Biology subjects

Bateman, N. W.

Publications and source records attributed to Bateman, N. W..

11 recordsLinked to original sources

bioRxiv2026

Proteomic Profiling Identifies CLDN3 as a Tumor-Selective Therapeutic Target in Small Cell Lung Cancer

Small cell lung cancer (SCLC) remains a highly lethal disease with limited targetable surface antigens beyond delta-like ligand 3 (DLL3). We sought to systematically identify and validate tumor-selective cell-surface targets in relapsed SCLC. To this end, we developed an integrated proteogenomic pipeline combining single-cell RNA sequencing, combinatorial optimization, mass spectrometry-based proteomics, and immunohistochemistry to systematically map the SCLC surfaceome of 49 tumors across 25 patients with relapsed SCLC. Using this approach we found claudin-3 (CLDN3) to be a consistently expressed and tumor-selective antigen, with broader coverage than DLL3, which is the current clinical benchmark. CLDN3 was highly expressed across various treatment states, while maintaining low expression in most nonmalignant tissues. Functional validation using a novel CLDN3-specific monoclonal antibody (ABN501) demonstrated NK cell-mediated cytotoxicity in vitro and tumor regression in vivo, as well as a favorable safety profile. These findings support clinical development of CLDN3-directed therapies and demonstrate the utility of integrated proteogenomic approaches for antigen discovery in solid tumors.

cancer biology↗

bioRxiv2025

ShapeUpLMD: An Automated Pipeline for Spatially Optimized Laser Microdissection in Multi-omic Tissue Profiling

Laser microdissection (LMD) enables enrichment of defined cellular populations from heterogeneous tissues, providing histologically-resolved molecular profiles with spatial resolution. However, the absence of standardized methods for generating optimized regions of interest (ROIs) limits reproducibility and scalability for spatial multi-omic workflows. To address this gap, we developed ShapeUpLMD, an open-source software tool and integrated workflow that automates the optimization of spatially defined ROIs for LMD, directly from digital pathology annotations. Fresh-frozen uterine serous carcinoma (USC) tumors (n = 11) were sectioned onto polyethylene naphthalate slides, scanned and subsequently annotated by an expert pathologist. Tumor and non-tumor ROIs were used to train tumor-specific classifiers in HALO (Indica Labs). Classified tumor or unbiased whole tissue ROIs were refined and optimized by ShapeUpLMD prior to automated collection on a Leica LMD7 microscope. Across the cohort, predicted tumor ROIs increased effective tumor purity by 86 {+/-} 6% relative to whole tissue while maintaining high spatial concordance between predicted and collected ROIs (accuracy = 0.9 {+/-} 0.05). Data-independent acquisition mass spectrometry quantified upwards of [~]6,500 proteins across spatially resolved ROIs, revealing regionally coherent clustering of adjacent tumor regions and abundance patterns consistent with tumor and non-tumor cell admixture in an unbiased spatial proteomic application. ShapeUpLMD provides an automated, reproducible, and scalable framework that bridges digital pathology with LMD enabling high-fidelity spatial enrichment for multi-omic analyses. This workflow increases throughput, reduces inter-operator variability, and supports standardized, regionally resolved tissue collection for spatial systems biology applications. The software is available at https://github.com/GYNCOE/ShapeUpLMD.

cancer biology↗

bioRxiv2025

Integrated proteogenomics uncovers ancestry-specific and shared molecular drivers in localized prostate cancer

Our integrative proteogenomics (genome, proteome and phosphoproteome) of localized prostate cancer (PCa) in an equal-access Military Health System patient cohort (57 Black and 55 White) revealed significant ancestry-associated differences. Somatic and germline regulatory differences converged on androgen, metabolic, PI3K/AKT/mTOR, and DNA damage response (DDR) pathways, with ancestry-specific immune- and stromal-associated signals. Black patients displayed greater genomic variability, enhanced androgen response, fatty-acid metabolism, and epithelial-mesenchymal transition, while White patients showed prevalent DDRG alterations, activated oncogenic signaling (MYC, E2F, mTORC1), and cell cycle regulation. Phosphoproteomics highlighted distinct kinase activities and candidate druggable dependencies. Multiomics integration revealed three exploratory tumor subtypes whose distinct biological programs were reproducibly validated. Ancestry-associated eQTLs supported inherited regulation of the proteome independent of CNAs. Ancestry-specific CNA and protein panels improved progression risk prediction beyond PSA and pathology models. These findings provide a framework for ancestry-informed prognostic models and generate testable hypotheses for precision therapies to reduce outcome disparities.

cancer biology↗

bioRxiv2024

A modified dual preparatory method for improved isolation of nucleic acids from laser microdissected fresh-frozen human cancer tissue specimens

