Citations

Background/Objectives: Glioblastoma surveillance after surgery and chemoradiation remains challenging because MRI findings of tumor recurrence can overlap with pseudo-progression, treatment-related effects, and postoperative tissue changes. Methods: We developed a seven-channel postoperative MRI framework using a deep learning transfer method to support non-invasive glioblastoma treatment-effect assessment. The model used T1 contrast-enhanced, FLAIR, T1, RSI-Cell, ADC, T2, and cerebral blood flow volumes from 124 postoperative glioblastoma patients (164 MRI timepoints) as input. Images were processed using a 3D ResNet18 encoder pretrained on 588 postoperative glioma samples and fine-tuned using task-specific classification heads. The cohort comprised 124 patients contributing 164 postoperative MRI timepoints, all acquired at 3T on scanners from a single vendor. Performance was evaluated with nested five-fold cross-validation stratified and assigned at the patient level, so that all timepoints from a given patient fell in one-fold and the training epoch was selected on an inner split rather than on the fold being reported. Because some of the clinical labels were incomplete, the number of evaluable timepoints differed by task (recurrence versus pseudo-progression, 164; MGMT, 99; short-term survival, 139). The whole procedure was repeated under three independent random seeds and results are reported as the mean and standard deviation across seeds. Results: The strongest clinical endpoint was recurrence versus pseudo-progression, where nested cross-validation across three random seeds gave a pooled out-of-fold AUC of 0.935 (SD = 0.014), area under the precision-recall curve of 0.973, balanced accuracy of 0.880, sensitivity of 0.917, and specificity of 0.843. No other endpoint reached reliable discrimination. Radiation decision reached an AUC of 0.658 (SD = 0.039), while MGMT promoter methylation (AUC = 0.532, SD = 0.082) and short-term survival (AUC = 0.514, SD = 0.027) were indistinguishable from chance. Conclusions: In this single-center proof-of-concept study, postoperative MRI successfully distinguished tumor recurrence from pseudo-progression. However, the models did not reliably predict the other three outcomes related to molecular status, treatment planning, and prognosis. Overall, the model learned imaging features specifically associated with recurrence, rather than a more general representation of the tumor that can predict many different clinical outcomes. These findings support technical feasibility for a single endpoint rather than clinical readiness, for which external multi-center validation is required.

Accurate estimation of Alzheimer’s disease (AD) severity from structural magnetic resonance imaging (MRI) remains difficult, as disease-associated anatomical alterations are often subtle and publicly available datasets are typically too small to support robust deep learning model training. This feasibility study sought to determine how much Alzheimer’s disease spectrum-related information could be extracted from a small structural MRI cohort using a deliberately lightweight two-dimensional convolutional neural network (2D CNN), and whether transfer learning improves model performance. This study was intended as a methodological proof of concept rather than the development of a clinically deployable diagnostic tool. Structural scans and Clinical Dementia Rating (CDR) labels from the OASIS-1 dataset were filtered to 214 subjects: 124 cognitively normal (CN), 65 with mild cognitive impairment (MCI; CDR = 0.5), and 25 with AD-level impairment (CDR ≥ 1). A compact 2D CNN trained from scratch and a transfer learning model (frozen ImageNet MobileNetV2 features) were evaluated on four binary tasks (CN vs. AD, MCI vs. AD, CN vs. MCI, and CN vs. any impairment) under identical pre-processing and subject-level repeated 5-fold cross-validation (10 repeats), with the decision threshold tuned only on an inner split. Discrimination was summarized by ROC-AUC with 95% confidence intervals (CIs), permutation tests against chance, and per-task sensitivity and specificity. The from-scratch CNN recovered only a broad normal-versus-impaired signal (CN vs. any impairment AUC 0.59) and was at chance on adjacent-stage tasks (MCI vs. AD 0.41; CN vs. MCI 0.51). Transfer learning improved every task: CN vs. AD AUC 0.745 (95% CI 0.730–0.763), CN vs. any impairment 0.642, CN vs. MCI 0.601, and MCI vs. AD 0.599. On an independent OASIS-2 cohort, the transfer learning CN vs. AD model retained AUC 0.748. In this small-data regime, transfer learning recovers substantially more Alzheimer-spectrum signals than a from-scratch CNN, but performance remains modest because it is bounded by CDR-based, non-biomarker-confirmed labels, suggesting the model separates CDR-defined cognitive-status groups rather than detecting AD pathology.

