Exploiting heat shock protein expression to develop a non-invasive diagnostic tool for breast cancer

Abstract

Leveraging the unique surface expression of heat shock protein 90 (Hsp90) in breast cancer provides an exciting opportunity to develop rapid diagnostic tests at the point-of-care setting. Hsp90 has previously been shown to have elevated expression levels across all breast cancer receptor subtypes. We have developed a non-destructive strategy using HS-27, a fluorescently-tethered Hsp90 inhibitor, to assay surface Hsp90 expression on intact tissue specimens and validated our approach in clinical samples from breast cancer patients across estrogen receptor positive, Her2-overexpressing, and triple negative receptor subtypes. Utilizing a pre-clinical biopsy model, we optimized three imaging parameters that may affect the specificity of HS-27 based diagnostics – time between tissue excision and staining, agent incubation time, and agent dose, and translated our strategy to clinical breast cancer samples. Findings indicated that HS-27 florescence was highest in tumor tissue, followed by benign tissue, and finally followed by mammoplasty negative control samples. Interestingly, fluorescence in tumor samples was highest in Her2+ and triple negative subtypes, and inversely correlated with the presence of tumor infiltrating lymphocytes indicating that HS-27 fluorescence increases in aggressive breast cancer phenotypes. Development of a Gaussian support vector machine classifier based on HS-27 fluorescence features resulted in a sensitivity and specificity of 82% and 100% respectively when classifying tumor and benign conditions, setting the stage for rapid and automated tissue diagnosis at the point-of-care.

Full text

1
SCIENTIFIC REPORTS | (2019) 9:3461 | https://doi.org/10.1038/s41598-019-40252-y
www.nature.com/scientificreports
Exploiting heat shock protein
expression to develop a non-
invasive diagnostic tool for
breast cancer
Brian T. Crouch1, Jennifer Gallagher2, Roujia Wang1, Joy Duer3, Allison Hall4, Mary Scott Soo5,
Philip Hughes6, Timothy Haystead6 & Nirmala Ramanujam1,6
Leveraging the unique surface expression of heat shock protein 90 (Hsp90) in breast cancer provides an
exciting opportunity to develop rapid diagnostic tests at the point-of-care setting. Hsp90 has previously
been shown to have elevated expression levels across all breast cancer receptor subtypes. We have
developed a non-destructive strategy using HS-27, a uorescently-tethered Hsp90 inhibitor, to assay
surface Hsp90 expression on intact tissue specimens and validated our approach in clinical samples
from breast cancer patients across estrogen receptor positive, Her2-overexpressing, and triple negative
receptor subtypes. Utilizing a pre-clinical biopsy model, we optimized three imaging parameters that
may aect the specicity of HS-27 based diagnostics – time between tissue excision and staining, agent
incubation time, and agent dose, and translated our strategy to clinical breast cancer samples. Findings
indicated that HS-27 orescence was highest in tumor tissue, followed by benign tissue, and nally
followed by mammoplasty negative control samples. Interestingly, uorescence in tumor samples was
highest in Her2+ and triple negative subtypes, and inversely correlated with the presence of tumor
inltrating lymphocytes indicating that HS-27 uorescence increases in aggressive breast cancer
phenotypes. Development of a Gaussian support vector machine classier based on HS-27 uorescence
features resulted in a sensitivity and specicity of 82% and 100% respectively when classifying tumor
and benign conditions, setting the stage for rapid and automated tissue diagnosis at the point-of-care.
Breast cancer management represents a complicated landscape, with therapy regimens often including a
mélange of chemotherapy, radiation therapy, and surgical procedures. Unfortunately, low to middle income
countries (LMICs), which shoulder most of the total breast cancer burden1, oen do not have the resources
to perform standard-of-care treatments, leading to higher mortality rates2. Moreover, access barriers to treat-
ment are higher in LMICs, leading to increased time between initial medical consultation and treatment2. In
high-income countries (HICs), when a woman presents with a suspicious lesion on her mammogram, she
undergoes diagnostic biopsy to determine what type of lesion is present by pathological analysis. is strategy
is not adoptable by LMICs, however, due to the scarcity of pathologists. For example, in sub-Saharan Africa the
pathologist-to-population ratio is 50 times less than in HICs at approximately one to one million3. e distinct
lack of reliable access to pathology in LMICs dictates a need for low-cost, automated methods for diagnosing
breast cancer at the point-of-care. Even in HICs there are opportunities to streamline breast cancer care. For
instance, in breast radiology, to ensure complete sampling of the lesion, radiologists currently take anywhere from
4–6 biopsies, which are then sent out for pathologic analysis, a process that can take up to a week. If the lesion was
not successfully sampled, the patient must return for a second set of biopsies, before nally determining diagnosis
and initial treatment. Similarly, in the case of Breast Conserving Surgery, evaluation of resected margins is always
performed post-operatively requiring a patient to come back for re-excision if positive margins are found.
1Department of Biomedical Engineering, Duke University, Durham, NC, USA. 2Department of Surgery, Duke
University Medical Center, Durham, NC, USA. 3Trinity College of Arts and Sciences, Duke University, Durham, NC,
USA. 4Department of Pathology, Duke University Medical Center, Durham, NC, USA. 5Department of Radiology,
Duke University Medical Center, Durham, NC, USA. 6Department of Pharmacology and Cancer Biology, Duke
University Medical Center, Durham, NC, USA. Correspondence and requests for materials should be addressed to
B.T.C. (email: brian.crouch@duke.edu)
Received: 19 September 2018
Accepted: 12 February 2019
Published: xx xx xxxx
OPEN
Content courtesy of Springer Nature, terms of use apply. Rights reserved

