Molecular cartography uncovers evolutionary and microenvironmental dynamics in sporadic colorectal tumors

Exomeres and supermeres: Current advances and perspectives

Recent studies have revealed a great diversity and complexity in extracellular vesicles and particles (EVPs). The developments in techniques and the growing awareness of the particle heterogeneity have spurred active research on new particle subsets. Latest discoveries highlighted unique features and roles of non-vesicular extracellular nanoparticles (NVEPs) as promising biomarkers and targets for diseases. These nanoparticles are distinct from extracellular vesicles (EVs) in terms of their smaller particle sizes and lack of a bilayer membrane structure and they are enriched with diverse bioactive molecules particularly proteins and RNAs, which are widely reported to be delivered and packaged in exosomes. This review is focused on the two recently identified membraneless NVEPs, exomeres and supermeres, to provide an overview of their biogenesis and contents, particularly those bioactive substances linked to their bio-properties. This review also explains the concepts and characteristics of these nanoparticles, to compare them with other EVPs, especially EVs, as well as to discuss their isolation and identification methods, research interests, potential clinical applications and open questions.

Multiomic spatial atlas shows deleted in malignant brain tumors 1 (DMBT1) glycoprotein is lost in colonic dysplasia

Colorectal cancer (CRC) is responsible for over 900,000 annual deaths worldwide. Emerging evidence supports pro-carcinogenic bacteria in the colonic microbiome are at least promotional in CRC development and may be causal. We previously showed toxigenic C. difficile from human CRC-associated bacterial biofilms accelerates tumorigenesis in ApcMin/+ mice, both in specific pathogen-free mice and in gnotobiotic mice colonized with a defined consortium of bacteria. To further understand host-microbe interactions during colonic tumorigenesis, we combined single-cell RNA-sequencing (scRNA-seq), spatial transcriptomics, and immunofluorescence to define the molecular spatial organization of colonic dysplasia in our consortium model with or without C. difficile. Our data show a striking bipartite regulation of Deleted in Malignant Brain Tumors 1 (DMBT1) in the inflamed versus dysplastic colon. From scRNA-seq, differential gene expression analysis of normal absorptive colonocytes at 2 weeks postinoculation showed DMBT1 upregulated by C. difficile compared to colonocytes from mice without C. difficile exposure. In contrast, our spatial transcriptomic analysis showed DMBT1 dramatically downregulated in dysplastic foci compared with normal-adjacent tissue. We further integrated our datasets to generate custom colonic dysplasia scores and ligand-receptor mapping. Validation with immunofluorescence showed DMBT1 protein downregulated in dysplastic foci from three mouse models of colonic tumorigenesis and in adenomatous dysplasia from human samples. Finally, we used mouse and human organoids to implicate WNT signaling in the downregulation of DMBT1 mRNA and protein. Together, our data reveal cell type-specific regulation of DMBT1, a potential mechanistic link between bacteria and colonic tumorigenesis. Published 2025. This article is a U.S. Government work and is in the public domain in the USA. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Targeting KIF18A triggers antitumor immunity and enhances efficiency of PD-1 blockade in colorectal cancer with chromosomal instability phenotype

Colorectal cancer with chromosomal instability (CIN+) phenotype is immunosuppressive and refractory to immune checkpoint blockade (ICB) therapy. Recently, KIF18A is found to be a mitotic vulnerability in chromosomally unstable cancers, but whether targeting KIF18A affects antitumor immunity in CIN+ colorectal cancer is unknown. In our study, western blot, cell viability assay, transwell migration and invasion assays, flow cytometry, animal model, immunohistochemistry (IHC) staining, reverse transcription-quantitative PCR (RT-qPCR) and ELISA assay were conducted to evaluate the potential function of KIF18A in CIN+ colorectal cancer. We found that KIF18A inhibition by short hairpin RNAs (ShRNAs) or small inhibitor AM-1882 suppressed proliferation, migration, invasion and tumor growth and metastasis of CIN+ colorectal cancer cells in vitro and in vivo. Moreover, targeting KIF18A disrupted cell-cycle progression and induced G2/M arrest in CIN+ colorectal cancer cells. In addition, KIF18A inhibition promoted immune infiltration and activation in CIN+ colorectal tumors. KIF18A inhibition suppressed proliferation of Tregs and increased infiltration and activation of cytotoxic CD8+ T cells in CIN+ colorectal tumors. Mechanically, KIF18A inhibition stimulated type I IFN signaling and cGAS-STING activation in CIN+ colorectal tumors. Finally, targeting KIF18A enhanced PD-1 blockade efficiency in CIN+ colorectal tumors through T cells. Our data elucidated a novel role of KIF18A in antitumor immunity of CIN+ colorectal cancer.

