Biology subjects

Santagata, S.

Publications and source records attributed to Santagata, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Modeling development of tertiary lymphoid structures in pulmonary tuberculosis by 3D profiling and trajectory analysis

Tertiary lymphoid structures (TLSs) are sites of immune organization in peripheral tissues that arise from chronic inflammation. They play important roles in infection control and cancer but the mechanisms controlling their formation remain only partly understood. Here, we combine high-plex imaging, serial-section 3D reconstruction, and optimal transport trajectory modeling to reconstruct TLSs in human lungs infected with Mycobacterium tuberculosis. We find that the extended and irregular shape of TLSs is poorly captured by 2D histopathology or spatial profiling, making assessment of developmental stage (early, primary, or secondary) error prone. In contrast, modeling TLS development in 3D using optimal transport reveals two trajectories that differ in the timing and coordination of follicular dendritic cell association, germinal center consolidation, and position within the tissue. These findings highlight the value of volumetric analysis in understanding immune organization and provide new insights into TLS biology. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=98 SRC="FIGDIR/small/725201v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@11b4daorg.highwire.dtl.DTLVardef@7871feorg.highwire.dtl.DTLVardef@1840c2dorg.highwire.dtl.DTLVardef@3b6028_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LI3D reconstruction of TB-infected lung reveals TLSs to be extended, heterogeneous structures that are frequently misclassified in 2D C_LIO_LITLS maturation follows continuous trajectories rather than discrete histological stages C_LIO_LIOptimal transport modeling identifies distinct maturation paths with divergent follicular dynamics C_LIO_LIGerminal center organization and Tfh-FDC interactions emerge along maturation trajectories C_LIO_LITLS maturation states are shaped by spatially localized microenvironmental niches. C_LI

immunology↗

bioRxiv2026

Compositional and interpretable representation of histology using AI foundation models and sparse autoencoders

Light microscopy of tissue sections stained with hematoxylin and eosin (H&E) has been the foundation of histopathology for over 150 years and remains essential for diagnosis and research. The development of high-plex spatial profiling approaches able to measure protein and RNA expression at single-cell resolution augments but does not replace H&E imaging, even in research. Computational pathology (CPath) models based on deep learning promise to further increase the value of H&E imaging but interpreting these models in biological terms remains challenging. As a result, they are not widely used in spatial profiling studies. Here we describe a human-in-the-loop computational framework that leverages CPath foundation models (FMs) and sparse autoencoders (SAEs) to decompose FM embeddings and automatically identify diverse, human-interpretable histopathology features in H&E images. When FM-SAE modeling was applied to pulmonary diseases such as tuberculosis and lung cancer, human-machine interaction augmented and accelerated expert interpretation. Moreover, the resulting annotations provide a morphology-aware approach to integrating 2D and 3D mesoscale tissue architectures with molecular spatial profiling.

bioinformatics↗

bioRxiv2026

Volumetric Cyclic Immunofluorescence for 3D Spatial Profiling of Immune Structures in Human FFPE Tissue

Mapping how parenchymal, immune, and stromal cells interact with extended structures such as blood vessels and nerves is challenging using conventional thin-section tissue imaging. Volumetric methods such as light-sheet fluorescence microscopy (LSFM) are ideal in these cases and LSFM is widely used in animal models such as fish and mice. This is enabled by well-established clearing methods, genetically encoded reporters, and vascular dyes. However, LSFM is rarely utilized for human tissue, which is primarily available as formaldehyde-fixed paraffin embedded (FFPE) specimens. Here, we present an approach to volumetric cyclic immunofluorescence (v-CyCIF) that enables 40-plex or greater imaging of immune and other cell types in human specimens up to 1 mm thick. We demonstrate the use of v-CyCIF to study neuroimmune interactions and lymphoid structures in normal and cancer tissues. Following LSFM imaging, specimens can be re-embedded and sectioned to ~50 {micro}m thick for high-resolution confocal imaging of punctate structures and cell-cell junctions. v-CyCIF therefore provides a flexible framework for multi-scale 3D profiling of clinical specimens across imaging formats and resolutions.

