Soluble SIGLEC5: A New Prognosis Marker in Colorectal Cancer Patients

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).

Integrated analysis of the microbiome and transcriptome in stomach adenocarcinoma

We aimed to characterize the stomach adenocarcinoma (STAD) microbiota and its clinical value using an integrated analysis of the microbiome and transcriptome. Microbiome and transcriptome data were downloaded from the Cancer Microbiome Atlas and the Cancer Genome Atlas databases. We identified nine differentially abundant microbial genera, including Helicobacter, Mycobacterium, and Streptococcus, which clustered patients into three subtypes with different survival rates. In total, 74 prognostic genes were screened from 925 feature genes of the subtypes, among which five genes were identified for prognostic model construction, including NTN5, MPV17L, MPLKIP, SIGLEC5, and SPAG16. The prognostic model could stratify patients into different risk groups. The high-risk group was associated with poor overall survival. A nomogram established using the prognostic risk score could accurately predict the 1, 3, and 5 year overall survival probabilities. The high-risk group had a higher proportion of histological grade 3 and recurrence samples. Immune infiltration analysis showed that samples in the high-risk group had a higher abundance of infiltrating neutrophils. The Notch signaling pathway activity showed a significant difference between the high- and low-risk groups. In conclusion, a prognostic model based on five feature genes of microbial subtypes could predict the overall survival for patients with STAD.

Colorectal Cancer Stem Cells Fuse with Monocytes to Form Tumour Hybrid Cells with the Ability to Migrate and Evade the Immune System

BACKGROUND

The cancer cell fusion theory could be one of the best explanations for the metastasis from primary tumours.

METHODS

Herein, we co-cultured colorectal cancer (CRC) stem cells with human monocytes and analysed the properties of the generated tumour hybrid cells (THCs). The presence of THCs in the bloodstream together with samples from primary and metastatic lesions and their clinical correlations were evaluated in CRC patients and were detected by both FACS and immunofluorescence methods. Additionally, the role of SIGLEC5 as an immune evasion molecule in colorectal cancer was evaluated.

RESULTS

Our data demonstrated the generation of THCs after the in vitro co-culture of CRC stem cells and monocytes. These cells, defined as CD45+CD14+EpCAM+, showed enhanced migratory and proliferative abilities. The THC-specific cell surface signature allows identification in matched primary tumour tissues and metastases as well as in the bloodstream from patients with CRC, thus functioning as a biomarker. Moreover, SIG-LEC5 expression on in vitro generated THCs has shown to be involved in the mechanism for immune evasion. Additionally, sSIGLEC5 levels correlated with THC numbers in the prospective cohort of patients.

CONCLUSIONS

Our results indicate the generation of a hybrid entity after the in vitro co-culture between CRC stem cells and human monocytes. Moreover, THC numbers present in patients are related to both prognosis and the later spread of metastases in CRC patients.

Butler R, Avendaño-Ortiz J, López-Collazo E. Differential Immune Checkpoint and Ig-like V-Type Receptor Profiles in COVID-19: Associations with Severity and Treatment

Identifying patients' immune system status has become critical to managing SARS-CoV-2 infection and avoiding the appearance of secondary infections during a hospital stay. Despite the high volume of research, robust severity and outcome markers are still lacking in COVID-19. We recruited 87 COVID-19 patients and analyzed, by unbiased automated software, 356 parameters at baseline emergency department admission including: high depth immune phenotyping and immune checkpoint expression by spectral flow cytometry, cytokines and other soluble molecules in plasma as well as routine clinical variables. We identified 69 baseline alterations in the expression of immune checkpoints, Ig-like V type receptors and other immune population markers associated with severity (O2 requirement). Thirty-four changes in these markers/populations were associated with secondary infection appearance. In addition, through a longitudinal sample collection, we described the changes which take place in the immune system of COVID-19 patients during secondary infections and in response to corticosteroid treatment. Our study provides information about immune checkpoint molecules and other less-studied receptors with Ig-like V-type domains such as CD108, CD226, HVEM (CD270), B7H3 (CD276), B7H5 (VISTA) and GITR (CD357), defining these as novel interesting molecules in severe and corticosteroids-treated acute infections.

