Large-scale tumor-associated collagen signatures identify high-risk breast cancer patients

Tension directs cancer cell migration over fiber alignment through energy minimization

Cell migration during many fundamental biological processes including metastasis requires cells to traverse tissue with heterogeneous mechanical cues that direct migration as well as determine force and energy requirements for motility. However, the influence of discrete structural and mechanical cues on migration remains challenging to determine as they are often coupled. Here, we decouple the pro-invasive cues of collagen fiber alignment and tension to study their individual impact on migration. When presented with both cues, cells preferentially travel in the axis of tension against fiber alignment. Computational and experimental data show applying tension perpendicular to alignment increases potential energy stored within collagen fibers, lowering requirements for cell-induced matrix deformation and energy usage during migration compared to motility in the direction of fiber alignment. Energy minimization directs migration trajectory, and tension can facilitate migration against fiber alignment. These findings provide a conceptual understanding of bioenergetics during migration through a fibrous matrix.

Label-Free Monitoring of Endometrial Cancer Progression Using Multiphoton Microscopy

Endometrial cancer is the most common gynecological cancer in the developed world. However, the accuracy of current diagnostic methods is still unsatisfactory and time-consuming. Here, we presented an alternate approach to monitoring the progression of endometrial cancer via multiphoton microscopy imaging and analysis of collagen, which is often overlooked in current endometrial cancer diagnosis protocols but can offer a crucial signature in cancer biology. Multiphoton microscopy (MPM) based on the second-harmonic generation and two-photon excited fluorescence was introduced to visualize the microenvironment of endometrium in normal, hyperplasia without atypia, atypical hyperplasia, and endometrial cancer specimens. Furthermore, automatic image analysis based on the MPM image processing algorithm was used to quantify the differences in the collagen morphological features among them. MPM enables the visualization of the morphological details and alterations of the glands in the development process of endometrial cancer, including irregular changes in the structure of the gland, increased ratio of the gland to the interstitium, and atypical changes in the glandular epithelial cells. Moreover, the destructed basement membrane caused by gland proliferation and fusion is clearly shown in SHG images, which is a key feature for identifying endometrial cancer progression. Quantitative analysis reveals that the formation of endometrial cancer is accompanied by an increase in collagen fiber length and width, a progressive linearization and loosening of interstitial collagen, and a more random arrangement of interstitial collagen. Observation and quantitative analysis of interstitial collagen provide invaluable information in monitoring the progression of endometrial cancer. Label-free multiphoton imaging reported here has the potential to become an in situ histological tool for effective and accurate early diagnosis and detection of malignant lesions in endometrial cancer.

The importance of 3D fibre architecture in cancer and implications for biomaterial model design

The need for improved prediction of clinical response is driving the development of cancer models with enhanced physiological relevance. A new concept of 'precision biomaterials' is emerging, encompassing patient-mimetic biomaterial models that seek to accurately detect, treat and model cancer by faithfully recapitulating key microenvironmental characteristics. Despite recent advances allowing tissue-mimetic stiffness and molecular composition to be replicated in vitro, approaches for reproducing the 3D fibre architectures found in tumour extracellular matrix (ECM) remain relatively unexplored. Although the precise influences of patient-specific fibre architecture are unclear, we summarize the known roles of tumour fibre architecture, underlining their implications in cell-matrix interactions and ultimately clinical outcome. We then explore the challenges in reproducing tissue-specific 3D fibre architecture(s) in vitro, highlighting relevant biomaterial fabrication techniques and their benefits and limitations. Finally, we discuss imaging and image analysis techniques (focussing on collagen I-optimized approaches) that could hold the key to mapping tumour-specific ECM into high-fidelity biomaterial models. We anticipate that an interdisciplinary approach, combining materials science, cancer research and image analysis, will elucidate the role of 3D fibre architecture in tumour development, leading to the next generation of patient-mimetic models for mechanistic studies and drug discovery.

Mechanoimmunology in the solid tumor microenvironment

The tumor microenvironment (TME) is a complex and dynamic ecosystem that adjoins the cancer cells within solid tumors and comprises distinct components such as extracellular matrix, stromal and immune cells, blood vessels, and an abundance of signaling molecules. In recent years, the mechanical properties of the TME have emerged as critical determinants of tumor progression and therapeutic response. Aberrant mechanical cues, including altered tissue architecture and stiffness, contribute to tumor progression, metastasis, and resistance to treatment. Moreover, burgeoning immunotherapies hold great promise for harnessing the immune system to target and eliminate solid malignancies; however, their success is hindered by the hostile mechanical landscape of the TME, which can impede immune cell infiltration, function, and persistence. Consequently, understanding TME mechanoimmunology - the interplay between mechanical forces and immune cell behavior - is essential for developing effective solid cancer therapies. Here, we review the role of TME mechanics in tumor immunology, focusing on recent therapeutic interventions aimed at modulating the mechanical properties of the TME to potentiate T cell immunotherapies, and innovative assays tailored to evaluate their clinical efficacy.

Prognostic significance of collagen signatures in pancreatic ductal adenocarcinoma obtained from second-harmonic generation imaging

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) ranks among the deadliest types of cancer, and it will be meaningful to search for new biomarkers with prognostic value to help clinicians tailor therapeutic strategies.

METHODS

Here we tried to use an advanced optical imaging technique, multiphoton microscopy (MPM) combining second-harmonic generation (SHG) and two-photon excited fluorescence (TPEF) imaging, for the label-free detection of PDAC tissues from a cohort of 149 patients. An automated image processing method was used to extract collagen features from SHG images and the Kaplan-Meier survival analysis and Cox proportional hazards regression were used to assess the prognostic value of collagen signatures.

RESULTS

SHG images clearly show the different characteristics of collagen fibers in tumor microenvironment. We gained eight collagen morphological features, and a Feature-score was derived for each patient by the combination of these features using ridge regression. Statistical analyses reveal that Feature-score is an independent factor, and can predict the overall survival of PDAC patients as well as provide well risk stratification.

CONCLUSIONS

SHG imaging technique can potentially be a tool for the accurate diagnosis of PDAC, and this optical biomarker (Feature-score) may help clinicians make more approximate treatment decisions.

Improving the diagnosis of ductal carcinoma in situ with microinvasion without immunohistochemistry: An innovative method with H&E-stained and multiphoton microscopy images

Ductal carcinoma in situ with microinvasion (DCISM) is a challenging subtype of breast cancer with controversial invasiveness and prognosis. Accurate diagnosis of DCISM from ductal carcinoma in situ (DCIS) is crucial for optimal treatment and improved clinical outcomes. However, there are often some suspicious small cancer nests in DCIS, and it is difficult to diagnose the presence of intact myoepithelium by conventional hematoxylin and eosin (H&E) stained images. Although a variety of biomarkers are available for immunohistochemical (IHC) staining of myoepithelial cells, no single biomarker is consistently sensitive to all tumor lesions. Here, we introduced a new diagnostic method that provides rapid and accurate diagnosis of DCISM using multiphoton microscopy (MPM). Suspicious foci in H&E-stained images were labeled as regions of interest (ROIs), and the nuclei within these ROIs were segmented using a deep learning model. MPM was used to capture images of the ROIs in H&E-stained sections. The intensity of two-photon excitation fluorescence (TPEF) in the myoepithelium was significantly different from that in tumor parenchyma and tumor stroma. Through the use of MPM, the myoepithelium and basement membrane can be easily observed via TPEF and second-harmonic generation (SHG), respectively. By fusing the nuclei in H&E-stained images with MPM images, DCISM can be differentiated from suspicious small cancer clusters in DCIS. The proposed method demonstrated good consistency with the cytokeratin 5/6 (CK5/6) myoepithelial staining method (kappa coefficient = 0.818).

