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Application of a hyperplexed fluorescence microscopy method (MultiOmyx ) to dissect proteomic biomarkers of (18)F-fluorodeoxy-glucose ((18)FDG) uptake in breast cancer
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Anup Sood1, Alexandra Miller2, Fiona Ginty1, Elizabeth McDonough1, Yunxia Sui1, Christopher Sevinsky1, Alexander Bordwell1, Qing Li1, Sireesha
Kaanumalle1, Zhengyu Pang1, Franklin Torres2, Edi Brogi2, Steven M. Larson2, Ingo K. Mellinghoff2
1GE Global Research, Niskayuna, NY, USA; 2Memorial Sloan Kettering Cancer Center, New York, NY, USA
BACKGROUND
•
•
RESULTS & DISCUSSION
Altered tumor metabolism and high glycolysis rate play a key role in tumor development and progression. This has been
leveraged in diagnostic imaging through use of 18F-labeled fluorodeoxy-glucose (FDG) PET for tumor and metastases
detection. Recent studies1 indicate potential value of using FDG PET for therapy monitoring in both adjuvant and
neoadjuvant settings. However, tumors have varying glycolysis rates and hence a wide range of FDG uptakes. In breast
cancer, FDG uptake has been reported to vary 20 fold2. Understanding the mechanisms3 behind these differences will be
critical for broader use of FDG or selecting alternative imaging targets.
The goal of the present study was to investigate pathways associated with FDG PET uptake in locally advanced human
breast cancer using a new hyperplex tissue analyses platform4, MultiOmyx. This platform provides quantitative protein
expression measurements of up to 60 proteins at the single cell resolution using only one unstained slide from a routinely
collected clinical sample (5 μm FFPE section).
Abstract # 2499
Figure 6: Marker distribution in different clusters
Concordance with standard IHC results
Marker expression for ER, PR and HER2 measured on the MultiOmyx platform was consistent with CLIA based IHC (ER, PR) & FISH (HER2
amplification) assays (Figure 3) in 18/18 cases for HER2, 18/18 cases for ER, and 17/18 cases for PR.
Figure 3: Concordance with CLIA Results
-ve by CLIA assay
+ve by CLIA assay
Table 1: Clinical Characteristics
Group
Low FDG
# of
samples
SUVmax
Mean
(Range)
Age
Mean
(Range)
11
4.3
(0.0-7.2)
50.5
(28-69)
4.0
(1.4-9.5)*
7
15.3
(9.7-22.1)
45.6
(29-61)
3.8
(2.1-8.5)**
High FDG
Tumor size
Histological
Mean (Range)
grade III
HER2
ER
amplified #,
positive Mean (Range)
Nuclear
grade III
6***
6
5**
6
10
2, 4.4
(3.4-5.4)
0
3, 4.1
(2.2-5.5)
+ve lymph
node
Log2 (Her2 expression)
7
Stain and quantify immunofluorescence
signal for each marker
Total number of measurements: >
324,000 per tumor (28-30 ROIs x
300-500 cells x 27 markers)
Total number of measurements in
dataset > 5,832,000 (18 x 324,000)
 Select 18 locally
advanced human Breast
Cancers with pre-op FDGPET
Patient 1
Patient 2
Cluster 1

Cluster 2 Cluster 3
Cluster 4 Cluster 5
Log2 (PR expression)
Correlation of biomarkers with FDG uptake
Figure 1: Study Design
Patient 2
Log2 (ER expression)
10
*one case with skeletal muscle involvement, **one case with two foci, ***2 cases of invasive lobular carcinoma
Patient 1
Cell Density
belonged to the same cohort as previously described by Palakas et.al.2. Imaging details and methods used to calculate SUVmax
values are described therein.
