Citations

e21054 Background: Up to 50% of patients undergoing resection for advanced melanoma experience recurrence. Identification of preoperative prognostic biomarkers is needed to ascertain risk of relapse and guide postoperative management. Lactate dehydrogenase (LDH) represents a strong prognostic factor in unresectable metastatic (stage IV) melanoma, but its relevance in patients with resected stage III or IV disease remains unknown. Methods: We retrospectively analyzed data from patients with stage III and IV melanoma who had undergone complete resection of disease and received follow-up treatment at Mayo Clinic, Rochester between January 1, 2000 and January 31, 2012. Clinical data were collected from electronic records. Survival data were estimated using the Kaplan-Meier method. Associations of preoperative LDH with time to relapse and death were evaluated using Cox proportional hazards regression models and summarized with hazard ratios and 95% confidence intervals. Results: A total of 154 subjects with resectable stage III or IV melanoma were included in the study. Median age at the time of resection was 58; 54 (35.1%) were female. One-hundred sixteen (75.3%) patients were classified as stage III and 38 (24.7%) stage IV. Adjuvant systemic treatment was administered in 75 (48.7%) patients and adjuvant radiation in 32 (20.7%). Median duration of follow-up was 4.0 years. Sixteen (10.3%) patients had preoperative LDH above the upper limit of normal. Each 50-unit increase in LDH was associated with a 15% increased risk of relapse (HR 1.15; p = 0.040) and 23% increased risk of death (HR 1.23; p = 0.001). After adjusting for age, gender, stage, number of sites, adjuvant systemic treatment, and adjuvant radiation, preoperative LDH remained associated with time to death (HR 1.25; p = 0.002). Preoperative LDH greater than the upper limit of normal was associated with increased hazard of death, both with univariate (HR 2.44; p = 0.005) and multivariate (HR 2.17; p = 0.017) analyses. Conclusions: This study supports the role of elevated preoperative LDH as a predictor of inferior outcomes in patients with advanced melanoma. Further study to correlate LDH to outcomes in the era of adjuvant immunotherapy is required.

Although immune checkpoint inhibitors have resulted in durable clinical benefits in a subset of patients with advanced cancer, some patients who did not respond to initial anti–PD-1 therapy have been found to benefit from the addition of salvage chemotherapy. However, the mechanism responsible for the successful chemoimmunotherapy is not completely understood. Here we show that a subset of circulating CD8 + T cells expressing the chemokine receptor CX3CR1 are able to withstand the toxicity of chemotherapy and are increased in patients with metastatic melanoma who responded to chemoimmunotherapy (paclitaxel and carboplatin plus PD-1 blockade). These CX3CR1 + CD8 + T cells have effector memory phenotypes and the ability to efflux chemotherapy drugs via the ABCB1 transporter. In line with clinical observation, our preclinical models identified an optimal sequencing of chemoimmunotherapy that resulted in an increase of CX3CR1 + CD8 + T cells. Taken together, we found a subset of PD-1 therapy–responsive CD8 + T cells that were capable of withstanding chemotherapy and executing tumor rejection with their unique abilities of drug efflux (ABCB1), cytolytic activity (granzyme B and perforin), and migration to and retention (CX3CR1 and CD11a) at tumor sites. Future strategies to monitor and increase the frequency of CX3CR1 + CD8 + T cells may help to design effective chemoimmunotherapy to overcome cancer resistance to immune checkpoint blockade therapy.

BackgroundPatients with regionally advanced melanoma are at high risk of distant failure and unlikely to be cured by surgery alone. Neoadjuvant therapy may provide benefit in these patients.ObjectivesTo evaluate our experience with neoadjuvant systemic therapy in high‐risk stage III patients.MethodsRetrospective review of patients with advanced stage III disease who received neoadjuvant therapy between August 2009 and August 2016 at Mayo Clinic Rochester.ResultsTwenty‐three cases met our inclusion criteria, 16 with resectable disease and 7 with unresectable disease. No patients with resectable disease and one patient with borderline resectable disease progressed regionally, prohibiting surgical resection. Five of seven patients with unresectable disease were down‐staged to a resectable state. Six of twenty‐three (26%) had a CR and five are alive at last follow‐up. Fifteen of twenty three patients (65%) progressed with a median progression free survival of 11 months; however, the 5 year overall survival estimate was 84%.ConclusionsNeoadjuvant systemic therapy is a reasonable approach for patients with advanced but resectable/borderline resectable disease and the risk of losing regional control is low. Our data also suggest some patients with unresectable disease will be converted to resectable and a complete clinical response to treatment can be obtained in approximately one quater of patients.

The peripheral blood lymphocyte-to-monocyte ratio (LMR) has been associated with prognosis in many malignancies including metastatic melanoma. However, it has not been studied in patients treated with immune checkpoint inhibitors. In this study, we analyzed the baseline LMR with progression-free survival (PFS) and overall survival (OS) in metastatic melanoma patients treated with pembrolizumab. A total of 133 patients with metastatic melanoma treated with pembrolizumab were included in this retrospective study. LMR was calculated from pretherapy peripheral blood counts and the optimal cutoff value was determined by a receiver operator characteristic curve. PFS and OS were evaluated using the Kaplan–Meier method and multivariate Cox proportional hazard modeling. Patients with an LMR of at least 1.7 showed improved PFS (hazard ratio=0.55; 95% confidence interval: 0.34–0.92; P=0.024) and OS (hazard ratio=0.29; 95% confidence interval: 0.15–0.59; P=0.0007). The baseline LMR is associated with PFS and OS in metastatic melanoma patients treated with pembrolizumab, and could represent a convenient and cost-effective prognostic biomarker. Validation of these findings in an independent cohort is needed.

