Alterations of the extracellular matrix in ovarian cancer studied by Second Harmonic Generation imaging microscopy

Topographic modification of the extracellular matrix precedes the onset of bladder cancer

Background

Non-muscle invasive bladder cancer (NMIBC) patients are affected by a high risk of recurrence. The topography of collagen fibers represents a hallmark of the neoplastic extracellular microenvironment.

Objective

Assess the topographic change associated with different stages of bladder cancer (from neoplastic lesions to bona fide tumor) and whether those changes favour the development of NMIBC.

Design Setting and Participants

Seventy-one clinical samples of urothelial carcinoma at different stages were used. Topographic changes preceding tumor onset and progression were evaluated in the rat bladder cancer model induced by nitrosamine (BBN), a bladder-specific carcinogen. The preclinical model of actinic cystitis was also used in combination with BBN. Validated hematoxylin-eosin sections were used to assess the topography of collagen fibrils associated with pre-tumoral steps, NMIBC, and MIBC.

Findings

Linearization of collagen fibers was higher in Cis and Ta vs. dysplastic urothelium, further increased in T1 and greatest in T2 tumors. In the BBN preclinical model, an increase in the linearization of collagen fibers was established since the beginning of inflammation, such as the onset of atypia of a non-univocal nature and dysplasia, and further increased in the presence of the tumor. Linearization of collagen fibers in the model of actinic cystitis was associated with earlier onset of BBN-induced tumor.

Conclusions

The topographic modification of the extracellular microenvironment occurs during the inflammatory processes preceding and favoring the onset of bladder cancer. The topographic reconfiguration of the stroma could represent a marker for identifying and treating the non-neoplastic tissue susceptible to tumor recurrence.

The Balance Between Shear Flow and Extracellular Matrix in Ovarian Cancer-on-Chip

Ovarian cancer is the most lethal gynecologic cancer in developed countries. In the tumor microenvironment, the extracellular matrix (ECM) and flow shear stress are key players in directing ovarian cancer cells invasion. Artificial ECM models based only on ECM proteins are used to build an ovarian tumor-on-chip to decipher the crosstalk between ECM and shear stress on the migratory behavior and cellular heterogeneity of ovarian tumor cells. This work shows that in the shear stress regime of the peritoneal cavity, the ECM plays a major role in driving individual or collective ovarian tumor cells migration. In the presence of basement membrane proteins, migration is more collective than on type I collagen regardless of shear stress. With increasing shear stress, individual cell migration is enhanced; while, no significant impact on collective migration is measured. This highlights the central position that ECM and flow shear stress should hold in in vitro ovarian cancer models to deepen understanding of cellular responses and improve development of ovarian cancer therapeutic platforms. In this frame, adding flow provides significant improvement in biological relevance over the authors' previous work. Further steps for enhanced clinical relevance require not only multiple cell lines but also patient-derived cells and sera.

Machine learning assisted mid-infrared spectrochemical fibrillar collagen imaging in clinical tissues

Significance

Label-free multimodal imaging methods that can provide complementary structural and chemical information from the same sample are critical for comprehensive tissue analyses. These methods are specifically needed to study the complex tumor-microenvironment where fibrillar collagen's architectural changes are associated with cancer progression. To address this need, we present a multimodal computational imaging method where mid-infrared spectral imaging (MIRSI) is employed with second harmonic generation (SHG) microscopy to identify fibrillar collagen in biological tissues.

Aim

To demonstrate a multimodal approach where a morphology-specific contrast mechanism guides a mid-infrared spectral imaging method to detect fibrillar collagen based on its chemical signatures.

Approach

We trained a supervised machine learning (ML) model using SHG images as ground truth collagen labels to classify fibrillar collagen in biological tissues based on their mid-infrared hyperspectral images. Five human pancreatic tissue samples (sizes are in the order of millimeters) were imaged by both MIRSI and SHG microscopes. In total, 2.8 million MIRSI spectra were used to train a random forest (RF) model. The remaining 68 million spectra were used to validate the collagen images generated by the RF-MIRSI model in terms of collagen segmentation, orientation, and alignment.

Results

Compared to the SHG ground truth, the generated MIRSI collagen images achieved a high average boundary F-score (0.8 at 4 pixels threshold) in the collagen distribution, high correlation (Pearson's R 0.82) in the collagen orientation, and similarly high correlation (Pearson's R 0.66) in the collagen alignment.

Conclusions

We showed the potential of ML-aided label-free mid-infrared hyperspectral imaging for collagen fiber and tumor microenvironment analysis in tumor pathology samples.

Mechanical activation and expression of HSP27 in epithelial ovarian cancer

Understanding the complex biomechanical tumor microenvironment (TME) is of critical importance in developing the next generation of anti-cancer treatment strategies. This is especially true in epithelial ovarian cancer (EOC), the deadliest of the gynecologic cancers due to recurrent disease or chemoresistance. However, current models of EOC progression provide little control or ability to monitor how changes in biomechanical parameters alter EOC cell behaviors. In this study, we present a microfluidic device designed to permit biomechanical investigations of the ovarian TME. Using this microtissue system, we describe how biomechanical stimulation in the form of tensile strains upregulate phosphorylation of HSP27, a heat shock protein implicated in ovarian cancer chemoresistance. Furthermore, EOC cells treated with strain demonstrate decreased response to paclitaxel in the in vitro vascularized TME model. The results provide a direct link to biomechanical regulation of HSP27 as a mediator of EOC chemoresistance, possibly explaining the failure of such therapies in some patients. The work presented here lays a foundation to elucidating mechanobiological regulation of EOC progression, including chemoresistance and could provide novel targets for anti-cancer therapeutics.

Collagen fiber centerline tracking in fibrotic tissue via deep neural networks with variational autoencoder-based synthetic training data generation

The role of fibrillar collagen in the tissue microenvironment is critical in disease contexts ranging from cancers to chronic inflammations, as evidenced by many studies. Quantifying fibrillar collagen organization has become a powerful approach for characterizing the topology of collagen fibers and studying the role of collagen fibers in disease progression. We present a deep learning-based pipeline to quantify collagen fibers' topological properties in microscopy-based collagen images from pathological tissue samples. Our method leverages deep neural networks to extract collagen fiber centerlines and deep generative models to create synthetic training data, addressing the current shortage of large-scale annotations. As a part of this effort, we have created and annotated a collagen fiber centerline dataset, with the hope of facilitating further research in this field. Quantitative measurements such as fiber orientation, alignment, density, and length can be derived based on the centerline extraction results. Our pipeline comprises three stages. Initially, a variational autoencoder is trained to generate synthetic centerlines possessing controllable topological properties. Subsequently, a conditional generative adversarial network synthesizes realistic collagen fiber images from the synthetic centerlines, yielding a synthetic training set of image-centerline pairs. Finally, we train a collagen fiber centerline extraction network using both the original and synthetic data. Evaluation using collagen fiber images from pancreas, liver, and breast cancer samples collected via second-harmonic generation microscopy demonstrates our pipeline's superiority over several popular fiber centerline extraction tools. Incorporating synthetic data into training further enhances the network's generalizability. Our code is available at https://github.com/uw-loci/collagen-fiber-metrics.

Comparative Tensile Properties and Collagen Patterns in Domestic Cat (Felis catus) and Dog (Canis lupus familiaris) Ovarian Cortical Tissues

The importance of the ovarian extracellular environment and tissue rigidity on follicle survival and development has gained attention in recent years. Our laboratory has anecdotally observed differences in the rigidity of domestic cat and dog ovarian cortical tissues, which have been postulated to underlie the differences in in vitro culture responses between the species, wherein cat ovarian tissues display higher survival in extended incubation. Here, the tensile strengths of cat and dog ovarian cortical tissues were compared via micropipette aspiration. The underlying collagen patterns, including fiber length, thickness, alignment, curvature, branch points and end points, and overall tissue lacunary and high-density matrix (HDM) were quantified via picrosirius red staining and TWOMBLI analysis. Finally, we explored the potential of MMP (-1 and -9) and TIMP1 supplementation in modulating tissue rigidity, collagen structure, and follicle activation in vitro. No differences in stiffness were observed between cat or dog cortical tissues, or pre- versus post-pubertal status. Cat ovarian collagen was characterized by an increased number of branch points, thinner fibers, and lower HDM compared with dog ovarian collagen, and cat tissues exposed to MMP9 in vitro displayed a reduced Young's modulus. Yet, MMP exposure had a minor impact on follicle development in vitro in either species. This study contributes to our growing understanding of the interactions among the physical properties of the ovarian microenvironment, collagen patterns, and follicle development in vitro.

