Faculty Bibliography

Background: There are no predictive biomarkers of ipilimumab (IPI) toxicity. Of metastatic melanoma (MM) patients (pts) receiving IPI (3 mg/kg), 35% require systemic therapies to treat immune-related adverse events (irAEs) and 20% must terminate treatment [1]. Here we tested the hypothesis that a pre-existing autoantibody (autoAb) profile is predictive of IPI irAEs.
Method(s): We measured autoAb levels in pre- and post-treatment sera from MM pts who received IPI (3 mg/kg) monotherapy on a proteome microarray containing ~ 20,000 unique full-length human proteins (HuProt array, CDI Laboratories). Clinical data were prospectively collected with protocol-driven follow-up. IrAEs were categorized by CTCAE guidelines as none (grade 0), mild (grade 12), or severe (grade 34). AutoAb levels were standardized using median quantile normalization and considered positive hits if > 2-SD above the peak array signal and differed by >= 2-fold with p < 0.05 between toxicity groups (Non-parametric Analysis/Wilcox test).
Result(s): Seventy-eight sera from 37 MM pts were analyzed. Antibodies against CTLA-4 were significantly elevated post IPI treatment (p < 0.0001), validating the assay. The pre-treatment levels of 190 IgG autoAbs were significantly different in pts who experienced irAEs (n = 28) compared to those with no irAEs (n = 9). Comparison of severe irAE (n = 9) and no irAE (n = 9) groups revealed 129 IgG auto- Abs that significantly differed in pre-treatment sera. Localization and pathway analysis (UniProt, KEGG, Reactome) showed 81/190 (43%) of the autoAbs targeted nuclear and mitochondrial antigens and were enriched in metabolic pathways (p = 0.015). AutoAbs associated with irAEs did not correlate with treatment response.
Conclusion(s): AutoAbs to antigens enriched in metabolic pathways prior to treatment may predict IPI-induced toxicity in MM. The subcellular localization of targeted antigens could explain the autoimmune toxicities associated with IPI. Studies in larger cohorts and in pts receiving other checkpoint inhibitors and/or combination therapies are essential to determine the validity of the data. If validated, our results would support the discovery of the first toxicity predictor in cancer immunotherapy

Background: Approximately 40% of metastatic cutaneous melanoma (CM) patients do not respond to the current immunotherapy (IT) regimens, pointing to other, yet unknown factors conferring IT resistance. In addition, > 60% of patients from single-line or combined treatment (COMBO) regimens present severe immune related adverse events (irAEs). In this study we have developed a novel genomic approach interrogating expression quantitative trait loci (eQTLs) to explore weather germline genetic variation can serve as novel personalized determinant of immunotherapy response and toxicity.
Method(s): By interrogating the genome wide expression data and SNP array datasets of healthy twin cohort (MuTHER), we have identified 85 eQTLs most significantly associated with the expression of 265 immune genes. Using the MassARRAY system, the 85 SNPs were genotyped in 138 anti-CTLA-4 treated patients, 87 PD-1 treated patients, and 69 patients from combined (COMBO) treatments, collected from multi-institutional collaborations. To test the association of SNPs with IT response and irAEs, logistic regression analyses were performed for each treatment group adjusting by demographic and clinical covariates.
Result(s): We found significant associations with COMBO IT resistance for and eQTL in IL10/IL19 (OR = 4.249, p = 0.0167), which we have recently identified for association with melanoma survival and which, interestingly, is an established locus associated with the risk of several autoimmune diseases. Additionally, we also identified eQTLs that are associated with IT sensitivity; IL1-beta with resistance to anti-CTLA-4 and SPI1 with resistance to anti-PD-1. Interestingly, genomic scan of 85 eQTLs has identified novel loci predictive of severe autoimmunity and site specific irAEs in patients treated with COMBO or single-line anti- CTLA4 IT.
Conclusion(s): In this study, we report that eQTLs from IL19/IL10 locus, previously shown to predict autoimmunity risk and CM survival, is also a surrogate marker of response to COMBO IT, indicating a strong relationship between interleukin pathways and tumor immunogenicity. Novel loci have been found as predictive markers for autoimmune toxicity, in patients treated with COMBO and anti-CTLA4 IT. This is a first evidence that immunomodulatory pathways modulated by germline genetic variation can impact susceptibility to irAEs as well as IT efficacy. Currently, a large scan is underway using genome-wide genetic screens to further test the functional validity of these findings in a large collaborative setting

