The value of Biobanks for personalized medicine in acute Leukemia
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Hematology Research Unit Helsinki
Biobanks and Personalized Medicine: The FHRB Biobank Concept
Kimmo Porkka Helsinki University Hospital Comprehensive Cancer Center
Department of Hematology and
Hematology Research Unit Helsinki
University of Helsinki
Biobanking as a resource for biomedical research, 9-13 Feb 2015
Hematology Research Unit Helsinki
Population-based vs. disease/research-oriented biobanks
• Population-based biobanks
• Large patient numbers, “simple” sample collection
• Pathology samples as the backbone
• Focused, disease-specific biobanks
• Smaller patient numbers
• Extensive sample collection/patient
• Multiple collection instances (diagnosis, remission, relapses)
• Need for both, not competitive, complementary
Hematology Research Unit Helsinki
Some challenges for (cancer) biobanks
• Site and temporal variation inherent to cancer
• Repeated sampling, multisite sampling
• Clinical annotation of biological/biobank samples
• No uniform, national/international quality registers for clinical care and research
• Annotation of drug response phenotypes
Hematology Research Unit Helsinki
Example: selecting biobank samples for drug research from clinical annotation data
• Commonly, new drugs are tested in a population of patients resistant to conventional therapy
• Search criteria: pick biobank samples from those patients who have first responded to conventional therapy, but then lost the response (secondary resistance)
• Current patient records/charts (FIN): not possible
• Other hospital databases (lab, pathology etc): not possible/difficult, indirect
• Only feasible with structured, disease-specific evaluation of therapy outcome
Hematology Research Unit Helsinki
Individualized systems medicine for leukemia
Individualized and improved therapy
Biobank Profiling and drug testing technologies
Research
Clinics
Example of a disease-specific population-based biobank
Finnish Hematology Registry and Biobank
Hematology Research Unit Helsinki
FHRB – owners and partners
• Finnish Red Cross Blood Service
• FIMM – Institute for Molecular Medicine Finland
• Finnish Association of Hematology
• Contractual partners: all hospital districts treating hematological patients (n=21)
Hematology Research Unit Helsinki
Finnish Hematology Registry and Biobank - FHRB
• National registry and biobanking effort that aims to include all university and regional medical centers by 2015 in a population-based manner.
• High-quality samples from all patients with a hematological disease diagnosis.
• Bone marrow aspirates and blood samples collected at diagnosis, remission and relapse(s). Skin biopsy taken at first sampling.
• Samples processed and coded by the Finnish Red Cross Blood Service and then stored at FIMM as viable frozen mononuclear cells, frozen plasma and DNA.
• Researchers (incl. pharma) apply to the FHRB board for use of samples.
• Data returned to FHRB database
Hematology Research Unit Helsinki
Flow chart
Sample processing Finnish Red Cross Blood Service
(centralized)
Biobanking Clinical registry
FIMM
FAH
Patient consent
Sample, data collection • 30 mL blood, 30 mL bone marrow, skin biopsy (opt.)
• at diagnosis, remission (opt.), relapses
• all university and central hospitals (n=21) Hospitals
Experiments, discovery
Research groups, pharma
Clinical applications
Diagnostics Imaging Targeted therapies
FHRB
Hematology Research Unit Helsinki
FHRB clinical quality register & sample inventory: real-time data entry
Hematology Research Unit Helsinki
FHRB: Samples biobanked/patient
50 sample tubes/patient/sampling: all samples stored in LN2
Hematology Research Unit Helsinki
FIMM: centralized LN2 based sample
storage system
• Current capacity >1M samples (2 ml
cryotubes); expandable
• Samples stored in LN2 vapour phase
(-180C)
• Trained personnel, audited processes
• IT-systems
• Sample management
• Storage conditions surveillance
• Safety procedures
13
Hematology Research Unit Helsinki
FHRB-project: biobanking timeline
• Started in Dec 5, 2011; currently all newly-diagnosed and relapse patients sampled (acute leukemias, MDS, myeloma, MPN, CLL, CML)
• All University hospitals: Helsinki, Turku, Tampere, Oulu, Kuopio collecting samples
• All other hospitals treating hematological patients by the end of 2015
• 300-500 patients/year (50 000 samples/year)
Hematology Research Unit Helsinki
FHRB is part of routine patient care
• A biobank lab request
– 453 €/sample (+157 € for Fridays)
• Data registry
• Sampling, transport
• Sample processing
• Biobanking (6-7 yrs)
• Costs included in the daily care of the patients (laboratory budget)
• ”good care of a hematological patient”
• Cost of one PCR test
• <3% of university clinic lab budget
Hematology Research Unit Helsinki
Continuous quality control: cell viability
FHRB operational group quality control round September 2013
Hematology Research Unit Helsinki
Status 04.FEB.2015
http://www.hematology.fi/fhrb-status
873
Total 39 799 tubes in biobank
Hematology Research Unit Helsinki
FHRB – a new biobank
• Finnish biobank act September 1, 2013
• TUKIJA (National Committee on Medical Research Ethics) approval November 2013
• VALVIRA (National Supervisory Authority for Welfare and Health) approval July 14, 2014
• Transfer of FHRB project samples to FHRB biobank (October 2014).
