Heterogeneity in Melanoma Batool Shannan, Michela Perego, Rajasekharan Somasundaram and Meenhard Herlyn
Abstract
Melanoma is among the most aggressive and therapy-resistant human cancers. While great strides in therapy have generated enthusiasm, many challenges remain. Heterogeneity is the most pressing issue for all types of therapy. This chapter summarizes the clinical classification of melanoma, of which the research community now adds additional layers of classifications for better diagnosis and prediction of therapy response. As the search for new biomarkers increases, we expect that biomarker analyses will be essential for all clinical trials to better select patient populations for optimal therapy. While individualized therapy that is based on extensive biomarker analyses is an option, we expect in the future genetic and biologic biomarkers will allow grouping of melanomas in such a way that we can predict therapy outcome. At this time, tumor heterogeneity continues to be the major challenge leading inevitably to relapse. To address heterogeneity therapeutically, we need to develop complex therapies that eliminate the bulk of the tumor and, at the same time, the critical subpopulations. Keywords
Melanoma
Heterogeneity Therapy
Batool Shannan and Michela Perego have equally contributed. B. Shannan M. Perego R. Somasundaram M. Herlyn (&) Molecular and Cellular Oncogenesis Program, Melanoma Research Center, The Wistar Institute, 3601 Spruce Street, Philadelphia, PA 19104, USA e-mail:
[email protected]
© Springer International Publishing Switzerland 2016 H.L. Kaufman and J.M. Mehnert (eds.), Melanoma, Cancer Treatment and Research 167, DOI 10.1007/978-3-319-22539-5_1
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Abbreviations
ABCB5 AKT ALCAM ALDH1 ARID1A BRAF CD CDK CDKN2A ERK Fbxw-7 gp100 HGF IGF JARID1B Kit MAPK MART-1/Melan-A MCAM MEK MITF mTOR NF NGF NGFR NRAS PI3K PTEN Rac1 RAF RAS SCF SEER TGF-β TICs TME TNF TP53 UV
ATP-binding cassette subfamily B5 V-akt murine thymoma viral oncogene homolog Activated leukocyte cell adhesion molecule Aldehyde dehydrogenase 1 AT-rich interactive domain-containing protein 1A V-raf murine sarcoma viral oncogene homolog B1 Cluster differentiation Cyclin-dependent Kinase Cyclin-dependent kinase inhibitor 2A Extracellular signal-regulated kinase F-box/WD repeat-containing protein 7 Glycoprotein 100 Hepatocyte growth factor Insulin-like growth factor Jumonji/ARID1 (JARID1) histone 3 K4 (H3K4) demethylases C-kit tyrosine kinase receptor Mitogen-activated protein kinase Melanoma antigen recognized by T cells-1/melanoma antigen A Melanoma cell adhesion molecule MAPK/ERK Kinase Microphthalmia-associated transcription factor Mammalian target of Rapamycin Neurofibromatosis Nerve growth factor Nerve growth factor receptor Neuroblastoma RAS viral (v-ras) oncogene homolog Phosphoinositide-3 Kinase Phosphatase and tensin homolog Ras-related C3 botulinum toxin substrate-1 RAS viral (v-raf) oncogene homolog RAS viral (v-ras) oncogene homolog Stem cell factor Surveillance, epidemiology, end results Transforming growth factor beta Tumor-initiating cells Tumor microenvironment Tumor necrosis factor Tumor protein p53 Ultraviolet
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Contents 1 Introduction ............................................................................................................................ 3 2 Clinical and Molecular Classification of Melanoma ............................................................ 3 3 Genetic Heterogeneity in Melanoma..................................................................................... 7 4 Biological Heterogeneity in Melanoma................................................................................. 8 5 Conclusions............................................................................................................................ 10 References .................................................................................................................................... 12
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Introduction
Melanoma is the most aggressive form of skin cancer and incidences continue to rise worldwide. According to the American Cancer Society, an estimated 76,100 new cases of melanoma will be diagnosed in the USA and 9710 people are expected to die of metastatic disease [48]. SEER data indicate the prevalence of melanoma in the older age group, particularly men over the age of 65. However, in recent years, young adults, particularly women between the ages of 25–39 years, have pronounced increases in incidence rates, often with severe outcomes [4, 39, 71]. Although intense intermittent sun exposure is a major risk factor for melanoma, family history of melanoma, genetic susceptibility, environmental factors, and immunosuppression are some of the other factors that influence incidence rates [48]. Efforts are underway to understand the biology of melanoma heterogeneity to better design strategies for more precise choices for targeting. In this chapter, we review clinical and genetic profiles in melanoma and discuss heterogeneity as one of the most significant causes for cancer therapy resistance.
