Status Of Tumor Microenvironment Research Articles

Utility of neutrophil-to-lymphocyte ratio as an indicator of tumor immune status in non-small cell lung cancer.

Neutrophil-to-lymphocyte ratio (NLR) has been reported as a prognostic biomarker in non-small cell lung cancer (NSCLC); however, the underlying biological rationale remains unclear. The present study aimed to explore the potential utility of NLR as a surrogate biomarker for immune response to cancer and to elucidate the underlying mechanism. This retrospective study included the medical records of 120 patients with NSCLC who underwent surgery at the study institution in 2012. NLR in peripheral blood was determined from blood test within 30days before surgery. Tumor immune status was evaluated using immunohistochemical staining to identify CD3+, CD8+ and FOXP3+ tumor-infiltrating lymphocytes (TILs), and the relationship of NLR, with clinicopathologic characteristics including 5-year overall survival (OS), and the tumor immune status was investigated. The median values of NLR and TIL count were used as cutoff points. The 5-year OS was significantly better in patients with low NLR (<2.2) than in those with high NLR (≥2.2) (70.1% vs. 56.8%, P=0.042) and in patients with high CD3+ TIL count (≥242) than in those with low CD3+ TIL count (<242) (70% vs. 56.8%, P=0.019). Additionally, the CD3+ TIL count was negatively correlated with preoperative NLR (P=0.005). NLR might potentially reflect the immune status of tumor microenvironment, explaining its impact on prognosis of patients with NSCLC.

Abstract PO5-21-05: An Endocrine Resistant-Related Gene Signature Revealing the Tumor Microenvironment to Predict the Prognosis of Hormone Receptor-Positive Breast Cancer Patients

Abstract Key words: breast cancer; hormone receptor-positive; predictive model; prognosis; endocrine therapy; drug resistance Hormone receptor positive (HR+) breast cancer is the most common type of breast cancer, accounting for approximately 70% of all breast cancer, in whom endocrine therapy plays a vital role. However, the limited effectiveness of endocrine therapy due to intricate mechanisms of drug resistance poses a significant challenge in treating certain patients. Therefore, it is imperative to identify novel indicators of endocrine resistance and develop new models for predicting recurrence-free survival (RFS) in patients with HR+ breast cancer. In this study, we successfully established an endocrine resistance-related risk model based on multicenter RNA sequencing data. This model could represent tumor microenvironment characteristics and provide valuable insights into predicting the prognosis of HR+ breast cancer patients. A seven gene risk model reflecting endocrine therapy resistance in 208 early HR+ breast cancer patients from two centers was constructed by LASSO analysis and multivariate cox regression. Based on the median value of risk score, patients were divided into high-risk group and low-risk group. Compared with the low-risk group, the high-risk group exhibited a significantly worse prognosis, which showed consistent across two external validation cohorts (TCGA-BRCA and METABRIC cohort). Time dependent ROC curve showed excellent capacity of gene model in predicting 1,3 and 5-year RFS. The IC50 analysis revealed that patients in low-risk group exhibited greater sensitivity to endocrine drugs, such as tamoxifen, fulvestrant and palbociclib, which strongly suggested that the identified gene signature can effectively differentiate between endocrine-resistant and -sensitive status. Gene-set enrichment analysis revealed regulation of cell killing, positive regulation of inflammatory response and T cell mediated cytotoxicity were enriched in low risk group, suggesting immune activation and a higher immune infiltration status. Conversely, the high-risk group demonstrated associations with collagen metabolic process, extracellular matrix structural constituent and collagen fibril organization, which were the indicative of the location and function of cancer-associated fibroblasts (CAFs). As expected, ssGSEA results revealed elevated expression levels of immune cells (e.g., natural killer cells, tumor-infiltrating lymphocytes, CD8+ T cells) and immune functions (e.g., T cell co-stimulation, inflammation-promoting, cytolytic activity) in the low-risk group. However, three diverse acknowledged methods (TIDE, EPIC and MCP-counter) consistently identified CAFs as predominantly content in the high-risk groups. Furthermore, considering that CD8+ T cell is the primary cell type responsible for exerting cytotoxic effects, we observed a negative correlation between CD8+ T cells and CAFs. This suggests that CAFs may inhibit the infiltration of CD8+ T cells, thereby weakening their cytotoxic capabilities, leading to a shorter RFS in high-risk patient groups. In conclusion, a novel gene signature constructed with 7 endocrine resistance response-related genes can be used for prognostic prediction and reflecting the tumor microenvironment status in HR+ early breast cancer. The low-risk group exhibited enhanced anti-tumor immunity, resulting in a favorable prognosis. In contrast, the high-risk group displayed pronounced infiltration of CAFs, indicating their potential to impede immune cell infiltration and impact the efficacy of endocrine drugs through the formation of physical barriers and recruitment of immunosuppressive factors. Consequently, patients in the high-risk group experienced a worse prognosis. Therefore, inhibiting these genes may be a therapeutic option for overcoming endocrine therapy resistance in HR+ breast cancer patients. Citation Format: Xiyu Kang, Jiaxiang Liu, Xin Wang. An Endocrine Resistant-Related Gene Signature Revealing the Tumor Microenvironment to Predict the Prognosis of Hormone Receptor-Positive Breast Cancer Patients [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-21-05.

