Ultra-Deep Sequencing Leads to Earlier and More Sensitive Detection of the TKI Resistance Mutation p.T315I in CML

Abstract Background: Chronic myeloid leukemia (CML) cells can acquire resistance to tyrosine kinase inhibitors (TKI) that in ~40% of cases is due to acquisition of mutations in the ABL1 kinase domain of the BCR-ABL1 transcript. The p.T315I (c.944C>T) mutation (mut) mediates resistance to most BCR-ABL1 TKIs (Imatinib, Dasatinib, Nilotinib and Bosutinib), whereas sensitivity to ponatinib has been demonstrated. Patients with p.T315Imut show a rapid increase in malignant cell burden and can progress to blast crisis. An earlier detection of the p.T315Imut may allow TKI treatment intervention ahead of disease progression. However, the sensitivity of conventional Sanger sequencing for detection of mutations is not less than 10-20%. Aim: To study the dynamics of evolution and progression of the p.T315Imut using ultra-deep sequencing (UDS) in comparison with Sanger sequencing. Patients and Methods: We selected 18 CML patients with high p.T315Imut levels originally detected by Sanger sequencing for routine diagnostics. Subsequently, we backtracked prior blood samples of all patients for a mean period of eight months (2-15 months) before detection of p.T315Imut by Sanger sequencing, analyzing 3-7 time points per patient. Patients (4 female and 14 male) had a median age of 60 years (18-84 years) and received treatment as follows: only Imatinib (n=3), only Nilotinib (n=3), only Dasatinib (n=1), treated with two prior (n=6) or three prior TKIs (n=5) by the time of p.T315Imut detection by Sanger sequencing. For more sensitive mutation detection, we amplified the BCR-ABL1 fusion transcript and designed two sequencing amplicons (550 bp and 575 bp) for UDS with the XL+ Kit for extended read length (Roche/454, Branford, CT). A minimal read coverage of 1,000 per base was reached. Our backtracking study by UDS was performed on samples sent in at intervals of approximately 3 months. Results: To prove high sensitivity of UDS with the 454 XL+ protocol we performed dilution experiments for three sequence variants and replicated sequencing experiments with low level mutations. The detection limit was at 1-2% mutation level and thus is 10-fold better than the sensitivity reached by Sanger sequencing. At the time point of initial routine diagnosis of p.T315Imut the median mutation load was 87.5% (30-100%) by Sanger sequencing and very similar by UDS (median: 84%; range: 40-99%; R2=0.7). In 6/18 patients backtracking identified a sample with a low p.T315I mutation level of <5% (1.9-13.6 months, median: 3.2 months) before a mutation load of >10% (Sanger sequencing detection level) was reached. Thus, in 33.3% of all cases a small, early clone of CML with p.T315Imut was identified. At subsequent time points, all 6 patients experienced a strong increase of the p.T315Imut level (>50%), which represents the very fast expansion of the mutated clone. In a second subset of 10 patients, the p.T315Imut load was already >30% when first detected by UDS. The median interval to the last p.T315I negative time point was 2.4 months (0.9-3.5) and no sample between the p.T315I negativity and high positivity was available. This subset confirms the fast outgrowth of the p.T315Imut positive clone. The p.T315Imut load had a median increase of 0.9% (0.2-3.1%) per day, when calculated as average increase from the last negative sample to the time point with maximum mutation load. The other 2 patients had high p.T315Imut levels (>40%) for our entire monitoring period. At the time of p.T315I detection by UDS, we observed eight patients with additional resistance mutations. The accumulation of mutations in one clone results in an extremely resistant CML. This was detected in one patient, where a p.T253H clone (Imatinib and Dasatinib resistant) gained the p.T315Imut. This clone expanded to 73% within 79 days. In contrast, we identified five cases with multiple CML clones carrying different mutations. However, the p.T315Imut clone was able to overgrow up to six other resistant clones. Conclusions: We showed: 1) the p.T315Imut rapidly increases upon occurrence, supporting the relevance of regular mutation monitoring in CML patients, when resistance to TKIs is suspected. 2) that small p.T315Imut clones in the 1-2% range can be sensitively detected by UDS in 33% of all samples if sampling intervals are within the 3 months range. 3) earlier detection of the p.T315Imut by UDS is a potentially valid method to allow a prompt change of TKIs before clonal expansion of the p.T315Imut cells. Disclosures Baer: MLL Munich Leukemia Laboratory: Employment; ARIAD Pharmaceuticals: Research Funding. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Mariathas:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Schnittger:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership; ARIAD Pharmaceuticals: Research Funding.

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Modulation of the Clonal Composition in Relapsed CLL: A Study Based on Targeted Deep-Sequencing of ATM, BIRC3, NOTCH1, POT1, SF3B1, SAMHD1 and TP53

Blood ◽

10.1182/blood.v126.23.1721.1721 ◽

2015 ◽

Vol 126 (23) ◽

pp. 1721-1721

Author(s):

Sabine Jeromin ◽

Wolfgang Kern ◽

Richard Schabath ◽

Tamara Alpermann ◽

Niroshan Nadarajah ◽

...

Keyword(s):

