Skin Cancer is one of the most common cancers and close to 4 million cases occur globally each year. According to Skin Cancer Foundation, in the last three decades, more people have had skin cancers than all other cancers combined. In spite of availability of large amounts of drugs and surgical treatments for melanoma and non-melanoma skin cancer, the 5-year survival rate is only 63% and 17% when the cancer reaches lymph nodes and metastasizes to other organs respectively.
Various studies like TCGA, ICGC, PCAWG and analysis techniques like GWAS contain large scale cohort information to better understand the disease diagnosis and prognosis. The web portals of TCGA, ICGC and PCAWG store information from various omics levels like genome, transcriptome, proteome, epigenome, metabolome etc. The information present at these various levels may help us in understanding the factors that regulate the flow of information in the cells.
This large volumes of data has enabled precision/personalized medicines or treatment regimens which can improve patient care system. Integrative analysis wherein the information from various cellular levels are analyzed together, may help us in identifying the missing gaps. Similarly, meta-analysis wherein data from various studies/platforms are combined and analyzed together can help us in comparing different population studies. Apart from the above mentioned analysis, predictive modeling approach to predict the risk of onset of cancer, progression of cancer, subtypes classification are useful methods that contributes towards better diagnosis of disease. Thus these analyses using the available data can create a positive impact in drug discovery and treatment of skin cancer.
We at Persistent LABS would like to hear your views on the gaps/challenges that prevails in the usage of available data and methods to improve our understanding of skin cancer biology. Leave your views/comments/suggestions/gaps/challenges in this post!
Various studies like TCGA, ICGC, PCAWG and analysis techniques like GWAS contain large scale cohort information to better understand the disease diagnosis and prognosis. The web portals of TCGA, ICGC and PCAWG store information from various omics levels like genome, transcriptome, proteome, epigenome, metabolome etc. The information present at these various levels may help us in understanding the factors that regulate the flow of information in the cells.
This large volumes of data has enabled precision/personalized medicines or treatment regimens which can improve patient care system. Integrative analysis wherein the information from various cellular levels are analyzed together, may help us in identifying the missing gaps. Similarly, meta-analysis wherein data from various studies/platforms are combined and analyzed together can help us in comparing different population studies. Apart from the above mentioned analysis, predictive modeling approach to predict the risk of onset of cancer, progression of cancer, subtypes classification are useful methods that contributes towards better diagnosis of disease. Thus these analyses using the available data can create a positive impact in drug discovery and treatment of skin cancer.
We at Persistent LABS would like to hear your views on the gaps/challenges that prevails in the usage of available data and methods to improve our understanding of skin cancer biology. Leave your views/comments/suggestions/gaps/challenges in this post!
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