Collagen organization of renal cell carcinoma differs between low and high grade tumors

Unbiased discovery of cancer pathways and therapeutics using Pathway Ensemble Tool and Benchmark

Correctly identifying perturbed biological pathways is a critical step in uncovering basic disease mechanisms and developing much-needed therapeutic strategies. However, whether current tools are optimal for unbiased discovery of relevant pathways remains unclear. Here, we create "Benchmark" to critically evaluate existing tools and find that most function sub-optimally. We thus develop the "Pathway Ensemble Tool" (PET), which outperforms existing methods. Deploying PET, we identify prognostic pathways across 12 cancer types. PET-identified prognostic pathways offer additional insights, with genes within these pathways serving as reliable biomarkers for clinical outcomes. Additionally, normalizing these pathways using drug repurposing strategies represents therapeutic opportunities. For example, the top predicted repurposed drug for bladder cancer, a CDK2/9 inhibitor, represses cell growth in vitro and in vivo. We anticipate that using Benchmark and PET for unbiased pathway discovery will offer additional insights into disease mechanisms across a spectrum of diseases, enabling biomarker discovery and therapeutic strategies.

Molecular mechanism of formation and destruction of a pseudo‑capsule in clear cell renal cell carcinoma

The process and molecular mechanisms underlying the formation and destruction of a pseudo-capsule (PC) in clear cell renal cell carcinoma (ccRCC) are poorly understood. In the present study, the PCs of surgical specimens from primary tumors and metastatic lesions in 169 patients with ccRCC, and carcinogen-induced ccRCC rat models were semi-quantified using the invasion of PC (i-Cap) score system. This was based on the relationship among the tumor, PC and adjacent normal tissue (NT) as follows: i-Cap 0, tumor has no PC and does not invade NT; i-Cap 1, tumor has a complete PC and does not invade into the PC; i-Cap 2, tumor with focal absences in the PC, which partially invades the PC but not completely through the PC; i-Cap 3, tumor crosses the PC and invades the NT; i-Cap 4, tumor directly invades the NT without a PC. The study suggested that PC formation was not observed without physical compression, and also revealed that tumor invasion into the PC was a prognostic factor for postoperative oncological outcomes. Higher i-Cap, Fuhrman grade and tumor size were independent poor prognostic factors for postoperative disease-free survival. mRNA expression arrays generated from carcinogen-induced ccRCC rat models were used to explore genes potentially associated with the formation and destruction of a PC. Subsequently, human ccRCC specimens were validated for four genes identified via expression array; the results revealed that collagen type 4A2, matrix metalloproteinase-7 and l-selectin were upregulated alongside the progression of i-Cap score. Conversely, endoglin was downregulated. In conclusion, the present study provides insights into the formation and destruction of a PC, and the results may aid the treatment and management of patients with ccRCC.

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.

Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumor-specificity and predictive potential of extracellular vesicles for cell invasion and proliferation - A meta-analysis

BACKGROUND

Although interest in the role of extracellular vesicles (EV) in oncology is growing, not all potential aspects have been investigated. In this meta-analysis, data regarding (i) the EV proteome and (ii) the invasion and proliferation capacity of the NCI-60 tumor cell lines (60 cell lines from nine different tumor types) were analyzed using machine learning methods.

METHODS

On the basis of the entire proteome or the proteins shared by all EV samples, 60 cell lines were classified into the nine tumor types using multiple logistic regression. Then, utilizing the Least Absolute Shrinkage and Selection Operator, we constructed a discriminative protein panel, upon which the samples were reclassified and pathway analyses were performed. These panels were validated using clinical data (n = 4,665) from Human Protein Atlas.

RESULTS

Classification models based on the entire proteome, shared proteins, and discriminative protein panel were able to distinguish the nine tumor types with 49.15%, 69.10%, and 91.68% accuracy, respectively. Invasion and proliferation capacity of the 60 cell lines were predicted with R2 = 0.68 and R2 = 0.62 (p < 0.0001). The results of the Reactome pathway analysis of the discriminative protein panel suggest that the molecular content of EVs might be indicative of tumor-specific biological processes.

CONCLUSION

Integrating in vitro EV proteomic data, cell physiological characteristics, and clinical data of various tumor types illuminates the diagnostic, prognostic, and therapeutic potential of EVs. Video Abstract.