Impacts of tumor microenvironment during neoadjuvant chemotherapy in patients with esophageal squamous cell carcinoma.

With the advent of immune checkpoint inhibitors (ICIs), a better understanding of tumor microenvironment (TME) is becoming crucial in managing esophageal squamous cell carcinoma (ESCC) patients. We investigated the survival impact of TME status and changes in patients with ESCC who underwent neoadjuvant chemotherapy (NAC) followed by surgery (n = 264). We examined immunohistochemical status (CD4+, CD8+, CD20+, Foxp3+, HLA class-1+, CD204+, and programmed death ligand-1 [PD-L1+]) on 264 pre-NAC and 204 paired post-NAC specimens. Patients were classified by their pre- and post-NAC immune cell status and their changes following NAC. Our findings showed that pre-NAC TME status was not significantly associated with survival outcomes. In contrast, post-NAC TME status, such as low level of T cells, CD4+ T cells, and high PD-L1 combined positive score (CPS), were significantly associated with poor overall survival (OS). Notably, TME changes through NAC exerted significant survival impacts; patients with consistently low levels of T cells, low levels of CD4+ T cells, or high levels of PD-L1 (CPS) had very poor OS (3-year OS: 35.5%, 40.2%, and 33.3%, respectively). Tumor microenvironment changes of consistently low T cells, low CD4+ T cells, and high PD-L1 were independent predictors of poor OS in multivariate Cox hazards analyses, while factors indicating post-NAC status (T cells, CD4+, and PD-L1 [CPS]) alone were not. Therefore, we suggest that the consistently low T/high PD-L1 group could benefit from additional therapies, such as ICIs, and the importance of stratification by the TME, which has recently been recognized.

Exploration of a noninvasive radiomics classifier for breast cancer tumor microenvironment categorization and prognostic outcome prediction

Rationale and Objectives: Breast cancer progression and treatment response are significantly influenced by the tumor microenvironment (TME). Traditional methods for assessing TME are invasive, posing a challenge for patient care. This study introduces a non-invasive approach to TME classification by integrating radiomics and machine learning, aiming to predict the TME status using imaging data, thereby aiding in prognostic outcome prediction. Materials and MethodsUtilizing multi-omics data from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), this study employed CIBERSORT and MCP-counter algorithms analyze immune infiltration in breast cancer. A radiomics classifier was developed using a random forest algorithm, leveraging quantitative features extracted from intratumoral and peritumoral regions of Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) scans. The classifer’s ability to predict diverse TME states were and their prognostic implications were evaluated using Kaplan-Meier survival curves. ResultsThree distinct TME states were identified using RNA-Seq data, each displaying unique prognostic and biological characteristics. Notably, patients with increased immune cell infiltration showed significantly improved prognoses (P < 0.05). The classifier, comprising 24 radiomic features, demonstrated high predictive accuracy (AUC of training set = 0.960, 95 % CI: 0.922, 0.997; AUC of testing set = 0.853, 95 % CI: 0.687, 1.000) in differentiating these TME states. Predictions from the classifier also correlated significantly with overall patient survival (P < 0.05). ConclusionThis study offers a detailed analysis of the complex TME states in breast cancer and presents a reliable, noninvasive radiomics classifier for TME assessment. The classifer’s accurate prediction of TME status and its correlation with prognosis highlight its potential as a tool in personalized breast cancer treatment, paving the way for more individualized and less invasive therapeutic strategies.

STING agonist inflames the cervical cancer immune microenvironment and overcomes anti-PD-1 therapy resistance.

