1.

Introduction

The extracellular matrix (ECM) in primary breast cancers is significantly altered compared with normal breast tissue and may provide new biomarkers for cancer diagnosis,^1–4 metastasis prediction,^5,6 and prognosis of survival.⁷ Cancer metastasis is a multistep process in which the tumor microenvironment (TME) facilitates cancer cell–ECM interactions that are necessary for metastasis to occur.⁸ The ECM in breast tumors is composed of a complex meshwork of fibrillar collagens, glycoproteins, and proteoglycans,⁹ which profoundly affect metastasis, as well as proliferation, angiogenesis, adhesion, migration, invasion, macromolecular transport, and drug delivery.^9–13 Multiple molecular mechanisms, such as ECM degradation by proteases secreted by cancer cells and dysregulated ECM synthesis by tumor-associated stromal cells, have been implicated in the formation of the significantly altered ECM of breast tumors⁸ that include structural changes in the fiber patterns of fiber-forming ECM components. Collagen I (Col1) fibers are the major structural ECM component in breast tumors,^5,6,9,14,15 and increased stromal Col1 has been found to facilitate breast tumor formation, invasion, and metastasis.^5,6

X-ray mammography, which detects radiodense fibroglandular tissue in the breast, demonstrated that women with high (50% to 74% versus $<5\%$) breast density have a 4.64-fold increased risk of developing breast carcinoma.^16,17 Histologic evaluation of biopsy and mastectomy specimens from x-ray mammographically dense breast regions revealed a significantly higher collagen density and extent of fibrosis in these regions.^14,15 High Col1 fiber density significantly increased mammary tumor initiation, progression, and lung metastasis.⁶ Cancer cell invasion in this mouse model started at the tumor–stromal interface in the primary tumor along radially aligned Col1 fibers, which can form avenues for metastasis.⁵ In a recent study, Col1 fibers that were oriented perpendicular to the cancer cell boundaries were found to serve as a prognostic signature for survival of breast carcinoma patients.⁷

Here we have, for the first time, performed a pilot study to test the relationship between lymph node metastasis and Col1 fiber texture and density in primary human breast cancers using second harmonic generation (SHG) microscopy. SHG microscopy detects an intrinsic signal generated by the noncentrosymmetric physical properties of Col1 fibers and does not require the use of an exogenous optical imaging agent.^3,4,18 We have performed a small retrospective SHG microscopy study to determine if the Col1 fiber signature in primary breast tumors is altered in patients presenting with lymph node metastasis (${\mathrm{LN}}^{+}$) compared to patients with uninvolved lymph nodes (${\mathrm{LN}}^{-}$). We used our in-house automated fiber analysis software as previously described,¹⁹ which is free of user variability, and observed that primary breast cancers in ${\mathrm{LN}}^{+}$ patients have significantly denser Col1 fiber signatures than primary breast cancer in ${\mathrm{LN}}^{-}$ patients. To validate our in-house software results, we additionally used two commonly used texture analysis techniques to analyze Col1 images:^20–22 the gray level co-occurrence matrix (GLCM) and the Fourier transform (FT). We observed that the GLCM parameters, co-relation and energy, and the FT parameters, aspect ratio and eccentricity, were significantly different for the Col1 fiber distribution in ${\mathrm{LN}}^{+}$ cases compared to the ${\mathrm{LN}}^{-}$ cases. We also demonstrated that tissue fixation and paraffin embedding had no effect on SHG Col1 fiber detection and quantification, indicating that Col1 fiber assessment by nondestructive SHG microscopy may be implemented in routine clinical pathology settings.

2.

Materials and Methods

2.5.

