Role of Collagen Fiber Morphology on Ovarian Cancer Cell Migration Using Image-Based Models of the Extracellular Matrix

Tumor-associated fibrosis: a unique mechanism promoting ovarian cancer metastasis and peritoneal dissemination

Epithelial ovarian cancer (EOC) is often diagnosed in advanced stage with peritoneal dissemination. Recent studies indicate that aberrant accumulation of collagen fibers in tumor stroma has a variety of effects on tumor progression. We refer to remodeled fibrous stroma with altered expression of collagen molecules, increased stiffness, and highly oriented collagen fibers as tumor-associated fibrosis (TAF). TAF contributes to EOC cell invasion and metastasis in the intraperitoneal cavity. However, an understanding of molecular events involved is only just beginning to emerge. Further development in this field will lead to new strategies to treat EOC. In this review, we focus on the recent findings on how the TAF contributes to EOC malignancy. Furthermore, we will review the recent initiatives and future therapeutic strategies for targeting TAF in EOC.

Ovarian cancer identification technology based on deep learning and second harmonic generation imaging

Ovarian cancer is among the most common gynecological cancers and the eighth leading cause of cancer-related deaths among women worldwide. Surgery is among the most important options for cancer treatment. During surgery, a biopsy is generally required to screen for lesions; however, traditional case examinations are time consuming and laborious and require extensive experience and knowledge from pathologists. Therefore, this study proposes a simple, fast, and label-free ovarian cancer diagnosis method that combines second harmonic generation (SHG) imaging and deep learning. Unstained fresh human ovarian tissues were subjected to SHG imaging and accurately characterized using the Pyramid Vision Transformer V2 (PVTv2) model. The results showed that the SHG imaged collagen fibers could quantify ovarian cancer. In addition, the PVTv2 model could accurately differentiate the 3240 SHG images obtained from our imaging collection into benign, normal, and malignant images, with a final accuracy of 98.4%. These results demonstrate the great potential of SHG imaging techniques combined with deep learning models for diagnosing the diseased ovarian tissues.

Multicompartmentalized microvascularized tumor-on-a-chip to study tumor-stroma interactions and drug resistance in ovarian cancer

Introduction

The majority of ovarian cancer (OC) patients receiving standard of care chemotherapy develop chemoresistance within 5 years. The tumor microenvironment (TME) is a dynamic and influential player in disease progression and therapeutic response. However, there is a lack of models that allow us to elucidate the compartmentalized nature of TME in a controllable, yet physiologically relevant manner and its critical role in modulating drug resistance.

Methods

We developed a 3D microvascularized multiniche tumor-on-a-chip formed by five chambers (central cancer chamber, flanked by two lateral stromal chambers and two external circulation chambers) to recapitulate OC-TME compartmentalization and study its influence on drug resistance. Stromal chambers included endothelial cells alone or cocultured with normal fibroblasts or cancer-associated fibroblasts (CAF).

Results

The tumor-on-a-chip recapitulated spatial TME compartmentalization including vessel-like structure, stromal-mediated extracellular matrix (ECM) remodeling, generation of oxygen gradients, and delayed drug diffusion/penetration from the circulation chamber towards the cancer chamber. The cancer chamber mimicked metastasis-like migration and increased drug resistance to carboplatin/paclitaxel treatment in the presence of CAF when compared to normal fibroblasts. CAF-mediated drug resistance was rescued by ECM targeted therapy. Critically, these results demonstrate that cellular crosstalk recreation and spatial organization through compartmentalization are essential to determining the effect of the compartmentalized OC-TME on drug resistance.

Conclusions

Our results present a functionally characterized microvascularized multiniche tumor-on-a-chip able to recapitulate TME compartmentalization influencing drug resistance. This technology holds the potential to guide the design of more effective and targeted therapeutic strategies to overcome chemoresistance in OC.