Cervical cancer poses a significant global threat to women's health. However, current therapeutic interventions, such as radiotherapy, chemotherapy, surgical resection, and immune checkpoint inhibitors, face limitations in the advanced stages of the disease. Given the immunosuppressive microenvironment in cervical cancer, it is imperative to explore novel perspectives. In this regard, STING agonists have emerged as promising candidates. The expression profiles and clinicopathological data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Prognostic analysis of STING downstream genes (CCL5, CXCL9, CXCL10) and immune infiltration analysis were conducted using Kaplan-Meier Plotter, ESTIMATE, and deconvo_CIBERSOR. Single-cell RNA-seq (scRNA-seq) analysis was conducted to evaluate the potential of MSA-2 in cervical cancer treatment employing SingleR, chi-squared test, and Gene Set Enrichment Analysis (GSEA). Cellular interaction analysis utilized the CellChat package to assess the potentiation of cellular interaction following MSA-2 administration. Murine tumor models involving U14 and TC-1, were conducted, and the IF of tissue was subsequently conducted to assess the tumor microenvironment status after treatment. Prognosis in cervical cancer correlated with elevated expression of STING downstream genes, indicating prolonged survival and reduced recurrence. These genes positively correlated with immune infiltration, influencing stromal scores, immune scores, and estimate scores. Specific immune cell populations, including CD8+ T cells, M1-type macrophages, NK cells, and T follicular helper cells, were associated with STING downstream genes. scRNA-seq in a classic immune-excluded model revealed that MSA-2 exerts priming and activating functions on vital components within TME, and intensifies their intercellular communications. The in vivo assay ultimately demonstrated that MSA-2, either as a standalone treatment or in combination with anti-PD-1, effectively suppressed the growth of subcutaneous cervical tumors. Moreover, the combination strategy significantly augmented efficacy compared to anti-PD-1 monotherapy by eliciting a robust antitumor immune response. This study highlights the pivotal role of the STING pathway and the potential of MSA-2 in reshaping the immune microenvironment in cervical cancer. Combining MSA-2 with immune checkpoint inhibitors presents a transformative approach, holding promise for improved prognosis. Further investigations are warranted to explore the broader immune landscape and potential long-term effects of MSA-2 in cervical cancer treatment.

Development and verification of a novel immunogenic cell death-related signature for predicting the prognosis and immune infiltration in triple-negative breast cancer.

Insufficient understanding of the pathogenesis and tumor immunology of triple-negative breast cancer (TNBC) has limited the development of immunotherapy. The importance of tumor microenvironment (TME) in immunotyping, prognostic assessment and immunotherapy efficacy of cancer has been emphasized, however, potential immunogenic cell death (ICD) related genes function in TME of TNBC has been rarely investigated. To initially explore the role and related mechanisms of ICD in TNBC, especially the role played in the TME of TNBC, and to identify different relevant subtypes based on ICD, and then develop an ICD-related risk score to predict each TNBC patient TME status, prognosis and immunotherapy response. In this study, we identified distinct ICD-related modification patterns based on 158 TNBC cases in the TCGA-TNBC cohort. We then investigated the possible correlation between ICD-related modification patterns and TME cell infiltration characteristics in TNBC. By using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis, we created a risk scoring system (ICD score) to quantifiably evaluate the impact of ICD-related modification patterns in individual TNBC patient. Two different ICD-related modification patterns were found with significant differences in immune infiltration. Lower ICD score was correlated with higher immune infiltration, tumor mutational burden and significantly enriched in immune-related pathways, indicating a strong ability to activate immune response, which might account for relatively favorable prognosis of TNBC patients and could serve as a predictor to select suitable candidates for immunotherapy. We used two independent cohorts, GSE58812 cohort and Metabric cohort to validate prognosis and immunohistochemistry for preliminary in vitro validation. This study evidenced that the ICD-related modification patterns might exert pivotal roles in the immune infiltration landscape of TNBC and ICD score might act as potential predictors of prognostic assessment and immunotherapy response. This research provides unique insights for individualize immune treatment strategies and promising immunotherapy candidates screening.

