Evaluation of anti-cancer therapy using in silico analysis of treatments for HER2&amp;#x002B; breast cancer

Ganic, Emir; Gundry, Stephen; Zou, Jianmin

doi:10.1109/memea.2014.6860059

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…The goal of pharmacogenomics is to develop accurate models and translate them to clinical practice. We calculate tumor growth model parameters from clinical and preclinical data for breast and ovarian cancer patients [9], [28]. Our software tools use bio-inspired artificial intelligence methods developed at the earlier stages of our research [11], [17], [21].…”

Section: Calculation Of Tumor Growth Parametersmentioning

confidence: 99%

“…Pharmacokinetics (PK) and pharmacodynamics (PD) models focus on the absorbtion, distribution, metabolism, excretion from drugs, and the characteristics for the anti-cancer drugs on tumor cell death [9], [22].…”

Section: Introductionmentioning

confidence: 99%

“…In our previous work we evaluated treatments recommended by the National Comprehensive Cancer Network (NCCN) for HER2+ breast cancer [9] and ovarian cancer [28] in pre-clinical settings. We showed that the chemotherapy regimens recommended by the NCCN were effective in reducing tumor volume.…”

Section: Introductionmentioning

confidence: 99%

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Toward Genomic Based Personalized Mathematical Models for Breast Cancer Tumor Growth

Saribudak

Ganic

Zou

et al. 2014

2014 IEEE International Conference on Bioinformatics and Bioengineering

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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.

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Section: Calculation Of Tumor Growth Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Toward Genomic Based Personalized Mathematical Models for Breast Cancer Tumor Growth

Saribudak

Ganic

Zou

et al. 2014

2014 IEEE International Conference on Bioinformatics and Bioengineering

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“…Empirical models describe tumor growth using mathematical equations without in-depth analysis of basal processes, whereas functional models concentrate on physiological hypotheses and biochemical processes [1], [2]. Exponential-linear tumor growth model [1] built based on experimental observations is a well-accepted empirical model.…”

Section: Introductionmentioning

confidence: 99%

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

Saribudak¹,

Dong²,

Hsieh³

et al. 2016

IJET

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Mathematical models for absorption and efficacy of ovarian cancer treatments

Zou

Gundry

Ganic

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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The creation of personal and individualized anti-cancer treatments has been a major goal in the progression of cancer discovery as evident by the continuous research efforts in genetics and population based PK/PD studies. In this paper we use our clinical decision support tool, called ChemoDSS, to evaluate the effectiveness of three treatments recommended by the NCCN guidelines for ovarian cancer using pre-clinical data from the literature. In particular, we analyze the treatments of PC (i.e., Paclitaxel and Cispaltin), DC (i.e., Docetaxel and Carboplatin), and PBC (i.e., Paclitaxel, Bevacizumab, and Carboplatin). Our in silico analysis of the ovarian cancer treatments shows that PC was the most effective regimen for treating ovarian cancer compared to DC and PBC, which is consistent with literature findings. We demonstrate that we can successfully evaluate the effectiveness of the selected ovarian cancer treatment regimens using ChemoDSS.

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Evaluation of anti-cancer therapy using in silico analysis of treatments for HER2+ breast cancer

Cited by 5 publications

References 21 publications

Toward Genomic Based Personalized Mathematical Models for Breast Cancer Tumor Growth

Toward Genomic Based Personalized Mathematical Models for Breast Cancer Tumor Growth

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

Mathematical models for absorption and efficacy of ovarian cancer treatments

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