TRAIL is a TNF family ligand that trimerizes TRAIL-R1 (DR4) or TRAIL-R2 (DR5) to induce apoptosis, necroptosis, and/or NF-κB activation in receptor-bearing cells. We previously identified TRAILshort as a splice variant of TRAIL that lacks cysteine 230, cannot trimerize, and acts as a dominant-negative ligand that blocks TRAIL-mediated apoptosis. TRAILshort is expressed on cell surfaces and within extracellular vesicles, enabling it to confer TRAIL resistance to both producing and bystander cells. In this study, we showed that elevated TRAILshort levels were associated with chronic viral infections, cancer, and autoimmune diseases, suggesting a link to impaired immune regulation. Using unbiased phosphoproteomics and mechanistic studies, we demonstrated that TRAILshort binding to DR5 recruited and activated the phosphatase Src homology region 2 domain–containing phosphatase 1 (SHP-1), leading to zeta-chain-associated protein kinase 70 (ZAP-70) dephosphorylation, disruption of ZAP-70–CD3ζ interactions, and impaired T cell receptor signaling, thereby reducing T cell activation, proliferation, and cytokine production in response to antigen or CD3/CD28 ligation. Genetic or pharmacologic SHP-1 inhibition reverses these effects. In humanized mouse models, TRAILshort promoted the persistence of transformed mouse embryonic fibroblasts (MEFs) and L428 and antagonized CD19-directed CAR T cell activity, revealing TRAILshort as an immunomodulator of T cell function with therapeutic implications, including blocking TRAILshort to restore T cell immunity or delivering TRAILshort to enforce tolerance. TRAILshort, a variant of TRAIL, suppresses T cell function and immune responses in cancer and infectious diseases, highlighting its potential as a therapeutic target.

Abstract Despite long-standing interest in combining topoisomerase I (TOP1) poisons with PARP inhibitors (PARPis), the mechanistic basis for the variable and often unpredictable response to this strategy remains unclear. Early preclinical studies suggested that PARP inhibition amplifies TOP1 poison-induced DNA damage, implying broad therapeutic potential; however, emerging translational and clinical data—particularly in ovarian cancer—indicate that synergy is context-dependent and governed by tumor-intrinsic features that have not been fully defined.Here we assessed determinants of response in five HR proficient ovarian cancer (OC) patient-derived xenografts (PDXs) previously treated with the SN-38 prodrug etirinotecan pegol (NKTR-102) in combination with the PARPi rucaparib (C. Nitschmann, Clin. Cancer Res. 22(2) Suppl. B50, 2016). Although all models were refractory to single-agent rucaparib, 50% exhibited enhancement of NKTR-102-induced tumor regression with the addition of rucaparib. Neither PARP1 abundance nor extent of rucaparib-mediated poly(ADP-ribose) suppression correlated with therapeutic benefit. Instead, sensitization was restricted to PDXs intrinsically responsive to NKTR-102 and was strongly associated with high levels of NKTR-102-induced TOP1-DNA covalent complexes. In further analysis, 2 of 3 resistant PDXs exhibited high levels of the ABC transporter ABCG2 despite lack of previous therapy, revealing a previously underappreciated mechanism of de novo resistance that limits effective TOP1 inhibitor activity independent of homologous recombination status. Notably, NKTR-102 response did not track with platinum/taxane sensitivity, highlighting the distinct molecular circuitry governing TOP1 poison susceptibility.Collectively, these findings demonstrate that rucaparib can potentiate NKTR-102-induced regression in tumors capable of forming abundant TOP1-DNA covalent complexes. This work identifies TOP1-DNA complex trapping and lack of ABCG2 expression as mechanistic predictors of benefit, providing an updated framework for selecting patients most likely to respond to TOP1 poison/PARPi combinations and challenging the long-standing assumption that PARP inhibition can universally resensitize tumors to TOP1 poisons. Citation Format: Annapoorna Venkatachalam, Caroline C. Nitschmann, Karen S. Flatten, Rachel Hurley, Xiaonan Hou, Cristina Correia, Paula A. Schenider, Krista Goergen, Matthew Maurer, Ethan P. Heinzen, Ann L. Oberg, S John Weroha, Paul Haluska, Scott H. Kaufmann, Elizabeth M. Swisher. TOP1-DNA protein complex as a predictive biomarker for NKTR-102/Rucaparib combination therapy in PARP-inhibitor resistant ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(8_Suppl):Abstract nr LB364.