Deep Sequencing ◽

Clonal Evolution ◽

Gene Mutations ◽

Wild Type ◽

Mutation Load ◽

Employment Equity ◽

Time Points ◽

Mutational Status ◽

Equity Ownership ◽

Time Point

Abstract Background: Relapse or refractory disease is a challenging clinical problem in the majority of chronic lymphocytic leukemia (CLL) patients. Treatment influences the clonal composition by selection and eventually induction of additional genetic abnormalities. Aim: To characterize the clonal evolution in relapsed CLL patients by deep-sequencing analysis of mutations in ATM, BIRC3, NOTCH1, POT1, SF3B1, SAMHD1 and TP53. Patients and Methods: Sequential samples of 20 relapsed CLL patients at three time-points were evaluated: A: at diagnosis (n=16) or in untreated state (n=4), B: at first relapse (n=20) and C: at second relapse (n=2). Patients were treated with diverse treatment schemes and had temporarily achieved either complete or partial remission during the course of the disease. The median time from diagnosis to first-line treatment was 13 months (1 - 169 months). All geneswere sequenced by a deep sequencing approach (Illumina, San Diego, CA). IGHV mutational status was determined by direct Sanger sequencing at time-point A. Chromosome banding analysis (CBA) and FISH data on del(17p), del(11q), trisomy 12 (+12), and del(13q) were available in 33/42 and 36/42 samples, respectively. Results: Initially, samples at first relapse were sequenced. Mutations in SF3B1 (6/20, 30%), TP53 (5/20, 25%), ATM (5/20, 25%), NOTCH1 (4/20, 20%), and SAMHD1 (3/20, 15%) were detected at high frequencies. No mutations were detected in BIRC3 and POT1. In total, 75% of cases presented with at least one mutation (Figure 1): 8 (40%) cases had one, 6 (30%) cases had two and one patient had three genes concomitantly mutated (mut). Patients were also analyzed for IGHV mutational status at diagnosis and presented with unmutated status at a frequency of 85% (17/20). Subsequently, samples from cases with mutations were analyzed at time-point A. In 12/15 (80%) cases the mutations at relapse were already detectable at time-point A with a similar load indicating presence of the main clone before and after chemotherapy. However, in 7/15 (47%) patients new gene mutations were acquired either additionally to existing mutations (n=4) or in previously wild-type cases (n=3). In 5/7 (71%) cases mutations were located in TP53. TP53 mut were the only mutations that were not detected in samples before treatment (sensitivity of 3%). Thus, TP53 mutations might have been initiated by chemotherapy or exist in a minor subclone subsequently selected by chemotherapy. Furthermore, only 4 cases had low-level mutations (3-6% mutation load) at diagnosis in either SAMHD1 or SF3B1 that eventually increased in their burden during disease course. Of note, in two patients a multibranching clonal evolution could be identified (#2, #9). For patient #2 three time-points were analyzed. At diagnosis 2 ATM mutations were detected with mutation loads of about 20%, each. In the course of the disease these mutations were lost, whereas SF3B1 mut showed a stable mutation load in all three time-points of about 40%. In contrast, mutation load of SAMHD1 increased over time from 4% to 87%. CBA was performed at diagnosis and detected independent clones with del(11q) and del(13q). Accordingly, del(11q) detected by FISH at diagnosis was lost and the percentage of cells with del(13q) increased from diagnosis to time-point C. Therefore, patient #2 shows different genetic subclones in parallel that were eradicated or selected by chemotherapy. In patient #9 two SF3B1 mutations were initially detected with the same mutation load of 10%. After treatment one mutation was lost, whereas the load of the second mutation increased indicating at least two different subclones with only one of them being sensitive to chemotherapy. This might be due to different additional aberrations. Indeed, CBA identified two clones: one with +12 alone and one in combination with del(13q). FISH revealed unchanged percentage of +12 at time-point B, whereas del(13q) positive cells were diminished. Conclusions: In 75% of relapsed CLL cases mutations in SF3B1, TP53, ATM, NOTCH1, and SAMHD1 are present at high frequencies. 80% of these mutations are already detectable before treatment initiation representing the main clone. Remarkably, TP53 mutations were the only mutations that were not detected before but only after chemotherapy. Figure 1. Distribution of gene mutations in 15 CLL cases with mutations at diagnosis or before treatment (D) and at relapse (R). Red = mutated, grey = wild-type, white = not analyzed. Figure 1. Distribution of gene mutations in 15 CLL cases with mutations at diagnosis or before treatment (D) and at relapse (R). Red = mutated, grey = wild-type, white = not analyzed. Disclosures Jeromin: MLL Munich Leukemia Laboratory: Employment. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Schabath:MLL Munich Leukemia Laboratory: Employment. Alpermann:MLL Munich Leukemia Laboratory: Employment. Nadarajah:MLL Munich Leukemia Laboratory: Employment. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Schnittger:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.

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First-Line Treatment and Management of Chronic Myeloid Leukemia (CML) in Clinical Practice: Update of > 1800 Patients (Pts) in the WORLD CML Registry

Blood ◽

10.1182/blood.v120.21.3750.3750 ◽

2012 ◽

Vol 120 (21) ◽

pp. 3750-3750

Author(s):

Jorge E. Cortes ◽

Ricardo Pasquini ◽

Hagop M. Kantarjian ◽

David Joske ◽

Luis A Meillon ◽

...

Keyword(s):

Clinical Practice ◽

Board Of Directors ◽

Research Funding ◽

Disease Status ◽

First Line ◽

Employment Equity ◽

Advisory Committees ◽

Time Points ◽

The World ◽

Equity Ownership

Abstract Abstract 3750 Background: The WORLD CML Registry is a multinational, prospective registry established to longitudinally assess global patterns of current and evolving methods for diagnosis, treatment, and clinical outcome measures in pts with CML and to compare clinical practice patterns to management recommendations provided by the European LeukemiaNet (ELN; Baccarani M, et al. J Clin Oncol. 2009;27:6041–6051). Here, we report overall efficacy and safety data from this registry, as well as clinical monitoring practices and outcomes in the subgroup of pts with CML in chronic phase (CP) treated with first-line imatinib. Methods: Pts (≥ 16 y of age) with CML in CP, accelerated phase (AP), or blast crisis (BC) within 6 mo + 2 weeks of confirmed CML diagnosis were enrolled at sites in Latin America, Asia-Pacific, the United States, Russia, Turkey, the Middle East, and Africa. Baseline demographics and medical history were collected at enrollment; disease status and management information were collected at approximate 6-mo intervals or when there was a change in disease status/management. Adverse events (AEs) were collected only if they resulted in a dose/regimen change, nonadherence to treatment, or death. Results: A total of 1837 of the 1889 pts enrolled between February 2008 and December 31, 2010, were evaluable (ie, had confirmed informed consent forms and no protocol deviations) and are the basis for this analysis. Median age was 47 y (range, 16–92 y), and 58% of pts were male. CML diagnosis was established using hematologic (91% of pts), bone marrow (82%), cytogenetic (83%), and molecular (polymerase chain reaction [PCR]; 53%) assessments. Nearly all pts (94%) were initially diagnosed in CP (Table). As of the data cutoff (December 31, 2010), median overall survival (OS) and median event-free survival (EFS) in all pts were not reached. Estimated OS and EFS rates at 3 y were 90.4% and 74.8%, respectively. AEs reported in ≥ 1% of pts were thrombocytopenia (3%) and neutropenia (2%). In the CML-CP subgroup, imatinib (Glivec®/Gleevec®) was administered as first-line therapy (in clinical practice or in a clinical trial) to 63% of pts (n = 1083). Disease burden in CML-CP pts on imatinib over time was most commonly assessed via blood counts (Table). Cytogenetic and molecular assessments were used in a minority of CML-CP pts at most time points. Only 50% of pts had a disease assessment at 3 mo (hematologic, 49%; cytogenetic, 10%; molecular, 15%). Of the pts on first-line imatinib outside of a clinical trial setting (n = 1024), 95 (9%) had their dose increased, 77 (8%) had their dose decreased, and 82 (8%) were switched to nilotinib or dasatinib. In all CML-CP pts treated with first-line imatinib, estimated OS and EFS rates at 3 y were 92.1% and 76.6%, respectively (Table). Estimated OS and EFS rates at 3 y were higher in pts who had higher imatinib exposure (treatment received ≥ 85% of total days) vs pts who received imatinib treatment on < 85% of days. Conclusions: The majority of CML-CP pts treated with first-line imatinib did not have cytogenetic or molecular assessments in accordance with current ELN recommendations, particularly at early time points. Additionally, pts who had higher drug exposure to imatinib had higher estimated OS and EFS rates at 3 y than those who did not. Disclosures: Cortes: Novartis: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Ariad: Consultancy, Research Funding. Kantarjian:Novartis Pharmaceuticals Corp: Consultancy, Research Funding; BMS: Research Funding; Pfizer: Research Funding. Piccolo:Novartis Pharma AG: Employment. Zernovak:Novartis Pharmaceuticals Corp: Employment, Equity Ownership. Sivarathinasami:Novartis Healthcare Pvt. Ltd,: Employment. Eng:Novartis Pharmaceuticals Corp: Employment, Equity Ownership. Kim:Novartis: Consultancy, Research Funding, Speakers Bureau; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; ARIAD: Research Funding; II-Yang: Consultancy, Honoraria, Research Funding. Hughes:Novartis Pharmaceuticals Corp: Consultancy, Honoraria, Research Funding; Bristol Myers Squibb: Consultancy, Honoraria, Research Funding; Ariad: Consultancy; CSL: Research Funding.

