Detection of Emergent Mechanical Properties of Biologically Complex Cellular States

项目摘要 尽管年龄、生殖系突变和癌症家族史是乳腺癌的重要危险因素，但它仍然是 不清楚为什么一个人的乳房细胞比另一个人的乳房细胞更容易感染这种疾病。 细胞和组织，使它们更容易发生癌症。我们公布的数据显示， 具有高危生殖系突变的年轻女性的乳房上皮细胞显示出加速衰老的 中间丝分布、生物钟加速和基质免疫细胞环境变化 与年龄比女性大20-40岁的女性相比。在我们目前资助的R01EB024989中，我们发现 正常乳腺上皮细胞的机械性能，通过我们的机械结节毛孔来衡量 感觉(机械-NPS)平台，在年轻和老年女性中不同，正常的上皮细胞来自 携带生殖系BRCA1、BRCA2或PALB2变异体的遗传高危女性在机械上“变老” 而不是他们的实际年龄。我们假设机械核动力源可以根据 从定义血统和血统的潜在分子网络中产生的新出现的机械特性 疾病状态。在这一竞争性更新应用中，我们将这一假设扩展到包括癌症检测 易感性或风险，这是到目前为止通过基因筛查无法检测到的。我们将创新机械核动力源和 改进了我们的设备的计算机模型，以增加它可以测量的物理参数的数量，从而 提供更完整的单个人类乳腺上皮细胞(HMECS)的肖像(目标1)。我们将建立一个 基于不同力学性能测量的机器学习癌症易感性检测系统 原发HMEC(年轻、老年、高危、乳腺癌家族史等)(目标2)。最后，我们将剖析 用我们先进的MANICANO-NPS平台测量机械状态的分子机制(目标3)。在 在上一个资助期，我们成功地设计、构建并验证了第一代机械核动力源平台，网址为 加州大学伯克利分校，并通过在希望之城建造第二个平台展示了它的便携性和健壮性。其影响 我们的竞争性续签申请的影响将更加深远。临床上有用的基因测试依赖于 少数已知的单基因风险特征，但我们假设，测量到的新出现的机械特性 只有几百个细胞，是构成癌症易感状态的生物学特征 那些本质上是多基因的或表观遗传的，在一个家庭中遗传的，但到目前为止还没有 定义。

Project Summary Although aging, germline mutations, and family history of cancer are significant risks for breast cancer, it is still unclear why one person’s breast cells are more susceptible to this disease than another person’s. Aging changes cells and tissues such that they become more susceptible to cancer initiation. Our published data show that breast epithelial cells from young women who have high-risk germline mutations show accelerated aging with intermediate filament distribution, biological clock acceleration, and stromal immune cell milieu changes comparable to those of women who are 20–40 years older. In our currently funded R01EB024989, we discovered that the mechanical properties of normal breast epithelial cells, as measured by our mechano-Node Pore Sensing (mechano-NPS) platform, differ among younger and older women and that normal epithelial cells from genetically high-risk women who carry germline BRCA1, BRCA2, or PALB2 variants are mechanically “older” than their chronological age. We hypothesized that mechano-NPS can detect disease states based on the emergent mechanical properties that arise from the underlying molecular networks that define lineage and disease states. In this competitive renewal application, we extend this hypothesis to include detection of cancer susceptibility or risk, which is so far not detectable with genetic screening. We will innovate mechano-NPS and advance an in silico model of our device to increase the number of physical parameters it can measure, thereby providing a more complete portrait of single human mammary epithelial cells (HMECS) (Aim 1). We will build a machine learning cancer susceptibility detection system based on measuring mechanical properties of different primary HMEC (young, old, high-risk, family history of breast cancer, etc.) (Aim 2). Finally, we will dissect the molecular mechanisms of mechanical states measured by our advanced mechano-NPS platform (Aim 3). In the last funding period, we successfully designed, built, and validated the first-generation mechano-NPS platform at UC Berkeley and showed it to be portable and robust by building a second platform at City of Hope. The impact of our competitive renewal application will be far more reaching. Clinically useful genetic testing relies on a handful of known monogenic risk traits, but we hypothesize that emergent mechanical properties, measured from just a few hundred cells, are a characteristic of the biology that underlies cancer susceptible states, even those that are polygenic or epigenetic in nature and are passed within a family but that so far have defied definition.