Signature In Tumor Microenvironment Research Articles

Abstract 4312: KRAS oncogenic activation in Gramd2 + AT1 cells form lung adenocarcinoma with distinct transcriptomic and tumor microenvironment signatures

Abstract Lung adenocarcinoma (LUAD) is the most prevalent subtype of lung cancer and presents with a high degree of biological heterogeneity and distinct clinical outcomes. The current paradigm of LUAD etiology recognizes alveolar epithelial type II (AT2) cells as the primary cell of origin, while the role of AT1 cells in LUAD oncogenesis remains relatively unknown. Here, we examined transformation via activation of the oncogenic KRASG12D mutation in a Gram-domain containing 2 (Gramd2)+ AT1 cell lineage tracing model system, which resulted in multifocal LUAD, primarily of papillary histology. Spatial transcriptomic sequencing revealed significant differences of transcriptomic signatures and signaling activities between Gramd2+ AT1 cell-derived and Sftpc+ AT2 cell-derived LUAD. Single-cell RNA sequencing of macrodissected LUAD lesions demonstrated heterogeneous cell composition of the tumor microenvironment (TME), with Gramd2+ AT1 cell-derived LUAD fostering a proinflammatory, immunoreactive TME and Sftpc+ AT2-derived LUAD presenting with an immunosuppressive TME. Collectively, our study suggests that cell of origin for LUAD is associated with histologic subtype, transcriptomic aberrations, and immune cell composition within LUAD that may influence response to therapy. Citation Format: Minxiao Yang, Jonathan Castillo, Edgar Gonzalez, Chunli Yan, Evanthia Roussos Torres, Zea Borok, Crystal Marconett. KRAS oncogenic activation in Gramd2 + AT1 cells form lung adenocarcinoma with distinct transcriptomic and tumor microenvironment signatures [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4312.

Abstract 3644: Uncover spatial signatures of tumor microenvironment and oncogenic pathways using 6,000-plex single-cell spatial molecular imaging on FFPE skin squamous cell carcinoma

Abstract Background: Tumor progression and therapeutic response are regulated by the tumor microenvironment. Understanding the spatial association and architecture of molecular characteristics and composition of tumor immune microenvironment at single-cell and subcellular resolution encourages improvements in clinical prognosis and immunotherapy benefits. Single-cell Spatial profiling technologies permit the study of transcriptional activity at the spatial, single-cell level and provide abundant, high-resolution information required for the identification of clinical-related features in immuno-oncology. Methods: We performed an ultra-high-plex RNA assay to detect 6,000 targets simultaneously in situ on FFPE human skin squamous cell carcinoma using the CosMx™ Spatial Molecular Imager (SMI). For the selection of regions of interest, four protein markers and DAPI were co-detected on the same tissue slide. Tertiary analysis algorithms were developed for cell typing, co-localization of genes and ligands, cell-cell interaction, and pathway analysis. Results: Thousands of transcripts were simultaneously detected with high sensitivity and specificity on the FFPE skin squamous cell carcinoma tissue section at single-cell subcellular resolution. We investigated the tumor microenvironment including cell types, their spatial distribution and proportion of diversified immune cells in the tumor compartment. The cell typing results revealed many cancerous subpopulations, which resolved spatially from cells that lacked any defining marker genes. We also assessed distances between immune cells and their nearest functional-related neighbors. Moreover, we revealed the ligands co-localization, as well as spatial patterns of direct cell-cell interactions and signaling pathways. We found that some genes have elevated expression in macrophages which are near cancer compared to those are far from cancer cells, empowering the molecular investigation of novel signaling between immune and cancer cells in tumor immune microenvironment. Conclusions: Single-cell spatial measurements of 6,000 RNA and four proteins on the same tissue section, along with a large viewing area on archival FFPE tissue, provide a novel tool to reveal the spatial signature of tumor microenvironment and oncogenic pathways, facilitating the next level of cancer and therapeutic research. FOR RESEARCH USE ONLY. Not for use in diagnostic procedures. FOR RESEARCH USE ONLY. Not for use in diagnostic procedures. Citation Format: Michael Patrick, Shanshan He, Saskia Ilcisin, David Kroeppler, Patrick Danaher, Jason Reeves, Mark Gregory, Haiyan Zhai, Michael Rhodes, Joseph Beechem. Uncover spatial signatures of tumor microenvironment and oncogenic pathways using 6,000-plex single-cell spatial molecular imaging on FFPE skin squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3644.

