Biology subjects

Zhao, N.

Publications and source records attributed to Zhao, N..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Exploring the Influence of Chemical Exposures in Breast Cancer Disparities: High-Throughput Transcriptomic Analysis in Normal Breast Cells from Diverse Donors

Racial disparities in the incidence of, and mortality from, aggressive breast cancers are a pressing public health issue. Many factors have been investigated in these inequities; however, the role of toxicant exposures is not well characterized. We and others have identified substantial inequities in chemical biomarker concentrations by race. The goal of this study was to test the hypothesis that exposure to these chemicals is linked to biological changes relevant to aggressive breast cancers, such as dysregulation of the Hallmarks of Cancer. We used high throughput transcriptomic profiling of normal primary human breast epithelial cells from diverse donors (n=6) to test effects of 8 chemicals (cadmium, lead, arsenic, copper, PFNA, BPA, BPS, p,p-DDE) with documented exposure disparities by race/ethnicity across 3 concentrations (100nM, 1{micro}M, 10{micro}M). Across chemicals, we identified that pathways related to cell cycle regulation and protein secretion were commonly affected. Through bioinformatic estimation of cell type proportions, we found that metals like lead and cadmium induced cell-type shifts, consistent with the dysregulated cellular plasticity cancer hallmark. Lead and arsenic response genes were enriched for genes associated with poor breast cancer survival in the Cancer Genome Atlas. Integrating concentration-response modeling and chemical biomonitoring data, BPA, p,p-DDE, copper, and lead elicited expression changes at concentrations relevant to the US population. Finally, we identified substantial interindividual heterogeneity in response to organic compounds, but less so in metals. These findings highlight the value of high-throughput transcriptomics as a New Approach Methodology (NAM) in quantifying how common exposures may impact aggressive breast cancer associated biological processes.

pharmacology and toxicology↗

bioRxiv2026

MERTK inhibition cooperates with immunomodulatory cyclophosphamide to induce CXCL9⁺ monocyte-macrophage programming and durable anti-tumor immunity in triple negative breast cancer

Triple-negative breast cancer (TNBC) has high rates of recurrence despite chemotherapy and immune checkpoint blockade (ICB). Tumor-associated macrophages (TAMs) can either suppress or support anti-tumor immunity, but the mechanisms governing these states and therapeutic targets remain unclear. Here, integrating public scRNAseq datasets with TNBC cohorts, we identify a prognostic myeloid signature defined by CXCL9hi/C1Qlow TAM programs, associated with improved survival and increased lymphocyte activation pathways. Using immunocompetent p53-null syngeneic TNBC models spanning basal-like (2153L) and claudin-low (T12) subtypes, we show that immunomodulatory cyclophosphamide (CTX) reprograms hematopoiesis toward the monocytic lineage and induces an interferon (IFN) conditioned tumor milieu that supports CXCL9 monocyte-derived macrophages (Mo.Macs) in basal-like disease. Combining CTX with the next generation MERTK-selective inhibitor UNC2371 (MRX-2843) drives complete remissions in both models, but durable long-term responses occurred selectively in the basal-like subtype model. The expansion of antigen-presenting CXCL9 Mo.Macs and reduction of C1q phagocytic TAMs are observed in responding tumors. Mechanistically, MERTK inhibition relieves MAPK/SOCS1 mediated restraint of IFN signaling driving a positive feedback loop of IRF7/STAT1/IRF1 driven CXCL9 induction. Functionally, tumor control requires CXCL9-CXCR3 dependent CD4 T cell recruitment, accumulation of stem-like memory CD4 T cells, and germinal center like immune organization in tumor-draining lymph nodes. PD-1 blockade further increases durability, preventing recurrence in most treated basal-like tumors. Together, these findings define an IFN licensed, MERTK regulated myeloid checkpoint that can be therapeutically targeted to convert suppressive TNBC microenvironments into durable adaptive immunity, supporting clinical translation of CTX + MRX-2843 based combinations in basal-like TNBC. SignificanceSuppressive myeloid programing limits effective adaptive immune engagement in TNBC usually resulting in ICB treatment resistance and tumor recurrence. This study identifies a therapeutically actionable myeloid interferon checkpoint in which MERTK inhibition stabilizes CXCL9 monocyte-macrophage programming to promote CD4 T cell dependent immune memory and durable tumor control in basal-like TNBC.

