Biology subjects

Jee, J.

Publications and source records attributed to Jee, J..

7 recordsLinked to original sources

bioRxiv2026

Systemic iron sequestration restricts the intratumoral CD8+ T cell landscape in pancreas cancer

Systemic iron sequestration occurs frequently in cancer due to inflammation-driven expression of the iron-regulatory hormone hepcidin. The impact of systemic iron availability on tumoral immunity is unclear. Here, we show that elevated serum hepcidin is associated with reduced survival and decreased intratumoral CD8+ T cells in patients with pancreas cancer. While hepcidin is not induced in murine tumor models, administration of a hepcidin mimetic phenocopies the T-cell-depleted tumor microenvironment seen in patients. Mechanistically, chronic antigen-driven mitochondrial dysfunction disrupts iron metabolism and selectively depletes high avidity CD8+ T cells during iron restriction. These findings establish a direct link between hepcidin-mediated iron sequestration and tumoral immunity and nominate systemic iron dysregulation as a therapeutic target to enhance anti-tumoral CD8+ T cell responses.

immunology↗

bioRxiv2026

SMARCA4, STK11, and KEAP1 co-inactivation associates with poor prognosis and upregulation of the TGF-β pathway in lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) is clinically and molecularly defined by oncogenic driver mutations, identification of which has led to the development of driver-targeted therapies and substantial improvements in prognosis for subsets of LUAD patients. Recent studies assessing clinical outcomes in the context of multigenic alterations have identified secondary mutations that might explain differential responses to targeted therapies, chemotherapies and immunotherapies. Genetic inactivation or loss of SMARCA4, which frequently co-occurs with loss-of-function mutations in STK11 and KEAP1, is especially predictive of poor prognosis and shorter overall survival in LUAD patients, regardless of driver status. We sought to examine the clinical and functional associations of SMARCA4 deficiency in LUAD, with or without co-associated STK11/KEAP1 loss-of-function. MethodsWe examined correlation between SMARCA4 loss, gene expression and prognosis through genomic and transcriptomic profiling of clinically annotated LUAD samples. We generated isogenic cell line models with genetic knockouts of SMARCA4 with or without concomitant STK11 and KEAP1 to profile mutationally-defined genotypes of interest in vitro and in vivo. Lastly, we interrogated the functional dependency of SMARCA4/STK11/KEAP1 triple mutant models on TGF-{beta} signaling to assess its potential as a therapeutic target. ResultsSMARCA4/STK11/KEAP1 triple mutant LUAD is associated with poor survival and high frequency of multisite metastasis. SMARCA4/STK11/KEAP1 triple knockout models showed enhanced migration and invasion in vitro, and diversified organotropism in an in vivo intracardiac xenograft metastasis assay. RNA-Seq and DNaseI-Seq of these in vitro models and clinical samples identified upregulation of TGF-{beta} signaling and EMT gene expression signatures, and corresponding changes in chromatin accessibility, in SMARCA4/STK11/KEAP1 triple mutant LUAD. ConclusionsWe identify SMARCA4/STK11/KEAP1 triple mutant LUAD as a prognostically significant disease subset and nominate TGF-{beta} signaling as a potential therapeutic target.

cancer biology↗

bioRxiv2026

Generalist large language models complement tailor-made predictors for tumor genomics interpretation

General-purpose large language models (LLMs) are trained on large corpora to acquire broad knowledge, but whether LLMs can replace, or augment, task-specific models is unclear. We evaluated LLMs on three real-world, clinically important tumor genomic interpretation tasks, in order of increasing difficulty: (i) distinguishing tumor from non-tumor mutations (n=34,415 variants), (ii) distinguishing driver from passenger mutations (n=13,469 variants), and (iii) inferring cancer type from tumor sequencing reports across multiple assays and institutions (n=102,791 samples). The best general-purpose LLMs performed as well as the benchmark tailor-made predictor for task (i). Ensembling tailor-made models with zero-shot LLMs improved their performance for tasks (i) and (ii). For task (iii), LLMs outperformed or supplemented tailor-made models on out-of-distribution data. Without fine-tuning, current LLMs already can be useful in clinical genomic interpretation by adding complementary expertise to tailor-made, state-of-the-art predictors.

genomics↗

bioRxiv2025

Integrated histopathologic modeling of detailed tumor subtypes and actionable biomarkers

Accurate cancer subtyping with accompanying molecular characterization is critical for precision oncology. While machine learning approaches have been applied to both digital pathology and cancer genomics, previous work has been limited in sample size and has typically aggregated granular cancer subtypes into coarse groupings, likely obfuscating informative molecular and prognostic associations and phenotypic variation of more detailed tumor subtypes. Accordingly, we collated 378,123 hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) with matched targeted DNA clinical sequencing results and OncoTree detailed cancer subtypes from a real-world cohort of 71,142 patients. Using this scaled, granular dataset and a cancer subtype knowledge graph, we developed Mosaic: a family of calibrated machine learning models using H&E WSI embeddings to classify tumors and identify molecular phenotypes across 163 detailed subtypes. The cancer subtyping module (Aeon) achieved an area under the receiver operating characteristic curve (AUROC) of 0.992 overall, with 161/163 subtypes reaching an AUROC [≥] 0.90 and improved performance over a state-of-the-art genomics-based classifier. The genomic inference module (Paladin) achieved an AUROC [≥] 0.80 for 167 pairs of detailed subtypes and genomic targets. We further used the learned histopathologic representations to i) identify key associations of the histopathologic embeddings with clinical biomarkers; ii) identify unsupervised sub-clusters of tumors with genomic determinants of tumor phenotype; iii) specify granular diagnoses for cancers of unknown primary, evaluated by genomic associations and expected clinical outcome distributions; iv) annotate functional significance for variants of uncertain significance (VUS); and v) identify cases that mimic the phenotypic effect of known DNA variants on H&E in the absence of detectable DNA alterations. Taken together, this work advances our understanding of phenotypic variation of granular tumor subtypes, their relevance to enhanced diagnostics, and their potential utility in risk stratification with multimodal machine learning in cancer.

