Associating somatic mutation with clinical outcomes through kernel regression and optimal transport

Somatic mutations in cancer patients are inherently sparse and potentially high dimensional. Cancer patients may share the same set of deregulated biological processes perturbed by different sets of somatically mutated genes. Therefore, when assessing the associations between somatic mutations and clinical outcomes, gene-by-gene analysis is often under-powered because it does not capture the complex disease mechanisms shared across cancer patients. Rather than testing genes one by one, an intuitive approach is to aggregate somatic mutation data of multiple genes to assess their joint association with clinical outcomes. The challenge is how to aggregate such information. Building on the optimal transport method, we propose a principled approach to estimate the similarity of somatic mutation profiles of multiple genes between tumor samples, while accounting for gene–gene similarities defined by gene annotations or empirical mutational patterns. Using such similarities, we can assess the associations between somatic mutations and clinical outcomes by kernel regression. We have applied our method to analyze somatic mutation data of 17 cancer types and identified at least five cancer types, where somatic mutations are associated with overall survival, progression-free interval, or cytolytic activity.     

ING1 inhibits Twist1 expression to block EMT and is antagonized by the HDAC inhibitor vorinostat

ING1 is a chromatin targeting subunit of the Sin3a histone deacetylase (HDAC) complex that alters chromatin structure to subsequently regulate gene expression. We find that ING1 knockdown increases expression of Twist1, Zeb 1&2, Snai1, Bmi1 and TSHZ1 drivers of EMT, promoting EMT and cell motility. ING1 expression had the opposite effect, promoting epithelial cell morphology and inhibiting basal and TGF-β-induced motility in 3D organoid cultures. ING1 binds the Twist1 promoter and Twist1 was largely responsible for the ability of ING1 to reduce cell migration. Consistent with ING1 inhibiting Twist1 expression in vivo, an inverse relationship between ING1 and Twist1 levels was seen in breast cancer samples from The Cancer Genome Atlas (TCGA). The HDAC inhibitor vorinostat is approved for treatment of multiple myeloma and cutaneous T cell lymphoma and is in clinical trials for solid tumours as adjuvant therapy. One molecular target of vorinostat is INhibitor of Growth 2 (ING2), that together with ING1 serve as targeting subunits of the Sin3a HDAC complex. Treatment with sublethal (LD25-LD50) levels of vorinostat promoted breast cancer cell migration several-fold, which increased further upon ING1 knockout. These observations indicate that correct targeting of the Sin3a HDAC complex, and HDAC activity in general decreases luminal and basal breast cancer cell motility, suggesting that use of HDAC inhibitors as adjuvant therapies in breast cancers that are prone to metastasize may not be optimal and requires further investigation.     

Bayesian estimation of two-part joint models for a longitudinal semicontinuous biomarker and a terminal event with INLA: Interests for cancer clinical trial evaluation

Two-part joint models for a longitudinal semicontinuous biomarker and a terminal event have been recently introduced based on frequentist estimation. The biomarker distribution is decomposed into a probability of positive value and the expected value among positive values. Shared random effects can represent the association structure between the biomarker and the terminal event. The computational burden increases compared to standard joint models with a single regression model for the biomarker. In this context, the frequentist estimation implemented in the R package frailtypack can be challenging for complex models (i.e., a large number of parameters and dimension of the random effects). As an alternative, we propose a Bayesian estimation of two-part joint models based on the Integrated Nested Laplace Approximation (INLA) algorithm to alleviate the computational burden and fit more complex models. Our simulation studies confirm that INLA provides accurate approximation of posterior estimates and to reduced computation time and variability of estimates compared to frailtypack in the situations considered. We contrast the Bayesian and frequentist approaches in the analysis of two randomized cancer clinical trials (GERCOR and PRIME studies), where INLA has a reduced variability for the association between the biomarker and the risk of event. Moreover, the Bayesian approach was able to characterize subgroups of patients associated with different responses to treatment in the PRIME study. Our study suggests that the Bayesian approach using the INLA algorithm enables to fit complex joint models that might be of interest in a wide range of clinical applications.     

