Association of the Collagen Signature in the Tumor Microenvironment With Recurrence and Survival of Patients With T4N0M0 Colon Cancer

Label-free quantification of imaging features in the extracellular matrix of left and right-sided colon cancer tissues

The molecular pathogenesis of colorectal cancer is known to differ between the right and left side of the colon. Several previous studies have focussed on the differences in clinicopathological features, proteomic and genetic biomarkers, the composition of gut microbiota, response to therapy, and the characteristics of the tumour microenvironment. However, the morphology and density of collagen in the extracellular matrix (ECM) have not been studied intensively. In this study, we employed 2-photon laser scanning microscopy (2PLSM) to visualise the intrinsic second-harmonic generation (SHG) signal emitted by collagen fibres in the heterogeneous ECM of human colon tumour tissues. Through texture analysis of the SHG signal, we quantitatively distinguished the imaging features generated by structural differences of collagen fibres in healthy colon and cancers and found marked differences. The fibres inside of tumours exhibited a loss of organisation, particularly pronounced in right-sided colon cancer (RSCC), where the chaotic regions were significantly increased. In addition, a higher collagen content was found in left-sided colon cancer (LSCC). In future, this might aid in subclassification and therapeutic decisions or even in designing new therapy regimens by taking into account the differences between collagen fibres features between colon tumours located at different sides.

A nomogram based on collagen signature for predicting the immunoscore in colorectal cancer

Objectives

The Immunoscore can categorize patients into high- and low-risk groups for prognostication in colorectal cancer (CRC). Collagen plays an important role in immunomodulatory functions in the tumor microenvironment (TME). However, the correlation between collagen and the Immunoscore in the TME is unclear. This study aimed to construct a collagen signature to illuminate the relationship between collagen structure and Immunoscore.

Methods

A total of 327 consecutive patients with stage I-III stage CRC were included in a training cohort. The fully quantitative collagen features were extracted at the tumor center and invasive margin of the specimens using multiphoton imaging. LASSO regression was applied to construct the collagen signature. The association of the collagen signature with Immunoscore was assessed. A collagen nomogram was developed by incorporating the collagen signature and clinicopathological predictors after multivariable logistic regression. The performance of the collagen nomogram was evaluated via calibration, discrimination, and clinical usefulness and then tested in an independent validation cohort. The prognostic values of the collagen nomogram were assessed using Cox regression and the Kaplan-Meier method.

Results

The collagen signature was constructed based on 16 collagen features, which included 6 collagen features from the tumor center and 10 collagen features from the invasive margin. Patients with a high collagen signature were more likely to show a low Immunoscore (Lo IS) in both cohorts (P<0.001). A collagen nomogram integrating the collagen signature and clinicopathological predictors was developed. The collagen nomogram yielded satisfactory discrimination and calibration, with an AUC of 0.925 (95% CI: 0.895-0.956) in the training cohort and 0.911 (95% CI: 0.872-0.949) in the validation cohort. Decision curve analysis confirmed that the collagen nomogram was clinically useful. Furthermore, the collagen nomogram-predicted subgroup was significantly associated with prognosis. Moreover, patients with a low-probability Lo IS, rather than a high-probability Lo IS, could benefit from chemotherapy in high-risk stage II and stage III CRC patients.

Conclusions

The collagen signature is significantly associated with the Immunoscore in the TME, and the collagen nomogram has the potential to individualize the prediction of the Immunoscore and identify CRC patients who could benefit from adjuvant chemotherapy.

Development and validation of a collagen signature to predict the prognosis of patients with stage II/III colorectal cancer

The tumor, nodes and metastasis (TNM) classification system provides useful but incomplete prognostic information and lacks the assessment of the tumor microenvironment (TME). Collagen, the main component of the TME extracellular matrix, plays a nonnegligible role in tumor invasion and metastasis. In this cohort study, we aimed to develop and validate a TME collagen signature (CS_TME) for prognostic prediction of stage II/III colorectal cancer (CRC) and to compare the prognostic values of "TNM stage + CS_TME" with that of TNM stage alone. Results indicated that the CS_TME was an independent prognostic risk factor for stage II/III CRC (hazard ratio: 2.939, 95% CI: 2.180-3.962, p < 0.0001), and the integration of the TNM stage and CS_TME had a better prognostic value than that of the TNM stage alone (AUC_(TNM+CSTME) = 0.772, AUC _TNM = 0.687, p < 0.0001). This study provided an application of "seed and soil" strategy for prognosis prediction and individualized therapy.

