Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology (original) (raw)
Related papers
Data-Driven Methods for Advancing Precision Oncology
Current Pharmacology Reports, 2018
Purpose of Review-This article discusses the advances, methods, challenges, and future directions of data-driven methods in advancing precision oncology for biomedical research, drug discovery, clinical research, and practice. Recent Findings-Precision oncology provides individually tailored cancer treatment by considering an individual's genetic makeup, clinical, environmental, social, and lifestyle information. Challenges include voluminous, heterogeneous, and disparate data generated by different technologies with multiple modalities such as Omics, electronic health records, clinical registries and repositories, medical imaging, demographics, wearables, and sensors. Statistical and machine learning methods have been continuously adapting to the ever-increasing size and complexity of data. Precision Oncology supportive analytics have improved turnaround time in biomarker discovery and time-to-application of new and repurposed drugs. Precision oncology additionally seeks to identify target patient populations based on genomic alterations that are sensitive or resistant to conventional or experimental treatments. Predictive models have been developed for cancer progression and survivorship, drug sensitivity and resistance, and identification of the most suitable combination treatments for individual patient scenarios. In the future, clinical decision support systems need to be revamped to better incorporate knowledge from precision oncology, thus enabling clinical practitioners to provide precision cancer care. Summary-Open Omics datasets, machine learning algorithms, and predictive models have enabled the advancement of precision oncology. Clinical decision support systems with integrated electronic health record and Omics data are needed to provide data-driven recommendations to assist clinicians in disease prevention, early identification, and individualized treatment. Additionally, as cancer is a constantly evolving disorder, clinical decision systems will need to be continually updated based on more recent knowledge and datasets.
Human Genetics
In the field of cancer genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intelligence (AI) approaches such as machine learning, deep learning, and natural language processing (NLP) to tackle the challenges of scalability and high dimensionality of data and to transform big data into clinically actionable knowledge is expanding and becoming the foundation of precision medicine. In this paper, we review the current status and future directions of AI application in cancer genomics within the context of workflows to integrate genomic analysis for precision cancer care. The existing solutions of AI and their limitations in cancer genetic testing and diagnostics such as variant calling and interpretation are critically analyzed. Publicly available tools or algorithms for key NLP technologies in the literature mining for evidencebased clinical recommendations are reviewed and compared. In addition, the present paper highlights the challenges to AI adoption in digital healthcare with regard to data requirements, algorithmic transparency, reproducibility, and real-world assessment, and discusses the importance of preparing patients and physicians for modern digitized healthcare. We believe that AI will remain the main driver to healthcare transformation toward precision medicine, yet the unprecedented challenges posed should be addressed to ensure safety and beneficial impact to healthcare.
2017
predicted that precision medicine "is going to change everything about how we understand health and disease." 2 The massive Precision Medicine Initiative (PMI), now renamed the "All of Us" Research Program, plans to study a cohort of more than one million Americans. Its hope is to expand our understanding of heredity and disease and revolutionize the treatment of disease and the improvement of human health. According to the White House, precision medicine is "health care tailored for you." 3 At its most fundamental level, precision medicine seeks to optimize treatments based on individual physiological characteristics, such as the ability to metabolize certain compounds or to respond to one or another set of drugs based on individual differences in genes, environments, and lifestyles. Precision medicine is being developed today within a complex landscape of medicine, science, public policy, law, and ethics. In December 2016, the
Frontiers in Oncology
Editorial on the Research Topic Artificial intelligence: A step forward in biomarker discovery and integration towards improved cancer diagnosis and treatment In cancer, a biomarker refers to a substance or process indicative of the presence of cancer in the body. However, the idea of "one-molecule (or process) marker" indicated by its presence, and the existence of an undergoing transforming cancer process is currently a utopia. During the past decade, there has been a fundamental shift in cancer research and clinical decision-making, moving from qualitative data to quantitative digital data. A large wealth of cancer biomarkers and images has come from research laboratories and clinical institutions worldwide. Moreover, the major bulk of information has arisen from genomics, proteomics, metabolomics, and other omics, but also from oncology clinics, imaging, epidemiology and more. Artificial Intelligence (AI) is a unique technology that is able to combine all the above, and particularly suited to establish novel therapies and predictive models of drug response (1, 2). The combination of several biomarkers, by means of Machine Learning (ML) algorithms, would reach unprecedented conclusions in diagnosis, prediction and general decision making of novel anticancer therapies (3-5). In addition, the multimodal temporal data collected from patients with cancers can feed to initialize and track a Digital Twin to experiment with multiple possible treatments in silico. This Research Topic has gathered 10 selected contributions in the area of ML tools, Deep Learning and Cancer Digital Twin technologies in the field of Precision Oncology, and contains one review, one minireview and eight original contributions. The review paper by Asada et al. emphasizes the relevance of Precision Oncology and the integration of whole genome sequencing analysis, epigenome analyses and the use of ML, and opens a discussion about future perspectives in the field.
