Identification of sSIGLEC5 and sLAG3 as New Relapse Predictors in Lung Cancer

Soluble immune checkpoints: implications for cancer prognosis and response to immune checkpoint therapy and conventional therapies

Longitudinal sampling of tumor tissue from patients with solid cancers, aside from melanoma and a few other cases, is often unfeasible, and thus may not capture the plasticity of interactions between the tumor and immune system under selective pressure of a given therapy. Peripheral blood analyses provide salient information about the human peripheral immunome while offering technical and practical advantages over traditional tumor biopsies, and should be utilized where possible alongside interrogation of the tumor. Some common blood-based biomarkers used to study the immune response include immune cell subsets, circulating tumor DNA, and protein analytes such as cytokines. With the recent explosion of immune checkpoint inhibitors (ICI) as a modality of treatment in multiple cancer types, soluble immune checkpoints have become a relevant area of investigation for peripheral immune-based biomarkers. However, the exact functions of soluble immune checkpoints and their roles in cancer for the most part remain unclear. This review discusses current literature on the production, function, and expression of nine soluble immune checkpoints - sPD-L1, sPD-1, sCTLA4, sCD80, sTIM3, sLAG3, sB7-H3, sBTLA, and sHVEM - in patients with solid tumors, and explores their role as biomarkers of response to ICI as well as to conventional therapies (chemotherapy, radiotherapy, targeted therapy, and surgery) in cancer patients.

Soluble lymphocyte activation gene-3 (sLAG3) and CD4/CD8 ratio dynamics as predictive biomarkers in patients undergoing immune checkpoint blockade for solid malignancies

BACKGROUND

The search for biomarkers to identify suitable candidates for immune checkpoint inhibitor (ICI) therapy remains ongoing. We evaluate how soluble levels of the next generation immune checkpoint Lymphocyte Activation Gene-3 (sLAG-3) and its association with circulating T lymphocyte subsets could pose as a novel biomarker to predict outcome to ICI therapy.

METHODS

Circulating levels of sLAG3 were analyzed using multiplex immunoassay in n = 84 patients undergoing ICI therapy for advanced solid cancer, accompanied by flow cytometry analyses of peripheral blood mononuclear cells (PBMCs).

RESULTS

Uni- and multivariate analysis shows that patients with higher sLAG3 concentrations before ICI therapy had a significantly impaired progression-free (PFS) and overall survival (OS) (HRPFS: 1.005 [95%CI: 1.000-1.009], p = 0.039; HROS: 1.006 [95%CI: 1.001-1.011], p = 0.015). The CD4/CD8 cell ratio and its dynamics during therapy were strong predictors of PFS and OS with patients with a decreasing ratio between baseline and after 1-2 cycles having an improved median OS compared to patients with increasing values (p = 0.012, HR: 3.32). An immunological score combining sLAG3 and the CD4/CD8 ratio showed the highest predictive potential (HROS: 10.3).

CONCLUSION

Pending prospective validation, sLAG3 and correlating circulating T-cell subsets can be used as a non-invasive predictive marker to predict outcome to ICI therapy to help identifying ideal ICI candidates in the future.

The prognostic impact of SIGLEC5-induced impairment of CD8+ T cell activation in sepsis

BACKGROUND

Sepsis is associated with T-cell exhaustion, which significantly reduces patient outcomes. Therefore, targeting of immune checkpoints (ICs) is deemed necessary for effective sepsis management. Here, we evaluated the role of SIGLEC5 as an IC ligand and explored its potential as a biomarker for sepsis.

METHODS

In vitro and in vivo assays were conducted to both analyse SIGLEC5's role as an IC ligand, as well as assess its impact on survival in sepsis. A multicentre prospective cohort study was conducted to evaluate the plasmatic soluble SIGLEC5 (sSIGLEC5) as a mortality predictor in the first 60 days after admission in sepsis patients. Recruitment included sepsis patients (n = 346), controls with systemic inflammatory response syndrome (n = 80), aneurism (n = 11), stroke (n = 16), and healthy volunteers (HVs, n = 100).

FINDINGS

SIGLEC5 expression on monocytes was increased by HIF1α and was higher in septic patients than in healthy volunteers after ex vivo LPS challenge. Furthermore, SIGLEC5-PSGL1 interaction inhibited CD8+ T-cell proliferation. Administration of sSIGLEC5r (0.8 mg/kg) had adverse effects in mouse endotoxemia models. Additionally, plasma sSIGLEC5 levels of septic patients were higher than HVs and ROC analysis revealed it as a mortality marker with an AUC of 0.713 (95% CI, 0.656-0.769; p < 0.0001). Kaplan-Meier survival curve showed a significant decrease in survival above the calculated cut-off (HR of 3.418, 95% CI, 2.380-4.907, p < 0.0001 by log-rank test) estimated by Youden Index (523.6 ng/mL).

INTERPRETATION

SIGLEC5 displays the hallmarks of an IC ligand, and plasma levels of sSIGLEC5 have been linked with increased mortality in septic patients.

FUNDING

Instituto de Salud Carlos III (ISCIII) and "Fondos FEDER" to ELC (PIE15/00065, PI18/00148, PI14/01234, PI21/00869), CDF (PI21/01178), RLR (FI19/00334) and JAO (CD21/00059).

Prediction of risk and overall survival of pancreatic cancer from blood soluble immune checkpoint-related proteins

Background

Immune checkpoint inhibition holds promise as a novel treatment for pancreatic ductal adenocarcinoma (PDAC). The clinical significance of soluble immune checkpoint (ICK) related proteins have not yet fully explored in PDAC.

Methods

We comprehensively profiled 14 soluble ICK-related proteins in plasma in 70 PDAC patients and 70 matched healthy controls. Epidemiological data of all subjects were obtained through structured interviews, and patients' clinical data were retrieved from electronical health records. We evaluated the associations between the biomarkers with the risk of PDAC using unconditional multivariate logistic regression. Consensus clustering (k-means algorithm) with significant biomarkers was performed to identify immune subtypes in PDAC patients. Prediction models for overall survival (OS) in PDAC patients were developed using multivariate Cox proportional hazards regression. Harrell's concordance index (C-index), time-dependent receiver operating characteristic (ROC) curve and calibration curve were utilized to evaluate performance of prediction models. Gene expressions of the identified ICK-related proteins in tumors from TCGA were analyzed to provide insight into underlying mechanisms.

Results

Soluble BTLA, CD28, CD137, GITR and LAG-3 were significantly upregulated in PDAC patients (all q < 0.05), and elevation of each of them was correlated with PDAC increased risk (all p < 0.05). PDAC patients were classified into soluble immune-high and soluble immune-low subtypes, using these 5 biomarkers. Patients in soluble immune-high subtype had significantly poorer OS than those in soluble immune-low subtype (log-rank p = 9.7E-03). The model with clinical variables and soluble immune subtypes had excellent predictive power (C-index = 0.809) for the OS of PDAC patients. Furthermore, the immune subtypes identified with corresponding genes' expression in PDAC tumor samples in TCGA showed an opposite correlation with OS to that of immune subtypes based on blood soluble ICK-related proteins (log-rank p =0.02). The immune-high subtype tumors displayed higher cytolytic activity (CYT) score than immune-low subtype tumors (p < 2E-16).

Conclusion

Five soluble ICK-related proteins were identified to be significantly associated with the risk and prognosis of PDAC. Patients who were classified as soluble immune-low subtype based on these biomarkers had better overall survival than those of the soluble immune-high subtype.