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| 1 | Systematic screening reveals synergistic interactions that overcome MAPK inhibitor resistance in cancer cells显示文摘Objective:Effective adjuvant therapeutic strategies are urgently needed to overcome MAPK inhibitor(MAPKi)resistance,which is one of the most common forms of resistance that has emerged in many types of cancers.Here,we aimed to systematically identify the genetic interactions underlying MAPKi resistance,and to further investigate the mechanisms that produce the genetic interactions that generate synergistic MAPKi resistance.Methods:We conducted a comprehensive pair-wise sgRNA-based high-throughput screening assay to identify synergistic interactions that sensitized cancer cells to MAPKi,and validated 3 genetic combinations through competitive growth,cell viability,and spheroid formation assays.We next conducted Kaplan-Meier survival analysis based on The Cancer Genome Atlas database and conducted immunohistochemistry to determine the clinical relevance of these synergistic combinations.We also investigated the MAPKi resistance mechanisms of these validated synergistic combinations by using co-immunoprecipitation,Western blot,qRTPCR,and immunofluorescence assays.Results:We constructed a systematic interaction network of MAPKi resistance and identified 3 novel synergistic combinations that effectively targeted MAPKi resistance(ITGB3+IGF1R,ITGB3+JNK,and HDGF+LGR5).We next analyzed their clinical relevance and the mechanisms by which they sensitized cancer cells to MAPKi exposure.Specifically,we discovered a novel protein complex,HDGF-LGR5,that adaptively responded to MAPKi to enhance cancer cell stemness,which was up-or downregulated by the inhibitors of ITGB3+JNK or ITGB3+IGF1R.Conclusions:Pair-wise sgRNA library screening provided systematic insights into elucidating MAPKi resistance in cancer cells.ITGB3-+IGF1R-targeting drugs(cilengitide+linsitinib)could be used as an effective therapy for suppressing the adaptive formation of the HDGF-LGR5 protein complex,which enhanced cancer stemness during MAPKi stress. | Yu Yu Minzhen Tao Libin Xu Lei Cao Baoyu Le Na An Jilin Dong Yajie Xu Baoxing Yang Wei Li Bing Liu Qiong Wu Yinying Lu Zhen Xie Xiaohua Lian | 2022 | Cancer Biology & Medicine2022，19，2： | 1 |
| 2 | Determination of Double Beta Decay Half-Life of 136Xe with the PandaX-4T Natural Xenon Detector显示文摘Precise measurement of two-neutrino double beta decay(DBD)half-life is an important step for the searches of Majorana neutrinos with neutrinoless double beta decay.We report the measurement of DBD half-life of 136xe using the Pandax-4T dual-phase Time Projection Chamber(TPC)with 3.7-tonne natural xenon and the first 94.9-day physics data release. | Lin Si Zhaokan Cheng Abdusalam Abdukerim Zihao Bo Wei Chen Xun Chen Yunhua Chen Chen Cheng Yunshan Cheng Xiangyi Cui Yingjie Fan Deqing Fang Changbo Fu Mengting Fu Lisheng Geng Karl Giboni Linhui Gu Xuyuan Guo Ke Han Changda He Jinrong He Di Huang Yanlin Huang Zhou Huang Ruquan Hou Xiangdong Ji Yonglin Ju Chenxiang Li Jiafu Li Mingchuan Li Shu Li Shuaijie Li Qing Lin Jianglai Liu Xiaoying Lu Lingyin Luo Yunyang Luo Wenbo Ma Yugang Ma Yujun Mao Yue Meng Nasir Shaheed Xiaofeng Shang Xuyang Ning Ningchun Qi Zhicheng Qian Xiangxiang Ren Changsong Shang Guofang Shen Wenliang Sun Andi Tan Yi Tao Anqing Wang Meng Wang Qiuhong Wang Shaobo Wang Siguang Wang Wei Wang Xiuli Wang Zhou Wang Yuehuan Wei Mengmeng Wu Weihao Wu Jingkai Xia Mengjiao Xiao Xiang Xiao Pengwei Xie Binbin Yan Xiyu Yan Yong Yang Chunxu Yu Jumin Yuan Ying Yuan Zhe Yuan Dan Zhang Minzhen Zhang Peng Zhang Shibo Zhang Shu Zhang Tao Zhang Li Zhao Qibin Zheng Jifang Zhou Ning Zhou Xiaopeng Zhou Yong Zhou | 2023 | Research2023，，2： | 0 |
