Multimodal Free-Water Imaging Links Cardiometabolic Risk to Periarterial Dysfunction and Amyloid Accumulation in Early Alzheimer’s

Research Square · 2026-05-07

Abstract 4737: Utilizing patient-derived organoid tumor models to combat chemo-immunotherapy resistance in NSCLC

Cancer Research · 2026-04-03

Abstract Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape for non-small cell lung cancer (NSCLC), yet only a subset of patients achieves durable benefit due to primary and acquired resistance. We previously reported elevated kynurenine (KYN) in cisplatin-resistant NSCLC. KYN, produced by indoleamine 2,3-dioxygenase 1 (IDO1) and tryptophan 2,3-dioxygenase (TDO2), promotes immune evasion by diminishing CD8+ T-cell responses and expanding regulatory T cells (Tregs). Clinical failure of selective IDO1 inhibition in cancer suggests compensatory TDO2 upregulation as one of the resistance mechanisms. To target redundant KYN production, we evaluated the efficacy of a dual IDO1/TDO2 inhibitor (AT-0174; Antido Therapeutics) in preclinical models of cisplatin-resistant NSCLC. Efficacy and mechanism were assessed in vivo via a syngeneic orthotopic mouse model. Dual inhibition significantly reduced tumor volume (n=10, p<0.01) and prolonged survival in the mouse model. These effects correlated with increased activity of CD8+ T cells and natural killer (NK) cells, as well as reduced frequencies of immunosuppressive Tregs and myeloid-derived suppressor cells (MDSCs). Combining AT-0174 with anti-PD-1 therapy produced a synergistic effect, further suppressing tumor growth and significantly improving survival in mice (log-rank, p=0.0001). We then developed a patient-derived organoid tumor (PDOT) platform that closely recapitulates the molecular and metabolic features of each patient's NSCLC. Single-cell RNA sequencing demonstrated ∼70% transcriptional similarity to original tumors, and metabolomic profiling confirmed comparable pathway activity. IDO1 protein was detected in all PDOTs. Co-culture with patient-matched PBMCs revealed that PDOTs with high baseline PD-L1 and kynurenine (KYN) expression were highly sensitive to dual IDO/TDO inhibition combined with pembrolizumab, resulting in increased immune cell infiltration (n = 10, p < 0.005). This microfluidic PDOT system enables functional, patient-specific assessment of KYN pathway reliance, supporting timely stratification of NSCLC patients and rational design of combination regimens integrating metabolic targeting with immune checkpoint inhibitors. These findings provide a strong rationale for clinical evaluation of dual IDO/TDO inhibitors alongside checkpoint blockade. Citation Format: Manojavan Nagarajan, Chunjing Wu, Dao M. Nguyen, Estelamari Rodriguez, Emily Kim, Diane Lim, George Theodore, Irving Vidaurre, Wei Sha, Adeline Murphy, Jose Gomez, Durga Prasad Gannamedi Hinder, David Lombard, Lynn G. Feun, Niramol Savaraj, Medhi Wangpaichitr. Utilizing patient-derived organoid tumor models to combat chemo-immunotherapy resistance in NSCLC [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 4737.

