Reorganized Collagen in the Tumor Microenvironment of Gastric Cancer and Its Association with Prognosis

Identification of collagen subtypes of gastric cancer for distinguishing patient prognosis and therapeutic response

Background

Gastric cancer is a highly heterogeneous disease, presenting a major obstacle to personalized treatment. Effective markers of the immune checkpoint blockade response are needed for precise patient classification. We, therefore, divided patients with gastric cancer according to collagen gene expression to indicate their prognosis and treatment response.

Methods

We collected data for 1250 patients with gastric cancer from four cohorts. For the TCGA-STAD cohort, we used consensus clustering to stratify patients based on expression levels of 44 collagen genes and compared the prognosis and clinical characteristics between collagen subtypes. We then identified distinct transcriptomic and genetic alteration signatures for the subtypes. We analyzed the associations of collagen subtypes with the responses to chemotherapy, immunotherapy, and targeted therapy. We also established a platform-independent collagen-subtype predictor. We verified the findings in three validation cohorts (GSE84433, GSE62254, and GSE15459) and compared the collagen subtyping method with other molecular subtyping methods.

Results

We identified two subtypes of gastric adenocarcinoma: a high-expression collagen subtype (CS-H) and a low-expression collagen subtype (CS-L). Collagen subtype was an independent prognostic factor, with better overall survival in the CS-L subgroup. The inflammatory response, angiogenesis, and phosphoinositide 3-kinase (PI3K)/Akt pathways were transcriptionally active in the CS-H subtype, while DNA repair activity was significantly greater in the CS-L subtype. PIK3CA was frequently amplified in the CS-H subtype, while PIK3C2A, PIK3C2G, and PIK3R1 were frequently deleted in the CS-L subtype. CS-H subtype tumors were more sensitive to fluorouracil, while CS-L subtype tumors were more sensitive to immune checkpoint blockade. CS-L subtype was predicted to be more sensitive to HER2-targeted drugs, and CS-H subtype was predicted to be more sensitive to vascular endothelial growth factor and PI3K pathway-targeting drugs. Collagen subtyping also has the potential to be combined with existing molecular subtyping methods for better patient classification.

Conclusions

We classified gastric cancers into two subtypes based on collagen gene expression and validated these subtypes in three validation cohorts. The collagen subgroups differed in terms of prognosis, clinical characteristics, transcriptome, and genetic alterations. The subtypes were closely related to patient responses to chemotherapy, immunotherapy, and targeted therapy.

Antitumor Activity of a Novel LAIR1 Antagonist in Combination with Anti-PD1 to Treat Collagen-Rich Solid Tumors

We recently reported that resistance to PD-1 blockade in a refractory lung cancer-derived model involved increased collagen deposition and the collagen-binding inhibitory receptor leukocyte-associated immunoglobulin-like receptor 1 (LAIR1). Thus, we hypothesized that LAIR1 and collagen cooperated to suppress therapeutic response. In this study, we report that LAIR1 is associated with tumor stroma and is highly expressed by intratumoral myeloid cells in both human tumors and mouse models of cancer. Stroma-associated myeloid cells exhibit a suppressive phenotype and correlate with LAIR1 expression in human cancer. NGM438, a novel humanized LAIR1 antagonist mAb, elicits myeloid inflammation and allogeneic T-cell responses by binding to LAIR1 and blocking collagen engagement. Furthermore, a mouse-reactive NGM438 surrogate antibody sensitized refractory KP mouse lung tumors to anti-PD-1 therapy and resulted in increased intratumoral CD8+ T-cell content and inflammatory gene expression. These data place LAIR1 at the intersection of stroma and suppressive myeloid cells and support the notion that blockade of the LAIR1/collagen axis can potentially address resistance to checkpoint inhibitor therapy in the clinic.

Extraction of collagen morphological features from second-harmonic generation microscopy images via GLCM and CT analyses: A cross-laboratory study

Second-harmonic generation (SHG) microscopy provides a high-resolution label-free approach for noninvasively detecting collagen organization and its pathological alterations. Up to date, several imaging analysis algorithms for extracting collagen morphological features from SHG images-such as fiber size and length, order and anisotropy-have been developed. However, the dependence of extracted features on experimental setting represents a significant obstacle for translating the methodology in the clinical practice. We tackled this problem by acquiring SHG images of the same kind of collagenous sample in various laboratories using different experimental setups and imaging conditions. The acquired images were analyzed by commonly used algorithms, such as gray-level co-occurrence matrix or curvelet transform; the extracted morphological features were compared, finding that they strongly depend on some experimental parameters, whereas they are almost independent from others. We conclude with useful suggestions for comparing results obtained in different labs using different experimental setups and conditions.

Contrast enhanced photoacoustic detection of fibrillar collagen in the near infrared region-I

Fibrillar collagen accumulation emerges as a promising biomarker in several diseases, such as desmoplastic tumors and unstable atherosclerotic plaque. Gold nanorods (GNRs) hold great potential as contrast agents in high-resolution, biomedically safe, and non-invasive photoacoustic imaging (PAI). This study presents the design and characterization of a specialized imaging tool which exploits GNR assisted targeted photoacoustic imaging that is tailored for the identification of fibrillar collagen. In addition to the photoacoustic characterization of collagen in the NIR 1 and 2 regions, we demonstrate the detailed steps of conjugating a decoy to GNRs. This study serves as a proof of concept, that demonstrates that conjugated collagenase-1 (MMP-1) generates a distinct and collagen-specific photoacoustic signal, facilitating real-time visualization in the wavelength range of 700-970 nm (NIR I). As most of the reported studies utilized the endogenous contrast of collagen in the NIR II wavelength that has major limitations to perform in vivo deep tissue imaging, the approach that we are proposing is unique and it highlights the promise of MMP-1 decoy-functionalized GNRs as novel contrast agents for photoacoustic imaging of collagen in the NIR 1 region. To our knowledge this is the first time functionalized GNRs are optimized for the detection of fibrillar collagen and utilized in the field of non-invasive photoacoustic imaging that can facilitate a better prognosis of desmoplastic tumors and broken atherosclerotic plaques.

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.

Decellularized tissues as platforms for digestive system cancer models

The extracellular matrix (ECM) is a multifunctional network of macromolecules that regulate various cellular functions and physically support the tissues. Besides physiological conditions, the ECM also changes during pathological conditions such as cancer. As tumor cells proliferate, notable changes occur in the quantity and makeup of the surrounding ECM. Therefore, the role of this noncellular component of tissues in studies of tumor microenvironments should be considered. So far, many attempts have been made to create 2-dimensional (2D) or 3-dimensional (3D) models that can replicate the intricate connections within the tumor microenvironment. Decellularized tissues are proper scaffolds that imitate the complex nature of native ECM. This review aims to summarize 3D models of digestive system cancers based on decellularized ECMs. These ECM-based scaffolds will enable us to study the interactive communication between cells and their surrounding environment which brings new potential for a better understanding of the pathophysiology of cancer.

