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Acute myeloid leukemia leads the way in molecular cancer genetics: precision medicine at reach?

MODERN PROBLEMS OF GENETICS dedicated to the 115 th anniversary of the birth of N. W. Timofeeff-Ressovsky Cancer genetics & Cancer therapy 4 th June 2015, St. Petersburg. Acute myeloid leukemia leads the way in molecular cancer genetics: precision medicine at reach?.

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Acute myeloid leukemia leads the way in molecular cancer genetics: precision medicine at reach?

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  1. MODERN PROBLEMS OF GENETICS dedicated to the 115th anniversary of the birth of N. W. Timofeeff-Ressovsky Cancer genetics & Cancer therapy 4th June 2015, St. Petersburg Acute myeloid leukemia leads the way in molecular cancer genetics: precision medicine at reach? Lars Bullinger University of Ulm / MDC Berlin-Buch

  2. IntroductionAcute Myeloid Leukemia (AML)

  3. Acute Myeloid Leukemia (AML) Definition: - Clonal expansion of myeloid blasts in bone marrow, blood or tissue. Epidemiology: - 3% of all cancers - Incidence increases with age Clinical course: NCI, SEER data base

  4. Overall survival 100 low-risk; n=261 80 60 intermediate-risk; n=698 40 high-risk; n=171 20 0 0 1 2 3 4 5 6 7 8 9 10 time (years) AML Cytogenetics • Low-risk • t(15;17) PML-RARA • t(8;21) AML1-ETO • inv(16)CBFB-MYH11 • Intermediate-risk • normal karyotype • t(9;11)MLL-AF9 • High-risk • inv(3) EVI1 • complex karyotype

  5. Omics and NGS in AML

  6. Approved treatment options for AML vs. other hematologic malignancies US approvals EU approvals Subsequently withdrawn • 1. NCI Drug Info. http://www.cancer.gov/cancertopics/druginfo. 2. EMA Drug Approvals. http://www.ema.europa.eu/ema/index.jsp?curl=pages/includes/ medicines/medicines_landing_page.jsp&mid=. 3. FDA Drug Approvals. http://www.accessdata.fda.gov/ scripts/cder/drugsatfda/index.cfm. 3. NCCN clinical practice guidelines in oncology: acute myeloid leukemia. National Comprehensive Cancer Network website. V.2.2014. http://www.nccn.org/professionals/physician_gls/PDF/aml.pdf.

  7. Improving outcome in AML remains a major challenge 100 75 18-45 yrs, n=1,574 Survival (%) 50 45-60 yrs, n=2,156 60-70 yrs, n=963 25 >70 yrs, n=437 0 0 1 2 3 4 5 6 7 8 9 10 Time (years) No. of pts: n=5,130; only pts considered eligible for intensive induction therapy

  8. Genomic landscape of de novo AML TCGA Research Network. N Engl J Med 2013

  9. Genomic landscape of de novo AML Myeloid transcription- factor genes RUNX1, CEBPA Transcription-factor fusion genes RUNX1-RUNX1T1, MYH11-CBFB 18% 22% Signaling genes FLT3, KIT, NRAS 59% TCGA Research Network. N Engl J Med 2013

  10. Genomic landscape of de novo AML Myeloid transcription- factor genes RUNX1, CEBPA Transcription-factor fusion genes RUNX1-RUNX1T1, MYH11-CBFB 18% 22% Signaling genes FLT3, KIT, NRAS 59% 27% NPM1 Tumor-suppressor genes TP53, WT1, PHF6 16% DNA-methylation- related genes DNMT3A, TET2, IDH1/2 44% 14% Spliceosome-complex genes SF3B1, U2AF1 30% Chromatin- modifying genes MLL-X, ASXL1, EZH2 13% Cohesin-complex genes SMC1A, SMC3, RAD21 TCGA Research Network. N Engl J Med 2013

  11. Genomic heterogeneity and evolution => improved understanding of clonal heterogeneity at diagnosis might provide means to prevent relapse caused by evolution of persisting subclones Welch et al. Cell 2012

  12. Clonal architecture and genetic heterogeneity Lindsleyand Ebert. Blood 2013

  13. Genomic heterogeneity and evolution Krönke et al. Blood 2013

  14. Exome sequencing of NPM1mutloss cases Diagnosis Remission Relapse pre-leukemic HSC persistence? time (years) pre-leukemic HSC ? 2000 2010 SureSelect Human All Exon 50Mb Average coverage: diagnosis sample, 73.6 fold; relapse sample, 88.9 fold

