© fimm - institiute for molecular medicine finland · labcyte inc (liquid handling) pfizer, roche...

Disclosures

› Research collaborations: Labcyte Inc (liquid handling)Pfizer, Roche IMI-Predect public-private project (10 companies)

› Off-label use of drugs will be discussed

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Genomic and molecular landscape in AML

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23 significantly mutated genes in 200 AML patient

samples

The Cancer Genome Atlas

(TCGA) dataset

Genomic and Epigenomic Landscapes of Adult De Novo Acute Myeloid Leukemia, New England Journal of Medicine 2013

Number of patient samples

Panoramic view of AML, Chen and Chen; Nature Genetics, June 2013

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Many leukemia patients die due to diseaseand/ortreatment toxicity

<50% survival-Resistancecommon (10% survival)-Classicalchemotherapyused (from1960s) with toxicity-Targetedtreatments notavailable-Mutationalprofile often not helpful

• AML genomes sequenced

• Clonal evolution characterized

• Increasing data on molecular pathogenesis

• Stem cell niche

Example of a translational gap in acute myeloid leukemia

Novel therapy targets and therapeutics(e.g. FLT-3, DNMTs)disease/patient-specific molecular markers

US National Academy of Sciences 2012

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Infrastructure and collaborative environment for individualized systems medicine in AML

Genomic & molecular profiles Drug sensitivity testing

Data integration & models

Individualized and improved therapy

NationalBiobank

Clinic

Why AML? Sampling over the course of disease

Cancer always accessibleEx-vivo functional drug testing easier

Finnish Hematology Society

Kimmo PorkkaSatu MustjokiMika Kontro

Krister WennerbergTero AittokallioCaroline HeckmanJonathan KnowlesOlli Kallioniemi

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Systems medicine to optimize drugs and drug combinations for individual AML patients

• Repeated sampling at diagnosis, therapy response, relapse and drug resistance

• Integration of genomic and molecular profiles and high-throughput ex-vivo drug testing for each patient (real-time)

• Feedback therapeutic insights back to the clinic for tailoring personalized therapy or guiding clinical trials with new agents

• Molecular and functional analysis of drug resistant samples after novel therapies

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Biobanking all leukemia patients across the country:

On average 46 sample aliquots per patient

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Exome seqp-proteomics

Drug testingExome seq

RNA seqp-proteomics


RNA seqp-proteomics


RNA seqp-proteomics

Diagnosis Treatment 1 Relapse 2 Relapse 3Relapse 1

Samples across time points provide molecular insights on disease progression in patients, including drug resistance

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Individualized systems medicine (ISM)

System medicine approach:- Data integration- Repeated sampling- Feedback to clinic- Learning system

Pemovska et al. Cancer Discovery, on-line Sep 20, 2013

Rapid data analysis, integration and interpretation to provide therapeutic insights to the clinic

Drug sensitivity testing Exome sequencing

Gene expression Phosphoproteomics Fusion genes

Integration & Interpretation &

Feedback to clinic(4 days to 2 weeks)

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High throughput screening infrastructure: - “repurposed” from functional genomics/drug discovery to drug

sensitivity testing for individualized medicineBeckmanCoulter integrated robotic system – fully automated assays

Labcyte Access Workstation – nl acoustic dispensing

96 384 1536 Micro-arrays

Plate readersImagingFlow cytometryGenomics

Wennerberg, Östling et al. HTB facility at FIMM 12

The most direct way to ascertain patient treatment: test drugs individually on patient cellsDrug sensitivity and resistance testing (DSRT) workflow

Pre-plated drug collection(1, 10, 100, 1000and 10 000 nM)

37°C, 72 h

Fluorescent dyes(HCS)

Measure cell growth/survival/ p-

proteins and calculate response

Leukemia cells isolated from blood or bone

marrow

30 x 106 cells

4 day turnaround

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Detailed dose-response curves for all oncology drugs and many