cancer biology↗

bioRxiv2026

Reconstruction of epithelial transcriptional trajectories reveals heterogeneous progression and early therapy-resistance programs in high-grade serous carcinomaprecursors

BackgroundSerous-tubal intraepithelial-carcinoma (STIC) is considered the principal precursor of tubo-ovarian high-grade serous-carcinoma (HGSC), yet its biological uniformity, progression risk and potential response to poly(ADP-ribose) polymerase-inhibitors (PARPi) remain poorly defined. MethodsWe performed trajectory-based transcriptomic reconstruction of Fallopian-tube epithelial regions spanning normal epithelium, precursor lesions, STIC, and invasive carcinoma using pseudotime inference and publicly available spatial transcriptomic data. Gene expression and pathway dynamics were defined along pseudotime, and interpatient heterogeneity examined at both lesion and patient levels. Transcriptional signatures associated with PARPi-resistance were quantified across STICs, BRCA-mutant and BRCA-wild type subtypes. ResultsTrajectory inference captured a continuous transcriptional progression from normal epithelium to HGSC, with STICs occupying heterogeneous evolutionary positions rather than a single precursor state. Incidental isolated STICs(STICi) spanned early to later pseudotime states and frequently aligned with loss-of-cilium organisation and less advanced epithelial phenotypes. STICs associated with concurrent cancer(STICc) exhibited more advanced malignant progression signatures, including cell-cycle activation, epithelial-to-mesenchymal transition (EMT), interferon signaling, and DNA-repair. Histologically similar lesions occupied divergent pseudotime positions with marked interpatient heterogeneity. PARPi resistance-associated signatures were variably enriched across precursor and precancer-stage lesions, with persistence into invasive disease. ConclusionsSTICs are heterogeneous and occupy distinct evolutionary positions along a continuum, highlighting potentially different progression risks from normal epithelium to HGSC. STICi differ from STICc which harbour signatures of more advanced malignant progression. Heterogeneity in PARPi resistance-associated programs in STICs cautions against uniform (non-stratified) use of PARPi-based primary prevention strategies. Future research should explore evolutionary-trajectory informed biomarkers for risk stratification and early interception strategies. Translational relevanceSerous tubal intraepithelial-carcinoma (STIC) represents a precursor of tubo-ovarian high-grade serous carcinoma (HGSC), offering unique translational opportunities for early detection, risk stratification, and prevention. However, its phenotypic uniformity, progression risk and therapeutic drug response are not fully understood. Our study shows that STICs are not a uniform precursor state, but instead occupy a range of evolutionary positions along a continuum from normal epithelium to invasive HGSC. This heterogeneity reflects continuous transcriptional variation rather than discrete precursor categories. We report gene expression dynamics and molecular signature changes across the malignant transformation timeline. We further illustrate that precursor lesions exhibit phenotypic variability that impacts therapeutic drug resistance-associated programs. Our data highlight potential disease biomarkers defined by evolutionary trajectory inference and transcriptional processes associated with drug resistance operating in precursor lesions. These findings may help improve our ability to distinguish clinically significant lesions and inform targeted interception strategies.

cancer biology↗

bioRxiv2026

In vivo evidence for bleb-induced survival signaling in metastatic melanoma

Bleb signaling is a cellular process in which pressure-driven plasma membrane protrusions generate localized micron-scale membrane curvature that recruits cytosolic septin complexes, assembling signaling hubs that promote cell survival1. Our prior work showed that this morphology-encoded signaling pathway is necessary to sustain anchorage-independent survival of BRAF and NRAS mutant melanoma cells in vitro. However, whether bleb-induced signaling occurs in vivo and contributes to cancer progression is unclear. Here, we develop complementary in vivo and ex vivo assays spanning human patient samples and mouse xenograft models to mechanistically interrogate bleb signaling directly in physiologic contexts. We observe that bleb-associated septin hubs are exclusively formed in poorly adherent amoeboid tumor cells at the invasive margin, within malignant effusions, and at distant metastatic sites, while well-adhered cells in the tumor interior show no signs of septin hub formation. Accordingly, pharmacological pathway disruption specifically kills disseminated tumor cells within these low-adhesion microenvironments while having no appreciable effect on adhered cells in the tumor interior, resulting in reduced metastatic burden and delayed disease recurrence in vivo. This work confirms septin-mediated bleb signaling as a previously unrecognized vulnerability of disseminated cancer cells and demonstrates that this morphology-encoded pathway operates in vivo to support disease progression, preserving cancer cell viability under conditions in which cell survival signals from the environment are muted. These findings suggest novel opportunities to target survival signaling in micrometastatic disseminated cancer cells. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/697601v1_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@1770384org.highwire.dtl.DTLVardef@87e567org.highwire.dtl.DTLVardef@1e5d6b2org.highwire.dtl.DTLVardef@724c5f_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Mechanisms of tumor persistence in metastatic melanoma following successful immunotherapy

