Faculty Bibliography

PURPOSE/OBJECTIVE:Immunotherapy has recently been shown to improve outcomes for advanced PD-L1-positive triple-negative breast cancer (TNBC) in the Impassion130 trial, leading to FDA approval of the first immune checkpoint inhibitor in combination with taxane chemotherapy. To further develop predictive biomarkers and improve therapeutic efficacy of the combination, interrogation of the tumor immune microenvironment before therapy as well as during each component of treatment is crucial. Here we use single-cell RNA sequencing (scRNA-seq) on tumor biopsies to assess immune cell changes from two patients with advanced TNBC treated in a prospective trial at predefined serial time points, before treatment, on taxane chemotherapy and on chemo-immunotherapy. METHODS:Both patients (one responder and one progressor) received the trial therapy, in cycle 1 nab-paclitaxel given as single agent, in cycle 2 nab-paclitaxel in combination with pembrolizumab. Tumor core biopsies were obtained at baseline, 3 weeks (after cycle 1, chemotherapy alone) and 6 weeks (after cycle 2, chemo-immunotherapy). Single-cell RNA sequencing (scRNA-seq) of both cancer cells and infiltrating immune cells isolated were performed from fresh tumor core biopsy specimens by 10 × chromium sequencing. RESULTS:). In contrast, tumors from the patient with rapid disease progression showed a prevalent and persistent myeloid compartment. CONCLUSIONS:Our study provides a deep cellular analysis of on-treatment changes during chemo-immunotherapy for advanced TNBC, demonstrating not only feasibility of single-cell analyses on serial tumor biopsies but also the heterogeneity of TNBC and differences in on-treatment changes in responder versus progressor.

T cell recognition of antigen and resulting proximal signaling are key steps in the initiation of the adaptive immune response. Previous studies targeting antigen binding site for enhancing T-cell responses to tumor antigens often lead to off-target effects and toxicity. Recently, we used nuclear magnetic resonance (NMR) spectroscopy, mutational analysis and computational docking to derive a 3D structure of the extracellular TCRCD3 assembly. Further, biomolecular force probe (BFP) measurements allowed us to determine how 2D affinity and force-modulated TCR-pMHC kinetics depend on TCR-CD3 interaction sites and affect transduction of extracellular pMHC-TCR ligation into T cell function. Based on our TCR-CD3 structural model and binding data, we generated TCR libraries for a melanoma-specific TCR (DMF5) using site-specific mutagenesis in the Cbhelix 3 and helix 4-F strand regions of the TCR to optimize the TCR-CD3 interaction and to select for mutants with enhanced T-cell effector function. One Cb helix 4-F strand mutant, NP202203AA showed increased T cell response to antigen and showed enhanced TCR-pMHC bond lifetime (catch-bonds) in BFP assays leading to prolonged T cell signaling. In the future, DMF5 TCR with reengineered CD3 binding regions will be used in tumor rejection in pre-clinical mouse melanoma models for eficacy and toxicity to develop more effective T cell therapies for human targets

BACKGROUND:High C reactive protein (CRP) levels have been reported to be associated with a poor clinical outcome in a number of malignancies and with programmed cell death protein 1 immune checkpoint blockade in patients with advanced cancer. Little is known about the direct effects of CRP on adaptive immunity in cancer. Therefore, we investigated how CRP impacted the function of T cells and dendritic cells (DCs) from patients with melanoma. METHODS:The effects of CRP on proliferation, function, gene expression and phenotype of patient T cells and DCs, and expansion of MART-1 antigen-specific T cells were analyzed by multicolor flow cytometry and RNA-seq. Additionally, serum CRP levels at baseline from patients with metastatic melanoma treated on the Checkmate-064 clinical trial were assessed by a Luminex assay. RESULTS:In vitro, CRP inhibited proliferation, activation-associated phenotypes and the effector function of activated CD4+ and CD8+ T cells from patients with melanoma. CRP-treated T cells expressed high levels of interleukin-1β, which is known to enhance CRP production from the liver. CRP also suppressed formation of the immune synapse and inhibited early events in T-cell receptor engagement. In addition, CRP downregulated the expression of costimulatory molecules on mature DCs and suppressed expansion of MART-1-specific CD8+ T cells in a dose-dependent manner by impacting on both T cells and antigen-presenting cells. High-serum CRP levels at baseline were significantly associated with a shorter survival in both nivolumab-treated and ipilimumab-treated patients. CONCLUSIONS:in melanoma and support the blockade of CRP as a therapeutic strategy to enhance immune checkpoint therapies in cancer. TRIAL REGISTRATION NUMBER/BACKGROUND:NCT01783938 and NCT02983006.

