ELN recommended reference gene copy numbers for CML MRD monitoring

Abbreviations: CML, chronic myeloid leukemia; ELN, European LeukemiaNet; IS, international scale; MRD, measurable residual disease; TKI, tyrosine kinase inhibitor.

ELN 2020 recommendations for treatment response evaluation in CML patients by monitoring BCR::ABL1%-on the international scale[18] [23] [24]

Abbreviations: ACAs, additional chromosomal aberrations; ELN, European LeukemiaNet; ELTS, EUTOS score for long-term survival; MMR, major molecular remission; NA, not applicable.

a Loss of MMR (BCR-ABL1 > 0.1%) indicates failure after TFR.

Digital PCR-Based MRD Monitoring

The digital PCR (dPCR) is one of the latest generation PCR technologies that is based on the principle of partitioning the PCR reaction and provide an absolute quantity of copies in numerical digits. It is becoming a popular and widely accepted method for the MRD molecular monitoring in hemato-oncology,[25] [26] [27] [28] which can be attributed to its increased accuracy and sensitivity. In comparison to RQ-PCR, the dPCR has the added advantage of providing absolute quantification without the requirement of reference standards.[29] [30] The dPCR can more efficiently monitor CML patients in TFR trial.[31] [32] [33] [34] [35]

The droplet digital PCR (ddPCR) is a type of dPCR based on the separation of a standard PCR reaction into many thousand nanoliter single droplets (specifically 20,000 droplets). Most of the droplet contain either one (or more) or no target copy. In each single droplet, the PCR amplification is performed and the original target copy number is calculated from the proportion of positive and negative droplets generated using the Poisson distribution statistics. Moreover, ddPCR is more resistant to nonspecific amplifications.[5] [36] The greater sensitivity of ddPCR has been established by a reasonable number of studies that have shown prompt detection of loss of deep molecular remission utilizing this technique in comparison to real-time PCR.[37] [38] [39] [40] The ddPCR platform has shown great promise in MRD monitoring, especially for lesser common BCR::ABL transcripts (e.g., e1a2, e19a2).

Microfluidic-Based MRD Monitoring

In recent times, various other monitoring techniques based on RNA have become available. One of the most used among these techniques is by Cepheid Xpert BCR::ABL1 ultra. It is a quantitative tests for BCR::ABL1 p210 transcripts. This is a sensitive, fully automated cartridge-based technique, which is based on the GeneXpert technology.[41] The technology uses the principle of microfluidics in a cartridge and performs RNA extraction, reverse transcription, and BCR::ABL1 quantification in a single reaction.[41] [42] To achieve uniformity of result, this kit also provides a conversion factor. Various previous studies have reported inability of this system to detect the BCR::ABL1 transcript level below 0.01%. However, recent advancement in kits has resolved such issue.[43]

MRD Monitoring in CML with Nonclassical BCR::ABL1 Transcripts

Rarely, in 1 to 2%-of CML cases, the nonclassical transcript (e19a2, e13a3/e14a3, and e1a2) are encountered. Conventional karyotyping, FISH, and reverse transcription PCR can be useful for such rare transcript detection, as documented in literateure.[44] [45] The probability of achieving CCyR and major molecular remission in such cases is less; in addition, lower rates of event-free survival and progression-free survival have also been reported.[45] The quantification of such transcripts poses a unique problem due to the lack of standardization among commercially available kits for MRD monitoring. Conventional cytogenetics, quantitative RQ-PCR, and ddPCR using patient-specific primers can provide an effective solution but it can be expensive.[46]

Flowcytometric-Based MRD Monitoring

The flowcytometric immunobead assay utilization for BCR::ABL1 fusion proteins detection has been used by few centers.[47] A concordance between reverse transcription PCR of fusion gene transcripts by approach of utilizing the anti-BCR catching antibody adhered to immunobead and fluorescently tagged anti-ABL1antibody is published in the literature. The limit of detection (LOD) of 1%-was derived using the sequentially diluting three different BCR::ABL1-harboring cell lines. The sensitivity is better than karyotyping (∼5%), equals to FISH (∼1–2%), and lower than the PCR-based platforms.[47] [48] [49] The proximity ligation assay is a flowcytometric-based approach that can quantify white blood cells having the BCR::ABL1 fusion at the proteomic level. The other approach is by the enzyme dipeptidyl-peptidase-IV (DPPIV/CD26), which are being detected in a specific type of CD34 +/CD38− leukemic cells along with CD26 positivity, which is not seen in normal hematopoietic stem cells (HSCs) and only present in CML leukemic stem cells (LSCs). This attribute is used in flowcytometry to capture CML-positive stem cells for MRD monitoring.[50] [51] [52] [53]

Novel Techniques for MRD Monitoring

Single-cell sequencing (SCS) technologies using the single-cell gene amplification for identifying the heterogeneity among the LSCs is a novel approach for MRD monitoring.[54] This technique uses a multiomics work approach. The transcriptome of CML LSCs, which are resistant to TKI, is biologically different from the normal HSCs, as demonstrated in Smart-seq2 study using SCS.[55]

