In:
Neuro-Oncology, Oxford University Press (OUP), Vol. 23, No. Supplement_1 ( 2021-06-01), p. i36-i36
Abstract:
Image-based phenotypic drug profiling is receiving increasing attention in drug discovery and precision medicine. Compared to classical end-point measurements quantifying drug response, image-based profiling enables both the quantification of drug response and characterization of disease entities and drug mechanisms of actions. In pediatric precision oncology, we aim to study drug response in patient-derived 3D spheroid tumor cell cultures and tackle the challenges of a lack of image-segmentation methods and limited patient-derived material. Methods We investigate deep transfer learning with patient-by-patient fine-tuning for cell-viability quantification. We fine-tune a convolutional neural network (pre-trained on ImageNet) with many cell-line-specific and few patient-specific assay controls. The method is validated using 3D cell cultures in 384-well microplates derived from cell lines with known drug sensitivities and tested with primary patient-derived samples. Network outputs at different drug concentrations are used for drug-sensitivity scoring; dense-layer activations are used in t-distributed stochastic neighbor embedding and clustering of drugs. Results Cell-line experiments confirm expected hits, such as effective treatment with BRAF inhibitors in a BRAF V600E mutated brain tumor model and NTRK inhibitors in a cell line harboring an NTRK-fusion, indicating the predictive power of deep learning to identify drug-hit candidates for individual patients. In patient-derived samples, clustering of drugs further confirms phenotypic similarity according to their mechanisms of actions. Combining drug scoring with phenotypic clustering may provide opportunities for complementary combination treatments. Conclusion Deep transfer learning with patient-by-patient fine-tuning is a promising, segmentation-free image-analysis approach for precision medicine and drug discovery based on 3D spheroid cell cultures.
Type of Medium:
Online Resource
ISSN:
1522-8517
,
1523-5866
DOI:
10.1093/neuonc/noab090.145
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2021
detail.hit.zdb_id:
2094060-9
