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. 2023 Dec 8;3(6):549-564.
doi: 10.1007/s43657-023-00136-8. eCollection 2023 Dec.

A Noninvasive Approach to Evaluate Tumor Immune Microenvironment and Predict Outcomes in Hepatocellular Carcinoma

Jianmin Wu #  1   2   3 , Wanmin Liu #  4 , Xinyao Qiu #  2   3   5 , Jing Li #  6 , Kairong Song  7 , Siyun Shen  2   3 , Lei Huo  7 , Lu Chen  2   3 , Mingshuang Xu  2   3 , Hongyang Wang  1   2   3   5 , Ningyang Jia  7 , Lei Chen  2   3
Affiliations

A Noninvasive Approach to Evaluate Tumor Immune Microenvironment and Predict Outcomes in Hepatocellular Carcinoma

Jianmin Wu et al. Phenomics. .

Abstract

It is widely recognized that tumor immune microenvironment (TIME) plays a crucial role in tumor progression, metastasis, and therapeutic response. Despite several noninvasive strategies have emerged for cancer diagnosis and prognosis, there are still lack of effective radiomic-based model to evaluate TIME status, let alone predict clinical outcome and immune checkpoint inhibitor (ICIs) response for hepatocellular carcinoma (HCC). In this study, we developed a radiomic model to evaluate TIME status within the tumor and predict prognosis and immunotherapy response. A total of 301 patients who underwent magnetic resonance imaging (MRI) examinations were enrolled in our study. The intra-tumoral expression of 17 immune-related molecules were evaluated using co-detection by indexing (CODEX) technology, and we construct Immunoscore (IS) with the least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression method to evaluate TIME. Of 6115 features extracted from MRI, five core features were filtered out, and the Radiomic Immunoscore (RIS) showed high accuracy in predicting TIME status in testing cohort (area under the curve = 0.753). More importantly, RIS model showed the capability of predicting therapeutic response to anti-programmed cell death 1 (PD-1) immunotherapy in an independent cohort with advanced HCC patients (area under the curve = 0.731). In comparison with previously radiomic-based models, our integrated RIS model exhibits not only higher accuracy in predicting prognosis but also the potential guiding significance to HCC immunotherapy.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-023-00136-8.

Keywords: Hepatocellular carcinoma; Immunotherapy response; Prognosis; Radiomic; Tumor immune microenvironment.

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Conflict of interest statement

Conflict of InterestThe authors declare no potential conflict of interest.

Figures

Fig. 1
Fig. 1
The establishment of radiomic immunoscore (RIS). Tissue microarray was incubated with 17 immune-related markers, and CODEX image was generated through image processing. Immunoscore (IS) was constructed based on the expression of immune-related markers. A predictive model for IS, referred as RIS was developed. The RIS was found to be associated with prognosis and could provide guidance for immunotherapy
Fig. 2
Fig. 2
Construction and prognosis value of IS. a Images of a single tissue region color for each antibody. Scale bar, 10 um. b Spatial interaction of each immune-related markers. c Correlation between 17 immune-related markers. d Tuning parameter (λ) selection in the LASSO model used via tenfold cross-validation. e LASSO coefficient profiles of the 17 markers. f Kaplan–Meier analyses of OS according to IS signature in discovery cohort (EHBH cohort 1). g Validation cohort (EHBH cohort 2)
Fig. 3
Fig. 3
Performance of RIS in predicting TIME and prognosis in HCC patients. a Receiver operating characteristic curves of RIS in training cohort (EHBH cohort 1). b Testing cohort (EHBH cohort 3). c Kaplan–Meier analyses of overall survival of RIS in EHBH cohort 4. d Combined cohort. e Representative patients with immune-activated TIME and immunosuppressive TIME, along with their MRI images and CODEX images
Fig. 4
Fig. 4
Nomogram based on RIS and clinicopathological factors and their calibration curve. a Nomogram predicting survival of HCC patients was established based on RIS and clinicopathological factors. b Calibration curves based on RIS for 5-year OS. c Calibration curves for 5-year OS based on RS and clinicopathological factors. d Time-dependent ROC for IS, RIS, clinical model, and radiomic model (RS)
Fig. 5
Fig. 5
The association between RIS and responses to anti-PD-1 immunotherapy. a ROC curves of RIS for predicting response to anti-PD-1 immunotherapy. b RIS distribution in groups of clinical benefit patients and disease progressed patients. c RIS and MRI images of patients with different responses to anti-PD-1 immunotherapy
See this image and copyright information in PMC

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