The study of microRNA (miRNA) in leukemogenesis is another area of interest in the present times. The translation of miRNAs from research to diagnostic setup in leukemogenesis is picking up the pace. The genomic profile of miRNAs responsible for oncogenesis ranges from expression analysis to mutation, deletion, and epigenetic changes.[56] The HSC differentiation and deregulated expression of several miRNAs such as miR-486–5p play a vital role in hematological malignancies, and these also get overexpressed in CML CD34+ progenitor cells.[57] [58] [59]

The monitoring of CML using DNA as an input template is sparsely utilized, and very few studies are published on this approach. Among the published literature, there are discordances documented among the detection of positive BCR::ABL1 copies by RNA and DNA. The use of RNA as an input material is widely accepted method; however, DNA-based methods provide more sensitive and specific results.[60] The LOD for DNA based method (10−6) is superior than RNA-based techniques. The disadvantage of DNA-based approach is requirement of pretesting characterization of breakpoints, which is a very laborious and time-consuming work. The newer techniques to overcome this problem have been under trial such as long-range PCR, multiplex PCR, Sanger sequencing, and next-generation sequencing (NGS).[61] [62] [63] [64] [65] Due to its greater sensitivity compared with RNA-based technique, it can be adopted for TFR trial in future.[66]

BCR::ABL1 TKI Resistance in CML

The first-generation TKI imatinib is offered upfront to majority of the newly diagnosed CML cases due to its widespread availability, efficacy, compliance, and cost. There is a significant number of CML cases that develop resistance to imatinib (10–15%-of cases) and are bound to shift to higher generations of TKI. It becomes essential to identify imatinib resistance at the earliest, to benefit these patients with dose escalation, higher generation TKI, or in, certain cases, HSC transplant.[18] [67]

Types of TKI Resistance

TKI resistance mechanism can be primarily segregated into two groups: innate (primary) resistance and acquired (secondary) resistance. Innate resistance may be suspected in the following scenario during CML treatment:

• Absence of complete hematological response or FISH Ph positivity of more than 95%-at 3 months.

• BCR::ABL1 copies greater than 10%-or FISH Ph positivity of more than 35%-at 6 months.

• BCR::ABL1 copies greater than 1%, FISH Ph positivity of more than or equal to 1%, or CCyR at 18 months.

Acquired resistance can be suspected whenever there is a forfeiture of a previously achieved hematological, cytogenetic, or molecular response during the course of TKI treatment.[68]

TKI Resistance due to BCR::ABL1 Tyrosine Kinase Domain Mutation

Among many myriad mechanisms, tyrosine kinase domain (TKD) mutations accounts for the majority among the causes of TKI resistance. These may be detected in up to approximately 60%-of patients with suboptimal TKI response. During the disease progression (accelerated/blast phase), these mutations are documented with higher frequencies. Recognition of these TKD mutation is very critical during the therapeutic phase of CML, as the change of TKI is predominantly dependent on the type of mutations.[69]

The ABL1 TKD has components such as P-loop, catalytic domain, and A-loop.[70] The usual binding of the TKI takes place between the mentioned TKDs. The TKD mutations may lead to ineffective binding of the drug moiety and result in TKI resistance in majority. In the literature, there are hundreds of variants documented with varying response to TKI therapy based on the location of mutation. P-loop mutations are the most common, accounting for nearly 50%-of the TKD mutations, and confer a poorer prognosis.[71] Compound TKD mutations are also documented, which are defined by the occurrence of more than one variant on the same DNA strand, and these are often associated with particularly high resistance to multiple generations of TKIs.[72] [73] The most frequent TKD mutations along with their location and resistance profile have been depicted in [Table 3].[74] [75]

Frequency of BCR::ABL1 TKD mutations and their response profile to the approved inhibitors[74] [75]

Abbreviation: TKD, tyrosine kinase domain.

The timing of BCR::ABL1 TKD mutations testing is critical, as early detection can be decisive. Various recommendations exist from ELN, European Society for Medical Oncology (ESMO), and National Comprehensive Cancer Network (NCCN) regarding the appropriate time point to perform the TKD mutation analysis and are summarized in [Table 4].[76] [77]

Recommended time points for TKD mutation analysis[76] [77]

Abbreviations: CML, chronic myeloid leukemia; IS, international scale; MMR, major molecular remission; TKD, tyrosine kinase domain; TKI, tyrosine kinase inhibitor.

Acquired TKI Resistance Mechanisms

TKI resistances not associated with TKD mutations can also have significant contribution toward suboptimal therapeutic response and are grouped as secondary factors. Many such secondary factors can range from (but is not limited to) variables such as treatment compliance, drug bioavailability, altered pharmacodynamics and pharmacokinetics, genomic instability, and BCR::ABL1 gene amplification/overexpression. Very rarely, causes such as alternate mechanism of signaling and mutations beyond the kinase domain of ABL1 gene can also lead to acquired resistance.[78]

Methodologies to Assess TKI Resistance

Currently, many molecular platforms with a variety of assays to detect TKD mutational profile having a significant clinical impact are readily available. These assays have their own merits and drawbacks as summarized in [Table 5]. The most commonly used techniques for BCR::ABL1 TKD mutation assays are discussed in brief in the following.

Advantages and drawbacks of various TKI mutation detection platforms[77] [79] [80]

Abbreviations: PCR, polymerase chain reaction; TKD, tyrosine kinase domain; TKI, tyrosine kinase inhibitor.

