Acute lymphoblastic leukemia (ALL) is the most common cancer in children [1]. It is a biologically heterogeneous hematologic malignancy—mainly characterized by chromosomal alterations, and some somatic and genetic mutations—that leads to the dysregulation of cytokine receptors, hematopoietic transcription factors and epigenetic modifiers ^[2][3][2,3]. Contemporary chemotherapy for childhood ALL has resulted in a cure rate of more than 85% in developed countries, representing one of the success stories in treatment of childhood malignancies [4]. This can be attributed to: (i) risk-directed therapy, based on clinical features such as age and initial white blood count, (ii) biologic and genetic features such as karyotype and identification of cryptic translocations, but most importantly (iii) the response to treatment evaluation with minimal/measurable residual disease (MRD) ^[5][6][5,6]. However, despite the observed improvement in survival rates, leukemia remains one of the leading causes of cancer-related deaths [7]. Given the improved cure rates, current research has focused on subgroups of patients with refractory/relapsed disease. Identification of proteins and pathways related to cancer and its environment provides the potential to develop effective individualized treatment, especially in this high-risk group. Proteomics is a cutting-edge technique and a useful tool for creating innovative and customized therapy providing new prospects for precision-medicine strategies [8].

2. Molecular Basis of Acute Lymphoblastic Leukemia

Acute lymphoblastic leukemia is a hematologic malignancy of lymphoid origin and the most common childhood cancer representing about 25% of cancer diagnoses [9]. The highest peak age of incidence is between two and five years [10]. It is more frequent in boys than in girls with an approximate ratio of 1.3:1. Children of Hispanic descent are more frequently affected followed by White, and to a lesser percentage, African Americans [11]. Classification, based on immunophenotype, consists of 80–85% B-cell and 15–20% of T-cell, increasing in adolescence. Leukemic cells initiate in the bone marrow (BM) and infiltrate extramedullary sites such as the liver, spleen, mediastinum and lymph nodes and also sanctuary sites, such as the central nervous system (CNS), ovaries in girls and testes in boys. Although environmental, immunologic, socioeconomic and epidemiologic factors have been evaluated rigorously as contributing factors to leukemogenesis, the exact underlying etiology remains unknown [12]. Before the advent of next-generation sequencing (NGS), only a small number of uncommon constitutional leukemia predisposition syndromes, such as Down syndrome (DS) and Li–Fraumeni syndrome, were associated with the development of ALL [13]. Other genetic syndromes linked to an increased risk of ALL include: Bloom syndrome, ataxia telangiectasia (AT), neurofibromatosis 1 and constitutional mismatch repair deficiency (CMMRD) [13]. The latter, and AT, have a preponderance to T-ALL, while B-ALL occurs almost exclusively in DS-ALL ^[14][15][14,15]. During the last decades, the landscape of germline and somatic mutations in childhood ALL has been unveiled with the aid of a microarray analysis of gene expression and ultra-high throughput sequencing technologies. Based on genomic analysis, childhood ALL is subdivided into genetic subgroups ^[16][17][18][16,17,18]. Gross chromosomal alterations in childhood ALL have been associated with outcome. Fluorescence in situ hybridization (FISH) assays and cytogenetics are used to identify structural chromosomal gains or losses in leukemic cells. High-hyperdiploidy (>50 chromosomes: 51–67 chromosomes) with trisomies of chromosomes 4,6,10,14,17,18,21, X occurs in almost 25% of childhood ALL cases and is associated with excellent outcome, even with reduced intensity chemotherapeutic regimens ^[19][20][19,20]. Low-hyperdiploidy with 47–50 chromosomes was traditionally associated with a poor outcome. However, contemporary therapy regimens have significantly improved clinical outcome ^[21][22][21,22]. Chromosomal translocations represent a molecular hallmark of childhood ALL and represent a significant prognostic factor. Chimeric fusion genes are created by chromosomal rearrangements and involve epigenetic modifiers, tyrosine kinases and transcription factors ^[23][25]. They can be identified with reverse-transcription polymerase chain reaction amplification of the fusion genes created, together with FISH assays. Routine cytogenetic analyses fail to detect some