StarTrace: A Multiplex Organoid Avatar Drug Testing Platform for Personalized Medicine
Khalili, S.; patel, S.; Patel, N.; Moy, V.; Lyons, S.; Gray, E.; Brents, R.; Banister, C. E.; Morrison, S. E.; Buckhaults, P. J.
Show abstract
The goal of precision medicine is to improve clinical outcomes of cancer patients by choosing treatments most likely to work. One idea is to match tumor response to cancer-causing somatic mutations, but this strategy still faces limitations in colorectal cancer due to poorly understood genetic influences on drug resistance. We describe here a simple direct drug sensitivity assay platform applied to mixtures of patient-derived organoid avatars as a practical solution for choosing therapy, sidestepping the need for exhaustive knowledge of drug-genetic interactions. This approach rank orders individual patient organoid avatars responses to various drugs to be used to guide treatment choices. The platform multiplexes organoids using a clonal barcoding method, called StarTrace, which simultaneously tests pools of multiple patients organoid avatars for sensitivity or resistance to small molecule inhibitors. We utilized both quantitative real-time PCR-based and single-molecule sequencing assays to track the relative Darwinian fitness of each barcoded organoid within the pool. StarTrace offers a rapid, cost effective and sensitive testing platform that could be useful for either preclinical drug development or tailoring personalized therapy.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A suspension technique for efficient large-scale cancer organoid culturing and perturbation screens 95%
- Long-term maintenance of patient-specific characteristics in tumoroids from six cancer indications in a common base culture media system 95%
- Detection of genomic alterations in breast cancer with circulating tumour DNA sequencing 94%
Similar papers in this journal
Similar papers in this journal
- GLUT1 inhibition blocks growth of RB1-positive Triple Negative Breast Cancer 93%
- XENTURION, a multidimensional resource of xenografts and tumoroids from metastatic colorectal cancer patients for population-level translational oncology 93%
- Artificial intelligence-based histopathology image analysis identifies a novel subset of endometrial cancers with distinct genomic features and unfavourable outcome 93%
Similar papers in this journal
- Deep learning uncovers histological patterns of YAP1/TEAD activity related to disease aggressiveness in cancer patients. 93%
- An Isogenic Cell Line Panel for Sequence-based Screening of Targeted Anti-cancer Drugs 93%
- Multiple instance learning to predict immune checkpoint blockade efficacy using neoantigen candidates 93%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.