Statistical methods for genomic analysis of heterogeneous tumors

项目摘要 在大多数癌症中，肿瘤样品内和之间的异质性细胞组成反映在复杂的细胞结构中。 在分子水平上的变化。这种分子复杂性包括转录变异和基因组变异。 复杂性，因为肿瘤不断进化并获得新的突变。因此，为了进一步了解 对于肿瘤的进化，研究癌症基因组之间的进化动力学是至关重要的， 转录组然而，由于癌细胞与其环境之间复杂的相互作用，这些 动力学仍然知之甚少，这是临床进展的主要瓶颈。 管理和治疗癌症患者。最近的多区域匹配DNA/RNA测序研究 在我们对癌症进化动力学的理解上取得了重大进展。然而，分析工具 在这些研究中使用的数据仅限于一种分子数据类型，这意味着错过了一个机会， 新的生物学发现本提案的总体目标是：1）在规模上量化 癌细胞中基因组和转录组变异之间的动态;以及2）将该数量与癌症联系起来 预后和治疗反应。在方法论方面，我们将开发一套综合的 用于匹配的基因组和转录组数据类型的解卷积模型。多角度接近 将在单独的统计模型中评价匹配的数据。在应用方面，我们将专注于临床 这些模型对前列腺癌（PCa）和甲状腺癌（THCa）治疗的影响。这两种癌症 疾病预防控制中心预计，到2021年， 我们对于这两种癌症，过度治疗是临床上最紧迫的问题，因为没有明确的方法来治疗 将低风险患者与高风险患者区分开来。我们假设，肿瘤的生物标志物 进化轨迹可以识别不需要进一步治疗的患者。这些生物标志物的鉴定 将大大提高临床实践的效率。我们的研究小组由经验丰富的 在肿瘤异质性和癌症临床管理方面具有互补专业知识的研究人员。 我们共同提出以下目标：1.开发综合反褶积模型，研究 癌细胞中的转录组，1A）在细胞类型和基因水平，1B）在单核苷酸变体水平，1C） 多样本设计的基因组异质性，和1D）多样本设计的转录组异质性 设计; 2.将综合模型应用于癌症患者，以发现2A）高危前列腺癌中的生物标志物 和2B）高风险甲状腺癌; 3.为癌症开发用户友好和计算效率高的软件工具 基因组学所提出的方法和工具有望通过使 肿瘤细胞类型在进化过程中的综合特征分析，并将这些值与临床结果相关联。 我们对前列腺癌和甲状腺癌的原理验证研究有可能发现新的综合治疗方法。 预测癌症预后和对治疗的反应的生物标志物。

Project Summary In most cancers, heterogeneous cell composition within and between tumor samples is mirrored in complex variations at a molecular level. This molecular complexity includes both transcriptional variation and genomic complexity, since tumors continually evolve and acquire new mutations. Therefore, to further our understanding of tumor evolution, it is essential to study the evolutionary dynamics between cancer genomes and transcriptomes. However, due to the complex interplay between cancer cells and their environment, these dynamics are still poorly understood, which presents a major bottleneck for the advancement of clinical management and treatment of cancer patients. Recent multi-region matched DNA/RNA sequencing studies have made significant advances in our understanding of cancer evolutionary dynamics. However, the analytical tools used in these studies were limited to one molecular data type at a time, representing a missed opportunity for novel biological discovery. The overall objective of this proposal is to 1) quantify, at scale, the evolutionary dynamics between genomic and transcriptomic variations in cancer cells; and 2) link this quantity to cancer prognosis and therapeutic response. On the methodological side, we will develop a suite of integrative deconvolution models for matched genomic and transcriptomic data types. Multiple angles to approach the matched data will be evaluated in separate statistical models. On the applied side, we will focus on the clinical impact of such models on the treatment of prostate (PCa) and thyroid cancers (THCa). These two cancers rank 3rd and 12th in prevalence and are projected by the CDC to present a total of 292,810 new cases in 2021 in the US. For both cancers, overtreatment is the most clinically urgent problem since there is no clear method to differentiate low-risk patients from those at high risk. We hypothesize that biomarkers informed by tumor evolutionary trajectory may identify patients who do not need further treatment. Identification of these biomarkers would significantly improve the efficiency of clinical practice. Our research group consists of experienced investigators with complementary expertise in tumor heterogeneity and clinical management of cancers. Together, we propose the following Aims: 1. Develop integrative deconvolution models to study the evolution of transcriptomes in cancer cells, 1A) at the cell-type and gene levels, 1B) at single-nucleotide-variant level, 1C) genomic heterogeneity over a multi-sample design, and 1D) transcriptomic heterogeneity over a multi-sample design; 2. Apply integrative models to cancer patients for biomarker discovery in 2A) high-risk prostate cancer and 2B) high-risk thyroid cancer; 3. Develop user-friendly and computationally efficient software tools for cancer genomics. The proposed methods and tools are expected to open new avenues to discovery by enabling comprehensive profiling of tumor cell types over evolution and associating these values with clinical outcomes. Our proof-of-principle investigation of prostate and thyroid cancers has the potential to identify new integrative biomarkers predictive of cancer prognosis and response to treatment.