Interdisciplinary Sciences: Computational Life Sciences https://doi.org/10.1007/s12539-019-00325-y

ORIGINAL RESEARCH ARTICLE

Most Variable and Transcription Factors in Acute Lymphoblastic Leukemia Patients

Anil Kumar Tomar1 · Rahul Agarwal2 · Bishwajit Kundu1

Received: 24 September 2018 / Revised: 21 January 2019 / Accepted: 26 February 2019 © International Association of Scientists in the Interdisciplinary Areas 2019

Abstract Acute lymphoblastic leukemia (ALL) is a hematologic tumor caused by cell cycle aberrations due to accumulating genetic disturbances in the expression of transcription factors (TFs), signaling oncogenes and tumor suppressors. Though survival rate in childhood ALL patients is increased up to 80% with recent medical advances, treatment of adults and childhood relapse cases still remains challenging. Here, we have performed bioinformatics analysis of 207 ALL patients’ mRNA expression data retrieved from the ICGC data portal with an objective to mark out the decisive genes and pathways responsible for ALL pathogenesis and aggression. For analysis, 3361 most variable genes, including 276 transcription factors (out of 16,807 genes) were sorted based on the coefcient of variance. Silhouette width analysis classifed 207 ALL patients into 6 subtypes and heat map analysis suggests a need of large and multicenter dataset for non-overlapping subtype classifcation. Overall, 265 GO terms and 32 KEGG pathways were enriched. The lists were dominated by cancer-associated entries and highlight crucial genes and pathways that can be targeted for designing more specifc ALL therapeutics. Diferential expression analysis identifed upregulation of two important genes, JCHAIN and CRLF2 in dead patients’ cohort suggesting their pos- sible involvement in diferent clinical outcomes in ALL patients undergoing the same treatment.

Keywords Gene expression · KEGG pathways · Leukemia · Most variable genes · Subtype classifcation

1 Introduction age, it is most common in children and adolescents. B-cell acute lymphocytic leukemia (B-ALL) accounts for about Leukemia, cancer of blood or bone morrow, is widely clas- 85% and 75% of childhood ALL and adult ALL cases, sifed into four major categories—acute myeloid leukemia respectively, with male predominance, while T-cell acute (AML), chronic myeloid leukemia (CML), acute lympho- lymphocytic leukemia (T-ALL) accounts for the remaining cytic leukemia (ALL) and chronic lymphocytic leukemia cases [1, 2]. With recent medical advances in treatment pro- (CLL). The basic parameters of this classifcation are rate tocols, global survival rate in childhood ALL is increased of cancer progression and site of cancer development (http:// substantially (> 80%); however, survival rate in adults still www.cance​rcent​er.com/). ALL is a blood malignancy char- remains less than 40% [3–5]. Also, survival in the ALL acterized by uncontrolled proliferation of lymphoblasts, patients who experience a relapse is very poor [6]. immature B and/or T cells. Though ALL can occur at any Uncontrolled cell proliferation due to loss of cell cycle control is the hallmark of cancer [7, 8]. Chromosomal rear- rangements are common genetic abnormalities in B-ALL, Electronic supplementary material The online version of this e.g., BCR-ABL1, ETV6-RUNX1 and TCF3-PBX1 [9]. Also, article (https​://doi.org/10.1007/s1253​9-019-00325​-y) contains aberrant expression of transcription factors associated with supplementary material, which is available to authorized users. lymphoid development, e.g., PAX5, EBF1 and IKZF1 has * Anil Kumar Tomar been reported in more than 60% B-ALL cases [10, 11]. [email protected] CRLF2 rearrangements and JAK mutations are also detected in B-ALL cases [12]. The genomic profling of high-risk 1 Kusuma School of Biological Sciences, Indian Institute ALL patients has identifed rearrangements of ABL1, JAK2, of Technology Delhi, Hauz Khas, New Delhi 110016, India PDGFRB, CRLF2 and EPOR, activating mutations of IL7R 2 Department of Reproductive Biology, All India Institute and FLT3 and deletion of SH2B3 [13]. Regardless of all the of Medical Sciences, New Delhi 110029, India

Vol.:(0123456789)1 3 Interdisciplinary Sciences: Computational Life Sciences advances at the molecular level understanding of the disease, Table 1 Sample details ALL remains a challenging and aggressive disease due to Description Percentage (number) high genetic heterogeneity among patients and its progno- sis is uncertain in relapse cases. For tailoring efective and Total samples 207 specifc therapies, it is essential to classify patients and rec- Sex Male 66.18% (137) ognize those with high probability of relapse at the time of Female 33.81% (70) disease diagnosis. Comprehensive subtype classifcation of Vital status Alive 33.81% (70) risk groups of a disease is important for more specifc treat- Deceased 25.12% (52) ment of patients and better therapeutic outcomes. No data 41.06% (85) The International Cancer Genome Consortium (ICGC) Age at diagnosis (years) 1–9 35.74% (74) coordinates a large number of projects elucidating the 10–19 63.28% (131) genomic changes in various cancer types. Through its data 20–29 0.96% (2) portal (https​://dcc.icgc.org/), ICGC has provided open access to the gene expression data of about 70 cancer pro- jects to research community worldwide. Here, we have per- genes. Overall 3361 most variable genes were sorted out for formed bioinformatics analysis of 207 ALL patients’ mRNA predicting the subtypes. Unsupervised hierarchical cluster- expression data (16,807 genes) retrieved from the ICGC data ing was done on these genes across all the 207 patient sam- portal. The primary objective was to delineate the crucial ples using Bioconductor R package ConsensusClusterPlus genes, transcription factors and pathways responsible for [16]. Final cluster attained the consensus after 1000 reitera- ALL pathogenesis. Also, diferential gene expression analy- tions. The number of clusters that represented the expression sis was performed (male vs. female and alive vs. dead) to data most signifcantly was selected by silhouette method identify genomic variability in patient subgroups. A male of KMeans clustering, a method that calculates the separa- predominance is well known in case of leukemia that led us tion distance between the resulting clusters. This method to perform diferential gene expression analysis in male vs. basically estimates how close each point in one of the clus- female B-ALL patients to identify decisive gene(s), if any, ters is to the points of the neighboring clusters. The value for high occurrence of ALL in males, while alive vs. dead of a silhouette coefcient always lies in the range of [− 1, patient cohorts were chosen for diferential gene expression 1]. Bioconductor R based package Cluster [17] was used to analysis to identify crucial genes that possibly can defne estimate these coefcients. Samples with positive silhou- disease aggression. Recent studies have shown interest in ette coefcient values were selected for further analysis. Top global profling of diferentially expressed genes (DEGs) in variable genes were obtained for each k = 1 to n subtypes ALL. Li et al. have identifed DEGs between diagnostic and by employing sam function from bioconductor package relapsed cases with an aim to explore the underlying mecha- siggenes [18]. Overall median survival analysis of predicted nism of relapsed ALL [14]. In another study, Sedek et al. B-ALL subtypes was performed using coxph model [19] have shown aberrant (over)expression of CD73, CD86 and and Kaplan–Meier (KM) curve was used for presenting the CD304 in a substantial percentage of B-ALL patients [15]. results [20].