Abstract 5506: HOST-Factor: A comprehensive platform for single-cell highplex immunofluorescence staining, digital imaging, spatial mapping, and quantitative analysis of the tumor microenvironment

Abstract Solid tumor complexity extends beyond the genetic and functional landscapes of heterogenous cancer cells, encompassing the tumor microenvironment (TME). Elucidating the TME’s complexity requires a comprehensive assessment of its cellular composition, functional states, and spatial distributions. We developed the Harmonic Output of Stromal Traits (HOST) to identify TME cells, and the HOST-Factor to quantify their functional states. The HOST-Factor is a numerical value that reflects the relative contribution of cancer-associated fibroblasts (CAFs) to tumor-suppressive or tumor-promoting functions. Our workflow combines automated cycling highplex immunofluorescent microscopy with artificial intelligence (AI)-guided image analysis. This generates HOST-Factor values for each identified TME cell within selected regions of interest, providing spatial distribution data. The TME signature encompasses 15 immune cells and 14 CAF antibody-detected biomarkers. We applied our workflow to ten human pancreatic cancer specimens, generating OME-TIFF output images. This cancer model was used due to its significant TME makeup. The 29 highplex AI-based digital image analysis was conducted using the Phenoplex™ workflow from Visiopharm. The workflow included deep-learning-based tissue morphologic and cellular segmentations, cellular phenotyping, and integration of spatial/location data. Biomarker subsets were visualized, and a user-trained algorithm was used for tissue segmentation. Nuclear segmentation was done using a pre-trained algorithm on a DAPI-labeled DNA channel. Cellular phenotyping was performed using thresholds based on visual assessment and confirmation of positivity. Spatial neighborhood plots and metrics, heatmaps and partitioned t-SNE plots were generated for the dataset for downstream analysis. Importantly, the workflow's visualization templates, pre-trained nuclear/cytoplasm segmentation tools, and neighborhood plots and metrics, are reusable and fully customizable for new datasets. Using HOST-Factor values, we successfully classified cancer and TME cells, along with their functional states, and spatial distributions. This AI-based computational approach and user-friendly workflow provides a simple and effective way to obtain single-cell information from multiplex immunofluorescence images, making spatial phenotyping of several cell populations in tissues more accessible to researchers, providing a fully amendable means for future clinical translation. Citation Format: Janusz Franco-Barraza, Fabian Schneider, Rasmus Norré-Sørensen, Rasmus Ahrenkiel-Lyngby, Caneta Brown, Dan Winkowski, James Robert Mansfield, Edna Cukierman. HOST-Factor: A comprehensive platform for single-cell highplex immunofluorescence staining, digital imaging, spatial mapping, and quantitative analysis of the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5506.

Multi-dimensional characterization of apoptosis in the tumor microenvironment and therapeutic relevance in melanoma.

Melanoma is widely utilized as a prominent model for the development of immunotherapy, thought an inadequate immune response can occur. Moreover, the development of apoptosis-related therapies and combinations with other therapeutic strategies is impeded by the limited understanding of apoptosis's role within diverse tumor immune microenvironments (TMEs). Here, we constructed an apoptosis-related tumor microenvironment signature (ATM) and employ multi-dimensional analysis to understand the roles of apoptosis in tumor microenvironment. We further assessed the clinical applications of ATM in nine independent cohorts, and anticipated the impact of ATM on cellular drug response in cultured cells. Our ATM model exhibits robust performance in survival prediction in multiple melanoma cohorts. Different ATM groups exhibited distinct molecular signatures and biological processes. The low ATM group exhibited significant enrichment in B cell activation-related pathways. What's more, plasma cells showed the lowest ATM score, highlighting their role as pivotal contributors in the ATM model. Mechanistically, the analysis of the interplay between plasma cells and other immune cells elucidated their crucial role in orchestrating an effective anti-tumor immune response. Significantly, the ATM signature exhibited associations with therapeutic efficacy of immune checkpoint blockade and the drug sensitivity of various agents, including FDA-approved and clinically utilized drugs targeting the VEGF signaling pathway. Finally, ATM was associated with tertiary lymphoid structures (TLS), exhibiting stronger patient stratification ability compared to classical "hot tumors". Our findings indicate that ATM is a prognostic factor and is associated with the immune response and drug sensitivity in melanoma.

