Annual Meeting Proceedings Part 1 - American Society of Clinical ...

516 Poster Discussion Session (Board #6), Sat, 1:15 PM-5:15 PM and
4:45 PM-5:45 PM
Mammostrat as an immunohistochemical multigene assay for prediction of
early relapse risk in the TEAM pathology study. Presenting Author: John
M. S. Bartlett, Ontario Institute for Cancer Research, Toronto, ON, Canada
Background: Some postmenopausal patients with hormone sensitive early
breast cancer remain at high risk of relapse despite endocrine therapy, and
might benefit additionally from adjuvant chemotherapy. The challenge is to
prospectively identify such patients. The Mammostrat test uses five
immunohistochemical markers to stratify patients regarding recurrence
risk, and may inform treatment decisions. We tested the efficacy of this
panel in the TEAM trial. Methods: Pathology blocks from 4598 TEAM
patients were collected and TMAs constructed. The cohort was 47% node
positive and 36% were also treated with adjuvant chemotherapy. Triplicate
0.6mm2 TMA cores were stained and positivity for p53, HTF9C, CEACAM5,
NDRG1, SLC7A5 assessed. Cases were assigned a Mammostrat risk score,
and distant relapse free (DRFS) and disease free survival (DFS) analysed.
Results: In multivariate regression analyses, corrected for conventional
clinicopathological markers, Mammostrat provided significant additional
information on DRFS after endocrine therapy in ER positive node negative
patients (N�1226) not receiving chemotherapy (p�0.004). Further analyses
in all patients not exposed to chemotherapy, irrespective of nodal status
(N�2559) and in the entire cohort (N�3837) showed Mammostrat scores
provide additional information on DRFS in these groups (p�0.001 and
p�0.0001 respectively; multivariate analyses). No differences were seen
between the two endocrine treatment regimens. Conclusions: The Mammostrat
score predicts DRFS for both exemestane and tamoxifenexemestane
treated patients irrespective of nodal status and chemotherapy.
The ability of this test to provide additional outcome data following
treatment provides further evidence for its’ utility in risk stratification of ER
positive postmenopausal breast cancer patients.
518 Poster Discussion Session (Board #8), Sat, 1:15 PM-5:15 PM and
4:45 PM-5:45 PM
Gene methylation in random FNA samples as biomarkers for breast cancer.
Presenting Author: Vered Stearns, Sidney Kimmel Comprehensive Cancer
Center at Johns Hopkins University, Baltimore, MD
Background: Current methods to determine breast cancer risk are insufficiently
sensitive to select women most likely to benefit from preventive
strategies. We hypothesized that candidate gene promoter hypermethylation
may provide an individualized risk profile. We performed a prospective
study to determine whether DNA cumulative methylation index (CMI)
varies by menstrual phase or menopausal status, and to correlate CMI with
established risk factors. Methods: We obtained random fine needle aspiration
(rFNA) samples from healthy women age 35-60 and determined their
menopausal and menstrual status, lifetime Gail risk, mammographic breast
density, and cytologic atypia assessed as the Masood score. We evaluated
CMI of 11 candidate genes in rFNA cells using the Quantitative Multiplex
Methylation-Specific PCR (QM-MSP) technique. We used Wilcoxon test
and ANOVA model to compare CMI across menopausal and menstrual
(follicular, mid-cycle, luteal) categories, respectively. We used linear
regression model to adjust for age and BMI. Methylation scores were
log-transformed in the analysis. Results: We enrolled 390 women at the
Avon Breast Centers at Johns Hopkins and Northwestern, the majority
through the Love/Avon Army of Women, and 380 completed study
procedures. Median age 50 (36-60), mean BMI 28 (18.7-50.8), 52% were
postmenopausal. Mean life-time Gail risk 14.6 (5.6-54.1), mean percent
mammographic density 19.6 (2.5-72.8), and mean Masood score (N�354)
13.6 (7-18). QM-MSP analysis was completed on 229 samples. We did not
observe differences in CMI among menopausal (P�0.4895) or menstrual
categories (P�0.2333). There was no association between CMI and
life-time Gail risk (P�0.706) or breast density (P�0.4116). We observed a
significant correlation between CMI and Masood score (P�0.0167).
Conclusions: CMI correlates with degree of cytologic atypia and is potentially
a robust indicator of breast cancer risk since it does not vary with
menstrual or menopausal status. Next, we will select genes that best reflect
changes in the clinical parameters to create a gene methylation signature
that will be validated in other studies and correlated with breast cancer risk.
Breast Cancer—HER2/ER
11s
517 Poster Discussion Session (Board #7), Sat, 1:15 PM-5:15 PM and
4:45 PM-5:45 PM
Validation of IHC4 algorithms for prediction of risk of recurrence in early
breast cancer using both conventional and quantitative IHC approaches.
