RESUMO
Skin cancer mortality rates continue to rise, and survival analysis is increasingly needed to understand who is at risk and what interventions improve outcomes. However, current statistical methods are limited by inability to synthesize multiple data types, such as patient genetics, clinical history, demographics, and pathology and reveal significant multimodal relationships through predictive algorithms. Advances in computing power and data science enabled the rise of artificial intelligence (AI), which synthesizes vast amounts of data and applies algorithms that enable personalized diagnostic approaches. Here, we analyze AI methods used in skin cancer survival analysis, focusing on supervised learning, unsupervised learning, deep learning, and natural language processing. We illustrate strengths and weaknesses of these approaches with examples. Our PubMed search yielded 14 publications meeting inclusion criteria for this scoping review. Most publications focused on melanoma, particularly histopathologic interpretation with deep learning. Such concentration on a single type of skin cancer amid increasing focus on deep learning highlight growing areas for innovation; however, it also demonstrates opportunity for additional analysis that addresses other types of cutaneous malignancies and expands the scope of prognostication to combine both genetic, histopathologic, and clinical data. Moreover, researchers may leverage multiple AI methods for enhanced benefit in analyses. Expanding AI to this arena may enable improved survival analysis, targeted treatments, and outcomes.
RESUMO
Hereditary leiomyomatosis and renal cell cancer (HLRCC) is a cancer syndrome caused by inactivating germline mutations in fumarate hydratase (FH) and subsequent accumulation of fumarate. Fumarate accumulation leads to profound epigenetic changes and the activation of an anti-oxidant response via nuclear translocation of the transcription factor NRF2. The extent to which chromatin remodeling shapes this anti-oxidant response is currently unknown. Here, we explored the effects of FH loss on the chromatin landscape to identify transcription factor networks involved in the remodeled chromatin landscape of FH-deficient cells. We identify FOXA2 as a key transcription factor that regulates anti-oxidant response genes and subsequent metabolic rewiring cooperating without direct interaction with the anti-oxidant regulator NRF2. The identification of FOXA2 as an anti-oxidant regulator provides additional insights into the molecular mechanisms behind cell responses to fumarate accumulation and potentially provides further avenues for therapeutic intervention for HLRCC.