Weighted Hypoxemia Index: An adaptable method for quantifying hypoxemia severity

PLoS ONE · 2025-07-10

OBJECTIVE: To quantitate hypoxemia severity. METHODS: We developed the Weighted Hypoxemia Index to be adapted to different clinical settings by applying 5 steps to the oxygen saturation curve: (1) Identify desaturation/resaturation event [Formula: see text] by setting the upper threshold; (2) Exclude events as artifact by setting a lower threshold; (3) Calculate weighted area for each [Formula: see text] as [Formula: see text]; (4) Calculate a normalization factor [Formula: see text] for each subject; (5) Calculate the Weighted Hypoxemia Index as the summation of all weighted areas multiplied by [Formula: see text]. We assessed the Weighted Hypoxemia Index predictive value for all-cause mortality and cardiovascular mortality using the Sleep Heart Health Study (enrollment 1995-1998, 11.1 years mean follow-up). RESULTS: We set varying upper thresholds at 92%, 90%, 88%, and 86%, a lower threshold of 50%, calculated area under the curve and area above the curve, with and without a linear weighted factor (duration of each event [Formula: see text]), and used the same normalization factor of total sleep time <90% divided by total sleep time. After excluding subjects with missing data, we analyzed 4,509 participants (Alive: N = 3,769; All-cause mortality: N = 1,071; cardiovascular mortality: N = 330). Since the Weighted Hypoxemia Index-Area Under the Curve set at upper threshold of 90% (WHI-AUC90) had the best results in predicting all-cause mortality, we then compared it to the Apnea-Hypopnea Index and Total Sleep Time <90%. WHI-AUC90 showed statistical significance across quintiles for all-cause mortality, but not cardiovascular mortality, in adjusted Cox regression models. CONCLUSION: The Weighted Hypoxemia Index offers a versatile and clinically relevant method for quantifying hypoxemia severity, with potential applications to evaluate mechanisms and outcomes across various patient populations.

Genome-wide gene by sleepiness interaction analysis for sleep apnea

SLEEP · 2025-07-24 · 1 citations

STUDY OBJECTIVES: Excessive daytime sleepiness (EDS), influenced by environmental and social-behavioral factors, is reported by a subset of patients with sleep apnea-a group that may be at elevated cardiovascular risk. However, it is unclear whether sleep apnea with and without EDS have distinct genetic underpinnings. In this study, we perform gene-by-EDS interaction analyses for apnea hypopnea index, a diagnostic marker of sleep apnea severity, to understand EDS's influence on its underlying genetic risk. METHODS: Discovery interaction analyses for common variants and gene-based rare variants were conducted respectively using multi-ethnic Trans-Omics for Precision Medicine (N = 11 619) data, followed by replication and subsequent meta-analysis in additional Trans-Omics for Precision Medicine-imputed data (N = 8904). The 1 degree-of-freedom (1df) G × E test and the 2df joint G,G × E tests were utilized. Sex-stratified analyses were additionally performed. RESULTS: Discovery analysis revealed two common intronic variants-rs13118183 (CCDC3) and rs281851 (MARCHF1)-and three rare variant gene sets mapped to SCUBE2, TMEM26, and CPS4FL-to exhibit interaction with EDS. Meta-analysis revealed EDS interaction with 11 rare variant gene sets mapped to UBLCP1, MED31, RAP1GAP, CPNE5, MYMX, YY1, ZNF773, YBEY, IQCB1, PI4K2B, and CORO1A. CONCLUSION: Genetic loci reveal connections to cardiovascular risk, insulin resistance, thiamine deficiency, and resveratrol mechanism. Discovered genetic signals may offer insight into pertinent biological pathways for sleep apnea patients with an excessively sleepy subtype. Statement of Significance Sleep apnea is a complex sleep disorder. Exemplifying this is the disparately varying estimates of presence of excessive daytime sleepiness (EDS) in patients, and persistent EDS that lingers despite treatment. Some data indicate that the excessively sleepy subtype of sleep apnea carries heightened cardiovascular risk. Whether EDS influences genetic risk factors underlying sleep apnea has not yet been investigated. This study addresses this gap, as the first genome-wide gene × EDS interaction study for apnea hypopnea index, the standard sleep apnea severity metric. Genetic loci that have been previously unconsidered for sleep apnea are revealed. Discovered interaction signals highlight pathways in metabolism, genes associated with cardiometabolic traits, and therapeutic agents influencing obesity, blood pressure, oxidative stress, and apnea hypopnea index.

