The human genome encodes ~1,900 secreted proteins, many of which mediate intercellular communication. Secreted proteins do not act cell-autonomously, limiting systematic approaches to characterize their functions. Here we introduce SecAct (Secreted Activity, https://secact.ccr.cancer.gov ), a computational framework that infers the signaling activities of 1,170 human secreted proteins from spatial, single-cell and bulk transcriptomic data. The inference model harnesses precomputed intercellular signaling signatures trained on 1,258 spatial transcriptomics samples spanning 37 cancer types. Transcriptomics data from antisecreted protein therapies validate SecAct's accuracy in predicting the repression of secreted protein activity following treatment. For spatial and single-cell transcriptomics data, SecAct provides interactive modules for analyzing secreted protein-mediated cell-cell communication. Applying SecAct to 54 cancer immunotherapy cohorts comprising 5,174 patients, we identified secreted proteins associated with tumor immunity. In vivo experiments validated lymphocyte antigen 86 (LY86), whose function in cancer was previously unknown, as an antitumor regulator.

Spatial transcriptomics (ST) assays are transforming our understanding of tumor heterogeneity, but their high cost limits their application in large-scale biomarker discovery. Here, we present “Path2Space,” a deep-learning model that predicts spatial gene expression directly from histopathology slides. Trained on extensive breast cancer ST data, Path2Space robustly predicts the spatial expression of thousands of genes, outperforming 21 established methods. Charting the tumor microenvironment (TME) of 976 breast cancer TCGA (The Cancer Genome Atlas) tumors, it accurately infers cell-type abundances and identifies three spatially defined breast cancer subgroups with distinct survival outcomes. Notably, the derived low-cost spatial TME landscapes enable more accurate predictions of patient response to chemotherapy and trastuzumab compared with costly conventional bulk-sequencing-based biomarkers. Path2Space thus offers a scalable, fast, and cost-effective alternative to molecular assays. It opens avenues for large cohort treatment biomarker discovery and translationally relevant insights into tumor biology, with potential applicability across many cancer indications.

Secreted proteins are central mediators of intercellular communications and can serve as therapeutic targets in diverse diseases. The ∼1,903 human genes encoding secreted proteins are difficult to study through common genetic approaches. To address this hurdle and, more generally, to discover cancer therapeutics, we developed the Cancer Immunology Data Engine (CIDE, https://cide.ccr.cancer.gov), which incorporates 90 omics datasets spanning 8,575 tumor profiles with immunotherapy outcomes from 17 solid tumor types. CIDE systematically identifies all genes associated with immunotherapy outcomes. Then, we focused on secreted proteins prioritized by CIDE without known cancer roles and validated regulatory effects on immune checkpoint blockade for AOAH, CR1L, COLQ, and ADAMTS7 in mouse models. The top hit, acyloxyacyl hydrolase (AOAH), potentiates immunotherapies in multiple tumor models by sensitizing T cell receptors to weak antigens and protecting dendritic cells through depleting immunosuppressive arachidonoyl phosphatidylcholines and oxidized derivatives.

Secreted proteins, derived from around 1900 human protein-coding genes, play a crucial role in mediating intercellular communication. Despite their significance, the downstream signaling effects of most secreted proteins in shaping antitumor immunity remain largely unexplored, except for extensively studied cytokines. Spatial transcriptomics (ST), which profiles gene expression in the spatial context of intact tissues, enables us to investigate spatial relationships between secreted proteins and their downstream targets. This technology provides insights into the activity and function of secreted proteins within the tumor microenvironment. The current study aims to leverage target gene signatures trained from ST data to identify novel secreted proteins modulating the efficacy of immunotherapy. We collected hundreds of tumor ST samples across over twenty cancer types. Using Moran’s I correlation, we assessed the spatial relationships between secreted proteins and potential target genes. After filtering out low-quality targets, approximately 1, 200 secreted protein signaling signatures were retained. Based on these data, we developed SecAct, a multiple regression framework to predict secreted protein signaling activity. Next, SecAct’s reliability was validated using clinical trial data from cytokine-blocking treatments. We observed that the inferred activity reduction of secreted proteins significantly correlates with patients' clinical responses in both tumor and inflammatory disease settings, demonstrating SecAct’s clinical relevance. Subsequently, we applied SecAct to analyze forty-two cancer immunotherapy cohorts treated with immune checkpoint blockades, identifying secreted proteins whose activity is strongly associated with therapeutic efficacy. This prioritized list includes both established cancer immunotherapy regulators and novel candidates without previously known roles in cancer. Several new high-ranked regulators were further validated through in vivo mouse models. Our framework provides a data-driven screening ground for identifying novel regulators of antitumor immunity, potentially guiding the development of new immunotherapies. Beibei Ru, Lanqi Gong, Emily Yang, Kenneth Aldape, Lalage Wakefield, Peng Jiang. Identification of novel secreted proteins modulating immunotherapy efficacy through spatial transcriptomics models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6279.

