Laboratory of Innovative Medicine and Agrobiotechnology, Moscow Institute of Physics and Technology (MIPT), Dolgoprudny, Moscow Region, Russia.
The Biomechanic Materials Lab, Technion Israel Institute of Technology, Haifa, Israel.
Sci Rep. 2023 Jul 29;13(1):12289. doi: 10.1038/s41598-023-33540-1.
Metastasis is the main cause of cancer-related mortality; therefore, the ability to predict its propensity can remarkably affect survival rate. Metastasis development is predicted nowadays by lymph-node status, tumor size, histopathology, and genetic testing. However, all these methods may have inaccuracies, and some require weeks to complete. Identifying novel prognostic markers will open an essential source for risk prediction, possibly guiding to elevated patient treatment by personalized strategies. Cancer cell invasion is a critical step in metastasis. The cytoskeletal mechanisms used by metastatic cells for the invasion process are very similar to the utilization of actin cytoskeleton in the endocytosis process. In the current study, the adhesion and encapsulation efficiency of low-cost carboxylate-modified fluorescent nanoparticles by breast cancer cells with high (HM) and low metastatic potential (LM) have been evaluated; benign cells were used as control. Using high-content fluorescence imaging and analysis, we have revealed (within a short time of 1 h), that efficiency of nanoparticles adherence and encapsulation is sufficiently higher in HM cells compared to LM cells, while benign cells are not encapsulating or adhering the particles during experiment time at all. We have utilized custom-made automatic image analysis algorithms to find quantitative co-localization (Pearson's coefficients) of the nanoparticles with the imaged cells. The method proposed here is straightforward; it does not require especial equipment or expensive materials nor complicated cell manipulations, it may be potentially applicable for various cells, including patient-derived cells. Effortless and quantitative determination of the metastatic likelihood has the potential to be performed using patient-specific biopsy/surgery sample, which will directly influence the choice of protocols for cancer patient's treatment and, as a result, increase their life expectancy.
转移是癌症相关死亡的主要原因;因此,预测其倾向的能力可以显著影响生存率。目前,转移发展是通过淋巴结状态、肿瘤大小、组织病理学和基因检测来预测的。然而,所有这些方法都可能存在不准确之处,有些方法需要数周时间才能完成。确定新的预后标志物将为风险预测开辟一个重要的来源,可能通过个性化策略来指导提高患者的治疗效果。癌细胞的侵袭是转移的关键步骤。转移性细胞用于侵袭过程的细胞骨架机制与它们在胞吞作用中利用肌动蛋白细胞骨架非常相似。在本研究中,通过高转移潜能(HM)和低转移潜能(LM)的乳腺癌细胞评估了低成本羧基修饰荧光纳米粒子的粘附和封装效率;良性细胞作为对照。使用高内涵荧光成像和分析,我们在短时间内(1 小时内)发现,与 LM 细胞相比,HM 细胞中纳米粒子的粘附和封装效率足够高,而良性细胞在实验过程中根本不包裹或粘附颗粒。我们利用定制的自动图像分析算法找到了与成像细胞共定位的纳米粒子的定量(皮尔逊系数)。这里提出的方法很简单;它不需要特殊的设备或昂贵的材料,也不需要复杂的细胞操作,它可能适用于各种细胞,包括患者来源的细胞。使用患者特定的活检/手术样本进行转移可能性的轻松和定量测定有可能直接影响癌症患者治疗方案的选择,并因此提高他们的预期寿命。