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Year : 2017  |  Volume : 60  |  Issue : 2  |  Page : 209-213
An integrated genomic profile that includes copy number alterations is highly predictive of minimal residual disease status in childhood precursor B-lineage acute lymphoblastic leukemia

1 Hematopathology Laboratory, Tata Memorial Centre, Mumbai, Maharashtra, India
2 Department of Cancer Cytogenetics, Tata Memorial Centre, Mumbai, Maharashtra, India
3 Department of Medical Oncology, Pediatric Haemato-lymphoid Disease Management Group, Mumbai, Maharashtra, India

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Date of Web Publication19-Jun-2017


Introduction: Copy number alterations (CNA) have been described in childhood precursor B-lineage acute lymphoblastic leukemia (B-ALL) which in conjunction with chromosomal abnormalities drive leukemogenesis. There is no consensus on the clinical incorporation of CNA in B-ALL. An integrated genomic classification (IGC) has been proposed which includes CNA and cytogenetics. Methods: We correlated this IGC with immunophenotypic minimal residual disease (MRD) as well as other standard criteria for 245 patients of B-ALL such as National Cancer Institute (NCI) risk, D+8 prednisolone response, cytogenetics, and ploidy status. Results: MRD was detectable in 81 patients (33.1%). The most common abnormalities were seen in CDKN2A/B (25.7%) followed by PAX5(20%), ETV6(16.7%), IKZF1(15.5%), Rb1(5.3%), BTG (3.3%), EBF1(2.0%), and PAR1(0.8%). On integrating CNA into the IGC, 170 patients (69.4%) were classified into good genomic risk (GEN-GR) whereas 75 (30.6%) belonged to the poor genomic risk (GEN-PR) category. The IGC showed a significant correlation with MRD and NCI risk. The presence of CNA predicted MRD clearance in intermediate cytogenetics group. Conclusion: These data seem to indicate that in addition to cytogenetics, CNA should be incorporated into routine clinical testing and risk algorithms for B-ALL. The IGC is of prognostic relevance and offers an additional avenue for prognostication and risk-adapted therapy.

Keywords: Copy number alteration, minimal residual disease, precursor B-lineage acute lymphoblastic leukemia

How to cite this article:
Patkar N, Subramanian P G, Tembhare P, Mandalia S, Chaterjee G, Rabade N, Kodgule R, Chopra K, Bibi A, Joshi S, Chaudhary S, Mascerhenas R, Kadam-Amare P, Narula G, Arora B, Banavali S, Gujral S. An integrated genomic profile that includes copy number alterations is highly predictive of minimal residual disease status in childhood precursor B-lineage acute lymphoblastic leukemia. Indian J Pathol Microbiol 2017;60:209-13

How to cite this URL:
Patkar N, Subramanian P G, Tembhare P, Mandalia S, Chaterjee G, Rabade N, Kodgule R, Chopra K, Bibi A, Joshi S, Chaudhary S, Mascerhenas R, Kadam-Amare P, Narula G, Arora B, Banavali S, Gujral S. An integrated genomic profile that includes copy number alterations is highly predictive of minimal residual disease status in childhood precursor B-lineage acute lymphoblastic leukemia. Indian J Pathol Microbiol [serial online] 2017 [cited 2022 Dec 8];60:209-13. Available from:

   Introduction Top

In the last 50 years, we have seen a revolution in the responses of childhood acute lymphoblastic leukemia (ALL) to chemotherapy with improving event-free survival from <10% in the 1950s to more than 90% in the last decade.[1] These changes have been brought about by an in-depth understanding of chemotherapy and leukemia biology as well as treatment of ALL patients based on risk stratification.[2] Current prognostic algorithms rely on prognostic variables such as age, white blood cell count, immunophenotype, the presence of minimal residual disease (MRD) as well as the presence of genetic abnormalities in the leukemic blasts. The current 2008 WHO classification for ALL includes abnormalities at a chromosomal level such as the presence of fusion transcripts resulting from chromosomal translocations (BCR-ABL1, ETV6-RUNX1, IL3-IGH, TCF3-PBX1) as well as abnormalities of chromosome number (hyperdiploidy as well as hypoploidy).[3] These genetic abnormalities, however, do not entirely account for the outcome as a fraction of patients with favorable genetic features relapse [4],[5] Similarly, a quarter of pediatric ALL patients who are standard risk eventually relapse.[6] This indicates that the above-mentioned clinical and genetic parameters are not ideal for risk stratification and therefore appropriate therapy.[4]