2
SCIENTIFIC REPORTS | (2019) 9:3461 | https://doi.org/10.1038/s41598-019-40252-y
www.nature.com/scientificreports
www.nature.com/scientificreports/
ere is an opportunity for a new era of low cost, point of care molecular diagnostics to serve as an eective
alternative to routine pathology. Despite its low specicity for distinguishing breast tumors from benign condi-
tions, portable ultrasound systems are currently being used as a screening tool in lieu of mammography for breast
cancer in LMICs4,5. A number of groups have developed methods to detect extracellular vesicles6 and exosomes7,8
extracted from blood with potential diagnostic applications for pancreatic cancer9 and glioblastoma10,11. Another
example is the adaption of smart phone cameras to be used as microscopes for applications in global health12–18.
Combining molecular diagnostics with low cost imaging technologies provides an opportunity to create low-cost,
point-of-carebreast cancer diagnostics for blood samples, cells, and biopsy samples.
Here, we investigated imaging Heat Shock Protein 90 (Hsp90) expression as a molecular diagnostic target
in breast cancer. Hsp90 is a chaperone protein that assists other proteins to fold properly, stabilizes proteins
against stress, and aids in protein degradation19. Hsp90 also stabilizes a number of proteins required for tumor
growth20,21, and is overexpressed in both DCIS and invasive breast cancers22–24. Hsp90 is also found on the surface
of many cancer types, including the breast20,25, and this ‘ectopic’ surface expression is specic to tumors21. Hsp90
inhibitors including geldanamycin analogues 17-AAG and 17-DMAG, SNX-5422 and SNX-2112, and others are
currently in clinical trials26–29.
We have developed a uorescently-tethered Hsp90 inhibitor, HS-27, made up of the core elements of SNX-
5422, an Hsp90 inhibitor currently in clinical trials, tethered via a PEG linker to a uorescein derivative (uores-
cein isothiocyanate or FITC), that binds to ectopically expressed Hsp90, and demonstrated its potential use in a
see-and-treat paradigm in breast cancer21,30. We found that HS-27 labels all receptor subtypes of breast cancer,
but not normal cells, and specically binds to Hsp90 expressed on the surface of breast cancer cells before being
internalized. IVIS and hyperspectral imaging aer systemic HS-27 injection revealed tumor selective uptake
in a xenogra model, with excised tumor cryosections verifying cellular uptake. We further demonstrated that
HS-27 can be used to treat aggressive Her2+ and triple negative (TNBC) breast cancers by degrading an Hsp90
client protein involved in cell metabolism, down-regulating both glycolytic and oxidative metabolism leading
to decreased cell proliferation. Finally, we demonstrated an ex vivo imaging strategy in clinical models of breast
cancer, showing all receptor subtypes of breast cancer take up HS-27 with increased uorescence from HS-27
corresponding to areas of invasive cancer. HS-27 is a suitable candidate for use in LMICs as it does not require
refrigeration and can be made inexpensively when made to scale.
In this study we focused on optimizing imaging parameters including post-excision window, incubation time,
and agent dose to rapidly translate HS-27 to clinical use by excising murine breast tumors (4T1) and staining
them ex vivo. With optimized imaging parameters of a 1 to 10-minute post-excision window, 1-minute incu-
bation time, and 100 µM dose, we then demonstrated the feasibility of our imaging strategy on standard of care
biopsies from patients presenting with a mammographic lesion, as well as a population of patients undergo-
ing breast reduction mammoplasty to interrogate HS-27 uptake by normal breast tissue. To determine potential
sources of HS-27 uorescence, we investigated correlations between HS-27 uorescence and the density of cancer
or tumor stromal cells to assess whether density of tumor cells and surface Hsp90 expression dictate uorescence
levels. We further examined correlations between HS-27 uorescence and the density of tumor inltrating lym-
phocytes (TILs), a positive prognostic marker in breast cancer, as well as breast cancer receptor subtypes to inves-
tigate whether or not surface Hsp90 is further up-regulated by aggressive tumors. Finally, we employed image
processing methods to extract HS-27 uorescence features to dierentiate tumor from benign tissues.
Results
Optimization of HS-27 incubation parameters for ex vivo imaging. We optimized three distinct
imaging parameters in preclinical studies that could potentially aect the specicity of HS-27 uptake by clinical
samples. e rst parameter we investigated was the time between excision and staining (1, 3, or 10 minutes) to
understand how ectopic Hsp90 expression changes as time between excision and application of the contrast agent
is increased. e second parameter was HS-27 incubation time (1, 5, or 10 minutes), which when increased may
increase non-specic HS-27 diusion into the tissue. Finally, we optimized agent dose (1, 10, 50, or 100 µM) to
round out our investigation. For optimization, the specicity of HS-27 uptake was dened by the ratio of HS-27
(specic signal) to HS-217 (non-specic HS-27 analog signal) uorescence.
Representative uorescence images of HS-27 or HS-217 biopsies from 4T1 murine breast tumors treated
with the optimized parameters, shown in Fig.1a, demonstrate that HS-27 signal is signicantly greater than
non-specic HS-217 signal. Representative images from post-excision window, incubation time, and dose exper-
iments can be found in Supplementary Fig.S1. Curves of HS-27 to HS-217 uorescence ratio fractions (survival
curves dened as 1 minus the cumulative probability), clearly indicate the optimal imaging parameters (Fig.1b–d).
e ratio of HS-27 to HS-217 signal showed no signicant changes when increasing the post-excision window
from 1 to 10 minutes, as shown in Fig.1b. Conversely, Fig.1c shows that increasing agent incubation time from
1 or 5-minutes to 10-minutes signicantly decreased specicity. Finally, 100 µM agent dose showed the greatest
specicity in Fig.1d. For all groups n = 4 biopsies. A 1-minute post-tissue excision time, 1-minute incubation,
and a 100 µM dose were established as the parameters to use in the clinical studies.
HS-27 uorescence is greater in tumor than non-tumor tissue. Next, the protocol we established
in pre-clinical studies was applied to biopsies obtained from patients undergoingultrasound guided core needle
biopsy (USGCNB). Typically the rst biopsy from each patient was imaged to increase the likelihood of obtain-
ing biopsies with cancer. Images were obtained in 1 mm increments along the biopsy prior to inking to ensure
proper orientation for site-level pathology, as previously described30. Representative biopsy images from an ER/
PR-positive tumor, Her2-overexpressing tumor, TNBC, benign lesion (broadenoma), and normal mammoplasty
tissue demonstrate greater HS-27 uorescence in tumor compared to benign and normal tissues as shown in
Fig.2. Histology H&E images from the sites that were imaged are shown below for comparison.
Content courtesy of Springer Nature, terms of use apply. Rights reserved

