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Ed Reznik, Cerise Tang, Amy Xie, Eric Minwei Liu, Fengshen Kuo, Minsoo Kim, Mahdi Golkaram, Yingbei Chen, Sounak Gupta, Robert Motzer, Paul Russo, Jonathan Coleman, Maria Carloa, Martin Voss, Ritesh Kotecha, Chung Han Lee, Wesley Tansey, Nikolaus Schultz, A Ari Hakimi, Functional and translational consequences of immunometabolic coevolution in ccRCC, The Oncologist, Volume 28, Issue Supplement_1, September 2023, Pages S1–S2, https://doi.org/10.1093/oncolo/oyad216.003
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Abstract
Tumor cell phenotypes and anti-tumor immune responses are shaped by local metabolite availability, but intratumoral metabolite heterogeneity (IMH) and its phenotypic consequences remain poorly understood. In vitro mechanistic studies have demonstrated that the anti-tumor activity of lymphoid and myeloid cell populations is mediated by metabolite availability and signaling in the TME, raising the possibility that the immune response and metabolism of ccRCC tumors coevolve and jointly influence the likelihood that a patient responds to therapy.. However, both the broad patterns of coordination between metabolite abundance and TME cellular composition, as well as the precise cell populations producing metabolic phenotypes of interest, remain unknown.
To study IMH, we multiregionally profiled the metabolome, transcriptome, and genome of 187 tumor/normal regions from 31 clear cell renal cell carcinoma (ccRCC) patients. Using these measurements and additional multimodal metabolomic/transcriptomic profiling of ccRCC and other diseases, we developed computational models that can be used to understand RNA-metabolite covariation and ultimately impute metabolite levels from RNA sequencing data.
Analysis of intratumoral metabolite-RNA covariation revealed that the immune composition of the microenvironment, and especially the abundance of myeloid cells, drove intratumoral metabolite variation. Motivated by the strength of RNA-metabolite covariation and the clinical significance of RNA biomarkers in ccRCC, we deployed and benchmarked a machine learning method (MIRTH) to impute metabolite levels directly from RNA sequencing data of primary and metastatic ccRCC tumors. We inferred metabolomic profiles from RNA sequencing data of ccRCC patients enrolled in 6 clinical trials, ultimately identifying specific metabolite biomarkers associated with response to anti-angiogenic agents.
Local metabolic phenotypes therefore emerge in tandem with the immune microenvironment and associate with therapeutic sensitivity.
CDMRP DOD Funding: yes