The discovery of immune checkpoint proteins such as PD-1/PDL-1 and CTLA-4 represents a significant breakthrough in the field of cancer immunotherapy. Therefore, humanized monoclonal antibodies, targeting these immune checkpoint proteins have been utilized successfully in patients with metastatic melanoma, renal cell carcinoma, head and neck cancers and non-small lung cancer. The US FDA has successfully approved three different categories of immune checkpoint inhibitors (ICIs) such as PD-1 inhibitors (Nivolumab, Pembrolizumab, and Cemiplimab), PDL-1 inhibitors (Atezolimumab, Durvalumab and Avelumab), and CTLA-4 inhibitor (Ipilimumab). Unfortunately, not all patients respond favourably to these drugs, highlighting the role of biomarkers such as Tumour mutation burden (TMB), PDL-1 expression, microbiome, hypoxia, interferon-γ, and ECM in predicting responses to ICIs-based immunotherapy. The current study aims to review the literature and updates on ICIs in cancer therapy.

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Immune Checkpoint Inhibitors in Cancer Therapy

Extracellular matrix-based cancer targeting.

Microvascular invasion (MVI) is a critical determinant of the early recurrence and poor prognosis of patients with hepatocellular carcinoma (HCC). Prediction of MVI status is clinically significant for the decision of treatment strategies and the assessment of patient’s prognosis. A deep learning (DL) model was developed to predict the MVI status and grade in HCC patients based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical parameters. HCC patients with pathologically confirmed MVI status from January to December 2016 were enrolled and preoperative DCE-MRI of these patients were collected in this study. Then they were randomly divided into the training and testing cohorts. A DL model with eight conventional neural network (CNN) branches for eight MRI sequences was built to predict the presence of MVI, and further combined with clinical parameters for better prediction. Among 601 HCC patients, 376 patients were pathologically MVI absent, and 225 patients were MVI present. To predict the presence of MVI, the DL model based only on images achieved an area under curve (AUC) of 0.915 in the testing cohort as compared to the radiomics model with an AUC of 0.731. The DL combined with clinical parameters (DLC) model yielded the best predictive performance with an AUC of 0.931. For the MVI-grade stratification, the DLC models achieved an overall accuracy of 0.793. Survival analysis demonstrated that the patients with DLC-predicted MVI status were associated with the poor overall survival (OS) and recurrence-free survival (RFS). Further investigation showed that hepatectomy with the wide resection margin contributes to better OS and RFS in the DLC-predicted MVI present patients. The proposed DLC model can provide a non-invasive approach to evaluate MVI before surgery, which can help surgeons make decisions of surgical strategies and assess patient’s prognosis.

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Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters.

Background Microvascular invasion (MVI) is a critical prognostic factor of hepatocellular carcinoma (HCC). However, it could only be obtained by postoperative histological examination. Purpose To develop an end-to-end deep-learning models based on MRI images for preoperative prediction of MVI in HCC patients who underwent surgical resection. Study type Retrospective. Population Two hundred and thirty-seven patients with histologically confirmed HCC. Field strength 1.5 T and 3.0 T. Sequence Axial T2 -weighted (T2 -w) with turbo spin echo sequence, T2 -Spectral Presaturation with Inversion Recovery (T2 -SPIR), and dynamic contrast-enhanced (DCE) imaging with fat suppressed enhanced T1 high-resolution isotropic volume examination. Assessment The patients were randomly divided into training (N = 158) and validation (N = 79) sets. Data augmentation by random rotation was performed on the training set and the sample size increased to 1940 for each MR sequence. A three-dimensional convolutional neural network (3D CNN) was used to develop four deep-learning models, including three single-layer models based on single-sequence, and fusion model combining three sequences. MVI status was obtained from the postoperative pathology reports. Statistical tests The dice similarity coefficient (DSC) and Hausdorff distance (HD) were applied to assess the similarity and reproducibility between the manual segmentations of tumor from two radiologists. Receiver operating characteristic curve analysis was used to evaluate model performance. MVI was identified in 92 (38.8%) patients. Good reproducibility with interobserver DSCs of 0.90, 0.89, and 0.89 and HDs of 4.09, 3.67, and 3.60 was observed for PVP, T2 WI, and T2 -SPIR, respectively. The fusion model achieved an area under the curve (AUC) of 0.81, sensitivity of 69%, and specificity of 79% in the training set and 0.72, sensitivity of 55%, and specificity of 81% in the validation set. Data conclusion 3D CNN model may serve as a noninvasive tool to predict MVI in HCC, whereas its accuracy needs to be enhanced with larger cohort. Level of evidence 3 TECHNICAL EFFICACY: Stage 2.

