High Immune Cell Infiltration Research Articles

Inhibition of CDK9 exhibits anticancer activity in hepatocellular carcinoma cells via targeting ribonucleotide reductase

Cyclin-dependent kinase 9 (CDK9) inhibitors are a novel category of anticancer treatment for cancers. However, their effects on hepatocellular carcinoma (HCC) are rarely investigated. Human ribonucleotide reductase (RR, which consists of RRM1 and RRM2 subunits) catalyzes the conversion of ribonucleoside diphosphate into 2′-deoxyribonucleoside diphosphate to maintain the homeostasis of nucleotide pools, which play essential roles in DNA synthesis and DNA repair. In this study, we identified that CDK9 protein expression in adjacent non-tumor tissues predicted HCC patients' overall and progression-free survivals. The anticancer activity of a CDK9-selective inhibitor, LDC000067, on HCC cells was positively associated with its ability to inhibit the expression of RRM1 and RRM2. LDC000067 downregulated RRM1 and RRM2 expression through post-transcriptional pathway. Specifically, LDC000067 triggered RRM2 protein degradation via multiple pathways, including proteasome-, lysosome-, and calcium-dependent pathways. Furthermore, CDK9 positively correlates with RRM1 or RRM2 expression in HCC patients, and the expressions of these three genes were associated with the higher infiltration of immune cells in HCC. Taken together, this study identified the prognostic relevance of CDK9 in HCC and the molecular mechanism for the anticancer effect of CDK9 inhibitors on HCC.

Identification of a novel gene expression signature as prognostic biomarker for human papillomavirus negative head and neck carcinoma

AbstractPurposeHead and neck squamous cell carcinomas (HNSCCs) are heterogeneous and complex malignancies. The present study aimed to explore potential molecular classifications and establish promising prognostic candidate biomarkers for human papillomavirus (HPV)‐negative HNSCC.MethodsWe performed unsupervised clustering in HPV‐negative patients from the TCGA‐HNSCC database and identified molecular subclusters based on HNSCC prognosis. Weighted gene co‐expression network analysis was used to identify cluster‐related gene modules and identify reliable candidate genes. Biomarker signature was established from these candidate genes via 10‐fold cross validation in a least absolute shrinkage and selection operator (LASSO) Cox regression model and validated in The Cancer Genome Atlas Program (TCGA) cohort and four external Gene Expression Omnibus (GEO) validation cohorts.ResultsWe identified three molecular subclusters (C1–C3), and tumour microenvironment and survival evaluation results showed that C3 had high immune cell infiltration and better prognosis. However, C1 and C2 had low immune cell infiltration, and C1 had a worse survival outcome. A 20‐gene signature was established from the input candidate genes via 10‐fold cross validation in the LASSO Cox regression model. The signature was first validated in a training cohort, followed by a testing cohort and the entire cohort. A total of 347 patients from four external GEO validation cohorts indicated great performance of the signature in predicting outcomes.ConclusionCompared with HPV‐positive patients, those with HPV‐negative HNSCC had an immunosuppressed tumour microenvironment. The 20‐gene signature was developed based on the characteristic unsupervised clustering molecular subclusters to predict survival outcomes in patients with HPV‐negative HNSCC.

Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma.

Glutamine metabolism (GM) is known to play a critical role in cancer development, including in lung adenocarcinoma (LUAD), although the exact contribution of GM to LUAD remains incompletely understood. In this study, we aimed to discover new targets for the treatment of LUAD patients by using machine learning algorithms to establish prognostic models based on GM-related genes (GMRGs). We used the AUCell and WGCNA algorithms, along with single-cell and bulk RNA-seq data, to identify the most prominent GMRGs associated with LUAD. Multiple machine learning algorithms were employed to develop risk models with optimal predictive performance. We validated our models using multiple external datasets and investigated disparities in the tumor microenvironment (TME), mutation landscape, enriched pathways, and response to immunotherapy across various risk groups. Additionally, we conducted in vitro and in vivo experiments to confirm the role of LGALS3 in LUAD. We identified 173 GMRGs strongly associated with GM activity and selected the Random Survival Forest (RSF) and Supervised Principal Components (SuperPC) methods to develop a prognostic model. Our model's performance was validated using multiple external datasets. Our analysis revealed that the low-risk group had higher immune cell infiltration and increased expression of immune checkpoints, indicating that this group may be more receptive to immunotherapy. Moreover, our experimental results confirmed that LGALS3 promoted the proliferation, invasion, and migration of LUAD cells. Our study established a prognostic model based on GMRGs that can predict the effectiveness of immunotherapy and provide novel approaches for the treatment of LUAD. Our findings also suggest that LGALS3 may be a potential therapeutic target for LUAD.

