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Identification of biomarkers for the prediction of relapse‑free survival in pediatric B‑precursor acute lymphoblastic leukemia

  • Authors:
    • Wei Jing
    • Jing Li

  • View Affiliations

  • Published online on: November 2, 2018     https://doi.org/10.3892/or.2018.6846
  • Pages: 659-667
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Abstract

B‑precursor acute lymphoblastic leukemia (B‑ALL) is the most common cancer diagnosed in children and adolescents. Despite the fact that the 5‑year survival rate has increased from 60 to 90%, approximately a quarter of children suffer from relapse with poor outcome. To improve the clinical management of B‑ALL, there is an urgent need for prognostic biomarkers for the prediction of B‑ALL outcomes. In the present study, we performed a comprehensive analysis of the gene expression data of 456 samples from five independent cohorts. We first sought to identify B‑ALL‑associated genes by differential gene expression analysis by applying linear models. Then, the statistical modelling was applied to identify candidates related to relapse‑free survival. We identified a total of 1,273 B‑ALL‑associated genes that have functions relevant to chemokine signaling. From these genes, 59 genes were identified as prognostic biomarkers. Based on expression patterns of these genes, we successfully distinguished high‑ and low‑risk groups of B‑ALL patients (log-rank test, P‑value=0.025). We further investigated the 59‑gene expression levels in ALL chemotherapy‑treated cohorts and identified 4 genes as potential drug targets associated with drug sensitivity. Our results provided a novel biomarker panel. By leveraging the large scale of data and statistical modelling, we believe this 59‑gene biomarker could help to unveil the mechanisms underlying B‑ALL progression and become potential drug targets.

Introduction

B-precursor acute lymphoblastic leukemia (B-ALL) is the most common cancer diagnosed in children and adolescents (1,2). One of the major causes of mortality is relapse despite intensive multi-agent chemotherapy (3). For the past two decades, several studies have reported that molecular abnormalities including TP53 mutations (4), deletion of INK4A/ARF (5) and TEL deletion (6) contribute to B-ALL relapse. However, the pathogenesis and biological mechanisms underlying relapsed ALL remain largely unknown. Thus, we sought to provide novel insights by identifying prognostic biomarkers from genome-wide expression profiling data generated by DNA microarrays.

Microarray technology has been developed more than a decade ago and is widely used in biomedical and clinical research. This high-throughput strategy enables profiling genome-wide expression simultaneously. Previously, based on committee neural networks, the leukemia gene expression data can be subcategorized into B-cell acute lymphoblastic leukemia, T-cell acute lymphoblastic leukemia and acute myeloid leukemia (7). Unsupervised hierarchal clustering of ALL gene expression could be used to reveal unique clusters with distinct cytogenetic, genomic and transcriptomic characterizations (8). By comparing gene expression profiles of specimens at the time of diagnosis vs. at relapse, or early- vs. late-relapse, several biological pathways such as cell cycle regulation, WNT and mitogen-activated protein kinase pathways (9,10) were identified to contribute to ALL relapse.

However, these findings lack connections to clinical practice, as the prediction of prognosis plays a crucial role in facilitating clinical decision-making. The purpose of this study was to develop a prognostic biomarker from gene expression profiles. Unlike the previously published study (11), we integrated data sets from multiple cohorts and implemented a comprehensive computational pipeline to identify a 59-gene biomarker that could serve as a B-ALL prognostic biomarker in practical applications.

Materials and methods

Gene expression data and preprocessing

We collected raw microarray data from two cohorts including TARGET-ALL study (11) and Microarray Innovations in LEukemia (MILE) (12,13). For the TAGET-ALL study, 207 high-risk B-precursor ALL patients between March 15, 2000 and April 25, 2003 were recruited from the Children's Oncology Group (COG) Clinical Trial P9906. RNA was first purified from samples with >80% blasts (131 bone marrow, 76 peripheral blood) and then hybridized to Affymetrix Human Genome U133 Plus 2.0 Array. The raw intensity *.CEL files were retrieved from NCBI Gene Expression Omnibus (http://www.ncbi.nih.gov/geo) under the accession number GSE11877. For the MILE study, there were in total 2,096 blood or bone marrow samples of acute and chronic leukemia patients from 11 participant centers. In this study, we restricted to take 74 non-leukemia healthy samples as controls. Consistent with the TARGET-ALL study, Affymetrix Human Genome U133 Plus 2.0 Array was also used for the gene expression profiling.