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Monitoring of Minimal Residual Disease Using Next-Generation Deep-Sequencing in 460 Acute Myeloid Leukemia Cases identifies RUNX1 Mutated Patients with Resistant Disease

Blood ◽

10.1182/blood.v118.21.747.747 ◽

2011 ◽

Vol 118 (21) ◽

pp. 747-747 ◽

Cited By ~ 2

Author(s):

Alexander Kohlmann ◽

Vera Grossmann ◽

Stefan Harbich ◽

Frank Dicker ◽

Tamara Alpermann ◽

...

Keyword(s):

Deep Sequencing ◽

Residual Disease ◽

Normal Karyotype ◽

Refractory Disease ◽

Employment Equity ◽

Clone Size ◽

Equity Ownership ◽

Conventional Methods ◽

Time Point

Abstract Abstract 747 Introduction: RUNX1 (runt-related transcription factor 1) deregulations constitute a disease-defining aberration in AML. RUNX1 mutations were proposed as clinically useful biomarkers to follow disease progression from MDS to s-AML, as well as to monitor minimal residual disease (MRD). Study design: First, a next-generation amplicon deep-sequencing (NGS) assay was developed and a validation study was performed on genomic DNA obtained from mononuclear cells on a longitudinal series of 116 retrospective samples obtained from 25 patients. These samples were collected between 11/2005 and 6/2010 and were characterized for RUNX1 mutations by DHPLC and Sanger sequencing (conventional methods). In median, 3,346 reads per amplicon were generated and in all cases NGS analyses concordantly detected the mutations known from conventional methods. Furthermore, in 2/25 (8%) cases, NGS detected additional low-level mutations with 0.9% and 3.2% of reads mutated that were not observed by standard Sanger technique. Concerning MRD monitoring, in 7/25 (28%) cases an increasing clone size, i.e. mutations as low as 0.2% - 7.0%, was detectable up to 9 months earlier than by conventional methods. This established assay then was applied to characterize an unselected prospectively collected cohort during the subsequent 12-months routine diagnostics period starting 07/2010. Results: In total, 2,705 NGS RUNX1 mutation analyses were performed on a variety of hematological malignancies. We report on analyses on 460 AML cases at diagnosis including 369 de novo AML, 57 s-AML, and 34 t-AML cases (median age: 68 years; females: 204; males 256). 51% of cases presented with a normal karyotype, 38% harbored non-complex cytogenetic alterations, 10% carried a complex aberrant karyotype, and 1% of patients were characterized by favorable cytogenetics. Overall, 141 RUNX1 mutations were observed in 24.3% (112/460) of cases. At diagnosis, the clone size ranged from 2% to 95% (median: 40%). 82% (92/112) of mutated patients carried one, whereas 18% (20/112) harbored two (n=17) or more (n=3) mutations. The 141 mutations were characterized as follows: 43% (60/141) frame-shift mutations, 34% (49/141) missense, 15% (21/141) nonsense, 5% (7/141) exon-skipping, and 3% (4/141) in-frame insertion/deletion alterations, respectively. The mutations were predominantly located in the RHD domain (54%) or TAD domain (20%). In subsequent serial NGS analyses 31/112 evaluable RUNX1 mutated cases were studied and in 88 individual samples the alterations detected at diagnosis were specifically investigated with high coverage. With a median sampling interval of 50 days for the NGS analyses between 2 and 9 samples per patient were analyzed during the first year of treatment. In this cohort, three categories of patients were detectable: (i) 55% (17/31) of patients responded to therapy and were characterized by a total clearance of the mutated clone at the first time point of follow-up (804-fold median sequencing coverage; sensitivity ∼1:800). (ii) A second group consisted of 10% (3/31) of patients with refractory disease that stayed mutated, but were excluded from further analyses since they underwent transplantation. (iii) The third group comprised 35% (11/31) of patients: None of these patients demonstrated a clone size reduction below 0.7% of reads at the first follow-up analysis (reduction to a median of 21% mutated reads; range 0.7% - 41%). Also, at the second time point (in median 108 days after initial diagnosis), mutated clones were still detectable (reduction to a median of 8% mutated reads; range 4% - 15%). Most of these cases (10/11) had refractory disease as assessed by cytomorphology or molecular analyses. 10/11 cases did harbor a normal karyotype; n=1 with del(7q). Further, 6 of these 11 patients with refractory disease, as defined using NGS, were found to carry RUNX1 double mutations. Finally, in all (3/3) cases with double mutations in the same domain and refractory disease a changing antiparallel distribution of the clone size from initial diagnosis to first follow-up was observed. Conclusions: NGS accurately detects and quantifies RUNX1 mutations in AML with high sensitivity. The technique of deep-sequencing was observed to be superior to current routine methods, in particular during follow-up and in detecting MRD and thus has the potential to enable an individualized monitoring of disease progression and treatment efficacy. Disclosures: Kohlmann: MLL Munich Leukemia Laboratory: Employment; Roche Diagnostics: Honoraria. Grossmann:MLL Munich Leukemia Laboratory: Employment. Harbich:MLL Munich Leukemia Laboratory: Employment. Dicker:MLL Munich Leukemia Laboratory: Employment. Alpermann:MLL Munich Leukemia Laboratory: Employment. Nadarajah:MLL Munich Leukemia Laboratory: Employment. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Schnittger:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.

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A 13-Gene Panel Targeted To Investigate CLL By Next-Generation Amplicon Deep-Sequencing Can Be Successfully Implemented In Routine Diagnostics

Blood ◽

10.1182/blood.v122.21.867.867 ◽

2013 ◽

Vol 122 (21) ◽

pp. 867-867 ◽

Cited By ~ 1

Author(s):

Alexander Kohlmann ◽

Andreas Roller ◽

Andreia Albuquerque ◽

Sabrina Kuznia ◽

Sandra Weissmann ◽

...

Keyword(s):