Computer-Aided Multiphoton Microscopy Diagnosis of 5 Different Primary Architecture Subtypes of Meningiomas

Meningiomas rank among the most common intracranial tumors, and surgery stands as the primary treatment modality for meningiomas. The precise subtyping and diagnosis of meningiomas, both before and during surgery, play a pivotal role in enabling neurosurgeons choose the optimal surgical program. In this study, we utilized multiphoton microscopy (MPM) based on 2-photon excited fluorescence and second-harmonic generation to identify 5 common meningioma subtypes. The morphological features of these subtypes were depicted using the MPM multichannel mode. Additionally, we developed 2 distinct programs to quantify collagen content and blood vessel density. Furthermore, the lambda mode of the MPM characterized architectural and spectral features, from which 3 quantitative indicators were extracted. Moreover, we employed machine learning to differentiate meningioma subtypes automatically, achieving high classification accuracy. These findings demonstrate the potential of MPM as a noninvasive diagnostic tool for meningioma subtyping and diagnosis, offering improved accuracy and resolution compared with traditional methods.

Biopathologic Characterization and Grade Assessment of Breast Cancer With 3-D Multiparametric Ultrasound Combining Shear Wave Elastography and Backscatter Tensor Imaging

OBJECTIVE

Despite recent improvements in medical imaging, the final diagnosis and biopathologic characterization of breast cancers currently still requires biopsies. Ultrasound is commonly used for clinical examination of breast masses. B-Mode and shear wave elastography (SWE) are already widely used to detect suspicious masses and differentiate benign lesions from cancers. But additional ultrasound modalities such as backscatter tensor imaging (BTI) could provide relevant biomarkers related to tissue organization. Here we describe a 3-D multiparametric ultrasound approach applied to breast carcinomas in the aims of (i) validating the ability of BTI to reveal the underlying organization of collagen fibers and (ii) assessing the complementarity of SWE and BTI to reveal biopathologic features of diagnostic interest.

METHODS

Three-dimensional SWE and BTI were performed ex vivo on 64 human breast carcinoma samples using a linear ultrasound probe moved by a set of motors. Here we describe a 3-D multiparametric representation of the breast masses and quantitative measurements combining B-mode, SWE and BTI.

RESULTS

Our results reveal for the first time that BTI can capture the orientation of the collagen fibers around tumors. BTI was found to be a relevant marker for assessing cancer stages, revealing a more tangent tissue orientation for in situ carcinomas than for invasive cancers. In invasive cases, the combination of BTI and SWE parameters allowed for classification of invasive tumors with respect to their grade with an accuracy of 95.7%.

CONCLUSION

Our results highlight the potential of 3-D multiparametric ultrasound imaging for biopathologic characterization of breast tumors.

Label-free assessment of pathological changes in pancreatic intraepithelial neoplasia by biomedical multiphoton microscopy

Pancreatic intraepithelial neoplasia (PanIN) is the most common precursor lesion that has the potential to progress to invasive pancreatic cancer, and early and rapid detection may offer patients a chance for treatment before the development of invasive carcinoma. Therefore, the identification of PanIN holds significant clinical importance. In this study, we first used multiphoton microscopy (MPM) combining two-photon excitation fluorescence and second-harmonic generation imaging to label-free detect PanIN and attempted to differentiate between normal pancreatic ducts and different grades of PanIN. Then, we also developed an automatic image processing strategy to extract eight morphological features of collagen fibers from MPM images to quantify the changes in collagen fibers surrounding the ducts. Experimental results demonstrate that the combination of MPM and quantitative information can accurately identify normal pancreatic ducts and different grades of PanIN. This study may contribute to the rapid diagnosis of pancreatic diseases and may lay the foundation for further clinical application of MPM.

Exploration of the relationship between tumor-infiltrating lymphocyte score and histological grade in breast cancer

BACKGROUND

The histological grade is an important factor in the prognosis of invasive breast cancer and is vital to accurately identify the histological grade and reclassify of Grade2 status in breast cancer patients.

METHODS

In this study, data were collected from 556 invasive breast cancer patients, and then randomly divided into training cohort (n = 335) and validation cohort (n = 221). All patients were divided into actual low risk group (Grade1) and high risk group (Grade2/3) based on traditional histological grade, and tumor-infiltrating lymphocyte score (TILs-score) obtained from multiphoton images, and the TILs assessment method proposed by International Immuno-Oncology Biomarker Working Group (TILs-WG) were also used to differentiate between high risk group and low risk group of histological grade in patients with invasive breast cancer. Furthermore, TILs-score was used to reclassify Grade2 (G2) into G2 /Low risk and G2/High risk. The coefficients for each TILs in the training cohort were retrieved using ridge regression and TILs-score was created based on the coefficients of the three kinds of TILs.

RESULTS

Statistical analysis shows that TILs-score is significantly correlated with histological grade, and is an independent predictor of histological grade (odds ratio [OR], 2.548; 95%CI, 1.648-3.941; P < 0.0001), but TILs-WG is not an independent predictive factor for grade (P > 0.05 in the univariate analysis). Moreover, the risk of G2/High risk group is higher than that of G2/Low risk group, and the survival rate of patients with G2/Low risk is similar to that of Grade1, while the survival rate of patients with G2/High risk is even worse than that of patients with G3.

CONCLUSION

Our results suggest that TILs-score can be used to predict the histological grade of breast cancer and potentially to guide the therapeutic management of breast cancer patients.

Nomograms integrating the collagen signature and systemic immune-inflammation index for predicting prognosis in rectal cancer patients

BACKGROUND

This study aimed to develop and validate a model based on the collagen signature and systemic immune-inflammation index to predict prognosis in rectal cancer patients who underwent neoadjuvant treatment.

METHODS

Patients with rectal cancer who had residual disease after neoadjuvant treatment at two Chinese institutions between 2010 and 2018 were selected, one used as a training cohort and the other as a validation cohort. In total, 142 fully quantitative collagen features were extracted using multiphoton imaging, and a collagen signature was generated by least absolute shrinkage and selection operator Cox regression. Nomograms were developed by multivariable Cox regression. The performance of the nomograms was assessed via calibration, discrimination and clinical usefulness. The outcomes of interest were overall survival and disease-free survival calculated at 1, 2 and 3 years.

RESULTS

Of 559 eligible patients, 421 were selected (238 for the training cohort and 183 for the validation cohort). The eight-collagen-features collagen signature was built and multivariable Cox analysis demonstrated that it was an independent prognostic factor of prognosis along with the systemic immune-inflammation index, lymph node status after neoadjuvant treatment stage and tumour regression grade. Then, two nomograms that included the four predictors were computed for disease-free survival and overall survival. The nomograms showed satisfactory discrimination and calibration with a C-index of 0.792 for disease-free survival and 0.788 for overall survival in the training cohort and 0.793 for disease-free survival and 0.802 for overall survival in the validation cohort. Decision curve analysis revealed that the nomograms could add more net benefit than the traditional clinical-pathological variables.

CONCLUSIONS

The study found that the collagen signature, systemic immune-inflammation index and nomograms were significantly associated with prognosis.

