《PLOS ONE》：狗DNA助力人类癌症新疗法开发

　　

生物通报道：根据美国国家癌症研究所（NCI）和翻译基因组学研究院（TGen）带领的一项概念验证研究表明，利用基因组分析研究犬类癌症，可以有助于开发人类癌症的新疗法。

相关研究结果发表在2014年3月17日《PLOS ONE》杂志。研究指出，经过数百年的人类干预，纯种犬的遗传学已经标准化，为设计临床试验提供了高度可预测的、有用的遗传学模型，在这些临床试验中，特定的药物与人类患者的分子特征相匹配。

TGen主席和这项研究的资深作者Jeffrey Trent博士称：“我们的伴侣犬不仅是‘人类最好的朋友’，而且我们的研究表明，犬类也能帮助人类患者对抗不同类型的癌症。不仅患有癌症的犬类会从这项研究受益，人类也是如此。”

相对来讲，患相同类型癌症的人之间有许多的遗传差异，但是同一品种的犬类之间遗传差异更少，这使我们能够更容易地鉴定和研究驱动犬类癌症的基因。

将自然发生的犬类癌症与人类癌症生物学和疗法的普遍研究一体化，被称为比较肿瘤学（comparative oncology）。根据个人的特定遗传学或分子组成，确定特定的药物治疗，通常被称为个性化用药（personalized medicine，PMed）。

在NCI比较肿瘤学试验联盟（Comparative Oncology Trials Consortium，COTC）组织的这项概念验证研究中，研究人员分析了来自31只犬类的遗传样本。这些遗传样本均采自肿瘤活检标本。在这项临床试验中，没有犬类以任何方式受伤。

本文的第一作者、COTC的前主任Melissa Paoloni博士称：“要想有效地评价PMed研究设计，就必需有复杂的模型，这一概念验证性试验，将癌症犬确定为临床评估PMed方法的模型。比较肿瘤学模型有潜力加快这种临床评估，并带来个性化用药的进步。”

这项COTC研究，是根据不同犬品种对特殊癌症类型的发展倾向而组织的。这项研究所用的癌症犬包括：患膀胱移行细胞癌的苏格兰犬、患淋巴瘤的金毛猎犬、患黑色素瘤的美国可卡犬和患有各种类型癌症的犬类。

在这项研究中，研究人员利用Affymetrix基因表达谱，将31份犬类肿瘤样本，与40份正常的犬类组织样本进行比较，作为评估基因表达差异的方法。这种分析的目标周转时间为7天，但是这项研究将这个分析过程平均为不超过5天。

研究人员指出，这项研究进一步支持，将癌症犬类异源群体作为个性化用药的临床前模型。未来优化PMed策略实施的比较肿瘤学研究，可有助于癌症药物的开发。（生物通：王英）

生物通推荐原文摘要：
Prospective Molecular Profiling of Canine Cancers Provides a Clinically Relevant Comparative Model for Evaluating Personalized Medicine (PMed) Trials
Abstract
Background
Molecularly-guided trials (i.e. PMed) now seek to aid clinical decision-making by matching cancer targets with therapeutic options. Progress has been hampered by the lack of cancer models that account for individual-to-individual heterogeneity within and across cancer types. Naturally occurring cancers in pet animals are heterogeneous and thus provide an opportunity to answer questions about these PMed strategies and optimize translation to human patients. In order to realize this opportunity, it is now necessary to demonstrate the feasibility of conducting molecularly-guided analysis of tumors from dogs with naturally occurring cancer in a clinically relevant setting.

Methodology
A proof-of-concept study was conducted by the Comparative Oncology Trials Consortium (COTC) to determine if tumor collection, prospective molecular profiling, and PMed report generation within 1 week was feasible in dogs. Thirty-one dogs with cancers of varying histologies were enrolled. Twenty-four of 31 samples (77%) successfully met all predefined QA/QC criteria and were analyzed via Affymetrix gene expression profiling. A subsequent bioinformatics workflow transformed genomic data into a personalized drug report. Average turnaround from biopsy to report generation was 116 hours (4.8 days). Unsupervised clustering of canine tumor expression data clustered by cancer type, but supervised clustering of tumors based on the personalized drug report clustered by drug class rather than cancer type.

Conclusions
Collection and turnaround of high quality canine tumor samples, centralized pathology, analyte generation, array hybridization, and bioinformatic analyses matching gene expression to therapeutic options is achievable in a practical clinical window (<1 week). Clustering data show robust signatures by cancer type but also showed patient-to-patient heterogeneity in drug predictions. This lends further support to the inclusion of a heterogeneous population of dogs with cancer into the preclinical modeling of personalized medicine. Future comparative oncology studies optimizing the delivery of PMed strategies may aid cancer drug development.