precision medicine at reach Lars Bullinger University of Ulm MDC Berlin Buch MODERN PROBLEMS OF GENETICS dedicated to the 115 th anniversary of the birth of N W TimofeeffRessovsky ID: 935216
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Slide1
Acute myeloid leukemia leads the way in molecular cancer genetics: precision medicine at reach?
Lars BullingerUniversity of Ulm / MDC Berlin-Buch
MODERN
PROBLEMS OF GENETICSdedicated to the 115th anniversary of the birth of N. W. Timofeeff-RessovskyCancer genetics & Cancer therapy4th June 2015, St. Petersburg
Slide2Introduction
Acute Myeloid Leukemia (AML)
Slide3Acute Myeloid Leukemia (AML)Epidemiology:- 3% of all cancers-
Incidence increases with age Clinical course:
CR
Survivalpts < 65 years70-80% ~35%
pts
> 65 years
40-60%
~5%
Definition:
- Clonal expansion of myeloid blasts in bone marrow, blood or tissue.
NCI, SEER data base
Slide4AML CytogeneticsLow-riskt(15;17) PML-RARA
t(8;21) AML1-ETOinv(16) CBFB-MYH11Intermediate-risk
normal karyotypet(9;11)
MLL-AF9High-riskinv(3) EVI1 complex karyotypeOverall survival
0
1
2
3
4
5
6
7
8
9
10
0
20
40
60
80
100
time (years)
low-risk; n=261
intermediate-risk; n=698
high-risk; n=171
Slide5Omics and NGS in AML
Slide6Approved
treatment options for AML
vs. other hematologic malignancies
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.
US approvals
EU approvals
Subsequently withdrawn
Slide718-45 yrs, n=1,57445-60 yrs, n=2,156
60-70 yrs, n=963>70 yrs, n=437
T
ime (years)Survival (%)
0
1
2
3
4
5
6
7
8
9
10
0
25
50
75
100
Improving
outcome
in AML
remains
a
major challenge
No. of pts: n=5,130; only pts considered eligible for intensive induction therapy
Slide8Genomic landscape of de novo AML
TCGA Research Network. N Engl J Med 2013
Slide9Genomic landscape of de novo AML
TCGA Research Network. N Engl J
Med 2013
59%18%22%Signaling genes FLT3, KIT, NRASMyeloid transcription-factor genesRUNX1, CEBPATranscription-factor fusion genes RUNX1-RUNX1T1, MYH11-CBFB
Slide10Genomic landscape of de novo AML
TCGA Research Network. N Engl J Med 2013
NPM1
Tumor-suppressor genesTP53, WT1, PHF6DNA-methylation-related genes DNMT3A, TET2, IDH1/2Signaling genes FLT3, KIT, NRASChromatin-modifying genesMLL-X, ASXL1, EZH2Myeloid transcription-
factor genes
RUNX1
,
CEBPA
Cohesin
-
complex genes SMC1A, SMC3, RAD2127%22%59%
44%
30%
16%13%14%Spliceosome-complex genesSF3B1, U2AF118%Transcription-factor fusion genes RUNX1-RUNX1T1, MYH11-CBFB
Slide11Genomic heterogeneity and evolution
=> improved understanding of clonal heterogeneity at diagnosis might provide means to prevent relapse caused by evolution of persisting subclonesWelch et
al. Cell
2012
Slide12Clonal architecture and genetic heterogeneity
Lindsley and Ebert. Blood 2013
Slide13Genomic heterogeneity and evolution
Krönke
et
al. Blood 2013
Slide14? DNMT3A
DNMT3A
DNMT3A
DNMT3ANPM1
Loss of
NPM1
BHMT2, CDH26, FSD1,
KAT6B, PDLIM1,
PSTPIP2,
TOP2B, ZBTB47
ARID4B, BRAF, CACNA1E, CHUK,
COL5A2, DDX58,
FAT2,
LINGO4,
MDN1, NF1, NPAT,
PARS2, PTPRO
Diagnosis
Relapse
2000
2010
pre
-leukemic HSC ?
time
(
years
)
SureSelect
Human All Exon 50Mb
Average coverage: diagnosis sample, 73.6 fold;
r
elapse
sample,
88.9 fold
pre-leukemic
HSC
persistence
?
Remission
Exome
sequencing
of
NPM1
mut
loss cases
Slide15DNMT3A
/
ABL1
x104NPM1/ABL1x104diagnosisinduction IICons. III
f-
up
(2,75
yrs
)
induction
IRelapseLoss of
NPM1mut
NPM1mut (BM)DNMT3Amut
AMLSG HD98A
trial
,
pt
.
died
, in
relapse
BM
PB
BM
BM
BM
MRD of
DNMT3A
mut
-R882H and
NPM1
mut
Slide16Identification of pre-leukemic HSCs in AML
Shlush
et
al. Nature 2014
DNMT3A
mutation precedes
NPM1
mutation in human
AML
DNMT3A
mutations present in stem/ progenitor cells at diagnosis and remission
Slide17Therapy related AML (t-AML)
Wong et al. Nature 2015The mutational burden in t-AML is similar to de novo AMLHSC clones harboring somatic TP53 mutations are detected
in patients before cytotoxic therapy exposure
Slide18Loss of TP53 confers a clonal advantage
Wong et al. Nature 2015
Slide19Clonal evolution model in t-AML/t-MDS
Bullinger. Hematotopics 2015
Slide20Targeted re-sequencing in AML
Basis for precision medicine?
