대한혈액학회 korean society of...
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I have no personal or financial interests to declare:
I have no financial support from an industry source at the current presentation.
Use the following slide to disclose any conflicts of interest
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대한혈액학회 Korean Society of Hematology
COI disclosureName of author : Liran I Shlush
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Liran I Shlush
Weizmann Institute of science Israel
Rambam Healthcare campus
Seoul Mar 2019
Predicting Leukemia Development from Preleukemic Clonal
Hematopoiesis
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Definition of a preleukemic mutation
Mutant allele frequency: Shlush et.al Nature 2014
DNMT3amut
Leukemic blasts
T cells
PreL-HSC
X
X
X
X
XY XX
X
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Age Related Clonal Hematopoiesis (ARCH)is driven by preleukemic mutaitons
Shlush LI Blood 2018
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Can leukemia be diagnosed earlier?
ARCH
Preleukemic Mutation
Pre-LeukemiaLeukemia
(AML MPN MDS etc)
Aging
Non Age Dependent
CHGenetic drift Non- recurrent
genetic variations
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Preleukemia versus ARCH using EPIC data (Precision medicine)
6
European Prospective Investigation into Cancer
and Nutrition (EPIC)
520,000 People
Targeted error corrected Seq of AML related genes
124 AML Cases677 Matched Controls
In Collaboration with George Vassiliou
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ARCH more prevalent in pre-AMLs with higher VAFs
ARCH=Restricted Gene list and specific positions VAF>0.5%
pre-AML CasesMatched Controls
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Pre-AML cases carry more mutations at younger age
pre-AML CasesMatched Controls
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Somatic mutations in specific genes are more predictive
We NEVER found:
NPM1cFLT3/ITDCEBPA
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Clonal dynamics in ARCH and Pre-AML
pre-AML casesMatched Controls
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p-value = 0.2861
Pre-AML clones grow in the same rate as ARCH clones
pre-AML CasesMatched Controls
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Mutations in specific genes contribute to AML risk differently
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A molecular predictive model on a validation cohort from the Sanger institute (Vasilliue group)
Abelson S & Collord G (Nature 2018)
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Desai et.al Nat Med 2018
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Long term follow-up (median 4 years) of 30 AML cases
Provide evidence for parallel evolution of preleukemic clones
Chapal-Ilani N et.al unpublished
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Summary So far
AgeYears
Preleukemia
Months Leukemia
Diagnosis
TherapeuticWindow
Pre-AML Control
30
60
67
FLT3-ITDNPM1cCEBPA
High penetranceMutations: IDH1/2SRSF2, U2AF1, TP53RUNX1
Low penetranceDNMT3a TET2
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AML Prediction Based on RDW in EPIC
P=0.008
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Electronic health record analysis
4.5 Million individual over 15
years1696 AMLs
Machine learning
Electronic health record (HER) basedAML prediction model
Mendelson Cohen N Niemeyer E Tanay A (Nature 2018)
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Modelling blood for 4.5 million patients
Projecting Red Blood Cells on the multi-parameter space
Smokers/Runners
Aging
NormalHigh
Normal/ Average
Normal/Low W
High Value
CBC Map of Israel (3.2 Million people)
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Modelling blood for 3.2 million patients
Projecting Hemoglobin on the multi-parameter space
Smokers/Runners
Aging/Anemia
NormalHigh
Normal/ Average
Normal/Low W
High Value
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Modelling blood for 4.5 million patients
Mean Cell Volume
Thalassemia minorAverage Hb low MCV
Microcytic AnemiaMacrocytic AnemiaMacocytosis WO AnemiaAlcohol/Medications
NormalHigh
Normal/ Average
Normal/Low W
High Value
Projecting MCV on the multi-parameter space
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NormalHigh RDW
AverageRDW
NormalLow RDWRDW
High RDW
Red Blood Cell Distribution Width (RDW)
Patients 1-15 years before AML
Iron Deficiency Anemia
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Changes in Blood Counts Before AML
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AML predictive model based on Electronic Health Records (HER)
Abelson S et.al Nature 2018
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Clinical Trial E7820Healthy Individuals
Clalit 750,000ARCH/CCUS Clalit
N=200
EHR prediction model to identify high risk
N=1000
ARCH/CCUSHospitals
N=200
Genetic testing
100 Positive for SRSF2 U2AF1
E7820 for 3 month end point reduction
in VAF>5%
Abdel-waheb Shlush Feldman Stein Eisai
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Acknowledgments
Sagi AbelsonJohn DickStanley Ng
John Dick Lab Shlush Lab
Collaborators
Mark Minden (PMH)Scott Bratman (PMH)Trevor Pugh (PMH)Lawrence Heisler (OICR)Philip Awadalla(OICR)Philip Zuzarte (OICR)Yogi Sundaravadanam (OICR)Paul Brennan (EPIC)Amos Tanay (WIS)Netta Mendelson-Cohen (WIS)Omer Weisboard (WIS)Stanly Ng (UOT)
Elisabeth NeimayerNoa Chapal-IlaniAviv De-MorganBarak OronNathali KaushanskyMax KushnirYoni MoskovitzAmos TuvalYoav WigelmanYael Morgenstern
GrantsLLS – quest for cureERC – Horizon 2020BIRAX
George Vassiliou LabGrace Collord (Sanger)
Moritz Gerstung
EMBL
Slide Number 1Predicting Leukemia Development from Preleukemic Clonal�HematopoiesisDefinition of a preleukemic mutationAge Related Clonal Hematopoiesis (ARCH)�is driven by preleukemic mutaitons Can leukemia be diagnosed earlier?Preleukemia versus ARCH �using EPIC data (Precision medicine)�ARCH more prevalent in pre-AMLs with higher VAFsPre-AML cases carry more mutations at younger ageSomatic mutations in specific genes are more �predictiveSlide Number 10Slide Number 11Mutations in specific genes contribute to AML risk differentlyA molecular predictive model on a validation cohort from the Sanger institute (Vasilliue group)Slide Number 14Long term follow-up (median 4 years) of 30 AML cases�Provide evidence for parallel evolution of preleukemic clones Summary So farAML Prediction Based on RDW in EPICSlide Number 18Slide Number 19Slide Number 20Slide Number 21Slide Number 22Changes in Blood Counts Before AMLAML predictive model based on Electronic Health Records (HER)Clinical Trial E7820Slide Number 26