The pathogenesis of acute lymphoblastic leukaemia (ALL)

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1.1. Cancer

Cancer is one of the leading causes of mortality and is the causal factor of as many as 25% of deaths in the United States (ACS, 2014). In general, cancer is a group of different diseases characterized by aberrant, unregulated cell growth. In cancer, due to loss of control of cell division and growth, formation of lumps or tissue masses, termed tumors, occurs. The cancer may also migrate to other parts of the body via the blood or lymphatic systems, leading to destruction of healthy tissues. The formation of blood cells fundamentally takes place in the bone marrow and is a closely regulated process involving proliferation, differentiation and cell survival. Cancers arise from a gradual buildup of genetic changes due to clonal expansion events in pre-malignant cell populations that are undergoing Darwinian selection process (Weinberg, 2007).

1.2. Hematopoiesis

Hematopoiesis is a crucial process in the growth and re-generation of blood cells, since these cells are essential for the regulation of various processes such as immune response, supply of oxygen to tissues and homeostasis. The hematopoietic system comprises of blood cells, bone marrow, lymph node, thymus, spleen and endothelial cells. The Precursor Hematopoietic stem cell (HSC) is a pluripotent stem cell, capable of dividing and giving rise to cells of different lineages. On receiving differentiation stimulus, HSC divides to give rise to Multipotent Progenitor cell (MPP), which does not have self-renewal capacity. This cell divides to give rise to precursor cells of lymphoid and myeloid lineages (Figure 1.1). The common lymphoid precursor cell in turn gives rise to Pro-B, Pro-T and Pro-NK cells which divide to form B-, T- and NK-cells. The common myeloid precuror cell gives rise to the megakaryotic/erythroid progenitor which further divides to form erythrocytes and platelets, Myelomonocytic progenitors which divide to form granulocytes and macrophages. Both the common lymphoid precursor and the common myeloid precursor cell may also give rise to Pro-dendritic cell which divides to form dendritic cell (Passegué et al., 2003). Carcinogenic stimuli may lead to alterations in this process, transforming a normal hematopoietic stem cell precursor to a leukemic stem cell. Thus, aberrant hematopoiesis leads to development of leukemias.

Hematopoiesis_Passegué et al

Figure 1.1. Schematic overview of hematopoiesis [LT-HSC= Long Term Hematopoiestic Stem Cell, ST-HSC= Short Term Hematopoietic Stem Cell, MPP= Multipotent Progenitor, CMP= Common Myeloid Progenitor, CLP= Common Lymphoid Progenitor, MEP= Megakaryotic/Erythroid Progenitor, GMP = Myelomonocytic Progenitor, Pro-DC = Dendritic Cell Progenitor, Pro-T = T-cell Progenitor, Pro-NK= Natural Killer cell Progenitor, Pro-B= B-cell Progenitor] (Source: Passegué et al., 2003)

1.3. Leukemia

Cancer has been known to affect many body organs and tissues, some of them being bones, stomach, lungs and blood. The cancer of the blood is called Leukemia. It is a subtype of a broad array of diseases commonly referenced as Hematological Malignancies. According to statistics provided by the Leukemia and Lymphoma Society, this disease is expected to be diagnosed in more than 52,380 people in the United States in 2014, and it has one of the top mortality rates among different types of cancer (Leukemia & Lymphoma Society, 2014). Based on the severity of the disease and the type of white blood cell affected, leukemia is classified into different types.

1.4. Types of leukemia

Most leukemias may be subdivided into two general groups: myeloid leukemia (~60% of the cases) and lymphocytic leukemia (~40% of the cases). Leukemias are also classified based on whether they are acute (~55%) or chronic (~45%). In acute leukemias, the malignant cells, or blasts, are immature cells that have lost their ability to differentiate and are thus incapable of performing their functions in immune response. The onset of acute leukemias is rapid, generally weeks, and, is mostly fatal, unless the treatment is initiated swiftly. Chronic leukemias occur in mature cells and result in reduction in their functioning capacity. These abnormal cells also proliferate at a slower rate, generally years. Thus, leukemias may be categorized into four main types: Acute Lymphocytic Leukemia (ALL), Chronic Lymphocytic Leukemia (CLL), Acute Myelogenous Leukemia (AML), Chronic Myelogenous Leukemia (CML), each of which further comprises several subtypes. Our study is focused on ALL.

