The 21st Century Cures Act, approved in the USA in December 2016, has encouraged the establishment of the national Precision Medicine Initiative and the augmentation of efforts to address disease prevention, diagnosis and treatment on the basis of a molecular understanding of disease. The Act adopts into law the formal process, developed by the FDA, of qualification of drug development tools, including biomarkers and clinical outcome assessments, to increase the efficiency of clinical trials and encourage an era of molecular medicine. The FDA and European Medicines Agency (EMA) have developed similar processes for the qualification of biomarkers intended for use as companion diagnostics or for development and regulatory approval of a drug or therapeutic. Biomarkers that are used exclusively for the diagnosis, monitoring or stratification of patients in clinical trials are not subject to regulatory approval, although their qualification can facilitate the conduct of a trial. In this Review, the salient features of biomarker discovery, analytical validation, clinical qualification and utilization are described in order to provide an understanding of the process of biomarker development and, through this understanding, convey an appreciation of their potential advantages and limitations.展开>><<收起

Nature Reviews Rheumatology,Published: 14 May 2018  0


A new method for early and accurate breast cancer screening has been developed by researchers at Ben-Gurion University of the Negev and Soroka University Medical Center, using commercially available technology. The researchers were able to isolate relevant data to more accurately identify breast cancer biomarkers using two different electronic nose gas sensors for breath, along with gas-chromatography mass spectrometry (GC-MS) to quantify substances found in urine. In their study published in Computers in Biology and Medicine, researchers detected breast cancer with more than 95 percent average accuracy using an inexpensive commercial electronic nose (e-nose) that identifies unique breath patterns in women with breast cancer. In addition, their revamped statistical analyses of urine samples submitted both by healthy patients and those diagnosed with breast cancer yielded 85 percent average accuracy. "Breast cancer survival is strongly tied to the sensitivity of tumor detection; accurate methods for detecting smaller, earlier tumors remains a priority," says Prof. Yehuda Zeiri, a member of Ben-Gurion University's Department of Biomedical Engineering. "Our new approach utilizing urine and exhaled breath samples, analyzed with inexpensive, commercially available processes, is non-invasive, accessible and may be easily implemented in a variety of settings." The study reports breast cancer is the most commonly diagnosed malignancy among females and the leading cause of death around the world. In 2016, breast cancer accounted for 29 percent of all new cancers identified in the United States and was responsible for 14 percent of all cancer-related deaths. Mammography screenings, which are proven to significantly reduce breast cancer mortality, are not always able to detect small tumors in dense breast tissue. In fact, typical mammography sensitivity, which is 75 to 85 percent accurate, decreases to 30 to 50 percent in dense tissue. Current diagnostic imaging detection for smaller tumors has significant drawbacks: dual-energy digital mammography, while effective, increases radiation exposure, and magnetic resonance imaging (MRI) is expensive. Biopsies and serum biomarker identification processes are invasive, equipment-intensive and require significant expertise.  "We've now shown that inexpensive, commercial electronic noses are sufficient for classifying cancer patients at early stages," says Prof. Zeiri. "With further study, it may also be possible to analyze exhaled breath and urine samples to identify other cancer types, as well." Story Source: Materials provided by American Associates, Ben-Gurion University of the Negev. Note: Content may be edited for style and length. Journal Reference: Or Herman-Saffar, Zvi Boger, Shai Libson, David Lieberman, Raphael Gonen, Yehuda Zeiri. Early non-invasive detection of breast cancer using exhaled breath and urine analysis. Computers in Biology and Medicine, 2018; 96: 227 DOI: 10.1016/j.compbiomed.2018.04.002详情>>