Immunogenic cell death-related classification reveals prognosis and effectiveness of immunotherapy in breast cancer

Lack of specific biomarkers and effective drug targets constrains therapeutic research in breast cancer (BC). In this regard, therapeutic modulation of damage-associated molecular patterns (DAMPs)-induced immunogenic cell death (ICD) may help improve the effect of immunotherapy in individuals with BC. The aim of this investigation was to develop biomarkers for ICD and to construct ICD-related risk estimation models to predict prognosis and immunotherapy outcomes of BC. RNA-seq transcriptome information and medical data from individuals with BC (n = 943) were obtained from TCGA. Expression data from a separate BC cohort (GEO: GSE20685) were used for validation. We identified subtypes of high and low ICD gene expression by consensus clustering and assessed the connection between ICD subtypes and tumor microenvironment (TME). In addition, different algorithms were used to construct ICD-based prognostic models of BC. BC samples were categorized into subtypes of high and low ICD expression depending on the expression of genes correlated with ICD. The subtype of ICD high-expression subtypes are correlated with poor prognosis in breast cancer, while ICD low-expression subtypes may predict better clinical outcomes. We also created and verified a predictive signature model depending on four ICD-related genes (ATG5, CD8A, CD8B, and HSP90AA1), which correlates with TME status and predicts clinical outcomes of BC patients. We highlight the connection of ICD subtypes with the dynamic evolution of TME in BC and present a novel ICD-based prognostic model of BC. In clinical practice, distinction of ICD subtype and assessment of ICD-related biomarkers should help guide treatment planning and improve the effectiveness of tumor immunotherapy.

A Lactic Acid Metabolism-Related Gene Signature for Predicting Clinical Outcome and Tumor Microenvironmental Status in Patients with Hepatocellular Carcinoma

This study aims to build a prognostic model based on lactic acid metabolism-related genes (LMRGs) to predict survival outcomes and tumor microenvironment status of Hepatocellular carcinoma (HCC) patients. The model was used to calculate riskscores of clinical samples. Survival analysis and Cox regression analysis were conducted to verify the independence and reliability of the riskscore to determine its clinical significance in prognosis evaluation of HCC. Additionally, we conducted a comprehensive analysis of tumor mutation burden (TMB), immune cell infiltration, and gene set molecular function in the high- and low-risk groups. We obtained 134 LMRGs mainly involved in cellular calcium homeostasis and calcium signaling pathways. The LMRGs in the risk assessment model included PFKFB4, SLC16A3, ADRA2B, SLC22A1, QRFPR, and PROK1. This study discovered much shorter overall survival and median survival time of patients with higher riskscores when compared to those with lower riskscores. It was indicated that for independent prediction of patients’ prognosis, the riskscore had a significant clinical value. A remarkable difference was also found regarding TMB between the two groups. Finally, cell experiments demonstrated that the knockout of PFKFB4 and SLC16A3 genes suppressed lactate. Our research demonstrated that the riskscore, established based on LMRGs, is a promising biomarker.

Impact of Mutations in Subunit Genes of the Mammalian SWI/SNF Complex on Immunological Tumor Microenvironment.

Recently, inactivating somatic mutations of SWI/SNF chromatin-remodeling genes in cancers have been reported. However, few studies have been performed regarding the immunological analysis of the tumor microenvironment (TME) in chromatin remodeling complex gene-mutated tumors. In the present study, we identified cancer patients harboring various mammalian SWI/SNF complex mutations and investigated the immunological features in those mutated cancers. Cancer patients harboring any type of chromatin remodeling complex gene mutation were selected and clinicopathological features were compared between chromatin remodeling complex gene expression-low and expression-high groups. Specifically, expression levels of immune response-associated genes and cancer-associated genes were compared between the SMARCA4 expression-low and expression-high groups using volcano plot analysis. Among cancers harboring PBRM1, SAMRACA4 and ARID2 gene mutations, T-cell marker and mature B-cell marker genes were up-regulated in the tumor. Specifically, T-cell effector genes (CD8B, CD40LG), central memory marker genes (CD27, CCR7) and mature B-cell marker genes (CD20, CD38, CD79 and IRF4) were up-regulated, and cancer-associated genes including MYB, MYC and AURKB genes were down-regulated in the SMARCA4 expression-low group. Remarkably, heatmap of gene expression and immunohistochemistry (IHC) data demonstrated that the tertiary lymphoid structure (TLS) gene signature of mature B cells was up-regulated in SMACA4 gene-mutated stomach cancers. These results suggest that immune tumor microenvironment status, such as mature B cell recruitment featuring the TLS gene signature and immune activation mediated by cancer signal down-regulation, might contribute to the classification of SMARCA4 gene-mutated tumors as immune checkpoint blockade therapy-sensitive target tumors.

