Differences In Tumor Microenvironment Research Articles

Insights into the heterogeneity of the tumor microenvironment in lung adenocarcinoma and squamous carcinoma through single‐cell transcriptomic analysis: Implications for distinct immunotherapy outcomes

AbstractBackgroundImmune checkpoint blockade has emerged as a key strategy to the therapy landscape of non‐small cell lung cancer (NSCLC). However, notable differences in immunotherapeutic outcomes exist between the two primary NSCLC subtypes: lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). This disparity may stem from the tumor immune microenvironment's heterogeneity at the transcriptome level.MethodsBy integrative analysis of transcriptomic characterization of 38 NSCLC patients by single‐cell RNA sequencing, the present study revealed a distinct tumor microenvironment (TME) between LUAD and LUSC, with relevant results further confirmed in bulk transcriptomic and multiplex immunofluorescence (mIF) validation cohort of neoadjuvant immunotherapy patients.ResultsLUAD exhibited a more active immune microenvironment compared to LUSC. This included highly expression of HLA I/II in cancer cells, reinforced antigen presentation potential of dendritic cells and enhanced cytotoxic activity observed in T/NK cells. In LUSC, cancer cells highly expressed genes belonging to the aldo‐keto reductases, glutathione S‐transferases and aldehyde dehydrogenase family, negatively correlating with immunotherapy outcomes in the validation cohort of our center. Further analysis revealed elevated infiltrated cancer‐associated fibroblasts (CAFs) in LUSC, which was corroborated in The Cancer Genome Atlas cohort. Corresponding increased infiltration of ADH1B+ CAFs in major pathologic response (MPR) patients and the higher presence of FAP+ CAFs in non‐MPR patients were demonstrated by multiplex mIF. Moreover, upregulating immunosuppressive extracellular matrix remodeling was identified in LUSC.ConclusionsThese comprehensive analyses advance the understanding of the differences in TME between LUAD and LUSC, offering insights for patient selection and developing subtype‐specific treatment strategies.

Integrative single-cell analysis of LUAD: elucidating immune cell dynamics and prognostic modeling based on exhausted CD8+ T cells.

The tumor microenvironment (TME) plays a pivotal role in the progression and metastasis of lung adenocarcinoma (LUAD). However, the detailed characteristics of LUAD and its associated microenvironment are yet to be extensively explored. This study aims to delineate a comprehensive profile of the immune cells within the LUAD microenvironment, including CD8+ T cells, CD4+ T cells, and myeloid cells. Subsequently, based on marker genes of exhausted CD8+ T cells, we aim to establish a prognostic model for LUAD. Utilizing the Seurat and Scanpy packages, we successfully constructed an immune microenvironment atlas for LUAD. The Monocle3 and PAGA algorithms were employed for pseudotime analysis, pySCENIC for transcription factor analysis, and CellChat for analyzing intercellular communication. Following this, a prognostic model for LUAD was developed, based on the marker genes of exhausted CD8+ T cells, enabling effective risk stratification in LUAD patients. Our study included a thorough analysis to identify differences in TME, mutation landscape, and enrichment across varying risk groups. Moreover, by integrating risk scores with clinical features, we developed a new nomogram. The expression of model genes was validated via RT-PCR, and a series of cellular experiments were conducted, elucidating the potential oncogenic mechanisms of GALNT2. Our study developed a single-cell atlas for LUAD from scRNA-seq data of 19 patients, examining crucial immune cells in LUAD's microenvironment. We underscored pDCs' role in antigen processing and established a Cox regression model based on CD8_Tex-LAYN genes for risk assessment. Additionally, we contrasted prognosis and tumor environments across risk groups, constructed a new nomogram integrating clinical features, validated the expression of model genes via RT-PCR, and confirmed GALNT2's function in LUAD through cellular experiments, thereby enhancing our understanding and approach to LUAD treatment. The creation of a LUAD single-cell atlas in our study offered new insights into its tumor microenvironment and immune cell interactions, highlighting the importance of key genes associated with exhausted CD8+ T cells. These discoveries have enabled the development of an effective prognostic model for LUAD and identified GALNT2 as a potential therapeutic target, significantly contributing to the improvement of LUAD diagnosis and treatment strategies.

Molecular phenotypes behind patient stratification based on TP53 and HPV status and potential for treatment personalization with first single-cell RNA sequencing atlas of penile cancer.