A Perspective Review: Analyzing Collagen Alterations in Ovarian Cancer by High-Resolution Optical Microscopy

High-grade serous ovarian cancer (HGSOC) is the predominant subtype of ovarian cancer (OC), occurring in more than 80% of patients diagnosed with this malignancy. Histological and genetic analysis have confirmed the secretory epithelial of the fallopian tube (FT) as a major site of origin of HGSOC. Although there have been significant strides in our understanding of this disease, early stage detection and diagnosis are still rare. Current clinical imaging modalities lack the ability to detect early stage pathogenesis in the fallopian tubes and the ovaries. However, there are several microscopic imaging techniques used to analyze the structural modifications in the extracellular matrix (ECM) protein collagen in ex vivo FT and ovarian tissues that potentially can be modified to fit the clinical setting. In this perspective, we evaluate and compare the myriad of optical tools available to visualize these alterations and the invaluable insights these data provide on HGSOC initiation. We also discuss the clinical implications of these findings and how these data may help novel tools for early diagnosis of HGSOC.

The consequences of manipulating relaxin family peptide receptor 1 (RXFP1) level in ovarian cancer cells

Deregulation of the relaxin family peptide system (RFPS) appears to increase the risk of range of cancers, including epithelial ovarian cancers (EOC). The present study examines the effect of relaxin family peptide receptor 1 (RXFP1) level on the biological properties of human epithelial ovarian adenocarcinoma cells (OVCAR4 and SKOV3). RXFP1 was downregulated (RXFP1↓) in the cells using the RXFP1 sgRNA CRISPR All-in-One Lentivirus set (pLenti-U6-sgRNA-SFFV-Cas9-2A-Puro), and upregulated (RXFP1↑) using the RXFP1 CRISPRa sgRNA Lentivector (pLenti-U6-sgRNA-PGK-Neo) kit, which activates the RXFP1 gene when paired with dCas9-SAM. The changes taking place during adhesion to extracellular matrix (ECM) proteins were assessed in multi-well plates coated with collagen, fibronectin, laminin and gelatin. Cellular viability was monitored based on mitochondrial metabolic activity (MTT Assay, Alamar Blue Assay) and adenosine triphosphate production (ATP Assay). The rate of cell proliferation was determined based on the percentage of Ki67 immunoreactive cells and the numbers of cells in particular cell-cycle phases. The mesenchymal-like (Boyden Chamber Assay) and amoeboid-like movements (Wound Healing Assay) of ovarian cancer cells were also analyzed after transfection. RXFP1 downregulation decreased the adhesion properties of ovarian cancer cells and increased the tendency for apoptosis under stressful conditions. In contrast, RXFP1 upregulation had pro-proliferative, pro-survival and promigratory effects. Our findings confirm that the relaxin-2/RXFP1 signaling pathway plays a role in the promotion of growth and progression of ovarian cancer.

Microengineering 3D Collagen Matrices with Tumor-Mimetic Gradients in Fiber Alignment

Collagen fibers in the 3D tumor microenvironment (TME) exhibit complex alignment landscapes that are critical in directing cell migration through a process called contact guidance. Previous in vitro work studying this phenomenon has focused on quantifying cell responses in uniformly aligned environments. However, the TME also features short-range gradients in fiber alignment that result from cell-induced traction forces. Although the influence of graded biophysical taxis cues is well established, cell responses to physiological alignment gradients remain largely unexplored. In this work, fiber alignment gradients in biopsy samples are characterized and recreated using a new microfluidic biofabrication technique to achieve tunable sub-millimeter to millimeter scale gradients. This study represents the first successful engineering of continuous alignment gradients in soft, natural biomaterials. Migration experiments on graded alignment show that HUVECs exhibit increased directionality, persistence, and speed compared to uniform and unaligned fiber architectures. Similarly, patterned MDA-MB-231 aggregates exhibit biased migration toward increasing fiber alignment, suggesting a role for alignment gradients as a taxis cue. This user-friendly approach, requiring no specialized equipment, is anticipated to offer new insights into the biophysical cues that cells interpret as they traverse the extracellular matrix, with broad applicability in healthy and diseased tissue environments.

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.

Integrative evaluation and experimental validation of the immune-modulating potential of dysregulated extracellular matrix genes in high-grade serous ovarian cancer prognosis

BACKGROUND

High-grade serous ovarian cancer (HGSOC) is a challenging malignancy characterized by complex interactions between tumor cells and the surrounding microenvironment. Understanding the immune landscape of HGSOC, particularly the role of the extracellular matrix (ECM), is crucial for improving prognosis and guiding therapeutic interventions.