Deep Learning With 3D Convolutional Neural Network for Noninvasive Prediction of Microvascular Invasion in Hepatocellular Carcinoma.

Abstract Tumors are comprised of both cancer cells and surrounding stromal components. As an essential part of the tumor microenvironment, the tumor stroma is highly dynamic, heterogeneous and commonly tumor-type specific, and it mainly includes noncellular compositions such as the extracellular matrix and the unique cancer-associated vascular system as well as a wide variety of cellular components including activated cancer-associated fibroblasts, mesenchymal stromal cells, pericytes. All these elements operate with each other in a coordinated fashion and collectively promote cancer initiation, progression, metastasis and therapeutic resistance. Over the past few decades, numerous studies have been conducted to study the interaction and crosstalk between stromal components and neoplastic cells. Meanwhile, we have also witnessed an exponential increase in the investigation and recognition of the critical roles of tumor stroma in solid tumors. A series of clinical trials targeting the tumor stroma have been launched continually. In this review, we introduce and discuss current advances in the understanding of various stromal elements and their roles in cancers. We also elaborate on potential novel approaches for tumor-stroma-based therapeutic targeting, with the aim to promote the leap from bench to bedside. 

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Targeting the tumor stroma for cancer therapy

The extracellular matrix (ECM) is one of the major components of tumors that plays multiple crucial roles, including mechanical support, modulation of the microenvironment, and a source of signaling molecules. The quantity and cross-linking status of ECM components are major factors determining tissue stiffness. During tumorigenesis, the interplay between cancer cells and the tumor microenvironment (TME) often results in the stiffness of the ECM, leading to aberrant mechanotransduction and further malignant transformation. Therefore, a comprehensive understanding of ECM dysregulation in the TME would contribute to the discovery of promising therapeutic targets for cancer treatment. Herein, we summarized the knowledge concerning the following: (1) major ECM constituents and their functions in both normal and malignant conditions; (2) the interplay between cancer cells and the ECM in the TME; (3) key receptors for mechanotransduction and their alteration during carcinogenesis; and (4) the current therapeutic strategies targeting aberrant ECM for cancer treatment.

/pdf/extracellular-matrix-and-its-therapeutic-potential-for-4mannczf4y.pdf

Extracellular matrix and its therapeutic potential for cancer treatment

Background The present study constructed and validated the use of contrast-enhanced computed tomography (CT)-based radiomics to preoperatively predict microvascular invasion (MVI) status (positive vs negative) and risk (low vs high) in patients with hepatocellular carcinoma (HCC) Methods We enrolled 637 patients from two independent institutions Patients from Institution I were randomly divided into a training cohort of 451 patients and a test cohort of 111 patients Patients from Institution II served as an independent validation set The LASSO algorithm was used for the selection of 798 radiomics features Two classifiers for predicting MVI status and MVI risk were developed using multivariable logistic regression We also performed a survival analysis to investigate the potentially prognostic value of the proposed MVI classifiers Results The developed radiomics signature predicted MVI status with an area under the receiver operating characteristic curve (AUC) of 780, 776, and 743 in the training, test, and independent validation cohorts, respectively The final MVI status classifier that integrated two clinical factors (age and α-fetoprotein level) achieved AUC of 806, 803, and 796 in the training, test, and independent validation cohorts, respectively For MVI risk stratification, the AUCs of the radiomics signature were 746, 664, and 700 in the training, test, and independent validation cohorts, respectively, and the AUCs of the final MVI risk classifier-integrated clinical stage were 783, 778, and 740, respectively Survival analysis showed that our MVI status classifier significantly stratified patients for short overall survival or early tumor recurrence Conclusions Our CT radiomics-based models were able to predict MVI status and MVI risk of HCC and might serve as a reliable preoperative evaluation tool

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ctm2.111

Contrast-enhanced CT radiomics for preoperative evaluation of microvascular invasion in hepatocellular carcinoma: A two-center study.

