Functional and translational consequences of immunometabolic coevolution in ccRCC Meeting Abstract


Authors: Reznik, E.; Tang, C.; Xie, A.; Liu, E. M.; Kuo, F.; Kim, M.; Golkaram, M.; Chen, Y.; Gupta, S.; Motzer, R.; Russo, P.; Coleman, J.; Carloa, M.; Voss, M.; Kotecha, R.; Lee, C. H.; Tansey, W.; Schultz, N.; Hakimi, A. A.
Abstract Title: Functional and translational consequences of immunometabolic coevolution in ccRCC
Meeting Title: 5th Kidney Cancer Research Summit (KCRS 2023)
Abstract: Background 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. Methods 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. Results 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. Conclusions Local metabolic phenotypes therefore emerge in tandem with the immune microenvironment and associate with therapeutic sensitivity. CDMRP DOD Funding: yes
Keywords: carcinoma, renal cell; massachusetts; metabolomics; metabolites; congresses and conferences -- massachusetts
Journal Title: The Oncologist
Volume: 28
Issue: Suppl. 1
Meeting Dates: 2023 Jul 13-14
Meeting Location: Boston, MA
ISSN: 1083-7159
Publisher: Oxford University Press  
Date Published: 2023-09-01
Start Page: S1
End Page: S2
Language: English
DOI: 10.1093/oncolo/oyad216.003
PROVIDER: EBSCOhost
PROVIDER: cinahl
DOI/URL:
Notes: Meeting Abstract: 40 -- This meeting was also held virtually -- Source: Cinahl
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MSK Authors
  1. Jonathan Coleman
    341 Coleman
  2. Paul Russo
    581 Russo
  3. Robert Motzer
    1243 Motzer
  4. Martin Henner Voss
    288 Voss
  5. Yingbei Chen
    393 Chen
  6. Nikolaus D Schultz
    486 Schultz
  7. Abraham Ari Hakimi
    323 Hakimi
  8. Eduard Reznik
    103 Reznik
  9. Chung-Han   Lee
    157 Lee
  10. Fengshen Kuo
    80 Kuo
  11. Minwei Liu
    24 Liu
  12. Ritesh Rajesh Kotecha
    91 Kotecha
  13. Wesley Tansey
    15 Tansey
  14. Minsoo Kim
    8 Kim
  15. Cerise Tang
    11 Tang
  16. Amy Xie
    6 Xie