Shilin Zhao
Jing Wang
Quanhu Sheng
Qi Liu
Yu Shyr
a Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville TN 37203, USA;
b Department of Biostatistics, Vanderbilt University Medical Center, Nashville TN 37203, USA
Funds: Cancer Center Support Grant (2P30 CA068485-19 to Y.S.). Funding for open access charge:National Cancer Institute (5U01 CA163056-05 to Y.S.).
The authors would like to acknowledge funding from National Cancer Institute (5U01 CA163056-05, U2C CA233291 and U54 CA217450 to Y.S.)
Received Date: 2020-12-15
Accepted Date:2021-03-17
Rev Recd Date:2021-03-15
Publish Date:2021-05-20
Abstract
Abstract
There are increasing studies aimed to reveal genomic hallmarks predictive of immune checkpoint blockade (ICB) treatment response, which generated a large number of data and provided an unprecedented opportunity to identify response-related features and evaluate their robustness across cohorts. However, those valuable data sets are not easily accessible to the research community. To take full advantage of existing large-scale immuno-genomic profiles, we developed Immu-Mela (http://bioinfo.vanderbilt.edu/database/Immu-Mela/), a multidimensional immuno-genomic portal that provides interactive exploration of associations between ICB responsiveness and multi-omics features in melanoma, including genetic, transcriptomics, immune cells, and single-cell populations. Immu-Mela also enables integrative analysis of any two genomic features. We demonstrated the value of Immu-Mela by identifying known and novel genomic features associated with ICB response. In addition, Immu-Mela allows users to upload their data sets (unrestricted to any cancer types) and co-analyze with existing data to identify and validate signatures of interest. Immu-Mela reduces barriers between researchers and complex genomic data, facilitating discoveries in cancer immunotherapy.Keywords: Immunotherapy,
PD-1/PD-L1 blockade,
CTLA-4 blockade,
Immune checkpoint,
Multidimensional genomics,
Biomarker,
Melanoma
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http://www.jgenetgenomics.org/article/exportPdf?id=3aa5510c-0fb9-46a3-9a45-a16a708a2431&language=en
