Dataset Information

Neuropilin-1 cooperates with PD-1 in CD8⁺ T cells predicting outcomes in melanoma patients treated with anti-PD1.

ABSTRACT: Targeting immune checkpoints, such as Programmed cell Death 1 (PD1), has improved survival in cancer patients by restoring antitumor immune responses. Most patients, however, relapse or are refractory to immune checkpoint blocking therapies. Neuropilin-1 (NRP1) is a transmembrane glycoprotein required for nervous system and angiogenesis embryonic development, also expressed in immune cells. We hypothesized that NRP1 could be an immune checkpoint co-receptor modulating CD8⁺ T cells activity in the context of the antitumor immune response. Here, we show that NRP1 is recruited in the cytolytic synapse of PD1⁺CD8⁺ T cells, cooperates and enhances PD-1 activity. In mice, CD8⁺ T cells specific deletion of Nrp1 improves anti-PD1 antibody antitumor immune responses. Likewise, in human metastatic melanoma, the expression of NRP1 in tumor infiltrating CD8⁺ T cells predicts poor outcome of patients treated with anti-PD1. NRP1 is a promising target to overcome resistance to anti-PD1 therapies.

SUBMITTER: Rossignol J

PROVIDER: S-EPMC9301874 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Neuropilin-1 cooperates with PD-1 in CD8<sup>+</sup> T cells predicting outcomes in melanoma patients treated with anti-PD1.

Rossignol Julien J Belaid Zakia Z Fouquet Guillemette G Guillem Flavia F Rignault Rachel R Milpied Pierre P Renand Amédée A Coman Tereza T D'Aveni Maud M Dussiot Michael M Colin Elia E Levy Jonathan J Carvalho Caroline C Goudin Nicolas N Cagnard Nicolas N Côté Francine F Babdor Joel J Bhukhai Kanit K Polivka Laura L Bigorgne Amélie E AE Halse Héloise H Marabelle Aurélien A Mouraud Séverine S Lepelletier Yves Y Maciel Thiago T TT Rubio Marie-Thérèse MT Heron Delphine D Robert Caroline C Girault Isabelle I Lebeherec Doris D Scoazec Jean-Yves JY Moura Ivan I Condon Louise L Weimershaus Mirjana M Pages Franck F Davoust Jean J Gross David D Hermine Olivier O

iScience 20220505 6

Targeting immune checkpoints, such as Programmed cell Death 1 (PD1), has improved survival in cancer patients by restoring antitumor immune responses. Most patients, however, relapse or are refractory to immune checkpoint blocking therapies. Neuropilin-1 (NRP1) is a transmembrane glycoprotein required for nervous system and angiogenesis embryonic development, also expressed in immune cells. We hypothesized that NRP1 could be an immune checkpoint co-receptor modulating CD8<sup>+</sup> T cells act ...[more]

PMID: 35874918

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Project description:BackgroundImmune checkpoint inhibitors (ICIs) have demonstrated significantly improved clinical efficacy in a minority of patients with advanced melanoma, whereas non-responders potentially suffer from severe side effects and delays in other treatment options. Predicting the response to anti-PD1 treatment in melanoma remains a challenge because the current FDA-approved gold standard, the nonsynonymous tumor mutation burden (nsTMB), offers limited accuracy.MethodsIn this study, we developed a multi-omics-based machine learning model that integrates genomic and transcriptomic biomarkers to predict the response to anti-PD1 treatment in patients with advanced melanoma. We employed least absolute shrinkage and selection operator (LASSO) regression with 49 biomarkers extracted from tumor-normal whole-exome and RNA sequencing as input features. The performance of the multi-omics AI model was thoroughly compared to that of nsTMB alone and to models that use only genomic or transcriptomic biomarkers.ResultsWe used publicly available DNA and RNA-seq datasets of melanoma patients for the training and validation of our model, forming a meta-cohort of 449 patients for which the outcome was recorded as a RECIST score. The model substantially improved the prediction of anti-PD1 outcomes compared to nsTMB alone, with an ROC AUC of 0.7 in the training set and an ROC AUC of 0.64 in the test set. Using SHAP values, we demonstrated the explainability of the model's predictions on a per-sample basis.ConclusionsWe demonstrated that models using only RNA-seq or multi-omics biomarkers outperformed nsTMB in predicting the response of melanoma patients to ICI. Furthermore, our machine learning approach improves clinical usability by providing explanations of its predictions on a per-patient basis. Our findings underscore the utility of multi-omics data for selecting patients for treatment with anti-PD1 drugs. However, to train clinical-grade AI models for routine applications, prospective studies collecting larger melanoma cohorts with consistent application of exome and RNA sequencing are required.