2.3 Pathway Analysis 2 Materials and Methods (GO) annotations and pathway analysis of 2.1 Retrieval of Patient Data 3000 most variable genes among 207 ALL patients was performed using Database for Annotation, Visualization Gene expression array matrices and associated clinical data and Integrated Discovery (DAVID) gene enrichment tool of 207 high-risk B-ALL patients were retrieved from ICGC with default settings [21]. GO annotations and pathways data portal (Project: ALL-US; DCC data release; December with FDR < 0.05 were considered signifcant. This program 7, 2016) and expression matrix of 16,807 genes for all of enlists enriched GO terms and pathways as an output along the patients was normalized. The sample details are given with many other important features. in Table 1. 2.4 Diferential Gene Expression 2.2 Subtype Classifcation and Survival Analysis Gene expression array data of 207 samples were pre-pro- To predict B-ALL subtypes using ICGC-ALL data, genes cessed and genes with more than 50% missing data were were fltered out based on the coefcient of variance (CV). A excluded. Those genes which have expression greater than CV value of ≥ 0.8 was used as cut-of to defne most variable 5 (in more than 80% of the samples) were used for further

1 3 Interdisciplinary Sciences: Computational Life Sciences analysis. The patient samples were grouped separately in Table 2 List of most variable genes and transcription factors among two diferent biological categories (as per the information 207 ALL patients provided in clinical data), male/female and dead/alive. Dif- S. no. Most variable genes Most variable TFs ferential gene expression analysis was performed by employ- ing gene expression data analysis limma package [22]. To 1. S100P FOXC1 account for multiple testing, adjusted p value was estimated 2. SFTPA1 FOXR1 using Benjamini–Hochberg method. Genes having log2- 3. GJB6 SOX8 fold change values > 1.5 (for up-regulation) and < − 1.5 4. LTF HOXA5 (for down-regulation) were considered as signifcantly dif- 5. IFNB1 NFIB ferentially expressed. Diferentially expressed genes (DEGs) 6. FOXC1 IRX3 in predicted subtypes (associated with least and maximum 7. CD1E MYT1L survival) were also identifed. 8. FOXR1 SIX3 9. MS4A3 SALL4 10. PTPRZ1 MEIS1 3 Results and Discussion 11. RBFOX2 ZNF521 12. MT1E IRX2 3.1 Variably Expressed Genes 13. S100A12 SOX11 14. SOX8 CEBPD As accessed by box plot analysis of randomly selected 50 15. MT1H ID1 samples, high gene expression variability was observed that 16. IFNA2 HES1 indicates tumor heterogeneity among the patients (Fig. 1). 17. HOXA5 CEBPB The signifcantly variable genes (3361 out of total 16,807) 18. IFI27 PBX1 among all the patients were shortlisted using coefcient of 19. CLDN5 WT1 variance method (Supplementary fle 1). The most variable 20. ANXA3 IRX1 genes (top 20) are listed in Table 2, including S100 calcium- binding (S100P), interferons (IFNB1, IFNA2), lac- totransferrin (LTF), forkhead box genes (FOXC1, FOXR1), S100P, a metastasis-inducing protein, has been associ- membrane spanning 4-domains A3 (MS4A3), protein tyros- ated with the regulation of cell cycle progression, difer- ine phosphatase, type Z1 (PTPRZ1), metallothio- entiation and poor patient survival [23, 24]. Interferons are neins (MT1E, MT1H), SRY-Box 8 (SOX8), A5 naturally produced by our immune system for (HOXA5), and annexin A3 (ANXA3). The encoded defense against viral infections. Additionally, they exhibit by these genes are linked with progression or suppression of anti-tumor activity [25]. LTF gene codes for lactotransfer- various tumors including leukemia. rin, a well-known iron-binding glycoprotein involved in

Fig. 1 Box plot analysis of randomly selected 50 ALL sam- ples. The plot shows high gene expression variability among the samples

1 3 Interdisciplinary Sciences: Computational Life Sciences several physiological and protective functions [26]. LTF of cancers, including colon, pancreatic, cervical, and breast and its peptides have been widely explored for their anti- cancers [52]. Due to this, it has been suggested as a potential cancer potential and found to prevent diferent cancer stages, target for cancer therapy. In addition, SLC6A14 has been including initiation and progression [27, 28]. It has been tested as a probable delivery system for drugs as well as for shown that bovine LTF induces apoptosis and kills human -based pro-drugs [53]. T-ALL cells [29–32]. MS4A3 participates in innate immune system pathway and acts as a tumor suppressor in CML [33]. 3.2 Gene Enrichment and Pathway Analysis PTPRZ1 is the receptor of a heparin-binding glycoprotein pleiotrophin (PTN), a crucial which regulates vari- GO terms (Biological processes, Molecular functions and ous physiological functions [34]. Over-expression of PTN Cellular components) and Pathways (KEGG, Reactome, was observed in various malignant tumors resulting in poor BBID and Biocarta) associated with 3000 most variable prognosis of patients [35, 36]. Similarly, aberrant expression genes were identifed by DAVID gene enrichment tool. The of PTPRZ1 was frequent in several cancer types [37–39]. GO analysis enriched 168 biological processes, 62 molecu- The metallothioneins (MTs) are cysteine-rich metal-binding lar functions and 35 cellular components (Supplementary proteins and MTs encoded by MT-1 genes, such as MT1E, fle 2). The enriched GO terms were sorted based on their MT1G and MT1H are closely associated with carcinogen- p values (lowest to highest). The top biological processes esis in various human tumors [40]. MT1H functions as a included infammatory responses, cell–cell signaling, cell tumor suppressor and was consistently down-regulated in adhesion, immune response, chemokine-mediated signaling, human malignancies including neuroblastoma, breast can- G-protein coupled receptor signaling, neutrophil chemotaxis, cer, lung cancer, colon cancer, prostate cancer, B-cell lym- homophilic cell adhesion via plasma membrane adhesion phoma and leukemia in comparison to healthy tissues [41, molecules, multicellular organism development and posi- 42]. ANXA3 is a reported prognostic biomarker of various tive regulation of ERK1 and ERK2 cascade. The molecular cancer types including breast [43], prostate [44] and gastric functions associated with these genes are binding (calcium, [45]; however, it is not much explored in leukemia. Four heparin, receptor, heme, protease, sequence-specifc DNA, out of 20 most variable genes (FOXC1, FOXR1, SOX8 and etc.), cytokine, growth factor, chemokine, hormone, oxygen HOXA5) were transcription factors. In addition, there were transporter and transcriptional activator activities. Overall several other important genes in the list of most variable 32 and dominantly cancer-related KEGG pathways were genes which were extensively studied and linked with devel- enriched, such as PI3K–Akt signaling, Jak–STAT signal- opment of diferent cancer types, such as DLX6 antisense ing, complement and coagulation cascades, transcriptional RNA 1 (DLX6-AS1), interleukin 32 (IL-32), sphingosine- misregulation in cancer, cytokine– inter- 1-phosphate receptor 2 (S1PR2), gastrokine 1 (GKN1), action, chemokine signaling, natural killer cell-mediated tubulin polymerization promoting protein family member 2 cytotoxicity, ECM–receptor interaction and transform- (TPPP2), polypeptide GalNAc transferase 6 (GALNT6) and ing growth factor-β (TGF-β) signaling (Supplementary solute carrier family 6 Member 14 (SLC6A14). Hypermeth- file 2). The most significant KEGG pathways enriched ylation of DLX6-AS1 was reported in aggressive metastatic were cytokine–cytokine receptor interaction (hsa04060), neuroblastoma in comparison to low-grade tumors [46]. systemic lupus erythematosus (hsa05322) and neuroac- IL-32 is a plausible chemotactic factor, which participates tive ligand–receptor interaction (hsa04080). To obtain in crosstalk between stromal and leukemia cells resulting in more insights, genes clusters of top KEGG pathways were chemo-resistance [47]. It has been found that normal epithe- reanalyzed. Total 79 genes were connected with KEGG lial cells are involved in tumor suppressive activity by sens- pathway hsa04060, including interleukins, tumor necrosis ing and actively eliminating the neighboring transformed factor (TNF) superfamily members, chemokines and their cells, but mechanism is largely unknown. A recent study receptors and interferon genes. Further analysis using Pan- has shown that S1PR2 mediates activation of Rho in the ther tools [54] revealed that these 79 genes are part of 14 normal epithelial cells and thus helps in apical extrusion of crucial pathways (Supplementary fle 3), most of which surrounding transformed cells [48]. GKN1 protein is under- have been widely studied in various cancer types includ- expressed in gastric tumor tissues and considered as a tumor ing leukemia. These pathways include interleukin signaling, suppressor because its over-expression induces apoptosis in interferon-gamma signaling, apoptosis, infammation medi- gastric cancer cells [49]. Also, its absence is associated with ated by chemokine and cytokine signaling, Wnt signaling, metastasis [50]. GALNT6 is hardly detectable in human nor- toll receptor signaling, TGF-beta signaling, CCKR signal- mal tissues and specifcally expressed in higher amounts in ing and PDGF signaling. Total 54 genes were clustered in several cancer types [51]. SLC6A14, an amino acid trans- hsa05322 which mostly include histone cluster genes and porter, helps cancer cells in managing their increased amino are involved in seven crucial pathways including Wnt sign- acid demand and was found over-expressed in many types aling, infammation mediated by chemokine and cytokine

