Spatial biology technologies offer a unique opportunity to link tissue composition with function. However, analytical methods for quantifying and interpreting highly complex spatial data remain limited. We present CROCHET (ChaRacterization Of Cellular HEterogeneity in Tissues), an end-to-end analysis pipeline for construction of spatially resolved cell atlases from raw data covering millions of cells across large sample cohorts. Its modular architecture supports the integration of diverse data modalities and novel analytical methods for image processing and segmentation, spatialomics quantification and downstream analyses. With comprehensive, open-source, user-friendly, interactive, and visual analysis modules, CROCHET aims to democratize spatial omics for a broad community of users.

Abstract Introduction: We present here a public website, omicology.com, which includes extensive information on omic research domains and features a specialized omics search engine. Omics started as a simple suffix for research based on molecular profiling of genes, proteins, and other (biological) molecules in aggregate. The etymology, from ancient Greek or possibly Sanskrit, is debated. Application of the suffix to dozens of research fields starting in the 1990s (Weinstein 1998) was often derided as jargon. “Where’s the hypothesis?” was a common critique - and reason for rejecting manuscripts or proposals. But given the draft human genome in 2001, synergy between omic and hypothesis-driven research gradually gained acceptance. In 2000, 2010, 2020, and 2025, there were 6, 61, 267, and >700 omics terms, respectively. More recently, high-throughput single-cell, spatially-resolved, and temporally-resolved omic technologies have provided new dimensions to our pursuit of precision medicine for cancer. Methods: Our starting point for development of the search engine database was 6,980,243 full-text Open Access PubMed Central articles plus various metadata. It included 5,699 omics terms. Our NLP, use of LLM resources, and careful manual curation have produced 702 omics terms. Refinement of such lists is complicated by eccentricities of language, misspellings, alternative spellings, suffixes like ‘nomics’ (e.g., in economics), not ‘omics,’ and ambiguous terms like ‘chromosomics.’ AI tools (Elicit, Cursor, Google, and ChatGPT) have provided useful design and content ideas for a static html prototype website. An agentic AI coding tool has assisted our development of a dynamic full-stack, human-curated version (currently 34,124 lines of code and content) based on the prototype (for publicly roll-out before AACR 2026). Representative Results: The most frequent omic terms are Genomics (257,617 articles), Proteomics (123,697), Metabolomics, (80,023), and Transcriptomics (78,279). Metagenomics, radiomics, lipidomics, epigenomics, pharmacogenomics, and phosphoproteomics complete the top 10. Our data on the top 702 terms currently include usage numbers for each document section, publication date, curation status, a brief LLM-generated description, and more. Conclusions: The open-source, updatable Omicology.com website will provide an expanding repertoire of information and perspectives on omics plus specialized omics search capabilities. Biomedical perspective will be aided by the senior author’s long-term experience in omic research beginning with his initiation and leadership of the first omic/multi-omic NCI project, molecular profiling of the NCI-60 (e.g., Weinstein, et al., Science, 1997). Methods used here for omics can provide a template for research on other hard-to-analyze fields, complementing the capabilities of such resources as PubMed Central. Citation Format: James M. Melott, John N. Weinstein, . Omicology: A comprehensive AI/LLM/NLP-based web resource for the ontology, phylogeny, and practical navigation of omics literature [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5451.

Mass spectrometry (MS) is indispensable for high-throughput quantitation of protein expression. But protein function is regulated by factors beyond abundance alone. Here, we evaluate two supercharging reagents, dimethyl sulfoxide (DMSO) and m-nitrobenzyl alcohol (mNBA), in narrow-window data-independent acquisition (nDIA)-MS. DMSO markedly enhances MS signal and protein identification, whereas mNBA primarily increases peptide identifications. Optimizating nDIA-MS with 3