Col1 Fiber Quantification

To quantify our data independent of SHG signal intensity and free of errors introduced by user variability, we used automated geometric analysis and texture analysis techniques. We used a geometric analysis technique in which the parameters of fiber volume and inter-fiber distance were calculated. Image analysis to quantify the Col1 inter-fiber distance and the Col1 fiber volume was performed using in-house software as previously described.¹⁹ This software was written in Matlab (version 7.4.0, TheMathWorks, Natick, MA) and was previously described in detail.¹⁹ Briefly, our software quantifies the median of all Col1 inter-fiber distances and the total Col1 fiber volume as outlined in Fig. 1. A representative $z$-plane of a raw image is shown in Fig. 1(a), which is preprocessed to exclude noise and nonfibrillar shapes as demonstrated in the schematic in Fig. 1(b), resulting in the extraction of Col1 fiber structures from the raw images as shown in Fig. 1(c). As indicated in Fig. 1(d), Euclidean distances from each empty voxel to the nearest Col1 fiber voxel were computed within each 3-D FOV to quantify the inter-fiber distances and the total fiber volume from the Col1 fiber mesh. Based on a previous report, we expected more tightly packed Col1 fibers and a higher fiber volume for ${\mathrm{LN}}^{+}$ cases as compared to ${\mathrm{LN}}^{-}$ cases.⁶ Therefore, a one-sided $t$-test ($\alpha =0.05$) was used to detect significant differences in the Col1 inter-fiber distance and the Col1 fiber volume between primary tumors from ${\mathrm{LN}}^{-}$ versus ${\mathrm{LN}}^{+}$ cases using Excel 2007 (Microsoft, Redmond, WA). $P\text{values of\hspace{0.17em}\hspace{0.17em}}<0.05$ were considered to be significant. $P\text{values of}0.10>P>0.05$ were considered to be a trend toward significance.²³

Fig. 1

Construction of inter-fiber distance and fiber volume. Composite image (a) of the collagen I (Col1) fibers (red) and cell nuclei (green); preprocessing using the optional filter (b) to identify fiber-like objects (red) among various non-fiber-like objects in the image to produce the corresponding Col1 fiber image (c); (d) computing the distances from each 3-D voxel to the surrounding fibers in 3-D to determine the nearest distance to a fiber in μm and the total fiber volume (%). One $z$-plane from a 3-D image stack is displayed in (a) and (c). Each image size is 339 by 339 μm. The scale bars represent 50 μm.

To validate our analysis results, we also used well-established texture analysis methods for Col1 fiber quantification,^20–22 to see whether the Col1 fiber distributions were significantly different for the ${\mathrm{LN}}^{-}$ and ${\mathrm{LN}}^{+}$ cases as observed in our geometric analysis. We used GLCM and FT texture analysis to analyze Col1 fiber distributions. Several textural features were extracted by GLCM, which is a statistical texture analysis method based on the second-order statistics of an image’s grayscale histogram.²⁴ This analysis was done in Matlab. The extracted features include (1) contrast, which is a measure of intensity variation, with a $\text{contrast}=0$ for a constant image, and a $\text{range}=[\begin{array}{cc}0& 1\end{array}]$; (2) co-relation, which is a measure of how a pixel is co-related to its neighboring pixel, with 1 for positively and $-1$ for negatively co-related pixels, and a $\text{range}=[\begin{array}{cc}-1& 1\end{array}]$; (3) energy, which is also known as uniformity or angular second moment, with an $\text{energy}=1$ for a constant image, and a $\text{range}=[\begin{array}{cc}0& 1\end{array}]$; and (4) homogeneity, which is a property measure of how homogeneous an image is, with a $\text{value}=1$ for a constant image, and a $\text{range}=[\begin{array}{cc}0& 1\end{array}]$.²⁵ We also calculated the 2-D FT for all images. For randomly distributed Col1 fibers, the resulting FT approximates a circle, whereas for a more organized fiber pattern, the FT looks elliptical. FT features were obtained by fitting an ellipse to the FT of each image and including (1) aspect ratio (AR), which is the ratio of the major axis to the minor axis of the ellipse; (2) orientation, which is the angle between the $x$-axis and the major axis of the ellipse; and (3) eccentricity, which is the ratio of the distance between the foci of the ellipse and the length of the major axis. For a circle, the eccentricity is 0 and its $\text{range}=[\begin{array}{cc}0& 1\end{array}]$. A one-sided $t$-test ($\alpha =0.05$) was used to detect significant differences in the Col1 texture for all above parameters between primary tumors from ${\mathrm{LN}}^{-}$ versus ${\mathrm{LN}}^{+}$ cases using Excel 2007. $P\text{values of}<0.05$ were considered to be significant.

