Platform combining statistical modeling and patient-derived organoids to facilitate personalized treatment of colorectal carcinoma

Abstract

Background We propose a new approach for designing personalized treatment for colorectal cancer (CRC) patients, by combining ex vivo organoid efficacy testing with mathematical modeling of the results. Methods The validated phenotypic approach called Therapeutically Guided Multidrug Optimization (TGMO) was used to identify four low-dose synergistic optimized drug combinations (ODC) in 3D human CRC models of cells that are either sensitive or resistant to first-line CRC chemotherapy (FOLFOXIRI). Our findings were obtained using second order linear regression and adaptive lasso. Results The activity of all ODCs was validated on patient-derived organoids (PDO) from cases with either primary or metastatic CRC. The CRC material was molecularly characterized using whole-exome sequencing and RNAseq. In PDO from patients with liver metastases (stage IV) identified as CMS4/CRIS-A, our ODCs consisting of regorafenib [1 mM], vemurafenib [11 mM], palbociclib [1 mM] and lapatinib [0.5 mM] inhibited cell viability up to 88%, which significantly outperforms FOLFOXIRI administered at clinical doses. Furthermore, we identified patient-specific TGMO-based ODCs that outperform the efficacy of the current chemotherapy standard of care, FOLFOXIRI. Conclusions Our approach allows the optimization of patient-tailored synergistic multi-drug combinations within a clinically relevant timeframe. Graphical Abstract

New Therapeutic Perspectives in Prostate Cancer: Patient-Derived Organoids and Patient-Derived Xenograft Models in Precision Medicine

“…A very interesting study involving a platform called therapeutically guided multidrug optimization (TGMO) has developed a workflow that allows rapid identification of multiple personalized drug combinations [95]. The platform uses a minimum number of experimental tests on organoids of colorectal cancer (CRC) patients, exploiting an in silico mathematical prediction on the efficacy of the predicted drugs [96].…”