Exploring disentangled generative factors of cancer transcriptomes

癌症是一种由遗传和环境因素引起的复杂疾病。癌症的治疗非常复杂，因为疾病的分子水平亚型并不总是像病理水平上的表型那样明显，因此需要对患者进行分层，甚至对个体进行分层。由于无法做到这一点，导致临床医生可用的大量肿瘤药物中的任何一种只能对一小部分患者产生积极反应。随着近年来高通量实验的进展，在基因组畸变、核苷酸突变、基因表达、蛋白质浓度和调控信息的水平上获得了大量的测量。人们普遍认为，我们获取数据的能力超过了我们从中做出准确和有用推断的能力。该项目将探索最先进的无监督学习方法的发展和应用，以帮助理解基因型测量和癌症表型之间的映射。生成模型捕捉复杂的肿瘤微环境的特性将被开发。我们的目标是解开影响肿瘤的各种因素的贡献，他们的进展和对治疗的反应。这项工作将基于一个丰富的肿瘤相关数据库：癌症基因组图谱。

Cancer is a complex disease with genetic and environmental causes. The treatment of cancer has enormous complexity because molecular-level subtypes of the disease are not always apparent as phenotypes at the pathological levels, thus requiring stratification of patients, even down to individuals. The inability to do this results in any one of the vast array of oncological drugs available to clinicians eliciting a positive response only on a small fraction of sufferers of the disease.With recent advances in high throughput experimentation, large number of measurements at the level of genome aberration, nucleotide mutations, gene expressions, protein concentrations and regulatory information is acquired. It is accepted that our ability to acquire data outstrips our ability to make accurate and useful inferences from them.This project will explore the development and application of state-of-the art unsupervised learning methods to help understand the mapping between genotypic measurements and cancer phenotypes. Generative models that capture properties of the intricate tumour micro-environment will be developed. We will aim to disentangle the contributions of various factors influencing tumors, their progression and response to treatment. The work will be based on a rich repository of tumor related database: The Cancer Genome Atlas.