8 Background: TP53 mutations and Human papillomavirus (HPV) have been suggested to impact penile squamous cell carcinoma (pSCC) prognosis and may predict treatment outcomes. Insights in differences in tumor micro-environment at single cell resolution may benefit personalized therapeutic development and are currently unavailable. Methods: We performed single cell RNA sequencing and targeted next generation sequencing (for TP53 loss-of-function mutations; p53LOF) on primary HPV-associated and HPV-independent (HPV+/-) pSCC lesions or healthy control samples of the coronal sulcus skin. Cell clusters were annotated based on known marker genes. Gene Set Enrichment Analysis (GSEA) and slingshot trajectory analysis was performed using R/Seurat package. Results: Twenty-two samples were analyzed: 4 tumors harbored p53LOF mutations (1 HPV+ p53LOF, 3 HPV- p53LOF), 6 HPV- p53WT, 6 HPV+ p53WT and 6 healthy controls. A total of 84.394 cells was annotated and we identified 68 distinct cell subtypes in pSCC. In epithelial cells, two distinct pseudotime trajectories stemming from the basal cells were identified characterized by either signals of partial epithelial-mesenchymal transition (pEMT; TGFB, ZEB2, SNAI2) or cornification ( ELF3, KRT19, PPL). Whilst the pEMT trajectory was enriched in p53LOF samples, cornification was enriched in HPV+ p53WT. HPV- p53WT samples displayed increase in metabolic changes and stemness hallmarks ( SOX2, HEY1, ZEB1). GSEA confirmed these findings and showed upregulation of pEMT gene sets in p53LOF; fatty acid metabolism and xenobiotic metabolism in HPV- p53WT, and cornification in HPV+ p53WT. We observed an immune suppressive environment in p53LOF patients with higher regulatory T-cell abundancy, and this correlated with abundancy in the epithelial pEMT clusters. Pseudo-bulk analysis on all T-cell subsets demonstrated a higher expression of inhibitory immune checkpoints ( PD-1, CTLA4, TIM3 and LAG3) in p53LOF and HPV- p53WT while HPV+ p53WT patients displayed higher abundancy of immunomodulatory and cytotoxic natural killer (NK) cells. Conclusions: We established the first comprehensive pSCC transcriptomic atlas at single cell resolution and illustrate differences in molecular phenotypes of pSCC according to TP53 mutation- and HPV status. p53LOF tumors are characterized by pEMT, which is a known driver of invasiveness, therapy resistance and metastasis. HPV- p53WT display hallmarks of stemness, which is related to and may explain chemotherapy-resistance often observed in these patients. p53LOF and HPV- p53WT display immune-evasion and exhaustion, while HPV+ p53WT are characterized by abundant cytotoxic innate-like lymphocytes. These observations provide deeper insights into cancer biology that will help advancing pSCC diagnosis and therapy.

Development of a novel prognostic signature based on single-cell combined bulk RNA analysis in breast cancer.

Breast cancer (BC), a malignant tumor, is a significant cause of death and disability among women globally. Recent research indicates that copy number variation plays a crucial role in tumor development. In this study, we employed the Single-Cell Variational Aneuploidy Analysis (SCEVAN) algorithm to differentiate between malignant and non-malignant cells, aiming to identify genetic signatures with prognostic relevance for predicting patient survival. We analyzed gene expression profiles and associated clinical data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Using the SCEVAN algorithm, we distinguished malignant from non-malignant cells and investigated cellular interactions within the tumor microenvironment (TME). We categorized TCGA samples based on differentially expressed genes (DEGs) between these cell types. Subsequent Kyoto Encyclopedia of Genes and Genomes pathway analysis was conducted. Additionally, we developed polygenic models for the DEGs using least absolute shrinkage and selection operator-penalized Cox regression analysis. To assess the prognostic accuracy of these characteristics, we generated Kaplan-Meier and receiver operating characteristic curves from training and validation datasets. We also monitored the expression variations of prognostic genes across the pseudotime of malignant cells. Patients were divided into high-risk and low-risk groups based on median risk scores to compare their TME and identify potential therapeutic agents. Lastly, polymerase chain reaction was used to validate seven pivotal genes. The SCEVAN algorithm identified distinct malignant and non-malignant cells in GSE180286. Cellchat analysis revealed significantly increased cellular communication, particularly between fibroblasts, endothelial cells and malignant cells. The DEGs were predominantly involved in immune-related pathways. TCGA samples were classified into clusters A and B based on these genes. Cluster A, enriched in immune pathways, was associated with poorer prognosis, whereas cluster B, predominantly involved in circadian rhythm pathways, showed better outcomes. We constructed a 14-gene prognostic signature, validated in a 1:1 internal TCGA cohort and external GEO datasets (GSE42568 and GSE146558). Kaplan-Meier analysis confirmed the prognostic signature's accuracy (p < 0.001). Receiver operating characteristic curve analysis demonstrated the predictive reliability of these prognostic features. Single-cell pseudotime analysis with monocle2 highlighted the distinct expression trends of these genes in malignant cells, underscoring the intratumoral heterogeneity. Furthermore, we explored the differences in TME between high- and low-risk groups and identified 16 significantly correlated drugs. Our findings suggest that the 14-gene prognostic signature could serve as a novel biomarker for forecasting the prognosis of BC patients. Additionally, the immune cells and pathways in different risk groups indicate that immunotherapy may be a crucial component of treatment strategies for BC patients.

A novel immune cell signature for predicting glioblastoma after radiotherapy prognosis and guiding therapy.