Long-lasting mRNA-encoded interleukin-2 restores CD8+ T cell neoantigen immunity in MHC class I-deficient cancers

Major histocompatibility complex (MHC) class I antigen presentation deficiency is a common cancer immune escape mechanism, but the mechanistic implications and potential strategies to address this challenge remain poorly understood. Studying β2-microglobulin (B2M) deficient mouse tumor models, we find that MHC class I loss leads to a substantial immune desertification of the tumor microenvironment (TME) and broad resistance to immune-, chemo-, and radiotherapy. We show that treatment with long-lasting mRNA-encoded interleukin-2 (IL-2) restores an immune cell infiltrated, IFNγ-promoted, highly proinflammatory TME signature, and when combined with a tumor-targeting monoclonal antibody (mAB), can overcome therapeutic resistance. Unexpectedly, the effectiveness of this treatment is driven by IFNγ-releasing CD8+ Tcells that recognize neoantigens cross-presented by TME-resident activated macrophages. These macrophages acquire augmented antigen presentation proficiency and other M1-phenotype-associated features under IL-2 treatment. Our findings highlight the importance of restoring neoantigen-specific immune responses in the treatment of cancers with MHC class I deficiencies.

Deciphering the tumor immune microenvironment of imatinib-resistance in advanced gastrointestinal stromal tumors at single-cell resolution

The heterogeneous nature of tumors presents a considerable obstacle in addressing imatinib resistance in advanced cases of gastrointestinal stromal tumors (GIST). To address this issue, we conducted single-cell RNA-sequencing in primary tumors as well as peritoneal and liver metastases from patients diagnosed with locally advanced or advanced GIST. Single-cell transcriptomic signatures of tumor microenvironment (TME) were analyzed. Immunohistochemistry and multiplex immunofluorescence staining were used to further validate it. This analysis revealed unique tumor evolutionary patterns, transcriptome features, dynamic cell-state changes, and different metabolic reprogramming. The findings indicate that in imatinib-resistant TME, tumor cells with activated immune and cytokine-mediated immune responses interacted with a higher proportion of Treg cells via the TIGIT-NECTIN2 axis. Future immunotherapeutic strategies targeting Treg may provide new directions for the treatment of imatinib-resistant patients. In addition, IDO1+ dendritic cells (DC) were highly enriched in imatinib-resistant TME, interacting with various myeloid cells via the BTLA-TNFRSF14 axis, while the interaction was not significant in imatinib-sensitive TME. Our study highlights the transcriptional heterogeneity and distinct immunosuppressive microenvironment of advanced GIST, which provides novel therapeutic strategies and innovative immunotherapeutic agents for imatinib resistance.

A human lymphoma organoid model for evaluating and targeting the follicular lymphoma tumor immune microenvironment

Heterogeneity in the tumor microenvironment (TME) of follicular lymphomas (FLs) can affect clinical outcomes. Current immunotherapeutic strategies, including antibody- and cell-based therapies, variably overcome pro-tumorigenic mechanisms for sustained disease control. Modeling the intact FL TME, with its native, syngeneic tumor-infiltrating leukocytes, is a major challenge. Here, we describe an organoid culture method for cultivating patient-derived lymphoma organoids (PDLOs), which include cells from the native FL TME. We define the robustness of this method by successfully culturing cryopreserved FL specimens from diverse patients and demonstrate the stability of TME cellular composition, tumor somatic mutations, gene expression profiles, and B/T cell receptor dynamics over 3weeks. PDLOs treated with CD3:CD19 and CD3:CD20 therapeutic bispecific antibodies showed B cell killing and Tcell activation. This stable system offers a robust platform for advancing precision medicine efforts in FL through patient-specific modeling, high-throughput screening, TME signature identification, and treatment response evaluation.

Clinical and Immunological Significance of ANKRD52 in Pan-Cancer.