• First national, disease-focused biobank in Finland
• Accepting applications for samples autumn 2015 – Academic research groups – Pharma
Hematology Research Unit Helsinki
FHRB - organization
Board of owners
Steering group
Executive officer Operative unit
Scientific advisory board
Administrative headquarters
Administrative and legal responsibility
Operative responsibility
Hospitals
Sample applications
Hematology Research Unit Helsinki
Infrastructure and collaborative environment required to practice individualized systems medicine in acute myeloid leukemia (AML)
Genomic & molecular profiles Drug sensitivity testing
Data integration & models
Individualized and improved therapy
National Biobank
Clinics
Why AML? Sampling over the course of disease
Cancer always accessible Ex-vivo functional drug testing easier
Finnish Hematology Association
Hematology Research Unit Helsinki
AML: survival from relapse by age
Rowe, J. M. et al. Blood 2010;116:3147-3156
Hematology Research Unit Helsinki
Genomic and molecular landscape in AML
22
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
Hematology Research Unit Helsinki
Molecular diversity: a challenge for drug development
100 newly diagnosed AML patients
AML genotype 1
AML genotype 2
AML genotype 3
AML genotype 5
AML genotype 6
AML genotype 7
AML genotype 98
AML genotype 99
AML genotype 100 …
New drug 1: Phase I-II (n=10-40)
Response rate 2/100= 2% Study fail End development
Hematology Research Unit Helsinki
Individualized Systems Medicine Overview
Systems medicine approach:
- Data integration
- Repeated sampling
- Feedback to clinic
- Learning system
FHRB biobank
Pemovska, Kontro et al. Cancer Discovery, 2013
Hematology Research Unit Helsinki
Leukemia sample work flow
Hematology Clinic FIMM
1 h 1 day
Biobank
DSRT
4 days
NGS
2-3 weeks
Proteomics
2 days
FIMM
Sample collection • Bone marrow aspirate
• Peripheral blood
• Skin biopsy
Sample processing • Mononuclear cell separation
• Protein lysates
• DNA extraction
• RNA extraction
Sample analysis • Drug screening
• Phospho-protein analysis
• Whole genome/exome sequencing
• RNA sequencing
Hematology Research Unit Helsinki
Augmenting molecular drug discovery/repurposing
• Genome/transcriptome-driven
Molecular profiling of leukemic cells
Disease-associated genomic variants
Molecularly targeted therapies
- Rarely directly clinically actionable
- 3-10+ years
Drug response profiling of leukemic cells
› Drug-response phenotype-driven
- Often directly clinically actionable
- Unbiased - Drug repurposing
- 1-5+ years
Disease-associated drug responses
Molecularly dissection of drug response
Molecularly targeted therapies
Hematology Research Unit Helsinki
1408
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Drug sensitivity and resistance testing
300 dose response curves
Oncology
collection
5 conc. 10,000-
fold conc. range
37°C,
72 h
Assay & detection
reagents
(CellTiter-Glo for viability)
Leukemic
patient cells
Calculate activity
Compare to controls
Novel biological
understanding of the
disease
Find responding
cells/patients.
