Heterogeneous Microenvironment Research Articles

Integrative analyses of bulk and single-cell transcriptomics reveals the infiltration and crosstalk of cancer-associated fibroblasts as a novel predictor for prognosis and microenvironment remodeling in intrahepatic cholangiocarcinoma

BackgroundIntrahepatic cholangiocarcinoma (ICC) is a highly malignant neoplasm and characterized by desmoplastic matrix. The heterogeneity and crosstalk of tumor microenvironment remain incompletely understood.MethodsTo address this gap, we performed Weighted Gene Co-expression Network Analysis (WGCNA) to identify and construct a cancer associated fibroblasts (CAFs) infiltration biomarker. We also depicted the intercellular communication network and important receptor-ligand complexes using the single-cell transcriptomics analysis of tumor and Adjacent normal tissue.ResultsThrough the intersection of TCGA DEGs and WGCNA module genes, 784 differential genes related to CAFs infiltration were obtained. After a series of regression analyses, the CAFs score was generated by integrating the expressions of EVA1A, APBA2, LRRTM4, GOLGA8M, BPIFB2, and their corresponding coefficients. In the TCGA-CHOL, GSE89748, and 107,943 cohorts, the high CAFs score group showed unfavorable survival prognosis (p < 0.001, p = 0.0074, p = 0.028, respectively). Additionally, a series of drugs have been predicted to be more sensitive to the high-risk group (p < 0.05). Subsequent to dimension reduction and clustering, thirteen clusters were identified to construct the single-cell atlas. Cell-cell interaction analysis unveiled significant enhancement of signal transduction in tumor tissues, particularly from fibroblasts to malignant cells via diverse pathways. Moreover, SCENIC analysis indicated that HOXA5, WT1, and LHX2 are fibroblast specific motifs.ConclusionsThis study reveals the key role of fibroblasts - oncocytes interaction in the remodeling of the immunosuppressive microenvironment in intrahepatic cholangiocarcinoma. Subsequently, it may trigger cascade activation of downstream signaling pathways such as PI3K-AKT and Notch in tumor, thus initiating tumorigenesis. Targeted drugs aimed at disrupting fibroblasts-tumor cell interaction, along with associated enrichment pathways, show potential in mitigating the immunosuppressive microenvironment that facilitates tumor progression.

Abstract PO3-14-01: Heterogeneity of tumor immune microenvironment to predict everolimus efficacy in premenopausal women with hormone receptor-positive/human epidermal growth factor receptor 2-negative adavanced breast cancer

Abstract Objective Everolimus (EVE), a mammalian target of rapamycin (mTOR) inhibitor, has been shown to prolong survival of patients with hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2-), advanced breast cancer (ABC) with endocrine resistance. Preclinical studies have suggested that mTOR is closely associated with the tumor immune microenvironment (TME). We aimed to explore the tumor heterogeneity of TME between patients with different response to everolimus (EVE), and identify potential markers to predict efficacy of EVE in premenopausal patients with HR+/HER2−, advanced breast cancer (ABC) suffering endocrine resistance. Methods Patients were divided into two groups according to the best response to EVE: the responsive subgroup (RS) and the non-responsive subgroup (NRS). Postoperative tumor tissue from patients who received EVE was collected for whole exome sequencing. Transcript abundance of 126 distinct tumor regions of interest (ROIs) were quantitated using digital spatial profiling, including 42 tumor cell zones (TCZs), 42 immune cell zones (ICZs), and 42 stroma cell zones (SCZs). Based on investigation in multiple discrete areas, differences in cell components, function and pathway, gene expression and immune cell infiltration between RS and NRS were investigated. Results Totally, 93 differentially expressed genes (DEGs) were identified in the ICZs, 174 DEGs in the TCZs, and 197 DEGs in the SCZs, using p&amp;lt; 0.05 and |log2FC &amp;gt;1| as the screening criterion. Among all the subgroup analyses on different ROIs, the statistical significance of PIP gene was the highest (ICZs: p=2.6E-12, TCZs: p=1.25E-20, SCZs: p=2.0E-18) in the comparison of RS and NRS. Moreover, we found that PIP was highly expressed in NRS and patients with a progression-free survival &amp;lt; 1 year. The receptor-legend analysis showed that, the intra-and inter-cellular communication in RS was mainly mediated by interaction between CCL-CCR family, while NRS mainly by COL family complex. Comparing all cell components extracted from each ROI, no statistical differences were found between RS and NRS, only a statistical trend in the SCZs (p=0.098). Further cell classification analysis showed fibroblasts highest in TCZs and SCZs, macrophages in ICZs. Compared to NRS, in TCZs, fibroblasts were significantly increased (p=0.01), and plasma cells (p=0.02) and neutrophils (p=0.03) were decreased in RS. In SCZs, the downregulated fibroblasts (p=0.04) and enhanced plasma cells (p=0.01) were observed in NRS. In ICZs, pDCs (p=0.048) and neutrophils (p=0.006) were significantly elevated in NRS compared with RS. Conclusion The utility of digital spatial gene expression profiling might provide some references to predict the efficacy of EVE in premenopausal patients with HR+/HER2- ABC with endocrine resistance. Citation Format: Yujing Tan, Fei Ma, Jiayu Wang, Pin Zhang, Binghe Xu, Liyan Xue, Ying Fan. Heterogeneity of tumor immune microenvironment to predict everolimus efficacy in premenopausal women with hormone receptor-positive/human epidermal growth factor receptor 2-negative adavanced breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-14-01.