Deposition of collagen III and alterations in basement membrane integrity as candidate prognostic markers in prostate cancer

The extracellular matrix surrounding the tumor undergoes changes in its organization during the metastasis process. The present study aims to quantify total collagen, collagen I (Col I) and collagen III (Col III), analyze the alignment of collagen fibers and assess the basement membrane integrity in samples from patients with metastatic and non-metastatic prostate cancer. Tissue samples from 60 patients were classified into groups based on prognostic parameters: better prognosis (n = 20), worse prognosis without metastasis (n = 23) and metastatic (n = 17). Picrosirius red with further analysis under polarizing microscope was used to quantify (with validation using immunohistochemistry) and analyze collagen alignment, and Periodic Acid Schiff staining was used to analyze the basement membrane integrity. The Col I/Col III ratio was found to be higher in the metastatic group than in the groups with better prognosis (p = 0.012) and worse prognosis without metastasis (p = 0.018). Basement membrane integrity constitution in malignant tumor tissue differed from that of adjacent non-tumor tissue (p < 0.001). Moreover, the worsening in the tumor tissue integrity was positively correlated with worse prognostic parameters. All in all, absence of Col III and basement membrane integrity might be indicators of poor prognosis in prostate cancer.

Prognostic significance of tumor budding, desmoplastic reaction, and lymphocytic infiltration in patients with gastric adenocarcinoma

BACKGROUND

Recent studies have shown that the tumor microenvironment significantly influences the behavior of solid tumors. In this context, Accumulated data suggests that pathological evaluation of tumor budding (TB), desmoplastic reaction (DR), and tumor-infiltrating lymphocytes (TILs) may be crucial in determining tumor behavior in the gastrointestinal tract. Regarding gastric adenocarcinoma (GAC), although some results suggest that TB and TILs may be effective in determining the course of the disease, the data do not agree. Moreover, very few studies have investigated the relationship between DR and survival. At present, the associations between tumor TB, DR and TILs in GAC patients have not been determined.

AIM

To establish the relationships between TB, DR, and TILs in patients with GAC and to assess their influence on prognosis.

METHODS

Our study group comprised 130 patients diagnosed with GAC. The definition of TB was established based on the International TB Consensus Conference. The DR was categorized into three groups according to the level of tumor stroma maturation. The assessment of TILs was conducted using a semiquantitative approach, employing a cutoff value of 5%. The statistical analysis of the whole group and 100 patients with an intestinal subtype of GAC was performed using SPSS version 27.

RESULTS

A significant correlation between peritumoral budding (PTB) and intratumoral budding (ITB) was noted (r = 0.943). Tumors with high PTBs and ITBs had a greater incidence of immature DRs and low TILs (P < 0.01). PTB and ITB were associated with histological subtype, lymph node metastasis (LNM), and stage (P < 0.01). ITB, PTB, LNM, DR, and stage were significant risk factors associated with poor prognosis. The multivariate Cox regression analysis identified ITB, PTB, and LNM as independent prognostic variables (P < 0.05). In intestinal-type adenocarcinomas, a positive correlation between PTB and ITB was noted (r = 0.972). While univariate analysis revealed that LNM, stage, PTB, ITB, and DR were strong parameters for predicting survival (P < 0.05), only PTB and ITB were found to be independent prognostic factors (P < 0.001).

CONCLUSION

TB may be a potential prognostic marker in GAC. However, further studies are needed to delineate its role in pathology reporting protocols and the predictive effects of DR and TILs.

[Screening of differentially expressed genes in gastric cancer based on GEO database and function and pathway enrichment analysis]

OBJECTIVE

To explore the core genes related to the diagnosis and prognosis of gastric cancer (GC) based on Gene Expression Omnibus (GEO) database and screen the molecular targets involved in the occurrence and development of GC.

METHODS

GC microarray data GSE118916, GSE54129 and GSE79973 were downloaded from GEO database, and the differentially expressed genes (DEGs) were screened. Enrichment analysis of the signaling pathways and molecular functions were preformed and protein-protein interaction networks (PPI) were constructed to identify the hub genes, whose expression levels and diagnostic and prognostic values were verifies based on gastric adenocarcinoma data from TCGA. The expression levels of these core genes were also detected in different GC cell lines using qRT- PCR.

RESULTS

Seventy-seven DEGs were identified, which encodes proteins located mainly in the extracellular matrix and basement membrane with activities of oxidoreductase and extracellular matrix receptor and ligand, involving the biological processes of digestion and hormone metabolism and the signaling pathways in retinol metabolism and gastric acid secretion. Nine hub genes were obtained, among which SPARC, TIMP1, THBS2, COL6A3 and THY1 were significantly up- regulated and TFF1, GKN1, TFF2 and PGC were significantly down-regulated in GC. The abnormal expressions of SPARC, TIMP1, THBS2, COL6A3, TFF2 and THY1 were significantly correlated with the survival time of GC patients. ROC curve analysis showed that aberrant expression of TIMP1 SPARC, THY1 and THBS2 had high diagnostic value for GC. High expressions of SPARC, TIMP1, THBS2 and COL6A3 were detected in GC tissues. In the GC cell lines, qRT- PCR revealed different expression patterns of these hub genes, but their expressions were largely consistent with those found in bioinformatics analyses.

CONCLUSION

SPARC, TIMP1, THBS2 and other DEGs are probably involved in GC occurrence and progression and may serve as potential candidate molecular markers for early diagnosis and prognostic evaluation of GC.

Collagen modifications predictive of lymph node metastasis in dogs with carcinoma in mixed tumours

Introduction

Mixed tumours in the canine mammary gland are the most common histological type in routine diagnosis. In general, these neoplasms have a favourable prognosis that does not evolve into metastatic disease. However, some cases develop into lymph node metastases and are associated with worse patient survival rates.

Methods

Here is a retrospective study of 46 samples of primary mixed tumours of the canine mammary gland: 15 cases of benign mixed tumours (BMT), 16 cases of carcinoma in mixed tumours without lymph node metastasis (CMT), and 15 cases of carcinomas in mixed tumours with lymph node metastasis (CMTM). In addition, we selected 23 cases of normal mammary glands (NMT) for comparison. The samples were collected from biopsies performed during nodulectomy, simple mastectomy, regional mastectomy, or unilateral/bilateral radical mastectomy. We used multiphoton microscopy, second harmonic generation, and two-photon excited fluorescence, to evaluate the characteristics of collagen fibres and cellular components in biopsies stained with haematoxylin and eosin. We performed Ki67, ER, PR, and HER-2 immunostaining to define the immunophenotype and COX-2. We showed that carcinomas that evolved into metastatic disease (CMTM) present shorter and wavier collagen fibres as compared to CMT.

Results and discussion

When compared to NMT and BMT the carcinomas present a smaller area of fibre coverage, a larger area of cellular coverage, and a larger number of individual fibres. Furthermore, we observed a correlation between the strong expression of COX-2 and a high rate of cell proliferation in carcinomas with a smaller area covered by cell fibres and a larger number of individual fibres. These findings highlight the fundamental role of collagen during tumour progression, especially in invasion and metastatic dissemination.

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.

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.