emerging cancer compoundsfor individual patient samples

Drug sensitivity and resistance testing withdose-response curves for each drug

Approved 137

Investigational 103

Probes66

(+)JQ1

2-methoxyestradiol

Abiraterone

ABT-751

Afatinib

Allopurinol

Altretamine

Alvespimycin

Alvocidib

Amifostine

Aminoglutethimide

Aminolevulinic acid

Amonafide

Anagrelide

Anastrozole

Arsenic trioxide

AS703026

AT9283

Axitinib

AZ 3146

Azacitidine

AZD1480

AZD4547

AZD7762

AZD8055

Barasertib

Belinostat

Bexarotene

BI 2536

BIIB021

Bimatoprost

BKM-120

Bleomycin

BMS-754807

Bortezomib

Bosutinib

Brivanib

Bryostatin 1

Busulfan

Cabozantinib

CAL-101

Camptothecin

Canertinib

Capecitabine

Carboplatin

Carfilzomib

Carmustine

Cediranib

Celecoxib

Chlorambucil

CI-994

Cisplatin

Cladribine

Clofarabine

Clomifene

Crizotinib

CUDC-101

Cyclophosphamide

Cytarabine

Dacarbazine

Dactinomycin

Danusertib

Dasatinib

Daunorubicin

Decitabine

Dexamethasone

Dexrazoxone

Docetaxel

Doramapimod

Dovitinib

Doxorubicin

EMD1214063

Entinostat

Enzastaurin

Erlotinib

Estramustine

Etoposide

Everolimus

Exemestane

Fasudil

Finasteride

Fingolimod

FK-866

Floxuridine

Fludarabine

Fluorouracil

Flutamide

Foretinib

Fulvestrant

Galiellalactone

GDC-0941

GDC-0980

Gefitinib

Gemcitabine

Goserelin

Gossypol

GSK1120212

Hydroxyurea

Idarubicin

Ifosfamide

Imatinib

Imiquimod

Indibulin

Iniparib

Irinotecan

Ixabepilone

JNJ-26481585

Lapatinib

Lasofoxifene

Lenalidomide

Lestaurtinib

Letrozole

Leucovorin calcium

Levamisole

Linifanib

Lomustine

LY2157299

LY2603618

LY2784544

Masitinib

Mechlorethamine

Megestrol

Melphalan

Mepacrine

Mercaptopurine

Methotrexate

Methoxsalen

Methylprednisolone

MGCD-265

Midostaurin

Mitomycin C

Mitotane

Mitoxantrone

MK-2206

MK1775

MLN8237

Mocetinostat

Motesanib

Navitoclax

Nelarabine

Neratinib

Nilotinib

Nilutamide

Nitrogen mustard

Nutlin-3

NVP-AUY922

NVP-BEZ235

NVP-LDE225

Obatoclax

Olaparib

Omacetaxine

OSI-027

OSI-906

Oxaliplatin

Paclitaxel

Panobinostat

Patupilone

Pazopanib

PD 0332991

Pemetrexed

Pentostatin

PF-00477736

PF-04691502

PF-04708671

Pilocarpine

Pipobroman

Plerixafor

Plicamycin

Ponatinib

Prednisolone

Prednisone

PRIMA-1(Met)

Procarbazine

PX-866

Quizartinib

RAF265

Raloxifene

RDEA119

Regorafenib

Ridaforolimus

Roscovitine

Ruboxistaurin

Rucaparib

Ruxolitinib

S-trityl-L-cysteine

Saracatinib

Selumetinib

Serdemetan

Sirolimus

SNS-032

Sorafenib

Sotrastaurin

Streptozocin

Sunitinib

Tacrolimus

Tamoxifen

Tandutinib

Tanespimycin

Tarenflurbil

Tasocitinib

Temozolomide

Temsirolimus

Teniposide

Thalidomide

Thioguanine

Thiotepa

Tipifarnib

Tivozanib

Topotecan

Toremifene

Tosedostat

Tretinoin

Triethylenemelamine

UCN-01

Uracil mustard

Valrubicin

Vandetanib

Vargatef

Vatalanib

Veliparib

Vemurafenib

VER 155008

Vinblastine

Vincristine

Vinorelbine

Vismodegib

Volasertib

Vorinostat

XAV-939

XL147

XL765

YM155

Zolendronic acid

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Identification of cancer-specific drug responses

› Kill cancer cells more than normal cells (cancer-selective response)

› We calculate DSS scores by comparing cancer’s response to controls (Healthy bone marrow)

-9 -8 -7 -6 -5

0

25

50

75

100 controlAML

Conc (log M)

% v

iabi

lity

Patient-specific cancer-selective drug response

High DSS value => cancer cells have higher sensitivity to the drug 16

Summary of ex-vivo drug testing data from 27 chemorefractory AML patients

Most frequent bioactive drugsin 27 AML patients

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Guidance for personalized medicine and clinical development of emerging drugs

D

1993

2095

1064

1145

_3

393_

139

3_3

800

1145

_111

45_6

718

1280

1497

600_

3

252_

225

2_3

1867

784_

178

4_2

250

600_

125

2_5

252_

156

0_1

560_

960

0_2

1690

4701

001

NavitoclaxRuxolitinibDexamethasoneMEKiPI3K/mTORiQuizartinib

Topoisomerase IIi

II III IV VI

$" & + '& + % & ' $ $ $ $ $ $ $ $ $ $ $ $ $ $ $

$" & +( ' ! $ ++)

$ " )) ) *& & #

DasatinibSunitinib

D D* R R D* D R D* R R D R R R D R R R R R R R R R D RDisease stage

FLT3-ITD

WT1

TP53

NRAS/KRASDNMT3A

PTPN11

RUNX1

NUP98-NSD1

MLL-X fusions

ETV6-NTRK3

NPM1

M1M2FAB subtypes M2 M2M2M2 M5M5 M5 M5 M5M5M5M5 M5M5M5 M1M1

KIT

IDH1/IDH2

Adverse karyotype

Correlating drug sensitivity with genomic data:• Predictive biomarkers• Novel therapeutics• Drug combinations• Novel drug targets• Insights on drug resistance Activating FLT3

mutations link tosensitivity to FLT3 inhibitors and dasatinib

MLL fusions link to MEK and PI3K inhibitor sensitivity

Link between MEK and JAK inhibition sensitivity in DSRT subtype II

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Individualized systems medicine for AMLto define optimal drugs for patients