Abstract PO4-26-03: Collagen deposition in the breast cancer microenvironment correlates with ER-positive, Histological Grade I and low proliferation tumors

Abstract Breast cancer is a highly heterogeneous neoplasm both at the molecular and histological levels. Breast cancer cells present extremely high levels of genetic heterogeneity concerning mutational burden, copy number variations (CNVs) and epigenetic signatures. While this level of heterogeneity is well established, other layers of heterogeneity, such as tumor microenvironment (TME) heterogeneity, remains to be elucidated. TME in breast cancer is an intricate network encompassing tumor cells, “normal” stroma cells such as fibroblasts, lymphocytes, and a plethora of extracellular matrix (ECM) proteins produced either by cancer cells or stromal cells. These three interacting modules (cancer cells, stromal cells and the ECM) can affect each other in a reciprocal way with profound effects on patient prognosis and treatment outcome. Major protein components of the TME are ECM-related collagens, that are produced by breast cancer cells or/and cancer associated fibroblasts. The ECM collagen in breast cancer, that is mainly composed of collagen types I, III and V, has multiple roles modulating tumor growth, cancer cell invasiveness and metastasis. Here by Masson Trichrome and Immunohistochemistry/Aniline Blue co-stains for collagen fibers and proliferation index (Ki-67) in breast cancer FFPE tissue samples, we identified a statistically significant correlation between collagen deposition and Estrogen Receptor (ER)) status (p&amp;lt; 0.05, proliferation index (p&amp;lt; 0.01) and histological Grade (p=0.01). Further, in order to expand and support our findings, we analyzed the METABRIC study through the cBioPortal hub in relevance to collagen genes and multiple clinicopathological parameters. We were able to identify a coregulated subset of collagen genes that presented a positive correlation with ER-positive (p&amp;lt; 10−10, q&amp;lt; 10−10), low proliferating (p&amp;lt; 10−10, q&amp;lt; 10−10), Grade I (p=2.35e−9, q&amp;lt; 1.07e−8) group of breast cancer cases, supporting our findings. In addition, analyses of collagen-high patients from the METABRIC study indicated that most cases presented with PIK3CA, CBFB and MAP3K1 mutations, while through the g.Profiler software we identified collagen-high relevant gene signatures that encompassed gene ontology terms related to tissue development, extracellular matrix organization and cell migration. Collectively, our preliminary results indicate that ECM collagen deposition in breast cancer is characteristic of low-grade tumors with a better differentiation and possibly with a favorable prognosis. Citation Format: Angelos Koutras, Chaido Sirinian, Olga Bogri, Martha Nifora, Maria Theakou, Soren Degn, Ioannis Mouratidis, Dimitrios Chaniotis, Stavros Peroukidis, Ilias Georgakopoulos-Soares, Anastasios Papanastasiou. Collagen deposition in the breast cancer microenvironment correlates with ER-positive, Histological Grade I and low proliferation tumors [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-26-03.

Abstract PO1-15-03: Spatially resolved analysis of tumor microenvironment in invasive lobular carcinoma