Background: 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, combine to affect the process of T cell recognition of tumor antigen and activation signaling, with the goal of understanding the basis of resistance to PD-1 blockade and the potential identification of new molecular targets to enable T cells to overcome dysfunction mediated by multiple inhibitory receptors.
Methods and Results: We show that Lck activity affects T cell sensitivity and influences the probability of inducing effector function [1]. Under non-activating conditions, Lck and Shp-1 phosphorylation and activity vary based on CD8+ memory T cell phenotype. Shp-1 interaction with Lck under non-activation conditions can also vary, as suggested by our results showing decreased Shp-1 S591 phosphorylation, which affects Shp-1 localization and correlates with increased Shp-1 colocalization with Lck. Further, we showed that Shp-1 directly influences Lck activity under non-activating conditions, as inhibition of Shp-1 leads to increased Lck activity. Importantly, inhibition of Shp- 1/2, a major mediator of PD-1 signaling, targeting CD28 and Lck [2], prior to activation leads to increased T cell cytotoxic effector function. Our proteomics-based analysis of patient T cells identified both mediators of PD-1 signaling and signaling components and pathways associated with blockade resistance. It has generally been thought that TCR and CD8 binding depend mainly on their ectodomain interactions with pMHC. We have shown, however, that Lck-CD8 binding [3] and Lck activity [4] are required for upregulated CD8 binding to prebound TCR-pMHC complex. Therefore, the cytoplasmic associations of Lck with CD8 and Zap-70, as well as CD3 with Zap-70 may influence formation and stability of the TCRpMHCCD8 complex. To determine the mechanistic basis of PD-1 inhibition of TCR-pMHCCD8 binding we utilized 2D affinity combined with Biomembrane Force Probe (BFP) measurements[5, 6] and showed that PD-1 directly suppresses TCR pMHCCD8 binding. Our data also revealed that TCR-pMHC binding was independent of PD-1-PD-L1, but TCRpMHCCD8 binding was suppressed by PD-1-PD-L1 binding demonstrating negative cooperativity, as fewer bonds formed than the sum of bonds formed by each interaction alone.
Conclusion(s): Together, our results show that the activities of TCRproximal signaling components affect T cell mechanosensing and sensitivity at the earliest stages of antigen recognition and are influenced by PD-1. Targeting these interactions may enhance tumor-specific T cell sensitivity for cancer immunotherapy and understanding the basis of resistance to PD-1 blockade to potentially allow identification of new molecular targets to enable T cells to overcome dysfunction mediated by multiple inhibitory receptors

Metastatic cancers produce exosomes that condition pre-metastatic niches in remote microenvironments to favor metastasis. In contrast, here we show that exosomes from poorly metastatic melanoma cells can potently inhibit metastasis to the lung. These "non-metastatic" exosomes stimulate an innate immune response through the expansion of Ly6Clow patrolling monocytes (PMo) in the bone marrow, which then cause cancer cell clearance at the pre-metastatic niche, via the recruitment of NK cells and TRAIL-dependent killing of melanoma cells by macrophages. These events require the induction of the Nr4a1 transcription factor and are dependent on pigment epithelium-derived factor (PEDF) on the outer surface of exosomes. Importantly, exosomes isolated from patients with non-metastatic primary melanomas have a similar ability to suppress lung metastasis. This study thus demonstrates that pre-metastatic tumors produce exosomes, which elicit a broad range of PMo-reliant innate immune responses via trigger(s) of immune surveillance, causing cancer cell clearance at the pre-metastatic niche.