General Guidelines for BCR:ABL1 TKD Mutation Analysis

The sample processing and RNA extraction should be done per the recommended standard protocols of laboratory. There should be a written policy to avoid cross contamination, especially for the nested PCR-based methods. Appropriate negative controls and NTC (no template controls) must be employed during each run. Generally, most TKD mutation detection strategies use methods that selectively amplify the ABL1 component of the BCR::ABL1 fusion product and should not amplify the nonmutated (wild-type ABL1 gene). Multiple transcripts of BCR::ABL1 fusion have been documented; therefore, it is of utmost importance to know the transcript of the patient before proceeding with these assays. The quality of the RNA should pass the recommended quality parameters. One of the important quality control parameters is that the sample should have BCR::ABL1 and ABL1 copy numbers >50 and >5,000, respectively; any suboptimal copies should not be tested and re-extraction is advised.[80]

Sanger Sequencing–Based TKD Mutation Analysis

Sanger sequencing is considered the gold standard assay for the detection and screening for TKD mutation screening. Due to recommendation by international guidelines and consensus panels, it is being employed in majority of laboratories. However, it has a drawback of relatively poor sensitivity (10–20%) and can lead to missing out some mutation (false negative). There are also limitations of Sanger sequencing since it cannot detect all existing mutations, such as compound, polyclonal mutations, and mutations present below the detection limit of the assay (variant allele frequency ≤ 20%). A common strategy employed is to selectively amplify the TK domain of ABL1 (exons 4–10) and use bidirectional Sanger sequencing with overlapping primers. This strategy is more effective, as every base gets sequenced at least two times.[81]

PCR (dPCR)-Based TKD Mutation Analysis

dPCR can also be potentially applied in TKD mutation analysis. The dPCR assay detects targeted TKD mutations by using specific primers and probes. It is more useful when limited mutation analysis is desired; it is technically less demanding and has a shorter turnaround time. A single-tube dPCR assay for the detection and quantification of common TKD mutations has been recently developed.[82] The “drop-phase” dPCR is one of the modified versions of dPCR, which utilizes droplet-based dPCR to identify compound mutations. This platform uses mutation-specific dual-color probes using which compound mutations can be detected as an increase in double-positive droplets.[83]

NGS-Based TKD Mutation Analysis

NGS is a high throughput molecular diagnostic modality that is gaining wide-reaching popularity in the detection of TKD mutations in CML patients and is particularly advantageous in detecting TKD variants at very low allelic frequencies.[84]

NGS of amplicons encompassing the TKD is capable of detecting single nucleotide variant, insertion and deletion variants in the BCR::ABL1 transcript. This approach can achieve sensitivities up to 1%-or even deeper. “Deep” or “ultra-deep sequencing” (UDS-NGS) is an application of NGS that is optimized for TKD mutation analysis, where a genetic region of interest is sequenced many times (hundreds to thousands), thus enabling it to achieve very high sensitivities.[85] Multiple studies have documented UDS-NGS can detect TKD mutations (including T315I variant) earlier as compared with Sanger sequencing and other highly sensitive assays. UDS-NGS can identify all TKD mutations including novel variants and can detect patients who harbored more than one resistance mutation.[86] CML patients harboring compound variants can be distinguished from those with polyclonal variants by the variation in read distribution using assays with longer amplicons design. To rule out false-positive results due to sequencing artifacts and chimeric reads, modified sequencing strategies (error-corrected sequencing, single molecule consensus sequencing) may be employed.[87] [88] In the year 2020, ELN advocated the use of NGS for those CML cases that did not adequately respond to standard TKIs.[18] More robust outcome-based evidence would further strengthen the importance of TKD mutation analysis by NGS and help in its wider utilization in the clinical setting.

D-HPLC-Based TKD Mutation Analysis

Denaturing high-performance liquid chromatography (D-HPLC) is another screening technique with a high-output capacity. This employs a heteroduplex formation by PCR products amplified from wild-type and mutant alleles. Subsequently, these heteroduplexes are then used to distinguish from homoduplexes under optimal denaturation conditions. D-HPLC is more sensitive compared with direct sequencing; however, it is not as widely available and prone to false-negative (homozygous) results at higher mutant cDNA concentrations. Positive results by the technique are required to be confirmed by sequencing; therefore, the main utility of D-HPLC seems to be as a screening method.[89]

TKD Mutation Analysis by Allele-Specific Oligonucleotide Reverse Transcription Quantitative PCR (ASO RQ-PCR)

ASO RQ-PCR is based on the principle of AS-PCR with subsequent quantification of the product in real time. This can be used only for detection of single mutation, and it reasonably failed to quantify compound mutations. The ASO RQ-PCR is reported to have high specificity and sensitivity, but the drawback of low throughput and tedious work process makes it less preferable for use in routine diagnostics.[90]

Conclusion and Future Insights

CML is one of the most studied and well-characterized hematological neoplasm. Different generations of TKI have made it possible to achieve near-normal life expectancy among patients. Despite such therapeutic advancements, there are challenges posed by residual disease and TKI resistance. Hence, a constant evolution is happening among various testing modalities from past, to the present and into the future, which always aimed to mitigate these problems. Novel techniques are endeavoring to reach even better accuracy and sensitivities, thus allowing these patients to achieve TFR.

