Biology subjects

Qian, X.

Publications and source records attributed to Qian, X..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Overexpression of TPM4 promotes cancer progression through EMT in anaplastic thyroid cancer

Anaplastic thyroid cancer (ATC) is an undifferentiated tumour originating from thyroid follicular epithelium and represents the most malignant subtype of thyroid cancer. However, the mechanisms underlying ATC development are not fully understood. This study aimed to investigate the mechanisms driving ATC invasion and metastasis to provide novel insights into its aggressive behavior. Through an integrated bioinformatics analysis of the GEO database, we identified Tropomyosin 4 (TPM4) as a differentially expressed gene in ATC. Bioinformatic correlation and pathway enrichment analyses suggested that TPM4 expression is potentially linked to tumor cell invasion and lymph node metastasis. We showed that TPM4 was highly expressed in both ATC tissues and cell lines, and its expression levels were positively correlated with enhanced cell migration and invasion. Mechanistic studies indicated that TPM4 likely enhances tumour cell migration and invasion by facilitating cytoskeletal remodeling and promoting the epithelial-mesenchymal transition (EMT). Our findings suggest that TPM4 represents a promising diagnostic and therapeutic biomarker for ATC.

cancer biology↗

bioRxiv2025

Adjacent Rare-Earth Separation via a Protein Atomic Ruler

The separation of rare-earth elements (REEs) poses a formidable challenge due to their nearly identical ionic radii and chemical properties. Here, we present a transformative strategy for REE separation using a multimetal ion-stacking metalloprotein framework (MIF) that functions as an atomic ruler, effectively amplifying subtle differences in REE ionic radii. This approach achieves exceptional selectivity, enabling the separation of all 13 adjacent REE pairs with ultrahigh purity (up to 99.999%). Remarkably, the MIF retains its lanthanide-binding capacity over 410 consecutive absorption-desorption cycles, demonstrating outstanding stability. Structural insights reveal that the MIFs densely packed REE coordination environment magnifies minute radius variations--a key mechanism underlying its unparalleled separation efficiency. Our MIF-based strategy offers a sustainable, scalable solution to rare-earth separation in industrial applications.

biophysics↗

bioRxiv2025

Programmable Aptamer-Embedded Circular RNAs for Targeted Antigen-Presenting Cells Immunotherapy

The targeted delivery of mRNA to specific cell types in vivo remains a major unmet need in mRNA-based therapeutics. Conventional strategies rely on nanocarriers engineered with various modifications. However, these exogenous materials could cause adverse reactions, and their complex manufacturing processes often hinder clinical application. By leveraging the innate programmability of RNA sequences, we herein report a carrier-free, scalable, and programmable aptamer-embedded circular RNAs (Apt-circRNAs) design that confers targeting capability through the strategic incorporation of different aptamers at varying densities, while retaining circular RNA stability and protein production capability. Experimental results show that antigen-loaded Apt-circRNAs enable targeted delivery to antigen-presenting cells (APCs), drive endogenous antigen expression, activate antigen-specific T cells, and elicit potent immune responses that mediate clearance of both early-and late-stage tumors in mouse models. In humanized models of colorectal cancer, the combination of Apt-circRNA and PD-1 blockade produced synergistic antitumor effects and significantly reshaped the tumor microenvironment. In a first-in-human (FIH) clinical trial, Apt-circRNA vaccine encoding KRAS G12D/G12V neoantigens exhibited a favorable safety profile and induced robust immune activation, T cell mobilization, and neoantigen-specific immune responses. With this study, we have established a new paradigm in carrier-free, APC-targeted RNA vaccine design, offering a safe and effective platform for next-generation cancer immunotherapy.

bioengineering↗

bioRxiv2025

Combining Selenocystine Based Fluorescence Assay with Structural Prediction for Enhanced Detection and Analysis of Cystine Stone Pathology

BackgroundCystine stones, a rare but recurrent type of kidney stones, primarily result from cystinuria, an inherited disorder caused by mutations in the genes SLC3A1 and SLC7A9, which encode renal cystine transporters. These mutations impair cystine reabsorption, resulting in elevated urinary cystine concentrations and stone formation. Current diagnostic methods are limited, particularly for detecting molecular-level dysfunctions. We aimed to develop a nonradioactive method for functional assessment of cystine transporters and mutation-specific pathologies. MethodsWe designed an innovative diagnostic approach combining a selenocystine-based fluorescence assay with structural predictions using AlphaFold to rapidly and accurately assess cystine transporter function and the molecular impacts of genetic mutations. ResultsOur assay demonstrated comparable transport efficiencies of cystine and selenocystine by the SLC3A1/SLC7A9 complex, and effectively differentiated mild, moderate, and severe functional impairments associated with known clinical mutations, including A354T and P482L. Structural modeling further provided mechanistic insights into mutation-induced dysfunctions. ConclusionThis integrated approach--combining a sensitive selenocystine fluorescence assay with AI-powered structural analysis--enables rapid, precise diagnosis of cystinuria variants and delivers mechanistic insights for personalized therapeutic strategies.