3.

Results

To test whether the SHG microscopic detection of Col1 fiber signatures depends on tissue processing, we compared fresh tissue samples with formaldehyde-fixed, Hoechst-stained whole-mount sections and FFPE samples from the same MCF-7 breast tumor xenograft. The data presented in Fig. 2 demonstrate that SHG microscopy effectively revealed Col1 fiber signatures in this breast tumor xenograft model. We found that the Col1 fiber distribution in tissues under these different preparations were fairly consistent. Figure 2(a) to 2(c) shows that the Col1 fiber structures were stable and remained unaltered in the same MCF-7 breast tumor xenograft that was first imaged fresh, and from which adjacent sections were re-imaged after 4% paraformaldehyde fixation and Hoechst 33342 staining, or after formalin fixation and paraffin embedding within FFPE blocks. The Col1 fiber volume [Fig. 2(d)] and inter-fiber distance [Fig. 2(e)] from five randomly selected FOVs detected from each sample preparation revealed no changes between the same tissue that was fresh, fixed, or paraffin-embedded. These results suggest that tissues with different preparations (fresh, fixed, or FFPE) can be used for SHG-Col1 imaging studies without introducing artifacts, although in practice, tissue processing within each experimental setup should be kept consistent to avoid any potential variations across different types of tissue preparations.

Fig. 2

The second harmonic generation (SHG) signal is robust and independent of tissue processing. We obtained comparable images from fresh (a), fixed and Hoechst-stained (b), and paraffin-embedded (c) MCF-7 breast tumor xenograft tissue, consistent with almost identical results obtained by fiber volume (d) and inter-fiber distance quantification (e) of five randomly selected fields of view (FOVs) from each differently processed tissue. Each image size is 339 by 339 μm. The scale bars represent 50 μm.

We compared the Col1 fiber signatures in the primary tumors of ${\mathrm{LN}}^{-}$ and ${\mathrm{LN}}^{+}$ patients in an initial proof-of-principle pilot study. As evident in Fig. 3, denser SHG Col1 fiber signatures were detected in primary tumor specimens from eight ${\mathrm{LN}}^{+}$ patients compared to those from six ${\mathrm{LN}}^{-}$ patients. We quantified 10 randomly selected, equidistant FOVs from each primary tumor to assess global Col1 changes. This approach accounts for the possibility that Col1 fiber architecture can be spatially heterogeneous within a given tumor. The image quantification software described above was used to quantify the Col1 fiber volume [Fig. 3(c)] and distance [Fig. 3(d)] in this geometric analysis. The Col1 fiber volume significantly ($P=0.0036$, $n=6$ to 8, values are $\text{mean}\pm \text{standard deviation}$) increased from $20.33\%\pm 5.56\%$ in ${\mathrm{LN}}^{-}$ to $29.22\%\pm 4.72\%$ in ${\mathrm{LN}}^{+}$ patients. The Col1 inter-fiber distance showed a trend toward significance ($P=0.0807$, $n=6$ to 8) and decreased from $2.88\pm 1.07\mu \mathrm{m}$ in ${\mathrm{LN}}^{-}$ to $2.25\pm 0.45\mu \mathrm{m}$ in ${\mathrm{LN}}^{+}$ patients (Fig. 3). The primary tumors of all patients were positive for ER and PR. Hence ER and PR status were the same in all patients and were not confounding factors in this comparison.