Rapid and comprehensive analysis of complex proteomes across large sample sets is vital for unlocking the potential of systems biology. We present a high-throughput mass spectrometry (MS) proteomics method that integrates narrow-window data-independent acquisition (nDIA) with short-gradient micro-flow chromatography, enabling profiling of >240 samples per day. This optimized MS approach identifies 6,201 and 7,466 human proteins with 1- and 2-min gradients, respectively. As a practical application, we analyzed 507 samples composed of 13 different tissues from mice treated with the enzyme-drug L-asparaginase (ASNase) or its glutaminase-free Q59L mutant, generating a quantitative profile of 11,472 proteins following drug treatment. The MS results confirmed the impact of ASNase on amino acid metabolism in solid tissues. Further analysis revealed broad suppression of anticoagulants and cholesterol metabolism and uncovered numerous tissue-specific dysregulated pathways. In summary, the optimized high-throughput proteomics method accelerates systems-level analysis of a preclinical model to generate biological insights and clinically actionable hypotheses.

Poor therapeutic index is a principal cause of drug attrition during development. A case in point is L-asparaginase (ASNase), an enzyme-drug approved for treatment of pediatric acute lymphoblastic leukemia (ALL) but too toxic for adults. To elucidate potentially targetable mechanisms for mitigation of ASNase toxicity, we performed multi-omic profiling of the response to sub-toxic and toxic doses of ASNase in mice. We collected whole blood samples longitudinally, processed them to plasma, and extracted metabolites, lipids, and proteins from a single 20-µL plasma sample. We analyzed the extracts using multiple reaction monitoring (MRM) of 500+ water soluble metabolites, 750+ lipids, and 375 peptides on a triple quadrupole LC-MS/MS platform. Metabolites, lipids, and peptides that were modulated in a dose-dependent manner appeared to converge on antioxidation, inflammation, autophagy, and cell death pathways, prompting the hypothesis that inhibiting those pathways might decrease ASNase toxicity while preserving anticancer activity. Overall, we provide here a streamlined, three-in-one LC-MS/MS workflow for targeted metabolomics, lipidomics, and proteomics and demonstrate its ability to generate new insights into mechanisms of drug toxicity.

Ion suppression is a major problem in mass spectrometry (MS)-based metabolomics; it can dramatically decrease measurement accuracy, precision, and sensitivity. Here we report a method, the IROA TruQuant Workflow, that uses a stable isotope-labeled internal standard (IROA-IS) library plus companion algorithms to: 1) measure and correct for ion suppression, and 2) perform Dual MSTUS normalization of MS metabolomic data. We evaluate the method across ion chromatography (IC), hydrophilic interaction liquid chromatography (HILIC), and reversed-phase liquid chromatography (RPLC)-MS systems in both positive and negative ionization modes, with clean and unclean ion sources, and across different biological matrices. Across the broad range of conditions tested, all detected metabolites exhibit ion suppression ranging from 1% to >90% and coefficients of variation ranging from 1% to 20%, but the Workflow and companion algorithms are highly effective at nulling out that suppression and error. To demonstrate a routine application of the Workflow, we employ the Workflow to study ovarian cancer cell response to the enzyme-drug L-asparaginase (ASNase). The IROA-normalized data reveal significant alterations in peptide metabolism, which have not been reported previously. Overall, the Workflow corrects ion suppression across diverse analytical conditions and produces robust normalization of non-targeted metabolomic data.

Deletion of the long q arm of chromosome 22 (22qDEL) is the most frequently identified recurrent somatic copy number alteration observed in papillary thyroid carcinoma (PTC). Since its role in PTC is not fully understood, we conducted a pooled analysis of genomic characteristics and clinical correlates in 1094 primary tumors from four published PTC genomic studies. The majority of PTC cases with 22qDEL exhibited arm-level loss of heterozygosity (86%); nearly all PTC cases with 22qDEL had losses in 22q12 and 13, which together constitute 70% of the q arm. Our analysis confirmed that 22qDEL occurs more frequently with RAS point mutations (50.4%), particularly HRAS (70.3%), compared with other PTC drivers (9.3%), supporting the conclusion that 22qDEL is unlikely to be a solitary driver of PTC but possibly an important co-factor in carcinogenesis, particularly in PTCs with RAS driver mutations. Differential RNA expression analyses revealed downregulation of most genes located on chromosome 22 in cases with 22qDEL compared to those without 22qDEL. Many differentially expressed genes are drawn from immune response and regulation pathways. These findings highlight the value of further investigations into the contributions of 22qDEL events to PTC, perhaps mediated through immune perturbations.

