prof dr edwin cuppen, umc utrecht and hubrecht institute
TRANSCRIPT
PERSONALIZED CANCER TREATMENTProf dr Edwin Cuppen, UMC Utrecht and Hubrecht Institute
11/6/2014 © 2012–2014 Healthcare Information and Management Systems Society (HIMSS) 2
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
1,000 dollar genome: January 2014
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
Changes in DNA are the basis for cancer
But also make every cancer patient unique
Personalized treatment
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
Biomarker discovery requires large cohorts
and systematic integration of genetic and treatment data
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)
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
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
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
Gene X mutated
Gene X wild type
Systematic biomarker discovery
Identify DNA changes associated with good or poor response
Treatment Z
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
‘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