DNA methylation signatures predicting liver metastasis of colorectal cancer: A proof-of-concept pilot study.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e16080-e16080
Author(s):  
Jianming Ying ◽  
Weihua Li ◽  
Kaihua Liu ◽  
Cong Xiao ◽  
Shuyu Wu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Early Detection ◽  
Predictive Model ◽  
Primary Tumor ◽  
Metastatic Tumor ◽  
Training Data ◽  
Training Dataset ◽  
Tissue Samples ◽  
Normal Tissues

e16080 Background: Liver metastasis (LIM) is the leading cause of death in colorectal cancer (CRC) patients. Early detection of LIM may improve outcome in CRC patients. The aim of this study was to evaluate the feasibility of predicting LIM of CRC using methylation profiles. Methods: We performed Roche targeted (~5.5 million methylation sites) bisulfite sequencing of matched primary, metastatic and their adjacent normal tissue samples from 5 CRC patients with LIM, 5 patients with lung metastasis (LUM) and 8 patients without metastasis in the training cohort (n = 48 samples). Differential methylation regions (DMR) of LUM were identified and a predictive model was developed. The model was further validated in primary tumor sample from nine patients (6 with LIM). Results: By comparing primary tumor vs adjacent normal tissues and metastatic tumor vs adjacent normal tissues in CRC patients with LIM, we identified 28954 common DMRs which indicating the methylation characteristic of CRC with LIM. Similarly, 16187 DMRs were identified in patients with LUM. 9179 DMRs are shared in both LIM and LUM comparisons which should be the common characteristic of CRC tumor tissue regardless of the location of metastasis. 7008 DMRs are LUM specific and 19775 DMRs are LIM specific. In order to predict LIM in primary, early changes in LIM specific DMRs should be identified. Hence, we further selected 4134 DMRs by chossing significantly differentically methylated regions between LIM primary tissues and LUM primary tissues. To increase the ability of distinguishing LIM from other normal tissues and non-matastasis CRC tumors, 1215 DMRs were finally selected which also showed increasing or decreasing trend of methylation level through the progression of CRC. The final 1215 biomarkers were used to construct a random forest model using methlylation profile of 5 CRC patients with LIM as positive training data and 5 CRC patients with LUM as well as 8 patients without metastasis as negative training data. Through the feature recursive elimination method, one methylation site (chr8.72468901-72469000) was identified with ROC of 0.9 in the training dataset. The predictive model was validated in an independent dataset which is composed of 6 patients with LIM and 3 patients without metastasis, and achieved an AUC of 0.87. Conclusions: Our findings demonstrate the utility of methylation biomarkers for the molecular characterization of metastatic precursors, with implications for prediction and early detection of liver metastasis in CRC.

scholarly journals Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Tenghui Han ◽  
Jun Zhu ◽  
Xiaoping Chen ◽  
Rujie Chen ◽  
Yu Jiang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Predictive Model ◽  
Area Under The Curve ◽  
Predictive Ability ◽  
Subsequent Treatment ◽  
Training Dataset ◽  
Discriminative Ability ◽  
Fundamental Parameters

Abstract Background Liver is the most common metastatic site of colorectal cancer (CRC) and liver metastasis (LM) determines subsequent treatment as well as prognosis of patients, especially in T1 patients. T1 CRC patients with LM are recommended to adopt surgery and systematic treatments rather than endoscopic therapy alone. Nevertheless, there is still no effective model to predict the risk of LM in T1 CRC patients. Hence, we aim to construct an accurate predictive model and an easy-to-use tool clinically. Methods We integrated two independent CRC cohorts from Surveillance Epidemiology and End Results database (SEER, training dataset) and Xijing hospital (testing dataset). Artificial intelligence (AI) and machine learning (ML) methods were adopted to establish the predictive model. Results A total of 16,785 and 326 T1 CRC patients from SEER database and Xijing hospital were incorporated respectively into the study. Every single ML model demonstrated great predictive capability, with an area under the curve (AUC) close to 0.95 and a stacking bagging model displaying the best performance (AUC = 0.9631). Expectedly, the stacking model exhibited a favorable discriminative ability and precisely screened out all eight LM cases from 326 T1 patients in the outer validation cohort. In the subgroup analysis, the stacking model also demonstrated a splendid predictive ability for patients with tumor size ranging from one to50mm (AUC = 0.956). Conclusion We successfully established an innovative and convenient AI model for predicting LM in T1 CRC patients, which was further verified in the external dataset. Ultimately, we designed a novel and easy-to-use decision tree, which only incorporated four fundamental parameters and could be successfully applied in clinical practice.