In the last few years, we have been provided with meaningful insights into the biology of many cancers including ALL. Many of these developments would not have occurred without technological advancements as single nucleotide polymorphism arrays and next-generation sequencing. We now recognize that there are recurrently occurring DNA sequence mutations as well as copy number alterations (CNAs) affecting key B-cell developmental pathways in B-lineage ALL (B-ALL).[7],[8] Mullighan et al. studied a large cohort of B-ALL patients and found recurrently occurring CNA in IKZF1, PAX5, and other genes. Importantly, they stated that ALL harboring IKZF1 mutations were associated with persistent MRD values and poor prognosis.[9]BCR-ABL1-like ALL was first described by Den Boer et al.[10] as well as by Mullighan et al.[9] These are a subset of ALL with a unique gene expression profile that resembles BCR-ABL1 rearranged ALL. Importantly, they are associated with a high-risk clinical features and are more frequently MRD positive.[9],[10],[11]BCR-ABL1-like ALL accounts for 10%–15% of all pediatric ALLs [11] is, therefore, an important subset of ALL that needs identification so that appropriate risk-adapted therapeutic strategies can be employed.

Unfortunately, there remains a lack of consensus among experts about the exact diagnostic criteria for the definition of BCR-ABL1-like ALL using gene expression profiling.[11],[12] Furthermore, the technology is expensive and is not particularly well suited for a clinical diagnostic workflow. Multiplex ligation-dependent probe amplification (MLPA) is a cost-effective technology that has been used for detection of CNAs in ALL.[13],[14] Recently, Moorman et al. described a genomic algorithm that integrated cytogenetics and an eight gene CNA profile.[12] We adapted this approach and validated it on our cohort of patients. In this manuscript, we demonstrate that this approach correlates well with standard risk factors and is highly predictive of end-induction flow cytometric MRD clearance in 245 patients of pediatric B-ALL. To the best of our knowledge, this is the first data from India that describe the incidence of CNA in B-ALL as well as correlates it with MRD.

   Methods Top


All consecutive patients of pediatric (<18 years) B-ALL who were treated at the Tata Memorial Centre, Mumbai, were accrued over a 12-month period from August 2014 to July 2015. Diagnostic samples (bone marrow/peripheral blood in case of high counts) of patients were referred to the Hematopathology Laboratory, Molecular Division for molecular testing. Patient records were assessed on the electronic medical records for relevant laboratory information. Patients were diagnosed according to the WHO 2008 criteria.[3]

Cytogenetics and ploidy

Fluorescence in situ hybridization (FISH) for recurrent cytogenetic abnormalities was done using standard techniques. Ploidy was determined using conventional karyotyping as well as using flow cytometric techniques established by our laboratory.[15] The latter was used for scoring here when conventional karyotyping did not yield adequate metaphases. Patients were classified into good-, intermediate-, and high-risk cytogenetic groups as per Moorman et al.[12]

Treatment protocol

The patients were treated on a uniform risk-stratified protocol, which allowed for treatment intensification from the baseline National Cancer Institute (NCI) risk group and cytogenetics based on central nervous system (CNS) status and day-8 prednisolone response. Positive CNS status and poor prednisolone response were treated as high-risk irrespective of NCI and cytogenetic risk categories.