Background: Glioblastoma, a highly aggressive brain tumor, poses a significant clinical challenge, particularly in the context of radiotherapy. In this study, we aimed to explore infiltrating immune cells and identify immune-related genes associated with glioblastoma radiotherapy prognosis. Subsequently, we constructed a signature based on these genes to discern differences in molecular and tumor microenvironment immune characteristics, ultimately informing potential therapeutic strategies for patients with varying risk profiles. Methods: We leveraged UCSC Xena and CGGA gene expression profiles from post-radiotherapy glioblastoma as verification cohorts. Infiltration ratios were stratified into high and low groups based on the median value. Differential gene expression was determined through Limma differential analysis. A signature comprising four genes was constructed, guided by Gene Ontology (GO) functional enrichment results and Kaplan-Meier survival analysis. We evaluated differences in cell infiltration levels, Immune Score, Stromal Score, and ESTIMATE Score and their Pearson correlations with the signature. Spearman's correlation was computed between the signature and patient drug sensitivity (IC50), predicted using Genomics of Drug Sensitivity in Cancer (GDSC) and CCLE databases. Results: Notably, the infiltration of central memory CD8+T cells exhibited a significant correlation with glioblastoma radiotherapy prognosis. Samples were dichotomized into high- and low-risk groups based on the optimal signature threshold (2.466642). Kaplan-Meier (K-M) survival analysis revealed that the high-risk group experienced a significantly poorer prognosis (p = .0068), with AUC values exceeding 0.82 at 1, 3, and 5 years, underscoring the robust predictive potential of the signature scoring system. Independent validation sets substantiated the validity of the signature. Statistically significant differences in tumor microenvironments (p < .05) were observed between high- and low-risk groups, and these differences were significantly correlated with the signature (p < .05). Furthermore, there were significant correlations between high and low-risk groups regarding immune checkpoint expressions, Immune Prognostic Score (IPS), and Tumor Immune Dysfunction and Exclusion (TIDE) scores. Conclusion: The immune cell signature, comprising SDC-1, PLAUR, FN1, and CXCL13, holds promise as a predictive tool for assessing glioblastoma prognosis following radiotherapy. This signature also offers valuable guidance for tailoring treatment strategies, emphasizing its potential clinical relevance in improving patient outcomes.

Integrated machine learning identifies a cellular senescence-related prognostic model to improve outcomes in uterine corpus endometrial carcinoma.

Uterine Corpus Endometrial Carcinoma (UCEC) stands as one of the prevalent malignancies impacting women globally. Given its heterogeneous nature, personalized therapeutic approaches are increasingly significant for optimizing patient outcomes. This study investigated the prognostic potential of cellular senescence genes(CSGs) in UCEC, utilizing machine learning techniques integrated with large-scale genomic data. A comprehensive analysis was conducted using transcriptomic and clinical data from 579 endometrial cancer patients sourced from the Cancer Genome Atlas (TCGA). A subset of 503 CSGs was assessed through weighted gene co-expression network analysis (WGCNA) alongside machine learning algorithms, including Gaussian Mixture Model (GMM), support vector machine - recursive feature elimination (SVM-RFE), Random Forest, and eXtreme Gradient Boosting (XGBoost), to identify key differentially expressed cellular senescence genes. These genes underwent further analysis to construct a prognostic model. Our analysis revealed two distinct molecular clusters of UCEC with significant differences in tumor microenvironment and survival outcomes. Utilizing cellular senescence genes, a prognostic model effectively stratified patients into high-risk and low-risk categories. Patients in the high-risk group exhibited compromised overall survival and presented distinct molecular and immune profiles indicative of tumor progression. Crucially, the prognostic model demonstrated robust predictive performance and underwent validation in an independent patient cohort. The study emphasized the significance of cellular senescence genes in UCEC progression and underscored the efficacy of machine learning in developing reliable prognostic models. Our findings suggested that targeting cellular senescence holds promise as a strategy in personalized UCEC treatment, thus warranting further clinical investigation.

Assessment tool based on fatty acid metabolic signatures for predicting the prognosis and treatment response in bladder cancer

BackgroundFatty acid metabolism (FAM) is closely connected with tumorigenesis as well as disease progression and affects the efficacy of platinum-based drugs. Exploring biomarkers related to FAM in bladder cancer (BLCA) is essential to improve cancer prognosis. MethodsHigh-throughput sequencing data from The Cancer Genome Atlas (TCGA) were bioinformatically resolved to identify molecular subtypes of fatty acid metabolic profiles in BLCA using coherent clustering analysis. Based on fatty acid metabolic profile, a prognostic model was created using COX and LASSO COX models. CIBERSORT, Estimation of STromal and Immune cells in MAlignant Tumours using Expression (ESTIMATE), MCP-Count, and single sample gene set enrichment analysis (ssGSEA) were used to assess the differences in tumor microenvironment (TME) among different molecular subtypes, prognostic groups. Kaplan-Meier (K-M) survival curve was plotted to assess patients’ prognosis. Receiver operating characteristic curve (ROC) and the clinical prognostic value of prognostic models was evaluated by the Nomogram. ResultsThree molecular subtypes (FAMC1, FAMC2, FAMC3) of fatty acid metabolic patterns were determined. FAMC1 showed significant prognostic advantage with immunoreactivity. Five key prognostic FAMGs were identified and RiskScore was developed. We found that patients with low RiskScore showed significantly better immune microenvironment status, survival and response to immunotherapy. Similarly, both Nomogram and RiskScore demonstrated excellent prognostic value. ConclusionsIn conclusion, our study showed that the RiskScore was closely related to the clinical traits of BLCA patients. The RiskScore may provide essential clinical guidance for predicting prognosis and treatment response in bladder cancer.

