Review
. 2020 Sep; 11:1177.
doi: 10.3389/fphar.2020.01177.

Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology

Sandip Kumar Patel 1 Bhawana George 2 Vineeta Rai 3 
Affiliations
  • PMID: 32903628
  •     201 References
  •     2 citations

Abstract

The multitude of multi-omics data generated cost-effectively using advanced high-throughput technologies has imposed challenging domain for research in Artificial Intelligence (AI). Data curation poses a significant challenge as different parameters, instruments, and sample preparations approaches are employed for generating these big data sets. AI could reduce the fuzziness and randomness in data handling and build a platform for the data ecosystem, and thus serve as the primary choice for data mining and big data analysis to make informed decisions. However, AI implication remains intricate for researchers/clinicians lacking specific training in computational tools and informatics. Cancer is a major cause of death worldwide, accounting for an estimated 9.6 million deaths in 2018. Certain cancers, such as pancreatic and gastric cancers, are detected only after they have reached their advanced stages with frequent relapses. Cancer is one of the most complex diseases affecting a range of organs with diverse disease progression mechanisms and the effectors ranging from gene-epigenetics to a wide array of metabolites. Hence a comprehensive study, including genomics, epi-genomics, transcriptomics, proteomics, and metabolomics, along with the medical/mass-spectrometry imaging, patient clinical history, treatments provided, genetics, and disease endemicity, is essential. Cancer Moonshot℠ Research Initiatives by NIH National Cancer Institute aims to collect as much information as possible from different regions of the world and make a cancer data repository. AI could play an immense role in (a) analysis of complex and heterogeneous data sets (multi-omics and/or inter-omics), (b) data integration to provide a holistic disease molecular mechanism, (c) identification of diagnostic and prognostic markers, and (d) monitor patient's response to drugs/treatments and recovery. AI enables precision disease management well beyond the prevalent disease stratification patterns, such as differential expression and supervised classification. This review highlights critical advances and challenges in omics data analysis, dealing with data variability from lab-to-lab, and data integration. We also describe methods used in data mining and AI methods to obtain robust results for precision medicine from "big" data. In the future, AI could be expanded to achieve ground-breaking progress in disease management.

Keywords: artificial intelligence (AI); cancer biomarkers; data integration; multi-omics; patient stratification.

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Comprehensive Genomic Profiling of Metastatic Squamous Cell Carcinoma of the Anal Canal.
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A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data.
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Cancer Subtype Discovery Using Prognosis-Enhanced Neural Network Classifier in Multigenomic Data.
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Visualizing Energy Charge in Breast Carcinoma Tissues by MALDI Mass-spectrometry Imaging Profiles of Low-molecular-weight Metabolites.
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Imaging mass spectrometry distinguished the cancer and stromal regions of oral squamous cell carcinoma by visualizing phosphatidylcholine (16:0/16:1) and phosphatidylcholine (18:1/20:4).
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Anal Bioanal Chem, 2013 Jun 04; 406(5). PMID: 23728729
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Machine Learning-Based Radiomics for Molecular Subtyping of Gliomas.
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Metabolomic profiling of prostate cancer by matrix assisted laser desorption/ionization-Fourier transform ion cyclotron resonance mass spectrometry imaging using Matrix Coating Assisted by an Electric Field (MCAEF).
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Lipid Heterogeneity Resulting from Fatty Acid Processing in the Human Breast Cancer Microenvironment Identified by GCIB-ToF-SIMS Imaging.
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Multisensor Imaging-From Sample Preparation to Integrated Multimodal Interpretation of LA-ICPMS and MALDI MS Imaging Data.
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TANDEM: a two-stage approach to maximize interpretability of drug response models based on multiple molecular data types.
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Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas.
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Developing a Prognostic Gene Panel of Epithelial Ovarian Cancer Patients by a Machine Learning Model.
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The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups.
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Matrix-assisted laser desorption/ionization mass spectrometry imaging of cell cultures for the lipidomic analysis of potential lipid markers in human breast cancer invasion.
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Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine.
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