Moretto R, Rossini D, Catteau A, Antoniotti C, Giordano M, Boccaccino A, Ugolini C, Proietti A, Conca V, Kassambara A, Pietrantonio F, Salvatore L, Lonardi S, Tamberi S, Tamburini E, Poma AM, Fieschi J, Fontanini G, Masi G, Galon J, Cremolini C. Dissecting tumor lymphocyte infiltration to predict benefit from immune-checkpoint inhibitors in metastatic colorectal cancer: lessons from the AtezoTRIBE study. Journal for Immunotherapy of Cancer. 2023. Download the PDF
Summary of the Study
Identifying predictive biomarkers for immune checkpoint inhibitor (ICI) efficacy in metastatic colorectal cancer (mCRC) remains a major clinical challenge, particularly for pMMR/MSS tumors.
This study used digital pathology tools—Immunoscore, Immunoscore-IC, tumor-infiltrating lymphocytes (TILs), and PD-L1 expression—to assess immune infiltration in tumors from patients enrolled in the AtezoTRIBE phase II trial, comparing:
FOLFOXIRI + bevacizumab + atezolizumab (anti-PD-L1)
versus FOLFOXIRI + bevacizumab alone
Out of 218 patients, most samples were analyzable for immune markers.
High Immunoscore or Immunoscore-IC were significantly associated with longer progression-free survival (PFS) in pMMR mCRC:
- Immunoscore-high: 16.4 vs 12.2 months (HR = 0.55; p = 0.049)
- Immunoscore-IC-high: 14.8 vs 11.5 months (HR = 0.55; p = 0.007)
A significant interaction was found between Immunoscore-IC status and treatment arm (p = 0.006), suggesting predictive value.
No predictive benefit was seen from TILs or PD-L1 expression alone.
In this study, the statistical analysis and modeling supporting the prognostic value of Immunoscore and Immunoscore-IC in the AtezoTRIBE cohort were performed by Alboukadel Kassambara, contributing to biomarker validation.
Citation
Publication: In Journal for Immunotherapy of Cancer
Date: April 1, 2023
Type: Journal Article
PDF: Download the PDF
Scientific Contributions
Here are more scientific abstracts authored or co-authored by Alboukadel Kassambara. These contributions span computational biology, bioinformatics, biostatistics, machine learning, and multi-omics, with a focus on immuno-oncology and translational research.
