Integrative radiomics analyses identify universal signature for predicting prognosis and therapeutic vulnerabilities across primary and secondary liver cancers: A multi-cohort study
As the hallmark of cancer, genetic and phenotypic heterogeneity leads to biomarkers that are typically tailored to specific cancer type or subtype. This specificity introduces complexities in facilitating streamlined evaluations across diverse cancer types and optimizing therapeutic outcomes. In thi...
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Elsevier
2024-12-01
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| Series: | Pharmacological Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1043661824004808 |
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| author | Hongjie Xin Qianwei Lai Yanping Liu Naying Liao Ying Wang Bihong Liao Keyang Zhou Yuchen Zhou Yang Bai Zhihua Chen Yuanping Zhou |
| author_facet | Hongjie Xin Qianwei Lai Yanping Liu Naying Liao Ying Wang Bihong Liao Keyang Zhou Yuchen Zhou Yang Bai Zhihua Chen Yuanping Zhou |
| author_sort | Hongjie Xin |
| collection | DOAJ |
| description | As the hallmark of cancer, genetic and phenotypic heterogeneity leads to biomarkers that are typically tailored to specific cancer type or subtype. This specificity introduces complexities in facilitating streamlined evaluations across diverse cancer types and optimizing therapeutic outcomes. In this study, we comprehensively characterized the radiological patterns underlying liver cancer (LC) by integrating radiomics profiles from computed tomography (CT) images of hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and colorectal cancer liver metastases (CRLM) through unsupervised clustering analysis. We identified three distinct radiomics clusters, displaying heterogeneity in prognosis. Subsequently, we formulated a shared prognosticator, the liver cancer radiomics signature (LCRS), by discovering and manifesting connectivity among radiomics phenotypes using GGI strategy. We validated that the LCRS is independent prognostic factor after adjusting for clinic-pathologic variables (all P < 0.05), with the LCRS-High group consistently associated with worse survival outcomes across HCC, ICC, and CRLM. However, the LCRS-High group showed clinical benefit from adjuvant chemotherapy, leading to reduced disease recurrence risk and improved survival. By contrast, the LCRS-Low group, including a subset of gastric cancer liver metastases (GCLM), exhibited more favorable response to immune checkpoint inhibitors (ICIs)-based combinational therapy (P = 0.02, hazard ratio (HR): 0.34 [95 % confidence interval (CI): 0.13–0.88]). Further analysis revealed that Notch signaling pathway was enriched in LCRS-High tumors, while LCRS-Low tumors exhibited higher infiltration of natural killer cell. These findings highlight the promise of this universal scoring model to personalize management strategies for patients with LC. |
| format | Article |
| id | doaj-art-45f1c1e431874bafa5d7a68e9c611811 |
| institution | Kabale University |
| issn | 1096-1186 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Pharmacological Research |
| spelling | doaj-art-45f1c1e431874bafa5d7a68e9c6118112024-12-18T08:47:26ZengElsevierPharmacological Research1096-11862024-12-01210107535Integrative radiomics analyses identify universal signature for predicting prognosis and therapeutic vulnerabilities across primary and secondary liver cancers: A multi-cohort studyHongjie Xin0Qianwei Lai1Yanping Liu2Naying Liao3Ying Wang4Bihong Liao5Keyang Zhou6Yuchen Zhou7Yang Bai8Zhihua Chen9Yuanping Zhou10Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, ChinaDepartment of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, ChinaDepartment of Gastroenterology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, ChinaDepartment of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, ChinaDepartment of Gastroenterology, The Fourth Hospital of Changsha, Hunan Normal University, Changsha, ChinaDepartment of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, ChinaDepartment of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, ChinaDepartment of General Surgery, Cancer Center, Integrated Hospital of Traditional Chinese Medicine, Southern Medical University, Guangzhou, ChinaDepartment of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China; Corresponding authors.Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China; Correspondence to: Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang 421001, China.Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China; Corresponding authors.As the hallmark of cancer, genetic and phenotypic heterogeneity leads to biomarkers that are typically tailored to specific cancer type or subtype. This specificity introduces complexities in facilitating streamlined evaluations across diverse cancer types and optimizing therapeutic outcomes. In this study, we comprehensively characterized the radiological patterns underlying liver cancer (LC) by integrating radiomics profiles from computed tomography (CT) images of hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and colorectal cancer liver metastases (CRLM) through unsupervised clustering analysis. We identified three distinct radiomics clusters, displaying heterogeneity in prognosis. Subsequently, we formulated a shared prognosticator, the liver cancer radiomics signature (LCRS), by discovering and manifesting connectivity among radiomics phenotypes using GGI strategy. We validated that the LCRS is independent prognostic factor after adjusting for clinic-pathologic variables (all P < 0.05), with the LCRS-High group consistently associated with worse survival outcomes across HCC, ICC, and CRLM. However, the LCRS-High group showed clinical benefit from adjuvant chemotherapy, leading to reduced disease recurrence risk and improved survival. By contrast, the LCRS-Low group, including a subset of gastric cancer liver metastases (GCLM), exhibited more favorable response to immune checkpoint inhibitors (ICIs)-based combinational therapy (P = 0.02, hazard ratio (HR): 0.34 [95 % confidence interval (CI): 0.13–0.88]). Further analysis revealed that Notch signaling pathway was enriched in LCRS-High tumors, while LCRS-Low tumors exhibited higher infiltration of natural killer cell. These findings highlight the promise of this universal scoring model to personalize management strategies for patients with LC.http://www.sciencedirect.com/science/article/pii/S1043661824004808Liver cancerMachine learningRadiomics profilingBiomarkerPrognosis |
| spellingShingle | Hongjie Xin Qianwei Lai Yanping Liu Naying Liao Ying Wang Bihong Liao Keyang Zhou Yuchen Zhou Yang Bai Zhihua Chen Yuanping Zhou Integrative radiomics analyses identify universal signature for predicting prognosis and therapeutic vulnerabilities across primary and secondary liver cancers: A multi-cohort study Pharmacological Research Liver cancer Machine learning Radiomics profiling Biomarker Prognosis |
| title | Integrative radiomics analyses identify universal signature for predicting prognosis and therapeutic vulnerabilities across primary and secondary liver cancers: A multi-cohort study |
| title_full | Integrative radiomics analyses identify universal signature for predicting prognosis and therapeutic vulnerabilities across primary and secondary liver cancers: A multi-cohort study |
| title_fullStr | Integrative radiomics analyses identify universal signature for predicting prognosis and therapeutic vulnerabilities across primary and secondary liver cancers: A multi-cohort study |
| title_full_unstemmed | Integrative radiomics analyses identify universal signature for predicting prognosis and therapeutic vulnerabilities across primary and secondary liver cancers: A multi-cohort study |
| title_short | Integrative radiomics analyses identify universal signature for predicting prognosis and therapeutic vulnerabilities across primary and secondary liver cancers: A multi-cohort study |
| title_sort | integrative radiomics analyses identify universal signature for predicting prognosis and therapeutic vulnerabilities across primary and secondary liver cancers a multi cohort study |
| topic | Liver cancer Machine learning Radiomics profiling Biomarker Prognosis |
| url | http://www.sciencedirect.com/science/article/pii/S1043661824004808 |
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