Molecular analysis of the extracellular microenvironment: from form to function

The extracellular matrix (ECM) proteome represents an important component of the tissue microenvironment that controls chemical flux and induces cell signaling through encoded structure. The analysis of the ECM represents an analytical challenge through high levels of post-translational modifications, protease-resistant structures, and crosslinked, insoluble proteins. This review provides a comprehensive overview of the analytical challenges involved in addressing the complexities of spatially profiling the extracellular matrix proteome. A synopsis of the process of synthesizing the ECM structure, detailing inherent chemical complexity, is included to present the scope of the analytical challenge. Current chromatographic and spatial techniques addressing these challenges are detailed. Capabilities for multimodal multiplexing with cellular populations are discussed with a perspective on developing a holistic view of disease processes that includes both the cellular and extracellular microenvironment.

Prognostic significance of collagen signatures at breast tumor boundary obtained by combining multiphoton imaging and imaging analysis

PURPOSE

Collagen features in breast tumor microenvironment is closely associated with the prognosis of patients. We aim to explore the prognostic significance of collagen features at breast tumor border by combining multiphoton imaging and imaging analysis.

METHODS

We used multiphoton microscopy (MPM) to label-freely image human breast tumor samples and then constructed an automatic classification model based on deep learning to identify collagen signatures from multiphoton images. We recognized three kinds of collagen signatures at tumor boundary (CSTB I-III) in a small-scale, and furthermore obtained a CSTB score for each patient based on the combined CSTB I-III by using the ridge regression analysis. The prognostic performance of CSTB score is assessed by the area under the receiver operating characteristic curve (AUC), Cox proportional hazard regression analysis, as well as Kaplan-Meier survival analysis.

RESULTS

As an independent prognostic factor, statistical results reveal that the prognostic performance of CSTB score is better than that of the clinical model combining three independent prognostic indicators, molecular subtype, tumor size, and lymph nodal metastasis (AUC, Training dataset: 0.773 vs. 0.749; External validation: 0.753 vs. 0.724; HR, Training dataset: 4.18 vs. 3.92; External validation: 4.98 vs. 4.16), and as an auxiliary indicator, it can greatly improve the accuracy of prognostic prediction. And furthermore, a nomogram combining the CSTB score with the clinical model is established for prognosis prediction and clinical decision making.

CONCLUSION

This standardized and automated imaging prognosticator may convince pathologists to adopt it as a prognostic factor, thereby customizing more effective treatment plans for patients.

Unveiling breast cancer metastasis through an advanced X-ray imaging approach

Breast cancer is a significant global health burden, causing a substantial number of deaths. Systemic metastatic tumour cell dissemination is a major cause of poor outcomes. Understanding the mechanisms underlying metastasis is crucial for effective interventions. Changes in the extracellular matrix play a pivotal role in breast cancer metastasis. In this work, we present an advanced multimodal X-ray computed tomography, by combining Small-angle X-ray Scattering Tensor Tomography (SAXS-TT) and X-ray Fluorescence Computed Tomography (XRF-CT). This approach likely brings out valuable information about the breast cancer metastasis cascade. Initial results from its application on a breast cancer specimen reveal the collective influence of key molecules in the metastatic mechanism, identifying a strong correlation between zinc accumulation (associated with matrix metalloproteinases MMPs) and highly oriented collagen. MMPs trigger collagen alignment, facilitating breast cancer cell intravasation, while iron accumulation, linked to angiogenesis and vascular endothelial growth factor VEGF, supports cell proliferation and metastasis. Therefore, these findings highlight the potential of the advanced multimodal X-ray computed tomography approach and pave the way for in-depth investigation of breast cancer metastasis, which may guide the development of novel therapeutic approaches and enable personalised treatment strategies, ultimately improving patient outcomes in breast cancer management.

Improving HCC Prognostic Models after Liver Resection by AI-Extracted Tissue Fiber Framework Analytics

Despite advances in diagnostic and treatment technologies, predicting outcomes of patients with hepatocellular carcinoma (HCC) remains a challenge. Prognostic models are further obscured by the variable impact of the tumor properties and the remaining liver parenchyma, often affected by cirrhosis or non-alcoholic fatty liver disease that tend to precede HCC. This study investigated the prognostic value of reticulin and collagen microarchitecture in liver resection samples. We analyzed 105 scanned tissue sections that were stained using a Gordon and Sweet's silver impregnation protocol combined with Picric Acid-Sirius Red. A convolutional neural network was utilized to segment the red-staining collagen and black linear reticulin strands, generating a detailed map of the fiber structure within the HCC and adjacent liver tissue. Subsequent hexagonal grid subsampling coupled with automated epithelial edge detection and computational fiber morphometry provided the foundation for region-specific tissue analysis. Two penalized Cox regression models using LASSO achieved a concordance index (C-index) greater than 0.7. These models incorporated variables such as patient age, tumor multifocality, and fiber-derived features from the epithelial edge in both the tumor and liver compartments. The prognostic value at the tumor edge was derived from the reticulin structure, while collagen characteristics were significant at the epithelial edge of peritumoral liver. The prognostic performance of these models was superior to models solely reliant on conventional clinicopathologic parameters, highlighting the utility of AI-extracted microarchitectural features for the management of HCC.

Dynamics of Fibril Collagen Remodeling by Tumor Cells: A Model of Tumor-Associated Collagen Signatures

Many solid tumors are characterized by a dense extracellular matrix (ECM) composed of various ECM fibril proteins. These proteins provide structural support and a biological context for the residing cells. The reciprocal interactions between growing and migrating tumor cells and the surrounding stroma result in dynamic changes in the ECM architecture and its properties. With the use of advanced imaging techniques, several specific patterns in the collagen surrounding the breast tumor have been identified in both tumor murine models and clinical histology images. These tumor-associated collagen signatures (TACS) include loosely organized fibrils far from the tumor and fibrils aligned either parallel or perpendicular to tumor colonies. They are correlated with tumor behavior, such as benign growth or invasive migration. However, it is not fully understood how one specific fibril pattern can be dynamically remodeled to form another alignment. Here, we present a novel multi-cellular lattice-free (MultiCell-LF) agent-based model of ECM that, in contrast to static histology images, can simulate dynamic changes between TACSs. This model allowed us to identify the rules of cell-ECM physical interplay and feedback that guided the emergence and transition among various TACSs.

Rapid label-free detection of early-stage lung adenocarcinoma and tumor boundary via multiphoton microscopy

Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer-related deaths in China. Rapid and precise evaluation of tumor tissue during lung cancer surgery can reduce operative time and improve negative-margin assessment, thus increasing disease-free and overall survival rates. This study aimed to explore the potential of label-free multiphoton microscopy (MPM) for imaging adenocarcinoma tissues, detecting histopathological features, and distinguishing between normal and cancerous lung tissues. We showed that second harmonic generation (SHG) signals exhibit significant specificity for collagen fibers, enabling the quantification of collagen features in lung adenocarcinomas. In addition, we developed a collagen score that could be used to distinguish between normal and tumor areas at the tumor boundary, showing good classification performance. Our findings demonstrate that MPM imaging technology combined with an image-based collagen feature extraction method can rapidly and accurately detect early-stage lung adenocarcinoma tissues.