In Vitro 3D Spheroid Model Preserves Tumor Microenvironment of Hot and Cold Breast Cancer Subtypes.

Dynamic interaction of cancer, immune, and stromal cells with extracellular matrix components modulates and resists the response of standard care therapies. To mimic this, an in vitro 3D spheroid model is designed using liquid overlay method to simulate hot (MDA-MB-231) and cold (MCF-7) breast tumor microenvironment (TME). This study shows increased mesenchymal phenotype, stemness, and suppressive microenvironment in MDA-MB-231-spheroids upon exposure to doxorubicin. Intriguingly, the presence of human dermal fibroblasts enhances cancer-associated fibroblast phenotype in MDA-MB-231-spheroids through increased expression of CXCL12 and FSP-1, leading to higher infiltration of immune cells (THP-1 monocytes). However, a suppressive TME is observed in both subtypes, as seen by upregulation of M2-macrophage-specific CD68 and CD206 markers. Specifically, increased PDL-1 expressing tumor-associated macrophages along with FoxP3 expressing T regulatory cells are found in MDA-MB-231-spheroids when cultured with peripheral blood mononuclear cells. Further, it is found that the addition of 1-methyl-tryptophan, a potent indoleamine-2,3-dioxygenase-1 inhibitor, subsides the suppressive phenotype by decreasing the M2 polarization via downregulation of tryptophan metabolism and IL10 expression, particularly in MCF-7 triculture spheroids. Thus, the in vitro 3D spheroid model of TME can be utilized in therapeutics to validate immunomodulatory drugs for various breast cancer subtypes.

Role of Single Nucleotide Polymorphism-Related Genes in Tumour Immune Cell Infiltration and Prognosis of Cutaneous Melanoma.

Advances in cancer research have allowed for early diagnosis and improved treatment of cutaneous melanoma (CM). However, its invasiveness and recurrent metastasis, along with rising resistance to newer therapies, have lent urgency to the search for novel biomarkers and the underlying molecular mechanisms of CM. Single nucleotide polymorphism- (SNP-) related genes were obtained from the sequencing data of 428 CM samples in The Cancer Genome Atlas. Functional enrichment of these genes was analysed in clusterProfiler. Additionally, a protein-protein interaction (PPI) network was constructed with the Search Tool for the Retrieval of Interacting Gene (STRING) database. Gene Expression Profiling Interactive Analysis (GEPIA) was used to identify the expression and prognostic value of mutated genes. Finally, the Tumour Immune Estimation Resource (TIMER) analysed the relationship between gene expression and immune cell infiltration. We constructed a PPI network from the top 60 SNP-related genes. Mutated genes were mainly involved in calcium and oxytocin signalling pathways, as well as circadian entrainment. In addition, three SNP-related genes, BRAF, FLG, and SORL1, were significantly associated with patient prognosis. BRAF and SORL1 were positively associated with infiltration abundance of B cells, CD8+ T cells, CD4+ T cells, neutrophils, and dendritic cells, whereas FLG expression was negatively associated. Furthermore, higher immune cell infiltration was positively correlated with good prognosis. Our study provides vital bioinformatic data and a relevant theoretical basis to further explore the molecular pathogenesis of CM and improve patient prognosis.

B-arrestin deletion abrogates the innate immune response in murine CVB3 myocarditis