The promising role of nanopore sequencing in cancer diagnostics and treatment

Cancer arises from genetic alterations that impact both the genome and transcriptome. The utilization of nanopore sequencing offers a powerful means of detecting these alterations due to its unique capacity for long single-molecule sequencing. In the context of DNA analysis, nanopore sequencing excels in identifying structural variations (SVs), copy number variations (CNVs), gene fusions within SVs, and mutations in specific genes, including those involving DNA modifications and DNA adducts. In the field of RNA research, nanopore sequencing proves invaluable in discerning differentially expressed transcripts, uncovering novel elements linked to transcriptional regulation, and identifying alternative splicing events and RNA modifications at the single-molecule level. Furthermore, nanopore sequencing extends its reach to detecting microorganisms, encompassing bacteria and viruses, that are intricately associated with tumorigenesis and the development of cancer. Consequently, the application prospects of nanopore sequencing in tumor diagnosis and personalized treatment are expansive, encompassing tasks such as tumor identification and classification, the tailoring of treatment strategies, and the screening of prospective patients. In essence, this technology stands poised to unearth novel mechanisms underlying tumorigenesis while providing dependable support for the diagnosis and treatment of cancer.

Seq-Scope: repurposing Illumina sequencing flow cells for high-resolution spatial transcriptomics

Spatial transcriptomics technologies aim to advance gene expression studies by profiling the entire transcriptome with intact spatial information from a single histological slide. However, the application of spatial transcriptomics is limited by low resolution, limited transcript coverage, complex procedures, poor scalability and high costs of initial setup and/or individual experiments. Seq-Scope repurposes the Illumina sequencing platform for high-resolution, high-content spatial transcriptome analysis, overcoming these limitations. It offers submicrometer resolution, high capture efficiency, rapid turnaround time and precise annotation of histopathology at a much lower cost than commercial alternatives. This protocol details the implementation of Seq-Scope with an Illumina NovaSeq 6000 sequencing flow cell, allowing the profiling of multiple tissue sections in an area of 7 mm × 7 mm or larger. We describe the preparation of a fresh-frozen tissue section for both histological imaging and sequencing library preparation and provide a streamlined computational pipeline with comprehensive instructions to integrate histological and transcriptomic data for high-resolution spatial analysis. This includes the use of conventional software tools for single-cell and spatial analysis, as well as our recently developed segmentation-free method for analyzing spatial data at submicrometer resolution. Aside from array production and sequencing, which can be done in batches, tissue processing, library preparation and running the computational pipeline can be completed within 3 days by researchers with experience in molecular biology, histology and basic Unix skills. Given its adaptability across various biological tissues, Seq-Scope establishes itself as an invaluable tool for researchers in molecular biology and histology.

Collagen remodeling-mediated signaling pathways and their impact on tumor therapy

In addition to their traditional roles in maintaining tissue morphology and organ development, emerging evidence suggests that collagen (COL) remodeling-referring to dynamic changes in the quantity, stiffness, arrangements, cleavage states, and homo-/hetero-trimerization of COLs-serves as a key signaling mechanism that governs tumor growth and metastasis. COL receptors act as switches, linking various forms of COL remodeling to different cell types during cancer progression, including cancer cells, immune cells, and cancer-associated fibroblasts. In this review, we summarize recent findings on the signaling pathways mediated by COL arrangement, cleavage, and trimerization states (both homo- and hetero-), as well as the roles of the primary COL receptors-integrin, DDR1/2, LAIR-1/2, MRC2, and GPVI-in cancer progression. We also discuss the latest therapeutic strategies targeting COL fragments, cancer-associated fibroblasts, and COL receptors, including integrins, DDR1/2, and LAIR1/2. Understanding the pathways modulated by COL remodeling and COL receptors in various pathological contexts will pave the way for developing new precision therapies.