cancer biology↗

bioRxiv2025

CAFT: A Compositional Log-Linear Model for Microbiome Data with Zero Cells

BackgroundDifferential abundance analysis is fundamental to microbiome research and provides valuable insights into host-microbe interactions. However, microbiome data are compositional, highly sparse (with many zero counts), and influenced by differential experimental biases across taxa. Standard statistical methods often overlook these features. Many approaches analyze relative abundances without accounting for compositionality or rely on pseudocounts, potentially leading to spurious associations and inadequate false discovery rate (FDR) control. MethodsWe introduce a novel framework for differential abundance analysis of microbiome data: the Compositional Accelerated Failure Time (CAFT) model. CAFT addresses zero read counts by treating them as censored observations that are below a detection limit. This approach is inherently resistant to multiplicative technical bias, eliminates the need for pseudocounts, and addresses compositional bias through the establishment of appropriate score test procedures. ResultsExtensive simulations show that CAFT outperforms competing compositional differential abundance methods, including LOCOM, LinDA, ANCOM-BC2, its robust variant, and LDM-clr by offering more robust type I error and FDR control with or without technical bias. Additionally, we applied CAFT to microbiome data on inflammatory bowel disease (IBD) and the upper respiratory tract (URT) to identify differentially abundant gut microbial taxa between IBD patients and healthy controls, as well as URT taxa distinguishing smokers from non-smokers. ConclusionWe present CAFT, a powerful, robust, and efficient approach for compositional differential abundance analysis. CAFT effectively controls Type I error and maintains FDR control, while demonstrating enhanced power in statistical testing. These capabilities render CAFT a useful tool for compositional microbiome data analysis. Availability and implementationThe R package and Vignette are available at https://github.com/mli171/CAFT.

bioinformatics↗

bioRxiv2025

VISTA-FISH: Video Imaging with Spatial-Temporal Analysis by Fluorescent In Situ Hybridization

Performing live-cell microscopy and high-dimensional gene expression measurements on the same cells is crucial for unraveling the molecular mechanisms underlying complex temporal phenotypes, yet this remains challenging using traditional approaches. To address these limitations, we developed Video Imaging with Spatial-Temporal Analysis by FISH (VISTA-FISH), a technique that matches live-cell recordings of cultured cells with the expression of thousands of genes in the same cell at the end of the video. Moreover, VISTA-FISH can simultaneously detect CRISPR guide RNAs in pooled screens, enabling investigation into the fundamental underpinnings of cellular activity, organelle transport, and other pivotal cellular functions. Using VISTA-FISH, we measured gene expression and neuron activity in the same differentiating neurons. This combined single-cell transcriptomic and video data allowed us to link differentiation stage, subcellular transcript localization, and cell subtype with neuron activity. Using these measurements, we built a model to predict activity from expression. Finally, we performed a pooled CRISPR interference screen with live-cell lysosome imaging, identifying alterations in lysosome movement and morphology as well as gene expression in perturbed neurons. Collectively, these studies position VISTA-FISH as a powerful new tool for elucidating the molecular mechanisms underlying neuron activity, organelle trafficking, and other complex temporal phenotypes.

molecular biology↗

bioRxiv2025

LabOS: The AI Co-Scientist That Sees and Works With Humans

Modern science advances fastest when thought meets action. LabOS represents the first AI co-scientist that unites computational reasoning with physical experimentation through multimodal perception, self-evolving agents, and XR-enabled, embodied human-AI collaboration. To empower agentic AI with embodied intelligence, we built LabSuperVision, the first vision-language-model (VLM) benchmark spanning biomedical and materials science laboratories, designed to evaluate scientific perception and reasoning. This endowed LabOS with visual reasoning capabilities that surpass those of general-purpose models on laboratory-specific tasks. By connecting multi-model AI agents, smart glasses, and human-AI-robot collaboration, LabOS allows AI to see what scientists see, understand experimental context, and participate in real-time execution through immersive XR interaction and robotic autonomation. Across applications-from cancer immunotherapy target discovery to cell engineering-LabOS shows that AI can move beyond computational design to participation, turning the laboratory into an intelligent, collaborative environment where human and machine discovery evolve together.