β-arrestins (βarrs), initially discovered as desensitizers of GPCR signaling, are now known to function as intracellular scaffolds and signaling transducers mediating a wide array of signaling cascades including immune responses to inflammatory stimuli. An important cause of immune cell infiltration and cardiac inflammation are viral pathogens such as enterovirus coxsackievirus B3 (CVB3) induced myocarditis. While viral myocarditis is a leading cause of dilated cardiomyopathy, the precise molecular mechanisms for the acute immune response to CVB3 infection are not well understood. Given the important role for βarrs in the acute immune response, we tested the hypothesis that βarrs are necessary for the activation of the acute immune response and mediate the progression and severity of cardiotropic CVB3-induced myocarditis in mice. Five to 7-week-old mice devoid of βarr1 (βarr1 KO) or βarr2 (βarr2 KO) were infected intraperitoneally with 1x103-1x104 plaque forming units (PFU) of CVB3 per animal. Wild type (WT) C57BL/6 mice infected with CVB3 were used as controls. The survival rate 7 days post infection (dpi) was significantly improved in male βarr1 KOs (80%) and βarr2 KOs (75%) compared to male WT control mice (35%, p=0.013). Female WT, βarr1 KO, and βarr2 KO mice showed similar survival rates 7 dpi., ranging from 70% to 80%. Early mortality among all groups before 5 dpi was associated with high viral titers (~6.0 ± 0.5 log10PFU/mg) in the liver compared to mortality after 5 dpi (~2.4 ± 1.7 log10PFU/mg) as determined by plaque assay. Viral titers in the heart (WT=3.7 ± 0.9 log10PFU/mg, βarr1KO=3.6 ± 1.3 log10PFU/mg, βarr2KO=3.5 ± 1.2log10PFU/mg) were similar among all groups suggesting that βarrs are not involved in viral replication. Morphometric analysis showed similar weight loss (~18%) across all groups at 7 dpi corroborated by viral cardiac PFU. Immunofluorescent staining of the heart with the pan-inflammatory marker, CD45, revealed high immune cell infiltration in infected WT mice (34.2% ± 4.5%), whereas immune cell infiltration was significantly blunted in infected βarr1 KO (6.0% ± 1.1%; p<0.0001) and βarr2 KO (8.5% ± 0.9%; p<0.0001) mice. The degree of immune cell infiltration in hearts of infected WT mice was directly correlated with an increase in heart viral titer, whereas immune cell infiltration remained blunted across a broad range of heart viral titers in both βarr1 and 2 KO mice. The blunted immune response in both βarr KOs compared to WT infected controls was consistent across multiple immune cell markers including CD4 for helper T cells and CD11b for innate immune cells. In conclusion, we demonstrate that βarrs are necessary for immune cell recruitment to the heart in CVB3 myocarditis and deletion of βarrs in male mice may be cardioprotective as indicated by an increase in survival rate. These data suggest that βarrs may serve as potential therapeutic targets in the treatment of acute viral myocarditis. This work was supported by the National Institutes of Health Grants HL056687 (to H.A.R.). This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Inflammatory response-based subtyping and potential therapeutic strategies for triple-negative breast cancer

Objective: Inflammatory response plays a crucial role in the development and treatment of cancer. However, the role of inflammatory response in triple-negative breast cancer (TNBC) remains unclear. Based on the heterogeneity of the inflammatory response, we classified TNBC, elucidated its subtype features, and revealed potential therapeutic strategies. Methods: We established inflammatory response subtyping based on the RNA sequencing data of TNBCs derived from a cohort at the Fudan University Shanghai Cancer Center (FUSCC). Next, we explored the features and potential therapeutic strategies for each subgroup by analyzing transcriptome data. Using a machine-learning method, we validated and generalized the TNBC inflammatory response subtypes in an external dataset. Results: A total of 360 TNBC samples and 88 normal tissues were collected from a cohort at FUSCC. Patients with TNBC were divided into four inflammatory response groups (IRGs) based on the expression of inflammatory response genes: high inflammatory response gene expression with pronounced pyroptosis phenotype and high immune cell infiltration (IRG 1), low inflammatory response gene expression and low immune cell infiltration (IRG 2), ITGB8 specific inflammatory response with a predominant proliferation phenotype (IRG 3), and low M1/M2 ratio with a marked angiogenesis phenotype (IRG 4). Relapse-free survival (RFS) was better in IRG 1 and 2 and worse in IRG 3 and 4. Owing to their poor prognosis, we mainly focused on IRG 3 and IRG 4 to investigate potential treatment strategies. ITGB8 was highly expressed in IRG 3; thus, targeting ITGB8 may be a potential therapeutic strategy for patients in IRG 3. IRG 4 had a lower M1/M2 ratio and a marked angiogenesis phenotype; therefore, therapeutic strategies, such as anti-angiogenesis or M2 to M1 repolarization of macrophages, could be recommended for these patients. Additionally, we validated and generalized the TNBC inflammatory response subtyping in an external dataset using a machine-learning method. Conclusion: TNBC patients with different inflammatory response subtypes have different characteristics and may need subtype-specific treatment strategies.

Neutrophil extracellular traps-mediated molecular subtypes characterize the hallmarks of tumor microenvironment and guide precision medicine in hepatocellular carcinoma