scholarly journals LncRNA MIR17HG promotes colorectal cancer liver metastasis by mediating a glycolysis-associated positive feedback circuit

Oncogene ◽  
2021 ◽  
Author(s):  
Senlin Zhao ◽  
Bingjie Guan ◽  
Yushuai Mi ◽  
Debing Shi ◽  
Ping Wei ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Positive Feedback ◽  
Feedback Loop ◽  
Metastatic Tumor ◽  
Feedback Circuit ◽  
Colorectal Cancer Liver Metastasis ◽  
Positive Feedback Circuit

AbstractGlycolysis plays a crucial role in reprogramming the metastatic tumor microenvironment. A series of lncRNAs have been identified to function as oncogenic molecules by regulating glycolysis. However, the roles of glycolysis-related lncRNAs in regulating colorectal cancer liver metastasis (CRLM) remain poorly understood. In the present study, the expression of the glycolysis-related lncRNA MIR17HG gradually increased from adjacent normal to CRC to the paired liver metastatic tissues, and high MIR17HG expression predicted poor survival, especially in patients with liver metastasis. Functionally, MIR17HG promoted glycolysis in CRC cells and enhanced their invasion and liver metastasis in vitro and in vivo. Mechanistically, MIR17HG functioned as a ceRNA to regulate HK1 expression by sponging miR-138-5p, resulting in glycolysis in CRC cells and leading to their invasion and liver metastasis. More interestingly, lactate accumulated via glycolysis activated the p38/Elk-1 signaling pathway to promote the transcriptional expression of MIR17HG in CRC cells, forming a positive feedback loop, which eventually resulted in persistent glycolysis and the invasion and liver metastasis of CRC cells. In conclusion, the present study indicates that the lactate-responsive lncRNA MIR17HG, acting as a ceRNA, promotes CRLM through a glycolysis-mediated positive feedback circuit and might be a novel biomarker and therapeutic target for CRLM.

scholarly journals Exosomal ANGPTL1 attenuates colorectal cancer liver metastasis by regulating Kupffer cell secretion pattern and impeding MMP9 induced vascular leakiness

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
Kai Jiang ◽  
Haiyan Chen ◽  
Yimin Fang ◽  
Liubo Chen ◽  
Chenhan Zhong ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Kupffer Cell ◽  
Cell Secretion ◽  
Gene Expressions ◽  
Protein Levels ◽  
Normal Tissues ◽  
Mmp9 Expression ◽  
Stat3 Signaling Pathway