Multiparameter interferometric polarization-enhanced imaging differentiates carcinoma in situ from inflammation of the bladder: an ex vivo study

Significance

Successful differentiation of carcinoma in situ (CIS) from inflammation in the bladder is key to preventing unnecessary biopsies and enabling accurate therapeutic decisions. Current standard-of-care diagnostic imaging techniques lack the specificity needed to differentiate these states, leading to false positives.

Aim

We introduce multiparameter interferometric polarization-enhanced (MultiPIPE) imaging as a promising technology to improve the specificity of detection for better biopsy guidance and clinical outcomes.

Approach

In this ex vivo study, we extract tissue attenuation-coefficient-based and birefringence-based parameters from MultiPIPE imaging data, collected with a bench-top system, to develop a classifier for the differentiation of benign and CIS tissues. We also analyze morphological features from second harmonic generation imaging and histology slides and perform imaging-to-morphology correlation analysis.

Results

MultiPIPE enhances specificity to differentiate CIS from benign tissues by nearly 20% and reduces the false-positive rate by more than four-fold over clinical standards. We also show that the MultiPIPE measurements correlate well with changes in morphological features in histological assessments.

Conclusions

The results of our study show the promise of MultiPIPE imaging to be used for better differentiation of bladder inflammation from flat tumors, leading to a fewer number of unnecessary procedures and shorter operating room (OR) time.

Characterization of pathological stomach tissue using polarization-sensitive second harmonic generation microscopy

Alterations in collagen ultrastructure between human gastric adenocarcinoma and normal gastric tissue were investigated using polarization-resolved second harmonic generation (PSHG) microscopy. Cylindrical and trigonal symmetries were assumed to extract quantitative PSHG parameters, ρ, κ and S, from each image pixel. Statistically significant variations in these values were observed for gastric adenocarcinoma, indicating a higher disorder of collagen. Numerical focal volume simulations of crossing fibrils indicate increased S parameter is due to more intersecting collagen fibrils of varying diameters. These parameters were also able to distinguish between different grades of gastric adenocarcinoma indicating that PSHG may be useful for automated cancer diagnosis.

Aberration free synthetic aperture second harmonic generation holography

Second harmonic generation (SHG) microscopy is a valuable tool for optical microscopy. SHG microscopy is normally performed as a point scanning imaging method, which lacks phase information and is limited in spatial resolution by the spatial frequency support of the illumination optics. In addition, aberrations in the illumination are difficult to remove. We propose and demonstrate SHG holographic synthetic aperture holographic imaging in both the forward (transmission) and backward (epi) imaging geometries. By taking a set of holograms with varying incident angle plane wave illumination, the spatial frequency support is increased and the input and output pupil phase aberrations are estimated and corrected - producing diffraction limited SHG imaging that combines the spatial frequency support of the input and output optics. The phase correction algorithm is computationally efficient and robust and can be applied to any set of measured field imaging data.

Evaluation of a polarization-enhanced laparoscopy prototype for improved intra-operative visualization of peritoneal metastases

Despite careful staging, the accuracy for preoperative detection of small distant metastases remains poor, creating a clinical need for enhanced operative staging to detect occult peritoneal metastases. This study evaluates a polarization-enhanced laparoscopy (PEL) prototype and assesses its potential for label-free contrast enhancement of peritoneal metastases. This is a first-in-human feasibility study, including 10 adult patients who underwent standard staging laparoscopy (SSL) for gastrointestinal malignancy along with PEL. Image frames of all detectable peritoneal lesions underwent analysis. Using Monte Carlo simulations, contrast enhancement based on the color dependence of PEL (mPEL) was assessed. The prototype performed safely, yet with limitations in illumination, fogging of the distal window, and image co-registration. Sixty-five lesions (56 presumed benign and 9 presumed malignant) from 3 patients represented the study sample. While most lesions were visible under human examination of both SSL and PEL videos, more lesions were apparent using SSL. However, this was likely due to reduced illumination under PEL. When controlling for such effects through direct comparisons of integrated (WLL) vs differential (PEL) polarization laparoscopy images, we found that PEL imaging yielded an over twofold Weber contrast enhancement over WLL. Further, enhancements in the discrimination between malignant and benign lesions were achieved by exploiting the PEL color contrast to enhance sensitivity to tissue scattering, influenced primarily by collagen. In conclusion, PEL appears safe and easy to integrate into the operating room. When controlling for the degree of illumination, image analysis suggested a potential for mPEL to provide improved visualization of metastases.

Novel oviduct endoscope combining optical coherence tomography with intratubal ultrasonography for fallopian tube exploration: An in vivo rabbit pilot study

BACKGROUND AND STUDY AIM

Currently, several limitations exist in the examination of the oviduct. In this study, the usefulness and feasibility of a novel ultrafine dual-modality oviduct endoscopy device for in vivo assessment of the oviduct were evaluated.

METHODS

Five Japanese white rabbits were selected to undergo oviduct probing using a combination of optical coherence tomography (OCT) and intratubal ultrasonography. The feasibility of the procedure was evaluated through 152 pairs of clear, clinically interpretable images obtained using spiral scanning via the pull-back method. OCT images were compared with the oviduct histopathology sections.

RESULTS

Visualization of the oviduct using both OCT and ultrasound revealed a differentiated three-layer tissue; however, ultrasound showed a poorer clarity than OCT. By comparing OCT images with the histological morphology of the oviduct, the inner low-reflective layer of the oviduct corresponds to the mucosal layer, the middle high-reflective layer corresponds to the fibrous muscle layer, and the outer low-reflective layer corresponds to the connective tissue layer. Postoperatively, the general condition of the animals was good.

CONCLUSION

This study demonstrated the feasibility and potential clinical value of the novel ultrafine dual-modality oviduct endoscope. Dual-modality imaging of OCT and intratubal ultrasonography can provide clearer microstructure of the oviduct wall.

Extracellular matrix educates an immunoregulatory tumor macrophage phenotype found in ovarian cancer metastasis

Recent studies have shown that the tumor extracellular matrix (ECM) associates with immunosuppression, and that targeting the ECM can improve immune infiltration and responsiveness to immunotherapy. A question that remains unresolved is whether the ECM directly educates the immune phenotypes seen in tumors. Here, we identify a tumor-associated macrophage (TAM) population associated with poor prognosis, interruption of the cancer immunity cycle, and tumor ECM composition. To investigate whether the ECM was capable of generating this TAM phenotype, we developed a decellularized tissue model that retains the native ECM architecture and composition. Macrophages cultured on decellularized ovarian metastasis shared transcriptional profiles with the TAMs found in human tissue. ECM-educated macrophages have a tissue-remodeling and immunoregulatory phenotype, inducing altered T cell marker expression and proliferation. We conclude that the tumor ECM directly educates this macrophage population found in cancer tissues. Therefore, current and emerging cancer therapies that target the tumor ECM may be tailored to improve macrophage phenotype and their downstream regulation of immunity.

