CRI iAtlas: an interactive portal for immuno-oncology research

The Cancer Research Institute (CRI) iAtlas is an interactive web platform for data exploration and discovery in the context of tumors and their interactions with the immune microenvironment. iAtlas allows researchers to study immune response characterizations and patterns for individual tumor types, tumor subtypes, and immune subtypes. iAtlas supports computation and visualization of correlations and statistics among features related to the tumor microenvironment, cell composition, immune expression signatures, tumor mutation burden, cancer driver mutations, adaptive cell clonality, patient survival, expression of key immunomodulators, and tumor infiltrating lymphocyte (TIL) spatial maps. iAtlas was launched to accompany the release of the TCGA PanCancer Atlas and has since been expanded to include new capabilities such as (1) user-defined loading of sample cohorts, (2) a tool for classifying expression data into immune subtypes, and (3) integration of TIL mapping from digital pathology images. We expect that the CRI iAtlas will accelerate discovery and improve patient outcomes by providing researchers access to standardized immunogenomics data to better understand the tumor immune microenvironment and its impact on patient responses to immunotherapy.

[1]  N. Meskin,et al.  Crosstalk between HER2 and PD-1/PD-L1 in Breast Cancer: From Clinical Applications to Mathematical Models , 2020, Cancers.

[2]  David L. Gibbs Robust classification of Immune Subtypes in Cancer , 2020, bioRxiv.

[3]  Kohske Takahashi,et al.  Welcome to the Tidyverse , 2019, J. Open Source Softw..

[4]  M. Kurosumi,et al.  Clinicopathological values of PD-L1 expression in HER2-positive breast cancer , 2019, Scientific Reports.

[5]  Vanessa M. Hubbard-Lucey,et al.  Immuno-oncology drug development goes global , 2019, Nature Reviews Drug Discovery.

[6]  T. Sun,et al.  PD-1 and PD-L1 correlated gene expression profiles and their association with clinical outcomes of breast cancer , 2019, Cancer Cell International.

[7]  Laura Pearce,et al.  Trends in the global immuno-oncology landscape , 2018, Nature Reviews Drug Discovery.

[8]  Vanessa M. Hubbard-Lucey,et al.  The global landscape of cancer cell therapy , 2018, Nature Reviews Drug Discovery.

[9]  C. Hutter,et al.  The Cancer Genome Atlas: Creating Lasting Value beyond Its Data , 2018, Cell.

[10]  Adrian V. Lee,et al.  An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics , 2018, Cell.

[11]  Rajarsi R. Gupta,et al.  Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. , 2018, Cell reports.

[12]  R. Weinberg,et al.  Understanding the tumor immune microenvironment (TIME) for effective therapy , 2018, Nature Medicine.

[13]  Steven J. M. Jones,et al.  The Immune Landscape of Cancer , 2018, Immunity.

[14]  Vanessa M. Hubbard-Lucey,et al.  Comprehensive analysis of the clinical immuno-oncology landscape , 2018, Annals of oncology : official journal of the European Society for Medical Oncology.

[15]  J. Schachter,et al.  Adoptive T cell therapy: An overview of obstacles and opportunities , 2017, Cancer.

[16]  Elaine R. Mardis,et al.  Applications of Immunogenomics to Cancer , 2017, Cell.

[17]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[18]  Gianluca Bontempi,et al.  TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data , 2015, Nucleic acids research.

[19]  Yihui Xie,et al.  A Wrapper of the JavaScript Library 'DataTables' , 2015 .

[20]  H. Wickham Simple, Consistent Wrappers for Common String Operations , 2015 .

[21]  C. Sautès-Fridman,et al.  The immune contexture in human tumours: impact on clinical outcome , 2012, Nature Reviews Cancer.

[22]  George Coukos,et al.  Cancer immunotherapy comes of age , 2011, Nature.

[23]  Daniel Q. Naiman,et al.  Classifying Gene Expression Profiles from Pairwise mRNA Comparisons , 2004, Statistical applications in genetics and molecular biology.