Label-free nonlinear optical signatures of extracellular vesicles in liquid and tissue biopsies of human breast cancer.

Extracellular vesicles (EVs) have been implicated in metastasis and proposed as cancer biomarkers. However, heterogeneity and small size makes assessments of EVs challenging. Often, EVs are isolated from biofluids, losing spatial and temporal context and thus lacking the ability to access EVs in situ in their native microenvironment. This work examines the capabilities of label-free nonlinear optical microscopy to extract biochemical optical metrics of EVs in ex vivo tissue and EVs isolated from biofluids in cases of human breast cancer, comparing these metrics within and between EV sources. Before surgery, fresh urine and blood serum samples were obtained from human participants scheduled for breast tumor surgery (24 malignant, 6 benign) or healthy participants scheduled for breast reduction surgery (4 control). EVs were directly imaged both in intact ex vivo tissue that was removed during surgery and in samples isolated from biofluids by differential ultracentrifugation. Isolated EVs and freshly excised ex vivo breast tissue samples were imaged with custom nonlinear optical microscopes to extract single-EV optical metabolic signatures of NAD(P)H and FAD autofluorescence. Optical metrics were significantly altered in cases of malignant breast cancer in biofluid-derived EVs and intact tissue EVs compared to control samples. Specifically, urinary isolated EVs showed elevated NAD(P)H fluorescence lifetime in cases of malignant cancer, serum-derived isolated EVs showed decreased optical redox ratio in stage II cancer, but not earlier stages, and ex vivo breast tissue showed an elevated number of EVs in cases of malignant cancer. Results further indicated significant differences in the measured optical metabolic signature based on EV source (urine, serum and tissue) within individuals.

Weakly supervised identification of microscopic human breast cancer-related optical signatures from normal-appearing breast tissue.

With the latest advancements in optical bioimaging, rich structural and functional information has been generated from biological samples, which calls for capable computational tools to identify patterns and uncover relationships between optical characteristics and various biomedical conditions. Constrained by the existing knowledge of the novel signals obtained by those bioimaging techniques, precise and accurate ground truth annotations can be difficult to obtain. Here we present a weakly supervised deep learning framework for optical signature discovery based on inexact and incomplete supervision. The framework consists of a multiple instance learning-based classifier for the identification of regions of interest in coarsely labeled images and model interpretation techniques for optical signature discovery. We applied this framework to investigate human breast cancer-related optical signatures based on virtual histopathology enabled by simultaneous label-free autofluorescence multiharmonic microscopy (SLAM), with the goal of exploring unconventional cancer-related optical signatures from normal-appearing breast tissues. The framework has achieved an average area under the curve (AUC) of 0.975 on the cancer diagnosis task. In addition to well-known cancer biomarkers, non-obvious cancer-related patterns were revealed by the framework, including NAD(P)H-rich extracellular vesicles observed in normal-appearing breast cancer tissue, which facilitate new insights into the tumor microenvironment and field cancerization. This framework can be further extended to diverse imaging modalities and optical signature discovery tasks.

Differentiation of breast tissue types for surgical margin assessment using machine learning and polarization-sensitive optical coherence tomography.

We report an automated differentiation model for classifying malignant tumor, fibro-adipose, and stroma in human breast tissues based on polarization-sensitive optical coherence tomography (PS-OCT). A total of 720 PS-OCT images from 72 sites of 41 patients with H&E histology-confirmed diagnoses as the gold standard were employed in this study. The differentiation model is trained by the features extracted from both one standard OCT-based metric (i.e., intensity) and four PS-OCT-based metrics (i.e., phase difference between two channels (PD), phase retardation (PR), local phase retardation (LPR), and degree of polarization uniformity (DOPU)). Further optimized by forward searching and validated by leave-one-site-out-cross-validation (LOSOCV) method, the best feature subset was acquired with the highest overall accuracy of 93.5% for the model. Furthermore, to show the superiority of our differentiation model based on PS-OCT images over standard OCT images, the best model trained by intensity-only features (usually obtained by standard OCT systems) was also obtained with an overall accuracy of 82.9%, demonstrating the significance of the polarization information in breast tissue differentiation. The high performance of our differentiation model suggests the potential of using PS-OCT for intraoperative human breast tissue differentiation during the surgical resection of breast cancer.

Intraoperative visualization of the tumor microenvironment and quantification of extracellular vesicles by label-free nonlinear imaging.

Characterization of the tumor microenvironment, including extracellular vesicles (EVs), is important for understanding cancer progression. EV studies have traditionally been performed on dissociated cells, lacking spatial information. Since the distribution of EVs in the tumor microenvironment is associated with cellular function, there is a strong need for visualizing EVs in freshly resected tissues. We intraoperatively imaged untreated human breast tissues using a custom nonlinear imaging system. Label-free optical contrasts of the tissue, correlated with histological findings, enabled point-of-procedure characterization of the tumor microenvironment. EV densities from 29 patients with breast cancer were found to increase with higher histologic grade and shorter tumor-to-margin distance and were significantly higher than those from 7 cancer-free patients undergoing breast reduction surgery. Acquisition and interpretation of these intraoperative images not only provide real-time visualization of the tumor microenvironment but also offer the potential to use EVs as a label-free biomarker for cancer diagnosis and prognosis.

Complementary use of polarization-sensitive and standard OCT metrics for enhanced intraoperative differentiation of breast cancer.

We report the development and implementation of an intraoperative polarization-sensitive optical coherence tomography (PS-OCT) system for enhancing breast cancer detection. A total of 3440 PS-OCT images were intraoperatively acquired from 9 human breast specimens diagnosed by H&E histology as healthy fibro-adipose tissue (n = 2), healthy stroma (n = 2), or invasive ductal carcinoma (IDC, n = 5). A standard OCT-based metric (coefficient of variation (CV)) and PS-OCT-based metrics sensitive to biological tissue from birefringence (i.e., retardation and degree of polarization uniformity (DOPU)) were derived from 398 statistically different and independent images selected by correlation coefficient analysis. We found the standard OCT-based metric and PS-OCT-based metrics were complementary for the differentiation of healthy fibro-adipose tissue, healthy stroma, and IDC. While the CV of fibro-adipose tissue was significantly higher (p<0.001) than those of either stroma or IDC, the CV difference between stroma and IDC was minimal. On the other hand, stroma was associated with significantly higher (p<0.001) retardation and significantly lower (p<0.001) DOPU as compared to IDC. By leveraging the complementary information acquired by the intraoperative PS-OCT system, healthy fibro-adipose tissue, healthy stroma, and IDC can be differentiated with an accuracy of 89.4%, demonstrating the potential of PS-OCT as an adjunct modality for enhanced intraoperative differentiation of human breast cancer.