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Prelaj A, Miskovic V, Zanitti M, Trovo F, Genova C, Viscardi G, Rebuzzi SE, Mazzeo L, Provenzano L, Kosta S, Favali M, Spagnoletti A, Castelo-Branco L, Dolezal J, Pearson AT, Lo Russo G, Proto C, Ganzinelli M, Giani C, Ambrosini E, Turajlic S, Au L, Koopman M, Delaloge S, Kather JN, de Braud F, Garassino MC, Pentheroudakis G, Spencer C, Pedrocchi ALG. Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review. Ann Oncol 2024; 35:29-65. [PMID: 37879443 DOI: 10.1016/j.annonc.2023.10.125] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/31/2023] [Accepted: 10/08/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. MATERIALS AND METHODS We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. RESULTS A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. CONCLUSION AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.
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Affiliation(s)
- A Prelaj
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy; ESMO Real World Data and Digital Health Working Group, ESMO, Lugano, Switzerland.
| | - V Miskovic
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - M Zanitti
- Department of Electronic Systems, Aalborg University Copenhagen, Denmark
| | - F Trovo
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - C Genova
- UO Clinica di Oncologia Medica, IRCCS Ospedale Policlinico San Martino, Genoa; Department of Internal Medicine and Medical Specialties (Di.M.I.), University of Genoa, Genoa
| | - G Viscardi
- Precision Medicine Department, Università degli Studi della Campania Luigi Vanvitelli, Naples
| | - S E Rebuzzi
- Department of Internal Medicine and Medical Specialties (Di.M.I.), University of Genoa, Genoa; Medical Oncology Unit, Ospedale San Paolo, Savona, Italy
| | - L Mazzeo
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - L Provenzano
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - S Kosta
- Department of Electronic Systems, Aalborg University Copenhagen, Denmark
| | - M Favali
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - A Spagnoletti
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - L Castelo-Branco
- ESMO European Society for Medical Oncology, Lugano, Switzerland; NOVA National School of Public Health, Lisboa, Portugal
| | - J Dolezal
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, USA
| | - A T Pearson
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, USA
| | - G Lo Russo
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - C Proto
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - M Ganzinelli
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - C Giani
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - E Ambrosini
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - S Turajlic
- Cancer Dynamics Laboratory, The Francis Crick Institute, London
| | - L Au
- Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London, UK; Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne; Sir Peter MacCallum Department of Medical Oncology, The University of Melbourne, Melbourne, Australia
| | - M Koopman
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands; ESMO Real World Data and Digital Health Working Group, ESMO, Lugano, Switzerland
| | - S Delaloge
- Department of Cancer Medicine, Gustave Roussy, Villejuif, France; ESMO Real World Data and Digital Health Working Group, ESMO, Lugano, Switzerland
| | - J N Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - F de Braud
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - M C Garassino
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, USA
| | | | - C Spencer
- Cancer Dynamics Laboratory, The Francis Crick Institute, London.
| | - A L G Pedrocchi
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
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Peng J, Zou D, Han L, Yin Z, Hu X. A Support Vector Machine Based on Liquid Immune Profiling Predicts Major Pathological Response to Chemotherapy Plus Anti-PD-1/PD-L1 as a Neoadjuvant Treatment for Patients With Resectable Non-Small Cell Lung Cancer. Front Immunol 2021; 12:778276. [PMID: 35095850 PMCID: PMC8797141 DOI: 10.3389/fimmu.2021.778276] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 11/30/2021] [Indexed: 01/08/2023] Open
Abstract
The biomarkers for the pathological response of neoadjuvant chemotherapy plus anti-programmed cell death protein-1/programmed cell death-ligand 1 (PD-1/PD-L1) (CAPD) are unclear in non-small cell lung cancer (NSCLC). Two hundred and eleven patients with stage Ib-IIIa NSCLC undergoing CAPD prior to surgical resection were enrolled, and 11 immune cell subsets in peripheral blood were prospectively analyzed using multicolor flow cytometry. Immune cell subtypes were selected by recursive feature elimination and least absolute shrinkage and selection operator methods. The support vector machine (SVM) was used to build a model. Multivariate analysis for major pathological response (MPR) was also performed. Finally, five immune cell subtypes were identified and an SVM based on liquid immune profiling (LIP-SVM) was developed. The LIP-SVM model achieved high accuracies in discovery and validation sets (AUC = 0.886, 95% CI: 0.823–0.949, P < 0.001; AUC = 0.874, 95% CI: 0.791–0.958, P < 0.001, respectively). Multivariate analysis revealed that age, radiological response, and LIP-SVM were independent factors for MPR in the two sets (each P < 0.05). The integration of LIP-SVM, clinical factors, and radiological response showed significantly high accuracies for predicting MPR in discovery and validation sets (AUC = 0.951, 95% CI: 0.916–0.986, P < 0.001; AUC = 0.943, 95% CI: 0.912–0.993, P < 0.001, respectively). Based on immune cell profiling of peripheral blood, our study developed a predictive model for the MPR of patients with NSCLC undergoing CAPD treatment that can potentially guide clinical therapy.
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Affiliation(s)
- Jie Peng
- Department of Oncology, The Second Affiliated Hospital, Guizhou Medical University, Kaili, China
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences, Hanzhou, China
- *Correspondence: Jie Peng,
| | - Dan Zou
- Department of Oncology, The Second Affiliated Hospital, Guizhou Medical University, Kaili, China
| | - Lijie Han
- Department of Hematology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zuomin Yin
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences, Hanzhou, China
| | - Xiao Hu
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences, Hanzhou, China
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