bioengineering↗

bioRxiv2025

Functional Electrical Stimulation of the Soleus Redistributes Lower-Limb Joint Work Distally in Young and Older Adults

Older adults walk with reduced ankle and greater hip mechanical output compared to young adults. This "distal-to-proximal redistribution" likely contributes to the greater metabolic energy expenditure during walking in older versus young adults. Due to the inverse relationship between ankle and hip use, functional electrical stimulation (FES) of the ankle extensors may increase ankle mechanical work and indirectly decrease hip mechanical work. Although FES increases stimulated muscle metabolism, bilateral soleus stimulation may restore more youthful walking kinetics without a detectable change in whole-body metabolism because ankle extension requires less metabolic energy than hip extension. Ten young adults and 10 older adults walked on a treadmill at 1.25 m/s with and without FES bilaterally applied over the respective legs soleus when the anterior-posterior ground reaction force exceeded 10% body weight. FES use altered walking mechanics and metabolic power similarly across age groups (all FES condition and age-group interactions p[≥]0.214). Across age groups, FES increased ankle mechanical power (p=0.041) and redistributed mechanical work production to occur relatively more at the ankle and less at the hip (p=0.010). The lower-limb joint redistribution ratio of older adults walking with FES was not different to that of young adults during baseline (p=0.785). Moreover, walking with FES increased metabolic power by 2% (p=0.037). FES attenuated older adult distal-to-proximal redistribution and modestly increased whole-body metabolic rate. FES applied to soleus muscles during the late stance of walking affects users similarly across the lifespan, indicating that FES interventions ought to consider a persons functional needs, regardless of age.

physiology↗

bioRxiv2025

MicroLive: An Image Processing Toolkit for Quantifying Live-cell Single-Molecule Microscopy

Advances in live-cell fluorescence microscopy have enabled us to visualize single molecules (such as mRNAs and nascent proteins) in real time with high spatiotemporal resolution. However, these experiments generate large datasets that require complex computational processing pipelines to derive meaningful and quantitative information, which is a technical barrier for many researchers. To address this barrier, here, we introduce MicroLive, an open-source Python-based application for quantifying live-cell microscopy images. MicroLive provides an interactive Graphical User Interface (GUI) to perform key tasks, including cell segmentation, photo-bleaching correction, single-particle detection/tracking, spot intensity quantification, inter-channel colocalization, and time-series correlation analysis. As a ground-truth testing dataset, we used synthetic live-cell imaging data generated with the rSNAPed toolkit, demonstrating accurate extraction of biologically relevant parameters. Microscopy images of U-2 OS cells expressing a gene construct smHA-KDM5B-BoxB-MS2 were used to demonstrate the use of this software. Availability and implementationMicroLive is distributed under a GPLv3 license and available on GitHub. https://github.com/ningzhaoAnschutz/microlive. Contactning.zhao@cuanschutz.edu.

biophysics↗

bioRxiv2025

Comparative Machine Learning Analysis of Saliva and Plaque Microbiomes in Children with Type 1 Diabetes

BackgroundType 1 diabetes (T1D) is associated with microbial dysbiosis. While most research has focused on the gut microbiome, limited data address the role of the oral microbiome in T1D. The oral and gut microbiomes overlap substantially, and the oral cavity may influence the gut microbial composition. Saliva and dental plaque represent two distinct oral niches with unique microbial communities, but it remains unclear which better reflects systemic disease states such as T1D. This study compared the performance of salivary and plaque microbiomes in classifying pediatric T1D status. MethodsPaired saliva and plaque samples were collected from 46 children (23 with T1D and 23 healthy controls). Microbial DNA was extracted and sequenced to target the 16S rRNA gene. The data were processed via QIIME 2 for taxonomic classification and centered log-ratio transformation. Alpha diversity, microbial abundance, and clustering analyses were performed to compare the oral microbiome between the T1D and control groups. Random forest classifiers were used to evaluate and compare the predictive accuracy of saliva- and plaque-based models, both with and without clinical metadata. ResultsSaliva samples presented lower alpha diversity than plaque samples did but presented significantly greater bacterial loads and total microbial abundances. Saliva-based models outperformed plaque-based models, achieving a classification accuracy of 94.2% with or without clinical metadata, compared with 73.3% accuracy for plaque-based models. ROC curve analysis further supported this difference, with saliva models reaching an AUC of approximately 0.94 versus 0.75 for plaque, indicating superior discriminative performance. UMAP clustering revealed more distinct separation of the T1D and control groups in terms of the salivary profiles than in the plaque profiles. Feature importance analysis revealed both unique and shared taxa predictive of T1D in each niche. The incorporation of clinical and demographic metadata did not enhance model performance, underscoring the robustness and predictive strength of microbiome data alone. ConclusionThe salivary microbiome is a more effective biospecimen than dental plaque for detecting T1D-associated microbial profiles in children. It offers superior classification accuracy and greater sensitivity in distinguishing T1D status, supporting salivas potential as a noninvasive, scalable medium for future microbiome-based monitoring.

