Recent advances in melanoma research via “omics” platforms

Rodríguez-Cerdeira, Carmen; Molares-Vila, Alberto; Carnero-Gregorio, Miguel; Corbalán-Rivas, Alberte

doi:10.1016/j.jprot.2017.11.005

Cited by 14 publications

(15 citation statements)

References 208 publications

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“…Most of these proteins were also upregulated in cultured melanoma cell lines. The roles of signalling pathways such as the PI3K-AKT, mTOR and MAPK have been previously described in melanoma (Rodríguez-Cerdeira et al 2018). In several studies, these pathways were activated in melanoma and other type of cancers.…”

Section: Understanding Melanoma By Mapping Proteomic Data On Biologicmentioning

confidence: 93%

Clinical protein science in translational medicine targeting malignant melanoma

et al. 2019

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Melanoma of the skin is the sixth most common type of cancer in Europe and accounts for 3.4% of all diagnosed cancers. More alarming is the degree of recurrence that occurs with approximately 20% of patients lethally relapsing following treatment. Malignant melanoma is a highly aggressive skin cancer and metastases rapidly extend to the regional lymph nodes (stage 3) and to distal organs (stage 4). Targeted oncotherapy is one of the standard treatment for progressive stage 4 melanoma, and BRAF inhibitors (e.g. vemurafenib, dabrafenib) combined with MEK inhibitor (e.g. trametinib) can effectively counter BRAFV600E-mutated melanomas. Compared to conventional chemotherapy, targeted BRAFV600E inhibition achieves a significantly higher response rate. After a period of cancer control, however, most responsive patients develop resistance to the therapy and lethal progression. The many underlying factors potentially causing resistance to BRAF inhibitors have been extensively studied. Nevertheless, the remaining unsolved clinical questions necessitate alternative research approaches to address the molecular mechanisms underlying metastatic and treatment-resistant melanoma. In broader terms, proteomics can address clinical questions far beyond the reach of genomics, by measuring, i.e. the relative abundance of protein products, post-translational modifications (PTMs), protein localisation, turnover, protein interactions and protein function. More specifically, proteomic analysis of body fluids and tissues in a given medical and clinical setting can aid in the identification of cancer biomarkers and novel therapeutic targets. Achieving this goal requires the development of a robust and reproducible clinical proteomic platform that encompasses automated biobanking of patient samples, tissue sectioning and histological examination, efficient protein extraction, enzymatic digestion, mass spectrometry-based quantitative protein analysis by label-free or labelling technologies and/or enrichment of peptides with specific PTMs. By combining data from, e.g. phosphoproteomics and acetylomics, the protein expression profiles of different melanoma stages can provide a solid framework for understanding the biology and progression of the disease. When complemented by proteogenomics, customised protein sequence databases generated from patient-specific genomic and transcriptomic data aid in interpreting clinical proteomic biomarker data to provide a deeper and more comprehensive molecular characterisation of cellular

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Section: Understanding Melanoma By Mapping Proteomic Data On Biologicmentioning

confidence: 93%

Clinical protein science in translational medicine targeting malignant melanoma

et al. 2019

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“…Molecular biomarkers can provide robust, objective, and quantitative measurements of the disease state (Buchbinder and Flaherty, 2016;Leachman et al, 2017;Rodríguez-Cerdeira et al,2018). One class of candidate biomarkers are small non-coding microRNAs (miRNAs).…”

Section: Introductionmentioning

confidence: 99%

MicroRNA Ratios Distinguish Melanomas from Nevi

Torres

Lang

Hejna

et al. 2020

Journal of Investigative Dermatology

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The use of microRNAs as biomarkers has been proposed for many diseases, including the diagnosis of melanoma. Although hundreds of microRNAs have been identified as differentially expressed in melanomas as compared to benign melanocytic lesions, a limited consensus has been achieved across studies, constraining the effective use of these potentially useful markers. In this study, we applied a machine learning-based pipeline to a dataset consisting of genetic features, clinical features, and next-generation microRNA sequencing from micro-dissected formalin-fixed paraffin embedded melanomas and their adjacent benign precursor nevi. We identified patient age and tumor cellularity as variables that frequently confound the measured expression of potentially diagnostic microRNAs. By employing the ratios of microRNAs that were either enriched or depleted in melanoma compared to the nevi as a normalization strategy, we developed a model that classified all the available published cohorts with an area under the receiver operating characteristic curve of 0.98. External validation on an independent cohort classified lesions with 81% sensitivity and 88% specificity and was uninfluenced by the tumor content of the sample or patient age.

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“…The aim of our study was to determine genomic biomarkers as adverse prognostic factors in 499 melanoma patients. Candidate genomic biomarkers for rapid clinical translation may be germline or somatic 31 , 32 , therefore we focused on loss-of-function germline mutations in cancer predisposition genes, as well as somatic biomarkers: BRAF mutation status and TMB. We used whole-exome sequencing (WES) data from a Queensland cohort as well as an independent cohort from The Cancer Genome Atlas Program (TCGA) to confirm our findings.…”

Section: Introductionmentioning

confidence: 99%

Pathogenic germline variants are associated with poor survival in stage III/IV melanoma patients

Aoude

Bonazzi

Brosda

et al. 2020

Sci Rep

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Patients with late stage resected cutaneous melanoma have poor overall survival (OS) and experience irreversible adverse events from systemic therapy. There is a clinical need to identify biomarkers to predict outcome. Performing germline/tumour whole-exome sequencing of 44 stage III/IV melanoma patients we identified pathogenic germline mutations in CDKN2A, CDK4, ATM, POLH, MRE11A, RECQL4 and XPC, affecting 7/44 patients. These mutations were associated with poor OS (p = 0.0082). We confirmed our findings in The Cancer Genome Atlas (TCGA) human skin cutaneous melanoma cohort where we identified pathogenic variants in 40/455 patients (p = 0.0203). Combining these cohorts (n = 499) further strengthened these findings showing germline carriers had worse OS (p = 0.0009). Additionally, we determined whether tumour mutation burden (TMB) or BRAF status were prognostic markers of survival. Low TMB rate (< 20 Mut/Mb; p = 0.0034) and BRAF p.V600 mutation (p = 0.0355) were associated with worse progression-free survival. Combining these biomarkers indicated that V600 mutant patients had significantly lower TMB (p = 0.0155). This was confirmed in the TCGA (n = 443, p = 0.0007). Integrative analysis showed germline mutation status conferred the highest risk (HR 5.2, 95% CI 1.72–15.7). Stage IV (HR 2.5, 0.74–8.6) and low TMB (HR 2.3, 0.57–9.4) were similar, whereas BRAF V600 status was the weakest prognostic biomarker (HR 1.5, 95% CI 0.44–5.2).

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Recent advances in melanoma research via “omics” platforms

Cited by 14 publications

References 208 publications

Clinical protein science in translational medicine targeting malignant melanoma

Clinical protein science in translational medicine targeting malignant melanoma

MicroRNA Ratios Distinguish Melanomas from Nevi

Pathogenic germline variants are associated with poor survival in stage III/IV melanoma patients

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