PT  - JOURNAL ARTICLE
AU  - Jonathan Fine
AU  - Deepika Dhawan
AU  - Sagar Utturkar
AU  - Phillip San Miguel
AU  - Gaurav Chopra
AU  - John Turek
AU  - David Nolte
AU  - Michael O. Childress
AU  - Nadia A. Lanman
TI  - Integration of Biodynamic Imaging and RNA-seq predicts chemotherapy response in canine diffuse large B-cell lymphoma
AID  - 10.1101/2020.09.11.290353
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.09.11.290353
4099  - http://biorxiv.org/content/early/2020/09/12/2020.09.11.290353.short
4100  - http://biorxiv.org/content/early/2020/09/12/2020.09.11.290353.full
AB  - Diffuse large B-cell lymphoma (DLBCL) is a common, aggressive cancer of notorious genotypic and phenotypic heterogeneity. A major challenge is predicting response to drug treatment that has typically been done using genomic tools alone with little success. A novel method that incorporates phenotypic profiling for predicting the effectiveness of therapy for individual patients is desperately needed. BioDynamic Imaging (BDI) is a technique for measuring time-dependent fluctuations in back-scattered light through living tumor tissues to identify critical changes in intracellular dynamics that are associated with phenotypic response to drugs. In this study, BDI and RNA sequencing (RNA-seq) data were collected on tumor samples from dogs with naturally occurring DLBCL, an animal model of increasingly recognized relevance to the human disease. BDI and RNA-seq data were combined to identify correlations between gene co-expression modules and linear combinations of biomarkers to provide biological mechanistic interpretations of BDI biomarkers. Using regularized multivariate logistic regression, we combined RNA-seq and BDI data to develop a novel machine learning model to accurately predict the clinical response of canine DLBCL to combination chemotherapy (i.e. CHOP). Our model incorporates data on the expression of 4 genes and 3 BDI-derived phenotypic biomarkers, capturing changes in transcription, microtubule related processes, and apoptosis. These results suggest that multi-scale genomic and phenotypic data integration can identify patients that respond to a given treatment a priori in a disease that has been difficult to treat. Our work provides an important framework for future development of strategies and treatments in precision cancer medicine.Key PointsCombined intracellular Doppler spectra and RNA-seq classify DLBCL samples as sensitive or resistance to CHOP chemotherapyKey dynamic features were identified that can be used to classify dogs with naturally-occurring DLBCL as CHOP-sensitive or -resistantCompeting Interest StatementDavid Nolte and John Turek have a financial interest in Animated Dynamics, Inc. that is seeking to commercialize biodynamic technology with a intellectual property license from Purdue University.