2018-04-27 00:00:00


Rheumatoid arthritis (RA) is an autoimmune disorder that occurs when the immune system mistakenly attacks the body's tissues. Unlike the wear-and-tear damage of osteoarthritis, rheumatoid arthritis affects the lining of the joints, causing painful swelling that can eventually result in bone erosion and joint deformity. Most RA patients are positive for anticitrullinated protein antibodies (ACPA), and these antibodies are highly specific for RA diagnosis. ACPA recognizes various citrullinated proteins, such as fibrinogen, vimentin and glucose- 6-phosphate isomerase. Citrullinated proteins are proteins that have the amino acid arginine converted into the citrulline, which is not one of the 20 standard amino acids encoded by DNA in the genetic code. Autoreactivity to citrullinated protein may increase susceptibility to RA. While many candidate citrullinated antigens have been identified in RA joints, the involvement of citrullinated proteins in blood serum remains mostly uninvestigated. To that end, a team of University of Tsukuba-centered researchers set out to explore the expression and commonality of citrullinated proteins in peptide glucose-6-phosphate isomerase-induced arthritis (pGIA) and patients with RA, and went one step further to investigate its correlation with RA disease activity. The researchers recently published their findings in Arthritis Research & Therapy. "We examined serum citrullinated proteins from pGIA by western blotting, and the sequence was identified by mass spectrometry. With the same methods, serum citrullinated proteins were analyzed in patients with RA, primary Sjögren's syndrome, systemic lupus erythematosus, and osteoarthritis as well as in healthy subjects," study corresponding author Isao Matsumoto explains. "In patients with RA, the relationship between the expression of the identified protein inter-alpha-trypsin inhibitor heavy chain 4 (ITIH4) and clinical features was also evaluated, and the levels of citrullinated ITIH4 were compared before and after biological treatment." The researchers found that citrullinated ITIH4 was highly specific to patients with RA, compared with patients with other autoimmune and arthritic diseases or in healthy subjects, indicating a potential role for citrullinated ITIH4 in RA pathogenesis. Notably, its levels were decreased in correlation with the reduction of disease activity score after effective treatment in patients with RA. Moreover, antibody response to citrullinated epitope in ITIH4 was specifically observed in patients with RA. "Our results suggest that citrullinated ITIH4 might be a novel biomarker to distinguish RA from other rheumatic diseases and for assessing disease activity in patients with RA," Matsumoto says. "To our knowledge, this is the first report of its kind in the literature."详情>>

