yang XH, Li M, Wang B, Zhu W, Desgardin A, Onel K, de Jong J, Chen J, Chen L, Cunningham JM - BMC Bioinformatics (2015)

Bottom Line: Genes that regulate stem cell function are suspected to exert adverse effects on prognosis in malignancy.Genes within each signature significantly share common biological processes and/or molecular functions (empirical p = 6e-5 and 0.03 respectively).We successfully validated the prognosis of both signatures in two independent cohorts of 91 and 242 patients respectively (log-rank p < 0.0015 and 0.05; empirical p < 0.015 and 0.08).

View Article: PubMed Central - PubMed

Affiliation: Department of Pediatrics, and Comer Children's Hospital, Section of Hematology/Oncology, The University of Chicago, 900 East 57th Street, KCBD Room 5121, Chicago, Illinois, 60637, USA. xyang2@uchicago.edu.

ABSTRACT

Background: Genes that regulate stem cell function are suspected to exert adverse effects on prognosis in malignancy. However, diverse cancer stem cell signatures are difficult for physicians to interpret and apply clinically. To connect the transcriptome and stem cell biology, with potential clinical applications, we propose a novel computational "gene-to-function, snapshot-to-dynamics, and biology-to-clinic" framework to uncover core functional gene-sets signatures. This framework incorporates three function-centric gene-set analysis strategies: a meta-analysis of both microarray and RNA-seq data, novel dynamic network mechanism (DNM) identification, and a personalized prognostic indicator analysis. This work uses complex disease acute myeloid leukemia (AML) as a research platform.

Results: We introduced an adjustable "soft threshold" to a functional gene-set algorithm and found that two different analysis methods identified distinct gene-set signatures from the same samples. We identified a 30-gene cluster that characterizes leukemic stem cell (LSC)-depleted cells and a 25-gene cluster that characterizes LSC-enriched cells in parallel; both mark favorable-prognosis in AML. Genes within each signature significantly share common biological processes and/or molecular functions (empirical p = 6e-5 and 0.03 respectively). The 25-gene signature reflects the abnormal development of stem cells in AML, such as AURKA over-expression. We subsequently determined that the clinical relevance of both signatures is independent of known clinical risk classifications in 214 patients with cytogenetically normal AML. We successfully validated the prognosis of both signatures in two independent cohorts of 91 and 242 patients respectively (log-rank p < 0.0015 and 0.05; empirical p < 0.015 and 0.08).

Conclusion: The proposed algorithms and computational framework will harness systems biology research because they efficiently translate gene-sets (rather than single genes) into biological discoveries about AML and other complex diseases.

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Related in: MedlinePlus

Prognostic analysis of patients with cytogenetically normal AML, based on DNM identified gene-set pairs. Kaplan–Meier plots of survival analysis on stratified samples with better outcome (green) or worse outcome (red). The Relative Effect Analysis with Gene-Set-Group Pairs (RXA-GSP) calculated a prognostic indicator by comparing three LSC- representative gene-sets (30 genes) with three normal control gene-sets (166 genes, Additional file 4: Table S2). The normalized FAIME.5 profiles are used. In each sub-panel, top bars mark the simulated p-values from which we estimated the empirical p-value for the actually observed log-rank p-value, the vertical line marked with an arrow. A RXA-GSP indicator I of less than 1 significantly indicates worse prognosis in the training cohort (GSE12417, Panel A) of cytogenetically normal AML patients and in two validation cohorts (GSE14468 and TCGA, Panels B and C) of cytogenetically normal AML patients.
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Fig4: Prognostic analysis of patients with cytogenetically normal AML, based on DNM identified gene-set pairs. Kaplan–Meier plots of survival analysis on stratified samples with better outcome (green) or worse outcome (red). The Relative Effect Analysis with Gene-Set-Group Pairs (RXA-GSP) calculated a prognostic indicator by comparing three LSC- representative gene-sets (30 genes) with three normal control gene-sets (166 genes, Additional file 4: Table S2). The normalized FAIME.5 profiles are used. In each sub-panel, top bars mark the simulated p-values from which we estimated the empirical p-value for the actually observed log-rank p-value, the vertical line marked with an arrow. A RXA-GSP indicator I of less than 1 significantly indicates worse prognosis in the training cohort (GSE12417, Panel A) of cytogenetically normal AML patients and in two validation cohorts (GSE14468 and TCGA, Panels B and C) of cytogenetically normal AML patients.

Mentions: Next, we investigated the clinical relevance in patients with cytogenetically normal AML. A subclass of intermediate-risk AML, cytogenetically normal AML has a variety of outcomes: some affected individuals respond well to standard treatment while others may require more intensive therapy. We set the largest cytogenetically normal AML cohort for training (GSE12417, n = 242 cytogenetically normal AML [35]), and the other two datasets as validation (GSE14468, n = 214 cytogenetically normal AML [33]; TCGA, n = 91 cytogenetically normal AML [34]). For the LSC- gene-sets, significance of three out of four of the control gene-sets was repeatable, based on the FAIME.5 profiles (Additional file 4: Table S2). The observed prognosis (log-rank p = 0.0014, 0.0012, and 0.00082 respectively; empirical p = 0.014, 0.0045, and 0.013, respectively, Figure 4) remains significant in multivariate analysis, independent of age, KRAS mutation, and ELN risk classification (Table 4).Figure 4


Systematic computation with functional gene-sets among leukemic and hematopoietic stem cells reveals a favorable prognostic signature for acute myeloid leukemia.

