Attagene

Abstract IntroductionThe incidence of early-onset colorectal cancer (EOCRC) is rising rapidly, with disproportionate health burdens falling on populations that experience both the steepest increases and the poorest outcomes. The phosphoinositide 3-kinase (PI3K) signaling pathway is a key oncogenic driver in colorectal cancer (CRC), influencing tumor growth, survival, and therapeutic resistance. Despite its biological significance, the role of PI3K pathway alterations in EOCRC remains poorly understood—particularly in underrepresented populations—due to limited diversity in genomic datasets and a lack of tools for integrative, pathway-specific analysis. To address this gap, we developed AI-HOPE-PI3K, a conversational artificial intelligence (AI) system designed to streamline clinical-genomic integration and enable real-time, population-aware analysis of PI3K dysregulation in CRC.MethodsAI-HOPE-PI3K is built on a fine-tuned biomedical LLaMA 3 large language model (LLM) and supports natural language queries that are translated into executable statistical pipelines. The platform harmonizes and integrates data from cBioPortal, encompassing key clinical features such as age, race/ethnicity, MSI status, tumor site, stage, treatment history, and survival outcomes. It automates cohort construction, survival modeling, mutation frequency comparison, and odds ratio analysis, delivering interpretive visual and tabular outputs. Validation was performed through the replication of known PI3K-related associations and comparative benchmarking against existing bioinformatics platforms.ResultsAI-HOPE-PI3K enabled real-time interrogation of CRC datasets, producing interpretable results without the need for programming. Among EOCRC patients, the frequency of PI3K alterations was similar across different population cohorts. However, colon tumors harboring PI3K alterations were associated with significantly worse survival compared to rectal tumors (p = 0.0177). High tumor mutational burden (TMB) predicted improved survival in FOLFIRI-treated CRC patients (p = 0.0032) and was enriched for MTOR mutations. Among MSI-high patients receiving pembrolizumab, survival did not differ significantly by PIK3CA mutation status. Notably, INPP4B mutations were significantly enriched in H/L EOCRC patients (OR = 3.57, p = 0.005), indicating a potential ancestry-linked biomarker. Analyses stratified by age and stage in PTEN- and PI3K-altered CRC cohorts revealed context-dependent trends in survival.ConclusionsAI-HOPE-PI3K is a first-in-class, conversational AI platform that enables natural language–based, PI3K-pathway-specific analysis of CRC genomics. By integrating multi-institutional datasets with clinical annotations, it democratizes access to complex analyses and enables equitable exploration of PI3K biology. The system reliably reproduced established findings and uncovered novel, population-specific genomic insights—particularly among H/L EOCRC patients. AI-HOPE-PI3K demonstrates the power of AI-driven platforms to advance precision oncology and address disproportionate health burdens through scalable, real-time, and hypothesis-driven clinical-genomic investigation.

Abstract Introduction Early-onset colorectal cancer (EOCRC) is rising rapidly, especially among populations at risk who experience disproportionate incidence and mortality. The TP53 pathway, frequently altered in CRC, regulates key processes such as DNA repair and apoptosis. Despite its clinical relevance, TP53 dysregulation remains understudied in EOCRC, particularly in populations at risk. Current tools lack support for pathway-specific, population-stratified, and treatment-aware analysis. To address this gap, we developed AI-HOPE-TP53—the first conversational AI agent designed to explore TP53 alterations in CRC using harmonized clinical-genomic data and natural language queries. Methods AI-HOPE-TP53 leverages large language models (LLMs) to translate natural language prompts into executable bioinformatics workflows operating on datasets from cBioPortal. It supports dynamic cohort stratification by TP53-related genes, age, MSI status, treatment, tumor stage, and ethnicity. Automated analyses include mutation frequency, co-mutation profiling, survival curves, and odds ratios. Results AI-HOPE-TP53 successfully replicated known molecular characterization, identifying higher TP53 alteration rates in EOCRC patients. It revealed trends toward improved survival in patients with TP53 mutations and identified significant enrichment of H/L individuals among early-onset, FOLFOX-treated cases (OR = 2.002, p < 0.0001). Additional analyses uncovered subsite-specific outcomes in ATM-mutant CRC, stage-specific effects in CHEK1-mutated tumors, and disproportionate health burdens in treatment exposure. Conclusions AI-HOPE-TP53 enables fast, natural language–driven exploration of TP53 pathway alterations in CRC. Its ability to integrate clinical and genomic data across diverse subgroups makes it a powerful tool for precision oncology and Community-engaged health research—supporting both hypothesis validation and discovery in EOCRC and beyond.

