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:Expression profiling of tumor samples obtained during CA209-038 (ClinicalTrials.gov Identifier: NCT01621490). The purpose of this study is to evaluate pharmacodynamic changes of nivolumab and nivolumab in combination with ipilimumab treatment on the biomarkers measured in the peripheral blood and tumor tissues of subjects with advanced melanoma (unresectable or advanced). Samples are rumor core needle biopsies obtained at trial enrolment (i.e. Screen) and/or at Cycle 1 Day 29 (i.e.Week 4) from Subjects With Advanced Melanoma (Unresectable or Metastatic) treated with nivolumab (BMS-936558,MDX-1106) 3 mg/kg solution intravenously every 2 weeks on Bristol-Myers Squibb clinical trial protocol CA209-038 Part 1 . Cohort 2 patients have progressed on anti-CTLA4 (ipilimumab) monoclonal antibody therapy. Values are from an interim lock of the trial data in July 2014. Best overall response (BOR) was defined using RECIST 1.1 criteria: tumor assessments between date of first dose and the date of first objectively documented progression, or the date of non-missing subsequent anti-cancer therapy (whichever occurs first) were used to derive BOR. MPCT is Maximum reduction in tumor size (index lesions only) up to first progression, and is the value typically shown on a waterfall plot.
Project description:The blossom of immunotherapy in melanoma highlights the need to delineate mechanisms of immune resistance. Recently, we have demonstrated that the RNA editing protein, adenosine deaminase acting on RNA-1 (ADAR1) is down-regulated during metastatic transition of melanoma, which enhances melanoma cell proliferation and tumorigenicity. Here we investigate the role of ADAR1 in melanoma immune resistance. Importantly, knockdown of ADAR1 in human melanoma cells induces resistance to tumor infiltrating lymphocytes in a cell contact-dependent mechanism. We show that ADAR1, in an editing-independent manner, regulates the biogenesis of miR-222 at the transcription level and thereby Intercellular Adhesion Molecule 1 (ICAM1) expression, which consequently affects melanoma immune resistance. ADAR1 thus has a novel, pivotal, role in cancer immune resistance. Corroborating with these results, the expression of miR-222 in melanoma tissue specimens was significantly higher in patients who had no clinical benefit from treatment with ipilimumab as compared to patients that responded clinically, suggesting that miR-222 could function as a biomarker for the prediction of response to ipilimumab. These results provide not only novel insights on melanoma immune resistance, but also pave the way to the development of innovative personalized tools to enable optimal drug selection and treatment. 13 formalin fixed paraffin embedded (FFPE) melanoma tissues were stained with hematoxilin and eosin for examination by an expert pathologist. Non-tumor tissue was removed. Total RNA was isolated using miRNeasy FFPE kit (Qiagen) according to the manufacture guidelines.
Project description:Severe immune-related adverse events (irAEs) occur in up to 60% of melanoma patients treated with immune checkpoint inhibitors (ICIs). However, it remains unknown whether a common baseline immunological state precedes irAE development. Leveraging CyTOF, single-cell RNA sequencing, bulk RNA sequencing, and T cell receptor sequencing to analyze pretreatment blood from metastatic melanoma patients treated with ICIs, we investigated cellular factors associated with severe irAE development regardless of organ system involvement. Our results demonstrate circulating T cell characteristics associated with ICI-induced toxicity, with implications for improved diagnostics and clinical management.
Project description:Severe immune-related adverse events (irAEs) occur in up to 60% of melanoma patients treated with immune checkpoint inhibitors (ICIs). However, it remains unknown whether a common baseline immunological state precedes irAE development. Leveraging CyTOF, single-cell RNA sequencing, bulk RNA sequencing, and T cell receptor sequencing to analyze pretreatment blood from metastatic melanoma patients treated with ICIs, we investigated cellular factors associated with severe irAE development regardless of organ system involvement. Our results demonstrate circulating T cell characteristics associated with ICI-induced toxicity, with implications for improved diagnostics and clinical management.