Slide21Clinical 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?Genetics guided therapeutic approaches:clinical practical challenges
Slide22Systematic characterization of
myeloid neoplasms
1540 adult patients with AML
Enrolled in 3 trials of the German-Austrian AML Study GroupTargeted re-sequencing of 111 genes involved in pathogenesis of myeloid neoplasms (SureSelect target enrichment)Objectives:Identify genetic lesions that contribute to disease pathogenesis and classificationIdentify secondary and tertiary gene-gene interactionsEvaluate prognostic and predictive
impact
E. Papaemmanuil, M.
Gerstung
, P. Campbell
R. Schlenk, K. Döhner, L. Bullinger, H. Döhner
Slide23Genomic 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. abnormalitiesDriver mutations significantly increased with age (p<0.001)
Slide24Timing of driver mutation acquisition
Genes
involved with epigenome modeling (
DNMT3A, ASXL1, TET2) were typically acquired earliestGenes involved in receptor tyrosine kinase (RTK) / RAS signaling occurred as late eventsvirtual time axisSubclonal heterogeneity and informative timings could be inferred for 690 (64%) of 1076 pts. with two or more mutations
Slide25Implication of genomics for classification
Formal statistical analysis (Bayesian latent class models)
11 non-overlapping molecular classes can be identified
NPM1 mutation, with significant contribution from DNA methylation / hydroxymethylation genes DNMT3A, TET2, IDH1, IDH2Biallelic CEBPA mutationTP53 mutation and / or chromosomal aneuploidies
Splicing factor genes or regulators of chromatin and transcription
DNMT3A
/
IDH2
(in the absence of
NPM1
)6 balanced rearrangements:
inv(16), t(15;17), t(8;21), t(11q23),
inv(3), t(6;9)
Slide26Implication of genomics for classification
11 “non-overlapping” molecular AML classes
1332/1540 (86%) of AML classified, with minimal overlap across categories
Slide27Reference map of gene-gene interactions
>200 significant interactions
CA,
chrom. aneuploidies I SF, splicing factor I RTK, receptor tyrosine kinases
Slide28Risk contributions in each patient
RED: short survival
BLUE: long survival
Slide29Unique constellations of risk
factors
Slide30Risk groups
Quartiles of predicted risk
121 intermediate-1 risk patients will be reclassified as very high risk case
very low low high very highFavorable 308 131 33 3inter-1 19 104 174 121inter-2 36 65 85 84Adverse 4 23 44 185
Slide31Personally
tailored cancer management based
on
genomic and clinical dataComprehensive 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 patientData 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
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
Slide32AML prediction tool
29-year old female patient with t(8;21)-positive AML
Intensive Chemotherapy
Allogeneic HCT in 1
st
CR
Slide33Targeted resequencing in the clinic?
using e.g. Illumina sequencing by synthesis technology (MiSeq
)
=> AML panel of 31 genes (560 target regions, 222kb)Library preparation e.g. HaloPlex (~500kb) ~6h
Cluster generation and sequencing
(2x100bp -> 1Gb)
~14h
Data analysis
(supported workflow)
<
2
h
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
Slide34NGS based targeted personalized therapy
Slide35Clinical implementation?
Current status
Slide36Translation into the clinic
Newly diagnosed AML
IC molecular screening
Registration AMLSG-BiO AMLSG BiO-ID
0-8 hours
BM&PB samples
Reference Lab
Overnight
Molecular screening
-
PML-RARA
-RUNX1-RUNX1T1
-CBF
-MYH11
-MLL-AF9
-
FLT3
-ITD
-
FLT3
-TKD
-NPM1
-CEBPA
overnight
24-48 hours
Genotype
adapted
strategy
APL
CBF
NPM1
mut
FLT3
-ITD
Other
Slide37Genetics guided AML therapy
Trial
Midostaurin
AMLSG 16-10ATRA +/- GO AMLSG 09-09NAPOLEON GIMEMA/AMLSG/SALAPOLLO +/- ATO-ATRA-Ida
+/-
Dasatinib
AMLSG 21-13
+/-
Panobinostat
AMLSG 22-14+/- Volasertib AMLSG 20-13
+/-
Crenolanib
AMLSG 19-13EPZ 5676 (DOT1L) Palbociclib (CDK6) AMLSG 23-14 Genotype
AML
FLT3
mut
CBF-AML [
KIT
]
Molecular
Screening
24-48
hrs
AML
NPM1
mut
Other
subtypes
,
mainly
high-
risk
APL
[
PML-RARA
]
AML
MLL
rearr
Slide38PML-RARA ATRA, ATOKIT mutations dasatinib, midostaurin
FLT3 mutations midostaurin, sorafenib; quizartinib, crenolanibIDH mutations AG-221MLL-rearranged DOT1L, CDK6 inhibitor
Epigenetic mutations /
azacitidine, decitabine, SGI-110alterations (?) OTX015, I-BET-762Selected targets for molecular therapy
Slide39Precision 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 toolsCurrently, 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 clinicMajor challenge: identification of predictive biomarkers that help selecting the appropriate therapy for an individual patient=> integrate biosampling, companion studiesEnter your patients, younger or older, on a clinical trial!
Slide40P. Campbell
E
. Papaemmanuil
CambridgeM. HeuserG. GöhringF. TholB. SchlegelbergerA. GanserMHH, Hannover
S.
Cocciardi
A.
Dolnik
V. Gaidzik
S. Kapp-Schwörer
J.
KrönkeK. Lang
F. KuchenbauerP. Paschka
F. RückerF. StegelmannD. WeberK. HolzmannK. DöhnerR. F. SchlenkS. StilgenbauerH. DöhnerUlm University
A.
Krivtsov
S. Armstrong
New York
G. Martinelli
I.
Iaccobucci
Bologna
P. Valk
B. Löwenberg
Rotterdam
SFB 1074
S. Fröhling
C.
Plass
P. Lichter
C. Scholl
Heidelberg
K.
Rajewsky
S. Sander
Berlin