genetics↗

bioRxiv2025

Identification and characterization of a novel exo-glycosidase activity of NGLY1 on ENGase-digested N-GlcNAc proteins in vitro

N-Glycanase 1 (NGLY1) is known for its activity to completely remove conjugated N-glycans from glycoproteins. Early studies illustrated that NGLY1 defects were associated with a rare disease named NGLY1-related congenital disorder of deglycosylation (NGLY1-CDDG). Although extensive research has been conducted over the past decade on the biological impact of NGLY1s endo-(N-)glycanase activity in cells and on disease, whether NGLY1 also exhibits an exo-(N-)glycanase activity remains open. In this study, an exo-(N-)glycanase activity of NGLY1 on the N-GlcNAc proteins was firstly reported and characterized in vitro. To distinguish for the conventional endo-(N-)glycanase activity and the newly reported exo-(N-)glycanase activity of NGLY1, the active sites of exo-NGLY1 were predicted and compared with those of endo-NGLY1. In addition, the correlation between NGLY1s exo-(N-)glycanase activity in vitro and NGLY1-CDDGs clinical characterization was investigated and discussed. The observation of NGLY1s novel exo-(N-)glycanase activity expanded the functional repertoire of NGLY1 beyond canonical endo-(N-)glycanase-mediated deglycosylation, and may provide a new framework to explain the clinical heterogeneity of NGLY1-CDDG.

biochemistry↗

bioRxiv2025

Light-up nanostructures with allosteric DNA mimics of GFP

In this study, we present a systematic approach for the rational design of synthetic allosteric DNA aptamers. This methodology enables precise control over the allosteric ON-OFF transition in fluorescent DNA aptamers, allowing for the engineering of aptamers with highly tunable fluorescent properties. When combined with toehold-mediated strand displacement, we have developed a series of allosteric aptamers in which the target sequence functions as a specific allosteric modulator. Furthermore, these aptamers have been applied in synthetic DNA computing and in the construction of responsive nanostructures that light up upon activation.

synthetic biology↗

bioRxiv2025

Rictor CRISPR Gene Editing by Lipid Nanoparticle Delivery Stimulates Anti-Tumor Immunity in Breast Cancer Liver Metastasis Model

Triple-negative breast cancer (TNBC) is a highly aggressive subtype, accounting for 10-15% of breast cancer cases in the United States. Liver metastases, common in advanced TNBC, are linked to especially poor outcomes, with a 5-year survival rate of just 11%. Although immune checkpoint inhibitors (ICIs) targeting PD-1 or PD-L1 show promise, durable responses in TNBC remain uncommon. This is largely due to a profoundly immunosuppressive tumor microenvironment (TME), driven by tumor-associated myeloid cells. Tumor-associated macrophages (TAMs) and neutrophils (TANs) polarize into immunosuppressive M2 and N2 phenotypes, respectively, suppressing T cell activity through cytokines, ROS, and checkpoint ligands such as VISTA. Myeloid-derived suppressor cells (MDSCs) further inhibit immunity by depleting nutrients and inducing regulatory T cells. As a result, despite its immunogenic features, TNBC remains resistant to immunotherapy due to persistent myeloid-mediated suppression. Here, we developed ionizable lipid nanoparticles (iLNPs) engineered to deliver the CRISPR-Cas12a ribonuclease complex targeting Rictor, a critical component of mTORC2, for in vivo reprograming of myeloid cells. The intravenous (IV) injection of CRISPR Rictor-targeting iLNP (CR-Ric-LNP) showed efficient uptake by circulating myeloid cells and accumulation into the breast cancer liver metastases. Notably, Rictor gene editing triggered pro-inflammatory activation of myeloid cells in the TME, enhancing antitumor responses. Single-cell RNA sequencing revealed that Rictor silencing treated samples showed induced rapid remodeling of the TME, with a significant reduction in immunosuppressive macrophages within 24 hours of treatment. Concurrently, cytotoxic T-cell populations exhibited increased interferon-gamma (Ifng) production, driving the emergence of specific myeloid clusters that were responsive to Interferon signaling, particularly in macrophages and neutrophils. A shift from an immunosuppressive to an inflammatory TME was further evidenced by an elevated Cxcl10/Spp1 ratio in myeloid cells. CR-Ric-LNP treatment also enhanced T-cell activation, reducing exhausted T cells and regulatory T cells (Tregs) while expanding natural killer (NK) cells, naive CD4+, and CD8+ T cells. These changes correlated with a decreased proportion of tumor cells and proliferating cells, ultimately leading to a significant survival benefit in a 4T1 breast cancer liver metastasis model. Our findings demonstrate that myeloid-targeted Rictor silencing reprograms the TME, promoting antitumor immunity and improving therapeutic outcomes. O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/661471v1_ufig1.gif" ALT="Figure 1"> View larger version (109K): org.highwire.dtl.DTLVardef@10a5e7aorg.highwire.dtl.DTLVardef@1c0c005org.highwire.dtl.DTLVardef@17ec6b5org.highwire.dtl.DTLVardef@27c507_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2025

