Noninvasive prognostic classification of ITH in HCC with multi-omics insights and therapeutic implications

Yangyang Xie^{1

2

3

4}, Fang Wang^{5

6

7

8}, Jingwei Wei^{9

10}, Zefeng Shen^{1

2

3

4}, Xue Song¹¹, Yali Wang^{1

2

3

4}, Hongjun Chen^{1

2

3

4}, Liye Tao^{1

2

3

4}, Junhao Zheng^{1

2

3

4}, Lanfen Lin¹², Ziwei Niu¹², Xiaojun Guan¹³, Tianhan Zhou¹⁴, Zhengao Xu^{1

2

3

4}, Yang Liu^{1

2

3

4}, Danwei Du¹⁵, Haoyu Pan^{1

2

3

4}, Shihao Li^{1

2

3

4}, Wenbin Ji¹⁶, Wei Zhou¹⁷, Yunjun Yang¹⁸, Jie Tian^{9

10

19

20}, Junjie Xu^{1

2

3

4}, Hongjie Hu⁸, Xiao Liang^{1

21

22}

Affiliations

¹ Zhejiang Key Laboratory of Multi-omics Precision Diagnosis and Treatment of Liver Diseases, Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, 310016 Hangzhou, China.
² Zhejiang Minimal Invasive Diagnosis and Treatment Technology Research Center of Severe Hepatobiliary Disease, Zhejiang Research and Development Engineering Laboratory of Minimally Invasive Technology and Equipment, 310016 Hangzhou, China.
³ Zhejiang University Cancer Center, 310058 Hangzhou, China.
⁴ Liangzhu Laboratory, Zhejiang University Medical Center, 311121 Hangzhou, China.
⁵ Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030 Wuhan, China.
⁶ Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, 430022 Wuhan, China.
⁷ Hubei Key Laboratory of Molecular Imaging, 430022 Wuhan, China.
⁸ Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 310016 Hangzhou, China.
⁹ Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, 100190 Beijing, China.
¹⁰ Beijing Key Laboratory of Molecular Imaging, 100190 Beijing, China.
¹¹ Department of Respiratory and Critical Care Medicine, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, 310007 Hangzhou, China.
¹² The College of Computer Science and Technology, Zhejiang University, 310027 Hangzhou, China.
¹³ Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China.
¹⁴ Department of General Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, 310007 Hangzhou, China.
¹⁵ Department of Anorectal, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, 310000 Hangzhou, China.
¹⁶ Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 317000 Taizhou, China.
¹⁷ Department of Radiology, Huzhou Central Hospital, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, 313000 Huzhou, China.
¹⁸ Department of Radiology, The First Affiliated Hospital, Wenzhou Medical University, 325006 Wenzhou, China.
¹⁹ Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, 100191 Beijing, China.
²⁰ Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, 710126 Xi'an, China.
²¹ School of Medicine, Shaoxing University, 312000 Shaoxing, China.
²² School of Basic Medical Sciences and Forensic Medicine, Hangzhou Medical College, 310000 Hangzhou, China.

PMID: 40315307
PMCID: PMC12047409
DOI: 10.1126/sciadv.ads8323

Noninvasive prognostic classification of ITH in HCC with multi-omics insights and therapeutic implications

Yangyang Xie et al. Sci Adv. 2025.

. 2025 May 2;11(18):eads8323.

doi: 10.1126/sciadv.ads8323. Epub 2025 May 2.

Authors

Affiliations

¹ Zhejiang Key Laboratory of Multi-omics Precision Diagnosis and Treatment of Liver Diseases, Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, 310016 Hangzhou, China.
² Zhejiang Minimal Invasive Diagnosis and Treatment Technology Research Center of Severe Hepatobiliary Disease, Zhejiang Research and Development Engineering Laboratory of Minimally Invasive Technology and Equipment, 310016 Hangzhou, China.
³ Zhejiang University Cancer Center, 310058 Hangzhou, China.
⁴ Liangzhu Laboratory, Zhejiang University Medical Center, 311121 Hangzhou, China.
⁵ Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030 Wuhan, China.
⁶ Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, 430022 Wuhan, China.
⁷ Hubei Key Laboratory of Molecular Imaging, 430022 Wuhan, China.
⁸ Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 310016 Hangzhou, China.
⁹ Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, 100190 Beijing, China.
¹⁰ Beijing Key Laboratory of Molecular Imaging, 100190 Beijing, China.
¹¹ Department of Respiratory and Critical Care Medicine, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, 310007 Hangzhou, China.
¹² The College of Computer Science and Technology, Zhejiang University, 310027 Hangzhou, China.
¹³ Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China.
¹⁴ Department of General Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, 310007 Hangzhou, China.
¹⁵ Department of Anorectal, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, 310000 Hangzhou, China.
¹⁶ Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, 317000 Taizhou, China.
¹⁷ Department of Radiology, Huzhou Central Hospital, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, 313000 Huzhou, China.
¹⁸ Department of Radiology, The First Affiliated Hospital, Wenzhou Medical University, 325006 Wenzhou, China.
¹⁹ Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, 100191 Beijing, China.
²⁰ Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, 710126 Xi'an, China.
²¹ School of Medicine, Shaoxing University, 312000 Shaoxing, China.
²² School of Basic Medical Sciences and Forensic Medicine, Hangzhou Medical College, 310000 Hangzhou, China.

