Digital droplet PCR as a predictive tool for successful discontinuation outcome in chronic myeloid leukemia: Is it time to introduce it in the clinical practice?

Abstract Background Oral BCR-ABL tyrosine kinase inhibitors (TKIs), including imatinib (IM), dasatinib (DAS) and nilotinib (NIL), have improved survival in chronic-phase chronic myeloid leukemia (CP-CML). Few data are available that compare TKIs in daily clinical practice across multiple regions. Methods SIMPLICITY is an ongoing observational cohort study of adult patients with newly diagnosed CP-CML receiving first-line treatment with IM, DAS or NIL in the USA and Europe (Eu) outside of clinical trials (NCT01244750). The primary objective is to assess effectiveness of these TKIs in clinical practice. The study includes three ‘prospective’ cohorts of patients treated with IM, DAS or NIL since 2010 (the study opened after first-line approval of all three TKIs) and a ‘historical’ cohort treated with IM since 2008. Preliminary baseline demographics are presented for prospective cohorts. Results 860 prospective patients (Eu: 32%, USA: 68%) were enrolled through June 20, 2013, receiving IM (n=399), DAS (n=229) or NIL (n=232). Median age at initiation of first-line TKI was 56 years, with significant differences in pairwise comparisons between DAS and IM and NIL and IM (Table). Demographics were consistent across cohorts. Only 30% of patients had Hasford or Sokal scores recorded. ECOG performance status (PS) was available in 54% of patients. The number of baseline comorbidities per patient (mean: 3.2 + 2.7) was balanced across cohorts; 51% of patients presented with ≥3 comorbidities. Patients in the IM cohort had a higher prevalence of gastrointestinal comorbidities (P=.006 and .007 for DAS vs IM and NIL vs IM, respectively), and the NIL cohort had a higher prevalence of musculoskeletal comorbidities than the DAS cohort (P=.015). The proportions of patients with cardiovascular comorbidities were 38%, 36% and 42% in the DAS, NIL and IM cohorts, respectively, consisting primarily of hypertension (31%) and hyperlipidemia (17%) (P>.05 across cohorts). Coronary artery disease was present in 9%, cardiac arrhythmias in 6%, myocardial infarction in 3% and peripheral arterial disease in 2% of patients. The proportion of patients with diabetes was 10%. Clinicians reported effectiveness as the most common reason for TKI selection; familiarity and cost were also cited as reasons for IM selection (P<.001 vs DAS and NIL). Comorbidities were not drivers of TKI selection in this analysis. Conclusions This is the first report from the prospective cohorts of SIMPLICITY. Demographics were consistent across cohorts. Overall, the SIMPLICITY population is older with potentially more comorbidities than patients enrolled in first-line clinical trials with restrictive inclusion criteria (NEJM 2003 348 994; NEJM 2010 362 2260; NEJM 2010 362 2251). Initial TKI selection does not appear to be driven by baseline comorbidity, rather by perceived effectiveness, cost and familiarity. Hasford/Sokal scores were not recorded in the majority of patients prior to starting first-line TKI therapy. Outcomes data are being collected across cohorts that will inform about a multi-region population treated outside clinical trials. Disclosures: Cortes: Ariad: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Teva: Consultancy, Honoraria, Research Funding. Hehlmann:Novartis: Consultancy; Bristol-Myers Squibb: Consultancy, Research Funding. Gambacorti-Passerini:Bristol-Myers Squibb: Consultancy; Pfizer: Honoraria, Research Funding. Goldberg:Bristol-Myers Squibb: Honoraria, Research Funding, Speakers Bureau; Novartis Oncology: Honoraria, Research Funding, Speakers Bureau; Ariad: Honoraria, Research Funding, Speakers Bureau. Khoury:Bristol-Myers Squibb: Honoraria; Pfizer: Honoraria; Ariad: Honoraria; Teva: Honoraria. Mauro:Novartis Oncology: Consultancy, Honoraria, Research Funding; Ariad: Consultancy, Honoraria, Research Funding, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria, Speakers Bureau. Michallet:Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Astellas: Consultancy, Honoraria, Research Funding; MSD: Consultancy, Honoraria, Research Funding; Genzyme: Consultancy, Honoraria, Research Funding. Paquette:Ariad: Consultancy; Incyte: Consultancy, Honoraria; Novartis: Consultancy. Foreman:ICON Clinical Research: Employment, My employer ICON Clinical Research receives research funding from pharmaceutical companies including manufacturers of CML drugs Other. Mohamed:Bristol-Myers Squibb: Employment. Zyczynski:Bristol-Myers Squibb: Employment. Hirji:Bristol-Myers Squibb: Employment. Davis:Bristol-Myers Squibb: Employment.

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PCN54 LOWER HEALTH CARE RESOURCE UTILIZATION ASSOCIATED WITH MANAGING NILOTINIB RELATED ADVERSE EVENTS IN CHRONIC MYELOID LEUKEMIA (CML) PATIENTS: EVIDENCE FROM A CLINICAL PRACTICE SETTING STUDY

Value in Health ◽

10.1016/s1098-3015(10)72143-4 ◽

2010 ◽

Vol 13 (3) ◽

pp. A33

Author(s):

A Guerin ◽

V Bollu ◽

D Williams

Keyword(s):

Health Care ◽

Clinical Practice ◽

Chronic Myeloid Leukemia ◽

Adverse Events ◽

Myeloid Leukemia ◽

Health Care Resource ◽

Practice Setting ◽

Health Care Resource Utilization ◽

Clinical Practice Setting ◽

Lower Health

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Flow cytometric analysis as an additional predictive tool of treatment response in children with chronic-phase chronic myeloid leukemia treated with imatinib

Pediatric Blood & Cancer ◽

10.1002/pbc.26478 ◽

2017 ◽

Vol 64 (9) ◽

pp. e26478

Author(s):

Haruko Shima ◽

Nobutaka Kiyokawa ◽

Masashi Miharu ◽

Akihiko Tanizawa ◽

Hidemitsu Kurosawa ◽

...