Comprehensive analysis of single-cell RNA-seq and bulk RNA-seq revealed the heterogeneity and convergence of the immune microenvironment in renal cell carcinoma

Substantial progress has been made in cancer biology and treatment in recent years, but the clinical outcome of patients with renal cell carcinoma (RCC) remains unsatisfactory. The tumor microenvironment (TME) is a potential target. By analyzing single-cell RNA sequencing (sc-RNAseq) data from six RCC tumor samples, this study identified 11 different cell types in the RCC cellular microenvironment, indicating a high degree of intratumoral heterogeneity. Through re-dimensionality reduction clustering of epithelial cells, neutrophils, macrophages, and T cells, we deeply reveal differences in the RCC tumor microenvironment. By analyzing differentially expressed genes in normal epithelial cells and malignant epithelial cells, we identify RNASET2 and GATM as potential prognostic biomarkers in RCC. In addition, by transcriptional factor analysis, we found significant differences in the expression of GZMK-CD8 T cell and B cell transcription factors between cancer tissues and normal tissues. By cell correlation analysis, we found significant correlations between neutrophils and macrophages and between IL7R-CD4 T cells and T regulatory (Treg) cells in RCC, which may be involved in the formation of immune TMEs. By cell developmental trajectory analysis, we showed that macrophages may be derived from neutrophils, whereas Treg cells may be derived from IL7R-CD4 T cells. By cell communication analysis, we found a clear interaction between macrophages and endothelial cells, neutrophils, and GZMK-CD8 T cells. In addition, we found that ADGRE5 signaling was mainly derived from mast cells and GZMK-CD8 T cells, and had a significant communication effect with neutrophils. The COLLAGEN signaling pathway is mainly derived from fibroblasts and has a significant communication effect with mast cells. Finally, we verified that RNASET2, which is highly expressed in epithelial cells, promotes proliferation and migration of RCC in vitro. RNASET2 is likely to be a potential target for renal cell carcinoma therapy. The results based on sc-RNAseq data analysis help to further elucidate the cellular microenvironment of RCC and provide help for cancer heterogeneity studies. This will help to provide more accurate personalized treatment for patients in clinical diagnosis.

Prostate cancer tissue classification by multiphoton imaging, automated image analysis and machine learning

Prostate carcinoma, a slow-growing and often indolent tumour, is the second most commonly diagnosed cancer among men worldwide. The prognosis is mainly based on the Gleason system through prostate biopsy analysis. However, new treatment and monitoring strategies depend on a more precise diagnosis. Here, we present results by multiphoton imaging for prostate tumour samples from 120 patients that allow to obtain quantitative parameters leading to specific tumour aggressiveness signatures. An automated image analysis was developed to recognise and quantify stromal fibre and neoplastic cell regions in each image. The set of metrics was able to distinguish between non-neoplastic tissue and carcinoma areas by linear discriminant analysis and random forest with accuracy of 89% ± 3%, but between Gleason groups of only 46% ± 6%. The reactive stroma analysis improved the accuracy to 65% ± 5%, clearly demonstrating that stromal parameters should be considered as additional criteria for a more accurate diagnosis.

Identification of different types of tumors based on photoacoustic spectral analysis: preclinical feasibility studies on skin tumors

Significance

Collagen and lipid are important components of tumor microenvironments (TME) and participates in tumor development and invasion. It has been reported that collagen and lipid can be used as a hallmark to diagnosis and differentiate tumors.

Aim

We aim to introduce photoacoustic spectral analysis (PASA) method that can provide both the content and structure distribution of endogenous chromophores in biological tissues to characterize the tumor-related features for identifying different types of tumors.

Approach

Ex vivo human tissues with suspected squamous cell carcinoma (SCC), suspected basal cell carcinoma (BCC), and normal tissue were used in this study. The relative lipid and collagen contents in the TME were assessed based on the PASA parameters and compared with histology. Support vector machine (SVM), one of the simplest machine learning tools, was applied for automatic skin cancer type detection.

Results

The PASA results showed that the lipid and collagen levels of the tumors were significantly lower than those of the normal tissue, and there was a statistical difference between SCC and BCC (p < 0.05 ), consistent with the histopathological results. The SVM-based categorization achieved diagnostic accuracies of 91.7% (normal), 93.3% (SCC), and 91.7% (BCC).

