The Prognosis and Immune Checkpoint Blockade Efficacy Prediction of Tumor-Infiltrating Immune Cells in Lung Cancer

Expression of lymphoid structure-associated cytokine/chemokine gene transcripts in tumor and protein in serum are prognostic of melanoma patient outcomes

Background

Proinflammatory chemokines/cytokines support development and maturation of tertiary lymphoid structures (TLS) within the tumor microenvironment (TME). In the current study, we sought to investigate the prognostic value of TLS-associated chemokines/cytokines (TLS-kines) expression levels in melanoma patients by performing serum protein and tissue transcriptomic analyses, and to then correlate these data with patients clinicopathological and TME characteristics.

Methods

Levels of TLS-kines in patients' sera were quantitated using a custom Luminex Multiplex Assay. The Cancer Genomic Atlas melanoma cohort (TCGA-SKCM) and a Moffitt Melanoma cohort were used for tissue transcriptomic analyses. Associations between target analytes and survival outcomes, clinicopathological variables, and correlations between TLS-kines were statistically analyzed.

Results

Serum of 95 patients with melanoma were evaluated; 48 (50%) female, median age of 63, IQR 51-70 years. Serum levels of APRIL/TNFSF13 were positively correlated with levels of both CXCL10 and CXCL13. In multivariate analyses, high levels of serum APRIL/TNFSF13 were associated with improved event-free survival after adjusting for age and stage (HR = 0.64, 95% CI 0.43-0.95; p = 0.03). High expression of APRIL/TNFSF13 tumor transcripts was significantly associated with improved OS in TCGA-SKCM (HR = 0.69, 95% CI 0.52-0.93; p = 0.01) and in Moffitt Melanoma patients (HR = 0.51, 95% CI: 0.32-0.82; p = 0.006). Further incorporation of CXCL13 and CXCL10 tumor transcript levels in a 3-gene index revealed that high APRIL/CXCL10/CXCL13 expression was associated with improved OS in the TCGA SKCM cohort (HR = 0.42, 95% CI 0.19-0.94; p = 0.035). Melanoma differentially expressed genes positively associated with high APRIL/CXCL10/CXCL13 tumor expression were linked to tumor infiltration by a diverse array of proinflammatory immune cell types.

Conclusion

Serum protein and tumor transcript levels of APRIL/TNFSF13 are associated with improved survival outcomes. Patients exhibiting high coordinate expression of APRIL/CXCL10/CXCL13 transcripts in their tumors displayed superior OS. Further investigation of TLS-kine expression profiles related to clinical outcomes in larger cohort studies is warranted.

MRPL12 Acts as A Novel Prognostic Biomarker Involved in Immune Cell Infiltration and Tumor Progression of Lung Adenocarcinoma

Mitochondrial ribosomal protein L7/L12 (MRPL12) is a member of the mitochondrial ribosomal proteins (MRPs). However, the biological function of MRPL12 in lung adenocarcinoma (LUAD) remains unclear. The expression and prognostic value of MRPL12 in LUAD were systematically analyzed using UALCAN, TIMER, HPA, Kaplan-Meier plotter, and GEPIA databases. The relationship between MRPL12 and immune infiltrates was investigated using TIMER and TISIDB databases. The clinical significance of MRPL12 in LUAD patients was validated using a tissue microarray (TMA). Cellular functional experiments were carried out to examine the influences of MRPL12 knockdown on cell proliferation, migration, and invasion. MRPL12 was significantly upregulated in LUAD samples, and high MRPL12 expression was correlated with worse prognosis. MRPL12 expression was markedly associated with immunomodulators, chemokines, and infiltration levels of multiple immune cells. Furthermore, TMA results confirm the upregulation of MRPL12 expression in LUAD, and MRPL12 was identified as an independent prognostic factor in LUAD patients. MRPL12 knockdown inhibited proliferation, migration, and invasion of LUAD cells. These data indicate that MRPL12 is a prognostic biomarker and correlated with immune infiltrates in LUAD. Therefore, MRPL12 shows potential as a therapeutic target for LUAD.

Immune subtype identification and multi-layer perceptron classifier construction for breast cancer

Introduction

Breast cancer is a heterogeneous tumor. Tumor microenvironment (TME) has an important effect on the proliferation, metastasis, treatment, and prognosis of breast cancer.

Methods

In this study, we calculated the relative proportion of tumor infiltrating immune cells (TIICs) in the breast cancer TME, and used the consensus clustering algorithm to cluster the breast cancer subtypes. We also developed a multi-layer perceptron (MLP) classifier based on a deep learning framework to detect breast cancer subtypes, which 70% of the breast cancer research cohort was used for the model training and 30% for validation.

