Impact of Biomarkers in the Management of Patients with Acute Myeloid Leukemia Hartmut Döhner Medical Director, Department of Internal Medicine III Director, Comprehensive Cancer Center Ulm Ulm University, Germany
Incidence of AML in Sweden (1997-2005) According to Age and Sex Cases per 100.000 inhabitants Median age at diagnosis: 72 years Juliusson G, et al. Blood. 2009:113(18):4179-4187.
Challenges in Leukemia Research Overall Poor Treatment Outcome in AML 100 80 Survival (%) 60 40 18-60 yrs, n=3459 20 >60 yrs, n=949 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Time (years) AMLSG data base
Age-Related Changes in AML Biomarkers 0.8 0.7 n=3,022 percentage 0.6 0.5 0.4 0.3 0.2 0.1 MDS-karyo t-aml NPM1 CEBPA inv(3) t(6;9) t(11q23) t(9;11) inv(16) t(8;21) 0 <45 45-60 60-75 >75 < 45 years 45-60 years 60-75 years >75 years AMLSG data base
Genetic Diversity of AML NPM1, CEBPA, FLT3, RUNX1, MLL, RAS, WT1, IDH1/2, TET2, DNMT3A, ASXL1, 45% normal karyotype 11% complex karyotype KIT RAS FLT3 CBL excl. APL 5% t(8;21) 6% inv(16) EVI1+ 2% t(9;11) 2% t(11q23) FLT3 1% t(6;9) 1% inv(3)/t(3;3) RAS 23% various e.g. +8, 9q-, +13, 20q- NPM1, FLT3, TP53, chromothripsis
Heterogeneity of Cytogenetically Normal AML Döhner H et al. Blood. 2010;115(3):453-474. NPM1, CEBPA, FLT3-ITD/-TKD, MLL, NRAS, WT1
Biomarkers Some Challenges Disease classification: does the biomarker define a disease entity? Prognosis: does the biomarker provide prognostic information, independent from others? Prediction: does the biomaker predict response to a specific therapy (novel agents)? Clinical diagnostics: which markers should be tested for? Molecular therapy: which molecular biomarker can be targeted therapeutically?
Prognosis: Genotypes Associated with Favorable Outcome in Cytogenetically Normal AML Schlenk RF, Döhner K, et al. N Engl J Med. 2008;358(18):1909-18.
Prediction: Impact of allogeneic HSCT in molecular subsets of cytogenetically normal AML Favorable molecular genotype Unfavorable molecular genotype incl. AML with FLT3-ITD Schlenk RF, Döhner K, et al. N Engl J Med. 2008;358(18):1909-18.
NPM1, CEBPA, FLT3 Entered Clinical Practice Fav Int-I t(8;21)(q22;q22); RUNX1-RUNX1T1 inv(16)(p13.1q22); CBFB-MYH11 Mutated NPM1 without FLT3-ITD (nl karyotype) Mutated CEBPA (nl karyotype) Mutated NPM1 and FLT3-ITD (nl karyotype) Wild type NPM1 and FLT3-ITD (nl karyotype) Wild type NPM1 without FLT3-ITD (nl karyotype) Int-II t(9;11)(p22;q23); MLLT3-MLL; cytogenetic abnormalities not classified as favorable or adverse Adv inv(3)(q21q26.2) or t(3;3)(q21;q26.2); RPN1-EVI1 t(6;9)(p23;q34); DEK-NUP214; t(v;11q23); MLL rearranged; -5 or del(5q); -7; abn(17p); complex karyotype ( 3) International AML Recommendations (ELN) Döhner H, et al. Blood. 2010;115:453-474. RUNX1 MLL WT1 IDH1 IDH2 ASXL1 SF3B1 TET2 DNMT3A TP53 EVI1 + MN1 + ERG + MLL5 + BAALC +
Translation into Biomarker-Driven Trials - Core-binding factor AML AML with inv(16)(p13.1q22); CBFB-MYH11 AML with t(8;21)(q22;q22); RUNX1T1-RUNX1 - AML with FLT3-ITD - AML with NPM1 mutation
Core-Binding Factor (CBF) AML Associated with KIT mutations exon 8 - inv(16)(p13.1q22); CBFB-MYH11 (30-35%) - t(8;21)(q22;q22); RUNX1-RUNX1T1 (30-35%) High KIT expression Impact on prognosis: in general poor eg, Care RS, et al. Br J Haematol. 2003; Schnittger S, et al. Blood. 2006; Paschka P, et al. J Clin Oncol. 2006. exon 17 Impact on therapy -> evaluation of KIT inhibitors eg, dasatinib, midostaurin, lestaurtinib