Abstract Background: Invasive lobular carcinoma (ILC) is the second most common histological breast cancer subtype; however, little is known about its tumor microenvironment (TME). Here, we aimed to study ILC TME using spatial transcriptomics (ST). Methods: We performed ST (Visium 10x Genomics) on frozen tumor samples from 43 primary hormone receptor positive (HR+), HER2-negative (HER2-) ILCs. Of note, 9 samples were coming from patients who experienced disease relapse. Relative hematoxylin/eosin (H&amp;E) slides were morphologically annotated (QuPath software). xCell was used to perform cell type enrichment analysis on sample pseudo-bulks. After cross-samples integration, ST spots were clustered using hierarchical clustering (STutility R package). intNMF algorithm was used to perform clustering at the patient-level by combining: RNA sequencing information (relative percentage of spot-level clusters in each sample), morphology and level of colocalization between cancer cells and the other cell types of the tumor microenvironment. METABRIC (HR+, HER2- ILC samples, n = 122) was used as external validation cohort. Survival analyses (univariable and multivariable adjusting for clinicopathological features) were performed using Cox proportional hazard models. Results: The patient-level classification revealed four groups showing different biological characteristics. Differences in terms of morphology (annotation) and pathway enrichment analysis based on marker genes (GSEA) were observed between groups. This information allowed us to annotate our groups as: proliferative (P, n = 12, enriched in tumor cells and proliferation-related pathways), normal-stroma enriched (NSE, n = 10, enriched in fibroblasts and carcinoma in situ), metabolic (M, n = 9, enriched in metabolic-related pathways) and metabolic-immune enriched (MIE, n = 10, enriched in adipose tissue, metabolic and immune-related pathways). Interestingly, a significantly higher presence of macrophages M2 was found in MIE group. Using group-specific gene signatures, we were able to reproduce the same 4 groups in the METABRIC. Of note, we observed significant differences in relapse-free survival (RFS) in METABRIC (p = 0.03), with NSE presenting better outcome, while P, M and MIE presented worse prognosis. Analysing the area of contact between adipocytes and cancer cells typical of MIE group, we noticed an enrichment in metabolic and M2 macrophages-related pathways. In doing so, we derived a 28 gene signature relative to the tumor-adipocytes contact area. Importantly, our signature was highly expressed in M and MIE groups, and it was not correlated with proliferation-related signatures. Interestingly, the adipocytes-related signature was significantly associated with shorter RFS in ILC patients in METABRIC (univariable: HR 1.4, p = 0.023; multivariable: HR 1.6, p = 0.005). Since both proliferation and metabolism showed to be key processes in defining prognosis in ILC, we built a prognostic index by integrating our adipocytes-related signature with genomic grade index (GGI, a proliferation-related signature). Our index outperformed other existing prognostic signatures (e.g., Oncotype DX, MammaPrint, EndoPredict, LobSig) in assessing prognosis (RFS) in ILC in METABRIC (univariable: HR 1.7, p &amp;lt; 0.001; multivariable: HR 1.7, p = 0.002). Conclusions: We identified 4 biologically driven HR+, HER2- ILC groups describing tumor microenvironment heterogeneity. Of note, two of the three groups associated to worse disease outcome were related to metabolism, highlighting the importance of such process in ILC biology and in the future development of new treatment strategies. Moreover, the prognostic power of our index has the potential to refine the assessment of the risk of relapse in ILC. Further validation is warranted. Citation Format: Matteo Serra, Mattia Rediti, Laetitia Collet, Frédéric Lifrange, David Venet, Nicola Occelli, Xiaoxiao Wang, Delphine Vincent, Ghizlane Rouas, Ligia Craciun, Denis Larsimont, Laurence Buisseret, Miikka Vikkula, François Duhoux, Françoise Rothé, Christos Sotiriou. Spatially resolved analysis of tumor microenvironment in invasive lobular carcinoma [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-15-03.

Unraveling the unfolded protein response signature: implications for tumor immune microenvironment heterogeneity and clinical prognosis in stomach cancer.

Stomach cancer is a leading cause of cancer-related deaths globally due to its high grade and poor response to treatment. Understanding the molecular network driving the rapid progression of stomach cancer is crucial for improving patient outcomes. This study aimed to investigate the role of unfolded protein response (UPR) related genes in stomach cancer and their potential as prognostic biomarkers. RNA expression data and clinical follow-up information were obtained from the TCGA and GEO databases. An unsupervised clustering algorithm was used to identify UPR genomic subtypes in stomach cancer. Functional enrichment analysis, immune landscape analysis, and chemotherapy benefit prediction were conducted for each subtype. A prognostic model based on UPR-related genes was developed and validated using LASSO-Cox regression, and a multivariate nomogram was created. Key gene expression analyses in pan-cancer and in vitro experiments were performed to further investigate the role of the identified genes in cancer progression. A total of 375 stomach cancer patients were included in this study. Analysis of 113 UPR-related genes revealed their close functional correlation and significant enrichment in protein modification, transport, and RNA degradation pathways. Unsupervised clustering identified two molecular subtypes with significant differences in prognosis and gene expression profiles. Immune landscape analysis showed that UPR may influence the composition of the tumor immune microenvironment. Chemotherapy sensitivity analysis indicated that patients in the C2 molecular subtype were more responsive to chemotherapy compared to those in the C1 molecular subtype. A prognostic signature consisting of seven UPR-related genes was constructed and validated, and an independent prognostic nomogram was developed. The gene IGFBP1, which had the highest weight coefficient in the prognostic signature, was found to promote the malignant phenotype of stomach cancer cells, suggesting its potential as a therapeutic target. The study developed a UPR-related gene classifier and risk signature for predicting survival in stomach cancer, identifying IGFBP1 as a key factor promoting the disease's malignancy and a potential therapeutic target. IGFBP1's role in enhancing cancer cell adaptation to endoplasmic reticulum stress suggests its importance in stomach cancer prognosis and treatment.

