Citations

Abstract Introduction: Diffuse large B-cell lymphoma (DLBCL) is the most common type of malignant lymphoma. Previous studies have shown that DLBCL cells are susceptible to GPX4 (Glutathione peroxidase 4)-regulated ferroptosis, an iron-dependent type of programmed cell death characterized by increased reactive oxygen species and excessive lipid peroxidation. In addition to GPX4, FSP1 (Ferroptosis suppressor protein 1, previously known as AIFM2) is a glutathione-independent repressor of ferroptosis that has shown prognostic significance in solid tumors. In this study, we aimed to reveal the significance of GPX4 and FSP1 in DLBCL. Patients and Methods: We performed immunohistochemistry (IHC) for GPX4 and FSP1 in a large cohorts of patients with de novo DLBCL, and evaluated their cytoplasmic and nuclear expression. Prognostic analysis was performed for FSP1 and GPX4 expression in patients treated with rituximab (R)-CHOP or CHOP chemotherapy, respective of TP53 mutation status, as p53 regulates ferroptosis and TP53 mutation is associated with poorer prognosis in DLBCL. Results: FSP1 expression with a ≥10% cutoff was positive in 42.3% of DLBCL cases. Patients with TP53 mutations had a non-significant trend of higher mean FSP1 expression than those with wild-type (Wt) TP53 (P=0.11). Prognostic analysis revealed that cytoplasmic FSP1+ expression was associated with significantly poorer overall survival and progression-free survival in Wt-TP53 DLBCLs treated with R-CHOP (P=0.016 and P=0.003, respectively), and only showed non-significant unfavorable trends in TP53 mutated DLBCLs and patients treated with CHOP. In contrast, GPX4 expression did not show a significant unfavorable prognostic effect in DLBCL, and in fact, was associated with a non-significant trend of better survival. Previously we have quantified immune cell abundance and PD-1/PD-L1 expression in the tumor microenvironment of the study cohort using multiplex fluorescent IHC. Correlative analysis found that FSP1+ patients had significantly higher mean and median abundance of CD68+ cells (both M1 and M2 macrophages) and CD11c+ cells than FSP1- patients. Further analysis in Wt-TP53 and mutated TP53 subcohorts found that only in the TP53 mutated DLBCL subcohort, FSP1 expression was associated with significantly higher mean and median CD163-CD68+ (M1) and overall CD68+ macrophages, whereas in the Wt-TP53 DLBCL subcohort, FSP1 expression was associated with significantly higher median (but not mean) CD163+CD68+ (M2) macrophages and CD11c+ cells. Summary: Cytoplasmic FSP1 expression but not GPX4 had significantly adverse prognostic effect in patients with Wt-TP53 DLBCL treated with standard immunochemotherapy. Our results also suggest that in addition to ferroptosis regulation, macrophage abundance was relevant for the prognostic effects of FSP1 expression. Citation Format: Beibei Lyu, Zijun Xu-Monette, Xiaoxian X. Zhao, Eric D. Hsi, Ming Chen, Carlo Visco, Alexandar Tzankov, Karen Dybkaer, Mu-En Wang, Chang Wang, Qingyan Au, Harry Nunns, Zenggang Pan, Benjamin Parsons, Santiago Montes-Moreno, Fenghuang Zhan, Michael B. Møller, Leon Bernal-Mizrachi, Youli Zu, Shanxiang Zhang, Weina Chen, Govind Bhagat, Yong Li, KEN H. YOUNG. Expression of the ferroptosis suppressor FSP1 but not GPX4 shows significant adverse prognostic effect in diffuse large B-cell lymphoma with wild-type TP53 [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 4661.

