PCM4EU and PRIME-ROSE: Collaboration for implementation of precision cancer medicine in Europe.

BACKGROUND: In the two European Union (EU)-funded projects, PCM4EU (Personalized Cancer Medicine for all EU citizens) and PRIME-ROSE (Precision Cancer Medicine Repurposing System Using Pragmatic Clinical Trials), we aim to facilitate implementation of precision cancer medicine (PCM) in Europe by leveraging the experience from ongoing national initiatives that have already been particularly successful. PATIENTS AND METHODS: PCM4EU and PRIME-ROSE gather 17 and 24 partners, respectively, from 19 European countries. The projects are based on a network of Drug Rediscovery Protocol (DRUP)-like clinical trials that are currently ongoing or soon to start in 11 different countries, and with more trials expected to be established soon. The main aims of both the projects are to improve implementation pathways from molecular diagnostics to treatment, and reimbursement of diagnostics and tumour-tailored therapies to provide examples of best practices for PCM in Europe. RESULTS: PCM4EU and PRIME-ROSE were launched in January and July 2023, respectively. Educational materials, including a podcast series, are already available from the PCM4EU website (http://www.pcm4eu.eu). The first reports, including an overview of requirements for the reimbursement systems in participating countries and a guide on patient involvement, are expected to be published in 2024. CONCLUSION: PCM4EU and PRIME-ROSE were launched in January and July 2023, respectively. Educational materials, including a podcast series, are already available from the PCM4EU website (http://www.pcm4eu.eu). The first reports, including an overview of requirements for the reimbursement systems in participating countries and a guide on patient involvement, are expected to be published in 2024. CONCLUSION: European collaboration can facilitate the implementation of PCM and thereby provide affordable and equitable access to precision diagnostics and matched therapies for more patients.

Immunotherapy drives mesenchymal tumor cell state shift and TME immune response in glioblastoma patients.

BACKGROUND: Glioblastoma is a highly aggressive type of brain tumor for which there is no curative treatment available. Immunotherapies have shown limited responses in unselected patients, and there is an urgent need to identify mechanisms of treatment resistance to design novel therapy strategies. METHODS: Here we investigated the phenotypic and transcriptional dynamics at single-cell resolution during nivolumab immune checkpoint treatment of glioblastoma patients. RESULTS: We present the integrative paired single-cell RNA-seq analysis of 76 tumor samples from patients in a clinical trial of the PD-1 inhibitor nivolumab and untreated patients. We identify a distinct aggressive phenotypic signature in both tumor cells and the tumor microenvironment in response to nivolumab. Moreover, nivolumab-treatment was associated with an increased transition to mesenchymal stem-like tumor cells, and an increase in TAMs and exhausted and proliferative T cells. We verify and extend our findings in large external glioblastoma dataset (n = 298), develop a latent immune signature and find 18% of primary glioblastoma samples to be latent immune, associated with mesenchymal tumor cell state and TME immune response. Finally, we show that latent immune glioblastoma patients are associated with shorter overall survival following immune checkpoint treatment (P = .0041). CONCLUSIONS: We find a resistance mechanism signature in one fifth of glioblastoma patients associated with a tumor-cell transition to a more aggressive mesenchymal-like state, increase in TAMs and proliferative and exhausted T cells in response to immunotherapy. These patients may instead benefit from neuro-oncology therapies targeting mesenchymal tumor cells.

Nivolumab reaches brain lesions in patients with recurrent glioblastoma and induces T-cell activity and upregulation of checkpoint pathways.

Glioblastoma (GBM) is an aggressive brain tumor with poor prognosis. Although immunotherapy is being explored as a potential treatment option for patients with GBM, it is unclear whether systemic immunotherapy can reach and modify the tumor microenvironment in the brain. We evaluated immune characteristics in patients receiving the anti-PD1 immune checkpoint inhibitor Nivolumab one week prior to surgery, compared to control patients receiving salvage resection without prior Nivolumab treatment. We observed saturating levels of Nivolumab bound to intratumorally- and tissue-resident T cells in the brain, implicating saturating levels of Nivolumab reaching brain tumors. Following Nivolumab treatment, significant changes in T-cell activation and proliferation were observed in the tumor resident T-cell population, and peripheral T cells upregulated chemokine receptors related to brain homing. A strong Nivolumab-driven upregulation in compensatory checkpoint inhibition molecules, TIGIT, LAG-3, TIM-3 and CTLA-4 was observed, potentially counteracting the treatment effect. Finally, tumor-reactive tumor-infiltrating lymphocytes (TILs) were found in a subset of Nivolumab-treated patients with prolonged survival, and neoantigen-reactive T cells were identified in both TILs and blood. This indicates a systemic response towards GBM in a subset of patients, which was further boosted by Nivolumab, with T-cell responses towards tumor-derived neoantigens. Our study demonstrates that Nivolumab does reach the GBM tumor lesion and enhances antitumor T-cell responses both intratumorally and systemically. However, various anti-inflammatory mechanisms mitigate the clinical efficacy of the anti-PD1 treatment.

IMPROVE: a feature model to predict neoepitope immunogenicity through broad-scale validation of T-cell recognition.

Background: Mutation-derived neoantigens are critical targets for tumor rejection in cancer immunotherapy, and better tools for neoepitope identification and prediction are needed to improve neoepitope targeting strategies. Computational tools have enabled the identification of patient-specific neoantigen candidates from sequencing data, but limited data availability has hindered their capacity to predict which of the many neoepitopes will most likely give rise to T cell recognition. Method: To address this, we make use of experimentally validated T cell recognition towards 17,500 neoepitope candidates, with 467 being T cell recognized, across 70 cancer patients undergoing immunotherapy. Results: We evaluated 27 neoepitope characteristics, and created a random forest model, IMPROVE, to predict neoepitope immunogenicity. The presence of hydrophobic and aromatic residues in the peptide binding core were the most important features for predicting neoepitope immunogenicity. Conclusion: Overall, IMPROVE was found to significantly advance the identification of neoepitopes compared to other current methods.