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Infiltration of myeloid-derived suppressor cells (MDSCs) leads to Immunosuppressive tumor microenvironment (TME), which is one of the major causes for low objective response rates of immune checkpoint blockade (ICB) therapy. Here, we report that chemical inhibition of p38α reverses this MDSC-induced immunosuppressive TME and improves the immunotherapy efficacy in triple negative breast cancer (TNBC). Firstly, by combining the tumor immunological phenotype (TIP) gene signature and high throughput sequencing based high throughput screening (HTS2), we identified that ponatinib significantly inhibits the expression of “cold” tumor associated chemokines CXCL1 and CXCL2 in cancer cells. This inhibition decreases the infiltration of MDSCs and consequently increased the accumulation of “hot” tumor associated T cells and NK cells and thus reverses the immunosuppressive TME. Then, by multiple preclinical models, we found that ponatinib significantly inhibits tumor growth in a TME-dependent manner and enhances the efficacy of anti-PD-L1 immunotherapy on TNBCin vivo. Notably, ponatinib exhibits no significant inhibition on immune cells in mouse spleens. Mechanistically, ponatinib directly inhibits the kinase activity of p38α, which results in the reduction of the phosphorylation of STAT1 at Ser727, and thus the decreased expression of CXCL1 and CXCL2 in cancer cells. Our study provided the therapeutic potential of combining p38α inhibition with ICB for the treatment of TNBC.

SummaryImmune checkpoint inhibitors (ICIs) are a breakthrough in oncology treatment, and studies of screening predictive biomarkers of ICIs are emerging. We developed a web server named IMPACT (http://impact.brbiotech.com/) to thoroughly explore immunotherapeutic predictive or prognostic biomarkers. IMPACT contains a large dataset of 6,276 patients treated with ICIs and integrates 11 well-designed function modules, enabling an in-depth solution for biomarkers exploration. Compared with the existing tools, IMPACT was implemented with one exclusive module for interaction analysis and several optimized conventional functions for discovering novel biomarkers. Specifically, the interaction analysis of biomarker-treatment effect is essential to determine whether a biomarker is predictive and/or prognostic for ICIs. Moreover, several optimized functions allow complicated biomarker exploration, including customized selections of variant types in more detail, automatically screening meaningful co-mutations among multiple genes, and selecting cut-off values for gene expression biomarkers. In summary, IMPACT is a comprehensive analysis resource to facilitate biomarker research of ICIs.

Deep learning is gaining significant traction in a wide range of areas. Whereas, recent studies have demonstrated that deep learning exhibits the fatal weakness on adversarial examples. Due to the black-box nature and un-transparency problem of deep learning, it is difficult to explain the reason for the existence of adversarial examples and also hard to defend against them. This study focuses on improving the adversarial robustness of convolutional neural networks. We first explore how adversarial examples behave inside the network through visualization. We find that adversarial examples produce perturbations in hidden activations, which forms an amplification effect to fool the network. Motivated by this observation, we propose an approach, termed as sanitizing hidden activations, to help the network correctly recognize adversarial examples by eliminating or reducing the perturbations in hidden activations. To demonstrate the effectiveness of our approach, we conduct experiments on three widely used datasets: MNIST, CIFAR-10 and ImageNet, and also compare with state-of-the-art defense techniques. The experimental results show that our sanitizing approach is more generalized to defend against different kinds of attacks and can effectively improve the adversarial robustness of convolutional neural networks.

Aerobic glycolysis, also known as the Warburg effect, is a hallmark of cancer and essential for metabolism in malignancies, but its regulation and modulation in cancer cells remain poorly understood. Here, using large-scale functional screening, we identified a tumor-associated and broadly expressed oncogenic long noncoding RNA LINC00973. Notably, knocking down LINC00973 significantly inhibits the proliferation of multiple types of cancer cells and reduces tumor growth in vivo. Mechanistically, LINC00973 directly binds to lactate dehydrogenase A (LDHA), an essential glycolytic enzyme, and enhances its enzymatic activity, thereby promoting glycolysis. Clinically, high expression of LINC00973 is significantly associated with poor prognosis in many types of human cancers. This work demonstrates that LINC00973 modulates cancer-specific regulation of the Warburg effect, and may represent a potential target for broad-acting anti-cancer therapies.