Current staging guidelines are insufficient to predict which patients with thin primary melanoma are at high risk of recurrence. Computer-assisted image analysis may allow for more practical and objective histopathological analysis of primary tumors than traditional light microscopy. We studied a prospective cohort of stage IB melanoma patients treated at NYU Langone Medical Center from 2002 to 2014. Primary tumor width, manual area, digital area, and conformation were evaluated in a patient subset via computer-assisted image analysis. The associations between histologic variables and survival were evaluated using Cox proportional hazards model. Logistic regressions were used to build a classifier with clinicopathological characteristics to predict recurrence status. Of the 655 patients with stage IB melanoma studied, a subset of 149 patient tumors (63 recurred, 86 did not recur) underwent computer-assisted histopathological analysis. Increasing tumor width (hazard ratios (HR): 1.17, P=0.01) and digital area (HR: 1.08, P<0.01) were significantly associated with worse recurrence-free survival, whereas non-contiguous conformation (HR: 0.57, P=0.05) was significantly associated with better recurrence-free survival. The novel histopathological classifier composed of digital area, conformation, and baseline variables effectively distinguished recurrent cases from non-recurrent cases (AUC: 0.733, 95% confidence interval (CI): 0.647-0.818), compared to the baseline classifier alone (AUC: 0.635, 95% CI: 0.545-0.724). Primary tumor cross-sectional area, width, and conformation measured via computer-assisted analysis may help identify high-risk patients with stage IB melanoma.Modern Pathology advance online publication, 21 July 2017; doi:10.1038/modpathol.2017.64.

Circular RNAs are a novel class of non-coding RNAs with functions that remain poorly characterized in normal and pathological conditions. CDR1as is a non-canonical circRNA observed to act as a sponge for miR-7 in brain tissues. Analysis of RNA-seq data of melanocytes and melanoma cell lines and short-term cultures revealed loss of CDR1as expression as a hallmark of melanoma cells. We confirmed silencing of CDR1as in melanoma cells and tissues by RT-qPCR using divergent primers. Clinically, we observed CDR1as loss associated with metastatic progression and poor patient outcomes in a cohort of fresh-frozen melanoma tissue samples. Depletion of CDR1as in melanoma cell lines enhanced invasion in vitro and lung metastasis in vivo, demonstrating functional significance of CDR1as silencing. Surprisingly, CDR1as depletion had no clear effect on miR-7 activity in melanoma cells, and miR-7 inhibition was insufficient to rescue CDR1as silencing-induced invasion. Moreover, GSEA analyses of proteomic profiling of melanoma cells depleted of CDR1as revealed reductions of proteins involved in oxidative phosphorylation (OXPHOS) and mitochondrial function, suggesting CDR1as loss may alter metabolism of melanoma cells. Mining of CLIP-Seq data sets and subsequent RIP-PCR revealed direct interactions of CDR1as with the IGF2BP family of proteins and TAR

Background: There are no predictive biomarkers of ipilimumab (IPI) toxicity. Of metastatic melanoma (MM) patients (pts) receiving IPI (3mg/kg), 35% require systemic therapies to treat immune-related adverse events (irAEs) and 20% must terminate treatment (Horvat et al., JCO 2015). Here we tested the hypothesis that a pre-existing autoantibody (autoAb) profile is predictive of IPI irAEs. Methods: We measured autoAb levels in pre- and post-treatment sera from mm pts who received IPI (3mg/kg) monotherapy on a proteome microarray containing ~20,000 unique full-length human proteins (HuProt array, CDI Laboratories). Clinical data were prospectively collected with protocol-driven follow-up. IrAEs were categorized by CTCAE guidelines as none (grade 0), mild (grade 1-2), or severe (grade 3-4). AutoAb levels were standardized using median quantile normalization and considered positive hits if > 2-SD above the peak array signal and differed by >=2 fold with p < 0.05 between toxicity groups (Non-parametric Analysis/Wilcox test). Results: Seventy-eight sera from 37 mm pts were analyzed. Antibodies against CTLA-4 were significantly elevated post IPI treatment (p < 0.0001), validating the assay. The pre-treatment levels of 190 IgG autoAbs were significantly diferent in pts who experienced irAEs (n = 28) compared to those with no irAEs (n = 9). Comparison of severe irAE (n = 9) and no irAE (n = 9) groups revealed 129 IgG autoAbs that significantly differed in pre-treatment sera. Localization and pathway analysis (UniProt, KEGG, Reactome) showed 81/190 (43%) of the autoAbs targeted nuclear and mitochondrial antigens and were enriched in metabolic pathways (p = 0.015). AutoAbs associated with irAEs did not correlate with treatment response. Conclusions: AutoAbs to antigens enriched in metabolic pathways prior to treatment may predict IPI-induced toxicity in MM. The subcellular localization of targeted antigens could explain the autoimmune toxicities associated with IPI. Studies in larger cohorts and in pts receiving other checkpoint inhibitors and/or combination therapies are essential to determine the validity of the data. If validated, our results would support the discovery of the first toxicity predictor in cancer immunotherapy