Bulk deconvolution with single-cell/nucleus RNA-seq data is critical for understanding heterogeneity in complex biological samples, yet the technological discrepancy across sequencing platforms limits deconvolution accuracy. To address this, we introduce an experimental design to match inter-platform biological signals, hence revealing the technological discrepancy, and then develop a deconvolution framework called DeMixSC using the better-matched, i.e., benchmark, data. Built upon a novel weighted nonnegative least-squares framework, DeMixSC identifies and adjusts genes with high technological discrepancy and aligns the benchmark data with large patient cohorts of matched-tissue-type for large-scale deconvolution. Our results using a benchmark dataset of healthy retinas suggest much-improved deconvolution accuracy. Further analysis of a cohort of 453 patients with age-related macular degeneration supports the broad applicability of DeMixSC. Our findings reveal the impact of technological discrepancy on deconvolution performance and underscore the importance of a well-matched dataset to resolve this challenge. The developed DeMixSC framework is generally applicable for deconvolving large cohorts of disease tissues, and potentially cancer.

Poor therapeutic indexes are a principal cause of drug attrition during development. To develop multiomic methods for elucidating potentially targetable mechanisms of drug toxicity, we performed profiling of the response to subtoxic and toxic doses of l-Asparaginase (ASNase) in immune-compromised mice. ASNase is an enzyme-drug approved for the treatment of pediatric acute lymphoblastic leukemia (ALL) but too toxic for use in adults, making it an ideal test case. We collected 20-μL whole blood samples longitudinally, processed them to plasma, and extracted three molecule types (metabolites, lipids, and proteins) from each sample. We then analyzed the extracts using multiple reaction monitoring (MRM) of 500+ water-soluble metabolites, 750+ lipids, and 375 peptides on a triple quadrupole LC-MS/MS platform. Metabolites, lipids, and peptides that were modulated in a dose-dependent manner appeared to converge on antioxidation, inflammation, autophagy, and cell death pathways, prompting the hypothesis that inhibiting one or more of those pathways might decrease ASNase toxicity while preserving anticancer activity. The present studies were not designed to address therapeutic index directly, because efficacy was not studied. We provide here a streamlined, three-in-one LC-MS/MS workflow for targeted metabolomics, lipidomics, and proteomics and, as a proof of principle, demonstrate its ability to generate new hypotheses about mechanisms of ASNase toxicity.