Yang XH, Li M, Wang B, Zhu W, Desgardin A, Onel K, de Jong J, Chen J, Chen L, Cunningham JM - BMC Bioinformatics (2015)

Prognostic analysis of patients with cytogenetically normal AML, based on DNM identified gene-set pairs. Kaplan–Meier plots of survival analysis on stratified samples with better outcome (green) or worse outcome (red). The Relative Effect Analysis with Gene-Set-Group Pairs (RXA-GSP) calculated a prognostic indicator by comparing three LSC- representative gene-sets (30 genes) with three normal control gene-sets (166 genes, Additional file 4: Table S2). The normalized FAIME.5 profiles are used. In each sub-panel, top bars mark the simulated p-values from which we estimated the empirical p-value for the actually observed log-rank p-value, the vertical line marked with an arrow. A RXA-GSP indicator I of less than 1 significantly indicates worse prognosis in the training cohort (GSE12417, Panel A) of cytogenetically normal AML patients and in two validation cohorts (GSE14468 and TCGA, Panels B and C) of cytogenetically normal AML patients.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4376348&req=5

Fig4: Prognostic analysis of patients with cytogenetically normal AML, based on DNM identified gene-set pairs. Kaplan–Meier plots of survival analysis on stratified samples with better outcome (green) or worse outcome (red). The Relative Effect Analysis with Gene-Set-Group Pairs (RXA-GSP) calculated a prognostic indicator by comparing three LSC- representative gene-sets (30 genes) with three normal control gene-sets (166 genes, Additional file 4: Table S2). The normalized FAIME.5 profiles are used. In each sub-panel, top bars mark the simulated p-values from which we estimated the empirical p-value for the actually observed log-rank p-value, the vertical line marked with an arrow. A RXA-GSP indicator I of less than 1 significantly indicates worse prognosis in the training cohort (GSE12417, Panel A) of cytogenetically normal AML patients and in two validation cohorts (GSE14468 and TCGA, Panels B and C) of cytogenetically normal AML patients.
Mentions: Next, we investigated the clinical relevance in patients with cytogenetically normal AML. A subclass of intermediate-risk AML, cytogenetically normal AML has a variety of outcomes: some affected individuals respond well to standard treatment while others may require more intensive therapy. We set the largest cytogenetically normal AML cohort for training (GSE12417, n = 242 cytogenetically normal AML [35]), and the other two datasets as validation (GSE14468, n = 214 cytogenetically normal AML [33]; TCGA, n = 91 cytogenetically normal AML [34]). For the LSC- gene-sets, significance of three out of four of the control gene-sets was repeatable, based on the FAIME.5 profiles (Additional file 4: Table S2). The observed prognosis (log-rank p = 0.0014, 0.0012, and 0.00082 respectively; empirical p = 0.014, 0.0045, and 0.013, respectively, Figure 4) remains significant in multivariate analysis, independent of age, KRAS mutation, and ELN risk classification (Table 4).Figure 4

Bottom Line: Genes that regulate stem cell function are suspected to exert adverse effects on prognosis in malignancy.Genes within each signature significantly share common biological processes and/or molecular functions (empirical p = 6e-5 and 0.03 respectively).We successfully validated the prognosis of both signatures in two independent cohorts of 91 and 242 patients respectively (log-rank p < 0.0015 and 0.05; empirical p < 0.015 and 0.08).

View Article: PubMed Central - PubMed

Affiliation: Department of Pediatrics, and Comer Children's Hospital, Section of Hematology/Oncology, The University of Chicago, 900 East 57th Street, KCBD Room 5121, Chicago, Illinois, 60637, USA. xyang2@uchicago.edu.

ABSTRACT

Background: Genes that regulate stem cell function are suspected to exert adverse effects on prognosis in malignancy. However, diverse cancer stem cell signatures are difficult for physicians to interpret and apply clinically. To connect the transcriptome and stem cell biology, with potential clinical applications, we propose a novel computational "gene-to-function, snapshot-to-dynamics, and biology-to-clinic" framework to uncover core functional gene-sets signatures. This framework incorporates three function-centric gene-set analysis strategies: a meta-analysis of both microarray and RNA-seq data, novel dynamic network mechanism (DNM) identification, and a personalized prognostic indicator analysis. This work uses complex disease acute myeloid leukemia (AML) as a research platform.

Results: We introduced an adjustable "soft threshold" to a functional gene-set algorithm and found that two different analysis methods identified distinct gene-set signatures from the same samples. We identified a 30-gene cluster that characterizes leukemic stem cell (LSC)-depleted cells and a 25-gene cluster that characterizes LSC-enriched cells in parallel; both mark favorable-prognosis in AML. Genes within each signature significantly share common biological processes and/or molecular functions (empirical p = 6e-5 and 0.03 respectively). The 25-gene signature reflects the abnormal development of stem cells in AML, such as AURKA over-expression. We subsequently determined that the clinical relevance of both signatures is independent of known clinical risk classifications in 214 patients with cytogenetically normal AML. We successfully validated the prognosis of both signatures in two independent cohorts of 91 and 242 patients respectively (log-rank p < 0.0015 and 0.05; empirical p < 0.015 and 0.08).

Conclusion: The proposed algorithms and computational framework will harness systems biology research because they efficiently translate gene-sets (rather than single genes) into biological discoveries about AML and other complex diseases.

Show MeSH
Related in: MedlinePlus