The Interaction between Collagen 1 and High Mannose Type CD133 Up-Regulates Glutamine Transporter SLC1A5 to Promote the Tumorigenesis of Glioblastoma Stem Cells

Targeting the niche components surrounding glioblastoma stem cells (GSCs) helps to develop more effective glioblastoma treatments. However, the mechanisms underlying the crosstalk between GSCs and microenvironment remain largely unknown. Clarifying the extracellular molecules binding to GSCs marker CD133 helps to elucidate the mechanism of the communication between GSCs and the microenvironment. Here, it is found that the extracellular domain of high mannose type CD133 physically interacts with Collagen 1 (COL1) in GSCs. COL1, mainly secreted by cancer-associated fibroblasts, is a niche component for GSCs. COL1 enhances the interaction between CD133 and p85 and activates Akt phosphorylation. Activation of Akt pathway increases transcription factor ATF4 protein level, subsequently enhances SLC1A5-dependent glutamine uptake and glutathione synthesis. The inhibition of CD133-COL1 interaction or down-regulation of SLC1A5 reduces COL1-accelerated GSCs self-renewal and tumorigenesis. Analysis of glioma samples reveals that the level of COL1 is correlated with histopathological grade of glioma and the expression of SLC1A5. Collectively, COL1, a niche component for GSCs, enhances the tumorigenesis of GSCs partially through CD133-Akt-SLC1A5 signaling axis, providing a new mechanism underlying the cross-talk between GSCs and extracellular matrix (ECM) microenvironment.

Benign and malignant prostate neoplasms show different spatial organization of collagen

AIM

To compare the indicators of the spatial organization of collagen and its regulating factors between benign and malignant prostate neoplasms.

METHODS

The study involved tumor tissue samples from 40 patients with stage II-III prostate cancer (PCa) and 20 patients with benign prostatic hyperplasia (BPH). The localization of collagen was determined with a Masson trichrome stain. To establish quantitative indicators of the spatial organization of collagen, morphometric studies were carried out with the CurveAlign and ImageJ programs.

RESULTS

PCa tissue had two times lower collagen density (P<0.0001) and 1.3 times lower levels of collagen alignment (P=0.018) compared with BPH tissue. In PCa tissue, collagen fibers were shorter (by 24.2%; P<0.001) and thicker (by 15.5%; P<0.001). PCa tissue samples showed significantly higher levels of metalloproteinase (MMP)-2 (by 2.4 times; P=0.001), MMP-8 (by 2.3 times; P=0.007), and MMP-13 (by 1.9 times; P=0.004).

CONCLUSIONS

Collagen matrix spatial organization features, as well as its regulatory factors, could be potential biomarkers of malignant prostate neoplasms.

A collagen-binding SIRPαFc fusion protein for targeted cancer immunotherapy

Collagen is abundant but exposed in tumor due to the abnormal tumor blood vessels, thus is considered as a tumor-specific target. The A3 domain of von Willebrand factor (vWF A3) is a kind of collagen-binding domain (CBD) which could bind collagen specifically. Previously we reported a chemosynthetic CBD-SIRPαFc conjugate, which could block CD47 and derived tumor-targeting ability by CBD. CBD-SIRPαFc conjugate represented improved anti-tumor efficacy with increased MHC II+ M1 macrophages, but the uncertain coupling ratio remained a problem. Herein, we produced a vWF A3-SIRPαFc fusion protein through eukaryotic expression system. It was examined at both molecular and cellular levels with its collagen affinity, uninfluenced original affinity to targets and phagocytosis-promoting function compared to unmodified SIRPαFc. Living imaging showed that vWF A3-SIRPαFc fusion protein derived the improved accumulation and retention in tumor than SIRPαFc. In the MC38 allograft model, vWF A3-SIRPαFc demonstrated a superior tumor-suppressing effect, characterized by increased MHC II+ M1 macrophages and T cells (particularly CD4+ T cells). These results revealed that vWF A3-SIRPαFc fusion protein derived tumor-targeting ability, leading to improved anti-tumor immunotherapeutic efficacy compared to SIRPαFc. Altogether, vWF A3 improved the anti-tumor efficacy and immune-activating function of SIRPαFc, supporting targeting tumor collagen as a possible targeted strategy.

Collagen-related gene expression level predicts the prognosis and immune therapy response

BACKGROUND

Gastric cancer patients responded differently to the same treatment strategy and had various prognoses for the lack of biomarkers to guide the therapy choice.

METHODS

RNA data of a local gastric cancer cohort with 103 patients were processed and used to explore potential treatment guiding factors. Cluster analysis was performed by non-negative matrix factorization. The expression level of collagen-related genes was evaluated by ssGSEA named collagen score (CS). Data from TCGA, ACRG, and an immune therapy cohort were utilized to explore prognosis and efficacy. Prognostic predictive power of CS was assessed using the nomogram.

RESULTS

In our study, local RNA data were processed by cluster analysis, and it was found that cluster 2 contained a worse tumor infiltration status. The GSEA result showed that collagen-related pathways were differentially activated in two clusters. In TCGA and ACRG cohorts, the CS can be used as an independent prognostic factor (TCGA OS: p = 0.018, HR = 3.5; ACRG OS: p = 0.014, HR = 4.88). An immunotherapy cohort showed that the patients with higher CS had a significantly worse ORR (p = 0.0025). The high CS group contained several cell death pathways down-regulated and contained the worse tumor microenvironment. The nomogram demonstrated the survival prediction capability of collagen score.

CONCLUSION

CS was verified as an independent prognostic factor and potentially reflected the therapeutic effect of immunotherapy. The CS could provide a new way to evaluate the clinical prognosis and response information helping develop the collagen-targeted treatment.

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

Significance

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

Aim

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

Approach

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

Results

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

Conclusions

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

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

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

Collagen code in tumor microenvironment: Functions, molecular mechanisms, and therapeutic implications

The tumor microenvironment (TME) is crucial in cancer progression, and the extracellular matrix (ECM) is an important TME component. Collagen is a major ECM component that contributes to tumor cell infiltration, expansion, and distant metastasis during cancer progression. Recent studies reported that collagen is deposited in the TME to form a collagen wall along which tumor cells can infiltrate and prevent drugs from working on the tumor cells. Collagen-tumor cell interaction is complex and requires the activation of multiple signaling pathways for biochemical and mechanical signaling interventions. In this review, we examine the effect of collagen deposition in the TME on tumor progression and discuss the interaction between collagen and tumor cells. This review aims to illustrate the functions and mechanisms of collagen in tumor progression in the TME and its role in tumor therapy. The findings indicated collagen in the TME appears to be a better target for cancer therapy.

Targeting collagen in tumor extracellular matrix as a novel targeted strategy in cancer immunotherapy

Collagen, the most abundant protein in mammal, is widely expressed in tissues and organs, as well as tumor extracellular matrix. Tumor collagen mainly accumulates in tumor stroma or beneath tumor blood vessel endothelium, and is exposed due to the fragmentary structure of tumor blood vessels. Through the blood vessels with enhanced permeability and retention (EPR) effect, collagen-binding macromolecules could easily bind to tumor collagen and accumulate within tumor, supporting tumor collagen to be a potential tumor-specific target. Recently, numerous studies have verified that targeting collagen within tumor extracellular matrix (TEM) would enhance the accumulation and retention of immunotherapy drugs at tumor, significantly improving their anti-tumor efficacy, as well as avoiding severe adverse effects. In this review, we would summarize the known collagen-binding domains (CBD) or proteins (CBP), their mechanism and application in tumor-targeting immunotherapy, and look forward to future development.

HNRNPC promotes collagen fiber alignment and immune evasion in breast cancer via activation of the VIRMA-mediated TFAP2A/DDR1 axis

BACKGROUND

Cancers aggressively reorganize collagen in their microenvironment, leading to the evasion of tumor cells from immune surveillance. However, the biological significance and molecular mechanism of collagen alignment in breast cancer (BC) have not been well established.