3
SCIENTIFIC REPORTS | (2019) 9:3461 | https://doi.org/10.1038/s41598-019-40252-y
www.nature.com/scientificreports
www.nature.com/scientificreports/
We next wanted to understand the potential sources of HS-27 uorescence within a biopsy image. ere
are three potential subsets of cell types present within a malignant biopsy – cancer cells, tumor associated stro-
mal cells, and surrounding benign cells. Our pathologist assessed each 1-mm site along the biopsy for percent
tumor area (PTA), tumor cellularity (the percentage of the tumor area made up of cancer cells), and stromal
area (1-tumor cellularity). e density of tumor inltrating lymphocytes (TILs) within the stromal area was also
provided. We began by investigating the relationship between mean HS-27 uorescence and tumor cellularity
and found that there was no correlation between the two endpoints, as shown in Fig.3a. Since tumor cellularity
did not correlate with HS-27 uorescence, we next examined how mean HS-27 uorescence varied with receptor
subtype, as shown in Fig.3b. HS-27 uorescence was highest in Her2+ tumors, followed by TNBC, and ER+.
Next, we looked at how the presence of various tissue types inuenced uorescence. Based on our previous study
suggesting surface Hsp90 is upregulated in particularly aggressive tumors30, we explored the relationship between
HS-27 uorescence and the percent of tumor inltrating lymphocytes (TILs), a positive prognostic factor in
Her2+ and TNBC receptor subtypes31–33. Because TILs are given as a percentage of stromal area covered by
TILs, we took the ratio of TIL% to stromal % to provide a more accurate density of TILs in the biopsy. HS-27
uorescence is inversely correlated with increased density of TILs in the tumor stroma across receptor subtypes,
as shown in Fig.3c–e. ese results suggest that receptor subtype and the density of TILs more strongly inuences
the mean uorescence than tumor cellularity.
HS-27 uorescence features accurately distinguish tumor from benign tissue. One of the major
challenges of traditional mammography is the ability to distinguish benign from malignant conditions, hence
the need for biopsies and subsequent histopathology. We wanted to examine whether or not features from HS-27
uorescence images could be used to distinguish benign from malignant tissues and serve as a potential alter-
native for histopathology. Fig.4a shows cumulative distributions (CDFs) of HS-27 uorescence intensity from
the full stitched image for tumor vs. benign vs. mammoplasty tissue. Because many of our lesion images contain
non-lesion regions, we utilized distributions to test cut-o thresholds to include all pixels or only the top 25%,
top 10%, or top 1% of pixels to increase the specicity of HS-27 based diagnostics. Clearly, for the top 1% of
pixels, there is an increase in separation between the curves, reected by decreasing p-values determined from
Kolmogorov-Smirnov (KS) testing. ough not signicant, tumor uorescence is greater than benign across all
bins, and signicantly dierent than mammoplasty control tissues across all bins. Benign is only signicantly
dierent from mammoplasty at the 1% pixel bin level.
Figure 1. A 1 to 10-minute post-excision window, 1-minute incubation time, and 100 µM dose maximizes the
HS-27 to HS-217 specicity ratio. 4T1 tumors were biopsied and incubated in either 100 µM HS-27 or 100 µM
HS-217 for 1-minute either 1-minute, 3-minutes, or 10-minutes post biopsy prior to uorescence imaging to
identify the optimal post-excision window, or 1-minute post excision for 1-minute, 5-minutes, or 10-minutes
to identify the optimal agent incubation time. To identify optimal dose, biopsies were incubated in either
1 µM, 10 µM, 50 µM, or 100 µM HS-27 or HS-217 1-minute post-excision for 1-minute prior to uorescence
imaging. (a) Representative uorescence images of 4T1 biopsies stained with 100 µM HS-217 or HS-27 for
1-minute within 1-minute of tissue excision. (b–d) Survival curves of the ratio of HS-27 to HS-217 uorescence
demonstrate no signicant dierences with increasing post-excision time (b), a signicant decrease in
the specicity ratio with increasing incubation time (c), and a signicant increase in specicity ratio with
increasing dose (d) by Kolmogorov-Smirnov (KS) test. For all groups n = 4 biopsies. Survival curves show the
mean ± SEM.
Content courtesy of Springer Nature, terms of use apply. Rights reserved

4
SCIENTIFIC REPORTS | (2019) 9:3461 | https://doi.org/10.1038/s41598-019-40252-y
www.nature.com/scientificreports
www.nature.com/scientificreports/
We created 12 dierent parameters from our uorescence images that could be used as optical predictors to
distinguish tumor from both benign lesions and normal breast tissue from mammoplasty cases. e rst 6 were
calculated by tting a logistic curve to each CDF from either all pixels or the top 1% of pixels with summary
variables A, B, and C, as shown in Supplementary Fig.S2. Parameter A controls the slope of the CDF, reecting
primarily the variance of pixel values within each image. Parameter B controls the le/right shi of the CDF,
reecting primarily the mean pixel value within each image. Parameter C controls the vertical shi of the CDF,
reecting both the mean and variance of the highest pixel values. e remaining 6 parameters were calculated as
summary parameters, namely the overall mean, variance, and ratio of the maximum to minimum uorescence for
all pixels and the top 1% of pixels to report on the average uorescence, uorescence spread, and dynamic range
respectively. Boxplots of the summary variables across all pixels and the top 1% of pixels are shown in Fig.4b,c
respectively.
We next explored how the CDF and summary parameters aect the accuracy of HS-27 based classication.
Since there are dierences (though not all signicant) between tumor, benign lesion, and mammoplasty tissue
types, we performed two sets of comparisons – tumor vs. mammoplasty and tumor vs. benign lesion. For the two
comparison groups, Gaussian support vector machine (GSVM) classiers were created and tested with 10-fold
cross-validation to create receiver operating characteristic curves (ROCs) using either the CDF t parameters for
all pixels, the CDF t parameters for the top 1% of pixels, the summary variables for all pixels, or the summary
Figure 2. HS-27 uptake is greater in tumor than non-tumor samples. USGCNB were obtained from patients
prior to imaging with our optimized parameters. Representative uorescence (top) and histology (bottom)
images of mammoplasty tissue, broadenoma, ER/PR-positive tumor, TNBC, and Her2-positive tumor.
Content courtesy of Springer Nature, terms of use apply. Rights reserved

5
SCIENTIFIC REPORTS | (2019) 9:3461 | https://doi.org/10.1038/s41598-019-40252-y
www.nature.com/scientificreports
www.nature.com/scientificreports/
variables for the top 1% of pixels. e sensitivity, specicity, and area under the curve (AUC) for the ROC for each
scenario are summarized in Table1.
Looking at the AUCs in Table1 reveals an interesting pattern. For tumor vs. mammoplasty comparisons,
utilizing the CDF t parameters from the top 1% of variables achieved a higher AUC than utilizing the CDF t
parameters from all pixels. e converse was true for tumor vs benign lesion comparisons. Similarly, utilizing
the summary parameters for all pixels achieved a higher AUC than the summary parameters from the top 1%
of pixels for tumor vs mammoplasty samples, with the opposite holding true for tumor vs benign comparisons.
Figure 3. Receptor subtype and presence of TILs aect HS-27 uorescence levels more than tumor cellularity.
(a) HS-27 uorescence does not correlate with tumor cellularity. (b) Mean uorescence varies with receptor
subtype and is signicantly lower in mammoplasty than all other tissue types. (c–e) Mean uorescence strongly
and inversely correlates with the density of TILs across receptor subtypes.
Figure 4. HS-27 uorescence is greater in tumor than non-tumor tissue. (a) CDFs of uorescence image
pixel intensities were created for each combined biopsy image of either all pixels or of only the top 25%, top
10%, or top 1% of pixels. Curves were stratied by histology type. Mammoplasty (black) survival curves were
signicantly dierent from tumor curves by KS testing across pixel bins. (b,c) Box plots of intensity summary
parameters mean, variance and max to min ratio for tumor (T), benign (b) and mammoplasty (M) biopsies
for (b) all pixels or (c) the top 1% of pixels. Sample sizes – n = 6 mammoplasty, n = 10 benign, n = 27 tumor.
*p < 0.05 by KS testing (CDFs) or one-way ANOVA with Tukey-Kramer post-hoc testing(box plots). Survival
curves show the mean ± SEM.
Content courtesy of Springer Nature, terms of use apply. Rights reserved