Abstract Background: Understanding the spatial interactions between tumor and immune cells in the heterogeneous tumor microenvironment is pivotal to improve clinical outcomes of immunotherapy. Various multiplexed imaging platforms have been recently developed to visualize the different immune cell subtypes with various distribution patterns that can impact the antitumor immunity. However, the lack of tools that allow for the spatial pattern modeling remains a major barrier for systematic analysis of cellular interactions in the TME. Methods: We used multiplexed immunohistochemistry (mIHC) to understand the cellular interplays in metastatic melanoma TME using FFPE section. This platform allows in situ quantitative single cell analysis with high-level of multiplexing while preserving the tumor heterogeneity. Single cell spatial data (position and location) in the context of TME can be obtained during mIHC, enabling further spatial modeling to characterize the cellular interactions between tumor immune cells. Novel spatial analysis algorithms are developed taking advantages of these spatial data. Results: Using R programming language, we developed two novel analytical approaches to reveal the spatial distributions of various immune cell subtypes relative to tumor cells. The cell neighborhood analysis algorithm uses the “applynbd()” function to traverse through every cellular point on a two-dimensional plane and creates neighborhoods of 24um in diameter centered by a tumor cell. This algorithm captures the counts of a specific type of immune cells that are in direct contact with a tumor cell, allowing the visualization and statistical analysis of cellular attraction/repulsion patterns. The cell aggregate analysis algorithm uses a sliding-window based approach to capture cell aggregates with certain densities and the areas within the TME covered by specific cell aggregates. Combined with the Ripley K-function, the cell aggregate algorithm captures the aggregation vs. segregation pattern between different cell types, enabling the clustering and distance analysis of immune cells relative to tumor cells. Our algorithms demonstrate that tumor cells either attract CD3+ tumor-infiltrating T cells or favor the aggregation of CD20+ B cell in peri-tumor areas depends on the levels of tumor cell human leukocyte antigen-1 (HLA-1) expression. Conclusion: Our novel spatial analysis algorithms enable the modeling of different interaction patterns between tumor and immune cells (direct contact vs. aggregation) at both single cell and TME level. It demonstrates that tumor cells with heterogeneous properties can impact the immune cells’ distributions in the TME with various biological outcomes. It also establishes tools that are necessary for systematic analysis of the TME, allowing the elucidation of the “homogeneous patterns” within the heterogeneous TME. Citation Format: Yiyi Yan, Alberto Santamaria-Pang, Michael Gerdes, Fiona Ginty, Anup Sood, Svetomir N. Markovic, Alexey Leontovich. Novel algorithms for spatial modeling of cellular interactions in the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 547. doi:10.1158/1538-7445.AM2017-547

Abstract The variable clinical success with immune checkpoint inhibitors in the treatment of solid tumors challenges us to further understand the regulatory mechanisms of immune cells infiltration – a critical step of tumor elimination – in a highly heterogenous tumor microenvironment (TME). Systemic analyses of the tumor-immune cell interface and interactions are of particular significance with direct clinical impact. We employed a multiplexed immunofluorescence (MxIF) method, a novel technology that allows in situ quantitative single cell characterization with high order protein multiplexing on whole tumor sections, to elucidate the key components in TME governing the immune cells' composition and function in metastatic melanoma lymph node metastases. Here, we show that the expression of tumor cell Human Leukocyte Antigen 1 (HLA-1) is highly heterogeneous both within the TME and between patients. The level of tumor HLA-1 expression directly correlates with the magnitude of cytotoxic T lymphocytes (CTLs) infiltration in the TME. In addition, decreased tumor HLA-1 expression is associated with accumulation of CD20+ cells at the edge of the tumor as well as an increased population of CD4+FOXP3+ cells. The association between tumor HLA-1 expression and immune cell distribution is highly consistent yet heterogenous across the entire tumor mass. Moreover, using quantitative single cell data, we developed algorithms for computational and spatial modeling of tumor-immune cell interfaces, allowing statistical comparison of cellular interactions in the context of tumor heterogeneity. Spatial analysis demonstrates that tumor HLA-1 expression favors CTL invasion, while lack of tumor HLA-1 expression results in CTL evasion. Furthermore, in a cohort of 166 Stage III melanoma patients, we confirm that CTLs infiltration is only present in regions with high tumor HLA-1 expressions, and that patients with tumors high in both tumor HLA-1 and CTLs have significantly improved progression free survival (PFS) compared with those with tumors low in HLA-1 and/or CTLs. Our study demonstrates that the heterogeneous tumor HLA-1 expression results in various immune cell distribution patterns in the TME, directly contributing to the varied antitumor immunities and ultimately influencing tumor outcomes. As part of the antigen presentation machinery, HLA-1 expression on tumors is necessary for CTLs infiltration and is indispensable for T cell mediated tumor elimination with direct clinical benefits. We also establish a novel platform for visualization and spatial representation of the cellular heterogeneity within the TME, providing data processing and modeling algorithms necessary for understanding the cellular interplay mediating tumor surveillance, which will ultimately improve therapeutic efficacy and clinical outcomes. Citation Format: Yiyi Yan, Alexey Leontovich, Thomas Flotte, Michael Gerdes, Fiona Ginty, Alberto Santamaria-Pang, Anup Sood, Keyur Desai, Chrystal Chadwick, Rong Zhang, Svetomir N. Markovic. Tumor HLA Class I expression influences immune cells infiltration in metastatic melanoma tumor microenvironment. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2016 Oct 20-23; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2017;5(3 Suppl):Abstract nr A71.