Next Generation Sequencing ◽

Deep Sequencing ◽

Sanger Sequencing ◽

Survival Prediction ◽

Next Generation ◽

Employment Equity ◽

Routine Diagnostics ◽

Evaluation Phase ◽

Equity Ownership ◽

Generation Sequencing

Abstract Introduction Massively parallel next-generation sequencing (NGS) data have changed the landscape of molecular mutations in chronic lymphocytic leukemia (CLL). The number of molecular markers continues to constantly increase. As such, physicians and laboratories face a great unmet yet challenging need to test panels of genes at a high level of sensitivity. Aim To develop an assay that is easily adoptable to adjust gene targets and amplicons according to current state-of-the-art evidence regarding the published landscape of mutations in CLL. Methods We developed a sensitive deep-sequencing assay for routine diagnostics. In total, 13 genes with relevance in CLL providing in part adverse prognostic information were chosen: ATM, BIRC3, BRAF (V600), FBXW7, KLHL6, KRAS, NOTCH1 (PEST domain), NRAS, MYD88, POT1, SF3B1 (HEAT repeats), TP53, and XPO1. Targets of interest comprised either complete coding gene regions or hotspots. In summary, 323 amplicons were designed with a median length of 204 bp (range 150-240 bp), representing a total target sequence of 39.36 kb. The sequencing library was constructed starting off 2.2 μg genomic DNA per patient using a single-plex microdroplet-based assay (RainDance, Lexington, MA). Sequencing data was generated using the MiSeq instrument (Illumina, San Diego, CA) loading up to 10 patients per run. The total turn-around time of the assay was less than 5 days. As a proof-of-principle cohort, 18 clinically well-annotated CLL patients were analyzed during the evaluation phase. The median age was 78 years (range: 52 – 87 years). Results Using the 500 cycles sequencing by-synthesis chemistry, in median 7,262 millions of paired-end reads were generated per run. This resulted in a median coverage per gene of 7,476 reads (range: 5,595 - 10,337). (1) In this cohort of 18 cases, a total of 71 mutation analyses had already been previously performed for eight of the 13 genes using either capillary Sanger sequencing or alternative amplicon deep-sequencing assays (454 LifeSciences or Illumina MiSeq). In detail, in these 8 genes these 71 assays detected 56 known polymorphisms or mutations in ATM (n=8), BIRC3 (n=6), FBXW7 (n=4), MYD88 (n=4), NOTCH1 (n=10), SF3B1 (n=5), TP53 (n=14), and XPO1 (n=4) and 28 analyses revealed a wild-type status. When comparing these results with data obtained using the 13-gene NGS panel, in all 84/84 (100%) parallel assessments concordant results were obtained underlining the robustness of this assay. (2) Overall and extending the previous results, the comprehensive 13-gene NGS panel then detected in 18/18 patients a total of 46 mutations in 10 of the 13 genes with a range of 1-5 mutations per case (median: 2). The mutation types comprised 22 missense, 4 nonsense, 16 frame-shifts, 3 insertions and 1 splice-site alterations. In median, the coverage per variant was 10,390-fold, thus enabling a sensitive detection of mutations at a lower limit of detection set at 3%. The mutation burden ranged from 3.0% to 62.0%. 18/46 (39.13%) mutations were detected with a clone size <20%, thus being detected only due to the higher sensitivity of this assay in comparison to direct capillary Sanger sequencing. With respect to the technical limit of detecting larger alterations, a 34 bp deletion variant (NOTCH1; c.7403_7436del) was successfully sequenced. Moreover, a common theme in hematological malignancies is the emergence of novel prognostic scoring systems, integrating molecular mutations and cytogenetic lesions into revised survival prediction models. Importantly, a number of patients (14/18) was detected to harbor mutations in genes reported to be associated with decreased overall survival, both in high-risk (e.g. TP53, BIRC3) and intermediate-risk (NOTCH1, SF3B1) categories according to Rossi et al., 2013 (Blood;121:1403-12). As such, detecting these adverse somatic alterations may influence the course of therapy for these patients underlining the utility of such a screening panel. Conclusion We demonstrated that microdroplet-based sample preparation enabled to robustly target 13 genes for next-generation sequencing in a routine diagnostics environment. This included also larger gene targets such as ATM, being represented by 119 amplicons. Thus, this approach provides the potential to screen for prognostically relevant mutations in all CLL patients in a fast and comprehensive way providing actionable information suitable to guide therapy. Disclosures: Kohlmann MLL Munich Leukemia Laboratory: Employment. Roller:MLL Munich Leukemia Laboratory: Employment. Albuquerque:MLL Munich Leukemia Laboratory: Employment. Kuznia:MLL Munich Leukemia Laboratory: Employment. Weissmann:MLL Munich Leukemia Laboratory: Employment. Jeromin:MLL Munich Leukemia Laboratory: Employment. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Schnittger:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.

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Development of Molecular Mutations during the Evolution of Therapy-Related MDS and Therapy-Related AML

Blood ◽

10.1182/blood.v124.21.4624.4624 ◽

2014 ◽

Vol 124 (21) ◽

pp. 4624-4624

Author(s):

Wolfgang Kern ◽

Manja Meggendorfer ◽

Tamara Alpermann ◽

Andreia de Albuquerque ◽

Claudia Haferlach ◽

...

Keyword(s):

Bone Marrow ◽

Peripheral Blood ◽

Gene Panel ◽

Employment Equity ◽

Time Points ◽

Number Of Patients ◽

Patients At Risk ◽

Previously Treated ◽

Equity Ownership ◽

Time Point

Abstract Introduction: Therapy-related myelodysplastic syndrome (t-MDS) and acute myeloid leukemia (t-AML) develop after the application of chemotherapy for malignancies in a significant number of patients (pts). Mutations in TP53 have been described recently to be present even before chemotherapy for the prior malignancy and thus also before any sign of t-MDS or t-AML. Data suggested that chemotherapy selected the TP53mutated clone which evolved to t-MDS/t-AML. More comprehensive genetic analyses, however, have been lacking so far. Aim: To identify molecular mutations by a comprehensive gene panel in pts at t-MDS/t-AML diagnosis and to backtrack them to prior time points. Patients and Methods: We searched our database for pts diagnosed with t-MDS or t-AML for whom in addition ≥1 prior peripheral blood or bone marrow sample from assessment of a previously treated malignancy was stored. Diagnosis of t-MDS and t-AML was performed by cytomorphology, cytochemistry and cytogenetics according to WHO classification 2008 in all cases. A total of 11 pts were identified (3/8 females/males; median age at t-MDS/t-AML diagnosis 72 years, range 50-81 years). 8 pts had t-MDS and 3 had t-AML. All pts had received chemotherapy for CLL before. All pts underwent mutation analysis at t-MDS/t-AML diagnosis by a 26 gene panel targeting ASXL1, BCOR, BRAF, CBL, DNMT3A, ETV6, EZH2, FLT3-TKD, GATA1, GATA2, IDH1, IDH2, JAK2, KIT, KRAS, MPL, NPM1, NRAS, PHF6, RUNX1, SF3B1, SRSF2, TET2, TP53, U2AF1, and WT1. The library was generated with the ThunderStorm (RainDance Technologies, Billerica, MA) and sequenced on MiSeq instruments (Illumina, San Diego, CA). Specific mutations identified at t-MDS/t-AML diagnosis were selectively analyzed in prior samples of the respective patients. Mutations were considered for this analysis only if they were present at t-MDS/t-AML diagnosis at mutation loads clearly higher than residual CLL infiltration. Accordingly, mutations were excluded from this analysis if their load was in the range of residual CLL infiltration or lower. One not yet described genetic variant was also excluded. Results: 13 mutations were identified at t-MDS/t-AML diagnosis in 8/11 pts. While in 3 pts no mutations were found, 5 pts had 1 mutation, 2 had 2, and 1 had 4 mutations. Mean number of mutations per pt was 1.6. TP53 was mutated most frequently (n=5), RUNX1 was mutated in 2 pts, and FLT3-TKD, IDH2, KRAS, NPM1, NRAS, and U2AF1 in 1 pt each. Mean mutation load was 27% (range 4-48%) while mean CLL infiltration at the same time point was 2% (range 0-4%). Thus, the attribution of the described mutations to t-MDS/t-AML is highly likely. We then analyzed a total of 13 samples (8 bone marrow, 5 peripheral blood) drawn prior to t-MDS/t-AML diagnosis from the 8 pts for the respective mutations identified at t-MDS/t-AML diagnosis. In 5/8 patients the respective specific mutations identified at t-MDS/t-AML diagnosis were found in at least one prior sample. Genes found mutated in the prior samples were TP53 in 2 cases and IDH2, KRAS, NPM1, RUNX1, and U2AF1 in 1 case each. Mutation loads in general were lower in prior samples as compared to samples at t-MDS/t-AML diagnosis (median 54-fold lower, range 1.5 to 205-fold), except for one sample with a similar load at both time points which both times was clearly higher than the residual CLL infiltration (50% and 42% vs. 9% and 4%). Specifically, in 3/4 patients with samples available from the time point of CLL diagnosis all of these mutations (n=4) were not detectable at a sensitivity level of 1% while in 1 patient 2 mutations were not detectable and a U2AF1mutation was identified with a 1.9% load. This further supports the concept of these mutations being related to a pre-malignant clone which in the majority of cases might have been present at undetectable levels at the time point of CLL diagnosis or which even developed only during chemotherapy and later evolved into t-MDS/t-AML. The mean interval from first detection of the respective mutations to t-MDS/t-AML diagnosis was 10 months (range 4-25 months). Conclusions: Mutational screening applying a 26 gene panel identified molecular mutations in the majority of pts. These mutations were present up to 2 years before t-MDS/t-AML diagnosis. Further studies focusing on patients at risk of t-MDS/t-AML should clarify the role of early molecular screening helping to potentially improve diagnosis and management of t-MDS/t-AML. Disclosures Kern: MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Alpermann:MLL Munich Leukemia Laboratory: Employment. de Albuquerque:MLL Munich Leukemia Laboratory: Employment. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Schnittger:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.