2018-04-27 00:00:00


4月18日,由树兰医疗集团管理有限公司、深圳华大基因科技有限公司共同发起的“基因医生计划”(GeneDoctor Project)在杭州良渚新城正式发布。该计划旨在推动精准医学新技术在医院的转化应用,通过培养复合型“基因医生”人才,为成果的快速转化“破局”,开创医疗的新模式,为我国精准医学的发展抢占先机。 本次发布的“基因医生计划”将以开放性项目平台致力推动包括基因组学、代谢组学、免疫组学、蛋白质组学等在内的跨组学(Trans-Omics)数据的获得与解读纳入临床常规,建立临床标准与指南,发现疾病新知,促进临床转化与应用,实现平台、技术、人才的共同发展,实现医疗模式的创新。 活动现场,树兰医疗发起人、中国工程院李兰娟院士,华大联合创始人、董事长汪建,余杭区委副书记、区长陈如根,副区长许玲娣,华大执行副总裁杨爽,华大执行副总裁、国内区域规划与发展中心主任路军,树兰医疗集团总裁郑杰,树兰(杭州)医院院长叶再元,华大国际高级副总裁、董事长特别助理何亦武等相关领导出席了发布会。 陈如根在致辞中表示,余杭将全力支持“基因医生计划”的实施与推广,推动基因科技成果在余杭的转化和应用,并提供一流的服务和环境,推动合作不断深化。   余杭区委副书记、区长陈如根致辞 李兰娟院士指出,十九大明确提出了实施“健康中国”的战略,在近期结束的海南博鳌亚洲论坛中,习总书记也提出大力发展健康事业的指导意见。树兰医疗希望通过“基因医生计划”践行总书记改革开放的要求,创建一些新的医学模式来满足人民群众不断增长的医疗卫生需求。未来,“基因医生计划”将构建个人生命健康云,提供全人全程、全方位全周期的健康医疗服务。   树兰医疗发起人、中国工程院李兰娟院士致辞 汪建强调,此次华大与树兰医疗合作,双方均具备优秀的团队和领先的科研技术,强强联合,优势互补,将能够为余杭区、杭州市乃至浙江省精准医学的发展起到核心支撑作用,继续助推我国医疗卫生健康事业的发展。   华大联合创始人、董事长汪建致辞 在发布会上,还进行了三项签约活动——余杭区政府、树兰医疗、华大共同签署战略合作意向书,在良渚国际生命科技小镇共同建设民生、科研、产业三环联动为承载的国际基因谷。树兰医疗与良渚新城管委会、良渚新城管委会与树兰旗下杭州医景股权投资基金管理有限公司也签署了合作协议。 基因科技应用临床 推动医疗模式创新转化 近年来,以基因检测为基石的“精准医学”愈发受到世界各国的高度重视,这种将个人基因、环境与生活习惯差异考虑在内的疾病预防与处置的新兴方法与医疗模式正在逐渐兴起。据了解,自2015年我国将“精准医学”计划上升为“国家战略”后,相关产业及市场正在基因组测序技术、生物医学分析技术和大数据分析工具的驱动之下不断快速发展。而相比全球,不少国家已经形成了以肿瘤为主要对象,覆盖从早期筛查、辅助诊断、伴随诊断到精准治疗全流程的精准医学产业。 业内人士则建议,我国“精准医学”应借助基因测序技术锁定个人病变基因,实现精准的预测预防、治疗和康复,同时结合“基因+质谱+临床”大数据,发现疾病新知,促进临床转化,推动医疗模式创新转化,为我国在精准医学领域的发展抢占先机。 “基因医生计划”启动 助力我国精准医学快速发展 正如业内人士所言,精准医学成果需要通过医院及医生等载体实现转化与应用,为普通民众、医院、医生和患者等提供辅助诊断的工具与平台,争取在全球推进的“精准医学”大产业中拥有话语权,最终为民族大健康事业服务。 此次树兰医疗与华大首提的“基因医生计划”在国际竞争中独树一帜。该计划的发起旨在推动精准医学新技术在医院转化应用,将通过多方共同参与,构建跨组学工具与平台,推动基因组学等检测纳入临床常规,通过对样本和基因组学等数据进行标准化存储、分析与解读,促进数据共享,为医生与患者提供个人健康状况的完整数据。同时,也将通过实践培养出一批复合型“基因医生”人才,实现平台、技术、人才共同发展,实现医学模式的创新,为成果的快速转化“破局”,为我国精准医学的发展抢占先机。 树兰医疗将为“基因医生计划”提供平台支撑 据悉,树兰将依托自身的医疗资源优势为“基因医生计划”提供平台支撑。同时,该计划将在余杭国际基因谷、良渚先导基地开展“健康余杭”万人队列研究项目,开展人群大样本的基于真实世界数据的生物信息、临床医学和卫生经济学研究。 树兰医疗作为医疗领域中国社会化办医的标杆,一直致力于积极探索大生命科学领域的研究、创新和孵化,培养以健康医学为核心的跨学科人才,建立“临床、科研、教学、产业”四位一体的生态体系,积极推动医疗健康产业生态的数据开放。目前已集聚了50余名临床医学院士级专家及2000余名国内外著名专家,目前已建设和委托管理的医院已达8家,运行良好,其中2家医院已通过国际JCI标准认证,托管的博鳌超级医院更是外界备受关注。树兰医疗建立专业的实验诊断中心和医疗智能信息化服务闭环,发起OMAHA联盟,推动健康医疗数据的标准化及共享,提高患者对自身健康医疗数据使用的完整性、可及性和可用性。 华大将为“基因医生计划”提供技术驱动 透过“基因医生计划”发布会得知,此次计划开启后,华大将提供工具、科技与人才支撑,并将推动跨组学数据与临床实践的全面融合,从提高就医个性化、精准化程度等多方面助推“基因医生计划”的开展。 据了解,作为全球领先的基因组学研发机构,华大近年来在基因测序领域取得了长足的发展,以科教、科研、科服、科普四大支柱服务于生命科学研究的各个领域。尤其在基因测序临床应用方面,华大已开发出一系列基于跨组学技术的检测服务,形成了贯穿生命孕育、出生、发育、成长、衰老等全过程的全时全景产品图谱,可用于出生缺陷防控、肿瘤精准诊疗与康复、传染感染性疾病精准治疗、心脑血管及代谢类疾病防控等。华大将充分发挥基因测序技术优势,为我国“精准医学”产业的发展贡献力量。 此次“基因医生计划”的正式发布具有划时代的意义,它将促进“基因医生”群体的蓬勃发展,帮助国家在精准医学领域实现跨越式发展,助力国家基因科技产业的发展与腾飞,并共同探索有效的医学模式,为人类健康服务。详情>>

