Abstract
Precision medicine is a medical approach to administer patients with a tailored dose of treatment by taking into consideration a person’s variability in genes, environment, and lifestyles. The accumulation of omics big sequence data led to the development of various genetic databases on which clinical stratification of high-risk populations may be conducted. In addition, because cancers are generally caused by tumor-specific mutations, large-scale systematic identification of single nucleotide polymorphisms (SNPs) in various tumors has propelled significant progress of tailored treatments of tumors (i.e., precision oncology). Machine learning (ML), a subfield of artificial intelligence in which computers learn through experience, has a great potential to be used in precision oncology chiefly to help physicians make diagnostic decisions based on tumor images. A promising venue of ML in precision oncology is the integration of all available data from images to multi-omics big data for the holistic care of patients and high-risk healthy subjects. In this review, we provide a focused overview of precision oncology and ML with attention to breast cancer and glioma as well as the Bayesian networks that have the flexibility and the ability to work with incomplete information. We also introduce some state-of-the-art attempts to use and incorporate ML and genetic information in precision oncology.
Highlights
The Human Genome Project decoded over 3 billion nucleotides between 1990 and 2003, providing meaningful information to biomedical researchers [1]
Precision medicine refers to a medical approach in which treatments are tailored to individual patients and/or unique subpopulations—because the genetic variation among people directly impacts their susceptibility to diseases, prognoses, and response to treatment methods
We aim to provide an overview of up-to-date precision oncology, including novel enlistment of computational biology, artificial intelligence (AI), and Machine learning (ML) to better create targeted therapies for patients diagnosed with cancer
Summary
The Human Genome Project decoded over 3 billion nucleotides between 1990 and 2003, providing meaningful information to biomedical researchers [1]. The primary methodologies of precision medicine include (1) identifying genes related to a particular disease and drug response, (2) predicting the risk of the disease based on the genetic information of subjects, and (3) addressing the technological issues involved in the treatment based on the genetic and phenotypic information of patients [9,10] This approach works well for cancers, in which family health history and genetic alterations have great impacts on risk prediction, diagnosis, and treatment. The major databases include The Cancer Genome Atlas (TCGA) (https://www.cancer.gov/tcga, accessed on 10 May 2021), the International Cancer Genome Consortium (ICGC) Data Portal (https://dcc.icgc.org, accessed on 10 May 2021), and the Catalogue of Somatic Mutations in Cancer (COSMIC) (https://cancer.sanger.ac.uk/cosmic, accessed on 10 May 2021) With these techniques, precision oncology, due to its specificity and tailored approach, will potentially be more beneficial for patients compared to one-drug-fits-all treatment methodologies [14]. SNP genotyping and gene expression profiling have circumvented the expenditures associated with analyzing the genome, transcriptome, and proteome of subjects
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