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Introduction: Lenvatinib plus an anti-PD-1 antibody has shown promising antitumor effects in patients with advanced hepatocellular carcinoma (HCC), but with clinical benefit limited to a subset of patients. We developed and validated a radiomic-based model to predict objective response to this combination therapy in advanced HCC patients. Methods: Patients (N = 170) who received first-line combination therapy with lenvatinib plus an anti-PD-1 antibody were retrospectively enrolled from 9 Chinese centers; 124 and 46 into the training and validation cohorts, respectively. Radiomic features were extracted from pretreatment contrast-enhanced MRI. After feature selection, clinicopathologic, radiomic, and clinicopathologic-radiomic models were built using a neural network. The performance of models, incremental predictive value of radiomic features compared with clinicopathologic features and relationship between radiomic features and survivals were assessed. Results: The clinicopathologic model modestly predicted objective response with an AUC of 0.748 (95% CI: 0.656–0.840) and 0.702 (95% CI: 0.547–0.884) in the training and validation cohorts, respectively. The radiomic model predicted response with an AUC of 0.886 (95% CI: 0.815–0.957) and 0.820 (95% CI: 0.648–0.984), respectively, with good calibration and clinical utility. The incremental predictive value of radiomic features to clinicopathologic features was confirmed with a net reclassification index of 47.9% (p < 0.001) and 41.5% (p = 0.025) in the training and validation cohorts, respectively. Furthermore, radiomic features were associated with overall survival and progression-free survival both in the training and validation cohorts, but modified albumin-bilirubin grade and neutrophil-to-lymphocyte ratio were not. Conclusion: Radiomic features extracted from pretreatment MRI can predict individualized objective response to combination therapy with lenvatinib plus an anti-PD-1 antibody in patients with unresectable or advanced HCC, provide incremental predictive value over clinicopathologic features, and are associated with overall survival and progression-free survival after initiation of this combination regimen. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer in adults. Previous studies in our laboratory found that long non-coding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) was upregulated in HCC cells, which could affect the metastasis and invasion of HCC. However, the underlying mechanism remains unknown. Herein, we studied the interaction between MALAT1 and miR-140 on the regulation of angiogenesis and immunosuppressive properties. We revealed that the expression of MALAT1 and VEGF-A was significantly increased in HCC cells. Knockdown of MALAT1 in HCC cells suppressed the production of VEGF-A, impaired the angiogenesis of HUVECs, and facilitated the polarization of macrophage toward the M1 subset. Mechanistically, the interaction between MALAT1 and miR-140 or between miR-140 and VEGF-A was confirmed by multiple assays. Besides, a negative correlation between MALAT1 and miR-140 was found in HCC tissues. Furthermore, miR-140 inhibition significantly increased VEGF-A expression, promoted angiogenesis of HUVECs, and redirected the polarization of macrophages toward the M2 subset. In addition, in vivo studies also verified the regulatory network of the MALAT1/miR-140 axis on VEGF-A in HCC progression. In summary, this study revealed the mechanism that MALAT1 worked as a putative HCC promotor via inhibiting miR-140. Therefore, targeting MALAT1 or miR-140 might alleviate the progression of HCC in the future.