Abstract Background Angiopoietin-like protein 1 (ANGPTL1) has been proved to suppress tumor metastasis in several cancers. However, its extracellular effects on the pre-metastatic niches (PMNs) are still unclear. ANGPTL1 has been identified in exosomes, while its function remains unknown. This study was designed to explore the role of exosomal ANGPTL1 on liver metastasis in colorectal cancer (CRC). Methods Exosomes were isolated by ultracentrifugation. The ANGPTL1 level was detected in exosomes derived from human CRC tissues. The effects of exosomal ANGPTL1 on CRC liver metastasis were explored by the intrasplenic injection mouse model. The liver PMN was examined by vascular permeability assays. Exosomal ANGPTL1 localization was validated by exosome labeling. The regulatory mechanisms of exosomal ANGPTL1 on Kupffer cells were determined by RNA sequencing. qRT-PCR, Western Blot, and ELISA analysis were conducted to examine gene expressions at mRNA and protein levels. Results ANGPTL1 protein level was significantly downregulated in the exosomes derived from CRC tumors compared with paired normal tissues. Besides, exosomal ANGPTL1 attenuated liver metastasis and impeded vascular leakiness in the liver PMN. Moreover, exosomal ANGPTL1 was mainly taken up by KCs and regulated the KCs secretion pattern, enormously decreasing the MMP9 expression, which finally prevented the liver vascular leakiness. In mechanism, exosomal ANGPTL1 downregulated MMP9 level in KCs by inhibiting the JAK2-STAT3 signaling pathway. Conclusions Taken together, exosomal ANGPTL1 attenuated CRC liver metastasis and impeded vascular leakiness in the liver PMN by reprogramming the Kupffer cell and decreasing the MMP9 expression. This study suggests a suppression role of exosomal ANGPTL1 on CRC liver metastasis and expands the approach of ANGPTL1 functioning.

Lung metastasis as predictor for prognosis in metastatic colorectal cancer with mutated KRAS.

2019 ◽  
Vol 37 (4_suppl) ◽  
pp. 636-636
Author(s):  
Ben Boursi ◽  
Einat shacham-Shmueli ◽  
Yaacov Richard Lawrence ◽  
Yu-Xiao Yang ◽  
Kim Anna Reiss ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Metastatic Colorectal Cancer ◽  
Lung Metastasis ◽  
Primary Tumor ◽  
Clinical Decision Making ◽  
Secondary Analysis ◽  
Tumor Location ◽  
Single Site ◽  
Primary Tumor Location

636 Background: Previous studies have shown that prognosis in metastatic colorectal cancer (mCRC) may vary according to sites of metastasis. We evaluated prognosis in individuals with single site metastasis, according to several clinical and genetic variables. Methods: Using the National Cancer Database we identified 58,044 mCRC patients with a synchronous single site of metastasis. We first examined the effect of metastasis site on prognosis. In a secondary analysis, among individuals who had not undergone surgery or received radiotherapy, we examined the prognostic value of chemotherapy intensity, KRAS status, primary tumor location and CEA levels. Results: Individuals with lung metastasis had the best prognosis (HR = 0.80, 0.77-0.83), followed by those with liver metastasis (HR = 1.11, 1.07-1.15), while those with bone or brain metastasis had the worse prognosis. In a subgroup analysis, we assessed prognosis among individuals who received multi-agent chemotherapy and had not undergone surgery or received radiotherapy. Individuals with lung metastasis and mutant KRAS had better prognosis compared with those with liver metastasis, (HR = 0.69, 0.54-0.88), regardless of primary tumor location or CEA levels. Conclusions: Single site metastasis to the lungs is associated with better prognosis in mCRC, specifically among KRAS mutant tumors. This survival advantage should be taken into consideration in clinical decision-making.

scholarly journals PD-L1 expression in liver metastasis: its clinical significance and discordance with primary tumor in colorectal cancer

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Xiao-Li Wei ◽  
Xuan Luo ◽  
Hui Sheng ◽  
Yun Wang ◽  
Dong-Liang Chen ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Liver Metastases ◽  
Primary Tumor ◽  
Checkpoint Inhibitors ◽  
Tissue Microarrays ◽  
Tumor Differentiation ◽  
Metastatic Tumors ◽  
Primary Tumors ◽  
Expression Cluster

