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Neurooncol Adv
Neurooncol Adv
noa
Neuro-Oncology Advances
2632-2498
Oxford University Press US

10.1093/noajnl/vdae090.034
vdae090.034
Final Category: Data Sciences/AI Advances
AcademicSubjects/MED00300
AcademicSubjects/MED00310
DSAI-01 TUMOR EVOLUTION OF BRAIN-SPECIFIC TROPISM IN METASTATIC RENAL CELL CARCINOMA
Elias Roy Johns Hopkins, Baltimore, USA

Kamson David Johns Hopkins, Baltimore, USA

Feinaj Ardit Johns Hopkins, Baltimore, USA

Pallavajjala Aparna Johns Hopkins, Baltimore, USA

Yerrapragada Anirudh Johns Hopkins, Baltimore, USA

Dreschner Max University of Maryland, Baltimore, USA

Patel Sunil Johns Hopkins, Baltimore, USA

Fertig Elana Johns Hopkins, Baltimore, USA

Markowski Mark Johns Hopkins, Baltimore, USA

Yegnasubramanian Srinivasan dkamson1@jhmi.edu, Baltimore, USA

Matoso Andres Johns Hopkins, Baltimore, USA

Baraban Ezra Johns Hopkins, Baltimore, USA

Bettegowda Chetan Johns Hopkins, Baltimore, USA

Ged Yasser Johns Hopkins, Baltimore, USA

Singla Nirmish Johns Hopkins, Baltimore, USA

8 2024
02 8 2024
02 8 2024
6 Suppl 1 2024 SNO/ASCO CNS Metastases Conference i11i11
© The Author(s) 2024. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

Abstract

BACKGROUND

Herein, we describe the molecular landscape of renal cell carcinoma (RCC) brain metastases (BrMs).

METHODS

Panel-based DNA (DNAseq) and whole-transcriptome sequencing (RNAseq) were used to assess BrM, non-BrM and matched primary renal tumors (PRT) from RCC patients who underwent resection.

RESULTS

95samples (BrM=54, nBrM=14, PRT=27) from 53patients were included (85% clear-cell histology). DNAseq of 85 samples (50pts) identified common mutations in VHL (18pts, 36%), TP53 (24%), PBRM1 (24%), SETD2 (24%), and BAP1 (8%). MTOR-pathway mutations were enriched, with 25pts (50%) having at least one mutation in PTEN (12pts, 24%), TSC1 (8%), TSC2 (10%), or MTOR (18%). Copy number analyses revealed frequent deletions in chr14q21(34pts, 68%) and chr9p21 (70%) and gains in chr20q13 (62%) and chr7p15 (62%). At the sample-level, PTEN mutations were more common in BrM (11pts, 22.9%) versus non-BrM (0) and PRT (12%, p=0.124), as were deletions in chr13q22 (BrM=26.5% [13pts], non-BrM=8.3%, PRT=4%; p=0.036) and gains in chr20q13 (BrM=61.2% [30pts], non-BrM=33.3%, PRT=32%; p=0.03). Chr9p21 deletions were enriched in BrM (32pts, 65.3%) and non-BrM (66.7%) relative to PRTs (28%, p=0.006). Other copy number alterations and somatic mutations had similar distribution across sites. Analysis of 86 samples (51pts) identified 806 genes expressed differentially between BrM and PRT (adjusted p<0.05), 399 between BrM and non-BrM (adj. p<0.05). Gene set enrichment analysis of MSigDB Hallmark gene sets revealed upregulation of MTORC1 signaling, glycolysis, and MYC targets in BrM comparted to PRT and non-BrM (adj. p<0.0001). Immune-related gene sets, e.g. interferon-alpha, interferon-gamma, TNF-alpha, were enriched in PRT relative to BrM (adj. p<0.0001), but not non-BrM (adj. p > 0.05). CIBERSORT cellular deconvolution analysis comparing BrM with PRT revealed increased proportions of M2 (p=0.0009), but decreased M1 macrophages (p=0.0003) and CD8+T-cells (p=0.0106).

CONCLUSIONS

RCC with BrM exhibit distinct MTOR-pathway enrichment and hyperactivation. The immunosuppressive milieu in BrM may be steroid-related. Further analyses are underway.
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pmc
