Leveraging Mammalian Cancers, Platinum-Quality Genome Assemblies, and Large-Scale Data to Identify Mechanisms of Rare Human Cancers

利用哺乳动物癌症、白金级基因组组装和大规模数据来识别罕见人类癌症的机制

基本信息

批准号：
10677546
负责人：
Kiley Graim
金额：
$ 29.09万
依托单位：
UNIVERSITY OF FLORIDA
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-05 至 2027-07-31
项目状态：
未结题

项目摘要

PROJECT SUMMARY Studying cancer across a diverse array of species provides a unique opportunity to interrogate factors underpinning cancer initiation and progression and facilitating the modeling of new therapeutic targets in the setting of spontaneous tumors complicated by comorbidities and metastases. While there is extensive reporting of the frequency and diversity of tumors in animals from zoos, it has not been systematically linked to the plethora of genomic resources available. To facilitate the transition of comparative oncology studies from human plus one or two other species, to pan-mammalian analyses, we propose building a pan-mammalian tumor compendium and portal to easily disseminate our resource. We will develop and apply machine learning tools to detect patterns of cancer emergence and cancer resistance in human and non-human mammalian tumors. Our approach provides opportunities to leverage the largely understudied mammalian tumor data jointly with human data to identify reciprocal links between evolution and cancer resistance. This will allow us to make new human cancer discoveries through integrative analysis of high-throughput biological data in the context of mammalian species. We offer a powerful approach combining high-quality reference genomes and genomic data from hundreds of mammalian species with machine learning, that has the promise to unearth the evolutionary genetic underpinnings that are cornerstones of cancer initiation and progression. We will develop and apply models to identify mammalian tumors that effectively mimic rare human cancers, work with collaborators to acquire tumor samples from strongest identified mammalian models, then sequence and analyze these samples to validate their effectiveness as models of human cancers.

项目摘要在各种物种中研究癌症为询问因素提供了独特的机会基础癌症的启动和进展，并促进了新的治疗靶标建模自发性肿瘤的设置因合并症和转移而复杂。虽然有广泛的报告在动物园的动物肿瘤的频率和多样性中，它尚未系统地联系到很多可用的基因组资源。为了促进比较肿瘤学研究从人类加一个或另外两个物种，为了进行泛哺乳动物分析，我们提出建立一个泛哺乳动物肿瘤纲要和门户，以轻松传播我们的资源。我们将开发和应用机器学习工具来检测人和非人类哺乳动物肿瘤中癌症出现和癌症抗性的模式。我们的方法提供了与人类共同利用经过大量研究的哺乳动物肿瘤数据的机会数据以识别进化和抗癌性之间的相互联系。这将使我们成为新人癌症通过在哺乳动物的背景下对高通量生物学数据的综合分析发现物种。我们提供了一种强大的方法，结合了高质量的参考基因组和基因组数据数百种具有机器学习的哺乳动物物种，有望发掘进化的遗传是癌症开始和进展的基石的基础。我们将开发并将模型应用于确定有效模仿稀有人类癌的哺乳动物肿瘤，与合作者合作获取肿瘤来自最强鉴定的哺乳动物模型的样品，然后序列并分析这些样品以验证它们作为人类癌症模型的有效性。