Tumor dormancy is thought to enable cancer recurrence, but the settings and mechanisms of dormancy in patients are poorly characterized. Both immunogenic tumor mass dormancy, involving continued tumor cell proliferation and balanced immune-mediated cell death, and cell-level quiescence have been observed in preclinical models. Here, we demonstrate the existence of mass dormancy rather than quiescence in long-term stable residual lesions in melanoma patients treated with immune checkpoint inhibitors (ICI). In a large stable lesion subjected to detailed spatial profiling, the proportion of proliferating tumor cells is similar to that in site-matched tumors from patients progressing after ICI. Residual stable lesions from other patients, judged to contain only scar tissue upon clinical pathology review, also contained nests of dividing tumor cells surrounded by active immune cells. These findings demonstrate the presence of viable tumor cells and mass dormancy in persistent stable lesions, a finding with implications for disease monitoring and management.

cancer biology↗

bioRxiv2025

Integrative single-cell and spatial mapping of oxidative stress response uncovers GCLC+ mesenchymal tumor cell state linked with favorable outcomes in triple negative breast cancer

Inhibiting oxidative stress response (OSR) proteins has been suggested as a therapeutic strategy in triple negative breast cancer (TNBC). However, the cell type specificity and spatial distribution of OSR genes and proteins, such as GCLC and NQO1, is unknown. Using single cell and spatial transcriptomics datasets we found that OSR genes were highly expressed in TNBC tumor cells, which localized in spatial clusters. Multiplex immunofluorescence imaging of 345 TNBC samples from 186 patients demonstrated that OSR proteins GCLC and NQO1 exhibit distinct expression profiles across tumor, immune, and stromal cell populations and are elevated in inflamed histological regions. Tumor cell OSR protein expression was associated with the composition of the adjacent cellular neighborhood. Furthermore, we identified GCLC and vimentin positive (GCLC+VIM+) mesenchymal-like tumor cells, residing near immune cells and exhibiting increased proliferation and decreased anastasis signatures, suggesting sensitivity to chemotherapy. Across a panel of thirteen TNBC cell lines, GCLC expression was positively correlated with sensitivity to cisplatin. Cox regression analysis revealed that patients with higher proportions of GCLC+VIM+ tumor cells had a longer overall survival. Collectively, our results demonstrate that individual OSR proteins are expressed in distinct microenvironments and tumor cell states, potentially contributing to patient outcomes.

cancer biology↗

bioRxiv2025

Morphology-Aware Profiling of Highly Multiplexed Tissue Images using Variational Autoencoders

Spatial proteomics (highly multiplexed tissue imaging) provides unprecedented insight into the types, states, and spatial organization of cells within preserved tissue environments. To enable single-cell analysis, high-plex images are typically segmented using algorithms that assign marker signals to individual cells. However, conventional segmentation is often imprecise and susceptible to signal spillover between adjacent cells, interfering with accurate cell type identification. Segmentation-based methods also fail to capture the morphological detail that histopathologists rely on for disease diagnosis and staging. Here, we present a method that combines unsupervised, pixel-level machine learning using autoencoders with traditional segmentation to generate single-cell data that captures information on protein abundance, morphology, and local neighborhood in a manner analogous to human experts while overcoming signal spillover. We demonstrate the generality of this approach by applying it to CyCIF, Lunaphore COMET, and CODEX data, and show that it can learn histological features across multiple spatial scales.

pathology↗

bioRxiv2025

Spatial determinants of tumor cell dedifferentiation and plasticity in primary cutaneous melanoma

