Precision medicine: In need of guidance and surveillance

Lin, Jianzhen; Long, Junyu; Wang, An-Qiang; Zheng, Ying; Zhao, Haitao

doi:10.3748/wjg.v23.i28.5045

Cited by 13 publications

(11 citation statements)

References 19 publications

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“…Much of the efforts put into precision medicine have been focused in the oncology arena, but benefits in terms of treatment choice, survival, or quality of life have been lacking, or at least lagging. [ 19,20 ] Far from being a sign of lack of promise, this merely highlights the difficulties in applying classical thinking to novel methodologies. The question of how one uses precision medicine to improve the diagnosis, prevention, and treatment of disease requires more experience and more humility in the possibilities.…”

Section: Successes and Challenges In Liver Disease And Transplantationmentioning

confidence: 99%

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Phenotypic Response and Personalized Medicine in Liver Cancer and Transplantation: Approaches to Complex Systems

Zarrinpar

Kim

Boominathan

2020

Advanced Therapeutics

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Rapid improvements in medical technology, big data analysis, and molecular medicine come with promises of revolutionizing medical care. They span the spectrum from diagnostics to genome-based drug selection to multi-biomarker analysis to deciphering large amounts of data. Below, recent developments in personalized and precision medicine are reviewed, focusing specifically on the liver, ranging from fatty liver disease to liver cancer treatment and liver transplantation. Furthermore, current technologies and their advantages and limitations are discussed, in addition to ways in which these disadvantages can be overcome, using phenotypic personalized medicine.

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Section: Successes and Challenges In Liver Disease And Transplantationmentioning

confidence: 99%

“…The question of how one uses precision medicine to improve the diagnosis, prevention, and treatment of disease requires more experience and more humility in the possibilities. [ 20 ]…”

Section: Successes and Challenges In Liver Disease And Transplantationmentioning

confidence: 99%

Phenotypic Response and Personalized Medicine in Liver Cancer and Transplantation: Approaches to Complex Systems

Zarrinpar

Kim

Boominathan

2020

Advanced Therapeutics

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“…To achieve this level, precision medicine can be viewed as a continuous process of data preprocessing/data mining (track 1), construction of diagnostic/prognostic models (track 2) and prediction of treatment response/disease progression (track 3) [ 1 ]. Whereas track 1 focuses on the identification of important observed and latent variables, tracks 2 and 3 require models with highly accurate predictions about disease status, prognosis or progression of a disease of a single individual [ 2 – 6 ]. Explained with (generalized) linear models as an example, based on the estimation of and inference on regression coefficients, tracks 2 and 3 aim at constructing models with their predictive ability as high as possible, measured in terms of some performance, e.g.…”

Section: Introductionmentioning

confidence: 99%

Empowering individual trait prediction using interactions for precision medicine

Gola

König

2021

BMC Bioinformatics

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Background One component of precision medicine is to construct prediction models with their predicitve ability as high as possible, e.g. to enable individual risk prediction. In genetic epidemiology, complex diseases like coronary artery disease, rheumatoid arthritis, and type 2 diabetes, have a polygenic basis and a common assumption is that biological and genetic features affect the outcome under consideration via interactions. In the case of omics data, the use of standard approaches such as generalized linear models may be suboptimal and machine learning methods are appealing to make individual predictions. However, most of these algorithms focus mostly on main or marginal effects of the single features in a dataset. On the other hand, the detection of interacting features is an active area of research in the realm of genetic epidemiology. One big class of algorithms to detect interacting features is based on the multifactor dimensionality reduction (MDR). Here, we further develop the model-based MDR (MB-MDR), a powerful extension of the original MDR algorithm, to enable interaction empowered individual prediction. Results Using a comprehensive simulation study we show that our new algorithm (median AUC: 0.66) can use information hidden in interactions and outperforms two other state-of-the-art algorithms, namely the Random Forest (median AUC: 0.54) and Elastic Net (median AUC: 0.50), if interactions are present in a scenario of two pairs of two features having small effects. The performance of these algorithms is comparable if no interactions are present. Further, we show that our new algorithm is applicable to real data by comparing the performance of the three algorithms on a dataset of rheumatoid arthritis cases and healthy controls. As our new algorithm is not only applicable to biological/genetic data but to all datasets with discrete features, it may have practical implications in other research fields where interactions between features have to be considered as well, and we made our method available as an R package (https://github.com/imbs-hl/MBMDRClassifieR). Conclusions The explicit use of interactions between features can improve the prediction performance and thus should be included in further attempts to move precision medicine forward.

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“…Deciding what therapy options to pursue can also be daunting, especially when tumors can harbor more than one potentially actionable aberration [11]. Further, different mutations/variants in a single gene may have different functional consequences, and response to targeted agents may be context dependent [12].…”

Section: Introductionmentioning

confidence: 99%

“…Precision medicine gradually aims toward accurate and effective precise treatment [13] by algorithm guidance. Emerging next generation sequencing (NGS) techniques and therapeutic drugs based on molecular profiling and genomic characteristics will help achieve that goal [12]. The Cancer Genome Atlas (TCGA) project (https://cancergenome.nih.gov/abouttcga/overview) has now provided detailed molecular compositions for over 11,000 cancers from at least 33 anatomic sites which can be used to define cancer subsets, including cell-of-origin patterns, oncogenic processes, and signaling pathways that could identify more homogeneous populations likely to benefit from similar interventions [14].…”

Section: Introductionmentioning

confidence: 99%

Computational Cancer Cell Models to Guide Precision Breast Cancer Medicine

Cheng

Majumdar

Stover

et al. 2020

Genes

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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.

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Precision medicine: In need of guidance and surveillance

Cited by 13 publications

References 19 publications

Phenotypic Response and Personalized Medicine in Liver Cancer and Transplantation: Approaches to Complex Systems

Phenotypic Response and Personalized Medicine in Liver Cancer and Transplantation: Approaches to Complex Systems

Empowering individual trait prediction using interactions for precision medicine

Computational Cancer Cell Models to Guide Precision Breast Cancer Medicine

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