METHODS AND RESULTS

Using univariate Cox regression analysis, we identified 71 ECM genes associated with prognosis in seven HGSOC populations. The ECMscore signature, consisting of 14 genes, was validated using Cox proportional hazards regression with a lasso penalty. Cox regression analyses demonstrated that ECMscore is an excellent indicator for prognostic classification in prevalent malignancies, including HGSOC. Moreover, patients with higher ECMscores exhibited more active stromal and carcinogenic activation pathways, including apical surface signaling, Notch signaling, apical junctions, Wnt signaling, epithelial-mesenchymal transition, TGF-beta signaling, and angiogenesis. In contrast, patients with relatively low ECMscores showed more active immune-related pathways, such as interferon alpha response, interferon-gamma response, and inflammatory response. The relationship between the ECMscore and genomic anomalies was further examined. Additionally, the correlation between ECMscore and immune microenvironment components and signals in HGSOC was examined in greater detail. Moreover, the expression of MGP, COL8A2, and PAPPA and its correlation with FAP were validated using qRT-PCR on samples from HGSOC. The utility of ECMscore in predicting the prospective clinical success of immunotherapy and its potential in guiding the selection of chemotherapeutic agents were also explored. Similar results were obtained from pan-cancer research.

CONCLUSION

The comprehensive evaluation of the ECM may help identify immune activation and assist patients in HGSOC and even pan-cancer in receiving proper therapy.

Microengineering 3D Collagen Matrices with Tumor-Mimetic Gradients in Fiber Alignment

In the tumor microenvironment (TME), collagen fibers facilitate tumor cell migration through the extracellular matrix. Previous studies have focused on studying the responses of cells on uniformly aligned or randomly aligned collagen fibers. However, the in vivo environment also features spatial gradients in alignment, which arise from the local reorganization of the matrix architecture due to cell-induced traction forces. Although there has been extensive research on how cells respond to graded biophysical cues, such as stiffness, porosity, and ligand density, the cellular responses to physiological fiber alignment gradients have been largely unexplored. This is due, in part, to a lack of robust experimental techniques to create controlled alignment gradients in natural materials. In this study, we image tumor biopsy samples and characterize the alignment gradients present in the TME. To replicate physiological gradients, we introduce a first-of-its-kind biofabrication technique that utilizes a microfluidic channel with constricting and expanding geometry to engineer 3D collagen hydrogels with tunable fiber alignment gradients that range from sub-millimeter to millimeter length scales. Our modular approach allows easy access to the microengineered gradient gels, and we demonstrate that HUVECs migrate in response to the fiber architecture. We provide preliminary evidence suggesting that MDA-MB-231 cell aggregates, patterned onto a specific location on the alignment gradient, exhibit preferential migration towards increasing alignment. This finding suggests that alignment gradients could serve as an additional taxis cue in the ECM. Importantly, our study represents the first successful engineering of continuous gradients of fiber alignment in soft, natural materials. We anticipate that our user-friendly platform, which needs no specialized equipment, will offer new experimental capabilities to study the impact of fiber-based contact guidance on directed cell migration.

Basal cell adhesion molecule promotes metastasis-associated processes in ovarian cancer

BACKGROUND

Basal cell adhesion molecule (BCAM) is a laminin α5 (LAMA5) binding membrane-bound protein with a putative role in cancer. Besides full-length BCAM1, an isoform lacking most of the cytoplasmic domain (BCAM2), and a soluble form (sBCAM) of unknown function are known. In ovarian carcinoma (OC), all BCAM forms are abundant and associated with poor survival, yet BCAM's contribution to peritoneal metastatic spread remains enigmatic.

METHODS

Biochemical, omics-based and real-time cell assays were employed to identify the source of sBCAM and metastasis-related functions of different BCAM forms. OC cells, explanted omentum and a mouse model of peritoneal colonisation were used in loss- and gain-of-function experiments.