BackgroundNeutrophil extracellular traps (NETs) are released by neutrophils to capture invading pathogens. Recent studies show that NETs also play a significant role in tumorigenesis and cancer progression. However, the clinical significance of NET-related genes (NETRGs) in hepatocellular carcinoma (HCC) has not yet been fully elucidated. MethodsThe transcriptomic and clinical data of HCC patients were extracted from The Cancer Genome Atlas, the International Cancer Genome Consortium, and Gene Expression Omnibus databases. The prognostic NETRGs were screened based on Kaplan-Meier and univariate COX analyses, and different molecular clusters corresponding to NETs-related modification patterns were identified by unsupervised clustering analyses. A NETs-related prognostic signature (NETRPS) was then constructed using least absolute shrinkage and selection operator regression method, and the patients were accordingly classified into the low-risk and high-risk groups. The molecular clusters and risk groups were compared in terms of the tumor microenvironment characteristics, prognosis, therapeutic response and clinical characteristics through various bioinformatic methods. ResultsThree distinct NETs-related clusters were identified, which were termed A, B and C. Notably, cluster C demonstrated high enrichment of infiltrating immune cells and stromal-related pathways, and was associated with worst prognosis. Furthermore, the low-risk group was characterized by better prognosis, stronger metabolic activation, higher immune cell infiltration, and greater responsiveness to immunotherapy, targeted and interventional therapies compared to the high-risk group. The nomogram based on NETRPS and other clinical information predicted patient prognosis with high accuracy. ConclusionNETs play a significant role in shaping the biological and immune landscape of the HCC microenvironment. NETRPS is a promising biomarker for predicting the prognosis and therapeutic responses of HCC patients.

Machine learning-based construction of immunogenic cell death-related score for improving prognosis and response to immunotherapy in melanoma.

Immunogenic cell death (ICD) is a form of regulated cell death (RCD) which could drive the activation of the innate and adaptive immune responses. In this work, we aimed to develop an ICD-related signature to facilitate the assessment of prognosis and immunotherapy response for melanoma patients. A set of machine learning methods, including consensus clustering, non-negative matrix factorization (NMF) method and least absolute shrinkage and selection operator (LASSO) logistic regression model, and bioinformatics analytic tools were integrated to construct an ICD-related risk score (ICDscore). CIBERSORT and ESTIMATE algorithm were used to evaluate the infiltration of immune cells. The 'pRRophetic' package in R and 6 cohorts of melanoma patients receiving immunotherapy were used for therapy sensitivity analyses. The predictive performance between ICDscore with other mRNA signatures were also compared. The ICDscore could predict prognosis and immunotherapy response in multiple cohorts, and displayed superior performance than other forms of cell death-related signatures or 52 published signatures. The melanoma patients with low ICDscore were marked with high infiltration of immune cells, high expression of immune checkpoint inhibitor-related genes, and increased tumor mutation burden. In conclusion, we constructed a stable and robust ICD-related signature for evaluating the prognosis and benefits of immunotherapy, and it could serve as a promising tool to guide decision-making and surveillance for individual melanoma patients.

Abstract 4229: Differential impact of rice bran based dietary interventions during inflammation-associated colorectal cancer on distinct immune infiltrates and their spatial distribution signature

Abstract Modulating the immune cell infiltrates in the colonic tissue via dietary intervention or modulation of the gut microbiota and its crosstalk with the components of the diet is now recognized as an effective strategy to protect against inflammation-associated colorectal cancer (CRC). In this regard, we recently reported, both gut microbiota-dependent and independent mechanisms for dietary rice bran (RB) meditated protective efficacy against inflammation-associated azoxymethane/dextran sodium sulfate (AOM/DSS)-induced CRC. We also reported that ex vivo fermentation of RB with beneficial microbe ‘Bifidobacterium longum’ did not exhibit similar protective benefits as RB in damaged colonic tissues in the absence of gut microbiota [germ-free (gf) mice]. The role of rice bran (RB) constituents as prebiotics and in preserving gut barrier and a healthy gut microbiota led to in-depth investigation into the protective efficacy of RB against CRC. Accordingly, in the present study, we performed multiplex imaging (9 color panel) to assess immune sub-typing and spatial phenotypic signature of the colonic tissue/tumor microenvironment in the AOM/DSSgf mice [following RB and fermented RB (FRB) dietary intervention]. Briefly, we determined the presence of T cells [CD3+], cytotoxic T cells [CD3+ CD8+], helper T cells [CD3+ CD8−], regulatory T (Treg) cells [CD3+CD8−FoxP3+), natural killer (NK) cells [CD49b+], NKT cells [CD3+CD49b+], macrophage (MΦ) [F4/80 +], M1 [F4/80+iNOS+CD206−] and M2 [F4/80+CD206+] subtypes and B cells [B220+]. Results indicated an overall high infiltration of immune cells in female AOM/DSSgf mice on control diet compared to males, which was positively correlated with severe pathological changes observed in the female colon tissues. Notably, RB and FRB diets showed a differential impact on the sub-type of immune cells and their spatial localization. Protective efficacy of RB was associated with decrease in infiltration of T cells (including their subtypes). Interestingly, while cytotoxic T cells were lower in FRB (AOM/DSSgf) female mice, there was a significant increase in pro-inflammatory M1 MΦ sub-types in colon tissue of mice marred by epithelial erosion which compromised FRB potential. Spatial distance analysis also indicated that immune cells were distantly spaced in RB intervention groups compared to FRB and AOM/DSSgf controls. Though, M2 MΦ were relatively higher in number in AOM/DSSgf controls, their presence did not have influence on protective efficacy or lack thereof. In general, it was inferenced that the protective efficacy of the RB diets in the absence of gut microbiota was positively correlated with modulation of T cell infiltration while inflammation-associated CRC and epithelial erosion in FRB diet groups were associated with skewed MΦ sub-types-with increased presence of M1MΦ. Citation Format: Robin Kumar, Akhilendra K. Maurya, Lakshmi S. Bugata, Md. I. Kabir, Munendra Tomar, Rajesh Agarwal, Elizabeth P. Ryan, Komal Raina. Differential impact of rice bran based dietary interventions during inflammation-associated colorectal cancer on distinct immune infiltrates and their spatial distribution signature. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4229.