A seven-step guide to spatial, agent-based modelling of tumour evolution.

Spatial agent-based models are frequently used to investigate the evolution of solid tumours subject to localized cell-cell interactions and microenvironmental heterogeneity. As spatial genomic, transcriptomic and proteomic technologies gain traction, spatial computational models are predicted to become ever more necessary for making sense of complex clinical and experimental data sets, for predicting clinical outcomes, and for optimizing treatment strategies. Here we present a non-technical step by step guide to developing such a model from first principles. Stressing the importance of tailoring the model structure to that of the biological system, we describe methods of increasing complexity, from the basic Eden growth model up to off-lattice simulations with diffusible factors. We examine choices that unavoidably arise in model design, such as implementation, parameterization, visualization and reproducibility. Each topic is illustrated with examples drawn from recent research studies and state of the art modelling platforms. We emphasize the benefits of simpler models that aim to match the complexity of the phenomena of interest, rather than that of the entire biological system. Our guide is aimed at both aspiring modellers and other biologists and oncologists who wish to understand the assumptions and limitations of the models on which major cancer studies now so often depend.

Single-cell analyzing of tumor microenvironment and cell adhesion between early and late-stage lung cancer

Lung adenocarcinoma (LUAD) is a highly heterogeneous disease that threaten human life with serious incidence and high mortality. High heterogeneity of tumor microenvironment (TME) was reported in multiple studies. However, the factor of controlling the tumor migration progression between eary and late-stage LUAD is still not fully understood. In this study, we conducted a comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data of LUAD obtained from the GEO database. The identification of cell clusters revealed significant expansion of epithelial cells in late-stage patients. Interpretation of the cell-cell communication results between early-stage and late-stage patient samples indicated that early tumor cells may interact with epithelial cells through the TGF-β pathway to promote tumor progression. The cell cycle analysis demonstrated a significant increase in the number of cells in the G2 and M phases in late-stage lung cancer. Further analysis using Non-negative Matrix Factorization (NMF) revealed early-stage cell-specific gene features involved in cell adhesion-related biological processes. Among these, the Tensin (TNS) gene family, particularly TNS1, showed high expression in epithelial cells and fibroblasts of early-stage samples, specifically associated with cell adhesion. Survival analysis using TCGA database for LUAD demonstrated that patients with high expression of TNS1 exhibited significantly higher overall survival rates compared to those with low expression. Immunofluorescence experiments have demonstrated co-expression of TNS1 with fibroblast and tumor cell markers (α-SMA and EPCAM). Immunohistochemistry experiments further validated the significantly higher expression levels of TNS1 in early-stage LUAD tissues compared to late-stage lung cancer tissues (P<0.05). Pathway experiments have shown that early-stage tumor patients with high expression of TNS1 exhibit stronger phosphorylation levels of Akt and mTOR, indicating a more potent activation of the Akt/mTOR signaling pathway. In conclusion, the results of this study demonstrate that TNS1 is an adhesive molecule in the immune microenvironment of early-stage tumor cells, and it may serve as a novel prognostic marker for lug cancer.

Establishment of Human Pituitary Neuroendocrine Tumor Derived Organoid and Its Pilot Application for Drug Screening.

Precision medicine for pituitary neuroendocrine tumors (PitNETs) is limited by the lack of reliable research models. To generate patient-derived organoids (PDOs), which could serve as a platform for personalized drug screening for PitNET patients. From July 2019 to May 2022, a total of 32 human PitNET specimens were collected for the establishment of organoids with an optimized culture protocol. This study was conducted at Sun Yat-Sen University Cancer Center. PitNET patients who were pathologically confirmed were enrolled in this study. Histological staining and whole-exome sequencing were utilized to confirm the pathologic and genomic features of PDOs. A drug response assay on PDOs was also performed. PDOs retained key genetic and morphological features of their parental tumors. PDOs were successfully established from various types of PitNET samples with an overall success rate of 87.5%. Clinical nonfunctioning PitNETs-derived organoids (22/23, 95.7%) showed a higher likelihood of successful generation compared to those from functioning PitNETs (6/9, 66.7%). Preservation of cellular structure, subtype-specific neuroendocrine profiles, mutational features, and tumor microenvironment heterogeneity from parental tumors was observed. A distinctive response profile in drug tests was observed among the organoids from patients with different subtypes of PitNETs. With the validation of key characteristics from parental tumors in histological, genomic, and microenvironment heterogeneity consistency assays, we demonstrated the predictive value of the PDOs in testing individual drugs. The established PDOs, retaining typical features of parental tumors, indicate a translational significance in innovating personalized treatment for refractory PitNETs.

