J Cancer Res Clin Oncol DOI 10.1007/s00432-014-1845-6
ORIGINAL ARTICLE - CLINICAL ONCOLOGY
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 Katerina Machova Polakova · Vojtech Kulvait · Adela Benesova · Jana Linhartova · Hana Klamova · Monika Jaruskova · Caterina de Benedittis · Torsten Haferlach · Michele Baccarani · Giovanni Martinelli · Tomas Stopka · Thomas Ernst · Andreas Hochhaus · Alexander Kohlmann · Simona Soverini Received: 19 September 2014 / Accepted: 26 September 2014 © Springer-Verlag Berlin Heidelberg 2014
Abstract Purpose Here, we studied whether amplicon next-generation deep sequencing (NGS) could improve the detection of emerging BCR-ABL1 kinase domain mutations in chronic phase chronic myeloid leukemia (CML) patients under tyrosine kinase inhibitor (TKI) treatment and discussed the clinical relevance of such sensitive mutational detection. Methods For NGS data evaluation including extraction of biologically relevant low-level variants from background error noise, we established and applied a robust and versatile bioinformatics approach. Results Results from a retrospective longitudinal analysis of 135 samples of 15 CML patients showed that NGS could have revealed emerging resistant mutants 2–11 months earlier than conventional sequencing. Interestingly, in cases who Katerina Machova Polakova and Vojtech Kulvait contributed equally to this manuscript. Electronic supplementary material The online version of this article (doi:10.1007/s00432-014-1845-6) contains supplementary material, which is available to authorized users. K. Machova Polakova (*) · A. Benesova · J. Linhartova · H. Klamova · M. Jaruskova Institute of Hematology and Blood Transfusion, Prague, Czech Republic e-mail:
[email protected] K. Machova Polakova · H. Klamova First Faculty of Medicine, Institute of Clinical and Experimental Hematology, Charles University, Prague, Czech Republic V. Kulvait · T. Stopka PersMed, Dolni Brezany, Czech Republic V. Kulvait · T. Stopka First Faculty of Medicine, Institute of Pathologic Physiology, Charles University, Prague, Czech Republic
later failed first-line imatinib treatment, NGS revealed that TKI-resistant mutations were already detectable at the time of major or deeper molecular response. Identification of emerging mutations by NGS was mirrored by BCR-ABL1 transcript level expressed either fluctuations around 0.1 %IS or by slight transcript level increase. NGS also allowed tracing mutations that emerged during second-line TKI therapy back to the time of switchover. Compound mutants could be detected in three cases, but were not found to outcompete single mutants. Conclusions This work points out, that next-generation deep sequencing, coupled with a robust bioinformatics approach for mutation calling, may be just in place to ensure reliable detection of emerging BCR-ABL1 mutations, allowing early therapy switch and selection of the most appropriate therapy. Further, prospective assessment of how to best integrate NGS in the molecular monitoring and clinical decision algorithms is warranted. Keywords CML · BCR-ABL mutation · Next-generation sequencing · Resistance
C. de Benedittis · M. Baccarani · G. Martinelli · S. Soverini Department of Experimental, Diagnostic and Specialty Medicine, Institute of Hematology “L. e A. Seràgnoli”, University of Bologna, Bologna, Italy T. Haferlach · A. Kohlmann MLL Munich Leukemia Laboratory, Munich, Germany T. Ernst · A. Hochhaus Abteilung für Hämatologie/Onkologie, Klinik für Innere Medizin II, Universitätsklinikum Jena, Jena, Germany A. Kohlmann AstraZeneca, Personalized Healthcare and Biomarkers, Innovative Medicines, Cambridge, UK
13
Introduction Excellent therapeutic results have been achieved with imatinib mesylate treatment in chronic myeloid leukemia (CML) patients. Still, up to 20–30 % of patients develop resistance due to additional mutagenesis of BCR-ABL1 that impairs inhibitor binding (Gorre et al. 2001; Soverini et al. 2011). Although more effective second- and thirdgeneration tyrosine kinase inhibitors (TKIs) have been developed, mutations may still emerge (Soverini et al. 2011; Baccarani et al. 2013). Currently, the Sanger capillary sequencing technique analyzing BCR-ABL1 is considered the gold standard for mutation detection in a clinical laboratory (Polakova et al. 2010) knowing that this assay has a sensitivity of 15–20 % (i.e., 15–20 mutated transcripts in 100 total BCR-ABL1 transcripts). However, in recent years evidence has been mounting that early detection of mutations may help stratifying patients and predict their therapeutic responsiveness (Polakova et al. 2010; Soverini et al. 2013; Parker et al. 2011; Khorashad et al. 2008). Next-generation sequencing (NGS) of an amplicon library improves sensitivity of mutation detection, enhances throughput and allows quantification of relative mutation burden. NGS is thus predicted to replace, in future, conventional Sanger sequencing for routine diagnostics. In CML and Philadelphia chromosome-positive (Ph+) acute lymphoblastic leukemia (ALL) patients who failed multiple lines of TKI therapy, NGS could identify mutations below the lower detection limits of Sanger sequencing and uncover complex clonal textures in many cases (Soverini et al. 2013). In this study, we extensively tested whether NGS may improve detection of BCR-ABL1 kinase domain mutations resistant to the TKI treatment in the setting of chronic phase (CP) CML patients receiving TKI therapy. We focused on critical time points such as the time of diagnosis, the first-line imatinib treatment, the time of therapy switch and post-therapy switching to second or subsequent-line treatment regimens. Additionally, whenever possible, we evaluated the presence of compound mutations. NGS data analysis was done using a newly established bioinformatics approach, herein described, allowing to call true low-level BCR-ABL1 mutations out of the background error noise with 99 % confidence.
