Stratifying ICIs-responsive tumor microenvironment in HCC: from parsing out immune-hypoxic crosstalk to clinically applicable MRI-radiomics models.

BACKGROUND: We aimed to redefine Immune checkpoint inhibitors (ICIs)-responsive "hot" TME and develop a corresponding stratification model to maximize ICIs-efficacy in Hepatocellular Carcinoma (HCC). METHODS: Hypoxic scores were designed, and the relevance to immunotherapy responses were validated in pan-cancers through single cell analysis. Multi-omics analysis using the hypoxic scores and immune infiltrate abundance was performed to redefine the ICIs-responsive TME subtype in HCC patients from TCGA (n = 363) and HCCDB database (n = 228). The immune hypoxic stress index (IHSI) was constructed to stratify the ICIs-responsive TME subtype, with exploring biological mechanism in vitro and in vivo. MRI-radiomics models were built for clinical applicability. RESULTS: The hypoxic scores were lower in the dominant cell-subclusters of responders in pan-cancers. The higher immune infiltrate-normoxic (HIN) subtype was redefined as the ICIs-responsive TME. Stratification of the HIN subtype using IHSI effectively identified ICIs-responders in Melanoma (n = 122) and urological cancer (n = 22). TRAF3IP3, the constituent gene of IHSI, was implicated in ICIs-relevant "immune-hypoxic" crosstalk by stimulating MAVS/IFN-I pathway under normoxic condition. MRI-radiomics models assessing TRAF3IP3 with HIF1A expression (AUC > 0.80) screened ICIs-Responders in HCC cohort (n = 75). CONCLUSION: The hypoxic-immune stratification redefined ICIs-responsive TME and provided MRI-Radiomics models for initial ICIs-responders screening, with IHSI facilitating further identification.

Characterization of tumor microbiome and associations with prognosis in intrahepatic cholangiocarcinoma.

BACKGROUND: The tumor microbiome has been characterized in several malignancies; however, no previous studies have investigated its role in intrahepatic cholangiocarcinoma (ICC). Hence, we explored the tumor microbiome and its association with prognosis in ICC. METHODS: One hundred and twenty-one ICC tumor samples and 89 adjacent normal tissues were profiled by 16S rRNA sequencing. Microbial differences between tumor and adjacent nontumoral liver tissues were assessed. Tumor microbial composition was then evaluated to detect its association with prognosis. Finally, a risk score calculated by the tumor microbiota was accessed by the least absolute shrinkage and selector operator method (Lasso) to predict prognosis of ICC. RESULTS: The tumor microbiome displayed a greater diversity than that in adjacent nontumoral liver tissues. Tumor samples were characterized by a higher abundance of Firmicutes, Actinobacteria, Bacteroidetes, and Acidobacteriota. Higher tumor microbial α diversity was associated with lymph node metastasis and predicted shortened overall survival (OS) and recurrence-free survival (RFS). A total of 11 bacteria were selected to generate the risk score by Lasso. This score showed potential in predicting OS, and was an independent risk factor for OS. CONCLUSION: In conclusion, our study characterized the tumor microbiome and revealed its role in predicting prognosis in ICC.

[Corrigendum] Macrophage­secreted IL­8 induces epithelial­mesen-chymal transition in hepatocellular carcinoma cells by activating the JAK2/STAT3/Snail pathway.

Subsequently to the publication of the above article, an interested reader drew to the authors' attention that a possible error had been identified in the selection of images in Figs. 1 and/or 7. After having consulted their original data, the authors realized that an erroneous image appeared on p. 593, in Fig. 7F [the 'Hep­G2 / IL­8 (5 ng/ml)' data panel], where part of this figure panel was overlapping with an image on p. 589 in Fig. 1C (the 'Hep­G2 Co­cultured' data panel). After a thorough review and verification of the data by all the authors, they have confirmed that the original data presented in the paper were accurate, and the error was solely due to the selection of an incorrect image during figure arrangement. The authors confirm that this mistake in image selection did not affect the overall conclusions reported in the article. A corrected version of Fig. 7, including the correct data for the 'Hep­G2 / IL­8 (5 ng/ml)' panel in Fig. 7F, is shown on the next page. The authors are grateful to the Editor of International Journal of Oncology for granting them the opportunity to publish this Corrigendum. All the authors agree to the publication of this Corrigendum, and apologize to the readership for any inconvenience caused. [International Journal of Oncology 46: 587­596, 2015; DOI: 10.3892/ijo.2014.2761].

Development and Validation of a Stromal-Immune Signature to Predict Prognosis in Intrahepatic Cholangiocarcinoma.

Background: Intrahepatic cholangiocarcinoma (ICC) is a highly desmoplastic tumor with poor prognosis even after curative resection. We investigated the associations between the composition of the ICC stroma and immune cell infiltration and aimed to develop a stromal-immune signature to predict prognosis in surgically treated ICC. Patients and methods: We recruited 359 ICC patients and performed immunohistochemistry to detect α-smooth muscle actin (α-SMA), CD3, CD4, CD8, Foxp3, CD68, and CD66b. Aniline was used to stain collagen deposition. Survival analyses were performed to detect prognostic values of these markers. Recursive partitioning for a discrete-time survival tree was applied to define a stromal-immune signature with distinct prognostic value. We delineated an integrated stromal-immune signature based on immune cell subpopulations and stromal composition to distinguish subgroups with different recurrence-free survival (RFS) and overall survival (OS) time. Results: We defined four major patterns of ICC stroma composition according to the distributions of α-SMA and collagen: dormant (α-SMAlow/collagenhigh), fibrogenic (α-SMAhigh/collagenhigh), inert (α-SMAlow/collagenlow), and fibrolytic (α-SMAhigh/collagenlow). The stroma types were characterized by distinct patterns of infiltration by immune cells. We divided patients into six classes. Class I, characterized by high CD8 expression and dormant stroma, displayed the longest RFS and OS, whereas Class VI, characterized by low CD8 expression and high CD66b expression, displayed the shortest RFS and OS. The integrated stromal-immune signature was consolidated in a validation cohort. Conclusion: We developed and validated a stromal-immune signature to predict prognosis in surgically treated ICC. These findings provide new insights into the stromal-immune response to ICC.