Abstract 59P
Background
Recently, cancer organoid-based drug sensitivity tests have been investigated to predict patient responses to anticancer drugs. The Area under the curve (AUC) or IC50 value of the dose-response curve (DRC) is typically used to differentiate between sensitive and resistant patient groups. This study proposes a multi-parameter analysis method (cancer organoid-based diagnosis reactivity prediction, CODRP) that considers cancer cell growth rate and the AUC of the DRC, to predict patient responses to anticancer drugs.
Methods
On the CODRP platform, patient-derived organoids (PDOs) recapitulating lung cancer patients were implemented using a mechanical dissociation method capable of high yields and proliferation rates. A disposable nozzle-type cell spotter with efficient high-throughput screening (HTS) has also been developed to dispense a limited number of patient cells. A drug sensitivity test was performed using PDOs from patient tissue, and the primary cancer characteristics of PDOs were confirmed through pathological comparison with tissue slides.
Results
The conventional index of drug sensitivity is the AUC of the DRC. In this study, we proposed the CODRP index for drug sensitivity tests through multi-parameter analyses considering cancer cell proliferation rate and AUC values. We tested PDOs from eight patients with lung cancer to validate the CODRP index. According to the anaplastic lymphoma kinase (ALK) rearrangement status, the conventional AUC index for three ALK-targeted drugs (crizotinib, alectinib, and brigatinib) did not differentiate between sensitive and resistant groups. The proposed CODRP index-based drug sensitivity test classified ALK-targeted drug responses according to ALK rearrangement status and demonstrated consistency with the clinical drug treatment response.
Conclusions
The PDO-based HTS and CODRP index drug sensitivity tests described in this study may be valuable for predicting and analyzing promising anticancer drug efficacy in lung cancer patients and can be integrated into a precision medicine platform.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.