Conclusions

We verified the potential use of collagen and lipid in the TME as biomarkers of tumor diversity and achieved accurate tumor classification based on the collagen and lipid content using PASA. The proposed method provides a new way to diagnose tumors.

Correlation Between Tumor-Associated Collagen Signature and Fibroblast Activation Protein Expression With Prognosis of Clear Cell Renal Cell Carcinoma Patient

Background

Despite recent promising findings from immunotherapy and other targeted medicines, individuals with metastatic clear cell renal cell carcinoma (mCCRCC) still have a poor prognosis. Biomarkers associated with metastatic status in CCRCC are important for early detection and for the identification of new therapeutic targets. The expression of fibroblast activation protein (FAP) is associated with the development of early metastases and worse cancer-specific survival. Tumor-associated collagen signature (TACS) is a type of collagen that develops during tumor growth and is associated with tumor invasion.

Methods

Twenty-six mCCRCC patients that underwent nephrectomy were admitted to this study. Data regarding age, sex, Fuhrman's grade, tumor diameter, staging, FAP expression, and TACS grading were collected. Spearman rho test was used to correlate FAP expression and TACS grading in both primary tumors and metastases and with the patient's age and sex.

Results

FAP manifestation correlated positively with TACS degree (Spearman rho test r = 0.51; P = 0.0001). FAP was positive in 25 (96%) of all intratumor samples and positive in 22 (84%) of all stromal samples.

Conclusions

FAP can be used as a prognostic factor in mCCRCC; its presence can predict the aggressiveness of mCRCC and poorer outcome in the patient. Furthermore, TACS can also be used for the prediction of aggressiveness and metastasis due to the changes necessary for a tumor to invade other organs.

Quantitative Image Analysis of Fibrillar Collagens Reveals Novel Diagnostic and Prognostic Biomarkers and Histotype-dependent Aberrant Mechanobiology in Lung Cancer

Fibrillar collagens are the most abundant extracellular matrix components in non-small cell lung cancer (NSCLC). Yet, the potential of collagen fiber descriptors as a source of clinically-relevant biomarkers in NSCLC is mainly unknown. Likewise, our understanding of the aberrant collagen organization and associated tumor-promoting effects needs to be better defined. To address these limitations, we identified a digital pathology approach that can be easily implemented in pathology units based on the Curvelet Transform filtering and single Fiber Reconstruction (CT-FIRE) software analysis of picrosirius (PSR) stains of fibrillar collagens imaged with polarized light (PL). CT-FIRE settings were pre-optimized to assess a panel of collagen fiber descriptors in PSR-PL images of tissue microarrays from surgical NSCLC patients (106 adenocarcinomas (ADC), 89 squamous cell carcinomas (SCC)). Using this approach, we identified straightness as the single high-accuracy diagnostic collagen fiber descriptor (average area under the curve AUC = 0.92) and fiber density as the single descriptor consistently associated with poor prognosis in both ADC and SCC independently of the gold standard based on tumor size, lymph node involvement and metastasis (TNM) staging (Hazard ratio HR = 2.69 (1.55-4.66), p < 0.001). Moreover, we found that collagen fibers were markedly straighter, longer, and more aligned in tumors compared to paired samples from uninvolved pulmonary tissue, particularly in ADC, which is indicative of increased tumor stiffening. Consistently, we observed an increase in a panel of stiffness-associated processes in the high collagen fiber density patient group selectively in ADC, including venous/lymphatic invasion, fibroblast activation (alpha-smooth muscle actin (α-SMA)), and immune evasion (programmed death-ligand 1 (PD-L1)). Likewise, transcriptional correlation analysis supported the potential involvement of the major Yes-associated protein 1 (YAP)/TAZ mechanobiology pathway in ADC. Our results provide a proof-of-principle to use CT-FIRE analysis of PSR-PL images to assess new collagen fiber-based diagnostic and prognostic biomarkers in pathology units, which may improve the clinical management of surgical NSCLC patients. Our findings also unveil an aberrant stiff microenvironment in lung ADC that may foster immune evasion and dissemination, encouraging future work to identify therapeutic opportunities.

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.

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.