Studies with promising future perspective about the disease monitoring in CML patients have documented the utility of whole-exome/genome sequencing, copy-number detection, SCS, and/or RNA sequencing in detecting novel gene variants, gene rearrangements, isoforms, and transcriptome in newly diagnosed CML patients. Integrating genomic and transcriptomic analysis in future will help further refine patient-specific risk-adapted therapeutic approaches.

Conflict of Interest

None declared.

• References

• 1 Bhutani M, Vora A, Kumar L, Kochupillai V. Lympho-hemopoietic malignancies in India. Med Oncol 2002; 19 (03) 141-150
  CrossrefPubMedGoogle Scholar
• 2 Bansal S, Prabhash K, Parikh P. Chronic myeloid leukemia data from India. Indian J Med Paediatr Oncol 2013; 34 (03) 154-158
  Article in Thieme ConnectPubMedGoogle Scholar
• 3 Shah NP. Advanced CML: therapeutic options for patients in accelerated and blast phases. J Natl Compr Canc Netw 2008; 6 (Suppl 2): S31-S36
  PubMedGoogle Scholar
• 4 Chereda B, Melo JV. Natural course and biology of CML. Ann Hematol 2015; 94 (Suppl 2): S107-S121
  CrossrefPubMedGoogle Scholar
• 5 Soverini S, Mancini M, Bavaro L, Cavo M, Martinelli G. Chronic myeloid leukemia: the paradigm of targeting oncogenic tyrosine kinase signaling and counteracting resistance for successful cancer therapy. Mol Cancer 2018; 17 (01) 49
  CrossrefPubMedGoogle Scholar
• 6 Kadam PR, Nanjangud GJ, Advani SH. et al. Chromosomal characteristics of chronic and blastic phase of chronic myeloid leukemia. A study of 100 patients in India. Cancer Genet Cytogenet 1991; 51 (02) 167-181
  CrossrefPubMedGoogle Scholar
• 7 Nanjangud G, Kadam PR, Saikia T. et al. Karyotypic findings as an independent prognostic marker in chronic myeloid leukaemia blast crisis. Leuk Res 1994; 18 (05) 385-392
  CrossrefPubMedGoogle Scholar
• 8 Krishna Chandran R, Geetha N, Sakthivel KM, Suresh Kumar R, Jagathnath Krishna KMN, Sreedharan H. Impact of Additional Chromosomal Aberrations on the Disease Progression of Chronic Myelogenous Leukemia. Front Oncol 2019; 9: 88
  CrossrefPubMedGoogle Scholar
• 9 Amare PS, Baisane C, Saikia T, Nair R, Gawade H, Advani S. Fluorescence in situ hybridization, highly efficient technique of molecular diagnosis, also a sensitive tool for prediction of future course of disease in patients with myeloid leukemias. Cancer Genet Cytogenet 2001; 131: 125-134
  PubMedGoogle Scholar
• 10 Luatti S, Castagnetti F, Marzocchi G. et al. Additional chromosomal abnormalities in Philadelphia-positive clone: adverse prognostic influence on frontline imatinib therapy: a GIMEMA Working Party on CML analysis [published correction appears in Blood. 2013 Jun 27;121(26):5259. Cambrin, Rege [corrected to Rege-Cambrin, Giovanna]]. Blood 2012; 120 (04) 761-767
  PubMedGoogle Scholar
• 11 Perrotti D, Jamieson C, Goldman J, Skorski T. Chronic myeloid leukemia: mechanisms of blastic transformation. J Clin Invest 2010; 120 (07) 2254-2264
  CrossrefPubMedGoogle Scholar
• 12 Hughes TP, Hochhaus A, Branford S. et al; IRIS investigators. Long-term prognostic significance of early molecular response to imatinib in newly diagnosed chronic myeloid leukemia: an analysis from the International Randomized Study of Interferon and STI571 (IRIS). Blood 2010; 116 (19) 3758-3765
  CrossrefPubMedGoogle Scholar
• 13 Landstrom AP, Ketterling RP, Knudson RA, Tefferi A. Utility of peripheral blood dual color, double fusion fluorescent in situ hybridization for BCR/ABL fusion to assess cytogenetic remission status in chronic myeloid leukemia. Leuk Lymphoma 2006; 47 (10) 2055-2061
  CrossrefPubMedGoogle Scholar
• 14 Kantarjian H, Schiffer C, Jones D, Cortes J. Monitoring the response and course of chronic myeloid leukemia in the modern era of BCR-ABL tyrosine kinase inhibitors: practical advice on the use and interpretation of monitoring methods. Blood 2008; 111 (04) 1774-1780
  CrossrefPubMedGoogle Scholar
• 15 Jabbour E, Kantarjian H. Chronic myeloid leukemia: 2014 update on diagnosis, monitoring, and management. Am J Hematol 2014; 89 (05) 547-556
  CrossrefPubMedGoogle Scholar