Abstract The CoMMpass study (NCT01454297) is a prospective, longitudinal observational study involving 1,141 newly diagnosed multiple myeloma (NDMM) patients. Bone marrow aspirates from the study participants were collected for comprehensive molecular characterization of tumor (using bulk RNA sequencing and whole genome sequencing) and tumor immune microenvironment using 3’ single-cell RNA sequencing (scRNA-seq). To support exploration of this rich multi-omic dataset, we developed CoMMpass Explorer (CE), an intuitive interactive platform that enables real-time analysis of clinical and genomic data from CoMMpass. A central feature of CE is cohort building, where users can filter and stratify patients by clinical, genomic, and survival data elements to create custom cohorts for analysis. CE provides five main views: Overall Summary for clinical feature distribution, Kaplan-Meier curves, and multivariate Cox proportional hazards models; Mutational Profile for visualizing and comparing somatic mutations; Tumor Profile for bulk differential expression and gene set enrichment; Immune Microenvironment for comparing cell type abundance and cell cycle dynamics between cohorts using scRNA-seq data; and Pseudo-Bulk, which aggregates single-cell expression by cell type to enable cohort-level comparisons of transcriptional programs using DEseq2. CE reproduces prior CoMMpass studies, including WEE1 expression being associated with poorer progression-free survival (Simhal, et al.) and different mutation frequencies between African American and European American patients (Manojlovic, et al.). We further demonstrate CE’s utility by interrogating the 2025 Consensus Genomic Staging (CGS) risk classification system (Avet-Loiseau, et al). We compared CGS high-risk (HR, n=249) vs. CGS standard-risk (SR, n=573) patients. HR patients have significantly worse progression-free survival, overall survival, and time to second-line therapy compared with SR patients. Univariate models recapitulated expected differences in the clinical features that define CGS risk. Bulk tumor RNA-seq and ssGSEA highlighted a more proliferative and genomically unstable transcriptional program in HR group, with upregulation of angiogenesis and cell-cycle-related pathways. In the immune microenvironment, monocytes were more abundant in the SR group. Pseudo-bulk RNA-seq of plasma cells showed overexpression of ANXA1, NSD2, IGF1R, MAF and related genes in HR. CE democratizes access to the CoMMpass multi-omic resource, allowing researchers to interrogate clinical and molecular heterogeneity. By enabling hypothesis generation and replication of published results, CE serves as a valuable tool for identifying prognostic biomarkers and meaningful patterns in gene expression, mutation profiles, and the immune microenvironment. Citation Format: Wanxing Zhang, Chaitanya R. Acharya, Steven M. Foltz, David E. Avigan, Samir Parekh, Ravi Vij, Shaji Kunnathu Kumar, Taxiarchis Kourelis, Sagar Lonial, Hearn Cho, Ioannis S. Vlachos, Sacha Gnjatic, Li Ding, Manoj Bhasin, George Mulligan. CoMMpass Explorer: An interactive platform to explore clinical and genomic data from newly diagnosed multiple myeloma patients from the landmark CoMMpass observational study [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 5504.

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.