Intratumoral fibrosis and patterns of immune infiltration in clear cell renal cell carcinoma

Background

Intratumoral fibrosis was positively correlated with histological grade of renal clear cell carcinoma (ccRCC) and intratumoral inflammation. However, the association of intratumoral fibrosis with the immune infiltration of ccRCC was few evaluated.

Methods

We used the second harmonic generation (SHG)-based imaging technology and evaluated the intratumoral fibrosis in ccRCC, and then divided the patients into the high fibrosis group (HF) and the low fibrosis group (LF). Meanwhile, the Kaplan–Meier survival curve analysis was performed to analyze the relationship between intratumoral fibrosis and the disease-free survival rate. Antibody arrays were used for seeking difference in cytokines and immune infiltration between the HF group (N = 11) and LF group (N = 11). The selected immune infiltration marker was then verified by immunohistochemistry (IHC) staining in 45 ccRCC samples.

Results

Out of 640 cytokines and immune infiltration markers, we identified 115 proteins that were significantly different in quantity between ccRCC and adjacent normal tissues. In addition, the Venn diagram indicated that six proteins, including Cytotoxic T-Lymphocyte Associated Protein 4 (CTLA4), were significantly associated with intratumoral fibrosis (p < 0.05). The GO/KEGG enrichment analysis indicated that the proteins associated with intratumoral fibrosis were involved in the immunity and tumor-infiltrating lymphocytes. The expression of the CTLA4 was negatively correlated with collagen level, confirmed by IHC staining of CTLA4 (p < 0.05).

Conclusions

The study indicated that the intratumoral fibrosis level was negatively correlated with the expression of CTLA4 in the tumor immune microenvironment of the ccRCC, which posed the potential value of targeting the stroma of the tumor, a supplement to immunotherapy. However, the specific mechanism of this association is still unclear and needs further investigation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09765-0.

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.

An integrated approach to investigate age-related modifications of morphological, mechanical and structural properties of type I collagen

The main propose of this study is to characterize the impact of chronological aging on mechanical, structural, biochemical, and morphological properties of type I collagen. We have developed an original approach combining a stress-strain measurement device with a portable Raman spectrometer to enable simultaneous measurement of Raman spectra during stress vs strain responses of young adult, adult and old rat tail tendon fascicles (RTTFs). Our data showed an increase in all mechanical properties such as Young's modulus, yield strength, and ultimate tensile strength with aging. At the molecular level, Raman data revealed that the most relevant frequency shift was observed at 938 cm^-1 in Old RTTFs, which is assigned to the C-C. This suggested a long axis deformation of the peptide chains in Old RTTFs during tensile stress. In addition, the intensity of the band at 872 cm^-1, corresponding to hydroxyproline decreased for young adult RTTFs and increased for the adult ones, while it remained unchanged for Old RTTFs during tensile stress. The amide III band (1242 and 1265 cm^-1) as well as the band ratios I₁₆₃₁/ I₁₆₆₃ and I₁₆₄₅ / I₁₆₆₃ responses to tensile stress were depending on mechanical phases (toe, elastic and plastic). The quantification of advanced glycation end-products by LC-MS/MS and spectrofluorometry showed an increase in their content with aging. This suggested that the accumulation of such products was correlated to the alterations observed in the mechanical and molecular properties of RTTFs. Analysis of the morphological properties of RTTFs by SHG combined with CT-FIRE software revealed an increase in length and straightness of collagen fibers, whereas their width and wavy fraction decreased. Our integrated study model could be useful to provide additional translational information to monitor progression of diseases related to collagen remodeling in musculoskeletal disorders. STATEMENT OF SIGNIFICANCE: Type I collagen is the major component of the extracellular matrix. Its architectural and structural organization plays an important role in the mechanical properties of many tissues at the physiological and pathological levels. The objective of this work is to develop an integrated approach to bring a new insight on the impact of chronological aging on the structural organization and mechanical properties of type I collagen. We combined a portable Raman spectrometer with a mechanical tensile testing device in order to monitor in real time the changes in the Raman fingerprint of type I collagen fibers during the mechanical stress. Raman spectroscopy allowed the identification of the type I collagen bonds that were affected by mechanical stress in a differential manner with aging.