1 3 Interdisciplinary Sciences: Computational Life Sciences signaling pathway, apoptosis, T cell activation, interleukin over-expressed NOTCH1-transduced T-ALL indicating their signaling pathway and interferon-gamma signaling. Simi- role in leukemia progression [64]. NOTCH1 signaling is the larly, 81 genes were associated with hsa04080 and deep prominent pathway in T-ALL, which promotes proliferation analysis shows that they were involved in 25 panther path- and inhibits apoptosis. Human WT1 gene can function as ways, including glutamate receptor pathways, infamma- both, an oncogene as well as a tumor suppressor and it has tion mediated by chemokine and cytokine signaling, blood been found over-expressed in leukemia and solid tumors. In coagulation and transcriptional regulation. In addition to addition, somatic mutations of WT1 were common in AML, widely studied pathways such as NOTCH1 signaling, JAK CML and ALL [65]. signaling, PI3K–AKT signaling and BCL-2 pathways, these Other crucial TFs were Kruppel like factor 4 (), pathways can also be explored and targeted in high-risk goosecoid homeobox (GSC), Ikaros family zinc fnger 3 B-ALL. As leukemia is a complex blood malignancy and (IKZF3), zinc fnger protein 300 (ZNF300), runt-domain heterogeneous in nature, integrative analysis of these mul- (RUNX3), CCAAT enhancer-binding tiple pathways can provide comprehensive disease insights protein alpha (CEBPA), thymocyte selection associ- to derive improved and specifc therapeutics. ated high mobility group box (TOX), NK2 homeobox 5 (NKX2-5), T-Box 21 (TBX21), and transcription fac- 3.3 Transcription Factors tor 2 (). KLF4 acts as a tumor suppressor in leukemic T cells as its over-expression induces apoptosis. A pre-req- Comprehensive analysis of TFs and their pathways is cru- uisite for early human T cell development and homeosta- cial for better understanding of disease regulation and thus, sis is down-regulated expression of KLF4 [66]. It is well we shortlisted transcription factors out of the 3361 most reported that IKZF3 regulates lymphopoiesis and IKZF3 variable genes. For this purpose, a database of 1474 human mutations may lead to speedy progression of leukemia and TFs (Supplementary fle 4) was retrieved from the Animal lymphoma [67, 68]. One of the studies has suggested that Transcription Factor DataBase (http://bioin​fo.life.hust.edu. ZNF300 plays a key role in leukemia development and cn/Anima​lTFDB​1.0) and used as a reference to search TFs progression [69]. The TFs of RUNX family (e.g., RUNX1, in the list of most variable genes (Supplementary fle 1). RUNX2, RUNX3) play imperative roles in hematopoiesis Overall 276 TFs were found among the most variable genes regulation [70]. RUNX3 is one of the master regulators (Supplementary fle 5). The top 20 variable TFs are listed in of gene expression in major developmental pathways and Table 2, including forkhead box genes (FOXC1, FOXR1), considered as a tumor suppressor in a number of cancer SRY-related HMG-box (SOX8), B (NFIB), types [71]. Like CEBPB and CEBPD, CEBPA is also homeobox genes (HOXA5, IRX1-3, SIX3, MEIS1, PBX1), recognized as a tumor repressor TF due to the fact that spalt-like TF 4 (SALL4), zinc fnger protein 521 (ZNF521), loss of-function mutations in CEBPA can contribute to CCAAT enhancer-binding proteins (CEBPD, CEBPB), AML development. In addition, expression of CEBPA was inhibitor Of DNA binding 1, HLH protein (ID1), hes family dysregulated in human cancers of various origins includ- BHLH TF1 (HES1) and Wilms tumor 1 (WT1). Deregulated ing liver, breast and lung [72]. TOX participates in T-cell expression of FOX proteins has been reported in human maturation [73]. Based on copy number alterations, TOX malignancies including leukemia [55]. FOXC1, which is was shown to be associated with relapse in pediatric ALL expressed in human AML patients but not in healthy popu- [74]. TBX21 is expressed in immune cells and plays vital lations, collaborates with a leukemic gene HOXA9 and role in the cytotoxic activity of NK cells [75–77]. accelerates onset of leukemia [56]. TFs of SOX family are Overall, fve KEGG pathways were enriched among the well established regulators of cell fate during development most variable TFs, viz. TGF-β signaling pathway, acute and their deregulation causes various diseases including can- myeloid leukemia, pathways in cancer, maturity onset dia- cers [57–59]. HOXA5, IRX1-3, SIX3, MEIS1, and PBX1 betes of the young (MODY) pathway and prostate cancer. It belong to highly conserved gene family of homeodomain is well known that TGF-β signaling pathway plays a com- (HOX) transcription factors and their aberrant expression plex role in cancer development, progression, and metasta- is associated with several malignancies including ALL and sis. The MODY pathway was surprising because no studies AML [60]. SALL4 regulates expression of BMI-1, a proto- were found suggesting its close and strong association with oncogene and a suggested prognostic marker of pediatric leukemia; thus, we reviewed literature for six genes of this ALL [61]. ZNF521 TF is expressed in human hematopoi- pathway, viz. HES1, BHLHA15, FOXA3, HNF4G, NKX2- etic cells and can act as both, a repressor or an activator. 2, NKX6-1 and found that all of these were closely linked Its translocation with PAX5 is linked with pediatric ALL with leukemia. More interestingly, HES1 plays a central role [62]. CEBPD and CEBPB play key roles in cell proliferation in the control of NOTCH1-induced leukemia cell survival and diferentiation and act as suppressors of leukemogen- [78] and its expression has been suggested as a useful prog- esis [63]. Several TFs including HES1 and ID1 were found nostic factor in AML patients [79].

