PT  - JOURNAL ARTICLE
AU  - Umesh Ghoshdastider
AU  - Marjan Mojtabavi Naeini
AU  - Neha Rohatgi
AU  - Egor Revkov
AU  - Angeline Wong
AU  - Sundar Solai
AU  - Tin Trung Nguyen
AU  - Joe Yeong
AU  - Jabed Iqbal
AU  - Puay Hoon Tan
AU  - Balram Chowbay
AU  - Ramanuj DasGupta
AU  - Anders Jacobsen Skanderup
TI  - Data-driven inference of crosstalk in the tumor microenvironment
AID  - 10.1101/835512
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 835512
4099  - http://biorxiv.org/content/early/2019/11/08/835512.short
4100  - http://biorxiv.org/content/early/2019/11/08/835512.full
AB  - Signaling between cancer and nonmalignant (stromal) cells in the tumor microenvironment (TME) is key to tumorigenesis yet challenging to decipher from tumor transcriptomes. Here, we report an unbiased, data-driven approach to deconvolute bulk tumor transcriptomes and predict crosstalk between ligands and receptors on cancer and stromal cells in the TME of 20 solid tumor types. Our approach recovers known transcriptional hallmarks of cancer and stromal cells and is concordant with single-cell and immunohistochemistry data, underlining its robustness. Pan-cancer analysis reveals previously unrecognized features of cancer-stromal crosstalk. We find that autocrine cancer cell cross-talk varied between tissues but often converged on known cancer signaling pathways. In contrast, many stromal cross-talk interactions were highly conserved across tumor types. Interestingly, the immune checkpoint ligand PD-L1 was overexpressed in stromal rather than cancer cells across all tumor types. Moreover, we predicted and experimentally validated aberrant ligand and receptor expression in cancer cells of basal and luminal breast cancer, respectively. Collectively, our findings validate a data-driven method for tumor transcriptome deconvolution and establishes a new resource for hypothesis generation and downstream functional interrogation of the TME in tumorigenesis and disease progression.