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BackgroundThe gut microbiome is associated with the response to immunotherapy for different cancers. However, the impact of the gut microbiome on hepatobiliary cancers receiving immunotherapy remains unknown. This study aims to investigate the relationship between the gut microbiome and the clinical response to anti-programmed cell death protein 1 (PD-1) immunotherapy in patients with advanced hepatobiliary cancers.MethodsPatients with unresectable hepatocellular carcinoma or advanced biliary tract cancers who have progressed from first-line chemotherapy (gemcitabine plus cisplatin) were enrolled. Fresh stool samples were collected before and during anti-PD-1 treatment and analyzed with metagenomic sequencing. Significantly differentially enriched taxa and prognosis associated taxa were identified. The Kyoto Encyclopedia of Genes and Genomes database and MetaCyc database were further applied to annotate the differentially enriched taxa to explore the potential mechanism of the gut microbiome influencing cancer immunotherapy.ResultsIn total, 65 patients with advanced hepatobiliary cancers receiving anti-PD-1 treatment were included in this study. Seventy-four taxa were significantly enriched in the clinical benefit response (CBR) group and 40 taxa were significantly enriched in the non-clinical benefit (NCB) group. Among these taxa, patients with higher abundance of Lachnospiraceae bacterium-GAM79 and Alistipes sp Marseille-P5997, which were significantly enriched in the CBR group, achieved longer progression-free survival (PFS) and overall survival (OS) than patients with lower abundance. Higher abundance of Ruminococcus calidus and Erysipelotichaceae bacterium-GAM147 enriched in the CBR group was also observed in patients with better PFS. In contrast, worse PFS and OS were found in patients with higher abundance of Veillonellaceae, which was significantly enriched in the NCB group. Functional annotation indicated that the taxa enriched in the CBR group were associated with energy metabolism while the taxa enriched in the NCB group were associated with amino acid metabolism, which may modulate the clinical response to immunotherapy in hepatobiliary cancers. In addition, immunotherapy-related adverse events were affected by the gut microbiome diversity and relative abundance.ConclusionsWe demonstrate that the gut microbiome is associated with the clinical response to anti-PD-1 immunotherapy in patients with hepatobiliary cancers. Taxonomic signatures enriched in responders are effective biomarkers to predict the clinical response and survival benefit of immunotherapy, which might provide a new therapeutic target to modulate the response to cancer immunotherapy.

Background: A therapeutic strategy involving combined treatment with lenvatinib plus pembrolizumab (LEP) has demonstrated a relatively high antitumor response in several solid tumors; however, the efficacy and safety of LEP in patients with refractory bile tract carcinoma (BTC) remains unknown.Methods: This is a single-arm study for a preliminary assessment of the efficacy and tolerability of LEP in patients who experienced progression from prior systemic treatments. Pre-treatment tumor tissues were collected to retrospectively evaluate the expression status of PDL1.Results: Thirty-two patients received second-line and above treatment with LEP. Overall, the objective response rate (ORR) was 25%, the disease control rate (DCR) was 78.1%, and the clinical benefit rate (CBR) was 40.5%. The median progression-free survival (PFS) was 4.9 months (95% CI: 4.7-5.2 months), and the median overall survival (OS) was 11.0 months (95% CI: 9.6-12.3 months). For tolerability, no grade 5 serious adverse events (AEs) were reported. All patients had any-grade AEs, and 59.3% of the patients experienced grade 3 AEs, while only 1 patient experienced a grade 4 AE of stomach bleeding. Fatigue was the most common AE, followed by hypertension and elevated aminotransferase levels. Retrospective analysis for PDL1 expression revealed that PDL1 positive tumor cells were associated with improved clinical benefits and survival outcomes.Conclusions: LEP is a promising alternative as a non-first-line therapeutic regimen for patients with refractory BTC. Furthermore, well-designed prospective clinical trials with a control arm are still needed to obtain more evidences to confirm the efficacy and safety of this particular regimen as well as the role of PDL1 expression.

In this study, we performed a comprehensively analysis of gene expression and DNA methylation data to establish diagnostic, prognostic, and recurrence models for hepatocellular carcinoma (HCC).Methods: We collected gene expression and DNA methylation datasets for over 1,200 clinical samples. Integrated analyses of RNA-sequencing and DNA methylation data were performed to identify DNA methylation-driven genes. These genes were utilized in univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses to build a prognostic model. Recurrence and diagnostic models for HCC were also constructed using the same genes.Results: A total of 123 DNA methylation-driven genes were identified. Two of these genes (SPP1 and LCAT) were chosen to construct the prognostic model. The high-risk group showed a markedly unfavorable prognosis compared to the low-risk group in both training (HR = 2.81; P < 0.001) and validation (HR = 3.06; P < 0.001) datasets. Multivariate Cox regression analysis indicated the prognostic model to be an independent predictor of prognosis (P < 0.05). Also, the recurrence model successfully distinguished the HCC recurrence rate between the high-risk and low-risk groups in both training (HR = 2.22; P < 0.001) and validation (HR = 2; P < 0.01) datasets. The two diagnostic models provided high accuracy for distinguishing HCC from normal samples and dysplastic nodules in the training and validation datasets, respectively.Conclusions: We identified and validated prognostic, recurrence, and diagnostic models that were constructed using two DNA methylation-driven genes in HCC. The results obtained by integrating multidimensional genomic data offer novel research directions for HCC biomarkers and new possibilities for individualized treatment of patients with HCC.