Histopathological Growth Pattern in Colorectal Liver Metastasis and The Tumor Immune Microenvironment

Prediction of transformation in the histopathological growth pattern of colorectal liver metastases after chemotherapy using CT-based radiomics

Chemotherapy alters the prognostic biomarker histopathological growth pattern (HGP) phenotype in colorectal liver metastases (CRLMs) patients. We aimed to develop a CT-based radiomics model to predict the transformation of the HGP phenotype after chemotherapy. This study included 181 patients with 298 CRLMs who underwent preoperative contrast-enhanced CT followed by partial hepatectomy between January 2007 and July 2022 at two institutions. HGPs were categorized as pure desmoplastic HGP (pdHGP) or non-pdHGP. The samples were allocated to training, internal validation, and external validation cohorts comprising 153, 65, and 29 CRLMs, respectively. Radiomics analysis was performed on pre-enhanced, arterial phase, portal venous phase (PVP), and fused images. The model was used to predict prechemotherapy HGPs in 112 CRLMs, and HGP transformation was analysed by comparing these findings with postchemotherapy HGPs determined pathologically. The prevalence of pdHGP was 19.8% (23/116) and 45.8% (70/153) in chemonaïve and postchemotherapy patients, respectively (P < 0.001). The PVP radiomics signature showed good performance in distinguishing pdHGP from non-pdHGPs (AUCs of 0.906, 0.877, and 0.805 in the training, internal validation, and external validation cohorts, respectively). The prevalence of prechemotherapy pdHGP predicted by the radiomics model was 33.0% (37/112), and the prevalence of postchemotherapy pdHGP according to the pathological analysis was 47.3% (53/112; P = 0.029). The transformation of HGP was bidirectional, with 15.2% (17/112) of CRLMs transforming from prechemotherapy pdHGP to postchemotherapy non-pdHGP and 30.4% (34/112) transforming from prechemotherapy non-pdHGP to postchemotherapy pdHGP (P = 0.005). CT-based radiomics method can be used to effectively predict the HGP transformation in chemotherapy-treated CRLM patients, thereby providing a basis for treatment decisions.

High Expression Level of TRIP6 is Correlated with Poor Prognosis in Colorectal Cancer and Promotes Tumor Cell Proliferation and Migration

Globally, colorectal cancer (CRC) is one of the leading causes of health problems. More reliable molecular biomarkers for early diagnosis in CRC patients are needed. A crucial role for thyroid hormone receptor interacting protein 6 (TRIP6) is played in tumorigenesis and tumor growth. Our study aims to determine the diagnostic and prognostic roles of TRIP6 at CRC. TRIP6 gene expression levels were analyzed in this study from public databases. The relationship between TRIP6 expression and clinicopathological characteristics was explored by logistic regression analysis. Based on Kaplan-Meier (K-M) survival curves and receiver operating characteristic curves (ROC) analysis, the prognostic and diagnostic values of TRIP6 were determined. Protein-protein interaction (PPI) networks analysis were performed using the STRING database. A Spearman's correlation analysis applied for examining the correlation between TRIP6 expression, immune cell infiltration, and immune checkpoint genes. Moreover, colony formation assay and transwell assay were used to investigate the functions of TRIP6. TRIP6 was highly expressed in CRC cancer tissues and cells. K-M survival analysis indicated that a high expression of TRIP6 was associated with poor prognosis. TRIP6 expression was obviously associated with immune cell infiltration and immune checkpoint gene expression. For validation, the results of collected clinical CRC samples show that TRIP6 levels in CRC tumor tissue were higher than those of paired adjacent colorectal tissues. Additionally, in vitro experiments suggested that TRIP6 knockdown suppressed proliferation and migration in CRC cell line RKO. TRIP6 overexpression promoted the proliferation and migration of normal colon cell line NCM460. High TRIP6 expression is associated with poor prognosis in colorectal cancer and promotes tumor cell proliferation and migration which may be a potential diagnostic and prognostic biomarker and a promising therapeutic target for CRC, providing new insights into its role in CRC.

Endoscopic Rectal Ultrasound-Based Radiomics Analysis for the Prediction of Synchronous Liver Metastasis in Patients With Primary Rectal Cancer

OBJECTIVES

To develop and validate an ultrasound-based radiomics model to predict synchronous liver metastases (SLM) in rectal cancer (RC) patients preoperatively.

METHODS

Two hundred and thirty-nine RC patients were included in this study and randomly divided into training and validation cohorts. A total of 5936 radiomics features were calculated on the basis of ultrasound images to build a radiomic model and obtain a radiomics score (Rad-score) using logistic regression. Meanwhile, clinical characteristics were collected to construct a clinical model. The radiomics-clinical model was developed and validated by integrating the radiomics features with the selected clinical characteristics. The performances of three models were evaluated and compared through their discrimination, calibration, and clinical usefulness.

RESULTS

The radiomics model was developed based on 13 radiomic features. The radiomics-clinical model, which incorporated Rad-score, CEA, and CA199, exhibited favorable discrimination and calibration with areas under the receiver operating characteristic curve (AUC) of 0.920 (95% CI: 0.874-0.965) in the training cohorts and 0.855 (95% CI: 0.759-0.951) in the validation cohorts. And the AUC of the radiomics-clinical model was 0.849 (95% CI: 0.771-0.927) for the training cohorts and 0.780 (95% CI: 0.655-0.905) for the validation cohorts, the clinical model was 0.811 (95% CI: 0.718-0.905) for the training cohorts and 0.805 (95% CI: 0.645-0.965) for the validation cohorts. Moreover, decision curve analysis (DCA) further confirmed the clinical utility of the radiomics-clinical model.

CONCLUSIONS

The radiomics-clinical model performed satisfactory predictive performance, which can help improve clinical diagnosis performance and outcome prediction for SLM in RC patients.

The Inhibition of Vessel Co-Option as an Emerging Strategy for Cancer Therapy

Vessel co-option (VCO) is a non-angiogenic mechanism of vascularization that has been associated to anti-angiogenic therapy. In VCO, cancer cells hijack the pre-existing blood vessels and use them to obtain oxygen and nutrients and invade adjacent tissue. Multiple primary tumors and metastases undergo VCO in highly vascularized tissues such as the lungs, liver or brain. VCO has been associated with a worse prognosis. The cellular and molecular mechanisms that undergo VCO are poorly understood. Recent studies have demonstrated that co-opted vessels show a quiescent phenotype in contrast to angiogenic tumor blood vessels. On the other hand, it is believed that during VCO, cancer cells are adhered to basement membrane from pre-existing blood vessels by using integrins, show enhanced motility and a mesenchymal phenotype. Other components of the tumor microenvironment (TME) such as extracellular matrix, immune cells or extracellular vesicles play important roles in vessel co-option maintenance. There are no strategies to inhibit VCO, and thus, to eliminate resistance to anti-angiogenic therapy. This review summarizes all the molecular mechanisms involved in vessel co-option analyzing the possible therapeutic strategies to inhibit this process.