Transcript Presentation

Personalized Health Care and Cancer
Therapeutics (Biomarkers and Treatment)
April 17,, 2011
Dr Howard L. McLeod
Eshelman Distinguished Professor and Director
Institute for Pharmacogenomics and Individualized Therapy (IPIT)
University of North Carolina – Chapel Hill, NC
“A surgeon who uses the wrong side of the
scalpel cuts her own fingers and not the
patient;
if the same applied to drugs they would
have been investigated very carefully a long
time ago”
Rudolph Bucheim
Beitrage zur Arzneimittellehre, 1849
Personalized medicine, schmersonalized medicine!
Medicine has always been personalized
Medicine is moving toward greater 'customer
accountability'
Medicine will never be personalized
it is a change in expectation as well as some practical,
process changes
Drivers of Personalized Medicine
Technology
– Significant new opportunities over the past 5 years
Patient financial burden
– When you are paying more, you want more say
Less personal care
– Who will be my 'doctor' today?
Cost of care
– Even the USA can't afford treating 100% to benefit 20%
Preemptive action is a clinical major weapon
Drug interactions
Renal dysfunction
Age
Vaccination
Antimalarial
TB
Mammography
colonoscopy
The clinical problem
•Multiple active regimens for the
treatment of most diseases
•Variation in response to therapy
•Unpredictable toxicity
$$$$$$$$$$$$$
With choice comes decision
Pharmacogenomic examples-2011
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bcr/abl or 9:22 translocation—imatinib mesylate*
HER2-neu—trastuzumab**
C-kit mutations—imatinib mesylate**
Epidermal growth factor receptor mutations—gefitinib
Thiopurine S-methyltransferase—mercaptopurine and
azathioprine*
UGT1A1-irinotecan**
CYP2D9/VKORC1-warfarin*
HLA-B*5701-abacavir *
HLA-B*1502-carbamazepine *
CYP2C19-clopidogrel
Cytochrome P-450 (CYP) 2D6—5-HT3 receptor
antagonists, antidepressants, ADHD drugs, and codeine
derivatives, tamoxifen*
What needs to be done to determine hope vs hype?
•Find the 'right' biomarkers
•Validate in robust datasets
•Apply them!
We do not know very much about drugs
Irinotecan
cell membrane
ABCB1
???? ????
Irinotecan
CES1
Irinotecan
CES2
CES1
CYP3A4
APC
CYP3A5
NPC
???? CES2
????
SN-38
SN-38
????
???? ????
????
SN-38
????
SN-38G
????
????
TOP1
???? ????
????
ADPRT
????
TDP1
XRCC1
????
????
????
NFKB1
CDC45L
????
Cell Death
4
Grade
3
2
1
HapMap
DB
A/
2
CB J
A/
J
PL
AK /J
NO R/J
D
BA /LtJ
LB
/
SW 2J
R/
C5 J
7L
R /J
NZ IIIS
B/ /J
B1
NJ
A/
J
0
Linkage
Model systems
Expression array
Discovery
Strategies
cases
controls
Association
What needs to be done to determine hope vs hype?
•Find the 'right' biomarkers
•Validate in robust datasets
•Apply them!
Correlative science: business as usual
Phase I
Phase II
Phase III
In vivo
Mechanism
Biomarker
assessment
Biomarker
validation
2011 Estimated US Cancer Cases*
C90401; n=1020
Prostate
33%
Lung and
bronchus
C30502;
n=270 13%
Colon and rectum
Men
710,040
Women
662,870
10%
7%
Melanoma of skin
5%
Non-Hodgkin
4%
3%
Leukemia
C10105; MDS
3%
Oral Cavity
3%
Pancreas
2%
C80303; n=528
All Other Sites
Lung and bronchus
C30502;
n=270
11%
Colon and rectum
Uterine corpus
C80203/80405;
n=2200
4%
Non-Hodgkin
lymphoma
4%
Melanoma
of skin
C50303; n=430
3%
C50303; n=430
lymphoma
Kidney
12%
6%
C80203/80405; n=2200
Urinary bladder
C40101;
n=4646
Breast
32%
Ovary
3%
Thyroid
2%
Urinary bladder
2%
Pancreas
21%
All Other Sites
C80303; n=528
17%
C80101 gastric; n=800
*Excludes basal and squamous cell skin cancers and in situ carcinomas except urinary bladder.
Source: American Cancer Society, 2005.