1049 Characterizing the Complexity of REM Sleep Across Pediatric Development: A Novel Metric Approach

SLEEP · 2025-05-01

Abstract Introduction Rapid eye movement (REM) sleep plays a critical role in neurodevelopment, yet current measures lack the sensitivity to capture age-specific REM dynamics that reflect evolving neural architecture. Chaos analytics involves analyzing complex, nonlinear, and dynamic systems where traditional methods may fail to capture the underlying patterns. Recurrence Analyses is one primary method of visualizing and quantifying this alternating complex pattern of “chaos” and “order” that recur. We established Multilevel Heterogeneous Recurrence Analysis (MHRA) to increase specificity and quantification of complex EEG patterns. MHRA offers a flexible framework for uncovering dynamic brain characteristics across multiple scales. With recurrence-based approach using chaos-driven metrics, we described age-specific REM sleep micro- and macrostructural features in pediatric populations. Methods REM sleep data from children and adolescents aged 6–18 years were analyzed using MHRA. For microstructural analysis, electroencephalogram (EEG) signals were extracted from REM epochs, normalized, and analyzed to quantify dynamic properties within each epoch. For macrostructural analysis, sleep stages (Wake, N1, N2, N3, REM) were segmented into 30-second epochs, producing symbolic sequences to represent stage transitions throughout the night. MHRA was applied to visualize dynamic recurrence patterns using Heterogeneous Recurrence Plots and Fractal Maps. Machine learning techniques were integrated to identify age-specific features across the micro- and macrostructural levels. Results Principal Component Analysis was performed to identify and explore age-related variations in REM EEG micro- and macrostructural features across pediatric age groups. The analysis revealed that the extracted REM micro- and macrostructural dynamics exhibited clear and distinct age-related patterns among children and adolescents aged 6 to 18 years. These findings demonstrate that the proposed methods successfully captured age-specific neurodevelopmental signatures, reflecting the evolving characteristics of REM sleep across developmental stages. Conclusion Our findings suggest that both micro- and macrostructures of REM sleep contain distinct developmental signatures across different age groups. This methodology offers promise as a diagnostic tool for detecting atypical neurodevelopment early in life, potentially enabling earlier therapeutic interventions and improved outcomes for children with developmental concerns. Support (if any)

Methodology of murine lung cancer mimics clinical lung adenocarcinoma progression and metastasis

Scientific Reports · 2025-02-28

Lung cancer is the leading cause of cancer-related deaths, of which adenocarcinoma is the most common subtype. Despite this, lung adenocarcinoma and its metastasis are poorly understood, due to difficulties in feasibly recapitulating disease progression and predicting clinical benefits of therapy. We outline a methodology to develop immunogenic orthotopic lung adenocarcinoma mouse models, by injecting cell-specific cre viruses into the lung of a genetically engineered mouse, which mirrors cancer progression defined by the International Association for the Study of Lung Cancer. Evaluation of different cre virus/concentrations models demonstrate remarkable consistency in cancer initiation and metastasis, allowing for high throughput, while showing differences in timing and severity, offering greater flexibility when selecting models. Histological and immune profiles reflect clinical observations suggesting similar mechanisms are recapitulated and preliminary data show resultant tumors to be responsive to clinical treatments. We present a clinically relevant, next-generation murine model for studying lung adenocarcinoma.

From stages to states: rethinking sleep from first principles

SLEEP · 2025-09-26

Abstract 3065: Targeting of metabolic reprogramming in lung cancer by utilizing patient-derived organoid tissue for precision medicine