Abstract 4524: Multiparametric characterization of early-stage SCLC human tumors reveals novel patient subgroups based on specific molecular to immune landscape associations

Abstract Small cell lung cancer (SCLC) is a lethal malignancy with few therapeutic options. Tissue resection or biopsy are unusual, so the scarce sample available has limited our knowledge on the biology of SCLC tumors. Recent advances propose novel molecular subtypes (SCLC-A, SCLC-N, SCLC-P, SCLC-I, NAPI classification) to classify SCLC patients and specific treatment susceptibilities of defined subgroups. In this line, we collected 119 tumor samples from a cohort of early-stage SCLC patients with complete clinical annotation, in order to find specific molecular and immune profiles that define new patient characteristics with potential impact on prognosis or therapy selection. For this, we subjected the tumor tissue to RNA-seq targeted panel of onco-immune-related genes and to immune characterization by IHC. In addition, we analyzed molecular aberrations by whole exome sequencing. All data, including clinical annotations, were integrated by multiparametric computational analysis. Based on the computational analysis of the onco-immune transcriptomic data, we found two major groups of patients with either pro-immunogenic or pro-tumorigenic profiles. The transcriptomic pro-immunogenic group shows better survival and higher infiltration of immune cells (CD4+ T cells, CD8+ T cells, B cells and Macrophages). In contrast, the pro-tumorigenic group presents worse survival and less immune cell infiltration. Using bioinformatics, we described a gene signature that can identify both subgroups. This gene signature includes immune cell markers (MS4A1, CD3D), antigen receptor complex (CD79A), adhesion and migration of T cells (CD2) and immunomodulatory genes (IDO1, TIGIT) expressed in the pro-immunogenic group, and, proliferation (MKI67), transcription regulator (TOP2A) and epithelial and mesenchymal transition-related genes (TWIST1) in the pro-tumorigenic group. Exome sequencing analysis resulted in the expected genomic heterogeneity of SCLC tumors beyond TP53 and RB1 mutations in most patients. However, we found a significant enrichment of XIRP2 gene alterations in the pro-tumorigenic group. Consistent with an early-stage of SCLC, most of the samples were described as SCLC-A subtype by the predominant expression of ASCL1. Interestingly, our findings suggest a relevant biological heterogeneity within SCLC-A tumors that impacts immune infiltrate and disease outcome. Here we show a gene signature that can subclassify early-stage SCLC patients according to their clinical, molecular and immune features, and provides prognostic value independently of the NAPI classification. Since immunogenicity of the tumor impacts response to immunotherapy, we speculate that this gene signature might predict therapy response and therefore contribute to tailored treatment of SCLC. Citation Format: Angel Nunez-Buiza, David Gómez-Sánchez, Jose Maria Gracia-Rodríguez, Esther Conde, Jose Luis Solorzano, Eva María Garrido-Martín, Jose Carlos Machado, Susana Guimarães, Ernest Nadal, Sonia Molina-Pinelo, Miguel Ángel Piris Pinilla, Nuria Romero-Laorden, Fernando Franco, Fernando López-Ríos Moreno, Álvaro Conrado Ucero, Luis Paz-Ares. Multiparametric characterization of early-stage SCLC human tumors reveals novel patient subgroups based on specific molecular to immune landscape associations. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4524.