RESULTS

We identified ADAM10 as a major BCAM sheddase produced by OC cells and identified proteolytic cleavage sites proximal to the transmembrane domain. Recombinant soluble BCAM inhibited single-cell adhesion and migration identically to membrane-bound isoforms, confirming its biological activity in OC. Intriguingly, this seemingly anti-tumorigenic potential of BCAM contrasts with a novel pro-metastatic function discovered in the present study. Thus, all queried BCAM forms decreased the compactness of tumour cell spheroids by inhibiting LAMA5 - integrin β1 interactions, promoted spheroid dispersion in a three-dimensional collagen matrix, induced clearance of mesothelial cells at spheroid attachment sites in vitro and enhanced invasion of spheroids into omental tissue both ex vivo and in vivo.

CONCLUSIONS

Membrane-bound BCAM as well as sBCAM shed by ADAM10 act as decoys rather than signalling receptors to modulate metastasis-related functions. While BCAM appears to have tumour-suppressive effects on single cells, it promotes the dispersion of OC cell spheroids by regulating LAMA5-integrin-β1-dependent compaction and thereby facilitating invasion of metastatic target sites. As peritoneal dissemination is majorly mediated by spheroids, these findings offer an explanation for the association of BCAM with a poor clinical outcome of OC, suggesting novel therapeutic options.

Improved segmentation of collagen second harmonic generation images with a deep learning convolutional neural network

Collagen fibers play an important role in both the structure and function of various tissues in the human body. Visualization and quantitative measurements of collagen fibers are possible through imaging modalities such as second harmonic generation (SHG), but accurate segmentation of collagen fibers is difficult for datasets involving variable imaging depths due to the effects of scattering and absorption. Therefore, an objective approach to segmentation is needed for datasets with images of variable SHG intensity. In this study, a U-Net convolutional neural network (CNN) was trained to accurately segment collagen-positive pixels throughout SHG z-stacks. CNN performance was benchmarked against other common thresholding techniques, and was found to outperform intensity-based segmentation algorithms within an independent dataset, particularly at deeper imaging depths. These results indicate that a trained CNN can accurately segment collagen-positive pixels within a wide range of imaging depths, which is useful for quantitative SHG imaging in thick tissues.

Assessing the roles of collagen fiber morphology and matrix stiffness on ovarian cancer cell migration dynamics using multiphoton fabricated orthogonal image-based models

Ovarian cancer remains the deadliest of the gynecological cancers, where this arises from poor screening and imaging tools that can detect early disease, and also limited understanding of the structural and functional aspects of the tumor microenvironment. To gain insight into the underlying cellular dynamics, we have used multiphoton excited fabrication to create Second Harmonic Generation (SHG) image-based orthogonal models from collagen/GelMA that represent both the collagen matrix morphology and stiffness (∼2-8 kPa) of normal ovarian stroma and high grade serous ovarian cancers (HGSOC). These scaffolds are used to study migration/cytoskeletal dynamics of normal (IOSE) and ovarian cancer (OVCA433) cell lines. We found that the highly aligned fiber morphology of HGSOC promotes aspects of motility (motility coefficient, motility, and focal adhesion expression) through a contact guidance mechanism and that stiffer matrix further promotes these same processes through a mechanosensitive mechanism, where these trends were similar for both normal and cancer cells. However, cell specific differences were found on these orthogonal models relative to those providing only morphology, showing the importance of presenting both morphology and stiffness cues. Moreover, we found increased cadherin expression and decreased cell alignment only for cancer cells on scaffolds of intermediate modulus suggesting different stiffness-dependent mechanotransduction mechanisms are engaged. This overall approach affords decoupling the roles of matrix morphology, stiffness and cell genotype and affords hypothesis testing of the factors giving rise to disease progression and metastasis. Further, more established fabrication techniques cannot simultaneously reproduce both the 3D collagen fiber morphology and stiffness. STATEMENT OF SIGNIFICANCE: Ovarian cancer metastasizes when lesions are small, where cells exfoliate from the surface of the ovary and reattach at distal sites in the peritoneum. The adhesion/migration dynamics are not well understood and there is a need for new 3D in vitro models of the extracellular matrix to study the biology. Here we use multiphoton excited crosslinking to fabricate ECM orthogonal models that represent the collagen morphology and stiffness in human ovarian tissues. These are then used to study ovarian cancer cell migration dynamics and we found that contact guidance and a mechanosensitive response and cell genotype all combine to affect the behavior. These models provide insight into disease etiology and progression not readily possible by other fabrication methods.

