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dc.creatorZeng, Q. (Qinghe)-
dc.creatorKlein, C. (Christophe)-
dc.creatorCaruso, S. (Stefano)-
dc.creatorMaille, P. (Pascale)-
dc.creatorAllende, D.S. (Daniela S.)-
dc.creatorMínguez, B. (Beatriz)-
dc.creatorIavarone, M. (Massimo)-
dc.creatorNingarhari, M. (Massih)-
dc.creatorCasadei-Gardini, A. (Andrea)-
dc.creatorPedica, F. (Federica)-
dc.creatorRimini, M. (Margherita)-
dc.creatorPerbellini, R. (Riccardo)-
dc.creatorBoulagnon-Rombi, C. (Camille)-
dc.creatorHeurgué, A. (Alexandra)-
dc.creatorMaggioni, M. (Marco)-
dc.creatorRela, M. (Mohamed)-
dc.creatorVij, M. (Mukul)-
dc.creatorBaulande, S. (Sylvain)-
dc.creatorLegoix, P. (Patricia)-
dc.creatorLameiras, S. (Sonia)-
dc.creatorBruges, L. (Léa)-
dc.creatorNault, J.C. (Jean-Charles)-
dc.creatorCampani, C. (Claudia)-
dc.creatorRhee, H. (Hyungjin)-
dc.creatorPark, Y.N. (Young Nyun)-
dc.creatorIñarrairaegui, M. (Mercedes)-
dc.creatorGarcia-Porrero, G. (Guillermo)-
dc.creatorArgemí, J. (Josepmaria)-
dc.creatorSangro, B. (Bruno)-
dc.creatorD’Alessio, A. (Antonio)-
dc.date.accessioned2024-02-09T13:10:35Z-
dc.date.available2024-02-09T13:10:35Z-
dc.date.issued2023-
dc.identifier.citationZeng, Q. (Qinghe); Klein, C. (Christophe); Caruso, S. (Stefano); et al. "Artificial intelligence-based pathology as a biomarker of sensitivity to atezolizumab–bevacizumab in patients with hepatocellular carcinoma: a multicentre retrospective study". The Lancet Oncology. 24, 2023, 1411 - 1422es
dc.identifier.issn1470-2045-
dc.identifier.urihttps://hdl.handle.net/10171/69002-
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. Additionally, 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 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.es_ES
dc.description.sponsorshipWe acknowledge the following institutions for supporting the study: Institut National du Cancer, Fondation ARC (TRANSCAN 2021 Joint Transnational Call for research proposals, project TANGERINE), Ligue Contre le Cancer du Val de Marne, Fondation de l’Avenir (project number AP-RM-20-011), Ipsen, and Fondation Bristol Myers Squibb Pour la Recherche en Immuno-Oncologie (project “Microenvironnement immunitaire du carcinome hépatocellulaire et intelligence artificielle”), and the China Scholarship Council (grant number 201908070052). The Institut Curie Genomics of Excellence Next Generation Sequencing Platform is supported by grants ANR-10-EQPX-03 (Equipex) and ANR-10-INBS-09-08 (France Génomique Consortium) from the Agence Nationale de la Recherche (Investissements d’Avenir programme), by the Aviesan Thematic Multi-Organization Institute for Cancer (Plan Cancer III) and by the Site de Recherche Intégrée sur le Cancer-Curie programme (SIRIC grants INCa-DGOS-465 and INCa-DGOS-Inserm_12554). DJP is supported by the Wellcome Trust Strategic Fund (grant number PS3416) and the Fondazione AIRC per la Ricerca sul Cancro (grant number AIRC MFAG 25697); and acknowledges grant support from the Cancer Treatment and Research Trust and the Foundation for Liver Research, and infrastructural support from the Imperial Experimental Cancer Medicine Centre and the National Institute for Health and Care Research Imperial Biomedical Research Centre. LDT is supported by the Associazione Italiana per la Ricerca sul Cancro (Individual Grant 2020; project reference 25087).es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relationANR-10-INBS-09-08es_ES
dc.rightsinfo:eu-repo/semantics/closedAccesses_ES
dc.subjectMaterias Investigacion::Ciencias de la Salud::Hepatologíaes_ES
dc.subjectHepatocellular carcinomaes_ES
dc.subjectArtificial intelligence-based pathologyes_ES
dc.subjectAtezolizumab–bevacizumabes_ES
dc.titleArtificial intelligence-based pathology as a biomarker of sensitivity to atezolizumab–bevacizumab in patients with hepatocellular carcinoma: a multicentre retrospective studyes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dadun.citation.endingPage1422es_ES
dadun.citation.publicationNameThe Lancet Oncologyes_ES
dadun.citation.startingPage1411es_ES
dadun.citation.volume24es_ES

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