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532 Intratumoral GABA reveals new resistance pathway to immune checkpoint inhibitors in tertiary lymphoid structures positive tumors
  1. Anne Calvez1,
  2. Isaias Hernández-Verdin2,
  3. Antoine Bougoüin2,
  4. Cheng-Ming Sun2,
  5. Asmat Ullah2,
  6. Yann Vano2,3,
  7. Antoine Italiano4,
  8. Stephane Oudard5,
  9. Catherine Sautes-Fridman6 and
  10. Wolf H Fridman2
  1. 1Cordeliers Research Centre, Paris, France
  2. 2Centre de Recherche des Cordeliers, INSERM, Université Paris Cité, Sorbonne Université, Paris, France
  3. 3Hôpital Européen Georges Pompidou, APHP, Paris, France
  4. 4Institut Bergonié, Bordeaux, France
  5. 5Georges Pompidou European Hospital, Paris, France
  6. 6Universite Paris-Cite, Paris, France
  • Journal for ImmunoTherapy of Cancer (JITC) preprint. The copyright holder for this preprint are the authors/funders, who have granted JITC permission to display the preprint. All rights reserved. No reuse allowed without permission.

Abstract

Background Tertiary lymphoid structures (TLS), organized immune aggregates located in the tumor microenvironment (TME), play a crucial role in supporting local anti-tumoral T and B cell immune responses. Their detection in routine clinical settings holds great promise due to their association with improved responses to immune checkpoint inhibitors (ICI) and better clinical outcomes across various cancers.1 2 Nevertheless, a significant proportion of patients with TLS-containing tumors still fail to respond to ICI,3 indicating the presence of resistance mechanisms impairing anti-tumor responses in such tumors.

Methods To address this question, we employed bulk RNA-seq, spatial transcriptomics (Visium, 10X Genomics), and highplex immunofluorescence (COMET, Lunaphore) on TLS-positive tumors from ICI-treated patients with metastatic clear cell renal cell carcinoma (ccRCC) and metastatic soft tissue sarcoma (STS).3 4 We also analyzed public single-cell RNAseq and spatial metabolomics data5 to further compare microenvironmental patterns between patients.

Results Comparative transcriptomic analysis between mccRCC TLS-positive tumors from responding and non-responding patients revealed gamma-aminobutyric acid (GABA) transport (p = 0.00034) as the top signature upregulated in tumors from non-responding patients (RECIST version 1.1). Differential gene expression analysis allowed to define GABA signatures significantly associated with non-response to ICI and poorer survival in both ccRCC and STS patients. Spatial transcriptomics showed that GABA was predominantly produced by a subpopulation of ccRCC tumor cells, with a profile close to that of renal proximal tubule cells, particularly present in non-responding patients. Interestingly, GABA-producing tumor cells were found in close proximity to TLS in non-responding patients (p = 0.014). Tumor, stromal, and immune cells within tumors expressed GABA receptors, suggesting that they could play a role in ICI response variability. TLS from non-responding patients exhibited reduced expression of genes involved in antigen presentation, elevated IgA and decreased IgG gene expression, suggesting dysfunctional immune activation. This result corroborates with the decrease in immune infiltration and activation within GABA high tumors observed by both bulk transcriptomic and multiplex-immunofluorescence.

Given GABA’s association with ICI resistance in TLS-positive tumors, a GABA synthesis inhibitor, 3-Mercaptopropionic acid, when intratumorally injected in a murine model of STS, significantly improved tumor control compared with anti-PD1 therapy alone (p = 0.029).

Conclusions Our study identifies GABA as a critical modulator of ICI resistance within TLS-positive tumors, unveiling potential strategies for patient stratification and therapeutic development to sensitize tumors and enhance immunotherapy efficacy.

References

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  2. Vanhersecke L, Brunet M, Guégan J-P, Rey C, Bougouin A, Cousin S, Le Moulec S, Besse B, Loriot Y, Larroquette M, et al. Mature tertiary lymphoid structures predict immune checkpoint inhibitor efficacy in solid tumors independently of PD-L1 expression. Nat Cancer 2021;2:794–802.

  3. Italiano A, Bessede A, Pulido M, Bompas E, Piperno-Neumann S, Chevreau C, Penel N, Bertucci F, Toulmonde M, Bellera C, et al. Pembrolizumab in soft-tissue sarcomas with tertiary lymphoid structures: a phase 2 PEMBROSARC trial cohort. Nat Med 2022;28:1199–1206.

  4. Vano Y-A, Elaidi R, Bennamoun M, Chevreau C, Borchiellini D, Pannier D, Maillet D, Gross-Goupil M, Tournigand C, Laguerre B, et al. Nivolumab, nivolumab–ipilimumab, and VEGFR-tyrosine kinase inhibitors as first-line treatment for metastatic clear-cell renal cell carcinoma (BIONIKK): a biomarker-driven, open-label, non-comparative, randomised, phase 2 trial. The Lancet Oncology 2022;23:612–624.

  5. Hu J, Wang S-G, Hou Y, Chen Z, Liu L, Li R, Li N, Zhou L, Yang Y, Wang L, et al. Multi-omic profiling of clear cell renal cell carcinoma identifies metabolic reprogramming associated with disease progression. Nat Genet 2024:1–16.

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