Extracellular Hsp90 Binds to and Aligns Collagen-1 to Enhance Breast Cancer Cell Invasiveness

Cancer cell-secreted eHsp90 binds and activates proteins in the tumor microenvironment crucial in cancer invasion. Therefore, targeting eHsp90 could inhibit invasion, preventing metastasis-the leading cause of cancer-related mortality. Previous eHsp90 studies have solely focused on its role in cancer invasion through the 2D basement membrane (BM), a form of extracellular matrix (ECM) that lines the epithelial compartment. However, its role in cancer invasion through the 3D Interstitial Matrix (IM), an ECM beyond the BM, remains unexplored. Using a Collagen-1 binding assay and second harmonic generation (SHG) imaging, we demonstrate that eHsp90 directly binds and aligns Collagen-1 fibers, the primary component of IM. Furthermore, we show that eHsp90 enhances Collagen-1 invasion of breast cancer cells in the Transwell assay. Using Hsp90 conformation mutants and inhibitors, we established that the Hsp90 dimer binds to Collagen-1 via its N-domain. We also demonstrated that while Collagen-1 binding and alignment are not influenced by Hsp90's ATPase activity attributed to the N-domain, its open conformation is crucial for increasing Collagen-1 alignment and promoting breast cancer cell invasion. These findings unveil a novel role for eHsp90 in invasion through the IM and offer valuable mechanistic insights into potential therapeutic approaches for inhibiting Hsp90 to suppress invasion and metastasis.

Two-photon imaging reveals histopathological changes in the gastric tumor microenvironment induced by neoadjuvant treatment

There is a close association between tumor response and survival in gastric cancer patients after receiving neoadjuvant treatment. An accurate and rapid assessment of therapeutic efficacy would be helpful for subsequent treatments and individual prognosis. At present, pathological examination is the gold standard for evaluating treatment response, however, it requires additional staining and the process is tedious, labor-intensive, as well as time-consuming. Here, we introduce a label-free imaging technique, two-photon imaging, to evaluate histopathological changes induced by pre-operative therapy, with a focus on assessing tumor regression as well as stromal response. Imaging data show that two-photon imaging allows label-free, rapid visualization of various aspects of pathological alterations in tumor microenvironment such as fibrotic reaction, inflammatory cell infiltration, mucinous response, isolated residual tumor cells. Moreover, a semi-automatic image processing approach is developed to extract the collagen morphological features, and statistical results show that there are significant differences in collagen area, length, width, cross-link space between the gastric cancer tissues with and without treatment. With the advent of a portable, miniaturized two-photon imaging device, we have enough reason to believe that this technique will become as an important auxiliary diagnostic tool in assessing neoadjuvant treatment response and thereby tailoring the most appropriate therapy strategies for the patients.

Quantitative Assessment of Hepatic Steatosis Using Label-Free Multiphoton Imaging and Customized Image Processing Program

Nonalcoholic fatty liver disease is rapidly becoming one of the most common causes of chronic liver disease worldwide and is the leading cause of liver-related morbidity and mortality. A quantitative assessment of the degree of steatosis would be more advantageous for diagnostic evaluation and exploring the patterns of disease progression. Here, multiphoton microscopy, based on the second harmonic generation and 2-photon excited fluorescence, was used to label-free image the samples of nonalcoholic fatty liver. Imaging results confirm that multiphoton microscopy is capable of directly visualizing important pathologic features such as normal hepatocytes, hepatic steatosis, Mallory bodies, necrosis, inflammation, collagen deposition, microvessel, and so on and is a reliable auxiliary tool for the diagnosis of nonalcoholic fatty liver disease. Furthermore, we developed an image segmentation algorithm to simultaneously assess hepatic steatosis and fibrotic changes, and quantitative results reveal that there is a correlation between the degree of steatosis and collagen content. We also developed a feature extraction program to precisely display the spatial distribution of hepatocyte steatosis in tissues. These studies may be beneficial for a better clinical understanding of the process of steatosis as well as for exploring possible therapeutic targets.

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

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

Prognostic value of tumor necrosis based on the evaluation of frequency in invasive breast cancer

BACKGROUND

Tumor necrosis (TN) was associated with poor prognosis. However, the traditional classification of TN ignored spatial intratumor heterogeneity, which may be associated with important prognosis. The purpose of this study was to propose a new method to reveal the hidden prognostic value of spatial heterogeneity of TN in invasive breast cancer (IBC).

METHODS

Multiphoton microscopy (MPM) was used to obtain multiphoton images from 471 patients. According to the relative spatial positions of TN, tumor cells, collagen fibers and myoepithelium, four spatial heterogeneities of TN (TN1-4) were defined. Based on the frequency of individual TN, TN-score was obtained to investigate the prognostic value of TN.

RESULTS

Patients with high-risk TN had worse 5-year disease-free survival (DFS) than patients with no necrosis (32.5% vs. 64.7%; P < 0.0001 in training set; 45.8% vs. 70.8%; P = 0.017 in validation set), while patients with low-risk TN had a 5-year DFS comparable to patients with no necrosis (60.0% vs. 64.7%; P = 0.497 in training set; 59.8% vs. 70.8%; P = 0.121 in validation set). Furthermore, high-risk TN "up-staged" the patients with IBC. Patients with high-risk TN and stage I tumors had a 5-year DFS comparable to patients with stage II tumors (55.6% vs. 62.0%; P = 0.565 in training set; 62.5% vs. 66.3%; P = 0.856 in validation set), as well as patients with high-risk TN and stage II tumors had a 5-year DFS comparable to patients with stage III tumors (33.3% vs. 24.6%; P = 0.271 in training set; 44.4% vs. 39.3%; P = 0.519 in validation set).

CONCLUSIONS

TN-score was an independent prognostic factor for 5-year DFS. Only high-risk TN was associated with poor prognosis. High-risk TN "up-staged" the patients with IBC. Incorporating TN-score into staging category could improve its performance to stratify patients.

Maternal obesity driven changes in collagen linearity of breast extracellular matrix induces invasive mammary epithelial cell phenotype

Obesity has been linked with numerous health issues as well as an increased risk of breast cancer. Although effects of direct obesity in patient outcomes is widely studied, effects of exposure to obesity-related systemic influences in utero have been overlooked. In this study, we investigated the effect of multigenerational obesity on epithelial cell migration and invasion using decellularized breast tissues explanted from normal female mouse pups from a diet induced multigenerational obesity mouse model. We first studied the effect of multigenerational diet on the mechanical properties, adipocyte size, and collagen structure of these mouse breast tissues, and then, examined the migration and invasion behavior of normal (KTB-21) and cancerous (MDA-MB-231) human mammary epithelial cells on the decellularized matrices from each diet group. Breast tissues of mice whose dams had been fed with high-fat diet exhibited larger adipocytes and thicker and curvier collagen fibers, but only slightly elevated elastic modulus and inflammatory cytokine levels. MDA-MB-231 cancer cell motility and invasion were significantly greater on the decellularized matrices from mice whose dams were fed with high-fat diet. A similar trend was observed with normal KTB-21 cells. Our results showed that the collagen curvature was the dominating factor on this enhanced motility and stretching the matrices to equalize the collagen fiber linearity of the matrices ameliorated the observed increase in cell migration and invasion in the mice that were exposed to a high-fat diet in utero. Previous studies indicated an increase in serum leptin concentration for those children born to an obese mother. We generated extracellular matrices using primary fibroblasts exposed to various concentrations of leptin. This produced curvier ECM and increased breast cancer cell motility for cells seeded on the decellularized ECM generated with increasing leptin concentration. Our study shows that exposure to obesity in utero is influential in determining the extracellular matrix structure, and that the resultant change in collagen curvature is a critical factor in regulating the migration and invasion of breast cancer cells.

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

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

Detection of pathological response of axillary lymph node metastasis after neoadjuvant chemotherapy in breast cancer using multiphoton microscopy

Neoadjuvant treatment is often considered in breast cancer patients with axillary lymph node involvement, but most of patients do not have a pathologic complete response to therapy. The detection of residual nodal disease has a significant impact on adjuvant therapy recommendations which may improve survival. Here, we investigate whether multiphoton microscopy (MPM) could identify the pathological changes of axillary lymphatic metastasis after neoadjuvant chemotherapy in breast cancer. And furthermore, we find that there are obvious differences in seven collagen morphological features between normal node and residual axillary disease by combining with a semi-automatic image processing method, and also find that there are significant differences in four collagen features between the effective and no-response treatment groups. These research results indicate that MPM may help estimate axillary treatment response in the neoadjuvant setting and thereby tailor more appropriate and personalized adjuvant treatments for breast cancer patients.

