1) Personalized medicine is driven by new technologies, rising healthcare costs, and a shift toward greater patient involvement in medical decisions. 2) Key factors driving personalized medicine include new technologies, patients paying more for healthcare and wanting more input, less continuity with physicians, and rising costs requiring more targeted treatment. 3) Determining the promise versus hype of personalized medicine requires identifying valid biomarkers, validating them in large datasets, and applying them in clinical practice.

1. Cancer Cure
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2. “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 Brought to you by
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3. 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 Brought to you by
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4. 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%
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5. Preemptive action is a clinical major weapon
Drug interactions
Renal dysfunction
Age
Vaccination
Antimalarial
TB
Mammography
colonoscopy
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6. Brought to you by
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8. The clinical problem
•Multiple active regimens for the
treatment of most diseases
•Variation in response to therapy
•Unpredictable toxicity
With choice comes decision
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10. Pharmacogenomic examples-2011
• 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*
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11. What needs to be done to determine hope vs hype?
•Find the 'right' biomarkers
•Validate in robust datasets
•Apply them!
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12. We do not know very much about drugs
Irinotecan
cell membrane
Irinotecan
Irinotecan
SN-38
SN-38
SN-38
TOP1
Cell Death
APC
SN-38G
ABCB1
CYP3A4
CYP3A5
CES1
CES2
UGT1A1
CES1
CES2
ABCC2
ABCG2
ABCC1
ADPRT
TDP1
CDC45L
XRCC1
NFKB1
NPC
ABCB1
????
????
????
????
????
???? ????
???? ????
????
????
????
???? ????
????
????
????
????
????
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13. Discovery
Strategies
HapMap
Linkage
cases controls
Association
DBA/2JCBA/J
PL/JAKR/JNOD/LtJBALB/2JSW
R/JC57L/JRIIIS/J
NZB/B1NJ
A/J
0
1
2
3
4
Grade
Model systems
Expression array Brought to you by

14. What needs to be done to determine hope vs hype?
•Find the 'right' biomarkers
•Validate in robust datasets
•Apply them!
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15. Phase I
In vivo
Mechanism
Phase II
Biomarker
assessment
Phase III
Biomarker
validation
Correlative science: business as usual
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16. 2011 Estimated US Cancer Cases*
*Excludes basal and squamous cell skin cancers and in situ carcinomas except urinary bladder.
Source: American Cancer Society, 2005.
Men
710,040
Women
662,870
32% Breast
12% Lung and bronchus
11% Colon and rectum
6% Uterine corpus
4% Non-Hodgkin
lymphoma
4% Melanoma
of skin
3% Ovary
3% Thyroid
2% Urinary bladder
2% Pancreas
21% All Other Sites
Prostate 33%
Lung and bronchus 13%
Colon and rectum 10%
Urinary bladder 7%
Melanoma of skin 5%
Non-Hodgkin 4%
lymphoma
Kidney 3%
Leukemia 3%
Oral Cavity 3%
Pancreas 2%
All Other Sites 17%
C90401; n=1020 C40101; n=4646
C80203/80405; n=2200 C80203/80405; n=2200
C50303; n=430
C50303; n=430
C10105; MDS
C80303; n=528
C80303; n=528
C80101 gastric; n=800
C30502; n=270 C30502; n=270
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17. 2010 Estimated US Cancer Cases*
*Excludes basal and squamous cell skin cancers and in situ carcinomas except urinary bladder.
Source: American Cancer Society, 2005.
Men
710,040
Women
662,870
32% Breast
12% Lung and bronchus
11% Colon and rectum
6% Uterine corpus
4% Non-Hodgkin
lymphoma
4% Melanoma
of skin
3% Ovary
3% Thyroid
2% Urinary bladder
2% Pancreas
21% All Other Sites
Prostate 33%
Lung and bronchus 13%
Colon and rectum 10%
Urinary bladder 7%
Melanoma of skin 5%
Non-Hodgkin 4%
lymphoma
Kidney 3%
Leukemia 3%
Oral Cavity 3%
Pancreas 2%
All Other Sites 17%
C90401; n=1020 C40101; n=4646
C80203/80405; n=2200 C80203/80405; n=2200
C50303; n=430
C50303; n=430
C10105; MDS
C80303; n=528
C80303; n=528
C80101 gastric; n=800
C30502; n=270 C30502; n=270
GWAS x 2GWAS
NextGEN
GWAS
GWAS
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18. What needs to be done to determine hope vs hype?