Localized cutaneous melanoma can be cured by excision but success critically depends on early detection and risk assessment of primary lesions. However, their initiation, progression, and immunology remain poorly understood, partly due to high intra- and inter-tumor heterogeneity. We studied this heterogeneity using spatial profiling in over 300 histological domains, each representing a single progression stage, and found that 200-600 cell neighborhoods from a single melanoma can be as different in RNA and protein expression as neighborhoods from different tumors. These differences are not stochastic, however, and disease progression can be mapped at the neighborhood level onto a cell state landscape defined by the activity of the melanocyte master regulator MITF and genes associated with a dedifferentiated neural crest phenotype. Position in this landscape is influenced by proximity to immune cells, perivascular environments, and other tissue features, but no single association is absolute, giving rise to complex spatial patterns.

cancer biology↗

bioRxiv2025

Multiomic analysis identifies suppressive myeloid cell populations in human TB granulomas.

Tuberculosis (TB) remains a major global health challenge, particularly in the context of multidrug-resistant (MDR) Mycobacterium tuberculosis (Mtb). Host-directed therapies (HDTs) have been proposed as adjunctive therapy to enhance immune control of infection. Recently, one such HDT, pharmacologic modulation of myeloid-derived suppressor cells (MDSCs), has been proposed to treat MDR-TB. While MDSCs have been well characterized in cancer, their role in TB pathogenesis remains unclear. To investigate whether MDSCs or other myeloid suppressor populations contribute to TB granuloma microenvironments (GME), we performed spatial transcriptional profiling and single-cell immunophenotyping on eighty-four granulomas in lung specimens from three individuals with active disease. Granulomas were histologically classified based on H&E staining, and transcriptional signatures were compared across regions of interest (ROIs) at different states of granuloma maturation. Our analysis revealed that immune suppression within granuloma was not primarily driven by classical MDSCs but rather by multiple myeloid cell subsets, including dendritic cells expressing indoleamine 2,3 dioxygenase-1 expressing (IDO1+ DCs). IDO1+ DCs were the most frequently observed suppressive myeloid cells, particularly in cellular regions, and their spatial proximity to activated T cells suggested localized immunosuppression. Importantly, granulomas at different stages contained distinct proportions of suppressor myeloid cells, with necrotic and cellular regions showing different myeloid phenotypes that may influence granuloma progression. Gene set enrichment analysis (GSEA) further indicated that elevated IDO1 expression was associated with a complex immune response that balanced suppressive signaling, immune activation, and cellular metabolism. These findings suggest that classical MDSCs, as defined in tumor microenvironments, likely play a minor role in TB, whereas IDO1+ DCs may be key regulators of immune suppression in granulomas influencing local Mtb control in infected lung. A deeper understanding of the role of IDO1+ suppressive myeloid cells in TB granulomas is essential to assessing their potential as therapeutic targets in TB treatment.

pathology↗

bioRxiv2024

ZNFX1 is a Novel Master Regulator in Epigenetically-induced Pathogen Mimicry and Inflammasome Signaling in Cancer

DNA methyltransferase and poly(ADP-ribose) polymerase inhibitors (DNMTis, PARPis) induce a stimulator of interferon (IFN) genes (STING)-dependent pathogen mimicry response (PMR) in ovarian (OC) and other cancers. We now show that combining DNMTis and PARPis upregulates expression of a little-studied nucleic-acid sensor, NFX1-type zinc finger-containing 1 protein (ZNFX1). We demonstrate that ZNFX1 is a novel master regulator for PMR induction in mitochondria, serving as a gateway for STING-dependent PMR. In patient OC databases, high ZNFX1 expression levels correlate with advanced stage disease. ZNFX1 expression alone significantly correlates with an increase in overall survival in a phase 3 trial for therapy-resistant OC patients receiving bevacizumab in combination with chemotherapy. In correlative RNA-seq data, inflammasome signaling through ZNFX1 correlates with abnormal vasculogenesis. ZNFX1 controls PMR signaling through the mitochondria and may serve as a biomarker to facilitate offering personalized therapy in OC patients, highlighting the strong translational significance of our findings. Significance statementDNA methyltransferase and poly(ADP-ribose) polymerase inhibitors upregulate expression of a novel nucleic-acid sensor, ZNFX1 that serves as a mitochondrial gateway to STING-dependent interferon/inflammasome signaling with tumor suppressor properties in ovarian cancer.

