Manning Lawton, Holmes Julia, Bonin Keith, Vidi Pierre-Alexandre
Department of Physics, Wake Forest University, Winston-Salem, NC, United States.
Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, United States.
Front Med (Lausanne). 2020 Jan 10;6:314. doi: 10.3389/fmed.2019.00314. eCollection 2019.
Preventing cancer is vastly better than treating the disease in terms of a patient's quality of life and healthcare costs. Yet, to screen for chemopreventative drugs or evaluate interventions aimed at lowering cancer risk, quantitative readouts of risk are needed. In the breast and in other organs of epithelial origin, apical-basal polarity is key to homeostasis and is one of the first tissue characteristics lost during cancer initiation. Therefore, apical-basal polarity may be leveraged as an "architectural" determinant of cancer risk. A classic approach to quantify the localization of epithelial polarity markers is visual scoring at the microscope by trained investigators. This approach is time-intensive and limited to low throughput. To increase the speed, accuracy, and scoring volume, we developed an algorithm that essentially replaces the human eye to objectively quantify epithelial polarity in microscopy images of breast glandular units (acini). Acini in culture are identified based on a nuclear stain and the corresponding masks are divided into concentric terraces of equal width. This positional information is used to calculate radial intensity profiles (RP) of polarity markers. Profiles with a steep slope represent polarized structures, whereas more horizontal curves are indicative of non-polarized acini. To compare treatment effects, RP curves are integrated into summary values of polarity. We envision applications of this method for primary cancer prevention research with acini organoids, specifically (1) to screen for chemoprevention drugs, (2) for toxicological assessment of suspected carcinogens and pharmacological hit compounds, and (3) for personalized evaluation of cancer risk and risk-reducing interventions. The RadialProfiler algorithm developed for the MATLAB computing environment and for users without prior informatics knowledge is publicly available on the Open Science Framework (OSF).
就患者的生活质量和医疗成本而言,预防癌症远比治疗癌症要好得多。然而,要筛选化学预防药物或评估旨在降低癌症风险的干预措施,就需要对风险进行定量读数。在乳腺和其他上皮起源的器官中,顶-基极性是维持内环境稳定的关键,也是癌症发生过程中最早丧失的组织特征之一。因此,顶-基极性可作为癌症风险的“结构”决定因素加以利用。一种量化上皮极性标记物定位的经典方法是由训练有素的研究人员在显微镜下进行视觉评分。这种方法耗时且通量低。为了提高速度、准确性和评分量,我们开发了一种算法,该算法基本上取代了人眼,能够客观地量化乳腺腺泡单元(腺泡)显微镜图像中的上皮极性。培养中的腺泡通过核染色进行识别,相应的掩码被划分为等宽的同心阶地。此位置信息用于计算极性标记物的径向强度分布(RP)。斜率陡峭的分布代表极化结构,而更水平的曲线则表示非极化腺泡。为了比较治疗效果,将RP曲线整合为极性的汇总值。我们设想将这种方法应用于腺泡类器官的原发性癌症预防研究,具体包括:(1)筛选化学预防药物;(2)对可疑致癌物和药理学活性化合物进行毒理学评估;(3)对癌症风险和降低风险干预措施进行个性化评估。为MATLAB计算环境开发的、供没有信息学知识的用户使用的RadialProfiler算法可在开放科学框架(OSF)上公开获取。