Integrating bulk and single-cell RNA sequencing data to establish necroptosis-related lncRNA risk model and analyze the immune microenvironment in hepatocellular carcinoma

BackgroundThe increasing evidence suggests that necroptosis mediates many behaviors of tumors, as well as the regulation of the tumor microenvironment. Long non-coding RNAs (lncRNAs) are involved in a variety of regulatory processes during tumor development and are significantly associated with patient prognosis. It suggests that necroptosis-related lncRNAs (NRlncRNAs) may serve as biomarkers for the prognosis of hepatocellular carcinoma (HCC). MethodslncRNA expression profiles of HCC were obtained from TCGA database. LncRNAs associated with necroptosis were extracted using correlation analysis. Prognostic models were constructed based on least absolute shrinkage and selection operator algorithm (LASSO) and multivariate Cox regression analysis. The differences of tumor microenvironment between high-risk and low-risk groups were further analyzed. Single-cell RNA sequencing data of HCC was performed to assess the enrichment of necroptosis-related genes in immune cell subsets. Finally, real-time RT-PCR was used to detect the prognosis-related lncRNAs expression in different HCC cell lines. ResultsWe constructed a prognostic signature based on 8 NRlncRNAs, which also showed good predictive accuracy. The model showed that the prognosis of patients with high-risk score was significantly worse than that of patients with low-risk score (P < 0.05). Combined with the clinical characteristics and risk score of HCC, Nomogram was drawn for reference in clinical practice. In addition, immune cell infiltration analysis and single cell RNA sequencing analysis showed that a low level of immune infiltration was observed in patients at high risk and that there was a significant correlation between NRlncRNAs and macrophages. The results of RT-qPCR also showed that 8 necroptosis-related lncRNAs were highly expressed in HCC cell lines and human liver cancer tissues. ConclusionThis prognostic signature based on the necroptosis-related lncRNAs may provide meaningful clinical insights for the prognosis and immunotherapy responses in patients with HCC.

Molecular characteristics, clinical significance, and immune landscape of extracellular matrix remodeling-associated genes in colorectal cancer.

Extracellular matrix (ECM) remodeling is one of the hallmark events in cancer and has been shown to be closely related to tumor immunity. Immunotherapy has evolved as an important tool to treat various cancers and improve patient prognosis. The positive response to immunotherapy relies on the unique interaction between cancer and the tumor microenvironment (TME). However, the relationship between ECM remodeling and clinical outcomes, immune cell infiltration, and immunotherapy in colorectal cancer (CRC) remains unknown. We systematically evaluated 69 ECM remodeling-associated genes (EAGs) and comprehensively identified interactions between ECM remodeling and prognosis and the immune microenvironment in CRC patients. The EAG_score was used to quantify the subtype of ECM remodeling in patients. We then assessed their value in predicting prognosis and responding to treatment in CRC. After elaborating the molecular characteristics of ECM remodeling-related genes in CRC patients, a model consisting of two ECM remodeling-related genes (MEIS2, SLC2A3) was developed for predicting the prognosis of CRC patients, Receiver Operating Characteristic (ROC) and Kaplan-Meier (K-M) analysis verified its reliable predictive ability. Furthermore, we created a highly reliable nomogram to enhance the clinical feasibility of the EAG_score. Significantly differences in TME and immune function, such as macrophages and CD8+ T cells, were observed between high- and low-risk CRC patients. In addition, drug sensitivity is also strongly related to EAG_score. Overall, we developed a prognostic model associated with ECM remodeling, provided meaningful clinical implications for immunotherapy, and facilitated individualized treatment for CRC patients. Further studies are needed to reveal the underlying mechanisms of ECM remodeling in CRC.

The integrated single-cell analysis developed a lactate metabolism-driven signature to improve outcomes and immunotherapy in lung adenocarcinoma.

It has been suggested that lactate metabolism (LM) is crucial for the development of cancer. Using integrated single-cell RNA sequencing (scRNA-seq) analysis, we built predictive models based on LM-related genes (LMRGs) to propose novel targets for the treatment of LUAD patients. The most significant genes for LM were identified through the use of the AUCell algorithm and correlation analysis in conjunction with scRNA-seq analysis. To build risk models with superior predictive performance, cox- and lasso-regression were utilized, and these models were validated on multiple external independent datasets. We then explored the differences in the tumor microenvironment (TME), immunotherapy, mutation landscape, and enriched pathways between different risk groups. Finally, cell experiments were conducted to verify the impact of AHSA1 in LUAD. A total of 590 genes that regulate LM were identified for subsequent analysis. Using cox- and lasso-regression, we constructed a 5-gene signature that can predict the prognosis of patients with LUAD. Notably, we observed differences in TME, immune cell infiltration levels, immune checkpoint levels, and mutation landscapes between different risk groups, which could have important implications for the clinical treatment of LUAD patients. Based on LMRGs, we constructed a prognostic model that can predict the efficacy of immunotherapy and provide a new direction for treating LUAD.