• 16 Hughes TP, Branford S. Monitoring disease response to tyrosine kinase inhibitor therapy in CML. Hematology (Am Soc Hematol Educ Program) 2009; 477-487
  CrossrefPubMedGoogle Scholar
• 17 Assouline S, Lipton JH. Monitoring response and resistance to treatment in chronic myeloid leukemia. Curr Oncol 2011; 18 (02) e71-e83
  CrossrefPubMedGoogle Scholar
• 18 Hochhaus A, Baccarani M, Silver RT. et al. European LeukemiaNet 2020 recommendations for treating chronic myeloid leukemia. Leukemia 2020; 34 (04) 966-984
  CrossrefPubMedGoogle Scholar
• 19 Gabert J, Beillard E, van der Velden VH. et al. Standardization and quality control studies of ‘real-time’ quantitative reverse transcriptase polymerase chain reaction of fusion gene transcripts for residual disease detection in leukemia - a Europe Against Cancer program. Leukemia 2003; 17 (12) 2318-2357
  CrossrefPubMedGoogle Scholar
• 20 Foroni L, Wilson G, Gerrard G. et al. Guidelines for the measurement of BCR-ABL1 transcripts in chronic myeloid leukaemia. Br J Haematol 2011; 153 (02) 179-190
  CrossrefPubMedGoogle Scholar
• 21 Costa JM, Ernault P, Olivi M, Gaillon T, Arar K. Chimeric LNA/DNA probes as a detection system for real-time PCR. Clin Biochem 2004; 37 (10) 930-932
  CrossrefPubMedGoogle Scholar
• 22 Baccarani M, Deininger MW, Rosti G. et al. European LeukemiaNet recommendations for the management of chronic myeloid leukemia: 2013. Blood 2013; 122 (06) 872-884
  CrossrefPubMedGoogle Scholar
• 23 Link-Lenczowska D, Pallisgaard N, Cordua S. et al. A comparison of qPCR and ddPCR used for quantification of the JAK2 V617F allele burden in Ph negative MPNs. Ann Hematol 2018; 97 (12) 2299-2308
  CrossrefPubMedGoogle Scholar
• 24 Hehlmann R. The new ELN recommendations for treating CML. J Clin Med 2020; 9 (11) 3671
  CrossrefPubMedGoogle Scholar
• 25 Bochicchio MT, Petiti J, Berchialla P. et al. Droplet digital PCR for BCR-ABL1 monitoring in diagnostic routine: ready to start?. Cancers (Basel) 2021; 13 (21) 5470
  CrossrefPubMedGoogle Scholar
• 26 Del Giudice I, Raponi S, Della Starza I. et al. Minimal residual disease in chronic lymphocytic leukemia: a new goal?. Front Oncol 2019; 9: 689
  CrossrefPubMedGoogle Scholar
• 27 Coccaro N, Anelli L, Zagaria A. et al. Droplet digital PCR is a robust tool for monitoring minimal residual disease in adult Philadelphia-positive acute lymphoblastic leukemia. J Mol Diagn 2018; 20 (04) 474-482
  CrossrefPubMedGoogle Scholar
• 28 Hindson CM, Chevillet JR, Briggs HA. et al. Absolute quantification by droplet digital PCR versus analog real-time PCR. Nat Methods 2013; 10 (10) 1003-1005
  CrossrefPubMedGoogle Scholar
• 29 Baker M. Digital PCR hits its stride. Nat Methods 2012; 9 (06) 541-544
  Google Scholar
• 30 Jennings LJ, George D, Czech J, Yu M, Joseph L. Detection and quantification of BCR-ABL1 fusion transcripts by droplet digital PCR. J Mol Diagn 2014; 16 (02) 174-179
  CrossrefPubMedGoogle Scholar
• 31 Goh HG, Lin M, Fukushima T. et al. Sensitive quantitation of minimal residual disease in chronic myeloid leukemia using nanofluidic digital polymerase chain reaction assay. Leuk Lymphoma 2011; 52 (05) 896-904
  CrossrefPubMedGoogle Scholar
• 32 Franke GN, Maier J, Wildenberger K. et al. Comparison of real-time quantitative PCR and digital droplet PCR for BCR-ABL1 monitoring in patients with chronic myeloid leukemia. J Mol Diagn 2020; 22 (01) 81-89
  PubMedGoogle Scholar
• 33 Franke GN, Maier J, Wildenberger K. et al. Comparison of Real-Time Quantitative PCR and Digital Droplet PCR for BCR-ABL1 Monitoring in Patients with Chronic Myeloid Leukemia. J Mol Diagn 2020; 22 (01) 81-89
  CrossrefPubMedGoogle Scholar
• 34 Bernardi S, Malagola M, Zanaglio C. et al. Digital PCR improves the quantitation of DMR and the selection of CML candidates to TKIs discontinuation. Cancer Med 2019; 8 (05) 2041-2055
  CrossrefPubMedGoogle Scholar
• 35 Vogelstein B, Kinzler KW. Digital PCR. Proc Natl Acad Sci U S A 1999; 96 (16) 9236-9241
  CrossrefPubMedGoogle Scholar
• 36 Foskett P, Gerrard G, Foroni L. Real-time quantification assay to monitor BCR-ABL1 transcripts in chronic myeloid leukemia. Methods Mol Biol 2014; 1160: 115-124
