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Artificial intelligence-based pathology as a biomarker of sensitivity to atezolizumab-bevacizumab in patients with hepatocellular carcinoma: a multicentre retrospective study

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dc.contributor.author박영년-
dc.contributor.author이형진-
dc.date.accessioned2024-02-15T06:47:45Z-
dc.date.available2024-02-15T06:47:45Z-
dc.date.issued2023-12-
dc.identifier.issn1470-2045-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/198041-
dc.description.abstractBACKGROUND: Clinical benefits of atezolizumab plus bevacizumab (atezolizumab-bevacizumab) are observed only in a subset of patients with hepatocellular carcinoma and the development of biomarkers is needed to improve therapeutic strategies. The atezolizumab-bevacizumab response signature (ABRS), assessed by molecular biology profiling techniques, has been shown to be associated with progression-free survival after treatment initiation. The primary objective of our study was to develop an artificial intelligence (AI) model able to estimate ABRS expression directly from histological slides, and to evaluate if model predictions were associated with progression-free survival. METHODS: In this multicentre retrospective study, we developed a model (ABRS-prediction; ABRS-P), which was derived from the previously published clustering-constrained attention multiple instance learning (or CLAM) pipeline. We trained the model fit for regression analysis using a multicentre dataset from The Cancer Genome Atlas (patients treated by surgical resection, n=336). The ABRS-P model was externally validated on two independent series of samples from patients with hepatocellular carcinoma (a surgical resection series, n=225; and a biopsy series, n=157). The predictive value of the model was further tested in a series of biopsy samples from a multicentre cohort of patients with hepatocellular carcinoma treated with atezolizumab-bevacizumab (n=122). All samples in the study were from adults (aged ≥18 years). The validation sets were sampled between Jan 1, 2008, to Jan 1, 2023. For the multicentre validation set, the primary objective was to assess the association of high versus low ABRS-P values, defined relative to cross-validation median split thresholds in the first biopsy series, with progression-free survival after treatment initiation. Finally, we performed spatial transcriptomics and matched prediction heatmaps with in situ expression profiles. FINDINGS: Of the 840 patients sampled, 641 (76%) were male and 199 (24%) were female. Across the development and validation datasets, hepatocellular carcinoma risk factors included alcohol intake, hepatitis B and C virus infections, and non-alcoholic steatohepatitis. Using cross-validation in the development series, the mean Pearson's correlation between ABRS-P values and ABRS score (mean expression of ABRS genes) was r=0·62 (SD 0·09; mean p<0·0001, SD<0·0001). The ABRS-P generalised well on the external validation series (surgical resection series, r=0·60 [95% CI 0·51-0·68], p<0·0001; biopsy series, r=0·53 [0·40-0·63], p<0·0001). In the 122 patients treated with atezolizumab-bevacizumab, those with ABRS-P-high tumours (n=74) showed significantly longer median progression-free survival than those with ABRS-P-low tumours (n=48) after treatment initiation (12 months [95% CI 7-not reached] vs 7 months [4-9]; p=0·014). Spatial transcriptomics showed significantly higher ABRS score, along with upregulation of various other immune effectors, in tumour areas with high ABRS-P values versus areas with low ABRS-P values. INTERPRETATION: Our study indicates that AI applied on hepatocellular carcinoma digital slides is able to serve as a biomarker for progression-free survival in patients treated with atezolizumab-bevacizumab. This approach could be used in the development of inexpensive and fast biomarkers for targeted therapies. The combination of AI heatmaps with spatial transcriptomics provides insight on the molecular features associated with predictions. This methodology could be applied to other cancers or diseases and improve understanding of the biological mechanisms that drive responses to treatments. FUNDING: Institut National du Cancer, Fondation ARC, China Scholarship Council, Ligue Contre le Cancer du Val de Marne, Fondation de l'Avenir, Ipsen, and Fondation Bristol Myers Squibb Pour la Recherche en Immuno-Oncologie. Copyright © 2023 Elsevier Ltd. All rights reserved.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherLancet Pub. Group-
dc.relation.isPartOfLANCET ONCOLOGY-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleArtificial intelligence-based pathology as a biomarker of sensitivity to atezolizumab-bevacizumab in patients with hepatocellular carcinoma: a multicentre retrospective study-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Pathology (병리학교실)-
dc.contributor.googleauthorQinghe Zeng-
dc.contributor.googleauthorChristophe Klein-
dc.contributor.googleauthorStefano Caruso-
dc.contributor.googleauthorPascale Maille-
dc.contributor.googleauthorDaniela S Allende-
dc.contributor.googleauthorBeatriz Mínguez-
dc.contributor.googleauthorMassimo Iavarone-
dc.contributor.googleauthorMassih Ningarhari-
dc.contributor.googleauthorAndrea Casadei-Gardini-
dc.contributor.googleauthorFederica Pedica-
dc.contributor.googleauthorMargherita Rimini-
dc.contributor.googleauthorRiccardo Perbellini-
dc.contributor.googleauthorCamille Boulagnon-Rombi-
dc.contributor.googleauthorAlexandra Heurgué-
dc.contributor.googleauthorMarco Maggioni-
dc.contributor.googleauthorMohamed Rela-
dc.contributor.googleauthorMukul Vij-
dc.contributor.googleauthorSylvain Baulande-
dc.contributor.googleauthorPatricia Legoix-
dc.contributor.googleauthorSonia Lameiras-
dc.contributor.googleauthorHCC-AI study group-
dc.contributor.googleauthorLéa Bruges-
dc.contributor.googleauthorViviane Gnemmi-
dc.contributor.googleauthorJean-Charles Nault-
dc.contributor.googleauthorClaudia Campani-
dc.contributor.googleauthorHyungjin Rhee-
dc.contributor.googleauthorYoung Nyun Park-
dc.contributor.googleauthorMercedes Iñarrairaegui-
dc.contributor.googleauthorGuillermo Garcia-Porrero-
dc.contributor.googleauthorJosepmaria Argemi-
dc.contributor.googleauthorBruno Sangro-
dc.contributor.googleauthorAntonio D'Alessio-
dc.contributor.googleauthorBernhard Scheiner-
dc.contributor.googleauthorDavid James Pinato-
dc.contributor.googleauthorMatthias Pinter-
dc.contributor.googleauthorValérie Paradis-
dc.contributor.googleauthorAurélie Beaufrère-
dc.contributor.googleauthorSimon Peter-
dc.contributor.googleauthorLorenza Rimassa-
dc.contributor.googleauthorLuca Di Tommaso-
dc.contributor.googleauthorArndt Vogel-
dc.contributor.googleauthorSophie Michalak-
dc.contributor.googleauthorJérôme Boursier-
dc.contributor.googleauthorNicolas Loménie-
dc.contributor.googleauthorMarianne Ziol-
dc.contributor.googleauthorJulien Calderaro-
dc.identifier.doi10.1016/S1470-2045(23)00468-0-
dc.contributor.localIdA01563-
dc.contributor.localIdA05171-
dc.relation.journalcodeJ02154-
dc.identifier.eissn1474-5488-
dc.identifier.pmid37951222-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S1470204523004680-
dc.contributor.alternativeNamePark, Young Nyun-
dc.contributor.affiliatedAuthor박영년-
dc.contributor.affiliatedAuthor이형진-
dc.citation.volume24-
dc.citation.number12-
dc.citation.startPage1411-
dc.citation.endPage1422-
dc.identifier.bibliographicCitationLANCET ONCOLOGY, Vol.24(12) : 1411-1422, 2023-12-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers

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