Identification of human ovarian cancer relying on collagen fiber coverage features by quantitative second harmonic generation imaging

Ovarian cancer has the highest mortality rate among all gynecological cancers, containing complicated heterogeneous histotypes, each with different treatment plans and prognoses. The lack of screening test makes new perspectives for the biomarker of ovarian cancer of great significance. As the main component of extracellular matrix, collagen fibers undergo dynamic remodeling caused by neoplastic activity. Second harmonic generation (SHG) enables label-free, non-destructive imaging of collagen fibers with submicron resolution and deep sectioning. In this study, we developed a new metric named local coverage to quantify morphologically localized distribution of collagen fibers and combined it with overall density to characterize 3D SHG images of collagen fibers from normal, benign and malignant human ovarian biopsies. An overall diagnosis accuracy of 96.3% in distinguishing these tissue types made local and overall density signatures a sensitive biomarker of tumor progression. Quantitative, multi-parametric SHG imaging might serve as a potential screening test tool for ovarian cancer.

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.

Immune-and Metabolism-Associated Molecular Classification of Ovarian Cancer

Ovarian cancer (OV) is a complex gynecological disease, and its molecular characteristics are not clear. In this study, the molecular characteristics of OV subtypes based on metabolic genes were explored through the comprehensive analysis of genomic data. A set of transcriptome data of 2752 known metabolic genes was used as a seed for performing non negative matrix factorization (NMF) clustering. Three subtypes of OV (C1, C2 and C3) were found in analysis. The proportion of various immune cells in C1 was higher than that in C2 and C3 subtypes. The expression level of immune checkpoint genes TNFRSF9 in C1 was higher than that of other subtypes. The activation scores of cell cycle, RTK-RAS, Wnt and angiogenesis pathway and ESTIMATE immune scores in C1 group were higher than those in C2 and C3 groups. In the validation set, grade was significantly correlated with OV subtype C1. Functional analysis showed that the extracellular matrix related items in C1 subtype were significantly different from other subtypes. Drug sensitivity analysis showed that C2 subtype was more sensitive to immunotherapy. Survival analysis of differential genes showed that the expression of PXDN and CXCL11 was significantly correlated with survival. The results of tissue microarray immunohistochemistry showed that the expression of PXDN was significantly correlated with tumor size and pathological grade. Based on the genomics of metabolic genes, a new OV typing method was developed, which improved our understanding of the molecular characteristics of human OV.

Extracellular matrix as a driver for intratumoral heterogeneity

The architecture of an organ is built through interactions between its native cells and its connective tissue consisting of stromal cells and the extracellular matrix (ECM). Upon transformation through tumorigenesis, such interactions are disrupted and replaced by a new set of intercommunications between malignantly transformed parenchyma, an altered stromal cell population, and a remodeled ECM. In this perspective, we propose that the intratumoral heterogeneity of cancer cell phenotypes is an emergent property of such reciprocal intercommunications, both biochemical and mechanical-physical, which engender and amplify the diversity of cell behavioral traits. An attempt to assimilate such findings within a framework of phenotypic plasticity furthers our understanding of cancer progression.

A Systematic Comparative Assessment of the Response of Ovarian Cancer Cells to the Chemotherapeutic Cisplatin in 3D Models of Various Structural and Biochemical Configurations-Does One Model Type Fit All?

Simple Summary

Epithelial Ovarian Cancer is considered to be a ‘silent killer’ and a challenge for gynaecological health across the world due to its asymptotic nature in the early stages, its late-stage diagnosis, high recurrence rate and resistance to currently available treatment methods (chemotherapy). These disheartening figures highlight the need for extensive in vitro studies to better understand this disease. A number of in vitro 3D models are currently available to aid in the study of ovarian cancer and its response to therapeutic methods. In this work, we report, for the first time, a comprehensive comparative study of three widely used 3D in vitro models for ovarian cancer, along with chemotherapy assessment of primary and metastatic cells. Our study highlights the importance of selecting an appropriate 3D in vitro platform, which is based on multiple factors including the origin of cells used, experimental time period and experimental design, even for one specific disease.