Development of a prognostic risk model for colorectal cancer and association of the prognostic model with cancer stem cell and immune cell infiltration

Background

The development of a prognostic model for patients with colorectal cancer (CRC) can facilitate the assessment of patient survival and the effectiveness of clinical treatments. A reasonable prognostic model can provide a basis for individualized treatment, prognostic risk stratification, and subsequent therapy for CRC patients. The aim of our study was to construct a prognostic model for patients with CRC using sequencing data derived from The Cancer Genome Atlas (TCGA) database.

Methods

Sequencing data of paracancerous tissues (n=51) and CRC samples (n=647) were downloaded from the TCGA database. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were employed to identify prognostic factors. A restricted cubic spline (RCS) model was used to assess the nonlinear relationship between risk score and poor overall survival (OS). The Genomics of Drug Sensitivity in Cancer (GDSC) database was accessed to evaluate the correlation between the prognostic model's risk score and drug sensitivity. The single-sample gene set enrichment analysis (ssGSEA), estimate, and CIBERSORT algorithms were applied to quantify the association between prognostic genes and immune cell infiltration in CRC.

Results

Our findings revealed that six genes, including Niemann-Pick C1-like 1 (NPC1L1) [hazard ratio (HR) =1.53; 95% confidence interval (CI): 1.08-2.17; P=0.02], glucagon-like peptide 2 receptor (GLP2R) (HR =0.68; 95% CI: 0.48-0.97; P=0.04), solute carrier family 8 member A3 (SLC8A3) (HR =0.67; 95% CI: 0.47-0.96; P=0.03), alpha-1-microglobulin/bikunin precursor (AMBP) (HR =0.64; 95% CI: 0.45-0.91; P=0.01), single-pass membrane protein with coiled-coil domains 2 (SMCO2) (HR =0.68; 95% CI: 0.48-0.97; P=0.03), and tetratricopeptide repeat domain 16 (TTC16) (HR =1.55; 95% CI: 1.09-2.20; P=0.02) function as independent prognostic factors for CRC. Based on these six genes, the developed prognostic assessment model identified a strong association between high risk score and poor OS (HR =2.43; 95% CI: 1.67-3.53; P<0.001) in patients with CRC. Furthermore, the analysis revealed a nonlinear relationship (P<0.001) between continuous variation in risk score and the risk of poor OS. Additionally, specific genes included in the prognostic model were found to be strongly associated with cancer stem cell and immune cell infiltration in CRC.

Conclusions

We developed a prognostic risk model incorporating a six-gene panel for patients with CRC. Our analysis revealed a nonlinear relationship between this prognostic model and OS in patients with CRC. A high risk score was associated with poor prognosis, indicating that the adverse outcomes observed in patients with CRC may be influenced by cancer stem cell and immune cell infiltration. Our model provides a promising predictive method for the prognosis of CRC patients, but it still needs to be validated in a larger sample size.

A constitutive interferon-high immunophenotype defines response to immunotherapy in colorectal cancer

Fewer than 50% of metastatic deficient mismatch repair (dMMR) colorectal cancer (CRC) patients respond to immune checkpoint inhibition (ICI). Identifying and expanding this patient population remains a pressing clinical need. Here, we report that an interferon-high immunophenotype locally enriched in cytotoxic lymphocytes and antigen-presenting macrophages is required for response. This immunophenotype is not exclusive to dMMR CRCs but comprises a subset of MMR proficient (pMMR) CRCs. Single-cell spatial analysis and in vitro cell co-cultures indicate that interferon-producing cytotoxic T cells induce overexpression of antigen presentation in adjacent macrophages and tumor cells, including MHC class II invariant chain CD74. dMMR CRCs expressing high levels of CD74 respond to ICI and a subset of CD74 high pMMR CRC patients show better progression free survival when treated with ICI. Therefore, CD74 abundance can identify the constitutive interferon-high immunophenotype determining clinical benefit in CRC, independently of tumor mutational burden or MMR status.

