Biology subjects

New, F.

Publications and source records attributed to New, F..

7 recordsLinked to original sources

bioRxiv2026

Spatial/molecular heterogeneity and treatment response in HER2+ early breast cancer: Translational analyses from the DAPHNe trial

Given the heterogeneity of HER2-positive breast cancer, reliable biomarkers to guide treatment are needed. We conducted multi-platform biomarker analyses of baseline tumor tissue from the DAPHNe trial of neoadjuvant paclitaxel/trastuzumab/pertuzumab (THP) for HER2-positive early breast cancer to characterize inter- and intra-tumor heterogeneity and to identify molecular predictors of response. A total of 98 patients with stage II-III HER2-positive breast cancer received neoadjuvant THP followed by surgery. Gene expression profiling, spatial protein profiling, and single-cell imaging (cyclic immunofluorescence) were performed on pre-treatment biopsies and a subset of residual disease specimens. Pathologic response was assessed using the residual cancer burden (RCB) score. Among HER2-positive patients included in the biomarker analysis, 34% of patients had node-positive breast cancer, and 66% had hormone receptor (HR)-positive tumors. HR-positive and HR-negative tumors differed significantly across gene expression, protein expression, and single-cell profiling. High ERBB2 gene signature, p53 gene signature, and HER2 protein expression predicted favorable response (RCB 0/1), while ESR1/PGR gene, estrogen receptor (ER) signaling gene signature, and ER alpha protein expression were associated with unfavorable response (RCB 2/3). Single-cell spatial analysis revealed that cancer cells clustered by shared HER2, ER, and PR expression, suggesting local expansion of phenotypically distinct subpopulations. ER-positive cancer cells were associated with lower HLA-A and PD-L1 expression, suggesting a less immunologically active tumor cell state. Baseline HER2 and ER expression are key predictors of response to neoadjuvant HER2-targeted therapy. Single-cell spatial profiling highlights intra-tumoral heterogeneity and suggests ER-driven immune exclusion as a potential mechanism of resistance.

cancer biology↗

bioRxiv2026

Spatiotemporal transcriptomics reveals distinct responses of ALDH1A1-positive and ALDH1A1-negative midbrain dopaminergic neurons to alpha-synuclein overexpression

Parkinsons disease is characterized by the progressive and preferential degeneration of dopaminergic neurons in the substantia nigra pars compacta, and intraneuronal alpha-synuclein (Syn) accumulation. Dopaminergic neurons (DANs) are anatomically and molecularly heterogeneous, but the impact of Syn pathology on distinct subpopulations is not well defined. One midbrain DAN sub-population expresses Aldehyde Dehydrogenase 1A1 (ALDH1A1), an enzyme that detoxifies aldehyde by-products of dopamine metabolism, and has been associated with differential vulnerability. Here, we applied GeoMx spatial transcriptomics to profile ALDH1A1-positive (ALDH1A1+) and ALDH1A1-negative (ALDH1A1-) DAN subpopulations in the mouse midbrain in situ at 3- and 8-weeks following adeno-associated virus (AAV)-mediated Syn overexpression. Analyzing 10,532 genes, we identified robust transcriptional differences between ALDH1A1+ and ALDH1A1- DANs under control conditions, supporting their characterization as distinct molecular subpopulations. In AAV-Syn-injected mice, we observed increased Snca expression and a reduction in ALDH1A1- DANs in the ipsilateral substantia nigra. Syn overexpression induced subpopulation-specific and time-dependent transcriptional responses, with dysregulation in ALDH1A1- DANs characterized by early down-regulation of pathways related to synaptic function, neurotransmitter handling, and bioenergetics, including glycolysis. In contrast, ALDH1A1+ DANs displayed later up-regulation of genes enriched for Acetyl-CoA and cholesterol metabolism pathways, reflecting subpopulation-specific adaptations to Syn overexpression. Analysis of human single nucleus RNA-sequencing data revealed partial conservation of the metabolic dysregulation signature. Together, our findings show that murine midbrain ALDH1A1+ and ALDH1A1- DANs represent molecularly distinct subpopulations with divergent temporal responses to Syn overexpression, emphasizing the importance of cell-type and disease-stage context in studies of Parkinsons disease mechanisms.

neuroscience↗

bioRxiv2024

Generalizability of Normative Models of Brain Morphometry Across Distinct Ethnoracial Groups

