Sarah-Jane Schramm

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In metastatic melanoma, it is vital to identify and validate biomarkers of prognosis. Previous studies have systematically evaluated protein biomarkers or mRNA-based expression signatures. No such analyses have been applied to microRNA (miRNA)-based prognostic signatures. As a first step, we identified two prognostic miRNA signatures from publicly available(More)
The role of microRNAs (miRNAs) in melanoma is unclear. We examined global miRNA expression profiles in fresh-frozen metastatic melanomas in relation to clinical outcome and BRAF mutation, with validation in independent cohorts of tumours and sera. We integrated miRNA and mRNA information from the same samples and elucidated networks associated with outcome(More)
Prediction of outcome for melanoma patients with surgically resected macroscopic nodal metastases is very imprecise. We performed a comprehensive clinico-pathologic assessment of fresh-frozen macroscopic nodal metastases and the preceding primary melanoma, somatic mutation profiling, and gene expression profiling to identify determinants of outcome in 79(More)
Despite intensive research efforts, within-stage survival rates for melanoma vary widely. Pursuit of molecular biomarkers with improved prognostic significance over clinicohistological measures has produced extensive literature. Reviews have synthesized these data, but none have systematically partitioned high-quality studies from the remainder across(More)
In melanoma, there is an urgent need to identify novel biomarkers with prognostic performance superior to traditional clinical and histological parameters. Gene expression-based prognostic signatures offer promise, but studies have been challenged by sample scarcity, cohort heterogeneity, and doubts about the efficacy of such signatures relative to current(More)
In patients with metastatic melanoma, the identification and validation of accurate prognostic biomarkers will assist rational treatment planning. Studies based on "-omics" technologies have focussed on a single high-throughput data type such as gene or microRNA transcripts. Occasionally, these features have been evaluated in conjunction with limited(More)
High-throughput '-omics' data can be combined with large-scale molecular interaction networks, for example, protein-protein interaction networks, to provide a unique framework for the investigation of human molecular biology. Interest in these integrative '-omics' methods is growing rapidly because of their potential to understand complexity and association(More)
Classical approaches to predicting patient clinical outcome via gene expression information are primarily based on differential expression of unrelated genes (single-gene approaches) or genes related by, for example, biologic pathway or function (gene-sets). Recently, network-based approaches utilising interaction information between genes have emerged. An(More)
For disseminated melanoma, new prognostic biomarkers and therapeutic targets are urgently needed. The organization of protein-protein interaction networks was assessed via the transcriptomes of four independent studies of metastatic melanoma and related to clinical outcome and MAP-kinase pathway mutations (BRAF/NRAS). We also examined patient(More)
Large-scale molecular interaction networks are dynamic in nature and are of special interest in the analysis of complex diseases, which are characterized by network-level perturbations rather than changes in individual genes/proteins. The methods developed for the identification of differentially expressed genes or gene sets are not suitable for(More)