6
SCIENTIFIC REPORTS | (2019) 9:3461 | https://doi.org/10.1038/s41598-019-40252-y
www.nature.com/scientificreports
www.nature.com/scientificreports/
We performed a sequential feature selection method to identify the optimal 2 parameters for tumor vs mam-
moplasty and tumor vs benign lesion comparisons, by testing all combinations of the 12 parameters using a
GSVM with 10-fold cross-validation. e optimal parameters were chosen as those that led to the highest AUC
for the corresponding ROC. A combination of a summary parameter from all pixels (variance) and a CDF param-
eter from the top 1% of pixels (CDF C) performed the best for tumor vs mammoplasty comparisons. GSVM
scores and the ROC for the optimal tumor vs mammoplasty GSVM are shown in Fig.5a. e optimal sensitivity
and specicity were determined by maximizing the Youden’s index, and were 86% and 100% respectively, with
an AUC of 0.96. In line with the results from Table1, the same variables in opposite pixel bins performed best for
tumor vs benign lesion comparisons (variance of the top 1% of pixels and the CDF C parameter for all pixels).
GSVM scores and the ROC for the optimal tumor vs benign GSVM are shown in Fig.5b. e optimal sensitivity
and specicity were again determined by maximizing the Youden’s index, and were 82% and 100% respectively,
with an AUC of 0.93.
Comparison CDFAll CDFTop 1% SummaryAll SummaryTop 1% Sens. Spec.
T v. M 0.8 52% 100%
T v. M 0.8 93% 67%
T v. M 0.95 89% 100%
T v. M 0.85 78% 100%
T v. B 0.78 67% 90%
T v. B 0.74 74% 70%
T v. B 0.44 93% 20%
T v. B 0.72 44% 100%
Table 1. Summary of GSVM performance for tumor vs mammoplasty (T v. M) and tumor vs benign (T v. B)
classiers. e AUC is shown in the box corresponding to the parameters used for classier development.
Figure 5. HS-27 features distinguish tumor from both mammoplasty and benign tissues. Gaussian support
vector machine (GSVM) classiers were developed for combinations of CDF and summary variables for both
tumor vs mammoplasty and tumor vs benign tissues. (a) GSVM scores and an ROC for a GSVM classier for
distinguishing tumor from mammoplasty tissue based on the variance of all pixels and the CDF C parameter
from the top 1% of pixels. (b) GSVM scores and an ROC for a GSVM classier for distinguishing tumor from
benign tissue based on the variance of the top 1% of pixels and the CDF C parameter from all pixels.
Content courtesy of Springer Nature, terms of use apply. Rights reserved

7
SCIENTIFIC REPORTS | (2019) 9:3461 | https://doi.org/10.1038/s41598-019-40252-y
www.nature.com/scientificreports
www.nature.com/scientificreports/
Discussion
With the complexity of breast cancer care continually increasing, and the associated cost burdens mounting,
it is more important to streamline care now more than ever before. When establishing our ex vivo diagnostic
methodology, simplicity and cost were two major considerations. We have created an ex vivo imaging strategy
to image surface Hsp90 expression in breast tumor biopsies. We optimized HS-27 uptake in pre-clinical models,
and found that a post-excision window of 1 to 10-minutes, incubation time of 1-minute, and dose of 100 µM
resulted in the greatest specicity ratio. Translating this protocol to clinical biopsy samples, we demonstrated
signicantly greater HS-27 uptake in tumor vs mammoplasty control tissues, and found that both cancerous and
tumor stromal cells contribute to HS-27 uorescence. GSVM analysis achieved an AUC of 0.93 with a sensitivity
and specicity of 82% and 100% respectively.
Interestingly, we found that benign breast conditions like broadenoma, abnormal ductal hyperplasia, and
cystic tissue showed higher HS-27 uorescence than mammoplasty control tissue, suggesting the presence of
surface Hsp90 in these samples, though at a lower level than in tumors. It is possible there is some surface Hsp90
expression in benign samples as indicated by HS-27 uorescence signal. Surface Hsp90 in benign samples may
be a mechanism of immune cell recruitment, as there have been numerous studies demonstrating the role Hsp90
plays during immune responses, both innate and adaptive34–37. For example, in innate immunity the presence of
Hsp90 extracellularly can signal a damage associated molecular pattern causing immune cell recruitment34. e
induction of surface Hsp90 expression to activate immune responses during benign conditions reduces the sensi-
tivity for identifying tumor lesions. at being said, we still found using a non-linear GSVM using both intensity
and spatial HS-27 uorescence based predictors yielded the highest sensitivity and specicity.
Our algorithm incorrectly classied 5 tumor biopsies as benign lesions. All of these biopsies came from women
with ER+ breast cancer, with one biopsy also showing over-expression of Her2. In our previous pre-clinical
studies, we have found that Her2+ and TNBC have greater surface Hsp90 expression that ER+ tumors30.
Even so, we still correctly classied 71% of our ER+ tumors. Deep learning techniques may be better suited to
address this limitation of our approach, however, due to the small sample size of this study, we are limited in the
machine learning techniques we can apply to our dataset. It is also important to note that the small sample size
dictates further larger scale studies to validate these results. In the future, we plan to develop more advanced
deep-learning non-linear strategies, like articial neural networks, to improve the overall performance of our
diagnostic platform. If there is insucient contrast between ER+ tumors and benign tissues, due to the relatively
low expression of Hsp90 in these tumors, a combination of contrast agents may be used to enhance sensitivity
extending the capabilities of our platform.
We noticed some heterogeneity in uptake both within and across biopsies. Each biopsy is comprised of many
dierent cell types that may have varying levels of surface Hsp90 expression (i.e. malignant cells, tumor asso-
ciated broblasts, tumor inltrating lymphocytes, and non-malignant cells such as adipocytes), which would
inuence HS-27 uptake and may cause some of the intra-biopsy heterogeneity in HS-27 uorescence. is is fur-
ther evidenced by the considerably greater homogeneity seen in the mammoplasty images, which are primarily
adipocytes.
In our clinical study we found variable HS-27 uptake within receptor subtypes, which, when coupled with
the established relationships between Hsp90 and immune responses, potentially provides an endpoint useful
for guiding treatment. For example, surface Hsp90 expression may be a useful surrogate marker for tumor inl-
trating lymphocytes (TILs), which are of particular importance, as increased density of TILs in patients with
early stage Her2+ breast cancer showed increased pathological complete response (pCR) when treated with
standard-of-care therapies trastuzumab and/or lapatinib32,33. Further, increased levels in TNBC have been
associated with improved patient outcomes following treatment with anthracycline-based chemotherapies31.
Interestingly, when binning Her2+ and TNBC samples together, we found a strong and signicant inverse cor-
relation between HS-27 uorescence and the density of stromal TILs (r = −0.63, p < 0.05). Despite promising
retrospective studies demonstrating the prognostic signicance of TILs, there are some limitations to using TIL
involvement as a prognostic or predictive biomarker in a clinical setting. Although eorts have been made to
standardize the assessment of TILs38, this assessment is still subject to inter-observer variability. e evidence
of Hsp90 involvement in immune regulation combined with our own ndings in Her2+ and TNBC tumors
provides a compelling opportunity to explore how surface Hsp90 expression on carcinoma cells relates to the
immune cell milieu in the tumor microenvironment.
Other groups are exploring molecular imaging techniques for applications in cancer39–41, including a group
performing ex vivo imaging of breast tumors for applications in margin assessment using Her2-targeted u-
orescent antibodies42. Utilizing a dual-probe approach with targeted and non-targeted antibodies at dierent
uorescence wavelengths allowed for highly accurate identication of tumors vs non-tumor tissue. Similar to the
optimization results in our study, they found that shorter incubation times yielded increased imaging specic-
ity. Other groups are utilizing quantum dots tethered to antibodies43 to identify protein biomarkers such as the
estrogen receptor as well as more ubiquitously expressed targets like EGFR44,45 for diagnostic purposes in estrogen
receptor positive patients. Our work builds on tumor-specic imaging by targeting surface Hsp90 expression,
which is ubiquitous to all receptor subtypes of cancer, increasing the potential population target from only Her2+
tumors (~20% of breast cancer diagnoses) to all patients with breast cancer. Additionally, by utilizing a small
molecule specic to Hsp90 rather than antibodies, our approach does not require any initial blocking steps to
prevent non-specic binding, reducing the required processing to tissue and imaging time. Finally, by reducing
the cost of both the molecular agent and imaging system, we are primed to provide rapid diagnostic information
to physicians even in settings where on-site pathology is not possible.
In high-income countries (HICs), tumor specic targeting with HS-27 will allow for rapid analysis of biopsies
during diagnostic biopsy and tumor margins in the OR. A careful examination of each tissue type (tumor, benign
lesion, mammoplasty) reveals dierent HS-27 uptake patterns necessitating dierent metrics to separate tumor
Content courtesy of Springer Nature, terms of use apply. Rights reserved

