Proteomic Profiling of Advanced Melanoma Patients to Predict Therapeutic Response to Anti-PD-1 Therapy.

PURPOSE: Despite high clinical need, there are no biomarkers that accurately predict the response of patients with metastatic melanoma to anti-PD-1 therapy. EXPERIMENTAL DESIGN: In this multicenter study, we applied protein depletion and enrichment methods prior to various proteomic techniques to analyze a serum discovery cohort (n = 56) and three independent serum validation cohorts (n = 80, n = 12, n = 17). Further validation analyses by literature and survival analysis followed. RESULTS: We identified several significantly regulated proteins as well as biological processes such as neutrophil degranulation, cell-substrate adhesion, and extracellular matrix organization. Analysis of the three independent serum validation cohorts confirmed the significant differences between responders (R) and nonresponders (NR) observed in the initial discovery cohort. In addition, literature-based validation highlighted 30 markers overlapping with previously published signatures. Survival analysis using the TCGA database showed that overexpression of 17 of the markers we identified correlated with lower overall survival in patients with melanoma. CONCLUSIONS: Ultimately, this multilayered serum analysis led to a potential marker signature with 10 key markers significantly altered in at least two independent serum cohorts: CRP, LYVE1, SAA2, C1RL, CFHR3, LBP, LDHB, S100A8, S100A9, and SAA1, which will serve as the basis for further investigation. In addition to patient serum, we analyzed primary melanoma tumor cells from NR and found a potential marker signature with four key markers: LAMC1, PXDN, SERPINE1, and VCAN.

Improved Survival Prediction by Combining Radiological Imaging and S-100B Levels Into a Multivariate Model in Metastatic Melanoma Patients Treated With Immune Checkpoint Inhibition.

Purpose: We explored imaging and blood bio-markers for survival prediction in a cohort of patients with metastatic melanoma treated with immune checkpoint inhibition. Materials and Methods: 94 consecutive metastatic melanoma patients treated with immune checkpoint inhibition were included into this study. PET/CT imaging was available at baseline (Tp0), 3 months (Tp1) and 6 months (Tp2) after start of immunotherapy. Radiological response at Tp2 was evaluated using iRECIST. Total tumor burden (TB) at each time-point was measured and relative change of TB compared to baseline was calculated. LDH, CRP and S-100B were also analyzed. Cox proportional hazards model and logistic regression were used for survival analysis. Results: iRECIST at Tp2 was significantly associated with overall survival (OS) with C-index=0.68. TB at baseline was not associated with OS, whereas TB at Tp1 and Tp2 provided similar predictive power with C-index of 0.67 and 0.71, respectively. Appearance of new metastatic lesions during follow-up was an independent prognostic factor (C-index=0.73). Elevated LDH and S-100B ratios at Tp2 were significantly associated with worse OS: C-index=0.73 for LDH and 0.73 for S-100B. Correlation of LDH with TB was weak (r=0.34). A multivariate model including TB change, S-100B, and appearance of new lesions showed the best predictive performance with C-index=0.83. Conclusion: Our analysis shows only a weak correlation between LDH and TB. Additionally, baseline TB was not a prognostic factor in our cohort. A multivariate model combining early blood and imaging biomarkers achieved the best predictive power with regard to survival, outperforming iRECIST.

Melanoma-derived small extracellular vesicles induce lymphangiogenesis and metastasis through an NGFR-dependent mechanism.

Secreted extracellular vesicles (EVs) influence the tumor microenvironment and promote distal metastasis. Here, we analyzed the involvement of melanoma-secreted EVs in lymph node pre-metastatic niche formation in murine models. We found that small EVs (sEVs) derived from metastatic melanoma cell lines were enriched in nerve growth factor receptor (NGFR, p75NTR), spread through the lymphatic system and were taken up by lymphatic endothelial cells, reinforcing lymph node metastasis. Remarkably, sEVs enhanced lymphangiogenesis and tumor cell adhesion by inducing ERK kinase, nuclear factor (NF)-κB activation and intracellular adhesion molecule (ICAM)-1 expression in lymphatic endothelial cells. Importantly, ablation or inhibition of NGFR in sEVs reversed the lymphangiogenic phenotype, decreased lymph node metastasis and extended survival in pre-clinical models. Furthermore, NGFR expression was augmented in human lymph node metastases relative to that in matched primary tumors, and the frequency of NGFR+ metastatic melanoma cells in lymph nodes correlated with patient survival. In summary, we found that NGFR is secreted in melanoma-derived sEVs, reinforcing lymph node pre-metastatic niche formation and metastasis.