Abstract P4-03-06: Clinical and socioeconomic disparities in treatment and survival between Hispanic and non-Hispanic Black women with non-metastatic Triple-Negative Breast Cancer

Abstract Background: Breast cancer(BC) is a common female malignancy [1]. Triple-negative breast cancer (TNBC) is the least common (10-15% of cases)[2], but the most aggressive subtype. TNBC accounts for 5% of all-cancer-related deaths every year[3, 4].Hispanic women are 1.3 more likely to develop TNBC [3, 5]. Whether Hispanic and non-Hispanic patients suffering from TNBC show different survival. We aim: (1) to evaluate the differences in overall (OS) and breast cancer specific survival(BCSS) between Hispanic(H) and non-Hispanic black (NHB) women in non-metastatic TNBC, and (2) to assess the contribution of sociodemographic, clinical, and neighborhood factors to TNBC survival disparities. Methods: With the SEER database of the National Cancer Institute, we identified 4271 (39%) H and 6594 (61%) NHB patients who were diagnosed with non-metastatic TNBC between 2010 and 2016. Patients with tumor in situ were excluded from the analysis. Logistic regression was used to identify odd ratios for treatment modalities. Kaplan-Meir methods were used to estimate OS and BCSS. Competing risk analysis was used to asses the association between race/ethnicity and risk of breast cancer mortality adjusting for age, insurance, social, SDI, rurality, BC stage, tumor grade, surgery, chemotherapy, and radiation therapy. Results: The mean age at diagnosis for H vs. NHB was 53 and 56 years, respectively (p &amp;lt; 0.001). 54% of Hispanics were married vs. 35 % of NHB, p &amp;lt; 0.001. Tumor size &amp;gt; 5 cm was present in 14% NHB vs. 12% H patients, p &amp;lt; 0.001. Grade I tumors were only 1 % in both groups, grade II were 13% and grade III-IV were 82% also in both groups, p &amp;gt;0.99. H (28% stage I, 50% stage II) had more advance disease as compared to NHB (30% stage I, 48% stage II) [p &amp;lt; 0.001]. Private insurance was more common in NHB than H (74% vs. 66%, p &amp;lt; 0.001). Rural residence was more common in NHB than H (8% vs. 3 %, p&amp;lt; 0.001). High level of SDI was more common in H as compared to NHB(43% vs. 36%, p &amp;lt; 0.001). Surgery was performed in 92% of NHB and 91% of H, p = 0.03. Radiation was given to 54% of NHB and 43% of H, p &amp;lt; 0.001, while chemotherapy was given to 79% of NHB and 79% of H patients, p = 0.54. After adjusting for clinicopathological features, the odds ratio (ORs) for receiving surgery, radiation, and chemotherapy for NHB as compared to H were 0.9 (p = 0.32), 1.57 (p &amp;lt; 0.001), and 0.89 (p = 0.03), respectively. The 5-year OS and BCSS rates were higher in H as compared to NHB(OS 76% vs. 72%, p &amp;lt; 0.001; BCSS 81% vs. 78%, p &amp;lt; 0.001). For stage I patients, neither 5-year OS (86% vs. 89%, p= 0.054) nor BCSS (91% vs. 93%, p= 0.37) was different between NHB and H patients. For stage II and III patients, the 5-year OS rates for Hispanics vs. NHB were (79% vs 74%, p &amp;lt; 0.001) and (51% vs 45%, p &amp;lt; 0.004), respectively. After adjusting for age, marital status, insurance, stage, grade, treatment, SDI, rurality; NHB have higher risk of BC death as compared to H patients (subdistribution Hazard Ratio (HR) = 1.39; 95% CI 1.2 – 1.56, p &amp;lt; 0.001). The association between race/ethnicity and risk of breast cancer death was not affected by age (p=0.44), insurance(p=0.89), SDI(p=0.40), or living in a rural area(p=0.14). The risk of BC death between NHB and H was even higher for patients who did not receive radiation (HR 1.5) than in those who received radiation (HR 1.2). The risk of BC death between NHB and H was also higher for patients who did not undergo surgery (HR 1.7) than in those who underwent surgery (HR 1.3). Conclusions: In patients with non-metastatic TNBC, H patients present with increased rates of high-grade and advanced stage tumors than NHB. NHB are 57% more likely to receive radiation and 11% less likely to receive chemotherapy than H women. NHB patients have a 39% higher risk of BC death as compared to H even after adjusting for clinicopathological, treatment and socioeconomic factors. Future studies need to assess potential etiologies for racial disparities in BC outcomes such as differences in tumor microenvironment, access to healthcare, tumor biology and treatment efficacy. Citation Format: Alvaro Alvarez Soto, Ana Maria Bernal, Jesus Anampa Mesias. Clinical and socioeconomic disparities in treatment and survival between Hispanic and non-Hispanic Black women with non-metastatic Triple-Negative Breast Cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-03-06.

A novel prognostic scoring model based on copper homeostasis and cuproptosis which indicates changes in tumor microenvironment and affects treatment response.

Background: Intracellular copper homeostasis requires a complex system. It has shown considerable prospects for intervening in the tumor microenvironment (TME) by regulating copper homeostasis and provoking cuproptosis. Their relationship with hepatocellular carcinoma (HCC) remains elusive. Methods: In TCGA and ICGC datasets, LASSO and multivariate Cox regression were applied to obtain the signature on the basis of genes associated with copper homeostasis and cuproptosis. Bioinformatic tools were utilized to reveal if the signature was correlated with HCC characteristics. Single-cell RNA sequencing data analysis identified differences in tumor and T cells' pathway activity and intercellular communication of immune-related cells. Real-time qPCR analysis was conducted to measure the genes' expression in HCC and adjacent normal tissue from 21 patients. CCK8 assay, scratch assay, transwell, and colony formation were conducted to reveal the effect of genes on in vitro cell proliferation, invasion, migration, and colony formation. Results: We constructed a five-gene scoring system in relation to copper homeostasis and cuproptosis. The high-risk score indicated poor clinical prognosis, enhanced tumor malignancy, and immune-suppressive tumor microenvironment. The T cell activity was markedly reduced in high-risk single-cell samples. The high-risk HCC patients had a better expectation of ICB response and reactivity to anti-PD-1 therapy. A total of 156 drugs were identified as potential signature-related drugs for HCC treatment, and most were sensitive to high-risk patients. Novel ligand-receptor pairs such as FASLG, CCL, CD40, IL2, and IFN-Ⅱ signaling pathways were revealed as cellular communication bridges, which may cause differences in TME and immune function. All crucial genes were differentially expressed between HCC and paired adjacent normal tissue. Model-constructed genes affected the phosphorylation of mTOR and AKT in both Huh7 and Hep3B cells. Knockdown of ZCRB1 impaired the proliferation, invasion, migration, and colony formation in HCC cell lines. Conclusion: We obtained a prognostic scoring system to forecast the TME changes and assist in choosing therapy strategies for HCC patients. In this study, we combined copper homeostasis and cuproptosis to show the overall potential risk of copper-related biological processes in HCC for the first time.