8
SCIENTIFIC REPORTS | (2019) 9:3461 | https://doi.org/10.1038/s41598-019-40252-y
www.nature.com/scientificreports
www.nature.com/scientificreports/
from benign lesions and tumor from healthy (mammoplasty) tissue. In the biopsy clinic, the possible tissue types
are either tumor or benign lesion, dictating use of the GSVM algorithm based on tumor vs. benign samples. For
margin assessment, the possible tissue types are either tumor or healthy tissue, dictating use of the GSVM algo-
rithm based on tumor vs. mammoplasty samples.
Performing our imaging ex vivo circumvents the need for the regulatory approvals required for in vivo appli-
cations in uorescence guided surgery, and decreases the risk of side eects to the patient. In our model, the pri-
mary tumor (or biopsy) will be rapidly assayed for the presence of disease, nding the equivalent of pathological
tumor on ink, normally necessitating a re-excision. Tumor cells will be selectively visualized using HS-27, and
localized by easily navigating back and forth between wide-eld and high-resolution imaging with our Pocket
mammoscope, a uorescence microscope adapted from our widely-used Pocket colposcope46–48. When disease is
found on the margin surface, the surgeon will go back and take additional shavings from the surgical cavity. is
strategy will be repeated until there is no signal on the surface of the margins.
Fortuitously, the ability to image tumor immune responses may fill an important niche in cancer prog-
nostics as well. Currently, for neoadjuvant and adjuvant treatment decisions oncologists use a combination of
clinical factors determined from either a diagnostic biopsy (neoadjuvant) or surgical specimen (adjuvant)49,50.
Unfortunately, current clinical factors such as hormone and/or growth factor receptor status are insucient to
predict which patients are likely to benet from additional therapies51. Without predictive tests, patients may
receive unnecessary and/or ineective treatments, which increases costs on an already overburdened healthcare
system, and exposes patients to unnecessary toxic side eects. Genetic tests including Oncotype DX have been
developed to assess whether ER+ breast cancer patients are likely to benet from adjuvant chemotherapy, reduc-
ing the use of potentially toxic therapies for women with low recurrence risk. However, for the 30% of patients
with either Her2+ or triple negative breast cancer (TNBC), there is no well-established predictive test to guide
treatment, beyond standard hormone receptor and Her2 testing. Being able to use Hsp90 expression as a surro-
gate for TIL levels may allow for a way to extend the prognostication available to patients with ER+ breast tumors
to patients with HER2+ or triple negative breast cancers.
ere is oen signicant patient attrition when multiple visits are required to diagnose and treat breast cancer
in LMICs. Integrating diagnostics with an eective treatment strategy into a single visit will improve outcomes
for patients in LMICs where standard of care pathology and surgical treatments are not feasible. e combination
of HS-27 with a low-cost microscopy system will provide a cost-eective and easily implementable diagnostic
platform for breast cancer as a rst step towards a single-visit see-and-treat strategy.
Methods
Cell culture. 4T1 murine breast cancer lines were used in the pre-clinical study, and were acquired from the
American Type Culture Collection and cultured under standard conditions free of contamination at 37 °C and 5%
CO2. Cells were maintained in RPMI-1640 (L-glutamine) medium supplemented with 10% FBS and 1% penicil-
lin-streptomycin. All cells were used for experiments within one month of rst passage.
Animal studies. All animal experiments were performed in accordance with protocol A216-15-08 approved
by the Duke University Institution for Animal Care and Use Committee. Animals were housed on-site with con-
tinual access to food and water under normal 12-hour light/dark cycles.
Flank tumor biopsy model. 4T1 tumors were grown in the ank of 11 athymic nude mice for optimizing
ex vivo imaging parameters. Specically, on passage two aer thaw, 106 4T1 cells suspended in 100 µL serum-free
medium were injected subcutaneously into the right ank to establish tumors. Tumors were allowed to grow to
a volume of 1 cm3 (tumor volume calculated as 0.5 × length × width2) to form a mass similar in size to those
evaluated in clinical radiology. We have previously described our biopsy procedure in detail30. Briey, mice were
anesthetized with a maximum of 1.5% isourane in room air. Prior to biopsy, scissors were used to make a small
incision to remove the skin over the tumor. Biopsies were taken using a 12 gauge Achieve programmable auto-
mated biopsy system. ree biopsies were taken from random locations within each tumor for 10 mice, with two
biopsies taken from the remaining mouse, yielding 32 biopsies for analysis.
Pre-clinical ex vivo imaging optimization. We identied and sequentially optimized three parameters
that could aect the specicity of HS-27 uptake: (1) time between tissue excision and staining, (2) agent incu-
bation time, and (3) agent dose. Because HS-27 is a small molecule rather than an antibody, no blocking steps
or specialized washes are required prior to agent incubation. For parameter 1, time between tissue excision and
staining was varied from 1, to 3, to 10-minutes while agent incubation time and dose were xed at 1-minute and
100 µM respectively. For parameter 2, incubation time was varied from 1, to 5, to 10-minutes while time aer
tissue excision and agent dose were xed at 1-minute and 100 µM respectively. Finally, for parameter 3, dose was
increased from 1, to 10, to 50, to 100 µM while time post excision and agent incubation time were both xed at
1-minute. 8 biopsies were used for each group in each experiment with 4 biopsies receiving HS-27 treatment and
4 biopsies receiving HS-217 treatment. HS-217 is a non-specic version of HS-27 that does not bind to Hsp90 and
serves as a negative control30. Aer HS-27 or HS-217 incubation, biopsies were thoroughly rinsed once with PBS
to remove unbound probe. Images were collected using a high-resolution microendoscope (HRME)52 every mm
along the length of the biopsy and stitched together for analysis as previously described30,53.
Clinical ex vivo biopsy imaging. All clinical imaging was performed in accordance with Duke IRB
approved protocol number Pro00008003. Aer giving informed consent, 34 adult patients undergoing standard
of care ultrasound guided core needle biopsy (USGCNB) and 4 adult patients undergoing breast reduction mam-
moplasty were enrolled in our study. Breast reduction mammoplasty patients serve as a negative control. Of the
34 USGCNB patients, one biopsy was imaged from each patient except in three patients where two biopsies were
Content courtesy of Springer Nature, terms of use apply. Rights reserved