of the cryptic translocations. Almost one fourth of standard-risk B-ALL harbor the cryptic t(12;21)(p13;q22), resulting in ETV6-RUNX1 (TEL-AML1) fusion. It should be noted that this fusion can also be detected in children who do not develop leukemia. It has also been detected in preserved blood spots from children who later develop ALL indicating a potential prenatal origin of leukemogenesis in association with additional necessary co-operating mutations for the development of leukemia ^[24][26]. A small group of “ETV6-RUNX1-like” B-ALL has also been reported. These cases lack the classic ETV6-RUNX1 rearrangement and are associated with other ETV6 fusions and with IKZF1 deletions. A unifying and prominent feature of a majority of prognostic studies in pediatric BCP ALL is that the different types of IKZF1 deletions have been constantly linked to an unfavorable clinical outcome of frontline treatment ^[25][27].  Other common rearrangements in B-ALL include: t(1;19)(q23;p13.3) resulting in TCF3-PBX1 (E2A-PBX1) fusion, rearrangement of KMT2A (formerly MLL; 11q23), and t(9;22)(q34;q11.2) (the Philadelphia chromosome) resulting in BCR-ABL1 fusion. While TCF3-PBX1 ALL was formerly linked to an intermediate or unfavorable prognosis, modern therapeutic regimens have enhanced outcome; thus, TCF3-PBX1 fusion is no longer considered for risk stratification ^[26][28]. Children with TCF3-PBX1 ALL appear to have higher risk of CNS relapse and may warrant intensification of CNS-directed therapy ^[27][29]. TCF3-HLF fusion resulting from t(17;19)(q22;p13.3) is very uncommon in patients with B-ALL and, despite its rarity, has been associated with extremely poor outcome ^[28][30].  An established molecular-targeted therapy paradigm in childhood ALL is Philadelphia positive (Ph+) ALL with the t(9;22)(q34;q11) resulting in BCR-ABL1 oncoprotein. Three BCR-ABL1 protein isoforms are encoded, the p210, the p190, and the p230, which have persistently enhanced tyrosine kinase (TK) activity. The BCR-ABL fusion gene of childhood and adult ALL have a different molecular basis, with the BCR-ABL fusion gene in adult ALL of the “p210” subtype resembling that found in chronic myeloid leukemia (CML), whereas the childhood subtype is mainly “p190” ^[29][34]. Historically, the best curative option has been hematopoietic stem cell transplantation (HSCT). The addition of tyrosine kinase inhibitors (TKIs) in the intensive chemotherapy backbone, has significantly improved event-free and overall survival ^[30][31][35,36]. BCR-ABL1-Like or Philadelphia chromosome-like ALL has recently been described as a subset of B-ALL defined by an activated kinase gene expression profile similar to that of Ph+ ALL and is associated with miscellaneous genetic alterations that activate cytokine receptor signaling pathways ^[32][37]. In spite of the heterogeneity in Ph-like kinase-activating alterations, JAK-STAT, ABL, Ras/MAPK signaling pathways are activated and can effectively be inhibited by relevant TKIs ^[33][38].  In contrast to B-ALL, the identified genetic alterations that occur in T-ALL do not add a substantial prognostic value in the established risk stratification of T-ALL based on CNS and MRD status ^[34][42]. Mutation of TAL1 (1p32) is a non-random genetic defect frequently present in childhood T-ALL. The SIL/TAL1 fusion product gives rise to inappropriate expression of TAL1, that may promote T-cell leukemogenesis. The clinical relevance and the prognostic value of this rearrangement remains to be further elucidated ^[35][43]. The most common dysregulated signaling pathway is the Notch pathway, either by upregulation with activating mutations of Notch1 or by loss of function of negative regulators such as FBXW7 ^[36][44].  The most common translocations in T-ALL involve fusion of T-cell receptor genes. Gene expression signatures define novel oncogenic pathways in T-cell ALL, but their prognostic significance remains unidentified. Although stimulating advances have occurred regarding the genomic characterization of T-ALL, development of precision medicine treatment approaches for T-ALL has proven more challenging ^[37][48]. Approximately 20% of pediatric ALL patients experience a relapse with a survival lagging behind newly diagnosed ALL. Major pathways of lymphoid development, kinase signaling, cell cycle regulation and epigenetic modification are involved in the genetic basis of relapsed ALL ^[38][49]. Molecular-targeted therapy has shown promising results in early trials, though not translated into improved survival ^[39][40][50,51].

