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Zanzotto, F. M.

Publications and source records attributed to Zanzotto, F. M..

2 recordsLinked to original sources

Microenvironment of metastatic site reveals key predictors of PD-1 blockade response in renal cell carcinoma

Immune checkpoint blockade (ICB) therapies have improved the overall survival (OS) of many patients with advanced cancers. However, the response rate to ICB varies widely among patients, exposing non-responders to potentially severe immune-related adverse events. The discovery of new biomarkers to identify patients responding to ICB is now a critical need in the clinic. We therefore investigated the tumor microenvironment (TME) of advanced clear cell renal cell carcinoma (ccRCC) samples from primary and metastatic sites to identify molecular and cellular markers of response to ICB. We revealed a significant discrepancy in treatment response between subgroups based on cell fractions inferred from metastatic sites. One of the subgroups was enriched in non-responders and harbored a lower fraction of CD8+ T cells and plasma cells, as well as a decreased expression of immunoglobulin genes. In addition, we developed the Tumor-Immunity Differential (TID) score which combines features from tumor cells and the TME to accurately predict response to anti-PD-1 immunotherapy (AUC-ROC=0.88, log-rank tests for PFS P < 0.0001, OS P = 0.01). Finally, we also defined TID-related genes (YWHAE, CXCR6 and BTF3), among which YWHAE was validated as a robust predictive marker of ICB response in independent cohorts of pre- or on-treatment biopsies of melanoma and lung cancers. Overall, these results provide a rationale to further explore variations in the cell composition of metastatic sites, and underlying gene signatures, to predict patient response to ICB treatments. One Sentence SummaryTumor microenvironment balance of metastasis and associated genes are key predictors of immunotherapy patient response in kidney cancer.

bioinformatics↗

A comprehensive library of canonical and non-canonical MHC class I antigens for cancer vaccine development.

A longstanding disconnect between the growing number of MHC Class I immunopeptidomic studies and genomic medicine hinders cancer vaccine design. We develop COD-dipp to genomically map the full spectrum of detected canonical and non-canonical (non-exonic) MHC Class I antigens from 26 cancer studies. We demonstrate that patient mutations in regions overlapping physically identified antigens better predict immunotherapy response when compared to neoantigen predictions. We suggest a vaccine design approach using 140,966 highly immune-visible regions of the genome annotated by their expression and haplotype frequency in the human population. These regions tend to be highly conserved, mutated in cancer and harbor 7.8 times more immunogenicity. Intersecting pan-cancer mutations with these immune surveilled regions revealed a potential to create off-the-shelf multi-epitope vaccines against public neoantigens. Here we release COD-dipp, a cancer vaccine toolkit as a web-application (https://www.proteogenomics.ca/COD-dipp) and open-source high-throughput resource.

bioinformatics↗