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.

Constructing 3D In Vitro Models of Heterocellular Solid Tumors and Stromal Tissues Using Extrusion-Based Bioprinting

Malignant tumor tissues exhibit inter- and intratumoral heterogeneities, aberrant development, dynamic stromal composition, diverse tissue phenotypes, and cell populations growing within localized mechanical stresses in hypoxic conditions. Experimental tumor models employing engineered systems that isolate and study these complex variables using in vitro techniques are under development as complementary methods to preclinical in vivo models. Here, advances in extrusion bioprinting as an enabling technology to recreate the three-dimensional tumor milieu and its complex heterogeneous characteristics are reviewed. Extrusion bioprinting allows for the deposition of multiple materials, or selected cell types and concentrations, into models based upon physiological features of the tumor. This affords the creation of complex samples with representative extracellular or stromal compositions that replicate the biology of patient tissue. Biomaterial engineering of printable materials that replicate specific features of the tumor microenvironment offer experimental reproducibility, throughput, and physiological relevance compared to animal models. In this review, we describe the potential of extrusion-based bioprinting to recreate the tumor microenvironment within in vitro models.

Targeting the secreted RGDKGE collagen fragment reduces PD‑L1 by a proteasome‑dependent mechanism and inhibits tumor growth

Structural alterations of collagen impact signaling that helps control tumor progression and the responses to therapeutic intervention. Integrins represent a class of receptors that include members that mediate collagen signaling. However, a strategy of directly targeting integrins to control tumor growth has demonstrated limited activity in the clinical setting. New molecular understanding of integrins have revealed that these receptors can regulate both pro‑ and anti‑tumorigenic functions in a cell type‑dependent manner. Therefore, designing strategies that block pro‑tumorigenic signaling, without impeding anti‑tumorigenic functions, may lead to development of more effective therapies. In the present study, evidence was provided for a novel signaling cascade in which β3‑integrin‑mediated binding to a secreted RGDKGE‑containing collagen fragment stimulates an autocrine‑like signaling pathway that differentially governs the activity of both YAP and (protein kinase‑A) PKA, ultimately leading to alterations in the levels of immune checkpoint molecule PD‑L1 by a proteasome dependent mechanism. Selectively targeting this collagen fragment, reduced nuclear YAP levels, and enhanced PKA and proteasome activity, while also exhibiting significant antitumor activity in vivo. The present findings not only provided new mechanistic insight into a previously unknown autocrine‑like signaling pathway that may provide tumor cells with the ability to regulate PD‑L1, but our findings may also help in the development of more effective strategies to control pro‑tumorigenic β3‑integrin signaling without disrupting its tumor suppressive functions in other cellular compartments.

Rapid and label-free detection of gastrointestinal stromal tumor via a combination of two-photon microscopy and imaging analysis

BACKGROUND

Gastrointestinal stromal tumor (GIST) is currently regarded as a potentially malignant tumor, and early diagnosis is the best way to improve its prognosis. Therefore, it will be meaningful to develop a new method for auxiliary diagnosis of this disease.

METHODS

Here we try out a new means to detect GIST by combining two-photon imaging with automatic image processing strategy.

RESULTS

Experimental results show that two-photon microscopy has the ability to label-freely identify the structural characteristics of GIST such as tumor cells, desmoplastic reaction, which are entirely different from those from gastric adenocarcinoma. Moreover, an image processing approach is used to extract eight collagen morphological features from tumor microenvironment and normal muscularis, and statistical analysis demonstrates that there are significant differences in three features-fiber area, density and cross-link density. The three morphological characteristics may be considered as optical imaging biomarkers to differentiate between normal and abnormal tissues.

CONCLUSION

With continued improvement and refinement of this technology, we believe that two-photon microscopy will be an efficient surveillance tool for GIST and lead to better management of this disease.

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

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

Survival analysis and prognosis of patients with breast cancer with pleural metastasis

Background

Breast cancer (BC) is the most common malignant cancer. The prognosis of patients differs according to the location of distant metastasis, with pleura being a common metastatic site in BC. Nonetheless, clinical data of patients with pleural metastasis (PM) as the only distant metastatic site at initial diagnosis of metastatic BC (MBC) are limited.

Patient cohort and methods

The medical records of patients who were hospitalized in Shandong Cancer Hospital between January 1, 2012 and December 31, 2021 were reviewed, and patients eligible for the study were selected. Survival analysis was conducted using Kaplan-Meier (KM) method. Univariate and multivariate Cox proportional-hazards models were used to identify prognostic factors. Finally, based on these selected factors, a nomogram was constructed and validated.

Results

In total, 182 patients were included; 58 (group A), 81 (group B), and 43 (group C) patients presented with only PM, only lung metastasis (LM), and PM combined with LM, respectively. The KM curves revealed no significant difference in overall survival (OS) among the three groups. However, in terms of survival after distant metastasis (M-OS), the difference was significant: patients with only PM exhibited the best prognosis, whereas those with PM combined with LM exhibited the worst prognosis (median M-OS: 65.9, 40.5, and 32.4 months, respectively; P = 0.0067). For patients with LM in groups A and C, those with malignant pleural effusion (MPE) exhibited significantly worse M-OS than those without MPE. Univariate and multivariate analyses indicated that primary cancer site, T stage, N stage, location of PM, and MPE were independent prognostic factors for patients with PM without other distant metastasis. A nomogram prediction model incorporating these variables was created. According to the C-index (0.776), the AUC values of the 3-, 5-, and 8-year M-OS (0.86, 0.86, and 0.90, respectively), and calibration curves, the predicted and actual M-OS were in good agreement.

Conclusion

BC patients with PM only at the first diagnosis of MBC exhibited a better prognosis than those with LM only or PM combined with LM. We identified five independent prognostic factors associated with M-OS in this subset of patients, and a nomogram model with good predictive efficacy was established.

Intratumor graph neural network recovers hidden prognostic value of multi-biomarker spatial heterogeneity

Biomarkers are indispensable for precision medicine. However, focused single-biomarker development using human tissue has been complicated by sample spatial heterogeneity. To address this challenge, we tested a representation of primary tumor that synergistically integrated multiple in situ biomarkers of extracellular matrix from multiple sampling regions into an intratumor graph neural network. Surprisingly, the differential prognostic value of this computational model over its conventional non-graph counterpart approximated that of combined routine prognostic biomarkers (tumor size, nodal status, histologic grade, molecular subtype, etc.) for 995 breast cancer patients under a retrospective study. This large prognostic value, originated from implicit but interpretable regional interactions among the graphically integrated in situ biomarkers, would otherwise be lost if they were separately developed into single conventional (spatially homogenized) biomarkers. Our study demonstrates an alternative route to cancer prognosis by taping the regional interactions among existing biomarkers rather than developing novel biomarkers.