3. The Era of Proteomics

Proteomics studies the structure along with the function of the proteome ^[41][54]. The term proteome describes the functional state of the total of proteins, which are responsible for the functional activity of different cells ^[42][55]. As a result, study of the proteome correlates the structural and functional multiplicity of proteins during the disease process. The field of proteomics has been developed since genomics is ineffective in unravelling the structure and dynamic state of proteins, which are gene products ^[43][56]. In contrast to the steady state of the genome, expression of a protein reflects a dynamic state of processes including RNA transcription, alternative splicing, and/or post-translational modifications (PTM) ^[44][57]. PTMs have fundamental regulatory properties, such as converting a protein from its inactive to its active state, or determining a protein’s half-life resulting from ubiquitination or acetylation; hence, defining its functional property in a cell and tissue-specific context that ultimately determines the resulting cellular phenotype and its biological significance ^[45][58]. The general strategy pursued in proteomics is to compare related samples from different disease stages considering that differences in their proteome could reflect a different disease stage. Proteomic studies are carried out mainly in body fluids such as the cerebrospinal fluid (CSF), peripheral blood (PB) and BM. Body fluids exhibit a great emerging potential for biomarker studies, in particular those that can be collected by minimally invasive techniques ^[46][47][60,61]. The high potential of serum/plasma as a source for protein biomarkers is reflective of the overall state of an organism ^[48][49][50][62,63,64].  Biomarkers are recognized using mass spectrometry (MS) ^[51][68]. Mass spectrometry requires a low-energy ionization source that transfers peptides from solid/liquid to gaseous states (matrix-assisted laser desorption/ionization, MALDI, and electrospray ionization, ESI). The two basic and commonest types of mass analyzers are time of flight (ToF) and ion trap resonance analyzers. Computer algorithms help vastly in the immense task of identifying peptides and ultimately the proteins from which they are derived. Computational methods and statistical algorithms can maximize the mining of proteomic data. Quantification MS-based methods are divided into the label-free and stable isotope label approach. In the label-free approach, individual samples are injected directly in the MS and the relative abundance of peptides is quantified ^[52][69]. The major advantage of this technique is minimal sample handling, but problems in reproducibility and accuracy are encountered ^[52][69]. Furthermore, the extensive instrument time required is a disadvantage for the large sample sets typical for biological and clinical studies. On the contrary, a stable isotope labeling strategy, such as an isobaric tag, allows for the mixing of multiple samples at different stages ^[53][70]. A workflow of proteomics is depicted in Figure 1.

