The document discusses breast cancer genomics and the Oncotype DX test. It provides examples of three patient cases where the Oncotype DX Recurrence Score helped determine treatment. For the first patient, a score of 4 meant she was low risk and hormonal therapy alone was sufficient. For the second patient, a high score of 34 helped convince her that chemotherapy was beneficial. The third patient's intermediate score of 25 meant the benefits of chemotherapy needed further consideration based on her individual factors. The document emphasizes how genomic assays like Oncotype DX can predict response to therapy and help determine the most appropriate treatment.

1. 1
Outstanding breast cancer
genomics
Dr. Sandeep Batra,
Medical Oncologist, Medicity Hospital

2. Breast Cancer
• One out of every eight women will
be diagnosed with breast cancer in
2011
• Fortunately, radical mastectomy
(surgical removal) is rarely needed
today with better treatment options
2
Breast cancer is second only to lung cancer as
a cause of cancer deaths in American women

3. Trends since 1950 in
age-standardized death
rates comparing breast
and selected other types
of cancer, among
women in the USA
EBCTCG, Lancet, 2010

4. BREAST CANCER IN THE WORLD
 1.15 million new cases
 Incidence increasing in most countries
 470 000 deaths
 Half of the global burden in low- and medium-
resourced countries

5. Breast Cancer Figures
 MOST COMMON CANCER in women all over India and
accounts for ASR 26% of all cancers in women in Indian cities.1
 AGE SHIFT, and the average age of developing breast cancer
has shifted from 50 - 70 years to 30 - 50 years; and cancers in
the young tend to be more aggressive.
 An estimated 70218 women died in India (1st) due to
breast cancer, more than any other country in the world.
(second: China - 47984 deaths and third: US - 43909
deaths ).2
1. (Source: PBCR 2009 - 2011 )
2. According to GLOBOCAN (WHO), for the year 2012,

6. Breast Cancer Progress Report
• Breast Cancer mortality
rates have decreased by
2.3% annually since
1990
Source: Breast Cancer Facts and Figures 2005-2006
National Center for Health Statistics data as analyzed by NCI
• The decline in
mortality is primarily
due to early
detection and new
treatment methods

7. Potential Applications for
Breast Cancer Biology
• Predict risk of cancer development
• Estimate prognosis for established cancer
• Predict response to therapy
• Identify therapeutic targets

8. Family history as a risk factor-
Hereditary Breast and Ovarian Cancer
Sporadic
Family clusters
Hereditary
Ovarian CancerBreast Cancer
5%–10% 5%–10%
15%-20%

9. Causes of Hereditary
Susceptibility to Breast Cancer
Gene
BRCA1
BRCA2
TP53
PTEN
Undiscovered genes
Contribution to
Hereditary Breast
Cancer
20%–40%
10%–30%
<1%
<1%
30%–70%
5 to 10% of breast cancers can be attributed to inherited factors

10. * Li-Fraumeni Syndrome, abnormal TP53 gene on ch17p,
associated with premenopausal breast cancer, childhood
sarcomas, brain tumors, leukemia, and adrenocortical
adenomas
*Cowden’s Syndrome, abnormal PTEN tumor
suppressor gene on ch10 associated with premenopausal
breast ca, gastrointestinal malignancies, and benign and
malignant

11. Features That Indicate Increased Likelihood of
Having BRCA Mutations
• Multiple cases of early onset breast ca
• Ovarian cancer (with family h/o of breast or ovarian ca)
• Breast and ovarian ca in the same woman
• Bilateral breast ca
• Ashkenazi Jewish heritage
• Male breast ca

12. BRCA1-Associated Cancers:
Lifetime Risk
Possible increased risk of other
cancers (e.g. prostate, colon)
Breast cancer 50%-85%
(often early age at onset, less than 40
years)
Second primary breast cancer 40%-60%
Ovarian cancer 15%-45%

13. BRCA2-Associated Cancers: Lifetime
Risk
Increased risk of prostate, laryngeal,
and pancreatic cancers (magnitude
unknown)
breast cancer
(50%-85%)
ovarian cancer
(10%-20%)
male breast cancer
(6%)

14. Comparing Breast Cancer Risk Estimates in BRCA
Mutation Carriers
Breast
cancer
risk (%)
General population
BRCA1+ carriers
(BCLC)
BRCA1+
carriers
(Ashkenazi
Jews)
AgeEaston DF et al. Am J Hum Genet 56:265, 1995
Struewing JP et al. N Engl J Med 336:1401, 1997
0
20
40
60
80
100
40 50 60 70 80