抑制CD36与Nogo-B表达抑制三阴性乳腺癌细胞增殖与迁移

分化聚类36（cluster of differentiation 36,CD36）是一种位于细胞表面的膜蛋白受体,可以结合并转运脂肪酸。内质网膜蛋白4B （Nogo-B）在肝脏中调控脂肪酸代谢而影响肝癌的发展。目前并不清楚CD36和Nogo-B的相互作用是否能够影响乳腺癌细胞的增殖和迁移。本研究在三阴性乳腺癌（triple-negative breast cancer,TNBC）细胞中同时干预CD36与Nogo-B的表达来探索它们对细胞增殖与迁移的影响。结果表明在三阴性乳腺癌细胞中,单独抑制CD36或Nogo-B的表达都能够抑制细胞的增殖与迁移;同时抑制CD36与Nogo-B的表达时,这种抑制效果更加明显,且Vimentin、B细胞淋巴瘤-2（B-cell lympoma-2,BCL2）和增殖细胞核抗原（proliferating cell nuclear antigen,PCNA）的表达受到抑制。在小鼠移植瘤模型中,E0771细胞转染CD36或Nogo-B的siRNA后成瘤能力降低;同时敲减CD36和Nogo-B时,肿瘤生长速度显著减慢。机制研究发现,抑制CD36和Nogo-B表达能够抑制脂肪酸结合蛋白4（fatty acid binding protein 4,FABP4）和脂肪酸转运蛋白4（fatty acid transport protein 4,FATP4） mRNA的含量,同时CD36和Nogo-B过表达刺激的细胞增殖被FABP4的siRNA降低,预示着抑制乳腺癌细胞中CD36与Nogo-B的表达可能通过抑制脂肪酸的吸收和转运而抑制细胞的生长和迁移。此外,抑制CD36与Nogo-B的表达可激活P53-P21-Rb信号通路,参与抑制CD36与Nogo-B表达而抑制的细胞增殖与迁移。本研究证明同时抑制CD36和Nogo-B的表达能够协同抑制三阴性乳腺癌细胞的增殖和迁移,为临床抗三阴性乳腺癌药物的开发提供了新的靶点。     

靶向紧密连接蛋白6的药物在癌症治疗中的研究进展

紧密连接蛋白6(Claudin6,CLDN6)是紧密连接蛋白(Claudins,CLDNs)家族的一员,在卵巢癌、睾丸癌、子宫颈内膜癌、肝癌和肺腺癌等多种癌症中特异性高表达,而在成人正常组织中几乎不表达。其能够激活多条通路参与肿瘤发生的多个过程,包括促进肿瘤生长、迁移和侵袭,且促进肿瘤化疗耐药。近年来,CLDN6作为癌症治疗的新靶点引起了研究人员的广泛关注,针对CLDN6靶点开发了多种类型的抗癌药物,包括抗体偶联药物(antibody-drug conjugate,ADC)、单克隆抗体、双特异性抗体和嵌合抗原受体T细胞免疫疗法(chimeric antigen receptor T-cell immunotherapy,CAR-T)。本文简要概述了CLDN6的蛋白结构、表达分布以及在肿瘤中的功能,并对其作为药靶开发的抗癌药物研发现状和研发思路进行了综述。     