Nonalcoholic fatty liver disease (NAFLD) is often accompanied by systemic metabolic disorders such as hyperglycemia, insulin resistance, and obesity. The relationship between NAFLD and systemic metabolic disorders has been well reviewed before, however, the metabolic changes that occur in hepatocyte itself have not been discussed. In NAFLD, many metabolic pathways have undergone significant changes in hepatocyte, such as enhanced glycolysis, gluconeogenesis, lactate production, tricarboxylic acid (TCA) cycle, and decreased ketone body production, mitochondrial respiration, and adenosine triphosphate (ATP) synthesis, which play a role in compensating or exacerbating disease progression, and there is close and complex interaction existed between these metabolic pathways. Among them, some metabolic pathways can be the potential therapeutic targets for NAFLD. A detailed summary of the metabolic characteristics of hepatocytes in the context of NAFLD helps us better understand the pathogenesis and outcomes of the disease.

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Metabolic Changes of Hepatocytes in NAFLD

Background: To compare the predictive power between radiomics and non-radiomics (conventional imaging and functional imaging methods) for preoperative evaluation of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) Methods: Comprehensive publications were screened in PubMed, Embase, and Cochrane Library Studies focusing on the discrimination values of imaging methods, including radiomics and non-radiomics methods, for MVI evaluation were included in our meta-analysis Results: Thirty-three imaging studies with 5,462 cases, focusing on preoperative evaluation of MVI status in HCC, were included The sensitivity and specificity of MVI prediction in HCC were 078 [95% confidence interval (CI): 075-080; I 2 = 707%] and 078 (95% CI: 076-081; I 2 = 00%) for radiomics, respectively, and were 073 (95% CI: 071-075; I 2 = 837%) and 082 (95% CI: 080-083; I 2 = 865%) for non-radiomics, respectively The areas under the receiver operation curves for radiomics and non-radiomics to predict MVI status in HCC were 08550 and 08601, respectively, showing no significant difference Conclusion: The imaging method is feasible to predict the MVI state of HCC Radiomics method based on medical image data is a promising application in clinical practice and can provide quantifiable image features With the help of these features, highly consistent prediction performance will be achieved in anticipation

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Preoperative Prediction Power of Imaging Methods for Microvascular Invasion in Hepatocellular Carcinoma: A Systemic Review and Meta-Analysis

Introduction: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide due to poor survival outcome. Thus, there is an urgent need to identify effective biomarkers for early diagnosis and prognosis prediction. Methods: A total of 389 differentially expressed genes (DEGs) between HCC samples and normal were selected based on the Robust Rank Aggregation (RRA) method. We combined DEGs expression and clinical traits to construct a gene co-expression network through WGCNA. Forty hub genes were selected from the key module. Among them, YWHAB, PPAT, NOL10 were eventually identified as prognostic biomarkers using multivariate Cox regression model. Biomarkers expression pattern was investigated by informatic analysis and verified by RNA-seq of 32 patients with HCC. DiseaseMeth 2.0, MEXPRESS, and Tumor Immune Estimation Resource (TIMER) were used to assess the methylation and immune status of biomarkers. GSVA, CCK8, colony formation assay, Edu imaging kit, wound-healing assay, and xenograft tumor model were utilized to investigate the effects of biomarkers on proliferation, metastasis of HCC cells in vitro, and in vivo. The Kaplan-Meier (KM) plotter and ROC curves were used to validate the prognostic and diagnostic value of biomarker expression. Results: All the selected biomarkers were upregulated in HCC samples and higher expression levels were associated with advanced tumor stages and T grades. The regulation of YWHAB, PPAT, NOL10 promoter methylation varied in tumors, and precancerous normal tissues. Immune infiltration analysis suggested that the abnormal regulations of these biomarkers were likely attributed to B cells and dendritic cells. GSVA for these biomarkers showed their great contributions to proliferation of HCC. Specific inhibition of their expression had strong effects on tumorigenesis in vitro and in vivo. ROC and KM curves confirmed their usefulness of diagnosis and prognosis of HCC. Conclusions: These findings identified YWHAB, PPAT, and NOL10 as novel biomarkers and validated their diagnostic and prognostic value for HCC.

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Jiacheng Huang

Papers

Extracellular matrix and its therapeutic potential for cancer treatment

Contrast-enhanced CT radiomics for preoperative evaluation of microvascular invasion in hepatocellular carcinoma: A two-center study.

Metabolic Changes of Hepatocytes in NAFLD

Preoperative Prediction Power of Imaging Methods for Microvascular Invasion in Hepatocellular Carcinoma: A Systemic Review and Meta-Analysis

Identification and Validation of Novel Biomarkers for Diagnosis and Prognosis of Hepatocellular Carcinoma.