Therapy Personalization Using Predictive Simulation Approach with Ex-Vivo Clinical Validations

Background

The epitome of cancer treatment personalization is N=1 segmentation where a custom therapy is designed for every patient. Because most cancer aberrations are not actionable mutations and tumors can have more than one actionable mutation, this one biomarker/one drug approach to cancer personalization has inherent limitations due to its over simplification.

Personalization 2.0 methodology creates a patient simulation avatar incorporating a patient’s genomic profile information holistically.

Methods

Bone marrow samples from two myeloma patients (P1 and P2) refractory to most recent treatment was collected, and P1’s sample was sorted into CD138+ and CD138- cells. The patient cells were analyzed for chromosomal alterations using Comparative Genomic Hybridization (aCGH) arrays by GenPath Diagnostics and cytogenetic chromosome analysis by Washington University School of Medicine and New York University (NYU), respectively.

Using this information, a predictive simulation avatar model of each patient was created by Cellworks based on genomic profile of patients. A digital functional library of over 80 FDA-approved drugs and agents currently in clinical trials were simulated individually and in combination using the two patient avatars to create a personalized treatment for each patient. The findings were prospectively validated using patient cells ex vivo as assessed by MTT assay at New York University.

Results

P1 aberrations included trisomy of CCND1 and deletion of TP53 along with single copy losses in different arms of chromosomes 1, 6, 8, 12, 13, 14, 16, 17 and 22 and gains in different arms and regions of chromosomes X, 1, 4, 7, 9, 17, 3, 5, 11, 15 and 19, indicating the presence of hyperdiploid clones. Using this information, 897 gene perturbations were included to model this patient simulation avatar. Simulation predicted high beta-catenin (CTNNB1) activity with increased hedgehog and NOTCH pathways that were inherent causes of Bortezomib resistance. Significant activation of STAT3 and STAT5 due to amplification of IL6 pathway, JAK2 and JAK3 was noted. Amplifications of MET, IGFR and FGFR converged at ERK and AKT signaling loops. Along with deletion of TP53, this profile had amplification of many anti-apoptotic genes including survivin, MCL1 and XIAP. Modeling predicted sensitivity to the JAK inhibitor Tofacitinib, a drug approved for rheumatoid arthritis. This was prospectively validated ex vivo, and the experimental data correlated with the prediction showing a reduction in viability.

P2 aberrations include losses in chromosomes X and 9 and a chromosome 11:14 translocation that is a common occurrence in MM. This translocation results in an amplification of CCND1 expression. The genomic aberrations reported include knockdown of tumor suppressors RXRA, TGFBR1, TJP2 and TSC1. TSC1 regulates the mTOR pathway, and its deletion causes an aberrant activation of mTOR and its downstream targets. Reduced expression of RXRA and TJP2 both in different manners leads to increase in AP1 activation. NFkB is also activated due to RXRA reduction. TGFBR1 reduction decreases the expression of cell cycle inhibitors via SMAD2/3 down-regulation. In this patient avatar, modeling predicted sensitivity to a combination of Sirolimus and Trametinib. Ex vivo validation confirmed this prediction of additive synergy of these two drug agents in the context of this patient.

Conclusions

This study demonstrates and validates the personalization of treatment through two patient cases based on creating predictive simulation avatar models using genomic profile information. This modeling holistically incorporates all genomic aberration information and is not limited to associating drugs to actionable mutations.