Results

By performing the K-means clustering algorithm, the research cohort was clustered into two subtypes. The Kaplan-Meier survival estimate analysis showed significant differences in the overall survival (OS) between the two identified subtypes. Estimating the difference in the relative proportion of TIICs showed that the two subtypes had significant differences in multiple immune cells, such as CD8, CD4, and regulatory T cells. Further, the expression level of immune checkpoint molecules (PDL1, CTLA4, LAG3, TIGIT, CD27, IDO1, ICOS) and tumor mutational burden (TMB) also showed significant differences between the two subtypes, indicating the clinical value of the two subtypes. Finally, we identified a 38-gene signature and developed a multilayer perceptron (MLP) classifier that combined multi-gene signature to identify breast cancer subtypes. The results showed that the classifier had an accuracy rate of 93.56% and can be robustly used for the breast cancer subtype diagnosis.

Conclusion

Identification of breast cancer subtypes based on the immune signature in the tumor microenvironment can assist clinicians to effectively and accurately assess the progression of breast cancer and formulate different treatment strategies for different subtypes.

Mechanisms of tumor resistance to immune checkpoint blockade and combination strategies to overcome resistance

Immune checkpoint blockade (ICB) has rapidly transformed the treatment paradigm for various cancer types. Multiple single or combinations of ICB treatments have been approved by the US Food and Drug Administration, providing more options for patients with advanced cancer. However, most patients could not benefit from these immunotherapies due to primary and acquired drug resistance. Thus, a better understanding of the mechanisms of ICB resistance is urgently needed to improve clinical outcomes. Here, we focused on the changes in the biological functions of CD8⁺ T cells to elucidate the underlying resistance mechanisms of ICB therapies and summarized the advanced coping strategies to increase ICB efficacy. Combinational ICB approaches and individualized immunotherapies require further in-depth investigation to facilitate longer-lasting efficacy and a more excellent safety of ICB in a broader range of patients.

A Novel Risk Score Model of Lactate Metabolism for Predicting over Survival and Immune Signature in Lung Adenocarcinoma

Simple Summary

Since the discovery of the WarBurg effect, the veil of the tumorigenic role of lactic acid has been gradually revealed. Recently, it was proposed that lactic acid that is produced by tumor cells was secreted into the extracellular space to create immunosuppressive tumor microenvironment (TME) in a variety of ways. However, the intersection genes and the association with immunotherapy are unclear. At present, we identified six lactate-metabolism-associated genes, which were thought to enable tumor progression, that were related to LUAD immunotherapy and we constructed an LAR-score risk model.

Abstract

Background: The role of lactate acid in tumor progression was well proved. Recently, it was found that lactate acid accumulation induced an immunosuppressive microenvironment. However, these results were based on a single gene and it was unclear that lactate acid genes were associated with immunotherapy and able to predict overall survival. Methods: Genes and survival data were acquired from TCGA, GEO and GENECARDS. PCA and TSNE were used to distinguish sample types according to lactate metabolism-associated gene expression. A Wilcox-test examined the expression differences between normal and tumor samples. The distribution in chromatin and mutant levels were displayed by Circo and MAfTools. The lactate metabolism-associated gene were divided into categories by consistent clustering and visualized by Cytoscape. Immune cell infiltration was evaluated by CIBERSORT and LM22 matrix. Enrichment analysis was performed by GSVA. We used the ConsensusClusterPlus package for consistent cluster analysis. A prognostic model was constructed by Univariate Cox regression and Lasso regression analysis. Clinical specimens were detected their expression of genes in model by IHC. Results: Most lactate metabolism-associated gene were significantly differently expressed between normal and tumor samples. There was a strong correlation between the expression of lactate metabolism-associated gene and the abundance of immune cells. We divided them into two clusters (lactate.cluster A,B) with significantly different survival. The two clusters showed a difference in signal, immune cells, immune signatures, chemokines, and clinical features. We identified 162 differential genes from the two clusters, by which the samples were divided into three categories (gene.cluster A,B,C). They also showed a difference in OS and immune infiltration. Finally, a risk score model that was composed of six genes was constructed. There was significant difference in the survival between the high and low risk groups. ROC curves of 1, 3, 5, and 10 years verified the model had good predictive efficiency. Gene expression were correlated with ORR and PFS in patients who received anti-PD-1/L1. Conclusion: The lactate metabolism-associated genes in LUAD were significantly associated with OS and immune signatures. The risk scoring model that was constructed by us was able to well identify and predict OS and were related with anti-PD-1/L1 therapy outcome.

Development and Validation of a 7-Gene Inflammatory Signature Forecasts Prognosis and Diverse Immune Landscape in Lung Adenocarcinoma

Background: Inflammatory responses are strongly linked with tumorigenesis and cancer development. This research aimed to construct and validate a novel inflammation response–related risk predictive signature for forecasting the prognosis of patients with LUAD.