Repositioning
Translate
directly to
patients
-9 -8 -7 -6 -5
0
25
50
75
100 control
AML
Conc (log M)
% v
iab
ility
4 day turnaround
Hematology Research Unit Helsinki
Assembling a concise and comprehensive functional screening collection
• Currently 306 oncology-focused active substances/drugs – Approved small molecule oncology substances – Oncology-related approved substances – Major investigational oncology compounds – Probe compounds with unique and cancer-relevant activities
• 135 approved • 171 investigational
• 65 conventional chemotherapeutics • 20 hormone therapy drugs • 119 kinase inhibitors • 33 epigenetic/differentiating drugs • 55 other targeted drugs • 10 immunosuppressants
Hematology Research Unit Helsinki
Unsupervised hierarchical clustering creates
functional taxonomy of patients AML patient samples can be divided into
five broad groups
Controls and patient samples
Dru
gs
693
105
4
10
67
134
6
153
4
207
1
220
0
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3
209
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RG
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Temozolomide Thio-TEPA Thioguanine Bryostatin 1 Enzastaurin Vatalanib Cediranib Plerixafor Tamoxifen Prima-1 Met Fasudil Lenalidomide Vemurafenib Chlorambucil Dexamethasone Prednisolone Methylprednisolone Linsitinib Navitoclax Tipifarnib Azacitidine Gefitinib Doramapimod Erlotinib Dovitinib Gandotinib Sotrastaurin Ruboxistaurin Capecitabine YM155 Pentostatin Crizotinib Obatoclax Decitabine Tretinoin Bexarotene Bortezomib Clofarabine Cladribine Midostaurin UCN-01 Serdemetan Carboplatin Uracil mustard Selumetinib Pimasertib Trametinib Refametinib Tacrolimus Tofacitinib Ruxolitinib 2-methoxyestradiol Gemcitabine Olaparib Motesanib Rucaparib Tacedinaline Nilotinib Plicamycin Cytarabine Ponatinib Lestaurtinib Temsirolimus Tanespimycin Alvespimycin BIIB021 NVP-AUY922 Entinostat Panobinostat Belinostat CUDC-101 Vorinostat AZD8055 PF-04691502 OSI-027 BMS-754807 Dactolisib MK-2206 GS-1101 Regorafenib Pictilisib Methotrexate Floxuridine Everolimus Sirolimus Imatinib Saracatinib Dasatinib Masitinib Tivozanib Axitinib Tandutinib Sorafenib Pazopanib Canertinib Fluorouracil Busulfan Vandetanib Amonafide Mitoxantrone Foretinib Etoposide Bleomycin MGCD-265 Sunitinib Daunorubicin Valrubicin Doxorubicin Idarubicin Teniposide Dactinomycin Quizartinib Fludarabine Cyclophosphamide Streptozocin Iniparib Fingolimod Nelarabine Raloxifene Vismodegib Exemestane Mitomycin C Carfilzomib
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NavitoclaxRuxolitinibDexamethasoneMEKiPI3K/mTORiQuizartinib
Topoisomerase IIi
II III IV VI
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
$" & + '& + % & ' $ $ $ $ $ $ $ $ $ $ $ $ $ $ $
$" & +( ' ! $ ++)
$ " ) ) ) *& & #
DasatinibSunitinib
IDH1/IDH2
Figure 3. Pemovska et al
Adverse karyotype
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
Pemovska, Kontro et al. Cancer Discovery, 2013
Hematology Research Unit Helsinki
Challenge with big data management, rapid integration and interpretation to give feedback to the clinic
Drug sensitivity testing Exome sequencing
Gene expression Phosphoproteomics Fusion genes
Integration & Interpretation &
Feedback to clinic (4 days to 2 weeks)
Hematology Research Unit Helsinki 09/02/2015
In personalized medicine, the patient (person) is at the center
Hematology Research Unit Helsinki
Patient case I: FHRB.600
• 50-year-old male, previously healthy
• AML FAB M5, normal karyotype, FLT3-ITD
• Failed 3 consecutive induction therapies
• Febrile, pancytopenia, WHO 3
• Molecular profiling: exome and transcriptome sequencing and targeted phospho-proteomics of serial samples
