bioRxivCancer Biology
An immunoevasive strategy through clinically-relevant pan cancer genomic and transcriptomic alterations of JAK-STAT signaling components
Abstract
Since its discovery almost three decades ago, the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway has paved the road for understanding inflammatory and immunity processes related to a wide range of human pathologies including cancer. Several studies have demonstrated the importance of JAK-STAT pathway components in regulating tumor initiation and metastatic progression, yet, the extent of how genetic alterations influence patient outcome is far from being understood. Focusing on 133 genes involved in JAK-STAT signaling, we found that copy number alterations underpin transcriptional dysregulation that differs within and between cancer types. Integrated analyses on over 18,000 tumors representing 21 cancer types revealed a core set of 28 JAK-STAT pathway genes that correlated with survival outcomes in brain, renal, lung and endometrial cancers. High JAK-STAT scores were associated with increased mortality rates in brain and renal cancers, but not in lung and endometrial cancers where hyperactive JAK-STAT signaling is a positive prognostic factor. Patients with aberrant JAK-STAT signaling demon-strated pan-cancer molecular features associated with misex-pression of genes in other oncogenic pathways (Wnt, MAPK, TGF-{beta}, PPAR and VEGF). Brain and renal tumors with hyperactive JAK-STAT signaling had increased regulatory T cell gene (Treg) expression. A combined model uniting JAK-STAT and Tregs allowed further delineation of risk groups where patients with high JAK-STAT and Treg scores consistently performed the worst. Providing a pan-cancer perspective of clinically-relevant JAK-STAT alterations, this study could serve as a framework for future research investigating anti-tumor immunity using combination therapy involving JAK-STAT and immune checkpoint inhibitors.
- organism: human
- assay: bulk RNA-seq
- research focus: cancer & tumor microenvironment
- research focus: cell-cell communication
- approach: statistical model
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