Abstract Background The outcomes of immune checkpoint inhibitors in cancer patients with liver metastases are poor, which may be related to a different tumor microenvironment in liver metastases from primary tumors. This study was aimed to analyze PD-L1 expression and the immune microenvironment status in liver metastases and compare the differences of PD-L1 expression between primary tumors and liver metastases of colorectal cancer. Methods 74 cases of pathologically confirmed colorectal cancer with liver metastasis underwent resection from our hospital were included. Tissue microarrays were used for the interpretation of PD-L1 expression, cluster of differentiation 4 (CD4) and CD8 density by immunohistochemistry. We evaluated the disparity between primary tumor and liver metastasis in PD-L1 expression, CD4 and CD8 density and analyzed the factors associated with obvious PD-L1 disparity. Results The expression of PD-L1 was positively related to the density of CD4 and CD8 in liver metastases. The expression of PD-L1 in liver metastases was higher than in primary tumors in certain subgroups, including patients with concurrent liver metastases (n = 63, p = 0.05), patients receiving concurrent resection of primary and metastatic tumors (n = 56, p = 0.04). The two subgroups generally reflected those without inconsistent external influences, such as treatment and temporal factors, between primary tumors and liver metastases. In these subgroups, the intrinsic differences of microenvironment between primary tumors and liver metastases could be identified. Furthermore, tumor differentiation [moderate vs. poor: OR = 0.23, 95% CI: 0.03–0.99, p = 0.05)] were demonstrated to be associated with obvious discordance of PD-L1 expression between primary tumors and liver metastases. Conclusions The expression of PD-L1 in liver metastases was higher than in primary tumors in subgroups, reflecting intrinsic microenvironment differences between primary and metastatic tumors. Obvious discordance of PD-L1 expression between primary tumor and liver metastasis was significantly related to the tumor differentiation.

scholarly journals Exosomal ANGPTL1 Attenuates Colorectal Cancer Liver Metastasis by Regulating Kupffer Cell Secretion Pattern and Impeding MMP9 Induced Vascular Leakiness

2020 ◽  
Author(s):  
Kai Jiang ◽  
Haiyan Chen ◽  
Yimin Fang ◽  
Liubo Chen ◽  
Chenhan Zhong ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Kupffer Cell ◽  
Cell Secretion ◽  
Gene Expressions ◽  
Protein Levels ◽  
Normal Tissues ◽  
Mmp9 Expression ◽  
Stat3 Signaling Pathway

Abstract Background: Angiopoietin-like protein 1 (ANGPTL1) has been proved to suppress tumor metastasis in several cancers. However, its extracellular effects on the pre-metastatic niches (PMNs) are still unclear. ANGPTL1 has been identified in exosomes, while its function remains unknown. This study was designed to explore the role of exosomal ANGPTL1 on liver metastasis in colorectal cancer (CRC).Methods: Exosomes were isolated by ultracentrifugation. The ANGPTL1 level was detected in exosomes derived from human CRC tissues. The effects of exosomal ANGPTL1 on CRC liver metastasis were explored by the intrasplenic injection mouse model. The liver PMN was examined by vascular permeability assays. Exosomal ANGPTL1 localization was validated by exosome labeling. The regulatory mechanisms of exosomal ANGPTL1 on Kupffer cells were determined by RNA sequencing. qRT-PCR, Western Blot, and ELISA analysis were conducted to examine gene expressions at mRNA and protein levels.Results: ANGPTL1 protein level was significantly downregulated in the exosomes derived from CRC tumors compared with paired normal tissues. Besides, exosomal ANGPTL1 attenuated liver metastasis and impeded vascular leakiness in the liver PMN. Moreover, exosomal ANGPTL1 was mainly taken up by KCs and regulated the KCs secretion pattern, enormously decreasing the MMP9 expression, which finally prevented the liver vascular leakiness. In mechanism, exosomal ANGPTL1 downregulated MMP9 level in KCs by inhibiting the JAK2-STAT3 signaling pathway.Conclusions: Taken together, exosomal ANGPTL1 attenuated CRC liver metastasis and impeded vascular leakiness in the liver PMN by reprogramming the Kupffer cell and decreasing the MMP9 expression. This study suggests a suppression role of exosomal ANGPTL1 on CRC liver metastasis and expands the approach of ANGPTL1 functioning.

