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Curated Papers > Recent high-impact research on "AI in Drug Discovery" (IF ≥ 30, last 5 years)

Artificial Intelligence in Cancer Research and Precision Medicine. ​

Literature Information ​

DOI10.1158/2159-8290.CD-21-0090
PMID33811123
JournalCancer discovery
Impact Factor33.3
JCR QuartileQ1
Publication Year2021
Times Cited283
KeywordsArtificial Intelligence, Cancer Research, Precision Medicine, Drug Discovery, Treatment Outcome Prediction
Literature TypeJournal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Review
ISSN2159-8274
Pages900-915
Issue11(4)
AuthorsBhavneet Bhinder, Coryandar Gilvary, Neel S Madhukar, Olivier Elemento

TL;DR ​

This research highlights the transformative impact of artificial intelligence (AI) on cancer research and personalized clinical care, driven by the availability of high-dimensional datasets and advanced deep learning techniques. It reviews significant advancements in AI applications across oncology, emphasizing its potential to enhance diagnosis, personalize treatment, and facilitate drug discovery, while also addressing existing limitations and outlining future directions for AI integration in clinical settings.

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Artificial Intelligence · Cancer Research · Precision Medicine · Drug Discovery · Treatment Outcome Prediction

Abstract ​

Artificial intelligence (AI) is rapidly reshaping cancer research and personalized clinical care. Availability of high-dimensionality datasets coupled with advances in high-performance computing, as well as innovative deep learning architectures, has led to an explosion of AI use in various aspects of oncology research. These applications range from detection and classification of cancer, to molecular characterization of tumors and their microenvironment, to drug discovery and repurposing, to predicting treatment outcomes for patients. As these advances start penetrating the clinic, we foresee a shifting paradigm in cancer care becoming strongly driven by AI. SIGNIFICANCE: AI has the potential to dramatically affect nearly all aspects of oncology-from enhancing diagnosis to personalizing treatment and discovering novel anticancer drugs. Here, we review the recent enormous progress in the application of AI to oncology, highlight limitations and pitfalls, and chart a path for adoption of AI in the cancer clinic.

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Primary Questions Addressed ​

  1. How can AI improve the accuracy of cancer detection and classification compared to traditional methods?
  2. What specific deep learning architectures have shown the most promise in molecular characterization of tumors?
  3. In what ways can AI contribute to the identification of novel anticancer drugs beyond existing approaches?
  4. What are the ethical considerations and potential biases associated with the use of AI in cancer research and treatment?
  5. How can high-dimensionality datasets be effectively utilized to enhance patient treatment outcomes through AI applications?

Key Findings ​

Research Background and Purpose ​

Artificial intelligence (AI) is transforming the landscape of cancer research and clinical care. The integration of high-dimensional datasets and advancements in computing power has accelerated AI applications in oncology. This review aims to explore the various applications of AI in cancer detection, classification, molecular characterization, drug discovery, and predicting treatment outcomes, while also discussing the implications for future cancer care.

Main Methods/Materials/Experimental Design ​

The review synthesizes recent literature on AI applications in oncology, focusing on various methodologies and technological frameworks. The following flowchart illustrates the key processes involved in the integration of AI in cancer research:

Mermaid diagram

Key Results and Findings ​

  • Detection and Classification: AI models have shown increased accuracy in identifying and classifying different cancer types compared to traditional methods.
  • Molecular Characterization: AI assists in analyzing tumor microenvironments and identifying molecular signatures, leading to better-targeted therapies.
  • Drug Discovery: AI accelerates the identification of potential drug candidates and repurposing existing drugs for new therapeutic uses.
  • Predicting Treatment Outcomes: Machine learning algorithms can predict patient responses to treatments, aiding in personalized medicine approaches.

Main Conclusions/Significance/Innovation ​

The review underscores that AI has the potential to revolutionize oncology by:

  • Enhancing diagnostic accuracy and speed.
  • Personalizing treatment plans based on individual patient data.
  • Streamlining drug discovery processes, thereby reducing time and costs associated with bringing new therapies to market. The shift towards AI-driven cancer care could lead to more effective and tailored treatment strategies, improving patient outcomes.