Proof of Concept of 3-D Backscatter Tensor Imaging Tomography for Non-invasive Assessment of Human Breast Cancer Collagen Organization

Tumor growth, similarly to several other pathologies, tends to change the structural orientation of soft tissue fibers, which can become relevant markers for diagnosis. Current diagnosis protocols may require a biopsy for histological analysis, which is an invasive, painful and stressful procedure with a minimum turnaround time of 2 d. Otherwise, diagnosis may involve the use of complex methods with limited availability such as diffusion tensor imaging (magnetic resonance diffusion tensor imaging), which is not widely used in medical practice. Conversely, advanced methodologies in ultrasound imaging such as backscatter tensor imaging (BTI) might become a routine procedure in clinical practice at a limited cost. This method evaluates the local organization of soft tissues based on the spatial coherence of their backscattered ultrasonic echoes. Previous work has proven that BTI applied with matrix probes enables measurement of the orientation of soft tissue fibers, especially in the myocardium. The aims of the study described here were (i) to present for the first time a methodology for performing BTI in a volume on ex vivo human breast tumors using a linear probe and (ii) to display a first proof of concept of the link between BTI measurements and the orientation of collagen fibers.

Modulating tumor physical microenvironment for fueling CAR-T cell therapy

Chimeric antigen receptor (CAR) T cell therapy has achieved unprecedented clinical success against hematologic malignancies. However, the transition of CAR-T cell therapies for solid tumors is limited by heterogenous antigen expression, immunosuppressive microenvironment (TME), immune adaptation of tumor cells and impeded CAR-T-cell infiltration/transportation. Recent studies increasingly reveal that tumor physical microenvironment could affect various aspects of tumor biology and impose profound impacts on the antitumor efficacy of CAR-T therapy. In this review, we discuss the critical roles of four physical cues in solid tumors for regulating the immune responses of CAR-T cells, which include solid stress, interstitial fluid pressure, stiffness and microarchitecture. We highlight new strategies exploiting these features to enhance the therapeutic potency of CAR-T cells in solid tumors by correlating with the state-of-the-art technologies in this field. A perspective on the future directions for developing new CAR-T therapies for solid tumor treatment is also provided.

Collagen signature as a novel biomarker to predict axillary lymph node metastasis in breast cancer using multiphoton microscopy

Accurate identification of axillary lymph node (ALN) status is crucial for tumor staging procedure and decision making. This retrospective study of 898 participants from two institutions was conducted. The aim of this study is to evaluate the diagnostic performance of clinical parameters combined with collagen signatures (tumor-associated collagen signatures [TACS] and the TACS corresponding microscopic features [TCMF]) in predicting the probability of ALN metastasis in patients with breast cancer. These findings suggest that TACS and TCMF in the breast tumor microenvironment are both novel and independent biomarkers for the estimation of ALN metastasis. The nomogram based on independent clinical parameters combined with TACS and TCMF yields good diagnostic performance in predicting ALN status.

Development and Validation of a Four Adenosine-to-Inosine RNA Editing Site-Relevant Prognostic Signature for Assessing Survival in Breast Cancer Patients

Background

Adenosine-to-inosine RNA editing (ATIRE) is increasingly being used to characterize cancer. However, no studies have been conducted to identify an ATIRE signature for predicting cancer survival.

Methods

Breast cancer (BRCA) samples with ATIRE profiles from The Cancer Genome Atlas were divided into training (n = 452) and internal validation cohorts (n = 311), and 197 additional BRCA patients were recruited as an external validation cohort. The ATIRE signature for BRCA overall survival (OS) and disease-free survival (DFS) were identified using forest algorithm analysis and experimentally verified by direct sequencing. An ATIRE-based risk score (AIRS) was established with these selected ATIRE sites. Significantly prognostic factors were incorporated to generate a nomogram that was evaluated using Harrell’s C-index and calibration plot for all cohorts.

Results

Seven ATIRE sites were revealed to be associated with both BRCA OS and DFS, of which four sites were experimentally confirmed. Patients with high AIRS displayed a higher risk of death than those with low AIRS in the training (hazard ratio (HR) = 3.142, 95%CI = 1.932–5.111), internal validation (HR = 2.097, 95%CI = 1.123–3.914), and external validation cohorts (HR = 2.680, 95%CI = 1.000–7.194). A similar hazard effect of high AIRS on DFS was also observed. The nomogram yielded Harrell’s C-indexes of 0.816 (95%CI = 0.784–0.847), 0.742 (95%CI = 0.684–0.799), and 0.869 (95%CI = 0.835–0.902) for predicting OS and 0.767 (95%CI = 0.708–0.826), 0.684 (95%CI = 0.605–0.763), and 0.635 (95%CI = 0.566–0.705) for predicting DFS in the three cohorts.

Conclusion

AIRS nomogram could help to predict OS and DFS of patients with BRCA.

Label-free multimodal nonlinear optical imaging of needle biopsy cores for intraoperative cancer diagnosis

SIGNIFICANCE

Needle biopsy (NB) procedures are important for the initial diagnosis of many types of cancer. However, the possibility of NB specimens being unable to provide diagnostic information, (i.e., non-diagnostic sampling) and the time-consuming histological evaluation process can cause delays in diagnoses that affect patient care.

AIM

We aim to demonstrate the advantages of this label-free multimodal nonlinear optical imaging (NLOI) technique as a non-destructive point-of-procedure evaluation method for NB tissue cores, for the visualization and characterization of the tissue microenvironment.

APPROACH

A portable, label-free, multimodal NLOI system combined second-harmonic generation (SHG) and third-harmonic generation and two- and three-photon autofluorescence (2PF, 3PF) microscopy. It was used for intraoperative imaging of fresh NB tissue cores acquired during canine cancer surgeries, which involved liver, lung, and mammary tumors as well as soft-tissue sarcoma; in total, eight canine patients were recruited. An added tissue culture chamber enabled the use of this NLOI system for longitudinal imaging of fresh NB tissue cores taken from an induced rat mammary tumor and healthy mouse livers.

RESULTS

The intraoperative NLOI system was used to assess fresh canine NB specimens during veterinary cancer surgeries. Histology-like morphological features were visualized by the combination of four NLOI modalities at the point-of-procedure. The NLOI results provided quantitative information on the tissue microenvironment such as the collagen fiber orientation using Fourier-domain SHG analysis and metabolic profiling by optical redox ratio (ORR) defined by 2PF/(2PF + 3PF). The analyses showed that the canine mammary tumor had more randomly oriented collagen fibers compared to the tumor margin, and hepatocarcinoma had a wider distribution of ORR with a lower mean value compared to the liver fibrosis and the normal-appearing liver. Moreover, the loss of metabolic information during tissue degradation of fresh murine NB specimens was shown by overall intensity decreases in all channels and an increase of mean ORR from 0.94 (standard deviation 0.099) to 0.97 (standard deviation 0.077) during 1-h longitudinal imaging of a rat mammary tumor NB specimen. The tissue response to staurosporine (STS), an apoptotic inducer, from fresh murine liver NB specimens was also observed. The mean ORR decreased from 0.86 to 0.74 in the first 40 min and then increased to 0.8 during the rest of the hour of imaging, compared to the imaging results without the addition of STS, which showed a continuous increase of ORR from 0.72 to 0.75.

CONCLUSIONS

A label-free, multimodal NLOI platform reveals microstructural and metabolic information of the fresh NB cores during intraoperative cancer imaging. This system has been demonstrated on animal models to show its potential to provide a more comprehensive histological assessment and a better understanding of the unperturbed tumor microenvironment. Considering tissue degradation, or loss of viability upon fixation, this intraoperative NLOI system has the advantage of immediate assessment of freshly excised tissue specimens at the point of procedure.