microbiology↗

bioRxiv2025

Telomere-to-telomere assemblies reveal complex adaptive variation of 3-ketoacyl-CoA-synthases in Populus trichocarpa likely driven by helitrons

BackgroundThe model woody plant Populus trichocarpa displays an atypical alkene-diverse wax cuticle likely driven by copy number variation (CNV) of 3-ketoacyl-CoA synthases (KCS), which has been difficult to confirm based on short-read assemblies. New long-read sequencing provides opportunities to develop telomere-to-telomere resources to detect cryptic variation, including CNVs, which are currently missed in traditional analyses. Integrating this information can improve genomic prediction for breeding and provide insights into the evolutionary basis of important traits. ResultsOur analysis of 78 telomere-to-telomere long-read haplotypes identified more than twice as many KCS genes as previously reported, along with numerous intragenic non-synonymous substitutions. Random forest predictive models highlighted the importance of Potri.010G079500 in producing very long chain alkenes; however, its absence did not predict previously reported alkene-deficient phenotypes. Instead, alkene levels are best predicted by the combinations of KCS copies. Amino acid substitutions clustered around ligand and donor binding pockets, suggesting they contribute to differing wax cuticle composition. Finally, each KCS gene and copy was linked to a helitron transposon. A phylogenetic analysis indicates they are the evolutionary mechanism for generating KCS tandem arrays. ConclusionsLong-read sequencing and telomere-to-telomere assembles revealed large-effect loci critical to genetic studies that are unattainable from short-reads. These approaches also have the potential to reveal novel insights into genome structure and function, such as the helitrons identified here. Our results highlight that, given current challenges in annotation and assembly, detailed and focused long-read sequences are key to interpreting complex genomic regions that contain tandem copy number variants.

genomics↗

bioRxiv2025

DTH: A nonparametric test for homogeneity of multivariate dispersions

Testing homogeneity across groups in multivariate data is an important scientific question in its own right, as well as well as an auxiliary step in verifying the assumptions of ANOVA. Existing methods either construct test statistics based on the distance of each observation from the group center, or as the mean of pairwise dissimilarities among observations in a group. Both approaches can fail when mean within-group distance is similar across groups but the distribution of the within-group distances are different. This is a pertinent question in high dimensional microbiome data, where outliers and overdispersion can distort the performance of a mean-dissimilarity-based test. We introduce the non-parametric Distance based Test for Homogeneity (DTH) which measures dissimilarity between groups by comparing the empirical distribution of within-group dissimilarities using a combination of the Kolmogorov-Smirnov and Wasserstein distances. For more than two groups, pairwise group tests are combined using a permutation-based p-value. Through simulations we show that our method has higher power than existing tests for homogeneity in certain situations and comparable power in other situations. We also provide a simple framework for extending the test to a continuous covariate.

genomics↗

bioRxiv2025

Anti-CSF-1R therapy with combined immuno- chemotherapy coordinate an adaptive immune response to eliminate macrophage enriched Triple Negative Breast Cancers

