Immune profile and mitotic index of metastatic melanoma lesions enhance clinical staging in predicting patient survival

Abstract

Although remission rates for metastatic melanoma are generally very poor, some patients can survive for prolonged periods following metastasis. We used gene expression profiling, mitotic index (MI), and quantification of tumor infiltrating leukocytes (TILs) and CD3+ cells in metastatic lesions to search for a molecular basis for this observation and to develop improved methods for predicting patient survival. We identified a group of 266 genes associated with postrecurrence survival. Genes positively associated with survival were predominantly immune response related (e.g., ICOS, CD3d, ZAP70, TRAT1, TARP, GZMK, LCK, CD2, CXCL13, CCL19, CCR7, VCAM1) while genes negatively associated with survival were cell proliferation related (e.g., PDE4D, CDK2, GREF1, NUSAP1, SPC24). Furthermore, any of the 4 parameters (prevalidated gene expression signature, TILs, CD3, and in particular MI) improved the ability of Tumor, Node, Metastasis (TNM) staging to predict postrecurrence survival; MI was the most significant contributor (HR = 2.13, P = 0.0008). An immune response gene expression signature and presence of TILs and CD3+ cells signify immune surveillance as a mechanism for prolonged survival in these patients and indicate improved patient subcategorization beyond current TNM staging.

Fig. 1.

Metastatic melanoma patient survival differ based on MI, TILs, and CD3 cell count. All available tissue specimens used for gene chip hybridization were also examined for the presence of mitoses; (A) reflects low and (B) reflects high levels of mitosis with bottom left corner showing a magnified section of the slide. They were also examined and scored for presence of TILs. (C) shows a representative view of low and (D) shows a high level of TILs. Paraffin embedded samples were also stained for CD3; (E) shows low levels and (F) shows high levels of CD3+ cells present in the melanoma sample. Kaplan-Meier survival curves for groups based on MI (G, P < 0.0001), TILs (H, P = 0.0163), CD3 cell count (I, P = 0.0134), and stage at recurrence/metastasis (J, overall P = 0.0006, but the separation of IIIb and IIIc is not significant P = 0.59).

Fig. 2.

MI, CD3 counts, and TILs aid staging of IIIb and IIIc patients in predicting their survival. Patients with staging of IIIb and IIIc are represented in (A). Their survival capabilities cannot be distinguished using only staging (P = 0.59). By incorporating MI, CD3, and TILs (B–D) in the model, it is possible to improve the ability to separate stage IIIb/IIIc patients based on their survival (P = 0.0009, P = 0.0139, and P = 0.0178, respectively).

Fig. 3.

Gene signature (PV) and MI are capable of improving current outcome prediction model through machine learning. Predicted high-risk and low-risk groups obtained using (A) prevalidated gene expression predictor (P = 0.027), (B) Stage alone (P = 0.086), (C) combination of Stage and prevalidated gene predictor (P = 0.015) and (D) combination of MI and prevalidated gene predictor (P = 0.0003).