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Increased Eligibility for Attempting Treatment-Free Remission (TFR) with Frontline Nilotinib (NIL) vs Imatinib (IM) in Patients with Philadelphia Chromosome-Positive (Ph+) Chronic Myeloid Leukemia (CML): Results of a Stochastic Simulation Model

Blood ◽

10.1182/blood.v126.23.5154.5154 ◽

2015 ◽

Vol 126 (23) ◽

pp. 5154-5154

Author(s):

Francois-Xavier Mahon ◽

An Tran-Duy ◽

Raechelle G. Ocampo ◽

David Ray ◽

Estella Mendelson ◽

...

Keyword(s):

Treatment Discontinuation ◽

Molecular Response ◽

Employment Equity ◽

Transcript Levels ◽

Time Points ◽

Pharmaceutical Corporation ◽

Rutgers University ◽

Equity Ownership ◽

Simulated Cohort ◽

Time Point

Abstract Background : Tyrosine kinase inhibitors (TKIs) have dramatically improved outcomes in patients with Ph+ CML, in part by allowing achievement of sustained, low levels of BCR-ABL1 transcripts (quantified on the International Scale [IS]). TFR studies (eg, STIM, ENESTfreedom) evaluate whether some of these patients can stop TKI therapy and maintain a therapeutic molecular response off treatment. Here, we evaluated BCR-ABL1IS transcript levels of patients treated with frontline NIL 300 mg twice daily or IM 400 mg once daily for ≥ 1 year to predict time to TFR eligibility according to the ENESTfreedom trial criteria. Methods : A statistical model was developed to predict the probability of future premature treatment discontinuation (due to adverse events, progression, or suboptimal response) and BCR-ABL1IS transcript levels at any time point after 1 year of treatment. The 5-year data from the ENESTnd clinical trial, in which BCR-ABL1IS transcript levels were assessed every 3 months, were the basis for this model. Probabilities of premature treatment discontinuation were modeled using parametric survival methods; early molecular response (EMR; BCR-ABL1IS ≤ 10% at 3 months) status was used as a predictor. For patients remaining on treatment, a second-order Markov chain model was used to predict probabilities of BCR-ABL1IS transcript levels being in each of 5 clinically relevant categories (≤ 0.0032% [MR4.5 ], > 0.0032% to ≤ 0.01% [MR4 ], > 0.01% to ≤ 0.1%, > 0.1% to ≤ 10%, and > 10%) at any time point after 1 year of therapy. Probabilities were a function of EMR status, the proportion of previous BCR-ABL1IS observations at or below MR4, and BCR-ABL1IS categories from the previous 2 assessments. A simulated cohort of 1000 patients was created to match the distribution of EMR status and BCR-ABL1IS categories in the first year of therapy in each of the trial populations (NIL or IM). Premature treatment discontinuation and BCR-ABL1IS categories were randomly drawn at each 3-month interval based on their corresponding predicted probabilities. Time to eligibility criteria for TFR was defined as: last BCR-ABL1IS assessment of MR4.5, none of the prior 3 assessments worse than MR4, and no more than 2 of the prior 3 assessments between MR4 and MR4.5. Results : For years 2 to 5 of the ENESTnd trial, the observed distribution of BCR-ABL1IS categories over time had reasonable agreement with the computer-simulated cohort. Simulation results (Figure) demonstrated that more patients on NIL than on IM were eligible for TFR by year 5 (52% vs 38%, respectively) and by year 10 (72% vs 64%, respectively; P < .0001 for both time points). Conclusion: Patients in our simulated cohort received a minimum of 3 years of frontline treatment with NIL or IM prior to TFR eligibility evaluation, similar to the current consensus in clinical disease management. Treatment with NIL resulted in significantly more patients becoming eligible for TFR by all time points vs treatment with IM. These data suggest that TFR as a therapeutic goal may be more attainable with NIL than IM. Studies evaluating the duration of TFR are presently being conducted. Disclosures Mahon: Novartis: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; ARIAD: Consultancy; Pfizer: Consultancy. Tran-Duy:Pharmerit International: Consultancy. Ray:Novartis Pharmaceutical Corporation/Rutgers University: Other: I am currently a fellow with Rutgers University, conducting my "field" experience at Novartis.. Mendelson:Novartis Pharmaceutical Corporation: Employment, Equity Ownership. Buchbinder:Novartis Pharmaceutical Corporation: Employment, Equity Ownership. Edrich:Novartis Pharma AG: Employment. Snedecor:Pharmerit International: Employment, Other: Institution received payment to conduct this study. Saglio:Pfizer: Consultancy, Honoraria; ARIAD: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Novartis Pharmaceutical Corporation: Consultancy, Honoraria.

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A Personalized Prediction Model to Risk Stratify Patients with Myelodysplastic Syndromes

Blood ◽

10.1182/blood-2018-99-114774 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 793-793 ◽

Cited By ~ 13

Author(s):

Aziz Nazha ◽

Rami S. Komrokji ◽

Manja Meggendorfer ◽

Sudipto Mukherjee ◽

Najla Al Ali ◽

...

Keyword(s):

