Publicaciones científicas

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

08-nov-2023 | Revista: The Lancet. Oncology

Qinghe Zeng  1 , Christophe Klein  2 , Stefano Caruso  3 , Pascale Maille  3 , Daniela S Allende  4 , Beatriz Mínguez  5 , Massimo Iavarone  6 , Massih Ningarhari  7 , Andrea Casadei-Gardini  8 , Federica Pedica  9 , Margherita Rimini  8 , Riccardo Perbellini  6 , Camille Boulagnon-Rombi  10 , Alexandra Heurgué  11 , Marco Maggioni  12 , Mohamed Rela  13 , Mukul Vij  14 , Sylvain Baulande  15 , Patricia Legoix  15 , Sonia Lameiras  15 ; HCC-AI study group; Léa Bruges  7 , Viviane Gnemmi  16 , Jean-Charles Nault  17 , Claudia Campani  18 , Hyungjin Rhee  19 , Young Nyun Park  20 , Mercedes Iñarrairaegui  21 , Guillermo Garcia-Porrero  22 , Josepmaria Argemi  23 , Bruno Sangro  23 , Antonio D'Alessio  24 , Bernhard Scheiner  25 , David James Pinato  26 , Matthias Pinter  25 , Valérie Paradis  27 , Aurélie Beaufrère  27 , Simon Peter  28 , Lorenza Rimassa  29 , Luca Di Tommaso  30 , Arndt Vogel  28 , Sophie Michalak  31 , Jérôme Boursier  32 , Nicolas Loménie  33 , Marianne Ziol  34 , Julien Calderaro  35

Background: 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.

CITA DEL ARTÍCULO  Lancet Oncol. 2023 Nov 8:S1470-2045(23)00468-0.  doi: 10.1016/S1470-2045(23)00468-0