Radiomics, Tumor Volume, and Blood Biomarkers for Early Prediction of Pseudoprogression in Patients with Metastatic Melanoma Treated with Immune Checkpoint Inhibition.

PURPOSE: We assessed the predictive potential of positron emission tomography (PET)/CT-based radiomics, lesion volume, and routine blood markers for early differentiation of pseudoprogression from true progression at 3 months. EXPERIMENTAL DESIGN: 112 patients with metastatic melanoma treated with immune checkpoint inhibition were included in our study. Median follow-up duration was 22 months. 716 metastases were segmented individually on CT and 2[18F]fluoro-2-deoxy-D-glucose (FDG)-PET imaging at three timepoints: baseline (TP0), 3 months (TP1), and 6 months (TP2). Response was defined on a lesion-individual level (RECIST 1.1) and retrospectively correlated with FDG-PET/CT radiomic features and the blood markers LDH/S100. Seven multivariate prediction model classes were generated. RESULTS: Two-year (median) overall survival, progression-free survival, and immune progression-free survival were 69% (not reached), 24% (6 months), and 42% (16 months), respectively. At 3 months, 106 (16%) lesions had progressed, of which 30 (5%) were identified as pseudoprogression at 6 months. Patients with pseudoprogressive lesions and without true progressive lesions had a similar outcome to responding patients and a significantly better 2-year overall survival of 100% (30 months), compared with 15% (10 months) in patients with true progressions/without pseudoprogression (P = 0.002). Patients with mixed progressive/pseudoprogressive lesions were in between at 53% (25 months). The blood prediction model (LDH+S100) achieved an AUC = 0.71. Higher LDH/S100 values indicated a low chance of pseudoprogression. Volume-based models: AUC = 0.72 (TP1) and AUC = 0.80 (delta-volume between TP0/TP1). Radiomics models (including/excluding volume-related features): AUC = 0.79/0.78. Combined blood/volume model: AUC = 0.79. Combined blood/radiomics model (including volume-related features): AUC = 0.78. The combined blood/radiomics model (excluding volume-related features) performed best: AUC = 0.82. CONCLUSIONS: Noninvasive PET/CT-based radiomics, especially in combination with blood parameters, are promising biomarkers for early differentiation of pseudoprogression, potentially avoiding added toxicity or delayed treatment switch.

Blood-based kinase activity profiling: a potential predictor of response to immune checkpoint inhibition in metastatic cancer.

BACKGROUND: Many cancer patients do not obtain clinical benefit from immune checkpoint inhibition. Checkpoint blockade targets T cells, suggesting that tyrosine kinase activity profiling of baseline peripheral blood mononuclear cells may predict clinical outcome. METHODS: Here a total of 160 patients with advanced melanoma or non-small-cell lung cancer (NSCLC), treated with anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA-4) or anti-programmed cell death 1 (anti-PD-1), were divided into five discovery and cross-validation cohorts. The kinase activity profile was generated by analyzing phosphorylation of peripheral blood mononuclear cell lysates in a microarray comprising of 144 peptides derived from sites that are substrates for protein tyrosine kinases. Binary grouping into patients with or without clinical benefit was based on Response Evaluation Criteria in Solid Tumors V.1.1. Predictive models were trained using partial least square discriminant analysis (PLS-DA), performance of the models was evaluated by estimating the correct classification rate (CCR) using cross-validation. RESULTS: The kinase phosphorylation signatures segregated responders from non-responders by differences in canonical pathways governing T-cell migration, infiltration and co-stimulation. PLS-DA resulted in a CCR of 100% and 93% in the anti-CTLA-4 and anti-PD1 melanoma discovery cohorts, respectively. Cross-validation cohorts to estimate the accuracy of the predictive models showed CCRs of 83% for anti-CTLA-4 and 78% or 68% for anti-PD-1 in melanoma or NSCLC, respectively. CONCLUSION: Blood-based kinase activity profiling for response prediction to immune checkpoint inhibitors in melanoma and NSCLC revealed increased kinase activity in pathways associated with T-cell function and led to a classification model with a highly accurate classification rate in cross-validation groups. The predictive value of kinase activity profiling is prospectively verified in an ongoing trial.