Abstract 5476: Veracyte Biopharma Atlas for colorectal cancer: Combining multi-parameter approach and machine learning to capture the complexity of the tumor immune contexture

Abstract (a) Understanding the immune contribution has deeply modified the standard of care of patients, especially with the advent of immunotherapies (ITs). However, ITs are still efficient only on a minority of the patients, especially for colorectal cancer (CRC), a high-incidence cancer. One hypothesis to explain the heterogeneity of the response to IT is that the presence or absence of immune factors within the tumor micro-environment (TME) may critically impact the efficacy of treatments. In this context, a deeper molecular understanding of the TME could be key to stratify patients. Here, we describe a proof of concept of the Veracyte Biopharma Atlas for CRC based on a multi-omics approach which integrates a unique range of assays allowing to decipher the immune contexture of tumors and offer a personalized and dynamic “fingerprint” of the interactions between the tumor and the immune system. (b) Veracyte Biopharma Atlas characterizes the tumor microenvironment by analyzing information from 3 different modalities: (i) proteomics (Brightplex® multiplex immunohistochemistry panels), (ii) transcriptomics (RNA sequencing) and (iii) genomics (somatic mutations by exome sequencing). This deep characterization of the tumor and its micro-environment requires the integration of thousands of features. The innovative Veracyte Biopharma Atlas approach relies on linear (multimodal factor analysis) and non-linear (self-organizing map) approaches with graphical representation of the data to integrate this multi-omics information. (c) Gene expression profiling, genomics analysis and proteomics by multiplex spatial technology were performed on 47 CRC FFPE samples to characterize the immune contexture of this disease. Multimodal analysis was used to derive a unique atlas map highlighting clusters distinguished by complex features and their immune tumor contexture. Indeed, as examples, the atlas map allows the identification of a group of patients displaying high immune cell infiltration, including T cell and macrophages. They are also characterized by an immune genes signature. By contrast, another group of patients is poorly infiltrated by immune cells but exhibits an elevated M2/M1 ratio, favorable to tumor development. (d) This proof of concept lays the foundations of an atlas for CRC that will be extended to samples associated with clinical data to build a map of the disease. Later-on, by projecting data from new specimens onto this referential map, it will become possible to establish an Immunogram of the tumor microenvironment of individual patients to define the most appropriate treatment but also facilitate the development of new therapies. Citation Format: Laurent Vanhille, Alboukadel Kassambara, Chafik Hamdad, Margaux Mercadal, Alexia Papadopoulos, Pernelle Outters, Théo Vasse, Vanina Leca, Jérôme Galon, Jacques Fieschi. Veracyte Biopharma Atlas for colorectal cancer: Combining multi-parameter approach and machine learning to capture the complexity of the tumor immune contexture. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5476.

Comprehensive analysis of scRNA-Seq and bulk RNA-Seq reveals dynamic changes in the tumor immune microenvironment of bladder cancer and establishes a prognostic model

BackgroundThe prognostic management of bladder cancer (BLCA) remains a great challenge for clinicians. Recently, bulk RNA-seq sequencing data have been used as a prognostic marker for many cancers but do not accurately detect core cellular and molecular functions in tumor cells. In the current study, bulk RNA-seq and single-cell RNA sequencing (scRNA-seq) data were combined to construct a prognostic model of BLCA.MethodsBLCA scRNA-seq data were downloaded from Gene Expression Omnibus (GEO) database. Bulk RNA-seq data were obtained from the UCSC Xena. The R package "Seurat" was used for scRNA-seq data processing, and the uniform manifold approximation and projection (UMAP) were utilized for downscaling and cluster identification. The FindAllMarkers function was used to identify marker genes for each cluster. The limma package was used to obtain differentially expressed genes (DEGs) affecting overall survival (OS) in BLCA patients. Weighted gene correlation network analysis (WGCNA) was used to identify BLCA key modules. The intersection of marker genes of core cells and genes of BLCA key modules and DEGs was used to construct a prognostic model by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, and chemotherapeutic drug sensitivity between the high and low-risk groups were also investigated.ResultsscRNA-seq data were analyzed to identify 19 cell subpopulations and 7 core cell types. The ssGSEA showed that all 7 core cell types were significantly downregulated in tumor samples of BLCA. We identified 474 marker genes from the scRNA-seq dataset, 1556 DEGs from the Bulk RNA-seq dataset, and 2334 genes associated with a key module identified by WGCNA. After performing intersection, univariate Cox, and LASSO analysis, we obtained a prognostic model based on the expression levels of 3 signature genes, namely MAP1B, PCOLCE2, and ELN. The feasibility of the model was validated by an internal training set and two external validation sets. Moreover, patients with high-risk scores are predisposed to experience poor OS, a larger prevalence of stage III-IV, a greater TMB, a higher infiltration of immune cells, and a lesser likelihood of responding favorably to immunotherapy.ConclusionBy integrating scRNA-seq and bulk RNA-seq data, we constructed a novel prognostic model to predict the survival of BLCA patients. The risk score is a promising independent prognostic factor that is closely correlated with the immune microenvironment and clinicopathological characteristics.