Label-free multiphoton imaging flow cytometry

Label-free imaging flow cytometry is a powerful tool for biological and medical research as it overcomes technical challenges in conventional fluorescence-based imaging flow cytometry that predominantly relies on fluorescent labeling. To date, two distinct types of label-free imaging flow cytometry have been developed, namely optofluidic time-stretch quantitative phase imaging flow cytometry and stimulated Raman scattering (SRS) imaging flow cytometry. Unfortunately, these two methods are incapable of probing some important molecules such as starch and collagen. Here, we present another type of label-free imaging flow cytometry, namely multiphoton imaging flow cytometry, for visualizing starch and collagen in live cells with high throughput. Our multiphoton imaging flow cytometer is based on nonlinear optical imaging whose image contrast is provided by two optical nonlinear effects: four-wave mixing (FWM) and second-harmonic generation (SHG). It is composed of a microfluidic chip with an acoustic focuser, a lab-made laser scanning SHG-FWM microscope, and a high-speed image acquisition circuit to simultaneously acquire FWM and SHG images of flowing cells. As a result, it acquires FWM and SHG images (100 × 100 pixels) with a spatial resolution of 500 nm and a field of view of 50 μm × 50 μm at a high event rate of four to five events per second, corresponding to a high throughput of 560-700 kb/s, where the event is defined by the passage of a cell or a cell-like particle. To show the utility of our multiphoton imaging flow cytometer, we used it to characterize Chromochloris zofingiensis (NIES-2175), a unicellular green alga that has recently attracted attention from the industrial sector for its ability to efficiently produce valuable materials for bioplastics, food, and biofuel. Our statistical image analysis found that starch was distributed at the center of the cells at the early cell cycle stage and became delocalized at the later stage. Multiphoton imaging flow cytometry is expected to be an effective tool for statistical high-content studies of biological functions and optimizing the evolution of highly productive cell strains.

Multiscale Label-Free Imaging of Fibrillar Collagen in the Tumor Microenvironment

With recent advances in cancer therapeutics, there is a great need for improved imaging methods for characterizing cancer onset and progression in a quantitative and actionable way. Collagen, the most abundant extracellular matrix protein in the tumor microenvironment (and the body in general), plays a multifaceted role, both hindering and promoting cancer invasion and progression. Collagen deposition can defend the tumor with immunosuppressive effects, while aligned collagen fiber structures can enable tumor cell migration, aiding invasion and metastasis. Given the complex role of collagen fiber organization and topology, imaging has been a tool of choice to characterize these changes on multiple spatial scales, from the organ and tumor scale to cellular and subcellular level. Macroscale density already aids in the detection and diagnosis of solid cancers, but progress is being made to integrate finer microscale features into the process. Here we review imaging modalities ranging from optical methods of second harmonic generation (SHG), polarized light microscopy (PLM), and optical coherence tomography (OCT) to the medical imaging approaches of ultrasound and magnetic resonance imaging (MRI). These methods have enabled scientists and clinicians to better understand the impact collagen structure has on the tumor environment, at both the bulk scale (density) and microscale (fibrillar structure) levels. We focus on imaging methods with the potential to both examine the collagen structure in as natural a state as possible and still be clinically amenable, with an emphasis on label-free strategies, exploiting intrinsic optical properties of collagen fibers.

In Vitro Models of Ovarian Cancer: Bridging the Gap between Pathophysiology and Mechanistic Models

Ovarian cancer (OC) is a disease of major concern with a survival rate of about 40% at five years. This is attributed to the lack of visible and reliable symptoms during the onset of the disease, which leads over 80% of patients to be diagnosed at advanced stages. This implies that metastatic activity has advanced to the peritoneal cavity. It is associated with both genetic and phenotypic heterogeneity, which considerably increase the risks of relapse and reduce the survival rate. To understand ovarian cancer pathophysiology and strengthen the ability for drug screening, further development of relevant in vitro models that recapitulate the complexity of OC microenvironment and dynamics of OC cell population is required. In this line, the recent advances of tridimensional (3D) cell culture and microfluidics have allowed the development of highly innovative models that could bridge the gap between pathophysiology and mechanistic models for clinical research. This review first describes the pathophysiology of OC before detailing the engineering strategies developed to recapitulate those main biological features.

Three-Dimensional Collagen Topology Shapes Cell Morphology, beyond Stiffness

Cellular heterogeneity is associated with many physiological processes, including pathological ones, such as morphogenesis and tumorigenesis. The extracellular matrix (ECM) is a key player in the generation of cellular heterogeneity. Advances in our understanding rely on our ability to provide relevant in vitro models. This requires obtainment of the characteristics of the tissues that are essential for controlling cell fate. To do this, we must consider the diversity of tissues, the diversity of physiological contexts, and the constant remodeling of the ECM along these processes. To this aim, we have fabricated a library of ECM models for reproducing the scaffold of connective tissues and the basement membrane by using different biofabrication routes based on the electrospinning and drop casting of biopolymers from the ECM. Using a combination of electron microscopy, multiphoton imaging, and AFM nanoindentation, we show that we can vary independently protein composition, topology, and stiffness of ECM models. This in turns allows one to generate the in vivo complexity of the phenotypic landscape of ovarian cancer cells. We show that, while this phenotypic shift cannot be directly correlated with a unique ECM feature, the three-dimensional collagen fibril topology patterns cell shape, beyond protein composition and stiffness of the ECM. On this line, this work is a further step toward the development of ECM models recapitulating the constantly remodeled environment that cells face and thus provides new insights for cancer model engineering and drug testing.

Unsupervised determination of lung tumor margin with widefield polarimetric second-harmonic generation microscopy

The extracellular matrix (ECM) is amongst many tissue components affected by cancer, however, morphological changes of the ECM are not well-understood and thus, often omitted from diagnostic considerations. Polarimetric second-harmonic generation (P-SHG) microscopy allows for visualization and characterization of collagen ultrastructure in the ECM, aiding in better understanding of the changes induced by cancer throughout the tissue. In this paper, a large region of hematoxylin and eosin (H&E) stained human lung section, encompassing a tumor margin, connecting a significant tumor portion to normal tissue was imaged with P-SHG microscopy. The resulting polarimetric parameters were utilized in principal components analysis and unsupervised K-Means clustering to separate normal- and tumor-like tissue. Consequently, a pseudo-color map of the clustered tissue regions is generated to highlight the irregularity of the ECM collagen structure throughout the region of interest and to identify the tumor margin, in the absence of morphological characteristics of the cells.

Differentiation of pancreatic ductal adenocarcinoma and chronic pancreatitis using graph neural networks on histopathology and collagen fiber features

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal human cancers. However, the symptoms and radiographic appearance of chronic pancreatitis (CP) mimics that of PDAC, and sometimes the 2 entities can also be difficult to differentiate microscopically. The need for accurate differentiation of PDAC and CP has become a major topic in pancreatic pathology. These 2 diseases can present similar histomorphological features, such as excessive deposition of fibrotic stroma in the tissue microenvironment and inflammatory cell infiltration. In this paper, we present a quantitative analysis pipeline empowered by graph neural networks (GNN) capable of automatic detection and differentiation of PDAC and CP in human histological specimens. Modeling histological images as graphs and deploying graph convolutions can enable the capture of histomorphological features at different scales, ranging from nuclear size to the organization of ducts. The analysis pipeline combines image features computed from co-registered hematoxylin and eosin (H&E) images and Second-Harmonic Generation (SHG) microscopy images, with the SHG images enabling the extraction of collagen fiber morphological features. Evaluating the analysis pipeline on a human tissue micro-array dataset consisting of 786 cores and a tissue region dataset consisting of 268 images, it attained 86.4% accuracy with an average area under the curve (AUC) of 0.954 and 88.9% accuracy with an average AUC of 0.957, respectively. Moreover, incorporating topological features of collagen fibers computed from SHG images into the model further increases the classification accuracy on the tissue region dataset to 91.3% with an average AUC of 0.962, suggesting that collagen characteristics are diagnostic features in PDAC and CP detection and differentiation.

