The Role of PTEN Loss in Immune Escape, Melanoma Prognosis and Therapy Response

Rita Cabrita, Shamik Mitra, Adriana Sanna, Henrik Ekedahl, Kristina Lövgren, Håkan Olsson, Christian Ingvar, Karolin Isaksson, Martin Lauss, Ana Carneiro, Göran Jönsson, Rita Cabrita, Shamik Mitra, Adriana Sanna, Henrik Ekedahl, Kristina Lövgren, Håkan Olsson, Christian Ingvar, Karolin Isaksson, Martin Lauss, Ana Carneiro, Göran Jönsson

Abstract

Checkpoint blockade therapies have changed the clinical management of metastatic melanoma patients considerably, showing survival benefits. Despite the clinical success, not all patients respond to treatment or they develop resistance. Although there are several treatment predictive biomarkers, understanding therapy resistance and the mechanisms of tumor immune evasion is crucial to increase the frequency of patients benefiting from treatment. The PTEN gene is thought to promote immune evasion and is frequently mutated in cancer and melanoma. Another feature of melanoma tumors that may affect the capacity of escaping T-cell recognition is melanoma cell dedifferentiation characterized by decreased expression of the microphtalmia-associated transcription factor (MITF) gene. In this study, we have explored the role of PTEN in prognosis, therapy response, and immune escape in the context of MITF expression using immunostaining and genomic data from a large cohort of metastatic melanoma. We confirmed in our cohort that PTEN alterations promote immune evasion highlighted by decreased frequency of T-cell infiltration in such tumors, resulting in a worse patient survival. More importantly, our results suggest that dedifferentiated PTEN negative melanoma tumors have poor patient outcome, no T-cell infiltration, and transcriptional properties rendering them resistant to targeted- and immuno-therapy.

Keywords: PTEN; immune evasion; melanoma.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Characterization of PTEN expression groups in melanoma tumors. (A) Immunostaining of HE, SOX10, and PTEN on tissue microarray representative cores. Sections were taken consecutively. A PTEN-negative case and a PTEN-positive case are shown. Arrowheads indicate tumor cells, and arrows indicate non-tumor cells. (B) Kaplan–Meier survival analysis using log-rank tests of PTEN. (C) Mutational pattern of representative genes of the MAPK and PI3K pathways in PTEN-positive and -negative tumors. Twelve tumors in the PTEN negative group had PTEN mutation; six cases had PIK3CA mutation; and one harbored PIK3R1 mutation. Among the PTEN-positive tumors, only two PTEN mutated tumors were found. (D) Mutational load across PTEN grouping. (E) Boxplot of gene expression of the PTEN gene between PTEN-positive and -negative tumors. p-values in boxplots were calculated using Wilcoxon analysis. (F) Average microenvironment cell populations (MCP) in PTEN-positive and PTEN-negative groups displayed in a heatmap. * FDR < 0.05. All others were non-significant.
Figure 2
Figure 2
Immunological characterization of PTEN/MITF groups. (A) Representative immunostaining of SOX10, CD8, MITF, and PTEN. Sections were taken consecutively. (B) Bar plots of the fraction of present or absent tumor-associated T-cells using immunostaining in the MITF/PTEN groups. (C) Gene expression heatmap of immune cell populations using the MCP algorithm across the MITF/PTEN groups. Each column is the average score across samples belonging to the respective MITF/PTEN group.
Figure 3
Figure 3
Molecular characterization of MITF/PTEN groups. (A) Mutational pattern of representative genes of the MAPK and PI3K pathways in MITF/PTEN groups. In MITFlow/PTENneg, two harbored PTEN mutation, three cases had PIK3CA mutation, and one had PIK3R1 mutation. In the MITFhigh/PTENneg group, ten had PTEN mutation and one had PIK3CA mutation. The two other group had one PTEN mutated case each. (B) Mutational load across MITF/PTEN grouping using the number of mutations across 1500 cancer genes. p > 0.5, Kruskal–Wallis analysis. (C) Kaplan–Meier survival analysis using log-rank tests of MITF and PTEN markers combined. Survival data were missing for two cases in both MITFlow/PTENneg and MITFhigh/PTENneg groups and five cases in the MITFhigh/PTENpos group.
Figure 4
Figure 4
Transcriptional analysis of MITFlow/PTENneg melanoma tumors. (A) SAM analysis identified genes (n = 276) characteristic of MITFlow/PTENneg melanoma tumors. (B) Bar plot showing results from the gene ontology analysis using the 276 genes differentiating MITFlow/PTENneg melanoma tumors from other melanomas. Notably, there are two cell adhesion molecule gene ontology terms. Genes downregulated (blue) include immune adhesion molecules, while upregulated genes include classical adhesion molecules such as N-cadherin. (C) Boxplots of the CTNNB1 (beta-catenin) and FZD1 gene expression value across the MITF/PTEN groups. CTNNB1 shows no significant difference (p = 0.24), while FZD1 expression shows a significant difference (p < 0.0001).
Figure 5
Figure 5
MITFlow/PTENneg gene signature predicts response to MAPK inhibitors and immune checkpoint blockade (ICB). (A) Heatmaps display the expression score of the MITFlow/PTENneg centroid (up- and down-regulated genes in the centroid separately) in the ICB and BRAF/MEK inhibitor datasets. (B) Response data show the number of patients that developed PD (partial disease) or CR (complete response) to BRAFi/MEKi treatment in the two transcriptional groups. p-values were calculated using Fishers exact test. (C) Kaplan–Meier survival analysis using log rank tests of MITFlow/PTENneg tumors and the unclassified group in ICB treated patients.

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