Abstract Background. Mitophagy plays a crucial role in removing dysfunctional mitochondria to maintain cellular homeostasis but also has been implicated in cancer tumorigenesis. Previously, we showed that genetic variation in mitophagy pathway genes holds a predictive value in metastatic CRC (mCRC) patients (pts) treated with bevacizumab-based chemotherapy. In this study, we analyzed the correlation between tumor expression profiles of genes associated with PINK1/Parkin and BNIP3/NIX/FUNDC1 mitophagy pathways with clinical outcome in a large phase III randomized clinical trial CALGB SWOG 80405. Method. We analyzed 433 pts with mCRC treated with first-line chemotherapy in combination with the targeted agents bevacizumab (Bev, n = 226) or cetuximab (Cet, n = 207). Tumor RNA isolated from FFPE samples was sequenced using the HiSeq 2500 platform (Illumina). Median Overall survival (mOS) and median progression-free survival (mPFS) were compared across pts groups divided into high, medium, and low tertiles based on mitophagy-related gene (BNIP3, BNIP3L, PINK1, PRKN) expression levels. Likelihood ratio tests, hazard ratios (HRs), and 95% confidence intervals (CIs) were calculated using multivariable Cox proportional hazards models, adjusting for age, sex, ECOG performance status, tumor location, number of metastatic sites, KRAS status, Consensus Molecular Subtypes, and treatment arm. Results: Overall, BNIP3L demonstrated significant prognostic value for both PFS and OS after adjustment for multiple testing (false discovery rate [FDR] < 0.05). Tumors with low BNIP3L expression showed longer PFS and OS compared to those with medium or high expression levels (mPFS 14.8 vs. 10.0 vs. 9.2 months, HR 1.16, 95% CI 1.04 - 1.29p = 0.0182; mOS: 37.4 vs. 30.2 vs. 24.4 months, HR 1.22, 95% CI 1.09 - 1.38, p = 0.0036), independent of treatment. In pts treated with Cet, high PRKN expression was associated with improved PFS and OS (mPFS: 8.9 vs. 10.3 vs. 14.6 months, HR 0.79; 95% CI 0.69 - 0.91, p = 7.3e-05; mOS: 21.2 vs. 27.7 vs. 41.1 months, HR 0.87; 95% CI 0.75 - 1.01, p = 0.00084), whereas low PINK1 expression was significantly associated with longer OS (mOS: 24.9 vs. 30.9 vs. 39.2 months, HR 1.23; 95% CI 1.04 -1.46, p = 0.013). No significant associations were observed in Bev-treated patients for PRKN or PINK1. No significant results were found for BNIP3 expression analysis. Conclusion. Mitophagy is a crucial player in CRC biology due to its tumor-intrinsic (mitochondrial bioenergetics) and microenvironment (interacts with immune components) effects. Our study highlights the prognostic value of BNIP3L and predictive potential of PINK1 and PRKN, suggesting a potential combinatorial therapeutic approach using mitochondrial targeting drugs and targeted agents e.g Cet, which warrants further investigation. Support: Genentech; Clinicaltrials.gov Identifier: NCT00265850 Citation Format: Shivani Soni, Yan Yang, Michela Bartolini, Pooja Mittal, Fang-Shu Ou, Lesly Torres-Gonzalez, Unnati Hemant Shah, Jae Ho Lo, Steve Soto Trujillo, Yitzhar Goretsky, Wu Zhang, Alan P. Venook, Sandra Algaze, Joshua Millstein, Heinz-Josef Lenz. Correlation of mitophagy related genes expression and clinical outcome in metastatic colorectal cancer patients enrolled in CALGB (Alliance)/SWOG 80405 [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 1011.