Ankyrin repeat domain 52 (ANKRD52) is a regulatory component of the protein phosphatase 6 (PP6) holoenzyme. Evidence has emerged to suggest involvement of ANKRD52 in tumor metastases and cancer cell escape from T cell-mediated elimination and immunotherapy but there has been no research across different cancer types. The current study explored the biological functions of ANKRD52 by combining data from many databases. The aim was to expose new diagnostic or treatment biomarkers for malignant tumors. The roles of ANKRD52 with respect to immunotherapy in 33 human cancer types were analyzed by combining data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE), UCSC Xena, the Tumor Immune Estimation Resource (TIMER), TISIDB and Cellminer. Bioinformatics methods were used to analyze the association between ANKRD52 expression and prognosis, immunological indicators (immune cell infiltration, ESTIMATE scores and tumor microenvironment (TME) signatures), tumor mutational burden (TMB), microsatellite instability (MSI) and drug sensitivity. ANKRD52 expression was generally higher in 24 tumor tissues than in normal tissues and was associated with poor prognosis, especially in kidney chromophobe (KICH). Lower expression was observed in advanced cancer. ANKRD52 expression was strongly linked to major immunological indicators, such as immune cell infiltration, ESTIMATE scores, TME signatures, as well as expression of immune and tumor-related genes. Expression was also associated with indicators of immunotherapy efficacy and outcome, such as TMB in 7 cancer types and MSI in 12. In addition, ANKRD52 expression was linked to sensitivity to a number of anticancer drugs. ANKRD52 had a distinct immune function in breast invasive carcinoma (BRCA) that correlated negatively with most immune indicators. Expression was enriched in proliferation-, differentiation- and metabolism-related pathways and linked to other immune cells and TME signatures. A nomogram to predict 3- or 5-year overall survival (OS) of patients with BRCA was constructed. ANKRD52 may have utility as an oncological and immunological biomarker. New insights into oncogenesis are presented and the development of ANKRD52-targeting to increase the therapeutic efficacy of immunotherapy combined with chemotherapy explored.

Immune landscape of hepatocellular carcinoma tumor microenvironment identifies a prognostic relevant model

BackgroundVarious studies highlighted that immune cell-mediated inflammatory processes play crucial roles in the progression and treatment of hepatocellular carcinoma (HCC). However, the immune microenvironment of HCC is still poorly characterized. Exploring the role of immune-related genes (IRGs) and describing the immune landscape in HCC would provide insights into tumor-immune co-evolution along HCC progression. MethodsWe integrated the datasets with complete prognostic information from the Cancer Genome Atlas (TCGA) database and GEO DataSets (GSE14520, GSE76427, and GSE54236) to construct a novel immune landscape based on the Cibersort algorithm and reveal the prognostic signature in HCC patients. ResultsTo describe the tumor microenvironment (TME) in HCC, immune infiltration patterns were defined using the CIBERSORT method, and a prognostic signature contains 5 types of immune cells, including 3 high-risk immune cells (T.cells. CD4. memory. resting, Macrophages.M0, Macrophages.M2) and 2 low-risk immune cells (Plasma. cells, T.cells.CD8), were finally constructed. A novel prognostic index, based on prognostic immune risk score (pIRG), was developed using the univariate Cox regression analyses and LASSO Cox regression algorithm. Furthermore, the ROC curve and KM curve showed that the TME signatures had a stable value in predicting the prognosis of HCC patients in the internal training cohort, internal validation, and external validation cohort. Differential genes analysis and qPCR experiment showed that the expression levels of AKR1B10, LAPTM4B, MMP9, and SPP1 were significantly increased in high-risk patients, while the expression of CD5L was lower. Further analysis found that AKR1B10 and MMP9 were associated with higher M0 macrophage infiltration, while CD5L was associated with higher plasma cell infiltration. ConclusionsTaken together, we performed a comprehensive evaluation of the immune landscape of HCC and constructed a novel and robust prognostic prediction model. AKR1B10, LAPTM4B, MMP9, SPP1, and CD5L were involved in important processes in the HCC tumor microenvironment and were expected to become HCC prediction markers and potential targets of treatment.

Immuno-genomic analysis reveals eosinophilic feature and favorable prognosis of female non-smoking esophageal squamous cell carcinomas

Most of esophageal squamous cell carcinoma (ESCC) develop in smoking males in Japan, but the genomic etiology and immunological characteristics of rare non-smoking female ECSS remain unclear. To elucidate the genomic and immunological features of ESCC in non-smoking females, we analyzed whole-genome or transcriptome sequencing data from 94 ESCCs, including 20 rare non-smoking female cases. In addition, 31,611 immune cells were extracted from four ESCC tissues and subject to single-cell RNA-seq. We compared their immuno-genomic and microbiome profiles between non-smoking female and smoking ESCCs. Non-smoking females showed much better prognosis. Whole-genome sequencing analysis showed no significant differences in driver genes or copy number alterations depending on smoking status. The mutational signatures specifically observed in non-smoking females ESCC could be attributed to aging. Immune profiling from RNA-seq revealed that ESCC in non-smoking females had high tumor microenvironment signatures and a high abundance of eosinophils with a favorable prognosis. Single-cell RNA-sequencing of intratumor immune cells revealed gender differences of eosinophils and their activation in female cases. ESCCs in non-smoking females have age-related mutational signatures and gender-specific tumor immune environment with eosinophils, which is likely to contribute to their favorable prognosis.