4. Application of Proteomics in Childhood ALL

Since the hallmark of ALL is the uncontrolled clonal proliferation of poorly differentiated lymphoid progenitor cells inside the bone marrow, interfering with the production of blood cells, serum and plasma may serve as rich sources of blood cancer-associated biomarkers. In an attempt to determine potential disease markers in childhood B-ALL, a Colombian exploratory study group performed a proteomic study implementing LC-MS/MS and quantification by label-free methods searching for proteins differentially expressed be-tween healthy children and children with B-ALL. They quantified 472 proteins in depleted blood plasma and found that 25 proteins were differentially expressed ^[54][73]. Moreover, differential proteins were analyzed by MALDI-TOF-MS and were identified in lymphocytes in patients with childhood ALL and healthy children, by Wang et al. ^[55][74]. Among the 25 differential proteins, eight provided a valuable insight into the molecular mechanism of leukemogenesis and could serve as candidate markers or drug targets. Cellular levels of GSTP in c-ALL samples were dramatically up-regulated and may be regarded as a biomarker and drug target together with PHB that was also up-regulated, suggesting that it might be associated with leukemogenesis ^[55][74]. Candidate biomarkers for early diagnosis of B-ALL were overexpressed in a proteomic analysis with lectin affinity chromatography LC-MS of serum from pediatric patients with B-ALL performed by Cavalcante et al. ^[56][75]. A total of 96 proteins were identified and among them leucine-rich alpha-2-glycoprotein 1 (LRG1), clusterin (CLU), thrombin (F2), heparin cofactor II (SERPIND1), alpha-2-macroglobulin (A2M), alpha-2-antiplasmin (SERPINF2), Alpha-1 antitrypsin (SERPINA1), complement factor B (CFB) and complement C3 (C3) were identified as candidate biomarkers for early diagnosis of B-ALL, as they were up-regulated in the B-ALL group compared to controls after induction therapy ^[56][75]. Identification of tumor autoantibodies may be utilized in early cancer diagnosis and immunotherapy. Serological proteome analysis (SERPA) is another proteomic approach and screening autoantibodies as serum biomarkers of B-ALL using SERPA with combination of 2-DE, immunoblotting and MS revealed that α-enolase and VDAC1 autoantibodies were promising biomarkers for children with B-ALL. Evaluation of serum autoantibodies against α-enolase and VDAC1 show promising clinical applications ^[57][76]. Resistance to chemotherapeutics used in ALL is a major challenge and involves com-plex cellular processes. Guzmán-Ortiz et al. ^[58][81] studied proteome changes in B-lineage pediatric ALL cell line CCRF-SB after adaptation to vincristine, a vinca alkaloid used in ALL therapy ^[58][81]. Vincristine, by interaction with tubulin, disrupts the microtubule polymerization, resulting in cell cycle arrest and apoptosis ^[59][82]. They found 135 proteins exclusively expressed in the presence of vincristine, indicating that signal transduction and mitochondrial ATP production may serve as potential therapeutic targets. Leukemia studies aim to decipher the mechanisms, pathways and the degree to which the proteome impacts leukemia subtypes and to identify whether disease stratification based on proteome features could provide precise targets for therapy. Strategies of phosphoproteomics can be used to profile the activation/deactivation of crucial molecules in signaling pathways which are key to the progression, remission and relapse of leukemia, since leukemogenesis is controlled via the regulation and interaction of signaling cascades ^[60][61][84,85]. A commonly mutated pathway in pediatric cancers is the receptor tyrosine kinase/ras (RTK/RAS) pathway. Mutations in this pathway are identified as possible targets for treatment and are mainly implicated on clonal evolution in high hyperdiploid ALL, a subtype of the most common childhood cancer ^[62][63][86,87]. Mutations of KRAS in signal transduction domains considerably affect the ability of proteins to accomplish their normal cell-signaling functions ^[63][87]. The JAK/STAT pathway have also been implicated in the oncogenesis of many cancers as well as of childhood leukemia ^[64][89]. A JAK mutant has been identified in childhood B-cell ALL leading to overactivity in cell proliferation ^[64][89]. Somatic mutations in tyrosine-protein phosphatase non-receptor type 11 (PTPN11) lead to hyperactivation of the catalytic activity instead of the normal inhibitory function ^[65][66][90,91]. The Notch signaling pathway is one of the most frequently overactivated signaling pathways in cancer, and mutations in Notch family proteins are detected in a majority of T-cell ALL ^[67][92]. Activation of γ-secretase is crucial in the activation of the Notch pathway and inhibitors can be applied to block this activation.  Phosphoproteomics is designed to provide information on pathway activation and signaling networks and offer opportunities for targeted therapy ^[68][100]. In a recent MS-based global phosphoproteomic profiling of 11 T-cell ALL cell lines targetable kinases were recognized ^[69][101]. Cordo et al. ^[68][100]. reported a comprehensive dataset consisting of 21,000 phosphosites on 4896 phosphoproteins, including 217 kinases.  Noteworthy, interesting results were depicted in a recent study by Leo et al. ^[70][104]. who performed a comprehensive multi-omic analysis of 49 readily available childhood ALL cell lines, using proteomics, transcriptomics, and pharmacoproteomic characterization. They connected the molecular phenotypes with drug responses to 528 oncology drugs, identifying drug correlations as well as lineage-dependent correlations ^[70][104]. Their observations indicate that both conventional lineage and oncogenic traits contribute to proteome-level differences in their cell line panel. Phenotypic profiling supports current clinical practice in leukemia stratification and suggests that MS-based proteomics could be an effective path to discover the drivers contributing to pathogenic phenotypes. They identified the diacylglycerol-analog bryostatin-1 as a therapeutic applicant in the MEF2D-HNRNPUL1 fusion high-risk subtype, for which this drug triggers pro-apoptotic ERK signaling linked to molecular mediators of pre-B cell negative selection ^[70][104].  Central nervous system involvement remains one of the major causes of ALL treatment failure ^[71][72][105,106]. Despite the therapeutic advances in ALL, CNS relapse occurs in 3–8% of the children with ALL and is associated with increased morbidity and mortality ^[73][107]. In normal physiological conditions, 80% of the protein in the CSF is hematogenic in origin ^[74][108]. Thus, protein content changes in CSF provide an attractive approach to study hematological malignancies ^[75][109]. Although CSF is obtained by using an invasive method, it is considered as the optimal fluid for diagnosis of CNS infiltration in ALL. Efforts have been made in recent years to detect novel biomarkers of hematologic malignancy in CSF ^[76][110]. As demonstrated in a pilot study, gel-free, label-free quantitative proteomics is feasible for profiling of CSF in pediatric leukemia/lymphoma ^[55][77][74,111].

5. Conclusions

Childhood ALL is a biologically heterogeneous disease characterized by structural alterations, genetic and somatic mutations through a process of complex protein-based signaling network pathway modifications. Changes in protein expression can partially be determined by the analysis of the static genome. Although implementation of next-generation genomic, transcriptomic, and epigenetic sequencing tools unveiled the genetic landscape of childhood ALL, uncovering the underlying dynamic changes at a protein level still remains a challenge. Proteomics offers complementary information to genomics and transcriptomics by analyzing protein structure, expression, and modification status. Advances in the field of MS enables high-throughput collection of global proteomic profiles representing an opportunity to discover new biological markers and druggable targets. Novel disease-specific biomarkers will provide an additional reference for predicting treatment responses and individual prognosis. Deeper characterization of biomarkers and molecular pathways, especially in patients with relapsed/refractory disease, will ultimately identify precision medicine candidates for application of translational precision therapy.