There is an increasing global interest to support research areas that can assist in understanding disease and improving patient care.,The National Cancer Institute (NIH) has identified precision medicine-based approaches as key research strategies to expedite advances in cancer research.,The Cancer Moonshot program (https://www.cancer.gov/research/key-initiatives/moonshot-cancer-initiative) is the largest cancer program of all time, and has been launched to accelerate cancer research that aims to increase the availability of therapies to more patients and, ultimately, to eradicate cancer.,Mass spectrometry-based proteomics has been extensively used to study the molecular mechanisms of cancer, to define molecular subtypes of tumors, to map cancer-associated protein interaction networks and post-translational modifications, and to aid in the development of new therapeutics and new diagnostic and prognostic tests.,To establish the basis for our melanoma studies, we have established the Southern Sweden Malignant Melanoma Biobank.,Tissues collected over many years have been accurately characterized with respect to the tumor and patient information.,The extreme variability displayed in the protein profiles and the detection of missense mutations has confirmed the complexity and heterogeneity of the disease.,It is envisaged that the combined analysis of clinical, histological, and proteomic data will provide patients with a more personalized medical treatment.,With respect to disease presentation, targeted treatment and medical mass spectrometry analysis and imaging, this overview report will outline and summarize the current achievements and status within malignant melanoma.,We present data generated by our cancer research center in Lund, Sweden, where we have built extensive capabilities in biobanking, proteogenomics, and patient treatments over an extensive time period.
We developed an automated approach for generating quantitative image analysis metrics (imaging biomarkers) that are then analysed with a set of 13 machine learning algorithms to generate an overall risk score that is called a Q‐score.,These methods were applied to a set of 120 “difficult” dermoscopy images of dysplastic nevi and melanomas that were subsequently excised/classified.,This approach yielded 98% sensitivity and 36% specificity for melanoma detection, approaching sensitivity/specificity of expert lesion evaluation.,Importantly, we found strong spectral dependence of many imaging biomarkers in blue or red colour channels, suggesting the need to optimize spectral evaluation of pigmented lesions.