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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.

项目总结在不同物种中研究癌症提供了一个独特的机会来询问各种因素支持癌症的发生和发展，并促进新的治疗靶点的建模自发性肿瘤合并合并症和转移的背景。虽然有大量的报道在动物园动物中肿瘤的频率和多样性中，还没有系统地与过多相关。可利用的基因组资源。促进比较肿瘤学研究从人类加一的过渡或其他两个物种，以泛哺乳动物分析，我们建议建立一个泛哺乳动物肿瘤纲要和门户，以方便地传播我们的资源。我们将开发和应用机器学习工具来检测人类和非人类哺乳动物肿瘤中癌症出现的模式和癌症耐药性。我们的该方法提供了与人类联合利用大部分未被研究的哺乳动物肿瘤数据的机会以确定进化和癌症耐药性之间的相互联系的数据。这将允许我们创造新的人类在哺乳动物的背景下通过高通量生物学数据的综合分析发现癌症物种。我们提供了一种强大的方法，将高质量的参考基因组和来自数百种哺乳动物物种通过机器学习，有望挖掘出进化中的基因基础是癌症发生和发展的基石。我们将开发和应用模型来识别有效模仿罕见人类癌症的哺乳动物肿瘤，与合作者合作获取肿瘤来自最强识别的哺乳动物模型的样本，然后对这些样本进行测序和分析以验证它们作为人类癌症模型的有效性。