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An Empirical Approach Leveraging Tumorgrafts to Dissect the Tumor Microenvironment in Renal Cell Carcinoma Identifies Missing Link to Prognostic Inflammatory Factors
- Source :
- Cancer Discovery. 8:1142-1155
- Publication Year :
- 2018
- Publisher :
- American Association for Cancer Research (AACR), 2018.
-
Abstract
- By leveraging tumorgraft (patient-derived xenograft) RNA-sequencing data, we developed an empirical approach, DisHet, to dissect the tumor microenvironment (eTME). We found that 65% of previously defined immune signature genes are not abundantly expressed in renal cell carcinoma (RCC) and identified 610 novel immune/stromal transcripts. Using eTME, genomics, pathology, and medical record data involving >1,000 patients, we established an inflamed pan-RCC subtype (IS) enriched for regulatory T cells, natural killer cells, TH1 cells, neutrophils, macrophages, B cells, and CD8+ T cells. IS is enriched for aggressive RCCs, including BAP1-deficient clear-cell and type 2 papillary tumors. The IS subtype correlated with systemic manifestations of inflammation such as thrombocytosis and anemia, which are enigmatic predictors of poor prognosis. Furthermore, IS was a strong predictor of poor survival. Our analyses suggest that tumor cells drive the stromal immune response. These data provide a missing link between tumor cells, the TME, and systemic factors. Significance: We undertook a novel empirical approach to dissect the renal cell carcinoma TME by leveraging tumorgrafts. The dissection and downstream analyses uncovered missing links between tumor cells, the TME, systemic manifestations of inflammation, and poor prognosis. Cancer Discov; 8(9); 1142–55. ©2018 AACR. This article is highlighted in the In This Issue feature, p. 1047
- Subjects :
- 0301 basic medicine
Stromal cell
Inflammation
Article
Mice
03 medical and health sciences
Immune system
Renal cell carcinoma
Exome Sequencing
Tumor Microenvironment
medicine
Carcinoma
Animals
Cluster Analysis
Humans
Gene Regulatory Networks
Carcinoma, Renal Cell
Tumor microenvironment
Sequence Analysis, RNA
business.industry
Gene Expression Profiling
High-Throughput Nucleotide Sequencing
Cancer
Prognosis
medicine.disease
Survival Analysis
Kidney Neoplasms
Gene Expression Regulation, Neoplastic
030104 developmental biology
Oncology
Cancer research
medicine.symptom
business
Neoplasm Transplantation
CD8
Unsupervised Machine Learning
Subjects
Details
- ISSN :
- 21598290 and 21598274
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- Cancer Discovery
- Accession number :
- edsair.doi.dedup.....30727601a9619585b3e0d4d3b8fb9c1a
- Full Text :
- https://doi.org/10.1158/2159-8290.cd-17-1246