Background Cancer cells reprogram metabolic pathways to meet increased energy and biosynthetic demands. Among those pathways, elevated asparagine metabolism regulated by asparagine synthetase (ASNS) has been linked to progression of various hematological cancers, driving cell proliferation, chemoresistance, and metastasis. ASNS inhibition represents a promising therapeutic strategy, but inhibitors have shown limited efficacy due to poor specificity and cell permeability. Methods We performed an unbiased phenotypic screen for inhibitors of the Wnt/β-catenin pathway. Subsequent mechanistic characterization via biochemical and cellular assays revealed that the resulting inhibitors were specifically targeted to asparagine synthetase (ASNS). We evaluated the anti-cancer activity of the top inhibitor alone and in combination with L-asparaginase (ASNase), a key component of childhood acute lymphoblastic leukemia therapy. We also assessed anticancer efficacy against the OCI-AML2 xenograft preclinical model of acute myeloid leukemia (AML). Results Phenotypic screening coupled with direct binding studies using purified human ASNS protein unveiled ASX-173 as a cell-permeable small molecule that inhibits ASNS at nanomolar concentrations (ki, inhibition constant of 0.4 nM). Using a paired cell line model consisting of ASNS-deficient RS4;11 cells (RS4;11) and their ASNS-expressing counterpart (RS4;11_ASNS), we found that increasing concentrations of ASX-173 effectively restored sensitivity of the ASNase-resistant RS4;11_ASNS line to ASNase. Mechanistically, the combination treatment disrupted nucleotide synthesis and induced cell cycle arrest and apoptosis. In a mouse model of AML, the combination significantly delayed the growth of OCI-AML2 xenografts. Although the combination therapy induced mild toxicity, evidenced by body weight loss (<15%), mice fully recovered after treatment cessation. Pharmacodynamic analysis revealed decreased plasma asparagine concentrations from approximately 60 µM baseline to 25 µM after two weeks of either ASNase alone or ASNase in combination with ASX-173, whereas ASX-173 alone had no effect on plasma asparagine. Conclusions There is a long-running history of cell lines reported to be sensitive to ASNase in vitro but not in vivo—a phenomenon that can be explained by the lack of ASNS inhibition in vivo. ASX-173 now enables such inhibition. In combination with ASNase, the resulting bicompartmental blockade of asparagine metabolism results in potent anticancer activity, a clear mechanism of action, and in vivo efficacy. Future studies to optimize pharmacokinetics, test rational combinations, and examine additional cancer models will help to realize the full therapeutic potential of targeting asparagine metabolism for the treatment of hematological malignancies.

Cancer cells reprogram metabolic pathways to meet increased energy and biosynthetic demands. Among those pathways, elevated asparagine metabolism regulated by asparagine synthetase (ASNS) has been linked to tumor progression in various cancers, driving cell proliferation, chemoresistance, and metastasis. ASNS inhibition represents a promising therapeutic strategy, but inhibitors have shown limited efficacy due to poor specificity and cell permeability. Through phenotypic screening, we identified ASX-173, a cell-permeable small molecule that inhibits ASNS at nanomolar concentrations. Biochemical and cellular assays confirm the specificity of ASX-173 activity and demonstrate its potentiation of the anti-cancer activity of L-asparaginase (ASNase), a key component of childhood acute lymphoblastic leukemia therapy. Mechanistically, the combination treatment disrupted nucleotide synthesis and induced cell cycle arrest and apoptosis. In a mouse model of acute myeloid leukemia, the combination significantly delayed the growth of OCI-AML2 xenografts. Analysis of data from The Cancer Genome Atlas (TCGA) revealed that ASNS mRNA expression is associated with poor survival in some cancer types and that ASNS protein levels are elevated in multiple solid tumors compared with the levels in normal tissues, suggesting possible broad utility of ASNS inhibition across the landscape of cancer. Together, these findings establish ASX-173 as a promising ASNS inhibitor and, for the first time, demonstrate a viable strategy to target ASNS therapeutically–an approach that has long remained elusive. ### Competing Interest Statement Roman Kombarov and Dmitry Genis are employees of Asinex Corporation. ASX173 was synthesized by Asinex Corporation. Victor Tatarskiy was a part-time employee of Asinex Corporation at the time this work was carried out. The other authors declare no conflict of interest. National Cancer Institute, https://ror.org/040gcmg81, CA143883, CA083639, CA235510, CA016672 Cancer Prevention and Research Institute of Texas, https://ror.org/0003xa228, RP130397 National Institutes of Health, S10OD012304-01, P30CA016672 Asinex Corporation

