Project description:<p>Blockade of T cell coinhibitory molecules such as CTLA-4 and PD-1, can activate T cell antitumor response. Although these immune checkpoint blockades (CTLA-4 blockade and PD-1 blockade) have shown durable response, response rate is modest. Therefore, there is a need to find stable biomarkers predictive of response to immune checkpoint blockades and to understand underlying resistance mechanisms. We collected longitudinal tumor biopsies from a cohort of metastatic melanoma patients treated with sequential immune checkpoint blockades and performed whole exome sequencing of this cohort. The comprehensive genomic characterization of tumors enabled identification of higher copy number loss burden as a resistance mechanism and clonal T cell repertoire as a predictive biomarker.</p>
Project description:To comprehensively characterize the changes within the TME during TREM1 deficiency and anti-PD-1 immune checkpoint blockade therapy, we performed scRNA-seq analysis of the CD45+ TICs in melanoma-bearing C57BL/6 mice receiving the various treatments. We analyzed approximately 8,249 CD45+ cells from the treatment groups with t-SNE analysis, identifying 10 distinct clusters of tumor-infiltrating immune cells
Project description:Treatment of late-stage melanoma patients with immune checkpoint blockade (ICB) is currently one of the most effective standard therapies. The response rates upon neoadjuvant ICB in stage III melanoma are higher as compared to stage IV disease. Given that successful ICB depends on systemic immune response, we hypothesized that systemic immune suppression might be a mechanism responsible for lower response rates in late-stage disease, and also potentially with disease recurrence in early-stage disease.
Project description:In this comprehensive study, the authors have developed concise models integrating clinical, genomic and transcriptomic features to predict intrinsic resistance to anti-PD1 Immune Checkpoint Blockade (ICB) treatment in individual tumors. It's important to note that their validation was performed in smaller, independent cohorts, constrained by data availability. The authors have developed two Logistic Regression based models for Ipilimumab treated and Ipilimumab naive patients with metastatic melanoma. The main predictive features for the Ipilimumab treated patients are MHC-II HLA, LDH at treatment initiation and the presence of lymph node metastases (LN met), chosen using forward selection methodology. The main predictive features for the Ipilimumab naive patients are tumor heterogeneity, tumor ploidy and tumor purity, chosen using forward selection methodology.
Please note that in these models, the output ‘1’ means progressive disease (PD) and ‘0’ means non-PD. The original GitHub repository can be accessed at https://github.com/vanallenlab/schadendorf-pd1
Project description:Immune checkpoint blockade (ICB) has demonstrated significant promise for the treatment of advanced malignancies. Anti-CTLA4 and ant-PD1 therapy can activate the immune system and result in durable control in diseases such as melanoma and non-small cell lung cancer.
Project description:We performed single-cell RNA-sequencing of tumor immune infiltrates and matched peripheral blood mononuclear cells of checkpoint inhibitor (CPI)-naive stage III-IV metastatic melanoma patients. After sample collection, the same patients received CPI-treatment and their response was assessed.
