Next Generation Sequencing in AML—On the Way to Becoming a New Standard for Treatment Initiation and/or Modulation?
Abstract
:1. Introduction
2. Next Generation Sequencing
2.1. Terminology
2.2. Transition of NGS from Research to Clinical Practice and Market Overview
3. NGS for the Diagnosis of AML
3.1. AML Classification and Pathogenesis
3.2. Current Guidelines Regarding NGS Analyses for the Diagnosis of AML
4. Prognostic Implication of Gene Mutations in AML
4.1. Overview of AML Prognosis and Age
4.2. Conventional Cytogenetics
4.3. Conventional Cytogenetics Refined with Analysis of Single Gene Mutations
4.4. Conventional Cytogenetics Refined with NGS Analysis of Multiple Genes
4.5. Using NGS to Predict Response to Hypomethylating Agents
4.6. Using NGS to Predict Outcome after Allogeneic Transplantation
5. Next Generation Sequencing to Guide Treatment in AML?
5.1. Overview of Current AML Treatment Strategies
5.2. Targeting FLT3 Mutations
5.2.1. Midostaurin (Novartis)
5.2.2. Gilteritinib (Astellas, Tokio, Japan)
5.2.3. Quizartinib (Daiichi Sankyo, Tokio, Japan)
5.3. Targeting IDH (Mutations)
5.3.1. Targeting IDH2 Mutations with Enasidenib (Celgene, Summit, NJ, USA)
5.3.2. Targeting IDH1 Mutations with Ivosidenib (Agios)
5.4. Targeting TP53 (Mutations) with Drugs Whose Mechanism of Action Is TP53 Independent
5.4.1. Overview of TP53 Mutations in AML
5.4.2. Targeting Bcl-2 with Venetoclax (AbbVie, Chicago, IL, USA)
5.5. Targeting JAK2 Mutations
Ruxolitinib (Novartis, Basel, Switzerland)
6. Next Generation Sequencing for Response Assessment and Disease Monitoring
6.1. Minimal Residual Disease
6.2. MRD Guided Treatment Modulation in Clinical Trials
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Qiagen Human Myeloid Neoplasms Panel 1 | Illumina AmpliSeq Myeloid Panel 2 | Quest Diagnostics LeukoVantage Panel 3 | Oxford Gene Technology SureSeq myPanel NGS Custom AML 1 |
---|---|---|---|
ASXL1 (full) | ASXL1 (full) | ASXL1 | ASXL1 (full) |
CEBPA (full) | CEBPA (full) | CEBPA | CEBPA (full) |
DNMT3A (full) | DNMT3A (hotspot) | DNMT3A | DNMT3A (full) |
FLT3 (full) | FLT3 (hotspot) | FLT3 | FLT3 (full) |
IDH1 (full) | IDH1 (hotspot) | IDH1 | IDH1 (full) |
IDH2 (full) | IDH2 (hotspot) | IDH2 | IDH2 (full) |
KIT (full) | KIT (hotspot) | KIT | KIT (full) |
KMT2A (full) | KMT2A (fusion) | KMT2A | KMT2A (full) |
KRAS (full) | KRAS (hotspot) | KRAS | KRAS (full) |
NPM1 (full) | NPM1 (hotspot) | NPM1 | NPM1 (full) |
NRAS (full) | NRAS (hotspot) | NRAS | NRAS (full) |
RUNX1 (full) | RUNX1 (full) | RUNX1 | RUNX1 (full) |
TET2 (full) | TET2 (full) | TET2 | TET2 (full) |