Materials and methods Samples Fifteen CP CML patients who developed TKI-resistant mutations while on treatment were retrospectively analyzed. The patient cohort consisted of out-study patients
13
J Cancer Res Clin Oncol Table 1 Patient’s characteristics Patients, total Median age, years (range) Pretreatment before IM start; n of patients HU, IFN or combination; median duration, months (range) No. of lines of therapy 2 TKIs 3 TKIs 2 or 3 TKIs and IFN SCT
15 50 (23–76) 6 14 (9–45)
11 2 2 2
SCT stem cell transplantation, HU hydroxyurea, IFN interferonalpha, TKIs tyrosine kinase inhibitors
from daily clinical practice at the Institute of Hematology and Blood Transfusion, Prague. Therapy responses were evaluated according to the European LeukemiaNet (ELN) recommendations (Baccarani et al. 2013). Based on availability of leftover white blood cell lysates collected for regular minimal residual disease monitoring, a total of 135 samples (median 8 samples per patient; range 5–19) were selected from the time of diagnosis onwards, throughout the individual treatment regimen. All the samples had already been analyzed by traditional Sanger sequencing. Patient characteristics are summarized in Table 1. Written informed consent was obtained from all patients in accordance with the declaration of Helsinki. This study received approval from the Institutional Ethical Committee. BCR‑ABL1 transcript quantification and mutation detection by Sanger sequencing BCR-ABL1 transcript levels were quantified by real-time RT-PCR with an ELN-standardized protocol and expressed on the international scale (IS) (Müller et al. 2009). Sanger sequencing of the cDNA region corresponding to the BCRABL1 kinase domain was performed as described previously (Polakova et al. 2010). Sample preparation for NGS Total RNA was isolated from stored lysates of 2 × 106 peripheral blood leukocytes. The mRNA was transcribed using random hexamers and SuperScript II enzyme (Invitrogen). SuperScript II was chosen among three different enzyme blends tested after performing comparison assays (see Supplementary Methods). In order to analyze the kinase domain of the translocated ABL1 only, a nested PCR approach was used. The first selective amplification was performed with a forward primer located on BCR exon13 (5′-TGACCAACTCGTGTGTGAAACTC-3′) and
J Cancer Res Clin Oncol
a reverse primer located on ABL1 exon11 (5′-ATCTCAGGCACGTCAGTGGT-3′). The second amplification step was subsequently performed with two alternative strategies generating either three or four overlapping fragments (amplicons) of the KD of BCR-ABL1 (Supplementary Methods: Fig. S2). These approaches are referred to as 3-amplicon or 4-amplicon assays. The 3-amplicon strategy was applied before availability of the 4-amplicon assays developed within the framework of the IRON-II phase study (Interlaboratory Robustness of Next-Generation Sequencing) (Kohlmann et al. 2013). The sequences of NGS fusion primers are listed in Supplementary Methods: Tab. S1. The selective amplification was performed using AccuPrimeSupermix I (Invitrogen). The second amplification was performed in all cases with the Fast Start High Fidelity PCR System Kit (Roche Diagnostics). The GS Junior instrument (Roche Diagnostics) was used for NGS analysis. Amplicon preparation procedure, emulsion PCR and sequencing were performed according to the manufacturer’s manuals (Roche Diagnostics). For multiple samples pooling, we used multiplexing with specific barcode adaptors (MID, multiplex identifier). To ensure uniform coverage across different samples, 10 samples were simultaneously analyzed within one GS Junior sequencing run when the 3-amplicon assay was used or seven samples when the 4-amplicon assay was used. Development of a bioinformatics approach to call BCR‑ABL1 kinase domain mutations at significant levels out of the background error noise To establish a robust bioinformatics approach to call lowabundance BCR-ABL1 kinase domain mutations and variants out of the background error noise of NGS, a set of experiments with non-mutated samples of healthy controls and non-mutated BCR-ABL1-positive cell line were performed. These experiments allowed us to: (1) derive the distribution of errors and how the main variables in the experimental pipeline influence it, (2) determine the sequencing depth and the lower detection limit below which true low-level BCR-ABL1 mutations/variants cannot be distinguished from the background error noise occurring during NGS and (3) statistically test the confidence in discovering true low-level variants. All NGS data were evaluated using our bioinformatics approach. The mutation and variant calling by the algorithm was cross-checked using default GS Amplicon Variant Analyzer algorithm from the manufacturer (AVA; Roche Diagnostics). Figure 1 shows a flowchart of the NGS experimental pipeline and NGS data processing using the established bioinformatics approach. Further details are presented in the Supplementary Methods.