2018-04-20 00:00:00


4月10日,Alzheimer’s & Dementi协会发布一份报告建议,“重新定义”阿尔兹海默症,即根据大脑的变化,而不是记忆衰退等认知症状定义这一疾病。这一改变有望建立“通用语言”,加快药物研发以及诊疗方案的及早干预。 “症状是疾病的结果,并不能成为疾病的定义。”报告者之一、梅奥诊所的脑成像专家Clifford Jack如此强调。这份由全球阿尔兹海默症协会、国家衰老研究所完成的新提议以论文的形式发表在《Alzheimer's & Dementia: The Journal of the Alzheimer's Association.》期刊。 科学家们建议,阿尔兹海默症患者应该依据体内生物标志物(例如β-淀粉样蛋白、Tau蛋白)分类。 DOI: https://doi.org/10.1016/j.jalz.2018.02.018 目的 2017年,FDA批准默沙东PD-1抗体Keytruda用于治疗携带一种特定基因特征的任何一种实体瘤,使其成为首个依据生物标志物进行区分的抗肿瘤疗法,在抗癌史上画上浓墨一笔。 现在,针对阿尔兹海默症的这一定义“变革”有望通过更客观的标准(例如脑部扫描)挑选患者进行相关研究,并有助于在疾病初期(症状尚未明显之前)筛选并给予有效干预。 This table shows the eight biomarker profiles (left column) and corresponding categories (right column) outlined in a new biomarker-based framework that could be used to group research participants. The biomarker profiles can be sorted into three broader categories: Normal Alzheimer's biomarkers, Alzheimer's continuum, and non-Alzheimer's pathologic change. [NIA-AA Research Framework] 意义 不可否认的是,“重新定义”将产生一个惊人的效果:更多的人会被考虑患有阿尔兹海默症,因为在症状出现之前的15-20年,大脑病变就已经发生。这也意味着,阿尔兹海默症患者数量将急剧增加。 “在70岁以上、无认知障碍的老年人中,有1/3人的大脑内实际上已经出现AD迹象。” Clifford Jack表示道。 全球约有5000万阿尔兹海默症患者。作为常见的神经性衰退疾病,AD已经成为威胁老年人健康和生命的主要病因。遗憾的是,抗AD药物研发一直鲜有成效,迄今已有超200项围绕AD药物的临床试验失败。这些研究多集中于治疗已经出现记忆丧失、交流困难等典型痴呆症状的患者。不少科学家们认为失败的一个原因可能是,当记忆衰退、认知退化等症状出现时,大脑中已经存在病斑了。 “依据典型病症确诊时,已经太晚了。” 衰老研究所的神经科学主任Eliezer Masliah博士说道。所以,他认为,阿尔兹海默症的诊疗时间应该“更早”。 这一指南“跳脱”认知衰退的传统范畴,通过生物标志物反映生病初期的大脑变化。但是,专家们强调,因为这一建议尚未得到证实,现在利用这些扫描、检测标准进行日常护理还为时尚早。目前,医生们仍将通过认知评估等主要方法诊断大多数病例。 责编:悠然 参考资料: Alzheimer’s Should be Characterized by Biomarkers: Report New way of defining Alzheimer's aims to find disease sooner详情>>