A miniaturized screening platform to identify novel regulators of extracellular matrix alignment

Extracellular matrix alignment contributes to metastasis in a number of cancers and is a known prognostic stromal factor; however, the mechanisms controlling matrix organization remain unclear. Cancer-associated fibroblasts (CAF) play a critical role in this process, particularly via matrix production and modulation of key signaling pathways controlling cell adhesion and contractility. Stroma normalization, as opposed to elimination, is a highly sought strategy, and screening for drugs that effectively alter extracellular matrix (ECM) alignment is a practical way to identify novel CAF-normalizing targets that modulate ECM organization. To meet this need, we developed a novel high-throughput screening platform in which fibroblast-derived matrices were produced in 384-well plates, imaged with automated confocal microscopy, and analyzed using a customized MATLAB script. This platform is a technical advance because it miniaturizes the assay, eliminates costly and time-consuming experimental steps, and streamlines data acquisition and analysis to enable high-throughput screening applications. As a proof of concept, this platform was used to screen a kinase inhibitor library to identify modulators of matrix alignment. A number of novel potential regulators were identified, including several receptor tyrosine kinases (c-MET, tropomyosin receptor kinase 1 (NTRK1), HER2/ERBB2) and the serine/threonine kinases protein kinase A, C, and G (PKA, PKC, and PKG). The expression of these regulators was analyzed in publicly available patient datasets to examine the association between stromal gene expression and patient outcomes.

The interplay between physical cues and mechanosensitive ion channels in cancer metastasis

Physical cues have emerged as critical influencers of cell function during physiological processes, like development and organogenesis, and throughout pathological abnormalities, including cancer progression and fibrosis. While ion channels have been implicated in maintaining cellular homeostasis, their cell surface localization often places them among the first few molecules to sense external cues. Mechanosensitive ion channels (MICs) are especially important transducers of physical stimuli into biochemical signals. In this review, we describe how physical cues in the tumor microenvironment are sensed by MICs and contribute to cancer metastasis. First, we highlight mechanical perturbations, by both solid and fluid surroundings typically found in the tumor microenvironment and during critical stages of cancer cell dissemination from the primary tumor. Next, we describe how Piezo1/2 and transient receptor potential (TRP) channels respond to these physical cues to regulate cancer cell behavior during different stages of metastasis. We conclude by proposing alternative mechanisms of MIC activation that work in tandem with cytoskeletal components and other ion channels to bestow cells with the capacity to sense, respond and navigate through the surrounding microenvironment. Collectively, this review provides a perspective for devising treatment strategies against cancer by targeting MICs that sense aberrant physical characteristics during metastasis, the most lethal aspect of cancer.

DDR2 Expression in Cancer-Associated Fibroblasts Promotes Ovarian Cancer Tumor Invasion and Metastasis through Periostin-ITGB1

Ovarian cancer has the highest mortality of all gynecologic malignancies. As such, there is a need to identify molecular mechanisms that underlie tumor metastasis in ovarian cancer. Increased expression of receptor tyrosine kinase, DDR2, has been associated with worse patient survival. Identifying downstream targets of DDR2 may allow specific modulation of ovarian cancer metastatic pathways. Additionally, stromal cells play a critical role in metastasis. The crosstalk between tumor and stromal cells can lead to tumor progression. We first identified that tumor cells co-cultured with DDR2-expressing fibroblasts had lower periostin expression when compared to tumor cells co-cultured with DDR2-depleted fibroblasts. We confirmed that DDR2 regulates POSTN expression in ovarian cancer-associated fibroblasts (CAFs). We found that mesothelial cell clearance and invasion by tumor cells were enhanced three-fold when DDR2 and POSTN-expressing CAFs were present compared to DDR2 and POSTN-depleted CAFs. Furthermore, DDR2-depleted and POSTN-overexpressing CAFs co-injected with ovarian tumor cells had increased tumor burden compared to mice injected with tumor cells and DDR2 and POSTN-depleted CAFs. Furthermore, we demonstrated that DDR2 regulates periostin expression through integrin B1 (ITGB1). Stromal DDR2 is highly correlated with stromal POSTN expression in ovarian cancer patient tumors. Thus, DDR2 expression in CAFs regulates the steps of ovarian cancer metastasis through periostin.

Injectable three-dimensional tumor microenvironments to study mechanobiology in ovarian cancer

Epithelial ovarian cancers are among the most aggressive forms of gynecological malignancies. Despite the advent of poly adenosine diphosphate-ribose polymerase (PARP) and checkpoint inhibitors, improvement to patient survival has been modest. Limited in part by clinical translation, beneficial therapeutic strategies remain elusive in ovarian cancers. Although elevated levels of extracellular proteins, including collagens, proteoglycans, and glycoproteins, have been linked to chemoresistance, they are often missing from the processes of drug- development and screening. Biophysical and biochemical signaling from the extracellular matrix (ECM) determine cellular phenotype and affect both tumor progression and therapeutic response. However, many state-of-the-art tumor models fail to mimic the complexities of the tumor microenvironment (TME) and omit key signaling components. In this article, two interpenetrating network (IPN) hydrogel scaffold platforms, comprising of alginate-collagen or agarose-collagen, have been characterized for use as 3D in vitro models of epithelial ovarian cancer ECM. These highly tunable, injection mold compatible, and inexpensive IPNs replicate the critical governing physical and chemical signaling present within the ovarian TME. Additionally, an effective and cell-friendly live-cell retrieval method has been established to recover cells post-encapsulation. Lastly, functional mechanotransduction in ovarian cancers was demonstrated by increasing scaffold stiffness within the 3D in vitro ECM models. With these features, the agarose-collagen and alginate-collagen hydrogels provide a robust TME for the study of mechanobiology in epithelial cancers. STATEMENT OF SIGNIFICANCE: Ovarian cancer is the most lethal gynecologic cancer afflicting women today. Here we present the development, characterization, and validation of 3D interpenetrating platforms to shift the paradigm in standard in vitro modeling. These models help elucidate the roles of biophysical and biochemical cues in ovarian cancer progression. The agarose-collagen and alginate-collagen interpenetrating network (IPN) hydrogels are simple to fabricate, inexpensive, and can be modified to create custom mechanical stiffnesses and concentrations of bio-adhesive motifs. Given that investigations into the roles of biophysical characteristics in ovarian cancers have provided incongruent results, we believe that the IPN platforms will be critically important to uncovering molecular drivers. We also expect these platforms to be broadly applicable to studies involving mechanobiology in solid tumors.

Machine learning-enabled cancer diagnostics with widefield polarimetric second-harmonic generation microscopy

The extracellular matrix (ECM) collagen undergoes major remodeling during tumorigenesis. However, alterations to the ECM are not widely considered in cancer diagnostics, due to mostly uniform appearance of collagen fibers in white light images of hematoxylin and eosin-stained (H&E) tissue sections. Polarimetric second-harmonic generation (P-SHG) microscopy enables label-free visualization and ultrastructural investigation of non-centrosymmetric molecules, which, when combined with texture analysis, provides multiparameter characterization of tissue collagen. This paper demonstrates whole slide imaging of breast tissue microarrays using high-throughput widefield P-SHG microscopy. The resulting P-SHG parameters are used in classification to differentiate tumor from normal tissue, resulting in 94.2% for both accuracy and F1-score, and 6.3% false discovery rate. Subsequently, the trained classifier is employed to predict tumor tissue with 91.3% accuracy, 90.7% F1-score, and 13.8% false omission rate. As such, we show that widefield P-SHG microscopy reveals collagen ultrastructure over large tissue regions and can be utilized as a sensitive biomarker for cancer diagnostics and prognostics studies.

Assessment of Ultra-Early-Stage Liver Fibrosis in Human Non-Alcoholic Fatty Liver Disease by Second-Harmonic Generation Microscopy

Non-alcoholic fatty liver disease (NAFLD) is associated with the chronic progression of fibrosis. In general, the progression of liver fibrosis is determined by a histopathological assessment with a collagen-stained section; however, the ultra-early stage of liver fibrosis is challenging to identify because of the low sensitivity in the collagen-selective staining method. In the present study, we demonstrate the feasibility of second-harmonic generation (SHG) microscopy in the histopathological diagnosis of the liver of NAFLD patients for the quantitative assessment of the ultra-early stage of fibrosis. We investigated four representative NAFLD patients with early stages of fibrosis. SHG microscopy visualised well-matured fibrotic structures and early fibrosis diffusely involving liver tissues, whereas early fibrosis is challenging to be identified by conventional histopathological methods. Furthermore, the SHG emission directionality analysis revealed the maturation of each collagen fibre of each patient. As a result, SHG microscopy is feasible for assessing liver fibrosis on NAFLD patients, including the ultra-early stage of liver fibrosis that is difficult to diagnose by the conventional histopathological method. The assessment method of the ultra-early fibrosis by using SHG microscopy may serve as a crucial means for pathological, clinical, and prognostic diagnosis of NAFLD patients.