  15. MRD of DNMT3Amut-R882H and NPM1mut PB BM BM BM BM DNMT3Amut DNMT3A/ABL1x104 Relapse Loss ofNPM1mut NPM1/ABL1x104 NPM1mut (BM) f-up (2,75 yrs) diagnosis induction I Cons. III induction II AMLSG HD98A trial, pt. died, in relapse

  16. Identification of pre-leukemic HSCs in AML • DNMT3A mutation precedes NPM1 mutation in human AML • DNMT3A mutations present in stem/ progenitor cells at diagnosis and remission Shlush et al. Nature 2014

  17. Therapy related AML (t-AML) • The mutational burden in t-AML is similar to de novo AML • HSC clones harboring somatic TP53mutations are detected in patients before cytotoxic therapy exposure Wong et al. Nature 2015

  18. Loss of TP53 confers a clonal advantage Wong et al. Nature 2015

  19. Clonal evolution model in t-AML/t-MDS Bullinger. Hematotopics 2015

  20. Targeted re-sequencing in AMLBasis for precision medicine?

  21. Genetics guided therapeutic approaches: clinical practical challenges • Clinical diagnostics: which biomarkers should be tested for? • Prognosis: which biomarkers provide prognostic information independent from others? • Prediction: which biomarkers are able to predict response to a specific therapy (novel agents)? • Molecular therapy: which molecular lesions can be targeted therapeutically?

  22. Systematic characterization of myeloid neoplasms • 1540 adult patients with AML • Enrolled in 3 trials of the German-Austrian AML Study Group • Targeted re-sequencing of 111 genes involved in pathogenesis of myeloid neoplasms (SureSelect target enrichment) • Objectives: • Identify genetic lesions that contribute to disease pathogenesis and classification • Identify secondary and tertiary gene-gene interactions • Evaluate prognostic and predictive impact E. Papaemmanuil, M. Gerstung, P. Campbell R. Schlenk, K. Döhner, L. Bullinger, H. Döhner

  23. Genomic landscape of AML • 6 genes in >10% of pts; 13 genes 5-10%; 24 genes 2-5%; 37 genes <2% • CN-AML => more gene mutations than in AML with chrom. abnormalities • Driver mutations significantly increased with age (p<0.001)

  24. Timing of driver mutation acquisition • Subclonal heterogeneity and informative timings could be inferred for 690 (64%) of 1076 pts. with two or more mutations • Genes involved with epigenome modeling (DNMT3A, ASXL1, TET2) were typically acquired earliest • Genes involved in receptor tyrosine kinase (RTK) / RAS signaling occurred as late events virtual time axis

  25. Implication of genomics for classification • Formal statistical analysis (Bayesian latent class models) • 11 non-overlapping molecular classes can be identified • 6 balanced rearrangements: • inv(16), t(15;17), t(8;21), t(11q23), inv(3), t(6;9) • NPM1 mutation, with significant contribution from DNA methylation / hydroxymethylation genes DNMT3A, TET2, IDH1, IDH2 • BiallelicCEBPA mutation • TP53 mutation and / or chromosomal aneuploidies • Splicing factor genes or regulators of chromatin and transcription • DNMT3A / IDH2 (in the absence of NPM1)

  26. Implication of genomics for classification • 11 “non-overlapping” molecular AML classes • 1332/1540 (86%) of AML classified, with minimal overlap across categories

  27. Reference map of gene-gene interactions • >200 significant interactions CA, chrom. aneuploidiesI SF, splicingfactorI RTK, receptortyrosinekinases

  28. Risk contributions in each patient RED: short survival BLUE: long survival

  29. Unique constellations of risk factors

  30. Risk groups • Quartiles of predicted risk • 121 intermediate-1 risk patients will be reclassified as very high risk case very lowlowhighvery high Favorable 308 131 33 3 inter-1 19 104 174 121 inter-2 36 65 85 84 Adverse 4 23 44 185

