Identification of Factors Modulating Immune System Response to Tumors
We are developing computational methods to identify factors that may be influencing the immune system response to tumors in the context of therapy, using genomic data from large-scale cancer studies. We are interested in tumor cell-intrinsic properties, as well as interactions between immune subsets and other stromal populations. These results will be made available as a resource for the wider community, as a basis for future experimental and clinical studies.
Atlas of clinically-distinct cell states and cellular ecosystems across human solid tumors.
Bogdan A Luca, Chloé B Steen, Armon Azizi, Magdalena Matusiak, Joanna Przybyl, Nastaran Neishaboori, Almudena Espín Pérez, Maximilian Diehn, Ash A Alizadeh, Matt van de Rijn, Andrew J Gentles, Aaron M Newman
Determining how cells vary with their local signaling environment and organize into distinct cellular communities is critical for understanding processes as diverse as development, aging, and cancer. Here we introduce EcoTyper, a new machine learning framework for large-scale identification and validation of cell states and multicellular communities from bulk, single-cell, and spatially-resolved gene expression data. When applied to 12 major cell lineages across nearly 6,000 tumor specimens from 16 types of human carcinoma, EcoTyper identified 69 transcriptionally-defined cell states. Most cell states were specific to neoplastic tissue, ubiquitous across tumor types, and significantly prognostic. By analyzing cell state co-occurrence patterns, we discovered 10 clinically-distinct multicellular communities with unexpectedly strong conservation, including four with unique myeloid and stromal elements, one enriched in normal tissue, and two associated with early cancer development. This study elucidates fundamental units of cellular organization in human carcinoma and provides a framework for large-scale profiling of cellular ecosystems in any tissue.