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

Qinghe Zeng, Christophe Klein, Stefano Caruso, Pascale Maille, Daniela S Allende, Beatriz Mínguez, Massimo Iavarone, Massih Ningarhari, Andrea Casadei-Gardini, Federica Pedica, Margherita Rimini, Riccardo Perbellini, Camille Boulagnon-Rombi, Alexandra Heurgué, Marco Maggioni, Mohamed Rela, Mukul Vij, Sylvain Baulande, Patricia Legoix, Sonia Lameiras, the HCC-AI study group*, Léa Bruges, Viviane Gnemmi, Jean-Charles Nault, Claudia Campani, Hyungjin Rhee, Young Nyun Park, Mercedes Iñarrairaegui, Guillermo Garcia-Porrero, Josepmaria Argemi, Bruno Sangro, Antonio D’Alessio, Bernhard Scheiner, David James Pinato, Matthias Pinter, Valérie Paradis, Aurélie Beaufrère, Simon Peter, Lorenza Rimassa, Luca Di Tommaso, Arndt Vogel, Sophie Michalak, Jérôme Boursier, Nicolas Loménie, Marianne Ziol, Julien Calderaro

Abstract: 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. Abstract: 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- 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. Read More

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides

Omar S. M. El Nahhas, Chiara M. L. Loeffler, Zunamys I. Carrero, Marko van Treeck, Fiona R. Kolbinger, Katherine J. Hewitt, Hannah S. Muti, Mara Graziani, Qinghe Zeng, Julien Calderaro, Nadina Ortiz-Brüchle, Tanwei Yuan, Michael Hoffmeister, Hermann Brenner, Alexander Brobeil, Jorge S. Reis-Filho, Jakob Nikolas Kather

Abstract: Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. Abstract: Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesized that regression-based DL outperforms classification-based DL. Therefore, we developed and evaluated a new self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from images in 11,671 patients across nine cancer types. We tested our method for multiple clinically and biologically relevant biomarkers: homologous repair deficiency (HRD) score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the interpretability of the results over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology. Read More

Selective internal radiation therapy for unresectable HCC: The SIRT downstaging study

Hélène Regnault, Julia Chalaye, Athena Galetto-Pregliasco, Clara Perrin, Haytham Derbel, Giuliana Amaddeo, Sébastien Mulé, Marie Lequoy, Hicham Kobeiter, Edouard Reizine, Emmanuel Itti, Christophe Duvoux, Alexis Laurent, Vincent Leroy, Daniele Sommacale, Diana Rasolonirina, Alain Luciani, Julien Calderaro, Vania Tacher, Raffaele Brustia

Abstract: Background Selective internal radiation therapy (SIRT) is recommended as a downstaging (DS) strategy for solitary unresectable HCC <8 cm. Abstract: Background Selective internal radiation therapy (SIRT) is recommended as a downstaging (DS) strategy for solitary unresectable HCC <8 cm. The aim of this study was to report the results of acquired experience in a tertiary center for all unresectable HCCs. Methods We conducted a retrospective, observational study using data collected from consecutive patients undergoing SIRT between October 2013 and June 2020. DS was considered achieved when a curative treatment could be proposed 6 months after SIRT. Results One hundred twenty-seven patients were included (male = 90%, 64 ± 11 y), of whom 112 (n = 88%) had cirrhosis. HCC was classified as BCLC stage C in 64 patients (50%), with a median diameter of 61 mm, an infiltrative pattern in 51 patients (40%), and portal vein invasion in 62 (49%) patients. Fifty patients (39%) achieved DS 6 months following SIRT, with 29 of them (23%) undergoing curative treatment in a median time of 4.3 months: 17 (13%) were transplanted, 11 (85%) had liver resection, and 1 patient had a radiofrequency ablation. The median overall survival of patients with or without DS was 51 versus 10 months, respectively ( p < 0.001). In patients who achieved DS, progression-free survival was higher in patients who underwent surgery: 47 versus 11 months ( p < 0.001). Four variables were independently associated with DS: age (OR: 0.96, 95% CI: [0.92, 0.99]; p = 0.032), baseline α-fetoprotein (OR: 1.00, 95% CI: [1.00, 1.00]; p = 0.034), HCC distribution (OR: 0.3, 95% CI: [0.11, 0.75]; p = 0.012), and ALBI grade (OR: 0.34. 95% CI: [0.14, 0.80]; p = 0.014). Conclusions These results suggest that SIRT in patients with unresectable HCC could be an effective treatment: DS was achieved for around 39% of the patients and more than half of these then underwent curative treatment. Read More