Abstract

Epithelial Ovarian Cancer (EOC) is a silent, deadly and aggressive gynaecological disease with a relatively low survival rate. This has been attributed, to some extent, to EOC’s high recurrence rate and resistance to currently available platinum-based chemotherapeutic treatment methods. Multiple groups have studied and reported the effect of chemotherapeutic agents on various EOC 3D in vitro models. However, there are very few studies wherein a direct comparative study has been carried out between the different in vitro 3D models of EOC and the effect of chemotherapy within them. Herein, we report, for the first time, a direct comprehensive systematic comparative study of three different 3D in vitro platforms, namely (i) spheroids, (ii) synthetic PeptiGels/hydrogels of various chemical configurations and (iii) polymeric scaffolds with coatings of various extracellular matrices (ECMs) on the cell growth and response to the chemotherapeutic (Cisplatin) for ovary-derived (A2780) and metastatic (SK-OV-3) EOC cell lines. We report that all three 3D models are able to support the growth of EOC, but for different time periods (varying from 7 days to 4 weeks). We have also reported that chemoresistance to Cisplatin, in vitro, observed especially for metastatic EOC cells, is platform-dependent, in terms of both the structural and biochemical composition of the model/platform. Our study highlights the importance of selecting an appropriate 3D platform for in vitro tumour model development. We have demonstrated that the selection of the best platform for producing in vitro tumour models depends on the cancer/cell type, the experimental time period and the application for which the model is intended.

Quantitative Biophysical Metrics for Rapid Evaluation of Ovarian Cancer Metastatic Potential

Ovarian cancer is routinely diagnosed long after the disease has metastasized through the fibrous sub-mesothelium. Despite extensive research in the field linking ovarian cancer progression to increasingly poor prognosis, there are currently no validated cellular markers or hallmarks of ovarian cancer that can predict metastatic potential. To discern disease progression across a syngeneic mouse ovarian cancer progression model, here, we fabricated extracellular-matrix mimicking suspended fiber networks: crosshatches of mismatch diameters for studying protrusion dynamics, aligned same diameter networks of varying inter-fiber spacing for studying migration, and aligned nanonets for measuring cell forces. We found that migration correlated with disease, while force-disease biphasic relationship exhibited f-actin stress-fiber network dependence. However, unique to suspended fibers, coiling occurring at tips of protrusions and not the length or breadth of protrusions displayed strongest correlation with metastatic potential. To confirm that our findings were more broadly applicable beyond the mouse model, we repeated our studies in human ovarian cancer cell lines and found that the biophysical trends were consistent with our mouse model results. Altogether, we report complementary high throughput and high content biophysical metrics capable of identifying ovarian cancer metastatic potential on time scale of hours. [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text] [Media: see text].

Extracellular Vesicle Molecular Signatures Characterize Metastatic Dynamicity in Ovarian Cancer

BACKGROUND

Late-stage diagnosis of ovarian cancer, a disease that originates in the ovaries and spreads to the peritoneal cavity, lowers 5-year survival rate from 90% to 30%. Early screening tools that can: i) detect with high specificity and sensitivity before conventional tools such as transvaginal ultrasound and CA-125, ii) use non-invasive sampling methods and iii) longitudinally significantly increase survival rates in ovarian cancer are needed. Studies that employ blood-based screening tools using circulating tumor-cells, -DNA, and most recently tumor-derived small extracellular vesicles (sEVs) have shown promise in non-invasive detection of cancer before standard of care. Our findings in this study show the promise of a sEV-derived signature as a non-invasive longitudinal screening tool in ovarian cancer.

METHODS

Human serum samples as well as plasma and ascites from a mouse model of ovarian cancer were collected at various disease stages. Small extracellular vesicles (sEVs) were extracted using a commercially available kit. RNA was isolated from lysed sEVs, and quantitative RT-PCR was performed to identify specific metastatic gene expression.