Cancer Research · 2024-03-22

Abstract In a preliminary pilot study, we reported elevated activity of indoleamine 2,3-dioxygenase 1 (IDO1) in serum from cisplatin-resistant (CR) lung cancer patients compared to cisplatin-sensitive (CS) patients. This heightened IDO1 activity was associated with increased tryptophan (TRP) catabolism, resulting in the upregulation of kynurenine (KYN) production. The detection of KYN can now be readily achieved through immunohistochemistry (IHC). We demonstrated that this upregulation contributed to increased Treg (regulatory T cell) and MDSC (myeloid-derived suppressor cell) populations resulting in an immunosuppressive tumor microenvironment. To investigate this effect, we used dual inhibition of IDO1/TDO2 (tryptophan2,3-dioxygenase 2) in an orthotopic mouse model of CR-Lewis lung carcinoma. This resulted in a significant reduction in tumor volume compared to LLC-CS (n=7, p&amp;lt;0.01), confirming the role of TRP catabolism in CR tumorigenesis. To determine the relevance of the data derived from this mouse model to clinical settings, we established 50 patient-derived organoid tissues (PDOT) from patients with NSCLC. PDOT is an experimental ex vivo tissue model developed to recapitulate the intricate features of lung architecture, including the lung acinus. Upon optimizing our collection and growth protocols, each PDOT successfully developed into a spherical body within 14 days. Furthermore, through bulk RNA-seq analysis, we showed that PDOT closely mirrors the expression of key molecular markers (PD-L1 and mutations), as observed in the original tumor tissues. PDOT from patients possessing high levels of PD-L1 and KYN were exquisitely sensitive to dual inhibition with 2-3-fold greater sensitivity compared to patients with undetectable KYN expression in growth inhibition assays (GI50=20±5.5μM vs. 65±7.5μM). Increased lipid peroxidation was detected via flow cytometry by accumulation of the C11-BODIPY reporter dye in the dual inhibition treatment group. Moreover, we integrated PDOT with a novel 3D microfluidic culture that can be supplied with patient-matched peripheral blood mononuclear cells (PBMC) to assess immune response. Using CyTOF, we showed that dual inhibitors with Atezolizumab (anti-PD-L1) enhanced immune effector (CD8+, NK) and suppressed immunosuppressive (Treg, MDSC) populations. We believe that our swift progress in PDOT development may bridge the divide between cancer biomarker expression and patient trials by timely complementing conventional drug studies based on cell lines and xenografts to facilitate the design of personalized therapies. As a result, the extensive reporting that we have contributed for KYN expression profiling in CR has the potential to serve as a valuable companion diagnostic tool in the future. This can assist in applying precision oncology by finding the most appropriate patients for immunotherapies. Citation Format: Chunjing Wu, Jonathan D. Nguyen, George Theodore, Estelamari Rodriguez, Sydney Spector, Emily Kim, Taranatee Khan, Pablo Puente, Yujie Wang, Dao M. Nguyen, Cheng-Bang Chen, Diane Lim, Lynn G. Feun, Niramol Savaraj, Medhi Wangpaichitr. Targeting of metabolic reprogramming in lung cancer by utilizing patient-derived organoid tissue for precision medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3065.

Orthotopic Models Using New, Murine Lung Adenocarcinoma Cell Lines Simulate Human Non-Small Cell Lung Cancer Treated with Immunotherapy

Cells · 2024-06-28 · 5 citations

Understanding tumor-host immune interactions and the mechanisms of lung cancer response to immunotherapy is crucial. Current preclinical models used to study this often fall short of capturing the complexities of human lung cancer and lead to inconclusive results. To bridge the gap, we introduce two new murine monoclonal lung cancer cell lines for use in immunocompetent orthotopic models. We demonstrate how our cell lines exhibit immunohistochemical protein expression (TTF-1, NapA, PD-L1) and common driver mutations (KRAS, p53, and p110α) seen in human lung adenocarcinoma patients, and how our orthotopic models respond to combination immunotherapy in vivo in a way that closely mirrors current clinical outcomes. These new lung adenocarcinoma cell lines provide an invaluable, clinically relevant platform for investigating the intricate dynamics between tumor and the immune system, and thus potentially contributes to a deeper understanding of immunotherapeutic approaches to lung cancer treatment.

Heterogeneous Recurrence Network Analysis: Unveiling Complex Dynamics for Emotion Recognition in EEG Signals

2024-10-01

This study presents a novel approach for emotion recognition through the analysis of EEG signals, employing Heterogeneous Recurrence Network Analysis (HRNA). Recognizing the complex and dynamic nature of brain activities, HRNA is adept at capturing these intricacies using a sophisticated network topology that integrates both heterogeneous and homogeneous properties. This integration enhances the potential for machine learning applications in deciphering the brain's complex dynamics. We deployed HRNA to the SITU Emotion EEG dataset (SEED-IV), aiming to uncover the multifaceted temporal patterns associated with emotional states. Our methodology involved constructing advanced network features that reflect the heterogeneity of brain signals, thereby providing a more nuanced understanding of emotional responses. Our HRNA-based approach achieved an average AUG of 0.73, demonstrating its effectiveness in distinguishing diverse emotional features within EEG data. These results indicate HRNA's potential as a powerful tool for accurately measuring and categorizing emotional states through EEG. The implications of our findings extend to providing objective support for the clinical diagnosis and treatment of mental health disorders, highlighting HRNA's relevance in both research and clinical settings.