Label-free 3D characterization of cardiac fibrosis in muscular dystrophy using SHG imaging of cleared tissue

BACKGROUND INFORMATION

Duchenne muscular dystrophy (DMD) is a neuromuscular disease caused by mutations in the gene encoding dystrophin. It leads to repeated cycles of muscle fiber necrosis and regeneration and progressive replacement of fibers by fibrotic and adipose tissue, with consequent muscle weakness and premature death. Fibrosis and, in particular, collagen accumulation are important pathological features of dystrophic muscle. A better understanding of the development of fibrosis is crucial to enable better management of DMD. Three-dimensional (3D) characterization of collagen organization by second harmonic generation (SHG) microscopy has already proven a highly informative means of studying the fibrotic network in tissue.

RESULTS

Here, we combine for the first-time tissue clearing with SHG microscopy to characterize in depth the 3D cardiac fibrosis network from DMD^mdx rat model. Heart sections (1-mm-thick) from 1-year-old wild-type (WT) and DMD^mdx rats were cleared using the CUBIC protocol. SHG microscopy revealed significantly greater collagen deposition in DMD^mdx versus WT sections. Analyses revealed a specific pattern of SHG⁺ segmented objects in DMD^mdx cardiac muscle, characterized by a less elongated shape and increased density. Compared with the observed alignment of SHG⁺ collagen fibers in WT rats, profound fiber disorganization was observed in DMD^mdx rats, in which we observed two distinct SHG⁺ collagen fiber profiles, which may reflect two distinct stages of the fibrotic process in DMD.

CONCLUSION AND SIGNIFICANCE

The current work highlights the interest to combine multiphoton SHG microscopy and tissue clearing for 3D fibrosis network characterization in label free organ. It could be a relevant tool to characterize the fibrotic tissue remodeling in relation to the disease progression and/or to evaluate the efficacy of therapeutic strategies in preclinical studies in DMD model or others fibrosis-related cardiomyopathies diseases. This article is protected by copyright. All rights reserved.

Stromal architecture directs early dissemination in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDA) is an extremely metastatic and lethal disease. Here, in both murine and human PDA, we demonstrate that extracellular matrix architecture regulates cell extrusion and subsequent invasion from intact ductal structures through tumor-associated collagen signatures (TACS). This results in early dissemination from histologically premalignant lesions and continual invasion from well-differentiated disease, and it suggests TACS as a biomarker to aid in the pathologic assessment of early disease. Furthermore, we show that pancreatitis results in invasion-conducive architectures, thus priming the stroma prior to malignant disease. Analysis in potentially novel microfluidic-derived microtissues and in vivo demonstrates decreased extrusion and invasion following focal adhesion kinase (FAK) inhibition, consistent with decreased metastasis. Thus, data suggest that targeting FAK or strategies to reengineer and normalize tumor microenvironments may have roles not only in very early disease, but also for limiting continued dissemination from unresectable disease. Likewise, it may be beneficial to employ stroma-targeting strategies to resolve precursor diseases such as pancreatitis in order to remove stromal architectures that increase risk for early dissemination.

Aligned forces: Origins and mechanisms of cancer dissemination guided by extracellular matrix architecture

Organized extracellular matrix (ECM), in the form of aligned architectures, is a critical mediator of directed cancer cell migration by contact guidance, leading to metastasis in solid tumors. Current models suggest anisotropic force generation through the engagement of key adhesion and cytoskeletal complexes drives contact-guided migration. Likewise, disrupting the balance between cell-cell and cell-ECM forces, driven by ECM engagement for cells at the tumor-stromal interface, initiates and drives local invasion. Furthermore, processes such as traction forces exerted by cancer and stromal cells, spontaneous reorientation of matrix-producing fibroblasts, and direct binding of ECM modifying proteins lead to the emergence of collagen alignment in tumors. Thus, as we obtain a deeper understanding of the origins of ECM alignment and the mechanisms by which it is maintained to direct invasion, we are poised to use the new paradigm of stroma-targeted therapies to disrupt this vital axis of disease progression in solid tumors.

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.

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.