• DSRT: Selection of an off-label drug-combination as per compassionate use based on ex vivo drug screening results
Hematology Research Unit Helsinki
FHRB.600: NUP98-NSD1 fusion
NUP98 (chr 11) NSD1 (chr 5)
Pemovska, Kontro et al. Cancer Discovery, 2013
Hematology Research Unit Helsinki
600: 1st DSRT
0 5 10 15 20
Pazopanib - TKI (VEGFR)
Axitinib - TKI (VEGFR)
Sorafenib - TKI (B-Raf/VEGFR)
Temsirolimus - Rapalog*
Navitoclax - Bcl-2/Bcl-xL inhibitor
Alvespimycin - HSP90 inhibitor
Mitoxantrone - Topoisomerase II inhibitor
PF-04691502 - mTOR/PI3K inhibitor
Foretinib - TKI (VEGFR2/MET)
Quizartinib - Flt3 inhibitor
Cytarabine - Antimetabolite
Entinostat - HDAC inhibitor
NVP-AUY922 - HSP90 inhibitor
Lestaurtinib - Broad TKI
Dasatinib - Broad TKI*
Tivozanib - TKI (VEGFR)
Tanespimycin - HSP90 inhibitor
Sunitinib - Broad TKI*
BIIB021 - HSP90 inhibitor
Ponatinib - Broad TKI
sDSS
-9 -8 -7 -6 -5
0
25
50
75
100 control
AML
Conc (log M)
% v
iab
ility
Differ DSS
DSS: Drug sensitivity score =
(AUC/Total Area)*100/log(100-
min)
Ex vivo DSRT results
Pemovska, Kontro, et al. Cancer Discovery, 2013
Hematology Research Unit Helsinki
Treatment response
0 10 20 30 40 500
20
40
60
0
1
2
3
Days from start of therapy
Bo
ne
ma
rro
w b
las
ts +
pro
mo
no
cy
te c
ou
nt
(%)
Ne
utro
ph
il co
un
t (10
9/L)
Dasatinib
Sunitinib
Temsirolimus
Infection
Sensitive sample Exome sequencing CNA RNAseq Expression Phophoproteomics
Resistant sample Exome sequencing CNA RNAseq Expression Phophoproteomics
Bone marrow blast cell %
Neutrophil count
Hematology Research Unit Helsinki
0 5 10 15 200
5
10
15
20
600_2 sDSS
600_3 s
DS
S
BIIB021
Tivozanib
Tanespimycin
Ponatinib
Sunitinib
Dasatinib
Lestaurtinib
Entinostat
Cytarabine
Alvespimycin
Pazopanib
PF-04691502
Quizartinib
AxitinibSorafenib
Temsirolimus
Bleomycin
VorinostatEtoposide
Valrubicin
Loss of ex vivo drug sensitivity
Pre treatment Post treatment
Pre treatment Post treatment
Pre treatment Post treatment
Pretreatment Postreatment
Pretreatment Postreatment
Pretreatment Postreatment
Temsirolimus
Dasatinib
Sunitinib
Hematology Research Unit Helsinki
Clonal evolution
A B
C D
0 5 10 15 200
5
10
15
20
BIIB021
Tivozanib
Tanespimycin
Ponatinib
Sunitinib
DasatinibLestaurtinib
EntinostatCytarabine
Alvespimycin
Pazopanib
PF-04691502
Quizartinib
AxitinibSorafenib
TemsirolimusBleomycin
VorinostatEtoposide
Valrubicin
0 50 100 150 180 200 2200
20
40
60
80
100
0.0
2.5
5.0
Chemotherapy
600_2 Selective DSS
60
0_
3 S
ele
ctive
DS
S
Chemotherapy Das-Sun-Tem
600_0 600_2 600_3
%
10
E9
Chemotherapy Das-Sun-Tem Das-Tem
Bone marrow blasts (%)
Neutrophils (x10E9)
600_2 600_30
5
10
15
20
600_2 600_3
600_2 600_30
5
10
15
Dasatinib Sunitinib
Temsirolimus
Se
lective
DS
S
Se
lective
DS
S
Se
lective
DS
S
0
5
10
15
20
NUP98-NSD1
600_0 600_1 600_3600_2
90%
5%
5%
40%
30%
12%
18%
70%
10%
20%FLT3-ITD #1
WT1 #2
FLT3-ITD #2
WT1 #1 WT1 #4
WT1 #3
Pemovska, Kontro, et al. Cancer Discovery, 2013
Novel candidate combination therapy for NUP98-NSD1-driven AML
Hematology Research Unit Helsinki
Functional investigation of NUP98-NSD1/FLT3 AML
Biobanked patient material: • 3 patients
Cell models: • Ba/F3 (murine, C3H, pro-B-cell,
BM)
o Empty vector
o NUP98-NSD1 variant 1
o NUP98-NSD1 variant 2
• 32D (murine, C3H, myeloblast-like, BM)
o Empty vector
o NUP98-NSD1 variant 1
o NUP98-NSD1 variant 2
• Balb/c BM Lin (-)
o Empty vector
o NUP98-NSD1 (pre)
o NUP98-NSD1/FLT3-ITD (pre)
o NUP98-NSD1/FLT3-ITD (M3)
• BL/6 BM Lin (-)
o Empty vector
o NUP98-NSD1/FLT3-ITD (M1)
Transgenic model
• In collaboration with Prof. Juerg Schwaller (Basel)
Hematology Research Unit Helsinki
Patient case: FHRB.1408 (CML BC T315I+)