Board Of Directors ◽

Myelodysplastic Syndromes ◽

Research Funding ◽

Validation Cohort ◽

Employment Equity ◽

Advisory Committees ◽

Training Cohort ◽

New Model ◽

Time Points ◽

Equity Ownership

Abstract Background Patients (pts) with myelodysplastic syndromes (MDS) have heterogeneous outcomes that can range from months for some pts to decades for others. Although several prognostic scoring systems have been developed to risk stratify MDS pts, survival varies even within discrete categories, which may lead to over- or under-treatment. Deficits in discriminatory power likely derive from analytic approaches or lack of incorporation of molecular data. Here, we developed a model that uses a machine learning approach to analyze genomic and clinical data to provide a personalized overall outcome that is patient-specific. Method Clinical and mutational data from MDS pts diagnosed according to 2008 WHO criteria were analyzed. The model was developed in a combined cohort from the Cleveland Clinic and Munich Leukemia Laboratory and validated in a separate cohort from the Moffitt Cancer Center. Next generation targeted deep sequencing of 40 gene mutations commonly found in myeloid malignancies was performed. Pts who underwent hematopoietic cell transplant (HCT) were censored at the time of transplant. A random survival forest (RSF) algorithm was used to build the model, in which clinical and molecular variables are randomly selected for inclusion in determining survival, thereby avoiding the shortcomings of traditional Cox step-wise regression in accounting for variable interactions. Survival prediction is thus specific to each pt's particular clinical and molecular characteristics. The accuracy of the proposed model, compared to other models, was assessed by concordance (c-) index. Results Of 2302 pts, 1471 were included in the training cohort and 831 in the validation cohort. In the training cohort, the median age was 71 years (range, 19-99), 230 pts (16%) progressed to AML, 156 (11%) had secondary/therapy-related MDS, and 130(9%) underwent HCT. Risk stratification by IPSS: 391 (27%) low, 626 (43%) intermediate-1, 280 (19%) intermediate-2, 104 (7%) high, 104 (7%) missing, and by IPSS-R: 749 (51%) very low/ low, 336 (23%) intermediate, 190 (13%) high, 92 (6%) very high, and 104 (7%) missing. Cytogenetic analysis by IPSS-R criteria: 65 (4%) very good, 1060 (72%) good, 193 (13%) intermediate, 60 (4%) poor, and 93 (6%) very poor. The most commonly mutated genes were: SF3B1 (26%), TET2 (25%), ASXL1 (20%), SRSF2 (15%), DNMT3A (12%), STAG2 (8%), RUNX1 (8%), and TP53 (8%). All clinical variables and mutations were included in the RSF algorithm. To identify the most important variables that impacted the outcome and the least number of variables that produced the best prediction, we conducted several feature extraction analyses which identified the following variables that impacted OS (ranked from the most important to the least): cytogenetic risk categories by IPSS-R, platelets, mutation number, hemoglobin, bone marrow blasts %, 2008 WHO diagnosis, WBC, age, ANC, absolute lymphocyte count (ALC), TP53, RUNX1, STAG2, ASXL1, absolute monocyte counts (AMC), SF3B1, SRSF2, RAD21, secondary vs. de novo MDS, NRAS, NPM1, TET2, and EZH2. The clinical and mutational variables can be entered into a web application that can run the trained model and provide OS and AML transformation probabilities at different time points that are specific for a pt, Figure 1. The C-index for the new model was .74 for OS and .81 for AML transformation. The new model outperformed IPSS (c-index .66, .73) and IPSS-R (.67, .73) for OS and AML transformation, respectively. The geno-clinical model outperformed mutations only (c-index .64, .72), mutations + cytogenetics (c-index .68, .74), and mutations + cytogenetics +age (c-index .69, .75) for OS and AML transformation, respectively. Addition of mutational variant allelic frequency did not significantly improve prediction accuracy. When applying the new model to the validation cohort, the c-index for OS and AML transformation were .80, and .78, respectively. Conclusion We built a personalized prediction model based on clinical and genomic data that outperformed IPSS and IPSS-R in predicting OS and AML transformation. The new model gives survival probabilities at different time points that are unique for a given pt. Incorporating clinical and mutational data outperformed a mutations only model even when cytogenetics and age were added. Disclosures Nazha: MEI: Consultancy. Komrokji:Celgene: Honoraria, Research Funding; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Celgene: Honoraria, Research Funding. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Walter:MLL Munich Leukemia Laboratory: Employment. Hutter:MLL Munich Leukemia Laboratory: Employment. Sallman:Celgene: Research Funding, Speakers Bureau. Roboz:Otsuka: Consultancy; Orsenix: Consultancy; Celgene Corporation: Consultancy; Daiichi Sankyo: Consultancy; Pfizer: Consultancy; Cellectis: Research Funding; Argenx: Consultancy; Roche/Genentech: Consultancy; Celltrion: Consultancy; Sandoz: Consultancy; Aphivena Therapeutics: Consultancy; Bayer: Consultancy; Pfizer: Consultancy; Aphivena Therapeutics: Consultancy; Eisai: Consultancy; Sandoz: Consultancy; Eisai: Consultancy; Roche/Genentech: Consultancy; AbbVie: Consultancy; Novartis: Consultancy; Janssen Pharmaceuticals: Consultancy; Bayer: Consultancy; Celltrion: Consultancy; Novartis: Consultancy; Janssen Pharmaceuticals: Consultancy; Astex Pharmaceuticals: Consultancy; Daiichi Sankyo: Consultancy; Celgene Corporation: Consultancy; Jazz Pharmaceuticals: Consultancy; Jazz Pharmaceuticals: Consultancy; Cellectis: Research Funding; Otsuka: Consultancy; Orsenix: Consultancy; Argenx: Consultancy; Astex Pharmaceuticals: Consultancy; AbbVie: Consultancy. List:Celgene: Research Funding. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Maciejewski:Apellis Pharmaceuticals: Consultancy; Ra Pharmaceuticals, Inc: Consultancy; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Ra Pharmaceuticals, Inc: Consultancy; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Apellis Pharmaceuticals: Consultancy. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Sekeres:Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees.

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Single-Cell Analysis Based Dissection of Clonality in Myelofibrosis

Blood ◽

10.1182/blood-2019-123367 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 469-469

Author(s):

Elena Mylonas ◽

Kenichi Yoshida ◽

Mareike Frick ◽

Kaja Hoyer ◽

Friederike Christen ◽

...

Keyword(s):