Hepatocellular carcinoma (HCC) is a leading malignant tumor globally and the second-highest cause of cancer-related mortality. The insidious nature and high recurrence rates of HCC largely necessitate systemic therapies for many patients, particularly those based on immune checkpoint inhibitors. For advanced HCC, the combination of atezolizumab and bevacizumab (Atezo+Beva) has become the preferred first-line treatment, providing superior survival benefits compared to sorafenib. However, the response rate to this therapy remains under 30%, with the underlying resistant mechanism still unclear. Therefore, identifying and characterizing predictive biomarkers associated with the response to Atezo+Beva therapy is essential for improving clinical prognosis in patients with advanced HCC. Here, we performed an in vivo genome-wide CRISPR screen model after anti-programmed death ligand 1 (anti-PD-L1) and anti-vascular endothelial growth factor a (anti-VEGFa) treatment to investigate genomic modulators of the immunotherapy response in HCC. Our screening identified serine and arginine rich splicing factor 9 (SRSF9) as a critical gene involved in the anti-immunotherapy regulation. Knockout of SRSF9 in mouse tumor cells significantly enhanced the sensitivity of primary tumors and the lung metastasis to anti-PD-L1 and anti-VEGFa treatment. This finding was further validated using SRSF9-overexpression mouse model. Functional assays indicated that without immunotherapy SRSF9 had a minimal direct effect on the proliferation of multiple HCC cell lines, aligning with the analytical results derived from the DepMap database. Clinically, SRSF9 expression was increased in multiple cancer types compared to normal counterparts and was positively correlated with poor survival in HCC patients following Atezo-Bev therapy. Furthermore, high SRSF9 expression could negate the prognostic advantage typically seen in patients with strong CD8+ T cell function. In addition, SRSF9 could decrease the secretion of interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α) in tumor environment. These suggested a potential role for SRSF9 in downregulating CD8+ T cell activity. The underlying mechanism of how SRSF9 inhibits the response of HCC patients to Atezo+Beva therapy still needs further investigation. In conclusion, our study reveals the significant role of SRSF9 in modulating the response to immunotherapy in HCC, indicating its substantial potential for expanding the cohort of patients who may benefit from Atezo+Beva therapy and enhancing their prognosis. Further exploration of the underlying mechanisms will be undertaken in the future. Nanzhou Yu, Jie Luo, Beibei Ru, Renyue Ji, Lanqi Gong, Peng Jiang, Xinyuan Guan. Genome-wide in vivo CRISPR screen identifies the role of SRSF9 in immunotherapy response regulation in hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4318.

Hematoxylin and Eosin staining (H&E) is widely used in clinical practice, but efficient and versatile image retrieval tools are lacking. We developed the H&E Retrieval Engine (HERE, https://hereapp.ccr.cancer.gov) to analyze patient cases based on image similarities to database records. Using H&E image regions as input, HERE searches 21.2 terabytes of whole-slide images from multiple tumor histopathology cohorts through a 12.1-gigabyte memory index, and returns top images containing regions similar to the query. HERE scans high-resolution images in the database using accurate artificial intelligence encoding and ultra-efficient hierarchical skip indexing. HERE demonstrated performance superior to existing image retrieval tools based on blinded pathologist scoring using benchmark queries that represent key image features of human tumors. By pairing spatial transcriptomics with H&E images, HERE also enables retrieving image features from gene transcriptomics input and identifies molecular pathways associated with tumor histologies.