The Immune Landscape and Molecular Subtypes of Pediatric Crohn's Disease: Results from In Silico Analysis.

Pediatric Crohn's disease (CD) presents a distinct phenotype from adult-onset disease. A dysregulated immune response is critical in CD pathogenesis; thus, it is clinically important to describe immune cell alterations and to identify a new molecular classification for pediatric CD. To this end, in this study, a RNA-seq derived dataset GSE101794-which contains the expression profiles of 254 treatment-naïve pediatric CD samples, including CIBERSORTx and weighted gene-co-expression network analysis (WGCNA)-were performed to estimate the ratio of immune cells and to identify modules and genes related to specific immune cell infiltration, respectively. Hub genes derived from WGCNA were further employed to create a molecular classification using unsupervised K-means clustering. In the pediatric CD samples, it was found that M2 macrophages, CD4+ memory resting T cells, CD8+ T cells, and resting mast cells were the most prominent immune cells in intestinal tissues. Then, 985 up-regulated genes and 860 down-regulated genes were identified in samples with high immune cell infiltration. Of these differential genes, 10 hub genes (APOA1, CYB5A, XPNPEP2, SLC1A7, SLC4A6, LIPE, G6PC, AGXT2, SLC13A1, and SOAT2) were associated with CD8+T cell infiltration. Clinically, the higher expression of these 10 hub genes was strongly associated with an earlier age of CD onset and colonic-type CD. Furthermore, based on these key genes, pediatric CD could be classified into three molecular subtypes, displaying a different immune landscape. Altogether, this in silico analysis provides a novel insight into the immune signature of pediatric CD, and a new classification of pediatric CD is presented, which may help us develop more personalized disease management and treatments for pediatric CD.

Definition of a Novel Immunogenic Cell Death-Relevant Gene Signature Associated with Immune Landscape in Gastric Cancer.

Immunogenic cell death (ICD) induces anti-tumor immunity and aids in dismantling the immunosuppressive immune microenvironment (TME), which belongs to a type of regulated cell death. The differentiation of gastric cancer (GC) subtypes and the discovery of prognostic biomarkers are crucial for its treatment because GC is a disease that is both highly heterogeneous and aggressive. However, although the induction of ICD in tumor cells is associated with afavorableprognosis, the exact mechanism of its role in GC remains unclear. Transcriptome profiling data and clinical data of GC patients were retrieved from The Cancer Genome Atlas (TCGA) database. Herein, patients were classified with the consensus clustering algorithm, and the associated biological functions and immune microenvironment infiltration were explored based on the expression of ICD-associated genes. A risk score signature consisting of 11 ICD-related genes was established via the least absolute shrinkage and selection operator regression (LASSO) method. We have retrieved similar studies in recent years and compared them with our study using the time-dependent receiver operating characteristic (ROC) curves. Gene set variation analysis (GSVA) and single sample gene set enrichment analysis (ssGSEA) were performed to explore the association between the signature and tumor microenvironment (TME). Two distinct subtypes associated with ICD in GC were identified, each with a different prognosis. The ICD-high expression subtype was associated with higher immune cell infiltration and a better prognosis. The ICD-related gene signature containing 11 genes (CGB5, Z84468.1, APOA5, EPHA8, CLEC18C, TLR7, MUC7, MUC15, CTLA4, CALB2, and UGT2B28), could independently and accurately predict the prognosis of GC. In this study, an ICD-based classification was conducted to assist in the diagnosis and personalized therapy for GC. The ICD-related genes risk score model was established to predict prognosis.

Infiltrating immune cells in prostate cancer tissue after androgen deprivation and radiotherapy.

Androgen deprivation therapy (ADT) has long been a cornerstone in treatment of advanced prostate cancer (PCa), and is known to improve the results of radiotherapy (RT) for high-risk disease. The purpose of our study was to use a multiplexed immunohistochemical (mIHC) approach to investigate the infiltration of immune cells in PCa tissue after eight weeks of ADT and/or RT with 10Gy. From a cohort of 48 patients divided into two treatment arms, we obtained biopsies before and after treatment and used a mIHC method with multispectral imaging to analyze the infiltration of immune cells in tumor stroma and tumor epithelium, focusing on areas with high infiltration. Tumor stroma showed a significantly higher infiltration of immune cells compared to tumor epithelium. The most prominent immune cells were CD20+ B-lymphocytes, followed by CD68+ macrophages, CD8+ cytotoxic T-cells, FOXP3+ regulatory T-cells (Tregs), and T-bet+ Th1-cells. Neoadjuvant ADT followed by RT significantly increased the infiltration of all five immune cells. Numbers of Th1-cells and Tregs significantly increased after single treatment with ADT or RT. In addition, ADT alone increased the number of cytotoxic T-cells and RT increased the number of B-cells. Neoadjuvant ADT in combination with RT results in a higher inflammatory response compared to RT or ADT alone. The mIHC method may be a useful tool for investigating infiltrating immune cells in PCa biopsies to understand how immunotherapeutic approaches can be combined with current PCa therapies.