Research Limitations and Future Directions ​

While the integration of AI in oncology is promising, several limitations and challenges remain:

  • Data Quality and Availability: High-quality, diverse datasets are crucial for training robust AI models.
  • Interpretability: Many AI algorithms operate as "black boxes," making it difficult for clinicians to understand decision-making processes.
  • Regulatory and Ethical Considerations: There is a need for clear guidelines on the use of AI in clinical settings to ensure patient safety and ethical standards.

Future Directions:

  • Development of standardized protocols for AI implementation in clinical practice.
  • Enhancing transparency and interpretability of AI models.
  • Conducting large-scale clinical trials to validate AI-driven approaches in diverse patient populations.

In summary, the review highlights the transformative potential of AI in oncology while also addressing the need for careful consideration of its limitations and ethical implications as it becomes more integrated into clinical practice.

References ​

  1. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. - T A Chan;M Yarchoan;E Jaffee;C Swanton;S A Quezada;A Stenzinger;S Peters - Annals of oncology : official journal of the European Society for Medical Oncology (2019)
  2. Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study. - Rikiya Yamashita;Jin Long;Teri Longacre;Lan Peng;Gerald Berry;Brock Martin;John Higgins;Daniel L Rubin;Jeanne Shen - The Lancet. Oncology (2021)
  3. Identification of 12 cancer types through genome deep learning. - Yingshuai Sun;Sitao Zhu;Kailong Ma;Weiqing Liu;Yao Yue;Gang Hu;Huifang Lu;Wenbin Chen - Scientific reports (2019)
  4. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. - Nicolas Coudray;Paolo Santiago Ocampo;Theodore Sakellaropoulos;Navneet Narula;Matija Snuderl;David Fenyö;Andre L Moreira;Narges Razavian;Aristotelis Tsirigos - Nature medicine (2018)
  5. PREDICT: a method for inferring novel drug indications with application to personalized medicine. - Assaf Gottlieb;Gideon Y Stein;Eytan Ruppin;Roded Sharan - Molecular systems biology (2011)
  6. Atypical patterns of response and progression in the era of immunotherapy combinations. - Roberto Ferrara;Ignacio Matos - Future oncology (London, England) (2020)
  7. The Human Cell Atlas. - Aviv Regev;Sarah A Teichmann;Eric S Lander;Ido Amit;Christophe Benoist;Ewan Birney;Bernd Bodenmiller;Peter Campbell;Piero Carninci;Menna Clatworthy;Hans Clevers;Bart Deplancke;Ian Dunham;James Eberwine;Roland Eils;Wolfgang Enard;Andrew Farmer;Lars Fugger;Berthold Göttgens;Nir Hacohen;Muzlifah Haniffa;Martin Hemberg;Seung Kim;Paul Klenerman;Arnold Kriegstein;Ed Lein;Sten Linnarsson;Emma Lundberg;Joakim Lundeberg;Partha Majumder;John C Marioni;Miriam Merad;Musa Mhlanga;Martijn Nawijn;Mihai Netea;Garry Nolan;Dana Pe'er;Anthony Phillipakis;Chris P Ponting;Stephen Quake;Wolf Reik;Orit Rozenblatt-Rosen;Joshua Sanes;Rahul Satija;Ton N Schumacher;Alex Shalek;Ehud Shapiro;Padmanee Sharma;Jay W Shin;Oliver Stegle;Michael Stratton;Michael J T Stubbington;Fabian J Theis;Matthias Uhlen;Alexander van Oudenaarden;Allon Wagner;Fiona Watt;Jonathan Weissman;Barbara Wold;Ramnik Xavier;Nir Yosef; - eLife (2017)
  8. MSI-H: a truly agnostic biomarker? - Peter Sidaway - Nature reviews. Clinical oncology (2020)
  9. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. - Jordi Barretina;Giordano Caponigro;Nicolas Stransky;Kavitha Venkatesan;Adam A Margolin;Sungjoon Kim;Christopher J Wilson;Joseph Lehár;Gregory V Kryukov;Dmitriy Sonkin;Anupama Reddy;Manway Liu;Lauren Murray;Michael F Berger;John E Monahan;Paula Morais;Jodi Meltzer;Adam Korejwa;Judit Jané-Valbuena;Felipa A Mapa;Joseph Thibault;Eva Bric-Furlong;Pichai Raman;Aaron Shipway;Ingo H Engels;Jill Cheng;Guoying K Yu;Jianjun Yu;Peter Aspesi;Melanie de Silva;Kalpana Jagtap;Michael D Jones;Li Wang;Charles Hatton;Emanuele Palescandolo;Supriya Gupta;Scott Mahan;Carrie Sougnez;Robert C Onofrio;Ted Liefeld;Laura MacConaill;Wendy Winckler;Michael Reich;Nanxin Li;Jill P Mesirov;Stacey B Gabriel;Gad Getz;Kristin Ardlie;Vivien Chan;Vic E Myer;Barbara L Weber;Jeff Porter;Markus Warmuth;Peter Finan;Jennifer L Harris;Matthew Meyerson;Todd R Golub;Michael P Morrissey;William R Sellers;Robert Schlegel;Levi A Garraway - Nature (2012)
  10. Predicting drug response of tumors from integrated genomic profiles by deep neural networks. - Yu-Chiao Chiu;Hung-I Harry Chen;Tinghe Zhang;Songyao Zhang;Aparna Gorthi;Li-Ju Wang;Yufei Huang;Yidong Chen - BMC medical genomics (2019)