Presenting Author: Jason Christiansen, HistoRx, Inc, Branford, CT
Background: Hormone receptors, HER2 and Ki67 are residual risk markers
in early breast cancer. Combining these markers into a unified algorithm
(IHC4) provides information on residual recurrence risk of patients treated
with hormone therapies. This study aimed to independently investigate the
validity of the IHC4 algorithm for residual risk prediction using both
conventional (DAB)-IHC and quantitative immunofluorescence (QIF-
AQUA). Methods: The TEAM pathology study recruited �4500 samples
from patients treated in the TEAM trial. TMAs were stained for ER, PgR,
HER2 and Ki67 using QIF-AQUA technology or DAB-based immunohistochemistry
(DAB-IHC). Central HER2 FISH was performed. Quantitative
image analysis was used to generate expression scores that were normalized
to produce “IHC4 algorithm” as well as novel algorithm scores.
Algorithm scores were compared with disease recurrence in univariate and
multivariate Cox Proportional Hazards models. Results: Both DAB-IHC and
QIF-AQUA IHC4 continuous models were significant (P�0.0001) for
prediction of disease recurrence with a continuous Hazard Ratio (HR) of
1.011 (1.010 – 1.013) for QIF-AQUA IHC4 versus 1.008 (1.007 – 1.010)
for the DAB-IHC IHC4 model using the published IHC4 algorithm (Cuzick
et al 2011). Binning continuous model scores (4 bins) by Kaplan-Meier
survival analysis was used to graphically illustrate these effects. De novo
models for both DAB-IHC and QIF-AQUA were also significantly (P�0.0001)
predictive of residual risk in early breast cancer. Additionally, all 4 models
were independent predictors of recurrence (P�0.0001) with other recognized
clinical prognostic factors in multivariate analysis. Although results
from DAB and QIF-AQUA were modestly correlated, the QIF-AQUA model
showed enhanced prediction of recurrence in both Cox Proportional
Hazards Modeling and C-index calculations. Conclusions: Either conventional
DAB or QIF-AQUA methods of IHC provided evidence supporting the
clinical utility of IHC4 algorithms in the context of the TEAM study. With
careful standardization, either of these IHC4 assays should be considered
for prediction of residual risk in early breast cancer.
519 Poster Discussion Session (Board #9), Sat, 1:15 PM-5:15 PM and
4:45 PM-5:45 PM
Dual effects of metformin on breast cancer proliferation in a randomized trial.
Presenting Author: Andrea De Censi, E. O. Ospedali Galliera, Genoa, Italy
Background: Metformin lowers breast cancer risk in observational studies in
diabetics, but evidence for its clinical activity is scanty. We studied the
metformin antiproliferative effect in a pre-surgical study. Since the antidiabetic
effect of metformin is heterogeneous according to obesity and insulin resistance
(IR), we also determined whether its antiproliferative effect was modified by risk
biomarkers. Methods: After tumor biopsy, we randomly allocated 200 nondiabetic
women with breast cancer to either metformin, 850 mg/bid (n�100) or
placebo (n�100) for 4 wks. The primary endpoint was the post-pretreatment
change in Ki-67 between arms. We explored effect modifications by STEPP and
tested biomarker thresholds that showed an interaction with treatment on Ki-67.
Results: Overall, median (IQR) Ki-67 was 19 (14-31) at baseline and 21 (14-32)
after 4 wks and 18 (12-29) at baseline and 20 (13-31) after 4 wks in the
metformin and placebo arm, respectively with mean increase of 4.0% (95%CI,
-5.6 to 14.4) on metformin versus placebo. Multivariate analyses showed an
increase of Ki-67 after 4 wks placebo in the following subgroups: HOMA� IR
threshold, IGFBP3� highest quartile, IGFBP1� lowest quintile, IGFratio (IGF1/
IGFBP3)� median, CRP� inflection point at STEPP analysis, HER2�ve tumors
(versus –ve). Metformin blunted the increase of Ki-67 noted on placebo in these
subgroups. Conclusions: Overall, metformin did not affect Ki-67 in most subjects
with breast cancer. However, in exploratory analysis we identified subgroups of
patients where metformin showed antiproliferative effect. Further studies to a
personalized approach are warranted with selection of study populations.
Post-pretreatment median change in Ki-67 by treatment arm and biomarker
thresholds.
Risk biomarker threshold Placebo Metformin p interaction
HOMA>2.8 0(n�29) 0 (n�24) 0.03
HOMA4614ng/ml 0(n�27) 0 (n�23) 0.04
IGFBP-32mg/L 2(n�25) 0 (n�37) 0.08
CRP