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Background Hepatocellular carcinoma (HCC) is among the most common forms of cancer and is associated with poor patient outcomes. The emergence of therapeutic resistance has hampered the efficacy of targeted treatments employed to treat HCC patients to date. In this study, we conducted a series of CRISPR/Cas9 screens to identify genes associated with synthetic lethality capable of improving HCC patient clinical responses. Methods CRISPR-based loss-of-function genetic screens were used to target 18,053 protein-coding genes in HCC cells to identify chemotherapy-related synthetic lethal genes in these cells. Synergistic effects were analyzed through in vitro and in vivo analyses, while related mechanisms were explored through RNA-seq and metabolomics analyses. Potential inhibitors of identified genetic targets were selected through high-throughput virtual screening. Results The inhibition of phosphoseryl-tRNA kinase (PSTK) was found to increase HCC cell sensitivity to chemotherapeutic treatment. PSTK was associated with the suppression of chemotherapy-induced ferroptosis in HCC cells, and the depletion of PSTK resulted in the inactivation of glutathione peroxidative 4 (GPX4) and the disruption of glutathione (GSH) metabolism owing to the inhibition of selenocysteine and cysteine synthesis, thus enhancing the induction of ferroptosis upon targeted chemotherapeutic treatment. Punicalin, an agent used to treat hepatitis B virus (HBV), was identified as a possible PSTK inhibitor that exhibited synergistic efficacy when applied together with Sorafenib to treat HCC in vitro and in vivo. Conclusions These results highlight a key role for PSTK as a mediator of resistance to targeted therapeutic treatment in HCC cells that functions by suppressing ferroptotic induction. PSTK inhibitors may thus represent ideal candidates for overcoming drug resistance in HCC.

Background Deep learning algorithms significantly improve the accuracy of pathological image classification, but the accuracy of breast cancer classification using only single-mode pathological images still cannot meet the needs of clinical practice. Inspired by the real scenario of pathologists reading pathological images for diagnosis, we integrate pathological images and structured data extracted from clinical electronic medical record (EMR) to further improve the accuracy of breast cancer classification. Methods In this paper, we propose a new richer fusion network for the classification of benign and malignant breast cancer based on multimodal data. To make pathological image can be integrated more sufficient with structured EMR data, we proposed a method to extract richer multilevel feature representation of the pathological image from multiple convolutional layers. Meanwhile, to minimize the information loss for each modality before data fusion, we use the denoising autoencoder as a way to increase the low-dimensional structured EMR data to high-dimensional, instead of reducing the high-dimensional image data to low-dimensional before data fusion. In addition, denoising autoencoder naturally generalizes our method to make the accurate prediction with partially missing structured EMR data. Results The experimental results show that the proposed method is superior to the most advanced method in terms of the average classification accuracy (92.9%). In addition, we have released a dataset containing structured data from 185 patients that were extracted from EMR and 3764 paired pathological images of breast cancer, which can be publicly downloaded from http://ear.ict.ac.cn/?page_id=1663. Conclusions We utilized a new richer fusion network to integrate highly heterogeneous data to leverage the structured EMR data to improve the accuracy of pathological image classification. Therefore, the application of automatic breast cancer classification algorithms in clinical practice becomes possible. Due to the generality of the proposed fusion method, it can be straightforwardly extended to the fusion of other structured data and unstructured data.

Immune checkpoint blockade (ICB) therapy is a treatment strategy for hepatocellular carcinoma (HCC); however, its clinical efficacy is limited to a select subset of patients. Next-generation sequencing has identified the value of tumor mutation burden (TMB) as a predictor for ICB efficacy in multiple types of tumor, including HCC. Specific driver gene mutations may be indicative of a high TMB (TMB-H) and analysis of such mutations may provide novel insights into the underlying mechanisms of TMB-H and potential therapeutic strategies. In the present study, a hybridization-capture method was used to target 1.45 Mb of the genomic sequence (coding sequence, 1 Mb), analyzing the somatic mutation landscape of 81 HCC tumor samples. Mutations in five genes were significantly associated with TMB-H, including mutations in tumor protein 53 (TP53), Catenin ® 1 (CTNNB1), AT-rich interactive domain-containing protein 1A (ARID1A), myeloid/lymphoid or mixed-lineage leukemia (MLL) and nuclear receptor co-repressor 1 (NCOR1). Further analysis using The Cancer Genome Atlas Liver Hepatocellular Carcinoma database showed that TP53, CTNNB1 and MLL mutations were positively correlated with TMB-H. Meanwhile, mutations in ARID1A, TP53 and MLL were associated with poor overall survival of patients with HCC. Overall, TMB-H and associated driver gene mutations may have potential as predictive biomarkers of ICB therapy efficacy for treatment of patients with HCC.

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