Claus, R. | bioRxiv Science

bioRxiv2026

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↗

bioRxiv2025

AI-driven analysis for real-time detection of unstained microscopic cell culture images

AI-based image recognition has significantly advanced the analysis of tissues and individual cells both in the context of translational studies and diagnostics. To date, recognition is primarily based on the identification of certain cell characteristics (e.g. by staining). The morphological assessment of unstained cells holds additional potential, as it allows for virtually real-time assessment without the need to manipulate the cells. This facilitates longitudinal observations, as required for drug testing, and forms a basis for autonomous experimental execution. A semi-automated cell culture system (AICE3, LabMaite) was used to culture myeloid leukemic cell lines (K562, HL-60, Kasumi-1). K562 cells were treated with hemin and PMA to induce erythroid and megakaryocytic differentiation, respectively. Cell images were acquired using automated bright field microscopy. Images were used to train an AI model using an NVIDIA DGX A100 GPU with Ultralytics YOLOv8. Morphologic features were extracted using RedTell. The model reliably distinguished K562 cells from HL-60 and Kasumi-1 using >400 images per class (average >15 cells/image). Bounding boxes were generated correctly (mAP@.5 >98%); precision and sensitivity exceeded 97%. Validation on an external K562 dataset confirmed these results. Classification of all three cell lines achieved >97% sensitivity/specificity and 94.6% precision. To test drug response, we used YOLOv8-s to distinguish untreated K562 cells from those undergoing erythroid or megakaryocytic differentiation (n >3,000 annotations). Precision, sensitivity, and specificity were >95%. RedTell identified 3 of 74 morphological traits contributing significantly to class separation. We demonstrate accurate, near real-time detection of unstained cells, enabling future AI-based drug testing.

cell biology↗