Normative modeling of brain morphometric data can inform about the clinical significance of deviations from typical patterns in brain structure. Their usefulness, however, is dependent on their applicability to diverse ethnoracial groups. With this in mind, we developed age- and sex-specific normative models for cortical thickness, surface area, and subcortical volumes using brain scans from 37,407 healthy individuals from a diverse international sample. Here we demonstrate the validity of these models in diverse and distinct populations. Specifically, we tested these pre-trained models on independent samples of healthy individuals that either self-identified as Black, South Asian, East Asian Chinese, East Asian Japanese, or we categorized as African, Admixed American, East Asian, and European based on their genetic ancestry. Regardless of ethnoracial definition, the performance of the pretrained models in these samples was exceptionally high; the relative mean absolute error for each regional brain morphometry measure was less than 10% across all the distinct ethnoracial groups. These findings affirm the broad applicability of our models, ensuring that brain morphometry assessments using these models are accurate and reliable for individuals regardless of background. This broad applicability has significant implications for advancing personalized medicine and improving health outcomes in diverse populations.

bioinformatics↗

bioRxiv2023

Brain-Age Prediction: Systematic Evaluation of Site Effects, and Sample Age Range and Size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of site harmonization, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2,101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 years and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1,600 participants. These findings have been incorporated into CentileBrain [https://centilebrain.org/#/brainAGE2], an open-science, web-based platform for individualized neuroimaging metrics.

bioinformatics↗

bioRxiv2023

Single nuclei chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry

Single nuclei analysis is allowing robust classification of cell types in an organ that helps to establish relationships between cell-type specific gene expression and chromatin accessibility status of gene regulatory regions. Using breast tissues of 92 healthy donors of various genetic ancestry, we have developed a comprehensive chromatin accessibility and gene expression atlas of human breast tissues. Integrated analysis revealed 10 distinct cell types in the healthy breast, which included three major epithelial cell subtypes (luminal hormone sensing, luminal adaptive secretory precursor, and basal-myoepithelial cells), two endothelial subtypes, two adipocyte subtypes, fibroblasts, T-cells, and macrophages. By integrating gene expression signatures derived from epithelial cell subtypes with spatial transcriptomics, we identify specific gene expression differences between lobular and ductal epithelial cells and age-associated changes in epithelial cell gene expression patterns and signaling networks. Among various cell types, luminal adaptive secretory cells and fibroblasts showed genetic ancestry dependent variability. A subpopulation of luminal adaptive secretory cells with alveolar progenitor (AP) cell state were enriched in Indigenous American (IA) ancestry and fibroblast populations were distinct in African ancestry. ESR1 expression pattern was distinctly different in cells from IA compared to the rest, with a high level of ESR1 expression extending to AP cells and crosstalk between growth factors and Estrogen Receptor signaling being evident in these AP cells. In general, cell subtype-specific gene expression did not uniformly correlate with cell-specific chromatin accessibility, suggesting that transcriptional regulation independent of chromatin accessibility governs cell type-specific gene expression in the breast.

cancer biology↗

bioRxiv2023

Normative Modeling of Brain Morphometry Across the Lifespan using CentileBrain: Algorithm Benchmarking and Model Optimization

We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3,000 study participants. The model and scripts described here are freely available through CentileBrain (https://centilebrain.org/).

bioinformatics↗

bioRxiv2022

Ultra High-Plex Spatial Proteogenomic Investigation of Giant Cell Glioblastoma Multiforme Immune Infiltrates Reveals Distinct Protein and RNA Expression Profiles

A deeper understanding of complex biological processes, including tumor development and immune response, requires ultra high-plex, spatial interrogation of multiple "omes". Here we present the development and implementation of a novel spatial proteogenomic (SPG) assay on the GeoMx(R) Digital Spatial Profiler platform with NGS readout that enables ultra high-plex digital quantitation of proteins (> 100-plex) and RNA (whole transcriptome, > 18,000-plex) from a single FFPE sample. This study highlighted the high concordance, R > 0.85, and <11% change in sensitivity between SPG assay and the single analyte -assays on various cell lines and tissues from human and mouse. Furthermore, we demonstrate that the SPG assay was reproducible across multiple users. When used in conjunction with advanced cellular neighborhood segmentation, distinct immune or tumor RNA and protein targets were spatially resolved within individual cell subpopulations in human colorectal cancer and non-small cell lung cancer. We used the SPG assay to interrogate 23 different glioblastoma multiforme samples across 4 pathologies. The study revealed distinct clustering of both RNA and protein based on pathology and anatomic location. The in-depth investigation of giant cell glioblastoma multiforme revealed distinct protein and RNA expression profiles compared to that of the more common glioblastoma multiforme. More importantly, the use of spatial proteogenomics allowed simultaneous interrogation of critical protein post-translational modifications alongside whole transcriptomic profiles within the same distinct cellular neighborhoods.

cancer biology↗