Three molecular subtypes and a five-gene signature for hepatocellular carcinoma based on m7G-related classification.

The current research investigated the heterogeneity of hepatocellular carcinoma (HCC) based on the expression of N7-methylguanosine (m7G)-related genes as a classification model and developed a risk model predictive of HCC prognosis, key pathological behaviors and molecular events of HCC. The RNA sequencing data of HCC were extracted from The Cancer Genome Atlas (TCGA)-live cancer (LIHC) database, hepatocellular carcinoman database (HCCDB) and Gene Expression Omnibus database, respectively. According to the expression level of 29 m7G-related genes, a consensus clustering analysis was conducted. The least absolute shrinkage and selection operator (LASSO) regression analysis and COX regression algorithm were applied to create a risk prediction model based on normalized expression of five characteristic genes weighted by coefficients. Tumor microenvironment (TME) analysis was performed using the MCP-Counter, TIMER, CIBERSORT and ESTIMATE algorithms. The Tumor Immune Dysfunction and Exclusion algorithm was applied to assess the responses to immunotherapy in different clusters and risk groups. In addition, patient sensitivity to common chemotherapeutic drugs was determined by the biochemical half-maximal inhibitory concentration using the R package pRRophetic. Three molecular subtypes of HCC were defined based on the expression level of m7G-associated genes, each of which had its specific survival rate, genomic variation status, TME status and immunotherapy response. In addition, drug sensitivity analysis showed that the C1 subtype was more sensitive to a number of conventional oncolytic drugs (including paclitaxel, imatinib, CGP-082996, pyrimethamine, salubrinal and vinorelbine). The current five-gene risk prediction model accurately predicted HCC prognosis and revealed the degree of somatic mutations, immune microenvironment status and specific biological events. In this study, three heterogeneous molecular subtypes of HCC were defined based on m7G-related genes as a classification model, and a five-gene risk prediction model was created for predicting HCC prognosis, providing a potential assessment tool for understanding the genomic variation, immune microenvironment status and key pathological mechanisms during HCC development.

High-plex spatial transcriptomic profiling reveals distinct immune components and the HLA class I/DNMT3A/CD8 modulatory axis in mismatch repair-deficient endometrial cancer.

Tumors bearing mismatch repair deficiency (MMRd) are characterized by a high load of neoantigens and are believed to trigger immunogenic reactions upon immune checkpoint blockade treatment such as anti-PD-1/PD-L1 therapy. However, the mechanisms are still ill-defined, as multiple cancers with MMRd exhibit variable responses to immune checkpoint inhibitors (ICIs). In endometrial cancer (EC), a distinct tumor microenvironment (TME) exists that may correspond to treatment-related efficacies. We aimed to characterize EC patients with aberrant MMR pathways to identify molecular subtypes predisposed to respond to ICI therapies. We applied digital spatial profiling, a high-plex spatial transcriptomic approach covering over 1,800 genes, to obtain a highly resolved TME landscape in 45 MMRd-EC patients. We cross-validated multiple biomarkers identified using immunohistochemistry and multiplexed immunofluorescence using in-study and independent cohorts totaling 123 MMRd-EC patients and validated our findings using external TCGA data from microsatellite instability endometrial cancer (MSI-EC) patients. High-plex spatial profiling identified a 14-gene signature in the MMRd tumor-enriched regions stratifying tumors into "hot", "intermediate" and "cold" groups according to their distinct immune profiles, a finding highly consistent with the corresponding CD8 + T-cell infiltration status. Our validation studies further corroborated an existing coregulatory network involving HLA class I and DNMT3A potentially bridged through dynamic crosstalk incorporating CCL5. Our study confirmed the heterogeneous TME status within MMRd-ECs and showed that these ECs can be stratified based on potential biomarkers such as HLA class I, DNMT3A and CD8 in pathological settings for improved ICI therapeutic efficacy in this subset of patients.

