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. 2024 Jun 10;12(6):e009039.
doi: 10.1136/jitc-2024-009039.

High-throughput transcriptome profiling indicates ribosomal RNAs to be associated with resistance to immunotherapy in non-small cell lung cancer (NSCLC)

Affiliations

High-throughput transcriptome profiling indicates ribosomal RNAs to be associated with resistance to immunotherapy in non-small cell lung cancer (NSCLC)

Myrto K Moutafi et al. J Immunother Cancer. .

Abstract

Background: Despite the impressive outcomes with immune checkpoint inhibitor (ICI) in non-small cell lung cancer (NSCLC), only a minority of the patients show long-term benefits from ICI. In this study, we used retrospective cohorts of ICI treated patients with NSCLC to discover and validate spatially resolved protein markers associated with resistance to programmed cell death protein-1 (PD-1) axis inhibition.

Methods: Pretreatment samples from 56 patients with NSCLC treated with ICI were collected and analyzed in a tissue microarray (TMA) format in including four different tumor regions per patient using the GeoMx platform for spatially informed transcriptomics. 34 patients had assessable tissue with tumor compartment in all 4 TMA spots, 22 with leukocyte compartment and 12 with CD68 compartment. The patients' tissue that was not assessable in fourfold redundancy in each compartment was designated as the validation cohort; cytokeratin (CK) (N=22), leukocytes CD45 (N=31), macrophages, CD68 (N=43). The human whole transcriptome, represented by~18,000 individual genes assessed by oligonucleotide-tagged in situ hybridization, was sequenced on the NovaSeq platform to quantify the RNAs present in each region of interest.

Results: 54,000 gene variables were generated per case, from them 25,740 were analyzed after removing targets with expression lower than a prespecified frequency. Cox proportional-hazards model analysis was performed for overall and progression-free survival (OS, PFS, respectively). After identifying genes significantly associated with limited survival benefit (HR>1)/progression per spot per patient, we used the intersection of them across the four TMA spots per patient. This resulted in a list of 12 genes in the tumor-cell compartment (RPL13A, GNL3, FAM83A, CYBA, ACSL4, SLC25A6, EPAS1, RPL5, APOL1, HSPD1, RPS4Y1, ADI1). RPL13A, GNL3 in tumor-cell compartment were also significantly associated with OS and PFS, respectively, in the validation cohort (CK: HR, 2.48; p=0.02 and HR, 5.33; p=0.04). In CD45 compartment, secreted frizzled-related protein 2, was associated with OS in the discovery cohort but not in the validation cohort. Similarly, in the CD68 compartment ARHGAP and PNN interacting serine and arginine rich protein were significantly associated with PFS and OS, respectively, in the majority but not all four spots per patient.

Conclusion: This work highlights RPL13A and GNL3 as potential indicative biomarkers of resistance to PD-1 axis blockade that might help to improve precision immunotherapy strategies for lung cancer.

Keywords: biomarker; immune checkpoint inhibitor; lung cancer.

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Conflict of interest statement

Competing interests: DLR has served as an advisor for AstraZeneca, Agendia, Amgen, BMS, Cell Signaling Technology, Cepheid, Danaher, Daiichi Sankyo, Genoptix/Novartis, GSK, Konica Minolta, Merck, NanoString, PAIGE.AI, Roche, and Sanofi. Amgen, Cepheid, NavigateBP, NextCure, and Konica Minolta fund or have funded research in DLR’s laboratory. KAS has served as a Consultant or advisor: AbbVie Inc.; Agenus Inc.; AstraZeneca; Bristol Myers Squibb; CDR-Life Inc.; Clinica Alemana de Santiago; EMD Serono Inc.; F.Hoffmann-La Roche; Genmab A/S; Indaptus Therapeutics; Janssen Pharmaceuticals, Inc.; Merck Sharpe & Dohme; Moderna, Inc.; Molecular Templates, Inc.; OnCusp Therapeutics; Parthenon/Incendia Therapeutics; Repertoire Therapeutics; Sanofi; Sensei Biotherapeutics; Shattuck Labs, Inc.; and Takeda Pharmaceutical Company Limited.

Figures

Figure 1
Figure 1
Workflow. (A) Four independent YTMA471 blocks, were analyzed; each block containing one non-adjacent tumor core per patient. (B) For the CK compartment 34 cores had evaluable CK+tissue in all four YTMA blocks (marked with magenta); 22 cores had evaluable CK+ tissue in at least one block and consisted the validation set. (C) Four independent Cox survival analysis were performed for each block. The intersection of the genes significantly associated with survival is shown in the middle. (D) The intersection of the 12 genes of the fourfold redundant cohorts with the validation set for the CK compartment consisted of 2 genes. (E) Kaplan-Meier graph of one of the two genes in the validation cohort. CK, cytokeratin; NSCLC, non-small cell lung cancer.
Figure 2
Figure 2
Volcano plots. (A) Volcano plot showing genes associations with survival in Block 1, (B) Block 2, (C) Block 3, (D) Block 4× axis logHR; HR>1 indicates shorter survival. (E) Taking the intersection of all the significantly associated RNAs with overall survival (OS) throughout the four YTMAs, RPL13A, GNL3, FAM83A, CYBA, ACSL4, SLC25A6, EPAS1, RPL5, APOL1, HSPD1, RPS4Y1 and ADI1 were correlated with shorter OS regardless the tumor core (HR>1, PLogRank <0.05). (F) Two of them ribosomal protein L13a (RPL13A), GNL3 (both participating in ribosomal RNA processing in the nucleolus and cytosol) they were also associated with shorter OS in the validation cohort (HR 2.48, PLogRank 0.02; HR 5.33, PLogRank 0.04). CK, cytokeratin.
Figure 3
Figure 3
Shown are Kaplan-Meier estimates of overall and progression-free survival, for CK compartment in the validation cohort. (A) Overall survival—RPL13A, (B). Overall survival—GNL3, (C). Progression-fee survival—RPL13A, (D) progression-free survival—GNL3. CK, cytokeratin; OS, overall survival; PFS, progression-fee survival.

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