Citations

Abstract Background While the radiation dose to left anterior descending (LAD) artery is associated with overall survival (OS) in NSCLC patients, manual segmentation is labor‐intensive. Several auto‐segmentation models were developed on average 4DCT with promising geometric agreement, yet their feasibility in clinical outcome analysis on lung CT remains unclear. Purpose This study evaluates whether automated LAD segmentation can reproduce, at the group level, the established dose‐survival association in a multi‐institutional clinical trial dataset. Methods Three deep learning‐based auto‐segmentation (DLAS) models for automatic segmentation of the LAD coronary artery were applied to 460 patients from the NRG Oncology/RTOG 0617 dataset. Kaplan–Meier curves and Cox proportional hazard ratio (HR) were calculated using each model's segmentation. The LAD V15 Gy was used to stratify patients into high‐and low‐risk groups, and the cut‐off values were determined by minimizing mean absolute error (MAE) between DLAS‐derived and manual OS curves. Results All three models demonstrated significantly different OS curves between the high risk and low risk groups ( p < 0.05). The HR was 1.40 (95% CI: 1.02–1.93; p = 0.0381; MAE = 7.1%), 1.32 (95% CI: 1.03–1.68; p = 0.0258; MAE = 9.6%), and 1.37 (95% CI: 1.03–1.84; p = 0.0331; MAE = 7.9%) for the three models using a common V15 Gy ≥ 10% cut‐off. After optimizing the cut‐off, one model aligned well with what was derived from manual segmentation (MAE = 4.3%). Conclusion Among the evaluated models, one DL model showed the strongest performance of survival groups separation and achieved group‐level overall survival stratification comparable to that previously reported using manual contours in the multi‐institutional cohort. Further validation with paired manual contours and pre‐specified cutoffs is warranted.

ABSTRACT Angioimmunoblastic T‐cell lymphoma (AITL) is an aggressive peripheral T‐cell lymphoma with poor outcomes, and the role of autologous stem cell transplantation (ASCT) as consolidation after frontline therapy remains controversial. We conducted a retrospective cohort study using the National Cancer Database, including adults diagnosed with AITL between 2004 and 2020 who received frontline systemic therapy. Patients were categorized as chemotherapy alone (Chemo) or chemotherapy followed by ASCT (Chemo + ASCT). To mitigate immortal time bias, prespecified landmark analyses were performed, with a 6‐month landmark analysis as the primary approach and a 9‐month landmark analysis as a sensitivity analysis. Multivariable Cox regression and propensity score weighting using inverse probability of treatment weighting for the average treatment effect were used to address measured confounding. Among 3996 patients receiving systemic therapy, 686 (17.2%) underwent ASCT. In the 6‐month landmark analysis, ASCT was associated with improved overall survival (HR, 0.53; 95% CI, 0.45–0.62; p < 0.001). Findings were consistent in the 9‐month landmark analysis (HR, 0.58; 95% CI, 0.49–0.69; p < 0.001) and IPTW‐adjusted model (HR, 0.51; 95% CI, 0.44–0.60; p < 0.001). In this large real‐world cohort, ASCT consolidation was consistently associated with improved survival in patients with AITL. Although treatment‐response data were unavailable and residual confounding cannot be excluded, these findings provide supportive real‐world evidence and warrant prospective studies with treatment‐timing and response data to better define patient selection.

