This document discusses using big data to advance personalized medicine in oncology. It notes that clinical studies and registries contain data from hundreds of cancer patients but it is fragmented across different organizations. The IMI2 HARMONY project aims to address this by creating a single big data platform to harmonize hematological malignancy data from over 45,000 patients across various clinical trials and registries. This will allow researchers to better understand patient subgroups and predictors of outcomes. The document outlines some of HARMONY's research projects and efforts to involve patients to help overcome challenges to large-scale data sharing and utilization.

1. Hot topics in oncology:
Big Data
Jan Geissler, IMI2 HARMONY

2. Personalized medicine …
 Best possible treatment may differ from one person
to another. This is partly due to biological
differences, such as genetic characteristics of the
tumor cells, or other individual factors.
 Databases from clinical studies and registries
contain clinical and biological data from hundreds of
cancer patients – but usually in distinct “data nests”
of
• (competing) pharmaceutical companies
• (competing) academic groups
• (unaligned) registries
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3. Personalized medicine …
The smaller the number of patients in clinical
trials or registries, the more difficult it is to…
 estimate frequency of rare events
 understand subgroups of patients and their
distinct characteristics
 make reliable predictions on the course of an
individual’s disease and their likelihood of
treatment response
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4. Personalized medicine … and big data
 “Big Data” means collecting and harmonizing
the data in one single platform, so
researchers can use it to
• understand heterogeneities and commonalities
• test research questions
 Big Data tools will enable clinicians and
doctors to rapidly select the most promising
treatment for a particular patient
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5. IMI2 “Big Data for Better Outcomes” Programme:
Improve health outcomes and healthcare systems in Europe by
maximising the potential of Big Data
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Design sets of standard
outcomes and
demonstrate value
Increase access to high
quality outcomes data
Use data to improve value
of HC delivery
THEMES/ENABLERS:
DISEASE-SPECIFIC PROJECTS:
ROADMAP: Alzheimer’s disease
HARMONY: Haematologic malignancies
BigData@Heart: Cardiovascular diseases
PIONEER: Prostate cancer
European Health Data & Evidence Network (EHDEN)
COORDINATING PROJECTS:
More to come….

6. Harmonization
of outcome
measures and
endpoint definitions
for HMs at European
level
Increase the
application
of omics data
in clinical practice
Speed up drug
development, access
pathways and
bench-to-bedside
process
Building a high-quality
Big Data platform on
hematological
malignancies
Funded by
One of Europe’s largest Big Data projects. €40m budget,
53 public-private partners from 11 countries

7. Moving towards personalized treatment according to the
presence of standard- and high-risk features in Multiple
Myeloma
Revised International staging system for multiple myeloma: evaluation of
the efficacy of different novel agents and treatment approaches in subsets
of patients with standard- and high-risk features
Project Partners
European Myeloma Network (EMN), uniting: Programa Español de Tratamientos en Hematología
(PETHEMA); Stichting Hemato-Oncologie voor Volwassenen Nederland (HOVON); Gruppo
Italiano Malattie EMatologiche dell’Adulto (GIMEMA); Università di Torino (UNITO); Università di
Bologna (UNIBO); Intergroupe Francophone du Myelome (IFM); German-Speaking Myeloma
Multicenter Group (GMMG); Nordic Myeloma Study Group (NMSG); Celgene; Takeda; Janssen.

8. ― The clinical outcome for
patients with Multiple Myeloma
is heterogeneous with wide-
ranging survival times.
― The “Revised-International
Staging System” stratifies
subgroups of patients with MM
with differing prognosis and
survival, however, this has only
partly guided therapeutic
choices.
Challenge Approach
― Identify suitable datasets:
15 academic clinical trials
enrolling NDMM treated with
novel agents (European
Cooperative groups)
― Data from large completed
Phase III studies from EFPIA
partners will be extremely
relevant (VISTA, FIRST trials….)
― Agree on variables to be
collected and harmonize data