9
SCIENTIFIC REPORTS | (2019) 9:3461 | https://doi.org/10.1038/s41598-019-40252-y
www.nature.com/scientificreports
www.nature.com/scientificreports/
imaged: one from each of two masses. Of the 4 reduction patients, two biopsies were imaged from each of two
patients and one from each of the remaining patients, resulting in 37 USGCNBs and 6 mammoplasty biopsies
for analysis. Of the 37 USGCNBs, 27 were invasive ductal cancer, of which 17 were ER/PR-positive, 4 were ER/
Her2-positive, 1 was ER/PR-negative but Her2-positive, and 4 were triple negative. e remaining 10 were benign
conditions and all mammoplasty samples were normal breast tissues. Table2 summarizes the demographic and
histologic information from our patient population.
Each biopsy was received within 5-minutes of tissue excision and had 100 µM HS-27 topically applied to the
biopsy for 1-minute prior to thorough rinsing with PBS. Images were collected using the HRME every mm along
the length of the biopsy and stitched together for analysis as previously described30,53. Biopsies were then inked
in three dierent colors and sent for standard pathologic review by a trained pathologist. e pathologist (AH)
provided specic diagnoses every mm along the biopsy for co-registration with HRME images, including the
percent tumor area (PTA), tumor cellularity, and the percent benign tissue area.
HS-27 uorescence quantication. All HRME images were processed using MATLAB (MathWorks).
Both pre-clinical and clinical images were calibrated using a uorescence slide to account for day-to-day sys-
tem variations. Because the HRME camera uses an automatic gain and exposure time, HRME images were
post-processed to correct for dierences between imaging sessions. For pre-clinical HS-27 uptake optimization,
non-specic uorescence was assessed by calculating the mean pixel intensity from the HS-217 images corre-
sponding to each optimization parameter and variable. e specicity ratio was then calculated by dividing each
HS-27 image by the corresponding mean HS-217 uorescence. Cumulative pixel distributions (CDFs) for each
image were averaged across biopsies within a group and used for statistical comparison.
Image processing, feature extraction and selection, and Gaussian support vector machine clas-
sication. e 37 USGCNB images were binned into either tumor (n = 27) or benign (n = 10) groups based
on their pathological diagnosis. 12 dierent parameters were created from our uorescence images to be used as
optical predictors. 6 were calculated as the mean pixel value, variance of pixel values, and maximum to minimum
pixel value ratio from either all or the top 1% of pixels. To take advantage of the intensity distributions within the
images, a logistic curve was t to the cumulative distribution function (CDF) to either all pixels or the top 1% of
pixels for each biopsy with three parameters per CDF that represent the CDF slope (A), the le/right shi (B), and
the vertical shi of the top of the CDF (C), as shown in Supplementary Fig.S2.
Next, we created Gaussian support vector machine (GSVM) classiers using the MATLAB Machine Learning
Toolbox to distinguish tumor from mammoplasty samples and tumor from benign lesion samples. A sequential
feature selection method was used to select the optimal set of features for each classier by testing each feature
individually, and then all possible pairs of features. e feature(s) resulting in optimal separation of tumor and
benign samples then underwent 10-fold cross validation. A receiver operating characteristic (ROC) curve was
Characteristic Biopsies
Number of Patients
Ultrasound (US) biopsy 37
Mammoplasty biopsy 6
Patient De mographics
Average Age (range) 55 (25–79)
Average BMI (range) 30 (18–54.4)
Pathology Breakdown
Malignant (US only) 27 (73%)
Benign (US only) 10 (27%)
Receptor Status (malignant only)
ER+,−22 (81%), 5 (19%)
PR+,−19 (70%), 8 (30%)
Her2+,−5 (19%), 22 (81%)
TNBC 4 (15%)
Menopausal Status
Pre-menopause 14 (37%)
Peri-menopause 1 (2%)
Post-menopause 23 (61%)
Breast Density
Fatty 4 (11%)
Scattered Fibroglandular 13 (35%)
Heterogeneous Density 15 (41%)
Extremely Dense 5 (13%)
Table 2. Demographic breakdown of patients enrolled in pilot clinical study.
Content courtesy of Springer Nature, terms of use apply. Rights reserved