CONCLUSION

This paper highlights the potential of sEVs in monitoring ovarian cancer progression and metastatic development. We identified a 7-gene panel in sEVs derived from plasma, serum, and ascites that overlapped with an established metastatic ovarian carcinoma signature. We found the 7-gene panel to be differentially expressed with tumor development and metastatic spread in a mouse model of ovarian cancer. The most notable finding was a significant change in the ascites-derived sEV gene signature that overlapped with that of the plasma-derived sEV signature at varying stages of disease progression. While there were quantifiable changes in genes from the 7-gene panel in serum-derived sEVs from ovarian cancer patients, we were unable to establish a definitive signature due to low sample number. Taken together our findings show that differential expression of metastatic genes derived from circulating sEVs present a minimally invasive screening tool for ovarian cancer detection and longitudinal monitoring of molecular changes associated with progression and metastatic spread.

Remodeling the ECM: Implications for Metastasis and Tumor Dormancy

While most primary tumors can be effectively treated, therapeutics fail to efficiently eliminate metastases. Metastases arise from cancer cells that leave the primary tumor and seed distant sites. Recent studies have shown that cancer cells disseminate early during tumor progression and can remain dormant for years before they resume growth. In these metastatic organs, cancer cells reside in microenvironments where they interact with other cells, but also with the extracellular matrix (ECM). The ECM was long considered to be an inert, non-cellular component of tissues, providing their architecture. However, in recent years, a growing body of evidence has shown that the ECM is a key driver of cancer progression, and it can exert effects on tumor cells, regulating their metastatic fate. ECM remodeling and degradation is required for the early steps of the metastatic cascade: invasion, tumor intravasation, and extravasation. Similarly, ECM molecules have been shown to be important for metastatic outgrowth. However, the role of ECM molecules on tumor dormancy and their contribution to the dormancy-supportive niches is not well understood. In this perspective article, we will summarize the current knowledge of ECM and its role in tumor metastasis and dormancy. We will discuss how a better understanding of the individual components of the ECM niche and their roles mediating the dormant state of disseminated tumor cells (DTCs) will advance the development of new therapies to target dormant cells and prevent metastasis outgrowth.

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.

Collagen Organization Does Not Influence T-Cell Distribution in Stroma of Human Pancreatic Cancer

Simple Summary

The excessive desmoplasia is the hallmark of human pancreatic cancer that influences the local T-cell-based immune response. In the present work, the stromal collagen organization in normal and malignant pancreatic tissues as well as its relationsship to T-cell distribution in pancreatic cancer were studied. It was found that differences in collagen organization do not change the spatial orientation of T-cell migration and do not influence the availability of tumor cells for T-cells. The results of the study do not support the concept of use of stroma collagen organization for improvement of spatial T-cell distribution in the tumor.

Abstract

The dominant intrastromal T-cell infiltration in pancreatic cancer is mainly caused by the contact guidance through the excessive desmoplastic reaction and could represent one of the obstacles to an effective immune response in this tumor type. This study analyzed the collagen organization in normal and malignant pancreatic tissues as well as its influence on T-cell distribution in pancreatic cancer. Human pancreatic tissue was analyzed using immunofluorescence staining and multiphoton and SHG microscopy supported by multistep image processing. The influence of collagen alignment on activated T-cells was studied using 3D matrices and time-lapse microscopy. It was found that the stroma of malignant and normal pancreatic tissues was characterized by complex individual organization. T-cells were heterogeneously distributed in pancreatic cancer and there was no relationship between T-cell distribution and collagen organization. There was a difference in the angular orientation of collagen alignment in the peritumoral and tumor-cell-distant stroma regions in the pancreatic ductal adenocarcinoma tissue, but there was no correlation in the T-cell densities between these regions. The grade of collagen alignment did not influence the directionality of T-cell migration in the 3D collagen matrix. It can be concluded that differences in collagen organization do not change the spatial orientation of T-cell migration or influence stromal T-cell distribution in human pancreatic cancer. The results of the present study do not support the rationale of remodeling of stroma collagen organization for improvement of T-cell–tumor cell contact in pancreatic ductal adenocarcinoma.

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.

A Nomogram Based on a Collagen Feature Support Vector Machine for Predicting the Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer Patients

BACKGROUND

The relationship between collagen features (CFs) in the tumor microenvironment and the treatment response to neoadjuvant chemoradiotherapy (nCRT) is still unknown. This study aimed to develop and validate a perdition model based on the CFs and clinicopathological characteristics to predict the treatment response to nCRT among locally advanced rectal cancer (LARC) patients.