Key interactive capabilities of NG-CHMs

Abstract Resolving tissue and proteomic heterogeneity is critical to decoding the structure and function of tumor-immune microenvironment (TIME). Such understanding requires profiling of tumor and immune cell proteomic features with spatial resolution at the single-cell level. Although such spatially resolved methods and data sets are becoming increasingly available, analytical and computational methods that can extract the highly complex features and interactions within TIME are lacking. To address that problem, we have developed a computational pipeline we call the Spatial Proteomics Analysis and Computational Evaluation Pipeline (SPACE). The SPACE pipeline is composed of many analysis modules for processing and mining highly multiplexed imaging-based data types to explore TIME composition, organization, and heterogeneity. The pipeline generates and interprets biomarker expression and positional information from multiplexed images using algorithms for image indexing, image registration, quality control, segmentation, identification and removal of non-specific signals, data normalization, automatic identification of missing data, and adjustment for left-over signals. The accurate intensity measurements at single cell level are then used to calculate the proposed spatial features that represent cellular interactions in TIME. A hierarchical decision tree of cell markers is used to annotate types and identities for individual cells. The SPACE enables statistical and differential analyses of complex spatial features as well as cell types/identities with respect to clinical annotations and genomic alterations. The visualization of spatial and imaging data is made possible through an open-source OMERO image repository and spatial maps that integrate diverse markers in a single representation. Here, we demonstrate the applications of our pipeline in diverse gastrointestinal tumor types (e.g., small bowel adenocarcinoma) and validate the importance of integrating tissue heterogeneity at spatial and single-cell level. The framework is applicable to nearly all highly multiplexed imaging data platforms, including but not limited to, CycIF, CODEX, and imaging mass cytometry. Citation Format: Behnaz Bozorgui, Zeynep Dereli, Guillaume Thibault, John N. Weinstein, Anil Korkut. Single cell spatial proteomics analysis and computational evaluation pipeline [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3765.

Ion suppression is a major problem in mass spectrometry (MS)-based metabolomics; it can dramatically decrease measurement accuracy, precision, and signal-to-noise sensitivity. Here we report a new method, the IROA TruQuant Workflow, that uses a stable isotope-labeled internal standard (IROA-IS) plus novel companion algorithms to 1) measure and correct for ion suppression, and 2) perform Dual MSTUS normalization of MS metabolomic data. We have evaluated the method across ion chromatography (IC), hydrophilic interaction liquid chromatography (HILIC), and reverse phase liquid chromatography (RPLC)-MS systems in both positive and negative ionization modes, with clean and unclean ion sources, and across different biological matrices. Across the broad range of conditions tested, all detected metabolites exhibited ion suppression ranging from 1% to 90+% and coefficient of variations ranging from 1% to 20%, but the Workflow and companion algorithms were highly effective at nulling out that suppression and error. Overall, the Workflow corrects ion suppression across diverse analytical conditions and produces robust normalization of non-targeted metabolomic data.

Figure showing the NG-CHM Generator in the Galaxy bioinformatics platform.