For the discovery of prognostic biomarkers, we used the gene expression data set from one previously published study (14), where 80 samples collected at the time of diagnosis were considered in this study. The RNA was hybridized to Affymetrix HG-U133A oligonucleotide microarrays. The raw intensity *CEL files were retrieved from EBI ArrayExpress (https://www.ebi.ac.uk/arrayexpress/) under the accession number E-MTAB-1216.

The raw intensity *.CEL files were preprocessed using the frma (15) package in R 3.4.1 (https://www.r-project.org) environment. Briefly, the frma package converted raw probe-level intensities into background-corrected gene-level intensities. The data was then normalized based on the estimation of probe-specific effects, which allowed us to combine data from various cohorts without batch effects.

Identification of B-ALL-associated genes

We sought to identify B-ALL-associated genes by performing differential gene expression analysis between B-ALL (n=207) and healthy samples (n=74). The R package limma (16) package was used to conduct the differential gene expression analysis. The expression data was first Log2 transformed and then fitted into linear models with Empirical Bayesian methods for the analysis. We filtered the results at Log2 fold-change ≥2, or ≤-2 and B-statistics ≥4.6, which indicates that the probabilities of the genes were differentially expressed were >99%. After filtering, 1,273 genes were identified. The gene set enrichment analysis was then conducted using the R package clusterProfiler (17). The enrichment P-value was Benjamini & Hochberg adjusted.

Prognostic biomarker identification for relapse-free survival (RFS)

For the development of prognostic gene signature, the 1,273 B-ALL-associated genes were first fitted with the Cox proportional hazards model in 80 samples. The prognostic significance of each gene test was assessed by log-rank test. We selected 59 at the cut-off P-value ≤0.05. The hierarchal clustering algorithm was then applied to the 59-gene expression profiles and the patients were divided into high- and low-risk groups. The Kaplan Meier-plot and log-rank test were used to test the prognostic significance of the two groups.

Results

In the present study, we sought to identify B-ALL RFS biomarkers using transcriptome data. We hypothesized that the biomarkers may be involved in B-ALL pathogenesis, thus the gene differential expression analysis was conducted by comparing 207 B-ALL samples with 74 healthy normal blood, or bone marrow samples. After applying the linear models and stringent cut-off [Log fold-change ≥2, ≤-2, and false discovery rate (FDR) ≤0.01], a total of 1,273 genes were identified as ALL-associated genes as they were significantly upregulated or downregulated in the ALL samples (Fig. 1).

The Gene Ontology (GO) enrichment analysis revealed that the B-ALL-associated genes were involved in various biological processes (Table I). Surprisingly, we found that the B-ALL-associated genes were most significantly enriched in the biological processes that respond to bacterium (GO:0009617). We further categorized these genes and found that they were involved in cytokine-cytokine receptor interaction pathway (Fig. 2). These genes include chemokine ligands (CCL5, CXCL1, CXCL16, CXCL2, CXCL3, CXCL8), tumor necrosis factor receptor superfamily (FAS, TNFRSF10C, TNFRSF1B, TNFSF8), interleukins (IL-18, CXCL8) and interleukin receptors (IL-10RA, IL-6R). These findings agreed with previous studies on B-ALL pathogenesis. Chemokines and their receptors are vital in many cellular activities such as migrations, targeting developing and mature leukocytes (18). It was previously reported that the CXCR5-CXCL13 axis plays a vital role in chronic lymphocytic leukemia (CLL) (19). There are also other chemokines such as CCR7, CXCR4 and CXCR5 (20), CXCR3 (21), CCL25 (22) and the CXCR4-CXCL12 axis (23) that have been identified as potential targets in leukemia treatment.

Table I.

Gene Ontology enrichment results of ALL-related genes.

Table I.

Gene Ontology enrichment results of ALL-related genes.

