Context: Testing for BCR-ABL1 kinase domain (KD) mutations should always be performed before tyrosine kinase inhibitor (TKI) changes. Next-generation sequencing (NGS) is the best approach to highlight emerging mutations in patients not responding adequately to TKI therapy. However, NGS requires sample centralization and batch analysis and has a non-negligible time to results. In this study, we set up and validated a novel droplet digital PCR (ddPCR)-based multiplex strategy for the detection and quantitation of transcripts harboring mutations impacting TKI selection. Methods: In collaboration with Bio-Rad, a 3-tube ddPCR strategy was designed that enables identification and quantitation of 16 nucleotide substitutions encoding the 13 mutations associated with resistance to one or more second-generation TKI (2GTKI). Primers and FAM-or FAM/HEX-labelled probes were grouped on a TKI-specific basis and generated clusters of droplets mapping to spatially distinct areas of the 2D plot based on resistance profiles. Each tube also incorporated primers and HEX-labelled probes for e13a2, e14a2, and e1a2 BCR::ABL1 fusion transcripts to express results as the percentage of mutation-positive over total BCR::ABL1 transcripts. For validation, a total of 101 RNA samples from healthy donors, TKI-sensitive and -resistant patients, and BCR::ABL1-positive and -negative cell lines were used. cDNA (125 ng) obtained with ABL1-specific primers was analyzed in duplicate on a QX200 ddPCR system (Bio-Rad). Results: The limit of blank was determined using 60 blank samples. Accuracy and specificity were confirmed using 48 samples positive for one or more 2GTKI-resistant mutations or mutations at nearby codons (to exclude cross-reactivity). Analysis of serial dilutions of cell line mixtures made using BCR::ABL1-positive mutation-positive cells, BCR::ABL1-positive unmutated cells, and BCR::ABL1-negative cells to mimic different mutation frequencies (70%, 5%, and 0.5%) and different transcript levels (MR0 to MR3) showed that a 0.5% lower detection limit could be consistently achieved irrespective of BCR::ABL1 levels. Conclusions: ddPCR proved highly sensitive and accurate. Total hands-on time was approximately 2 hrs, and time from sample to results was 2 days. Therefore, ddPCR may be integrated into diagnostic algorithms of CML (and Ph+ ALL) patients as a convenient first-level screening tool for mutations impacting TKI selection.

CML-184 A Novel Droplet Digital PCR Strategy for Rapid and Sensitive Detection of BCR::ABL1 Kinase Domain Mutations Conferring Resistance to Second-Generation Tyrosine Kinase Inhibitors / de Santis S.; Monaldi C.; Martelli M.; Mancini M.; Bruno S.; Castagnetti F.; Gugliotta G.; Polakova K.M.; Ernst T.; Maar D.; Corner A.; Cavo M.; Soverini S.. - In: CLINICAL LYMPHOMA MYELOMA & LEUKEMIA. - ISSN 2152-2650. - STAMPA. - 22:Suppl. 2(2022), pp. 289-290. [10.1016/S2152-2650(22)01367-2]

CML-184 A Novel Droplet Digital PCR Strategy for Rapid and Sensitive Detection of BCR::ABL1 Kinase Domain Mutations Conferring Resistance to Second-Generation Tyrosine Kinase Inhibitors

de Santis S.;Monaldi C.;Martelli M.;Mancini M.;Bruno S.;Castagnetti F.;Gugliotta G.;Cavo M.;Soverini S.
2022

Abstract

Context: Testing for BCR-ABL1 kinase domain (KD) mutations should always be performed before tyrosine kinase inhibitor (TKI) changes. Next-generation sequencing (NGS) is the best approach to highlight emerging mutations in patients not responding adequately to TKI therapy. However, NGS requires sample centralization and batch analysis and has a non-negligible time to results. In this study, we set up and validated a novel droplet digital PCR (ddPCR)-based multiplex strategy for the detection and quantitation of transcripts harboring mutations impacting TKI selection. Methods: In collaboration with Bio-Rad, a 3-tube ddPCR strategy was designed that enables identification and quantitation of 16 nucleotide substitutions encoding the 13 mutations associated with resistance to one or more second-generation TKI (2GTKI). Primers and FAM-or FAM/HEX-labelled probes were grouped on a TKI-specific basis and generated clusters of droplets mapping to spatially distinct areas of the 2D plot based on resistance profiles. Each tube also incorporated primers and HEX-labelled probes for e13a2, e14a2, and e1a2 BCR::ABL1 fusion transcripts to express results as the percentage of mutation-positive over total BCR::ABL1 transcripts. For validation, a total of 101 RNA samples from healthy donors, TKI-sensitive and -resistant patients, and BCR::ABL1-positive and -negative cell lines were used. cDNA (125 ng) obtained with ABL1-specific primers was analyzed in duplicate on a QX200 ddPCR system (Bio-Rad). Results: The limit of blank was determined using 60 blank samples. Accuracy and specificity were confirmed using 48 samples positive for one or more 2GTKI-resistant mutations or mutations at nearby codons (to exclude cross-reactivity). Analysis of serial dilutions of cell line mixtures made using BCR::ABL1-positive mutation-positive cells, BCR::ABL1-positive unmutated cells, and BCR::ABL1-negative cells to mimic different mutation frequencies (70%, 5%, and 0.5%) and different transcript levels (MR0 to MR3) showed that a 0.5% lower detection limit could be consistently achieved irrespective of BCR::ABL1 levels. Conclusions: ddPCR proved highly sensitive and accurate. Total hands-on time was approximately 2 hrs, and time from sample to results was 2 days. Therefore, ddPCR may be integrated into diagnostic algorithms of CML (and Ph+ ALL) patients as a convenient first-level screening tool for mutations impacting TKI selection.
2022
CML-184 A Novel Droplet Digital PCR Strategy for Rapid and Sensitive Detection of BCR::ABL1 Kinase Domain Mutations Conferring Resistance to Second-Generation Tyrosine Kinase Inhibitors / de Santis S.; Monaldi C.; Martelli M.; Mancini M.; Bruno S.; Castagnetti F.; Gugliotta G.; Polakova K.M.; Ernst T.; Maar D.; Corner A.; Cavo M.; Soverini S.. - In: CLINICAL LYMPHOMA MYELOMA & LEUKEMIA. - ISSN 2152-2650. - STAMPA. - 22:Suppl. 2(2022), pp. 289-290. [10.1016/S2152-2650(22)01367-2]
de Santis S.; Monaldi C.; Martelli M.; Mancini M.; Bruno S.; Castagnetti F.; Gugliotta G.; Polakova K.M.; Ernst T.; Maar D.; Corner A.; Cavo M.; Soverini S.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/904978
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