Plasma galectins and metabolites in advanced head and neck carcinomas: evidence of distinct immune characteristics linked to hypopharyngeal tumors

Abstract

Extra-cellular galectins 1, 3 and 9 (gal-1, -3 and -9) are known to act as soluble immunosuppressive agents in various malignancies. Previous publications have suggested that their expression is dependent on the metabolic status of producing cells and reciprocally that they can influence metabolic pathways in their target cells. Very little is known about the status of gal-1, -3 and -9 in patients bearing head and neck squamous cell carcinomas (HNSCC) and about their relationships with the systemic metabolic condition. This study was conducted in plasma samples from a prospective cohort of 83 HNSCC patients with advanced disease. These samples were used to explore the distribution of gal-1, -3 and -9 and simultaneously to profile a series of 87 metabolites assessed by mass spectrometry. We identified galectin and metabolic patterns within five disease categories defined according to the primary site and human papillomavirus (HPV) status (HPV-positive and -negative oropharyngeal carcinomas, carcinomas of the oral cavity, hypopharynx and larynx carcinomas). Remarkably, samples related to hypopharyngeal carcinomas displayed the highest average concentration of gal-9 (p = .017) and a trend toward higher concentrations of kynurenine, a potential factor of tumor growth and immune suppression. In contrast, there was a tendency toward higher concentrations of fatty acids in samples related to oral cavity. These observations emphasize the diversity of HPV-negative HNSCCs. Depending on their primary site, they evolve into distinct types of immune and metabolic landscapes that seem to be congruent with specific oncogenic mechanisms.

Keywords: Head and neck carcinomas; kynurenine; plasma galectins; plasma metabolites.

Figure 1.

Overall distribution and distribution by tumor categories of gal-1, −3 and −9 concentrations in plasma samples from 83 HNSCC patients. A Overall distribution in all samples of the cohort and comparison with healthy donors. Plasma concentrations of galectins were determined by ELISA as explained in the Materials and Methods section. Values obtained for patients and healthy donors are shown as black and red dots, respectively. For gal-1, we had 15 measurements made in samples from 5 donors (lowest and highest concentrations 7.44 and 22.81 ng/ml, respectively; median: 11.64 ng/ml). For gal-3, we had 13 measurements made in samples from 5 donors (lowest and highest concentrations 1.51 and 8.70 ng/ml, respectively; median: 5.33 ng/ml). For gal-9, we had 13 measurements in samples from 4 donors (lowest and highest concentrations 4.28 and 13.39 ng/ml, respectively; median 6.57 ng/ml). For both patients and healthy donors, concentrations were normalized on the median concentration of the healthy donors. Fifty eight out of 83 patients, 50/83 and 19/83 patients had plasma concentrations above the highest value recorded in healthy donors for gal-1, gal-3 and gal-9 respectively. b, c and d Comparison of plasma galectin concentrations for five tumor categories: oropharyngeal (oro) HPV-pos and neg, oral cavity, hypopharynx, larynx. Kruskal-Wallis tests: gal-1: p = .88; gal-3: p = .79; gal-9: p = .018. For gal-9, all possible pairwise comparisons were tested using the Dunn’s multiple comparisons test. The only significant difference regarding the distribution of plasma gal-9 concentrations was between hypopharynx and HPV-neg oropharynx carcinomas (**p = .012). e, f and g Comparison of plasma galectin concentrations recorded for hypopharyngeal carcinomas versus the four other categories taken as a single group. In accordance with data presented in B, C and D, the difference is statistically significant only for gal-9 (Mann–Whitney test: gal-1: p = .93; gal-3: p = .46; gal-9: p = .0020).

Figure 2.

Distribution of plasma galectin concentrations according to the number of metastatic sites. M0: absence of any detectable metastatic site (n = 33); M+: only one metastatic site (n = 20); M++: two or more metastatic sites (n = 30). a Gal-1 (Kruskal–Wallis test: p = .85). b Gal-3 (Kruskal–Wallis test: p = .45). c Gal-9 (Kruskal–Wallis test: p = .028); all possible pairwise comparisons were tested using the Dunn’s multiple comparisons test; the only significant difference regarding the distribution of plasma gal-9 was between M++ vs M0 patients (*p = .022). In contrast, the difference was not significant when comparing M+ and M0 patients (p = .69).

Figure 3.