Association of the Collagen Signature with Pathological Complete Response in Rectal Cancer Patients

Collagen in the tumor microenvironment is recognized as a potential biomarker for predicting treatment response. This study investigated whether the collagen features are associated with pathological complete response (pCR) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) and develop and validate a prediction model for individualized prediction of pCR. The prediction model was developed in a primary cohort (353 consecutive patients). In total, 142 collagen features were extracted from the multiphoton image of pretreatment biopsy, and the least absolute shrinkage and selection operator (Lasso) regression was applied for feature selection and collagen signature building. A nomogram was developed using multivariable analysis. The performance of the nomogram was assessed with respect to its discrimination, calibration, and clinical utility. An independent cohort (163 consecutive patients) was used to validate the model. The collagen signature comprised four collagen features significantly associated with pCR both in the primary and validation cohorts (p < 0.001). Predictors in the individualized prediction nomogram included the collagen signature and clinicopathological predictors. The nomogram showed good discrimination with area under the ROC curve (AUC) of 0.891 in the primary cohort and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (AUC = 0.908) and good calibration. Decision curve analysis demonstrated that the nomogram was clinically useful. In conclusion, the collagen signature in the tumor microenvironment of pretreatment biopsy is significantly associated with pCR. The nomogram based on the collagen signature and clinicopathological predictors could be used for individualized prediction of pCR in LARC patients before nCRT.

Predictive value of collagen in cancer

Cancer is a complex disease and a significant cause of mortality worldwide. Over the course of nearly all cancer types, collagen within the tumor microenvironment influences emergence, progression, and metastasis. This review discusses collagen regulation within the tumor microenvironment, pathological involvement of collagen, and predictive values of collagen and related extracellular matrix components in main cancer types. A survey of predictive tests leveraging collagen assays using clinical cohorts is presented. A conclusion is that collagen has high predictive value in monitoring cancer processes and stratifying by outcomes. New approaches should be considered that continue to define molecular facets of collagen related to cancer.

Multiphoton microscopy providing pathological-level quantification of myocardial fibrosis in transplanted human heart

Multiphoton microscopy (MPM), a high-resolution laser scanning technique, has been shown to provide detailed real-time information on fibrosis assessment in animal models. But the value of MPM in human histology, especially in heart tissue, has not been fully explored. We aimed to evaluate the association between myocardial fibrosis measured by MPM and that measured by histological staining in the transplanted human heart. One hundred and twenty samples of heart tissue were obtained from 20 patients consisting of 10 dilated cardiomyopathies (DCM) and 10 ischemic cardiomyopathies (ICM). MPM and picrosirius red staining were performed to quantify collagen volume fraction (CVF) in explanted hearts postoperatively. Cardiomyocyte and myocardial fibrosis could be clearly visualized by MPM. Although patients with ICM had significantly greater MPM-derived CVF than patients with DCM (25.33  ± 12.65 % vs. 19.82  ± 8.62 %, p = 0.006), there was a substantial overlap of CVF values between them. MPM-derived CVF was comparable to that derived from picrosirius red staining based on all samples (22.58 ± 11.13% vs. 21.19 ± 11.79%, p = 0.348), as well as in DCM samples and ICM samples. MPM-derived CVF was correlated strongly with the magnitude of staining-derived CVF in both all samples and DCM samples and ICM samples (r = 0.972, r = 0.963, r = 0.973, respectively; all p < 0.001). Intra- and inter-observer reproducibility for MPM-derived CVF and staining-derived CVF were 0.995, 0.989, 0.995, and 0.985, respectively. Our data demonstrated that MPM can provide a pathological-level assessment of myocardial microstructure in transplanted human heart.

Label-free metabolic and structural profiling of dynamic biological samples using multimodal optical microscopy with sensorless adaptive optics

Label-free optical microscopy has matured as a noninvasive tool for biological imaging; yet, it is criticized for its lack of specificity, slow acquisition and processing times, and weak and noisy optical signals that lead to inaccuracies in quantification. We introduce FOCALS (Fast Optical Coherence, Autofluorescence Lifetime imaging, and Second harmonic generation) microscopy capable of generating NAD(P)H fluorescence lifetime, second harmonic generation (SHG), and polarization-sensitive optical coherence microscopy (OCM) images simultaneously. Multimodal imaging generates quantitative metabolic and morphological profiles of biological samples in vitro, ex vivo, and in vivo. Fast analog detection of fluorescence lifetime and real-time processing on a graphical processing unit enables longitudinal imaging of biological dynamics. We detail the effect of optical aberrations on the accuracy of FLIM beyond the context of undistorting image features. To compensate for the sample-induced aberrations, we implemented a closed-loop single-shot sensorless adaptive optics solution, which uses computational adaptive optics of OCM for wavefront estimation within 2 s and improves the quality of quantitative fluorescence imaging in thick tissues. Multimodal imaging with complementary contrasts improves the specificity and enables multidimensional quantification of the optical signatures in vitro, ex vivo, and in vivo, fast acquisition and real-time processing improve imaging speed by 4-40 × while maintaining enough signal for quantitative nonlinear microscopy, and adaptive optics improves the overall versatility, which enable FOCALS microscopy to overcome the limits of traditional label-free imaging techniques.

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

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

Targeting the extracellular matrix for immunomodulation: applications in drug delivery and cell therapies

Host immune cells interact bi-directionally with their extracellular matrix (ECM) to receive and deposit molecular signals, which orchestrate cellular activation, proliferation, differentiation, and function to maintain healthy tissue homeostasis. In response to pathogens or damage, immune cells infiltrate diseased sites and synthesize critical ECM molecules such as glycoproteins, proteoglycans, and glycosaminoglycans to promote healing. When the immune system misidentifies pathogens or fails to survey damaged cells effectively, maladies such as chronic inflammation, autoimmune diseases, and cancer can develop. In these conditions, it is essential to restore balance to the body through modulation of the immune system and the ECM. This review details the components of dysregulated ECM implicated in pathogenic environments and therapeutic approaches to restore tissue homeostasis. We evaluate emerging strategies to overcome inflamed, immune inhibitory, and otherwise diseased microenvironments, including mechanical stimulation, targeted proteases, adoptive cell therapy, mechanomedicine, and biomaterial-based cell therapeutics. We highlight various strategies that have produced efficacious responses in both pre-clinical and human trials and identify additional opportunities to develop next-generation interventions. Significantly, we identify a need for therapies to address dense or fibrotic tissue for the treatment of organ tissue damage and various cancer subtypes. Finally, we conclude that therapeutic techniques that disrupt, evade, or specifically target the pathogenic microenvironment have a high potential for improving therapeutic outcomes and should be considered a priority for immediate exploration. A schematic showing the various methods of extracellular matrix disruption/targeting in both fibrotic and cancerous environments. a Biomaterial-based cell therapy can be used to deliver anti-inflammatory cytokines, chemotherapeutics, or other factors for localized, slow release of therapeutics. b Mechanotherapeutics can be used to inhibit the deposition of molecules such as collagen that affect stiffness. c Ablation of the ECM and target tissue can be accomplished via mechanical degradation such as focused ultrasound. d Proteases can be used to improve the distribution of therapies such as oncolytic virus. e Localization of therapeutics such as checkpoint inhibitors can be improved with the targeting of specific ECM components, reducing off-target effects and toxicity.

Computer-assisted quantification of tumor-associated collagen signatures to improve the prognosis prediction of breast cancer

BACKGROUND

Collagen fibers play an important role in tumor initiation, progression, and invasion. Our previous research has already shown that large-scale tumor-associated collagen signatures (TACS) are powerful prognostic biomarkers independent of clinicopathological factors in invasive breast cancer. However, they are observed on a macroscale and are more suitable for identifying high-risk patients. It is necessary to investigate the effect of the corresponding microscopic features of TACS so as to more accurately and comprehensively predict the prognosis of breast cancer patients.