• February 2012: male aged 35 diagnosed of CML in lymphatic blast phase
• Imatinib 600 mg monotherapy, hematologic response
• April 2012: hematologic relapse, switch to dasatinib 140 mg x1, no response. HD chemorherapy => partial response
• BCR-ABL1 KD sequencing: BCR-ABL1T315I 100%
• August 2012: AlloHSCT
• Current status (May 2014): BM in CR
• Exome sequencing (100x): missense mutations in
RUNX1, ABL1, NRK, ABCA13
Hematology Research Unit Helsinki
20 most selective drugs
PI3K/mTOR KI
Namt inhibitor
VEGFR-1/2/3 TKI
mTORC2/mTORC1 inhibitor
p53-activating drug PI3K/mTOR KI
Multikinase inhibitor
EGFR/HER-2 inhibitor
PI3K/mTOR KI Aurora A/B/C KI MEK-inhibitor MEK-inhibitor
HDAC-inhibitor
Hematology Research Unit Helsinki
0 5 10 150
5
10
15
20
25
RQ
-PC
R fo
r T
31
5I (
%)
Day
Clinical translation
Axitinib 5 mg x2
Confirmation of results from similar samples in the FHRB biobank Formal Phase I/II study; combination therapy; MOA – in partnership with Pfizer New, already approved drug for T315I-mutated leukemia in 2-3 years
Pemovska T, et al. Nature 2015 (in press)
Hematology Research Unit Helsinki
Multiple myeloma sample analysis
DSRT ex vivo drug sensitivity
Exome sequence SNVs, CNVs
RNA sequence gene expression profile, expressed
fusion genes, small RNAs, lncRNAs
Methyl sequence epigenomic profile
Hematology Research Unit Helsinki
CD138+ cell selection
DNA (CD138+ cells and skin biopsy)
RNA (CD138+ cells)
Drug sensitivity testing
Next generation DNA and RNA sequencing
Clinical data Finnish
Hematology Registry and
Biobank
Bone marrow aspirate
Biobanked samples
Hematology Research Unit Helsinki
Conclusions
• Disease-specific deep biobanks are key components
in the development of personalized cancer care
• ISM strategy is one way to implement personalized
cancer therapy
• Novel drug candidates for difficult to treat cancers
• Drug repurposing: patient selection for formal Phase I-II
studies. Patenting opportunities
• Formal Phase II clinical study necessary to validate
clinical usefulness (in collaboration with
pharmaceutical companies and research consortia)
Hematology Research Unit Helsinki
Tiina Vesterinen Kimmo Pitkänen Caroline Heckman Janna Saarela Kyösti Sutinen Olli Kallioniemi
Steering group Members Patients
Kari Aranko Tiina Wahlfors Henna Jalovaara Outi Huoponen Eeva Mainio
http://www.hematology.fi/fhrb
FHRB
Suomen Syöpäpotilaat ry Finnish Cancer Patient Association
Aimo Strömberg Mika Peltovaara Timo Laukkio
Hematology Research Unit Helsinki
FIMM
Chemical Systems Biology
Krister Wennerberg
Tea Pemovska
Arjan van Adrichem
Computational Systems Biology
Tero Aittokallio
Petteri Hintsanen
Agnieszka Szwajda
Bhagwan Yadav
Technology Center
Janna Saarela
Evgeny Kulesskiy
Laura Turunen
Anna Lehto
Ida LIndenschmidt
Pekka Ellonen
Maija Lepistö
Sonja Lagström
Sari Hannula
Pirkko Mattila
Aino Palva
Leukemia Network Helsinki
HUCH/HRU Kimmo Porkka
Satu Mustjoki
Pekka Anttila
Mika Kontro
Erkki Elonen
Hanna Koskela
Mette Ilander
Emma Anderson
Paavo Pietarinen
Jaakko Vartia
Minna Lehto
Mervi Saari
Kuopio University Central Hospital
Raija Silvennoinen
Turku University Central Hospital
Tuija Lundán
Tampere University Central Hospital
Hannele Rintala
Tero Pirttinen
Marja Sankelo
University of Bergen
Bjørn Tore Gjertsen
Personalized Cancer Medicine
Caroline Heckman
Jonathan Knowles
Samuli Eldfors
Riikka Karjalainen
Jarno Kivioja
Ashwini Kumar
Heikki Kuusanmäki
Muntasir Mamun Majumder
Alun Parsons
Minna Suvela
Individualized Systems Medicine
Olli Kallioniemi
Taija af Hällström
Henrik Edgren
Poojitha Kota Venkata
Disha Malani
John Patrick Mpindi
Astrid Murumägi
Päivi Östling
Maija Wolf
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