Melanoma cells display distinct intrinsic phenotypic states. Here, we seek to characterize the molecular regulation of these states using multi-omic analyses of whole exome, transcriptome, microRNA, long non-coding RNA and DNA methylation data together with reverse-phase protein array data on a panel of 68 highly annotated early passage melanoma cell lines. We demonstrate that clearly defined cancer cell intrinsic transcriptomic programs are maintained in melanoma cells ex vivo and remain highly conserved within melanoma tumors, are associated with distinct immune features within tumors, and differentially correlate with checkpoint inhibitor and adoptive T cell therapy efficacy. Through integrative analyses we demonstrate highly complex multi-omic regulation of melanoma cell intrinsic programs that provide key insights into the molecular maintenance of phenotypic states. These findings have implications for cancer biology and the identification of new therapeutic strategies. Further, these deeply characterized cell lines will serve as an invaluable resource for future research in the field.

Assessing Erb-b2 receptor tyrosine kinase 2 (ERBB2) amplification status in breast and gastric cancer is necessary for deciding the best therapeutic strategy. Immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) are currently used for assessing protein levels and gene copy number (CN), respectively. The use of next-generation sequencing (NGS) to measure ERBB2 CN in breast cancer is approved by the United States Federal Drug Administration as a companion diagnostic. However, a CN of less than 8 is evaluated as “equivocal”, which means that some ERBB2 amplification cases diagnosed as “HER2 negative” might be false-negative cases. We reviewed the results of gene profiling targeting 160 cancer-related genes in breast (N = 90) and non-breast (N = 19) cancer tissue, and compared the ERBB2 CN results with the IHC/FISH scores. We obtained an estimated CN from the measured CN by factoring in the histological proportion of tumor cells and found that an ERBB2-estimated CN of 3.2 or higher was concordant with the combined IHC/FISH outcome in 98.4% (88/90) of breast cancer cases, while this was not always evident among non-breast cancer cases. Therefore, NGS-estimated ERBB2 CN could be considered a diagnostic test for anti-HER2 therapy after the completion of adequate prospective clinical trials.

Innate inflammatory features have been found in melanoma tumors from patients at all stages, and molecular analysis has identified definitive inflammatory proteins expressed by tumors cells in patients who presents the worst prognosis. We have previously observed weakened outcomes in patients with constitutive expression of inducible nitric oxide synthase (iNOS), macrophage migration inhibitory factor (MIF) and improved outcomes with CD74 expression in stage III melanoma. In our current study, we tested our hypothesis on CD74-regulated inflammatory markers’ expression in stage IV melanoma tumors whether the signature is associated with survival outcome and/or risk of developing CNS metastasis. We retrospectively identified 315 patients with stage IV melanoma. In a tissue microarray (TMA), we examined the expression of cells with CD74, its receptor MIF, and downstream inflammatory markers iNOS, nitrotyrosine (NT), cyclooxygenase (COX)-2 and microsomal prostaglandin E synthase-1 (mPGES1). We analyzed the association of those inflammatory markers with overall survival time (OS) and time to CNS metastasis using Kaplan–Meier survival analyses. Our data validates CD74 as a useful prognostic tumor cell protein marker associated with favorable OS as in stage III melanomas, while the tumor NT expression strongly predicts an increased risk of developing CNS metastasis (p = 0.0008) in those patients.

CD10 has been widely used in cancer diagnosis. We previously demonstrated that its expression in melanoma increased with tumor progression and predicted poor patient survival. However, the mechanism by which CD10 promotes melanoma progression remains unclear. In order to elucidate the role of CD10 in melanoma, we established CD10-overexpressing A375 melanoma cells and performed DNA microarray and qRT–PCR analyses to identify changes in the gene expression profile. The microarray analysis revealed that up-regulated genes in CD10-A375 were mostly involved in cell proliferation, angiogenesis, and resistance to apoptosis; down-regulated genes mostly belonged to the categories associated with cell adhesion and migration. Accordingly, in functional experiments, CD10-A375 showed significantly greater cell proliferation in vitro and higher tumorigenicity in vivo; CD10 enzymatic inhibitors, thiorphan and phosphoramidon, significantly blocked the tumor growth of CD10-A375 in mice. In migration and invasion assays, CD10-A375 displayed lower migratory and invasive capacity than mock-A375. CD10 augmented melanoma cell resistance to apoptosis mediated by etoposide and gemcitabine. These findings indicate that CD10 may promote tumor progression by regulating the expression profiles of genes related to cell proliferation, angiogenesis, and resistance to apoptosis.

Modern clinical oncology faces an unprecedented data complexity that exceeds human analytical capacity, making artificial intelligence (AI) integration essential rather than optional. This review examines the dual impact of AI on productivity enhancement and creative discovery in cancer care. We trace the evolution from traditional machine learning to deep learning and transformer-based foundation models, analyzing their clinical applications. AI enhances productivity by automating diagnostic tasks, streamlining documentation, and accelerating research workflows across imaging modalities and clinical data processing. More importantly, AI enables creative discovery by integrating multimodal data to identify computational biomarkers, performing unsupervised phenotyping to reveal hidden patient subgroups, and accelerating drug development. Finally, we introduce the FUTURE-AI framework, outlining the essential requirements for translating AI models into clinical practice. This ensures the responsible deployment of AI, which augments rather than replaces clinical judgment, while maintaining patient-centered care.