Small bowel adenocarcinoma (SBA) is a rare malignancy marked with a poor prognosis. The cellular and proteomic heterogeneity within the tumor immune microenvironment (TIME) of SBA is a likely driver of prognosis, disease progression and response to therapy. We have addressed a major gap in knowledge of the TIME in SBA using highly multiplexed, protein imaging of the SBA tumor-immune ecosystem generating a comprehensive, single-cell level, spatial, proteomic atlas of TIME in > 600,000 cells from 136 tumor and matched normal samples from clinically and genomically annotated SBA patients (N=37). The SBA TIME Atlas informs on spatial distribution and interactions of tumor-intrinsic processes, diverse immune cell types, immune checkpoints, and vascularization. Enrichment of proliferating epithelial tissue, stem cells, and likely pro-tumor immune signatures in the tumor niche is contrasted by the representation of naive and early-effector T-cells in the epithelial compartments of adjacent normal tissues. Epithelial-stem-immune cell spatial architectures within tumor and matched-normal niches were strongly associated with patient survival, suggesting malignancy is driven by spatial architecture beyond the tumor microenvironment. The blueprints of therapeutically actionable immune checkpoints at the interface between epithelial and microenvironmental T-cells as well as macrophages have established a guideline for precision immunotherapies tailored to the TIME composition in SBA. We expect that this SBA atlas will contribute to a deeper understanding of the immune contexture in this rare disease as well as other gastrointestinal cancers and help guide future precision immunooncology strategies. ### Competing Interest Statement Gordon Mills: Relationship: Consultant/Scientific Advisory Board Companies: Amphista, Astex, AstraZeneca, BlueDot, Ellipses Pharmaceuticals, ImmunoMET, Ionis, Leapfrog Bio, Bruker/Nanostring, Neophore, Nerviano, Nuvectis, Pangea, PDX Pharmaceuticals, Qureator, Rybodyne, Signalchem Lifesciences, Tarveda, Turbine, Zentalis Pharmaceuticals **= travel reimbursement only Relationship: Stock/Options/Financial Companies: Bluedot, Catena Pharmaceuticals, ImmunoMet, Nuvectis, RyboDyne, SignalChem Lifesciences, Tarveda, Turbine Relationship: Licensed Technology Companies: HRD assay to Myriad Genetics DSP to Nanostring Foundation support: Adelson Medical Research Foundation, Breast Cancer Research Foundation, Komen Research Foundation, Ovarian Cancer Research Foundation, Prospect Creek Foundation Sponsored research AstraZeneca, Zentalis, Nanostring, Ionis (Provision of tool compounds) Clinical trials support (funding or in kind) AstraZeneca, Genentech, GSK, Lilly Entire List Amphista, Astex, AstraZeneca, BlueDot, Ellipses Pharmaceuticals, ImmunoMET, Ionis, Leapfrog Bio, Bruker/Nanostring, Neophore, Nerviano, Nuvectis, Pangea, PDX Pharmaceuticals, Qureator, Rybodyne, Signalchem Lifesciences, Tarveda, Turbine, Zentalis Pharmaceuticals; Stock/Options/Financial Companies: Bluedot, Catena Pharmaceuticals, ImmunoMet, Nuvectis, RyboDyne, SignalChem Lifesciences, Tarveda, Turbine; Licensed Technology Companies: HRD assay to Myriad Genetics DSP to Nanostring; Foundation support: Adelson Medical Research Foundation, Breast Cancer Research Foundation, Komen Research Foundation, Ovarian Cancer Research Foundation, Prospect Creek Foundation; Sponsored research; AstraZeneca, Zentalis, Nanostring, Ionis (Provision of tool compounds); Clinical trials support (funding or in kind) AstraZeneca, Genentech, GSK, Lilly Zeynep Dereli Shareholder: ViVoz Biolabs

The journal and authors wish to retract the article entitled ‘Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images’ cited above [...]

Rapid and comprehensive analysis of complex proteomes across large sample sets is vital for unlocking the potential of systems biology. We present UFP-MS, an ultra-fast mass spectrometry (MS) proteomics method that integrates narrow-window data-independent acquisition (nDIA) with short-gradient micro-flow chromatography, enabling profiling of >240 samples per day. This optimized MS approach identifies 6,201 and 7,466 human proteins with 1- and 2-min gradients, respectively. Our streamlined sample preparation workflow features high-throughput homogenization, adaptive focused acoustics (AFA)-assisted proteolysis, and Evotip-accelerated desalting, allowing for the processing of up to 96 tissue samples in 5 h. As a practical application, we analyzed 507 samples from 13 mouse tissues treated with the enzyme-drug L-asparaginase (ASNase) or its glutaminase-free Q59L mutant, generating a quantitative profile of 11,472 proteins following drug treatment. The MS results confirmed the impact of ASNase on amino acid metabolism in solid tissues. Further analysis revealed broad suppression of anticoagulants and cholesterol metabolism and uncovered numerous tissue-specific dysregulated pathways. In summary, the UFP-MS method greatly accelerates the generation of biological insights and clinically actionable hypotheses into tissue-specific vulnerabilities targeted by ASNase.

Supplementary Table 1 from Nonclassic Functions of Human Topoisomerase I: Genome-Wide and Pharmacologic Analyses