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Stojanovic Guzvic, N.

Publications and source records attributed to Stojanovic Guzvic, N..

2 recordsLinked to original sources

Ex vivo drug testing in metastatic biopsies reveals patient-specific vulnerabilities to cancer targeting and immune activating drugs

Biomarker-guided therapies in oncology often fail to induce considerable responses in patients with advanced cancer. As a complementary approach, direct drug testing on individual patient samples is highly attractive yet is currently hampered by the lack of assays that combine (i) fast reporting, (ii) the ability to inform about immune-mediated responses, (iii) robust quantification, and (iv) scalability for parallel assessment of multiple drugs. Here, we introduce our patient-derived ex vivo drug response assay (PEDRA) that fulfills all these requirements. Using malignant pleural effusions (MPEs) from five non-small cell lung cancer (NSCLC) patients with detailed clinical treatment histories, we tested 52 guideline-recommended therapies and eight investigational antibody-drug conjugates (ADCs). In all patients, PEDRA identified treatment options that outperformed the therapies the patients had received. The results reflected clinical observations as well as expectations derived from mutational profiling and disease courses. To extend the applicability of PEDRA beyond MPEs to other metastatic lesions, we generated a protocol starting from core needle biopsies. Owing to its reproducible and quantitative nature, PEDRA may provide a valuable diagnostic tool to guide time-sensitive clinical therapy decisions. Additionally, PEDRA has great potential for preclinical testing of investigational drugs, thereby reducing the need for animal experiments.

cancer biology↗

Plastimos: a live cell imaging-based framework to study dynamics of EMT-mediated cellular plasticity in breast cancer

Cancer cell plasticity, primarily mediated by the process of epithelial-mesenchymal transition (EMT), plays a critical role in promoting therapeutic resistance, tumor heterogeneity, and metastasis. EMT-mediated plasticity enables cancer cells to undergo molecular and phenotypic changes, which alter their invasiveness and resistance to treatments. This study aims to develop a quantitative, high-throughput system to assess EMT-mediated plasticity, which can inform therapeutic strategies for metastatic cancers. To accomplish this, we developed Plastimos, a semi-automated imaging analysis pipeline. This framework utilizes time-series images to track live cells through deep learning-based segmentation and employs a greedy algorithm to map cell trajectories, enabling the extraction of cellular phenotypic features. These features are used to study the EMT-mediated plasticity state of cells in response to the well-known EMT-inducing factors EGF and TGF-{beta}1. We selected two breast cancer cell lines, MCF7 and MDA-MB-231, representing classical epithelial-like and mesenchymal-like cell. The pipeline assigns a Plasticity Index based on various parameters, including motility, morphology, and proliferation, thus providing a quantitative estimate for the plasticity of deviating from the epithelial state. Our results indicate that epithelial-like cells respond to EMT-inducing factors at both molecular and phenotypic levels, while mesenchymal-like cells responses are only seen phenotypically. The Plasticity Index classifies cells along the EMT spectrum, converting to a Plasticity Score that reflects mesenchymal proportions. Availability and implementationThe pipeline implementation and the source code of Plastimos can be found at: https://github.com/Durdam/Plastimos.git

bioinformatics↗