GWAS x 2
GWAS
2010 Estimated US Cancer Cases*
NextGEN
C90401; n=1020
Prostate
33%
GWAS
Lung
and
bronchus
C30502;
n=270 13%
Colon and rectum
Men
710,040
Women
662,870
10%
7%
Melanoma of skin
5%
Non-Hodgkin
4%
3%
Leukemia
C10105; MDS
3%
Oral Cavity
3%
Pancreas
2%
GWAS
C80303; n=528
All Other Sites
Lung and bronchus
C30502;
n=270
11%
Colon and rectum
Uterine corpus
C80203/80405;
n=2200
4%
Non-Hodgkin
lymphoma
4%
Melanoma
of skin
C50303; n=430
3%
C50303; n=430
lymphoma
Kidney
12%
6%
C80203/80405; n=2200
Urinary bladder
C40101;
n=4646
Breast
32%
Ovary
3%
Thyroid
2%
Urinary bladder
2%
Pancreas
21%
All Other Sites
C80303; n=528
17%
C80101 gastric; n=800
*Excludes basal and squamous cell skin cancers and in situ carcinomas except urinary bladder.
Source: American Cancer Society, 2005.
What needs to be done to determine hope vs hype?
•Find the 'right' biomarkers
•Validate in robust datasets
•Apply them!
Fundamental questions
When is surgery enough?
Should we use chemotherapy?
difficult to reverse practice
Which treatment should we use?
toxicity-many 'equal' therapies
efficacy
dosage
When should we use chemotherapy?
Tumor tissue
DNA Chip Analysis
“Gene signature”
Single value on
“Gene signature”
Relapse Hazard Score
Assay result:
• low- or high- risk group
• probability of distant relapse
1.0
Prediction of disease recurrence after surgery in Stage II colon cancer
0.6
Good prognosis
0.4
N = 16
0.2
Poor prognosis
P-value = 0.0001
0.0
Distant Relapse-free Survival
0.8
N = 20
0
20
40
Time (months)
60
80
patients with stage II disease
Treat with therapy
Watch and wait
Not Predisposed to relapse
Development and Validation of a Multi-Gene
RT-PCR Colon Cancer Assay
Colon Cancer Technical Feasibility
• NSABP and CCF
Collaborations 761 genes studied in
1,851 patients to select
genes which predict
recurrence and/or
differential 5FU/LV
benefit
• Clinical Validation of
final assay in a large,
prospectively-designed
independent study
Development Studies
Surgery Alone
Development Studies
Surgery + 5FU/LV
NSABP C-01/C-02 (n=270)
NSABP C-04 (n=308)
CCF (n = 765)
NSABP C-06 (n=508)
Selection of Final Gene List & Algorithm
Validation of Analytical Methods
Clinical Validation Study – Stage II Colon Cancer
QUASAR (n=1,436)
Test Prognosis and Treatment Benefit
QUASAR RESULTS: Colon Cancer Recurrence
Score Predicts Recurrence Following Surgery
Prospectively-Defined Primary Analysis in Stage II Colon Cancer (n=711)
RECURRENCE SCORE
STROMAL
FAP
INHBA
BGN
CELL CYCLE
Ki-67
c-MYC
MYBL2
GADD45B
REFERENCE
ATP5E
GPX1
PGK1
UBB
VDAC2
Risk of recurrence at 3 years
Calculated from Tumor
Gene Expression
35%
Group Risk (by Kaplan-Meier)
22%
12%
18%
30%
25%
20%
15%
10%
p=0.004
5%
0%
0
10
20
30
40
50
Recurrence Score
60
70
Fundamental questions
When is surgery enough?
Should we use chemotherapy?
difficult to reverse practice
Which treatment should we use?
toxicity-many 'equal' therapies
efficacy
dosage
The Epidermal Growth Factor
Receptor Pathway
Shc
Grb2
PI3-K
Sos-1
Ras
AKT
MEKK-1
Raf
MEK
mTOR
MKK-7
ERK
JNK
Apoptosis
Resistance
Proliferation
Angiogenesis
Metastasis
Retrospective studies supporting K-ras and lack of
anti-EGFR response
Single agent panitumumab: N=208
K-Ras Mutation
Wild-Type K-Ras
Panitumumab registration trial
Amado RG, et al. J Clin Oncol. 2008;26:1626-1634.
Mutations aplenty!
Your patient with stage III sigmoid
mucin neg adenocarcinoma has
mutations in KRAS, BRAF, FGFR3, and
CDK4
WHAT DO YOU DO?
Patient biology
Lymph node status
Tumor biology
Cancer
Outcome
Distant metastasis
Surgical technique
Access to care
Comprehensive optimization of patient care
Disease
Genotypes
Infection
Defense
Genotypes
Toxicity-risk
Genotypes
Supportive
Care
Genotypes