  31. Personally tailored cancer management based on genomic and clinical data • Comprehensive genomic profiles of 111 cancer genes and cytogenetic data from 1,540 AML patient can be used in conjunction with clinical data sets to accurately predict outcome for each patient • Incorporating all genomic driver mutations into prognostic models outperforms models using conventional prognostication schemes • Genomic data account for approx. 2/3 of the predicted risk of overall survival • Data can be used to compute absolute probabilities of competing events such as relapse, non-relapse mortality or salvage rates on an individual level for each patient and under different treatment options -> basis for rationalized clinical decision support

  32. AML prediction tool 29-year old female patient with t(8;21)-positive AML Intensive Chemotherapy Allogeneic HCT in 1st CR

  33. Targeted resequencingin the clinic? • using e.g. Illumina sequencing by synthesis technology (MiSeq) • => AML panel of 31 genes (560 targetregions, 222kb) ASXL1, CBL, CEBPA, CTCF, DNMT1, DNMT3A, ETV6, EZH2, FLT3, GATA2, HIPK2, IDH1, IDH2, JAK2, KIT, KRAS, MLL3, MLL5, NF1, NPM1, NRAS, NSD1, PHF6, RAD21, RUNX1, SF3B1, SFPQ, TET1, TET2, TP53, WT1 Library preparation e.g. HaloPlex (~500kb) ~6h Cluster generation and sequencing (2x100bp -> 1Gb) ~14h Data analysis (supported workflow) <2h

  34. NGS based targeted personalized therapy

  35. Clinical implementation?Current status

  36. Translation into the clinic Genotypeadaptedstrategy Newly diagnosed AML Molecular screening -PML-RARA -RUNX1-RUNX1T1 -CBF-MYH11 -MLL-AF9-FLT3-ITD -FLT3-TKD -NPM1 -CEBPA IC molecular screeningRegistration AMLSG-BiO APL CBF NPM1mut FLT3-ITD Other  AMLSG BiO-ID BM&PB samples  Reference LabOvernight overnight 24-48 hours 0-8 hours

  37. Genetics guided AML therapy Genotype Trial NAPOLEONGIMEMA/AMLSG/SAL APOLLO+/- ATO-ATRA-Ida APL [PML-RARA] +/- DasatinibAMLSG 21-13 CBF-AML [KIT] MidostaurinAMLSG 16-10 AMLFLT3mut +/- CrenolanibAMLSG 19-13 AMLNPM1mut ATRA +/- GOAMLSG 09-09 EPZ 5676 (DOT1L) AMLMLLrearr Palbociclib (CDK6)AMLSG 23-14 Molecular Screening 24-48 hrs • +/- VolasertibAMLSG 20-13 Other subtypes, mainly high-risk +/- PanobinostatAMLSG 22-14

  38. Selected targets for molecular therapy PML-RARAATRA, ATO KIT mutations dasatinib, midostaurin FLT3 mutations midostaurin, sorafenib; quizartinib, crenolanib IDH mutations AG-221 MLL-rearranged DOT1L, CDK6 inhibitor Epigenetic mutations / azacitidine, decitabine, SGI-110 alterations (?)OTX015, I-BET-762

  39. Precision medicine in AML: fact or fiction? • We have entered a new era in leukemia genomics • => however, large gene panel testing and whole exome/genome sequencing remain research tools • Currently, cytogenetics and NPM1, CEBPA, FLT3-ITD mutational screening are standard of care (WHO / ELN update in 2016) • The explosion of knowledge has yet to be translated into therapeutic benefit • => however, a number of novel compounds are at the horizon that hold promise to enter the clinic • Major challenge: identification of predictive biomarkers that help selecting the appropriate therapy for an individual patient • => integrate biosampling, companion studies • Enter your patients, younger or older, on a clinical trial!

  40. S. Cocciardi A. Dolnik V. Gaidzik S. Kapp-Schwörer J. Krönke K. Lang F. Kuchenbauer P. Paschka F. Rücker F. Stegelmann D. Weber K. Holzmann K. Döhner R. F. Schlenk S. Stilgenbauer H. Döhner Ulm University M. Heuser G. Göhring F. Thol B. Schlegelberger A. Ganser MHH, Hannover G. Martinelli I. Iaccobucci Bologna A. Krivtsov S. Armstrong New York S. Fröhling C. Plass P. Lichter C. Scholl Heidelberg P. Valk B. Löwenberg Rotterdam P. Campbell E. Papaemmanuil Cambridge K. Rajewsky S. Sander Berlin SFB 1074

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