Integrative lactylation and tumor microenvironment signature as prognostic and therapeutic biomarkers in skin cutaneous melanoma

Integrative lactylation and tumor microenvironment signature as prognostic and therapeutic biomarkers in skin cutaneous melanoma

59P Correlation of tumor microenvironment signature in advanced stage non-small cell lung cancer with EGFR mutation who received EGFR-TKIs

59P Correlation of tumor microenvironment signature in advanced stage non-small cell lung cancer with EGFR mutation who received EGFR-TKIs

Tumor microenvironment characterization in colorectal cancer to identify prognostic and immunotherapy genes signature

BackgroundThe tumor microenvironment (TME) plays a crucial role in tumorigenesis, progression, and therapeutic response in many cancers. This study aimed to comprehensively investigate the role of TME in colorectal cancer (CRC) by generating a TMEscore based on gene expression.MethodsThe TME patterns of CRC datasets were investigated, and the TMEscores were calculated. An unsupervised clustering method was used to divide samples into clusters. The associations between TMEscores and clinical features, prognosis, immune score, gene mutations, and immune checkpoint inhibitors were analyzed. A TME signature was constructed using the TMEscore-related genes. The results were validated using external and clinical cohorts.ResultsThe TME pattern landscape was for CRC was examined using 960 samples, and then the TMEscore pattern of CRC datasets was evaluated. Two TMEscore clusters were identified, and the high TMEscore cluster was associated with early-stage CRC and better prognosis in patients with CRC when compared with the low TMEscore clusters. The high TMEscore cluster indicated elevated tumor cell scores and tumor gene mutation burden, and decreased tumor purity, when compared with the low TMEscore cluster. Patients with high TMEscore were more likely to respond to immune checkpoint therapy than those with low TMEscore. A TME signature was constructed using the TMEscore-related genes superimposing the results of two machine learning methods (LASSO and XGBoost algorithms), and a TMEscore-related four-gene signature was established, which had a high predictive value for discriminating patients from different TMEscore clusters. The prognostic value of the TMEscore was validated in two independent cohorts, and the expression of TME signature genes was verified in four external cohorts and clinical samples.ConclusionOur study provides a comprehensive description of TME characteristics in CRC and demonstrates that the TMEscore is a reliable prognostic biomarker and predictive indicator for patients with CRC undergoing immunotherapy.

Reanalysis of single-cell data reveals macrophage subsets associated with the immunotherapy response and prognosis of patients with endometrial cancer

Endometrial cancer (EC) is an aggressive gynecological malignancy with an increased incidence rate. The immune landscape crucially affects immunotherapy efficacy and prognosis in EC patients. Here, we characterized the distinct tumor microenvironment signatures of EC tumors by analyzing single-cell RNA sequencing data from Gene Expression Omnibus and bulk RNA sequencing data from The Cancer Genome Atlas, which were compared with normal endometrium. Three macrophage subsets were identified, and two of them showed tissue-specific distribution. One of the macrophage subsets was dominant in macrophages derived from EC and exhibited characteristic behaviors such as promoting tumor growth and metastasis. One of the other macrophage subsets was mainly found in normal endometrium and served functions related to antigen presentation. We also identified a macrophage subset that was found in both EC and normal endometrial tissue. However, the pathway and cellular cross-talk of this subset were completely different based on the respective origin, suggesting a tumor-related differentiation mechanism of macrophages. Additionally, the tumor-enriched macrophage subset was found to predict immunotherapy responses in EC. Notably, we selected six genes from macrophage subset markers that could predict the survival of EC patients, SCL8A1, TXN, ANXA5, CST3, CD74 and NANS, and constructed a prognostic signature. To verify the signature, we identified immunohistochemistry for the tumor samples of 83 EC patients based on the selected genes and further followed up with the survival of the patients. Our results provide strong evidence that the signature can effectively predict the prognosis of EC patients.

Transcriptomic Signatures of MSI-high Metastatic Colorectal Cancer Predict Efficacy of Immune Checkpoint Inhibitors.