Five-hub genes identify potential mechanisms for the progression of asthma to lung cancer.

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

Identification of immune related gene signature for predicting prognosis of cholangiocarcinoma patients.

To identify the gene subtypes related to immune cells of cholangiocarcinoma and construct an immune score model to predict the immunotherapy efficacy and prognosis for cholangiocarcinoma. Based on principal component analysis (PCA) algorithm, The Cancer Genome Atlas (TCGA)-cholangiocarcinoma, GSE107943 and E-MTAB-6389 datasets were combined as Joint data. Immune genes were downloaded from ImmPort. Univariate Cox survival analysis filtered prognostically associated immune genes, which would identify immune-related subtypes of cholangiocarcinoma. Least absolute shrinkage and selection operator (LASSO) further screened immune genes with prognosis values, and tumor immune score was calculated for patients with cholangiocarcinoma after the combination of the three datasets. Kaplan-Meier curve analysis determined the optimal cut-off value, which was applied for dividing cholangiocarcinoma patients into low and high immune score group. To explore the differences in tumor microenvironment and immunotherapy between immune cell-related subtypes and immune score groups of cholangiocarcinoma. 34 prognostic immune genes and three immunocell-related subtypes with statistically significant prognosis (IC1, IC2 and IC3) were identified. Among them, IC1 and IC3 showed higher immune cell infiltration, and IC3 may be more suitable for immunotherapy and chemotherapy. 10 immune genes with prognostic significance were screened by LASSO regression analysis, and a tumor immune score model was constructed. Kaplan-Meier (KM) and receiver operating characteristic (ROC) analysis showed that RiskScore had excellent prognostic prediction ability. Immunohistochemical analysis showed that 6 gene (NLRX1, AKT1, CSRP1, LEP, MUC4 and SEMA4B) of 10 genes were abnormal expressions between cancer and paracancer tissue. Immune cells infiltration in high immune score group was generally increased, and it was more suitable for chemotherapy. In GSE112366-Crohn's disease dataset, 6 of 10 immune genes had expression differences between Crohn's disease and healthy control. The area under ROC obtained 0.671 based on 10-immune gene signature. Moreover, the model had a sound performance in Crohn's disease. The prediction of tumor immune score model in predicting immune microenvironment, immunotherapy and chemotherapy in patients with cholangiocarcinoma has shown its potential for indicating the effect of immunotherapy on patients with cholangiocarcinoma.

Comparative analysis of tumor immune microenvironment in primary tumors vs metastatic tissue in microsatellite stable metastatic colorectal cancer.

187 Background: We previously demonstrated differences in responses to checkpoint inhibitors between liver and non-liver metastases (mets) in microsatellite stable (MSS) metastatic colorectal cancer (mCRC). Here, we investigate differences in tumor microenvironment (TME) and mutational landscape of primary and mCRC sites. Methods: We used the Tempus clinicogenomic database to select de-identified cases of MSS mCRC which underwent Tempus xT testing. Tempus xT is a 648-gene NGS panel that detects somatic alterations and includes RNA sequencing. Gene expression patterns of different immune cells were used to predict their relative abundance within the tumor. Analyzed tumors were divided into 4 categories based: primary tumor (colon), liver, lung, and peritoneal mets. Demographics, genomic alterations, tumor mutational burden (TMB), PD-L1 expression, and proportions of B, T (CD4+, CD8+), NK cells, and macrophages were compared between all 4 categories. Chi-squared/Fisher’s exact tests or Kruskal-Wallis tests were used to assess statistical significance. Results: Genomic profiles from 4,345 unique mCRC patients were analyzed (Primary CRC = 1734; Liver = 1825; Lung = 443; Peritoneal = 343). Median TMB and PD-L1 expressions had small differences across sites that were statistically significant. KRAS mutations were more frequent and SMAD4 mutations were less prevalent in lung mets. RNA-Seq revealed significant differences in immune cell composition. Lung mets exhibited a higher percentage of B cells and lower percentage of macrophages than liver and peritoneum (p&lt; 0.001), similar to primary tumors. CD4+ T cells were highest in primary tumors, whereas CD8+ T cells were highest in lung mets (p &lt; 0.001). Conclusions: The TME of lung mets has a more favorable immune composition (increased CD8+ T cells and B cells, decreased macrophages) than that of liver and peritoneal mets, suggesting that MSS mCRC to the lung may potentially be more responsive to immunotherapy.[Table: see text]

Ethnic disparities in the immune microenvironment of triple negative breast cancer and its role in therapeutic outcomes.