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 efficacy 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 infiltration or immunotherapy efficacy. Results: Analysis of 365 HCC samples identified 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 infiltration 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, reflect the immune microenvironment, and predict the efficacy of immunotherapy among HCC patients.

Autoimmune diseases (ADs) are at a significantly higher risk of cancers with unclear mechanism. By searching GWAS catalog database and Medline, susceptible genes for five common ADs, including systemic lupus erythematosus (SLE), rheumatoid arthritis, Sjögren syndrome, systemic sclerosis, and idiopathic inflammatory myopathies, were collected and then were overlapped with cancer driver genes. Single-cell transcriptome analysis was performed in the comparation between SLE and related cancer. We identified 45 carcinogenic autoimmune disease risk (CAD) genes, which were mainly enriched in T cell signaling pathway and B cell signaling pathway. Integrated single-cell analysis revealed immune cell signaling was significantly downregulated in renal cancer compared with SLE, while stemness signature was significantly enriched in both renal cancer or lymphoma and SLE in specific subpopulations. Drugs targeting CAD genes were shared between ADs and cancer. Our study highlights the common and specific features between ADs and related cancers, and sheds light on a new discovery of treatments.

Metastasis, the development of secondary malignant growths at a distance from a primary tumor, is the cause of death for 90% of cancer patients, but little is known about how metastatic cancer cells adapt to and colonize new tissue environments. Here, using clinical samples, patient-derived xenograft (PDX) samples, PDX cells, and primary/metastatic cell lines, we discovered that liver metastatic colorectal cancer (CRC) cells lose their colon-specific gene transcription program yet gain a liver-specific gene transcription program. We showed that this transcription reprogramming is driven by a reshaped epigenetic landscape of both typical enhancers and super-enhancers. Further, we identified that the liver-specific transcription factors FOXA2 and HNF1A can bind to the gained enhancers and activate the liver-specific gene transcription, thereby driving CRC liver metastasis. Importantly, similar transcription reprogramming can be observed in multiple cancer types. Our data suggest that reprogrammed tissue-specific transcription promotes metastasis and should be targeted therapeutically.

Cancer cells evolve various mechanisms to overcome cellular stresses and maintain progression. Protein kinase R (PKR) and its protein activator (PACT) are the initial responders in monitoring diverse stress signals and lead to inhibition of cell proliferation and cell apoptosis in consequence. However, the regulation of PACT-PKR pathway in cancer cells remains largely unknown. Herein, we identify that the long non-coding RNA (lncRNA) aspartyl-tRNA synthetase antisense RNA 1 (DARS-AS1) is directly involved in the inhibition of the PACT-PKR pathway and promotes the proliferation of cancer cells. Using large-scale CRISPRi functional screening of 971 cancer-associated lncRNAs, we find that DARS-AS1 is associated with significantly enhanced proliferation of cancer cells. Accordingly, knocking down DARS-AS1 inhibits cell proliferation of multiple cancer cell lines and promotes cancer cell apoptosis in vitro and significantly reduces tumor growth in vivo. Mechanistically, DARS-AS1 directly binds to the activator domain of PACT and prevents PACT-PKR interaction, thereby decreasing PKR activation, eIF2α phosphorylation and inhibiting apoptotic cell death. Clinically, DARS-AS1 is broadly expressed across multiple cancers and the increased expression of this lncRNA indicates poor prognosis. This study elucidates the lncRNA DARS-AS1 directed cancer-specific modulation of the PACT-PKR pathway and provides another target for cancer prognosis and therapeutic treatment.