Patients diagnosed with metastatic triple negative breast cancer (mTNBC) have limited treatment options, are more prone to develop resistance and are associated with high mortality. A cold tumor immune microenvironment (TIME) characterized by low T cells and high tumor associated macrophages (TAMs) in mTNBC is associated with the failure of standard-of-care chemotherapy and immune checkpoint blockade (ICB) treatment. We demonstrate that the combination of immunomodulatory metronomic Cyclophosphamide (CTX) coupled with anti-CSF-1R antibody targeted therapy (SNDX-ms6352) and anti-PD-1 (ICB), was highly effective against aggressive metastatic syngeneic Trp53 null TNBC genetically engineered mouse models (GEMMs) that present with high macrophage infiltration. Mechanistically, CSF-1R inhibition along with CTX disrupted the M-CSF/CSF-1R axis which upregulated IL-17, IL-15 and type II interferon resulting in elevated B- and T cell infiltration. Addition of an anti-PD-1 maintenance dose helped overcome de novo PD-L1 intra-tumoral heterogeneity (ITH) associated recurrence in lung and liver mTNBC.

cancer biology↗

bioRxiv2025

APOE Genotype Influences on The Brain Metabolome of Aging Mice - Role for Mitochondrial Energetics in Mechanisms of Resilience in APOE2 Genotype.

Alzheimers disease (AD) risk and progression are significantly influenced by APOE genotype with APOE4 increasing and APOE2 decreasing susceptibility compared to APOE3. While the effect of those genotypes was extensively studied on blood metabolome, less is known about their impact in the brain. Here we investigated the impacts of APOE genotypes and aging on brain metabolic profiles across the lifespan, using human APOE-targeted replacement mice. Biocrates P180 targeted metabolomics platform was used to measure a broad range of metabolites probing various metabolic processes. In all genotypes investigated we report changes in acylcarnitines, biogenic amines, amino acids, phospholipids and sphingomyelins during aging. The decreased ratio of medium to long-chain acylcarnitine suggests a reduced level of fatty acid {beta}-oxidation and thus the possibility of mitochondrial dysfunction as these animals age. Additionally, aging APOE2/2 mice had altered branch-chain amino acids (BCAA) profile and increased their downstream metabolite C5 acylcarnitine, indicating increased branched-chain amino acid utilization in TCA cycle and better energetic profile endowed by this protective genotype. We compared these results with human dorsolateral prefrontal cortex metabolomic data from the Religious Orders Study/Memory and Aging Project, and we found that the carriers of APOE2/3 genotype had lower markers of impaired BCAA katabolism, including tiglyl carnitine, methylmalonate and 3-methylglutaconate. In summary, these results suggest a potential involvement of the APOE2 genotype in BCAA utilization in the TCA cycle and nominate these humanized APOE mouse models for further study of APOE in AD, brain aging, and brain BCAA utilization for energy. We have previously shown lower plasma BCAA to be associated with incident dementia, and their higher levels in brain with AD pathology and cognitive impairment. Those findings together with our current results could potentially explain the AD-protective effect of APOE2 genotype by enabling higher utilization of BCAA for energy during the decline of fatty acid {beta}-oxidation.

systems biology↗

bioRxiv2025

Paired primary-metastasis patient-derived organoids and mouse models identify phenotypic evolution and druggable dependencies of peritoneal metastasis from appendiceal cancer

Peritoneal carcinomatosis is a common yet deadly manifestation of gastrointestinal cancers, with few effective treatments. To identify targetable determinants of peritoneal metastasis, we focused on appendiceal adenocarcinoma (AC), a gastrointestinal cancer that metastasizes almost exclusively to the peritoneum. Current treatments are extrapolated from colorectal cancer (CRC), yet AC has distinct genomic alterations, mucinous morphology and peritoneum restricted metastatic pattern. Further, no stable preclinical models of AC exist, limiting drug discovery and representing an unmet clinical need. We establish a first-in-class stable biobank of 16 long-term cultured AC patient-derived organoids (PDOs), including 3 matched, simultaneously resected primary AC-peritoneal carcinomatosis (AC-PC) pairs. By enriching for cancer cells, AC PDOs enable accurate genomic characterization relative to paucicellular AC tissue. We establish an organoid orthotopic intraperitoneal xenograft model that recapitulates diffuse peritoneal carcinomatosis and show that PC-organoids retain increased metastatic capacity, decreased growth factor dependency and sensitivity to standard of care chemotherapy relative to matched primary AC organoids. Single cell profiling of AC-PC pairs reveals dedifferentiation from mucinous differentiated states in primary AC into intestinal stem cell and fetal progenitor states in AC-PC, with upregulation of oncogenic signaling pathways. Through hypothesis-driven drug testing, we identify KRASMULTI-ON inhibitor RMC-7977 and Wnt-targeting tyrosine kinase inhibitor WNTinib as novel, clinically actionable strategies to target AC-PC more effectively.