2018-04-17 00:00:00


On April 12, the FDA finalized two guidances designed to enhance collaboration among researchers, and drive the efficient development of novel next generation sequencing (NGS)-based tests. The guidances outline innovative regulatory approaches for the oversight of medical technologies that play an important role in the continued advancement of individualized, genetic-based medicine. The first guidance, “Use of Public Human Genetic Variant Databases to Support Clinical Validity for Genetic and Genomic-Based In Vitro Diagnostics,” describes an approach where test developers may rely on clinical evidence from FDA-recognized public databases to support clinical claims for their tests and provide assurance of the accurate clinical evaluation of genomic test results. Using FDA-recognized databases will provide test developers with an efficient path for marketing clearance or approval of a new test. The second guidance, “Considerations for Design, Development, and Analytical Validation of Next Generation Sequencing (NGS)–Based In Vitro Diagnostics (IVDs) Intended to Aid in the Diagnosis of Suspected Germline Diseases,” provides recommendations for designing, developing, and validating NGS-based tests used to diagnose individuals with suspected genetic diseases.  It clarifies how the FDA evaluates premarket submissions to determine a test’s analytical validity, including how well it detects the presence or absence of a particular genomic change. On Thursday, May 24 from 2:00 – 3:30PM ET, the FDA will host a webinar for product developers, database administrators, and others interested in learning more about these final guidances. More information about the webinar is available at http://www.fda.gov/CDRHwebinar.详情>>

