The prognostic landscape of interactive biological processes presents treatment responses in cancer.

EBioMedicine
Bin HeQuentin Liu

Abstract

Differential gene expression patterns are commonly used as biomarkers to predict treatment responses among heterogeneous tumors. However, the link between response biomarkers and treatment-targeting biological processes remain poorly understood. Here, we develop a prognosis-guided approach to establish the determinants of treatment response. The prognoses of biological processes were evaluated by integrating the transcriptomes and clinical outcomes of ~26,000 cases across 39 malignancies. Gene-prognosis scores of 39 malignancies (GEO datasets) were used for examining the prognoses, and TCGA datasets were selected for validation. The Oncomine and GEO datasets were used to establish and validate transcriptional signatures for treatment responses. The prognostic landscape of biological processes was established across 39 malignancies. Notably, the prognoses of biological processes varied among cancer types, and transcriptional features underlying these prognostic patterns distinguished response to treatment targeting specific biological process. Applying this metric, we found that low tumor proliferation rates predicted favorable prognosis, whereas elevated cellular stress response signatures signified resistance to anti-prolifera...Continue Reading

Citations

Nov 6, 2020·Clinical Cancer Research : an Official Journal of the American Association for Cancer Research·Axel Stenmark TullbergPer Karlsson

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Datasets Mentioned

BETA
GSE10141-LIHC
GSE76360
GSE91061
GSE67501
GSE78220

Methods Mentioned

BETA
RNA-seq
PCR
transfection
Profiler
reverse transcription PCR

Software Mentioned

CycleLead
ssGSEAProjection
TreeView
PRECOG
Immunet
R
ssGSEA Projection
cBioPortal
ImmuneLead
GSEA

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