Synthetic viability induces resistance to immune checkpoint inhibitors in cancer cells
Menée à l'aide notamment de données multi-omiques portant sur 859 lignées cellulaires, de données génomiques du projet "The Cancer Genome Atlas" portant sur 32 types de cancer, de donnés cliniques portant sur 121 patients atteints d'un mélanome et d'une technique de criblage génétique utilisant la technologie d'édition CRISPR/Cas9, cette étude identifie une signature, basée sur six paires de gènes impliqués dans la viabilité synthétique, pour prédire la résistance des cellules cancéreuses aux inhibiteurs de point de contrôle immunitaire
Background : Immune checkpoint inhibitors (ICI) have revolutionized the treatment for multiple cancers. However, most of patients encounter resistance. Synthetic viability (SV) between genes could induce resistance. In this study, we established SV signature to predict the efficacy of ICI treatment for melanoma.
Methods : We collected features and predicted SV gene pairs by random forest classifier. This work prioritized SV gene pairs based on CRISPR/Cas9 screens. SV gene pairs signature were constructed to predict the response to ICI for melanoma patients.
Results : This study predicted robust SV gene pairs based on 14 features. Filtered by CRISPR/Cas9 screens, we identified 1,861 SV gene pairs, which were also related with prognosis across multiple cancer types. Next, we constructed the six SV pairs signature to predict resistance to ICI for melanoma patients. This study applied the six SV pairs signature to divide melanoma patients into high-risk and low-risk. High-risk melanoma patients were associated with worse response after ICI treatment. Immune landscape analysis revealed that high-risk melanoma patients had lower natural killer cells and CD8+ T cells infiltration.
Conclusions : In summary, the 14 features classifier accurately predicted robust SV gene pairs for cancer. The six SV pairs signature could predict resistance to ICI.
British Journal of Cancer , article en libre accès, 2023