FLT-PET At 6 Weeks Predicts Response Assessed by CT at 12 Weeks in Melanoma Patients Treated With Pembrolizumab.

PURPOSE: Investigate the ability of F-fluorothymidine (FLT) PET combined with CT at 6 weeks to predict treatment response at 12 weeks after treatment with pembrolizumab. METHODS: Five patients with unresectable stage IV melanoma were included in this single-institution pilot study. Patients underwent FLT-PET/CT (baseline and 6 weeks) and CT (baseline and 12 weeks). FLT-PET/CT response and CT response were assessed using PET Response Criteria in Solid Tumors and immune Response Evaluation Criteria in Solid Tumors, respectively. Patients were categorized as responders (complete response, partial response) and nonresponders (stable disease, progressive disease). Agreement between 6-week FLT-PET/CT and 12-week CT was calculated using Cohen kappa's agreement. Eight baseline FLT-PET/CT parameters were extracted: SUVmax, SUVpeak, SUVSD, SUVmean, proliferative tumor volume, total lesion proliferation, bone marrow-to-liver SUVmax ratio, and spleen-to-liver SUVmax ratio. Eight delta-parameters were extracted at 6 weeks by calculating variation in FLT uptake as percentage change from baseline. RESULTS: Agreement between 6-week FLT-PET/CT and 12-week CT was kappa = 0.615, P = 0.025. Three of 5 patients were categorized as responders on CT by immune Response Evaluation Criteria in Solid Tumors. At baseline, responders had a lower mean proliferative tumor volume and a higher bone marrow-to-liver SUVmax ratio. At 6 weeks, responders demonstrated a decrease in tumor volume and tumor proliferation. CONCLUSIONS: Our study illustrates the potential for FLT-PET/CT as an early predictor of response for patients with metastatic melanoma on anti-PD1 immunotherapy. Larger studies are indicated to confirm these findings.

Linking Transcriptomic and Imaging Data Defines Features of a Favorable Tumor Immune Microenvironment and Identifies a Combination Biomarker for Primary Melanoma.

Patients with resected stage II-III melanoma have approximately a 35% chance of death from their disease. A deeper understanding of the tumor immune microenvironment (TIME) is required to stratify patients and identify factors leading to therapy resistance. We previously identified that the melanoma immune profile (MIP), an IFN-based gene signature, and the ratio of CD8+ cytotoxic T lymphocytes (CTL) to CD68+ macrophages both predict disease-specific survival (DSS). Here, we compared primary with metastatic tumors and found that the nuclei of tumor cells were significantly larger in metastases. The CTL/macrophage ratio was significantly different between primary tumors without distant metastatic recurrence (DMR) and metastases. Patients without DMR had higher degrees of clustering between tumor cells and CTLs, and between tumor cells and HLA-DR+ macrophages, but not HLA-DR- macrophages. The HLA-DR- subset coexpressed CD163+CSF1R+ at higher levels than CD68+HLA-DR+ macrophages, consistent with an M2 phenotype. Finally, combined transcriptomic and multiplex data revealed that densities of CD8 and M1 macrophages correlated with their respective cell phenotype signatures. Combination of the MIP signature with the CTL/macrophage ratio stratified patients into three risk groups that were predictive of DSS, highlighting the potential use of combination biomarkers for adjuvant therapy. SIGNIFICANCE: These findings provide a deeper understanding of the tumor immune microenvironment by combining multiple modalities to stratify patients into risk groups, a critical step to improving the management of patients with melanoma.

Prognostic and Predictive Immunohistochemistry-Based Biomarkers in Cancer and Immunotherapy.

Immunotherapy has drastically improved the prognosis of many patients with cancer, but it can also lead to severe immune-related adverse events. Biomarkers, which are molecular markers that indicate a patient's disease outcome or a patient's response to treatment, are therefore crucial to helping clinicians weigh the potential benefits of immunotherapy against its potential toxicities. Immunohistochemistry (IHC) has thus far been a powerful technique for discovery and use of biomarkers such as CD8+ tumor-infiltrating lymphocytes. However, IHC has limited reproducibility. Thus, if more IHC-based biomarkers are to reach the clinic, refinement of the technique using multiplexing or automation is key.

Validation of Melanoma Immune Profile (MIP), a Prognostic Immune Gene Prediction Score for Stage II-III Melanoma.

PURPOSE: Biomarkers are needed to stratify patients with stage II-III melanoma for clinical trials of adjuvant therapy because, while immunotherapy is protective, it also confers the risk of severe toxicity. We previously defined and validated a 53-immune gene melanoma immune profile (MIP) predictive both of distant metastatic recurrence and of disease-specific survival (DSS). Here, we test MIP on a third independent population. EXPERIMENTAL DESIGN: A retrospective cohort of 78 patients with stage II-III primary melanoma was analyzed using the NanoString assay to measure expression of 53 target genes, and MIP score was calculated. Statistical analysis correlating MIP with DSS, overall survival, distant metastatic recurrence, and distant metastasis-free interval was performed using ROC curves, Kaplan-Meier curves, and standard univariable and multivariable Cox proportional hazards models. RESULTS: MIP significantly distinguished patients with distant metastatic recurrence from those without distant metastatic recurrence using ROC curve analysis (AUC = 0.695; P = 0.008). We defined high- and low-risk groups based on the cutoff defined by this ROC curve and find that MIP correlates with both DSS and overall survival by ROC curve analysis (AUC = 0.719; P = 0.004 and AUC = 0.698; P = 0.004, respectively). Univariable Cox regression reveals that a high-risk MIP score correlates with DSS (P = 0.015; HR = 3.2). CONCLUSIONS: MIP identifies patients with low risk of death from melanoma and may constitute a clinical tool to stratify patients with stage II-III melanoma for enrollment in clinical trials.