METHODS

In this study, BC-related RNA-Seq data were obtained from the TCGA database to analyze the correlation between DDR1 and immune cells. Mouse BC cells EO771 were selected for in vitro validation, and dual-luciferase experiments were conducted to examine the effect of TFAP2A on DDR1 promoter transcription activity. ChIP experiments were performed to assess TFAP2A enrichment on the DDR1 promoter, while Me-RIP experiments were conducted to detect TFAP2A mRNA m6A modification levels, and PAR-CLIP experiments were conducted to determine VIRMA's binding to TFAP2A mRNA and RIP experiments to investigate HNRNPC's recognition of m6A modification on TFAP2A mRNA. Additionally, an in vivo mouse BC transplant model and the micro-physiological system was constructed for validation, and Masson staining was used to assess collagen fiber arrangement. Immunohistochemistry was conducted to identify the number of CD8-positive cells in mouse BC tumors and Collagen IV content in ECM, while CD8 + T cell migration experiments were performed to measure CD8 + T cell migration.

RESULTS

Bioinformatics analysis showed that DDR1 was highly expressed in BC and negatively correlated with the proportion of anti-tumor immune cell infiltration. In vitro cell experiments indicated that VIRMA, HNRNPC, TFAP2A, and DDR1 were highly expressed in BC cells. In addition, HNRNPC promoted TFAP2A expression and, therefore, DDR1 transcription by recognizing the m6A modification of TFAP2A mRNA by VIRMA. In vivo animal experiments further confirmed that VIRMA and HNRNPC enhanced the TFAP2A/DDR1 axis, promoting collagen fiber alignment, reducing anti-tumor immune cell infiltration, and promoting immune escape in BC.

CONCLUSION

This study demonstrated that HNRNPC promoted DDR1 transcription by recognizing VIRMA-unveiled m6A modification of TFAP2A mRNA, which enhanced collagen fiber alignment and ultimately resulted in the reduction of anti-tumor immune cell infiltration and promotion of immune escape in BC.

Targeting extracellular matrix through phytochemicals: a promising approach of multi-step actions on the treatment and prevention of cancer

Extracellular matrix (ECM) plays a pivotal and dynamic role in the construction of tumor microenvironment (TME), becoming the focus in cancer research and treatment. Multiple cell signaling in ECM remodeling contribute to uncontrolled proliferation, metastasis, immune evasion and drug resistance of cancer. Targeting trilogy of ECM remodeling could be a new strategy during the early-, middle-, advanced-stages of cancer and overcoming drug resistance. Currently nearly 60% of the alternative anticancer drugs are derived from natural products or active ingredients or structural analogs isolated from plants. According to the characteristics of ECM, this manuscript proposes three phases of whole-process management of cancer, including prevention of cancer development in the early stage of cancer (Phase I); prevent the metastasis of tumor in the middle stage of cancer (Phase II); provide a novel method in the use of immunotherapy for advanced cancer (Phase III), and present novel insights on the contribution of natural products use as innovative strategies to exert anticancer effects by targeting components in ECM. Herein, we focus on trilogy of ECM remodeling and the interaction among ECM, cancer-associated fibroblasts (CAFs) and tumor-associated macrophages (TAMs), and sort out the intervention effects of natural products on the ECM and related targets in the tumor progression, provide a reference for the development of new drugs against tumor metastasis and recurrence.

Galectin-1 promotes gastric cancer peritoneal metastasis through peritoneal fibrosis

BACKGROUND

Peritoneal metastasis is one of the main causes of death in patients with gastric cancer (GC). Galectin-1 regulates various undesirable biological behaviors in GC and may be key in GC peritoneal metastasis.

METHODS

In this study, we elucidated the regulatory role of galectin-1 in GC cell peritoneal metastasis. GC and peritoneal tissues underwent hematoxylin-eosin (HE), immunohistochemical (IHC), and Masson trichrome staining to analyze the difference in galectin-1 expression and peritoneal collagen deposition in different GC clinical stages. The regulatory role of galectin-1 in GC cell adhesion to mesenchymal cells and in collagen expression was determined using HMrSV5 human peritoneal mesothelial cells (HPMCs). Collagen and corresponding mRNA expression were detected with western blotting and reverse transcription PCR, respectively. The promoting effect of galectin-1 on GC peritoneal metastasis was verified in vivo. Collagen deposition and collagen I, collagen III, and fibronectin 1 (FN1) expression in the peritoneum of the animal models were detected by Masson trichrome and IHC staining.

RESULTS

Galectin-1 and collagen deposition in the peritoneal tissues was correlated with GC clinical staging and were positively correlated. Galectin-1 enhanced the ability of GC cells to adhere to the HMrSV5 cells by promoting collagen I, collagen III, and FN1 expression. The in vivo experiments confirmed that galectin-1 promoted GC peritoneal metastasis by promoting peritoneal collagen deposition.

CONCLUSION

Galectin-1-induced peritoneal fibrosis may create a favorable environment for GC cell peritoneal metastasis.

Construction of an endoplasmic reticulum stress-related signature in lung adenocarcinoma by comprehensive bioinformatics analysis

BACKGROUND

Lung Adenocarcinoma (LUAD) is a major component of lung cancer. Endoplasmic reticulum stress (ERS) has emerged as a new target for some tumor treatments.

METHODS

The expression and clinical data of LUAD samples were downloaded from The Cancer Genome Atlas (TCGA) and The Gene Expression Omnibus (GEO) database, followed by acquiring ERS-related genes (ERSGs) from the GeneCards database. Differentially expressed endoplasmic reticulum stress-related genes (DE-ERSGs) were screened and used to construct a risk model by Cox regression analysis. Kaplan-Meier (K-M) curves and receiver operating characteristic (ROC) curves were plotted to determine the risk validity of the model. Moreover, enrichment analysis of differentially expressed genes (DEGs) between the high- and low- risk groups was conducted to investigate the functions related to the risk model. Furthermore, the differences in ERS status, vascular-related genes, tumor mutation burden (TMB), immunotherapy response, chemotherapy drug sensitivity and other indicators between the high- and low- risk groups were studied. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the mRNA expression levels of prognostic model genes.

RESULTS

A total of 81 DE-ERSGs were identified in the TCGA-LUAD dataset, and a risk model, including HSPD1, PCSK9, GRIA1, MAOB, COL1A1, and CAV1, was constructed by Cox regression analysis. K-M and ROC analyses showed that the high-risk group had a low survival, and the Area Under Curve (AUC) of ROC curves of 1-, 3- and 5-years overall survival was all greater than 0.6. In addition, functional enrichment analysis suggested that the risk model was related to collagen and extracellular matrix. Furthermore, differential analysis showed vascular-related genes FLT1, TMB, neoantigen, PD-L1 protein (CD274), Tumor Immune Dysfunction and Exclusion (TIDE), and T cell exclusion score were significantly different between the high- and low-risk groups. Finally, qRT-PCR results showed that the mRNA expression levels of 6 prognostic genes were consistent with the analysis.

CONCLUSION

A novel ERS-related risk model, including HSPD1, PCSK9, GRIA1, MAOB, COL1A1, and CAV1, was developed and validated, which provided a theoretical basis and reference value for ERS-related fields in the study and treatment of LUAD.