Project description:Background: Immune checkpoint blockers (ICBs) are revolutionized therapeutic strategies for cancer, but most patients with solid neoplasms remain resistant to ICBs, partly because of the difficulty in reversing the highly immunosuppressive tumor microenvironment (TME). Exploring the strategies for tumor immunotherapy is highly dependent on the discovery of molecular mechanisms of tumor immune escape and potential therapeutic target. Krüppel-like Factor 5 (KLF5) is a cell-intrinsic oncogene to promote tumorigenesis. However, the cell-extrinsic effects of KLF5 on suppressing the immune response to cancer remain unclear. Methods: We analyzed the immunosuppressive role of KLF5 in mice models transplanted with KLF5-deleted/overexpressing tumor cells. We performed RNA sequencing, immunohistochemistry, western blotting, real time-PCR, ELISA, luciferase assay, chromatin immunoprecipitation (ChIP), and flow cytometry to demonstrate the effects of KLF5 on CD8+ T cell infiltration and related molecular mechanism. Single-cell RNA sequencing and spatial transcriptomics analysis were applied to further decipher the association between KLF5 expression and infiltrating immune cells. The efficacy of KLF5/COX2 inhibitors combined with anti-programmed cell death protein 1 (anti-PD1) therapy were explored in pre-clinical models. Finally, a gene-expression signature depending on KLF5/COX2 axis and associated immune markers was created to predict patient survival. Results: KLF5 inactivation decelerated basal-like breast tumor growth in a CD8+ T-cell-dependent manner. Transcriptomic profiling revealed that KLF5 loss in tumors increases the number and activated function of T lymphocytes. Mechanistically, KLF5 binds to the promoter of the COX2 gene and promotes COX2 transcription; subsequently, KLF5 deficiency decreases prostaglandin E2 (PGE2) release from tumor cells by reducing COX2 expression. Inhibition of the KLF5/COX2 axis increases the number and functionality of intratumoral antitumor T cells to synergize the antitumorigenic effects of anti-PD1 therapy. Analysis of patient datasets at single-cell and spatial resolution shows that low expression of KLF5 is associated with an immune-supportive TME. Finally, we generate a KLF5/COX2-associated immune score (KC-IS) to predict patient survival. Conclusions: Our results identified a novel mechanism responsible for KLF5-mediated immunosuppression in TME, and targeting the KLF5/COX2/PGE2 axis is a critical immunotherapy sensitizer.