Engineering T cells to enhance 3D migration through structurally and mechanically complex tumor microenvironments

Defining the principles of T cell migration in structurally and mechanically complex tumor microenvironments is critical to understanding escape from antitumor immunity and optimizing T cell-related therapeutic strategies. Here, we engineered nanotextured elastic platforms to study and enhance T cell migration through complex microenvironments and define how the balance between contractility localization-dependent T cell phenotypes influences migration in response to tumor-mimetic structural and mechanical cues. Using these platforms, we characterize a mechanical optimum for migration that can be perturbed by manipulating an axis between microtubule stability and force generation. In 3D environments and live tumors, we demonstrate that microtubule instability, leading to increased Rho pathway-dependent cortical contractility, promotes migration whereas clinically used microtubule-stabilizing chemotherapies profoundly decrease effective migration. We show that rational manipulation of the microtubule-contractility axis, either pharmacologically or through genome engineering, results in engineered T cells that more effectively move through and interrogate 3D matrix and tumor volumes. Thus, engineering cells to better navigate through 3D microenvironments could be part of an effective strategy to enhance efficacy of immune therapeutics.

The mechanics of the migration of T cells into tumours is an important aspect of tumour immunity. Here the authors engineer complex 3D environments to explore functions of microtubules and cell contractility as strategies to enhance T cell migration in tumour microenvironments.

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.

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.

Peri-tumoural stroma collagen organization of invasive ductal carcinoma assessed by polarized light microscopy differs between OncotypeDX risk group

A commercially available genomic test, OncotypeDX has emerged as a useful postsurgical treatment guide for early stage breast cancer. Despite widespread clinical adoption, there remain logistical issues with its implementation. Collagenous stromal architecture has been shown to hold prognostic value that may complement OncotypeDX. Polarimetric analysis of breast cancer surgical samples allows for the quantification of collagenous stroma abundance and organization. We examine intratumoural collagen abundance and alignment along the tumor-host interface for 45 human samples of invasive ductal carcinoma categorized as low or higher risk by OncotypeDX. Furthermore, we probe the separatory power of collagen alignment patterns to classify unlabeled samples as low or higher OncotypeDX risk group using a linear discriminant (LD) model. No significant difference in mean collagen abundance was found between the two risk groups. However, collagen alignment along the tumor boundary was found to be significantly lower in higher risk samples. The LD model achieved a 71% total accuracy and 81% sensitivity to higher risk samples. Prognostic information extracted from the stromal morphology has potential to complement OncotypeDX as an easy-to-implement prescreening methodology.

Canine mammary cancer diagnosis from quantitative properties of nonlinear optical images

We present nonlinear microscopy imaging results and analysis from canine mammary cancer biopsies. Second harmonic generation imaging allows information of the collagen structure in the extracellular matrix that together with the fluorescence of the cell regions of the biopsies form a base for comprehensive image analysis. We demonstrate an automated image analysis method to classify the histological type of canine mammary cancer using a range of parameters extracted from the images. The software developed for image processing and analysis allows for the extraction of the collagen fibre network and the cell regions of the images. Thus, the tissue properties are obtained after the segmentation of the image and the metrics are measured specifically for the collagen and the cell regions. A linear discriminant analysis including all the extracted metrics allowed to clearly separate between the healthy and cancerous tissue with a 91%-accuracy. Also, a 61%-accuracy was achieved for a comparison of healthy and three histological cancer subtypes studied.

Intratumoral collagen signatures predict clinical outcomes in feline mammary carcinoma

Breast cancer is the most common cause of cancer-related deaths in women worldwide. Identification of reliable prognostic indicators and therapeutic targets is critical for improving patient outcome. Cancer in companion animals often strongly resembles human cancers and a comparative approach to identify prognostic markers can improve clinical care across species. Feline mammary tumors (FMT) serve as models for extremely aggressive triple negative breast cancer (TNBC) in humans, with high rates of local and distant recurrence after resection. Despite the aggressive clinical behavior of most FMT, current prognostic indicators are insufficient for accurately predicting outcome, similar to human patients. Given significant heterogeneity of mammary tumors, there has been a recent focus on identification of universal tumor-permissive stromal features that can predict biologic behavior and provide therapeutic targets to improve outcome. As in human and canine patients, collagen signatures appear to play a key role in directing mammary tumor behavior in feline patients. We find that patients bearing FMTs with denser collagen, as well as longer, thicker and straighter fibers and less identifiable tumor-stromal boundaries had poorer outcomes, independent of the clinical variables grade and surgical margins. Most importantly, including the collagen parameters increased the predictive power of the clinical model. Thus, our data suggest that similarities with respect to the stromal microenvironment between species may allow this model to predict outcome and develop novel therapeutic targets within the tumor stroma that would benefit both veterinary and human patients with aggressive mammary tumors.