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.

Poly(ADP-ribose) polymerase (PARP) inhibitors (PARPis) are an important therapy for high-grade serous ovarian cancer (HGSOC). However, PARPi resistance frequently emerges, necessitating previously unrecognized approaches to improve HGSOC responses. Here, we showed that the anaplastic lymphoma kinase (ALK) inhibitor brigatinib enhances PARPi activity in HGSOC cells by disrupting an adaptive survival mechanism orchestrated by Fos-related antigen 1 (FRA1) in response to PARPi. This effect of brigatinib occurred through an ALK-independent pathway, wherein brigatinib induced a dual blockade of focal adhesion kinase (FAK) and EPH receptor A2 (EPHA2) tyrosine kinases, leading to the suppression of protein kinase B (Akt) and extracellular-regulated kinase (ERK) signaling accompanied by disruption of a phosphorylation event crucial for FRA1 protein stability. Moreover, in HGSOC patient-derived xenograft (PDX) models, brigatinib and PARPi combination therapy induced tumor regression and improved overall survival compared with PARPi alone, particularly in models with high FAK and EPHA2. These findings support dual targeting of FAK and EPHA2 as a strategy to achieve effective and durable PARPi responses and identify a promising biomarker-based combinatorial approach using brigatinib and PARPi for HGSOC, particularly the subset characterized by high FAK and EPHA2.

Clinical trial enrichment is the targeted recruitment of prospective individual patients with defined clinical characteristics who are likely to benefit from newly developed or repurposed drugs. This process is central to the success of clinical trials together with patient management and regulatory compliance. A main challenge in clinical trial enrichment lies in the recognition of a priori clinical parameters and information that informs drug efficacy or toxicity, particularly when intended for a broader unseen population. Although Artificial Intelligence (AI) approaches, especially large language models (LLMs), have been employed in many aspects of clinical trials, to our knowledge, there is no AI method that has been developed which offers a prospective prediction and assesses the extent to which a given therapeutic intervention benefits an unseen population. Here, we offer an outlook on how to build Artificial Clinic Intelligence (ACI), a generative AI (GAI)-powered modeling platform for modeling clinical trial enrichment. ACI generates synthetic patient data and models clinical trial enrichment to inform clinicians on key clinical parameters that are enriched in prospective patients prior to accrual.

Abstract BRCA2 mutations contribute to the pathogenesis and treatment sensitivity of a subset of ovarian, breast, prostate, and pancreatic cancers. When these cancers become therapy resistant, secondary mutations that restore the BRCA2 open reading frame are found in half the cases, but other causes of resistance remain incompletely understood. Here, we identified translational readthrough of a premature termination codon (PTC) as a cause of resistance to poly(ADP-ribose) polymerase inhibitors (PARPis) and cisplatin in cells derived from the BRCA2-mutated ovarian cancer line PEO1 by PARPi selection. Despite persistence of the signature 4965C > G (p.Y1655X) BRCA2 mutation, low-level expression of full-length BRCA2 protein was detectable in these cells by immunoblotting and tandem mass spectrometry. Either BRCA2 knockdown or gene interruption 5′ or 3′ to the PTC restored treatment sensitivity, implicating BRCA2 in the resistance. Reporter assays demonstrated UAG-selective readthrough in the resistant clones but not parental cells. Moreover, custom searching of global proteomic data indicated readthrough of stop codons, particularly UAGs, in additional proteins in the resistant clones. Finally, multi-omic analysis identified multiple changes in the nonsense-mediated decay and termination machineries that favor readthrough. Accordingly, the present results identify PTC readthrough as a potential mechanism of drug resistance in cells with BRCA2 nonsense mutations.