Artificial Intelligence for Predictive Biomarker Discovery in Immuno-Oncology: A Systematic Review

Prelaj, A.; Miskovic, V.; Zanitti, M.; Trovo, F.; Genova, C.; Viscardi, G.; Rebuzzi, S.E.; Mazzeo, L.; Provenzano, L.; Kosta, S.; Favali, M.; Spagnoletti, A.; Castelo-Branco, L.; Dolezal, J.; Pearson, A.T.; Lo Russo, G.; Proto, C.; Ganzinelli, M.; Giani, C.; Ambrosini, E.; Turajlic, S.; Au, L.; Koopman, M.; Delaloge, S.; Kather, J.N.; de Braud, F.; Garassino, M.C.; Pentheroudakis, G.; Spencer, C.; Pedrocchi, A.L.G.

doi:10.1016/j.annonc.2023.10.125

Cited by 342Cancer Immunotherapy and BiomarkersRadiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and Education

Artificial Intelligence for Predictive Biomarker Discovery in Immuno-Oncology: A Systematic Review

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Prelaj, A., Miskovic, V., Zanitti, M., Trovo, F., Genova, C., Viscardi, G., Rebuzzi, S.E., Mazzeo, L. +22 more·2024·Annals of Oncology

DOI 10.1016/j.annonc.2023.10.125

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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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Multimodal deep learning integrates histopathology, radiology, genomics, and clinical records to create unified patient-level representations.
“The most active line of work is multimodal deep learning (MDL), which integrates histopathology (“pathomics”), radiology (“radiomics”), genomics, and clinical records to create unified patient-level representations [70,71,72] (Figure 4).”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [71]

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Authors: Prelaj, A., Miskovic, V., Zanitti, M., Trovo, F., Genova, C., Viscardi, G., Rebuzzi, S. E., Mazzeo, L., Provenzano, L., Kosta, S., Favali, M., Spagnoletti, A., Castelo-Branco, L., Dolezal, J., Pearson, A. T., Lo Russo, G., Proto, C., Ganzinelli, M., Giani, C., Ambrosini, E., Turajlic, S., Au, L., Koopman, M., Delaloge, S., Kather, J. N., de Braud, F., Garassino, M. C., Pentheroudakis, G., Spencer, C., & Pedrocchi, A. L. G.
Affiliations: European Society for Medical Oncology (CH)
Fondazione Politecnico di Milano (IT)
Aalborg University (DK)
Ospedale Policlinico San Martino (IT)
University of Campania "Luigi Vanvitelli" (IT)
Ospedale San Paolo (IT)
Fondazione IRCCS Istituto Nazionale dei Tumori (IT)
Politecnico di Milano (IT)
University of Chicago (US)
The Francis Crick Institute (GB)
Royal Marsden NHS Foundation Trust (GB)
Else Kröner Fresenius Center for Digital Health (DE)
Corresponding author: Prelaj, A.
ORCID: Prelaj, A.
Miskovic, V.
Genova, C.
Viscardi, G.
Rebuzzi, S.E.
Mazzeo, L.
Provenzano, L.
Kosta, S.
Spagnoletti, A.
Dolezal, J.
Pearson, A.T.
Proto, C.
Ganzinelli, M.
Ambrosini, E.
Turajlic, S.
Au, L.
Koopman, M.
Delaloge, S.
Kather, J.N.
Garassino, M.C.
Pentheroudakis, G.
Pedrocchi, A.L.G.
Venue: Annals of Oncology
Publisher: Elsevier BV
Published: 2024-01
Volume: 35
Issue: 1
Pages: 29-65
ISSN: 0923-7534 (ISSN-L)
DOI: 10.1016/j.annonc.2023.10.125
OpenAlex: W4387881611
Cited by: 340
References: 150
Citations by year: 2026: 131 · 2025: 149 · 2024: 60
FWCI: 11.08
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 366 d)
Locations: Annals of Oncology (publishedVersion)
PubMed (publishedVersion)
VBN Forskningsportal (Aalborg Universitet) (acceptedVersion, other-oa)
Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion, other-oa)
CINECA IRIS Institutial Research Information System (University of Genoa) (submittedVersion)
VBN Forskningsportal (Aalborg Universitet) (submittedVersion)
Funders: Gilead Sciences
AbbVie
Cancer Research Foundation
Topics: Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords: artificial intelligence, predictive biomarkers, immune checkpoint inhibitors, cancer immunotherapy, immunotherapy response prediction, radiomics, digital pathology, pathomics, genomics, transcriptomics, epigenomics, multimodal data integration, non-small cell lung cancer, melanoma, precision oncology
MeSH terms: Artificial Intelligence, Carcinoma, Non-Small-Cell Lung, Humans, Lung Neoplasms, Medical Oncology, Prospective Studies

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Artificial Intelligence for Predictive Biomarker Discovery in Immuno-Oncology: A Systematic Review

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