Gene OntologyBiological processP-value
GO:0009617Response to bacterium 1.39×10−14
GO:0050900Leukocyte migration 2.31×10−10
GO:0002274Myeloid leukocyte activation 7.56×10−10
GO:0006778 Porphyrin-containing compound metabolic process 7.71×10−10
GO:0042168Heme metabolic process 8.40×10−10
GO:0002237Response to molecule of bacterial origin 2.55×10−9
GO:0006783Heme biosynthetic process 3.53×10−9
GO:0032496Response to lipopolysaccharide 3.53×10−9
GO:0046501Protoporphyrinogen IX metabolic process 3.53×10−9
GO:0042742Defense response to bacterium 4.61×10−9
Gene OntologyCellular componentP-value
GO:0030141Secretory granule 1.95×10−6
GO:0099503Secretory vesicle 5.15×10−5
GO:0098857Membrane microdomain 5.15×10−5
GO:0030667Secretory granule membrane 5.15×10−5
GO:0031091Platelet alpha granule 6.73×10−5
GO:0045121Membrane raft 6.73×10−5
GO:0098589Membrane region 7.00×10−4
GO:0031092Platelet alpha granule membrane 9.60×10−4
GO:0098552Side of membrane 9.60×10−4
GO:0009897External side of plasma membrane 1.30×10−3
Gene OntologyMolecular functionP-value
GO:0030246Carbohydrate binding 3.98×10−5
GO:0008329Signaling pattern recognition receptor activity 2.94×10−4
GO:0038187Pattern recognition receptor activity 2.94×10−4
GO:0017171Serine hydrolase activity 2.94×10−4
GO:0008236Serine-type peptidase activity 4.53×10−4
GO:0042379Chemokine receptor binding 6.71×10−4
GO:0004252Serine-type endopeptidase activity 1.14×10−3
GO:0019865Immunoglobulin binding 1.14×10−3
GO:0004601Peroxidase activity 2.59×10−3
GO:0016684Oxidoreductase activity, acting on peroxide as acceptor 4.99×10−3

[i] Top 10 significantly enriched Gene Ontology (Biological process, Cellular component, Molecular function) terms of the identified B-ALL-associated genes. P-values were Benjamini & Hochberg adjusted. B-ALL, B-precursor acute lymphoblastic leukemia.

Then, we applied Cox proportional hazards modelling to the B-ALL-associated genes and selected 59 top ranked genes based on log-rank test P-values as B-ALL RFS genes (Table II). After applying the hierarchal clustering to this 59-gene profile, the high- (n=20), and low-risk (n=60) samples were identified from the B-ALL cohort with significant (log-rank test P=0.025) different relapse-free survival outcome (Fig. 3). The B-ALL RFS biomarkers included genes from various families, such as glycosyltransferases (C1GALT1 and B4GALT5), RAS oncogene GTPases (RAB27B and RAB7A), nuclear hormone receptors (NR3C1 and RORA), RNA binding proteins (RBM6 and U2SURP) and Zinc finger proteins (PLAGL2, SP1 and ZNF91). We also found that among these biomarkers, 4 genes, B4GALT5, CDK6, PDZD8 and RAB7A, were candidates that were associated with chemotherapy response (Fig. 4) in two independent ALL cohorts, GSE19143 (n=52) (24) and GSE13280 (n=44) (25) where patients were treated with prednisolone. Indeed, the B4GALT family is involved in mediating drug resistance in human leukemia cells by regulating the Hedgehog pathway (26). This suggest that the 59-gene biomarker could also indicate drug sensitivity in ALL treatment.

Table II.

List of 59 prognostic genes for RFS in ALL.

Table II.

List of 59 prognostic genes for RFS in ALL.

