Molecular Predictors in Clinical
Decision Making for Breast Cancer
George W. Sledge, Jr. M.D.
Indiana University
Simon Cancer Center
2000: The Problem of ER-Positive,
Lymph-Node Negative Breast Cancer
• Common: ~ 137,000 diagnosed annually
• Significant benefit fromhormonotherapy
• Small absolute benefit (~3-5%) from
chemotherapy
• Chemotherapy recommended for all with
T >1cm
Breast Cancer: not one disease, but
criminals sharing the same house
Questions Regarding Gene
Array Utility
• How good are current clinical predictors?
• Do gene arrays add to current clinical
predictors of benefit?
• Do gene arrays replace current clinical
predictors of benefit?
– Lymph node positive tumors
• Where Aren’t They Useful?
• Future directions and challenges
How Good Are Current Clinical
Predictors?
% of cases
Node-Negative Breast Cancer Grows in 5mm
Increments! (SEER 1995, 1996)
Tumor Size (in mm)
Tumor Grade
Rakha et al. Breast Cancer Res
2010, 12: 207.
Reproducibility of tumor
histological grade
Kappa:
0.43-0.83 for
inter-observer
variability
Despite the objective improvements that have been made to breast cancer grading methods,
any assessment of morphological characteristics inevitably retains a subjective element and
is heavily dependent on the pre-analytical parameters.
Rakha et al. Breast Cancer Res 2010, 12: 207.
How Good are Current Clinical
Predictors of Benefit?
• They aren’t useless (i.e., stage and grade
mean something)
• But they lack reproducibility
• And they aren’t perfect predictors
Do gene arrays add to current clinical
predictors of benefit?
Gene Arrays All Measure the Same Thing
all patients
from the
NKI 295
data set
(results are
identical if
only ER+
patients
are used)
Mammaprint:
n = 295 patients
survival
metastases-free
Kaplan-Meier Survival Curves
time (years)
time (years)
Does 70-gene Signature have
Independent Prognostic Value?
 Gene signature adds independent prognostic information to that provided by various risk
classifications
 The signature remained a statistically significant prognostic factor for time to distant metastases &
OS even after adjustment for various risk classifications (HR 2.15 & 2.15, respectively)
Buyse, M. J of NCI, 2006.
Oncotype DX 21 Gene
Recurrence Score (RS) Assay
16 Cancer and 5 Reference Genes From 3 Studies
PROLIFERATION
Ki-67
STK15
Survivin
Cyclin B1
MYBL2
ESTROGEN
ER
PR
Bcl2
SCUBE2
GSTM1
INVASION
Stromolysin 3
Cathepsin L2
HER2
GRB7
HER2
BAG1
CD68
REFERENCE
Beta-actin
GAPDH
RPLPO
GUS
TFRC
Category
RS (0 – 100)
Low risk
RS < 18
Int risk
RS ≥ 18 and < 31
High risk
RS ≥ 31
Levels of Gene Expression Determine
Recurrence Score
21-gene assay = 16 outcome-related genes + 5 reference genes
Higher expression levels of
“favorable” genes = ↓ RS
Higher expression levels of
“unfavorable” genes = ↑ RS
Cutoff points chosen based on
Results of NSABP trial B-20
A risk score is calculated from 0 -100
Sparano, J & Paik, S. JCO, 2008.
Recurrence Score and Distant
Recurrence-Free Survival
Low
RS < 18
Rec. Rate = 6.8%
C.I. = 4.0% - 9.6%
Rate of Distant Recurrence at 10 years
40
35
Intermediate
RS 18 - 31
Rec. Rate = 14.3%
C.I. = 8.3% - 20.3%
High
RS  31
Rec. Rate = 30.5%
C.I. = 23.6% - 37.4%
30
25
20
15
Recurrence Rate
95% C.I.