Abstract Background: Spatial cellular ecosystems in the tumor microenvironment (TME) form dynamic signaling hubs that critically influence cancer disease progression and response to therapy, including response to immune checkpoint inhibitors (ICIs). However, tumor sampling bias and the impracticality of acquiring serial tumor biopsies have made it challenging to profile the TME clinically. To address this challenge, we developed Liquid EcoTyper, a multi-analyte AI framework to quantify TME spatial cellular ecosystems—termed spatial ecotypes (SEs)—noninvasively from cell-free DNA (cfDNA) methylation data. Methods: In previous work, we discovered nine spatial ecotypes, including SEs associated with ICI response (e.g., SE7 with benefit, SE4 with resistance), and quantified them from 1,249 bulk tumor RNA-seq profiles of melanoma, non-small cell lung cancer (NSCLC), and bladder cancer (BC) patients (Cancer Res (2025) 85 (8_Supplement_1): 153). Here we trained Liquid EcoTyper, an interpretable deep learning model, to transfer SE profiling to cell-free DNA using CpG methylation profiles. Performance was quantified using simulated and real data, including methylation profiles (EM-seq) of plasma cfDNA paired with tumor biopsy-derived SE levels determined by EM-seq (n=20 pairs) or 10x Visium (n=15 pairs). Clinical outcome prediction was evaluated using pretreatment plasma from patients treated with ICI alone (n=78 melanoma pts), chemotherapy and ICI (n=25 NSCLC pts), and enfortumab vedotin and ICI (n=10 localized muscle-invasive BC (MIBC) pts). Tumor mutational burden (TMB), tumor PD-L1, or circulating tumor DNA (ctDNA) were profiled as a comparator where possible. Results: We observed striking concordance between plasma-derived SE levels and tumor biopsy-confirmed SE levels from the same patients, demonstrating that liquid biopsy analysis of cfDNA methylomes can recapitulate TME spatial biology. Underscoring specificity, no reliable SE signal was detectable from peripheral blood mononuclear cells. In pre-ICI plasma, liquid SE levels were significantly associated with ICI-based response (SE7) versus resistance (SE4) in patients with melanoma, NSCLC, and MIBC, with a mean cross-cancer area under the curve (AUC) of 0.87 for SE7 (range 0.8-0.97) and 0.81 for SE4 (range 0.76-0.85)—far surpassing TMB, tumor PD-L1, and ctDNA in evaluable patients, both in binary analyses of response and multivariable models of survival. Our results also showed potential to predict pathologic complete response in the neoadjuvant setting. Conclusion: Liquid EcoTyper is a scalable noninvasive AI-based framework for spatiotemporal assessment of the TME. By enabling TME-informed risk stratification without surgical biopsies, Liquid EcoTyper has potential to enable more precise and actionable decision making in patients with solid tumors. Citation Format: Erin L. Brown, Wubing Zhang, Abul Usmani, Noah Earland, Ayesha Hashmi, Chibuzor Olelewe, Anushka Viswanathan, Pradeep S. Chauhan, Minji Kang, Chloé B. Steen, Hyun Soo Jeon, Susanna Avagyan, Irfan Alahi, Nicholas P. Semenkovich, Matteo Bergsagel, Janella C. Schwab, Chloe M. Sachs, Faridi Qaium, Peter K. Harris, Antonella Bacchiocchi, Qingyuan Cai, Andrew J. Gentles, Rondell P. Graham, Peter C. Lucas, Ryan C. Fields, Jacob J. Orme, Aaron S. Mansfield, Mario Sznol, Ruth Halaban, David Y. Chen, Aaron M. Newman, Aadel A. Chaudhuri. Liquid biopsy profiling of the tumor microenvironment to determine response to immunotherapy regimens across solid tumors [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 94.

The patient is a male in his 60s with morbid obesity and end-stage renal disease secondary to hypertension and diabetes. After a lifelong struggle with weight management, he was approved for bariatric surgery. Following completion of the bariatric clinic courses, he underwent laparoscopic Roux-en-Y gastric bypass. There were no intraoperative complications nor divergences from our routine practice. Postoperatively, the patient resumed peritoneal dialysis (PD) on postoperative day zero. He tolerated the operation and dialysis well, noting that the drained abdominal fluid remained serosanguinous for 5 days after surgery. While the traditional practice after intra-abdominal surgery is to temporarily transition to haemodialysis in patients who require dialysis, we demonstrate that immediate resumption of PD may be safe and efficacious. However, patients should be educated regarding the temporary change in colour of the drained intra-abdominal fluid. More robust data is required for further research and future practice guideline development.

Abstract Methods that map genetic risk to cells do not directly test whether spatially organized ligand–receptor (LR) gene annotations carry conditional heritability association. Here we introduce EdgeMap, which scores each gene by the spatial activity of its LR contexts in spatial transcriptomics data, maps these scores alongside cell-intrinsic annotations to SNP-level LD scores, and tests both jointly against GWAS summary statistics. Across 17 traits and five human tissues, edge z -scores are systematically higher in biologically matched tissues: all 15 traits with both matched and unmatched tissue classes show this ordering (median Δ z = 1.51 ; sign P = 3.1 × 10 −5 ), and 11 of 13 nominal-positive associations concentrate in the biologically matched set ( P = 1 × 10 −4 ). The pattern survives broad LR-gene-class and cell-type composition controls. Donor-level cross-section summaries, independent GWAS replication for LDL and CAD, and cell-segmented Visium HD liver data provide convergent support. A secondary analysis prioritizes constituent genes within active LR contexts; sixteen of 31 prioritized genes are absent from standard gene-level methods. These results establish spatially weighted LR-gene annotations as a complementary layer for interpreting complex-trait genetic architecture.

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.