Background: Myelodysplastic Syndromes (MDS) are hematopoietic stem cells malignancies whose pathogenesis is, in part, driven by chronic inflammation of the bone marrow. This inflammation fosters malignant clone expansion while suppressing normal hematopoiesis resulting in the characteristic marrow dysplasia and peripheral cytopenias observed in these patients. Although gene expression immune signatures have been suggested as prognostic indicators in MDS, to our knowledge, correlations between protein secretion and gene expression have not been extensively explored. Here, we performed RNA sequencing of bone marrow cells and matched multiplexed cytokine analysis of bone marrow plasma from participants enrolled on the Comprehensive Molecular and Clinical Evaluation of Pediatric and Adult MDS (NCT05350748) study at the National Cancer Institute. Methods: Bone marrow aspirates were collected from participants and total RNA was isolated from cells after RBC lysis. RNAseq libraries were prepared using the TruSeq RNA Exome kit and sequenced on either NextSeq 550DX or NovaSeq 6000. Reads were aligned to the human genome using STAR, and read quantification was performed with RSEM. Raw counts were normalized to log2-transformed transcripts per million (TPM). Low expressing genes were filtered out and only those with log2(TPM) > 0.5 in at least two patients were retained. Bone marrow plasma separated from cells by centrifugation was used for cytokine analysis using the Olink® Target 48 Immune Surveillance Reagent and Target 48 Cytokine Reagent multiplex kits. Normalized protein expression (NPX) levels are reported. Spearman correlation analysis was performed to assess correlations between RNAseq expression, protein abundance, and disease states. P-values less than 0.05 were considered statistically significant. Results: We analyzed the plasma levels of 88 cytokines in 27 cases (4 healthy donors, 2 cases found to have no malignancy, 4 with CHIP or CCUS, 13 with MDS, and 4 AML cases). Unsupervised hierarchal clustering grouped healthy donors with non-malignancy cases and those with CHIP or CCUS together; while MDS and AML cases clustered together. Notably, CHIP or CCUS patients grouped either with non-malignant individuals, or those who had hematologic malignancy depending on the individual cytokine being tested. We then correlated RNAseq data with plasma cytokine levels in 18 participants (9 cases with MDS or MDS/MPN, 3 cases with AML, 4 with CHIP or CCUS, and 2 cases without malignancy). Correlation analysis included 57 cytokines that had paired RNAseq data. Principal component analysis demonstrated that RNAseq better discriminated disease states compared to plasma cytokine levels. Expression at both the gene and protein level of only OLR1, LIF, FLT3LG, LTA, and PDCD1 were positively and significantly correlated (rho=0.70, 0.68, 0.60, 0.58, and 0.55, respectively). Gene expression levels of TNFSF14, TNFSF10, OLR1, IL16, AREG, IFNG, IL1RN, CSF3, KRT18, CD28, IL32, and LTA were significantly correlated with disease state (comparing no malignancy, CHIP or CCUS, MDS/MDS-MPN, and AML) (rho = -0.69, -0.64, -0.63, -0.62, 0.56, -0.56, -0,56, 0.52, 0.51, -0.51, -0.50, and -0.49, respectively.) Plasma protein levels of LTA, HGF, CD80, TNFSF10, FLT3LG, CCL3, IL15 and IL6 were significantly correlated with disease state (rho=-0.72, 0.68, 0.63, -0.59, -0.55, 0.52, 0.49, 0.48, respectively). Interestingly, LTA was the only analyte for which both gene and protein expressions levels associated with disease state. Importantly, both lower LTA gene and protein levels were associated with the more advanced disease states (MDS and AML). LTA gene expression (log2TPM) was 1.24, 1.23, 1.19, and 0.34 in no malignancy, CHIP/CCUS, MDS/MDS-MPN, and AML, respectively while normalized protein values were 5.61, 5.96, 5.29, and 4.84, respectively. Conclusions: LTA (lymphotoxin alpha), also known as TNFbeta, is a member of the tumor necrosis factor family that heterodimerizes with lymphotoxin beta to modulate a variety of inflammatory processes. Our data suggests that LTA may have a protective role in hematologic malignancy and disease progression warranting further investigation. Given the lack of gene expression signatures that have been useful for patient stratification, our results underscore the value of paired secreted protein levels and gene expression patterns to help inform stratification and mechanistic understanding in myeloid malignancies.