Detection of minimal residual disease

MRD was determined using modification of our previously published technique [16],[17] into a single tube nine color assay. MRD was detected using nine-color flow cytometry that utilized CD19, CD20, CD10, CD45, CD38, CD66c, CD123, CD34, and CD58 (Beckman Coulter, USA) on an end of induction bone marrow sample. Samples were acquired on one of two 10 color Beckman Coulter Navios Instruments. In every case, attempt was made to acquire 1,000,000 events. Syto 16 dye was used to correct the MRD value. Flow cytometry data (.fcs files) were analyzed with Kaluza (v1.3)(Beckman Coulter, Indianapolis, USA). MRD values <0.01% were called as negative.

Detection of copy number alterations

Genomic DNA was extracted from the diagnostic material using QIAAmp DNA Blood Mini Kit (Qiagen, Germany). The SALSA MLPA P335 (MRC-Holland, the Netherlands) was used to detect CNA in B-ALL following the manufacturers recommendations. Data were analyzed using the Coffalyzer software(MRC-Holland, Amsterdam, The Netherlands). Patients were divided into good- and poor-risk genetic abnormalities to stratify them according to the integrated genetic profile as described by Moorman et al.[12] Cytogenetic abnormalities took precedence over CNA abnormalities as has been described.[12]

Statistical analysis

Chi-square test was used to correlate numerous factors with the integrated genomic profile as well as CNA using Graph Pad Prism 6 (GraphPad Software, Inc. San Diego, USA).

   Results Top

Conventional genetic classification

A total of 245 patients were accrued during this 12-month period. Of these, a majority of patients harbored good-risk chromosomal abnormalities (n = 120, 48.9%) and a minority were classified into the high-risk group (n = 18, 7.3%). The rest (n = 107, 43.7%) were classified as intermediate cytogenetic risk.

Detection of minimal residual disease

Immunophenotypic MRD was detectable in 81 patients (33.1%), whereas the rest (n = 164, 66.9%) were negative for MRD at the end of induction. MRD values ranged from 0.01% to 97% (median = 0.1%).

Detection of copy number alterations

The most common deletions were seen in CDKN2A/B (25.7%) followed by PAX5(20%), ETV6(16.7%), IKZF1(15.5%), Rb1(5.3%), BTG (3.3%), EBF1(2.0%), and PAR1(0.8%). Based on these abnormalities, patients could be classified into good-risk CNA (106, 43.3%), intermediate-risk CNA (86, 35.1%), and poor-risk CNA (53, 21.6%). On integrating this information, according to the integrated genomic profile, 170 patients (69.4%) were classified as good integrated genomic risk (GEN-GR) whereas 75 (30.6%) were poor integrated genomic risk (GEN-PR).

Correlation of integrated genomic profile with standard variables

[Table 1] shows the correlation of ALL patients who have been risk stratified into good and poor genetic risk profiles. The data seem to indicate that classification based on this scheme correlated with MRD risk and NCI risk. Importantly, in the intermediate cytogenetics group risk stratification according to CNAs was correlated with MRD clearance. Integration of cytogenetic risk groups and CNA profile is depicted in [Figure 1]. [Table 2] shows that among all CNAs, deletions in IKZF1 and EBF genes are of prognostic relevance and are associated with increased MRD positivity. Similarly, a trend was observed that patients harboring CNA in CDKN2A/B may have lower MRD positivity.
Table 1: Correlation of integrated genomic risk group with NCI risk group, total leucocyte count, end of induction MRD status and prednisolone response

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Figure 1: Cytogenetic and copy number alterations risk stratification, copy number status of eight genes in acute lymphoblastic leukemia cases

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Table 2: Correlation of minimal residual disease values with deletions of individual genes/loci

