Abstract 97P
Background
Colorectal cancer is the third most common cancer type worldwide. Despite promising treatment options, the applied therapy approaches can be optimized. Patient derived organoids (PDOs) are emerging as an advantageous tool in precision medicine, enable scientists to assess the effects of novel treatments on each patient’s tumor cells. In this study, we implemented a new bioinformatic feature-extraction based approach to predict the outcome of available treatments on each patient in-silico, based on his/her own RNA expression profile.
Methods
Colorectal cancer FFPE samples as well as PDOs were established from surgical specimens from tumor and healthy tissues and their gene expression profile was generated using 3’mRNA Sequencing (MACE) technology. The mutation status of KRAS and NRAS was determined using ddPCR, while BRAF status was determined by pyrosequencing. Using public RNA-Seq data from clinical studies and their clinically relevant features, the generated expression profile was used for consensus molecular subtypes (CMS) scoring and to perform in silico drug sensitivity prediction analysis. Finally, based on the results, a therapeutic decision-making support (“ClinXPro-Report) was compiled.
Results
Using machine learning algorithms, we have generated a refined classifier to predict the outcome of treatment for organoids based on their gene expression profile. The outcome is reported as a score which reflects the probability of an organoid responding to a specific treatment. We screened the possible reaction of organoids to about 450 drugs. Most of the treatments had low score for the tested organoids. However, compounds targeting Kras-Raf signalling pathway showed promising effect for PDOs in native (non-mutated) RAS-RAF genes, while PDOs with mutation in RAS-RAF had low scores for these compounds. To empirically evaluate ex vivo this approach, established PDOs are being tested by proposed treatments.
Conclusions
In this study we developed a method which together with 3’mRNA sequencing of PDOs is a promising and applicable approach in development of personalized therapy.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
GenXPro GmbH.
Funding
European Union’s Horizon 2020 research and innovation programme under grant agreement No 848098.
Disclosure
A. Fahim, L. Feist, M. Alkhatib, S. Khan-Chadhry, N. Cahyono: Financial Interests, Institutional, Member: GenXPro B. Rotter: Financial Interests, Institutional, Ownership Interest: GenXPro. All other authors have declared no conflicts of interest.