Zhang, Cheng

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.

Abstract Cellular senescence is a dynamic, multistage biological program that exerts context-dependent effects on tumor initiation, progression, and overall carcinogenesis. Our laboratory has previously demonstrated that the early accumulation of p16Ink4a-expressing (representing strong cyclin-dependent kinase inhibition) senescent alveolar macrophages during tumorigenesis actively facilitates tumor formation and progression by inhibiting the infiltration of cytotoxic T lymphocytes. However, considering the dynamic nature of cellular senescence, distinct senescent cell populations could express high amounts of the alternative cell cycle inhibitor p21Waf1/Cip1 independently of p16Ink4a to potentially catalyze distinct functions. To test this, we have employed transgenic p21-ATTAC (p21-driven apoptosis through targeted activation of caspase 8) mice to investigate the role of p21-expressing cells during Kras-driven adenoma formation. In agreement with our previous observations following clearance of p16-expressing senescent cells, we found that removal of p21+ cells attenuates early tumor development. Western blot and immunofluorescence analyses corroborates a reduction in p21, p16, and multiple members of a putative senescence-associated secretory phenotype (SASP). Although senescent alveolar macrophages remain a critical therapeutic target, here we found that senescent endothelial cells also exhibited elevated expression of p21, p16, and SASP with a high senolytic response. Future studies will include identifying the predominant p21-expressing senescent endothelial subpopulations and elucidating the mechanisms by which endothelial senescence contributes to lung tumorigenesis. Collectively, our findings have unveiled additional senescent cell populations with the potential to drive lung tumorigenesis. Citation Format: Yuanning Du, Luis I. Prieto, Karthikbabu Jeganathan, Cheng Zhang, Hu Li, Darren Baker. Elucidating the role of p21 + senescent cells in early-stage lung tumorigenesis [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 2268.