Cell Density
Samples: Samples from 18 patients with SUVmax values ranging from 0-22 were included in this study. These samples
Cell Density
METHODS
Univariate analysis of the entire data set including all ROIs, showed that several markers were correlated with FDG uptake (Figure 4). ER
expression was the strongest predictor of FDG uptake (odds ratio 0f 0.26 and p=0.02) which didn’t change (odds ratio=0.32, p=0.02) when
analysis was limited to IDC only ROIs. Proliferation rate measured by Ki67 expression also showed strong correlation with FDG uptake with an
odds ratio of 9.3, p=0.04 (odds ratio 11.8, p=0.04 for IDC only ROIs). A trend towards higher variability in Glut1 expression in patients with higher
FDG uptake was observed in the results based on IDC only ROIs, but it failed to attain significance (p=0.08) . No correlation was apparent with
HK2 expression. In multivariate analysis ER status remained the only biomarker that was significantly correlated with FDG uptake.
Figure 4: Single Marker Association with FDG-uptake
 Unsupervised clustering of
all IF measurements (5.8 x
106) to identify patterns of
protein co-expression
P < 0.05
Select one
unstained FFPE
slide for each
patient
Patient 1
Patient 2
Patient 3
Patient 4
 Identify 28-30 ROIs
on the slide, each
containing 300-500
cells
 Percent
composition of each
cluster within each
tumor
Staining & Imaging: A single 5 µm section of breast carcinoma from each of 18 patients underwent an iterative cycle of staining,
imaging and signal inactivation (Figure 1) with 27 fluorescently labeled antibodies. The list of targets is shown below. Prior to
staining, 28-30 representative regions of histological interest (ROI) were randomly selected for imaging from different regions of
the tumor. Each ROI consisted of 300-500 carcinoma cells.
NS
Image processing: Once staining was complete, the images were registered using DAPI and had autoflourescence removed
(Figure 2). Finally they were separated into histopathological and subcellular compartments based on the staining of pancytokeratin, NaKATPase, pan-cadherin, S6, and DAPI. A breast pathologist assessed and annotated the histologic composition
(invasive versus in situ carcinoma) of each ROI.
Image QC: Segmented images were visually inspected to remove poorly segmented images (# of ROIs removed: 2%). Further
clean up included cells from the necrotic regions, and artificial cells created by over segmentation. Finally, for each round of
staining, only those cells that perfectly registered with the cells observed in the previous round were included for analysis. This
removed ~1% of the cells after 1st round which progressively increased to ~10% after 20 rounds of imaging. Further analysis
focused on ROIs (n=390) containing only invasive ductal carcinoma without ILS, admixed DCIS, or normal breast tissue.
Data Analysis: Patients were divided into low and high FDG uptake groups with a cutoff of 8 for the SUVmax value. Uni- and
multi-variate analyses between marker expression and FDG uptake were performed using logistic regression models. Cells were
also clustered into groups based on their biomarker expression using K-medians clustering. Random forest classification method
identified a 5-cluster set as the best predictor of FDG uptake and was analyzed for marker expression and pathway activation.
SAMPLE ANALYSIS WORKFLOW
Figure 2: Sample analysis workflow
High SUVmax
Low SUVmax
Biomarker clustering
• K-median clustering identified several cluster sets that correlated well (AUC ~0.95) with FDG uptake (Figure 5a). A 5-Cluster set was
selected for evaluation over the other two sets as latter two contained over 40 clusters and were deemed to be over fitted.
• In the 5-cluster set, the individual clusters resembled known breast cancer subtypes and included: (1.) an ER, PR & HER2 low cluster
(cluster 1), (2.) an ER, PR low & HER2 high cluster (cluster 4), (3.) two hormone receptor high clusters (clusters 2 & 3), the latter with
activation of MAPK pathway and higher Ki67 expression. Most protein expression in the cluster 5 was relatively lower indicating
potentially a normal or “normal like” phenotype. FDG high samples predominantly contained cluster 1, clusters 4 or a mixture of two.
The other clusters were heterogeneously distributed among low FDG sample (Figure 5b).
Figure 5: Cluster analysis, a) Sets of proteomic clusters (i.e. cells with similar expression
pattern) predicting high FDG uptake, b) Cluster distribution in FDG high and low
samples
5-cluster set
1
3
Cluster 5: No distinctive
reactivity
Cluster 1: high in p53, Ki67, Glut1 ,
cMyc
Cluster 3: Similar to cluster 2 but
with higher Glut1 & MAPK
pathway activation
DISCUSSION
This feasibility study investigated 27 proteomic markers associated with FDG-PET uptake in human breast cancer .