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3.4 Classifcation of ALL Subtypes and Patient Survival Analysis

For predicting disease subtypes, we applied unsupervised hierarchical clustering method with Pearson correlation using signifcantly variable 3361 genes. Each subtype predicted here attained a fnal cluster after going through bootstrapping (n = 1000) and output was generated for k number of subtypes (k = 1 to n). The specifc k subtypes were selected based on silhouette width, cumulative dis- tribution function and the heat map plot. Silhouette width analysis of expression data estimated highest silhouette width for k = 6 (Fig. 2). The heat map generated through stratifcation of B-ALL patients based on gene expression data predicted six distinct subtypes, designated as A, B, C, D, E and F (Fig. 3). These subtypes accommodated all of the 207 patient samples, A (103), B (13), C (39), D (20), E (18) and F (14) (Supplementary fle 6) and over- all analysis based on gene expression variability shows that subtypes are well separated. Median survival analy- sis shows that subtype C patients have maximum survival Fig. 3 Heat map of gene expression in predicted ALL subtypes chances (4151 days), while subtype B patients have least (508 days) as compared to patients belonging to other pre- dicted subtypes (Fig. 4). The comparative gene expres- 3.5 Diferential Gene Expression sion analysis between subtypes B and C patients identi- fed 412 (p value cut-of ≤ 0.05) and 237 (p value cut-of Sorted on the basis of log2-fold change > 1.5 (for up-reg- ≤ 0.01) DEGs. Out of 412, 400 were upregulated and 2 ulation) and < − 1.5 (for down-regulation), DEGs in two were down-regulated in subtype C. Top 10 DEGs were selected groups, male vs. female and alive vs. dead, are listed CYTL1, SHANK3, IFI44L, DLL1, CCND2, CMTM2, in Supplementary fle 7. Overall, 13 genes were found dif- ITGA6, PDE4B, SH3BP5 and EGFL7 and all these genes ferentially expressed and 11 of these were over-expressed in were upregulated in subtype C. males. Some of these genes have been studied in context of leukemia in general and their associations are established. However, it would be biased here to correlate these genes with either high rate of B-ALL incidence in males or low rate of incidence in females as genes over-expressed in male patients were all Y associated while those over- expressed in female patients were located on . To ascertain their association with B-ALL incidence/pro- gression, their expression needs to be evaluated in compari- son to healthy controls. In alive vs. dead cohorts, only two genes were found signifcantly diferentially expressed, viz. joining chain of multimeric IgA and IgM (JCHAIN; fold change = 2.52; p value = 7.00E−05) and cytokine receptor-like factor 2 (CRLF2; fold change = 1.77; p value = 1.52E−04) and interestingly, both were upregulated in dead patients’ cohort. Adjusted p values that account for multiple test- ing (Benjamini–Hochberg method) were slightly higher (0.14 and 0.16), possibly due to high heterogeneity among the samples of same class. JCHAIN and CRLF2 encode for immunoglobulin J chain and cytokine receptor-like Fig. 2 Silhouette width plot. Here, maximum silhouette width is for factor 2, respectively. JCHAIN links monomer units of k = 6 and thus, ALL patients can likely be classifed into six subtypes IgA and IgM and also helps them to bind with secretory

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Fig. 4 Overall survival analysis of ALL patients. Kaplan–Meier survival analysis of six subtypes (A, B, C, D, E and F) was per- formed based on clinical data of the patients

components. CRLF2 along with thymic stromal lym- that three of these proteins (PAX5, PAX8 and SPI1) are phopoietin (TSLP) and interleukin 7 receptor (IL7R) associated with transcriptional misregulation in cancer. On activates three important pathways—STAT3, STAT5 and the other hand, CRLF2 interacts with JAK 1, JAK2, JAK3, JAK2. These pathways are known to control various pro- IL3, IL7, etc., and KEGG pathway analysis returned 14 cesses such as cell proliferation and hematopoietic system pathways (Fig. 5b, Supplementary fle 8). Interestingly, all development. Several previous studies have linked over- of them (CRLF2 and its functional partners) participate in expression of CRLF2 and JCHAIN with low treatment JAK–STAT signaling pathway. Another important pathway response and poor survival in ALL patients [12, 80–82]. term was PI3K–Akt signaling and associated proteins were The functional partners of JCHAIN and CRLF2 were pre- IL3, IL7, IL7R, JAK1, JAK2, JAK3, OSM and PRL. Their dicted and analyzed by string interactions [83]. Ten func- involvement in cancer progression pathways is a sugges- tional partners were predicted for JCHAIN (alias IGJ), tive evidence that upregulation of JCHAIN and CRLF2 including CD79A, PAX5, PAX8, SPI1, and IL2 (Fig. 5a, genes in dead patients’ cohort is likely associated with Supplementary fle 8). KEGG pathway analysis reveals ALL aggression.

Fig. 5 Protein–protein interac- tion networks of diferentially expressed genes (alive vs. dead): a CRLF2 and b JCHAIN