Phase II Study of Chemotherapy + Dasatinib in Patients with Newly Diagnosed Core-Binding Factor (CBF) AML - AMLSG 11-08 Induction Consolidation x 4 Maintenance CBF Mutation Screening Within 48 Hours n=90 Daunorubicin Cytarabine + Dasatinib High-Dose Cytarabine* + Dasatinib Dasatinib 1 year Companion studies, e.g.: Gene mutation / expression analyses Minimal residual disease assessment Pharmacodynamics studies (KIT plasma inhibitory activity) * Cytarabine: 18-60yrs: 3g/m 2, q12hr, d1-3; >60yrs: 1g/m 2, q12hr, d1-3 ClinicalTrials.gov Identifier: NCT00850382 (AMLSG), NCT01238211 (CALGB)
AML with FLT3 Internal Tandem Duplication Found ~ 25% of adult patients with AML JM TK-1 Impact on prognosis: poor Impact of mutant-to-wild type ITD allelic ratio Whitman SP, et al. Cancer Res. 2001; Thiede C, et al. Blood. 2002; Kottaridis PD et al. Blood. 2002; Gale R et al. Blood. 2008; Schnittger S, et al. Genes Chromosomes Cancer 2012; Kayser et al., ASH 2012. Impact of ITD insertion site (JM vs. TK-1 domain) Breitenbuecher F et al. Blood. 2009; Kayser S, et al. Blood. 2009; Breitenbuecher F, et al. Blood. 2009. Impact on therapy: FLT3 inhibitors in clinical development eg, midostaurin (PKC412), lestaurtinib; quizartinib (AC220) Allogeneic HSCT may improve outcome
Choose the right compound: Selectivity and Potency FLT3 quizartinib lestaurtinib midostaurin Zarrinkar PP, et al. Blood. 2009;114(14):2984-2992.
Phase II Study of Chemotherapy + Continuous Midostaurin Followed by Allogeneic HSCT and Midostaurin Maintenance in AML with FLT3-ITD AMLSG 16-10 Trial 1 st priority Early Allogeneic HSCT 1-yr maintenance Start: 30 d after allo FLT3-ITD Mutation Screening Within 48 Hours Dauno Cytarabine midostaurin* High-Dose Cytarabine** 2 nd priority 3x High-Dose Cytarabine 1-yr maintenance * Midostaurin: start on day 8, thereafter continuous dosing ** Optional 1 st consolidation before allo HSCT PI: R.F. Schlenk; CI: S. Kayser; supported by Novartis
AMLSG Biomarker-Driven Treatment Trials APL [t(15;17)] ~10% CBF AML ~13% ATO+ATRA APL0406 GIMEMA / AMLSG / SAL Dasatinib AMLSG 11-08 AML FLT3-ITD ~20 % Midostaurin AMLSG 16-10 Molecular Screening 24-48 hrs AML NPM1 mut ~17% ~35% Other subtypes (mainly high-risk) ATRA +/- GO AMLSG 09-09 Azacitidine + induction, Allo HSCT (1 st priority), or HiDAC + AZA maintenance AMLSG 12-09 C-IARA (Clofarabine), Allo HSCT (or HiDAC) AMLSG 13-09
Fast Biomarker Screening / Biobanking Trial Centers (n=65) AMLSG BiO registry Informed Consent Courier Express BM&PB samples Reference Lab Ulm Molecular screening - PML-RARA - RUNX1-RUNX1T1 - CBFB-MYH11 - NPM1 - FLT3-ITD - FLT3-TKD - MLL-AF9 - CEBPA Web-based system Trial Center Clinical Trial Informed Consent Standard Care 0 Hours Overnight 24-48 hrs / 7d week
Systematic Characterization of Biomarkers in Myeloid Neoplasms - Outline Genomic study findings > 2000 Acute myeloid leukemias Molecular classification Phenotype and clinical correlates Genome targets: Myeloid genes Cancer census selected Common fusion genes Sample criteria: Demographic information Clinical trial patients Working protocol: DNA: 100-200ng / WGA Capture arrays (Agilent) Next-generation sequencing
Myeloid & Cancer Genes (n=112)
A. Corbacioglu V. Gaidzik S. Gröschel S. Kayser J. Krönke M. Kühn P. Paschka F. Rücker F. Kuchenbauer L. Bullinger R.F. Schlenk K. Döhner Ulm University J. Krauter F. Damm M. Heuser G. Göhring B. Schlegelberger A. Ganser MHH, Hannover J. Korbel S. Pfister P. Lichter DKFZ, EMBL Heidelberg P. Valk R. Delwel B. Löwenberg Rotterdam R. Larson G. Marcucci C. Bloomfield CALGB E. Papaemmanuil P. Campbell Cambridge I. Radtke J. Downing Memphis