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) is the most lethal common cancer (5-year survival ∼13%), emerging through years-to-decades progression involving chronic fibro-inflammatory injury and precursor transformation processes including ADM and PanIN. KRAS-driven acinar-to-ductal metaplasia (ADM) is among the earliest experimentally tractable and potentially interceptable events; KRAS (mutated in &gt;90% of PDAC) has remained difficult to therapeutically target, particularly for prevention; emerging KRAS inhibitors are developed for treatment of established malignancy rather than early interception. We aimed to prioritize KRAS-axis interception candidates via AI-guided network analysis of TCGA-PAAD restricted to a curated KRAS-driven ADM/PanIN-initiation mechanism module. Methods: The 38-gene module (n=183 TCGA-PAAD samples; 37 genes available, PRSS2 absent) spans KRAS-MAPK, RTKs, TGF-β/SMAD, ECM, inflammation, transcription, tumor suppressors, and ductal/acinar genes from canonical pancreatic-cancer-progression biology, linking established-tumor transcriptomes to early-interception biology. A stability-selected Spearman-correlation network served as the network backbone (PCMCI+/NOTEARS extensions ongoing), paired with patient-level bootstrap filtering (B=200 resamples; ≥70% retention). A composite Interception Score I(v) = α·CB + β·Σ|w| + γ·U + δ·R integrating betweenness centrality, summed edge-weight influence, druggability (literature-curated drug-target prior), and bootstrap robustness ranked candidate interception nodes (unoptimized weights; tuning ongoing). Results: Spearman correlation yielded a 37-node, 248-edge network baseline; bootstrap resampling (B=200) evaluated 856 distinct candidate edges, of which 194 (22.7%) achieved ≥70% retention, defining the stable subgraph used for scoring. The Interception Score placed 7 KRAS-axis genes among the top-10 candidates: ERBB2 (0.953), KRAS (0.743), ERBB3 (0.742), MAPK1 (0.708), EGFR (0.675), MAP2K2 (0.605), and BRAF (0.589). Inflammation (IL6, 0.755), TGF-β/SMAD2 (0.726), and ECM/MMP2 (0.638) also ranked highly, recapitulating PDAC initiation mechanisms (stromal-epithelial crosstalk; inflammatory-fibrotic priming). ERBB2 (HER2), a clinically druggable RTK with approved inhibitors in other malignancies, emerged as the top-ranked candidate, supporting evaluation of HER2-axis signaling for interception in molecularly selected PDAC subsets. These findings align with canonical PDAC biology, supporting biological plausibility prior to formal weight optimization and external validation. Conclusions: AI-guided network analysis of TCGA-PAAD prioritizes biologically coherent KRAS-axis and stromal-inflammatory candidates with clinically available HER2-, EGFR-, MEK-, BRAF-, and KRAS-pathway inhibitors, supporting hypothesis generation for future pancreatic cancer interception studies. Stage-resolved analysis, causal-inference extensions, and sex-/age-stratified validation are ongoing. Citation Format: Jianfu Li, Yingyun Yang, Michael Wallace, Cui Tao, Yan Bi. AI-Driven Network-Based Discovery on TCGA-PAAD Identifies KRAS-Axis Drug-Repurposing Targets for Pancreatic Cancer Interception [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr A029.

Abstract Background: The limited efficacy of oncolytic virotherapy (OV) in gastrointestinal malignancies has been attributed to an immunologically inert tumor microenvironment. While cisplatin is widely used as a cytotoxic agent, its immunomodulatory effects remain incompletely defined. We hypothesized that cisplatin may reprogram the tumor immune landscape to enable more effective OV-mediated anti-tumor responses. Methods: We evaluated the effects of cisplatin in combination with two distinct oncolytic platforms, vesicular stomatitis virus (VSV) and adenovirus (AdV), across both in vitro and in vivo colorectal cancer models. In vivo efficacy was assessed using MC38 subcutaneous and peritoneal metastatic models, with analysis of tumor growth, survival, and toxicity. Immune profiling was performed using single-cell RNA sequencing (scRNA-seq), flow cytometry, and bulk RNA sequencing to interrogate changes in CD8+ T cell states and functional programs. Guided by these preclinical findings, a clinical trial combining XELOX, PD-1 blockade, and OV therapy was initiated (ChiCTR2500107607). Results: Cisplatin markedly enhanced OV-mediated anti-tumor efficacy across models, resulting in sustained tumor control and improved survival in both subcutaneous and peritoneal disease settings. This effect was conserved across viral platforms, indicating a virus-agnostic interaction. Mechanistically, cisplatin induced a robust interferon (IFN)–dominant transcriptional program, characterized by upregulation of interferon-stimulated genes, including IRF7, IRF9, and ISG15. Single-cell analysis revealed that combination therapy reshaped the CD8+ T cell compartment, promoting the expansion of a population with transcriptional and functional features of central memory T cells (Tcm), alongside reduced exhaustion signatures. These changes were accompanied by enhanced cytokine signaling pathways associated with T cell maintenance and function, including IL-2, IL-7, and IL-15. In vitro, the combination of cisplatin and OV increased tumor cell death and interferon-γ production, supporting a model in which cytotoxic and immunostimulatory effects are mechanistically linked. A phase I clinical trial has been initiated. In gastric cancer, patients with retroperitoneal metastases are being treated with OV in combination with PD-1 blockade (ongoing). In colorectal cancer, the strategy is being evaluated in the neoadjuvant setting with an rectal preservation approach. Conclusions: Our findings identify a previously unrecognized role for cisplatin in priming an interferon-driven immune program that promotes Tcm differentiation and function, thereby enabling durable responses to oncolytic virotherapy. This work provides a mechanistic rationale for leveraging chemotherapy-induced immune reprogramming to enhance viro-immunotherapy and supports the clinical translation of this strategy in gastrointestinal malignancies. Citation Format: Joseph Brandon. Parker, Zhongyi Dong, Tao Liu, Qisen Guo, Haoyu Zhang, Bo Ni, Long Bai, Yiwen Chen, Jiasheng Zong, Xiang Xia, Jiaojiao Zheng, Hoang Tran Pham, Aya Elalfy, Demarcus Ingram, Ahmed Abdel-Hakeem, Muhammad Ali, Shannon Hartzell, Zizhen Zhang, Vivienne Yang. Cisplatin primes an interferon-driven CD8+ central memory T cell program to enable oncolytic virotherapy in gastrointestinal cancer malignancies [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr B024.