Keyword(s):

Chronic Myeloid Leukemia ◽

Treatment Response ◽

Myeloid Leukemia ◽

Flow Cytometric Analysis ◽

Chronic Phase ◽

Predictive Tool ◽

Flow Cytometric

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DNA-Based Methods for Measurable Residual Disease Detection in NPM1-Mutated Acute Myeloid Leukemia; Establishment of Cut-Offs for qPCR, Digital Droplet PCR and Targeted Deep Sequencing

Blood ◽

10.1182/blood-2020-138675 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 6-6

Author(s):

Louise Pettersson ◽

Sofie Johansson Alm ◽

Alvar Almstedt ◽

Vladimir Lazarevic ◽

Gustav Orrsjö ◽

...

Keyword(s):

Risk Stratification ◽

Gold Standard ◽

Myeloid Leukemia ◽

Residual Disease ◽

Npm1 Mutation ◽

Digital Droplet Pcr ◽

Droplet Pcr ◽

Targeted Deep Sequencing ◽

Measurable Residual Disease

For detection of measurable residual disease (MRD) in acute myeloid leukemia with NPM1 mutations, RT-qPCR with quantification of leukemic transcripts is currently considered the method of choice; however, MRD can also be determined with DNA-based methods, offering certain advantages. For example, digital droplet PCR (ddPCR) and targeted deep sequencing (deep seq) do neither require standard curves nor reference genes and are thus less labor intense than RT-qPCR. Also, deep seq allows for quantification independently of type of NPM1 mutation. In addition, DNA-based techniques enable MRD assessment of other mutations, beyond the reach of RT-qPCR, which is limited to analyses of highly expressed genes or fusion transcripts (e.g. core binding factor leukemias). With the rapid development of highly sensitive DNA-based techniques for MRD detection, there is a need to establish clinically relevant cut-offs for accurate interpretation of MRD results and risk stratification. Here, we compare and provide MRD cut-offs for three different DNA-based MRD methods for NPM1 mutations: quantitative PCR (qPCR), ddPCR and deep seq. To compare the DNA-based methods with RT-qPCR, we analyzed 110 follow-up peripheral blood (PB) or bone marrow (BM) samples from 32 AML patients harboring NPM1 mutation type A. First, we compared the mere detectability of leukemic signals (without reference to specific MRD cut-off points). We found a high correlation between results from RT-qPCR and the three DNA-based methods (Rs=0.936 for RT-qPCR vs qPCR, Rs=0.774 for RT-qPCR vs ddPCR and Rs=0.743 for RT-qPCR vs deep seq, p<0.001). As expected, RT-qPCR was the most sensitive method. Among the DNA-based methods, qPCR was the most sensitive, detecting leukemic DNA in 95% (55/58) of the RT-qPCR positive samples, compared to 72% (42/58) and 62% (36/58) for ddPCR and deep seq, respectively. Interestingly, the transcript level for a given amount of measurable leukemic DNA (RNA copy number per leukemic DNA molecule) fluctuated considerably between different follow-up samples for certain patients. In some cases, the RNA/DNA ratio exceeded a hundredfold difference between different follow-up time points in both PB and BM. Hence, transcript analysis may be more complex than just a simple measurement of leukemic cell burden, which in turn may influence accurate risk stratification and treatment decisions, if relying on RT-qPCR measurements alone. To select adequate DNA MRD cut-offs, we performed ROC curve analyses for each method at various DNA cut-offs, comparing them with the gold standard RT-qPCR cut-off. In BM, this cut-off can be defined as a less than 3 log reduction of mutated NPM1 transcripts vs diagnosis, separating MRDhigh from MRDlow/undetectable (sometimes inaccurately termed "MRD-positivity" and "MRD-negativity", respectively). In PB, the mere detectability of mutated NPM1 transcripts is considered the relevant cut-off. DNA cut-offs were chosen based on the area under the curve (AUC) for the ROC analyses (Table 1), and influenced by available literature including recommendations of prognostically relevant MRD levels. For qPCR, a cut-off at 0.1% leukemic DNA was judged relevant in BM. For ddPCR and deep seq, 0.05% was chosen to adjust for measuring allelic ratio (variant allele frequency (VAF)) rather than mutant DNA alone. In PB, the selected cut-off was detectable leukemic signal irrespective of DNA method. We next determined the accuracy of the selected cut-offs, for identification of samples with clinically relevant MRD, by comparing them with the gold standard RT-qPCR. In general, the selected DNA cut-off values generated high specificity as well as high positive and negative predictive values (Table 1). The vast majority of all MRD analyses (93% (368/395)) showed concordant results irrespective of MRD method. In BM samples, MRD assessment by the DNA based methods agreed with MRD status as determined by RT-qPCR (MRDhigh high vs MRDlow/undetectable) in 93% (62/67) of the analyses for qPCR, 96% (64/67) for ddPCR, and 97% (65/67) for deep seq. In PB, the agreement was 95% (41/43), 88% (38/43) and 86% (37/43), respectively. In summary, we found strong agreement between different MRD methods and based on this could provide clinically relevant cut-offs for risk stratification. Thus, in BM follow-up samples from AML patients with NPM1 mutation, we propose 0.1% leukemic DNA as cut-off for qPCR and 0.05% VAF for ddPCR and deep seq. Disclosures No relevant conflicts of interest to declare.

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