1.5. Acute Lymphocytic Leukemia (ALL)

Acute lymphoblastic (lymphocytic) leukemia (ALL) comprises of a group of lymphoid neoplasms that have identical morphological and immunophentoypical characteristics to precursor cells of B- and T-lineages. These neoplasms may present largely as a complete leukemic process, with widespread involvement of the bone marrow and peripheral blood cells or they may be restricted to tissue penetration, with very little (<25%) to no bone marrow involvement. The former case is classically designated as lymphoblastic lymphomas (LBLs). ALL and LBLs appear to comprise a biologic gamut, although they may demonstrate distinct clinical features. The current World Health Organization (WHO) Classification of hematopoietic cancers delegates these disorders as B- or T-lymphoblastic leukemia/lymphoma (Swerdlow et al., 2008).

1.6. Clinical symptoms and diagnosis of ALL

The main symptoms of ALL include: fever, anaemia, increased bleeding and bruising, shortness of breath, infections and bone and joint pain. Initial diagnosis of ALL includes assessment of complete blood picture with analysis of total count of red blood cell, white blood cell and platelet numbers, as these numbers are generally altered in ALL (Daly et al., 2010). Generally, Children with ALL have low platelet count with low red blood cell (haemoglobin) levels and concomitantly high white cell count, with a surfeit of immature blast cells. These blast cells do not differentiate and mature; instead they proliferate in excessive numbers and also prevent development and functioning of normal unaltered blood cells. ALL is confirmed via assessment of the percentage of blast cells in the patient’s.bone marrow. Under normal physiological conditions, there are less than 5% blast cells present in healthy individuals. In ALL patients, blast cell range between 20% - 95% is generally reported (Daly et al., 2010). Of the two types of white blood cells affected by ALL, B-cell ALL constitutes about ~85% and T-cell ALL constitutes ~15% of ALL cases. Both of these subtypes are diagnosed by assessing the morphology of cells in blood or bone marrow specimens collected from the patients (Daly et al., 2010). ALL has been differentiated into subtypes based on morphology, cytochemistry and immunophenotyping. The conventional criteria used to classify ALL are based on classification system of the French-American-British (FAB) group (Bennet et al., 1976). The FAB group defines the 3 subtypes of ALL (L1, L2 and L3) based on the morphological features of the blasts when viewed under a microscope. This classification is based on ratio of nucleolus to cytoplasm, presence and size of nucleolus, degree of consistency in the shape of the nuclear membrane and size of the cell (Bennett et al., 1981). The ALL-L1 subtype comprises mainly small size blast cells and is found in 70-80% of childhood ALL cases. ALL-L2 subtype comprises of a mixed group of small and large blast cells, with a higher percentage of large sized cells. The ALL-L3 subtype comprises of medium to large sized blast cells. In contrast to this classification system, The European Group for the Immunological Classification of Leukemias (EGIL) classifies acute leukemias exclusively on the basis of immunophenotyping (Abdul-Hamid, 2011).

1.7. Incidences of Acute Lymphoblastic Leukemia (ALL)

According to WHO Report in 2003, 250,000 cases of leukemia were reported. En masse, leukemias account for about 31% of all childhood cancers and affect about 2,200 American children and young adults each year and results in death in 3.0% (618 under the age of 19) of these cases. In England and Wales about 400 children are diagnosed each year and about 100 die due to leukemia (Shah and Coleman, 2007). The average incidence of leukemia in children in the European Region was 46.7 cases per million per year (WHO, 2009). ALL is the most commonly diagnosed cancer in children, accounting for 26% of cancers diagnosed in those aged birth to 14 years. ALL is more common in industrialized countries than in developing countries. The incidence rate of childhood ALL in USA is about 35 to 40 cases per million (Howlader et al., 2013). The incidence rates of a few ethnicities, cities and countries have been represented in Table 1.1 (Ross et al., 2011). In India, about 25 people per million population are affected annually (about 500 cases per year) with relative proportion of ALL varying between 60 and 85% of all leukemias found in children. The mortality rate is reported to be high with only 33% surviving at five years (Bombay Cancer Registry 1996, Arora et al., 2009). Arora et al. (2009), through their meta-analysis, reported a higher incidence of T-ALL (20-50%) in India when compared to the developed countries, along with the more frequent presence of hypodiploidy and t(1;19), t(9;22), and t(4;11) translocations in Indian childhood ALL. Also, based on their analysis of the cancer registry, they observed a slightly higher incidence of ALL in male children than in female children.