10
5
0
0
5
10
15
20
25
30
Recurrence Score
35
40
45
50
Paik .S. et al. N Engl J Med 2004;351:2817-26
B-20: Absolute % Increase in DRFS at 10 Years
• Benefit of Chemo Depends on RS
n = 353
Low
RS<18
n = 134
Int
RS18-30
n = 164
High
RS≥31
0
10%
20%
30%
40%
% Increase in DRFS at 10 Yrs (mean ± SE)
21-Gene Array adds to
St. Gallen Risk Groups
St. Gallen Risk
Groups
Low Risk
(n = 53): 95.3%
(95% CI: 86.2%,
100%)
Intermediate Risk
(n = 222): 90.8%
(95% CI: 86.8%,
94.7%)
High Risk
(n = 393): 80.8%
(95% CI: 76.7%,
84.8%)
Oncotype DX™ RS
Groups
Subtotal
Events
10-year
DRFS
95% CI
Low
38
0
100%
100%,100%
Intermediate
12
2
81.8%
59%, 100%
High
3
1
66.7%
13.3%, 100%
Low
134
9
93.5%
89.1%, 97.9%
Intermediate
51
3
93.5%
86.4%, 100%
High
37
9
77.8%
64.3%, 91.4%
Low
166
19
91.5%
87.1%, 95.9%
Intermediate
86
20
82.1%
73.5%, 90.7%
High
141
46
67.4%
59.4%, 75.3%
Do Gene Arrays Add To Standard
Predictors?
• YES
• Highly reproducible, robust assays
• Among ER-positive node-neg. patients, they:
– are prognostic for recurrence
– are predictive of chemotherapy benefit
Do gene arrays replace current
clinical predictors of benefit?
Value of Gene Arrays and Classic
Pathologic Features (Treated Patients)
Wirapati et al. Breast Cancer Res 10:R65 (doi:10.1186/bcr2124), 2008
TransATAC Study: Recurrence Score Vs
Adjuvant! Online
Prediction of Relapse Rate:
Multivariate model
Correlation
40
30
RS Risk (%)*
ΔLog Rank
Chi-Square
P-Value
Risk Distant
Recurrence
from RS
21.9
<.001
Risk Relapse
from Adjuvant!
21.9
<.001
Variable
20
10
• RS and Adjuvant! are each:
0
0
10
20
30
40
– Highly significant
Adjuvant! Risk Using Central Grade (%)**
– Independent
• RS and Adjuvant! correlate
weakly (Spearman R=0.234)
– Predict relapse in both node
negative and node positive
patients
Dowsett M, et al. SABCS 2008. Abstract 53.
• Similar results for mortality
Nodal Status and Recurrence Score:
TransATAC
Dowsett, M et al. J ClinOncol 28: 1829-34, 2010
Trans ATAC: Multivariate Analysis in
Node-Positive ER-Positive Patients
Dowsett, M et al. J ClinOncol 28: 1829-34, 2010
Phase III SWOG 8814 (TBCI 0100)
Postmenopausal, N+, ER+
RANDOMIZE
tamoxifen x 5 yrs
(n = 361)
n = 1477
CAF x 6, then
tamoxifen
CAF x 6, with
concurrent tam
(n = 550)
(n = 566)
Superior Disease-Free Survival
(DFS) and Overall Survival (OS)
over 10 Years
Albain, et al. Breast Cancer Res Treat 2005
Outcome Based on Recurrence Score:
Hormonal Therapy +/- Chemotherapy
SWOG 8814/TBCI 0100 : 10-Year DFS Point
Estimates
Recurrence Score
Risk Category
Tamoxifen
Alone
CAF followed
by Tamoxifen
Low (< 18)
60%
64%
Intermediate (18-30)
49%
N.S.