Microsatellite instability (MSI) is currently the only predictive biomarker of efficacy of immune checkpoint inhibitors (ICI) in metastatic colorectal cancers (mCRC). However, 10-40% of patients with MSI mCRC will experience a primary resistance to ICI. In 2 cohorts of patients with MSI mCRC treated with ICI (exploratory: N=103, validation: N=35), 3'RNAseq was performed from primary tumors. Previously described single-cell transcriptomic signatures of tumor microenvironment (TME) were analysed. In the exploratory cohort, the unsupervised clustering allowed the identification of three clusters of tumors with distinct transcriptional profiles: cluster A ("stromalHIGH-proliferationLOW"), cluster B ("stromalHIGH-proliferationMED"), and cluster C ("stromalLOW-proliferationHIGH"), with an enrichment of patients progressing at first disease assessment under ICI in the cluster A (30% vs 12% in cluster B and 8.1% in cluster C, p=0.074). Progression-free survival (PFS) was also significantly shorter in patients belonging to cluster A, compared to clusters B or C (p<0.001) with 2-year PFS rates of 33.5%, 80.5% and 78.3%, respectively. In multivariate analysis, PFS was still significantly longer in patients belonging to cluster B (HR: 0.19 95%CI 0.08-0.45, p<0.001) and cluster C (HR: 0.25 95%CI 0.10-0.59, p=0.02), compared to patients belonging to cluster A. The association of this clustering with PFS under ICI was confirmed in the validation cohort. PFS related to non-ICI-based regimens was not significantly different according to cluster. This unsupervised transcriptomic classification identified three groups of MSI mCRCs with different compositions of TME cells and proliferative capacities of TME/tumor cells. The "stromalHIGH-proliferationLOW" cluster is associated with a poorer prognosis with ICI treatment.

THE GENETIC SUBTYPES AND THE TUMOR MICROENVIRONMENT SIGNATURES ARE ASSOCIATED WITH DISTINCT OUTCOMES IN PERIPHERAL T‐CELL LYMPHOMA

Background: Emerging evidence suggests the prognostic impact of the tumor microenvironment (TME) in peripheral T-cell lymphomas (PTCLs). To better understand PTCL pathobiology, we performed an integrative multi-omics study to explore the genetic subtypes and the TME signatures of PTCLs, especially nodal T-follicular helper T-cell lymphomas (nTFHLs) and PTCL, not otherwise specified (PTCL-NOS). Method: We performed whole-exome sequencing of 92 nTFHLs and 37 PTCL-NOSs, a total of 129 cases. Ninety-two nTFHLs included 85 angioimmunoblastic T-cell lymphomas (AITLs), four nPTCL with TFH phenotypes, two follicular T-cell lymphomas, and one unclassifiable nTFHL. Genetic drivers were integrated using non-negative matrix factorization consensus clustering (NMF). We estimated the proportion of immune cell fractions from bulk RNA sequencing data (n = 57) using CIBERSORTx and classified the TME signatures using hierarchical clustering. Results: NMF clustering analysis revealed three genetic subtypes within our cohort, except for 10 cases lacking recurrent genetic abnormalities (denoted as cluster 0). C2 was characterized by TP53 mutation, CDKN2A loss, PTEN loss, and chromosomal instability, corresponding to the previously described GATA3-PTCL subtype. C1 and C3 shared TFH-related genomic alterations, but C3 had significantly higher frequencies of chromosome (Chr) 5 gain, Chr 21 gain, RHOA, IDH2, and CD28 mutations. A higher frequency of rash and higher C-reactive protein levels than the other AITLs characterized C3-AITL. Pairwise association analysis of genomic alterations revealed that multiple TET2 mutations, rather than a single TET2 mutation, significantly co-occurred with IDH2 and RHOA mutations. A Bradley-Terry model estimated that the first TET2 and DNMT3A mutations occurred earlier, and the second or later TET2, RHOA, and IDH2 mutations occurred later. Compared to C1, significantly inferior survival was observed in C2 (hazard ratio (HR), 2.52; 95% CI, 1.37–4.63) and C3(HR, 2.14; 95% CI, 1.17–3.89; Figure A). The TME signatures were divided into TME1 (B-cells and follicular helper T-cells), TME2 (activated memory CD4 T-cells, M2 macrophages, and CD8 T-cells), and TME3 (naïve CD4 T-cells and activated mast cells). mTORC1 signaling was enriched in the TME2 signature compared to TME1 and TME3 signatures. The TME2 signature was significantly associated with shorter survival than the TME3 signature (HR: 3.4, 95% CI 1.6–7.5; Figure B). C2 had significantly more TME2 signature than C1 (64.3% vs. 7.7%), while C1 had significantly more TME3 signature than C2 (80.8% vs. 28.6%, p = 0.006; Figure C). Keywords: genomics, epigenomics, and other -omics, microenvironment Encore Abstract—previously submitted to EHA 2023 The research was funded by: Japan Society for the Promotion of Science KAKENHI grants (grant numbers: 19K23879, 22K19451, and 21H02945) and Japan Agency for Medical Research and Development (AMED) grants (20ck0106544 and 21ck0106644)