Single Cell ◽

Research Funding ◽

Rapid Expansion ◽

Disease Stage ◽

Neutral Evolution ◽

Time Points ◽

Sequential Samples ◽

Equity Ownership ◽

Cell Fractions ◽

Time Point

Introduction Large-scale sequencing studies have unraveled the mutational landscape of myelofibrosis (MF), demonstrating clonal heterogeneity and importance of genetically defined subgroups in disease prognosis and progression. In order to elucidate the genetics of MF progression and its molecular drivers during JAK inhibition therapy, we performed in-depth genetic studies on longitudinal blood samples from 15 MF patients covering a disease span of 3 to 5 years after initiation of ruxolitinib. Methods Sequential samples from 15 MF patients (PMF n=8; post-ET/PV-MF n=7) accounting for a total of 42 time points representing 58.5 years of ruxolitinib treatment were investigated by whole-exome sequencing (WES). Additionally, we performed targeted deep sequencing of patient-specific mutations in flow-sorted cell fractions to study clonal repartition within the hematopoietic differentiation tree. Finally, we genotyped more than 5000 Lin-CD34+ progenitor cells using a single-cell multiplexed qPCR approach on a micro-fluidic platform (Fluidigm) to infer MF phylogeny. Results WES identified a median of 14 non-silent somatic mutations per patient at initiation of ruxolitinib treatment (=baseline WES; Figure 1A). When comparing mutations between first and last investigated time points, the majority of baseline mutations (162/201=81%) could be detected also at a later disease stage. A total of 39 mutations were lost and 80 new mutations were detected at the last time point. All patients showed at least one gained/ lost mutation in sequential samples. We noted frequent acquisition of mutations in genes of the RAS/RTK pathways in one third of patients. Two patients with a JAK2 V617F mutation achieved a molecular remission at a level of persisting residual disease of 1x10-3 with ruxolitinib therapy. In one of them, a total of 13 mutations were detected at baseline. In the second sample, taken three years later, a completely different set of mutations was identified and at the last time point, four years after initiation of therapy, none of the mutations were detected. This likely represents genetic drift during neutral evolution as a consequence of a rapid expansion after JAK inhibition. All other 13 patients showed only a modest - if any - decrease of 10-20% JAK2/CALR allele burden which was often accompanied with the expansion of JAK2/CALR-wildtype clones due to positive selection and/or freed clonal space under treatment. However, in some patients with durable response to ruxolitinib, we noted opposing dynamics of clones questioning a common origin. The three patients who progressed to leukemia showed a higher number of mutations at baseline and all of them acquired mutations in KRAS or NRAS over time. As one example, MPN18 harbored mutations in ASXL1, ETV6, and SRSF2 at baseline. Thereafter, and in addition to other driver genes (IDH2,KRAS) a second JAK2 Mutation at codon R867 was acquired, which has been reported to confer treatment resistance to JAK Inhibitors (Marty, Blood 2014). Mutation analysis in flow-sorted cell fractions showed a higher allelic mutation load in the myeloid compared to the lymphoid compartment with only few mutations being detected at low allele frequency in lymphocytes. Interestingly, some patients showed evidence of differential expansion among different myeloid cell lineages (Figure 1B). Next, we sorted 480 CD34+ single-cells per sample from 12 time points from 8 patients which allowed identification of subclones at ≥2% frequency based on priori power calculations. Sorting errors (e.g.cell doublets, empty wells)determined the mean cell sorting failure rate to be 12.5%. We employed a heuristic search algorithm to select a phylogenetic tree with Maximum Likelihood under a finite site model of evolution. Loss of heterozygosity (LOH) events were found in 7/8 patients and were not restricted to the JAK2 locus. In some patients, LOH of JAK2 occurred independently in two subclones, a phenomenon of convergent evolution (Figure 1C). We also noted cases with multiple 9pUPDs, of which one got selected during therapy. LOH events gave rise to both, a mutant homozygous but also reversion to a wildtype genotype. Conclusions Comprehensive serial genotyping of MF patients treated with ruxolitinib revealed heterogeneous patterns of clonal composition and evolution. Our data support LOH as a major determination factor for clonal diversification in MF. EM, KY, and MF contributed equally Figure 1 Disclosures Zenz: Abbvie: Consultancy, Honoraria, Other: Travel support; Roche: Consultancy, Other: Travel support; Janssen: Consultancy; Takeda: Consultancy; Gilead: Honoraria. Bullinger:Pfizer: Honoraria; Astellas: Honoraria; Amgen: Honoraria; Abbvie: Honoraria; Bayer: Other: Financing of scientific research; Seattle Genetics: Honoraria; Sanofi: Honoraria; Novartis: Honoraria; Menarini: Honoraria; Jazz Pharmaceuticals: Honoraria; Janssen: Honoraria; Hexal: Honoraria; Gilead: Honoraria; Daiichi Sankyo: Honoraria; Celgene: Honoraria; Bristol-Myers Squibb: Honoraria. Le Coutre:Novartis: Honoraria, Speakers Bureau; Pfizer: Honoraria, Speakers Bureau; Bristol-Myers Squibb: Honoraria, Speakers Bureau; Incyte: Honoraria, Speakers Bureau. Ogawa:Kan Research Laboratory, Inc.: Consultancy; Asahi Genomics: Equity Ownership; Qiagen Corporation: Patents & Royalties; RegCell Corporation: Equity Ownership; ChordiaTherapeutics, Inc.: Consultancy, Equity Ownership; Dainippon-Sumitomo Pharmaceutical, Inc.: Research Funding. Damm:Novartis: Research Funding; AbbVie: Other: Travel support.

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Detection of BCR-ABL1 Compound and Polyclonal Mutants in Chronic Myeloid Leukemia Patients Using a Novel Next Generation Sequencing Approach That Minimises PCR and Sequencing Errors

Blood ◽

10.1182/blood.v124.21.399.399 ◽

2014 ◽

Vol 124 (21) ◽

pp. 399-399 ◽

Cited By ~ 1

Author(s):

Wendy T Parker ◽

Stuart R Phillis ◽

David T Yeung ◽

David Lawrence ◽

Andreas Schreiber ◽

...

Keyword(s):

Research Funding ◽

Sanger Sequencing ◽

Consensus Sequence ◽

Ion Torrent ◽

Employment Equity ◽

Advisory Committees ◽

Low Level ◽

Sequencing Errors ◽

Equity Ownership ◽

Generation Sequencing

Abstract Background BCR-ABL1 kinase domain (KD) mutations are the most common known cause of resistance to tyrosine kinase inhibitors (TKIs) in CML. Mutation analysis is critical for selection of subsequent TKI therapy after treatment failure. Low level and compound mutants (>1 KD mutation in the same molecule) may also lead to therapy failure. However, compound and multiple polyclonal mutants cannot be distinguished by conventional methods as they determine the average genotype of all molecules. Next generation sequencing (NGS) has the potential to sensitively detect these mutants, however sequencing and PCR errors confound the detection of true, low level mutants using current approaches. Indeed, we demonstrated that the reported frequency of BCR-ABL1 compound mutants may be over estimated due to PCR recombination artifacts that mimic compound mutations (Parker Blood 2014). More reliable methods are needed to appropriately assess the impact of various mutations on patient (pt) outcome. Aim To develop a clinically applicable NGS assay that can robustly distinguish BCR-ABL1 compound and polyclonal mutants. Method We have developed a novel NGS assay termed Single Molecule Consensus Sequencing (SMCS) that involves tagging individual BCR-ABL1 cDNA molecules before library amplification, enabling identification and elimination of most PCR and sequencing errors. NGS was performed on the Illumina MiSeq, 2 x 300 bp; aa 244 - 407 of the KD was examined. Reads derived from an initial BCR-ABL1 molecule are identified bioinformatically by virtue of sharing the same tag sequence. The consensus sequence of reads with the same tag is determined using automated variant calling and filtering algorithms. The consensus sequence represents the sequence of the initial BCR-ABL1 cDNA molecule (Fig A). Results To test the validity of SMCS, we examined 10 samples lacking KD mutations and 5 mock samples created by mixing compound mutant plasmids or pt samples. Examination of raw sequencing reads revealed a complex spectrum of mutants, similar to previous clinical reports. SMCS enabled bioinformatic filtering of these artifacts, largely eliminating PCR and sequencing error, and exclusively reported the compound and polyclonal mutants known to be present in the mock samples. We estimated the background error rate to be ~2x10-5 per base. The error spectrum was consistent with DNA damage causing first round PCR errors. SMCS was used to retrospectively examine samples of 46 pts (36 CP, 2 AP, 8 BP) who were resistant to ≤4 TKIs (1st and 2nd generation). 71 mutations were previously detected by Sanger sequencing in these samples, collected before starting next line TKI. Within the region examined using SMCS, there was 100% detection concordance with Sanger sequencing. We compared the results of SMCS with an amplicon NGS method performed at another centre for 24/46 pts (Ion Torrent, depth ~10000). Ion Torrent detected 34 compound mutants in 24 pts. Of the 30/34 that were within the region examined by SMCS we only detected 8. Based on observations in Parker Blood 2014, 14 of the 22 compound mutants not detected by SMCS were likely to be PCR recombination artifacts. The other 8/22 were low level (1 - 4%) and most (6/8) involved mutations rarely/never reported in TKI resistant pts so may also be artifacts (Fig B). We detected 3 additional compound mutants in these 24 pts, plus 5 in the remaining 22/46 pts. The compound mutants detected by SMCS were consistent with the pts' TKI treatment history. Conclusion We demonstrated detection of BCR-ABL1 compound and polyclonal mutants in pt samples using a novel NGS assay that has the potential to overcome technical artifacts generated with other published methods. Whilst there is no gold standard method that can accurately detect low level compound mutations, SMCS has correctly identified sequencing and PCR recombination artifacts using mock samples. The accuracy and clinical utility of SMCS for sensitive compound and polyclonal mutant detection is currently being validated in another group of 200 imatinib resistant pts. The frequency of compound mutants detected in pts with >1 mutation by SMCS in the current analysis (35%) is approximately half of that reported previously, which suggests the published frequency may have been overestimated. Our novel assay takes an important step towards enabling a more concrete understanding of the mutation spectra in pts and their association with resistance. Figure 1 Figure 1. Disclosures Yeung: Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Lustgarten:ARIAD Pharmaceuticals Inc: Employment, Equity Ownership. Hodgson:ARIAD Pharmaceuticals, Inc.: Employment, Equity Ownership. Rivera:ARIAD Pharmaceuticals Inc: Employment, Equity Ownership. Hughes:Novartis: Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria, Research Funding; Ariad: Honoraria, Research Funding. Branford:Novartis: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria, Research Funding; Ariad: Honoraria, Research Funding; Otsuka: Honoraria, Research Funding.