•Find the 'right' biomarkers
•Validate in robust datasets
•Apply them!
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19. 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 Brought to you by
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20. DNA Chip Analysis
“Gene signature”
Assay result:
• low- or high- risk group
• probability of distant relapse
Tumor tissue Single value on
“Gene signature”
Relapse Hazard Score
When should we use chemotherapy?
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21. Time (months)
DistantRelapse-freeSurvival
0 20 40 60 80
0.00.20.40.60.81.0
P-value = 0.0001
N = 20
N = 16
Good prognosis
Poor prognosis
Prediction of disease recurrence after surgery in Stage II colon cancer
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22. Watch and wait
Not Predisposed to relapse
patients with stage II disease
Treat with therapy
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23. Colon Cancer Technical Feasibility
Development Studies
Surgery Alone
NSABP C-01/C-02 (n=270)
CCF (n = 765)
Selection of Final Gene List & Algorithm
Development Studies
Surgery + 5FU/LV
NSABP C-04 (n=308)
NSABP C-06 (n=508)
Clinical Validation Study – Stage II Colon Cancer
QUASAR (n=1,436)
Test Prognosis and Treatment Benefit
Development and Validation of a Multi-
Gene RT-PCR Colon Cancer Assay
Validation of Analytical Methods
• 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
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24. p=0.004
0%
5%
10%
15%
20%
25%
30%
35%
0 10 20 30 40 50 60 70
Recurrence Score
Riskofrecurrenceat3years
QUASAR RESULTS: Colon Cancer Recurrence
Score Predicts Recurrence Following Surgery
STROMAL
FAP
INHBA
BGN
CELL CYCLE
Ki-67
c-MYC
MYBL2
REFERENCE
ATP5E
GPX1
PGK1
UBB
VDAC2
GADD45B
RECURRENCE SCORE
Calculated from Tumor
Gene Expression
Prospectively-Defined Primary Analysis in Stage II Colon Cancer (n=711)
Group Risk (by Kaplan-Meier)
12% 18% 22%
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25. 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 Brought to you by
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26. Proliferation MetastasisAngiogenesisApoptosis
Resistance
Shc
PI3-K
Raf
MEKK-1
MEK
MKK-7
JNK
ERK
Ras
mTOR
Grb2
AKT
Sos-1
The Epidermal Growth Factor Receptor
Pathway
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27. Retrospective studies supporting K-ras and lack of
anti-EGFR response
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28. Single agent panitumumab: N=208
K-Ras Mutation Wild-Type K-Ras
Amado RG, et al. J Clin Oncol. 2008;26:1626-1634.
Panitumumab registration trial
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29. Mutations aplenty!
Your patient with stage III sigmoid
mucin neg adenocarcinoma has
mutations in KRAS, BRAF, FGFR3,
and CDK4
WHAT DO YOU DO?
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30. Cancer
Outcome
Lymph node status
Distant metastasis Surgical technique
Patient biology
Tumor biology
Access to care
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31. Toxicity-risk
Genotypes
Supportive
Care
Genotypes
Infection
Defense
Genotypes
Disease
Genotypes
Comprehensive optimization of patient care
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32. This platform has been started by Parveen Kumar Chadha
with the vision that nobody should suffer the way he has
suffered because of lack and improper healthcare facilities in
India. We need lots of funds manpower etc. to make this
vision a reality please contact us. Join us as a member for a
noble cause.
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33. Our views have increased the mark of
the 90,000
 Thank you viewers
 Looking forward for franchise, collaboration,
partners.
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