Noninvasive Radiation Markers Based on Lung Cancer CT Images and Dosimetry Features to Predict the Immune Response to Radiotherapy in Tumor Microenvironment

For lung cancer patients with complex conditions, doctors need to determine treatment plan in combination with changes of immune response during radiotherapy. It was urgent to find a strategy to predict immune response to intervene the treatment at an early stage. The radiotherapy process and tumor microenvironment status of 105 lung cancer patients were tracked and recorded in our hospital from 2019 to 2022, public database TCGA-LUAD/LUSC queue of TCIA image data was downloaded as external verification set. Data was analyzed according to the following methods: First, the 3D slicer software was used to process the CT imageomics data, and the doseomics information was calculated by DVHmetrics package from R software. Then Python was brought in to develop a non-invasive radiation scoring system through machine learning methods (T-test, LASSO, random forest and Xgboost). Finally, combined with the clinical information related to the patient's immune response, a nomogram was made to study the correlation between the score markers and the ratio of CD4 cells/CD8 cells in the tumor microenvironment, the prognosis of lung cancer, as well as the incidence of radiation pneumonia. The radiation markers score formula was obtained with CT imageomics and DVH dosimics features, which achieved an AUC of 0.67-0.75 in predicting CD4/CD8 condition in tumor microenvironment after radiotherapy. It was worth noticing that in predicting the prognosis of lung cancer, it was difficult to distinguish between the radiation score and the patient's survival status (P>0.05). However, we found that the radiation pneumonitis of lung cancer patients in the low score group (42.6%) was significantly higher than that in the high score group (13.4%). Radiation markers score was a non-invasive method to evaluate the immune response of patients with lung cancer, which can be used to predict the incidence of radiation pneumonitis.

A seven-immune-genes risk model predicts the survival and suitable treatments for patients with skin cutaneous melanoma

BackgroundSkin cutaneous melanoma is characterized by high malignancy and prognostic heterogeneity. Immune cell networks are critical to the biological progression of melanoma through the tumor microenvironment. Thus, identifying effective biomarkers for skin cutaneous melanoma from the perspective of the tumor microenvironment may offer strategies for precise prognosis prediction and treatment selection. MethodsA total of 470 cases from The Cancer Genome Atlas and 214 from the Gene Expression Omnibus were systematically evaluated to construct an optimal independent immune cell risk model with predictive value using weighted gene co-expression network analysis, Cox regression, and least absolute shrinkage and selection operator assay. The predictive power of the developed model was estimated through receiver operating characteristic curves and Kaplan-Meier analysis. The association of the model with tumor microenvironment status, immune checkpoints, and mutation burden was assessed using multiple algorithms. Additionally, the sensitivity of immune and chemotherapeutics was evaluated using the ImmunophenScore and pRRophetic algorithm. Furthermore, the expression profiles of risk genes were validated using gene expression profiling interactive analysis and Human Protein Atlas resources. ResultsThe risk model integrated seven immune-related genes: ARNTL, N4BP2L1, PARP11, NUB1, GSDMD, HAPLN3, and IRX3. The model demonstrated considerable predictive ability and was positively associated with clinical and molecular characteristics. It can be utilized as a prognostic factor for skin cutaneous melanoma, where a high-risk score was linked to a poor prognosis and indicated an immunosuppressive microenvironment. Furthermore, the model revealed several potential target checkpoints and predicted the therapeutic benefits of multiple clinically used drugs. ConclusionOur findings provide a comprehensive landscape of the tumor immune microenvironment in skin cutaneous melanoma and identify prognostic markers that may serve as efficient clinical diagnosis and treatment selection tools.

Construction and validation of a PANoptosis-related lncRNA signature for predicting prognosis and targeted drug response in thyroid cancer.

Thyroid cancer (TC) is the most prevalent malignancy of the endocrine system. PANoptosis, a newly discovered cell death pathway, is of interest in tumor research. However, the relationship between PANoptosis-related lncRNAs (PRlncRNAs) and TC remains unclear. The study aimed to develop a prognostic model based on PRlncRNAs in TC. Gene expression data of PANoptosis-associated genes and clinical information on TC from The Cancer Genome Atlas (TCGA) database were analyzed by Pearson correlation analysis, univariate/multivariate Cox analysis, and Lasso Cox regression analysis. A PRlncRNA signature was constructed and used to develop a nomogram to predict overall survival (OS). We further explored the correlation between the risk score and tumor immune microenvironment, immune checkpoints, and drug sensitivity. Moreover, we verified the expression and biological function of lncRNAs in TC cell lines. Finally, seven PRlncRNAs were used to construct a prognostic model for predicting the OS of TC patients. We found that the risk score was associated with the tumor microenvironment (TME) and the expression of critical immune checkpoints. In addition, we screened for drugs that high- or low-risk TC groups might be sensitive to. Quantitative real-time polymerase chain reaction (qRT-PCR) results showed differential expression of four PRlncRNAs (GAPLINC, IDI2-AS1, LINC02154, and RBPMS-AS1) between tumor and normal tissues. Besides, a GEO database (GSE33630) was used to verify the expression differences of PRLncRNAs in THCA tissues and normal tissues. Finally, RBPMS-AS1 was found to inhibit the proliferation and migration of TC cells. In conclusion, we developed a PANoptosis-related lncRNA prognostic risk model that offers a comprehensive understanding of TME status in patients with TC and establishes a foundation for the choice of sensitive medications and immunotherapy.