Abstract Background The left anterior descending (LAD) coronary artery is a critical organ‐at‐risk in thoracic radiotherapy. In thoracic stereotactic body radiation therapy (SBRT), sharp dose gradients and poor LAD contrast on 4DCT scans introduce uncertainty in LAD dose assessment. Methods We evaluated the performance of an AI‐generated LAD region structure as a surrogate for manual LAD contours in early‐stage NSCLC. We used deep inspiration breath hold (DIBH) scans from 49 patients with manually delineated LADs; planning risk volume contours were expanded by 3 mm. Then, 96 lung SBRT dose maps were registered by matching the left anterior portion of the heart contours. Dice similarity coefficient (DSC), inclusion ratios (IR), dose–volume histogram (DVH) metrics, linear regression, and linear mixed models were used to compare the structures. The correlation of the AI LAD region and LAD dose to mean heart dose (MHD) was assessed. Results The AI LAD region showed strong correlation with manual LAD contours for maximum dose (D1%, R 2 = 0.93) and volume receiving ≥10 Gy (V10Gy, R 2 = 0.81). The planning risk volume also demonstrated high correlation to the AI LAD region. MHD was a poor predictor of LAD dose ( R 2 ≈0.4). Additional dose metric correlations were assessed. Discussion To investigate dose volume effects to the LAD during radiotherapy for stage I NSCLC, a robust segmentation paradigm must be established. It must be easily implementable on typical imaging acquired during treatment, with an emphasis on clinical translatability. Our model has shown that the AI LAD region has a strong correlation with LAD dose under typical lung SBRT doses and supports its use for further investigation into LAD dose volume constraints during lung SBRT. Conclusion AI LAD region provides a clinically feasible and scalable alternative to manual LAD contouring for dose estimation in lung SBRT. Its strong correlation with key dose metrics validates its use in large‐scale retrospective and prospective studies.

Abstract The minichromosome maintenance 2-7 (MCM2-7) proteins form a hexameric protein complex that is the structural core of replicative helicase, which is essential for accurate DNA replication. While their role in replication is well-established, the biological functions of excess chromatin-bound MCM2-7 proteins remain unclear. Here, we identify a transcriptional regulatory function of MCM2-7 through its interaction with nuclear factor κB (NF-κB) p65. We demonstrate that MCM2-7 proteins and NF-κB p65 reciprocally recruit each other to target gene promoters. Consequently, MCM2-7 proteins facilitate the localization of RNA Pol II to NF-κB target gene promoters and along the gene body, thereby promoting transcription of NF-κB target genes. These findings reveal MCM2-7 proteins promote NF-κB target gene transcription by facilitating p65 and RNA Pol II recruitment, offering a potential avenue for fine-tuning NF-κB target gene transcription.