PhenoGraph: A Multi-Agent Framework for Phenotype-driven Discovery in Spatial Transcriptomics Data Augmented with Knowledge Graphs

Spatial transcriptomics (ST) provides powerful insights into gene expression patterns within tissue structures, enabling the discovery of molecular mechanisms in complex tumor microenvironments (TMEs). Phenotype-based discovery in ST data holds transformative potential for linking spatial molecular expression patterns to clinical outcomes; however, appropriate ST data analysis remains fundamentally fragmented and highly labor-intensive. Due to its limited scalability considering the size of typical ST data in large cohorts, researchers must rely on other phenotypeannotated omics data modalities (e.g. bulk RNAsequencing) and align them with ST data to extract clinically meaningful spatial patterns. Yet, this process requires manually identifying relevant cohorts, aligning multi-modal data, selecting and tuning analysis pipelines, and interpreting results--typically without any built-in support for biologically context-aware reasoning. In this paper, we present PhenoGraph, a large language model (LLM) based multi-agent system, that automates the full pipeline for phenotype-driven ST data analysis, augmented by biological knowledge graphs for enhanced interpretability. Built on a modular agent architecture, PhenoGraph dynamically selects, executes, and corrects phenotype analysis pipelines based on user-defined queries. We showcase the flexibility and effectiveness of PhenoGraph across a variety of TME ST datasets and phenotype classes, highlighting its potential to enhance biological discovery efficacy.

bioinformatics↗

bioRxiv2025

Computational nanobody design using graph neural networks and Metropolis Monte Carlo sampling

Nanobodies have emerged as promising protein therapeutics due to their high-stability, low immunogenicity, and ease of production. However, experimental screening of high-affinity nanobodies for specific antigens and their post optimization remain costly and time-consuming, mainly due to the large number of possible variants. Here, we developed a computational approach that integrates graph neural networks (GNNs) with Monte Carlo Metropolis algorithm for nanobody design. We constructed a GNN model, AiPPA, to predict the protein-protein binding free energy (BFE) without requiring the complex structure, achieving a Pearson correlation of 0.62 on benchmark. We then combined AiPPA with Metropolis importance sampling to design low-BFE nanobodies from a non-affinity template. We applied this method to the antigen TL1A, and generated two affinity nanobodies. This work establishes a physics-informed deep learning method for computational nanobody design, providing a novel development strategy for protein therapeutics.

bioinformatics↗

bioRxiv2025

Contrastive Learning-Enhanced Drug Metabolite Prediction Drives the Discovery of an Orally Bioavailable RSK4 Inhibitor for Esophageal Cancer