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4 Conclusions 7. Ferrando AA, Neuberg DS, Staunton J, Loh ML, Huard C, Rai- mondi SC, Behm FG, Pui CH, Downing JR, Gilliland DG, Lander ES, Golub TR, Look AT (2002) Gene expression signatures defne This study presents gene expression analysis in 207 high- novel oncogenic pathways in T cell acute lymphoblastic leukemia. risk B-ALL patients. The gene sorting based on the vari- Cancer Cell 1:75–87 able expression among patients and associated clinical 8. Sherr CJ (1996) Cancer cell cycles. Science 274:1672–1677 9. Pui CH, Robison LL, Look AT (2008) Acute lymphoblastic leu- information has exposed a number of interesting genes and kaemia. Lancet 371:1030–1043. https​://doi.org/10.1016/S0140​ pathways that can be exploited in future for better under- -6736(08)60457​-2 standing of disease pathogenesis as well as for designing 10. Kuiper RP, Schoenmakers EF, van Reijmersdal SV, Hehir-Kwa specifc B-ALL therapy. The most variable TFs identifed JY, van Kessel AG, van Leeuwen FN, Hoogerbrugge PM (2007) High-resolution genomic profling of childhood ALL reveals and associated pathways may help us to draw a compre- novel recurrent genetic lesions afecting pathways involved in hensive map B-ALL regulation. Six subtypes were identi- lymphocyte diferentiation and cell cycle progression. Leukemia fed based on the most variable genes and the patients of 21:1258–1266. https​://doi.org/10.1038/sj.leu.24046​91 subtype C and B had the highest and lowest probability 11. Mullighan CG, Su X, Zhang J, Radtke I, Phillips LA, Miller CB, Ma J, Liu W, Cheng C, Schulman BA, Harvey RC, Chen IM, to survive, respectively, as per the clinical data. Difer- Cliford RJ, Carroll WL, Reaman G, Bowman WP, Devidas M, ential gene expression analysis revealed over-expression Gerhard DS, Yang W, Relling MV, Shurtlef SA, Campana D, of JCHAIN and CRLF2 genes in dead patients’ cohort in Borowitz MJ, Pui CH, Smith M, Hunger SP, Willman CL, Down- comparison to alive patients’ cohort. We believe that these ing JR, Children’s Oncology Group (2009) Deletion of IKZF1 and prognosis in acute lymphoblastic leukemia. N Engl J Med genes can further be explored and targeted in high-risk 360:470–480. https​://doi.org/10.1056/NEJMo​a0808​253 B-ALL relapse cases. 12. Harvey RC, Mullighan CG, Chen IM, Wharton W, Mikhail FM, Carroll AJ, Kang H, Liu W, Dobbin KK, Smith MA, Carroll WL, Acknowledgements This work was supported by grants received Devidas M, Bowman WP, Camitta BM, Reaman GH, Hunger SP, by AKT from Science and Engineering Research Board (SERB), Downing JR, Willman CL (2010) Rearrangement of CRLF2 is Department of Science & Technology, Govt. of India, New Delhi, associated with mutation of JAK kinases, alteration of IKZF1, under National-Postdoctoral Fellowship Scheme (File Number: Hispanic/Latino ethnicity, and a poor outcome in pediatric B-pro- PDF/2015/000979). Authors also thank the IIT Delhi HPC facility for genitor acute lymphoblastic leukemia. Blood 115:5312–5321. computational resources. https​://doi.org/10.1182/blood​-2009-09-24594​4 13. Roberts KG, Morin RD, Zhang J, Hirst M, Zhao Y, Su X, Chen Compliance with Ethical Standards SC, Payne-Turner D, Churchman ML, Harvey RC, Chen X, Kasap C, Yan C, Becksfort J, Finney RP, Teachey DT, Maude SL, Tse K, Moore R, Jones S, Mungall K, Birol I, Edmonson MN, Hu Conflict of interest Authors declare no confict of interest. Y, Buetow KE, Chen IM, Carroll WL, Wei L, Ma J, Kleppe M, Levine RL, Garcia-Manero G, Larsen E, Shah NP, Devidas M, Reaman G, Smith M, Paugh SW, Evans WE, Grupp SA, Jeha S, Pui CH, Gerhard DS, Downing JR, Willman CL, Loh M, Hunger SP, Marra MA, Mullighan CG (2012) Genetic alterations acti- References vating kinase and cytokine receptor signaling in high-risk acute lymphoblastic leukemia. Cancer Cell 22:153–166. https​://doi. 1. Chiaretti S, Foa R (2009) T-cell acute lymphoblastic leukemia. org/10.1016/j.ccr.2012.06.005 Haematologica 94:160–162. https​://doi.org/10.3324/haema​ 14. Li S, Wang C, Wang W, Liu W, Zhang G (2018) Abnormally tol.2008.00415​0 high expression of POLD1, MCM2, and PLK4 promotes 2. Pui CH, Behm FG, Singh B, Schell MJ, Williams DL, Rivera GK, relapse of acute lymphoblastic leukemia. Medicine (Baltimore) Kalwinsky DK, Sandlund JT, Crist WM, Raimondi SC (1990) 97(20):e10734. https​://doi.org/10.1097/MD.00000​00000​01073​4 Heterogeneity of presenting features and their relation to treat- 15. Sędek Ł, Theunissen P, Sobral da Costa E, van der Sluijs-Gelling ment outcome in 120 children with T-cell acute lymphoblastic A, Mejstrikova E, Gaipa G, Sonsala A, Twardoch M, Oliveira E, leukemia. Blood 75:174–179 Novakova M, Buracchi C, van Dongen JJM, Orfao A, van der 3. Paul S, Kantarjian H, Jabbour EJ (2016) Adult acute lympho- Velden VHJ, Szczepański T, EuroFlow Consortium (2018) Dif- blastic leukemia. Mayo Clin Proc 91:1645–1666. https​://doi. ferential expression of CD73, CD86 and CD304 in normal vs. leu- org/10.1016/j.mayoc​p.2016.09.010 kemic B-cell precursors and their utility as stable minimal residual 4. Redaelli A, Laskin BL, Stephens JM, Botteman MF, Pashos CL disease markers in childhood B-cell precursor acute lymphoblas- (2005) A systematic literature review of the clinical and epide- tic leukemia. J Immunol Methods. https​://doi.org/10.1016/j. miological burden of acute lymphoblastic leukaemia (ALL). jim.2018.03.005 Eur J Cancer Care (Engl) 14:53–62. https​://doi.org/10.111 16. Wilkerson MD, Hayes DN (2010) ConsensusClusterPlus: a class 1/j.1365-2354.2005.00513​.x discovery tool with confdence assessments and item tracking. 5. You MJ, Medeiros LJ, Hsi ED (2015) T-lymphoblastic leuke- Bioinformatics 26:1572–1573. https://doi.org/10.1093/bioin​ forma​ ​ mia/lymphoma. Am J Clin Pathol 144:411–422. https​://doi. tics/btq17​0 org/10.1309/AJCPM​F03LV​SBLHP​J 17. Maechler M, Rousseeuw P, Struyf A, Hubert M, Hornik K (2013) 6. Salzer WL, Devidas M, Carroll WL, Winick N, Pullen J, Hunger cluster: Cluster analysis basics and extensions. R package v1.14.4 SP, Camitta BA (2010) Long-term results of the pediatric oncol- edn. https​://www.rdocu​menta​tion.org/packa​ges/clust​er ogy group studies for childhood acute lymphoblastic leukemia 18. Schwender H (2012) siggenes: Multiple testing using SAM and 1984–2001: a report from the children’s oncology group. Leuke- Efron’s empirical Bayes approaches. R package v1.46.0 edn. https​ mia 24:355–370. https​://doi.org/10.1038/leu.2009.261 ://www.rdocu​menta​tion.org/packa​ges/sigge​nes