Frontiers in Precision Medicine II: Cancer, Big Data and the Public
SSRN Electronic Journal, 2016
predicted that precision medicine "is going to change everything about how we understand health and disease." 2 The massive Precision Medicine Initiative (PMI), now renamed the "All of Us" Research Program, plans to study a cohort of more than one million Americans. Its hope is to expand our understanding of heredity and disease and revolutionize the treatment of disease and the improvement of human health. According to the White House, precision medicine is "health care tailored for you." 3 At its most fundamental level, precision medicine seeks to optimize treatments based on individual physiological characteristics, such as the ability to metabolize certain compounds or to respond to one or another set of drugs based on individual differences in genes, environments, and lifestyles. Precision medicine is being developed today within a complex landscape of medicine, science, public policy, law, and ethics. In December 2016, the
Experimental Hematology & Oncology
Since U.S. President Barack Obama announced the Precision Medicine Initiative in his New Year’s State of the Union address in 2015, the establishment of a precision medicine system has been emphasized worldwide, particularly in the field of oncology. With the advent of next-generation sequencers specifically, genome analysis technology has made remarkable progress, and there are active efforts to apply genome information to diagnosis and treatment. Generally, in the process of feeding back the results of next-generation sequencing analysis to patients, a molecular tumor board (MTB), consisting of experts in clinical oncology, genetic medicine, etc., is established to discuss the results. On the other hand, an MTB currently involves a large amount of work, with humans searching through vast databases and literature, selecting the best drug candidates, and manually confirming the status of available clinical trials. In addition, as personalized medicine advances, the burden on MTB mem...
Proceedings of the International Conferences on WWW/Internet 2021 and Applied Computing 2021, 2021
Oncology is one of the most dynamic branches of medicine. As a result of numerous oncology studies, there has been a significant increase in scientific and clinical data that the human brain cannot store. Advances in artificial intelligence (AI) technology have led to its rapid clinical application. In this paper, we wanted to see the role of the use of artificial intelligence (AI) in oncology. We conducted an unsystematic search of databases (Pub Med, MEDLINE, and Google Scholar) using the keywords: artificial intelligence, deep learning, machine learning, oncology, personalized medicine. From a large number of articles available to us, we singled out review articles and clinical trial results according to their clarity and innovation regarding the use of artificial intelligence in oncology. Of particular importance to us was the ability to apply their results in everyday clinical work. The possibilities of using artificial intelligence in oncology are innumerable. Thus, AI can be used for diagnostic purposes (malignant screening, histopathology, and molecular diagnostics), therapeutic purposes (personalized treatment, prediction of treatment side effects and response to therapy, treatment decisions) as well as for prognostic purposes (risk stratification, 5-year survival, monitoring). The implementation of AI in clinical practice presents new challenges for clinicians. Namely, in the era of evidence-based and patient-centered medicine, they will have to master statistical as well as computer skills in addition to clinical ones. Therefore, it is necessary to start educating future doctors about the importance of AI in medicine as soon as possible.
Precision medicine: recent progress in cancer therapy
Mediterr J Pharm Pharm Sci, 2021
This review was aimed to describe a new approach of healthcare performance strategy based on individual genetic variants. Personalized medicine is a model for health care which is a combination of preventive, personalized, participatory and predictive measures. It is an approach for a better treatment by identifying the disease causing genomics makeup of an individual. This work features key advancements in the improvement of empowering advances that further the objective of customized and precision medication and the remaining difficulties that, when tended to, may produce phenomenal abilities in acknowledging genuinely individualized patient consideration. Customized treatment for patients determined to have strong tumors has brought about a few advances as of late. To improve a multi-drug approach ready to coordinate DNA and RNA adjustment, proteomics and metabolomics will be essential. The execution of translational examinations dependent on fluid biopsy and organoids or xenografts to assess molecular changes because of clonal weight produced because of the utilization of target specialists or tumor heterogeneity would help in the recognition of systems of opposition, proposing opportunities for novel mixes. The investigation of massive data in oncology can profit altogether from being engaged by artificial intelligence and machine learning strategies.
Yearbook of Medical Informatics, 2019
Objective: To summarize significant research contributions on cancer informatics published in 2018. Methods: An extensive search using PubMed/Medline, Google Scholar, and manual review was conducted to identify the scientific contributions published in 2018 that address topics in cancer informatics. The selection process comprised three steps: (i) 15 candidate best papers were first selected by the two section editors, (ii) external reviewers from internationally renowned research teams reviewed each candidate best paper, and (iii) the final selection of four best papers was conducted by the editorial board of the International Medical Informatics Association (IMIA) Yearbook. Results: The four selected best papers present studies addressing many facets of cancer informatics, with immediate applicability in the translational and clinical domains. Conclusion: Cancer informatics is a broad and vigorous subfield of biomedical informatics. Progress in cancer genomics, artificial intellig...
Bridging the Gap in Personalized Oncology using Omics Data and Epidemiology
Novel Approaches in Cancer Study, 2018
While the scientific community has largely accepted the utility of sequencing for research purposes, the use of the next-generation sequencing (NGS) technology in a clinical setting has yet to be fully addressed and applied clinically [7]. To effectively advance personalized medicine, it is necessary to be able to test for patients' genet-Abstract As Personalized Medicine tailored the field of precision oncology, many challenges have been arising to fulfill the dream of a full personalized health integrated system in cancer therapy. Personalized oncology has been addressed through the past decades in multiple disease and various stages using high throughput technology. This review gives hand on recent advances of personalized oncology in several cancer disease models including leukemia, melanoma, breast cancer, lung cancer, colorectal cancer, and prostate cancer. Moreover, the review enumerates technology-based assessment of personalized biomarkers, including chip micro-array, organ on chip, and next generation sequencing. Meanwhile addressing challenges faced in implementing true personalized health cancer in oncology setting, this review focuses on bridging the gap between omics data analytics and epidemiology to overcome the true challenge of direct application.