Melanoma Microenvironment Analysis Through Gene Expression Profiling

One strategy that has been pursued to gain a broad array of information regarding the melanoma tumor microenvironment and potential associations with clinical outcome to immunotherapies is gene expression profiling. Marincola and colleagues were the first to investigate whether patterns of transcripts in the melanoma tumor site might correlate with clinical response in patients. Using cDNA arrays applied to retrospective fine needle aspirate samples, they identified a set of transcripts present in pretreatment lesions that appeared to be linked with a subsequent response to therapy. These included multiple immunologically relevant genes, including EBI3, TIA1, IRF2, and IFI27, suggesting features of interferon (IFN) responsiveness and cytolytic potential [7]. The expression of interferon-response genes suggests the possibility that innate immune signals mediated through host type I IFNs might contribute to generating a supportive tumor microenvironment for an adaptive immune response. This hypothesis is currently being explored in preclinical models and is consistent with prior mouse model experiments indicated the importance of host Stat1 signaling for the induction of antitumor CTL [8].

In our own laboratory we have utilized the Affymetrix oligonucleotide array platform to analyze a series of melanoma biopsies and cell lines. We opted to study samples obtained by excisional or core biopsy (as opposed to fine needle aspiration) in an attempt to reflect more accurately the totality of the microenvironment. The melanoma cell lines were included to obtain inferential information regarding the likely expression of genes in the melanoma tumor cells themselves versus the stromal compartment. Non-supervised hierarchical clustering revealed two major groups, with a second major division within one group, generating three major clusters. Interestingly, the differences between these three groups were largely accounted for by differential expression of immunologically relevant transcripts. In particular, one cluster of samples expressed transcripts unique to T cells and B cells, a second cluster expressed some inflammatory genes but lacked a lymphocyte signature, and the third cluster was characterized by absence of these inflammatory transcripts (Harlin et al., manuscript submitted). Thus, not all melanoma metastases are the same, and a major distinction seems to be the presence or absence of lymphocyte recruitment.

A second critical observation in this study was a transcriptional profile suggesting an immunosuppressive microenvironment in the tumors that do contain T cells. The tryptophan-catabolizing enzyme indoleamine-2,3-dioxygenase (IDO) was most frequently found in the T cell-rich sample cluster. Interestingly, a second amino acid catabolizing gene, Arginase I, was found in a second group of tumors, indicating that these two factors are often expressed in a mutually exclusive set of tumors. We also saw expression of the inhibitory ligand, PD-L1/B7-H1, and the presence of FoxP3+ regulatory T cells (Tregs) in the T cell-containing tumors. These observations suggest that a rich network of immunosuppressive factors operates in concert to control the effector phase of the antitumor immune response in melanoma patients. Also noteworthy is the relative absence of expression of the T cell costimulatory ligands B7-1 and B7-2, suggesting the possibility of an anergy-promoting environment as well. These features of the metastatic melanoma microenvironment that are most relevant to the host immune response will be discussed in more detail below.

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