9. Myeloma data already received & harmonized by HARMONY
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- EMN01: MPR vs CPR vs Rd induction followed by R vs Rp maint in NTE MM patients
- GIMEMA-MM-03-05: VMPT followed by VT maint vs VMP in NTE MM patients
- IST-CAR-506: KCd continuous in NTE MM patients
- MMY2069: VCp vs VP vs VMP induction followed by V maint in NTE MM patients
- PAD-Mel100: Pad induction + Mel100/ASCT + Rp consolidation + R maint in TE MM patients
- RV-MM-PI-209: Rd induction + MPR vs HDM/ASCT consolidation + R vs no maint in TE MM patients
- RV-MM-EMN-441: Rd induction + CRd vs HDM/ASCT consolidation + R vs Rp maint in TE MM patients
- HOVON 65MM/GMMGHD4: VAD vs PAD induction + HDM/ASCT + T maint if VAD arm or V maint in PAD arm in TE MM patients
- HOVON 87/NSMG18: MPT induction followed by T maint vs MPR induction followed by T maint in NTE MM patients
- GEM05MENO65 : VBMCP/VBAD/B vs Td vs VTd induction + HDM/ASCT + IFN𝛂2b vs T vs VT maint in TE MM patients
- GEM05MAS65: VMp vs VTp induction + Vp vs VT maint in NTE MM patients
- GEM2010MAS65: VMP-Rd sequential vs VMP-Rd alternating in NTE MM patients
- MM-BO2005: VTd vs Td induction + HDM/ASCT + consolidation with same induction arm + Dex maint in TE MM patients
5089 patient cases from academic partners received by EMN
+ 2314 patient cases from industry partners pending
• VISTA (Takeda, Janssen) 682 patients
• FIRST (Celgene) 1632 patients

10. HARMONY Research Projects on ~45.000 patient cases
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Open issues in the management of APL
European knowledge bank
for evaluating risk factors in ALL
Large-scale mutation analysis - Novel
prognostic/predictive scheme for improved risk
stratification aimed at personalized medicine
Revised International Staging System for MM in the
European clinical trial population and evaluation of the
efficacy of different novel agents and treatment approaches
Identification of gene-gene interactions
and impact on disease outcome
The role of hypomethylating agents in high-risk MDS
AML
CLL
MDS
MM
ALL
APL
T-Cell Lymphomas: Bridging the Gap between Molecular
Understanding andTreatment Reality
NHL
4 500+
cases
5 000+
cases
4 000
cases
8 000+
cases
20 000
cases
5 000
cases
8 000
cases

11. Personalized prediction of risks & outcomes based on gene
profiles vs. current risk scores: Importance of large numbers
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Live screen captures of
HARMONY Big Data
Platform (14/6/2019)

12. Personalized prediction of risks & outcomes based on gene
profiles vs. current risk scores: Importance of large numbers
12/07/2019 12
Live screen captures of
HARMONY Big Data
Platform (14/6/2019)

13. De-facto anonymization process and data life-cycle
in HARMONY
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DATA PROVIDER TRUSTED
THIRD PARTY
Procedural, Contractual and Security Measures
Organizational Measures & Access Control
HARMONY PLATFORM
ID1’ De-identified registry
ID2’ De-identified registry
ID3’ De-identified registry
Storage
De-identified registry
ID2 De-identified registry
ID3 De-identified registry
ID1’
ID2’
ID3’
ID1
ID2
ID3
ID1
DI & QI Processing Code Change
First brokerage Second
brokerage
Eventually deleted
Data analysis
Harmonisation
Access restrictions and data managementReidentification
Risk Assessment
Research Questions
RESULTS

14. Is this what patients wanted researchers to do
when they invested their life into studies?
While everyone seems convinced of the importance of sharing
data…
 1 year for negotiating data sharing agreements, based on very
irrational legal concerns?
 Months until a valid data set is provided?
 Potential academic rivalry and competitive fears?
12/07/2019 14

15. Harmonization
of outcome
measures and
endpoint definitions
for HMs at European
level
Increase the
application
of omics data
in clinical practice
Speed up drug
development, access
pathways and
bench-to-bedside
process
Building a high-quality
Big Data platform on
hematological
malignancies
Funded by
Our Patient involvement in HARMONY
• Work package 6 co-lead to involve patient orgs, regulators, HTA, value framework, stakeholder forum
• Input into research project proposals
• Input into legal/ethics framework and de-identification procedure
• Definition of “Core Outcome Set definition” (Delphi)

16. So why do we engage as patient advocates?
 Big data has huge potential to better understand the biology of cancers,
subgroups of patients to build individual predictors for risk, response to
treatment and course of disease
 We can help to overcome formal challenges, e.g. Data anonymization and
privacy, legalistic overkill (to protect us!), as well as academic and commercial
rivalry (my data! my drug!) -- and a “too scientific view” on core outcome sets
 We have a huge role to play to make Big Data happen, e.g. provide input
into the research questions, ethical and legal questions, data sharing
willingness, and the regulatory framework
Jan Geissler <jan@patvocates.net>
12/07/2019 16