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Clinical and Molecular Classification of Melanoma
Clinical and histological classifications of melanoma have been extensively described (Fig. 1a, b). When dividing melanomas into those derived from cells within epithelia, there are four categories: (1) lentigo and desmoplastic melanomas (from areas on the head and neck with high ultraviolet (UV) exposure); (2) low UV exposure areas (gives rise to superficial spreading and spitzoid melanomas which also includes non-malignant lesions such as acquired and dysplastic nevi, Spitz nevi, and atypical Spitz tumors); (3) mucosal melanomas (those of the genital track); and (4) lesions of palms, soles, and nails giving rise to acral melanomas (Fig. 1a). Melanomas arising in areas outside of epithelia represent the second major group. The group is comprised of melanomas in the eye and internal organs such as the gut (Fig. 1b). Dermis-derived melanomas in the skin include blue nevus-like melanomas and those arising within congenital nevi. It is speculated that this latter group of lesions arise from neural crest-like stem cells in the dermis [70], but experimental proof has yet to be determined. Alternatively, findings in other cancers such as leukemia/lymphoma, breast cancer, or various brain tumors suggest
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(a) Epithelium-Associated
High-UV (head and neck)
Lentigo Melanoma
Low-UV
Desmoplastic Melanoma
Acquired Nevi
Spitz Nevi
Dysplastic Nevi
Atypical Spitz Tumor
Non-CSD Melanoma
Spitzoid Melanoma
Mucosal
Palm/Sole/ Nail
Mucosal Melanoma
Acral Melanoma
(b) Non-Epithelium Associated
Eye
Internal Organs
Uveal Nevus
Melanocytoma
Skin
Blue Nevus
Congenital Nevus
Atypical Blue Nevus
Uveal Melanoma
Visceral Melanoma
Blue Nevuslike Melanoma
Melanoma in Congenital Nevus
Fig. 1 Clinical grouping of melanoma. There are distinct patterns of clinical appearances of melanoma that led to the distinction of the histogenetic types in the first classification system in 1973. a Melanomas arising from epithelium-associated melanocytes. The relationship between sun exposure and melanoma has been established for decades (high UV melanoma); however, it was later discovered that melanomas can also occur in areas that are well-protected from UV exposure (low UV, mucosal, and palm/sole/nail melanomas). b Melanomas arising from non-epitheliumassociated melanocytes. These melanomas fall into the categories of intradermal melanocytic neoplasms (blue nevi, uveal melanoma). These types of melanoma harbor mutations of G protein alpha subunits of Gq family. These mutations are virtually absent from epithelium-associated melanocytes. Melanocytomas are neoplasms of the central nervous system and they closely resemble blue nevi. However, they can pose differential problems to melanoma metastases diagnosis. CSD chronic sun damage. Adapted from Bastian [4]
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that most, if not all, tumors arise from the respective stem cell populations. In human skin, there are two populations of neural crest-like stem cells, one in the dermis [28] and the other in the bulge region of the hair follicle [68]. Each stem cell can differentiate into multiple cell types including neuronal cells, melanocytes, smooth muscle cells, adipocytes, chondrocytes, osteoblasts, or Schwann cells [28]. Which of the two stem cells is more important in melanoma development is not clear, although there is a general lack of clinical and pathological information on melanomas arising from hair follicles. Interestingly, melanocytes can dedifferentiate to neural crest-like stem cells when Notch signaling is activated [42] suggesting a fluid transition from one state of cellular differentiation to the other, making it very difficult to trace the origin of melanomas. As tumors become more aggressive, they often lose their pigmentation markers and dedifferentiate acquiring stem cell features, which make them more resistant to therapy. The clinical classifications are the results of long-standing observations. They have been critical in making therapy decisions, although their usefulness as guides has been controversial. For practicing oncologists, a distinction between benign and malignant has been most critical, with intermediate stages generating controversial discussions, some of which have been ongoing for decades without a clear resolution. One major reason for this is that any suspected lesion is surgically removed for extensive diagnostic evaluation. Thus, follow-up of existing lesions has rarely been done, leading to considerable variations in risk estimates for dysplastic nevi, as well as biologically early melanomas that progress to aggressive tumors. Genetic analyses of melanocytic lesions have for the first time allowed a more detailed classification, but such new classifications are at an early stage as we know of very few drivers in the disease. Still, the first genetic analyses are becoming routine in making clinical decisions for melanoma therapy. It is important to discuss the major mutations in melanoma and their affected pathways, while acknowledging that of all human cancers, melanomas carry the most mutations, generally more than 10 per Mb with lung cancer following as the second most non-euploid tumor [33]. The mitogen-activated protein kinase (MAPK) pathway is one of the major signaling cascades involved in the control of cell growth and migration. The RAS/RAF/MEK/ERK pathway regulates cell properties downstream of tyrosine kinase receptors and heterodimeric G protein-coupled receptors. Melanomas are addicted to MAPK activation, regardless of whether or not tumors carry mutations in genes coding for proteins in this pathway. In normal melanocytes, the pathway is activated by growth factors/ligands such as stem cell factor (SCF), fibroblast growth factor, and hepatocyte growth factor (HGF). In melanoma, the same growth factors (except SCF) are produced for autocrine stimulation. Most important for melanoma cells is the constitutive activation of the MAPK pathway through activating mutations of BRAF (*50 % of melanomas) or NRAS (20–25 % of mutations; Fig. 2). This allows cells to vigorously grow, even in the absence of ligand. For example, the V600E mutation in BRAF upregulates the pathway 800-fold when compared to the inactive forms [63]. Mutations in BRAF can already occur in nevi, but they generally lead to the induction of senescence because cells are unable to cope with such tremendous
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NRAS (activation, ~20-25%) BRAF (activation, ~50%) KIT (activation, ~3%) Cyclin D1 (amplification, ~11%)
Gain of Function
CDK4 (amplification/mutation, ~3%) MITF (amplification, ~4%)
Genetic Alterations in Melanoma
RAC1 (amplification, ~5%) p53 (amplification, ~5%) FBXW7 (amplification/activation ~3%) CDKN2A (p16) (deletion/mutation,