1 3 Interdisciplinary Sciences: Computational Life Sciences

19. Cox DR (1972) Regression models and life tables. J R Stat Soc B peritoneal permeability leading to peritoneal fbrosis. Kidney Int 34:187–220 81:160–169. https​://doi.org/10.1038/ki.2011.305 20. Kaplan E, Meier P (1958) Nonparametric estimation from incom- 35. Chang Y, Zuka M, Perez-Pinera P, Astudillo A, Mortimer J, Ber- plete observations. J Am Stat Assoc 53:457–481. https​://doi. enson JR, Deuel TF (2007) Secretion of pleiotrophin stimulates org/10.2307/22818​68 breast cancer progression through remodeling of the tumor micro- 21. Huang DW, Sherman BT, Tan Q, Kir J, Liu D, Bryant D, Guo Y, environment. Proc Natl Acad Sci USA 104:10888–10893. https://​ Stephens R, Baseler MW, Lane HC, Lempicki RA (2007) DAVID doi.org/10.1073/pnas.07043​66104​ bioinformatics resources: expanded annotation database and novel 36. Du ZY, Shi MH, Ji CH, Yu Y (2015) Serum pleiotrophin could be algorithms to better extract biology from large gene lists. Nucleic an early indicator for diagnosis and prognosis of non-small cell Acids Res 35:W169–W175. https​://doi.org/10.1093/nar/gkm41​5 lung cancer. Asian Pac J Cancer Prev 16:1421–1425 22. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK 37. Ma Y, Ye F, Xie X, Zhou C, Lu W (2011) Signifcance of PTPRZ1 (2015) limma powers diferential expression analyses for RNA- and CIN85 expression in cervical . Arch Gynecol sequencing and microarray studies. Nucleic Acids Res 43:e47. Obstet 284:699–704. https://doi.org/10.1007/s0040​ 4-010-1693-9​ https​://doi.org/10.1093/nar/gkv00​7 38. Makinoshima H, Ishii G, Kojima M, Fujii S, Higuchi Y, Kuwata T, 23. Ishii Y, Kasukabe T, Honma Y (2005) Immediate up-regulation Ochiai A (2012) PTPRZ1 regulates calmodulin phosphorylation of the calcium-binding protein S100P and its involvement in and tumor progression in small-cell lung carcinoma. BMC Cancer the cytokinin-induced differentiation of human myeloid leu- 12:537. https​://doi.org/10.1186/1471-2407-12-537 kemia cells. Biochim Biophys Acta 1745:156–165. https​://doi. 39. Shi Y, Ping YF, Zhou W, He ZC, Chen C, Bian BS, Zhang L, org/10.1016/j.bbamc​r.2005.01.005 Chen L, Lan X, Zhang XC, Zhou K, Liu Q, Long H, Fu TW, 24. Clarke C, Gross SR, Ismail TM, Rudland PS, Al-Medhtiy M, Zhang XN, Cao MF, Huang Z, Fang X, Wang X, Feng H, Yao Santangeli M, Barraclough R (2017) Activation of tissue plasmi- XH, Yu SC, Cui YH, Zhang X, Rich JN, Bao S, Bian XW (2017) nogen activator by metastasis-inducing S100P protein. Biochem Tumour-associated macrophages secrete pleiotrophin to promote J 474(19):3227–3240. https​://doi.org/10.1042/BCJ20​17057​8 PTPRZ1 signalling in glioblastoma stem cells for tumour growth. 25. Westcott MM, Liu J, Rajani K, D’Agostino R Jr, Lyles DS, Poros- Nat Commun 8:15080. https​://doi.org/10.1038/ncomm​s1508​0 nicu M (2015) Interferon beta and interferon alpha 2a diferen- 40. Thirumoorthy N, Shyam Sunder A, Manisenthil Kumar K, Senthil tially protect head and neck cancer cells from vesicular stoma- Kumar M, Ganesh G, Chatterjee M (2011) A review of metal- titis virus-induced oncolysis. J Virol 89:7944–7954. https​://doi. lothionein isoforms and their role in pathophysiology. World J org/10.1128/JVI.00757​-15 Surg Oncol 9:54. https​://doi.org/10.1186/1477-7819-9-54 26. Giansanti F, Panella G, Lebofe L, Antonini G (2016) Lactoferrin 41. Han YC, Zheng ZL, Zuo ZH, Yu YP, Chen R, Tseng GC, Nelson from milk: nutraceutical and pharmacological properties. Pharma- JB, Luo JH (2013) Metallothionein 1 h tumour suppressor activity ceuticals (Basel) 9(4):E61. https​://doi.org/10.3390/ph904​0061 in prostate cancer is mediated by euchromatin methyltransferase 27. Benaissa M, Peyrat JP, Hornez L, Mariller C, Mazurier J, Pierce 1. J Pathol 230:184–193. https​://doi.org/10.1002/path.4169 A (2005) Expression and prognostic value of lactoferrin mRNA 42. Zheng Y, Jiang L, Hu Y, Xiao C, Xu N, Zhou J, Zhou X (2017) isoforms in human breast cancer. Int J Cancer 114:299–306. https​ Metallothionein 1H (MT1H) functions as a tumor suppressor in ://doi.org/10.1002/ijc.20728​ hepatocellular carcinoma through regulating Wnt/beta-catenin 28. Hoedt E, Hardiville S, Mariller C, Elass E, Perraudin JP, Pierce signaling pathway. BMC Cancer 17:161. https://doi.org/10.1186/​ A (2010) Discrimination and evaluation of lactoferrin and delta- s1288​5-017-3139-2 lactoferrin gene expression levels in cancer cells and under 43. Zhou T, Li Y, Yang L, Tang T, Zhang L, Shi J (2017) Annexin infammatory stimuli using TaqMan real-time PCR. Biometals A3 as a prognostic biomarker for breast cancer: a retro- 23:441–452. https​://doi.org/10.1007/s1053​4-010-9305-5 spective study. Biomed Res Int 2017:2603685. https​://doi. 29. Lee SH, Hwang HM, Pyo CW, Hahm DH, Choi SY (2010) - org/10.1155/2017/26036​85 directed activation of Bcl-2 is correlated with lactoferrin-induced 44. Hamelin-Peyron C, Vlaeminck-Guillem V, Haidous H, Schwall apoptosis in Jurkat leukemia T lymphocytes. Biometals 23:507– GP, Poznanovic S, Gorius-Gallet E, Michel S, Larue A, Guillotte 514. https​://doi.org/10.1007/s1053​4-010-9341-1 M, Rufon A, Choquet-Kastylevsky G, Ataman-Onal Y (2014) 30. Lu Y, Zhang TF, Shi Y, Zhou HW, Chen Q, Wei BY, Wang X, Prostate cancer biomarker annexin A3 detected in urines obtained Yang TX, Chinn YE, Kang J, Fu CY (2016) PFR peptide, one following digital rectal examination presents antigenic variability. of the antimicrobial peptides identifed from the derivatives of Clin Biochem 47:901–908. https​://doi.org/10.1016/j.clinb​ioche​ lactoferrin, induces necrosis in leukemia cells. Sci Rep 6:20823. m.2014.05.063 https​://doi.org/10.1038/srep2​0823 45. Wang K, Li J (2016) Overexpression of ANXA3 is an independent 31. Mader JS, Salsman J, Conrad DM, Hoskin DW (2005) Bovine prognostic indicator in gastric cancer and its depletion suppresses lactoferricin selectively induces apoptosis in human leukemia cell proliferation and tumor growth. Oncotarget 7:86972–86984. and carcinoma cell lines. Mol Cancer Ther 4:612–624. https​:// https​://doi.org/10.18632​/oncot​arget​.13493​ doi.org/10.1158/1535-7163.MCT-04-0077 46. Olsson M, Beck S, Kogner P, Martinsson T, Caren H (2016) 32. Richardson A, de Antueno R, Duncan R, Hoskin DW (2009) Genome-wide methylation profling identifes novel methylated Intracellular delivery of bovine lactoferricin’s antimicrobial core genes in neuroblastoma tumors. Epigenetics 11:74–84. https​:// (RRWQWR) kills T-leukemia cells. Biochem Biophys Res Com- doi.org/10.1080/15592​294.2016.11381​95 mun 388:736–741. https​://doi.org/10.1016/j.bbrc.2009.08.083 47. Lopes MR, Pereira JK, de Melo Campos P, Machado-Neto JA, 33. Eiring AM, Khorashad JS, Agarwal A, Mason CC, Yu F, Red- Traina F, Saad ST, Favaro P (2017) De novo AML exhibits wine HM, Bowler AD, Gantz KC, Reynolds KR, Clair PM (2015) greater microenvironment dysregulation compared to AML with MS4A3 improves imatinib response and survival in BCR-ABL1 myelodysplasia-related changes. Sci Rep 7:40707. https​://doi. primary TKI resistance and in blastic transformation of chronic org/10.1038/srep4​0707 myeloid leukemia. Blood 126:14 48. Yamamoto S, Yako Y, Fujioka Y, Kajita M, Kameyama T, Kon S, 34. Yokoi H, Kasahara M, Mori K, Ogawa Y, Kuwabara T, Imamaki Ishikawa S, Ohba Y, Ohno Y, Kihara A, Fujita Y (2016) A role of H, Kawanishi T, Koga K, Ishii A, Kato Y, Mori KP, Toda N, the sphingosine-1-phosphate (S1P)-S1P receptor 2 pathway in epi- Ohno S, Muramatsu H, Muramatsu T, Sugawara A, Mukoyama M, thelial defense against cancer (EDAC). Mol Biol Cell 27:491–499. Nakao K (2012) Pleiotrophin triggers infammation and increased https​://doi.org/10.1091/mbc.E15-03-0161