Gene symbolGene IDChromosome locationGene nameHR (95% CI)P-value
ADAM987548p11.22ADAM metallopeptidase domain 90.51 (0.27–0.94) 2.83×10−2
AOAH3137p14.2Acyloxyacyl hydrolase2.06 (1.12–3.78) 1.63×10−2
APP35121q21.3Amyloid β precursor protein0.53 (0.34–0.84) 5.49×10−3
B4GALT5933420q13.13 β-1,4-galactosyltransferase 50.43 (0.20–0.95) 4.53×10−2
BSG68219p13.3Basigin (Ok blood group)2.05 (1.05–4.00) 3.37×10−2
C1GALT1569137p22.1-p21.3Core 1 synthase, glycoprotein-N-acetylgalactosamine 3-β-galactosyltransferase 17.40 (1.68–32.52) 7.47×10−3
CDK610217q21.2Cyclin-dependent kinase 63.06 (1.14–8.24) 2.71×10−2
CHPT15699412q23.2Choline phosphotransferase 10.41 (0.24–0.69) 5.35×10−4
CKAP22658613q14.3Cytoskeleton associated protein 20.42 (0.19–0.97) 4.20×10−2
CLC117819q13.2Charcot-Leyden crystal galectin1.52 (1.02–2.29) 3.43×10−2
CLINT196855q33.3Clathrin interactor 10.28 (0.09–0.88) 2.72×10−2
CLU11918p21.1Clusterin12.51 (1.36–115.07) 2.42×10−2
COL17A1130810q25.1Collagen type XVII α 1 chain18.63 (1.35–256.93) 2.69×10−2
CRTAP104913p22.3Cartilage associated protein0.20 (0.05–0.82) 2.71×10−2
CTSA547620q13.12Cathepsin A2.55 (1.22–5.30) 1.33×10−2
DACH1160213q21.33Dachshund family transcription factor 12.04 (1.26–3.31) 1.68×10−3
DNAJB94189 7q31.1;14q24.2-q24.3DnaJ heat shock protein family (Hsp40) member B90.40 (0.18–0.91) 3.24×10−2
DNTT179110q24.1DNA nucleotidylexotransferase0.82 (0.67–0.99) 3.74×10−2
ECRP64333214q11.2Ribonuclease A family member 2 pseudogene2.77 (1.20–6.40) 1.27×10−2
FCGR1B22101p11.2Fc fragment of IgG receptor Ib1.79 (1.18–2.70) 1.96×10−3
GALNT10555685q33.2Polypeptide N-acetylgalactosaminyl transferase 104.01 (1.46–11.02) 6.34×10−3
GPI282119q13.11Glucose-6-phosphate isomerase2.37 (1.03–5.43) 4.16×10−2
JCHAIN35124q13.3Joining chain of multimeric IgA and IgM1.39 (1.13–1.71) 1.10×10−3
KYNU89422q22.2Kynureninase2.33 (1.10–4.93) 1.88×10−2
LPP40263q27.3-q28LIM domain containing preferred translocation partner in lipoma6.42 (1.19–34.48) 2.92×10−2
MICALL2797787p22.3MICAL-like 219.04 (3.85–94.09) 2.70×10−4
MME43113q25.2Membrane metalloendopeptidase0.83 (0.69–1.00) 4.42×10−2
MTMR11109031q21.2Myotubularin related protein 111.65 (1.01–2.70) 3.78×10−2
MZT2B800972q21.1Mitotic spindle organizing protein 2B14.10 (3.05–65.23) 5.59×10−4
NR3C129085q31.3Nuclear receptor subfamily 3 group C member 10.56 (0.36–0.86) 8.11×10−3
NRBF22998210q21.3Nuclear receptor binding factor 20.28 (0.10–0.74) 1.00×10−2
PDZD811898710q25.3-q26.11PDZ domain containing 80.42 (0.21–0.85) 1.49×10−2
PGRMC2104244q28.2Progesterone receptor membrane component 20.37 (0.15–0.92) 3.05×10−2
PIP4K2C7983712q13.3 Phosphatidylinositol-5-phosphate 4-kinase type 2 γ2.36 (1.01–5.48) 4.44×10−2
PLAGL2532620q11.21PLAG1 like zinc finger 20.13 (0.02–0.84) 2.93×10−2
PLD153373q26.31Phospholipase D149.97 (3.91–637.92) 2.28×10−3
PRR115577117q22Proline rich 118.79 (1.49–51.87) 1.56×10−2
PRSS211094216p13.3Protease, serine 211.88 (1.18–2.99) 5.29×10−3
PTAFR57241p35.3Platelet activating factor receptor4.12 (1.20–14.20) 2.46×10−2
QKI94446q26QKI, KH domain containing RNA binding0.50 (0.26–0.98) 4.47×10−2
RAB27B587418q21.2RAB27B, member RAS oncogene family112.03 (3.02–4156.89) 1.05×10−2
RAB7A78793q21.3RAB7A, member RAS oncogene family0.02 (0.00–0.39) 8.24×10−3
RBM6101803p21.31RNA binding motif protein 63.35 (1.08–10.37) 3.49×10−2
RNASE3603714q11.2Ribonuclease A family member 32.49 (1.58–3.92) 1.89×10−6
RORA609515q22.2RAR-related orphan receptor A2.29 (1.12–4.66) 2.06×10−2
RRAGD585286q15Ras-related GTP binding D0.41 (0.23–0.73) 1.96×10−3
S100A1062811q21.3S100 calcium binding protein A101.57 (1.10 to 2.24) 1.18×10−2
SAR1B511285q31.1Secretion associated Ras related GTPase 1B0.42 (0.18 to 0.97) 4.49×10−2
CAVIN284362q32.3Caveolae associated protein 213.41 (1.33 to 135.33) 2.71×10−2
SLAMF7578231q23.3SLAM family member 75.91 (1.01 to 34.70) 4.79×10−2
SLC25A16803410q21.3Solute carrier family 25 member 1693.59 (1.42 to 6174.64) 3.47×10−2
SOCS3902117q25.3Suppressor of cytokine signaling 36.70 (1.35 to 33.25) 2.00×10−2
SORT16272 1p13.3;1p21.3-p13.1Sortilin 15.74 (1.24 to 26.54) 2.28×10−2
SP1666712q13.13Sp1 transcription factor0.10 (0.01 to 0.65) 1.60×10−2
STEAP3552402q14.2STEAP3 metalloreductase3.08 (1.00 to 9.43) 4.95×10−2
TOB21076622q13.2Transducer of ERBB2, 20.38 (0.17 to 0.85) 2.01×10−2
U2SURP233503q23U2 snRNP associated SURP domain containing0.31 (0.13 to 0.77) 1.17×10−2
UBE2D1732110q21.1Ubiquitin conjugating enzyme E2 D10.15 (0.02 to 0.95) 4.14×10−2
ZNF91764419p12Zinc finger protein 910.59 (0.35 to 0.99) 4.80×10−2