Project description:Severe immune-related adverse events (irAEs) occur in up to 60% of melanoma patients treated with immune checkpoint inhibitors (ICIs). However, it remains unknown whether a common baseline immunological state precedes irAE development. Leveraging CyTOF, single-cell RNA sequencing, bulk RNA sequencing, and T cell receptor sequencing to analyze pretreatment blood from metastatic melanoma patients treated with ICIs, we investigated cellular factors associated with severe irAE development regardless of organ system involvement. Our results demonstrate circulating T cell characteristics associated with ICI-induced toxicity, with implications for improved diagnostics and clinical management.
Project description:Metastatic uveal melanoma (UM) remains challenging to treat, with objective response rates to immune checkpoint blockade (ICB) much lower than in primary cutaneous melanoma (CM). Besides a lower mutational burden, the overall immune-excluded tumor microenvironment of UM might contribute to the poor response rate. We therefore aimed at targeting deficiency in argininosuccinate synthase 1, which is a key metabolic feature of UM. This study aimed at investigating safety and tolerability of a triple combination consisting of ipilimumab and nivolumab immunotherapy and the metabolic therapy ADI-PEG 20. 9 patients were enrolled in this pilot study. The combination therapy was safe and tolerable with absence of immune related adverse events (irAE) of special interest but with 4 of 9 patients experiencing a CTCAE grade 3 AE. No objective responses were observed. All except one patient developed anti-drug antibodies (ADA) within a month of treatment initiation and therefore did not maintain arginine depletion. Further, an IFNg-dependent inflammatory signature was observed in metastatic lesions in patients pre-treated with ICB compared with patients with no pretreatment. Multiplex immunohistochemistry demonstrated variable presence of tumor infiltrating CD8 lymphocytes and PD-L1 expression at baseline in metastases.
Project description:The blossom of immunotherapy in melanoma highlights the need to delineate mechanisms of immune resistance. Recently, we have demonstrated that the RNA editing protein, adenosine deaminase acting on RNA-1 (ADAR1) is down-regulated during metastatic transition of melanoma, which enhances melanoma cell proliferation and tumorigenicity. Here we investigate the role of ADAR1 in melanoma immune resistance. Importantly, knockdown of ADAR1 in human melanoma cells induces resistance to tumor infiltrating lymphocytes in a cell contact-dependent mechanism. We show that ADAR1, in an editing-independent manner, regulates the biogenesis of miR-222 at the transcription level and thereby Intercellular Adhesion Molecule 1 (ICAM1) expression, which consequently affects melanoma immune resistance. ADAR1 thus has a novel, pivotal, role in cancer immune resistance. Corroborating with these results, the expression of miR-222 in melanoma tissue specimens was significantly higher in patients who had no clinical benefit from treatment with ipilimumab as compared to patients that responded clinically, suggesting that miR-222 could function as a biomarker for the prediction of response to ipilimumab. These results provide not only novel insights on melanoma immune resistance, but also pave the way to the development of innovative personalized tools to enable optimal drug selection and treatment.
Project description:The clinical success of immune-checkpoint inhibitors (ICI) in both resected and metastatic melanoma has confirmed the validity of therapeutic strategies that boost the immune system to counteract cancer. However, half of patients with metastatic disease treated with even the most aggressive regimen do not derive durable clinical benefit. Thus, there is a critical need for predictive biomarkers that can identify individuals who are unlikely to benefit with high accuracy, so that these patients may be spared the toxicity of treatment without the likely benefit of response. Ideally, such an assay would have a fast turnaround time and minimal invasiveness. Here, we utilize a novel platform that combines mass spectrometry with an artificial intelligence-based data processing engine to interrogate the blood glycoproteome in melanoma patients before receiving ICI therapy. We identify 143 biomarkers that demonstrate a difference in expression between the patients who died within six months of starting ICI treatment and those who remained progression-free for three years. We then develop a glycoproteomic classifier that predicts benefit of immunotherapy (HR=2.7; p=0.026) and achieves a significant separation of patients in an independent cohort (HR=5.6; p=0.027). To understand how circulating glycoproteins may affect efficacy of treatment, we analyze the differences in glycosylation structure and discover a fucosylation signature in patients with shorter overall survival (OS). We then develop a fucosylation-based model that effectively stratifies patients (HR=3.5; p=0.0066). Together, our data demonstrate the utility of plasma glycoproteomics for biomarker discovery and prediction of ICI benefit in patients with metastatic melanoma and suggest that protein fucosylation may be a determinant of anti-tumor immunity.