Molecular subtypes and scoring tools related to Foxo signaling pathway for assessing hepatocellular carcinoma prognosis and treatment responsiveness.

Background: Transcription factors in Foxo signaling pathway influence hepatocellular carcinoma metastasis through epithelial mesenchymal transition-related pathways. Prognostic factors in the Foxo signaling pathway are feasible for HCC prognosis and therapeutic management. Methods: Based on the differentially expressed genes and Foxo signaling pathway genes in HCC, the ConsensusClusterPlus package was conducted to identify Foxo signaling pathway-related molecular subtypes in HCC. Based on the DEGs in the FMSs, the optimal prognostic factors in HCC were screened by cox and least absolute shrinkage and selection operator (LASSO) cox analysis to form the Foxo prognosis score (FPS). The prognostic predictive effectiveness of FPS was assessed by Kaplan Meier (K-M) analysis and Receiver Operating Characteristic (ROC) analysis. Additionally, tumor microenvironment (TME) score, tumor mutation burden (TMB) and treatment sensitivity differences in FMSs and FPS groups were also evaluated. Results: There were low, medium and high Foxo signaling pathway activity molecular subtypes in HCC named FMS 1, FMS 2 and FMS 3, respectively. FMS 1 with lowest Foxo signaling pathway activity presented an excellent survival advantage, while FMS 3 with highest Foxo signaling pathway activity exhibited an inhibitory TME status. According to FPS grouping, low FPS exhibited favorable survival, low TMB and anti-tumor activity. Patients in the low FPS group were mostly in the early stage of cancer. Moreover, we found that patients with high and low FPS exhibited different sensitivity to chemotherapy, and patients with low FPS were more sensitive to immunotherapy. Conclusion: We revealed a novel molecular subtype and prognostic tool based on Foxo signaling pathway signature, which could potentially provide a direction for accurate and effective assessment of potential personalized treatment options and prognostic management for HCC patients.

Classification of Brainstem Gliomas Based on Tumor Microenvironment Status.

The inter-tumor heterogeneity of the tumor microenvironment (TME) and how it correlates with clinical profiles and biological characteristics in brainstem gliomas (BSGs) remain unknown, dampening the development of novel therapeutics against BSGs. The TME status was determined with a list of pan-cancer conserved gene expression signatures using a single-sample gene set enrichment analysis (ssGSEA) and was subsequently clustered via consensus clustering. BSGs exhibited a high inter-tumor TME heterogeneity and were classified into four clusters: "immune-enriched, fibrotic", "immune-enriched, non-fibrotic", "fibrotic", and "depleted". The "fibrotic" cluster had a higher proportion of diffuse intrinsic pontine gliomas (p = 0.041), and "PA-like" tumors were more likely to be "immune-enriched, fibrotic" (p = 0.044). The four TME clusters exhibited distinct overall survival (p < 0.001) and independently impacted BSG outcomes. A four-gene panel as well as a radiomics approach were constructed to identify the TME clusters and achieved high accuracy for determining the classification. Together, BSGs exhibited high inter-tumor heterogeneity in the TME and were classified into four clusters with distinct clinical outcomes and tumor biological properties. The TME classification was accurately identified using a four-gene panel that can potentially be examined with the immunohistochemical method and a non-invasive radiomics method, facilitating its clinical application.

Pyroptosis-related lncRNA prognostic signatures for cutaneous melanoma and tumor microenvironment status.