The role of eIF4F-driven mRNA translation in regulating the tumour microenvironment

Cells can rapidly adjust their proteomes in dynamic environments by regulating mRNA translation. There is mounting evidence that dysregulation of mRNA translation supports the survival and adaptation of cancer cells, which has stimulated clinical interest in targeting elements of the translation machinery and, in particular, components of the eukaryotic initiation factor 4F (eIF4F) complex such as eIF4E. However, the effect of targeting mRNA translation on infiltrating immune cells and stromal cells in the tumour microenvironment (TME) has, until recently, remained unexplored. In this Perspective article, we discuss how eIF4F-sensitive mRNA translation controls the phenotypes of key non-transformed cells in the TME, with an emphasis on the underlying therapeutic implications of targeting eIF4F in cancer. As eIF4F-targeting agents are in clinical trials, we propose that a broader understanding of their effect on gene expression in the TME will reveal unappreciated therapeutic vulnerabilities that could be used to improve the efficacy of existing cancer therapies.

Association of collagen deep learning classifier with prognosis and chemotherapy benefits in stage II-III colon cancer

The current tumor-node-metastasis staging system does not provide sufficient prognostic prediction or adjuvant chemotherapy benefit information for stage II-III colon cancer (CC) patients. Collagen in the tumor microenvironment affects the biological behaviors and chemotherapy response of cancer cells. Hence, in this study, we proposed a collagen deep learning (collagen^DL) classifier based on the 50-layer residual network model for predicting disease-free survival (DFS) and overall survival (OS). The collagen^DL classifier was significantly associated with DFS and OS (P < 0.001). The collagen^DL nomogram, integrating the collagen^DL classifier and three clinicopathologic predictors, improved the prediction performance, which showed satisfactory discrimination and calibration. These results were independently validated in the internal and external validation cohorts. In addition, high-risk stage II and III CC patients with high-collagen^DL classifier, rather than low-collagen^DL classifier, exhibited a favorable response to adjuvant chemotherapy. In conclusion, the collagen^DL classifier could predict prognosis and adjuvant chemotherapy benefits in stage II-III CC patients.

Overcoming the Fibrotic Fortress in Pancreatic Ductal Adenocarcinoma: Challenges and Opportunities

An overabundance of desmoplasia in the tumour microenvironment (TME) is one of the defining features that influences pancreatic ductal adenocarcinoma (PDAC) development, progression, metastasis, and treatment resistance. Desmoplasia is characterised by the recruitment and activation of fibroblasts, heightened extracellular matrix deposition (ECM) and reduced blood supply, as well as increased inflammation through an influx of inflammatory cells and cytokines, creating an intrinsically immunosuppressive TME with low immunogenic potential. Herein, we review the development of PDAC, the drivers that initiate and/or sustain the progression of the disease and the complex and interwoven nature of the cellular and acellular components that come together to make PDAC one of the most aggressive and difficult to treat cancers. We review the challenges in delivering drugs into the fortress of PDAC tumours in concentrations that are therapeutic due to the presence of a highly fibrotic and immunosuppressive TME. Taken together, we present further support for continued/renewed efforts focusing on aspects of the extremely dense and complex TME of PDAC to improve the efficacy of therapy for better patient outcomes.

Effects of Age and Multiple Vaginal Births on Lower Urinary Tract Structure and Function in Nonhuman Primates

Purpose: The relative roles of urinary sphincter damage, aging, and childbirth in stress urinary incontinence (SUI), have not been established. This study was performed to elucidate the roles of these factors.Methods: The study included: (1) 8 female cynomolgus monkeys (17–19 years of age and 7–8 vaginal births each); (2) six 5-yearold nulliparous monkeys with surgically created chronic urinary sphincter dysfunction; and (3) six 5-year-old, nulliparous, nosurgery controls. Sedated abdominal leak point pressure (ALPP) and maximum urethral sphincter pressures (MUP) were measured. Sphincters, bladders, and pelvic support muscles were quantified for collagen content. Additionally, bladders were analyzed for collagen fiber thickness, length, and angle using CT-FIRE analysis of Picrosirius red-stained tissues.Results: Resting MUP values were similar in the controls and older multiparous monkeys (P>0.05). However, aging and multiple births reduced pudendal nerve-stimulated increases in MUP (P<0.05 vs. controls). ALPP values were lower in the older multiparous versus younger groups of monkeys (P<0.05). Sphincter collagen content was greater, and muscle content less, in the injury model (P<0.05 vs. controls). However, these measures were not affected by age and childbirth (P>0.05 vs. young groups). Bladder collagen content was greater, and muscle content less, in the old multiparous monkeys (P<0.05 vs. younger groups). Additionally, collagen fibers were thicker and more angular in the bladders of the older multiparous monkeys than in the other nonhuman primate groups (P<0.05). Pelvic support muscles had higher collagen and lower muscle content in the older multiparous monkeys than in the younger groups of monkeys (P<0.05).Conclusions: SUI, associated with aging and multiple childbirths, appeared to be more strongly associated with bladder dysfunction, reduced pelvic muscle support, and the compensatory response to neural stimulation than with selective urinary sphincter dysfunction.

Colorectal Cancer Bioengineered Microtissues as a Model to Replicate Tumor-ECM Crosstalk and Assess Drug Delivery Systems In Vitro

Current 3D cancer models (in vitro) fail to reproduce complex cancer cell extracellular matrices (ECMs) and the interrelationships occurring (in vivo) in the tumor microenvironment (TME). Herein, we propose 3D in vitro colorectal cancer microtissues (3D CRC μTs), which reproduce the TME more faithfully in vitro. Normal human fibroblasts were seeded onto porous biodegradable gelatin microbeads (GPMs) and were continuously induced to synthesize and assemble their own ECMs (3D Stroma μTs) in a spinner flask bioreactor. Then, human colon cancer cells were dynamically seeded onto the 3D Stroma μTs to achieve the 3D CRC μTs. Morphological characterization of the 3D CRC μTs was performed to assess the presence of different complex macromolecular components that feature in vivo in the ECM. The results showed the 3D CRC μTs recapitulated the TME in terms of ECM remodeling, cell growth, and the activation of normal fibroblasts toward an activated phenotype. Then, the microtissues were assessed as a drug screening platform by evaluating the effect of 5-Fluorouracil (5-FU), curcumin-loaded nanoemulsions (CT-NE-Curc), and the combination of the two. When taken together, the results showed that our microtissues are promising in that they can help clarify complex cancer–ECM interactions and evaluate the efficacy of therapies. Moreover, they may be combined with tissue-on-chip technologies aimed at addressing further studies in cancer progression and drug discovery.

A Polymeric Extracellular Matrix Nanoremodeler for Activatable Cancer Photo-Immunotherapy

Cancer immunotherapy has shown tremendous potential to train the intrinsic immune system against malignancy in the clinic. However, the extracellular matrix (ECM) in tumor microenvironment is a formidable barrier that not only restricts the penetration of therapeutic drugs but also prevents the infiltration of antitumor immune cells. We herein report a semiconducting polymer-based ECM nanoremodeler (SPNcb) to combine photodynamic antitumor activity with cancer-specific inhibition of collagen-crosslinking enzymes (lysyl oxidase (LOX) family) for activatable cancer photo-immunotherapy. SPNcb is self-assembled from an amphiphilic semiconducting polymer conjugated with a LOX inhibitor (β-aminopropionitrile, BAPN) via a cancer biomarker (cathepsin B, CatB)-cleavable segment. BAPN can be exclusively activated to inhibit LOX activity in the presence of the tumor-overexpressed CatB, thus blocking collagen crosslinking and decreasing ECM stiffness. Such an ECM nanoremodeler synergizes immunogenic phototherapy and checkpoint blockade immunotherapy to improve the tumor infiltration of cytotoxic T cells, inhibiting tumor growth and metastasis.