Immune-tumor interaction dictates spatially directed evolution of esophageal squamous cell carcinoma

Abstract Esophageal squamous cell carcinoma (ESCC) is a poor-prognostic cancer type with extensive intra- and inter-patient heterogeneity in both genomic variations and tumor microenvironment (TME). However, the patterns and drivers of spatial genomic and microenvironmental heterogeneity of ESCC remain largely unknown. Here, we generated a spatial multi-omic atlas by whole-exome, transcriptome, and methylome sequencing of 507 tumor samples from 103 patients. We identified a novel tumor suppressor PREX2, accounting for 22% ESCCs with frequent somatic mutations or hyper-methylation, which promoted migration and invasion of ESCC cells in vitro. Analysis of the tumor microenvironment and quantification of subclonal expansion indicated that ESCCs undergo spatially directed evolution, where subclones mostly originated from the tumor center but had a biased clonal expansion to the upper direction of the esophagus. Interestingly, we found upper regions of ESCCs often underwent stronger immunoediting with increased selective fitness, suggesting more stringent immune selection. In addition, distinct TMEs were associated with variable genomic and clinical outcomes. Of which, hot TME was associated with high immune evasion and subclonal heterogeneity. We also found that immunoediting, instead of CD8+ T cell abundance, acts as an independent prognostic factor of ESCCs. Importantly, we found significant heterogeneity in previously considered potential therapeutic targets, as well as BRCAness characteristics in a subset of patients, emphasizing the importance of focusing on heterogeneity in ESCC targeted therapy. Collectively, these findings provide novel insights into the mechanisms of the spatial evolution of ESCC and inform precision therapeutic strategies.

A patient-specific lung cancer assembloid model with heterogeneous tumor microenvironments.

Cancer models play critical roles in basic cancer research and precision medicine. However, current in vitro cancer models are limited by their inability to mimic the three-dimensional architecture and heterogeneous tumor microenvironments (TME) of in vivo tumors. Here, we develop an innovative patient-specific lung cancer assembloid (LCA) model by using droplet microfluidic technology based on a microinjection strategy. This method enables precise manipulation of clinical microsamples and rapid generation of LCAs with good intra-batch consistency in size and cell composition by evenly encapsulating patient tumor-derived TME cells and lung cancer organoids inside microgels. LCAs recapitulate the inter- and intratumoral heterogeneity, TME cellular diversity, and genomic and transcriptomic landscape of their parental tumors. LCA model could reconstruct the functional heterogeneity of cancer-associated fibroblasts and reflect the influence of TME on drug responses compared to cancer organoids. Notably, LCAs accurately replicate the clinical outcomes of patients, suggesting the potential of the LCA model to predict personalized treatments. Collectively, our studies provide a valuable method for precisely fabricating cancer assembloids and a promising LCA model for cancer research and personalized medicine.

Abstract P20: Unveiling the Influence of Tumor Microenvironment and Spatial Heterogeneity on Temozolomide Resistance in Glioblastoma using an Advanced Human in vitro Model of the Blood-brain Barrier and Glioblastoma

Abstract Glioblastoma (GBM) is the most common primary malignant brain cancer in adults, with a dismal prognosis of 15 months. This is despite decades of research and improvements in surgery and radiotherapy. Temozolomide (TMZ) is the only first-in-line chemotherapeutic; however, resistance is frequent and multifactorial. While many molecular and genetic factors have been linked to TMZ resistance, the role of the solid tumor morphology and the tumor microenvironment (TME), particularly the blood-brain barrier (BBB), is unknown. Complex 3D in vitro models are able to recapitulate aspects of clinical disease such as the tumor microenvironment, such that research into the role of the TME and how it affects tumor biology and therapy resistance can be studied more easily and accurately. We have developed a complex 3D in vitro model for GBM and its surrounding BBB, that recapitulates important clinical features such as a dense tumor core with tumor cells that invade along the perivascular space; and a perfusable BBB with a physiological permeability with morphology that is altered in the presence of a tumor spheroid. We demonstrate that TMZ sensitivity decreases with increasing cancer cell spatial organization, and that the BBB can contribute to TMZ resistance. Proteomic analysis of cultured microtissues using low volume sample workflows revealed proteins linked to aggressive tumors and TMZ resistance. Notably, proteomic analysis revealed a potential role for cell cycle proteins in TMZ resistance. Preliminary data with tumor spheroids formed by patient derived GBM progenitor cells revealed a similar trend of an increase in proteins associated with tumor aggressiveness upon co-culture with a BBB. The data demonstrate the utility of combining complex physiological in vitro models and next generation mass spectrometry for interrogating the tumour microenvironment, tumor biology and accurate in vitro therapy validation. Citation Format: Maxine SY Lam, Joey JY Aw, Damien Tan, Ragavi Vijayakumar, Hui Yi Grace Lim, Swathi Yada, Qing You Pang, Nick Barker, Carol Tang, Beng Ti Ang, Radoslaw M. Sobota, Andrea Pavesi. Unveiling the Influence of Tumor Microenvironment and Spatial Heterogeneity on Temozolomide Resistance in Glioblastoma using an Advanced Human in vitro Model of the Blood-brain Barrier and Glioblastoma [abstract]. In: Proceedings of Frontiers in Cancer Science; 2023 Nov 6-8; Singapore. Philadelphia (PA): AACR; Cancer Res 2024;84(8_Suppl):Abstract nr P20.

