Long-term response to ipilimumab after nivolumab failure in a case of anorectal melanoma with an intermediate tumor mutation burden and negative for PD-L1 expression

Multicenter Evaluation of Radiation and Immune Checkpoint Inhibitor Therapy in Mucosal Melanoma and Review of Recent Literature

Purpose

Optimal integration of local therapy and systemic immune therapy for patients with mucosal melanoma (MM) is uncertain. We evaluated treatment patterns and outcomes following radiation therapy (RT) in combination with immune checkpoint inhibition (ICI) in MM.

Methods and Materials

Thirty-seven patients with localized (n = 32, 87%) or node-positive (n = 5, 14%) MM were treated across 4 institutions with RT to the primary tumor with or without oncologic resection (n = 28, 76%) and ICI from 2012 to 2020. Recurrence rates were estimated using cumulative incidence in the presence of the competing risk of death.

Results

Mucosal sites were head/neck (n = 29, 78%), vaginal (n = 7, 19%), and anorectal (n = 1, 3%). Patients received ICI prior to or concurrent with RT (n = 14, 38%), following RT (n = 5, 14%), or at recurrence (n = 18, 49%). The objective response rate for evaluable patients was 31% for ICI as initial treatment (95% CI, 11%-59%) and 19% for ICI at recurrence (95% CI, 4%-46%). Median follow-up was 26 months for living patients; median overall survival (OS) was 54 months (95% CI, 31 months-not reached). Two-year OS was 85%; distant metastasis-free survival 44%. The 2-year cumulative incidence of local recurrence (LR) was 26% (95% CI, 13%-41%). For 9 patients with unresectable disease, 2-year OS was 88% (95% CI, 35%-98%); LR was 25% (95% CI, 3%-58%). For 5 patients with positive nodes at diagnosis, 2-year OS was 100%; LR was 0%.

Conclusions

High rates of local control were achieved with RT with or without oncologic resection and ICI for localized and locally advanced MM. In particular, favorable local control was possible even for patients with unresectable or node-positive disease. Although risk of distant failure remains high, patients with MM may benefit from aggressive local therapy including RT in the setting of immunotherapy treatment.

Artificial intelligence-based immunoprofiling serves as a potentially predictive biomarker of nivolumab treatment for advanced hepatocellular carcinoma

Immune checkpoint inhibitors (ICI) have been applied in treating advanced hepatocellular carcinoma (aHCC) patients, but few patients exhibit stable and lasting responses. Moreover, identifying aHCC patients suitable for ICI treatment is still challenged. This study aimed to evaluate whether dissecting peripheral immune cell subsets by Mann-Whitney U test and artificial intelligence (AI) algorithms could serve as predictive biomarkers of nivolumab treatment for aHCC. Disease control group carried significantly increased percentages of PD-L1+ monocytes, PD-L1+ CD8 T cells, PD-L1+ CD8 NKT cells, and decreased percentages of PD-L1+ CD8 NKT cells via Mann-Whitney U test. By recursive feature elimination method, five featured subsets (CD4 NKTreg, PD-1+ CD8 T cells, PD-1+ CD8 NKT cells, PD-L1+ CD8 T cells and PD-L1+ monocytes) were selected for AI training. The featured subsets were highly overlapping with ones identified via Mann-Whitney U test. Trained AI algorithms committed valuable AUC from 0.8417 to 0.875 to significantly separate disease control group from disease progression group, and SHAP value ranking also revealed PD-L1+ monocytes and PD-L1+ CD8 T cells exclusively and significantly contributed to this discrimination. In summary, the current study demonstrated that integrally analyzing immune cell profiling with AI algorithms could serve as predictive biomarkers of ICI treatment.