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Alternative combination models according for the relative impact of LDH, REC, RLC and the pattern of visceral metastasis. A nomogram was developed using the nomogram function of R (A). For each patient a linear predictor score (risk score) considering the relative impact of four single factors was calculated as follows: LDH-ratio >2.5 - 100 points; presence of visceral metastasis other than lung - 96 points; REC

Calibration of combination models. Calibration was calculated after 12 and 24 months using the calibrate function in the rms package of R for combination model 1 (A) and combination model 2 (B). Bootstrapping (1000 repeats) was performed to obtain bias-corrected estimates of predicted vs. observed values. Non-convergence reduced the number of included bootstrapping steps for combination model 2 to 981 or 990 after 12 or 24 months, respectively. "Predicted" survival probabilities at 12 or 24 months are those predicted by the Cox model, and "observed" refers to the corresponding Kaplan-Meier survival estimate at the given time-point. Mean absolute error in predictions, the mean squared error, and the 0.9 quantile of the absolute error is reported. "Error" refers to the difference between the predicted values and the corresponding bias-corrected calibrated values. Mean error was

Supplementary Figures S1-S2 from Homeostatic Proliferation Plus Regulatory T-Cell Depletion Promotes Potent Rejection of B16 Melanoma

Multivariate Models including only patients receiving 3 mg/kg ipilimumab. Lactate dehydrogenase (LDH), regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSCs), hazard ratio (HR). * Relative lymphocyte count, absolute monocyte count, absolute and relative eosinophil count

Overview of factors, cut-offs, and analysis of overall survival according to cohorts

Discriminatory ability of combination models. The concordance index (c-index, y-axis) was calculated for the combination of factors with independent impact according to Cox regression analysis (model 6.1) and 13 alternative combination models considering 5, 7, or 8 factors (A). The numbers refer to the rows in A. The c-indices are presented according to the number of combined factors (B). The combination model with highest discriminatory ability (7.4), which considered regulatory T cells in addition to the 6 factors with independent impact according to Cox regression analysis was chosen as combination model 1. No further increase of the c-index compared to combination model 1 was observed if one of the 3 remaining factors was additionally considered (models 8.1, 8.2, 8.3). C-indices were calculated for different combination models accounting for the number of unfavorable values of all factors considered in the given model (C). All possible models derived from combinations of the five routine factors were considered. The c-indices are presented according to the number of considered factors (D). The model with highest discriminatory ability (4.1) was selected.

Overall survival (OS) following pembrolizumab according to the count of favorable baseline factors considering death from any reason as "event". In total, 194 patients with complete data of the four baseline markers as considered in the combination model died during follow up. The reasons of death were progressive melanoma (n=189), voluntary physician-assisted ending of life (n=2), or cerebral insult (n=3). In contrast to all other analyses throughout the manuscript, Kaplan-Meier curves of OS according to the number of favorable baseline factors are presented considering death from any reason as "event". The discriminatory ability was marginally higher compared to the analysis of melanoma-specific OS (c-index 0.783 vs. 0.782), and differences in OS remained significant in all pairwise comparisons between the five categories (log rank p=0.046 regarding the difference between patients with 4 or 3 favorable factors, p=0.009 regarding the difference between patients with 1 and 0 favorable factors, p

Spectrum of factors, cut-offs, and differences in overall survival according to biomarkers in the identification and the confirmation cohort. Absolute (Abs.), Relative (Rel.), Lactate dehydrogenase (LDH), regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSCs). * Green characters indicate the category associated with better survival in the identification cohort. ** Green cells indicate significant differences in overall survival (p < 0.05). Red cells indicate non-significant findings.

Correlations between adverse events and overall survival, clinical response, or biomarker categories. Overall survival was not different between patients stratified according to the occurrence of adverse events (AEs) in general (A) or immune-related AEs (irAEs). (B). Kaplan-Meier analysis is presented and censoring is indicated by vertical lines; p-values were calculated by log rank statistics (A&B). No correlations were observed between the occurrence of irAEs during ipilimumab treatment and the best tumor response (C, D) nor with the proposed combination groups of baseline biomarkers according to the combination model 1 (E) or combination model 2 (F). The best overall tumor response according to immune-related response criteria (irRC) was analyzed either as the rate of patients with an irRC response (sum of those with complete or partial responses) or irRC benefit (sum of those with complete responses, partial responses and stable disease). Differences were not statistically significant.

Panels of antibodies used for flow cytometry. * Cells were fixed and permeabilized with FoxP3 buffer (BD). Only frequencies of CD14+ cells from MDSC panel 1 and frequencies of CD4+, CD8+ T cells, as well as their ratio, were included from Treg panel 1. 1 Panel 1 (n=25), 2 Panel 2 (n=184).