Integrative co-registration of elemental imaging and histopathology for enhanced spatial multimodal analysis of tissue sections through TRACE

Summary

Elemental imaging provides detailed profiling of metal bioaccumulation, offering more precision than bulk analysis by targeting specific tissue areas. However, accurately identifying comparable tissue regions from elemental maps is challenging, requiring the integration of hematoxylin and eosin (H&E) slides for effective comparison. Facilitating the streamlined co-registration of whole slide images (WSI) and elemental maps, TRACE enhances the analysis of tissue regions and elemental abundance in various pathological conditions. Through an interactive containerized web application, TRACE features real-time annotation editing, advanced statistical tools, and data export, supporting comprehensive spatial analysis. Notably, it allows for comparison of elemental abundances across annotated tissue structures and enables integration with other spatial data types through WSI co-registration.

Availability and implementation

Available on the following platforms-GitHub: jlevy44/trace_app, PyPI: trace_app, Docker: joshualevy44/trace_app, Singularity: docker://joshualevy44/trace_app.

stSNV: a comprehensive resource of SNVs in spatial transcriptome

Single nucleotide variants (SNVs), as important components of genetic variation, affect gene expression, function and phenotype. Mining and summarizing the spatial distribution of SNVs in diseased and normal tissues for a better understanding of their characteristics and potential roles in cell-lineage determination, aging, or disease occurrence is significant. Herein, we have developed a comprehensive spatial mutation resource stSNV (http://bio-bigdata.hrbmu.edu.cn/stSNV/index.jsp), which provides an atlas of spatial SNVs in major diseased and normal tissues of human and mouse. stSNV documents 42 202 spatial mutated genes involving 898 908 SNVs called from 730 067 spots within 450 slices from 19 diseased and 28 normal tissues. Importantly, potential characteristics of SNVs are explored and provided by analyzing the perturbation of the SNVs to gene expression, spatial communication, biological function, region-specific mutated genes, spatial mutant signatures, SNV-cell co-localization and mutation core region. All these spatial mutation data and in-depth analyses have been integrated into a user-friendly interface, visualized through intuitive tables and various image formats. Flexible tools are developed to explore co-localization among clusters, genes, cell types and SNVs in the same slice. In summary, stSNV as a valuable resource helps to dissect intra-tissue genetic heterogeneity and lays the groundwork for understanding the SNVs' biological regulatory mechanisms.

Challenges and Opportunities in the Clinical Translation of High-Resolution Spatial Transcriptomics

Pathology has always been fueled by technological advances. Histology powered the study of tissue architecture at single-cell resolution and remains a cornerstone of clinical pathology today. In the last decade, next-generation sequencing has become informative for the targeted treatment of many diseases, demonstrating the importance of genome-scale molecular information for personalized medicine. Today, revolutionary developments in spatial transcriptomics technologies digitalize gene expression at subcellular resolution in intact tissue sections, enabling the computational analysis of cell types, cellular phenotypes, and cell-cell communication in routinely collected and archival clinical samples. Here we review how such molecular microscopes work, highlight their potential to identify disease mechanisms and guide personalized therapies, and provide guidance for clinical study design. Finally, we discuss remaining challenges to the swift translation of high-resolution spatial transcriptomics technologies and how integration of multimodal readouts and deep learning approaches is bringing us closer to a holistic understanding of tissue biology and pathology.

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.