Emerging evidence suggests that cancer cells may disseminate early, prior to the formation of traditional macro-metastases. However, the mechanisms underlying the seeding and transition of early disseminated cancer cells (DCCs) into metastatic tumors remain poorly understood. Through single-cell RNA sequencing, we show that early lung DCCs from esophageal squamous cell carcinoma (ESCC) exhibit a trophoblast-like ‘tumor implantation’ phenotype, which enhances their dissemination and supports metastatic growth. Notably, ESCC cells overexpressing GPRC5A demonstrate improved implantation and persistence, resulting in macro-metastases in the lungs. Clinically, elevated GPRC5A level is associated with poorer outcomes in a cohort of 148 ESCC patients. Mechanistically, GPRC5A is found to potentially interact with WWP1, facilitating the polyubiquitination and degradation of LATS1, thereby activating YAP1 signaling pathways essential for metastasis. Importantly, targeting YAP1 axis with CA3 or TED-347 significantly diminishes early implantation and macro-metastases. Thus, the GPRC5A/WWP1/LATS1/YAP1 pathway represents a crucial target for therapeutic intervention in ESCC lung metastases. The mechanisms allowing early disseminated cancer cells colonize other tissues remain largely unknown. Here, authors show that GPRC5A axis drives esophageal squamous cell carcinoma lung seeding and metastasis, in a mechanism resembling trophoblast behavior during embryo implantation.

Metastasis is the biggest obstacle to esophageal squamous cell carcinoma (ESCC) treatment. Single-cell RNA sequencing analyses are applied to investigate lung metastatic ESCC cells isolated from pulmonary metastasis mouse model at multiple timepoints to characterize early metastatic microenvironment. A small population of parental KYSE30 cell line (Cluster S) resembling metastasis-initiating cells (MICs) is identified because they survive and colonize at lung metastatic sites. Differential expression profile comparisons between Cluster S and other subpopulations identified a panel of 7 metastasis-initiating signature genes (MIS), including CD44 and TACSTD2, to represent MICs in ESCC. Functional studies demonstrated MICs (CD44(high)) exhibited significantly enhanced cell survival (resistances to oxidative stress and apoptosis), migration, invasion, stemness, and in vivo lung metastasis capabilities, while bioinformatics analyses revealed enhanced organ development, stress responses, and neuron development, potentially remodel early metastasis microenvironment. Meanwhile, early metastasizing cells demonstrate quasi-epithelial-mesenchymal phenotype to support both invasion and anchorage. Multiplex immunohistochemistry (mIHC) staining of 4 MISs (CD44, S100A14, RHOD, and TACSTD2) in ESCC clinical samples demonstrated differential MIS expression scores (dMISs) predict lymph node metastasis, overall survival, and risk of carcinothrombosis.

Uncovering the composition and structure of the tumor microenvironment is critical to a mechanistic understanding of tumorigenesis and therapeutic resistance. Spatial transcriptomics (ST) technology has enabled profiling the molecular features of tumor tissue with position information. However, the spatial probe of various ST strategies with a 10~100 μm diameter might capture a mixture of transcripts from multiple cells or cell lineages. Cell type deconvolution in ST data of tumor tissues remains challenging for existing methods, which are designed to decompose general ST or bulk data. We develop the Spatial Cellular Estimator for Tumors (SpaCET) to infer cell identities from tumor ST data. Without the need of inputting cell references, SpaCET estimates malignant and non-malignant cell abundance by using a gene pattern dictionary of copy number alterations in common malignancies and a hierarchical atlas of immune/stromal cells, respectively. SpaCET provides higher accuracy than existing methods based on eight ST datasets on seven cancer types with matched double-blind histopathology annotations as ground truth. Furthermore, SpaCET can reveal the potential intercellular interactions at the tumor-immune interface by integrating inferred cell fractions with the ligand-receptor interaction network. We expect that SpaCET will be a valuable tool for spatial cancer biology. Citation Format: Beibei Ru, Jinlin Huang, Yu Zhang, Kenneth Aldape, Peng Jiang. Exploring the tumor microenvironment in a spatial context with SpaCET [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB151.