Literatures Citing This Work ​

  1. Preclinical models as patients' avatars for precision medicine in colorectal cancer: past and future challenges. - Erika Durinikova;Kristi Buzo;Sabrina Arena - Journal of experimental & clinical cancer research : CR (2021)
  2. Application of Machine Learning for Predicting Anastomotic Leakage in Patients with Gastric Adenocarcinoma Who Received Total or Proximal Gastrectomy. - Shengli Shao;Lu Liu;Yufeng Zhao;Lei Mu;Qiyi Lu;Jichao Qin - Journal of personalized medicine (2021)
  3. Harnessing multimodal data integration to advance precision oncology. - Kevin M Boehm;Pegah Khosravi;Rami Vanguri;Jianjiong Gao;Sohrab P Shah - Nature reviews. Cancer (2022)
  4. Can Systems Biology Advance Clinical Precision Oncology? - Andrea Rocca;Boris N Kholodenko - Cancers (2021)
  5. Understanding Drug Sensitivity and Tackling Resistance in Cancer. - Jeffrey W Tyner;Franziska Haderk;Anbarasu Kumaraswamy;Linda B Baughn;Brian Van Ness;Song Liu;Himangi Marathe;Joshi J Alumkal;Trever G Bivona;Keith Syson Chan;Brian J Druker;Alan D Hutson;Peter S Nelson;Charles L Sawyers;Christopher D Willey - Cancer research (2022)
  6. Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient's Stratification. - Octav Ginghina;Ariana Hudita;Marius Zamfir;Andrada Spanu;Mara Mardare;Irina Bondoc;Laura Buburuzan;Sergiu Emil Georgescu;Marieta Costache;Carolina Negrei;Cornelia Nitipir;Bianca Galateanu - Frontiers in oncology (2022)
  7. Artificial Intelligence and Machine Learning Technologies in Cancer Care: Addressing Disparities, Bias, and Data Diversity. - Irene Dankwa-Mullan;Dilhan Weeraratne - Cancer discovery (2022)
  8. Next-generation sequencing: unraveling genetic mechanisms that shape cancer immunotherapy efficacy. - Ahmed Halima;Winston Vuong;Timothy A Chan - The Journal of clinical investigation (2022)
  9. Targeting Cell Cycle Progression in HER2+ Breast Cancer: An Emerging Treatment Opportunity. - Nischal Koirala;Nandini Dey;Jennifer Aske;Pradip De - International journal of molecular sciences (2022)
  10. A convolutional neural network based tool for predicting protein AMPylation sites from binary profile representation. - Sayed Mehedi Azim;Alok Sharma;Iman Noshadi;Swakkhar Shatabda;Iman Dehzangi - Scientific reports (2022)

... (273 more literatures)

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