Although much clinical progress has been made in harnessing the immune system to recognize and target cancer, there is still a significant lack of an understanding of how tumors evade immune recognition and the mechanisms that drive tumor resistance to both T-cell and checkpoint blockade immunotherapy. Our objective is to understand how tumor-mediated signaling through inhibitory receptors, including PD-1, combines to affect the process of T-cell recognition of tumor antigen and activation signaling. This has the goal of understanding the basis of resistance to PD-1 blockade and potentially identifying new molecular targets to enable T-cells to overcome dysfunction mediated by multiple inhibitory receptors. Biomembrane Force Probe (BFP) measurements show that that the activities of TCR-proximal signaling components affect T-cell mechanosensing and sensitivity at the earliest stages of antigen recognition and are influenced by PD-1 and other inhibitory receptors via Shp-1/2 by targeting CD28 and Lck to directly suppress TCR-pMHC-CD8 binding. Phospho-proteomics and flow cytometry-based analysis of patient-derived T-cells from PD-1 responders and nonresponders identified additional mediators, signaling components and pathways associated with PD-1 checkpoint blockade resistance. Targeting these interactions and understanding the basis of resistance to PD-1 blockade would potentially allow identification of novel biomarkers of resistance or new molecular targets to enable T-cells to overcome dysfunction during PD-1 checkpoint blockade

Background Autoantibody landscapes are very specific to the individual, can remain stable for many years, and contain unique features reported in association with cancer, autoimmunity, infection, neurologic conditions, CD8+ T cell behavior, and checkpoint blockade adverse events [1-11]. The goal of this work was to determine whether pre-existing antigenspecific features in melanoma patient autoantibody landscapes would associate with clinical outcomes following checkpoint blockade. Methods Pre-treatment serum samples were collected from 117 melanoma patients prior to checkpoint blockade with anti-CTLA4 (N=60), anti-PD1 (N=38), or both in combination (N=16). All data was collected with approval of the NYU Institutional Review Board at the NYU Perlmutter Cancer Center with informed consent [11]. Serum samples were run on HuProt Human Proteome Microarrays containing >19,000 human proteins by CDI Laboratories. Raw serum IgG signal intensities were processed across staining cohorts via interquartile range normalization. Pre-existing antibody responses were defined as patient-specific IgG signals >3.5 median absolute deviations above cohort median IgG background (modified Z-score). Group statistics were computed (GraphPad Prism), and gene ontology enrichment analysis was performed (Enrichr) [12]. Results Several pre-existing antigen-specific IgG autoantibody targets were observed to have associations with good outcomes (SD/PR) or objective clinical responses (PR/CR) versus patients with progressive disease (POD). While final determination of the most predictive subsets is ongoing, many targets represent genes in an axis surrounding immune signaling pathways, hereditary neurodegenerative disease, and the ubiquitin proteasome pathway (ie, UBQLN1, UBQLN2). An exemplary example was observed in the autoantibody responses shared by >10% of all patients regardless of clinical outcome. Gene ontology enrichment analysis of these shared melanoma-patient autoantibodies versus KEGG 2019 [12] demonstrates this set of proteins is strongly enriched for neurotrophin signaling-associated proteins after multi-sample correction (P=0.004) (Table 1). Several other associations were observed cohort-wide for ontologies with tissuespecific enrichment in the brain, neurons, and neuronal processes. Conclusions In this pilot study, we found strong associations across the cohort for autoantibodies against nerve-growth-inducing neurotrophins and genes like UBQLN1 and UBQLN2 which have strong associations with amyotrophic lateral sclerosis, frontotemporal dementia, Parkinson's, and Alzheimer's - neurodegenerative diseases that are known to have incidences which correlate with melanoma [14-16]; this hints at a potential immunologic connection between the conditions, perhaps related to an antitumor / autoimmune axis involving the targets reported here. (Table Presented)

BACKGROUND:Immune checkpoint inhibitors (anti-CTLA-4, anti-PD-1, or the combination) enhance anti-tumor immune responses, yielding durable clinical benefit in several cancer types, including melanoma. However, a subset of patients experience immune-related adverse events (irAEs), which can be severe and result in treatment termination. To date, no biomarker exists that can predict development of irAEs. METHODS:We hypothesized that pre-treatment antibody profiles identify a subset of patients who possess a sub-clinical autoimmune phenotype that predisposes them to develop severe irAEs following immune system disinhibition. Using a HuProt human proteome array, we profiled baseline antibody levels in sera from melanoma patients treated with anti-CTLA-4, anti-PD-1, or the combination, and used support vector machine models to identify pre-treatment antibody signatures that predict irAE development. RESULTS:We identified distinct pre-treatment serum antibody profiles associated with severe irAEs for each therapy group. Support vector machine classifier models identified antibody signatures that could effectively discriminate between toxicity groups with > 90% accuracy, sensitivity, and specificity. Pathway analyses revealed significant enrichment of antibody targets associated with immunity/autoimmunity, including TNFα signaling, toll-like receptor signaling and microRNA biogenesis. CONCLUSIONS:Our results provide the first evidence supporting a predisposition to develop severe irAEs upon immune system disinhibition, which requires further independent validation in a clinical trial setting.