Results Development of a bioinformatics algorithm for data analysis and low‑level variants calling Prospective diagnostic use of amplicon deep sequencing for BCR-ABL1 KD mutation detection mandates a robust bioinformatics pipeline of analysis allowing to filter out low-level single nucleotide substitutions (SNSs) not reflecting biologically true variants. Briefly, NGS of the KD of ABL1 in healthy donors, where any low-level SNS detectable was postulated to be an enzyme-induced misincorporation, showed that the frequency and distribution of errors strongly depends on the sequence context and that certain nucleotide changes are more likely than others to be errors (Campbell et al. 2008; Schmitt et al. 2012; Bracho et al. 1998). The observed error rate was indeed much higher in case of nucleotide transitions (purine to purine and pyrimidine to pyrimidine) in comparison to transversions (purine to pyrimidine or vice versa) (Supplementary Methods Fig. S3 and Tab. S2 B.), which is in agreement with other studies (Campbell et al. 2008; Grossmann et al. 2013). In addition, we could observe slightly different error frequencies among the type of single nucleotide substitutions. We also observed that the error frequency is approximately two times higher after a nested PCR (the approach commonly used to amplify the BCR-ABL1 KD before conventional Sanger sequencing or NGS) in comparison to a single-step PCR (Supplementary Methods Fig. S5 and Tab. S2 B.). Finally, we found that the error frequency is also influenced by the amplicon length. Based on these findings, we developed and optimized a bioinformatics algorithm allowing to correct for the variability and biases introduced by the experimental pipeline and to call true low-level variants out of a background of errors (detailed in Supplementary Methods). NGS reveals clinically relevant mutations earlier than conventional sequencing Twelve patients failed therapy, defined as failure to achieve cytogenetic response, failure to achieve complete cytogenetic response (CCgR), or loss of CCgR, and developed TKIresistant mutations detected by Sanger sequencing during first-line imatinib treatment. In 7/12 patients, a single mutation was detected, while in 5/12 patients, 2–3 mutations were detected. In three patients, mutations were not detected by Sanger sequencing during imatinib therapy and rather developed under second line or subsequent lines of treatment. We compared NGS and Sanger sequencing results as described below and summarized in Table 2 and Fig. 2. We found an overall good concordance in mutation quantification between conventional sequencing and NGS
13
J Cancer Res Clin Oncol
Fig. 1 Flowchart of the NGS experimental pipeline and NGS data processing using the established bioinformatics approach
in samples with mutations at levels ≥20 % (i.e., ≥20 mutated transcripts in 100 total BCR-ABL1 transcript) and with total transcript level measured by standardized realtime PCR ≥1 %IS. Based on reproducibility and repeatability tests of BCR-ABL1 mutation detection by Sanger sequencing, we have previously shown that at BCR-ABL1 transcript levels or = 0.1% (IS) in patients with CML responding to imatinib with complete cytogenetic remission may indicate mutation analysis. Exp Hematol 38:20–26 Schmitt MW, Kennedy SR, Salk JJ, Fox EJ, Hiatt JB, Loeb LA (2012) Detection of ultra-rare mutation by next-generation sequencing. PNAS 109:14508–14513 Soverini S, Hochhaus A, Nicolini FE, Gruber F, Lange T, Saglio G et al (2011) BCR-ABL1 kinase domain mutation analysis in chronic myeloid leukemia patients treated with tyrosine kinase inhibitors: recommendations from an expert panel on behalf of European LeukemiaNet. Blood 118:1208–1215
J Cancer Res Clin Oncol Soverini S, De Benedittis C, Polakova KM, Brouckova A, Horner D, Iacono M et al (2013) Unraveling the complexity of tyrosine kinase inhibitor-resistant populations by ultra-deep sequencing of the BCR-ABL1 kinase domain. Blood 122:1364–1648 Zabriskie MS, Eide CA, Tantravahi SK, Vellore NA, Estrada J, Nicolini FE et al (2014) BCR-ABL1 compound mutations combining
key kinase domain positions confer clinical resistance to ponatinib in Ph chromosome-positive Leukemia. Cancer Cell. doi:10.1016/j.ccr.2014.07.006
13