cancer biology↗

bioRxiv2025

Integrative Epigenomic Landscape of Alzheimer's Disease Brains Reveals Oligodendrocyte Molecular Perturbations Associated with Tau

Alzheimers disease (AD) brains are characterized by neuropathologic and biochemical changes that are highly variable across individuals. Capturing epigenetic factors that associate with this variability can reveal novel biological insights into AD pathophysiology. We conducted an epigenome-wide association study of DNA methylation (DNAm) in 472 AD brains with neuropathologic measures (Braak stage, Thal phase, and cerebral amyloid angiopathy score) and brain biochemical levels of five proteins (APOE, amyloid-{beta} (A{beta})40, A{beta}42, tau, and p-tau) core to AD pathogenesis. Using a novel regional methylation (rCpGm) approach, we identified 5,478 significant associations, 99.7% of which were with brain tau biochemical measures. Of the tau-associated rCpGms, 93 had concordant associations in external datasets comprising 1,337 brain samples. Integrative transcriptome-methylome analyses uncovered 535 significant gene expression associations for these 93 rCpGms. Genes with concurrent transcriptome-methylome perturbations were enriched in oligodendrocyte marker genes, including known AD risk genes such as BIN1, myelination genes MYRF, MBP and MAG previously implicated in AD, as well as novel genes like LDB3. We further annotated the top oligodendrocyte genes in an additional 6 brain single cell and 2 bulk transcriptome datasets from AD and two other tauopathies, Picks disease and progressive supranuclear palsy (PSP). Our findings support consistent rCpGm and gene expression associations with these tauopathies and tau-related phenotypes in both bulk brain tissue and oligodendrocyte clusters. In summary, we uncover the integrative epigenomic landscape of AD and demonstrate tau-related oligodendrocyte gene perturbations as a common potential pathomechanism across different tauopathies.

genomics↗

bioRxiv2025

AI-assisted protein design to rapidly convert antibody sequences to intrabodies targeting diverse peptides and histone modifications

Intrabodies are engineered antibodies that function inside living cells, enabling therapeutic, diagnostic, and imaging applications. While powerful, their development has been hindered by challenges associated with their folding, solubility, and stability in the reduced intracellular environment. Here, we present an AI-driven pipeline integrating AlphaFold2, ProteinMPNN, and live-cell screening to optimize antibody framework regions while preserving epitope-binding complementarity-determining regions. Using this approach, we successfully converted 19 out of 26 antibody sequences into functional single-chain variable fragment (scFv) intrabodies, including a panel targeting diverse histone modifications for real-time imaging of chromatin dynamics and gene regulation. Notably, 18 of these 19 sequences had failed to convert using the standard approach, demonstrating the unique effectiveness of our method. As antibody sequence databases expand, our method will accelerate intrabody design, making their development easier, more cost-effective, and broadly accessible for biological research.

cell biology↗

bioRxiv2024

Suaeda Salsa Hyperspectral Index (SSHI) for mapping S. salsa in coastal wetlands using hyperspectral satellite imagery

Suaeda Salsa (S. salsa), a pioneer species with short and red-purplish plants in the intertidal zones, has significant ecological, economic, recreational and tourism values. Timely monitoring of S. salsa is crucial for understanding its dynamics and sustainable management of coastal wetlands. Hyperspectral satellite offers valuable opportunities due to its detailed spectral information. This study proposed a Suaeda Salsa Hyperspectral Index (SSHI) for S. salsa mapping based on hyperspectral satellite imagery. The SSHI was developed by considering the large within-class spectral variations of cover types at coastal wetlands, accounting for the spectral correlations in hyperspectral data, and employing dynamic band selection on a per-pixel basis to optimize the separation of S. salsa from other land covers. [Formula] (1+ L), where i[isin][499nm, 611nm], j[isin][628nm, 851nm], L= 0.5. We applied SSHI on ZY1-02D/E AHSI images over Yellow River Delta (YRD) and Liao River Delta (LRD), China during 2021 to 2023. Based on SSHI, a simple thresholding method and a random forest (RF) model were used to map S. salsa. Our results showed that the overall accuracies of S. salsa maps achieved 87.96%[~]89.43% (YRD) and 92.03%[~]93.36% (LRD) using thresholds, and 92.62%[~]94.05% (YRD) and 94.74%[~]94.79% (LRD) using RF. For RF, incorporating SSHI improves S. salsa producers accuracies (users accuracies) by 1.02%[~]6.85% (2.26%[~]6.25%) compared to those without SSHI, proving the effectiveness of SSHI. The S. salsa maps reveal notable temporal variations, reflecting the impacts of climate change and human activities. SSHI is also applicable to other hyperspectral imagery, such as GF-5B and PRISMA.