Methods: Differential expression analysis, univariate Cox, LASSO, and multivariate Cox regression analyses of 200 inflammatory response–related genes (IRRG) were performed to establish a risk predictive model in the TCGA training cohort. The performance of the IRRG model was verified in eight GEO datasets. GSEA analysis, ESTIMATE algorithms, and ssGSEA analysis were applied to elucidate the possible mechanisms. Furthermore, the relationship analysis between risk score, model genes, and chemosensitivity was performed. Last, we verified the protein expression of seven model genes by immunohistochemical staining or Western blotting.

Results: We constructed a novel inflammatory response–related 7-gene signature (MMP14, BTG2, LAMP3, CCL20, TLR2, IL7R, and PCDH7). Patients in the high-risk group presented markedly decreased survival time in the TCGA cohort and eight GEO cohorts than the low-risk group. Interestingly, multiple pathways related to immune response were suppressed in high-risk groups. The low infiltration levels of B cell, dendritic cell, natural killer cell, and eosinophil can significantly affect the unsatisfactory prognosis of the high-risk group in LUAD. Moreover, the tumor cells’ sensitivity to anticancer drugs was markedly related to risk scores and model genes. The protein expression of seven model genes was consistent with the mRNA expression.

Conclusion: Our IRRG prognostic model can effectively forecast LUAD prognosis and is tightly related to immune infiltration.

BCL2A1 and CCL18 Are Predictive Biomarkers of Cisplatin Chemotherapy and Immunotherapy in Colon Cancer Patients

Background: Cisplatin enhances the antitumor T cell response, and the combination of PD-L1 blockade produces a synergistic therapeutic effect. However, the clinical correlation between cisplatin and immunotherapy in colon cancer (CC) is unknown.

Methods: Using the “pRRophetic” package, we calculated the IC50 of cisplatin. The correlation between cisplatin IC50, cisplatin resistance–related genes (CCL18 and BCL2A1), and immunotherapy were preliminarily verified in TCGA and further validated in independent cohorts (GSE39582 and GSE17538), cisplatin-resistant CC cell line DLD1, and our own clinical specimens. Classification performance was evaluated using the AUC value of the ROC curve. Scores of immune signatures, autophagy, ferroptosis, and stemness were quantified using the ssGSEA algorithm.

Results: Based on respective medians of three CC cohorts, patients were divided into high- and low-IC50 groups. Compared with the high IC50 group, the low-IC50 group had significantly higher tumor microenvironment (TME) scores and lower tumor purity. Most co-signaling molecules were upregulated in low IC50 group. CC patients with good immunotherapy efficacy (MSI, dMMR, and more TMB) were more attributable to the low-IC50 group. Among seven shared differentially expressed cisplatin resistance–related genes, CCL18 and BCL2A1 had the best predictive efficacy of the above immunotherapy biomarkers. For wet experimental verification, compared with cisplatin-resistant DLD1, similar to PD-L1, CCL18 and BCL2A1 were significantly upregulated in wild-type DLD1. In our own CC tissues, the mRNA expression of CCL18, BCL2A1, and PD-L1 in dMMR were significantly increased. The high group of CCL18 or BCL2A1 had a higher proportion of MSI, dMMR, and more TMB. IC50, CCL18, BCL2A1, and PD-L1 were closely related to scores of immune-related pathways, immune signatures, autophagy, ferroptosis, and stemness. The microRNA shared by BCL2A1 and PD-L1, hsa-miR-137, were significantly associated with CCL18, BCL2A1, and PD-L1, and downregulated in low-IC50 group. The activity of the TOLL-like receptor signaling pathway affected the sensitivity of CC patients to cisplatin and immunotherapy. For subtype analysis, immune C2, immune C6, HM-indel, HM-SNV, C18, and C20 were equally sensitive to cisplatin chemotherapy and immunotherapy.

Conclusions: CC patients sensitive to cisplatin chemotherapy were also sensitive to immunotherapy. CCL18 and BCL2A1 were novel biomarkers for cisplatin and immunotherapy.

