Correia, Cristina Belo

Abstract Neuroblastoma (NB) accounts for 10% of pediatric cancer-related deaths. High-risk NB is frequently metastatic at diagnosis and has a poor 5-year survival rate (<50%) despite aggressive treatment. GD2, a disialoganglioside glycolipid that is expressed in nearly all NBs, has been validated as a promising immunotherapy target. Although GD2-CAR-T cells have been clinically evaluated, their efficacy has been limited by poor tumor infiltration and the immunosuppressive tumor microenvironment (TME), highlighting the need for alternative immune-based strategies. With inherent capacity of macrophages to infiltrate tumors, phagocytose cancer cells, and modulate TME, they represent a promising alternative strategy for immunotherapy in NB. In this study, we developed and evaluated GD2-CAR-macrophages (GD2-CAR-Ms) as a novel therapeutic approach for NB. GD2-specific CAR constructs were designed, validated, and introduced into multiple human and murine monocyte/macrophage cell lines via lentiviral transduction. Fluorescent microscopy and flow cytometry confirmed a significant enhancement in the phagocytosis of GD2-high NB cells by GD2-CAR-Ms containing Hu3F8 single-chain variable fragment (scFv). A strong positive correlation was observed between GD2 expression levels on the target cells and phagocytosis efficiency of GD2-CAR-Ms, indicating that the phagocytic activity of GD2-CAR-Ms is antigen-specific. Anti-tumor efficacy was further evaluated in vivo using an immunocompetent Mycn-expressing NB non-genetically engineered mouse model (MYCN-NGEMM). Mice treated with GD2-CAR-Ms exhibited significantly reduced tumor burden and prolonged survival compared to control groups. Notably, treatment of mice bearing GD2-high tumors resulted in markedly improved survival, including one complete remission among ten treated animals, underscoring the therapeutic potential of GD2-targeted macrophage therapy. Histological analysis showed increased deposition of connective tissues and decreased tumor cells density, suggesting TME remodeling. Furthermore, immunohistochemistry reveals increased immune cell infiltration, particularly of macrophages, neutrophils and T cells, indicating a favorable shift in the immune TME from “cold” to “hot” following GD2-CAR-M treatment. Additionally, inhibition of tumor growth was also observed in our zebrafish NB model after GD2-CAR-Ms treatment, further supporting their therapeutic potential. Together, our findings demonstrate that GD2-CAR-Ms exhibit potent antitumor activity and reshape the tumor immune microenvironment, supporting their translational potential as a therapeutic strategy for high-risk NB. Citation Format: Ke-En Tan, Kok Siong Yeo, Cheng Zhang, Caleb Baker, Shyang Hong Tan, Alexis M. Sosic, Yat-Yuen Lim, Choong-Yong Ung, Cristina Correia, Stephanie F. Polites, Yi Lin, William A. Weiss, Hu Li, Shizhen Zhu. Engineering macrophages as a novel therapeutic approach for high-risk neuroblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7810.

Abstract Background: Symmetry principles, a long foundational framework in physics and chemistry, have rarely been applied to understand biological phenotypes especially in cancers. Here we examine whether symmetric relationships in gene expression can characterize and differentiate healthy from disease conditions. Method: To test this concept, we built a hybrid machine-learning framework, Learning-Based Invariant Feature Engineering (LIFE), that applies two symmetric invariant feature functions, IFF1 and IFF2, to all possible gene pairs in bulk transcriptomic datasets to identify invariant feature genes (IFGs) - gene pairs whose transformed expression values by either IFF1 or IFF2 produce quasi-constant single-value outputs within a phenotype despite inter-individual variability. Results: Using bulk transcriptomes from 25 normal organs (GTEx) and 25 cancer types (TCGA), we computed IFF values for all normally distributed gene pairs, selected the 1000 most stable pairs per phenotype, and evaluated their performance in multiclass classification with five-fold cross-validation and independent hold-out testing. IFGs generated >70% accuracy across organs and cancers, establishing the existence of robust phenotype-specific symmetry “fingerprints.” Mapping approved and experimental drug targets onto networks construction from IFGs (IF-Nets) showed strong enrichment of hubs in cancer networks and highlighted the use of IF-Nets as drug discovery platforms in cancer treatment. Conclusion: Our findings demonstrate that gene-expression symmetry as a unifying organizing principle for phenotype definition and illustrate how IFGs and IF-Nets can guide biomarker design and symmetry-aware pharmacological intervention via “symmetry breaking.” Citation Format: Cristina Correia, Choong-Yong Ung, Cheng Zhang, Shizhen Zhu, Hu Li, . Learning-based invariant feature engineering reveals symmetry-encoded fingerprints of cancers that facilitate drug discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5516.