Drug metabolism liabilities are a major bottleneck in drug development, necessitating accurate metabolite prediction tools. While AI-driven methods have advanced, challenges remain in achieving high-precision, robustness, broad reaction coverage and seamless integration into the drug development workflow. To address this, we first constructed the most comprehensive human-specific drug metabolism database to date, encompassing 11,665 human metabolic reactions and 2,497 high-quality rules. Leveraging this resource, we developed ConMeter (Contrastive learning and feature interaction based Metabolic Reaction prediction), a novel model integrating chemical feature interaction and contrastive learning. ConMeter demonstrates significant improvements, achieving [~]80% accuracy in identifying at least one metabolite within the top 5 predictions and showing an average 25% relative enhancement in ranking true positives compared to state-of-the-art methods. The comprehensive database also expands the range of predictable metabolic reactions. To demonstrate the utility of ConMeter, we integrated it into a metabolite prediction-guided drug design strategy aimed at addressing the low oral bioavailability of a metabolically vulnerable RSK4 lead compound -- a pervasive issue among known RSK inhibitors. This strategy successfully yielded R636, a novel RSK4 inhibitor exhibiting remarkably improved bioavailability (a 63-fold increase, reaching 63% absolute bioavailability) while maintaining its potent activity. Notably, R636 showed favorable safety profile and significant anti-tumor efficacy against esophageal squamous cell carcinoma (ESCC) in vitro and in two distinct patient-derived xenograft (PDX) mouse models. This work introduces ConMeter as a powerful and practical tool for high-accuracy metabolite prediction, demonstrating its potential to accelerate lead optimization, exemplified by the discovery of R636 as a promising orally bioavailable candidate for ESCC.

cancer biology↗

bioRxiv2025

De novo design of protein-binding aptamers through deep reinforcement learning assembly of nucleic acid fragments

Nucleic acid aptamers targeting proteins are becoming increasingly important in biopharmaceuticals and molecular diagnostics. Traditionally, aptamers are discovered through labor-intensive screening of nucleic acid libraries using the SELEX method. However, de novo design approaches without experimental screening remain a significant challenge. Here, we employed deep reinforcement learning to develop an artificial intelligence (AI) agent capable of de novo aptamer design, termed AiDTA (AI-driven Docking-Then-Assembling). First, nucleic acid fragments were docked to the target protein to identify target-binding fragments. Then, AiDTA automatically assembled these fragments into aptamers using the Monte Carlo tree search algorithm and a policy-value neural network to guide the agent in generating aptamers with secondary structures similar to the original constituent fragments. Experimental validation demonstrated that the AiDTA-designed DNA aptamers targeting disease-related proteins exhibited high binding affinities in the nanomolar range, achieving the de novo design of protein-binding aptamers for the first time. Our study establishes a new approach to obtaining protein-binding aptamers for potential applications in biopharmaceuticals and diagnostics.

bioinformatics↗

bioRxiv2025

AI-driven computational pathology for recurrence risk assessment in early-stage breast cancer

Recurrence related to poor prognosis is a leading cause of mortality in patients with breast cancer (BC). The MammaPrint (MP) genomic assay is designed to stratify recurrence risk and evaluate chemotherapy benefits for early-stage HR+/HER2- BC patients. However, MP fails to reveal spatial tumor morphology and is limited by high costs. In this study, we establish a BC MP cohort and develop CPMP, a weakly supervised agent-attention transformer model, to predict MP recurrence risk from annotation-free BC histopathological slides. CPMP achieves an AUROC of 0.824 {+/-} 0.03 in predicting MP risk groups. We further leveraged CPMP for spatial and morphological analyses to explore histological patterns associated with MP risk groups. The model reveals tumor spatial localization at the whole-slide level and highlights distinct intercellular interaction patterns of MP groups. It also characterizes the diversity in tumor morphology and uncovers MP High-specific, Low-specific, and colocalized morphological phenotypes that differ in quantitative cellular composition. Prognostic evaluation in the external cohort exhibits significant stratification of distant metastasis risk (HR: 3.14, p-value = 0.0014), underscoring the prognostic power of CPMP. These findings demonstrate the capability of CPMP in MP risk prediction, offering a flexible supplement to genomic risk assessment in early-stage BC.

pathology↗

bioRxiv2025

Intranasal sarbecovirus vaccine booster elicits cross-clade, durable and protective systemic and mucosal immunity