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.

Multiphoton Microscopic Study of the Renal Cell Carcinoma Pseudocapsule: Implications for Tumour Enucleation

OBJECTIVE

To utilize Multiphoton Microscopy (MPM) as a novel imaging technique to characterize and quantify collagen at the Renal Cell Carcinoma Pseudocapsule, to assess for both intra-tumoral and inter-tumoral variation of collagen characteristics. MPM combines Second Harmonic Generation and Two Photon Excitation Fluorescence to image extracellular matrix architecture.

METHODS

Twenty partial nephrectomy specimen tissues were retrieved, cut into 5-micron sections, mounted on slides and deparaffinized. The pseudocapsules (PCs) were imaged with 2X and 20X objective at selected Regions of Interest. Corresponding clinical information was retrieved. PC thickness was determined. Collagen parameters measured included quantification by the Collagen Area Ratio, and qualitative measurements by the Collagen Fiber Density and Collagen Reticulation Index.

RESULTS

The boundaries between tumor, PC and normal renal parenchyma were distinguished by MPM without need for staining. In the thickest areas of the PC, collagen content and density were quantitatively higher compared to the thinnest areas. Median Collagen Area Ratio was higher in the thickest compared to the thinnest areas of the PC (P = .01). Clear Cell RCC specimens had a consistently higher Collagen Fiber Density in both the thickest and thinnest areas compared to non-Clear Cell RCC specimens (P = .02).

CONCLUSIONS

We demonstrated the ability of MPM to quantify collagen characteristics of PCs without fluorescent labeling. Tumor enucleation for Renal Cell Carcinoma along its PC remains debatable with regards to oncological safety. Even with a complete and intact PC, the PC is not a homogenous structure, and varies in its thickness and its collagen characteristics within, and between tumors.

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.

RUNX1 Is a Driver of Renal Cell Carcinoma Correlating with Clinical Outcome

The recurring association of specific genetic lesions with particular types of cancer is a fascinating and largely unexplained area of cancer biology. This is particularly true of clear cell renal cell carcinoma (ccRCC) where, although key mutations such as loss of VHL is an almost ubiquitous finding, there remains a conspicuous lack of targetable genetic drivers. In this study, we have identified a previously unknown protumorigenic role for the RUNX genes in this disease setting. Analysis of patient tumor biopsies together with loss-of-function studies in preclinical models established the importance of RUNX1 and RUNX2 in ccRCC. Patients with high RUNX1 (and RUNX2) expression exhibited significantly poorer clinical survival compared with patients with low expression. This was functionally relevant, as deletion of RUNX1 in ccRCC cell lines reduced tumor cell growth and viability in vitro and in vivo. Transcriptional profiling of RUNX1-CRISPR-deleted cells revealed a gene signature dominated by extracellular matrix remodeling, notably affecting STMN3, SERPINH1, and EPHRIN signaling. Finally, RUNX1 deletion in a genetic mouse model of kidney cancer improved overall survival and reduced tumor cell proliferation. In summary, these data attest to the validity of targeting a RUNX1-transcriptional program in ccRCC. SIGNIFICANCE: These data reveal a novel unexplored oncogenic role for RUNX genes in kidney cancer and indicate that targeting the effects of RUNX transcriptional activity could be relevant for clinical intervention in ccRCC.