Mechanomimetic 3D Scaffolds as a Humanized In Vitro Model for Ovarian Cancer

The mechanical homeostasis of tissues can be altered in response to trauma or disease, such as cancer, resulting in altered mechanotransduction pathways that have been shown to impact tumor development, progression, and the efficacy of therapeutic approaches. Specifically, ovarian cancer progression is parallel to an increase in tissue stiffness and fibrosis. With in vivo models proving difficult to study, tying tissue mechanics to altered cellular and molecular properties necessitate advanced, tunable, in vitro 3D models able to mimic normal and tumor mechanic features. First, we characterized normal human ovary and high-grade serous (HGSC) ovarian cancer tissue stiffness to precisely mimic their mechanical features on collagen I-based sponge scaffolds, soft (NS) and stiff (MS), respectively. We utilized three ovarian cancer cell lines (OVCAR-3, Caov-3, and SKOV3) to evaluate changes in viability, morphology, proliferation, and sensitivity to doxorubicin and liposomal doxorubicin treatment in response to a mechanically different microenvironment. High substrate stiffness promoted the proliferation of Caov-3 and SKOV3 cells without changing their morphology, and upregulated mechanosensors YAP/TAZ only in SKOV3 cells. After 7 days in culture, both OVCAR3 and SKOV3 decreased the MS scaffold storage modulus (stiffness), suggesting a link between cell proliferation and the softening of the matrix. Finally, high matrix stiffness resulted in higher OVCAR-3 and SKOV3 cell cytotoxicity in response to doxorubicin. This study demonstrates the promise of biomimetic porous scaffolds for effective inclusion of mechanical parameters in 3D cancer modeling. Furthermore, this work establishes the use of porous scaffolds for studying ovarian cancer cells response to mechanical changes in the microenvironment and as a meaningful platform from which to investigate chemoresistance and drug response.

Second-Harmonic Generation Imaging Reveals Changes in Breast Tumor Collagen Induced by Neoadjuvant Chemotherapy

Breast cancer is the most common invasive cancer in women, with most deaths attributed to metastases. Neoadjuvant chemotherapy (NACT) may be prescribed prior to surgical removal of the tumor for subsets of breast cancer patients but can have diverse undesired and off-target effects, including the increased appearance of the 'tumor microenvironment of metastasis', image-based multicellular signatures that are prognostic of breast tumor metastasis. To assess whether NACT can induce changes in two other image-based prognostic/predictive signatures derived from tumor collagen, we quantified second-harmonic generation (SHG) directionality and fiber alignment in formalin-fixed, paraffin-embedded sections of core needle biopsies and primary tumor excisions from 22 human epidermal growth factor receptor 2-overexpressing (HER2+) and 22 triple-negative breast cancers. In both subtypes, we found that SHG directionality (i.e., the forward-to-backward scattering ratio, or F/B) is increased by NACT in the bulk of the tumor, but not the adjacent tumor-stroma interface. Overall collagen fiber alignment is increased by NACT in triple-negative but not HER2+ breast tumors. These results suggest that NACT impacts the collagenous extracellular matrix in a complex and subtype-specific manner, with some prognostic features being unchanged while others are altered in a manner suggestive of a more metastatic phenotype.

Polarization enhanced laparoscope for improved visualization of tissue structural changes associated with peritoneal cancer metastasis

A polarization enhanced laparoscopy (PEL) imaging system was developed to examine the feasibility of utilizing PEL to augment conventional white light laparoscopy (WLL) in the visualization of peritoneal cancer metastases. The system includes a modified tip to illuminate tissue with linearly polarized light and elements in the detection path enabling recording of corresponding images linearly co- and cross-polarized relative to the incident light. WLL and PEL images from optical tissue phantoms with features of distinct scattering cross-section confirm the enhanced sensitivity of PEL to such characteristics. Additional comparisons based on images acquired from collagen gels with different levels of fiber alignment highlight another source of PEL contrast. Finally, PEL and WLL images of ex vivo human tissue illustrate the potential of PEL to improve visualization of cancerous tissue surrounded by healthy peritoneum. Given the simplicity of the approach and its potential for seamless integration with current clinical practice, our results provide motivation for clinical translation.

The Functional Role of Extracellular Matrix Proteins in Cancer

The extracellular matrix (ECM) is highly dynamic as it is constantly deposited, remodeled and degraded to maintain tissue homeostasis. ECM is a major structural component of the tumor microenvironment, and cancer development and progression require its extensive reorganization. Cancerized ECM is biochemically different in its composition and is stiffer compared to normal ECM. The abnormal ECM affects cancer progression by directly promoting cell proliferation, survival, migration and differentiation. The restructured extracellular matrix and its degradation fragments (matrikines) also modulate the signaling cascades mediated by the interaction with cell-surface receptors, deregulate the stromal cell behavior and lead to emergence of an oncogenic microenvironment. Here, we summarize the current state of understanding how the composition and structure of ECM changes during cancer progression. We also describe the functional role of key proteins, especially tenascin C and fibronectin, and signaling molecules involved in the formation of the tumor microenvironment, as well as the signaling pathways that they activate in cancer cells.

Collagen I dysregulation is pivotal for ovarian cancer progression

As a principal matrisomal protein, collagen is involved in the regulation of the structural framework of extracellular matrix (ECM) and therefore is potentially crucial in determining the biophysical character of the ECM. It has been suggested that collagen architecture plays a role in ovarian cancer development, progression and therapeutic responses which led us to examine the collagen morphology in normal and cancerous ovarian tissue. Also, the behaviour of ovarian cancer cells cultured in four qualitatively different collagen gels was investigated. The results here provide evidence that collagen I morphology in the cancerous ovary is distinct from that in the normal ovary. Tumour-associated collagen I showed streams or channels of thick elongated collagen I fibrils. Moreover, fibril alignment was significantly more prevalent in endometrioid and clear cell cancers than other ovarian cancer subtypes. In this work, for the first-time collagen I architecture profiling (CAP) was introduced using histochemical staining, which distinguished between the collagen I morphologies of ovarian cancer subtypes. Immunohistochemical examination of ovarian normal and cancerous tissues also supported the notion that focal adhesion and Rho signalling are upregulated in ovarian cancers, especially in the high-grade serous tumours, as indicated by higher expression of p-FAK and p190RhoGEF. The results also support the concept that collagen I architecture, which might be collagen I concentration-dependent, influences proliferation in ovarian cancer cells. The study provides evidence that modification of collagen I architecture integrity is associated with ovarian cancer development and therapeutic responses.

TGFBI Production by Macrophages Contributes to an Immunosuppressive Microenvironment in Ovarian Cancer

The tumor microenvironment evolves during malignant progression, with major changes in nonmalignant cells, cytokine networks, and the extracellular matrix (ECM). In this study, we aimed to understand how the ECM changes during neoplastic transformation of serous tubal intraepithelial carcinoma lesions (STIC) into high-grade serous ovarian cancers (HGSOC). Analysis of the mechanical properties of human fallopian tubes (FT) and ovaries revealed that normal FT and fimbria had a lower tissue modulus, a measure of stiffness, than normal or diseased ovaries. Proteomic analysis of the matrisome fraction between FT, fimbria, and ovaries showed significant differences in the ECM protein TGF beta induced (TGFBI, also known as βig-h3). STIC lesions in the fimbria expressed high levels of TGFBI, which was predominantly produced by CD163-positive macrophages proximal to STIC epithelial cells. In vitro stimulation of macrophages with TGFβ and IL4 induced secretion of TGFBI, whereas IFNγ/LPS downregulated macrophage TGFBI expression. Immortalized FT secretory epithelial cells carrying clinically relevant TP53 mutations stimulated macrophages to secrete TGFBI and upregulated integrin αvβ3, a putative TGFBI receptor. Transcriptomic HGSOC datasets showed a significant correlation between TGFBI expression and alternatively activated macrophage signatures. Fibroblasts in HGSOC metastases expressed TGFBI and stimulated macrophage TGFBI production in vitro. Treatment of orthotopic mouse HGSOC tumors with an anti-TGFBI antibody reduced peritoneal tumor size, increased tumor monocytes, and activated β3-expressing unconventional T cells. In conclusion, TGFBI may favor an immunosuppressive microenvironment in STICs that persists in advanced HGSOC. Furthermore, TGFBI may be an effector of the tumor-promoting actions of TGFβ and a potential therapeutic target. SIGNIFICANCE: Analysis of ECM changes during neoplastic transformation reveals a role for TGFBI secreted by macrophages in immunosuppression in early ovarian cancer.

