Institute of Nanotechnology, National Research Council, Lecce 73100, Italy.
Dipartimento di Matematica e Fisica Ennio De Giorgi, Università del Salento, Lecce 73100, Italy.
Proc Natl Acad Sci U S A. 2023 Mar 14;120(11):e2122352120. doi: 10.1073/pnas.2122352120. Epub 2023 Mar 10.
A crucial challenge in medicine is choosing which drug (or combination) will be the most advantageous for a particular patient. Usually, drug response rates differ substantially, and the reasons for this response unpredictability remain ambiguous. Consequently, it is central to classify features that contribute to the observed drug response variability. Pancreatic cancer is one of the deadliest cancers with limited therapeutic achievements due to the massive presence of stroma that generates an environment that enables tumor growth, metastasis, and drug resistance. To understand the cancer-stroma cross talk within the tumor microenvironment and to develop personalized adjuvant therapies, there is a necessity for effective approaches that offer measurable data to monitor the effect of drugs at the single-cell level. Here, we develop a computational approach, based on cell imaging, that quantifies the cellular cross talk between pancreatic tumor cells (L3.6pl or AsPC1) and pancreatic stellate cells (PSCs), coordinating their kinetics in presence of the chemotherapeutic agent gemcitabine. We report significant heterogeneity in the organization of cellular interactions in response to the drug. For L3.6pl cells, gemcitabine sensibly decreases stroma-stroma interactions but increases stroma-cancer interactions, overall enhancing motility and crowding. In the AsPC1 case, gemcitabine promotes the interactions among tumor cells, but it does not affect stroma-cancer interplay, possibly suggesting a milder effect of the drug on cell dynamics.
医学面临的一个关键挑战是选择哪种药物（或组合）对特定患者最有利。通常，药物反应率有很大差异，而这种反应不可预测的原因仍然不清楚。因此，对有助于观察到的药物反应变异性的特征进行分类至关重要。胰腺癌是最致命的癌症之一，由于基质的大量存在，治疗效果有限，基质会产生促进肿瘤生长、转移和耐药的环境。为了了解肿瘤微环境中的癌症-基质相互作用，并开发个性化的辅助治疗方法，有必要采用有效的方法，提供可衡量的数据来监测药物在单细胞水平上的效果。在这里，我们开发了一种基于细胞成像的计算方法，该方法可以量化胰腺癌细胞（L3.6pl 或 AsPC1）和胰腺星状细胞（PSCs）之间的细胞串扰，并协调它们在化疗药物吉西他滨存在时的动力学。我们报告了药物反应中细胞相互作用的显著异质性。对于 L3.6pl 细胞，吉西他滨明显降低了基质-基质相互作用，但增加了基质-癌症相互作用，总体上增强了迁移和聚集。在 AsPC1 的情况下，吉西他滨促进了肿瘤细胞之间的相互作用，但它不会影响基质-癌症相互作用，这可能表明药物对细胞动力学的影响较轻。