Predicting the immunotherapy response based on molecular subtypes of pan-immune signatures in gastrointestinal stromal tumor.

e23512 Background: Gastrointestinal stromal tumor (GIST) is the most common sarcoma, often originate in the stomach or the small intestine. Although the application of imatinib has brought a breakthrough in the clinical treatment of GIST, not all GIST patients are effective, and most GIST patients who receive imatinib sensitivity will develop drug resistance and eventually malignant progression. The tumor microenvironment (TME) is a key mediator of cancer progression and treatment outcome, and plays an important role in predicting cancer prognosis and treatment response. This study aims to investigate the role of TME in the treatment of GIST and the response to immunotherapy. Methods: Raw microarray data of 199 GIST patients were downloaded from GEO, including 159 KIT-mutant, 21 PDGFRA-mutant and 19 WT. All affymetrix datasets were re-normalized using the gcRMA package with default parameters. We integrated 275 TME and malignant signaling pathways related signatures from different database and previous researches, including CIBERSORT, MCP-counter, TIMER, and others. Fisher exact test's was used to identify signatures that differential between KIT-mutant, PDGFRA-mutant, and WT (p &lt; 0.05), and 85 signatures were finally obtained. To classify the molecular profiles, the expression patterns were assessed in all 199 GIST patients using unsupervised clustering based on their ssGSEA scores, and can be clustered into three distinct microenvironments. Results: Using GSEA with all TME signatures genes ranked by the log2(fold change) between the threee clustes, we found that gene sets were considered immune favorable in cluster A, including gene sets related to activated CD8+ T cells (p = 0.0003)and activated CD4 T cell signaling pathway (p = 0.009). Cluster B enriched to the epithelial mesenchymal transformation pathway (p = 0.04) and TGFb signaling pathway (p = 0.003), cluster C enriched into DNA-repair pathway (p = 0.02). In an independent skin cutaneous melanoma cohorts (n = 86) treated with anti-PD-1 therapy, patients were classified into three molecular clusters, which significantly corrleated with response to ipilimumab (p = 0.007). The percentage of responders to anti-PD-1 therapy in cluster A was 75% in contrast to only 10% in cluster B. The three molecular clusters discern a potential responder versus non-responder (AUC = 0.87; p = 0.0015), with essentially a 80% chance of correctly predicting the response, which supporting the utilization of the classification method. Conclusions: In this study, the immuno-infiltrating landscape was characterized based on pan-immune signatures using transcriptomic profile in GIST. The molecular subtypes derived from pan-immune signatures have the potential to predict the efficacy of immunotherapy.

Transcriptomic signatures of MSI-high metastatic colorectal cancer to predict efficacy of immune checkpoint inhibitors.

3570 Background: Microsatellite instability (MSI) is currently the only predictive biomarker of efficacy of immune checkpoint inhibitors (ICI) in metastatic colorectal cancers (mCRC). However, 10-40% of patients with MSI mCRC will experience a primary resistance to ICI. Methods: In a cohort of 103 patients with MSI mCRC treated with ICI, 3’RNAseq was performed from primary tumors resected before the beginning of ICI. Previously described single-cell transcriptomic signatures of tumor microenvironment (TME) were analysed. Results: The unsupervised clustering of this cohort allowed the identification of three clusters of tumors with distinct transcriptional profiles: cluster A (“stromalHIGH-proliferationLOW”), cluster B (“stromalHIGH-proliferationMED”), and cluster C (“stromalLOW-proliferationHIGH”), with an enrichment of patients progressing at first disease assessment under ICI in the cluster A (30% vs 12% in cluster B and 8.1% in cluster C, p = 0.074). Progression-free survival (PFS) was also significantly shorter in patients belonging to cluster A, compared to clusters B or C (p &lt; 0.001) with 2-year PFS rates of 33.5%, 80.5% and 78.3%, respectively. Similar results were observed for overall survival (OS).In multivariate analysis, PFS was still significantly longer in patients belonging to cluster B (HR: 0.26 95%CI 0.11-0.58, p = 0.001) and cluster C (HR: 0.35 95%CI 0.16-0.78, p = 0.01), compared to patients belonging to cluster A (Table). No association of identified clusters with PFS during non-ICI-based regimens was identified. Conclusions: This unsupervised transcriptomic classification identified three groups of MSI mCRCs with different compositions of TME cells and proliferative capacities of TME/tumor cells. The “stromalHIGH-proliferationLOW” cluster is associated with a lower efficacy of ICI. [Table: see text]

