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Open AccessArticle

Computational Cancer Cell Models to Guide Precision Breast Cancer Medicine

Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
Department of Occupational and Environmental Health, School of Public Health, XinJiang Medical University, Urumqi 830011, China
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Genes 2020, 11(3), 263;
Received: 27 November 2019 / Revised: 20 February 2020 / Accepted: 25 February 2020 / Published: 28 February 2020
Background: Large-scale screening of drug sensitivity on cancer cell models can mimic in vivo cellular behavior providing wider scope for biological research on cancer. Since the therapeutic effect of a single drug or drug combination depends on the individual patient’s genome characteristics and cancer cells integration reaction, the identification of an effective agent in an in vitro model by using large number of cancer cell models is a promising approach for the development of targeted treatments. Precision cancer medicine is to select the most appropriate treatment or treatments for an individual patient. However, it still lacks the tools to bridge the gap between conventional in vitro cancer cell models and clinical patient response to inhibitors. Methods: An optimal two-layer decision system model is developed to identify the cancer cells that most closely resemble an individual tumor for optimum therapeutic interventions in precision cancer medicine. Accordingly, an optimal grid parameters selection is designed to seek the highest accordance for treatment selection to the patient’s preference for drug response and in vitro cancer cell drug screening. The optimal two-layer decision system model overcomes the challenge of heterology data comparison between the tumor and the cancer cells, as well as between the continual variation of drug responses in vitro and the discrete ones in clinical practice. We simulated the model accuracy using 681 cancer cells’ mRNA and associated 481 drug screenings and validated our results on 315 breast cancer patients drug selection across seven drugs (docetaxel, doxorubicin, fluorouracil, paclitaxel, tamoxifen, cyclophosphamide, lapitinib). Results: Comparing with the real response of a drug in clinical patients, the novel model obtained an overall average accordance over 90.8% across the seven drugs. At the same time, the optimal cancer cells and the associated optimal therapeutic efficacy of cancer drugs are recommended. The novel optimal two-layer decision system model was used on 1097 patients with breast cancer in guiding precision medicine for a recommendation of their optimal cancer cells (30 cancer cells) and associated efficacy of certain cancer drugs. Our model can detect the most similar cancer cells for each individual patient. Conclusion: A successful clinical translation model (optimal two-layer decision system model) was developed to bridge in-vitro basic science to clinical practice in a therapeutic intervention application for the first time. The novel tool kills two birds with one stone. It can help basic science to seek optimal cancer cell models for an individual tumor, while prioritizing clinical drugs’ recommendations in practice. Tool associated platform website: We extended the breast cancer research to 32 more types of cancers across 45 therapy predictions. View Full-Text
Keywords: drug recommendation; precision cancer medicine; cancer cells drug recommendation; precision cancer medicine; cancer cells
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MDPI and ACS Style

Cheng, L.; Majumdar, A.; Stover, D.; Wu, S.; Lu, Y.; Li, L. Computational Cancer Cell Models to Guide Precision Breast Cancer Medicine. Genes 2020, 11, 263.

AMA Style

Cheng L, Majumdar A, Stover D, Wu S, Lu Y, Li L. Computational Cancer Cell Models to Guide Precision Breast Cancer Medicine. Genes. 2020; 11(3):263.

Chicago/Turabian Style

Cheng, Lijun; Majumdar, Abhishek; Stover, Daniel; Wu, Shaofeng; Lu, Yaoqin; Li, Lang. 2020. "Computational Cancer Cell Models to Guide Precision Breast Cancer Medicine" Genes 11, no. 3: 263.

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