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ENESTnext Final Results: Deep Molecular Response (MR) with Nilotinib (NIL) in Patients (pts) with Newly Diagnosed Chronic Myeloid Leukemia in Chronic Phase (CML-CP)

Blood ◽

10.1182/blood.v126.23.4048.4048 ◽

2015 ◽

Vol 126 (23) ◽

pp. 4048-4048

Author(s):

Jesus G. Berdeja ◽

Michael Heinrich ◽

Shaker Dakhil ◽

Stuart L. Goldberg ◽

Martha Wadleigh ◽

...

Keyword(s):

Myocardial Infarction ◽

Research Funding ◽

Digital Pcr ◽

Molecular Response ◽

Newly Diagnosed ◽

Employment Equity ◽

Pharmaceutical Corporation ◽

Equity Ownership ◽

Time Point ◽

Advisory Role

Abstract Background : Regular monitoring of MR by real-time quantitative polymerase chain reaction (RQ-PCR) on the International Scale (IS) is critical for proper management of pts with CML-CP, and achievement of deep MR is a key criterion for enrollment in treatment-free remission studies. In addition, newer techniques have been developed for evaluating residual disease below the level of detection of conventional RQ-PCR. The BCR-ABL1 tyrosine kinase inhibitor NIL elicits higher rates of deep MR than imatinib (IM) in pts with newly diagnosed CML-CP. Prior results from ENESTnext in this pt population demonstrated rapid achievement of deep MR by conventional RQ-PCR and further reductions in BCR-ABL1 transcript levels using a microfluidic digital PCR platform in pts who achieved confirmed MR4.5 (BCR-ABL1IS ≤ 0.0032%) with NIL. Final results are presented here. Methods : ENESTnext (NCT01227577) was a single-arm, open-label, multicenter study in adult pts with Philadelphia-chromosome-positive CML-CP (diagnosed within ≤ 6 months of enrollment) treated with NIL 300 mg twice daily (BID) for up to 2 years. The primary endpoint was the rate of confirmed (≥ 2 samples taken 3 months apart) MR4.5 with up to 2 years of NIL therapy. Secondary endpoints included the rate of major MR (MMR; BCR-ABL1IS ≤ 0.1%). RQ-PCR evaluation of peripheral blood samples was performed monthly for the first 3 months and every 3 months thereafter by a central laboratory and according to the IS. In an exploratory analysis, samples from pts with confirmed MR4.5 (limit of detection of the RQ-PCR assay used) were also evaluated using the Fluidigm digital PCR platform (detection limit is approximately 1 positive cell in 1,000,000 negative cells), which is > 1 log more sensitive than conventional RQ-PCR. Samples were analyzed by both digital PCR and RQ-PCR for each pt upon achievement of confirmed MR4.5. Results : A total of 128 pts were enrolled (median age, 56.5 years; male, n = 64 [50.0%]; Caucasian, n = 103 [80.5%]), and 93 pts (72.7%) completed the study per protocol. With up to 2 years of treatment, 94 pts (73.4%) achieved MMR and 34 pts (26.6%) achieved confirmed MR4.5 (Table). Overall, 13 of 94 pts (13.8%) lost MMR and 6 of 34 pts (17.6%) lost MR4.5; among the pts who gained and lost MMR (n = 13) or MR4.5 (n = 6), the mean duration of response was 4.9 and 8.0 months, respectively. All pts who achieved MR4.5 had BCR-ABL1IS ≤ 10% at 3 months. Digital PCR analysis was performed on 195 samples from 33 pts with confirmed MR4.5 by RQ-PCR. Results of digital PCR detection of BCR-ABL1 transcripts in the first and last time point samples from each pt are shown in the Table; among pts for whom both the first and last time point samples showed detectable BCR-ABL1 transcripts by digital PCR, levels decreased over time with continued NIL therapy. The most common (≥ 4 pts) all-cause grade 3/4 adverse events were increased lipase (n = 16), thrombocytopenia (n = 11), neutropenia (n = 8), hypophosphatemia (n = 5), and nausea (n = 5). The most common cardiac disorders were palpitations (6.3%) and atrial fibrillation, myocardial infarction, and tachycardia (2.3% each). Ischemic cardiovascular events included myocardial infarction (2.3%) and cerebrovascular accident and transient ischemic attack (0.8% each). Conclusions : Rapid achievement of MR4.5 was observed in pts with newly diagnosed CML-CP receiving frontline NIL 300 mg BID in ENESTnext; rates of MR were also consistent with those from the ENESTnd study of frontline IM vs NIL with 2 years of follow-up. In ENESTnext, 39% of samples analyzed by digital PCR had detectable levels of BCR-ABL1 transcripts that were not detectable by conventional RQ-PCR, suggesting the potential for detection of even deeper levels of MR using this novel method. Disclosures Berdeja: BMS: Research Funding; Abbvie: Research Funding; Takeda: Research Funding; Onyx: Research Funding; Celgene: Research Funding; Acetylon: Research Funding; MEI: Research Funding; Curis: Research Funding; Novartis: Research Funding; Janssen: Research Funding; Array: Research Funding. Heinrich:BMS: Research Funding; Pfizer: Consultancy, Other: Consulting or Advisory Role; Blueprint Pharmaceuticals: Consultancy, Other: CONSULTING OR ADVISORY ROLE; ARIAD Pharmaceuticals Inc.: Consultancy, Other: Consulting or Advisory Role, Research Funding; Novartis: Consultancy, Other: Consulting & Advisory Role, Research Funding; MolecularMD: Other: Consulting or Advisory Role; MolecularMD: Other: Stock/Shareholder ; Novartis: Other: Expert Testimony; Onyx: Other: Consulting or Advisory Role; Bayer: Research Funding. Goldberg:BMS: Research Funding, Speakers Bureau; Ariad: Research Funding, Speakers Bureau; COTA: Employment, Equity Ownership, Other: Leadership, Stock; Novartis: Research Funding, Speakers Bureau; Pfizer: Research Funding. Kuriakose:Kedrion: Speakers Bureau. Cortes:Astellas: Consultancy, Research Funding; BerGenBio AS: Research Funding; Teva: Research Funding; Novartis: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Ariad: Consultancy, Research Funding; Ambit: Consultancy, Research Funding; Arog: Research Funding; Celator: Research Funding; Jenssen: Consultancy. Rizzieri:Novartis: Membership on an entity's Board of Directors or advisory committees. Bonifacio:Novartis Pharmaceutical Corporation: Employment, Equity Ownership. Dautaj:Novartis Pharmaceutical Corporation: Employment. Warsi:Novartis Pharmaceutical Corporation: Employment. Mauro:Ariad: Consultancy; Bristol-Myers Squibb: Consultancy; Novartis Pharmaceutical Corporation: Consultancy, Research Funding; Pfizer: Consultancy.

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