Background Considering the heterogeneity of tumors, it is a key issue in precision medicine to predict the drug response of each individual. The accumulation of various types of drug informatics and multi-omics data facilitates the development of efficient models for drug response prediction. However, the selection of high-quality data sources and the design of suitable methods remain a challenge. Methods In this paper, we design NeRD, a multidimensional data integration model based on the PRISM drug response database, to predict the cellular response of drugs. Four feature extractors, including drug structure extractor (DSE), molecular fingerprint extractor (MFE), miRNA expression extractor (mEE), and copy number extractor (CNE), are designed for different types and dimensions of data. A fully connected network is used to fuse all features and make predictions. Results Experimental results demonstrate the effective integration of the global and local structural features of drugs, as well as the features of cell lines from different omics data. For all metrics tested on the PRISM database, NeRD surpassed previous approaches. We also verified that NeRD has strong reliability in the prediction results of new samples. Moreover, unlike other algorithms, when the amount of training data was reduced, NeRD maintained stable performance. Conclusions NeRD’s feature fusion provides a new idea for drug response prediction, which is of great significance for precise cancer treatment.

Drug response prediction in cancer cell lines is of great significance in personalized medicine. In this study, we propose GADRP, a cancer drug response prediction model based on graph convolutional networks (GCNs) and autoencoders (AEs). We first use a stacked deep AE to extract low-dimensional representations from cell line features, and then construct a sparse drug cell line pair (DCP) network incorporating drug, cell line, and DCP similarity information. Later, initial residual and layer attention-based GCN (ILGCN) that can alleviate over-smoothing problem is utilized to learn DCP features. And finally, fully connected network is employed to make prediction. Benchmarking results demonstrate that GADRP can significantly improve prediction performance on all metrics compared with baselines on five datasets. Particularly, experiments of predictions of unknown DCP responses, drug-cancer tissue associations， and drug-pathway associations illustrate the predictive power of GADRP. All results highlight the effectiveness of GADRP in predicting drug responses, and its potential value in guiding anti-cancer drug selection.

Background Excessive extracellular matrix deposition and increased stiffness are typical features of solid tumors such as hepatocellular carcinoma (HCC) and pancreatic ductal adenocarcinoma (PDAC). These conditions create confined spaces for tumor cell migration and metastasis. The regulatory mechanism of confined migration remains unclear. Methods LC–MS was applied to determine the differentially expressed proteins between HCC tissues and corresponding adjacent tissue. Collective migration and single cell migration microfluidic devices with 6 μm-high confined channels were designed and fabricated to mimic the in vivo confined space. 3D invasion assay was created by Matrigel and Collagen I mixture treat to adherent cells. 3D spheroid formation under various stiffness environment was developed by different substitution percentage GelMA. Immunoprecipitation was performed to pull down the LH1-binding proteins, which were identified by LC–MS. Immunofluorescent staining, FRET, RT-PCR, Western blotting, FRAP, CCK-8, transwell cell migration, wound healing, orthotopic liver injection mouse model and in vivo imaging were used to evaluate the target expression and cellular phenotype. Results Lysyl hydroxylase 1 (LH1) promoted the confined migration of cancer cells at both collective and single cell levels. In addition, LH1 enhanced cell invasion in a 3D biomimetic model and spheroid formation in stiffer environments. High LH1 expression correlated with poor prognosis of both HCC and PDAC patients, while it also promoted in vivo metastasis. Mechanistically, LH1 bound and stabilized Septin2 (SEPT2) to enhance actin polymerization, depending on the hydroxylase domain. Finally, the subpopulation with high expression of both LH1 and SEPT2 had the poorest prognosis. Conclusions LH1 promotes the confined migration and metastasis of cancer cells by stabilizing SEPT2 and thus facilitating actin polymerization.

Sorafenib is one a first-line therapeutic drugs for advanced hepatocellular carcinoma (HCC). However, only 30% of patients benefit from sorafenib due to drug resistance.We and other groups have revealed that nuclear factor I B (NFIB) regulates liver regeneration and carcinogenesis, but its role in drug resistance is poorly known. We found that NFIB was more upregulated in sorafenib-resistant SMMC-7721 cells compared to parental cells. NFIB knockdown not only sensitized drug-resistant cells to sorafenib but also inhibited the proliferation and invasion of these cells. Meanwhile, NFIB promoted the proliferation and invasion of HCC cells in vitro and facilitated tumor growth and metastasis in vivo. Knocking down NFIB synergetically inhibited tumor growth with sorafenib. Mechanically, gene expression profiling and subsequent verification experiments proved that NFIB could bind with the promoter region of a complex I inhibitor NDUFA4L2 and promote its transcription. Transcriptional upregulation of NDUFA4L2 by NFIB could thus inhibit the sorafenib-induced reactive oxygen species accumulation. Finally, we found that NFIB was highly expressed in HCC tissues, and high NFIB expression level was associated with macrovascular invasion, advanced tumor stage, and poor prognosis of HCC patients (n = 156). In summary, we demonstrated that NFIB could transcriptionally upregulate NDUFA4L2 to enhance both intrinsic and acquired sorafenib resistance of HCC cells by reducing reactive oxygen species induction.

The tetraspanins (TSPANs) are a family of four-transmembrane proteins with 33 members in mammals. They are variably expressed on the cell surface, various intracellular organelles and vesicles in nearly all cell types. Different from the majority of cell membrane proteins, TSPANs do not have natural ligands. TSPANs typically organize laterally with other membrane proteins to form tetraspanin-enriched microdomains (TEMs) to influence cell adhesion, migration, invasion, survival and induce downstream signaling. Emerging evidence shows that TSPANs can regulate not only cancer cell growth, metastasis, stemness, drug resistance, but also biogenesis of extracellular vesicles (exosomes and migrasomes), and immunomicroenvironment. This review summarizes recent studies that have shown the versatile function of TSPANs in cancer development and progression, or the molecular mechanism of TSPANs. These findings support the potential of TSPANs as novel therapeutic targets against cancer.