2018-04-16 00:00:00


这是癌症研究领域的一个重要里程碑!科学家们完成了对11,000多名患者肿瘤的基因测序和分析,提出了全新的肿瘤分类策略。研究人员称,是时候重新编写关于癌症的教科书了! 很长时间以来,癌症主要是依据其体内的“发源位置”来进行分类,如乳腺癌、胃癌等等。然而,一项在2012年发起的、名为Pan-Cancer Initiative的合作项目计划从另一个角度来研究癌症。之前的初步分析显示,起源于不同器官的癌症实际上在分子水平有着共同之处,而起源于同一组织的癌症也可能具有大不相同的基因组特征。 最全面的跨癌症分析 近日,Pan-Cancer Initiative发布了更大规模的基因组和分子数据的分析结果,涉及来自一万多名患者的33种不同的癌症。相关成果以27篇论文的形式发布在Cell、Cancer Cell、Cell Reports等杂志上。这是迄今为止最全面的跨癌症分析(cross-cancer analysis),也是癌症基因组图谱(The Cancer Genome Atlas)计划最终的成果。 The UCSC Tumor Map helps researchers visualize the dominant patterns found in the TCGA data, such as the cell of origin, molecular histology, 'stemness' or differentiation status, specific altered genetic pathways, and the immune system component of the tumors. Credit: UC Santa Cruz Genomics Institute 颠覆癌症分类方式 Pan-Cancer Initiative的组织者是加州大学圣克鲁兹分校的Josh Stuart教授。加州大学旧金山分校的临床肿瘤学家Christopher Benz教授是他的主要合作者之一。 早在2013年9月,Stuart等就已完成了第一波跨肿瘤比较。当时,他们分析了12种不同类型的肿瘤。Stuart说:“当我们发现了不同类型的癌症之间有相似之处时,大家希望能够做一个更全面的分析。” 2014年,他以共同作者的身份发表了一篇相关论文。在这一研究中,科学家们使用肿瘤分子数据的统计分析,将肿瘤按照亚型或簇(clusters)进行分类。 之后,通过与国际科学家团队合作,他们对TCGA肿瘤数据的完整集合进行了全面的分子分析。最新发表的结果显示,根据肿瘤的细胞和基因组成,而不是它们的起源部位,被分析的33种肿瘤可以被重新划分为28种不同的分子类型。 Researchers analyzed more than 10,000 tumor samples from 33 cancers to reveal a new molecular classification system. Credit: Hoadley, KA., Laird, PW., et al., "Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer" Cell, April 5, 2018. 意义深远的里程碑进展 Stuart教授说:“这些论文是里程碑式的成果。现在,我们能够看到癌症的全貌,这让我对‘理解癌症的复杂性’充满了希望。我也相信,一旦科学家开始仔细研究这些数据,其临床影响就不会落后太远。” Benz教授则表示,这些发现将为未来的癌症研究和临床试验提供新的基础。如果患者的肿瘤能够首先根据基因组和分子组成来分类,那么,他们将有最好的机会获得最佳的治疗。 同时,Benz教授也指出,这些成果还支持了一种逐渐被认可的观点,即,FDA批准用于治疗一种癌症的特定免疫疗法可能会使多种其他类型的癌症患者受益。PD-1抗体就是最好的例证。除了可用于治疗包括黑色素瘤、非小细胞肺癌、肾细胞癌、经典型霍奇金淋巴瘤、头颈癌、膀胱癌、结直肠癌、肝癌、胃癌在内的多种癌症,这类药物还是FDA批准的首个不区分肿瘤来源的抗癌疗法【详细】。 鉴定出了约300个驱动肿瘤生长的基因 除了癌症分类方面的颠覆,在同期发表的系列成果中,华盛顿大学的Li Ding博士带来的科学家小组还鉴定出了约300个驱动肿瘤生长的基因。值得注意的是,所有被分析的肿瘤中,超过一半以上携带基因突变。 Briefly, the outer text in blue indicates different cancer types. The predicted driver genes unique to that cancer type are listed in black text. The top right section shows all genes found to be important in multiple cancer types. Credit: Cell(点击链接查看大图:http://pic.biodiscover.com/files/6/t4/biodiscover1523357494.7890156.jpg) Ding博士说:“通过对上万个肿瘤样本的分析,我们从细节上了解了驱动癌症的遗传性突变以及随年龄增长而不断积累的基因错误。这些基因错误使得肿瘤形成了特定的、能够指导治疗的分子特征。” “我们的发现也支持了一种观点,即,任何携带大量突变的肿瘤(这类肿瘤通常对化疗具有耐药性)都容易受到检查点抑制剂(癌症免疫疗法的一种)的影响。这是因为,高突变的肿瘤会产生相对更畸形的蛋白质,而这些蛋白会触发免疫响应。虽然为了防止造成自身免疫,人体形成了抑制这种免疫响应的机制,但检查点抑制剂能够消除这种机制,让免疫系统更有效地对抗肿瘤。”她补充道。 值得一提的是,新研究还进一步调查了驱动乳腺癌和卵巢癌的BRCA1基因。Ding博士说:“我们很早就知道,BRCA1是癌症发展中的一个重要基因,但一直很难弄清楚,BRCA1的哪些基因突变驱动了癌症发生,哪些突变是无害的。在最新的研究中,我们在乳腺癌中发现了21种致病的BRCA1和BRCA2突变体,在宫颈癌中发现了3种,在结直肠癌中发现了1种,在恶性胶质瘤中发现了1种,在卵巢癌中发现了38种。” Ding博士表示,先前,大多数早期临床试验的设计并没有考虑到基因组学。现在,我们可以对患者的肿瘤样本进行测序,以寻找其基因组学与药物疗效之间的相关性,这将帮助设计更好的疗法。 附:部分相关论文截图 参考资料: New 'Pan-Cancer' analysis reveals the common roots of different cancers Major milestone reached in effort to ID cancers' genetic roots详情>>