In 2020, newly diagnosed breast cancer (BC) cases surpassed that of lung cancer among women, making it the most common female cancer globally. In spite of recent increases in incidence rates, mortality due to BC has declined since 1989. These declines have been attributed to advancements in treatment modalities as well as increased mammography surveillance. Despite these advances, African American (AA) women are 40% more likely to die from BC than Caucasian women. Multifactorial etiology has been implicated in the disparity of BC mortality rates among AA women. As an example, AA women have a disproportionate incidence of triple negative breast cancer (TNBC), which has a poor prognosis and marginal treatment options. Increasingly, the tumor microenvironment (TME) has gained relevance as it relates to primary tumor progression, metastasis and treatment possibilities. The treatment outcomes or pathological complete response (pCR) in TNBC among AA women are affected by differences in TME. The TME of AA women exhibit several variances in acellular and cellular components associated with pro-tumorigenic effects. For example, increased levels of the adipocyte-related hormone, resistin, the pro-inflammatory cytokine, IL-6, and the CC chemokine, CCL2, within the TME of AA women gives rise to an increased density of M2 macrophages, also known as tumor-associated macrophages. Elevated levels of vascular endothelial growth factor in the TME of AA women increase the vascular density or vascularity, which facilitate aggressive tumor growth and metastasis. Furthermore, a pro-tumorigenic TME is supported by increased levels of the CXC chemokine, CXCL12 that results in the recruitment of regulatory T lymphocytes (Tregs ). Due to these and other differences in the TME of AA women, precision oncology can target specific aspects of the TME that may contribute to a poorer prognosis. In addition to the discrepancies in the TME, AA women face socio-economic barriers that limit their ability to access state-of-the-art, novel therapies against metastatic TNBC. In this review, we will provide a brief overview of the tumor immune microenvironment, immune-based treatment options for TNBC and their potential to decrease health disparities due to ethnicity.

Pancancer landscape analysis of the thymosin family identified TMSB10 as a potential prognostic biomarker and immunotherapy target in glioma.

BackgroundThymosin family genes (TMSs), biologically important peptides with diverse intracellular and extracellular functions, have been shown to promote the progression of multiple cancers. However, multiomics characterization of TMSs and their role in human cancer prognosis has not been systematically performed.MethodsWe performed a comprehensive analysis of TMSs and thymosin β10 (TMSB10) using multiomics data from more than 10,000 tumor samples of 33 cancer types from The Cancer Genome Atlas (TCGA). We used single-sample gene set enrichment analysis (ssGSEA) and the gene set variation analysis (GSVA) algorithm to investigate the differences in tumor microenvironment (TME) cell infiltration and functional annotation for individual tumor samples, respectively. The role of TMSB10 in the malignant progression of glioma, the promotion of macrophage infiltration,and immunosuppressive polarization, and the combination drug efficacy were assessed via biological function assays.ResultsWe comprehensively assessed genomic mutations, expression dysregulation, prognosis and immunotherapeutic response across 33 human cancer samples and showed that TMSB10 is specifically overexpressed in almost all types of cancer tissues. Further pan-cancer analysis showed that TMSB10 is closely related to the biological function, immune regulation and prognosis of glioma. Similar results were also found in several public glioma cohorts and our Qilu local cohort. Further integration with other biological experiments revealed the key roles of TMSB10 in the malignant progression of glioma, the promotion of macrophage infiltration and immunosuppressive polarization. We also identified multiple drugs targeting cells with high TMSB10 expression and validated that knockdown of TMSB10 improved the efficacy of selumetinib (a MEK1/2 inhibitor approved by the FDA for the treatment of neurofibromatosis-associated tumors) and anti-PD1 treatment in glioma.ConclusionThese results indicate that TMSB10 holds promise as a novel prognostic marker and therapeutic target, providing a theoretical basis for the development of more effective and targeted clinical treatment strategies for glioma patients.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12935-022-02698-5.

Identification of prognostic and immunotherapy-related eRNA ID2-AS1 in bladder cancer.

Enhancer RNAs (eRNAs) can participate in enhancer regulation and target gene transcription, thus affecting the occurrence and development of tumors. In this study, we identified eRNAs closely related to bladder cancer (BLCA).Gene expression profiles and clinical information from The Cancer Genome Atlas (TCGA) database were used in this study. The Atlas of Noncoding RNAs in Cancer (TANRIC) co-expression data was also studied to evaluate correlations between the inferred levels of eRNA and its predicted target genes. Moreover, we evaluated differences in tumor microenvironment between high and low ID2-AS1 expression groups, and predicted the response of high- and low-expression groups to immune checkpoint inhibitor (ICI) treatment. Finally, we analyzed the prognostic value of ID2-AS1 in different tumors.ID2-AS1 and ID2 were identified as eRNAs and target genes related to the prognosis of BLCA. Low ID2-AS1 levels were associated with advanced age, low overall survival, high histological grade, and late BLCA staging. ID2-AS1 appeared to regulate epithelial mesenchymal transition, mitotic spindle assembly, and angiogenesis, thereby affecting BLCA progression. The ID2-AS1 high-expression group had better ICI treatment response. In addition, ID2-AS1 also had prognostic value in other cancers.ID2-AS1 helps predict prognostic and immunotherapeutic effects in BLCA.

