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. 2024 Sep 10;25(18):9777.
doi: 10.3390/ijms25189777.

Discriminating Benign from Malignant Lung Diseases Using Plasma Glycosaminoglycans and Cell-Free DNA

Alvida Qvick  1 , Sinisa Bratulic  2 , Jessica Carlsson  3 , Bianca Stenmark  1 , Christina Karlsson  4 , Jens Nielsen  2   5 , Francesco Gatto  2   6 , Gisela Helenius  1
Affiliations

Discriminating Benign from Malignant Lung Diseases Using Plasma Glycosaminoglycans and Cell-Free DNA

Alvida Qvick et al. Int J Mol Sci. .

Abstract

We aimed to investigate the use of free glycosaminoglycan profiles (GAGomes) and cfDNA in plasma to differentiate between lung cancer and benign lung disease, in a cohort of 113 patients initially suspected of lung cancer. GAGomes were analyzed in all samples using the MIRAM® Free Glycosaminoglycan Kit with ultra-high-performance liquid chromatography and electrospray ionization triple quadrupole mass spectrometry. In a subset of samples, cfDNA concentration and NGS-data was available. We detected two GAGome features, 0S chondroitin sulfate (CS), and 4S CS, with cancer-specific changes. Based on the observed GAGome changes, we devised a model to predict lung cancer. The model, named the GAGome score, could detect lung cancer with 41.2% sensitivity (95% CI: 9.2-54.2%) at 96.4% specificity (95% CI: 95.2-100.0%, n = 113). When we combined the GAGome score with a cfDNA-based model, the sensitivity increased from 42.6% (95% CI: 31.7-60.6%, cfDNA alone) to 70.5% (95% CI: 57.4-81.5%) at 95% specificity (95% CI: 75.1-100%, n = 74). Notably, the combined GAGome and cfDNA testing improved the sensitivity, compared to cfDNA alone, especially in ASCL stage I (55.6% vs 11.1%). Our findings show that plasma GAGome profiles can enhance cfDNA testing performance, highlighting the applicability of a multiomics approach in lung cancer diagnostics.

Keywords: GAGome; cfDNA; glycosaminoglycans; lung cancer; multiomics.

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Conflict of interest statement

F. Gatto and J. Nielsen are shareholders in Elypta AB. F. Gatto and S. Bratulic are employed by Elypta AB. J. Nielsen is a board member at Elypta AB. Elypta AB has a commercial interest in part of the technology described in this study. The remaining authors have no conflicts to declare.

Figures

Figure 1
Figure 1
Study overview. (A) Cohort inclusion for GAGome and cfDNA analysis. (B) Development of scores using GAGome and cfDNA.
Figure 2
Figure 2
Plasma GAGome profiles. Concentrations (A) and fractions (B) of plasma GAGs disaccharides in cases (n = 85) and controls (n = 28). CS: chondroitin sulfate.
Figure 3
Figure 3
The performance of the GAGome score in discriminating between cases and controls in (A) the whole cohort and (B) by IASLC stage. Controls are shown in blue (NControl = 28) and cases in orange (NCase = 85; NStageI = 9, NStageII/III = 24, NStageIV = 58). IASLC: International Association for the Study of Lung Cancer.
Figure 4
Figure 4
The performance of the combined test. (A) The performance and cut-offs of the cfDNA and GAGome scores separately, and (B) the sensitivity when combined (by IASCL stage). Color scheme is: controls (blue), stage I (yellow), stage II/III (green), and stage IV (red). IASCL: International Association for the Study of Lung Cancer.
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