10
SCIENTIFIC REPORTS | (2019) 9:3461 | https://doi.org/10.1038/s41598-019-40252-y
www.nature.com/scientificreports
www.nature.com/scientificreports/
generated for the optimized GSVM classier, and the optimal sensitivity and specicity were determined by
maximizing the Youden’s index.
Statistical analysis. A two-sided student’s t-test was used for experiments comparing only two groups.
A one-way ANOVA with Tukey-Kramer post-hoc testing was used for experiments comparing more than two
groups. CDFs were compared using a Kolmogorov Smirnov (KS) test. Pearson’s linear correlations were used
to calculate correlation coecients. Comparisons and correlations were considered signicant on a 95% con-
dence interval with a p-value of 0.05 or less. All statistical testing was performed using the Statistics Toolbox in
MATLAB (MathWorks).
Data Availability
e datasets generated during and/or analyzed during the current study are available from the corresponding
author on reasonable request.
References
1. Ferlay, J. et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer
136, E359–386, https://doi.org/10.1002/ijc.29210 (2015).
2. Unger-Saldana, . Challenges to the early diagnosis and treatment of breast cancer in developing countries. World J Clin Oncol 5,
465–477, https://doi.org/10.5306/wjco.v5.i3.465 (2014).
3. Wilson, M. L. et al. Access to pathology and laboratory medicine services: a crucial gap. Lancet 391, 1927–1938, https://doi.
org/10.1016/S0140-6736(18)30458-6 (2018).
4. Anderson, B. O., Ilbawi, A. M. & El Saghir, N. S. Breast cancer in low and middle income countries (LMICs): a shiing tide in global
health. Breast J 21, 111–118, https://doi.org/10.1111/tbj.12357 (2015).
5. Eisenbrey, J. ., Dave, J. . & Forsberg, F. ecent technological advancements in breast ultrasound. Ultrasonics 70, 183–190, https://
doi.org/10.1016/j.ultras.2016.04.021 (2016).
6. Shao, H. et al. New Technologies for Analysis of Extracellular Vesicles. Chem Rev 118, 1917–1950, https://doi.org/10.1021/acs.
chemrev.7b00534 (2018).
7. Im, H., Lee, ., Weissleder, ., Lee, H. & Castro, C. M. Novel nanosensing technologies for exosome detection and proling. Lab
Chip 17, 2892–2898, https://doi.org/10.1039/c7lc00247e (2017).
8. Im, H. et al. Label-free detection and molecular proling of exosomes with a nano-plasmonic sensor. Nat Biotechnol 32, 490–495,
https://doi.org/10.1038/nbt.2886 (2014).
9. Yang, . S. et al. Multiparametric plasma EV proling facilitates diagnosis of pancreatic malignancy. Sci Transl Med 9, https://doi.
org/10.1126/scitranslmed.aal3226 (2017).
10. Shao, H. et al. Protein typing of circulating microvesicles allows real-time monitoring of glioblastoma therapy. Nat Med 18,
1835–1840, https://doi.org/10.1038/nm.2994 (2012).
11. Shao, H. et al. Chip-based analysis of exosomal mNA mediating drug resistance in glioblastoma. Nat Commun 6, 6999, https://doi.
org/10.1038/ncomms7999 (2015).
12. Coulibaly, J. T. et al. Accuracy of Mobile Phone and Handheld Light Microscopy for the Diagnosis of Schistosomiasis and Intestinal
Protozoa Infections in Cote d’Ivoire. PLoS Negl Trop Dis 10, e0004768, https://doi.org/10.1371/journal.pntd.0004768 (2016).
13. D’Ambrosio, M. V. et al. Point-of-care quantication of blood-borne larial parasites with a mobile phone microscope. Sci Transl
Med 7, 286re284, https://doi.org/10.1126/scitranslmed.aaa3480 (2015).
14. Ephraim, . . et al. Diagnosis of Schistosoma haematobium infection with a mobile phone-mounted Foldscope and a reversed-lens
CellScope in Ghana. Am J Trop Med Hyg 92, 1253–1256, https://doi.org/10.4269/ajtmh.14-0741 (2015).
15. Switz, N. A., D’Ambrosio, M. V. & Fletcher, D. A. Low-cost mobile phone microscopy with a reversed mobile phone camera lens.
PLoS One 9, e95330, https://doi.org/10.1371/journal.pone.0095330 (2014).
16. Sandarajah, A., eber, C. D., Switz, N. A. & Fletcher, D. A. Quantitative imaging with a mobile phone microscope. PLoS One 9,
e96906, https://doi.org/10.1371/journal.pone.0096906 (2014).
17. Maamari, . N., eenan, J. D., Fletcher, D. A. & Margolis, T. P. A mobile phone-based retinal camera for portable wide eld imaging.
Br J Ophthalmol 98, 438–441, https://doi.org/10.1136/bjophthalmol-2013-303797 (2014).
18. Breslauer, D. N., Maamari, . N., Switz, N. A., Lam, W. A. & Fletcher, D. A. Mobile phone based clinical microscopy for global health
applications. PLoS One 4, e6320, https://doi.org/10.1371/journal.pone.0006320 (2009).
19. Whitesell, L. & Lindquist, S. L. HSP90 and the chaperoning of cancer. Nat Rev Cancer 5, 761–772, https://doi.org/10.1038/nrc1716
(2005).
20. Barrott, J. J. & Haystead, T. A. Hsp90, an unliely ally in the war on cancer. FEBS J 280, 1381–1396, https://doi.org/10.1111/
febs.12147 (2013).
21. Barrott, J. J. et al. Optical and radioiodinated tethered Hsp90 inhibitors reveal selective internalization of ectopic Hsp90 in malig nant
breast tumor cells. Chem Biol 20, 1187–1197, https://doi.org/10.1016/j.chembiol.2013.08.004 (2013).
22. Zagouri, F. et al. Hsp90 in the continuum of breast ductal carcinogenesis: Evaluation in precursors, preinvasive and ductal carcinoma
lesions. BMC Cancer 10, 353, https://doi.org/10.1186/1471-2407-10-353 (2010).
23. Cheng, Q. et al. Amplication and high-level expression of heat shoc protein 90 mars aggressive phenotypes of human epidermal
growth factor receptor 2 negative breast cancer. Breast Cancer Res 14, 62, https://doi.org/10.1186/bcr3168 (2012).
24. Pic, E. et al. High HSP90 expression is associated with decreased survival in breast cancer. Cancer Res 67, 2932–2937, https://doi.
org/10.1158/0008-5472.CAN-06-4511 (2007).
25. Becer, B. et al . Induction of Hsp90 protein expression in malignant melanomas and melanoma metastases. Exp Dermatol 13, 27–32,
https://doi.org/10.1111/j.0906-6705.2004.00114.x (2004).
26. Institute, N. C. Erlotinib Hydrochloride and Hsp90 Inhibitor AT13387 in Treating Patients With Recurrent or Metastatic Non-small Cell
Lung Cancer, https://clinicaltrials.gov/ct2/show/NCT02535338?term=hsp90+inhibitor+AND+cancer&ran=4.
27. Institute, N. C. Hsp90 Inhibitor AT13387 in Treating Patients With Locoregionally Advanced Squamous Cell Carcinoma of the Head and Neck
Receiving Radiation Therapy and Cisplatin, https://clinicaltrials.gov/ct2/show/NCT02381535?term=hsp90+inhibitor+AND+
cancer&ran=14.
28. Institute, N. C. HSP90 Inhibitor AT13387 and Paclitaxel in Treating Patients With Advanced Triple Negative Breast Cancer, https://
clinicaltrials.gov/ct2/show/NCT02474173?term=hsp90+inhibitor+AND+cancer&ran=1.
29. Institute, N. C. Talazoparib and HSP90 Inhibitor AT13387 in Treating Patients With Metastatic Advanced Solid Tumor or Recurrent Ovarian,
Fallopian Tube, Primary Peritoneal, or Triple Negative Breast Cancer, https://clinicaltrials.gov/ct2/show/
NCT02627430?term=hsp90+inhibitor+AND+cancer&ran=3.
30. Crouch, B. et al. Leveraging ectopic Hsp90 expression to assay the presence of tumor cells and aggressive tumor phenotypes in breast
specimens. Scientic Reports 7, 17487, https://doi.org/10.1038/s41598-017-17832-x (2017).
Content courtesy of Springer Nature, terms of use apply. Rights reserved