63%
P < 0.05
High (≥ 31)
43%
P < 0.05
K Albain et al: Lancet Oncology 2010
55%
ATAC: Hazard Ratios for Recurrence Score
by Nodes and Treatment Arm
All(n=1231)
Node Negative, Both arms (n=872)
Tamoxifen (n=432)
Anastrozole (n= 440)
Size of symbol
proportional to
number of events
Node Positive, Both arms (n=306)
Tamoxifen (n=152)
Anastrozole (n=154)
0.1
1.0
4.35
10.0
100.0
Hazard Ratio*
*Hazard ratio for RS/50 adjusted for tumour size, grade and age
Dowsett et al. SABCS 2008 #53
Association between 70 gene assay test recurrence score and
pathologic CR in a neoadjuvant T-AT study
Almost all pCR occurred in the high RS group.
Most patients with high RS did not have a pCR.
<18 low RS
>31 high RS
Cases with pathologic CR after paclitaxel / paclitaxel-doxorubicin
Cases with residual cancer after paclitaxel / paclitaxel-doxorubicin
L Gianni et al., JCO 23:7265-77, 2005
Pre-Op Chemotherapy: Relationship between
prognostic predictors and first generation
chemotherapy sensitivity predictors
• Predictors of Pathologic CR
These very same variables
– High Recurrence Score
predict for worse survival !
– High tumor grade
– ER-negative cancer
(even after chemotherapy)
– Younger age
– HER-2 amplification
This is because even if most of pCRs occur
in these poor prognosis groups, most
patients do not achieve pCR and their
prognosis brings down the overall survival
of the entire group.
An inconvenient truth about
biomarkers
• Survival of individual patients with stage I-III
breast cancer depends on
– Baseline prognosis
– Efficacy of chemotherapy
– Efficacy of endocrine therapy
Many known biomarkers interact separately
with each of these outcome variables!
Gene Arrays in Node-Positive ERPositive Patients
• Size and lymph node status matter: big tumors
and multiple nodes kill
• Gene arrays define a high-risk population that
does not benefit from chemotherapy
• Gene arrays DO NOT replace standard
predictors
Gene Arrays Predict Early Recurrence
and Chemotherapy Benefit
Hazard Ratios over Time
for High vs. Low RS
2y
5y
10y
Blows, FM et al. PLOS Med 7(5): e1000279, 2010
17 (8.4-34)
4.0 (2.0-8.0)
1.7 (.84-3.4)
Lau, KF. J ClinOncol 27:15s, 2009
(suppl; abstr 11085)
SWOG 8814: “The effect of the RS on treatment is not constant over
time. In the first five years, RS predicts chemotherapy benefit
(interaction p=0.029), but not after five years (p=0.58).”
Albain et al. Lancet Oncol 11: 55-65, 2010
Gene Arrays Predict Early Recurrence
and Chemotherapy Benefit
Hazard Ratios over Time
for High vs. Low RS ER+
2y
5y
10y
Blows, FM et al. PLOS Med 7(5): e1000279, 2010
17 (8.4-34)
4.0 (2.0-8.0)
1.7 (.84-3.4)
Lau, KF. J ClinOncol 27:15s, 2009
(suppl; abstr 11085)
SWOG 8814: “The effect of the RS on treatment is not constant over
time. In the first five years, RS predicts chemotherapy benefit
(interaction p=0.029), but not after five years (p=0.58).”
Albain et al. Lancet Oncol 11: 55-65, 2010
Gene Arrays Predict Early Recurrence
and Chemotherapy Benefit
Hazard Ratios over Time
for High vs. Low RS
2y
5y
10y
Blows, FM et al. PLOS Med 7(5): e1000279, 2010
17 (8.4-34)
4.0 (2.0-8.0)
1.7 (.84-3.4)
Lau, KF. J ClinOncol 27:15s, 2009
(suppl; abstr 11085)
SWOG 8814: “The effect of the RS on treatment is not constant over
time. In the first five years, RS predicts chemotherapy benefit
(interaction p=0.029), but not after five years (p=0.58).”
Albain et al. Lancet Oncol 11: 55-65, 2010
Risk of Breast Cancer Recurrence:
Two Cell Populations
0.3
1)Proliferating Micromets
Recurrence hazard rate
ER/PgR+
ER/PgR–
0.2
2)Relapsing Dormant Cells
0.1
0
0
1
2
3
4
PgR = progesterone receptor.