Background Nasopharyngeal carcinoma (NPC) is an EBV-related and highly inflamed malignancy of strategic importance in Asia and Africa. Only 0.1–1% NPC incidences are diagnosed in children, and pediatric NPC exhibits superior prognosis and immunotherapy outcomes. Microenvironmental characteristics might contribute to such discrepancies between adult and pediatric NPC. Thus, we apply scRNA-seq and Visium spatial RNA-seq to primary pediatric NPC with paired blood, and incorporate our data with multi-central NPC cohorts. We develop a computational framework to infer spatial cellular constitutes and signaling. We report low lipid metabolism, weak interaction between NPC cells and Tregs/CD8+ T cells, and enriched T memory stem cells (Tscm) collectively result in stronger and long-lasting immunity in pediatric NPC and as new immunotherapeutic vulnerabilities (figure 1). Methods We applied scRNA-seq to 5 primary pediatric NPC tissues with paired blood samples, and spatial-seq to 4 paired pediatric and 7 adult NPC tissues. We established a multi-central NPC cohort with 69 patient samples by integrating our scRNA-seq data with three adult NPC cohorts.1–3 A personalized pipeline incorporated with Seurat,4 Spacexr5 and SpaCET6 was used to analyze single-cell spatial data. Key findings were validated by multiplex staining, flow cytometry, and blood tests on patient samples. Results We established a systematic NPC scRNA-seq and spatial-seq cohort containing 503,021 cells from 69 samples, and 15,222 spatial spots from 11 samples. We developed a personalized computational framework to characterize spatial co-localization and regionally enriched signaling. We unveiled that fatty acid (FA)/cholesterol metabolism were elevated in the tumor-T cell core in adult NPC (p=2.12e-5), and such metabolic aberrance was validated by Oil Red O staining and blood cholesterol/FA tests (p=0.012). Strong co-localizations between NPC cells and Tregs (r=0.47, p<2.2e-22)/exhausted T cells (r=0.29, p<2.2e-22) were found in adult NPC, with spatially enriched CD70-CD27, LGALS9-TIM3 and Nectin-4 interactions. This finding was corroborated by multiplex staining of KRT19+ NPC cells, CD4+/FOXP3+ Tregs, and CD8+/PD-1/TIM-3+ exhausted T cells. We identified novel IL7R+/ANXA1+ Tscm populations in pediatric NPC, with strong resilience to immunosuppressive cues, including TGF-β, PEG2 and adenosine, and had a positive impact on long-term immunity and immunotherapeutic outcomes (p=7.49e-9). Conclusions We demonstrate that higher immunosuppression and exhaustion caused by lipid metabolism and tumor-intrinsic interactions, and a lower age-associated Tscm pool, are the vital drivers of immunotherapeutic discrepancies in adult and pediatric NPC patients. Pre-treatment screening of Tscm abundance in NPC patients might help stratify immunotherapy responders, and targeting metabolic and interacting vulnerabilities might generate therapeutic benefits. References Chen YP, Yin JH, Li WF, Li HJ, Chen DP, Zhang CJ, Lv JW, Wang YQ, Li XM, Li JY, et al. Single-cell transcriptomics reveals regulators underlying immune cell diversity and immune subtypes associated with prognosis in nasopharyngeal carcinoma. Cell Res. 2020;30(11):1024–1042. Gong L, Kwong DL, Dai W, Wu P, Li S, Yan Q, Zhang Y, Zhang B, Fang X, Liu L, et al. Comprehensive single-cell sequencing reveals the stromal dynamics and tumor-specific characteristics in the microenvironment of nasopharyngeal carcinoma. Nat Commun. 2021;12(1):1540. Liu Y, He S, Wang XL, Peng W, Chen QY, Chi DM, Chen JR, Han BW, Lin GW, Li YQ, et al. Tumour heterogeneity and intercellular networks of nasopharyngeal carcinoma at single cell resolution. Nat Commun. 2021;12(1):741. Hao Y, Hao S, Andersen-Nissen E, Mauck WM, 3rd, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M, et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184(13):3573–3587 e3529. Cable DM, Murray E, Zou LS, Goeva A, Macosko EZ, Chen F, Irizarry RA. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat Biotechnol. 2022;40(4):517–526. Ru B, Huang J, Zhang Y, Aldape K, Jiang P. Estimation of cell lineages in tumors from spatial transcriptomics data. Nat Commun. 2023;14(1):568. Ethics Approval The study was approved by the ethics committee at the University of Hong Kong. We complied with all related ethical regulations. Written informed consent was obtained from adult and pediatric NPC patients for their tissues and blood samples to be used in the study. Consent Written informed consent was obtained from adult and pediatric NPC patients for publication of this abstract and any accompanying images. A copy of the written consent is available for review by the Editor of this journal.