Table 1.1. Incidence rates of Acute Lymphoblastic Leukemia (ALL) (Ross et al., 2011)


Incidence rate/million

US Hispanics


Costa Rica


US Whites






The Netherlands




US Blacks






1.8. Genetic Aspects of Acute Lymphoblastic Leukemia (ALL)

Present diagnostic methods reveal that genetic aberrations occur in approximately 90% of ALL patients. In most cases these aberrations were found to be specific to the leukemia type and also to immunological or morphological leukemia subtypes. In ALL, many of the genetic perturbations are clearly different in children and adults (Ma et al., 1999). Research on ALL, using genetic, proteomic, expression and genome wide association studies, have shown that the normal biological processes and cell development and differentiation pathways such as cell cycle, proliferation, cellular signaling, hematopoiesis, epigenetic regulation are deregulated in leukemic cells due to alterations in the genes and proteins involved in these processes (Pui et al., 2012). Focal deletions and mutations in the transcription factors PAX5, IKZF1 (Mullighan et al., 2007), transcription regulator CREBBP (Mullighan et al., 2011), protein tyrosine kinases JAK1, JAK2 (Mullighan et al., 2009), cell cycle regulator and tumor suppressor TP53 (Hof et al., 2011), chromosomal rearrangements in CRLF2 (involved in hematopoiesis) (Mullighan et al., 2011) have been observed in leukemic cells. Further, alterations in cell cycle regulators such as cyclin D1 (Aref et al., 2006) and hematopoietic regulators such as Notch1 (frequently mutated in T-ALL, Weng et al., 2004) have also been reported. Zhang et al., (2011) also reported mutations in RAS signaling, JAK/STAT and B-cell development pathway. Studies have also shown that alterations in FLT3 gene play an important role in leukemogenesis (Reddy et al., 2006a). Single nucleotide polymorphisms (SNPs) in genes such as folate metabolizing MTHFR (Reddy and Jamil, 2006b), RFC1, NNMT (de Jonge et al., 2009), xenobiotic metabolizing CYP1A1*2A, GSTM1 null type (Krajinovic et al., 1999; Reddy and Jamil 2006c), NAT2 (Krajinovic et al., 2000), CYP2E1, MPO, NQO1 (Krajinovic et al., 2002), GSTT1, immune function genes IL12A (Chang et al., 2010), HLA-DPB1*0201 (Taylor et al., 2002) were found to be associated with increased risk of developing ALL, especially in children. These studies emphasize the critical role played by alterations in genes and proteins in neoplastic transformation of ALL and hence point to the need to better understand the biomolecules involved in the regulation of crucial deregulated pathways such as cell cycle and hematopoiesis that are commonly aberrated in ALL.

1.9. Cytogenetic alterations of Acute Lymphoblastic Leukemia (ALL)

Cytogenetic aberrations are currently one of the major prognostic factors in ALL. Cytogenetic studies have reported numerous chromosomal aberrations in patients with ALL. These include alterations in chromosome number resulting in High Hyperdiploidy with 51 to 65 chromosomes per cell and Hypodiploidy with less than 44 chromosomes. Alterations in chromosomal structure, mainly translocations, have also been observed, including ETV6-RUNX1 (t(12;21), Philadelphia chromosome (t(9;22), MLL translocations and TCF3-PBX1 (also known as E2A-PBX1; t(1;19)). These chromosomal number and structure alterations, together with the other prognostic factors, have been observed to affect treatment response.