Fig. 3

Primary breast tumors from lymph node positive (${\mathrm{LN}}^{+}$) patients contained denser Col1 fibers than primary tumors from ${\mathrm{LN}}^{-}$ patients. Shown are representative SHG Col1 fiber (red) images from an ${\mathrm{LN}}^{-}$ patient (a) and an ${\mathrm{LN}}^{+}$ patient (b). Nuclei were counterstained with Hoechst (green). Each image size is 339 by 339 μm; one $z$-plane is displayed. The scale bars represent 50 μm. ${\mathrm{LN}}^{-}$ patients ($n=6$) displayed significantly ($P=0.0036$) decreased fiber volume (c) and a trend ($P=0.0807$) toward increased inter-fiber distance (d) compared to ${\mathrm{LN}}^{+}$ patients ($n=8$). Values are $\text{mean}\pm \text{standard deviation}$.

To validate our geometric analysis of the differences observed in Col1 fiber distribution of ${\mathrm{LN}}^{-}$ versus ${\mathrm{LN}}^{+}$ patients, we used different texture analysis methods to quantify the fiber patterns in a manner that is independent of the SHG signal intensity. We analyzed the same FOVs as for the geometric analysis using GLCM and FT methods. We observed that the GLCM parameters co-relation and energy [Fig. 4(a)] significantly separated the ${\mathrm{LN}}^{-}$ and ${\mathrm{LN}}^{+}$ patients ($P=0.0007$ and 0.0178, respectively). The Col1 fibers in ${\mathrm{LN}}^{-}$ samples had a significantly lower co-relation (${\mathrm{LN}}^{-}\text{mean}=0.44$, ${\mathrm{LN}}^{+}\text{mean}=0.67$, $P=0.0007$) and a significantly higher energy value (${\mathrm{LN}}^{-}\text{mean}=0.47$, ${\mathrm{LN}}^{+}\text{mean}=0.28$, $P=0.0178$) than the fibers in ${\mathrm{LN}}^{+}$ samples. These parameters of higher co-relation and lower energy in ${\mathrm{LN}}^{+}$ versus ${\mathrm{LN}}^{-}$ cases indicate that ${\mathrm{LN}}^{+}$ samples had more and denser Col1 fibers than ${\mathrm{LN}}^{-}$ samples, which is consistent with our geometric analysis results. In Fig. 4(b), we show that the FT parameters AR and eccentricity were significantly different for the ${\mathrm{LN}}^{-}$ and ${\mathrm{LN}}^{+}$ patient samples ($P=0.0557$ and 0.0462, respectively). The ${\mathrm{LN}}^{-}$ samples had a significantly higher AR (${\mathrm{LN}}^{-}\text{mean}=1.9$, ${\mathrm{LN}}^{+}\text{mean}=1.45$, $P=0.0557$) and a significantly higher eccentricity (${\mathrm{LN}}^{-}\text{mean}=0.74$, ${\mathrm{LN}}^{+}\text{mean}=0.66$, $P=0.0462$) than the ${\mathrm{LN}}^{+}$ samples. The FT analysis parameters AR and eccentricity were able to distinguish between ${\mathrm{LN}}^{+}$ and ${\mathrm{LN}}^{-}$ samples. However, because the spatial information is lost in FT analysis, it was not possible in our case to interpret the decrease in AR and eccentricity in ${\mathrm{LN}}^{+}$ versus ${\mathrm{LN}}^{-}$ patients in terms of geometric fiber parameters.