PMID: 40315307
PMCID: PMC12047409
DOI: 10.1126/sciadv.ads8323

Abstract

Intratumoral heterogeneity (ITH) is a critical factor associated with treatment failure and disease relapse in hepatocellular carcinoma (HCC). However, decoding ITH in a noninvasive and comprehensive manner remains a notable challenge. In this study involving 851 patients from five centers, we developed a noninvasive prognostic classification for ITH using radiomics based on multisequence MRI, termed radiomics ITH (RITH) phenotypes. The RITH phenotypes highly correlated with prognosis and pathological ITH. In addition, through an integrated multi-omics analysis, we uncovered the molecular mechanisms underlying RITH, notably enhancing its biological interpretability. Specifically, high-RITH tumors demonstrated an enrichment of cancer-associated fibroblasts and activation of extracellular matrix remodeling. Our approach facilitates the noninvasive refined classification of ITH using radiomics and multi-omics, paving the way for tailored treatment strategies in HCC. Extracellular matrix-receptor interaction could be a potential therapeutic target in patients with high-RITH tumors. Given the routine use of radiologic imaging in oncology, our methodology ignites versatile framework for broader application to other solid tumors.

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Figures

**Fig. 1.. Study design of the imaging-based multi-omics analysis.**
(A) Study population. (B) Imaging phenotypes identification. (C) Survival analysis. (D) Imaging and pathological ITH. (E) Multi-omics explanation. (F) Therapeutic targets.

**Fig. 2.. Identification of RITH phenotypes in HCC.**
(A) Heatmap displaying unsupervised clustering of radiomic features associated with heterogeneity using SNF, including features notably correlated with the RITH subtype. Grade, MVI status, and T stage are annotated. (B) Principal components (PC) analysis plot of samples from three different subtypes. (C to F) HI in the SRRSH (C), Taizhou (D), Huzhou (E), and Wenzhou (F) centers. Median HI values: SRRSH (high: 255.1, low: −222.5), Taizhou (high: 229.5, low: −223.1), Huzhou (high: 123.4, low: −31.9), Wenzhou (high: 125.3, low: −34.6). (G) Representative AP-MRI habitat imaging illustrating low-, intermediate-, and high-RITH. LHR, low heterogeneity related; HHR, high heterogeneity related. ***P < 0.001.

**Fig. 3.. Prognostic classification in different RITH phenotypes.**
(A and B) Kaplan-Meier survival curves of OS (A) and RFS (B) based on RITH phenotypes within the SRRSH cohort. (C and D) Kaplan-Meier survival curves of OS (C) and RFS (D) grouped by HI in the SRRSH cohort. (E and F) Kaplan-Meier survival curves according to RITH phenotypes for OS (E) and RFS (F) in the Taizhou center. (G and H) Kaplan-Meier survival curves according to RITH phenotypes for RFS in the Huzhou (G) and Wenzhou (H) centers.

**Fig. 4.. Correlation between imaging and pathological ITH.**
(A) Flowchart presenting the extraction and utilization of pathological ITH. (B to D) Cellular morphological ITH scores of representative features: IntensityMax (B), CurvStd (C), and CurvMean (D) extracted from tumor cells. (E) Proportion of different grade stages across the low-, intermediate-, and high-RITH phenotypes. (F to H) Representative pathological images of tumor samples with low- (F), intermediate- (G), and high-RITH (H). ***P < 0.001, **P < 0.01, and *P < 0.05.

**Fig. 5.. The biological interpretation of RITH phenotypes.**
(A) Volcano plots showing DEGs in high-RITH and low-RITH samples in the SRRSH dataset. (B) KEGG pathway analysis based on the DEGs in the SRRSH dataset. (C) GSEA identifying the up-regulation of the ECM-receptor interaction pathway in the high-RITH phenotype in the SRRSH cohort. (D) ECM score in the SRRSH cohort. (E) Bar plot showing the top enriched pathways ordered by the NES in the high-RITH phenotype (red) and low-RITH phenotype (blue) in the SRRSH dataset. **P < 0.01. IgA, immunoglobulin A. PPAR, peroxisome proliferator–activated receptor.

**Fig. 6.. Identification of therapeutic targets through bulk and single-cell transcriptomics.**
(A) Boxplot of immune cell abundance. (B) t-distributed stochastic neighbor embedding (t-SNE) plot of cells from 15 HCC patients with eight clusters. (C) Bar plots showing the proportion of cell types in different clusters. (D) GSVA of CAFs according to RITH phenotypes. (E) Representative multiplexed immunohistochemistry images of high-RITH and low-RITH tumors, showing CD3, CD8, PD-L1, and α–smooth muscle actin (α-SMA) expression. TAMs, tumor-associated macrophages; TECs, tumor-associated endothelial cells.

**Fig. 7.. Correlation between ECM-related genes and radiomic features.**
(A) Sankey plot illustrating the correlation between ECM-related genes and radiomic features (P < 0.01), with line thickness indicating the strength of the correlation. (B) Bubble plot showing the correlation between ADAMTS5 expression and radiomic features. (C and D) Kaplan-Meier survival curves of OS (C) and RFS (D) grouped by ADAMTS5 expression in the SRRSH cohort. (E and F) Kaplan-Meier survival curves of OS (E) and RFS (F) grouped by HI and ADAMTS5 expression in the SRRSH cohort.

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Noninvasive prognostic classification of ITH in HCC with multi-omics insights and therapeutic implications

Affiliations

Noninvasive prognostic classification of ITH in HCC with multi-omics insights and therapeutic implications

Authors

Affiliations

Abstract

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References

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Medical