Biomedical subjects

Yuan Chen

Publications and source records attributed to Yuan Chen.

4 recordsLinked to original sources

PubMed2026

Personalized functional topography-based multisite brain age prediction modeling reveals divergent neurodevelopment in major depression.

Major depressive disorder (MDD) is associated with widespread alterations in functional brain networks across the lifespan. However, heterogeneity in atypical brain development among patients with MDD remains largely uncharacterized. Using a multisite resting-state functional MRI dataset consisting of 1,105 MDD patients and 1,065 healthy controls, we constructed a harmonized multicenter brain age prediction model based on individualized functional topography and identified two patient subgroups with positive or negative brain age gaps (BAGs). In patients with a positive BAG (BAG+), expansion of the salience network (SAL) into the dorsolateral prefrontal and ventrolateral prefrontal cortices, in addition to contraction of the sensorimotor and dorsal attention networks (DAN), contributes to accelerated brain aging. Conversely, in the negative BAG (BAG-) group, SAL expansion into the orbitofrontal cortex (OFC) and contraction of the visual and sensorimotor networks (SMN) were linked to delayed brain development. These subgroups also exhibited distinct neurodevelopmental trajectories. Clinically, BAG+ patients showed stronger associations between higher-order network topography and mood symptoms, whereas BAG- patients exhibited links between visual/default mode network topography and insomnia. At the molecular level, both groups showed enrichment of genes related to synaptic signaling but displayed distinct expression patterns and divergent expression trajectories in key neurodevelopmental gene sets. Notably, antidepressant treatment modulated the brain in ways that were specific to each subgroup. These findings reveal heterogeneous neurodevelopmental profiles in MDD with distinct biological and clinical signatures, offering insights into personalized precision medicine for this disorder.

Humans↗

PubMed2025

Unveiling non-small cell lung cancer treatment effect heterogeneity: a comparative analysis of statistical methods.

BACKGROUND: For patients with advanced non-small cell lung cancer lacking targetable genomic alterations, the impact of clinicogenomic characteristics on the effectiveness of combining chemotherapy with immunotherapy is unclear. METHODS: We evaluated 4 statistical methods for detecting heterogeneous treatment effects related to clinical factors, including programmed death-ligand 1 expression, tumor mutation burden, and stage at diagnosis, using the American Association for Cancer Research Project Genomics Evidence Neoplasia Exchange BioPharma Collaborative dataset supplemented with institutional data collected under the same data curation model. A 2-sided P value of no more than .05 was used to denote statistical significance for all analyses. RESULTS: The mixture model revealed 2 latent subgroups: in one subgroup, there was no meaningful treatment effect, with average progression-free survival (PFS) only 5% longer with immunotherapy alone (95% confidence interval [CI] = -19% to 35%); in the second subgroup, immunotherapy alone was associated with a 35% decrease in average PFS (95% CI = -59% to 2%), corresponding to a ratio in treatment effects of 1.62 (95% CI = 1.02 to 2.57). There was a marginal association between lower tumor mutation burden levels and membership in the subgroup with improved PFS following receipt of chemoimmunotherapy. The causal survival forest highlighted the importance of tumor mutation burden (variable importance ranking: 1) and programmed death-ligand 1 (variable importance ranking: 3) when assessing heterogeneity. In contrast, the accelerated failure time and Cox proportional hazards models did not detect any statistically significant heterogeneous treatment effects. In simulations, the mixture model identified heterogeneous treatment effects more frequently than other methods, especially with weak covariate relationships, demonstrating its utility for informing personalized treatment approaches. CONCLUSIONS: The application of novel statistical methods to large scale clinico-genomic databases offers an opportunity to more accurately identify heterogeneous treatment effects in some settings as compared to traditional statistical methods. Applying such methods to the AACR Project GENIE BPC non-small cell lung cancer data indicated a potential association between decreasing tumor mutation burden and improved outcomes with chemoimmunotherapy as compared to immunotherapy alone.

Humans↗

PubMed2024

Pan-cancer analysis of biallelic inactivation in tumor suppressor genes identifies KEAP1 zygosity as a predictive biomarker in lung cancer.

The canonical model of tumor suppressor gene (TSG)-mediated oncogenesis posits that loss of both alleles is necessary for inactivation. Here, through allele-specific analysis of sequencing data from 48,179 cancer patients, we define the prevalence, selective pressure for, and functional consequences of biallelic inactivation across TSGs. TSGs largely assort into distinct classes associated with either pan-cancer (Class 1) or lineage-specific (Class 2) patterns of selection for biallelic loss, although some TSGs are predominantly monoallelically inactivated (Class 3/4). We demonstrate that selection for biallelic inactivation can be utilized to identify driver genes in non-canonical contexts, including among variants of unknown significance (VUSs) of several TSGs such as KEAP1. Genomic, functional, and clinical data collectively indicate that KEAP1 VUSs phenocopy established KEAP1 oncogenic alleles and that zygosity, rather than variant classification, is predictive of therapeutic response. TSG zygosity is therefore a fundamental determinant of disease etiology and therapeutic sensitivity.

Kelch-Like ECH-Associated Protein 1↗

PubMed2024

Shared differential factors underlying individual spontaneous neural activity abnormalities in major depressive disorder.

BACKGROUND: In contemporary neuroimaging studies, it has been observed that patients with major depressive disorder (MDD) exhibit aberrant spontaneous neural activity, commonly quantified through the amplitude of low-frequency fluctuations (ALFF). However, the substantial individual heterogeneity among patients poses a challenge to reaching a unified conclusion. METHODS: To address this variability, our study adopts a novel framework to parse individualized ALFF abnormalities. We hypothesize that individualized ALFF abnormalities can be portrayed as a unique linear combination of shared differential factors. Our study involved two large multi-center datasets, comprising 2424 patients with MDD and 2183 healthy controls. In patients, individualized ALFF abnormalities were derived through normative modeling and further deconstructed into differential factors using non-negative matrix factorization. RESULTS: Two positive and two negative factors were identified. These factors were closely linked to clinical characteristics and explained group-level ALFF abnormalities in the two datasets. Moreover, these factors exhibited distinct associations with the distribution of neurotransmitter receptors/transporters, transcriptional profiles of inflammation-related genes, and connectome-informed epicenters, underscoring their neurobiological relevance. Additionally, factor compositions facilitated the identification of four distinct depressive subtypes, each characterized by unique abnormal ALFF patterns and clinical features. Importantly, these findings were successfully replicated in another dataset with different acquisition equipment, protocols, preprocessing strategies, and medication statuses, validating their robustness and generalizability. CONCLUSIONS: This research identifies shared differential factors underlying individual spontaneous neural activity abnormalities in MDD and contributes novel insights into the heterogeneity of spontaneous neural activity abnormalities in MDD.

Humans↗