HLA antigens are candidate markers for prediction of lymph node metastasis in gastric cancer

Human leukocyte antigen class II-based immune risk model for recurrence evaluation in stage I-III small cell lung cancer

Background

Immunotherapy has revolutionized therapeutic patterns of small cell lung cancer (SCLC). Human leukocyte antigen class II (HLA class II) is related to antitumor immunity. However, the implications of HLA class II in SCLC remain incompletely understood.

Materials and methods

We investigated the expression patterns of HLA class II on tumor cells and tumor-infiltrating lymphocytes (TILs) by immunohistochemistry staining and its association with clinical parameters, immune markers, and recurrence-free survival (RFS) in 102 patients with stage I–III SCLC with radical surgery. Additionally, an HLA class II-based immune risk model was established by least absolute shrinkage and selection operator regression. With bioinformatics methods, we investigated HLA class II-related enrichment pathways and immune infiltration landscape in SCLC.

Results

HLA class II on tumor cells and TILs was positively expressed in 9 (8.8%) and 45 (44.1%) patients with SCLC, respectively. HLA class II on TILs was negatively associated with lymph node metastasis and positively correlated with programmed death-ligand 1 (PD-L1) on TILs (p<0.001) and multiple immune markers (CD3, CD4, CD8, FOXP3; p<0.001). Lymph node metastasis (OR 0.314, 95% CI 0.118 to 0.838, p=0.021) and PD-L1 on TILs (OR 3.233, 95% CI 1.051 to 9.95, p=0.041) were independent predictive factors of HLA class II on TILs. HLA class II positivity on TILs prompted a longer RFS (40.2 months, 95% CI 31.7 to 48.7 vs 28.8 months, 95% CI 21.4 to 36.3, p=0.014). HLA class II on TILs, PD-L1 on TILs, CD4, and FOXP3 were enrolled in the immune risk model, which categorized patients into high-risk and low-risk groups and had better power for predicting the recurrence than tumor stage. Pathway enrichment analyses showed that patients with high HLA class II expression demonstrated signatures of transmembrane transportation, channel activity, and neuroactive ligand–receptor interaction. High-risk SCLC patients had a higher proportion of T follicular helper cells (p=0.034) and a lower proportion of activated memory CD4-positive T cells (p=0.040) and resting dendritic cells (p=0.045) versus low-risk patients.

Conclusions

HLA class II plays a crucial role in tumor immune microenvironment and recurrence prediction. This work demonstrates the prognostic and clinical values of HLA class II in patients with SCLC.

Cloud Computing Based Immunopeptidomics Utilizing Community Curated Variant Libraries Simplifies and Improves Neo-Antigen Discovery in Metastatic Melanoma

Unique peptide neo-antigens presented on the cell surface are attractive targets for researchers in nearly all areas of personalized medicine. Cells presenting peptides with mutated or other non-canonical sequences can be utilized for both targeted therapies and diagnostics. Today's state-of-the-art pipelines utilize complementary proteogenomic approaches where RNA or ribosomal sequencing data helps to create libraries from which tandem mass spectrometry data can be compared. In this study, we present an alternative approach whereby cloud computing is utilized to power neo-antigen searches against community curated databases containing more than 7 million human sequence variants. Using these expansive databases of high-quality sequences as a reference, we reanalyze the original data from two previously reported studies to identify neo-antigen targets in metastatic melanoma. Using our approach, we identify 79 percent of the non-canonical peptides reported by previous genomic analyses of these files. Furthermore, we report 18-fold more non-canonical peptides than previously reported. The novel neo-antigens we report herein can be corroborated by secondary analyses such as high predicted binding affinity, when analyzed by well-established tools such as NetMHC. Finally, we report 738 non-canonical peptides shared by at least five patient samples, and 3258 shared across the two studies. This illustrates the depth of data that is present, but typically missed by lower statistical power proteogenomic approaches. This large list of shared peptides across the two studies, their annotation, non-canonical origin, as well as MS/MS spectra from the two studies are made available on a web portal for community analysis.

Major Histocompatibility Complex Class I-Related Chain A (MICA) Allelic Variants Associate With Susceptibility and Prognosis of Gastric Cancer

Gastric cancer (GC) is the fifth most prevalent type of cancer worldwide. Gastric tumor cells express MICA protein, a ligand to NKG2D receptor that triggers natural killer (NK) cells effector functions for early tumor elimination. MICA gene is highly polymorphic, thus originating alleles that encode protein variants with a controversial role in cancer. The main goal of this work was to study MICA gene polymorphisms and their relationship with the susceptibility and prognosis of GC. Fifty patients with GC and 50 healthy volunteers were included in this study. MICA alleles were identified using Sanger sequencing methods. The analysis of MICA gene sequence revealed 13 MICA sequences and 5 MICA-short tandem repeats (STR) alleles in the studied cohorts We identified MICA^*002 (^*A9) as the most frequent allele in both, patients and controls, followed by MICA^*008 allele (^*A5.1). MICA^*009/049 allele was significantly associated with increased risk of GC (OR: 5.11 [95% CI: 1.39–18.74], p = 0.014). The analysis of MICA-STR alleles revealed a higher frequency of MICA^*A5 in healthy individuals than GC patients (OR = 0.34 [95% CI: 0.12–0.98], p = 0.046). Survival analysis after gastrectomy showed that patients with MICA^*002/002 or MICA^*002/004 alleles had significantly higher survival rates than those patients bearing MICA^*002/008 (p = 0.014) or MICA^*002/009 (MICA^*002/049) alleles (p = 0.040). The presence of threonine in the position MICA-181 (MICA^*009/049 allele) was more frequent in GC patients than controls (p = 0.023). Molecular analysis of MICA-181 showed that the presence of threonine provides greater mobility to the protein than arginine in the same position (MICA^*004), which could explain, at least in part, some immune evasion mechanisms developed by the tumor. In conclusion, our findings suggest that the study of MICA alleles is crucial to search for new therapeutic approaches and may be useful for the evaluation of risk and prognosis of GC and personalized therapy.

The increase of MICA gene A9 allele associated with gastric cancer and less schirrous change

Since surgical resection is the principal treatment of gastric cancer, early detection is the only effective strategy against this disease at present. Recently, a new polymorphic gene family, the major histocompatibility complex class I chain-related (MIC) genes located about 40 kb centromeric to HLA-B gene has been proposed. This family consists of five genes (A, B, C, D and E). Among them, MICA has five various alleles (A4, A5, A5.1, A6 and A9), which can be used as a polymorphic marker for genetic mapping and for disease susceptibility. The MICA polymorphism was studied in our gastric cancer patients to see if there is any possible correlation with genetic predisposition and clinicopathological factors. Genomic DNA was extracted from fresh or frozen peripheral blood leukocytes in 107 patients with gastric adenocarcinoma who underwent gastrectomy in our hospital and 351 noncancer controls. MICA polymorphism was analysed by using PCR-based technique. The results showed both phenotypic and allele frequencies of allele A9 in patients with gastric cancer were significantly higher than controls (33 vs 17.6%, P=0.005; 17 vs 9.9%, P=0.02). Gastric adenocarcinoma with allele A9 was associated with less schirrous change than those without (P=0.014). MICA gene A9 allele might confer the risk of gastric cancer and associate with less schirrous change. The mechanisms among them deserve further investigation.