  CrossrefPubMedGoogle Scholar
• 37 Wang WJ, Zheng CF, Liu Z. et al. Droplet digital PCR for BCR/ABL(P210) detection of chronic myeloid leukemia: a high sensitive method of the minimal residual disease and disease progression. Eur J Haematol 2018; 101 (03) 291-296
  CrossrefPubMedGoogle Scholar
• 38 Mori S, Vagge E, le Coutre P. et al. Age and dPCR can predict relapse in CML patients who discontinued imatinib: the ISAV study. Am J Hematol 2015; 90 (10) 910-914
  CrossrefPubMedGoogle Scholar
• 39 Atallah E, Schiffer CA, Weinfurt KP. et al. Design and rationale for the life after stopping tyrosine kinase inhibitors (LAST) study, a prospective, single-group longitudinal study in patients with chronic myeloid leukemia. BMC Cancer 2018; 18 (01) 359
  CrossrefPubMedGoogle Scholar
• 40 Bernardi S, Foroni C, Zanaglio C. et al. Feasibility of tumor‑derived exosome enrichment in the onco‑hematology leukemic model of chronic myeloid leukemia. Int J Mol Med 2019; 44 (06) 2133-2144
  PubMedGoogle Scholar
• 41 Enjeti A, Granter N, Ashraf A. et al. A longitudinal evaluation of performance of automated BCR-ABL1 quantitation using cartridge-based detection system. Pathology 2015; 47 (06) 570-574
  CrossrefPubMedGoogle Scholar
• 42 Gerrard G, Foong HE, Mudge K, Alikian M, Apperley JF, Foroni L. Cepheid xpert monitor platform for the confirmation of BCR-ABL1 IS conversion factors for the molecular monitoring of chronic myeloid leukaemia. Leuk Res 2016; 49: 47-50
  CrossrefPubMedGoogle Scholar
• 43 O'Dwyer ME, Swords R, Nagler A. et al. Nilotinib 300 mg BID as frontline treatment of CML: prospective analysis of the Xpert BCR-ABL monitor system and significance of 3-month molecular response. Leuk Res 2014; 38 (03) 310-315
  CrossrefPubMedGoogle Scholar
• 44 Qin YZ, Jiang Q, Jiang H. et al. Prevalence and outcomes of uncommon BCR-ABL1 fusion transcripts in patients with chronic myeloid leukaemia: data from a single centre. Br J Haematol 2018; 182 (05) 693-700
  CrossrefPubMedGoogle Scholar
• 45 Duan MH, Li H, Cai H. A rare e13a3 (b2a3) BCR-ABL1 fusion transcript with normal karyotype in chronic myeloid leukemia: the challenges in diagnosis and monitoring minimal residual disease (MRD). Leuk Res 2017; 59: 8-11
  CrossrefPubMedGoogle Scholar
• 46 Tong YQ, Zhao ZJ, Liu B. et al. New rapid method to detect BCR-ABL fusion genes with multiplex RT-qPCR in one-tube at a time. Leuk Res 2018; 69: 47-53
  CrossrefPubMedGoogle Scholar
• 47 Raponi S, De Propris MS, Wai H. et al. An accurate and rapid flow cytometric diagnosis of BCR-ABL positive acute lymphoblastic leukemia. Haematologica 2009; 94 (12) 1767-1770
  CrossrefPubMedGoogle Scholar
• 48 Yujie W, Yu Z, Sixuan Q. et al. Detection of BCR-ABL fusion proteins in patients with leukemia using a cytometric bead array. Leuk Lymphoma 2012; 53 (03) 451-455
  CrossrefPubMedGoogle Scholar
• 49 Löf L, Arngården L, Olsson-Strömberg U. et al. Flow cytometric measurement of blood cells with BCR-ABL1 fusion protein in chronic myeloid leukemia. Sci Rep 2017; 7 (01) 623
  CrossrefPubMedGoogle Scholar
• 50 Herrmann H, Sadovnik I, Cerny-Reiterer S. et al. Dipeptidylpeptidase IV (CD26) defines leukemic stem cells (LSC) in chronic myeloid leukemia. Blood 2014; 123 (25) 3951-3962
  CrossrefPubMedGoogle Scholar
• 51 Valent P, Sadovnik I, Ráčil Z. et al. DPPIV (CD26) as a novel stem cell marker in Ph+ chronic myeloid leukaemia. Eur J Clin Invest 2014; 44 (12) 1239-1245
  CrossrefPubMedGoogle Scholar
• 52 Bocchia M, Sicuranza A, Abruzzese E. et al. Residual peripheral blood CD26+ leukemic stem cells in chronic myeloid leukemia patients during TKI therapy and during treatment-free remission. Front Oncol 2018; 8: 194
  CrossrefPubMedGoogle Scholar
• 53 Herrmann H, Cerny-Reiterer S, Gleixner KV. et al. CD34(+)/CD38(-) stem cells in chronic myeloid leukemia express Siglec-3 (CD33) and are responsive to the CD33-targeting drug gemtuzumab/ozogamicin. Haematologica 2012; 97 (02) 219-226
  CrossrefPubMedGoogle Scholar
• 54 Giustacchini A, Thongjuea S, Barkas N. et al. Single-cell transcriptomics uncovers distinct molecular signatures of stem cells in chronic myeloid leukemia. Nat Med 2017; 23 (06) 692-702