Development of nanobodies targeting hepatocellular carcinoma and application of nanobody-based CAR-T technology

BackgroundChimeric antigen receptor T (CAR-T) cell therapy, as an emerging anti-tumor treatment, has garnered extensive attention in the study of targeted therapy of multiple tumor-associated antigens in hepatocellular carcinoma (HCC). However, the suppressive microenvironment and individual heterogeneity results in downregulation of these antigens in certain patients’ cancer cells. Therefore, optimizing CAR-T cell therapy for HCC is imperative.MethodsIn this study, we administered FGFR4-ferritin (FGFR4-HPF) nanoparticles to the alpaca and constructed a phage library of nanobodies (Nbs) derived from alpaca, following which we screened for Nbs targeting FGFR4. Then, we conducted the functional validation of Nbs. Furthermore, we developed Nb-derived CAR-T cells and evaluated their anti-tumor ability against HCC through in vitro and in vivo validation.ResultsOur findings demonstrated that we successfully obtained high specificity and high affinity Nbs targeting FGFR4 after screening. And the specificity of Nbs targeting FGFR4 was markedly superior to their binding to other members of the FGFR family proteins. Furthermore, the Nb-derived CAR-T cells, targeting FGFR4, exhibited significantly enhanced anti-tumor efficacy in both experiments when in vitro and in vivo.ConclusionsIn summary, the results of this study suggest that the CAR-T cells derived from high specificity and high affinity Nbs, targeting FGFR4, exhibited significantly enhanced anti-tumor efficacy in vitro and in vivo. This is an exploration of FGFR4 in the field of Nb-derived CAR-T cell therapy for HCC, holding promise for enhancing safety and effectiveness in the clinical treatment of HCC in the future.

Integration of 3D bioprinting and multi-algorithm machine learning identified glioma susceptibilities and microenvironment characteristics

Glioma, with its heterogeneous microenvironments and genetic subtypes, presents substantial challenges for treatment prediction and development. We integrated 3D bioprinting and multi-algorithm machine learning as a novel approach to enhance the assessment and understanding of glioma treatment responses and microenvironment characteristics. The bioprinted patient-derived glioma tissues successfully recapitulated molecular properties and drug responses of native tumors. We then developed GlioML, a machine learning workflow incorporating nine distinct algorithms and a weighted ensemble model that generated robust gene expression-based predictors, each reflecting the diverse action mechanisms of various compounds and drugs. The ensemble model superseded the performance of all individual algorithms across diverse in vitro systems, including sphere cultures, complex 3D bioprinted multicellular models, and 3D patient-derived tissues. By integrating bioprinting, the evaluative scope of the treatment expanded to T cell-related therapy and anti-angiogenesis targeted therapy. We identified promising compounds and drugs for glioma treatment and revealed distinct immunosuppressive or angiogenic myeloid-infiltrated tumor microenvironments. These insights pave the way for enhanced therapeutic development for glioma and potentially for other cancers, highlighting the broad application potential of this integrative and translational approach.

IFI35 regulates non-canonical NF-κB signaling to maintain glioblastoma stem cells and recruit tumor-associated macrophages.

Glioblastoma (GBM) is the most aggressive malignant primary brain tumor characterized by a highly heterogeneous and immunosuppressive tumor microenvironment (TME). The symbiotic interactions between glioblastoma stem cells (GSCs) and tumor-associated macrophages (TAM) in the TME are critical for tumor progression. Here, we identified that IFI35, a transcriptional regulatory factor, plays both cell-intrinsic and cell-extrinsic roles in maintaining GSCs and the immunosuppressive TME. IFI35 induced non-canonical NF-kB signaling through proteasomal processing of p105 to the DNA-binding transcription factor p50, which heterodimerizes with RELB (RELB/p50), and activated cell chemotaxis in a cell-autonomous manner. Further, IFI35 induced recruitment and maintenance of M2-like TAMs in TME in a paracrine manner. Targeting IFI35 effectively suppressed in vivo tumor growth and prolonged survival of orthotopic xenograft-bearing mice. Collectively, these findings reveal the tumor-promoting functions of IFI35 and suggest that targeting IFI35 or its downstream effectors may provide effective approaches to improve GBM treatment.