Fig. 4

Gray level co-occurrence matrix (GLCM) and Fourier transform (FT) texture analyses of Col1 fibers in primary breast tumors were able to distinguish between ${\mathrm{LN}}^{+}$ and ${\mathrm{LN}}^{-}$ patients. (a), GLCM parameters showed that ${\mathrm{LN}}^{-}$ patients ($n=6$) displayed a significantly decreased inter-fiber co-relation ($P=0.0007$) and significantly higher energy uniformity ($P=0.0178$) compared to ${\mathrm{LN}}^{+}$ patients ($n=8$). (b), Texture analysis using FT demonstrated that the Col1 fiber distribution of ${\mathrm{LN}}^{-}$ patients ($n=6$) had a significantly higher aspect ratio (AR, $P=0.0557$) and significantly higher eccentricity value ($P=0.0462$) compared to ${\mathrm{LN}}^{+}$ patients ($n=8$). Values are $\text{mean}\pm \text{standard deviation}$.

To assess whether local Col1 fiber differences in a given tumor affect global Col1 fiber measurements obtained by analyzing and quantifying 10 randomly selected, equidistant FOVs from each primary tumor ($n=14$), we determined the spatial heterogeneity of the Col1 fiber architecture in breast tumor specimens. This assessment is necessary because with microscopic techniques such as SHG Col1 fiber imaging, the FOV is in the range of hundreds of microns, whereas clinical breast tumors typically span larger diameters. Therefore, it is necessary to test whether the differences in Col1 fiber density globally span the tumor or are local phenomena. Our data presented in Fig. 5 show that the Col1 inter-fiber distance distribution and fiber volume are almost identical in four biopsy passes that were obtained from the same human breast tumor specimen. In addition, the overall averages of a Col1 fiber volume of $25\%\pm 3\%$ and distance of $3.0\pm 0.4\mu \mathrm{m}$ for the tile-imaged biopsy pass B1 are comparable to the averages from six FOVs per biopsy pass (Fig. 5). Comparable results were obtained for a total of four primary breast tumor cases, in which the Col1 fiber volumes and inter-fiber distances for six equidistant FOVs from four separate biopsy passes from each one of the four tumors gave comparable results (null hypothesis of the $t$-test was true, i.e., $P>0.05$ for all breast tumor cases; $n=4$). Although there is some heterogeneity in the Col1 fiber architecture in breast tumor specimens, our approach of measuring and averaging over several randomly selected, equidistant FOVs that cover the entire tumor/biopsy averages out local fiber heterogeneity and detects overall global changes in Col1 fiber signature. Our data indicate that global Col1 fiber alterations in breast tumors dominate local Col1 fiber differences.

Fig. 5

SHG tile imaging covering breast tumor biopsy B1 (a) and the adjacent hematoxylin and eosin–stained section (b). Fiber volume (c) and fiber distance (d) were quantified with our software on an FOV-by-FOV basis covering the entire biopsy pass B1 to visualize the spatial heterogeneity by heat maps. Representative, average fiber volume and inter-fiber distance of the 3-D data stack is displayed in 2-D [(c) and (d)]. Six randomly selected FOVs (marked in red) in this biopsy were analyzed for their Col1 fiber distribution. Four biopsy passes B1 (shown), B2, B3, and B4 taken from the same ${\mathrm{LN}}^{+}$ breast tumor were analyzed for their Col1 fiber volume (e) and distance (f) and displayed close to identical values for both parameters. Values are $\text{mean}\pm \text{standard deviation}$. These findings demonstrate that robust global changes in Col1 fiber architecture occur in malignant breast tumors, which can be reliably quantified by randomly selecting six FOVs. Each image size is 9862 by 4080 μm. The scale bars represent 500 μm.

4.