Insight into dipeptidase 1: structure, function, and mechanism in gastrointestinal cancer diseases

Dipeptidase 1 (DPEP1), initially identified as a renal membrane enzyme in mature human kidneys, plays a pivotal role in various cellular processes. It facilitates the exchange of materials and signal transduction across cell membranes, contributing significantly to dipeptide hydrolysis, glucose and lipid metabolism, immune inflammation, and ferroptosis, among other cellular functions. Extensive research has delineated the complex role of DPEP1 in oncogenesis and tumor progression, with its influence being context dependent. DPEP1 has been observed to promote oncogenic activities in hepatocellular carcinoma, non-small cell lung cancer, colorectal cancer, and lymphoblastic malignancies and is hypothesized to participate in multiple biological processes, including tumor cell invasion, metastatic spread, cellular signaling pathways, cell-matrix interactions, and evasion of immune surveillance. Conversely, DPEP1 has been identified as a tumor suppressor in pancreatic adenocarcinoma, lobular breast carcinoma, and Wilms tumor. Moreover, the role of DPEP1 in colorectal cancer has been increasingly recognized in recent research. Emerging evidence suggests that DPEP1 substantially augments the metastatic and invasive potential of colorectal cancer cells, facilitates immune evasion, and confers resistance to chemotherapeutic agents. Despite these findings, the precise molecular mechanisms remain to be fully characterized. This systematic review endeavors to elucidate the structural and functional attributes of the DPEP1 protein, with the aim to clarify its regulatory mechanisms and assess its clinical relevance in oncology. Gaining a thorough understanding of the physiological role and molecular underpinnings of DPEP1 is critical to informing the diagnostic, therapeutic, and prognostic paradigms of related pathologies. It is anticipated that these insights will facilitate the discovery of novel therapeutic targets and generate new investigative trajectories, particularly in the clinical management of colorectal cancer.

Analysis of diagnostic genes and molecular mechanisms of Crohn's disease and colon cancer based on machine learning algorithms

Crohn's disease (CD) is a chronic inflammatory bowel condition, and colon adenocarcinoma (COAD), as one of the most prevalent malignant tumors of the digestive tract, has been indicated by research to have a close association with CD. This study employs bioinformatics techniques to uncover the potential molecular links between CD and COAD. In this study, two data series related to CD were identified from the Gene Expression Omnibus (GEO) database under specific criteria, and relevant COAD gene data were obtained from The Cancer Genome Atlas (TCGA). Weighted Gene Co-expression Network Analysis (WGCNA), differentially expressed genes (DEGs), and protein-protein interaction (PPI) network analysis were conducted. A diagnostic model was established using machine learning. The accuracy of the diagnosis was validated using methods such as the construction of Receiver Operating Characteristic (ROC) curves and nomograms. Gene Set Enrichment Analysis (GSEA) was also employed to enrich the relevant pathways and biological processes. This study identified three genes through machine learning selection: DPEP1, MMP3, and MMP13. The ROC curves demonstrated that the machine learning model constructed with these three genes has a high level of accuracy, confirming their potential as biomarkers. Furthermore, GSEA elucidated that the pathways associated with these three key genes are closely related to cytokines and other factors. This study has identified key biomarker genes for CD and COAD: DPEP1, MMP3, and MMP13, providing additional molecular mechanism associations between the two diseases. It also offers more connections and pathways for reference regarding the progression of CD to COAD.

Emerging strategies to investigate the biology of early cancer

Early detection and intervention of cancer or precancerous lesions hold great promise to improve patient survival. However, the processes of cancer initiation and the normal-precancer-cancer progression within a non-cancerous tissue context remain poorly understood. This is, in part, due to the scarcity of early-stage clinical samples or suitable models to study early cancer. In this Review, we introduce clinical samples and model systems, such as autochthonous mice and organoid-derived or stem cell-derived models that allow longitudinal analysis of early cancer development. We also present the emerging techniques and computational tools that enhance our understanding of cancer initiation and early progression, including direct imaging, lineage tracing, single-cell and spatial multi-omics, and artificial intelligence models. Together, these models and techniques facilitate a more comprehensive understanding of the poorly characterized early malignant transformation cascade, holding great potential to unveil key drivers and early biomarkers for cancer development. Finally, we discuss how these new insights can potentially be translated into mechanism-based strategies for early cancer detection and prevention.