Spatial transcriptomics (ST) technology through in situ capturing has enabled topographical gene expression profiling of tumor tissues. However, each capturing spot may contain diverse immune and malignant cells, with different cell densities across tissue regions. Cell type deconvolution in tumor ST data remains challenging for existing methods designed to decompose general ST or bulk tumor data. We develop the Spatial Cellular Estimator for Tumors (SpaCET) to infer cell identities from tumor ST data. SpaCET first estimates cancer cell abundance by integrating a gene pattern dictionary of copy number alterations and expression changes in common malignancies. A constrained regression model then calibrates local cell densities and determines immune and stromal cell lineage fractions. SpaCET provides higher accuracy than existing methods based on simulation and real ST data with matched double-blind histopathology annotations as ground truth. Further, coupling cell fractions with ligand-receptor coexpression analysis, SpaCET reveals how intercellular interactions at the tumor-immune interface promote cancer progression.

Abstract Background: Nasopharyngeal carcinoma (NPC) is an EBV-positive malignancy with high immune infiltrates but also high immunosuppressive activities. Pediatric NPC only constitutes 0.1-1% of the total NPC incidences and exhibits superior treatment responses and prognosis compared to adult NPC. Clinically, we consider that the differences in the tumor landscape between adult and pediatric NPC patients are the vital driver of such immune discrepancies. Therefore, we apply scRNA-seq and Visium spatial sequencing to primary pediatric NPC tissues and paired peripheral blood samples, and integrate our data with multiple independent adult NPC scRNA-seq cohorts. Furthermore, we establish a bioinformatics analysis pipeline that can calculate spatial cellular compositions and biological activities. Here, we demonstrate that low lipid metabolism, disrupted interactions between NPC cells and regulatory T cells (Tregs)/CD8+ T cells, and high infiltration of central stem memory T cells, contribute to more potent and enduring anti-tumor immunity in pediatric NPC patients, and these microenvironmental features might serve as new immunotherapeutic vulnerabilities. Method: 5' scRNA-seq coupled with TCR/BCR profiling was performed on 5 primary and treatment-naïve pediatric NPC tissues with 5 paired peripheral blood samples. Visium spatial-seq was performed on 4 paired pediatric and 7 adult NPC tissues. We incorporated our scRNA-seq cohort with three independent adult NPC cohorts. Our computational framework was integrated with existing analysis packages, including Seurat, Spacexr, and SpaCET, to analyze single-cell-referenced spatial data. The key results in our study were validated by multiplex IHC staining and flow cytometry on patient samples. Results: We established a multi-center NPC scRNA spatial cohort containing 503,021 cells from 69 samples, and 15,222 spatial spots from 11 samples. We developed a computational framework to calculate spatial co-localization and biological activities. We unveiled that lipid metabolism was elevated in the tumor-T cell core in adult NPC. Strong co-localizations between NPC cells and Tregs/CD8+ T cells were found in adult NPC, with spatially enriched CD70-CD27 and LGALS9-TIM3 interactions. This finding was corroborated by multiplex IHC staining of KRT19+ NPC cells, CD4+/FOXP3+ Tregs, and CD8+/PD-1/TIM-3+ exhausted T cells. We also identified and characterized novel IL7R+ central stem memory T cells in pediatric NPC tissues and blood, with strong resilience to immunosuppressive cues, including TGF-β, PEG2, and cyclic AMP, and could lead to enduring immunity and anti-PD-1/PD-L1 responses. Conclusion: We demonstrate that higher T-cell immunosuppression and exhaustion caused by aberrant lipid metabolism, tumor-T interactions, and a lower age-associated central stem memory T-cell pool, are vital drivers of immune discrepancies in adult and pediatric NPC patients. Thus, screening and targeting these molecular microenvironmental characteristics might help stratify immunotherapy responders and generate therapeutic benefits in NPC patients. Citation Format: Lanqi Gong, Yu Zhang, Grace Guan, Beibei Ru, Peng Jiang. Single-cell spatial analysis reveals microenvironmental features that contribute to immune discrepancies between adult and pediatric nasopharyngeal carcinomas [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr PR002.