Should Hypervascular Incidentalomas Detected on Per-Interventional Cone Beam Computed Tomography during Intra-Arterial Therapies for Hepatocellular Carcinoma Impact the Treatment Plan in Patients Waiting for Liver Transplantation?

Haytham Derbel, Athena Galletto Pregliasco, Sébastien Mulé, Julien Calderaro, Alexis Laurent, Laetitia Saccenti, Mario Ghosn, Edouard Reizine, Maxime Blain, Vania Tacher, Raffaele Brustia, Vincent Leroy, Giuliana Amaddeo, Alain Luciani, Hicham Kobeiter

Abstract: Simple Summary Discovering hypervascular incidentalomas (HVIs) during intra-arterial therapies (IATs) for hepatocellular carcinoma (HCC) is a common condition, Abstract: Simple Summary Discovering hypervascular incidentalomas (HVIs) during intra-arterial therapies (IATs) for hepatocellular carcinoma (HCC) is a common condition, but guidelines lack precise management suggestions. This study examines whether to include HVIs in IAT for HCC patients awaiting liver transplantation. A retrospective study analyzed liver-transplanted HCC patients who received TACE or TARE before LT from 2014 to 2018. The study compared HCC detection rates between pre-interventional imaging and per-interventional CBCT and investigated correlations between HVIs and poor prognosis criteria. Results showed higher nodule detection with CBCT and no significant correlations between HVIs and poor prognosis criteria, tumor recurrence, or mortality. Kaplan–Meier analysis found no significant impact of HVIs on recurrence-free, recurrence-related, or overall survival. These data may indicate that the treatment plan during IAT should not be impacted or modified in response to HVI detection in patients awaiting LT. Abstract Background: Current guidelines do not indicate any comprehensive management of hepatic hypervascular incidentalomas (HVIs) discovered in hepatocellular carcinoma (HCC) patients during intra-arterial therapies (IATs). This study aims to evaluate the prognostic value of HVIs detected on per-interventional cone beam computed tomography (CBCT) during IAT for HCC in patients waiting for liver transplantation (LT). Material and methods: In this retrospective single-institutional study, all liver-transplanted HCC patients between January 2014 and December 2018 who received transarterial chemoembolization (TACE) or radioembolization (TARE) before LT were included. The number of ≥10 mm HCCs diagnosed on contrast-enhanced pre-interventional imaging (PII) was compared with that detected on per-interventional CBCT with a nonparametric Wilcoxon test. The correlation between the presence of an HVI and histopathological criteria associated with poor prognosis (HPP) on liver explants was investigated using the chi-square test. Tumor recurrence (TR) and TR-related mortality were investigated using the chi-square test. Recurrence-free survival (RFS), TR-related survival (TRRS), and overall survival (OS) were assessed according to the presence of HVI using Kaplan–Meier analysis. Results: Among 63 included patients (average age: 59 ± 7 years, H/F = 50/13), 36 presented HVIs on per-interventional CBCT. The overall nodule detection rate of per-interventional CBCT was superior to that of PII (median at 3 [Q1:2, Q3:5] vs. 2 [Q1:1, Q3:3], respectively, p < 0.001). No significant correlation was shown between the presence of HVI and HPP (p = 0.34), TR (p = 0.095), and TR-related mortality (0.22). Kaplan–Meier analysis did not show a significant impact of the presence of HVI on RFS (p = 0.07), TRRS (0.48), or OS (p = 0.14). Conclusions: These results may indicate that the treatment plan during IAT should not be impacted or modified in response to HVI detection. Read More