Human diseases are complex and arise from the coordinated action of multiple genes and their protein products. Genes’ behaviors extend beyond genetic variants, mutations, and differential expressions. Their coordinated activity across biological scales (molecules, cells, tissues, organs) produces emergent behaviors that shape health and disease. These emergent behaviors span time and space and are often hard to measure directly from observation when using standard experimental measurements. Yet these “hidden” or latent gene characteristics can be powerful drivers of disease. We propose a Mini-Galaxy Model (MGM), a systems-level AI-driven network framework that models cells as “mini-galaxies” composed of multilayered biological information, with each layer encoding a different dimension of genes’ behavior. Here, we delineate a strategy on how to construct and compare MGMs across health and disease and map their etiological relatedness. We also operationalize the MGM as a discovery platform for translational medicine, offering modules to allow target prioritization and editing. By reframing human diseases as the result of emergent behavior of multilayered multimode biological networks and their perturbations, the MGM yields actionable rules to streamline biomarker discovery, guide target selection and enable rational design of combinatorial interventions, and accelerate drug repurposing.

Abstract Glioblastoma multiforme (GBM) is a particularly aggressive primary brain tumor that is relatively common in adults. The disease comes with a dismal prognosis, often with expected survival just over a year when treated with surgery, chemotherapy, and radiation. A promising addition to our GBM treatment arsenal are oncolytic viruses (OVs), a class of immunotherapy now in clinical trials. The most effective OVs home in on tumor cells, killing them and releasing tumor-associated antigens that elicit antitumor immunity. This effectiveness, however, varies depending on the cell state—a property that may be influenced by the composition of the extracellular matrix (ECM) of GBM. Cellular communication network factor 1 (CCN1) is found in the ECM of the majority of GBMs and is predictive of resistance to OVs, particularly those derived from herpes simplex virus type 1 (HSV-1). In this study, our aim is to understand how extracellular CCN1 alters the GBM intracellular state in a manner that confers resistance to OVs. We began by using our NetDecoder platform to build CCN1-specific prioritized protein-protein interaction (PPI) networks from published LN229 human GBM transcriptomic datasets. From this network, we identified 12 PPI edges that dominate the network flow in CCN1-high LN229 cells when compared to CCN1-low controls. The 21 unique nodes that comprise these binary PPI edges were extracted and subjected to downstream biological process overrepresentation analyses using Gene Ontology. We found that the cytokine-mediated signaling pathway (FDR = 4.2 x 10-11; enrichment ratio = 15.8) and the cellular response to cytokine stimulus (FDR = 3.1 x 10-9; enrichment ratio = 10.9) were highly enriched when mapped to these nodes. We conclude that CCN1, acting as a paracrine signal in the GBM tumor microenvironment, primes the cell to inhibit HSV-1 and other candidate OV replication prior to any virus interactions, thus contributing to immunovirotherapy resistance. While CCN1 and its cell surface integrin binding partners are ostensible drug targets for overcoming this resistance, pharmacologic interventions that target CCN1-specific high flow edges in our prioritized PPI network may be more effective. Citation Format: Dileep D. Monie, Cristina Correia, Cheng Zhang, Choong Ung, Richard G. Vile, Hu Li. Innate immunity and cytokine signaling pathways drive resistance to HSV-1 oncolytic immunovirotherapy in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1679.