2018-04-12 00:00:00


Matching unique genetic information from cancer patients' tumors with treatment options -- an emerging area of precision medicine efforts -- often fails to identify all patients who may respond to certain therapies. Other molecular information from patients may reveal these so-called "hidden responders," according to a Penn Medicine study in Cell Reports this week. The findings are published alongside several papers in other Cell journals this week examining molecular pathways using The Cancer Genome Atlas (TCGA). "Targeted sequencing can find individuals with certain mutations that are thought to confer susceptibility to anti-cancer drugs," said senior author Casey Greene, PhD, an assistant professor of Pharmacology in the Perelman School of Medicine at the University of Pennsylvania. "But many people may lack these mutations, and as machine learning approaches improve they may help guide these patients to appropriate therapies." Greene and first author and doctoral student Gregory P. Way used machine learning to classify abnormal protein activity in tumors. This branch of artificial intelligence develops computer programs that can use new data to learn and make predictions. The algorithm they devised to search TCGA integrates genetic data from 33 different cancer types. Greene and Way used information from the transcriptome, the grand total of all messenger RNAs expressed within an individual. They specifically applied their model to the Ras pathway, a family of genes that make proteins that govern cell replication and death. Changes in the normal function of Ras proteins -- mutations which are responsible for 30 percent of all cancers -- can power cancer cells to grow and spread. These mutations are often referred to as the "undruggable Ras," having beaten back a variety of investigational inhibitor drugs and vaccine-based therapies. "This model was trained on genetic data from human tumors in The Cancer Genome Atlas and was able to predict response to certain inhibitors that affect cancers with overactive Ras signaling in an encyclopedia of cancer cell lines," Greene said. The upshot is that the transcriptome is underused in bringing precision to oncology, but when combined with machine learning it can aid in identifying potential hidden responders. The Penn team collaborated with coauthor Yolanda Sanchez, PhD, a cancer biologist from the Geisel School of Medicine at Dartmouth College. They are working together to mesh her identification of compounds that target tumors with runaway Ras activity and tumor data (analyzed by machine learning) to find patients who could benefit from these potential cancer drugs. "For precision medicine to benefit individuals in real time, we must develop robust models to efficiently test efficacy of potential therapies," Sanchez said. "We can use this very powerful combined approach of machine learning-guided drug discovery using Avatars, which are mice carrying identical copies of a patient's tumors. The Avatars allow our interdisciplinary team to identify the tumors with runaway Ras activity and evaluate and compare multiple therapies in real time." Story Source: Materials provided by University of Pennsylvania School of Medicine. Note: Content may be edited for style and length. Journal Reference: Gregory P. Way et al. Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas. Cell Reports, 2018; 23 (1): 172 DOI: 10.1016/j.celrep.2018.03.046详情>>

2018-04-10 00:00:00


香港科技大学深圳研究院“分子神经科学和药物创新研究孔雀”团队在阿尔茨海默症研究领域取得突破性进展,针对中国的患病人群首次进行全基因组测序研究,发现了与疾病发生发展有密切关系的新风险基因位点,揭示了人体免疫系统失调与阿尔茨海默症病变的关系。该项重要成果于 2018 年 2 月 5 日在《美国国家科学院院刊》(PNAS)上发表,填补了国际上关于中国阿尔茨海默症人群全基因组数据的空白,对于阿尔茨海默症的早期诊断、生物标志物研究和药物开发具有重要意义。 阿尔茨海默症(Alzheimer’sdisease,AD),俗称老年痴呆症,是一种以认知、记忆损伤为特征的神经退化性疾病,也是导致老年人痴呆症状发生的主要因素,在 65 岁以上的人群具有很高的发病率。目前中国 AD 患者超过 700 万,居世界首位,而且预期患者数量将随着人口老龄化的加剧而激增。然而关于阿尔茨海默症的发病机制尚未完全明确,也缺乏有效的诊断和治疗方法。国际上关于阿尔茨海默症的研究主要集中于高加索人群,还尚未有中国人群的全基因组测序数据。鉴于遗传背景、环境和生活习惯等方面的差异,高加索人群的研究结果并不完全适用于中国人群。 由香港科技大学副校长、中科院院士叶玉如带领国际化团队,包括了来自香港科技大学、香港科技大学深圳研究院、复旦大学附属华山医院、中国科学院深圳先进技术研究院、英国伦敦大学学院、美国北卡罗来纳大学教堂山分校的科学家。研究团队选取了 2007-2016 年间收集的不同程度阿尔兹海默症患者和对应年龄的健康人群作为研究对象,进行了全基因组测序研究,发现了阿尔兹海默症的新风险基因,例如 GCH1 和 KCNJ15 基因。研究团队在非亚洲人群的阿尔茨海默症患者中也验证了 GCH1 和 KCNJ15 基因的变异与病变的关系,并且发现这两个基因的变异与阿尔茨海默症患者血浆生物标志物的表达有密切关联。研究团队的进一步分析发现了这些阿尔茨海默症风险基因与人体免疫信号存在相互作用,揭示了免疫系统功能失调与阿尔茨海默症病变的关系。 叶玉如表示:此次研究是首个针对中国阿尔茨海默症患者的全基因组测序研究,不但发现了新的遗传风险因子,而且提出了基因变异导致病变的内在生物学机制,对于阿尔茨海默症的早诊早治和精准医学研究有重要意义。我们非常感谢科技部 973 计划、国家自然科学基金、广东省重点实验室、深圳市孔雀计划等对该项目的支持。深圳是我国沿海地区最为发达的城市之一,经济的高速腾飞和人口老龄化的增速,亦使深圳在卫生、社会经济方面面临着巨大压力。该研究成果将有助于提升深圳市及粤港澳大湾区脑科学基础研究水平,推动生物医药产业的发展,保障人民群众身体健康,提高生活质量。  详情>>