TP53 (full) | TP53 (full) | TP53 | TP53 (full) |
U2AF1 (full) | U2AF1 (hotspot) | U2AF1 | U2AF1 (full) |
WT1 (full) | WT1 (hotspot) | WT1 | WT1 (full) |
BCOR (full) | BCOR (full) | - | BCOR (full) |
CALR (full) | CALR (full) | CALR | - |
CBL (full) | CBL (hotspot) | CBL | - |
CSF3R (full) | CSF3R (hotspot) | CSF3R | - |
ETV6 (full) | ETV6 (full) | - | ETV6 (full) |
EZH2 (full) | EZH2 (full) | EZH2 | - |
GATA1 (full) | - | GATA1 | GATA1 (full) |
JAK2 (full) | JAK2 (hotspot) | JAK2 | - |
MPL (full) | MPL (hotspot) | MPL | - |
PHF6 (full) | PHF6 (full) | - | PHF6 (full) |
PTPN11 (full) | PTPN11 (hotspot) | PTPN11 | - |
SETBP1 (full) | SETBP1 (hotspot) | SETBP1 | - |
SF3B1 (full) | SF3B1 (hotspot) | SF3B1 | - |
SRSF2 (full) | SRSF2 (hotspot) | SRSF2 | - |
ZRSR2 (full) | ZRSR2 (full) | ZRSR2 | - |
ABL1 (full) | ABL1 (hotspot) | - | - |
BRAF (full) | BRAF (hotspot) | - | - |
CREBBP (full) | CREBBP (fusion) | - | - |
DDX41 (full) | - | DDX41 | - |
EGFR (full) | EGFR (fusion) | - | - |
GATA2 (full) | GATA2 (hotspot) | - | - |
HRAS (full) | HRAS (hotspot) | - | - |
IKZF1 (full) | IKZF1(full) | - | - |
KMD6A (full) | - | KMD6A | - |
MYC (full) | MYC (expression) | - | - |
MYD88 (full) | MYD88 (hotspot) | - | - |
NF1 (full) | NF1 (full) | - | - |
NTRK3 (full) | NTRK3 (fusion) | - | - |
PDGFRA (full) | PDGFRA (fusion) | - | - |
PRPF8 (full) | PRPF8 (full) | - | - |
RB1 (full) | RB1 (full) | - | - |
SH2B3 (full) | SH2B3 (full) | - | - |
SMC1A (full) | SMC1A (expression) | - | - |
STAG2 (full) | STAG2 (full) | - | - |
91 genes available |
Author | Papaemanuil [1] | Tefferi [14] | Gangat [15] | Han Lin [16] | Hussaini [17] | Ruffalo [18] | Ley [2] | Lindsley [19] | Lindsley [19] | Lindsley [19] | Chun Ha [20] | Wang [21] | Welch [22] |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Year Published | 2016 | 2017 | 2018 | 2016 | 2018 | 2015 | 2013 | 2014 | 2014 | 2014 | 2016 | 2016 | 2016 |
n pts | 1540 | 179 | 300 | 112 | 187 | 274 | 200 | 93 1 | no data 2 | 101 3 | 60 | 95 | 54 |
Median Age | 18–65 | 73 | <70 | 43 | no data | 61,9 | 55 | 62 | 62 | 62 | 50 | 45 | 74 |
n-Genes in Panel | 111 | 27 | 27 | 260 | 21 | 71 | WGS/WES | 82 | 82 | 82 | 54 | 410 | 264 |
Gene | %pts | %pts | %pts | %pts | %pts | %pts | %pts | %pts | %pts | %pts | %pts | %pts | %pts |
TET2 | 13.3 | 25 | 26 | 10 | 15.3 | 14.0 | 8.0 | 20.0 | 9.0 | 14.0 | 8.0 | 9.5 | 14.8 |
ASXL1 | 4.61 | 30 | 27 | 16 | 20.7 | 5.0 | 2.5 | 32.0 | 3.0 | no data | 8.0 | 9.5 | 11.1 |
SMC1A | - | - | - | no data | - | - | no data | 3.0 | 4.0 | 3.0 | no data | no data | 3.7 |