Development and Experimental Validation of a Novel Prognostic Signature for Gastric Cancer

Gastric cancer is a malignant tumor with high morbidity and mortality. Therefore, the accurate recognition of prognostic molecular markers is the key to improving treatment efficacy and prognosis. In this study, we developed a stable and robust signature through a series of processes using machine-learning approaches. This PRGS was further experimentally validated in clinical samples and a gastric cancer cell line. The PRGS is an independent risk factor for overall survival that performs reliably and has a robust utility. Notably, PRGS proteins promote cancer cell proliferation by regulating the cell cycle. Besides, the high-risk group displayed a lower tumor purity, higher immune cell infiltration, and lower oncogenic mutation than the low-PRGS group. This PRGS could be a powerful and robust tool to improve clinical outcomes for individual gastric cancer patients.

LRP1B mutation is associated with tumor immune microenvironment and progression-free survival in lung adenocarcinoma treated with immune checkpoint inhibitors.

Only a fraction of lung adenocarcinoma (LUAD) patients are eligible for immunotherapy. The identification of biomarkers for immunotherapy is crucial to improve patient outcomes. This study aims to systemically analyze LRP1B mutation and its association with the tumor immune microenvironment (TIME) and immunotherapy. A cohort of immune checkpoint inhibitors (ICIs)-treated LUAD patients was analyzed to assess the association of LRP1B mutation with immunotherapy prognosis. Another cohort of LUAD patients with genetic and transcriptomic data was also obtained from The Cancer Genome Atlas (TCGA). By investigating the ICIs and the TCGA-LUAD cohorts, we compared the differences in mutation profiles, immunogenicity, TIME, and DNA damage repair (DDR) mutations between the LRP1B-mutated and LRP1B wild-type groups. Additionally, we performed multiplex immunohistochemistry (mIHC) to validate the differences in the tumor microenvironment. Our results revealed that LRP1B mutation is associated with multiple immune-related pathways. Analysis of TIME indicated that LUAD patients with LRP1B mutation expressed significant levels of genes involved in antigen presentation, cytotoxicity, chemokines, and pro-inflammatory mediators, whereas a few immune checkpoint genes were highly expressed in the LRP1B-mutated group as well. Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) analysis indicated that LRP1B-mutated LUAD patients had higher infiltration of active immune cells. Multiplex IHC analysis showed that LRP1B-mutated LUAD patients had elevated programmed death ligand-1 (PD-L1) expression and immune cell infiltration. Patients with LRP1B mutation had higher tumor mutation burden, neoantigens, as well as more mutated genes in the DDR-related pathways. Finally, LRP1B-mutated LUAD patients showed a significant prolongation of progression-free survival (PFS) in the ICIs cohort and could be effectively predicted by our constructed nomogram. Our study suggests that LRP1B mutation is associated with higher immune cell infiltration and elevated immune gene expression in TIME and potentially serves as a prognostic biomarker for LUAD patients treated with ICIs.

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.

Cancer functional states-based molecular subtypes of gastric cancer

BackgroundThe treatment of gastric cancer remains a challenge.MethodsWe divided gastric cancer into three subtypes based on 14 cancer functional states. We investigated differences between subtypes through multi-omics data, especially at the single-cell level, which allowed us to analyze differences from the perspective of each type of cell rather than the whole.ResultsThe cluster 1 is characterized by high levels of tumor progression-related cancer functional status, worst survival outcomes, low metabolic level, high infiltration of immunosuppressive cells, high copy number variations (CNV), and low tumor mutational burden (TMB). The cluster 2 is characterized by low levels of tumor progression-related cancer functional status, favorable prognosis, moderate metabolic level, low immune cell infiltration, high CNV, and moderate TMB. Then, the cluster 3 is characterized by the high level of all cancer functional status, high metabolic level, low CNV, high TMB, high infiltration of immune cells with high cytotoxicity, and better response to immunotherapy. We also established a prognostic model based on cancer functional status and validated its robustness.ConclusionsCollectively, our study identified gastric cancer subtypes and provided new insights into the clinical treatment of gastric cancer.