Algorithm for cut-off point definition

Purpose: To investigate the antitumor efficacy of T-cell anergy reversal through homeostatic proliferation and regulatory T-cell (Treg) depletion in a clinically relevant murine adoptive immunotherapy model.Experimental Design: B16 melanoma cells were engineered to express the model SIYRYYGL (SIY) antigen to enable immune monitoring. Tumor-specific T cells expanded in tumor-challenged wild-type hosts but became hyporesponsive. To examine whether lymphopenia-induced homeostatic proliferation could reverse tumor-induced T-cell anergy, total splenic T cells were transferred into lymphopenic RAG2−/− mice or control P14/RAG2−/− mice. Tumor growth was measured, and SIY-specific immune responses were monitored using ELISPOT and SIY/Kb tetramers. To determine whether Treg depletion could synergize with homeostatic proliferation, RAG2−/− mice received total or CD25-depleted T cells, followed or preceded by B16.SIY challenge. This approach was further investigated in wild-type mice lymphodepleted with sublethal total body irradiation.Results: Adoptive transfer of total splenic T cells into RAG2−/− mice moderately affected the growth rate of B16.SIY. As Treg expansion occurred in tumor-bearing mice, CD25+ T cells were depleted from total T cells before adoptive transfer. Interestingly, transfer of CD25-depleted T cells into RAG2−/− mice resulted in potent rejection of B16 melanoma in both prophylactic and short-term preimplanted tumor settings and was associated with maintained T-cell effector function. Using a clinically applicable approach, wild-type mice were lymphodepleted using sublethal total body irradiation, which similarly supported tumor rejection upon transfer of CD25-depleted T cells.Conclusions: Our results indicate that combined CD25 depletion and homeostatic proliferation support a potent antitumor immune response—an approach with potential for clinical translation.

Purpose: To identify baseline peripheral blood biomarkers associated with clinical outcome following ipilimumab treatment in advanced melanoma patients.Experimental Design: Frequencies of myeloid-derived suppressor cells (MDSC) and regulatory T cells (Treg), serum lactate dehydrogenase (LDH), routine blood counts, and clinical characteristics were assessed in 209 patients. Endpoints were overall survival (OS) and best overall response. Statistical calculations were done by Kaplan–Meier and Cox regression analysis, including calibration and discrimination by C-statistics.Results: Low baseline LDH, absolute monocyte counts (AMC), Lin−CD14+HLA-DR−/low-MDSC frequencies, and high absolute eosinophil counts (AEC), relative lymphocyte counts (RLC), and CD4+CD25+FoxP3+-Treg frequencies were significantly associated with better survival, and were considered in a combination model. Patients (43.5%) presenting with the best biomarker signature had a 30% response rate and median survival of 16 months. In contrast, patients with the worst biomarkers (27.5%) had only a 3% response rate and median survival of 4 months. The occurrence of adverse events correlated with neither baseline biomarker signatures nor the clinical benefit of ipilimumab. In another model, limited to the routine parameters LDH, AMC, AEC, and RLC, the number of favorable factors (4 vs. 3 vs. 2–0) was also associated with OS (P < 0.001 for all pairwise comparisons) in the main study and additionally in an independent validation cohort.Conclusions: A baseline signature of low LDH, AMC, and MDSCs as well as high AEC, Tregs, and RLC is associated with favorable outcome following ipilimumab. Prospective investigation of the predictive impact of these markers following ipilimumab and other treatments, e.g., PD-1 antibodies, is warranted. Clin Cancer Res; 22(12); 2908–18. ©2016 AACR.

Purpose: Biomarkers for outcome after immune-checkpoint blockade are strongly needed as these may influence individual treatment selection or sequence. We aimed to identify baseline factors associated with overall survival (OS) after pembrolizumab treatment in melanoma patients.Experimental Design: Serum lactate dehydrogenase (LDH), routine blood count parameters, and clinical characteristics were investigated in 616 patients. Endpoints were OS and best overall response following pembrolizumab treatment. Kaplan–Meier analysis and Cox regression were applied for survival analysis.Results: Relative eosinophil count (REC) ≥1.5%, relative lymphocyte count (RLC) ≥17.5%, ≤2.5-fold elevation of LDH, and the absence of metastasis other than soft-tissue/lung were associated with favorable OS in the discovery (n = 177) and the confirmation (n = 182) cohort and had independent positive impact (all P < 0.001). Their independent role was subsequently confirmed in the validation cohort (n = 257; all P < 0.01). The number of favorable factors was strongly associated with prognosis. One-year OS probabilities of 83.9% versus 14.7% and response rates of 58.3% versus 3.3% were observed in patients with four of four compared to those with none of four favorable baseline factors present, respectively.Conclusions: High REC and RLC, low LDH, and absence of metastasis other than soft-tissue/lung are independent baseline characteristics associated with favorable OS of patients with melanoma treated with pembrolizumab. Presence of four favorable factors in combination identifies a subgroup with excellent prognosis. In contrast, patients with no favorable factors present have a poor prognosis, despite pembrolizumab, and additional treatment advances are still needed. A potential predictive impact needs to be further investigated. Clin Cancer Res; 22(22); 5487–96. ©2016 AACR.