1.10. Rationale for Bioinformatics approach

In recent years, experimental research has been supplemented to a large extent with the use of computational approaches. The application of bioinformatics methodologies has helped in gaining new insights into disease biology, especially in cancers, through feasibility of large scale data analysis in lesser time. Also, in silico methodologies offer a vast array of data analysis tools that make it feasible to examine biological data via application of multi-parameter testing. Phylogenetics software have helped in understanding evolutionary patterns of genes among the different living organisms. These patterns may hold the clue to decipher the alterations of genes in human disease, through a comparative study of similar genes in other organisms as demonstrated in our studies (Jayaraman et al., 2011; Jayaraman and Jamil, 2012). Further, computational methods are especially useful in analysis of microarray expression data. Data generated from expression studies are generally voluminous and their inference manually would require a lot of time and resources and may also be subject to manual errors. The use of computational algorithms ensures the availability of multiple analysis parameters that search the data for patterns quickly and thoroughly and thus help in generating extensive results that can be interpreted more accurately. Also, application of gene prioritization algorithms have become extremely useful in shortlisting new gene targets in disease from an extensive set of plausible candidates, thus narrowing down the search for identifying new therapeutic targets in disease research. Further, in many complex diseases, especially cancers, genes do not function in isolation, but are part of immense interconnected network pathways that act as a disease highway and lead to a cascade of pathogenetic changes. Understanding the players in these networks through bioinformatics tools is a more feasible approach since mapping each of these connections experimentally would only be possible through the use of exhaustive resources and extensive time period. Computational prediction of networking integrates multiple information sources such as available experimental data, data from related species and occurrence upstream or downstream of a particular pathway and similar functions to map genes and their proteins to network modules. We have utilized in silico protein networking to elucidate interactors of TP53 and NOTCH1 and their role in leukemogenesis (Jamil et al., 2012; Jayaraman and Jamil, 2013).

Pharmacogenomics is a field that correlates genetic/genomic information of individuals to pharmacological information to determine the best therapeutic regimen for a particular person. This tailor made therapy is extremely essential since not all individuals respond the same way to treatment. Pharmacogenomics has been extremely useful in Oncology in identification of drugs and determination of drug efficacies, e.g. in identification of efficacy of the drug Trastuzumab in HER2 positive breast cancer with metastasis (Shak, 1999), non-efficacy of the drug 6-Mercaptopurine in leukemia and lymphoma patients with certain polymorphisms in TPMT enzyme, identification of inefficacy of Cetuximab, targeting EGFR, in patients with KRAS-induced cancers (Weng et al., 2013). The application of bioinformatics techniques in pharmacogenomics has been extremely useful as computational approaches have furthered our understanding of the genes/proteins involved in disease and also aided identification of new drugs. Homology modeling and in silico molecular docking using computational software have provided the means to visualize protein and DNA structure in three-dimensional space, study whether a particular drug may bind to the protein or DNA and if so, check how effective the binding is and visualize the interactions of the drug with the amino acids of the protein and the nucleotides of the DNA. Computational approaches have become indispensable in drug discovery research, since traditional drug discovery methods usually take a very long time from identification of new drug to release in the market. Application of bioinformatics approaches to drug discovery have helped speed up release of effective drugs in the market and have also helped in cost reduction by significant levels (Song et al., 2009). Further, they have been immensely useful in predicting potential toxicity of drugs, thus helping in development of more potent but less toxic drugs. Also, the application of bioinformatics methods as an initial step in disease research would help in narrowing down the research questions prior to experimental work and thus help in expediting research studies and saving resources. Thus, the use of bioinformatics tools and techniques has been proved to be very useful in furthering our understanding of disease biology and hence has been applied in our study to infer information about leukemogenesis in ALL. Hence, the objective of this study was to analyze the genes and pathways that are known to be deregulated in ALL using bioinformatics tools to infer new information with regard to their role in leukemogenesis, to profile new genes based on interconnectivity with already existing leukemia associated genes and explore their use as potential markers of prognostic and diagnostic interest and to perform in silico drug binding studies to infer new targets for therapy.