Saphner et al. J Clin Oncol. 1996;14:2738.
5
6
Years
7
8
9
10
11
12
Future Directions:
Integration of Gene Arrays and
Standard Predictors
• Tang et al. (ASCO 2010 Abstract 509)
combined RS with pathologic and clinical
information (RSPC Index)
• RSPC risk index is superior to RS alone at
predicting 10-year distant recurrence rates
• RSPC risk index reduces patients in the
intermediate category (18% vs 26%, p = 0.001)
Where Aren’t They Useful?
• “Special Type” Cancers
– Most have excellent prognosis
– Some have specific mutational events
• ER-Negative and HER2-Positive cancers
– Most require treatment
• Inflammatory cancer
Inflammatory Breast Cancer:
Not a Genomically Homogenous
Population
Bertucci et al., Cancer 116: 2783-93, 2010
Future Directions
Proportion of patients in low RS category
affects the power of adjuvant chemotherapy trials
in ER-positive breast cancer
0.5
0.6
0.7
0.8
0.9
80%
85%
90%
95%
0.4
Probability of Statistical Significance
1.0
Base power
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Proportion of Low Recurrence Score Patients
Pusztai et al JCO 26:4679-4683, 2008
Prospective Validation of
Mammaprint: The MINDACT Trial
Risk assessed via
Clinicopathological
Factors (adjuvant)
+ Mammaprint
6,000 LN- patients
High risk 
Chemo +
Endocrine
Low risk 
Endocrine
Discordant cases:
random assignment
to follow genomic
vs clinicopathologic
result
Accrual started 2/07 and is expected to be finished within 3 years
Cardoso, F. JCO, 2008.
Prospective Validation of Oncotype DX:
The TAILORX Trial
11,248 ER+/LN- patients
Low RS:
Hormonal
Therapy
High RS:
Chemo +
Hormonal
Therapy
Hormonal Therapy
Chemo + Hormonal
Dowsett, M. & Dunbier, A. Clin Cancer Res, 2008.
E2100: Role of Anti-angiogenic
Therapy in Metastatic Breast Cancer
Eligibility:
- No prior Rx for mets
- Adjuvant taxane if >12
mos.
Exclusion:
- Her-2 +
- CNS mets
- Proteinuria
- Uncontrolled HTN
R
A
N
D
O
M
I
Z
E
Arm A: Paclitaxel (q wk) + rhuMAb
VEGF
Arm B: Paclitaxel (90 mg/m2 q wk)
Progression Free Survival
1.0
Pac. + Bev. 11.8 months
Paclitaxel
5.9 months
DFS Proportion
0.8
0.6
HR = 0.60
0.4
Log Rank Test p<0.001
0.2
0.0
0
6
12
Patients at risk:
P+B 347
323
P
326
159
167
89
18
47
100 53
20
24
30
25
12
14
6
Months
36
42
7
2
0
48
2
0
54
1
VEGF -2578 AA & -1154 AA genotypes in
combination arm outperformed control
p=0.047
p=0.035
25.2 mo 37.0 mo
Median OS
Control arm=25.2 mo
Combination arm=26.7 mo
Combination arm AA=37.0 mo
25.2 mo
Median OS
Control arm=25.2 mo
Combination arm=26.7 mo
Combination arm AA=46.5 mo
46.5 mo
Challenges for Gene Arrays
• T1a,b ER-positive tumors: how low do you go?
• ER-negative, node-negative tumors: who
doesn’t need chemotherapy?
• Prediction of late relapse in ER-positive
patients (tumor dormancy)
• Prediction of benefit for specific drugs or
regimens
Conclusions
• Multi-gene assays provide consistent
prognostic results and probably measure the
same biology
• Arrays add to but do not replace current
clinical predictors
• Much work remains
.
Thank You!