Application of Artificial Intelligence in a Real-World Research for Predicting the Risk of Liver Metastasis in T1 Colorectal Cancer

2021 ◽  
Author(s):  
Tenghui Han ◽  
Jun Zhu ◽  
Dong Xu ◽  
Rujie Chen ◽  
Shuai Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Predictive Model ◽  
Tumor Size ◽  
Area Under The Curve ◽  
Subsequent Treatment ◽  
Tumor Type ◽  
Discriminative Ability ◽  
N Stage

Abstract Background: The liver is the most common metastatic site of colorectal cancer (CRC) and liver metastasis (LM) determines subsequent treatment as well as prognosis of patients, especially in T1 patients. T1 CRC patients with LM are recommended to adopt surgery and systematic treatments rather than endoscopic therapy alone. However, there is still no effective model to predict the risk of LM in T1 CRC patients and we aim to develop a novel and accurate predictive model.Methods: We integrated two independent CRC cohorts from Surveillance Epidemiology and End Results database (SEER) and Xijing hospital. Artificial intelligence (AI) and machine learning methods were adopted to establish the predictive model.Results: A total of 16785 and 326 T1 CRC patients from SEER database and our hospital were incorporated respectively in the study. We found that age, gender, married status, primary site, tumor size, carcinoembryonic antigen (CEA), tumor type, grade, N stage and perineural invasion were significant independent factors for predicting the presence of LM, among which tumor size is the most important. The stacking bagging model showed the best predictive capability, achieving a sensitivity of 0.8452, a specificity of 0.9566, and an area under the curve of 0.9631. In addition, the stacking model had an excellent discriminative ability and accurately screened out eight LM cases from 326 T1 patients in the outer validation cohort. Ultimately, we authenticated the prognostic value of the stacking model, which is consistent with the predictive result of LM.Conclusion: We successfully established an innovative and convenient AI model for predicting LM in T1 CRC patients, which was further verified in our dataset.

PET-CT Standard uptake value predict bevacizumab-containing chemotherapy response and prognosis for unresectable colorectal cancer liver metastasis

2019 ◽  
Author(s):  
Yang Lv ◽  
QingYang Feng ◽  
WenTao Tang ◽  
YuQiu Xu ◽  
SongBin Lin ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Tumor Size ◽  
Metastatic Tumor ◽  
Standard Uptake Value ◽  
Primary Lesion ◽  
First Line ◽  
Colorectal Cancer Liver Metastasis ◽  
Pet Ct ◽  
Line Chemotherapy

Abstract Background: Standard uptake value (SUV) of PET-CT is an indicator of tumor metabolic response. In this paper, we aim to explore the clinical value of SUV on the unresectable colorectal cancer liver metastasis (CRLM) patients receiving bevacizumab-containing chemotherapy. Method: This study was performed retrospectively. A total of 185 CRLM patients between April 2011 to December 2015 with complete clinical data were included in this study. All the enrolled patients were assigned into two treatment cohorts (bevacizumab plus first-line chemotherapy cohort and chemotherapy only cohort). A blindly, independent radiologist evaluated images for RECIST and morphologic response. All clinical variables, and various PET/CT parameters were statistically compared with progression-free survival (PFS) and overall survival (OS). Primary and Metastatic tumor SUV were selected for analysis. Results: Among the 185 patients, 101 patients received first line chemotherapy plus bevacizumab (beva cohort), 84 patients only received first-line chemotherapy (CMT cohort). Baseline characteristics of two cohorts showed no statistical difference (P>0.05). Primary SUV level was correlated with primary tumor size, while metastatic SUV was statistically correlated with metastatic tumor number and tumor size (P=0.000). Primary lesion, metastatic lesion SUV and elevation of SUV demonstrated prognostic role for OS (P<0.05). SUV gap were statistically associated with optimal response in bevacizumab cohort (P=0.03) and no-PD status in chemotherapy cohort (P=0.019), respectively. After multivariate analysis, elevated SUV is an independent risk factor for OS (P=0.000). Besides, elevation of SUV between metastatic and primary lesion can be a predictive factor for bevacizumab survival benefit. Conclusion: PET-CT scan is important for CRLM patients. Our study demonstrated that an elevation of SUV was a better prognostic and predictive marker for CRLM patients.

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