cancer biology↗

bioRxiv2025

Clinico-genomic features predict distinct metastatic phenotypes in cutaneous melanoma

Metastasis drives mortality and morbidity in cancer. While some patients develop broad metastatic disease across multiple organs, others exhibit organ-specific spread. To identify mechanisms underlying metastatic organotropism, we analyzed clinico-genomic data from over 7,000 patients with metastatic cutaneous melanoma in three independent cohorts (one primary discovery and two validation cohorts including a nationwide electronic health record-derived deidentified database), leveraging machine learning approaches to clinical data. We found that female sex and increased tumor mutational burden associate with decreased metastatic potential, while older age associates with increased lung and adrenal metastases. Using unsupervised analyses, patients clustered into five metastatic patterns: a "highly metastatic" cluster characterized by involvement of many organs, a "low metastatic" cluster characterized by few metastatic sites (mostly lymph node metastases), and three additional clusters each characterized by metastasis to specific sites (brain, lung, liver). Mutations in B2M and PTEN associated with increased overall metastatic potential. PTEN mutations were also associated with brain metastases but were enriched only in the "highly metastatic" cluster and not the brain-specific cluster. Mutations in GNAQ or GNA11 (GNA) associated with increased liver metastasis. To validate this association, we tested and demonstrated liver tropism in two GNA-mutant genetically engineered cutaneous melanoma mouse models of metastasis. Overall, our study elucidates distinct phenotypes of metastasis in patients with melanoma and identifies novel clinical and genomic associations that illuminate the drivers of clinical metastatic organotropism.

cancer biology↗

bioRxiv2024

Machine learning predictions improve identification of real-world cancer driver mutations

Characterizing and validating which mutations influence development of cancer is challenging. Machine learning has delivered significant advances in protein structure prediction, but its utility for identifying cancer drivers is less explored. We evaluated multiple computational methods for identifying cancer driver alterations. For identifying known drivers, methods incorporating protein structure or functional genomic data outperformed methods trained only on evolutionary data. We further validated VUSs annotated as pathogenic by testing their association with overall survival in two cohorts of patients with non-small cell lung cancer (N=7,965 and 977). "Pathogenic" VUSs in KEAP1 and SMARCA4 identified by several methods were associated with worse survival, unlike "benign" VUSs. "Pathogenic" VUSs exhibited mutual exclusivity with known oncogenic alterations at the pathway level, further suggesting biological validity. Despite training primarily on germline, rather than somatic, mutation data, computational predictions contribute to a more comprehensive understanding of tumor genetics as validated by real-world data.

genomics↗

bioRxiv2019

Transcriptomic analysis of brain hypometabolism links the cholinergic and gingipain hypotheses of Alzheimer's disease pathogenesis

Alzheimers disease (AD) starts decades before clinical symptoms appear. Low glucose utilization in regions of the cerebral cortex marks early AD and is clinically useful. To identify these regions, we conducted a voxel-wise meta-analysis of positron emission tomography studies that compared AD patients with healthy controls. This meta-analysis included 27 studies that assayed glucose utilization in 915 AD patients and 715 healthy controls. The resulting map marks hypometabolism in the posterior cingulate, middle frontal, angular gyrus, middle and inferior temporal regions. Using the Allen Human Brain Atlas, we identified genes with expression patterns associated with this hypometabolism pattern in the cerebral cortex. Of the six brains in the Atlas, one demonstrated a strong spatial association with the hypometabolism pattern. Previous neuropathological assessment of this brain from a 39-year-old male noted a neurofibrillary tangle in the entorhinal cortex. Using the transcriptomic data, we estimate lower proportions of neurons and more microglia in the hypometabolic regions when compared with the other five brains. Within this single brain, signal recognition particle (SRP)-dependent cotranslational protein targeting genes, which primarily encode cytosolic ribosome proteins, are highly expressed in the hypometabolic regions. Analyses of human and mouse data show that expression of these genes progressively increases across AD-associated states of microglial activation. In addition, genes involved in cell killing, chronic inflammation, ubiquitination, tRNA aminoacylation, and vacuole sorting are associated with the hypometabolism map. These genes suggest disruption of the protein life cycle and neuroimmune activation. Taken together, our molecular characterization of cortical hypometabolism reveals a molecular link to AD associated hypometabolism that may be relevant to preclinical stages.

bioinformatics↗