Overview and correlation mapping of the distribution of 87 metabolites detected by GC-MS and five selected proteins (gal-1, −3 and −9, CRP, CXCL9) in plasma samples from HNSCC patients. The correlation matrix was generated using the “correlation” function of the MetaboAnalyst software (https://www.metaboanalyst.ca). Data related to metabolites and proteins were analyzed simultaneously using the same software although they were obtained using distinct assays, GC/MS and ELISA respectively. The color scale is a function of Spearman coefficients of correlation. Areas of red colors signal biomolecules occurring with concomitant high abundance in substantial numbers of plasma samples. Five prominent clusters designated a, b, c, d, e were delimited on the basis of color contrasts and to a lesser extent dendrogram arborescence. Clusters a and b mainly consisted of free proteinogenic amino-acids including branched (valine, leucine, isoleucine) and aromatic (tryptophan, phenylalanine, tyrosine) amino-acids, proline, asparagine, serine and threonine as well as two non-proteinogenic amino-acids (citrulline and ornithine). Cluster c was heterogeneous including a polyamine, putrescine, glutamine and shikimic acid, a metabolite derived from plants and microorganisms. Cluster d consisted of 3 fatty acids (oleic, linoleic, palmitoleic acids) and the related 3-hydroxybutyric acid. Like cluster c, cluster e was heterogenous containing gal-9, kynurenine (Kyn), uric acid and 4 putative plant-derived metabolites (threonic and ferulic acids, arbitol and erythritol). While gal-9 was clearly linked to cluster e, just adjacent to Kyn, gal-1, gal-3, CRP and CXCL9 were not associated to well delimited clusters.

Figure 4.

Supervised heatmap showing relative concentrations of metabolites and selected proteins in connection with tumor/disease categories. This heatmap was generated using the “heatmap” function of the MetaboAnalyst software. The color scale is a function of the relative concentration of each biomolecule (m/z peak areas for metabolites and pg/ml or ng/ml for proteins). It was assessed according to the positive or negative distance from the mean concentration recorded for each analyte in the 83 sample series. For better view and reading, the heatmap was split in two parts. “Oro” stands for oropharyngeal carcinomas. Overall, according to Anova statistical analysis, none of the metabolites on this map showed a significant difference of distribution between the 5 disease categories. However, it is noteworthy that a large number of metabolites (38/87) were at higher relative concentrations in plasma samples related to hypopharyngeal carcinomas. Most components of cluster E identified in Figure 4, especially one protein (gal-9; bold blue arrow) and several metabolites (kynurenine, arabitol, threonic acid; thin blue arrows, part 2)) were abundant in samples from hypopharyngeal carcinomas. In contrast, three components of cluster D (Figure 4) – linoleic, palmitoleic and 3-hydroxybutyric acid (thin blue arrows; part 1) – were abundant in samples from carcinomas of the oral cavity.

Figure 5.

Variations of plasma kynurenine (Kyn) concentrations in connection with some clinical and biological characteristics of HNSCCs. a Comparison of the plasma Kyn concentrations according to the five tumor categories: oropharyngeal (oro) HPV-pos and neg, oral cavity, hypopharynx, larynx. Kyn concentrations were estimated from the areas under the corresponding m/z peak curves corrected according to quality control data. There was a trend toward higher concentrations for plasma samples related to hypopharyngeal and laryngeal carcinomas (Kruskal-Wallis p = .18). b Comparisons of the ratios of Tryptophan/Kyn concentrations in the five tumor categories. c Distribution of plasma Kyn concentrations according to the number of metastatic sites; M0: absence of any detectable metastatic site (n = 33); M+: only one metastatic site (n = 20); M++: two or more metastatic sites (n = 30) (Kruskal–Wallis test: p = .016 – Dunn’s multiple comparisons test: M++ vs M0 p = .015); d Correlations of gal-9 concentrations (based on ELISA) with Kyn concentrations (based on mass spectrometry) for plasma samples of the whole HNSCC cohort (83 patients)(Spearman test). e and f Same correlations restricted to plasma samples related to hypopharyngeal (e) and laryngeal (f) carcinomas.

Figure 6.

Status of gal-9, IDO1 and HIF1α transcripts in the various categories of HNSCCs according to TCGA online data (access through C-BioPortal – Firehose Legacy cohort). Oro ND: oropharyngeal carcinomas with unknown HPV status (n = 39); oro/HPV⁺: HPV-positive oropharyngeal carcinomas (n = 33); oro/HPV⁻: HPV-negative oropharyngeal carcinomas (n = 8); carcinomas of the oral cavity (n = 316); hypopharynx (n = 10) and larynx (n = 116). Total number of cases: 522. Upper panel: there are significant differences in the distribution of the Gal-9, IDO1 and HIF1α transcripts through the various categories of HNSCCs (Kruskal–Wallis test: p = .001)(error bars: standard error of the mean). Pairwise comparisons have been made using Dunn’s tests. Brackets and asterisks are shown only for significant comparisons involving oro/HPV⁺ (* p < .05; ** p < .01; *** p < .001; **** p < .0001). We found no significant comparisons involving hypopharyngeal carcinomas. Lower panel: correlations between the amounts of gal-9, IDO1 and HIF1α transcripts in the overall cohort of HNSCCs given using V2 RSEM values (RNA-seq by Expectation-Maximization). A positive correlation is found between the tumor amounts of gal-9 and IDO1 mRNAs. In contrast, there is a negative correlation between HIF1α and Gal-9 as well as HIF1α and IDO1 transcripts.