  CrossrefPubMedGoogle Scholar
• 55 Warfvinge R, Geironson L, Sommarin MNE. et al. Single-cell molecular analysis defines therapy response and immunophenotype of stem cell subpopulations in CML. Blood 2017; 129 (17) 2384-2394
  CrossrefPubMedGoogle Scholar
• 56 Huang XP, Hou J, Shen XY. et al. MicroRNA-486-5p, which is downregulated in hepatocellular carcinoma, suppresses tumor growth by targeting PIK3R1. FEBS J 2015; 282 (03) 579-594
  CrossrefPubMedGoogle Scholar
• 57 Gordon JE, Wong JJ, Rasko JE. MicroRNAs in myeloid malignancies. Br J Haematol 2013; 162 (02) 162-176
  CrossrefPubMedGoogle Scholar
• 58 Jiang M, Li X, Quan X. et al. MiR-486 as an effective biomarker in cancer diagnosis and prognosis: a systematic review and meta-analysis. Oncotarget 2018; 9 (17) 13948-13958
  CrossrefPubMedGoogle Scholar
• 59 Ninawe A, Guru SA, Yadav P. et al. miR-486-5p: a prognostic biomarker for chronic myeloid leukemia. ACS Omega 2021; 6 (11) 7711-7718
  CrossrefPubMedGoogle Scholar
• 60 Waller CF, Dennebaum G, Feldmann C, Lange W. Long-template DNA polymerase chain reaction for the detection of the bcr/abl translocation in patients with chronic myelogenous leukemia. Clin Cancer Res 1999; 5 (12) 4146-4151
  PubMedGoogle Scholar
• 61 Siebert PD, Chenchik A, Kellogg DE, Lukyanov KA, Lukyanov SA. An improved PCR method for walking in uncloned genomic DNA. Nucleic Acids Res 1995; 23 (06) 1087-1088
  CrossrefPubMedGoogle Scholar
• 62 Mattarucchi E, Spinelli O, Rambaldi A. et al. Molecular monitoring of residual disease in chronic myeloid leukemia by genomic DNA compared with conventional mRNA analysis. J Mol Diagn 2009; 11 (05) 482-487
  CrossrefPubMedGoogle Scholar
• 63 Pagani IS, Spinelli O, Mattarucchi E. et al. Genomic quantitative real-time PCR proves residual disease positivity in more than 30%-samples with negative mRNA-based qRT-PCR in chronic myeloid leukemia. Oncoscience 2014; 1 (07) 510-521
  CrossrefPubMedGoogle Scholar
• 64 Bartley PA, Martin-Harris MH, Budgen BJ, Ross DM, Morley AA. Rapid isolation of translocation breakpoints in chronic myeloid and acute promyelocytic leukaemia. Br J Haematol 2010; 149 (02) 231-236
  CrossrefPubMedGoogle Scholar
• 65 Bartley PA, Latham S, Budgen B. et al. A DNA real-time quantitative PCR method suitable for routine monitoring of low levels of minimal residual disease in chronic myeloid leukemia. J Mol Diagn 2015; 17 (02) 185-192
  CrossrefPubMedGoogle Scholar
• 66 Bartley PA, Ross DM, Latham S. et al. Sensitive detection and quantification of minimal residual disease in chronic myeloid leukaemia using nested quantitative PCR for BCR-ABL DNA. Int J Lab Hematol 2010; 32 (6 Pt 1): e222-e228
  CrossrefPubMedGoogle Scholar
• 67 Cortes J, Lang F. Third-line therapy for chronic myeloid leukemia: current status and future directions. J Hematol Oncol 2021; 14 (01) 44
  CrossrefPubMedGoogle Scholar
• 68 Kaleem B, Shahab S, Ahmed N, Shamsi TS. Chronic myeloid leukemia--prognostic value of mutations. Asian Pac J Cancer Prev 2015; 16 (17) 7415-7423
  CrossrefPubMedGoogle Scholar
• 69 Baccarani M, Soverini S, De Benedittis C. Molecular monitoring and mutations in chronic myeloid leukemia: how to get the most out of your tyrosine kinase inhibitor. Am Soc Clin Oncol Educ Book 2014; 167-175
  CrossrefPubMedGoogle Scholar
• 70 Nardi V, Azam M, Daley GQ. Mechanisms and implications of imatinib resistance mutations in BCR-ABL. Curr Opin Hematol 2004; 11 (01) 35-43
  CrossrefPubMedGoogle Scholar
• 71 Bommannan KB, Naseem S, Binota J, Varma N, Malhotra P, Varma S. Tyrosine kinase domain mutations in chronic myelogenous leukemia patients: a single center experience. J Postgrad Med 2022; 68 (02) 93-97
  CrossrefPubMedGoogle Scholar
• 72 Khorashad JS, Kelley TW, Szankasi P. et al. BCR-ABL1 compound mutations in tyrosine kinase inhibitor-resistant CML: frequency and clonal relationships. Blood 2013; 121 (03) 489-498
  CrossrefPubMedGoogle Scholar
• 73 Zabriskie MS, Eide CA, Tantravahi SK. et al. BCR-ABL1 compound mutations combining key kinase domain positions confer clinical resistance to ponatinib in Ph chromosome-positive leukemia. Cancer Cell 2014; 26 (03) 428-442
