Gaisa, N. T. | bioRxiv Science

bioRxiv2025

Pancreatic cancer patient-derived organoids capture therapy response and tumor evolution

Patient-derived organoids (PDOs) reflect parental tumor features and may represent promising avatars for prognosticating drug response. Here, we recruited 169 patients with pancreatic cancer (PC) and established a living biobank including 83 pharmacotyped PDOs isolated from primary and metastatic, treatment-naive and pretreated PCs. In a core facility setting, the pharmacotyping success rate was 61.5%, with an unmet turnaround time of 32 days. Forty-six patients who underwent a total of 94 therapeutic lines were analyzed, resulting in a pharmacotyping-patient response matching rate of 73.4%. Sensitivity, specificity, positive and negative predictive values were 85.0%, 64.8%, 64.2%, and 85.4%, respectively. Tracing clonal evolution in longitudinal biopsies uncovered therapy-induced genetic alterations and single-nucleus multiomics identified transcriptomic and epigenetic changes associated with abnormal FGF signaling during treatment in one particular tracked study case. Our findings highlight the potential of PDOs as robust tools for drug response prediction and patient modeling to advance functional precision medicine.

cancer biology↗

bioRxiv2021

Benchmarking artificial intelligence methods for end-to-end computational pathology

Artificial intelligence (AI) can extract subtle visual information from digitized histopathology slides and yield scientific insight on genotype-phenotype interactions as well as clinically actionable recommendations. Classical weakly supervised pipelines use an end-to-end approach with residual neural networks (ResNets), modern convolutional neural networks such as EfficientNet, or non-convolutional architectures such as vision transformers (ViT). In addition, multiple-instance learning (MIL) and clustering-constrained attention MIL (CLAM) are being used for pathology image analysis. However, it is unclear how these different approaches perform relative to each other. Here, we implement and systematically compare all five methods in six clinically relevant end-to-end prediction tasks using data from N=4848 patients with rigorous external validation. We show that histological tumor subtyping of renal cell carcinoma is an easy task which approaches successfully solved with an area under the receiver operating curve (AUROC) of above 0.9 without any significant differences between approaches. In contrast, we report significant performance differences for mutation prediction in colorectal, gastric and bladder cancer. Weakly supervised ResNet-and ViT-based workflows significantly outperformed other methods, in particular MIL and CLAM for mutation prediction. As a reason for this higher performance we identify the ability of ResNet and ViT to assign high prediction scores to highly informative image regions with plausible histopathological image features. We make all source codes publicly available at https://github.com/KatherLab/HIA, allowing easy application of all methods on any end-to-end problem in computational pathology.

bioinformatics↗