cancer biology↗

bioRxiv2024

Multimodal Spatial Profiling Reveals Immune Suppression and Microenvironment Remodeling in Fallopian Tube Precursors to High-Grade Serous Ovarian Carcinoma

High-Grade Serous Ovarian Cancer (HGSOC) originates from fallopian tube (FT) precursors. However, the molecular changes that occur as precancerous lesions progress to HGSOC are not well understood. To address this, we integrated high-plex imaging and spatial transcriptomics to analyze human tissue samples at different stages of HGSOC development, including p53 signatures, serous tubal intraepithelial carcinomas (STIC), and invasive HGSOC. Our findings reveal immune modulating mechanisms within precursor epithelium, characterized by chromosomal instability, persistent interferon (IFN) signaling, and dysregulated innate and adaptive immunity. FT precursors display elevated expression of MHC-class I, including HLA-E, and IFN-stimulated genes, typically linked to later-stage tumorigenesis. These molecular alterations coincide with progressive shifts in the tumor microenvironment, transitioning from immune surveillance in early STICs to immune suppression in advanced STICs and cancer. These insights identify potential biomarkers and therapeutic targets for HGSOC interception and clarify the molecular transitions from precancer to cancer. STATEMENT OF SIGNIFICANCEThis study maps the immune response in fallopian tube precursors of high-grade serous ovarian cancer, highlighting localized interferon signaling, CIN, and competing immune surveillance and suppression along the progression axis. It provides an explorable public spatial profiling atlas for investigating precancer mechanisms, biomarkers, and early detection and interception strategies.

cancer biology↗

bioRxiv2024

Integrating spatial profiles and cancer genomics to identify immune-infiltrated mismatch repair proficient colorectal cancers

Predicting the progression of solid cancers based solely on genetics is challenging due to the influence of the tumor microenvironment (TME). For colorectal cancer (CRC), tumors deficient in mismatch repair (dMMR) are more immune infiltrated than mismatch repair proficient (pMMR) tumors and have better prognosis following resection. Here we quantify features of the CRC TME by combining spatial profiling with genetic analysis and release our findings via a spatially enhanced version of cBioPortal that facilitates multi-modal data exploration and analysis. We find that [~]20% of pMMR tumors exhibit similar levels of T cell infiltration as dMMR tumors and that this is associated with better survival but not any specific somatic mutation. These T cell-infiltrated pMMR (tipMMR) tumors contain abundant cells expressing PD1 and PDL1 as well as T regulatory cells, consistent with a suppressed immune response. Thus, like dMMR CRC, tipMMR CRC may benefit from immune checkpoint inhibitor therapy. SIGNIFICANCEpMMR tumors with high T cell infiltration and active immunosuppression are identifiable with a mid-plex imaging assay whose clinical deployment might double the number of treatment-naive CRCs eligible for ICIs. Moreover, the low tumor mutational burden in tipMMR CRC shows that MMR status is not the only factor promoting immune infiltration.

cancer biology↗

bioRxiv2024

Spatial Profiling of Metals through Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging

The spatial-omic analysis of biomolecules such as nucleic acids, lipids, metabolites, and proteins is advancing the study of biological systems and processes in a physio-pathological context. Here, we describe an innovative matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) method to detect metals within biological tissues using instrumentation that is widely available in research and clinical laboratories. We characterize the spatial distribution of metals in diverse settings including mouse embryogenesis, genetic disorders leading to abnormal metal accumulation, and preclinical testing for improved platinum-based chemotherapy delivery through focused ultrasound across the blood-brain barrier. Spatial metal profiling will advance research studies and the clinical analysis of metal-related diseases, enabling more precise use of metal-based therapies and advances in diverse scientific fields beyond biomedicine. One-Sentence SummarySpatial metallomic profiling maps native metals or those coordinated to xenobiotics, antibodies, and biomolecules in tissues.