Esophageal squamous cell carcinoma (ESCC) metastatic-initiating cells are heterogenous subpopulations that are hypothesized to promote distal metastasis. Pulmonary metastatic human-origin ESCC mice model was established in our laboratory, chronological flow cytometry collection of tumor cells isolated from metastatic mice lungs at different timepoints inoculated via intravenous injection was followed by 10X genomics multiplex single-cell transcriptomic sequencing (scRNA-seq). Bioinformatics analyses denoted a 7-signature panel previously introduced with preliminary data, follow-up validations of signature-enriched subpopulation in silico, in vitro, and in vivo were performed in this study. In silico bulk transcriptomic sequencing (RNA-seq) of single signature-enriched KYSE30 subpopulation followed by gene ontology (GO) enrichment analyses indicated significantly (FDR<0.05) enhanced biological processes involved in cell motility, cell migration and cell adhesion, compared to signature-low subpopulation. In vitro western blotting showed elevated mesenchymal markers expression, while diminished epithelial markers expression. In vitro real-time qPCR revealed single signature-enriched subpopulation has co-enrichment of other signature marker RNAs compared to control group. Further in vitro functional assays demonstrated signature-enriched subpopulation possesses strengthened spheroid formation ability (p<0.05) and soft agar colony formation ability (p<0.05) as compared to signature-low subpopulation. In addition to previous preliminary data on early survival ability in vivo, signature-enriched subpopulation was significantly more favorable in sustained cell survival and proliferation, and formation of larger size of metastatic lesions in mice lungs at 2 months post-intravenous injection as illustrated by flow cytometry (p<0.05) and immunohistochemistry (IHC) staining analyses (p<0.05) respectively as compared to signature-low subpopulation. To validate the protein expression of signature markers, 6-colour multiplex IHC staining was performed on KYSE30 cell line and metastatic mice lungs collected at different timepoints. Multiplex IHC staining exhibited that these signature markers were expressed in subpopulation of cells as well as expressed on inoculated tumor cells in metastatic mice lungs with timeline pattern. In summary, signature-enriched subpopulation manifested the metastatic-initiating properties in ESCC via various in silico, in vitro, and in vivo experimental analyses. Further multiplex staining in clinical tissue microarray would be performed and analyzed to examine feasibility of adopting these signature markers for facilitating clinical diagnosis and molecular targeting of metastatic ESCC. Citation Format: Ching Ngar Wong, Yu Zhang, Beibei Ru, Kwong Yiu Lam, Hongyu Zhou, Xinyuan Guan. Metastatic-initiating signature-enriched subpopulation identified from single-cell transcriptomic sequencing of multi-timepoint esophageal squamous cell carcinoma pulmonary metastatic mice model [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 2102.