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.

Extracellular matrix physical properties govern the diffusion of nanoparticles in tumor microenvironment

Nanoparticles (NPs) are confronted with limited and disappointing delivery efficiency in tumors clinically. The tumor extracellular matrix (ECM), whose physical traits have recently been recognized as new hallmarks of cancer, forms a main steric obstacle for NP diffusion, yet the role of tumor ECM physical traits in NP diffusion remains largely unexplored. Here, we characterized the physical properties of clinical gastric tumor samples and observed limited distribution of NPs in decellularized tumor tissues. We also performed molecular dynamics simulations and in vitro hydrogel experiments through single-particle tracking to investigate the diffusion mechanism of NPs and understand the influence of tumor ECM physical properties on NP diffusion both individually and collectively. Furthermore, we developed an estimation matrix model with evaluation scores of NP diffusion efficiency through comprehensive analyses of the data. Thus, beyond finding that loose and soft ECM with aligned structure contribute to efficient diffusion, we now have a systemic model to predict NP diffusion efficiency based on ECM physical traits and provide critical guidance for personalized tumor diagnosis and treatment.

Comprehensive Profiling of Early Neoplastic Gastric Microenvironment Modifications and Biodynamics in Impaired BMP-Signaling FoxL1+-Telocytes

FoxL1⁺telocytes (TC^FoxL1+) are novel gastrointestinal subepithelial cells that form a communication axis between the mesenchyme and epithelium. TC^FoxL1+ are strategically positioned to be key contributors to the microenvironment through production and secretion of growth factors and extracellular matrix (ECM) proteins. In recent years, the alteration of the bone morphogenetic protein (BMP) signaling in TC^FoxL1+ was demonstrated to trigger a toxic microenvironment with ECM remodeling that leads to the development of pre-neoplastic gastric lesions. However, a comprehensive analysis of variations in the ECM composition and its associated proteins in gastric neoplasia linked to TC^FoxL1+ dysregulation has never been performed. This study provides a better understanding of how TC^FoxL1+ defective BMP signaling participates in the gastric pre-neoplastic microenvironment. Using a proteomic approach, we determined the changes in the complete matrisome of BmpR1a^△FoxL1+ and control mice, both in total antrum as well as in isolated mesenchyme-enriched antrum fractions. Comparative proteomic analysis revealed that the deconstruction of the gastric antrum led to a more comprehensive analysis of the ECM fraction of gastric tissues microenvironment. These results show that TC^FoxL1+ are key members of the mesenchymal cell population and actively participate in the establishment of the matrisomic fraction of the microenvironment, thus influencing epithelial cell behavior.

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

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

Unravelling cell migration: defining movement from the cell surface

Cell motility is essential for life and development. Unfortunately, cell migration is also linked to several pathological processes, such as cancer metastasis. Cells' ability to migrate relies on many actors. Cells change their migratory strategy based on their phenotype and the properties of the surrounding microenvironment. Cell migration is, therefore, an extremely complex phenomenon. Researchers have investigated cell motility for more than a century. Recent discoveries have uncovered some of the mysteries associated with the mechanisms involved in cell migration, such as intracellular signaling and cell mechanics. These findings involve different players, including transmembrane receptors, adhesive complexes, cytoskeletal components , the nucleus, and the extracellular matrix. This review aims to give a global overview of our current understanding of cell migration.

The Study of the Extracellular Matrix in Chronic Inflammation: A Way to Prevent Cancer Initiation?

Bidirectional communication between cells and their microenvironment has a key function in normal tissue homeostasis, and in disease initiation, progression and a patient's prognosis, at the very least. The extracellular matrix (ECM), as an element of all tissues and cellular microenvironment, is a frequently overlooked component implicated in the pathogenesis and progression of several diseases. In the inflammatory microenvironment (IME), different alterations resulting from remodeling processes can affect ECM, progressively inducing cancer initiation and the passage toward a tumor microenvironment (TME). Indeed, it has been demonstrated that altered ECM components interact with a variety of surface receptors triggering intracellular signaling that affect cellular pathways in turn. This review aims to support the notion that the ECM and its alterations actively participate in the promotion of chronic inflammation and cancer initiation. In conclusion, some data obtained in cancer research with the employment of decellularized ECM (dECM) models are described. The reported results encourage the application of dECM models to investigate the short circuits contributing to the creation of distinct IME, thus representing a potential tool to avoid the progression toward a malignant lesion.

N6-methylandenosine-related immune genes correlate with prognosis and immune landscapes in gastric cancer

OBJECTIVES

This study aimed to probe into the significance of N6-methyladenosine (m6A)-related immune genes (m6AIGs) in predicting prognoses and immune landscapes of patients with gastric cancer (GC).

METHODS

The clinical data and transcriptomic matrix of GC patients were acquired from The Cancer Genome Atlas database. The clinically meaningful m6AIGs were acquired by univariate Cox regression analysis. GC patients were stratified into different clusters via consensus clustering analysis and different risk subgroups via m6AIGs prognostic signature. The clinicopathological features and tumor microenvironment (TME) in the different clusters and different risk subgroups were explored. The predictive performance was evaluated using the KM method, ROC curves, and univariate and multivariate regression analyses. Moreover, we fabricated a nomogram based on risk scores and clinical risk characteristics. Biological functional analysis was performed based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. The connectivity map was used to screen out potential small molecule drugs for GC patients.

RESULTS

A total of 14 prognostic m6AIGs and two clusters based on 14 prognostic m6AIGs were identified. A prognostic signature based on 4 m6AIGs and a nomogram based on independent prognostic factors was constructed and validated. Different clusters and different risk subgroups were significantly correlated with TME scores, the distribution of immune cells, and the expression of immune checkpoint genes. Some malignant and immune biological processes and pathways were correlated with the patients with poor prognosis. Ten small molecular drugs with potential therapeutic effect were screened out.

CONCLUSIONS

This study revealed the prognostic role and significant values of m6AIGs in GC, which enhanced the understanding of m6AIGs and paved the way for developing predictive biomarkers and therapeutic targets for GC.

Bioinformatics Prediction and Machine Learning on Gene Expression Data Identifies Novel Gene Candidates in Gastric Cancer

Gastric cancer (GC) is one of the five most common cancers in the world and unfortunately has a high mortality rate. To date, the pathogenesis and disease genes of GC are unclear, so the need for new diagnostic and prognostic strategies for GC is undeniable. Despite particular findings in this regard, a holistic approach encompassing molecular data from different biological levels for GC has been lacking. To translate Big Data into system-level biomarkers, in this study, we integrated three different GC gene expression data with three different biological networks for the first time and captured biologically significant (i.e., reporter) transcripts, hub proteins, transcription factors, and receptor molecules of GC. We analyzed the revealed biomolecules with independent RNA-seq data for their diagnostic and prognostic capabilities. While this holistic approach uncovered biomolecules already associated with GC, it also revealed novel system biomarker candidates for GC. Classification performances of novel candidate biomarkers with machine learning approaches were investigated. With this study, AES, CEBPZ, GRK6, HPGDS, SKIL, and SP3 were identified for the first time as diagnostic and/or prognostic biomarker candidates for GC. Consequently, we have provided valuable data for further experimental and clinical efforts that may be useful for the diagnosis and/or prognosis of GC.

