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
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
Mathematical modeling and bio-mapping
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