METHODS

In this retrospective and multicenter study, we included 942 invasive breast cancer patients in both a training cohort (n = 355) and an internal validation cohort (n = 334) from one clinical center and in an external validation cohort (n = 253) from a different clinical center. TACS corresponding microscopic features (TCMFs) were firstly extracted from multiphoton images for each patient, and then least absolute shrinkage and selection operator (LASSO) regression was applied to select the most robust features to build a TCMF-score. Finally, the Cox proportional hazard regression analysis was used to evaluate the association of TCMF-score with disease-free survival (DFS).

RESULTS

TCMF-score is significantly associated with DFS in univariate Cox proportional hazard regression analysis. After adjusting for clinical variables by multivariate Cox regression analysis, the TCMF-score remains an independent prognostic indicator. Remarkably, the TCMF model performs better than the clinical (CLI) model in the three cohorts and is particularly outstanding in the ER-positive and lower-risk subgroups. By contrast, the TACS model is more suitable for the ER-negative and higher-risk subgroups. When the TACS and TCMF are combined, they could complement each other and perform well in all patients. As expected, the full model (CLI+TCMF+TACS) achieves the best performance (AUC 0.905, [0.873-0.938]; 0.896, [0.860-0.931]; 0.882, [0.840-0.925] in the three cohorts).

CONCLUSION

These results demonstrate that the TCMF-score is an independent prognostic factor for breast cancer, and the increased prognostic performance (TCMF+TACS-score) may help us develop more appropriate treatment protocols.

Single-Cell Proteomic Analysis Dissects the Complexity of Tumor Microenvironment in Muscle Invasive Bladder Cancer

Simple Summary

The tumor microenvironment (TME) is considered to play a key role in the development of many types of tumors. Muscle invasive bladder cancer (MIBC), which is well known for its heterogeneity, has a highly complex TME. Herein, we integrated mass cytometry and imaging mass cytometry to systematically investigate the complexity of the MIBC TME. Our investigation revealed tumor and immune cells with diverse phenotypes. We identified a specific cancer stem-like cell cluster (ALDH⁺PD-L1⁺ER-β⁻), which is associated with poor prognosis and highlighted the importance of the spatial distribution patterns of MIBC TME components. The present study comprehensively elucidated the complexity of the MIBC TME and provides potentially valuable information for future research.

Abstract

Muscle invasive bladder cancer (MIBC) is a malignancy with considerable heterogeneity. The MIBC tumor microenvironment (TME) is highly complex, comprising diverse phenotypes and spatial architectures. The complexity of the MIBC TME must be characterized to provide potential targets for precision therapy. Herein, an integrated combination of mass cytometry and imaging mass cytometry was used to analyze tumor cells, immune cells, and TME spatial characteristics of 44 MIBC patients. We detected tumor and immune cell clusters with abnormal phenotypes. In particular, we identified a previously overlooked cancer stem-like cell cluster (ALDH⁺PD-L1⁺ER-β⁻) that was strongly associated with poor prognosis. We elucidated the different spatial architectures of immune cells (excluded, infiltrated, and deserted) and tumor-associated collagens (curved, stretched, directionally distributed, and chaotic) in the MIBC TME. The present study is the first to provide in-depth insight into the complexity of the MIBC TME at the single-cell level. Our results will improve the general understanding of the heterogeneous characteristics of MIBC, potentially facilitating patient stratification and personalized therapy.

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.

Nomogram model combining macro and micro tumor-associated collagen signatures obtained from multiphoton images to predict the histologic grade in breast cancer

The purpose of this study is to develop and validate a new nomogram model combining macro and micro tumor-associated collagen signatures obtained from multiphoton images to differentiate tumor grade in patients with invasive breast cancer. A total of 543 patients were included in this study. We used computer-generated random numbers to assign 328 of these patients to the training cohort and 215 patients to the validation cohort. Macroscopic tumor-associated collagen signatures (TACS1-8) were obtained by multiphoton microscopy at the invasion front and inside of the breast primary tumor. TACS corresponding microscopic features (TCMF) including morphology and texture features were extracted from the segmented regions of interest using Matlab 2016b. Using ridge regression analysis, we obtained a TACS-score for each patient based on the combined TACS1-8, and the least absolute shrinkage and selection operator (LASSO) regression was applied to select the most robust TCMF features to build a TCMF-score. Univariate logistic regression analysis demonstrates that the TACS-score and TCMF-score are significantly associated with histologic grade (odds ratio, 2.994; 95% CI, 2.013-4.452; P < 0.001; 4.245, 2.876-6.264, P < 0.001 in the training cohort). The nomogram (collagen) model combining the TACS-score and TCMF-score could stratify patients into Grade1 and Grade2/3 groups with the AUC of 0.859 and 0.863 in the training and validation cohorts. The predictive performance can be further improved by combining the clinical factors, achieving the AUC of 0.874 in both data cohorts. The nomogram model combining the TACS-score and TCMF-score can be useful in differentiating breast tumor patients with Grade1 and Grade2/3.

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.

Characterizing the Invasive Tumor Front of Aggressive Uterine Adenocarcinoma and Leiomyosarcoma

The invasive tumor front (the tumor–host interface) is vitally important in malignant cell progression and metastasis. Tumor cell interactions with resident and infiltrating host cells and with the surrounding extracellular matrix and secreted factors ultimately determine the fate of the tumor. Herein we focus on the invasive tumor front, making an in-depth characterization of reticular fiber scaffolding, infiltrating immune cells, gene expression, and epigenetic profiles of classified aggressive primary uterine adenocarcinomas (24 patients) and leiomyosarcomas (11 patients). Sections of formalin-fixed samples before and after microdissection were scanned and studied. Reticular fiber architecture and immune cell infiltration were analyzed by automatized algorithms in colocalized regions of interest. Despite morphometric resemblance between reticular fibers and high presence of macrophages, we found some variance in other immune cell populations and distinctive gene expression and cell adhesion-related methylation signatures. Although no evident overall differences in immune response were detected at the gene expression and methylation level, impaired antimicrobial humoral response might be involved in uterine leiomyosarcoma spread. Similarities found at the invasive tumor front of uterine adenocarcinomas and leiomyosarcomas could facilitate the use of common biomarkers and therapies. Furthermore, molecular and architectural characterization of the invasive front of uterine malignancies may provide additional prognostic information beyond established prognostic factors.

Machine-Learning-Based Evaluation of Intratumoral Heterogeneity and Tumor-Stroma Interface for Clinical Guidance

Assessment of intratumoral heterogeneity and tumor-host interaction within the tumor microenvironment is becoming increasingly important for innovative cancer therapy decisions because of the unique information it can generate about the state of the disease. However, its assessment and quantification are limited by ambiguous definitions of the tumor-host interface and by human cognitive capacity in current pathology practice. Advances in machine learning and artificial intelligence have opened the field of digital pathology to novel tissue image analytics and feature extraction for generation of high-capacity computational disease management models. A particular benefit is expected from machine-learning applications that can perform extraction and quantification of subvisual features of both intratumoral heterogeneity and tumor microenvironment aspects. These methods generate information about cancer cell subpopulation heterogeneity, potential tumor-host interactions, and tissue microarchitecture, derived from morphologically resolved content using both explicit and implicit features. Several studies have achieved promising diagnostic, prognostic, and predictive artificial intelligence models that often outperform current clinical and pathology criteria. However, further effort is needed for clinical adoption of such methods through development of standardizable high-capacity workflows and proper validation studies.