Abstract Remote oligometastases are the predominant roots of the unsatisfactory 5-year survival rate in esophageal squamous cell carcinoma (ESCC) patients. The title of metastatic-initiating cells (MICs) has been designated to describe tumor subpopulation with heterogeneous genetic background and behaviors that potentially displays stemness features and favors metastatic lesion formation. However, preceding studies have overpassed the early surviving MICs due to limitations in bulk transcriptomic sequencing at macrometastatic stage. With the aim of denoting and characterizing MICs in ESCC, we established the pulmonary metastatic mice model via intravenous injection and followed by multiplex single-cell RNA sequencing (scRNA-Seq) of tumor cells collected from lung tissues along longitudinal end-points. The scRNA-Seq results uncover a 7-gene surface marker signature panel that could depict the early surviving MICs of ESCC, including CD44, TACSTD2, TM4SF1, S100A14, RHOD, CST6, and C19orf33. In silico gene ontology (GO) enrichment of the early surviving MICs subpopulation reveals numerous migration and adhesion-related biological processes (p<0.05). In addition to scRNA-Seq, immunohistochemistry (IHC) staining of tumor cells in mice lungs collected in sequential order evidenced the survival heterogeneity of tumor subpopulations. Likewise, flow cytometry fluorescence-activated cell sorting (FACS) of signature-enriched tumor cells was performed to resemble the target MICs. CD44Pos ESCC cells are found to show distinctively greater wound-healing (p<0.01), foci formation (p<0.05), migration (p<0.01) and invasion (p<0.01) ability than CD44Neg cells in vitro, and longer survival patterns (p<0.05) upon implantation in vivo. Multi-color immunofluorescence (mIF) staining of these gene signatures is underway in mice models and clinical samples for comprehensive validation. We anticipate this surface marker signature to accelerate development for clinical diagnosis together with molecular targeting in metastatic ESCC patients. Citation Format: Ching Ngar Wong, Yu Zhang, Beibei Ru, Hong Yu Zhou, Kwong Yiu Lam, Xin Yuan Guan. Single-cell RNA sequencing (scRNA-Seq) from mice model reveals metastatic and stemness signatures in esophageal squamous cell carcinoma (ESCC) [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 5982.

Cancer metastasis remains the key barrier for patient’s cure. Tumor metastasis initiating from a single disseminated cell into the invasive nodule in the foreign microenvironment is poorly understand. In this study, we established the intravenous injection pulmonary metastatic model followed by single-cell RNA sequencing of disseminating cells from mice lungs at different timepoints, we identified a cluster of SLPI-high esophageal squamous cell carcinoma (ESCC) cells that mediates the lung metastasis evolution and predicts worse overall survival of ESCC patients. GSEA analysis reveals that the SLPI-high subset is functionally associated with epithelial-to-mesenchymal transition and neutrophil activation during lung metastasis. Immune infiltration prediction from TIMER also confirmed that high SLPI is tightly correlated with more abundance of neutrophils in the ESCC tumor microenvironment. SLPI is reported to drive the metastasis progression through regulating the vascular mimicry. In the next step, further in-vitro and in-vivo studies is required to explore the role of SLPI in ESCC cells in promoting lung metastasis evolution. We identified a SLPI+ ESCC cells with high pluripotency that particularly initiates the metastasis process. Citation Format: Yu Zhang, Ching Ngar Wong, Lanqi Gong, Xin-yuan Guan, Beibei Ru, Beilei Liu. Single cell RNA sequencing uncovers high-plasticity cell state during tumor metastasis [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 5981.

COPYRIGHT © 2022 Huang, Takakusagi and Ru. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Phage display: Technique and applications

Cytokines are critical for intercellular communication in human health and disease, but the investigation of cytokine signaling activity has remained challenging due to the short half-lives of cytokines and the complexity/redundancy of cytokine functions. To address these challenges, we developed the Cytokine Signaling Analyzer (CytoSig; https://cytosig.ccr.cancer.gov/ ), providing both a database of target genes modulated by cytokines and a predictive model of cytokine signaling cascades from transcriptomic profiles. We collected 20,591 transcriptome profiles for human cytokine, chemokine and growth factor responses. This atlas of transcriptional patterns induced by cytokines enabled the reliable prediction of signaling activities in distinct cell populations in infectious diseases, chronic inflammation and cancer using bulk and single-cell transcriptomic data. CytoSig revealed previously unidentified roles of many cytokines, such as BMP6 as an anti-inflammatory factor, and identified candidate therapeutic targets in human inflammatory diseases, such as CXCL8 for severe coronavirus disease 2019.