| 3 | Construction of a CRISPR-based paired-sgRNA library for chromosomal deletion of long non-coding RNAs显示文摘Background:Derived from an adaptive bacterial immune system,the clustered regularly interspaced palindromic repeats(CRISPR)/CRISPR-associated 9(Cas9)system has shown great potential in high-throughput functional genomic screening,especially for protein-coding genes.However,it is still challenging to apply the similar strategy to study non-coding genomic elements such as long non-coding RNAs(lncRNAs)or clusters of microRNAs,because short insertions or deletions may not be sufficient to generate loss-of-function phenotypes.Methods:Here,we presented a systematic strategy for designing a CRISPR-based paired-sgRNA library for highthroughput screening in non-coding regions.Due to the abundance of lncRNAs and their diverse regulatory roles in vivo,we repurposed microarray datasets to select 600 highly expressed lncRNAs in non-small-cell lung cancer and designed two schemes for lncRNA deletion with^20 paired-sgRNAs for each lncRNA.Through Golden-Gate assembly,we generated a pooled CRISPR-based library with a total of 12,878 sgRNA pairs.Results:Over 80%of paired-sgRNAs were recovered from final pooled library with a relarively even distribution.Cleavage efficiency of sgRNA pairs was validated through experiments of transient transfection and viral infection.Moreover,randomly selected paired-sgRNAs showed that efficient deletion of genomic DNA could be achieved with a deletion size within the range of 500 to 3000 bp.Conclusions:In summary,we have demonstrated a strategy to design and construct a pooled paired-sgRNA library to generate genomic deletion in the lncRNA regions,validated their deletion efficiency and explored the relationship of deletion efficiency with respect to deletion size.This method would be also suitable for investigation of other uncharacterized non-coding genomic regions in mammalian cells in an efficient and cost-efTective manner. | Minzhen Tao Qiaochu Mu Yurui Zhang Zhen Xie | 2020 | Quantitative Biology2020，8，1： | 0 |
| 4 | Hybrid model enabling highly efficient follicular segmentation in thyroid cytopathological whole slide image显示文摘Background The prevalence of thyroid cancer is growing rapidly.Early and precise diagnosis is critical in thy-roid cancer caring.An automatic thyroid cancer diagnostic tool can be valuable to achieve early detection and diagnostic consistency.Only the follicular areas in the sample contain useful information to the thyroid cancer diagnosis based on fine needle aspiration(FNA).This study aimed to develop a highly efficient accurate method for follicular cell areas segmentation(FCAS)of thyroid cytopathological whole slide images(WSIs).Methods A total of 96 cell samples from July 2017 to July 2018 were collected in one hospital in Beijing,China.Forty-three WSIs were selected and manually labeled,including 17 cases of papillary thyroid carci-noma sample and 26 cases of benign sample.Six thousand and nine hundred cropped typical image patches(available on http://gffzz188fe103f8f1460as5b9f6k9nv56x6u96.ffgz.tsg.suse.edu.cn/bupt-ai-cz/Hybrid-Model-Enabling-Highly-Efficient-Follicular-Segmentation)of 1024×1024 pixels from 13 large WSIs were used for patch-level model training and testing and all of the 13 large WSIs were papillary thyroid carcinoma samples.Thirty testing WSIs with an average size 36,217×29,400(from 10,240×10,240 to 81,920×61,440)were used to test the effectiveness of the hybrid model.Based on the traditional semantic segmentation model deeplabv3,we constructed a hybrid segmentation architecture by adding a classification branch into the segmentation scheme to improve efficiency.Accuracy was used to measure the performance of the classification model;pixel accuracy(pAcc),mean accuracy(mAcc),mean intersection over union(mIoU),and frequency weighted intersection over union(fwIoU)were used to measure the performance of the segmentation model,respectively.Results Using this method,up to 93%WSI segmentation time was reduced by skipping the colloidal areas and the blank background areas.The average processing time of 30 WSI was 49.49 s.On the patch dataset,this hybrid model might reach pAcc=98.65%,mAcc=85.60%,mIoU=79.61%,and fwIoU=97.54%.On the WSI dataset,this model might reach pAcc=99.30%,mAcc=68.94%,mIoU=58.21%,and fwIoU=99.50%.Conclusion The proposed hybrid method might significantly improve previous solutions and achieve the superior performance of efficiency and accuracy. | Chuang Zhu Siyan Tao Huang Chen Minzhen Li Ying Wang Jun Liu Mulan Jin | 2021 | Intelligent Medicine2021，1，2： | 0 |
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