Comparing Patient-Derived Xenograft and Computational Response Prediction for Targeted Therapy in Patients of Early-Stage Large Cell Lung Cancer

Abstract

Purpose: Targeted therapy (TT) provides highly effective cancer treatment for appropriately selected individuals. A major challenge of TT is to select patients who would benefit most.Experimental Design: The study uses cancer material from 25 patients primarily diagnosed with non-small cell lung cancer (NSCLC). Patient-derived xenografts (PDXs) are treated with cetuximab and erlotinib. Treatment response is measured by tumor shrinkage comparing tumor volume at day 25 (V 25 ) with tumor volume at baseline (V 0 ). Shrinkage below 40% is considered as treatment response: V 25 /V 0 < 0.4 (<40%). Furthermore, RNA-seq data from each tumor sample are used to predict tumor response to either treatment using an in silico molecular signaling map (MSM) approach.Results Conclusions: For NSCLC patients, this proof-of-concept study shows a considerable agreement in response prediction from MSM and PDX approaches, but MSM saves time and laboratory resources. Our result indicates the potential of MSM-based approach for clinical decision making when selecting cancer TTs.

Tissue-directed Implantation Using Ultrasound Visualization for Development of Biologically Relevant Metastatic Tumor Xenografts

“…Significantly, these data provide two newly established NB patient-derived orthotopic xenografts (UMNBL001, UMNBL002), a rare model for future preclinical therapeutic testing and molecular studies. This model, similar to other patient-derived cancer models had overall high tumor engraftment and locally invasive growth at the orthotopic site (23). UMNBL002 patientderived orthotopic xenografts faithfully produced robust, infiltrative tumors that were metastatic to lung and lymph nodes.…”

DRAP: a toolbox for drug response analysis and visualization tailored for preclinical drug testing on patient-derived xenograft models

“…T*A*N : This pattern could be regarded as extended versions of the above three patterns, and could be applied in various situations. While applied for evaluating drug efficacies of multiple treatment arms based on a PDX collection, the analysis is consistent with that of T*1*N pattern [26]. While applied for testing drug responses of multiple tumors to a series of treatments, the analysis is similar to 1*A*N pattern [17].…”

Computational modeling of methionine cycle-based metabolism and DNA methylation and the implications for anti-cancer drug response prediction

“…Based on our experience in previous studies [ 27 – 29 ], we think that many studies of gene signature are premature in analyzing the pre-clinical and clinical outcome. While big data such as gene expression contains a multitude of valuable information, intricate biological regulation mechanisms such as transcriptional and translational regulation, feedback-control regulation, ligand-competitor mechanism and others are not included within any kind of big data.…”

“…Using an in silico approach and integrating molecular models with genetic information such as gene expression data we were able to effectively handle complex issues such as prediction of targeted treatment outcome. Moreover, it was also shown that the costs of an in silico approach both in regard to time and materials is much lower compared to conventional in vitro based studies such as cell line, xenograft and other experimental settings [ 27 – 29 ]. However, the MSM did not consider any aspects of metabolism and is therefore not able to fully reflect the metabolic regulation of carcinogenesis.…”