Post-operative anastomotic leakage and collagen changes in patients with rectal cancer undergoing neoadjuvant chemotherapy vs chemoradiotherapy

Background

A significant difference in the anastomotic leakage (AL) rate has been observed between patients with locally advanced rectal cancer who have undergone preoperative chemotherapy and those undergoing preoperative chemoradiotherapy. This study aimed to quantitatively analyse collagen structural changes caused by preoperative chemoradiotherapy and illuminate the relationship between collagen changes and AL.

Methods

Anastomotic distal and proximal "doughnut" specimens from the Sixth Affiliated Hospital of Sun Yat-sen University (Guangzhou, China) were quantitatively assessed for collagen structural changes between patients with and without preoperative radiotherapy using multiphoton imaging. Then, patients treated with preoperative chemoradiotherapy were used as a training cohort to construct an AL-SVM classifier by the Mann-Whitney U test and support vector machine (SVM). An independent test cohort from the Fujian Province Cancer Hospital (Fuzhou, China) was used to validate the AL-SVM classifier.

Results

A total of 207 patients were included from the Sixth Affiliated Hospital of Sun Yat-sen University. The AL rate in the preoperative chemoradiotherapy group (n = 107) was significantly higher than that in the preoperative chemotherapy group (n = 100) (21.5% vs 7.0%, P = 0.003). A fully quantitative analysis showed notable morphological and spatial distribution feature changes in collagen in the preoperative chemoradiotherapy group. Then, the patients who received preoperative chemoradiotherapy were used as a training cohort to construct the AL-SVM classifier based on five collagen features and the tumor distance from the anus. The AL-SVM classifier showed satisfactory discrimination and calibration with areas under the curve of 0.907 and 0.856 in the training and test cohorts, respectively.

Conclusions

The collagen structure may be notably altered by preoperative radiotherapy. The AL-SVM classifier was useful for the individualized prediction of AL in rectal cancer patients undergoing preoperative chemoradiotherapy.

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.

The Functional Role of Extracellular Matrix Proteins in Cancer

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

Collagen Score in the Tumor Microenvironment Predicts the Prognosis of Rectal Cancer Patients after Neoadjuvant Chemoradiotherapy

BACKGROUND AND PURPOSE

Little is known about the relationship between collagen and the prognosis of patients with locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy (nCRT). This study aimed to quantitatively analyze collagen alterations, establish a collagen score (CS) in the tumor microenvironment, and evaluate and validate the relationship of the CS with prognosis in these patients.

MATERIALS AND METHODS

A total of 365 primary patients diagnosed with LARC after nCRT between 2011 and 2018 were retrospectively reviewed (training cohort: 210; independent validation cohort: 155). Multiple collagen features of two fields in the tumor microenvironment, the core of the tumor (CT) and the invasive margin (IM), were derived from multiphoton imaging, and the CS_IM-CT was generated using least absolute shrinkage and selection operator (LASSO) Cox regression analysis.

RESULTS

The CS_IM-CT was created based on 3 features: collagen area, number of collagen fibers and a Gabor textural feature. In the training cohort, the CS_IM-CT predicted 3-year disease-free survival (DFS) with an area under the receiver operating characteristic curve (AUROC) of 0.765 (0.675-0.854) and an overall survival (OS) with AUROC of 0.822 (0.734-0.909). Additionally, the CS_IM-CT was significantly associated with DFS and OS in the two cohorts. A nomogram with the CS_IM-CT was developed and showed good prognostic value predicting a 3-year DFS with an AUROC of 0.826 (0.748-0.905) and an OS with AUROC of 0.882 (0.803-0.960).

CONCLUSIONS

The CS_IM-CT is an effective prognostic marker in patients with LARC after nCRT, and the nomogram with the CS_IM-CT can be used to accurately predict the individual prognosis of these patients.