Toward Genomic Based Personalized Mathematical Models for Breast Cancer Tumor Growth

Abstract

Our Genomic Relevance Parameterization (GReP) model aims to explore a possible relationship between gene expression values from breast cancer patients and mathematical tumor growth modeling parameters calculated using data from clinical and preclinical measurements. We introduce two methods to relate genomic information and the tumor growth measurements. One method explores the impact of exponentiation of gene expression values, whereas the other utilizes the correlation between co-regulated genes and the growth parameters. As inputs to our GReP model, we used patient tumor volume measurements and genomic information for 74 breast cancer related genes from the I-SPY 1 TRIAL. We performed a preliminary validation of GReP model using experimental data from literature including MDA-MB-231 cell line, MDA-MB-231 cell line with CXCL12 gene over-expressed, and the MDA-MB-231 sub-cell lines 1834 and 4175. Tumor growth curves generated by GReP model, for the initial exponential phase of tumor growth closely match the pre-clinical data reported in the literature. These promising results show that it may be possible to build tools combining clinical information and genomic data to model cancerous tumor growth.

Modeling Tumor Growth for Kidney Cancer Based on Nuclei Clusters of Pathology Slides

“…We build artificial intelligence based mathematical models to compute tumor growth and related parameters including exponential-linear model parameters. In our earlier research [4], we have studied the relationship between the genetic information from breast cancer patients and the growth parameters, based on the genetic data retrieved from 79 breast cancer patients with ER+ status Modeling Tumor Growth for Kidney Cancer Based on Nuclei Clusters of Pathology Slides provided in I-SPY 1 TRIAL database [12]. Using the expression values of 74 breast cancer related genes from these patients and the tumor volume measurements obtained from NBIA database [13] as inputs to our computational models, we have computed tumor growth parameters.…”

“…In exponential-linear model, tumor growth is expressed as a function of rate constants and tumor weight or volume when no drug is administered [1]. In our previous work [4], we computed tumor growth curves using genetic information from breast cancer patients in our artificial intelligence based methods. In this paper, we study the generation of tumor growth models based on stained tissue samples of pathology slides from kidney cancer patients.…”

A gene expression-based mathematical modeling approach for breast cancer tumor growth and shrinkage

“…In the earlier phase of our research (Saribudak et al 2014), we used 74 breast cancer-related genes which are reported as being significantly expressed in breast tissues (van't Veer et al 2002;Minn et al 2005;Ma et al 2009). van't Veer et al (2002) identified 70 prognostic marker genes to analyze their significance of expressions quantitatively in tumorous breast tissues.…”

“…For the 74 breast cancer-related genes reported as significant in literature (van't Veer et al 2002;Ma et al 2009;Minn et al 2005), we retrieved the gene expressions from I-SPY 1 TRIAL database (Edgar et al 2002) for 149 breast cancer patients. In this article, based on the promising results we obtained for tumor growth in Saribudak et al (2014), we extend our model with the inclusion of tumor shrinkage component of the mathematical model. Here, we present Personalized Relevance Parametrization (PReP-G) model which is capable of computing the tumor shrinkage behavior for a given set of chemotherapy regimens administered to xenograft models.…”

“…In our previous work, we examine the relationship between alterations in gene expressions of breast cancer patients and growth and shrinkage behavior of tumor utilizing phase-specific parameters of the models. In Saribudak et al (2014), we introduced a mathematical approach to generate tumor growth parameters of exponential-linear model using the gene expression data of breast cancer patients and their MRI images available in the National Biomedical Imaging Archive (NBIA) database which is built to provide medical images to support research studies (Nicholas et al 2012). For the 74 breast cancer-related genes reported as significant in literature (van't Veer et al 2002;Ma et al 2009;Minn et al 2005), we retrieved the gene expressions from I-SPY 1 TRIAL database (Edgar et al 2002) for 149 breast cancer patients.…”