1 3 Interdisciplinary Sciences: Computational Life Sciences

49. Altieri F, Di Stadio CS, Federico A, Miselli G, De Palma M, 64. Chadwick N, Zeef L, Portillo V, Fennessy C, Warrander F, Rippa E, Arcari P (2017) Epigenetic alterations of gastrokine 1 Hoyle S, Buckle AM (2009) Identifcation of novel Notch tar- gene expression in gastric cancer. Oncotarget 8:16899–16911. get genes in T cell leukaemia. Mol Cancer 8:35. https​://doi. https​://doi.org/10.18632​/oncot​arget​.14817​ org/10.1186/1476-4598-8-35 50. Xing R, Cui JT, Xia N, Lu YY (2015) GKN1 inhibits cell inva- 65. Bielinska E, Matiakowska K, Haus O (2017) Heterogeneity of sion in gastric cancer by inactivating the NF-kappaB pathway. human WT1 gene. Postepy Hig Med Dosw (Online) 71:595–601 Discov Med 19:65–71 66. Shen Y, Park CS, Suppipat K, Mistretta TA, Puppi M, Horton 51. Park JH, Nishidate T, Kijima K, Ohashi T, Takegawa K, Fuji- TM, Rabin K, Gray NS, Meijerink JP, Lacorazza HD (2017) Inac- kane T, Hirata K, Nakamura Y, Katagiri T (2010) Critical tivation of KLF4 promotes T-cell acute lymphoblastic leukemia roles of mucin 1 glycosylation by transactivated polypeptide and activates the MAP2K7 pathway. Leukemia 31(6):1314–1324. N-acetylgalactosaminyltransferase 6 in mammary carcinogen- https​://doi.org/10.1038/leu.2016.339 esis. Cancer Res 70:2759–2769. https​://doi.org/10.1158/0008- 67. Kronke J, Hurst SN, Ebert BL (2014) Lenalidomide induces 5472.CAN-09-3911 degradation of IKZF1 and IKZF3. Oncoimmunology 3:e941742. 52. Bhutia YD, Babu E, Prasad PD, Ganapathy V (2014) The amino https​://doi.org/10.4161/21624​011.2014.94174​2 acid transporter SLC6A14 in cancer and its potential use in chem- 68. Winandy S, Wu P, Georgopoulos K (1995) A dominant mutation otherapy. Asian J Pharm Sci 9:293–303. https://doi.org/10.1016/j.​ in the Ikaros gene leads to rapid development of leukemia and ajps.2014.04.004 lymphoma. Cell 83:289–299 53. Ganapathy ME, Ganapathy V (2005) Amino acid transporter 69. Xu JH, Wang T, Wang XG, Wu XP, Zhao ZZ, Zhu CG, Qiu HL, ATB0,+ as a delivery system for drugs and prodrugs. Curr Drug Xue L, Shao HJ, Guo MX, Li WX (2010) PU.1 can regulate the Targets Immune Endocr Metabol Disord 5:357–364 ZNF300 promoter in APL-derived promyelocytes HL-60. Leuk 54. Mi H, Huang X, Muruganujan A, Tang H, Mills C, Kang D, Res 34:1636–1646. https://doi.org/10.1016/j.leukr​ es.2010.04.009​ Thomas PD (2017) PANTHER version 11: expanded annotation 70. de Bruijn M, Dzierzak E (2017) Runx transcription factors in the data from gene ontology and reactome pathways, and data analysis development and function of the defnitive hematopoietic system. tool enhancements. Nucleic Acids Res 45:D183–D189. https​:// Blood 129:2061–2069. https​://doi.org/10.1182/blood​-2016-12- doi.org/10.1093/nar/gkw11​38 68910​9 55. Zhu H (2014) Targeting forkhead box transcription factors 71. Selvarajan V, Osato M, Nah GS, Yan J, Chung TH, Voon DC, Ito FOXM1 and FOXO in leukemia (Review). Oncol Rep 32:1327– Y, Ham MF, Salto-Tellez M, Shimizu N, Choo SN, Fan S, Chng 1334. https​://doi.org/10.3892/or.2014.3357 WJ, Ng SB (2017) RUNX3 is oncogenic in natural killer/T-cell 56. Somerville TD, Wiseman DH, Spencer GJ, Huang X, Lynch JT, lymphoma and is transcriptionally regulated by . Leukemia Leong HS, Williams EL, Cheesman E, Somervaille TC (2015) 31(10):2219–2227. https​://doi.org/10.1038/leu.2017.40 Frequent derepression of the mesenchymal transcription factor 72. Lourenco AR, Cofer PJ (2017) A tumor suppressor role for C/ gene FOXC1 in acute myeloid leukemia. Cancer Cell 28:329–342. EBPalpha in solid tumors: more than fat and blood. Oncogene https​://doi.org/10.1016/j.ccell​.2015.07.017 36(37):5221–5230. https​://doi.org/10.1038/onc.2017.151 57. Sarkar A, Hochedlinger K (2013) The sox family of transcription 73. Wilkinson B, Chen JY, Han P, Rufner KM, Goularte OD, Kaye factors: versatile regulators of stem and progenitor cell fate. Cell J (2002) TOX: an HMG box protein implicated in the regula- Stem Cell 12:15–30. https​://doi.org/10.1016/j.stem.2012.12.007 tion of thymocyte selection. Nat Immunol 3:272–280. https://doi.​ 58. Oliemuller E, Kogata N, Bland P, Kriplani D, Daley F, Haider S, org/10.1038/ni767​ Shah V, Sawyer EJ, Howard BA (2017) SOX11 promotes inva- 74. Mullighan CG, Phillips LA, Su X, Ma J, Miller CB, Shurtlef SA, sive growth and ductal carcinoma in situ progression. J Pathol Downing JR (2008) Genomic analysis of the clonal origins of 243(2):193–207. https​://doi.org/10.1002/path.4939 relapsed acute lymphoblastic leukemia. Science 322:1377–1380. 