Short-lived, clade-specific immune responses with limited mucosal priming are limitations faced by current COVID-19 mRNA vaccines against sarbecoviruses. We have developed a nasal booster vaccine candidate that induced robust and sustained, cross-clade, systemic and mucosal protective immunity. Two recombinant Clec9A-specific monoclonal antibodies fused to the Receptor Binding Domain (RBD) from Omicron XBB.1.5 and SARS-CoV-1, respectively were generated. In Comirnaty mRNA-vaccinated mice, boosting with each individual Clec9A-RBD construct induced immune responses that either were limited in breadth or waned over time; while boosting with both constructs combined (Clec9AOMNI) elicited robust cross-clade neutralizing antibodies (nAb) and T cell responses that were significantly more sustained compared to Bivalent Comirnaty (BC) mRNA vaccine booster. The persistence of RBD-specific follicular helper CD4+ T cells, germinal centre B cells, and long-lived plasma cells that facilitated affinity maturation in Clec9AOMNI-boosted mice, correlated with the detection of triple cross-reactive B cells that bind to ancestral SARS-CoV-2 ancestral, SARS-CoV-2 XBB.1.5 and SARS-CoV-1 RBD. Remarkably, intranasal boosting with Clec9AOMNI generated robust and sustained mucosal immune responses in the upper and lower respiratory compartments, including RBD-specific IgA, cross-clade nAb and cellular immunity together with functional tissue-resident memory T cells, without compromising the systemic immune responses. Correspondingly, Clec9AOMNI booster conferred superior protection against Omicron BA.1 compared to BC booster when challenge was performed at six months post-boost. Hence, Clec9AOMNI is a promising nasal booster vaccine candidate that has the potential to mitigate pandemic threats from emerging sarbecoviruses. One Sentence SummaryNasal booster immunization with dendritic cell-targeting vaccine candidate in mRNA-vaccinated mice induced cross-clade, sustained, systemic and mucosal protective immunity.

immunology↗

bioRxiv2025

High-throughput protein target mapping enables accelerated bioactivity discovery for ToxCast and PFAS compounds

Chemical pollution is a global threat to human health, yet the toxicity mechanism of most contaminants remains unknown. Here, we applied an ultrahigh-throughput affinity-selection mass spectrometry (AS-MS) platform to systematically identify protein targets of prioritized chemical contaminants. After benchmarking the platform, we screened 50 human proteins against 481 prioritized chemicals, including 446 ToxCast chemicals and 35 per-and polyfluoroalkyl substances (PFAS). Among 24,050 interactions assessed, we discovered 35 novel interactions involving 14 proteins, with fatty acid-binding proteins (FABPs) emerging as the most ligandable protein family. Given this, we selected FABPs for further validation, which revealed a distinct PFAS binding pattern: legacy PFAS selectively bound to FABP1, whereas replacement compounds, PFECAs, unexpectedly interacted with all FABPs. X-ray crystallography further revealed that the ether group enhances molecular flexibility of alternative PFAS, to accommodate the binding pockets of FABPs. Our findings demonstrate that AS-MS is a robust platform for the discovery of novel protein targets beyond the scope of the ToxCast program and highlight the broader protein-binding spectrum of alternative PFAS as potential regrettable substitutes.

pharmacology and toxicology↗

bioRxiv2025

Obesity Increases Atherosclerosis Susceptibility via Inter-tissue miR-30e-SLC7A11 Axis

BackgroundWith obesity as a risk factor for atherosclerotic disease, recent research suggests that adipose tissue in obese animal models and humans can generate endocrine-like molecules that affect arterial health later on. Previous studies showed that microRNA-30e (miR-30e) level is elevated in atherosclerosis and solute carrier family 7 member 11 (SLC7A11), a cystine/glutamate transporter, is involved in atherogenesis. However, whether an endocrine-like link between the adipose-derived miR-30e-5p and SLC7A11 in the vascular endothelium can lead to obesity-caused atherosclerosis is unknown. MethodsMiRNA data mining and RT-PCR validations were used to demonstrate the positive association among serum level of miR-30e-5p, obesity, and coronary arterial disease in human patients. Transcriptomics (RNA-seq and single-nucleus RNA-seq), metabolomics, and in silico analysis were used to establish a miR-30e-5p-SLC7A11 regulation of central carbon metabolism, mitochondrial and endothelial cell (EC) function. Mouse models with EC-specific Slc7a11 knockout (EC-Slc7a11-/-) and gain or loss of function of miR-30e-5p were used to elucidate the detrimental role of this endocrine-like axis in obesity-related atherosclerosis. ResultsThe level of adipocyte-derived miR-30e-5p was significantly upregulated in obese humans with coronary artery disease and obese and atherosclerotic mice. Via serum exosomes, the adipocyte-generated miR-30e-5p targeted SLC7A11 mRNA in vascular ECs. SLC7A11 deficiency due to miR-30e-5p targeting dysregulated glutamate/cystine metabolism increased glycolysis, reduced oxidative phosphorylation, and impaired mitochondrial function. The EC dysfunction could be rectified by SLC7A11 overexpression or miR-30e-5p antagonism. Exogenously delivered miR-30e-5p phenocopied the increased atherosclerosis in EC-Slc7a11-/- mice. In contrast, miR-30e-5p antagomir treatment reduced atherosclerosis in Apoe-/- and ob/ob mice. ConclusionOur multi-omics approaches demonstrates that the adipose-derived miR-30e-5p downregulated SLC7A11 mRNA in ECs via tissue crosstalk. The resulting EC dysfunction led to obesity-related atherosclerosis in mice. These findings underscore a causality between obesity and atherosclerosis in the context of cardiovascular-kidney-metabolic syndrome.