BCOR | 2.34 | - | - | no data | - | 2.0 | no data | 8.0 | 2.0 | 1.0 | no data | - | 5.6 |
DNMT3A | 24.9 | 10 | 13 | 15 | 14.8 | 21.0 | 26.0 | 19.0 | 28.0 | 27.0 | 8.0 | 16.8 | 14.8 |
IDH2 | 9.9 | 6 | 4 | 12 | 12 | 8.0 | 10.0 | 11.0 | 11.0 | 17.0 | 14.0 | 11.6 | 16.7 |
TP53-Others | 7.21 | 13 | 12 | no data | 14.4 | 8.0 | 8.0 | 15.0 | 9.0 | 23.0 | 8.0 | 5.3 | 25.9 |
EZH2 | 3.12 | 4 | 3 | no data | - | 3.0 | 1.5 | 9.0 | 2.0 | 3.0 | 2.0 | no data | 1.9 |
KAT6A | - | - | - | no data | - | - | no data | - | - | - | - | - | 0.0 |
IDH1 | 6.88 | 3 | 3 | no data | 10 | 8.0 | 9.5 | 11.0 | 11.0 | 17.0 | 6.0 | 4.2 | 9.3 |
JAK3 | no data | - | - | no data | - | - | no data | - | - | - | no data | no data | 0.0 |
KIT | 4.61 | 2 | no data | no data | 10 | 2.0 | 4.0 | 3.0 | no data | 2.0 | 4.0 | 2.1 | 5.6 |
RUNX1 | 9.8 | 11 | 10 | no data | 15.2 | 9.0 | 10.0 | 31.0 | 11.0 | 11.0 | - | 5.3 | 16.7 |
SRSF2 | 6.04 | 16 | 13 | no data | - | 8.0 | no data | 20.0 | 1.0 | 10.0 | no data | 4.2 | 18.5 |
NF1 | 2.53 | - | - | no data | - | 3.0 | no data | 6.0 | 4.0 | 4.0 | - | 1.1 | 1.9 |
BCORL1 | - | - | - | no data | - | - | no data | no data | no data | no data | no data | - | 0.0 |
WT1 | 5.26 | - | - | 11 | - | 4.0 | 6.0 | no data | no data | 3.0 | no data | 11.6 | 7.4 |
FLT3 others | 37.4 | 0.5 | no data | 21 | 11 | 16.0 | 28.0 | 19.0 | 28.0 | 16.0 | 32.0 | 18.9 | 5.6 |
NPM1 | 28.6 | no data | no data | no data | 11 | 16.0 | 27.0 | 5.0 | 30.0 | 16.0 | 24.0 | 21.5 | 11.1 |
IKZF1 | no data | no data | no data | no data | - | - | no data | no data | no data | no data | 2.0 | 1.1 | 0.0 |
KRAS | 5.19 | - | - | no data | - | 4.0 | 12.0 | 8.0 | 4.0 | 11.0 | no data | 3.2 | 3.7 |
NRAS | 19.0 | no data | no data | no data | 11.9 | 6.0 | 12.0 | 23.0 | 8.0 | 13.0 | 2.0 | 12.6 | 9.3 |
ATRX | 0.39 | - | - | no data | - | - | no data | no data | no data | no data | - | - | 0.0 |
ZRSR2 | 0.78 | no data | no data | no data | 10 | - | no data | 8.0 | no data | 1.0 | 2.0 | - | 0.0 |
SF3B1 | 2.60 | 20 | 30 | no data | 10 | 5.0 | no data | 11.0 | 1.0 | 3.0 | 2.0 | 1.1 | 7.4 |
STAG2 | 4.48 | - | - | no data | - | 4.0 | no data | 14.0 | 2.0 | 6.0 | 4.0 | 5.3 | 5.6 |
U2AF1 | 2.47 | 16 | no data | no data | 10 | 6.0 | no data | 16.0 | 4.0 | 5.0 | no data | 7.4 | 9.3 |
SETBP1 | - | 3 | 3 | no data | 10 | - | no data | 5.0 | no data | 3.0 | 2.0 | no data | 3.7 |
PTPN11 | 8.51 | no data | no data | no data | - | 4.0 | 4.0 | 5.0 | 5.0 | 9.0 | 2.0 | 4.2 | 1.9 |
ABL1 | - | - | - | no data | - | - | no data | - | - | - | - | no data | - |
SMC3 | - | - | - | no data | - | 2.0 | no data | 2.0 | 4.0 | 2.0 | 4.0 | 2.1 | 1.9 |