Immune cell-related prognostic risk model and tumor immune environment modulation in esophageal carcinoma based on single-cell and bulk RNA sequencing.

Immune cells play a pivotal role in the tumor microenvironment, exerting significant influence on tumor progression and patient outcomes, but the current biomarkers are insufficient to fully capture the complex and diverse tumor immune microenvironment and the impact of immunotherapy. The advent of single-cell sequencing allows us to explore the tumor microenvironment at an unprecedented resolution, enabling the identification and characterization of distinct subsets of immune cells, thereby paving the way for the development of prognostic models using immune cells. Leveraging single-cell data, our study deeply investigated the intricacies of immune microenvironment heterogeneity in esophageal carcinoma. We elucidated the composition, functionality, evolution, and intercellular communication patterns of immune cells, culminating in the construction of an independent prognostic model at the single-cell level. Furthermore, we conducted a comprehensive analysis of disparities in immune infiltration and immune checkpoint expression between patients categorized into high- and low-risk groups, which may impact patient prognosis. In summary, our study harnessed multiomics data to delineate the immune profile of esophageal carcinoma patients, provide a method for leveraging molecular signatures of immune cells to identify potential biomarkers, while concurrently providing evidence for the potential benefits of immunotherapy.

Abstract LB120: Stem cell-derived ACUA CAR-NKT allogeneic cell therapies harness orthogonal mechanisms of action for the treatment of solid tumors

Abstract Despite the remarkable success of chimeric antigen receptor T cell (CAR-T) therapies in treating hematological malignancies, their use in solid tumor indications has been met with distinct challenges. The immunosuppressive tumor microenvironment (TME) and tumor antigen heterogeneity are two significant factors that may adversely impact the efficacy of these ground-breaking therapies. Engineered natural killer T (NKT) cells harbor biological features distinct from CAR-T cell or CAR-NK cell therapies and thus represent a novel therapeutic approach to deploy “off the shelf” to patients. Appia Bio’s ACUA platform is capable of generating large quantities of highly pure CAR-NKT cells from cord blood CD34+ hematopoietic stem and progenitor cells (HSPCs) for clinical use. We have demonstrated that multiple distinct mechanisms of anti-tumor activity are operative in CAR-NKT cells, including CAR-mediated killing, NK receptor-mediated killing, adjuvant effects on antigen-specific T cells and immature dendritic cells, as well as the ability to deplete of pro-tumorigenic macrophages.When transduced with a CAR, ACUA-derived CAR-NKT cells show potent CAR-mediated tumor cell killing, similar to CAR-T cells. In contrast to conventional T cells, CAR-NKT cells also demonstrate potent CAR antigen-independent tumor cell killing, mediated partially through NKG2D and DNAM-1 activating NK receptors. CAR-NKT cells, but not conventional CAR-T cells, enhanced the function of NY-ESO specific T cells in controlling multiple rounds of tumor rechallenge, through the secretion of soluble components. In regard to other adjuvant effects which may provide additional benefit in vivo, ACUA-derived NKT cells enhanced the activation of peripheral blood monocyte-derived dendritic cells, which can regulate beneficial Th1 type immune responses. To explore how ACUA-derived NKT cells could reshape the TME, we generated immunosuppressive M2-polarized macrophages and observed that ACUA-derived NKT cells can either kill or reprogram these cells to a pro-inflammatory phenotype following co-culture. Notably, these immune cell adjuvant mechanisms are specific to ACUA-derived NKT cells and are not observed with conventional CAR-T cells. In summary, these mechanistic studies underscore the advantages of ACUA-derived NKT cells over conventional CAR-T cells and highlight their potential as a promising differentiated allogeneic therapy for the treatment of solid tumors. Citation Format: Xi Wang, Andrew J. Hou, Michael A. Christopher, Yannick Bulliard, Jason S. Damiano. Stem cell-derived ACUA CAR-NKT allogeneic cell therapies harness orthogonal mechanisms of action for the treatment of solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB120.

DNA nanomachines reveal an adaptive energy mode in confinement-induced amoeboid migration powered by polarized mitochondrial distribution

Energy metabolism is highly interdependent with adaptive cell migration in vivo. Mechanical confinement is a critical physical cue that induces switchable migration modes of the mesenchymal-to-amoeboid transition (MAT). However, the energy states in distinct migration modes, especially amoeboid-like stable bleb (A2) movement, remain unclear. In this report, we developed multivalent DNA framework-based nanomachines to explore strategical mitochondrial trafficking and differential ATP levels during cell migration in mechanically heterogeneous microenvironments. Through single-particle tracking and metabolomic analysis, we revealed that fast A2-moving cells driven by biomimetic confinement recruited back-end positioning of mitochondria for powering highly polarized cytoskeletal networks, preferentially adopting an energy-saving mode compared with a mesenchymal mode of cell migration. We present a versatile DNA nanotool for cellular energy exploration and highlight that adaptive energy strategies coordinately support switchable migration modes for facilitating efficient metastatic escape, offering a unique perspective for therapeutic interventions in cancer metastasis.