59. Xie C, Han Y, Liu Y, Han L, Liu J (2014) miRNA-124 down- https​://doi.org/10.1126/scien​ce.11642​66 regulates SOX8 expression and suppresses cell proliferation in 75. Gordon SM, Chaix J, Rupp LJ, Wu J, Madera S, Sun JC, Lindsten non-small cell lung cancer. Int J Clin Exp Pathol 7:7518–7526 T, Reiner SL (2012) The transcription factors T-bet and Eomes 60. Alharbi RA, Pettengell R, Pandha HS, Morgan R (2013) The role control key checkpoints of natural killer cell maturation. Immunity of HOX genes in normal hematopoiesis and acute leukemia. Leu- 36:55–67. https​://doi.org/10.1016/j.immun​i.2011.11.016 kemia 27:1000–1008. https​://doi.org/10.1038/leu.2012.356 76. Lazarevic V, Glimcher LH, Lord GM (2013) T-bet: a bridge 61. Peng HX, Liu XD, Luo ZY, Zhang XH, Luo XQ, Chen X, Jiang H, between innate and adaptive immunity. Nat Rev Immunol 13:777– Xu L (2017) Upregulation of the proto-oncogene Bmi-1 predicts a 789. https​://doi.org/10.1038/nri35​36 poor prognosis in pediatric acute lymphoblastic leukemia. BMC 77. Yu H, Yang J, Jiao S, Li Y, Zhang W, Wang J (2014) T-box tran- Cancer 17:76. https​://doi.org/10.1186/s1288​5-017-3049-3 scription factor 21 expression in breast cancer and its relationship 62. Yu M, Al-Dallal S, Al-Haj L, Panjwani S, McCartney AS, with prognosis. Int J Clin Exp Pathol 7:6906–6913 Edwards SM, Manjunath P, Walker C, Awgulewitsch A, Hent- 78. Schnell SA, Ambesi-Impiombato A, Sanchez-Martin M, Belver ges KE (2016) Transcriptional regulation of the proto-oncogene L, Xu L, Qin Y, Kageyama R, Ferrando AA (2015) Therapeu- Zfp521 by SPI1 (PU.1) and HOXC13. Genesis 54:519–533. https​ tic targeting of HES1 transcriptional programs in T-ALL. Blood ://doi.org/10.1002/dvg.22963​ 125:2806–2814. https​://doi.org/10.1182/blood​-2014-10-60844​8 63. Akasaka T, Balasas T, Russell LJ, Sugimoto KJ, Majid A, 79. Tian C, Tang Y, Wang T, Yu Y, Wang X, Wang Y, Zhang Y (2015) Walewska R, Karran EL, Brown DG, Cain K, Harder L, Gesk HES1 is an independent prognostic factor for acute myeloid leuke- S, Martin-Subero JI, Atherton MG, Bruggemann M, Calasanz mia. Onco Targets Ther 8:899–904. https://doi.org/10.2147/OTT.​ MJ, Davies T, Haas OA, Hagemeijer A, Kempski H, Lessard S8351​1 M, Lillington DM, Moore S, Nguyen-Khac F, Radford-Weiss I, 80. Dou H, Chen X, Huang Y, Su Y, Lu L, Yu J, Yin Y, Bao L (2017) Schoch C, Struski S, Talley P, Welham MJ, Worley H, Streford Prognostic signifcance of P2RY8-CRLF2 and CRLF2 overex- JC, Harrison CJ, Siebert R, Dyer MJ (2007) Five members of the pression may vary across risk subgroups of childhood B-cell acute CEBP transcription factor family are targeted by recurrent IGH lymphoblastic leukemia. Genes Cancer 56:135– translocations in B-cell precursor acute lymphoblastic leukemia 146. https​://doi.org/10.1002/gcc.22421​ (BCP-ALL). Blood 109:3451–3461. https://doi.org/10.1182/blood​ ​ 81. Palmi C, Savino AM, Silvestri D, Bronzini I, Cario G, Paganin -2006-08-04101​2 M, Buldini B, Galbiati M, Muckenthaler MU, Bugarin C, Della

1 3 Interdisciplinary Sciences: Computational Life Sciences

Mina P, Nagel S, Barisone E, Casale F, Locatelli F, Lo Nigro L, family and IGJ genes signature as predictor of low induction treat- Micalizzi C, Parasole R, Pession A, Putti MC, Santoro N, Testi ment response and worst survival in adult Hispanic patients with AM, Ziino O, Kulozik AE, Zimmermann M, Schrappe M, Villa A, B-acute lymphoblastic leukemia. J Exp Clin Cancer Res 35:64. Gaipa G, Basso G, Biondi A, Valsecchi MG, Stanulla M, Conter https​://doi.org/10.1186/s1304​6-016-0333-z V, Te Kronnie G, Cazzaniga G (2016) CRLF2 over-expression is 83. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, a poor prognostic marker in children with high risk T-cell acute Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, lymphoblastic leukemia. Oncotarget 7:59260–59272. https​://doi. Kuhn M, Bork P, Jensen LJ, von Mering C (2015) STRING v10: org/10.18632​/oncot​arget​.10610​ protein–protein interaction networks, integrated over the tree of 82. Cruz-Rodriguez N, Combita AL, Enciso LJ, Quijano SM, Pinzon life. Nucleic Acids Res 43:D447–D452. https​://doi.org/10.1093/ PL, Lozano OC, Castillo JS, Li L, Bareno J, Cardozo C, Solano nar/gku10​03 J, Herrera MV, Cudris J, Zabaleta J (2016) High expression of ID

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