Triple-modality co-registered endoscope featuring wide-field reflectance imaging, and high-resolution multiphoton and optical coherence microscopy

We present the design and feasibility testing of a multimodal co-registered endoscope based on a dual-path optical system integrated with a scanning piezo. This endoscope incorporates three different imaging modalities. A large field of view reflectance imaging system enables visualization of objects several millimeters in front of the endoscope, while optical coherence microscopy and multiphoton microscopy are employed in contact with tissue to further analyze suspicious areas. The optical system allows multiple different imaging modalities by employing a dual optical path. One path features a low numerical aperture and wide field of view to allow reflectance imaging of distant objects. The other path features a high numerical aperture and short working distance to allow microscopy techniques such as optical coherence microscopy and multiphoton microscopy. Images of test targets were obtained with each imaging modality to verify and characterize the imaging capabilities of the endoscope. The reflectance modality was demonstrated with a 561 nm laser to allow high contrast with blood vessels. It achieved a lateral resolution of 24.8 μm at 5 mm and a working distance from 5 mm to 30 mm. Optical coherence microscopy (OCM) was performed with a 1300 nm super-luminescent diode since this wavelength experiences low relative scattering to allow for deeper tissue imaging. Measured OCM lateral and axial resolution was 4.0 μm and 14.2 μm, respectively. Multiphoton microscopy (MPM) was performed with a custom 1400 nm femtosecond fiber laser, a wavelength suitable for exciting multiple exogenous and some endogenous fluorophores, as well as providing information on tissue composition through harmonic generation processes. A 4.0 μm MPM lateral resolution was measured.

Real-time polarization microscopy of fibrillar collagen in histopathology

Over the past two decades, fibrillar collagen reorganization parameters such as the amount of collagen deposition, fiber angle and alignment have been widely explored in numerous studies. These parameters are now widely accepted as stromal biomarkers and linked to disease progression and survival time in several cancer types. Despite all these advances, there has not been a significant effort to make it possible for clinicians to explore these biomarkers without adding steps to the clinical workflow or by requiring high-cost imaging systems. In this paper, we evaluate previously described polychromatic polarization microscope (PPM) to visualize collagen fibers with an optically generated color representation of fiber orientation and alignment when inspecting the sample by a regular microscope with minor modifications. This system does not require stained slides, but is compatible with histological stains such as H&E. Consequently, it can be easily accommodated as part of regular pathology review of tissue slides, while providing clinically useful insight into stromal composition.

Personalized models of heterogeneous 3D epithelial tumor microenvironments: Ovarian cancer as a model

Intractable human diseases such as cancers, are context dependent, unique to both the individual patient and to the specific tumor microenvironment. However, conventional cancer treatments are often nonspecific, targeting global similarities rather than unique drivers. This limits treatment efficacy across heterogeneous patient populations and even at different tumor locations within the same patient. Ultimately, this poor efficacy can lead to adverse clinical outcomes and the development of treatment-resistant relapse. To prevent this and improve outcomes, it is necessary to be selective when choosing a patient's optimal adjuvant treatment. In this review, we posit the use of personalized, tumor-specific models (TSM) as tools to achieve this remarkable feat. First, using ovarian cancer as a model disease, we outline the heterogeneity and complexity of both the cellular and extracellular components in the tumor microenvironment. Then we examine the advantages and disadvantages of contemporary cancer models and the rationale for personalized TSM. We discuss how to generate precision 3D models through careful and detailed analysis of patient biopsies. Finally, we provide clinically relevant applications of these versatile personalized cancer models to highlight their potential impact. These models are ideal for a myriad of fundamental cancer biology and translational studies. Importantly, these approaches can be extended to other carcinomas, facilitating the discovery of new therapeutics that more effectively target the unique aspects of each individual patient's TME. STATEMENT OF SIGNIFICANCE: In this article, we have presented the case for the application of biomaterials in developing personalized models of complex diseases such as cancers. TSM could bring about breakthroughs in the promise of precision medicine. The critical components of the diverse tumor microenvironments, that lead to treatment failures, include cellular- and extracellular matrix- heterogeneity, and biophysical signals to the cells. Therefore, we have described these dynamic components of the tumor microenvironments, and have highlighted how contemporary biomaterials can be utilized to create personalized in vitro models of cancers. We have also described the application of the TSM to predict the dynamic patterns of disease progression, and predict effective therapies that can produce durable responses, limit relapses, and treat any minimal residual disease.

Machine learning-based rapid diagnosis of human borderline ovarian cancer on second-harmonic generation images

Regarding growth pattern and cytological characteristics, borderline ovarian tumors fall between benign and malignant, but they tend to develop malignancy. Currently, it is difficult to accurately diagnose ovarian cancer using common medical imaging methods, and histopathological examination is routinely used to obtain a definitive diagnosis. However, such examination requires experienced pathologists, being labor-intensive, time-consuming, and possibly leading to interobserver bias. By using second-harmonic generation imaging and k-nearest neighbors classifier in conjunction with automated machine learning tree-based pipeline optimization tool, we developed a computer-aided diagnosis method to classify ovarian tissues as being malignant, benign, borderline, and normal, obtaining areas under the receiver operating characteristic curve of 1.00, 0.99, 0.98, and 0.97, respectively. These results suggest that diagnosis based on second-harmonic generation images and machine learning can support the rapid and accurate detection of ovarian cancer in clinical practice.

Causal contributors to tissue stiffness and clinical relevance in urology

Mechanomedicine is an emerging field focused on characterizing mechanical changes in cells and tissues coupled with a specific disease. Understanding the mechanical cues that drive disease progression, and whether tissue stiffening can precede disease development, is crucial in order to define new mechanical biomarkers to improve and develop diagnostic and prognostic tools. Classically known stromal regulators, such as fibroblasts, and more recently acknowledged factors such as the microbiome and extracellular vesicles, play a crucial role in modifications to the stroma and extracellular matrix (ECM). These modifications ultimately lead to an alteration of the mechanical properties (stiffness) of the tissue, contributing to disease onset and progression. We describe here classic and emerging mediators of ECM remodeling, and discuss state-of-the-art studies characterizing mechanical fingerprints of urological diseases, showing a general trend between increased tissue stiffness and severity of disease. Finally, we point to the clinical potential of tissue stiffness as a diagnostic and prognostic factor in the urological field, as well as a possible target for new innovative drugs.

Optical Microscopy and the Extracellular Matrix Structure: A Review

Biological tissues are not uniquely composed of cells. A substantial part of their volume is extracellular space, which is primarily filled by an intricate network of macromolecules constituting the extracellular matrix (ECM). The ECM serves as the scaffolding for tissues and organs throughout the body, playing an essential role in their structural and functional integrity. Understanding the intimate interaction between the cells and their structural microenvironment is central to our understanding of the factors driving the formation of normal versus remodelled tissue, including the processes involved in chronic fibrotic diseases. The visualization of the ECM is a key factor to track such changes successfully. This review is focused on presenting several optical imaging microscopy modalities used to characterize different ECM components. In this review, we describe and provide examples of applications of a vast gamut of microscopy techniques, such as widefield fluorescence, total internal reflection fluorescence, laser scanning confocal microscopy, multipoint/slit confocal microscopy, two-photon excited fluorescence (TPEF), second and third harmonic generation (SHG, THG), coherent anti-Stokes Raman scattering (CARS), fluorescence lifetime imaging microscopy (FLIM), structured illumination microscopy (SIM), stimulated emission depletion microscopy (STED), ground-state depletion microscopy (GSD), and photoactivated localization microscopy (PALM/fPALM), as well as their main advantages, limitations.