METHODS

In this multicenter, retrospective analysis, 428 patients were included and randomly divided into a training cohort (299 patients) and validation cohort (129 patients) [7:3 ratio]. A total of 11 CFs were extracted from a multiphoton image of pretreatment biopsy, and a support vector machine (SVM) was then used to construct a CFs-SVM classifier. A prediction model was developed and presented with a nomogram using multivariable analysis. Further validation of the nomogram was performed in the validation cohort.

RESULTS

The CFs-SVM classifier, which integrated collagen area, straightness, and crosslink density, was significantly associated with treatment response. Predictors contained in the nomogram included the CFs-SVM classifier and clinicopathological characteristics by multivariable analysis. The CFs nomogram demonstrated good discrimination, with area under the receiver operating characteristic curves (AUROCs) of 0.834 in the training cohort and 0.854 in the validation cohort. Decision curve analysis indicated that the CFs nomogram was clinically useful. Moreover, compared with the traditional clinicopathological model, the CFs nomogram showed more powerful discrimination in determining the response to nCRT.

CONCLUSIONS

The CFs-SVM classifier based on CFs in the tumor microenvironment is associated with treatment response, and the CFs nomogram integrating the CFs-SVM classifier and clinicopathological characteristics is useful for individualized prediction of the treatment response to nCRT among LARC patients.

Multiscale anisotropy analysis of second-harmonic generation collagen imaging of mouse skin

SIGNIFICANCE

Morphological collagen signatures are important for tissue function, particularly in the tumor microenvironment. A single algorithmic framework with quantitative, multiscale morphological collagen feature extraction may further the use of collagen signatures in understanding fundamental tumor progression.

AIM

A modification of the 2D wavelet transform modulus maxima (WTMM) anisotropy method was applied to both digitally simulated collagen fibers and second-harmonic-generation imaged collagen fibers of mouse skin to calculate a multiscale anisotropy factor to detect collagen fiber organization.

APPROACH

The modified 2D WTMM anisotropy method was initially validated on synthetic calibration images to establish the robustness and sensitivity of the multiscale fiber organization tool. Upon validation, the algorithm was applied to collagen fiber organization in normal wild-type skin, melanoma stimulated skin, and integrin α10KO skin.

RESULTS

Normal wild-type skin collagen fibers have an increased anisotropy factor at all sizes scales. Interestingly, the multiscale anisotropy differences highlight important dissimilarities between collagen fiber organization in normal wild-type skin, melanoma stimulated, and integrin α10KO skin. At small scales (∼2 to 3  μm), the integrin α10KO skin was vastly different than normal skin (p-value  ∼  10  -  8), whereas the melanoma stimulated skin was vastly different than normal at large scales (∼30 to 40  μm, p-value  ∼  10  -  15).

CONCLUSIONS

This objective computational collagen fiber organization algorithm is sensitive to collagen fiber organization across multiple scales for effective exploration of collagen morphological alterations associated with melanoma and the lack of α10 integrin binding.

Engineered Collagen Matrices

Collagen is the most abundant protein in mammals, accounting for approximately one-third of the total protein in the human body. Thus, it is a logical choice for the creation of biomimetic environments, and there is a long history of using collagen matrices for various tissue engineering applications. However, from a biomaterial perspective, the use of collagen-only scaffolds is associated with many challenges. Namely, the mechanical properties of collagen matrices can be difficult to tune across a wide range of values, and collagen itself is not highly amenable to direct chemical modification without affecting its architecture or bioactivity. Thus, many approaches have been pursued to design scaffold environments that display critical features of collagen but enable improved tunability of physical and biological characteristics. This paper provides a brief overview of approaches that have been employed to create such engineered collagen matrices. Specifically, these approaches include blending of collagen with other natural or synthetic polymers, chemical modifications of denatured collagen, de novo creation of collagen-mimetic chains, and reductionist methods to incorporate collagen moieties into other materials. These advancements in the creation of tunable, engineered collagen matrices will continue to enable the interrogation of novel and increasingly complex biological questions.