Graphical abstract Coronavirus disease-2019 (COVID-19) has become a major global epidemic. Facilitated by HTS2 technology, we evaluated the effects of 578 herbs and all 338 reported anti-COVID-19 TCM formulae on cytokine storm-related signaling pathways, and identified the key targets of the relevant pathways and potential active ingredients in these herbs. This large-scale transcriptional study innovatively combines HTS2 technology with bioinformatics methods and computer-aided drug design. For the first time, it systematically explores the molecular mechanism of TCM in regulating the COVID-19-related cytokine storm, providing an important scientific basis for elucidating the mechanism of action of TCM in treating COVID-19.

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.

Graphical abstract, As one of the classical traditional Chinese medicine (TCM) prescriptions in treating gynecological tumors, Guizhi Fuling Decoction (GFD) has been used to treat breast cancer (BRCA). Nonetheless, the potential molecular mechanism remains unclear so far. Therefore, systems pharmacology was used in combination with high throughput sequencing-based high throughput screening (HTS2) assay and bioinformatic technologies in this study to investigate the molecular mechanisms of GFD in treating BRCA. By computationally analyzing 76 active ingredients in GFD, 38 potential therapeutic targets were predicted and significantly enriched in the “pathways in cancer”. Meanwhile, experimental analysis was carried out to examine changes in the expression levels of 308 genes involved in the “pathways in cancer” in BRCA cells treated by five herbs of GFD utilizing HTS2 platform, and 5 key therapeutic targets, including HRAS, EGFR, PTK2, SOS1, and ITGB1, were identified. The binding mode of active compounds to these five targets was analyzed by molecular docking and molecular dynamics simulation. It was found after integrating the computational and experimental data that, GFD possessed the anti-proliferation, pro-apoptosis, and anti-angiogenesis activities mainly through regulating the PI3K and the MAPK signaling pathways to inhibit BRCA. Besides, consistent with the TCM theory about the synergy of Cinnamomi Ramulus (Guizhi) by Cortex Moutan (Mudanpi) in GFD, both of these two herbs acted on the same targets and pathways. Taken together, the combined application of computational systems pharmacology techniques and experimental HTS2 platform provides a practical research strategy to investigate the functional and biological mechanisms of the complicated TCM prescriptions.

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 anti-metastatic treatment. In our study, high-throughput sequencing-based high-throughput screening (HTS2) 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. Electronic supplementary material The online version of this article (10.1007/s13238-018-0533-8) contains supplementary material, which is available to authorized users.

Vital signs are the indicators which evaluate the existence of health status and life quality. Hospitals may provide medical services for acute and chronic diseases and injuries. However, continuous monitoring and long-term treatment becomes difficult in such type diseases. In this paper, continuous vital signs monitoring system CVSMS based on wireless body area network WBAN is designed. And the gathered data is transmitted to a mobile phone via Bluetooth and then transferred to a remote server and stored in the database. In this way, a variety of vital signs such as body temperature, pulse rate, blood pressure, and ECG information would be acquired. Through the analysis and assessment of CVSMS, the results showed that the measurements are accurate and it provides an effective method for continuous health monitoring.

We describe a method for removing mixed noise from digital images which are contaminated by salt and pepper noise and Gaussian noise, based on cellular automata and Gaussian scale mixture. First we learn some rules by training on the salt and pepper noise images. These rules can then be used on the mixed noise images and remove the salt and pepper noise by CA filtering, after this, we decompose the image into subbands using the steerable pyramid, and then model the neighborhoods of coefficients using the Gaussian scale mixture: the product of a Gaussian random vector and an independent hidden random scalar multiplier. With this model, Bayesian least squares estimator is used to remove the residual noise. Denoising by this method can preserve the edges and details better than others.

Nowadays, Web Search Engines have become an indispensable tool for people to find internet resources. However, current Web Search Engines still have many drawbacks. They serve all people in the same way, regardless of the individual needs of each user, which obviously cannot satisfy most of the users. Personalized Search is proposed to solve this problem and to improve the retrieve quality. This paper deeply investigates the approach for personalized search, and has proposed a practical and effective method.