Extracellular vesicles and nanoparticles at a glance

Cells can communicate with neighboring and more distant cells by secretion of extracellular vesicles (EVs). EVs are lipid bilayer membrane-bound structures that can be packaged with proteins, nucleic acids and lipids that mediate cell-cell signaling. EVs are increasingly recognized to play numerous important roles in both normal physiological processes and pathological conditions. Steady progress in the field has uncovered a great diversity and heterogeneity of distinct vesicle types that appear to be secreted from most, if not all, cell types. Recently, it has become apparent that cells also release non-vesicular extracellular nanoparticles (NVEPs), including the newly discovered exomeres and supermeres. In this Cell Science at a Glance article and the accompanying poster, we provide an overview of the diversity of EVs and nanoparticles that are released from cells into the extracellular space, highlighting recent advances in the field.

Endoplasmic reticulum-targeted biomimetic nanoparticles induce apoptosis and ferroptosis by regulating endoplasmic reticulum function in colon cancer

Colorectal cancer (CRC) is a major threat to human health, as it is one of the most common malignancies with a high incidence and mortality rate. The cancer cell membrane (CCM) has significant potential in targeted tumor drug delivery due to its membrane antigen-mediated homologous targeting ability. The endoplasmic reticulum (ER) in cancer cells plays a crucial role in apoptosis and ferroptosis. In this study, we developed an ER-targeted peptide-modified CCM-biomimetic nanoparticle-delivered lovastatin (LOV) nanomedicine delivery system (EMPP-LOV) for cancer treatment. Both in vitro and in vivo experiments demonstrated that EMPP could effectively target cancer cells and localize within the ER. EMPP-LOV modulated ER function to promote apoptosis and ferroptosis in tumor cells. Furthermore, synergistic antitumor efficacy was observed in both in vitro and in vivo models. EMPP-LOV induced apoptosis in CRC cells by over-activating endoplasmic reticulum stress and promoted ferroptosis by inhibiting the mevalonate pathway, leading to synergistic tumor growth inhibition with minimal toxicity to major organs. Overall, the EMPP-LOV delivery system, with its subcellular targeting capability within tumor cells, presents a promising therapeutic platform for CRC treatment.

Defining precancer: a grand challenge for the cancer community

The term 'precancer' typically refers to an early stage of neoplastic development that is distinguishable from normal tissue owing to molecular and phenotypic alterations, resulting in abnormal cells that are at least partially self-sustaining and function outside of normal cellular cues that constrain cell proliferation and survival. Although such cells are often histologically distinct from both the corresponding normal and invasive cancer cells of the same tissue origin, defining precancer remains a challenge for both the research and clinical communities. Once sufficient molecular and phenotypic changes have occurred in the precancer, the tissue is identified as a 'cancer' by a histopathologist. While even diagnosing cancer can at times be challenging, the determination of invasive cancer is generally less ambiguous and suggests a high likelihood of and potential for metastatic disease. The 'hallmarks of cancer' set out the fundamental organizing principles of malignant transformation but exactly how many of these hallmarks and in what configuration they define precancer has not been clearly and consistently determined. In this Expert Recommendation, we provide a starting point for a conceptual framework for defining precancer, which is based on molecular, pathological, clinical and epidemiological criteria, with the goal of advancing our understanding of the initial changes that occur and opportunities to intervene at the earliest possible time point.

Spatiotemporal lineage tracing reveals the dynamic spatial architecture of tumor growth and metastasis

Tumor progression is driven by dynamic interactions between cancer cells and their surrounding microenvironment. Investigating the spatiotemporal evolution of tumors can provide crucial insights into how intrinsic changes within cancer cells and extrinsic alterations in the microenvironment cooperate to drive different stages of tumor progression. Here, we integrate high-resolution spatial transcriptomics and evolving lineage tracing technologies to elucidate how tumor expansion, plasticity, and metastasis co-evolve with microenvironmental remodeling in a Kras;p53-driven mouse model of lung adenocarcinoma. We find that rapid tumor expansion contributes to a hypoxic, immunosuppressive, and fibrotic microenvironment that is associated with the emergence of pro-metastatic cancer cell states. Furthermore, metastases arise from spatially-confined subclones of primary tumors and remodel the distant metastatic niche into a fibrotic, collagen-rich microenvironment. Together, we present a comprehensive dataset integrating spatial assays and lineage tracing to elucidate how sequential changes in cancer cell state and microenvironmental structures cooperate to promote tumor progression.

