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Mutations in the parkin gene, which encodes a ubiquitin ligase, are a major genetic cause of parkinsonism. Interestingly, parkin also plays a role in cancer as a putative tumor suppressor, and the gene is frequently targeted by deletion and inactivation in human malignant tumors. Here, we investigated a potential tumor suppressor role for parkin in gliomas. We found that parkin expression was dramatically reduced in glioma cells. Restoration of parkin expression promoted G1 phase cell-cycle arrest and mitigated the proliferation rate of glioma cells in vitro and in vivo. Notably, parkin-expressing glioma cells showed a reduction in levels of cyclin D1, but not cyclin E, and a selective downregulation of Akt serine-473 phosphorylation and VEGF receptor levels. In accordance, cells derived from a parkin-null mouse model exhibited increased levels of cyclin D1, VEGF receptor, and Akt phosphorylation, and divided significantly faster when compared with wild-type cells, with suppression of these changes following parkin reintroduction. Clinically, analysis of parkin pathway activation was predictive for the survival outcome of patients with glioma. Taken together, our study provides mechanistic insight into the tumor suppressor function of parkin in brain tumors and suggests that measurement of parkin pathway activation may be used clinically as a prognostic tool in patients with brain tumor. Cancer Res; 72(10); 2543–53. ©2012 AACR.

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Neoantigens act as surrogates for T cell immunoreactivity and have been shown to correlate with patients’ response to immune checkpoint inhibitors (ICI). Here, we have developed a multi-allele convolutional neural network-based model, MINERVA, for predicting HLA class I (HLA-I) presentation of tumor antigenic peptides using transfer learning with immunopeptidome data from cancer patients. We show that MINERVA recapitulates physicochemical properties of naturally presented peptides that are relevant to tumor biology and is more sensitive to tumor-derived HLA presented peptides than existing algorithms. Without a priori knowledge provided to MINERVA, the model was able to learn the determinants of peptide-HLA presentation and we utilized the learnt features to understand the impact of mutations on neoantigen presentation. Furthermore, we show that the diversity of tumor peptides being presented by a pair of alleles are significantly correlated with HLA sequence divergence. In conclusion, MINERVA is an open source software that can greatly facilitate tumor neoantigen discovery and guide cancer immunotherapies.

BackgroundTertiary Lymphoid Structures (TLS) are highly organized ectopic lymphoid structures found in inflamed or tumor tissues, acting as sites of lymphoid recruitment and immune activation. A high TLS density within the tumor is commonly associated with an increased prognostic effect of TILs and with an improved disease free survival and overall survival for patients.1 However, the existence of conflicting studies suggest that multiple TLS features should be taken into account when assessing their prognostic value, such as their location, cellular composition, maturation stage and spatial organisation, as those may affect their functionalities.2MethodsWith the aim of gaining insights into TLS biology and evaluating the prognostic role of TLS in Non-Small Cell Lung Carcinoma according to their multiple features, we developed a TLS multiplex immunofluorescent (mIF) panel that includes T cells (CD3, CD8), B cells (CD20), Follicular Dendritic cells (CD21, CD23) and mature dendritic cells (DC-LAMP) markers. We deployed this panel across a cohort of primary tumors from NSCLC patients (n=408) and established a mIF image analysis workstream to assess the status and spatial location of each cell within the tissue. A H&E staining of the same tissue section was performed to evaluate mIF spatial data in relation to the tumor context. Additional multi-omics assessments were conducted across the same cohort including; whole exome sequencing, NanoString transcriptomics, and immunohistochemistry (e.g. PD-L1, FOXP3, NKp46, LKB1, CTLA4). We have leveraged clinical metadata, including demographics (e.g. age, sex, smoking status) and clinical risk factors (e.g. stage, grade, Standard of Care treatment) with clinical follow up (e.g. OS, PFS) for prevalence analysis, novel biomarker identification, and survival association.ResultsAssessment of the prevalence of each cell phenotype within the tumor tissue and TLS, the cell-cell interactions, the distance between each cell type, and the distance of non-TLS immune cells to the closest TLS will be described, demonstrating the different types of lymphoid aggregates and TLS and their functional status. An integrative analysis combining spatial biology data with multi-omics and clinical data will be presented evaluating the prognostic value of TLS composition, maturation status and spatial organization, in correlation with additional biomarkers and clinical characteristics.ConclusionsThis exploratory study using cutting-edge technologies enables us to better understand how TLS orchestrate an organised anti-tumour response, defining TLS spatial biomarker signatures, TLS gene signatures, and TLS features associated with patient outcomes to evaluate in the clinic.ReferencesMarie-Caroline Dieu-Nosjean, Jérémy Goc, Nicolas A Giraldo, Catherine Sautès-Fridman, Wolf Herman Fridman. Tertiary lymphoid structures in cancer and beyond. Trends Immunol 2014;35(11):571–580.Catherine Sautès-Fridman, Florent Petitprez, Julien Calderaro, Wolf Herman Fridman. Tertiary lymphoid structures in the era of cancer immunotherapy. Nat Rev Cancer 2019;19(6):307–325.Ethics ApprovalThe study was approved by AstraZeneca.