Pharmacogenomics in Colorectal Cancer

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Transcript Pharmacogenomics in Colorectal Cancer

Cancer Care Engineering
Colorectal Cancer
Gabriela Chiorean, M.D.
June 26, 2009
Rationale in colorectal
cancer
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Perform OMICs of healthy, polyps, cancer
Compare OMICs between cancer, polyps and healthy:
develop new screening and risk assessment tools
Analyse changes in OMICs with treatment and correlate
with response/toxicity: predictive markers
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Mathematical modeling and bio-mapping
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Cancer care delivery
Rationale: CCE now
GENOMICS
METABOLOMICS
GLYCOPROTEOMICS
BIOMAP
C
R
C
LIPIDOMICS
Mathematical
modeling
Schema IUCRO-0221
CCE in CRC
active April 2009
N=270
Stratification:
-Healthy (n=90)
-Polyps (n=90)
-Cancer (n=90)
stg 1/2
stg 3
S
A
M
P
L
E
S
stg 4 metastatic
8-hr fasting
Blood (Serum)
7 mL red top
Metab, vit D
Blood (Plasma)
21 mL purple top
Genomics, lipidomics,
glycoproteomics
Tissue
10 mg polyp or
50 mg cancer /
50 mg normal tissue
S
H
I
P
D
R
Y
I
C
E
Samples Collection
N= 5
Healthy Controls
Sign ICS (RN)
Screening Colonoscopy – GI Clinic
Collect by RN/processing CRS
Blood 1x 7 mL glass red top
3 x 7 mL plastic lavender
Questionnaires
diet/environmental exposures
Label specimens
Healthy
if no polyps/tumor
Samples Collection
N= 3
Adenomatous Polyps
Sign ICS (RN)
Polyps identified
Screening Colonoscopy – GI Clinic
Collect by RN/processing CRS
Blood 1x 7 mL glass red top
3 x 7 mL plastic lavender
Questionnaires
diet/environmental exposures
Label specimens
Polyp
Tissue procurement/Research specialist
-Polyp cut in ½
-Place in tube with no preservative
-Freeze at -70oC
Samples Collection
N= 8
Cancer
Sign ICS (RN)
Call tissue procurement
-Tumor tissue ~ 50 mg
-Normal mucosa ~ 50 mg
-Place in tube with no preservative
-Freeze at -70oC
Surgery
Chemotherapy
Follow-up
Collect by RN/processing tissue procurement
Blood: 1 x 7 mL red top glass tube
3 x 7 mL lavender plastic tubes
Questionnaires: diet/environmental exposures
Every 3 months
Up to 24 months
CCE Blood Acquisition Protocols
Glass Red Top Tube (1)
Volume = 7mL
Following blood draw,
patients and care givers
administered diet and life
style questionnaire
Glass Purple Top Tubes (EDTA) (3)
Volume = 7mL /tube
Page: Amber Allen (page #) for transport to laboratory (RT) and processing
Maximum time at RT from draw to centrifugation: 45-60 min.
Centrifuge: 1500g, RT, 15 min
0.2 mL (2) Whole Blood into freezing tubes containing
comet assay solution, mix, place on dry ice, FREEZE (-80oC)
Maximum time at RT from draw to Whole Blood Removal: 20 min.
Remaining whole blood
Serum ( ~ 3mL), place on wet ice
Maximum time at RT from draw to centrifuge: 30 min.
Centrifuge: 1750g, RT, 15 min
REGULAR EPPENDORF TUBES
0.3 mL (2) FREEZE (-80 oC) Metabolomics NMR
0.2 mL (2) FREEZE (-80 oC) Metabolomics MS
0.5 mL (2) FREEZE (-80 oC) Vitamin D Analysis
Plasma (~ 6mL), place on wet ice
SILICONIZED EPPENDORF TUBES
0.2 mL (2) FREEZE (-80 oC) Lipidomics
0.5 mL (2) LONG TERM STORAGE (LIQUID N2)
Pellets (2); resuspend (1), combine with second
pellet, re-centrifuge 1750g RT 5 min, decant, place
on dry ice: FREEZE (-80 oC) SNP
REGULAR EPPENDORF TUBES
1.5 mL (1) FREEZE (-80 oC)Glycoproteomics
0.2 mL (1) FREEZE (-80 oC) Proteomics
1.5 mL (1) LONG TERM STORAGE (LIQUID N2); Regular Eppendorf Tubes
0.2 mL (12) LONG TERM STORAGE (LIQUID N2); Siliconized Eppendorf Tubes
Metabolomics
Typical 2D GCxGC/MS data from a colon cancer patient serum sample.
After derivitization, approximately 800 metabolites are observed (many of the
lower intensity peaks are not evident in this figure). Dan Raftery-Purdue
Metabolomics
Combination of the GC PCA data with NMR PCA data improves the
classification to 95%. In the figure, 2 PCs from the GCxGC/TOF dataset
are combined with 1 PC from the NMR data. Oblong shapes are used to
indicate 95% confidence limits.
Schema IUCRO-0198
Metabolomics in CRC
N=150
Stratification:
-Healthy (n=30)
-Polyps (n=30)
-Cancer (n=90)
stg 1/2
stg 3
S
A
M
P
L
E
S
stg 4 metastatic
8-hr fasting
Blood (Serum)
7 mL red top
Urine
10 mL
Tissue
10 mg polyp or
50 mg cancer /
50 mg normal tissue
S
H
I
P
D
R
Y
I
C
E
Principle Component Analysis of Metabolites
in serum in IUCRO-0198
Dan Raftery, Lingyan Liu - Purdue
Investigators:
Indiana University
Gabriela Chiorean - Oncology
Pat Loehrer – Oncology
Stephen Williams - Oncology
Yan Xu - Lipidomics
Jim Klaunig - Genomics
Bruce Robb - Surgery
Eric Wiebke - Surgery
Doug Rex - GI
Mike Chiorean - GI
Charles Kahi - GI
Peter Johnstone – Rad Onc
Oscar Cummings - Pathology
Purdue University
Marietta Harrison - Chemistry
Daniel Raftery – Metabolomics
Fred Regnier – Proteomics
- Glycoproteomics
Dorothy Teegarden – Vitamin D
Min Zhang – Statistical Modeling
Jake Chen – Biological Modeling