Genetic and Environmental Determinants of Immune Response to Cutaneous Melanoma.

The immune response to melanoma improves the survival in untreated patients and predicts the response to immune checkpoint blockade. Here, we report genetic and environmental predictors of the immune response in a large primary cutaneous melanoma cohort. Bioinformatic analysis of 703 tumor transcriptomes was used to infer immune cell infiltration and to categorize tumors into immune subgroups, which were then investigated for association with biological pathways, clinicopathologic factors, and copy number alterations. Three subgroups, with "low", "intermediate", and "high" immune signals, were identified in primary tumors and replicated in metastatic tumors. Genes in the low subgroup were enriched for cell-cycle and metabolic pathways, whereas genes in the high subgroup were enriched for IFN and NF-κB signaling. We identified high MYC expression partially driven by amplification, HLA-B downregulation, and deletion of IFNγ and NF-κB pathway genes as the regulators of immune suppression. Furthermore, we showed that cigarette smoking, a globally detrimental environmental factor, modulates immunity, reducing the survival primarily in patients with a strong immune response. Together, these analyses identify a set of factors that can be easily assessed that may serve as predictors of response to immunotherapy in patients with melanoma. SIGNIFICANCE: These findings identify novel genetic and environmental modulators of the immune response against primary cutaneous melanoma and predict their impact on patient survival.See related commentary by Anichini, p. 2457.

Peripheral Blood TCR Repertoire Profiling May Facilitate Patient Stratification for Immunotherapy against Melanoma.

Many metastatic melanoma patients experience durable responses to anti-PD1 and/or anti-CTLA4; however, a significant proportion (over 50%) do not benefit from the therapies. In this study, we sought to assess pretreatment liquid biopsies for biomarkers that may correlate with response to checkpoint blockade. We measured the combinatorial diversity evenness of the T-cell receptor (TCR) repertoire (the DE50, with low values corresponding to more clonality and lack of TCR diversity) in pretreatment peripheral blood mononuclear cells from melanoma patients treated with anti-CTLA4 (n = 42) or anti-PD1 (n = 38) using a multi-N-plex PCR assay on genomic DNA (gDNA). A receiver operating characteristic curve determined the optimal threshold for a dichotomized analysis according to objective responses as defined by RECIST1.1. Correlations between treatment outcome, clinical variables, and DE50 were assessed in multivariate regression models and confirmed with Fisher exact tests. In samples obtained prior to treatment initiation, we showed that low DE50 values were predictive of a longer progression-free survival and good responses to PD-1 blockade, but, on the other hand, predicted a poor response to CTLA4 inhibition. Multivariate logistic regression models identified DE50 as the only independent predictive factor for response to anti-CTLA4 therapy (P = 0.03) and anti-PD1 therapy (P = 0.001). Fisher exact tests confirmed the association of low DE50 with response in the anti-CTLA4 (P = 0.041) and the anti-PD1 cohort (P = 0.0016). Thus, the evaluation of basal TCR repertoire diversity in peripheral blood, using a PCR-based method, could help predict responses to anti-PD1 and anti-CTLA4 therapies.

Melanoma Immunotherapy: Next-Generation Biomarkers.

The recent emergence of cancer immunotherapies initiated a significant shift in the clinical management of metastatic melanoma. Prior to 2011, melanoma patients only had palliative treatment solutions which offered little to no survival benefit. In 2018, with immunotherapy, melanoma patients can now contemplate durable or even complete remission. Treatment with novel immune checkpoint inhibitors, anti-cytotoxic T-lymphocyte protein 4 and anti-programmed cell death protein 1, clearly result in superior median and long-term survivals compared to standard chemotherapy; however, more than half of the patients do not respond to immune checkpoint blockade. Currently, clinicians do not have any effective way to stratify melanoma patients for immunotherapies. Research is now focusing on identifying biomarkers which could predict a patient's response prior treatment initiation (or very early during treatment course), in order to maximize therapeutic efficacy, avoid unnecessary costs, and undesirable heavy side effects for the patient. Given the rapid developments in this field and the translational potential for some of the biomarkers, we will summarize the current state of biomarker research for immunotherapy in melanoma, with an emphasis on omics technologies such as next-generation sequencing and mass cytometry (CyTOF).