1.
•
Our study confirms the reported3a inverse correlation between estrogen receptor (ER) expression and FDG uptake.
Radiotracers directly interrogating estrogen receptor status, such as 18F estradiol (FES), may be more suitable to image
these tumors with PET.
2.
3.
Ki67 was positively correlated to FDG uptake, as reported previously in other studies5 in breast cancer as well as other
tumor types.
•
In agreement with gene array based analysis, there was a trend toward higher cMyc expression and activation of PI3K
pathway in high SUVmax patients. We did not detect a correlation between FDG uptake and protein expression of Glut1
or HK2. This may be due to more subtle transcriptional co-regulation of many glycolysis pathway members which were
not included in our study. Contradictory reports6 on correlation of FDG uptake and Glut1 or HK2 exist in the literature.
•
Unsupervised cluster analysis of all proteomic measurements points identifies clusters of cells with similar protein
coexpression patterns. One of the two clusters associated with ER-positivity, shows higher expression of Ki67, cMyc, and
growth factor receptor activity, and perhaps represents a more aggressive subtype of ER-positive breast cancer.
CONCLUSIONS
• Staining results with the MultiOmyx platform were consistent with results from CLIA based IHC (ER, PR) & FISH (HER2) assays.
• The MultiOmyx platform provides quantitative single cell measurements of protein expression that may be useful to identify distinct
cell populations within heterogeneous tumors and monitor their evolution during cancer progression and treatment. Compared to
ER(+) breast carcinomas, HER2-positive or triple-negative breast carcinomas show a more uniform pattern of protein marker coexpression.
• Unsupervised clustering of over 5 million proteomic measurements identifies loss of ER expression as the most significant correlate
of high FDG-uptake in human breast cancer.
Gerdes, Sood, Sevinsky et al. (2013) PNAS
For Research Use Only
Cluster 4: high in hypoxia markers, &
PI3K pathway activation
downstream of mTOR
REFERENCES
•
•
Cluster 2: high in hypoxia
markers, pPDK1 & PTEN
4.
5.
6.
a) Groheux D et. al., Performance of FDG PET/CT in the clinical management of breast cancer,
Radiology 2013, v 266(2), pp 388-405. b) Flanagan FL et. al., PET in breast cancer, Seminars in
Nuclear Medicine 1998, v 28, pp 290-302.
Palaskas N et. al., 18F-Fluorodeoxy-glucose positron emission tomography marks Mycoverexpressing human basal-like breast cancers, Cancer Res 2011, v 71, pp 5164-5174.
a) Osborne JR et. al., 18F-FDG PET of locally invasive breast cancer and association of estrogen
receptor status with standardized uptake value: Microarray and immunohistochemical analysis, J
Nucl Med 2010, v 51(4), pp 543-550. b) Basu S et. al., Comparison of triple-negative and estrogen
receptor-positive/progesterone receptor-positive/HER2-negative breast carcinoma using
quantitative fluorine-18 fluorodeoxyglucose/positron emission tomography imaging parameters: a
potentially useful method for disease characterization, Cancer 2008, v 112(5), pp 995-1000. c)
Dogan BE & Turnbull LW, Imaging of triple-negative breast cancer, Ann Oncol 2012, v 23(6), pp vi23vi29.
Gerdes MJ et. al., Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded
cancer tissue, PNAS 2013, v 110 (29), pp 11982-11987.
Tchou J et. al., Degree of tumor FDG uptake correlates with proliferation index in triple negative
breast cancer, Mol Imaging Biol 2010, v 12(6), pp 657-62.
Avril N. Glut1 expression in tissue and 18F-FDG uptake, J Nucl Med 2004, v 45(6), pp 930-932.
FINANCIAL SUPPORT
This work was supported by funding from GE Global Research, GE
Healthcare & in part under P50 CA 086438 “MSKCC In Vivo
Molecular and Cellular Imaging Center (ICMIC)” to Ingo K.
Mellinghoff and Steven M. Larson
© 2014 General Electric Company ― All rights reserved.
GE and the GE Monogram are trademarks of General Electric Company.
MultiOmyx is a trademark of General Electric Company or one of its subsidiaries.
December 2014 JB26524US

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