  CrossrefPubMedGoogle Scholar
• 74 Alves R, Gonçalves AC, Rutella S. et al. Resistance to tyrosine kinase inhibitors in chronic myeloid leukemia-from molecular mechanisms to clinical relevance. Cancers (Basel) 2021; 13 (19) 4820
  CrossrefPubMedGoogle Scholar
• 75 O'Hare T, Shakespeare WC, Zhu X. et al. AP24534, a pan-BCR-ABL inhibitor for chronic myeloid leukemia, potently inhibits the T315I mutant and overcomes mutation-based resistance. Cancer Cell 2009; 16 (05) 401-412
  CrossrefPubMedGoogle Scholar
• 76 NCCN. . NCCN Clinical Practice Guidelines in Oncology. NCCN Chronic Myelogenous Leukemia Guidelines Version 4. NCCN; 2013
• 77 Soverini S, Branford S, Nicolini FE. et al. Implications of BCR-ABL1 kinase domain-mediated resistance in chronic myeloid leukemia. Leuk Res 2014; 38 (01) 10-20
  CrossrefPubMedGoogle Scholar
• 78 Patkar N, Ghodke K, Joshi S. et al. Characteristics of BCR-ABL kinase domain mutations in chronic myeloid leukemia from India: not just missense mutations but insertions and deletions are also associated with TKI resistance. Leuk Lymphoma 2016; 57 (11) 2653-2660
  CrossrefPubMedGoogle Scholar
• 79 Lavallade H, Kizilors A. The importance of mutational analysis in chronic myeloid leukaemia for treatment choice. EMJ Oncol 2016; 4 (01) 86-95
  Google Scholar
• 80 Alikian M, Gerrard G, Subramanian PG. et al. BCR-ABL1 kinase domain mutations: methodology and clinical evaluation. Am J Hematol 2012; 87 (03) 298-304
  CrossrefPubMedGoogle Scholar
• 81 Sorel N, Mayeur-Rousse C, Deverrière S. et al. Comprehensive characterization of a novel intronic pseudo-exon inserted within an e14/a2 BCR-ABL rearrangement in a patient with chronic myeloid leukemia. J Mol Diagn 2010; 12 (04) 520-524
  CrossrefPubMedGoogle Scholar
• 82 Soverini S, Bernardi S, Galimberti S. Molecular testing in CML between old and new methods: are we at a turning point?. J Clin Med 2020; 9 (12) 3865
  CrossrefPubMedGoogle Scholar
• 83 Vannuffel P, Bavaro L, Nollet F. et al. Droplet Digital PCR Phasing (DROP-PHASE): A Novel Method for Straightforward Detection of BCR-ABL1 Compound Mutations in Tyrosine Kinase Inhibitors Resistant Chronic Myeloid Leukemia (CML) and Acute Lymphoblastic Leukemia (ALL). Blood 2019; 134 (Supplement_1): 4660
  Google Scholar
• 84 Soverini S, Abruzzese E, Bocchia M. et al. Next-generation sequencing for BCR-ABL1 kinase domain mutation testing in patients with chronic myeloid leukemia: a position paper. J Hematol Oncol 2019; 12 (01) 131
  CrossrefPubMedGoogle Scholar
• 85 Yohe S, Thyagarajan B. Review of clinical next-generation sequencing. Arch Pathol Lab Med 2017; 141 (11) 1544-1557
  CrossrefPubMedGoogle Scholar
• 86 Machova Polakova K, Kulvait V, Benesova A. et al. Next-generation deep sequencing improves detection of BCR-ABL1 kinase domain mutations emerging under tyrosine kinase inhibitor treatment of chronic myeloid leukemia patients in chronic phase. J Cancer Res Clin Oncol 2015; 141 (05) 887-899
  CrossrefPubMedGoogle Scholar
• 87 Parker WT, Phillis SR, Yeung DT, Hughes TP, Scott HS, Branford S. Many BCR-ABL1 compound mutations reported in chronic myeloid leukemia patients may actually be artifacts due to PCR-mediated recombination. Blood 2014; 124 (01) 153-155
  CrossrefPubMedGoogle Scholar
• 88 Schmitt MW, Pritchard JR, Leighow SM. et al. Single-Molecule Sequencing Reveals Patterns of Preexisting Drug Resistance That Suggest Treatment Strategies in Philadelphia-Positive Leukemias. Clin Cancer Res 2018; 24 (21) 5321-5334
  CrossrefPubMedGoogle Scholar
• 89 Deininger MW, McGreevey L, Willis S, Bainbridge TM, Druker BJ, Heinrich MC. Detection of ABL kinase domain mutations with denaturing high-performance liquid chromatography. Leukemia 2004; 18 (04) 864-871
  CrossrefPubMedGoogle Scholar
• 90 Wongboonma W, Thongnoppakhun W, Auewarakul CU. BCR-ABL kinase domain mutations in tyrosine kinase inhibitors-naïve and -exposed Southeast Asian chronic myeloid leukemia patients. Exp Mol Pathol 2012; 92 (02) 259-265

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27 November 2023

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