Spine‑specific downregulation of LAPTM5 expression promotes the progression and spinal metastasis of estrogen receptor‑positive breast cancer by activating glutamine‑dependent mTOR signaling

Estrogen receptor-positive (ER⁺) breast cancer (BC) is a malignancy that is prone to metastasis to the spine, which is difficult to treat and often results in poor prognosis. However, the mechanism underlying the tumorigenesis and spinal metastasis of ER⁺ BC remains unclear. Lysosomal protein transmembrane 5 (LAPTM5) has been reported as a tumor suppressor in several types of cancer, but its role in ER⁺ BC has not been described. Here, by analyzing a gene sequencing dataset and ER⁺ BC tissues, tumor-adjacent normal tissues and spinal metastatic tissues from patients and mouse models, we found that LAPTM5 expression is negatively related to the progression and spinal metastasis of ER⁺ BC. Subsequently, in vitro experiments demonstrated that downregulation of LAPTM5 expression promoted the proliferation, migration, and chemoresistance of ER⁺ BC cells by activating glutamine-dependent mTOR signaling. A high level of CX3CL1 could inhibit LAPTM5 expression, explaining how ER⁺ BC metastasized to the spine. Thus, we found that LAPTM5 functions as a tumor suppressor in ER⁺ BC and that the CX3CL/CX3CR1/LAPTM5/glutamine axis mediates the spinal metastasis of ER⁺ BC. This axis may be a promising therapeutic target for ER⁺ BC.

Comprehensive Characterization of Human Lung Large Cell Carcinoma Identifies Transcriptomic Signatures with Potential Implications in Response to Immunotherapy

Lung cancer is the leading cause of cancer mortality worldwide, with non-small cell lung cancer (NSCLC) being the most prevalent histology. While immunotherapy with checkpoint inhibitors has shown outstanding results in NSCLC, the precise identification of responders remains a major challenge. Most studies attempting to overcome this handicap have focused on adenocarcinomas or squamous cell carcinomas. Among NSCLC subtypes, the molecular and immune characteristics of lung large cell carcinoma (LCC), which represents 10% of NSCLC cases, are not well defined. We hypothesized that specific molecular aberrations may impact the immune microenvironment in LCC and, consequently, the response to immunotherapy. To that end, it is particularly relevant to thoroughly describe the molecular genotype-immunophenotype association in LCC-to identify robust predictive biomarkers and improve potential benefits from immunotherapy. We established a cohort of 18 early-stage, clinically annotated, LCC cases. Their molecular and immune features were comprehensively characterized by genomic and immune-targeted sequencing panels along with immunohistochemistry of immune cell populations. Unbiased clustering defined two novel subgroups of LCC. Pro-immunogenic tumors accumulated certain molecular alterations, showed higher immune infiltration and upregulated genes involved in potentiating immune responses when compared to pro-tumorigenic samples, which favored tumoral progression. This classification identified a set of biomarkers that could potentially predict response to immunotherapy. These results could improve patient selection and expand potential benefits from immunotherapy.

Tumor-Associated Regulatory T Cell Expression of LAIR2 Is Prognostic in Lung Adenocarcinoma

Simple Summary

Understanding how the immune system navigate the tumor microenvironment is vital to developing effective drugs to treat cancer. Using gene and functional studies, we found that the collagen receptor LAIR2 is an important component of cancer regulation. When expressed in regulatory T cells, a LAIR2 containing gene signature is adversely prognostic in lung cancer. This study highlights the importance of microenvironment regulation of immune cells and provides a unique target for future therapeutic development.

Abstract

Cancer development requires a permissive microenvironment that is shaped by interactions between tumor cells, stroma, and the surrounding matrix. As collagen receptors, the leukocyte-associated immunoglobulin-like receptor (LAIR) family allows the immune system to interact with the extracellular matrix. However, little is known about their role in regulating tumor immunity and cancer progression. Methods: Genetic analysis of resected human lung adenocarcinoma was correlated to clinical-pathological characteristics, gene ontologies, and single cell RNA sequencing (scRNASeq). LAIR2 production was determined in subsets of immune cells isolated from blood leukocytes and lung adenocarcinoma tumor. Functional assays were used to determine the role of LAIR2 in tumorigenesis. Results: LAIR2 expression was adversely prognostic in lung adenocarcinoma. LAIR2 was preferentially produced by activated CD4⁺ T cells and enhanced in vitro tumor invasion into collagen. scRNASeq analysis of tumor infiltrating T cells revealed that LAIR2 expression co-localized with FOXP3 expressing cells and shared a transcriptional signature with tumor-associated regulatory T (T_reg) cells. A CD4⁺ LAIR2⁺ T_reg gene signature was prognostically significant in the TCGA dataset (n = 439; hazard ratio (HR) = 1.37; 95% confidence interval (CI), 1.05–1.77, p = 0.018) and validated in NCI Director’s Challenge lung adenocarcinoma dataset (n = 488; HR = 1.54; 95% CI, 1.14–2.09, p = 0.0045). Conclusions: Our data support a role for LAIR2 in lung adenocarcinoma tumorigenesis and identify a CD4⁺ LAIR2⁺ T_reg gene signature in lung adenocarcinoma prognosis. LAIR2 provides a novel target for development of immunotherapies.