Multi-Tensor Decompositions for Personalized Cancer Diagnostics and Prognostics

 描述（由申请人提供）：复发性DNA拷贝数改变（CNA）已被认为是癌症的标志超过100年，但这些改变对肿瘤的发病机制和患者的诊断，预后和治疗的含义仍然知之甚少。尽管越来越多的大型多维数据集记录了单一疾病的不同方面，例如，在癌症基因组图谱（TCGA）中，并且由于对数学框架的基本需求，该数学框架可以从以匹配列的多个张量布置的这样的多个数据集创建一个相干模型，例如，患者、平台和组织，但是独立的行，例如，probes. 例如，我们最近对来自TCGA的患者匹配的胶质母细胞瘤（GBM）脑肿瘤和正常血液基因组图谱（排列在两个矩阵中，匹配的列但独立的行）进行比较建模（通过使用数据驱动的双矩阵光谱分解），发现了一种以前未知的肿瘤特异性CNA的全球模式，该模式与GBM生存和对化疗的反应相关，并可能与之因果相关。自2008年以来，这些数据一直是公开的，但直到我们在2012年应用比较模型之前，这个特征仍然未知。生存分析显示，并且通过计算验证，该签名的表现优于年龄，并且在统计学上独立于年龄，年龄是GBM生存>50年的最佳指标，以及现有的GBM病理学实验室测试。基于该特征的GBM的新测试正在等待联合区域和大学病理学家实验室（ARUP）的实验重新验证，犹他州大学病理学系的一个非盈利参考实验室。 在这个NCI U 01项目中，我们来自生物工程、数学和病理学系、科学计算和成像（SCI）研究所和犹他州大学亨茨曼癌症研究所（HCI）的多学科研究人员团队旨在（i）定义和研究数据驱动的多张量谱分解的性质;（ii）使用这些来模拟患者匹配、平台匹配和组织匹配但不依赖于探针的TCGA基因组谱，并获得对低级星形细胞瘤（LGA）脑癌、卵巢浆液性囊腺癌（OV）和肺鳞状细胞癌中的基因型-表型关系的生物学和医学见解;以及（iii）通过使用犹他州样本实验性地测试现有GBM模型以及新LGA和OV模型的计算预测，使得能够将这些见解转化为病理学实验室测试。 最终，该项目将使医生更接近有一天能够预测和控制细胞分裂和癌症的进展，就像NASA工程师今天绘制航天器的轨迹一样。

 DESCRIPTION (provided by applicant): Recurring DNA copy-number alterations (CNAs) have been recognized as a hallmark of cancer for >100 years, yet what these alterations imply about a tumor's pathogenesis and a patient's diagnosis, prognosis, and treatment remains poorly understood. This is despite the growing number of large-scale multidimensional datasets recording different aspects of a single disease, e.g., in the Cancer Genome Atlas (TCGA), and due to a fundamental need for mathematical frameworks that can create one coherent model from such multiple datasets arranged in multiple tensors of matched columns, e.g., patients, platforms, and tissues, but independent rows, e.g., probes. For example, our recent comparative modeling (by using a data-driven two-matrix spectral decomposition) of patient-matched glioblastoma (GBM) brain tumor and normal blood genomic profiles from TCGA (arranged in two matrices, of matched columns but independent rows) uncovered a previously unknown global pattern of tumor-exclusive CNAs that is correlated with, and possibly causally related to, GBM survival and response to chemotherapy. The data had been publicly available since 2008, but this signature remained unknown until we applied our comparative modeling in 2012. Survival analyses showed, and computationally validated, that the signature performs better than, and is statistically independent of, age, the best indicator of GBM survival for >50 years, and existing GBM pathology laboratory tests. A new test for GBM based upon this signature is pending an experimental re-validation at the Associated Regional and University Pathologists (ARUP) Laboratories, Inc., a nonprofit reference laboratory of the Department of Pathology at the University of Utah. In this NCI U01 project, our multidisciplinary team of researchers from the Departments of Bioengineering, Mathematics, and Pathology, the Scientific Computing and Imaging (SCI) Institute, and the Huntsman Cancer Institute (HCI) at the University of Utah, aims to (i) define, and study the properties of data- driven multi-tensor spectral decompositions; (ii) use these to model patient-, platform-, and tissue-matched but probe-independent TCGA genomic profiles, and gain biological and medical insights into the genotype- phenotype relations in lower-grade astrocytoma (LGA) brain cancer, ovarian serous cystadenocarcinoma (OV), and lung squamous cell carcinoma; and (iii) enable translation of these insights into pathology laboratory tests, by experimentally testing the computational predictions of the existing GBM model, as well as the novel LGA and OV models by using Utah samples. Ultimately, this project will bring physicians a step closer to one day being able to predict and control the progression of cell division and cancer as readily as NASA engineers plot the trajectories of spacecraft today.