15. Established Prognostic Markers for Breast
Cancer
•Axillary lymph nodes
•Tumor size
•Histological grade
•Histological tumor type
•Steroid receptor status
•Age
NIH Consensus Conference 2000

16. Potential Applications for
Breast Cancer Biology
• Predict risk of cancer development
• Estimate prognosis for established cancer
• Predict response to therapy
• Identify therapeutic targets

17. Molecular Portrait of Breast Cancers
HER-2Basal-
like
Luminal A
Luminal B“Normal”
Sorlie T et al, PNAS 2001

18. Subtypes and Prognosis
Sorlie T et al, PNAS 2001

19. Potential Applications for
Breast Cancer Biology
• Predict risk of cancer development
• Estimate prognosis for established cancer
• Predict response to therapy
• Identify therapeutic targets

20. Applications of Expression Microarrays
in Predicting Response to Therapy
• Different profile of sporadic vs hereditary breast cancer
(Heldenfalk, NEJM 2001)
• Identify subset of young women with poor prognosis early
breast cancer (van’t Veer, Nature 2002)
• Subset outcomes for women with node-negative & ER-positive
breast cancer treated with tamoxifen (Paik, NEJM 2004, SABCS
2004)

21. Now available--$$$
Should we use it?
For whom?
How?
So What Good is All this Molecular Analysis??

22. Candidate Gene Selection
From ~40,000 genes
250
cancer-related
candidate genes
*Sources include:
1) Van 't Veer et al, Nature 415:530, 2002
2) Sorlie et al, Proc. Natl. Acad. Sci. USA 98:10869, 2001
3) Ramaswamy et al, Nature Genetics 33:4, 2003
4) Gruvberger et al, Cancer Res. 61:5979, 2001
Paik et al, SABCS 2003

23. Three Breast Cancer Studies Used to Select
16 Cancer and 5 Reference Genes
PROLIFERATION
Ki-67
STK15
Survivin
Cyclin B1
MYBL2
ESTROGEN
ER
PGR
Bcl2
SCUBE2
INVASION
Stromelysin 3
Cathepsin L2
HER2
GRB7
HER2
GSTM1
REFERENCE
Beta-actin
GAPDH
RPLPO
GUS
TFRC
Best RT-PCR performance
and most robust predictors
CD68
BAG1
Paik et al NEJM 2004

24. Three Breast Cancer Studies Used to
Develop Recurrence Score (RS) Algorithm
Paik et al, SABCS 2003
RS = + 0.47 x HER2 Group Score
- 0.34 x ER Group Score
+ 1.04 x Proliferation Group Score
+ 0.10 x Invasion Group Score
+ 0.05 x CD68
- 0.08 x GSTM1
- 0.07 x BAG1
Paik et al, SABCS 2003
Recurrence
Category
RS (0 – 100)
Low risk < 18
Intermediate risk 18 – 30
High risk ≥ 31

25. The Genomic Assay Recommended for Consideration in
NCCN Clinical Practice Guidelines

26. Patient Cases
Patient Cases: 1

27. Cont..
• Patient was identified
as low risk by
Oncotype DX® with a
Recurrence Score ®
result of 4
• Patient received
hormonal therapy
since she was in a
group in which
chemotherapy does
not provide benefit

28. Patient Cases: 2

29. • Patient was identified as
high risk by Oncotype
DX® with a Recurrence
Score® result of 34
• The Recurrence Score
helped convince the
patient on the likely
benefits of taking
chemotherapy given the
biology of her disease
• Patient received
chemotherapy and
hormonal therapy
Cont..

30. Patient Cases: 3

31. • Patient was identified
as intermediate risk
by Oncotype DX® with
a Recurrence Score®
result of 25
• Is there benefit from
chemotherapy for this
patient?
• Need to decide based
on pts PS, clinical
condition & life
expectancy
Cont..

32. Conclusions

33. Genomic Assays: Predictive Role
Recurrence Score® in N-, ER+ patients
1) Paik et al. NEJM 2004, 2) Habel et al. Breast Cancer Research 2006
3) Paik et al. JCO 2006, 4) Gianni et al. JCO 2005
Lower RS’s
• Lower likelihood of recurrence
• Minimal, if any, chemotherapy benefit
Higher RS’s
• Greater likelihood of recurrence
• Clear chemotherapy benefit

34. Potential Applications for
Breast Cancer Biology
• Predict risk of cancer development
• Estimate prognosis for established cancer
• Predict response to therapy
• Identify therapeutic targets