靶向蛋白质泛素修饰及降解在前列腺癌治疗中的机遇与挑战

前列腺癌是中国发病率增长最快的男性肿瘤,抗雄激素治疗耐药是导致前列腺癌患者预后差的主要原因。因此,解决耐药性难题是前列腺癌转化研究的关键问题。哺乳动物细胞利用泛素-蛋白酶体系统实现蛋白质的靶向降解。因此,前列腺癌中关键的癌基因如雄激素受体（AR）的上游泛素化调控因子（如去泛素化酶）是潜在的治疗靶点。然而,这些酶具有较广的底物谱系,存在脱靶的可能性。近来,基于泛素-蛋白酶体系统开发的蛋白质降解靶向嵌合体（proteolysis-targeting chimeras,PROTAC）技术是最具前景和革命性的新型抗癌药物研发技术,能够利用特定E3泛素连接酶对靶蛋白进行降解而不影响其他底物。与传统小分子抑制剂相比,PROTAC分子在克服耐药性以及针对不可成药的靶点方面拥有巨大优势。目前,针对AR的PROTAC降解剂已在II期临床取得了成功,靶向蛋白质泛素化及降解途径的新技术将有望为前列腺癌的临床治疗带来新的突破。     

胰腺癌早期检测的生物标志物（英文）

胰腺癌症是最难诊断和治疗的恶性肿瘤之一,其特点是发病隐匿、进展迅速、预后差。目前,手术治疗仍然是首选治疗方法。然而由于缺乏早期症状,大约70%的患者在确诊时已经出现局部扩散或远端转移,从而无法进行手术治疗。由此看来,早期检测是提高患者治疗效果和预后的有效途径。临床上使用的成像方法 （CT、MRI、EUS等）通常无法检测早期病变,并且很容易受到操作员的影响。常规临床标志物如CA19-9、CA125、CA242和CEA受到限制,其敏感性或特异性不令人满意。因此,寻找新的具有高敏感性和特异性的标志物是实现胰腺癌早期检测的关键。近年来,对生物标志物的广泛研究主要集中在遗传学、转录组学和蛋白质组学上。特别是由microRNA(miRNA)、long non-coding RNA(lncRNA)和circRNA(circRNA)组成的非蛋白质编码RNA(non-protein coding RNA,ncRNA)为胰腺癌的早期检测提出了许多新思路。然而,其中绝大多数仍处于实验室研究阶段。而一项成熟的生物标志物研究应该整合基因组学、转录组学、蛋白质组学或代谢组学的数据,并结合患者的个体特征（如体重指数...     

白花蛇舌草的化学成分研究

为研究白花蛇舌草(Hedyotis diffusa)的化学成分,采用硅胶柱色谱、制备HPLC色谱等方法,从白花蛇舌草全草的70%乙醇提取物中分离得到9个化合物。根据理化性质及其波谱或光谱数据,结构分别鉴定为：豆甾醇(1)、β-谷甾醇(2)、rubiadin(3)、2,6-二羟基-1-甲氧基蒽醌(4)、β-谷甾醇-3-O-β-D-葡萄糖苷(5)、对香豆酸(6)、山奈酚(7)、槲皮素(8)和2-羟基-3-甲氧基-7-甲基蒽醌(9)。化合物4为新化合物;氯仿提取部位的Fr.4、Fr.4-2及Fr.4-4流分具有较强体外抗人子宫内膜癌Ishikawa细胞活性,其IC₅₀值分别为52.8、78.1和27.5μg/mL。     

广西北部湾海岸带、海岛的外来入侵植物

为保护广西北部湾海岸带、海岛的植物多样性和生态系统多样性,通过样方、样带法野外实地调查并结合文献资料,对其外来入侵植物的物种组成、原产地、生活型、入侵途径和危害状况等进行了分析。结果表明,广西北部湾海岸带、海岛共有入侵植物64种,隶属28科55属,其中菊科(Asteraceae)最多(15种)。草本植物最多,有48种(75.00%)。原产地来自美洲的植物最多,有49种。入侵风险等级可划分为5个等级,其中Ⅰ级严重危害的有8种(12.50%)。与广西、广东、海南及华南地区的外来入侵植物在物种组成、生活型和原产地等方面呈现出较强的相似性;北部湾与广西中越边境内陆地区来自美洲的入侵植物都超过60%。因此推测广西外来入侵植物有两条可能的入侵线路：一是从海南登录,二是从中越边境跨入。广西北部湾海岸带、海岛的外来入侵植物总数(相对整个广西)虽较少,但其8种Ⅰ级严重危害植物的防治,仍需引起重视。