A central theme in cancer research is to increase our understanding of the cancer tissue microenvironment (TME), which is comprised of a complex and spatially heterogeneous ecosystem of malignant and non-malignant cells, both of which actively contribute to an intervening extracellular matrix. Laser microdissection (LMD) enables histology selective harvest of cellular subpopulations from the tissue microenvironment for their independent molecular investigation, such as by high-throughput DNA and RNA sequencing. Although enabling, LMD often requires a labor-intensive investment to harvest enough cells to achieve the necessary DNA and/or RNA input requirements for conventional next generation sequencing workflows. To increase efficiencies, we sought to use a commonplace dual preparatory (DP) procedure to isolate DNA and RNA from the same LMD harvested tissue samples. While the yield of DNA from the DP protocol was satisfactory, the RNA yield from the LMD harvested tissue samples was significantly poorer compared to a dedicated RNA preparation procedure. We identified that this low yield of RNA was due to incomplete partitioning of RNA in this widely used DP protocol. Here we describe a modified DP protocol that effectively partitions nucleic acids and results in significantly improved RNA yields from LMD harvested cells.

biochemistry↗

bioRxiv2024

Deep Quantitative Proteomics Identifies Conserved Proteome Alterations in Low and High-Grade Serous Ovarian Cancers

BackgroundLow-grade serous ovarian carcinoma (LGSOC) is a rare and largely chemoresistant subtype of epithelial ovarian cancer. Unlike treatment for high-grade serous ovarian cancer (HGSOC), management options for LGSOC patients are limited, in part, due to a lack of deep molecular characterization of this disease. To address this limitation, we aimed to define highly conserved proteome alterations in LGSOC by performing deep quantitative proteomic analysis of tumors collected from LGSOC (n=12) and HGSOC (n=24) patients or normal fallopian tube tissues (n=12) and validating proteins within two independent proteomic datasets of LGSOC (n=25) and HGSOC (n=49) tumors. ResultsOur efforts identified 275 protein alterations conserved between LGSOC and HGSOC tumors that exhibit high quantitative correlation between discovery and validation cohorts (Spearman Rho [≥] 0.82, P < 1E-4). Conserved proteins elevated in LGSOC tumors were enriched for pathways regulating cell adhesion and defective cellular apoptosis signaling and candidates mapping as putative drug targets included 5-nucleotidase/ cluster of differentiation 73 (NT5E/CD73). We also identified MUC16 (CA125) as significantly elevated in LGSOC versus HGSOC tumors and confirmed this by immunohistochemistry analysis between three independent reviewers. We also find that MUC16 exhibits a more apical versus membrane-staining pattern in LGSOC tumors, suggesting unique regulation of MUC16 in this disease subtype. ConclusionOur efforts define highly conserved protein alterations distinguishing LGSOC from HGSOC tumors, including CD73, as well as the novel identification that MUC16 is elevated and exhibits more apical staining pattern in LGSOC tumor tissues. These findings deepen our molecular understanding of LGSOC and provide unique insights into highly conserved proteome alterations in LGSOC tumors.

cancer biology↗

bioRxiv2024

Microenvironment Shapes Cell State, Plasticity, and Heterogeneity of Small Cell Lung Cancer

Small-cell lung cancer (SCLC) is the most fatal form of lung cancer. Intra-tumoral heterogeneity, marked by neuroendocrine (NE) and non-neuroendocrine (non-NE) cell states, defines SCLC, but the drivers of SCLC plasticity are poorly understood. To map the landscape of SCLC tumor microenvironment (TME), we apply spatially resolved transcriptomics and quantitative mass spectrometry-based proteomics to metastatic SCLC tumors obtained via rapid autopsy. The phenotype and overall composition of non-malignant cells in the tumor microenvironment (TME) exhibits substantial variability, closely mirroring the tumor phenotype, suggesting TME-driven reprogramming of NE cell states. We identify cancer-associated fibroblasts (CAF) as a crucial element of SCLC TME heterogeneity, contributing to immune exclusion, and predicting exceptionally poor prognosis. Together, our work provides a comprehensive map of SCLC tumor and TME ecosystems, emphasizing their pivotal role in SCLCs adaptable nature, opening possibilities for re-programming the intercellular communications that shape SCLC tumor states.

cancer biology↗

bioRxiv2023

ProteoMixture: A Cell Type Deconvolution Tool for Bulk Tissue Proteomics Data

Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome and transcriptome data generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture in two commonly used deconvolution algorithms, ESTIMATE and ConsensusTME (r [≥] 0.63). Here we have developed and optimized protein-based signatures to estimate cell admixture proportions and benchmarked these using bulk tumor proteomics data from over 150 HGSOC patients. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomics data are available at (https://lmdomics.org/ProteoMixture/.

bioinformatics↗

bioRxiv2020

Peptide Ancestry Informative Markers in Uterine Neoplasms from Women of European, African and Asian Ancestry