Discussion

The presence or absence of metastases in axillary lymph nodes is currently the most important predictor of prognosis in breast carcinoma and an integral component of the breast cancer staging system.^26,27 Using ex vivo primary tumor specimens from patients, we observed increased Col1 fiber density in ${\mathrm{LN}}^{+}$ compared to ${\mathrm{LN}}^{-}$ patients ($P=0.0036$, $n=14$) and significantly varied fiber distributions as obtained by our texture analysis (co-relation, $P=0.0007$; energy, $P=0.0178$; AR, $P=0.0557$; eccentricity, $P=0.0462$; $n=14$). Our data are in good agreement with a recent study that identified a tumor-associated Col1 signature (TACS) of bundles of straightened and aligned Col1 fibers that are oriented perpendicular to the tumor boundary as an independent prognostic marker for poor survival in patients with this TACS in their primary tumors.⁷ While this larger clinical study scored for disease-free survival in 196 patient samples on human breast carcinoma tissue microarrays, our study more specifically compared primary tumors from ${\mathrm{LN}}^{+}$ and ${\mathrm{LN}}^{-}$ patients in an initial proof-of-principle study. Unlike the study by Conklin et al.⁷ that relied on a qualitative yes/no judgment performed by trained persons without software involvement or quantification, data analysis in our study was done by using in-house automated software that quantifies inter-fiber distance and fiber volume from raw Col1-SHG images. A major advantage of this automated analysis approach is that it is free of errors from subjective judgement, and it can give quantitative results in real time. By averaging over 6 to 10 FOVs that were equidistantly spread throughout each breast tumor specimen, we were able to obtain robust quantitative results ($n=14$, total number of tissue samples). Our data are also in good agreement with preclinical findings in a bi-transgenic mouse model, in which high Col1 fiber density promoted mammary tumor initiation, progression, and metastasis,⁶ and provide the first patient data linking lymph node metastasis with Col1 fiber density. In addition, recent studies demonstrated that high Col1 fiber density in primary tumors can facilitate metastasis by providing dense Col1 fiber avenues along which cancer cells migrate to ultimately establish distant colonies.^5,6 In these studies, the initial invasion of breast cancer cells at the tumor–stromal interface was shown to occur along radially aligned Col1 fibers in the primary tumor.^5,6 Breast cancer cells invade into Col1 matrices by overexpressing integrins on their cell surface, which enable these cells to adhere to the Col1-containing ECM.^28,29 Cancer cells migrate through lymphatic vessels to the connected sentinel lymph node to establish metastatic growth.^30–33 Because lymphatic vessels are attached to collagen through anchoring filaments,^34–36 future studies should focus on identifying if Col1 fibers guide migrating cancer cells into tumor-associated lymphatic vessels within the primary tumor.

In the clinic, lymph node status is typically assessed by a sentinel lymph node biopsy, where the sentinel nodes draining the lymphatic basin containing the tumor are identified and excised. This biopsy is typically performed at about 3 weeks after the initial breast biopsy²⁷ to identify and histologically examine axillary lymph nodes that are at high risk of containing cancer cells. There is a 25% adverse event rate after sentinel lymph node biopsy, with 9% of patients having axillary paresthesia, 6% lymphedema, 6% axillary seromas, and 3% wound infections.²⁷ Avoiding the sentinel lymph node biopsy procedure would eliminate these risks for those patients that are ${\mathrm{LN}}^{-}$. If the sentinel lymph nodes cannot be identified, additional axillary lymph node dissection is performed, which causes even more adverse surgical effects than the sentinel lymph node biopsy alone.²⁷ Therefore, novel biomarkers that alone or when integrated with other standard markers accurately predict for lymph node metastasis are critically needed. Our data suggest that SHG imaging of Col1 fibers may serve as a surrogate marker to predict the presence of lymph node metastasis, which is in good agreement with the study by Conklin et al.⁷ that showed that a specific TACS was able to predict survival. Col1 fibers were detected by optical SHG microscopy, a highly sensitive and nondestructive technique that images intrinsic signal from Col1 fibers.^4,18,37,38 Several other breast cancer markers have been associated with axillary lymph node metastasis such as CCND1, CD44, COX-2, EGFR, HER2/NEU, HPA, and LYVE-1 (reviewed in Ref. 39). In addition, increased expression of vascular endothelial growth factor C (VEGF-C) or VEGF-A, both of which are able to promote lymphangiogenesis in primary tumors, was correlated with an increased incidence of regional lymph node as well as distant metastases in humans and animals.^30–32 Different techniques assessing protein expression, mRNA expression, chromosome alterations, and DNA copy number changes are being used to evaluate these markers of lymph node metastasis in primary breast tumors.³⁹ The advantage of SHG detection of Col1 fibers as a biomarker of lymph node metastasis is that the technique is nondestructive and can be used to interrogate live tissues.