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

Lung cancer (LC) continues to be the leading cause of cancer-related deaths in both men and women worldwide. After complete tumour resection, around half of the patients suffer from disease relapse, emphasising the critical need for robust relapse predictors in this disease. In search of such biomarkers, 83 patients with non-microcytic lung cancer and 67 healthy volunteers were studied. Pre-operative levels of sSIGLEC5 along with other soluble immune-checkpoints were measured and correlated with their clinical outcome. Soluble SIGLEC5 (sSIGLEC5) levels were higher in plasma from patients with LC compared with healthy volunteers. Looking into those patients who suffered relapse, sSIGLEC5 and sLAG3 were found to be strong relapse predictors. Following a binary logistic regression model, a sSIGLEC5 + sLAG3 score was established for disease relapse prediction (area under the curve 0.8803, 95% confidence intervals 0.7955−0.9652, cut-off > 2.782) in these patients. Based on score cut-off, a Kaplan−Meier analysis showed that patients with high sSIGLEC5 + sLAG3 score had significantly shorter relapse-free survival (p ≤ 0.0001) than those with low sSIGLEC5 + sLAG3 score.Our study suggests that pre-operative sSIGLEC5 + sLAG3 score is a robust relapse predictor in LC patients.

Next Generation Plasma Proteomics Identifies High-Precision Biomarker Candidates for Ovarian Cancer

Simple Summary

Ovarian cancer is the eighth most common cancer among women and has a 5-year survival of only 30–50%. The survival is close to 90% for patients in stage I but only 20% for patients in stage IV. The presently available biomarkers have insufficient sensitivity and specificity for early detection and there is an urgent need to identify novel biomarkers. The aim of our study was to broadly measure protein biomarkers to find tests for the early detection of ovarian cancer. We found that combinations of 4–7 protein biomarkers can provide highly accurate detection of early- and late-stage ovarian cancer compared to benign conditions. The performance of the tests was then validated in a second independent cohort.

Abstract

Background: Ovarian cancer is the eighth most common cancer among women and has a 5-year survival of only 30–50%. The survival is close to 90% for patients in stage I but only 20% for patients in stage IV. The presently available biomarkers have insufficient sensitivity and specificity for early detection and there is an urgent need to identify novel biomarkers. Methods: We employed the Explore PEA technology for high-precision analysis of 1463 plasma proteins and conducted a discovery and replication study using two clinical cohorts of previously untreated patients with benign or malignant ovarian tumours (N = 111 and N = 37). Results: The discovery analysis identified 32 proteins that had significantly higher levels in malignant cases as compared to benign diagnoses, and for 28 of these, the association was replicated in the second cohort. Multivariate modelling identified three highly accurate models based on 4 to 7 proteins each for separating benign tumours from early-stage and/or late-stage ovarian cancers, all with AUCs above 0.96 in the replication cohort. We also developed a model for separating the early-stage from the late-stage achieving an AUC of 0.81 in the replication cohort. These models were based on eleven proteins in total (ALPP, CXCL8, DPY30, IL6, IL12, KRT19, PAEP, TSPAN1, SIGLEC5, VTCN1, and WFDC2), notably without MUCIN-16. The majority of the associated proteins have been connected to ovarian cancer but not identified as potential biomarkers. Conclusions: The results show the ability of using high-precision proteomics for the identification of novel plasma protein biomarker candidates for the early detection of ovarian cancer.

Siglecs as Therapeutic Targets in Cancer

Hypersialylation is a common post-translational modification of protein and lipids found on cancer cell surfaces, which participate in cell-cell interactions and in the regulation of immune responses. Sialic acids are a family of nine-carbon α-keto acids found at the outermost ends of glycans attached to cell surfaces. Given their locations on cell surfaces, tumor cells aberrantly overexpress sialic acids, which are recognized by Siglec receptors found on immune cells to mediate broad immunomodulatory signaling. Enhanced sialylation exposed on cancer cell surfaces is exemplified as "self-associated molecular pattern" (SAMP), which tricks Siglec receptors found on leukocytes to greatly down-regulate immune responsiveness, leading to tumor growth. In this review, we focused on all 15 human Siglecs (including Siglec XII), many of which still remain understudied. We also highlighted strategies that disrupt the course of Siglec-sialic acid interactions, such as antibody-based therapies and sialic acid mimetics leading to tumor cell depletion. Herein, we introduced the central roles of Siglecs in mediating pro-tumor immunity and discussed strategies that target these receptors, which could benefit improved cancer immunotherapy.