Dataset Information

Positive Expression of Programmed Death Ligand 1 in Peritumoral Liver Tissue is Associated with Poor Survival after Curative Resection of Hepatocellular Carcinoma.

ABSTRACT:

Background

Recurrence or metastasis of hepatocellular carcinoma (HCC) is mainly intrahepatic after curative resection, demonstrating that the peritumoral environment is important but often neglected. Programmed death ligand 1 (PD-L1) in intratumoral liver tissues is a poor prognosis factor whose impact is removed after curative resection. However, PD-L1 expression remains in the peritumoral liver tissues and its distribution and prognostic value are still not clear.

Methods

We assessed the expression of PD-L1 by immunohistochemistry in peritumoral liver tissues from 90 HCC patients who underwent curative hepatectomy. The results were validated in an independent cohort of additional 90 HCC patients.

Results

We found PD-L1 positive expression in 31.11% (28/90) of peritumoral tissues. Peritumoral PD-L1 expression was associated with a significantly worse overall survival (OS) (P=.000) and disease-free survival (DFS) (P=.001) compared to the negative expression group. Additionally, peritumoral PD-L1 positivity significantly correlated with vascular invasion and a lower albumin level (?35 g/L). Univariate and multivariate Cox regression models both revealed peritumoral PD-L1 as an independent prognostic factor for OS (HR=2.853, P=.002) and DFS (HR=2.362, P=.003). The prognostic value of PD-L1 positivity was validated in the independent data set.

Conclusions

Our data suggest PD-L1 expression in peritumoral hepatocytes is an independent prognostic factor for OS and DFS. This implies that future anti-cancer therapy should target not only residual tumor cells but also the "soil" for promoting tumor growth. Peritumoral PD-L1 could be a good target for adjuvant therapy after hepatectomy.

SUBMITTER: Dai X

PROVIDER: S-EPMC5447656 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Publications

Positive Expression of Programmed Death Ligand 1 in Peritumoral Liver Tissue is Associated with Poor Survival after Curative Resection of Hepatocellular Carcinoma.

Dai Xiaomeng X Xue Jun J Hu Jianli J Yang Sheng-Li SL Chen George G GG Lai Paul B S PBS Yu Chao C Zeng Cui C Fang Xiefan X Pan Xiaoli X Zhang Tao T

Translational oncology 20170527 4

<h4>Background</h4>Recurrence or metastasis of hepatocellular carcinoma (HCC) is mainly intrahepatic after curative resection, demonstrating that the peritumoral environment is important but often neglected. Programmed death ligand 1 (PD-L1) in intratumoral liver tissues is a poor prognosis factor whose impact is removed after curative resection. However, PD-L1 expression remains in the peritumoral liver tissues and its distribution and prognostic value are still not clear.<h4>Methods</h4>We ass ...[more]

PMID: 28558264

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Project description:OBJECTIVE:To construct a prediction model based on peritumoral radiomics signatures from CT images and investigate its efficiency in predicting early recurrence (ER) of hepatocellular carcinoma (HCC) after curative treatment. MATERIALS AND METHODS:In total, 156 patients with primary HCC were randomly divided into the training cohort (109 patients) and the validation cohort (47 patients). From the pretreatment CT images, we extracted 3-phase two-dimensional images from the largest cross-sectional area of the tumor. A region of interest (ROI) was manually delineated around the lesion for tumoral radiomics (T-RO) feature extraction, and another ROI was outlined with an additional 2 cm peritumoral area for peritumoral radiomics (PT-RO) feature extraction. The least absolute shrinkage and selection operator (LASSO) logistic regression model was applied for feature selection and model construction. The T-RO and PT-RO models were constructed. In the validation cohort, the prediction efficiencies of the two models and peritumoral enhancement (PT-E) were evaluated qualitatively by receiver operating characteristic (ROC) curves, calibration curves and decision curves and quantitatively by area under the curve (AUC), the category-free net reclassification index (cfNRI) and integrated discrimination improvement values (IDI). RESULTS:By comparing AUC values, the prediction accuracy in the validation cohort was good for the PT-RO model (0.80 vs. 0.79, P = 0.47) but poor for the T-RO model (0.82 vs. 0.62, P < 0.01), which was significantly overfitted. In the validation cohort, the ROC curves, calibration curves and decision curves indicated that the PT-RO model had better calibration efficiency and provided greater clinical benefits. CfNRI indicated that the PT-RO model correctly reclassified 47% of ER patients and 32% of non-ER patients compared to the T-RO model (P < 0.01); additionally, the PT-RO model correctly reclassified 24% of ER patients and 41% of non-ER patients compared to PT-E (P = 0.02). IDI indicated that the PT-RO model could improve prediction accuracy by 0.22 (P < 0.01) compared to the T-RO model and by 0.20 (P = 0.01) compared to PT-E. CONCLUSION:The CT-based PT-RO model can effectively predict the ER of HCC and is more efficient than the T-RO model and the conventional imaging feature PT-E.

Dataset Information

Positive Expression of Programmed Death Ligand 1 in Peritumoral Liver Tissue is Associated with Poor Survival after Curative Resection of Hepatocellular Carcinoma.

Background

Methods

Results

Conclusions

Publications

Positive Expression of Programmed Death Ligand 1 in Peritumoral Liver Tissue is Associated with Poor Survival after Curative Resection of Hepatocellular Carcinoma.

Similar Datasets

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