Comprehensive molecular classification predicted microenvironment profiles and therapy response for hepatocellular carcinoma.

Tumor microenvironment (TME) heterogeneity leads to a discrepancy in survival prognosis and clinical treatment response for hepatocellular carcinoma (HCC) patients. The clinical applications of documented molecular subtypes are constrained by several issues. We integrated three single-cell datasets to describe the TME landscape and identified six prognosis-related cell subclusters. Unsupervised clustering of subcluster-specific markers was performed to generate transcriptomic subtypes. The predictive value of these molecular subtypes for prognosis and treatment response was explored in multiple external HCC cohorts and the Xiangya HCC cohort. TME features were estimated using single-cell immune repertoire sequencing, mass cytometry and multiplex immunofluorescence. The prognosis-related score (PRS) was constructed based on machine learning algorithm. Comprehensive single-cell analysis described TME heterogeneity in HCC. The five transcriptomic subtypes possessed different clinical prognoses, stemness characteristics, immune landscapes and therapeutic responses. Class 1 exhibited an inflamed phenotype with better clinical outcomes, while Classes 2 and 4 were characterized by a lack of T cell infiltration. Classes 5 and 3 indicated an inhibitory tumor immune microenvironment. Analysis of multiple therapeutic cohorts suggested that Classes 5 and 3 were sensitive to ICB and targeted therapy, whereas Classes 1 and 2 were more responsive to transcatheter arterial chemoembolization treatment. Class 4 displayed resistant to all conventional HCC therapies. Three potential therapeutic agents and four targets were further identified for high-PRS HCC patients. Our study generated a clinically valid molecular classification to guide precision medicine in patients with HCC.

Development of Transmission Ambient Pressure Laser Desorption Ionization/Postphotoionization Mass Spectrometry Imaging.

Laser-based high-resolution mass spectrometry imaging at ambient conditions has promising applications in life science. However, the ion yield during laser desorption/ablation is poor. Here, transmission atmospheric pressure laser desorption ionization combined with a compact postphotoionization (t-AP-LDI/PI) assembly with a krypton discharge lamp was developed for the untargeted imaging of various biomolecules. The spatial distributions of numerous lipid classes, fatty acids, neurotransmitters, and amino acids in the subregions of mouse cerebellum tissue were obtained. Compared with single laser ablation, the sensitivities for most analytes were increased by 1 to 3 orders of magnitude by dopant-assisted postphotoionization. After careful optimization, a spatial resolution of 4 μm could be achieved for the metabolites in mouse hippocampus tissue. Finally, the melanoma tissue slices were analyzed using t-AP-LDI/PI MSI, which revealed the metabolic heterogeneity of the melanoma microenvironment and exhibited the phenomenon of abnormal proliferation and invasion trends in tumor cells.

Emerging measurements for tumor-infiltrating lymphocytes in breast cancer.

Tumor-infiltrating lymphocytes are a general term for lymphocytes or immune cells infiltrating the tumor microenvironment. Numerous studies have demonstrated tumor-infiltrating lymphocytes to be robust prognostic and predictive biomarkers in breast cancer. Recently, immune checkpoint inhibitors, which directly target tumor-infiltrating lymphocytes, have become part of standard of care treatment for triple-negative breast cancer. Surprisingly, tumor-infiltrating lymphocytes quantified by conventional methods do not predict response to immune checkpoint inhibitors, which highlights the heterogeneity of tumor-infiltrating lymphocytes and the complexity of the immune network in the tumor microenvironment. Tumor-infiltrating lymphocytes are composed of diverse immune cell populations, including cytotoxic CD8-positive T lymphocytes, B cells and myeloid cells. Traditionally, tumor-infiltrating lymphocytes in tumor stroma have been evaluated by histology. However, the standardization of this approach is limited, necessitating the use of various novel technologies to elucidate the heterogeneity in the tumor microenvironment. This review outlines the evaluation methods for tumor-infiltrating lymphocytes from conventional pathological approaches that evaluate intratumoral and stromal tumor-infiltrating lymphocytes such as immunohistochemistry, to the more recent advancements in computer tissue imaging using artificial intelligence, flow cytometry sorting and multi-omics analyses using high-throughput assays to estimate tumor-infiltrating lymphocytes from bulk tumor using immune signatures or deconvolution tools. We also discuss higher resolution technologies that enable the analysis of tumor-infiltrating lymphocytes heterogeneity such as single-cell analysis and spatial transcriptomics. As we approach the era of personalized medicine, it is important for clinicians to understand these technologies.