Patients treated with pembrolizumab according to clinical site

Overall survival according to the relative lymphocyte and eosinophil counts in 3 subgroups of patients defined by LDH and the pattern of visceral metastasis. Kaplan Meier curves for OS are presented according to relative lymphocyte count (left), relative eosinophil counts (middle) and the count of favorable pre-treatment values considering both factors (right). In patients 132 patients with LDH-ratio {less than or equal to}2.5 AND no visceral metastasis other than lung (A) OS was significantly longer in patients with RLC {greater than or equal to}17.5% (p=0.003) and in those with 2 vs 0 favorable pretreatment values (p=0.022). In 310 patients with either LDH-ratio {less than or equal to}2.5 OR no visceral metastasis other than lung (B) OS was significantly in all pairwise comparisons of the three biomarker categories (all log rank p2.5 AND visceral metastasis other than lung (C) the differences in OS between patients with 2 vs 0 favorable pretreatment factors was not statistically significant (log rank p=0.108).

Relative proportion and survival analyses of all possible combination groups accounting for LDH, pattern of visceral metastasis, RLC and REC

Detailed gating strategy for quantification of subsets of monocytes and myeloid-derived suppressor cells (MDSCs), T cells and regulatory T cells (Tregs). Total cells were selected by gating on Time vs. SSC-A. Duplicates were removed via progressive gating on FSC-H vs. FSC-A and SSC-H vs. SSC-A. Dead cells were excluded by considering only EMA-negative cells. (A) A lineage cocktail (CD3, CD19, CD56) was used to avoid cross-contamination. Previously described MDSC populations were identified as Lin-CD14+HLA-DRlow and Lin-CD14-CD15+CD11b+ within the all-cell gate. Overall monocytes were defined as CD14+, while subsets were separated into classical monocytes (Lin-CD14+CD16-HLA-DR+), non-classical monocytes (Lin-CD14-CD16+HLA-DR+) and Lin- CD14-CD16dimHLA-DR+ monocytes within the all-cell gate. (B) A morphological gate was used to identify the population of lymphocytes. Next, CD3+ cells were selected and further separated into CD4+ and CD8+ cells. Ki67 expression was investigated on CD4+ and CD8+ cells. CD8+ T cells with suppressive potential were defined as CD103+. Previously described phenotypes of Tregs were defined as CD4+CD25+FoxP3+ and CD4+CD127lowCD25+FoxP3+. These were further subdivided into proliferating (Ki67+CD45RA-) and non-proliferating Tregs (Ki67-CD45RA+).

Overall survival and distribution after first dose of ipilimumab according to subsequent treatments. Of 209 patients, 71 received at least one additional systemic line of treatment after ipilimumab. 137 individuals did not receive further therapy and data were not available for one patient. 47 (combination model 1) or 67 (combination model 2) of 71 patients had complete data for classification according to biomarker combination models. The representation of PD-1/PD-L1-treated patients in the biomarker groups was shifted towards favorable biomarker combination groups for both combination models compared to those without subsequent PD-1/PD-L1 treatment. Therefore, a confounding effect of subsequent treatment with PD-1/PD-L1 antibodies on the biomarker results of this study cannot be ruled out (A). To investigate the potential confounding impact on OS and biomarker findings, subsequent treatments were categorized into three different groups: BRAF/MEK inhibitors (n=24), PD-1/PD-L1 antibodies (n=28), and chemotherapy/other treatments (n=33) and analyzed by the Kaplan-Meier method (B). Patients treated with PD-1/PD-L1 antibodies had a significant better survival compared to all 71 patients (p=0.006), while no significant difference was observed for the other two groups. Kaplan Meier analysis of overall survival of patients classified according to combination model 1 (C) or combination model 2 (D) is presented after exclusion of individuals who received subsequent treatment with anti-PD-1 or PD-L1 antibodies, as a confounding effect could not be ruled out. However, the prognostic impact of the proposed biomarker combinations at baseline of ipilimumab treatment remained strong (p