Background: Hepatocellular carcinoma (HCC) ranks the fourth in terms of cancer-related mortality globally. Herein, in this research, we attempted to develop a novel immune-related gene signature that could predict survival and e cacy of immunotherapy for HCC patients. Methods: The transcriptomic and clinical data of HCC samples were downloaded from The Cancer Genome Atlas (TCGA) and GSE14520 datasets, followed by acquisition of immune-related genes from the ImmPort database. Afterwards, an immune-related gene-based prognostic index (IRGPI) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. Kaplan-Meier survival curves as well as time-dependent receiver operating characteristic (ROC) curve were performed to evaluate its predictive capability. Besides, both univariate and multivariate analysis on overall survival for the IRGPI and multiple clinicopathologic factors were carried out, followed by the construction of nomogram. Finally, we explored the possible correlation of IRGPI with immune cell in ltration or immunotherapy e cacy. Results: Analysis of 365 HCC samples identi ed 11 differentially expressed genes, which were selected to establish the IRGPI. Notably, it can predict survival of HCC patients more accurately than published biomarkers. Furthermore, IRGPI can predict the in ltration of immune cells in the tumor microenvironment of HCC, as well as the response of immunotherapy. Conclusion: Collectively, the currently established IRGPI can accurately predict survival, re ect the immune microenvironment, and predict the e cacy of immunotherapy among HCC patients.

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Combination immunotherapy is promising to overcome the limited objective response rates of immune checkpoint blockade (ICB) therapy. Here, a tumor immunological phenotype (TIP) gene signature and high-throughput sequencing–based high-throughput screening (HTS2) were combined to identify combination immunotherapy compounds. We firstly defined a TIP gene signature distinguishing “cold” tumors from “hot” tumors. After screening thousands of compounds, we identified that aurora kinase inhibitors (AKIs) could reprogram the expression pattern of TIP genes in triple-negative breast cancer (TNBC) cells. AKIs treatments up-regulate expression of chemokine genes CXCL10 and CXCL11 through inhibiting aurora kinase A (AURKA)–signal transducer and activator of transcription 3 (STAT3) signaling pathway, which promotes effective T cells infiltrating into tumor microenvironment and improves anti-programmed cell death 1 (PD-1) efficacy in preclinical models. Our study established a novel strategy to discover combination immunotherapy compounds and suggested the therapeutic potential of combining AKIs with ICB for the treatment of TNBC.

The methodologies for evaluating similarities between gene expression profiles of different perturbagens are the key to understanding mechanisms of actions (MoAs) of unknown compounds and finding new indications for existing drugs. L1000-based next-generation Connectivity Map (CMap) data is more than a thousand-fold scale-up of the CMap pilot dataset. Although several systematic evaluations have been performed individually to assess the accuracy of the methodologies for the CMap pilot study, the performance of these methodologies needs to be re-evaluated for the L1000 data. Here, using the drug–drug similarities from the Drug Repurposing Hub database as a benchmark standard, we evaluated six popular published methods for the prediction performance of drug–drug relationships based on the partial area under the receiver operating characteristic (ROC) curve at false positive rates of 0.001, 0.005 and 0.01 (AUC0.001, AUC0.005 and AUC0.01). The similarity evaluating algorithm called ZhangScore was generally superior to other methods and exhibited the highest accuracy at the gene signature sizes ranging from 10 to 200. Further, we tested these methods with an experimentally derived gene signature related to estrogen in breast cancer cells, and the results confirmed that ZhangScore was more accurate than other methods. Moreover, based on scoring results of ZhangScore for the gene signature of TOP2A knockdown, in addition to well-known TOP2A inhibitors, we identified a number of potential inhibitors and at least two of them were the subject of previous investigation. Our studies provide potential guidelines for researchers to choose the suitable connectivity method. The six connectivity methods used in this report have been implemented in R package (https://github.com/Jasonlinchina/RCSM).

Metastasis is the leading cause of human cancer deaths. Unfortunately, no approved drugs are available for antimetastatic treatment. In our study, high-throughput sequencing-based high-throughput screening (HTS 2 ) and a breast cancer lung metastasis (BCLM)-associated gene signature were combined to discover anti-metastatic drugs. After screening of thousands of compounds, we identified Ponatinib as a BCLM inhibitor. Ponatinib significantly inhibited the migration and mammosphere formation of breast cancer cells in vitro and blocked BCLM in multiple mouse models. Mechanistically, Ponatinib represses the expression of BCLM-associated genes mainly through the ERK/c-Jun signaling pathway by inhibiting the transcription of JUN and accelerating the degradation of c-Jun protein. Notably, JUN expression levels were positively correlated with BCLM-associated gene expression and lung metastases in breast cancer patients. Collectively, we established a novel approach for the discovery of anti-metastatic drugs, identified Ponatinib as a new drug to inhibit BCLM and revealed c-Jun as a crucial factor and potential drug target for BCLM. Our study may facilitate the therapeutic treatment of BCLM as well as other metastases.