Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information

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Abstract

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 members is expected to increase in the future. Under these circumstances, introducing cutting-edge artificial intelligence (AI) technology and information and communication technology to MTBs while reducing the burden on MTB members and building a platform that enables more accurate and personalized medical care would be of great benefit to patients. In this review, we introduced the latest status of elemental technologies that have potential for AI utilization in MTB, and discussed issues that may arise in the future as we progress with AI implementation.

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Most cited references 189

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Drew M Pardoll (2012)

Among the most promising approaches to activating therapeutic antitumour immunity is the blockade of immune checkpoints. Immune checkpoints refer to a plethora of inhibitory pathways hardwired into the immune system that are crucial for maintaining self-tolerance and modulating the duration and amplitude of physiological immune responses in peripheral tissues in order to minimize collateral tissue damage. It is now clear that tumours co-opt certain immune-checkpoint pathways as a major mechanism of immune resistance, particularly against T cells that are specific for tumour antigens. Because many of the immune checkpoints are initiated by ligand-receptor interactions, they can be readily blocked by antibodies or modulated by recombinant forms of ligands or receptors. Cytotoxic T-lymphocyte-associated antigen 4 (CTLA4) antibodies were the first of this class of immunotherapeutics to achieve US Food and Drug Administration (FDA) approval. Preliminary clinical findings with blockers of additional immune-checkpoint proteins, such as programmed cell death protein 1 (PD1), indicate broad and diverse opportunities to enhance antitumour immunity with the potential to produce durable clinical responses.

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The Ensembl Variant Effect Predictor

William McLaren, Laurent Gil, Sarah Hunt … (2016)

The Ensembl Variant Effect Predictor is a powerful toolset for the analysis, annotation, and prioritization of genomic variants in coding and non-coding regions. It provides access to an extensive collection of genomic annotation, with a variety of interfaces to suit different requirements, and simple options for configuring and extending analysis. It is open source, free to use, and supports full reproducibility of results. The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.

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Human-level control through deep reinforcement learning.

Volodymyr Mnih, Koray Kavukcuoglu, David Silver … (2015)

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

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Author and article information

Contributors

Ryuji Hamamoto: rhamamot@ncc.go.jp

Journal

Journal ID (nlm-ta): Exp Hematol Oncol

Journal ID (iso-abbrev): Exp Hematol Oncol

Title: Experimental Hematology & Oncology

Publisher: BioMed Central (London )

ISSN (Electronic): 2162-3619

Publication date (Electronic): 31 October 2022

Publication date PMC-release: 31 October 2022

Publication date Collection: 2022

Volume: 11

Electronic Location Identifier: 82

Affiliations

[1 ]GRID grid.272242.3, ISNI 0000 0001 2168 5385, Division of Medical AI Research and Development, , National Cancer Center Research Institute, ; 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan

[2 ]GRID grid.509456.b, Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, ; 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027 Japan

[3 ]GRID grid.272242.3, ISNI 0000 0001 2168 5385, Department of Experimental Therapeutics, , National Cancer Center Hospital, ; 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan

[4 ]GRID grid.258799.8, ISNI 0000 0004 0372 2033, Department of Surgery, Graduate School of Medicine, , Kyoto University, ; Yoshida-konoe-cho, Sakyo-ku, Kyoto, 606-8303 Japan

[5 ]GRID grid.417547.4, ISNI 0000 0004 1763 9564, Research and Development Group, , Hitachi, Ltd., ; 1-280 Higashi-koigakubo, Kokubunji, Tokyo, 185-8601 Japan

[6 ]GRID grid.272242.3, ISNI 0000 0001 2168 5385, Department of Medical Oncology, , National Cancer Center Hospital, ; 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan

[7 ]GRID grid.272242.3, ISNI 0000 0001 2168 5385, Department of Genetic Medicine and Services, , National Cancer Center Hospital, ; 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan

[8 ]GRID grid.272242.3, ISNI 0000 0001 2168 5385, Department of Laboratory Medicine, , National Cancer Center Hospital, ; 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan

[9 ]GRID grid.272242.3, ISNI 0000 0001 2168 5385, Department of Diagnostic Pathology, , National Cancer Center Hospital, ; 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan

[10 ]GRID grid.272242.3, ISNI 0000 0001 2168 5385, Division of Molecular Pathology, , National Cancer Center Research Institute, ; 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045 Japan

Article

Publisher ID: 333

DOI: 10.1186/s40164-022-00333-7

PMC ID: 9620610

PubMed ID: 36316731

SO-VID: 10c00236-4ddb-4382-a2e3-3e279ba58cb3

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 31 August 2022

Date accepted : 5 October 2022

Funding

Funded by: MHLW ICT infrastructure establishment and implementation of artificial intelligence for clinical and medical research program

Award ID: JP21AC5001

Funded by: RIKEN Center for the Advanced Intelligence Project.

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ScienceOpen disciplines: Oncology & Radiotherapy

Keywords: molecular tumor board,precision medicine,artificial intelligence,next-generation sequencing,natural language processing

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ScienceOpen disciplines: Oncology & Radiotherapy

Keywords: molecular tumor board, precision medicine, artificial intelligence, next-generation sequencing, natural language processing

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Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information

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Radiology and Natural Language Processing

Most cited references 189

The blockade of immune checkpoints in cancer immunotherapy.

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Human-level control through deep reinforcement learning.

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