Examining lysyl oxidase-like modulation of collagen architecture in 3D spheroid models of idiopathic pulmonary fibrosis via second-harmonic generation microscopy

SIGNIFICANCE

Idiopathic pulmonary fibrosis (IPF) patients have a poor prognosis with short lifespan following diagnosis as there are limited effective treatment options. Despite matrix stiffening being the hallmark of the disease there remains a lack of knowledge surrounding the underlying collagen alterations in the disease. Specifically, while increased collagen crosslinking has been implicated, the resulting effects on collagen macro/supramolecular changes have not been explored.

AIM

We sought to determine if second-harmonic generation (SHG) microscopy could characterize differences in the collagen architecture in 3D spheroid models of IPF grown under different crosslinking modulation conditions (promotion and inhibition).

APPROACH

We used SHG metrics based on the fiber morphology, relative SHG brightness, and macro/supramolecular structure by SHG polarization analyses to compare the structure of the IPF spheroids.

RESULTS

Comparison of the fiber morphology of the spheroids showed that the control group had the longest, straightest, and thickest fibers. The spheroids with crosslink enhancement and inhibition had the highest and lowest SHG conversion efficiencies, respectively, consistent with the resulting harmonophore density. SHG polarization analyses showed that the peptide pitch angle, alignment of collagen molecules, and overall chirality were altered upon crosslink modulation and were also consistent with reduced organization relative to the control group.

CONCLUSIONS

While no single SHG signature is associated with crosslinking, we show that the suite of metrics used here is effective in delineating alterations across the collagen architecture sizescales. The results largely mirror those of human tissues and demonstrate that the combination of 3D spheroid models and SHG analysis is a powerful approach for hypothesis testing the roles of operative cellular and molecular factors in IPF.

Emerging technologies provide insights on cancer extracellular matrix biology and therapeutics

Recent engineering technologies have transformed traditional perspectives of cancer to include the important role of the extracellular matrix (ECM) in recapitulating the malignant behaviors of cancer cells. Novel biomaterials and imaging technologies have advanced our understanding of the role of ECM density, structure, mechanics, and remodeling in tumor cell-ECM interactions in cancer biology and have provided new approaches in the development of cancer therapeutics. Here, we review emerging technologies in cancer ECM biology and recent advances in engineered systems for evaluating cancer therapeutics and provide new perspectives on how engineering tools present an opportunity for advancing the modeling and treatment of cancer. This review offers the cell biology and cancer cell biology communities insight into how engineering tools can improve our understanding of cancer ECM biology and therapeutic development.

Multiphoton tomography in differentiation of morphological and molecular subtypes of breast cancer: A quantitative analysis

In this study multiphoton tomography, based on second harmonic generation (SHG), and two-photon-excited fluorescence (TPEF) was used to visualize both the extracellular matrix and tumor cells in different morphological and molecular subtypes of human breast cancer. It was shown, that quantified assessment of the SHG based imaging data has great potential to reveal differences of collagen quantity, organization and uniformity in both low- and highly- aggressive invasive breast cancers. The values of quantity and uniformity of the collagen fibers distribution were significantly higher in low-aggressive breast cancer compared to the highly-aggressive subtypes, while the value representing collagen organization was lower in the former type. Additionally, it was shown, that TPEF detection of elastin fibers and amyloid protein may be used as a biomarker of detection the low-aggressive breast cancer subtype. Thus, TPEF/SHG imaging offers the possibility of becoming a useful tool for the rapid diagnosis of various subtypes of breast cancer during biopsy as well as for the intraoperative determinination of tumor-positive resection margins.

Tumor extracellular matrix: lessons from the second-harmonic generation microscopy

Extracellular matrix (ECM) represents more than a mere intercellular cement. It is physiologically active in cell communication, adhesion and proliferation. Collagen is the most abundant protein, making up to 90% of ECM, and 30% of total protein weight in humans. Second-harmonic generation (SHG) microscopy represents an important tool to study collagen organization of ECM in freshly unfixed tissues and paraffin-embedded tissue samples. This manuscript aims to review some of the applications of SHG microscopy in Oncologic Pathology, mainly in the study of ECM of epithelial tumors. It is shown how collagen parameters measured by this technique can aid in the differential diagnosis and in prognostic stratification. There is a tendency to associate higher amount, lower organization and higher linearity of collagen fibers with tumor progression and metastasizing. These represent complex processes, in which matrix remodeling plays a central role, together with cancer cell genetic modifications. Integration of studies on cancer cell biology and ECM are highly advantageous to give us a more complete picture of these processes. As microscopic techniques provide topographic information allied with biologic characteristics of tissue components, they represent important tools for a more complete understanding of cancer progression. In this context, SHG has provided significant insights in human tumor specimens, readily available for Pathologists.

Applying limiting entropy to quantify the alignment of collagen fibers by polarized light imaging

Collagen alignment has shown clinical significance in a variety of diseases. For instance, vulvar lichen sclerosus (VLS) is characterized by homogenization of collagen fibers with increasing risk of malignant transformation. To date, a variety of imaging techniques have been developed to visualize collagen fibers. However, few works focused on quantifying the alignment quality of collagen fiber. To assess the level of disorder of local fiber orientation, the homogeneity index (HI) based on limiting entropy is proposed as an indicator of disorder. Our proposed methods are validated by verification experiments on Poly Lactic Acid (PLA) filament phantoms with controlled alignment quality of fibers. A case study on 20 VLS tissue biopsies and 14 normal tissue biopsies shows that HI can effectively characterize VLS tissue from normal tissue (P < 0.01). The classification results are very promising with a sensitivity of 93% and a specificity of 95%, which indicated that our method can provide quantitative assessment for the alignment quality of collagen fibers in VLS tissue and aid in improving histopathological examination of VLS.

Recent Advancements in Optical Harmonic Generation Microscopy: Applications and Perspectives

Second harmonic generation (SHG) and third harmonic generation (THG) microscopies have emerged as powerful imaging modalities to examine structural properties of a wide range of biological tissues. Although SHG and THG arise from very different contrast mechanisms, the two are complimentary and can often be collected simultaneously using a modified multiphoton microscope. In this review, we discuss the needed instrumentation for these modalities as well as the underlying theoretical principles of SHG and THG in tissue and describe how these can be leveraged to extract unique structural information. We provide an overview of recent advances showing how SHG microscopy has been used to evaluate collagen alterations in the extracellular matrix and how this has been used to advance our knowledge of cancers, fibroses, and the cornea, as well as in tissue engineering applications. Specific examples using polarization-resolved approaches and machine learning algorithms are highlighted. Similarly, we review how THG has enabled developmental biology and skin cancer studies due to its sensitivity to changes in refractive index, which are ubiquitous in all cell and tissue assemblies. Lastly, we offer perspectives and outlooks on future directions of SHG and THG microscopies and present unresolved questions, especially in terms of overall miniaturization and the development of microendoscopy instrumentation.

Navigating the Collagen Jungle: The Biomedical Potential of Fiber Organization in Cancer

Recent research has highlighted the importance of key tumor microenvironment features, notably the collagen-rich extracellular matrix (ECM) in characterizing tumor invasion and progression. This led to great interest from both basic researchers and clinicians, including pathologists, to include collagen fiber evaluation as part of the investigation of cancer development and progression. Fibrillar collagen is the most abundant in the normal extracellular matrix, and was revealed to be upregulated in many cancers. Recent studies suggested an emerging theme across multiple cancer types in which specific collagen fiber organization patterns differ between benign and malignant tissue and also appear to be associated with disease stage, prognosis, treatment response, and other clinical features. There is great potential for developing image-based collagen fiber biomarkers for clinical applications, but its adoption in standard clinical practice is dependent on further translational and clinical evaluations. Here, we offer a comprehensive review of the current literature of fibrillar collagen structure and organization as a candidate cancer biomarker, and new perspectives on the challenges and next steps for researchers and clinicians seeking to exploit this information in biomedical research and clinical workflows.