Impact of Extracellular Matrix Components to Renal Cell Carcinoma Behavior

Renal cell carcinoma (RCC) represents the main renal tumors and are highly metastatic. They are heterogeneous tumors and are subdivided in 12 different subtypes where clear cell RCC (ccRCC) represents the main subtype. Tumor extracellular matrix (ECM) is composed, in RCC, mainly of different fibrillar collagens, fibronectin, and components of the basement membrane such as laminin, collagen IV, and heparan sulfate proteoglycan. Little is known about the role of these ECM components on RCC cell behavior. Analysis from The Human Protein Atlas dataset shows that high collagen 1 or 4A2, fibronectin, entactin, or syndecan 3 expression is associated with poor prognosis whereas high collagen 4A3, syndecan 4, or glypican 4 expression is associated with increased patient survival. We then analyzed the impact of collagen 1, fibronectin 1 or Matrigel on three different RCC cell lines (Renca, 786-O and Caki-2) in vitro. We found that all the different matrices have little effect on RCC cell proliferation. The three cell lines adhere differently on the three matrices, suggesting the involvement of a different set of integrins. Among the 3 matrices tested, collagen 1 is the only component able to increase migration in the three cell lines as well as MMP-2 and 9 activity. Moreover, collagen 1 induces MMP-2 mRNA expression and is implicated in the epithelial to mesenchymal transition of two RCC cell lines via Zeb2 (Renca) or Snail 2 (Caki-2) mRNA expression. Taken together, our results show that collagen 1 is the main component of the ECM that enhances tumor cell invasion in RCC, which is important for the metastasic process.

Fibrillar Collagen Quantification With Curvelet Transform Based Computational Methods

Quantification of fibrillar collagen organization has given new insight into the possible role of collagen topology in many diseases and has also identified candidate image-based bio-markers in breast cancer and pancreatic cancer. We have been developing collagen quantification tools based on the curvelet transform (CT) algorithm and have demonstrated this to be a powerful multiscale image representation method due to its unique features in collagen image denoising and fiber edge enhancement. In this paper, we present our CT-based collagen quantification software platform with a focus on new features and also giving a detailed description of curvelet-based fiber representation. These new features include C++-based code optimization for fast individual fiber tracking, Java-based synthetic fiber generator module for method validation, automatic tumor boundary generation for fiber relative quantification, parallel computing for large-scale batch mode processing, region-of-interest analysis for user-specified quantification, and pre- and post-processing modules for individual fiber visualization. We present a validation of the tracking of individual fibers and fiber orientations by using synthesized fibers generated by the synthetic fiber generator. In addition, we provide a comparison of the fiber orientation calculation on pancreatic tissue images between our tool and three other quantitative approaches. Lastly, we demonstrate the use of our software tool for the automatic tumor boundary creation and the relative alignment quantification of collagen fibers in human breast cancer pathology images, as well as the alignment quantification of in vivo mouse xenograft breast cancer images.

Intensity-based registration of bright-field and second-harmonic generation images of histopathology tissue sections

The use of second-harmonic generation (SHG) microscopy in biomedical research is rapidly increasing. This is due in large part to the wide spread interest of using this imaging technique to examine the role of fibrillar collagen organization in diseases such as cancer. The co-examination of SHG images and traditional bright-field (BF) images of hematoxylin and eosin (H&E) stained tissue as a gold standard clinical validation is usually required. However, image registration of these two modalities has been mostly done by manually selecting corresponding landmarks which is labor intensive and error prone. We designed, implemented, and validated the first image intensity-based registration method capable of automatically aligning SHG images and BF images. In our algorithmic approach, a feature extractor is used to pre-process the BF image to block the content features not visible in SHG images and the output image is then aligned with the SHG image by maximizing the common image features. An alignment matrix maximizing the image mutual information is found by evolutionary optimization and the optimization is facilitated using a hierarchical multiresolution framework. The automatic registration results were compared to traditional manual registration to assess the performance of the algorithm. The proposed algorithm has been successfully used in several biomedical studies such as pancreatic and kidney cancer studies and shown great efficacy.

Super-resolution recurrent convolutional neural networks for learning with multi-resolution whole slide images

We study a problem scenario of super-resolution (SR) algorithms in the context of whole slide imaging (WSI), a popular imaging modality in digital pathology. Instead of just one pair of high- and low-resolution images, which is typically the setup in which SR algorithms are designed, we are given multiple intermediate resolutions of the same image as well. The question remains how to best utilize such data to make the transformation learning problem inherent to SR more tractable and address the unique challenges that arises in this biomedical application. We propose a recurrent convolutional neural network model, to generate SR images from such multi-resolution WSI datasets. Specifically, we show that having such intermediate resolutions is highly effective in making the learning problem easily trainable and address large resolution difference in the low and high-resolution images common in WSI, even without the availability of a large size training data. Experimental results show state-of-the-art performance on three WSI histopathology cancer datasets, across a number of metrics.