Project description:BackgroundThe therapeutic potential of immune checkpoint blockade (ICB) extends across various cancers; however, its effectiveness in treating hepatocellular carcinoma (HCC) is frequently curtailed by both inherent and developed resistance.ObjectiveThis research explored the effectiveness of integrating anlotinib (a broad-spectrum tyrosine kinase inhibitor) with programmed death-1 (PD-1) blockade and offers mechanistic insights into more effective strategies for treating HCC.MethodsUsing patient-derived organotypic tissue spheroids and orthotopic HCC mouse models, we assessed the effectiveness of anlotinib combined with PD-1 blockade. The impact on the tumour immune microenvironment and underlying mechanisms were assessed using time-of-flight mass cytometry, RNA sequencing, and proteomics across cell lines, mouse models, and HCC patient samples.ResultsThe combination of anlotinib with an anti-PD-1 antibody enhanced the immune response against HCC in preclinical models. Anlotinib remarkably suppressed the expression of transferrin receptor (TFRC) via the VEGFR2/AKT/HIF-1α signaling axis. CD8+ T-cell infiltration into the tumour microenvironment correlated with low expression of TFRC. Anlotinib additionally increased the levels of the chemokine CXCL14, crucial for attracting CD8+ T cells. CXCL14 emerged as a downstream effector of TFRC, exhibiting elevated expression following the silencing of TFRC. Importantly, low TFRC expression was also associated with a better prognosis, enhanced sensitivity to combination therapy, and a favourable response to anti-PD-1 therapy in patients with HCC.ConclusionsOur findings highlight anlotinib's potential to augment the efficacy of anti-PD-1 immunotherapy in HCC by targeting TFRC and enhancing CXCL14-mediated CD8+ T-cell infiltration. This study contributes to developing novel therapeutic strategies for HCC, emphasizing the role of precision medicine in oncology.HighlightsSynergistic effects of anlotinib and anti-PD-1 immunotherapy demonstrated in HCC preclinical models. Anlotinib inhibits TFRC expression via the VEGFR2/AKT/HIF-1α pathway. CXCL14 upregulation via TFRC suppression boosts CD8+ T-cell recruitment. TFRC emerges as a potential biomarker for evaluating prognosis and predicting response to anti-PD-1-based therapies in advanced HCC patients.

Project description:The blockade of programmed cell death protein 1 (PD-1) as monotherapy has been widely used in melanoma, but to identify melanoma patients with survival benefit from anti-PD-1 monotherapy is still a big challenge. There is an urgent need for prognostic signatures improving the prediction of immunotherapy responses of these patients. We analyzed transcriptomic data of pre-treatment tumor biopsies and clinical profiles in advanced melanoma patients receiving only anti-PD-1 monotherapy (nivolumab or pembrolizumab) from the PRJNA356761 and PRJEB23709 data sets as the training and validation cohort, respectively. Weighted gene co-expression network analysis was used to identify the key module, then least absolute shrinkage and selection operator was conducted to determine prognostic-related long noncoding RNAs (lncRNAs). Subsequently, the differentially expressed genes between different clusters were identified, and their function and pathway annotation were performed. In this investigation, 92 melanoma patients with complete survival information (51 from training cohort and 41 from validation cohort) were included in our analyses. We initiallyidentified the key module (skyblue) by weighted gene co-expression network analysis, and then identified a 15 predictive lncRNAs (AC010904.2, LINC01126, AC012360.1, AC024933.1, AL442128.2, AC022211.4, AC022211.2, AC127496.5, NARF-AS1, AP000919.3, AP005329.2, AC023983.1, AC023983.2, AC139100.1, and AC012615.4) signature in melanoma patients treated with anti-PD-1 monotherapy by least absolute shrinkage and selection operator in the training cohort. These results were then validated in the validation cohort. Finally, enrichment analysis showed that the functions of differentially expressed genes between two consensus clusters were mainly related to the immune process and treatment. In summary, the 15 lncRNAs signature is a novel effective predictor for prognosis in advanced melanoma patients treated with anti-PD-1 monotherapy.

Dataset Information

Neuropilin-1 cooperates with PD-1 in CD8⁺ T cells predicting outcomes in melanoma patients treated with anti-PD1.

Publications

Neuropilin-1 cooperates with PD-1 in CD8<sup>+</sup> T cells predicting outcomes in melanoma patients treated with anti-PD1.

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Dataset Information

Neuropilin-1 cooperates with PD-1 in CD8+ T cells predicting outcomes in melanoma patients treated with anti-PD1.

Publications

Neuropilin-1 cooperates with PD-1 in CD8<sup>+</sup> T cells predicting outcomes in melanoma patients treated with anti-PD1.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Neuropilin-1 cooperates with PD-1 in CD8⁺ T cells predicting outcomes in melanoma patients treated with anti-PD1.