Temporal recording of mammalian development and precancer

Temporal ordering of cellular events offers fundamental insights into biological phenomena. Although this is traditionally achieved through continuous direct observations1,2, an alternative solution leverages irreversible genetic changes, such as naturally occurring mutations, to create indelible marks that enables retrospective temporal ordering3-5. Using a multipurpose, single-cell CRISPR platform, we developed a molecular clock approach to record the timing of cellular events and clonality in vivo, with incorporation of cell state and lineage information. Using this approach, we uncovered precise timing of tissue-specific cell expansion during mouse embryonic development, unconventional developmental relationships between cell types and new epithelial progenitor states by their unique genetic histories. Analysis of mouse adenomas, coupled to multiomic and single-cell profiling of human precancers, with clonal analysis of 418 human polyps, demonstrated the occurrence of polyclonal initiation in 15-30% of colonic precancers, showing their origins from multiple normal founders. Our study presents a multimodal framework that lays the foundation for in vivo recording, integrating synthetic or natural indelible genetic changes with single-cell analyses, to explore the origins and timing of development and tumorigenesis in mammalian systems.

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.

SIGNIFICANCE

pMMR 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-naïve 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.

Hypoxia-induced TGFBI maintains glioma stem cells by stabilizing EphA2

Rationale: Glioma stem cells (GSCs) have emerged as pivotal drivers of tumor malignancy, sustained by various microenvironmental factors, including immune molecules and hypoxia. In our previous study, we elucidated the significant role of transforming growth factor beta-induced protein (TGFBI), a protein secreted by M2-like tumor-associated macrophages, in promoting the malignant behavior of glioblastoma (GBM) under normoxic conditions. Building upon these findings, the objective of this study was to comprehensively explore the crucial role and underlying mechanisms of autocrine TGFBI in GSCs under hypoxic conditions. Methods: We quantified TGFBI expression in glioma specimens and datasets. In vitro and in vivo assays were employed to investigate the effects of TGFBI on sustaining self-renewal and tumorigenesis of GSCs under hypoxia. RNA-seq and LC-MS/MS were conducted to explore TGFBI signaling mechanisms. Results: TGFBI is preferentially expressed in GSCs under hypoxic conditions. Targeting TGFBI impair GSCs self-renewal and tumorigenesis. Mechanistically, TGFBI was upregulated by HIF1α in GSCs and predominantly activates the AKT-c-MYC signaling pathway in GSCs by stabilizing the EphA2 protein through preventing its degradation. Conclusion: TGFBI plays a crucial role in maintaining the stem cell properties of GSCs in the hypoxic microenvironment. Targeting the TGFBI/EphA2 axis emerges as a promising and innovative strategy for GBM treatment, with the potential to improve the clinical outcomes of patients.

PoweREST: Statistical Power Estimation for Spatial Transcriptomics Experiments to Detect Differentially Expressed Genes Between Two Conditions

Recent advancements in Spatial Transcriptomics (ST) have significantly enhanced biological research in various domains. However, the high cost of current ST data generation techniques restricts its application in large-scale population studies. Consequently, there is a pressing need to maximize the use of available resources to achieve robust statistical power. One fundamental question in ST analysis is to detect differentially expressed genes (DEGs) among different conditions using ST data. Such DEG analysis is often performed but the associated power calculation is rarely discussed in the literature. To address this gap, we introduce, PoweREST (https://github.com/lanshui98/PoweREST), a power estimation tool designed to support power calculation of DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments or after preliminary data are collected, making it suitable for a wide variety of power analyses in ST studies. We also provide a user-friendly, program-free web application (https://lanshui.shinyapps.io/PoweREST/), allowing users to interactively calculate and visualize the study power along with relevant the parameters.

