Background Despite recent advances in cancer immunotherapy, the efficacy of these therapies for the treatment of human prostate cancer patients is low due to the complex immune evasion mechanisms (IEMs) of prostate cancer and the lack of predictive biomarkers for patient responses. Methods To understand the IEMs in prostate cancer and apply such understanding to the design of personalized immunotherapies, we analyzed the RNA-seq data for prostate adenocarcinoma from The Cancer Genome Atlas (TCGA) using a combination of biclustering, differential expression analysis, immune cell typing, and machine learning methods. Results The integrative analysis identified eight clusters with different IEM combinations and predictive biomarkers for each immune evasion cluster. Prostate tumors employ different combinations of IEMs. The majority of prostate cancer patients were identified with immunological ignorance (89.8%), upregulated cytotoxic T lymphocyte-associated protein 4 (CTLA4) (58.8%), and upregulated decoy receptor 3 (DcR3) (51.6%). Among patients with immunologic ignorance, 41.4% displayed upregulated DcR3 expression, 43.26% had upregulated CTLA4, and 11.4% had a combination of all three mechanisms. Since upregulated programmed cell death 1 (PD-1) and/or CTLA4 often co-occur with other IEMs, these results provide a plausible explanation for the failure of immune checkpoint inhibitor monotherapy for prostate cancer. Conclusion These findings indicate that human prostate cancer specimens are mostly immunologically cold tumors that do not respond well to mono-immunotherapy. With such identified biomarkers, more precise treatment strategies can be developed to improve therapeutic efficacy through a greater understanding of a patient’s immune evasion mechanisms.

In order to solve the many shortcomings of the current servo system using mechanical position sensors, this article combines the traditional fuzzy PI control and sliding mode observer, and proposes a control method of PMSM based on fuzzy PI and sliding mode observer. Simulink is used for simulation analysis, and the results are compared. It can be seen from the simulation results that the designed control method can efficiently control the permanent magnet synchronous speed control system, so that the system has excellent starting characteristics and dynamic stability.

Large volumes of data have been generated in biomedical field every day and made publicly accessible. A significant portion of the data are highly under-analyzed. On the other hands, biomedical researchers having problems that can be solved by these data do not have the expertise to access and analyze the data. The BioKDE (Biomedical Knowledge Discovery Engine) platform aims to bridge this gap by accelerating biomedical discovery through integration and mining of large volumes of public genomic data. We have integrated a large volume of genomics data from TCGA (the Cancer Genome Atlas) and GEO (Gene Expression Omnibus) database, which are linked with patient clinical information. Powerful and versatile query functions were built for the integrated data. Basic and advanced analysis tools have been implemented with user-friendly interfaces. Pipelines can be built by chaining the query tools with analysis tools to perform sophisticated data acquisition and analysis tasks very conveniently. Pipelines can be saved and shared among researchers to quickly replicate certain analyses using the same dataset or different datasets. A large number of novel discoveries have been made, which can be used directly to write scientific research papers using these discoveries alone or by combining with additional experimental validation studies. We will present a few case studies published recently to illustrate the power of the BioKDE. We are looking for collaborators to write papers together for the discoveries we made. The platform can be accessed at http://www.insilicom.com. Citation Format: Xiaodong Pang, Mayassa J. Bou-Dargham, Yuhang Liu, Zihan Cui, Linlin Sha, Tingting Zhao, Jinfeng Zhang. Accelerating cancer research using big data with BioKDE platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2247.

A sliding mode control method of surface mount permannent magnet synchronous motor (SPMSM) based on disturbance observer was proposed to solve the problem of internal parameter disturbance caused by temperature change during PMSM operation. Firstly, a disturbance observer based on parametric disturbance is established. Secondly, a new double power approach law is used to design the speed ring controller, and the stability of the controller and observer is proved by Lyapunov stability theory. The results turns out that the new method can clearly improve the performance of the motor, weaken chattering, enhance the robustness and anti-interference ability.