JAK2 | 0.71 | 1 | no data | no data | 10 | - | no data | no data | no data | no data | 2.0 | 1.1 | 5.6 |
ETV6 | 1.43 | - | - | no data | 10 | 2.0 | no data | no data | no data | no data | 2.0 | 1.1 | 3.7 |
PRPF40B | no data | - | - | no data | - | - | no data | no data | no data | no data | - | no data | 0.0 |
MLL | no data | - | - | no data | - | 2.0 | no data | no data | no data | no data | - | 1.1 | 1.9 |
RAD21 | 3.70 | - | - | no data | - | 2.0 | no data | 2.0 | 3.0 | 4.0 | 4.0 | 3.2 | 1.9 |
GNAS | no data | - | - | no data | - | - | no data | no data | no data | no data | no data | - | 0.0 |
CBL | 2.73 | 1 | 3 | no data | 10 | 3.0 | no data | 5.0 | 2.0 | 4.0 | 2.0 | no data | 5.6 |
PHF6 | 3.05 | - | - | no data | 10 | 4.0 | 3.0 | 5.0 | no data | 1.0 | no data | 1.1 | 7.4 |
SUZ12 | - | no data | no data | no data | - | - | no data | no data | no data | no data | - | - | 0.0 |
CBLB | no data | - | no data | - | - | no data | no data | no data | no data | - | 2.1 | 0.0 | |
MPL | no data | no data | no data | no data | 0 | - | no data | no data | no data | no data | no data | no data | 0.0 |
SF3A1 | no data | - | no data | - | - | no data | no data | no data | no data | - | - | 0.0 | |
SH2B3 | no data | no data | no data | no data | - | - | no data | no data | no data | no data | - | no data | 0.0 |
U2AF2 | 0.13 | - | 14 | no data | - | - | no data | no data | no data | no data | - | no data | 0.0 |
DAXX | - | - | - | no data | - | - | no data | - | - | - | - | - | 0.0 |
EED | - | - | - | no data | - | - | no data | no data | no data | no data | - | - | 0.0 |
RB1 | no data | - | - | no data | - | - | no data | - | - | - | - | no data | 0.0 |
GATA1 | no data | - | - | no data | - | - | no data | no data | no data | no data | no data | no data | 0.0 |
SF1 | no data | - | - | no data | - | - | no data | no data | no data | no data | no data | - | 0.0 |
JAK1 | - | - | - | no data | - | - | no data | - | - | - | - | no data | 0.0 |
CBFB | - | - | - | no data | - | - | no data | - | - | - | - | no data | 1.9 |
CEBPA | 8.18 | 3 | 3 | 15 | 10 | 2.0 | 6.0 | 3.0 | 7.0 | 5.0 | 6.0 | 29.5 | 3.7 |
Substance Group | Agent | Target | Ph | Patient Cohort | Schedule | ORR (%) | PFS (m) | OS (m) | A |
---|---|---|---|---|---|---|---|---|---|
FLT 3 Inhibitors | Midostaurin [61,62,63] | FLT3 (non mutated) | I | R/R or unfit | + AZA | 21 | NR | 6 | Y |
FLT 3 mutated or WT | I | De novo | + CTx | 80 | NR | NR | |||
FLT3 mutated or WT | II | R/R or unfit | M | 71 (mutated) 42 (WT) | NR | 4.3 | |||
FLT3 mutated | III | De novo | + CTx | 59/53 | 26.7/15.5 | 74.7/25.6 | |||