Spatial analysis of stromal signatures identifies invasive front carcinoma-associated fibroblasts as suppressors of anti-tumor immune response in esophageal cancer

BackgroundIncreasing evidence indicates that the tumor microenvironment (TME) is a crucial determinant of cancer progression. However, the clinical and pathobiological significance of stromal signatures in the TME, as a complex dynamic entity, is still unclear in esophageal squamous cell carcinoma (ESCC).MethodsHerein, we used single-cell transcriptome sequencing data, imaging mass cytometry (IMC) and multiplex immunofluorescence staining to characterize the stromal signatures in ESCC and evaluate their prognostic values in this aggressive disease. An automated quantitative pathology imaging system determined the locations of the lamina propria, stroma, and invasive front. Subsequently, IMC spatial analyses further uncovered spatial interaction and distribution. Additionally, bioinformatics analysis was performed to explore the TME remodeling mechanism in ESCC. To define a new molecular prognostic model, we calculated the risk score of each patient based on their TME signatures and pTNM stages.ResultsWe demonstrate that the presence of fibroblasts at the tumor invasive front was associated with the invasive depth and poor prognosis. Furthermore, the amount of α-smooth muscle actin (α-SMA)+ fibroblasts at the tumor invasive front positively correlated with the number of macrophages (MØs), but negatively correlated with that of tumor-infiltrating granzyme B+ immune cells, and CD4+ and CD8+ T cells. Spatial analyses uncovered a significant spatial interaction between α-SMA+ fibroblasts and CD163+ MØs in the TME, which resulted in spatially exclusive interactions to anti-tumor immune cells. We further validated the laminin and collagen signaling network contributions to TME remodeling. Moreover, compared with pTNM staging, a molecular prognostic model, based on expression of α-SMA+ fibroblasts at the invasive front, and CD163+ MØs, showed higher accuracy in predicting survival or recurrence in ESCC patients. Regression analysis confirmed this model is an independent predictor for survival, which also identifies a high-risk group of ESCC patients that can benefit from adjuvant therapy.ConclusionsOur newly defined biomarker signature may serve as a complement for current clinical risk stratification approaches and provide potential therapeutic targets for reversing the fibroblast-mediated immunosuppressive microenvironment.

Integrated machine learning and bioinformatic analyses used to construct a copper-induced cell death-related classifier for prognosis and immunotherapeutic response of hepatocellular carcinoma patients.

Background: Copper as phytonutrient has powerful activity against health diseases. A newly discovered mechanism of cell death that affects energy metabolism by copper ("cuproptosis") can induce multiple cuproptosis-related genes. Hepatocellular carcinoma (HCC) is a poorly prognosed widespread cancer having danger of advanced metastasis. Therefore, earlier diagnosis followed by the specific targeted therapy are required for improved prognosis. The work herein constructed scoring system built on ten cuproptosis-related genes (CRGs) to predict progression of tumor and metastasis more accurately and test patient reaction toward immunotherapy. Methods: A comprehensive assessment of cuproptosis patterns in HCC samples from two databases and a real-world cohort was performed on ten CRGs, that were linked to immune cell infiltration signatures of TME (tumor microenvironment). Risk signatures were created for quantifying effect of cuproptosis on HCC, and the effects of related genes on cellular function of HCC were investigated, in addition to the effects of immunotherapy and targeted therapy drugs. Results: Two distinct cuproptosis-associated mutational patterns were identified, with distinct immune cell infiltration characteristics and survival likelihood. Studies have shown that assessment of cuproptosis-induced tumor mutational patterns can help predict tumor stage, phenotype, stromal activity, genetic diversity, and patient prognosis. High risk scores are characterized by lower survival and worse treatment with anti-PD-L1/CTAL4 immunotherapy and first-line targeted drugs. Cytological functional assays show that CDKN2A and GLS promote proliferation, migration and inhibit copper-dependent death of HCC cells. Conclusion: HCC patients with high-risk scores exhibit significant treatment disadvantage and survival rates. Cuproptosis plays a non-negligible role in the development of HCC. Quantifying cuproptosis-related designs of tumors will aid in phenotypic categorization, leading to efficient personalized and targeted therapeutics and precise prediction of prognosis and metastasis.