prof dr edwin cuppen, umc utrecht and hubrecht institute
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PERSONALIZED CANCER TREATMENTProf dr Edwin Cuppen, UMC Utrecht and Hubrecht Institute
11/6/2014 © 2012–2014 Healthcare Information and Management Systems Society (HIMSS) 2
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organism cell chromosome DNA
Deoxyribo Nucleic Acid (DNA)
Changes in DNA (mutations) can cause disease- Early in embryogenesis: congenital disease- In somatic tissue: cancer
~3 billion letters: G, A, T, C
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1,000 dollar genome: January 2014
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Applications of Personal Genomes in Clinical CareFrom cradle to grave / From pre-womb to tomb
-2
Birth planning- Screening carriership
-0.5
Pregnancy- NIPT (trisomy 13/18/21, gender, carriership)
Newborn- Replacing heel prick: detectionrare congenital disease
0 0-10
Diagnostics congenital disease- De novo mutation screening- Whole genome scan
10-20
Disease prevention- BRCA, CFTR
Cancer- Personalized treatment
>50
Pharmacogenetics-drug/dose choice
60
Aging- Understanding healthyaging
>100
Death- Genetic autopsy unexplaineddisease cause
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Changes in DNA are the basis for cancer
But also make every cancer patient unique
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Personalized treatment
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Tumor growth requires changes of multiple characteristics
- Drugs have been or are being designed to target various biological processes- Many drugs only work in part of patients
- No biomarkers are available for most drug sensitivity or resistance
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Biomarker discovery requires large cohorts
and systematic integration of genetic and treatment data
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founded in 2010: UMCU, EUR, NKI
UMC Groningen
VuMC AmsterdamMeander
MUMC Maastricht
Radboud Nijmegen
LUMC Leiden
AMC Amsterdam
Center for Personalized Cancer Treatment (www.cpct.nl)
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Personalized Cancer Treatment
Obtain patient biopsy
Bioinformatics and Systems Biology to identify affected pathways and select drugs
Treat patient with selected drug(s)
until disease progression
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Patient-Centered Analysis
Longitudinal data monitoring system allows observation of patient’s molecular and clinical changes over time, as new conditions develop, drugs administered and lab tests taken
Medications,Surgeries, etc.
Events -Metastases
Samples & Molecular Data
Lab measurements
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Validated Clinically-Actionable Markers
Additional Markers with Potential Clinical
Benefit
Patient-Centered Reporting
Generating individual patient reports for clinicians summarizing prognostic and predictive markers identified in patient’s sample
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Gene X mutated
Gene X wild type
Systematic biomarker discovery
Identify DNA changes associated with good or poor response
Treatment Z
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Patientdata
Treatmentand respons
Pathologyand lab
DNA sequences
eZIS/EPD eCRF LMSpathology
Research DB
Patient report
Biomarker discovery
Publicdata
SYSTEMATIC DATA INTEGRATION AND MINING
PalGAIKNL, etc
Medical Specialist
Stak
e-h
old
ers
PatientFuture patient
Insurance company
Pharma Society
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‘BIG data’ challenges Systematic and large scale data collection is valuable for improving quality and
efficacy of care
Personalized cancer treatment is already possible
Only for some agents/indications: need for routine diagnostic testing
Systematic data collection and research required for others
Footprints one-dimensional datasets are large
ICT infrastructure
Whole genome information part of EMR
Multi-dimensional integration is required
coupling of heterogeneous data sources
use of standards/ontologies
Data security and data access needs to be guaranteed
Safety and misuse
Who owns this information