ecology↗

bioRxiv2024

Detecting Clinically Relevant Topological Structures in Multiplexed Spatial Proteomics Imaging Using TopKAT

Novel multiplexed spatial proteomics imaging platforms expose the spatial architecture of cells in the tumor microenvironment (TME). The diverse cell population in the TME, including its spatial context, has been shown to have important clinical implications, correlating with disease prognosis and treatment response. The accelerating implementation of spatial proteomic technologies motivates new statistical models to test if cell-level images associate with patient-level endpoints. Few existing methods can robustly characterize the geometry of the spatial arrangement of cells and also yield both a valid and powerful test for association with patient-level outcomes. We propose a topology-based approach that combines persistent homology with kernel testing to determine if topological structures created by cells predict continuous, binary, or survival clinical endpoints. We term our method TopKAT (Topological Kernel Association Test) and show that it can be more powerful than statistical tests grounded in the spatial point process model, particularly when cells arise along the boundary of a ring. We demonstrate the properties of TopKAT through simulation studies and apply it to two studies of triple negative breast cancer where we show that TopKAT recovers clinically relevant topological structures in the spatial distribution of immune and tumor cells.

bioinformatics↗

bioRxiv2024

Patient-Derived Xenografts of Triple-Negative Breast Cancer Enable Deconvolution and Prediction of Chemotherapy Responses

Chemotherapy regimens for triple-negative breast cancer (TNBC) combine agents without knowing which agents drive response. Consequently, predictors derived from multi-agent regimens cannot be assumed to generalize to individual drugs or other regimens, motivating development of treatment-matched predictors. Here, we used TNBC patient-derived xenografts (PDXs) treated with carboplatin, docetaxel, or the combination to deconvolute drug-specific responses and identify associated molecular features. Combination treatment rarely improved upon the best single agent, with enhanced responses in only 13% of PDXs and antagonism in a comparable fraction. Proteogenomic analyses identified high cytokeratin-5 (KRT5) as a general marker of chemotherapy responsiveness and KRT5 immunohistochemistry discriminated responsive PDXs (AUROC, 0.83). To train treatment-specific predictors, we integrated these data with independent PDX and clinical cohorts with responses assessed after anthracycline-free platinum, taxane, or platinum-taxane therapy, ensuring response corresponded to the modeled treatment. Four feature selection strategies yielded largely nonoverlapping biomarker panels converging on treatment-relevant pathways. On independent test data, RNA-based predictors of complete response (CR) to platinum-based (carboplatin or cisplatin) and taxane-based (docetaxel or paclitaxel) chemotherapy achieved AUROCs of 0.80 and 0.86, respectively. For platinum-taxane regimens (carboplatin plus docetaxel or paclitaxel), proteomic-guided feature selection generated a 10-biomarker, protein- informed RNA predictor of pathologic complete response (pCR) that outperformed RNA-only feature selection and achieved an AUROC of 0.85 in an independent clinical cohort, while retaining practicality as an RNA-based assay. Treatment-matched integration of multi-omic PDX and clinical datasets provides a framework for chemotherapy-specific predictors with clinically relevant performance, supporting biomarker-guided precision selection and treatment optimization for patients with TNBC. Statement of significanceIntegration of multi-omic data from patient-derived xenografts with treatment-matched clinical cohorts yielded three retrospectively validated predictors of platinum, taxane, and platinum+taxane response, enabling biomarker-guided chemotherapy selection for patients with triple-negative breast cancer.

cancer biology↗