Type XXII Collagen Complements Fibrillar Collagens in the Serological Assessment of Tumor Fibrosis and the Outcome in Pancreatic Cancer

Circulating fragments of type III collagen, measured by PRO-C3, has shown promising results as a tumor fibrosis biomarker. However, the fibrotic tumor microenvironment consists of many other collagens with diverse functions and unexplored biomarker potential. One example hereof is type XXII collagen (COL22). In this study, we investigated the biomarker potential of COL22 by measuring this in serum. An ELISA, named PRO-C22, was developed and measured in two serum cohorts consisting of patients with various solid tumors (n = 220) and healthy subjects (n = 33) (Cohort 1), and patients with pancreatic ductal adenocarcinoma (PDAC) (n = 34), and healthy subjects (n = 20) (Cohort 2). In Cohort 1, PRO-C22 was elevated in the serum from patients with solid tumors, compared to healthy subjects (p < 0.01 to p < 0.0001), and the diagnostic accuracy (AUROC) ranged from 0.87 to 0.98, p < 0.0001. In Cohort 2, the high levels of PRO-C22, in patients with PDAC, were predictive of a worse overall survival (HR = 4.52, 95% CI 1.90−10.7, p = 0.0006) and this remained significant after adjusting for PRO-C3 (HR = 4.27, 95% CI 1.24−10.4, p = 0.0013). In conclusion, PRO-C22 has diagnostic biomarker potential in various solid tumor types and prognostic biomarker potential in PDAC. Furthermore, PRO-C22 complemented PRO-C3 in predicting mortality, suggesting an additive prognostic value when quantifying different collagens.

Integrating digital pathology with transcriptomic and epigenomic tools for predicting metastatic uterine tumor aggressiveness

The incidence of new cancer cases is expected to increase significantly in the future, posing a worldwide problem. In this regard, precision oncology and its diagnostic tools are essential for developing personalized cancer treatments. Digital pathology (DP) is a particularly key strategy to study the interactions of tumor cells and the tumor microenvironment (TME), which play a crucial role in tumor initiation, progression and metastasis. The purpose of this study was to integrate data on the digital patterns of reticulin fiber scaffolding and the immune cell infiltrate, transcriptomic and epigenetic profiles in aggressive uterine adenocarcinoma (uADC), uterine leiomyosarcoma (uLMS) and their respective lung metastases, with the aim of obtaining key TME biomarkers that can help improve metastatic prediction and shed light on potential therapeutic targets. Automatized algorithms were used to analyze reticulin fiber architecture and immune infiltration in colocalized regions of interest (ROIs) of 133 invasive tumor front (ITF), 89 tumor niches and 70 target tissues in a total of six paired samples of uADC and nine of uLMS. Microdissected tissue from the ITF was employed for transcriptomic and epigenetic studies in primary and metastatic tumors. Reticulin fiber scaffolding was characterized by a large and loose reticular fiber network in uADC, while dense bundles were found in uLMS. Notably, more similarities between reticulin fibers were observed in paired uLMS then paired uADCs. Transcriptomic and multiplex immunofluorescence-based immune profiling showed a higher abundance of T and B cells in primary tumor and in metastatic uADC than uLMS. Moreover, the epigenetic signature of paired samples in uADCs showed more differences than paired samples in uLMS. Some epigenetic variation was also found between the ITF of metastatic uADC and uLMS. Altogether, our data suggest a correlation between morphological and molecular changes at the ITF and the degree of aggressiveness. The use of DP tools for characterizing reticulin scaffolding and immune cell infiltration at the ITF in paired samples together with information provided by omics analyses in a large cohort will hopefully help validate novel biomarkers of tumor aggressiveness, develop new drugs and improve patient quality of life in a much more efficient way.

The Tumor Microenvironment of Clear-Cell Ovarian Cancer

Some patients with advanced clear-cell ovarian cancer (CCOC) respond to immunotherapy; however, little is known about the tumor microenvironment (TME) of this relatively rare disease. Here, we describe a comprehensive quantitative and topographical analysis of biopsies from 45 patients, 9 with Federation Internationale des Gynaecologistes et Obstetristes (FIGO) stage I/II (early CCOC) and 36 with FIGO stage III/IV (advanced CCOC). We investigated 14 immune cell phenotype markers, PD-1 and ligands, and collagen structure and texture. We interrogated a microarray data set from a second cohort of 29 patients and compared the TMEs of ARID1A-wildtype (ARID1Awt) versus ARID1A-mutant (ARID1Amut) disease. We found significant variations in immune cell frequency and phenotype, checkpoint expression, and collagen matrix between the malignant cell area (MCA), leading edge (LE), and stroma. The MCA had the largest population of CD138+ plasma cells, the LE had more CD20+ B cells and T cells, whereas the stroma had more mast cells and αSMA+ fibroblasts. PD-L2 was expressed predominantly on malignant cells and was the dominant PD-1 ligand. Compared with early CCOC, advanced-stage disease had significantly more fibroblasts and a more complex collagen matrix, with microarray analysis indicating "TGFβ remodeling of the extracellular matrix" as the most significantly enriched pathway. Data showed significant differences in immune cell populations, collagen matrix, and cytokine expression between ARID1Awt and ARID1Amut CCOC, which may reflect different paths of tumorigenesis and the relationship to endometriosis. Increased infiltration of CD8+ T cells within the MCA and CD4+ T cells at the LE and stroma significantly associated with decreased overall survival.

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

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

A brief glimpse of a tangled web in a small world: Tumor microenvironment

A tumor is a result of stepwise accumulation of genetic and epigenetic alterations. This notion has deepened the understanding of cancer biology and has introduced the era of targeted therapies. On the other hand, there have been a series of attempts of using the immune system to treat tumors, dating back to ancient history, to sporadic reports of inflamed tumors undergoing spontaneous regression. This was succeeded by modern immunotherapies and immune checkpoint inhibitors. The recent breakthrough has broadened the sight to other players within tumor tissue. Tumor microenvironment is a niche or a system orchestrating reciprocal and dynamic interaction of various types of cells including tumor cells and non-cellular components. The output of this complex communication dictates the functions of the constituent elements present within it. More complicated factors are biochemical and biophysical settings unique to TME. This mini review provides a brief guide on a range of factors to consider in the TME research.

Immunohistochemistry and Bioinformatics Identify GPX8 as a Potential Prognostic Biomarker and Target in Human Gastric Cancer

Background

Glutathione peroxidase 8 (GPX8) is a type II transmembrane protein with rare structural features belonging to the glutathione peroxidase family. The function of GPX8 in stomach adenocarcinoma has not been discovered clearly.