[i] Fifty-nine genes associated with B-ALL relapse-free survival. HR, hazard ratio; CI, confidence interval; B-ALL, B-precursor acute lymphoblastic leukemia; RFS, recurrence-free survival.

Discussion

The major challenge in clinical treatments of B-precursor acute lymphoblastic leukemia (B-ALL) is relapse after chemotherapy; thus or the development of alternative treatments is critical. Here, we demonstrated the ability of gene expression profiling to reveal not only biological mechanisms but also clinical diagnostic markers.

Among the 59 genes, several genes have been characterized as being involved in the progression of leukemia and solid tumors. For instance, MME, which is also known as CD10, or CALLA, encodes a type II transmembrane glycoprotein and a common acute lymphocytic leukemia antigen. It is an important cell surface marker in the diagnosis of human acute lymphocytic leukemia (27) has been well-documented in many leukemia-related studies (28–32). SOCS3 is a member of the suppressor of cytokine signaling (SOCS) family and negatively regulates JAK2 kinase. Its altered expression is associated with leukemia (33,34) and solid tumors including melanoma (35–38), cervical cancer (39–41), renal cell carcinoma (42–45), prostate cancer (46,47) and gastric cancer (48). CDK6 is a serine/threonine protein that is important for cell cycle G1 phase progression and G1/S transition. CDK6 is dysregulated or disrupted in many types of cancer (49–54), and it is previously reported to be in a three-way rearrangement including other two elements: MLL and AF-4 in a case of infant ALL (55). This indicates the biological relevance of the 59-gene biomarker to B-ALL and could propose new directions for investigations.

However, we do realize that there is merely a one gene (JCHAIN known as IGJ) overlap of our 59-gene biomarker with a previously published 38-gene classifier (11). We reasoned that this discordance is due to differences in the analysis strategy. While in the previous study, the biomarker selection was implemented directly on all of the microarray probe sets, we pre-filtered the candidate list to B-ALL-associated genes by differential gene expression analysis. We also optimized the process of preprocessing the microarray data by using up-to-date algorithms and software, which could lead to higher confidence in producing final results.

In conclusion, our systematic approach provided an intriguing guideline for the identification of B-ALL prognostic biomarkers and revealed their potential roles in chemosensitivity. Further investigations are expected to validate the performances of these biomarkers before being applied to clinical management. As the RNA-seq technologies are trending in transcriptome profiling, we will also collect RNA-seq data to re-train and optimize our model. We will also try to reduce the number of biomarkers while maintaining the predictive power, so that the application in clinical management is more feasible.

Acknowledgements

Not applicable.

Funding

No funding was received.

Availability of data and materials

The datasets used during the present study are available from NCBI GEO Database with corresponding accession numbers.

Authors' contributions

WJ and JL conceived and designed the study. WJ collected the data and performed the data analysis. WJ and JL wrote and edited the manuscript. Both authors read and approved the manuscript and agreed to be accountable for all aspects of the research in ensuring that the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors do not have any competing interests.

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January-2019
Volume 41 Issue 1

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Jing W and Li J: Identification of biomarkers for the prediction of relapse‑free survival in pediatric B‑precursor acute lymphoblastic leukemia. Oncol Rep 41: 659-667, 2019.
APA
Jing, W., & Li, J. (2019). Identification of biomarkers for the prediction of relapse‑free survival in pediatric B‑precursor acute lymphoblastic leukemia. Oncology Reports, 41, 659-667. https://doi.org/10.3892/or.2018.6846
MLA
Jing, W., Li, J."Identification of biomarkers for the prediction of relapse‑free survival in pediatric B‑precursor acute lymphoblastic leukemia". Oncology Reports 41.1 (2019): 659-667.
Chicago
Jing, W., Li, J."Identification of biomarkers for the prediction of relapse‑free survival in pediatric B‑precursor acute lymphoblastic leukemia". Oncology Reports 41, no. 1 (2019): 659-667. https://doi.org/10.3892/or.2018.6846