Abstract 612: Microbial determinants of immune checkpoint inhibition response in a murine model of obesity and metastatic basal-like breast cancer

Abstract Introduction: Breast cancer (BC) is the second leading cause of cancer-related mortality among American women and is exacerbated by obesity. The basal-like (BL) subtype accounts for 10-15% of all BCs and is a particularly aggressive disease due to its extreme intrinsic diversity compared with other subtypes. Paradoxically, obesity promotes immune checkpoint inhibition (ICI) response in some cancers. Further, obesity promotes altered intestinal microbial composition, and recent clinical studies of fecal microbiota transplants suggest causal links between the microbiome and immunotherapy responses. Hence, we evaluated the correlation of tumoral and cecal microbiota with anti-Programmed Cell Death Protein 1 (PD1) immunotherapy response in a murine model of BLBC and obesity. Methods: In an initial study, normoweight female C57BL/6J mice (n=8/group) received orthotopic injection of one of two novel C3TAg BLBC cell lines (1.02 and 2.51) syngeneic to C57BL/6J derived from a spontaneous tumor excised from B6-C3TAg transgenic mice. Gene expression profiling (Affymetrix arrays) with Gene Set Enrichment Analysis (GSEA) was performed on the resulting transplanted tumors. In a second study, female C57BL/6J mice were divided into a diet-induced obesity (DIO) group (n=30), promoted by high-fat diet feeding, and a lean control group (n=30). After 24 weeks, 2.51 cells were orthotopically injected into the fourth mammary fat pad of all mice, followed by treatment with anti-PD1 or IgG control antibodies (n=15/diet group). Tumor and cecal DNA was extracted and subjected to 16S rRNA amplicon sequencing. Results: GSEA from the initial study revealed that orthotopically transplanted line 2.51 tumors, relative to line 1.02 tumors, had lower levels of stromal remodeling as indicated by significant enrichment of gene sets related to extracellular matrix proteoglycans, collagen chain trimerization, and collagen biosynthesis and modification. Gene sets related to insulin and IGF-1 signaling were also lower in line 2.51 tumors than line 1.02 tumors. The in-life portion of the second study revealed a heterogeneous response to ICI therapies among lean and obese mice. To understand this diversity of response, 16S rRNA sequencing analysis of tumoral and cecal microbiota in response to DIO and ICI therapy is underway. Discussion: These data reveal striking differences in tumor microenvironment and nutrient sensing as being potentially important determinants of differential tumor growth in BLBC. Ultimately, this research may contribute to the development of precision nutrition approaches using pro-/pre-biotics to promote microbial communities, targeting the tumor microenvironment, which may improve immunotherapy response and disrupt the obesity-associated exacerbation of breast cancer. Financial Support: This work was supported by R35CA197627 to SDH. Citation Format: Tori L. McFarlane, Meredith S. Carson, Elaine M. Glenny, Violet A. Kiesel, Ashlee Taylor, Daniel Roth, Jody Albright, Melissa VerHague, John E. French, Michael F. Coleman, Stephen D. Hursting. Microbial determinants of immune checkpoint inhibition response in a murine model of obesity and metastatic basal-like breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 612.

Differences in tumor microenvironment between HER2-positive and HER2-negative breast cancer.

e12562 Background: The tumor microenvironment of breast cancer is extremely complex, which containing different types of lymphocytes, macrophages, NK cells and other immune cell subtypes. At present, the relationship between immune cell subtypes and molecular subtypes of breast cancer is still unclear. Methods: Tumor tissue samples of 20 breast cancer patients were collected from January 2020. Then the density (number of positive cells/mm2) and ratio of immune cells in intratumoral area and stromal area were detected by multiple fluorescence immunohistochemistry (mIHC), the detection range was included CD3+T cells, CD4+T cells,CD8+T cells,PD-1+CD8+T cells,Tregs,M1/M2 tumor associated macrophages (TAM), PD-L1+CD68+TAM,B cells,CD56bright NK cells and CD56dim NK cells and so on. The immune cell subtypes of all area were included both intratumoral area and stromal area. Graphpad prism (version 7.01) was used for t test and the statistical significance was set at p=0.05. Results: These 20 patients were divided into two groups, HER2 positive (n=8) and HER2 negative group (n=12). The density of CD3+T cells in all area (including intratumoral area and stromal area) of tumor tissue in HER2 positive breast cancer patients were significantly higher than that of HER2 negative breast cancer patients (median density, HER2 positive group vs. HER2 negative group=2998 vs. 1520/mm², p=0.031) as well as the CD8+T cells (median density, HER2 positive group vs. HER2 negative group=536 vs. 175/mm², p=0.015). Meanwhile, the density of Tregs in all area of tumor tissue in HER2 positive breast cancer patients was also significantly higher (median density, HER2 positive group vs. HER2 negative group=119 vs. 27/mm², p=0.022).However, there was no significant difference in the other subtypes of immune cells infiltrated in tumor tissue between HER2 positive and HER2 negative samples, such as CD4+T cells, M1/M2 macrophages, B cells and so on (p＞0.05). Conclusions: Our research indicates that the patterns of immune cell subtypes in different types of early breast cancer are different. Further exploration of the immune microenvironment in different molecular types of breast cancer may provide additional targets for immunotherapy.