Quizartinib [64,65,66,67] | FLT3 mutated | I | R/R | M | 30 | NR | 3.5 | Y | |
FLT3 mutated or WT | II | R/R | M | 47 | 2.2 | 6.7 | |||
FLT3 mutated or WT | II | R/R | M | 74–77 | 3 | 6 | |||
FLT3 mutated | III | R/R | M vs. CTx | 48 vs. 27 | 4 vs. 1.2 | 6.2 vs. 4.7 | |||
Gilteritinib [68] | FLT3 (mutated) | I | R/R | M | 40 | 4.25 | 6.25 | Y | |
Lestaurtinib [69] | FLT3 (mutated) | III | De novo | + CTx | 97 | 40 vs. 36% (NS) | 5 y OS 46 vs. 45% (NS) | N | |
Sunitinib [70] | FLT 3 (mutated) | I/II | De novo elderly | + CTx | 59 | 12 | 18 | N | |
Crenolanib [71,72,73,74,75] | FLT ITD and D835 | II | De novo | + CTx | 96 | NR | Nre | N | |
II | De novo | + CTx | 83 | NR | Nre | ||||
II | R/R | + CTx | 67 | NR | NR | ||||
II | R/R | + CTx | 36 | NR | 9.25 | ||||
II | R/R | M | 47 | 2 | 4.75 | ||||
Sorafenib [76,77,78,79,80,81] | Multiple kinases | II | De novo | + CTx | 60 vs. 59 | 9 vs. 21 | Nre | N | |
I | R/R | M | 10 | NR | NR | ||||
I | After AlloTx in FLT3-ITD | M | NR | 85% at 12 Mo | 95% at 12 Mo | ||||
I | R/R after AlloTx with FLT3-ITD | + AZA | 50 | NR | 322 days | ||||
III | Maintenance After alloTx | M | NR | Nre vs. 30.9 | NR | ||||
IDH Inhibitors | Enasidenib [82] | IDH 2 (mutated) | I/II | R/R | M | 40 | 6.4 | 9.3 | Y |
Ivosidenib [83] | IDH 1 (mutated) | I | R/R | M | 41 | NR | NR | Y | |
Bcl-2 Inhibitors | Venetoclax [84,85] | Bcl-2 | II | R/R or unfit | M | 19 | 2.5 | 4.7 | Y |
Ib | unfit | + AZA or DAC | 73 | NR | 18 | ||||
Obatoclax [86] | Bcl-2 family | I/II | unfit | M | 0 | NR | NR | N |
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Leisch, M.; Jansko, B.; Zaborsky, N.; Greil, R.; Pleyer, L. Next Generation Sequencing in AML—On the Way to Becoming a New Standard for Treatment Initiation and/or Modulation? Cancers 2019, 11, 252. https://doi.org/10.3390/cancers11020252
Leisch M, Jansko B, Zaborsky N, Greil R, Pleyer L. Next Generation Sequencing in AML—On the Way to Becoming a New Standard for Treatment Initiation and/or Modulation? Cancers. 2019; 11(2):252. https://doi.org/10.3390/cancers11020252
Chicago/Turabian StyleLeisch, Michael, Bettina Jansko, Nadja Zaborsky, Richard Greil, and Lisa Pleyer. 2019. "Next Generation Sequencing in AML—On the Way to Becoming a New Standard for Treatment Initiation and/or Modulation?" Cancers 11, no. 2: 252. https://doi.org/10.3390/cancers11020252
APA StyleLeisch, M., Jansko, B., Zaborsky, N., Greil, R., & Pleyer, L. (2019). Next Generation Sequencing in AML—On the Way to Becoming a New Standard for Treatment Initiation and/or Modulation? Cancers, 11(2), 252. https://doi.org/10.3390/cancers11020252