cancer biology↗

bioRxiv2023

Cell Spotter (CSPOT): A machine-learning approach to automated cell spotting and quantification of highly multiplexed tissue images

Highly multiplexed tissue imaging and in situ spatial profiling aim to extract single-cell data from specimens containing closely packed cells of diverse morphology. This is challenging due to the difficulty of accurately assigning boundaries between cells (segmentation) and then generating per-cell staining intensities. Existing methods use gating to convert per-cell intensity data to positive and negative scores; this is a common approach in flow cytometry, but one that is problematic in imaging. In contrast, human experts identify cells in crowded environments using morphological, neighborhood, and intensity information. Here we describe a computational approach (Cell Spotter or CSPOT) that uses supervised machine learning in combination with classical segmentation to perform automated cell type calling. CSPOT is robust to artifacts that commonly afflict tissue imaging and can replace conventional gating. The end-to-end Python implementation of CSPOT can be integrated into cloud-based image processing pipelines to substantially improve the speed, accuracy, and reproducibility of single-cell spatial data.

bioinformatics↗

bioRxiv2023

Multiplexed 3D Analysis of Cell Plasticity and Immune Niches in Melanoma

Diseases like cancer involve alterations in in cell proportions, states, and local interactions as well as complex changes in 3D tissue architecture. However, disease diagnosis and most multiplexed spatial profiling studies rely on inspecting thin (4-5 micron) tissue specimens. Here, we use confocal microscopy and cyclic immunofluorescence (3D CyCIF) to show that few if any cells are intact in these thin sections; this reduces the accuracy of cell phenotyping and interaction analysis. In contrast, high-plex 3D CyCIF imaging of intact cells in thick tissue sections enables accurate quantification of marker proteins and detailed analysis of intracellular structures and organelles. Precise imaging of cell membranes also makes it possible to detect juxtacrine signalling among interacting tumour and immune cells and reveals the formation of spatially-restricted cytokine niches. Thus, 3D CyCIF provides insights into cell states and morphologies in preserved human tissues at a level of detail previously limited to cultured cells.

cancer biology↗

bioRxiv2023

2D and 3D multiplexed subcellular profiling of nuclear instability in human cancer

Nuclear atypia, including altered nuclear size, contour, and chromatin organization, is ubiquitous in cancer cells. Atypical primary nuclei and micronuclei can rupture during interphase; however, the frequency, causes, and consequences of nuclear rupture are unknown in most cancers. We demonstrate that nuclear envelope rupture is surprisingly common in many human cancers, particularly glioblastoma. Using highly-multiplexed 2D and super-resolution 3D-imaging of glioblastoma tissues and patient-derived xenografts and cells, we link primary nuclear rupture with reduced lamin A/C and micronuclear rupture with reduced lamin B1. Moreover, ruptured glioblastoma cells activate cGAS-STING-signaling involved in innate immunity. We observe that local patterning of cell states influences tumor spatial organization and is linked to both lamin expression and rupture frequency, with neural-progenitor-cell-like states exhibiting the lowest lamin A/C levels and greatest susceptibility to primary nuclear rupture. Our study reveals that nuclear instability is a core feature of cancer, and links nuclear integrity, cell state, and immune signaling.

cancer biology↗

bioRxiv2023

Quality Control for Single Cell Analysis of High-plex Tissue Profiles using CyLinter

Tumors are complex assemblies of cellular and acellular structures patterned on spatial scales from microns to centimeters. Study of these assemblies has advanced dramatically with the introduction of high-plex spatial profiling. Image-based profiling methods reveal the intensities and spatial distributions of 20-100 proteins at subcellular resolution in 103-107 cells per specimen. Despite extensive work on methods for extracting single-cell data from these images, all tissue images contain artefacts such as folds, debris, antibody aggregates, optical aberrations and image processing errors that arise from imperfections in specimen preparation, data acquisition, image assembly, and feature extraction. We show that these artefacts dramatically impact single-cell data analysis, obscuring meaningful biological interpretation. We describe an interactive quality control software tool, CyLinter, that identifies and removes data associated with imaging artefacts. CyLinter greatly improves single-cell analysis, especially for archival specimens sectioned many years prior to data collection, such as those from clinical trials.

pathology↗