Using drugs as biological probes => direct opportunity for translation19

Cancer-selective drugs

Ineffective drugs

Drug sensitivity testing report: summary of drugs showing selective cancer efficacy in an individual AML patients based on ex vivo testing

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Clinical data: A treatment-refractory AML patient receiving a novel combination of clinical drugs based on ex-vivo profiling: Complete clinical response and subsequent progression

Ex-vivo drug response data after relapse

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Leukemia‐selective responses observed in ex‐vivo drug testingDrugs available for human use (on‐ or off‐label)

Kontro et al, EHA 2013

2 x CRi , 2 x leukemia free (hypoplasia)40% response rate

14/17 (81%): ex‐vivo guided treatment possible

10 patients received personalized combinatorial treatments designed according to predictions

Clinical implementation of ex-vivo drug-testing data in chemorefractory AML patients

Drug combinations for patients: - Clo-Dasatinib-Vin- Sorafenib-Clo- Dasatinib-Sunitinib-Temsirolimus- Azacytidine-Sunitinib

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Individualized systems medicine for AML to define optimal drugs for patients

Molecular profiling to understand drug resistance and tx failure Drug-testing to understand drug (cross)-resistance and new vulnerabilities

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- Genomics as a measure of drug response in patients - Understand resistance mechanisms and evolution of multiple

subclones=> The clones causing clinical resistance often pre-exist before therapy

Assesment of treatment response: clonal evolution by next-gen seq after treatment with targeted drugs

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Evidence of vulnerability to previously ineffective drugs after resistance has emerged in patients=> An opportunity to design curative drug combinations

sample 2relapse

sample 1diagnosis

Compare drug vulnerabilities after relapse

Drugs whose

efficacy isnot altered

by the clinical therapy resistance

Sensitivity of cancer cells before resistance

Sen

sitiv

ity o

f can

cer c

ells

afte

r res

ista

nce New drug vulnerabilities

= Therapeutic optionsfor patients

Cross-resistance to other drugs

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Understanding altered cell signaling after treatment

Kinase signalling networks predicted from drug response

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Systems medicine to optimize drugs and drug combinations for individual AML patients

• Repeated sampling at diagnosis, therapy response, relapse and drug resistance

• Integration of genomic and molecular profiles and high-throughput ex-vivo drug testing for each patient (real-time)

• Feedback therapeutic insights back to the clinic for tailoring personalized therapy or guiding clinical trials with new agents

• Molecular and functional analysis of drug resistant samples after novel therapies

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Future development

Clinical: › Drug sensitivity testing in primary AML

› Stratified, biomarker driven trials for new drugs in AML

› Comparing treatment paradigms: Individualized vs. conventional clinical therapy

Biological:› Progenitor / single cell assays

› Mimicking the bone-marrow niche

› Larger libraries, combinations to facilitate drug repositioning

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WIN 2014 Symposium • 23-24 June • Paris • France

Slide content not available for publication

Primary prostate cancerorganoid cultures in 3D(Clevers protocol)

Tissue slice cultures to assay short-term drug effects(e.g. DNA damage, phosphorylation events)

Reprogrammed primary cell culture (Rock-inhibitor& feeder layers)

Towards analysis of solid tumors?

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FIMM

Chemical Systems BiologyKrister WennerbergTea PemovskaArjan van Adrichem

Computational Systems BiologyTero AittokallioPetteri HintsanenAgnieszka SzwajdaBhagwan Yadav

Technology CenterJanna SaarelaEvgeny KulesskiyLaura TurunenAnna LehtoIda LIndenschmidtPekka EllonenMaija LepistöSonja LagströmSari HannulaPirkko MattilaAino Palva

Precision Cancer Medicine NetworkHUCH/HRUKimmo PorkkaSatu MustjokiPekka AnttilaMika KontroErkki ElonenHanna KoskelaMette IlanderEmma AndersonPaavo PietarinenJaakko VartiaMinna LehtoMervi SaariKuopio University Central HospitalRaija SilvennoinenTurku University Central HospitalTuija LundánTampere University Central HospitalHannele RintalaTero PirttinenMarja SankeloUniversity of BergenBjørn Tore Gjertsen

Personalized Cancer MedicineCaroline HeckmanJonathan KnowlesSamuli EldforsRiikka KarjalainenJarno KiviojaAshwini KumarHeikki KuusanmäkiMuntasir Mamun MajumderAlun ParsonsMinna Suvela

Individualized Systems MedicineOlli KallioniemiTaija af HällströmHenrik EdgrenPoojitha Kota VenkataDisha MalaniJohn Patrick MpindiAstrid MurumägiPäivi ÖstlingMaija Wolf

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