Aims: To explore whether the expression of pyroptosis-related lncRNAsmakes a difference in the prognosis and antitumor immunity of cutaneous melanoma (CM) patients. Methods: A series of analyses were conducted to establish a prognostic risk model and validate its accuracy. Immune-related analyses were performed to further assess the associations among immune status, tumor microenvironmentand theprognostic risk model. Results: Eight pyroptosis-related lncRNAs relevant toprognosis were ascertained and applied to establish the prognostic risk model. The low-risk group had a higher overall survival rate. Conclusion: The established prognostic risk model presents better prediction ability for the prognosis of CM patients and provides new possibletherapeutic targets for CM.

Liposomal co-delivery of toll-like receptors 3 and 7 agonists induce a hot triple-negative breast cancer immune environment

Triple-negative breast cancer (TNBC) is highly aggressive and has no standard treatment. Although being considered as an alternative to conventional treatments for TNBC, immunotherapy has to deal with many challenges that hinder its efficacy, particularly the poor immunogenic condition of the tumor microenvironment (TME). Herein, we designed a liposomal nanoparticle (LN) platform that delivers simultaneously toll-like receptor 7 (imiquimod, IQ) and toll-like receptor 3 (poly(I:C), IC) agonists to take advantage of the different toll-like receptor (TLR) signaling pathways, which enhances the condition of TME from a “cold” to a “hot” immunogenic state. The optimized IQ/IC-loaded LN (IQ/IC-LN) was effectively internalized by cancer cells, macrophages, and dendritic cells, followed by the release of the delivered drugs and subsequent stimulation of the TLR3 and TLR7 signaling pathways. This stimulation encouraged the secretion of type I interferon (IFN-α, IFN-β) and CXCLl0, a T-cell and antigen-presenting cells (APCs) recruitment chemokine, from both cancer cells and macrophages and polarized macrophages to the M1 subtype in in vitro studies. Notably, systemic administration of IQ/IC-LN allowed for the high accumulation of drug content in the tumor, followed by the effective uptake by immune cells in the TME. IQ/IC-LN treatment comprehensively enhanced the immunogenic condition in the TME, which robustly inhibited tumor growth in tumor-bearing mice. Furthermore, synergistic antitumor efficacy was obtained when the IQ/IC-LN-induced immunogenic state in TME was combined with anti-PD1 antibody therapy. Thus, our results suggest the potential of combining 2 TLR agonists to reform the TME from a “cold” to a “hot” state, supporting the therapeutic efficacy of immune checkpoint inhibitors.

Tumor microenvironment phenotypes and prognostic evaluation tools for osteosarcoma characterized by different prognostic outcomes and immunotherapy responses.

The physiological and immunological characteristics of the tumor microenvironment (TME) have a profound impact on the effectiveness of immunotherapy. The present study aimed to define the TME subtype of osteosarcoma according to the signatures representing the global TME of the tumor, as well as create a new prognostic assessment tool to monitor the prognosis, TME activity and immunotherapy response of patients with osteosarcoma. The enrichment scores of 29 functional gene expression signatures in osteosarcoma samples were calculated by single sample gene set enrichment analysis (ssGSEA). TME classification of osteosarcoma was performed and a prognostic assessment tool was created based on 29 ssGSEA scores to comprehensively correlate them with TME components, immunotherapy efficacy and prognosis of osteosarcoma. Three TME subtypes were generated that differed in survival, TME activity and immunotherapeutic response. Four differentially expressed genes between TME subtypes were involved in the development of prognostic assessment tools. The established prognosis assessment tool had strong performance in both training and verification cohorts, could be effectively applied to the survival prediction of samples of different ages, genders and transfer states, and could well distinguish the TME status of different samples. The present study describes three different TME phenotypes in osteosarcoma, provides a risk stratification tool for osteosarcoma prognosis and TME status assessment, and provides additional information for clinical decision-making of immunotherapy.

Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics

The tumor microenvironment (TME) plays a critical role in disease progression and is a key determinant of therapeutic response in cancer patients. Here, we propose a noninvasive approach to predict the TME status from radiological images by combining radiomics and deep learning analyses. Using multi-institution cohorts of 2,686 patients with gastric cancer, we show that the radiological model accurately predicted the TME status and is an independent prognostic factor beyond clinicopathologic variables. The model further predicts the benefit from adjuvant chemotherapy for patients with localized disease. In patients treated with checkpoint blockade immunotherapy, the model predicts clinical response and further improves predictive accuracy when combined with existing biomarkers. Our approach enables noninvasive assessment of the TME, which opens the door for longitudinal monitoring and tracking response to cancer therapy. Given the routine use of radiologic imaging in oncology, our approach can be extended to many other solid tumor types.