molecular biology↗

bioRxiv2025

Autism-Associated Genes and Neighboring lncRNAs Converge on Key Gene Regulatory Networks

Autism spectrum disorder (ASD) is highly heritable, and mutations in hundreds of genes have been implicated as individually rare causes of ASD1-3. Understanding how disruptions to these functionally diverse genes lead to the core features of ASD remains a major challenge4. Moreover, ASD is three- to four-fold more common in males than females5, and autistic females tend to carry more autosomal risk alleles for ASD compared to autistic males6,7, but the biological basis of this "female protective effect" (FPE) is unknown8,9. Here we show that individual perturbations of 18 ASD genes in human neural progenitor cells converge on shared effects on gene expression, including widespread downregulation of other ASD genes. De novo reconstruction of a gene regulatory network (GRN) enabled the identification of central transcriptional regulators, including the prominent ASD gene CHD8 as well as novel candidates such as REST, that drive this transcriptomic convergence. Furthermore, the X-linked transcription factor ZFX, which is expressed from both the active and the inactive X chromosomes in females10, emerged as a key activator of many ASD genes: we propose that the higher ZFX expression level observed in female brain can buffer damaging mutations in diverse ASD genes, contributing to the FPE. Together, these results reveal how key GRNs can become broadly and similarly dysregulated upon disruption of individual ASD genes and provide molecular insight into the female protective effect in ASD.

genetics↗

bioRxiv2024

Optimization of protocols for immunohistochemical assessment of enteric nervous system in formalin fixed human tissue.

Gastrointestinal (GI) motility is regulated in a large part by the cells of the enteric nervous system (ENS), suggesting that ENS dysfunctions either associate with, or drive GI dysmotility in patients. However, except for select diseases such as Hirschsprungs Disease or Achalasia that show a significant loss of all neurons or a subset of neurons, our understanding of human ENS histopathology is extremely limited. Recent endoscopic advances allow biopsying patients full thickness gut tissues, which makes capturing ENS tissues simpler than biopsying other neuronal tissues, such as the brain. Yet, our understanding of ENS aberrations observed in GI dysmotility patients lags behind our understanding of central nervous system aberrations observed in patients with neurological disease. Paucity of optimized methods for histopathological assessment of ENS in pathological specimens represent an important bottleneck in ascertaining how the ENS is altered in diverse GI dysmotility conditions. While recent studies have interrogated ENS structure in surgically resected whole mount human gut, most pathological specimens are banked as formalin fixed paraffin embedded (FFPE) tissue blocks - suggesting that methods to interrogate ENS in FFPE tissue blocks would provide the biggest impetus for ENS histopathology in a clinical setting. In this report, we present optimized methods for immunohistochemical interrogation of the human ENS tissue on the basis of >25 important protein markers that include proteins expressed by all neurons, subset of neurons, hormones, and neurotransmitter receptors. This report provides a resource which will help pathologists and investigators assess ENS aberrations in patients with various GI dysmotility conditions.

pathology↗

bioRxiv2024

Oncogene-induced senescence and senescence-associated secretory phenotype (SASP) determine the efficacy of palbociclib in PIK3CA mutated colorectal cancer

Palbociclib is an excellent CDK4/6 inhibitor, but its clinical application is mainly limited to the treatment of advanced ER+/HR+ and HER2- breast cancer. Its efficacy in colorectal cancer (CRC) is evaluated in multiple trials and so far remains undetermined. We found that the PIK3CA-mutant CRC cells were insensitive to palbociclib treatment compared with the PIK3CA wild-type counterparts, and they were in an OIS (oncogene-induced senescence) state which was predisposed to a strong senescence-associated secretory phenotype (SASP) upon treatment. These senescent cells excessively secreted various SASP factors LCN2, and ultimately caused palbociclib-resistance via upregulation of EGFR in the non-senescent CRC cells. Importantly, a drug combination screen identified that erlotinib could synergize with palbociclib to overcome the SASP-induced resistance. Overall, we found that PIK3CA mutation-induced senescence compromised the efficacy of palbociclib treatment in CRC but co-targeting EGFR could minimize the OIS-associated side effects while preserving the beneficial effects.

cancer biology↗