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   Discussion Top

A comprehensive list of modalities that is required for more accurate risk stratification of B-ALL includes morphology, immunophenotyping, karyotyping, and FISH. It was hoped that gene expression profiling would be used to better this classification.[18] Indeed, the recognition of BCR-ABL-like ALL has been predominantly due to the contributions made by these technologies.[7],[10],[19] Unfortunately, even after many years, we do not have a clear consensus on how to incorporate these findings into an diagnostic algorithm that uses gene expression profiling.[12] MLPA has been demonstrated to have utility in B-ALL to screen for CNA.[13],[14] Schwab et al. studied a large cohort of B-ALL patients for the presence of CNA and found that deletions of CDKN2A/B were the most common (27%).[14] We are in agreement with this data and found a similar percentage of CDKN2A/B deletions (26.9%). In fact, as compared to their data, we detected similar frequencies of deletions in PAX5 (19% vs. 20% in our series) but slightly higher frequencies of deletions in IKZF1(13% vs. 15.5%) and EBF1(2% vs. 5.7%). However, deletions of ETV6(22% vs. 16.7%), Rb1(7% vs. 5.3%), BTG1(6% vs. 4.1%), and PAR1(4% vs. 0.8% in our series) occurred at a slightly lower frequency. Overall, these data seem to indicate that there may not be differences at a geographical level between cohorts. Although Schwab et al.[14] described a large cohort of ALL and elegantly dissected their results in each cytogenetic cohort, they did not discuss the relevance of their findings in terms of clinical outcome or correlation with MRD. This creates a dilemma for clinical application because a high frequency of CNAs can be seen in ALL in various combinations creating confusion about the exact algorithm to be adopted for prognostication. In a cohort of 162 Indian patients, Gupta et al. reported CNA in 70% cases, with CDKN2A, PAX5, and IKZF1 as the most commonly affected genes. They also reported better outcome and better potential risk stratification based on CNA profile.[20]

Moorman's elegant “overarching” solution to integrate the CNA with cytogenetics into a unified algorithm addresses an unmet need. This approach promises a solution that can be incorporated into clinical practice readily without esoteric instrumentation or data analytics. In their data, comparison of the integrated genomic profile groups is done with MRD measured by polymerase chain reaction-based methods.[12] Here, for the first time, we have validated these results with immunophenotypic MRD and suggest that this method indeed can be used in the clinic. Furthermore, we feel that in the intermediate cytogenetics group [Table 1], risk stratification based on CNA will offer additional information for prognostication [Figure 1]. In our series, we could demonstrate a strong correlation with MRD in this subset, similar to observations made by Moorman et al.[12]

It must be realized that CNA are secondary events in leukemogenesis. In a landmark paper, Mullighan et al. identified a high frequency of kinase-activating alterations arising from fusions involving JAK2 and ABL genes.[21] These are putative drivers of oncogenesis in BCR-ABL1-like ALL and are highly amenable to targeted therapy with TK and JAK inhibitors.[11],[22] It has been said that with MRD-directed therapy, it may not be necessary to treat these patients differently or even test for the presence of BCR-ABL1-like ALL.[23] However, such incorporation of targeted treatment may also contribute to reduced toxicity and long-term complications of intense therapy.[22] Targeted RNA sequencing may be more beneficial in the identification of these fusion transcripts.[11],[24]

   Conclusion Top

In this manuscript, for the first time from India, we describe the baseline frequencies of CNAs for B-ALL and show that the integrated genomic profile correlates with standard risk criteria including the presence of immunophenotypic MRD. This score will be of maximum utility in the intermediate-risk and perhaps high-risk ALL subgroups. This makes a case of incorporation of this algorithm into clinical practice.

Financial support and sponsorship

Dr Nikhil Patkar is supported by the Wellcome trust-DBT/India Alliance through an intermediate Fellowship for Clinicians and Public Health Researchers.

Conflicts of interest

There are no conflicts of interest.

   References Top

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Correspondence Address:
Nikhil Patkar
Molecular Division, Hematopathology Laboratory, KS-231, Khanolkar Shodika, ACTREC, Tata Memorial Centre, Mumbai - 410 210, Maharashtra
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/IJPM.IJPM_466_16

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  [Figure 1]

  [Table 1], [Table 2]

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