Characterization of ancestry-linked peptide variants in disease-relevant patient tissues represents a foundational step to connect patient ancestry with molecular disease pathogenesis. Nonsynonymous single nucleotide polymorphisms (SNPs) encoding missense substitutions within tryptic peptides exhibiting high allele frequencies in European, African, and East Asian populations, termed peptide ancestry informative markers (pAIMs), were prioritized from 1000 genomes. In silico analysis shows that as few as 20 pAIMs can determine ancestry proportions similarly to >260K SNPs (R2=0.9905). Multiplexed proteomic analysis of >100 human endometrial cancer cell lines and uterine leiomyoma tissues resulted in the quantitation of 62 pAIMs that correlate with self-described race and genotype-confirmed patient ancestry. Candidates include a D451E substitution in GC vitamin D-binding protein previously associated with altered vitamin D levels in African and European populations. These efforts describe a generalized set of markers for proteoancestry assessment that will further support studies investigating the impact of ancestry on the human proteome and how this relates to the pathogenesis of uterine neoplasms.

cancer biology↗

bioRxiv2020

Standardization and Harmonization of Distributed Multi-National Proteotype Analysis supporting Precision Medicine Studies

Cancer has no borders: Generation and analysis of molecular data across multiple centers worldwide is necessary to gain statistically significant clinical insights for the benefit of patients. Here we conceived and standardized a proteotype data generation and analysis workflow enabling distributed data generation and evaluated the quantitative data generated across laboratories of the international Cancer Moonshot consortium. Using harmonized mass spectrometry (MS) instrument platforms and standardized data acquisition procedures, we demonstrated robust, sensitive, and reproducible data generation across eleven sites in nine countries on seven consecutive days in a 24/7 operation mode. The data presented from the high-resolution MS1-based quantitative data-independent acquisition (HRMS1-DIA) workflow shows that coordinated proteotype data acquisition is feasible from clinical specimens using such standardized strategies. This work paves the way for the distributed multi-omic digitization of large clinical specimen cohorts across multiple sites as a prerequisite for turning molecular precision medicine into reality.

molecular biology↗

bioRxiv2019

Extensive Intratumor Proteogenomic Heterogeneity Revealed by Multiregion Sampling in a High-Grade Serous Ovarian Tumor Specimen

Enriched tumor epithelium, tumor-associated stroma, and whole tissue were collected by laser microdissection from thin sections across spatially separated levels of ten primary high-grade serous ovarian tumors and analyzed using proteomics (mass spectrometry and reverse phase protein microarray) and RNA-sequencing analyses. Comparative analyses of transcript and protein abundances revealed independent clustering of enriched stroma and enriched tumor epithelium, with whole tumor tissue clustering between purified collections, driven by overall tumor purity. Comparison of historic prognostic molecular subtypes for HGSOC revealed protein and transcript expression from tumor epithelium correlated most strongly with the differentiated molecular subtype, whereas stromal proteins and transcripts most strongly correlated with mesenchymal subtype. Protein and transcript abundance in tumor epithelium and stromal collections from neighboring sections exhibited decreased correlation in samples collected just hundreds of microns apart. These data reveal substantial protein and transcript expression heterogeneity within the tumor microenvironment that directly bears on prognostic signatures and underscore the need to enrich cellular subpopulations for expression profiling.

cancer biology↗

bioRxiv2019

Hematological and Neurological Expressed 1 (HN1): a predictive and pharmacodynamic biomarker of metformin response in endometrial cancers.

Preoperative use of metformin in obese women with endometrioid endometrial cancer (EEC) reduces tumor proliferation and inhibits the mammalian target of rapamycin (mTOR) pathway, though is only effective in select cases. This study sought to identify a predictive and/or pharmacodynamic proteomic signature of metformin response to tailor its pharmacologic use. Matched pre- and post-metformin treated tumor tissues from a recently completed phase 0 window trial of metformin in EEC patients (ClinicalTrials.gov: NCT01911247), were analyzed by mass spectrometry (MS)-based proteomic and immunohistochemical analyses. Hematological and neurological expressed 1 (HN1) was significantly elevated in metformin responders (n=13) versus non-responders (n=7), which was also found to decrease in abundance in metformin responders following treatment; observations that were verified by immunohistochemical staining for HN1. Metformin response and loss of HN1 was assessed in RL95-2 and ACI-181 endometrial cancer cell lines. We further identified that silencing of HN1 abundance does not alter cellular response to metformin or basal cell proliferation, but that HN1 abundance does decrease in response to metformin treatment in RL95-2 and ACI-181 endometrial cancer cell lines. These data suggest that HN1 represents a predictive and pharmacodynamic biomarker of metformin response that, if validated in larger patient populations, may enable pre-operative EEC patient stratification to metformin treatment and the ability to monitor patient response.\n\nNovelty and ImpactHematological and Neurological Expressed 1 (HN1) is elevated in endometrioid endometrial cancer (EEC) patients that respond to metformin vs non-responders and decreases after treatment. HN1 decreases after metformin treatment of EEC cells in vitro, but is not necessary for metformin response or proliferation. We report HN1 as a novel predictive and pharmacodynamic biomarker of metformin response in EEC that may enable pre-operative EEC patient stratification to metformin treatment and the ability to monitor patient response.

cancer biology↗