11
SCIENTIFIC REPORTS | (2019) 9:3461 | https://doi.org/10.1038/s41598-019-40252-y
www.nature.com/scientificreports
www.nature.com/scientificreports/
31. Loi, S. et al. Prognostic and predictive value of tumor-inltrating lymphocytes in a phase III randomized adjuvant breast cancer trial
in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicin-based chemotherapy: BIG
02-98. J Clin Oncol 31, 860–867, https://doi.org/10.1200/JCO.2011.41.0902 (2013).
32. Loi, S. et al . Tumor inltrating lymphocytes are prognostic in triple negative breast cancer and predictive for trastuzumab benet in
early breast cancer: results from the FinHE trial. Ann Oncol 25, 1544–1550, https://doi.org/10.1093/annonc/mdu112 (2014).
33. Salgado,  . et al . Tumor-Inltrating Lymphocytes and Associations With Pathological Complete esponse and Event-Free Survival
in HE2-Positive Early-Stage Breast Cancer Treated With Lapatinib and Trastuzumab: A Secondary Analysis of the NeoALTTO
Tri al. JAMA Oncol 1, 448–454, https://doi.org/10.1001/jamaoncol.2015.0830 (2015).
34. Wallin, . P. et al. Heat-shoc proteins as activators of the innate immune system. Trends Immunol 23, 130–135 (2002).
35. Tsan, M. F. & Gao, B. Heat shoc proteins and immune system. J Leukoc Biol 85, 905–910, https://doi.org/10.1189/jlb.0109005
(2009).
36. ono, H. & oc, . L. How dying cells alert the immune system to danger. Nat Rev Immunol 8, 279–289, https://doi.org/10.1038/
nri2215 (2008).
37. Graner, M. W. HSP90 and Immune Modulation in Cancer. Hsp90 in Cancer: Beyond the Usual Suspects 129, 191–224, https://doi.
org/10.1016/bs.acr.2015.10.001 (2016).
38. Salgado, . et al. e evaluation of tumor-inltrating lymphocytes (TILs) in breast cancer: recommendations by an International
TILs Woring Group 2014. Ann Oncol 26, 259–271, https://doi.org/10.1093/annonc/mdu450 (2015).
39. Miller, J. P., Maji, D., Lam, J., Tromberg, B. J. & Achilefu, S. Noninvasive depth estimation using tissue optical properties and a dual-
wavelength uorescent molecular probe in vivo. Biomed Opt E xpress 8, 3095–3109, https://doi.org/10.1364/BOE.8.003095 (2017).
40. Samoe, . S. et al. Application of Fluorescence-Guided Surgery to Subsurface Cancers equiring Wide Local Excision: Literature eview
and Novel Developments Toward Indirect Visualization. Cancer Control 25, 1073274817752332, https://doi.org/10.1177/
1073274817752332 (2018).
41. Pantel, A. . & Mano, D. A. Molecular imaging to guide systemic cancer therapy: Illustrative examples of PET imaging cancer
biomarers. Cancer Lett 387, 25–31, https://doi.org/10.1016/j.canlet.2016.05.008 (2017).
42. Barth, C. W., Schaefer, J. M., ossi, V. M., Davis, S. C. & Gibbs, S. L. Optimizing fresh specimen staining for rapid identication of
tumor biomarers during surgery. eranostics 7, 4722–4734, https://doi.org/10.7150/thno.21527 (2017).
43. Yezhelyev, M. V. et al. Emerging use of nanoparticles in diagnosis and treatment of breast cancer. Lancet Oncol 7, 657–667, https://
doi.org/10.1016/S1470-2045(06)70793-8 (2006).
44. e, S. et al. Near-infrared optical imaging of epidermal growth factor receptor in breast cancer xenogras. Cancer Res 63, 7870–7875
(2003).
45. Lamberts, L. E. et al. Tumor-Specic Uptae of Fluorescent Bevacizumab-IDye800CW Microdosing in Patients with Primary
Breast Cancer: A Phase I Feasibility Study. Clin Cancer Res 23, 2730–2741, https://doi.org/10.1158/1078-0432.CC-16-0437 (2017).
46. Lam, C. T. et al. Design of a Novel Low Cost Point of Care Tampon (POCeT) Colposcope for Use in esource Limited Settings.
PLoS One 10, e0135869, https://doi.org/10.1371/journal.pone.0135869 (2015).
47. Lam, C. T. et al. An integrated strategy for improving contrast, durability, and portability of a Pocet Colposcope for cervical cancer
screening and diagnosis. PLoS One 13, e0192530, https://doi.org/10.1371/journal.pone.0192530 (2018).
48. Mueller, J. L. et al. International Image Concordance Study to Compare a Point-of-Care Tampon Colposcope With a Standard-of-
Care Colposcope. J Low Genit Tract Dis 21, 112–119, https://doi.org/10.1097/LGT.0000000000000306 (2017).
49. Senus, E. et al. Primary breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol
26(Suppl 5), v8–30, https://doi.org/10.1093/annonc/mdv298 (2015).
50. Coates, A. S. et al. Tailoring therapies–improving the management of early breast cancer: St Gallen International Expert Consensus
on the Primary erapy of Early Breast Cancer 2015. Ann Oncol 26, 1533–1546, https://doi.org/10.1093/annonc/mdv221 (2015).
51. Yersal, O. & Barutca, S. Biological subtypes of breast cancer: Prognostic and therapeutic implications. World J Clin Oncol 5, 412–424,
https://doi.org/10.5306/wjco.v5.i3.412 (2014).
52. Muldoon, T. J. et al. Subcellular-resolution molecular imaging within living tissue by fiber microendoscopy. Opt Express 15,
16413–16423 (2007).
53. Mueller, J. L. et al. apid staining and imaging of subnuclear features to dierentiate between malignant and benign breast tissues at
a point-of-care setting. J Cancer Res Clin Oncol 142, 1475–1486, https://doi.org/10.1007/s00432-016-2165-9 (2016).
Acknowledgements
The authors would like to thank Dr. Rebecca Richards-Kortum and her lab group for providing the high-
resolution microendoscope. is work was supported by generous funding from the NIH National Institute for
Biomedical Imaging and Bioengineering (1R21EB02500801 and T32-EB001040). e funders had no role in
study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Author Contributions
Conception and design – B.C., J.G., A.H., M.S., T.H. and N.R. Development of methodology – B.C., J.G. and N.R.
Acquisition of data – B.C., J.G., R.W. and J.D. Analysis and interpretation of data – B.C., J.G., A.H., M.S., T.H.
and N.R. Writing, review and/or revision of the manuscript – B.C. and N.R. with input from all authors Study
supervision – T.H. and N.R.
Additional Information
Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-019-40252-y.
Competing Interests: Dr. Ramanujam has founded a company called Zenalux Biomedical and she and other
team members have developed technologies related to this work where the investigators or Duke may benet
nancially if this system is sold commercially.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional aliations.
Content courtesy of Springer Nature, terms of use apply. Rights reserved

12
SCIENTIFIC REPORTS | (2019) 9:3461 | https://doi.org/10.1038/s41598-019-40252-y
www.nature.com/scientificreports
www.nature.com/scientificreports/
Open Access This article is licensed under a Creative Commons Attribution 4.0 International
License, which permits use, sharing, adaptation, distribution and reproduction in any medium or
format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre-
ative Commons license, and indicate if changes were made. e images or other third party material in this
article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons license and your intended use is not per-
mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the
copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
© e Author(s) 2019
Content courtesy of Springer Nature, terms of use apply. Rights reserved

1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com