Methods

In this study, we comprehensively analyzed the expression of GPX8 in stomach adenocarcinoma and discovered that it is a potential target in the treatment of stomach adenocarcinoma. The immunohistochemical staining of GPX8 and survival analysis were performed in carcinoma tissue and adjacent tissues of 83 gastric cancer patients. The Gene Expression Profiling Interactive Analysis (GEPIA) database and Kaplan–Meier plotter database were used to evaluate the prognostic survival of GPX8 in stomach adenocarcinoma. The Cancer Genome Atlas (TCGA) database was used to download the microarray mRNA data of GPX8 and clinical information for cancer patients. The TIMER database and GSEA database were used to systematically evaluate the association of GPX8 and tumor-infiltrating lymphocytes in adenocarcinoma carcinoma. The STRING database was used to analyze protein-to-protein interactions of GPX8. The ROC curve was used to analyze the diagnostic effect of GPX8 in distinguishing outcomes between different subgroups, and a nomogram was constructed based on GPX8. Top transcription factor binding sites were analyzed using the QIAGEN database in the GPX8 gene promoter, and the functional enrichment analysis of GPX8 was done by GO and KEGG pathway enrichment analyses.

Result

Based on the GEPIA and TCGA databases, the mRNA expression of GPX8 was significantly higher in stomach adenocarcinoma compared with the adjacent normal tissues. The GEPIA and Kaplan–Meier plotter databases showed that a higher GPX8 expression level was correlated with poor prognosis of stomach adenocarcinoma, suggesting that GPX8 was a risk factor of poor prognosis in stomach adenocarcinoma. The TIMER database showed that the GPX8 expression level was positively correlated with infiltrating levels of CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells in stomach adenocarcinoma. The GSEA database indicated that GPX8 was positively correlated with B cells, dendritic cells, CD4+ T cells, CD8+ T cells, macrophages, mast cells, monocytes, and natural killer cells. At last, GO analysis indicated that the biological processes were enriched in collagen fibril organization, endodermal cell differentiation, collagen metabolic process, extracellular matrix organization, etc. KEGG signaling pathway analysis showed that GPX8 was correlated with protein digestion and absorption, extracellular matrix receptor interaction, AGE/RAGE signaling pathway, etc. The GSEA database showed that GPX8 was positively associated with angiogenesis, epithelial mesenchymal transition, hedgehog signaling, etc. The immunohistochemical staining of GPX8 and survival analysis in 83 gastric cancer patients showed that the OS rate of patients with a high GPX8 expression was significantly lower than that of the low GPX8 expression group.

Conclusion

GPX8 is an important factor which might be a potential target in the treatment of stomach adenocarcinoma.

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

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

Overcoming the limitations of cytokines to improve cancer therapy

Cytokines are pleiotropic soluble proteins used by immune cells to orchestrate a coordinated response against pathogens and malignancies. In cancer immunotherapy, cytokine-based drugs can be developed potentiating pro-inflammatory cytokines or blocking immunosuppressive cytokines. However, the complexity of the mechanisms of action of cytokines requires the use of biotechnological strategies to minimize systemic toxicity, while potentiating the antitumor response. Sequence mutagenesis, fusion proteins and gene therapy strategies are employed to enhance the half-life in circulation, target the desired bioactivity to the tumor microenvironment, and to optimize the therapeutic window of cytokines. In this review, we provide an overview of the different strategies currently being pursued in pre-clinical and clinical studies to make the most of cytokines for cancer immunotherapy.

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.

A SIRPαFc Fusion Protein Conjugated With the Collagen-Binding Domain for Targeted Immunotherapy of Non-Small Cell Lung Cancer

The SIRPαFc fusion protein can block the immunosuppressive CD47-SIRPα signal between macrophages and tumor cells as a decoy receptor and has demonstrated its immunotherapeutic efficacy in various tumors. However, its clinical application was limited because of the potential hematologic toxicity. The heptapeptide “TKKTLRT” is a collagen-binding domain (CBD) which can bind collagen specifically. Herein, we aim to improve the tumor targeting of SIRPαFc and therefore avoid its unnecessary exposure to normal cells through synthesizing a TKKTLRT–SIRPαFc conjugate. Experiments at molecular and cellular levels indicate that the TKKTLRT–SIRPαFc conjugate-derived collagen-binding affinity and the introduction of CBD did not impact the CD47-binding affinity as well as its phagocytosis-promoting effect on NSCLC cells. In vivo distribution experiments showed that CBD–SIRPαFc accumulated in tumor tissue more effectively compared to unmodified SIRPαFc, probably due to the exposed collagen in the tumor vascular endothelium and stroma resulting from the abnormal vessel structure. On an A549 NSCLC nude mouse xenograft model, CBD–SIRPαFc presented more stable and effective antitumor efficacy than SIRPαFc, along with significantly increased CD11b⁺F4/80⁺ macrophages especially MHC II⁺ M1 macrophages within tumors. All of these results revealed that CBD brought a tumor-targeting ability to the SIRPαFc fusion protein, which contributed to the enhanced antitumor immune response. Altogether, the CBD–SIRPαFc conjugate may have the potential to be an effective tumor immunotherapy with improved antitumor efficacy but less non-tumor-targeted side effect.

Diffuse gastric cancer: Emerging mechanisms of tumor initiation and progression

Gastric cancer is globally the fourth leading cause of cancer-related deaths. Patients with diffuse-type gastric cancer (DGC) particularly have a poor prognosis that only marginally improved over the last decades, as conventional chemotherapies are frequently ineffective and specific therapies are unavailable. Early-stage DGC is characterized by intramucosal lesions of discohesive cells, which can be present for many years before the emergence of advanced DGC consisting of highly proliferative and invasive cells. The mechanisms underlying the key steps of DGC development and transition to aggressive tumors are starting to emerge. Novel mouse- and organoid models for DGC, together with multi-omic analyses of DGC tumors, revealed contributions of both tumor cell-intrinsic alterations and gradual changes in the tumor microenvironment to DGC progression. In this review, we will discuss how these recent findings are leading towards an understanding of the cellular and molecular mechanisms responsible for DGC initiation and malignancy, which may provide opportunities for targeted therapies.

Adenosine Deaminase 1 Overexpression Enhances the Antitumor Efficacy of Chimeric Antigen Receptor-Engineered T Cells

Chimeric antigen receptor (CAR) T cell therapy mediates unprecedented benefit in certain leukemias and lymphomas, but has yet to achieve similar success in combating solid tumors. A substantial body of work indicates that the accumulation of adenosine in the solid tumor microenvironment (TME) plays a crucial role in abrogating immunotherapies. Adenosine deaminase 1 (ADA) catabolizes adenosine into inosine and is indispensable for a functional immune system. We have, for the first time, engineered CAR T cells to overexpress ADA. To potentially improve the pharmacokinetic profile of ADA, we have modified the overexpressed ADA in two ways, through the incorporation of a (1) albumin-binding domain or (2) collagen-binding domain. ADA and modified ADA were successfully expressed by CAR T cells and augmented CAR T cell exhaustion resistance. In a preclinical engineered ovarian carcinoma xenograft model, ADA and collagen-binding ADA overexpression significantly enhanced CAR T cell expansion, tumor tissue infiltration, tumor growth control, and overall survival, whereas albumin-binding ADA overexpression did not. Furthermore, in a syngeneic colon cancer solid tumor model, the overexpression of mouse ADA by cancer cells significantly reduced tumor burden and remodeled the TME to favor antitumor immunity. The overexpression of ADA for enhanced cell therapy is a safe, straightforward, reproducible genetic modification that can be utilized in current CAR T cell constructs to result in an armored CAR T product with superior therapeutic potential.