2018-04-02 00:00:00


In work that brings researchers closer to the goal of precision medicine approaches to treating glaucoma and other neurodegenerative vision diseases, a new IUPUI study has, for the first time, been able to identify a wide variety of previously unknown cell subtypes in the human eye. The cells -- called retinal ganglion cells, also known as RGCs -- are the neurons that take visual information from the eye to the brain for processing and interpretation, which is how we see things. "Although RGCs have been extensively studied in the past, they are not all the same. There are more than 30 different subtypes of these cells," said study senior author Jason Meyer, associate professor of biology in the School of Science at IUPUI and a primary investigator with the Stark Neurosciences Research Institute at the Indiana University School of Medicine. "Each of these subtypes is thought to have very different functions, and they respond differently in glaucoma and other diseases that affect RGCs. Some of these cell subtypes are more susceptible to damage than others." "With our new comprehensive understanding of the diversity of RGCs, we have set the stage for future studies to look at these cells through a more critical lens, with the ultimate goal of more-tailored drug development and treatment strategies for cells that are damaged or lost in glaucoma and other neurodegenerative vision disorders," Meyer said. The researchers studied RGCs that they derived from pluripotent stem cells. In past work, the Meyer laboratory in the School of Science successfully demonstrated the ability to turn stem cells derived from human skin cells into RGCs. "The methods used in this work will allow us to study how neurodegenerative diseases or optic-nerve injuries -- like those suffered by soldiers in combat or athletes in contact sports -- affect different subtypes of RGCs," Meyer said. "In the future, we will likely be able to customize cell-replacement strategies to replace those specific RGC subtypes for therapies." Prior to the study, knowledge of RGC subtypes in humans had been limited. Through methods developed by Kirstin Langer, the IUPUI doctoral student who is the first author of the new study, the researchers were able to identify and characterize these major RGC subtypes. "The study of different RGC subtypes in human-derived cells allows for more in-depth studies of how these RGCs develop, along with things like how these RGC subtypes may be differently affected by diseases or injuries of the eye," Langer said. "We hope this will allow us to develop better-targeted treatments for patients in the future." "Retinal Ganglion Cell Diversity and Subtype Specification from Human Pluripotent Stem Cells" is published in the peer-reviewed journal Stem Cell Reports. Co-authors, in addition to Meyer and Langer, are Sarah K. Ohlemacher and Clarisse M. Fligor of IUPUI and M. Joseph Phillips, Peng Jiang and David M. Gamm of the University of Wisconsin. The study was funded by the National Eye Institute and the Indiana Department of Health. Story Source: Materials provided by Indiana University-Purdue University Indianapolis School of Science. Note: Content may be edited for style and length. Journal Reference: Kirstin B. Langer, Sarah K. Ohlemacher, M. Joseph Phillips, Clarisse M. Fligor, Peng Jiang, David M. Gamm, Jason S. Meyer. Retinal Ganglion Cell Diversity and Subtype Specification from Human Pluripotent Stem Cells. Stem Cell Reports, 2018; DOI: 10.1016/j.stemcr.2018.02.010详情>>

2018-04-02 00:00:00