Intratumoral heterogeneity of second-harmonic generation scattering from tumor collagen and its effects on metastatic risk prediction

Background

Metastases are the leading cause of breast cancer-related deaths. The tumor microenvironment impacts cancer progression and metastatic ability. Fibrillar collagen, a major extracellular matrix component, can be studied using the light scattering phenomenon known as second-harmonic generation (SHG). The ratio of forward- to backward-scattered SHG photons (F/B) is sensitive to collagen fiber internal structure and has been shown to be an independent prognostic indicator of metastasis-free survival time (MFS). Here we assess the effects of heterogeneity in the tumor matrix on the possible use of F/B as a prognostic tool.

Methods

SHG imaging was performed on sectioned primary tumor excisions from 95 untreated, estrogen receptor-positive, lymph node negative invasive ductal carcinoma patients. We identified two distinct regions whose collagen displayed different average F/B values, indicative of spatial heterogeneity: the cellular tumor bulk and surrounding tumor-stroma interface. To evaluate the impact of heterogeneity on F/B’s prognostic ability, we performed SHG imaging in the tumor bulk and tumor-stroma interface, calculated a 21-gene recurrence score (surrogate for OncotypeDX®, or S-ODX) for each patient and evaluated their combined prognostic ability.

Results

We found that F/B measured in tumor-stroma interface, but not tumor bulk, is prognostic of MFS using three methods to select pixels for analysis: an intensity threshold selected by a blinded observer, a histogram-based thresholding method, and an adaptive thresholding method. Using both regression trees and Random Survival Forests for MFS outcome, we obtained data-driven prediction rules that show F/B from tumor-stroma interface, but not tumor bulk, and S-ODX both contribute to predicting MFS in this patient cohort. We also separated patients into low-intermediate (S-ODX < 26) and high risk (S-ODX ≥26) groups. In the low-intermediate risk group, comprised of patients not typically recommended for adjuvant chemotherapy, we find that F/B from the tumor-stroma interface is prognostic of MFS and can identify a patient cohort with poor outcomes.

Conclusions

These data demonstrate that intratumoral heterogeneity in F/B values can play an important role in its possible use as a prognostic marker, and that F/B from tumor-stroma interface of primary tumor excisions may provide useful information to stratify patients by metastatic risk.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-020-07713-4.

Cancer Extracellular Matrix Proteins Regulate Tumour Immunity

The extracellular matrix (ECM) plays an increasingly recognised role in the development and progression of cancer. Whilst significant progress has been made in targeting aspects of the tumour microenvironment such as tumour immunity and angiogenesis, there are no therapies that address the cancer ECM. Importantly, immune function relies heavily on the structure, physics and composition of the ECM, indicating that cancer ECM and immunity are mechanistically inseparable. In this review we highlight mechanisms by which the ECM shapes tumour immunity, identifying potential therapeutic targets within the ECM. These data indicate that to fully realise the potential of cancer immunotherapy, the cancer ECM requires simultaneous consideration.

Expanded Chinese hamster organ and cell line proteomics profiling reveals tissue-specific functionalities

Chinese hamster ovary (CHO) cells are the predominant production vehicle for biotherapeutics. Quantitative proteomics data were obtained from two CHO cell lines (CHO-S and CHO DG44) and compared with seven Chinese hamster (Cricetulus griseus) tissues (brain, heart, kidney, liver, lung, ovary and spleen) by tandem mass tag (TMT) labeling followed by mass spectrometry, providing a comprehensive hamster tissue and cell line proteomics atlas. Of the 8470 unique proteins identified, high similarity was observed between CHO-S and CHO DG44 and included increases in proteins involved in DNA replication, cell cycle, RNA processing, and chromosome processing. Alternatively, gene ontology and pathway analysis in tissues indicated increased protein intensities related to important tissue functionalities. Proteins enriched in the brain included those involved in acidic amino acid metabolism, Golgi apparatus, and ion and phospholipid transport. The lung showed enrichment in proteins involved in BCAA catabolism, ROS metabolism, vesicle trafficking, and lipid synthesis while the ovary exhibited enrichments in extracellular matrix and adhesion proteins. The heart proteome included vasoconstriction, complement activation, and lipoprotein metabolism enrichments. These detailed comparisons of CHO cell lines and hamster tissues will enhance understanding of the relationship between proteins and tissue function and pinpoint potential pathways of biotechnological relevance for future cell engineering.

Non-disruptive collagen characterization in clinical histopathology using cross-modality image synthesis

The importance of fibrillar collagen topology and organization in disease progression and prognostication in different types of cancer has been characterized extensively in many research studies. These explorations have either used specialized imaging approaches, such as specific stains (e.g., picrosirius red), or advanced and costly imaging modalities (e.g., second harmonic generation imaging (SHG)) that are not currently in the clinical workflow. To facilitate the analysis of stromal biomarkers in clinical workflows, it would be ideal to have technical approaches that can characterize fibrillar collagen on standard H&E stained slides produced during routine diagnostic work. Here, we present a machine learning-based stromal collagen image synthesis algorithm that can be incorporated into existing H&E-based histopathology workflow. Specifically, this solution applies a convolutional neural network (CNN) directly onto clinically standard H&E bright field images to extract information about collagen fiber arrangement and alignment, without requiring additional specialized imaging stains, systems or equipment.

Tumor-associated collagen signatures: pushing tumor boundaries

In 2006, a new model of invasive breast tumor emerged and, since 2011, is gaining recognition and research momentum. "Tumor-associated collagen signatures" describe 3 distinct layers of collagen which radiate outward in shells from the main body of the tumor. The outermost layer (TACS3) features branches of collagen radiating away from the tumor, 90° perpendicular to the tumor surface. TACS3 increases tumor span and correlates directly with metastasis, though presently difficult to detect in breast tissue. TACS is an emerging model but has been validated by multiple labs in vitro and in vivo, specifically for breast cancer prognostics. Newly recognized and accepted tumor borders will impact both R0 resections and downstream surgical reconstruction. This review aims to comprehensively introduce and connect the ranging literature on linearized collagen of invasive tumor borders. Using PubMed keyword searches containing "aligned," "linear," "oriented," and "organized," we have gathered the studies on TACS, integrated the concept into the clinic, and projected future platforms.

The Physical Microenvironment of Tumors: Characterization and Clinical Impact

The tumor microenvironment plays a critical role in tumorigenesis and metastasis. As tightly controlled extracellular matrix homeostasis is lost during tumor progression, a dysregulated extracellular matrix can significantly alter cellular phenotype and drive malignancy. Altered physical properties of the tumor microenvironment alter cancer cell behavior, limit delivery and efficacy of therapies, and correlate with tumorigenesis and patient prognosis. The physical features of the extracellular matrix during tumor progression have been characterized; however, a wide range of methods have been used between studies and cancer types resulting in a large range of reported values. Here, we discuss the significant mechanical and structural properties of the tumor microenvironment, summarizing their reported values and clinical impact across cancer type and grade. We attempt to integrate the values in the literature to identify sources of reported differences and commonalities to better understand how aberrant extracellular matrix dynamics contribute to cancer progression. An intimate understanding of altered matrix properties during malignant transformation will be crucial in effectively detecting, monitoring, and treating cancer.

Label-free characterization of collagen fibers in cancerous esophagus tissues using ratiometric nonlinear optical microscopy

IMPACT STATEMENT

The issue of classifying esophageal cancer at various developmental stages is crucial for determining the optimized treatment protocol for the patients, as well as the prognosis. Precision improvement in staging esophageal cancer keeps seeking quantitative and analytical imaging methods that could augment histopathological techniques. In this work, we used nonlinear optical microscopy for ratiometric analysis on the intrinsic signal of two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) from single collagen fibers only in submucosa of esophageal squamous cell carcinoma (ESCC). The blind tests of TPEF/SHG and forward (F)/backward (B) SHG were demonstrated to compare with the histology conclusion. The discussion of sensitivity and specificity was provided via statistical comparison between the four stages of esophageal cancer. To the best of our knowledge, this is the first study of using these two ratios in combination for staging ESCC.