Our data show that the global Col1 fiber architecture in a given breast cancer is an important parameter that dominates local Col1 fiber differences within the tissue. Our approach of probing several randomly selected, equidistant FOVs covering the entire tumor/biopsy to average out local fiber heterogeneity ensures that we are measuring global Col1 fiber architecture, which is quantified by the parameters Col1 fiber volume and inter-fiber distance. We previously observed in an orthotopic human breast tumor xenograft model that hypoxic tumor microenvironments reduce Col1 fiber density and increase Col1 inter-fiber distance.¹⁹ Some of the occurring local heterogeneity in the Col1 fiber architecture of human breast cancers may be due to hypoxic pockets in these tumors. To implement SHG microscopy of Col1 fibers in the breast pathology workflow, it would be feasible to image the SHG Col1 fiber signatures using paraffin blocks generated from excised tumors of patients as was done in Fig. 3 or from biopsies as shown in Fig. 5. As SHG microscopy is nondestructive, the unaltered paraffin blocks can be returned to the pathology archive after SHG microscopic investigation. Another major advantage of SHG microscopy is that it can be performed through an ultra-compact fiber-optic endomicroscope,^40–45 which can be integrated with a 14-gauge breast biopsy needle. Such “through-needle” SHG endomicroscopy may, in the future, allow for SHG Col1 imaging to be performed during the initial diagnostic mammography-guided breast biopsy in the clinic. We have also demonstrated the feasibility of using automated software to analyze Col1 fibers and obtain robust quantitative results that avoid human subjectivity and error. Our software has the potential to provide real-time results during fiber-optic endomicroscopy scanning.

Col1 fibers are the target of recently developed stromal depletion therapies to treat desmoplasmic tumors.⁴⁶ Stromal depletion therapies are currently going into clinical trials in pancreatic cancer.⁴⁶ Imaging biomarkers that are able to assess the response to such therapies in patients will be important. The SHG-detected Col1 fiber signature may provide such a monitoring tool for stromal depletion therapies in the future, and this may be realized either by nondestructive SHG microscopy during the pathology workflow using FFPE blocks or by using SHG endomicroscopic imaging of Col1 fibers in patients. Col1 fibers also play an important role in drug delivery across the ECM of tumors, for which SHG imaging can serve as an assessment tool.¹⁸ Tumor delivery of nanotherapeutics may in fact be improved by strategies aimed at reducing the Col1 fiber matrix, as recently demonstrated with a Col1 synthesis inhibitor in desmoplasmic models of human breast, pancreatic, and skin tumors using SHG microscopy.⁴⁷

5.

Conclusions

In summary, the results obtained here have identified a potential biomarker that may be useful in the assessment and staging of breast cancer and confirmed the importance of Col1 fibers in the metastatic process. Larger scale studies using breast and other cancer tissues are necessary to validate the sensitivity and specificity of Col1 fibers detected with SHG microscopy to predict the presence of cancer cells in lymph nodes. Such studies can lead to the development of an imaging biomarker to evaluate lymph node metastasis at the time of presentation and may result in the formulation of novel Col1 fiber-depletion strategies to reduce metastatic spread. Detection and quantification of Col1 fibers can easily be implemented in clinical pathological laboratory routines to provide additional valuable information for potentially predicting lymph node involvement in breast cancer. Automated software can be used to quantify the Col1 fiber distribution to give objective results, with the potential to perform real-time quantification during endomicroscopy.