Identification of Molecular Subtypes of Melanoma

One of the main challenges for medicine in the early part of 21st century is to incorporate information derived from the human genome into the clinical management of patients, thereby yielding more precise diagnoses and mechanism-based deployment of therapy. Nowhere is this need more pressing than in the treatment of cancer. Although melanoma accounts for only 4% of all skin cancers, it is responsible for 80% of skin cancer mortality. Melanoma displays considerable clinical, morphological, and biological heterogeneity. We hypothesize that the variability and the unpredictability in the clinical course and in therapeutic responses of melanoma is determined by heretofore largely unrecognized molecular heterogeneity of the disease. Given this heterogeneity, we propose here to identify novel molecular subtypes carrying distinct genetic alterations, which drive specific clinical and biological features of melanomas. With the access to a unique human melanoma tissue collection, annotated with extensive clinical information, to advanced computational and biostatistical technologies, to data already generated by complementary high-end genomic approaches (gene expression arrays, single nucleotide polymorphism (SNP) arrays, comparative genomic hybridization (CGH) arrays), and to in vitro and in vivo models of the disease, this project provides the opportunity to filter and prioritize genetic and molecular alterations occurring in human melanoma and to identify novel molecular subgroups of the disease. The guiding principles of the proposed plan have been organized into three Specific Aims: Aim 1: Identification of genes with an outlier profile in melanoma defining novel molecular subgroups of the disease. We plan to use several bioinformatics approaches developed to specifically identify candidate genes with extreme expression levels (outliers) in an already generated melanoma expression array dataset. Aim 2: Integrative exploration of outlier genes with genome-wide information obtained by SNP and CGH arrays and with clinical information. By combining the information obtained in Aim 1 with genomic information obtained in the same melanoma sample set (by SNP / CGH arrays), we expect to identify a set of outlier genes with underlying genomic alterations. Candidate genes will be further analyzed for their biological and clinical significance by integration with the extensive clinical information derived from the tissue collection. Aim 3: Functional validation of outlier candidates in melanoma, in relevant in vitro and in vivo models of the disease. Candidate genes will further be validated functionally to select for genetic alterations with true biological relevance. To accomplish these goals, we have assembled a multidisciplinary team of investigators with complementary expertise. At the conclusion of this proposal, we expect to have discovered and validated novel molecular subtypes of melanoma as identified by genes that drive the respective pathology, which may serve as markers or targets for improved individualized patient management and development of pathology-adjusted therapies.

One of the main challenges for medicine in the early part of 21st century is to incorporate information derived from the human genome into the clinical management of patients, thereby yielding more precise diagnoses and mechanism-based deployment of therapy. Nowhere is this need more pressing than in the treatment of cancer. Although melanoma accounts for only 4% of all skin cancers, it is responsible for 80% of skin cancer mortality. Melanoma displays considerable molecular heterogeneity that translates into its remarkable clinical, morphological, and biological diversity. Given this molecular heterogeneity, we had proposed to identify novel molecular subtypes carrying distinct genetic alterations, which drive specific clinical and biological features of melanomas. With access to a unique human melanoma tissue collection, annotated with extensive clinical information, to advanced computational and biostatistical technologies, to corresponding genomic data (gene expression, SNP, cGH arrays), and to in vitro and in vivo models of the disease, this project provided the opportunity to filter and prioritize genetic and molecular alterations occurring in human melanoma and to identify novel molecular subgroups of the disease.In a multidisciplinary team of international investigators, we have developed a mathematical algorithm, which we named INDEGO (INtegrated DEtection of Genomic Outliers) and used for a search across human tumor samples for genes with transcript outlier data points and associated gene copy number variations that are correlated with patients survival. In complementary in vivo and in vitro gain and loss of function assays we could identify one such gene as a metastasis driver in a subset of human melanomas. Interestingly, preliminary genomic and protein data suggest that a similar molecular subtype may also exist in many other human cancers. INDEGO may be useful to detect further molecular subgroups not only in cancer, but also other diseases.We have also significantly contributed to the TCGA cancer gene atlas for human melanoma, which displays a catalogue of gene mutations obtained by whole exome sequencing. These data are freely accessible to the scientific community and the public via cBioPortal for Cancer Genomics.We further detected diverse genomic alterations of genes that not completely fulfilled the criteria of INDEGO, but were of potential interest for the cell biology of melanoma either suggesting completely novel (RRM2 in oncogene-induced senescence; CRM1-mediated nucleocytoplasmic transport) or novel additive targets (CDK2/4, AMPK, PLK-1 and Smoothened) for therapeutic interference with the disease. These data were complemented by an attempt to detect biomarkers (EVL, CD24) for the prediction of metastasis in melanoma patients at the time of first diagnosis. In 10 peer-reviewed publications in highly ranked scientific journals, the project describes novel tools to detect molecular subtypes in melanoma/cancer which can serve as markers or targets for improved individualized patient management and development of pathology-adjusted therapies.