Electrical & Computer Engineering, University of Virginia, Charlottesville, VA, 22904, USA.
Electrical & Computer Engineering, University of Virginia, Charlottesville, VA, 22904, USA.
Biosens Bioelectron. 2023 Jul 1;231:115262. doi: 10.1016/j.bios.2023.115262. Epub 2023 Mar 30.
Chemotherapy failure in pancreatic cancer patients is widely attributed to cancer cell reprogramming towards drug resistance by cancer associated fibroblasts (CAFs), which are the abundant cell type in the tumor microenvironment. Association of drug resistance to specific cancer cell phenotypes within multicellular tumors can advance isolation protocols for enabling cell-type specific gene expression markers to identify drug resistance. This requires the distinction of drug resistant cancer cells versus CAFs, which is challenging since permeabilization of CAF cells during drug treatment can cause non-specific uptake of cancer cell-specific stains. Cellular biophysical metrics, on the other hand, can provide multiparametric information to assess the gradual alteration of target cancer cells towards drug resistance, but these phenotypes need to be distinguished versus CAFs. Using pancreatic cancer cells and CAFs from a metastatic patient-derived tumor that exhibits cancer cell drug resistance under CAF co-culture, the biophysical metrics from multifrequency single-cell impedance cytometry are utilized for distinction of the subpopulation of viable cancer cells versus CAFs, before and after gemcitabine treatment. This is accomplished through supervised machine learning after training the model using key impedance metrics for cancer cells and CAFs from transwell co-cultures, so that an optimized classifier model can recognize each cell type and predict their respective proportions in multicellular tumor samples, before and after gemcitabine treatment, as validated by their confusion matrix and flow cytometry assays. In this manner, an aggregate of the distinguishing biophysical metrics of viable cancer cells after gemcitabine treatment in co-cultures with CAFs can be used in longitudinal studies, to classify and isolate the drug resistant subpopulation for identifying markers.
胰腺癌患者的化疗失败广泛归因于癌症相关成纤维细胞（CAFs）使癌细胞通过重编程对药物产生耐药性，CAFs 是肿瘤微环境中丰富的细胞类型。在多细胞肿瘤中，将药物耐药性与特定癌细胞表型相关联可以推进分离方案，以确定细胞类型特异性基因表达标志物来识别耐药性。这需要区分耐药性癌细胞与 CAFs，这是具有挑战性的，因为在药物治疗期间 CAF 细胞的通透性会导致癌细胞特异性染色的非特异性摄取。另一方面，细胞生物物理指标可以提供多参数信息来评估靶癌细胞对药物耐药性的逐渐改变，但这些表型需要与 CAFs 区分开来。使用来自转移性患者衍生肿瘤的胰腺癌细胞和 CAFs，该肿瘤在 CAF 共培养下表现出癌细胞药物耐药性，从多频单细胞阻抗细胞术获得的生物物理指标用于区分活癌细胞与 CAFs 的亚群，在吉西他滨治疗前后。这是通过使用跨孔共培养中的癌细胞和 CAFs 的关键阻抗指标对模型进行训练后，通过监督机器学习来实现的，以便优化的分类器模型可以识别每种细胞类型，并预测它们在吉西他滨治疗前后的多细胞肿瘤样本中的各自比例，通过混淆矩阵和流式细胞术测定进行验证。通过这种方式，在与 CAFs 共培养中吉西他滨处理后区分活癌细胞的生物物理指标的总和可以用于纵向研究，以分类和分离药物耐药亚群以识别标志物。