Popularized on social media, hand-moldable plastics are formed by consumers into tools, trinkets, and dental prosthetics. Despite the anticipated dermal and oral contact, manufacturers share little information with consumers about these materials, which are typically sold as microplastic-sized resin pellets. Inherent to their function, moldable plastics pose a risk of dermal and oral exposure to unknown leachable substances. We analyzed 12 moldable plastics advertised for modeling and dental applications and determined them to be polycaprolactone (PCL) or thermoplastic polyurethane (TPU). The bioactivities of the most popular brands advertised for modeling applications of each type of polymer were evaluated using a zebrafish embryo bioassay. While water-borne exposure to the TPU pellets did not affect the targeted developmental end points at any concentration tested, the PCL pellets were acutely toxic above 1 pellet/mL. The aqueous leachates of the PCL pellets demonstrated similar toxicity. Methanolic extracts from the PCL pellets were assayed for their bioactivity using the Attagene FACTORIAL platform. Of the 69 measured end points, the extracts activated nuclear receptors and transcription factors for xenobiotic metabolism (pregnane X receptor, PXR), lipid metabolism (peroxisome proliferator-activated receptor γ, PPARγ), and oxidative stress (nuclear factor erythroid 2-related factor 2, NRF2). By nontargeted high-resolution comprehensive two-dimensional gas chromatography (GC × GC-HRT), we tentatively identified several compounds in the methanolic extracts, including PCL oligomers, a phenolic antioxidant, and residues of suspected antihydrolysis and cross-linking additives. In a follow-up zebrafish embryo bioassay, because of its stated high purity, biomedical grade PCL was tested to mitigate any confounding effects due to chemical additives in the PCL pellets; it elicited comparable acute toxicity. From these orthogonal and complementary experiments, we suggest that the toxicity was due to oligomers and nanoplastics released from the PCL rather than chemical additives. These results challenge the perceived and assumed inertness of plastics and highlight their multiple sources of toxicity.

The transcription factor nuclear factor (NF)-kappaB controls the expression of genes involved in inflammation, cell proliferation, apoptosis, and differentiation. Impaired regulation of NF-kappaB has been associated with many diseases; thus, there is significant interest in therapeutic approaches based on modulation of this transcription factor. NF-kappaB activity is controlled by numerous signaling molecules, many of which are potentially to be identified. Monocytes are principal effectors of the immune system, and monocyte adherence is the first step leading to their activation and differentiation. Adherence induces activation of NF-kappaB, resulting in the induction of proinflammatory genes as well as anti-inflammatory genes, which counterbalance and limit the intensity and duration of NF-kappaB activation. Here, to identify novel mediators of NF-kappaB signaling, we used the model of monocyte adherence to perform a systematic, genome-wide survey of adherence-induced genes. Having isolated mRNAs from nonadherent and adherent primary human monocytes, we constructed suppressive subtraction hybridization libraries containing cDNAs, which were differentially regulated by adherence. Of 366 identified differentially expressed genes, most were found to be up-regulated by adherence. Having analyzed a subset of these genes, we found that the library was enriched with inhibitors of NF-kappaB. Three of those (an orphan nuclear receptor NUR77, a guanosine 5'-diphosphate/guanosine 5'-triphosphate exchange factor RABEX5, and a PRK1-associated protein AWP1) were particularly potent inhibitors of NF-kappaB activation. Thus, the collection of monocyte adherence-regulated genes represents a rich source for the identification of novel components of the machinery that controls NF-kappaB activation.