Overcoming the Tumor Collagen Barriers: A Multistage Drug Delivery Strategy for DDR1-Mediated Resistant Colorectal Cancer Therapy

The extracellular matrix (ECM) is critical for drug resistance in colorectal cancer (CRC). The abundant collagen within the ECM significantly influences tumor progression and matrix-mediated drug resistance (MMDR) by binding to discoidin domain receptor 1 (DDR1), but the specific mechanisms by which tumor cells modulate ECM via DDR1 and ultimately regulate TME remain poorly understand. Furthermore, overcoming drug resistance by modulating the tumor ECM remains a challenge in CRC treatment. In this study, a novel mechanism is elucidated by which DDR1 mediates the interactions between tumor cells and collagen, enhances collagen barriers, inhibits immune infiltration, promotes drug efflux, and leads to MMDR in CRC. To address this issue, a multistage drug delivery system carrying DDR1-siRNA and chemotherapeutic agents is employed to disrupt collagen barriers by silencing DDR1 in tumor, enhancing chemotherapy drugs diffusion and facilitating immune infiltration. These findings not only revealed a novel role for collagen-rich matrix mediated by DDR1 in tumor resistance, but also introduced a promising CRC treatment strategy.

Identification and multimodal characterization of a specialized epithelial cell type associated with Crohn's disease

Crohn's disease (CD) is a complex chronic inflammatory disorder with both gastrointestinal and extra-intestinal manifestations associated immune dysregulation. Analyzing 202,359 cells from 170 specimens across 83 patients, we identify a distinct epithelial cell type in both terminal ileum and ascending colon (hereon as 'LND') with high expression of LCN2, NOS2, and DUOX2 and genes related to antimicrobial response and immunoregulation. LND cells, confirmed by in-situ RNA and protein imaging, are rare in non-IBD controls but expand in active CD, and actively interact with immune cells and specifically express IBD/CD susceptibility genes, suggesting a possible function in CD immunopathogenesis. Furthermore, we discover early and late LND subpopulations with different origins and developmental potential. A higher ratio of late-to-early LND cells correlates with better response to anti-TNF treatment. Our findings thus suggest a potential pathogenic role for LND cells in both Crohn's ileitis and colitis.

Advancing early onset colorectal cancer research: research advocacy, health disparities, and scientific imperatives

Early onset colorectal cancer (EOCRC) emerged as the fourth foremost contributor to cancer-related mortality among both genders in the late 1990s. Presently, EOCRC (<50) ranks as the leading cause of cancer mortality in men and the second leading cause in women within the United States. Similar trends are now also evident globally, particularly in developed countries. Furthermore, there is strong evidence confirming that health disparities persist in the diagnosis and treatment of EOCRC, with signs indicating that these gaps may worsen in specific cases. These alarming trends highlight the critical need for research to inform evidence-based interventions to reduce the burden of EOCRC globally. Fight Colorectal Cancer (Fight CRC) is the leading patient advocacy group in the United States providing information on colon and rectal cancer research, prevention, treatment, and policy. It is the opinion of Fight CRC that an international, coordinated effort with the medical, research, scientific, advocacy, industry and funding community is needed to advance impactful research. Fight CRC, in partnership with José Perea, MD, PhD, of the Institute of Biomedical Research of Salamanca (IBSAL) in Spain, and partners, are working together to address this global phenomenon and are presenting a multi-faceted research approach to move the field forward.

Clonal expansion of cancer driver gene mutants investigated using advanced sequencing technologies

Advanced sequencing technologies (ASTs) have revolutionized the quantitation of cancer driver mutations (CDMs) as rare events, which has utility in clinical oncology, cancer research, and cancer risk assessment. This review focuses on studies that have used ASTs to characterize clonal expansion (CE) of cells carrying CDMs and to explicate the selective pressures that shape CE. Importantly, high-sensitivity ASTs have made possible the characterization of mutant clones and CE in histologically normal tissue samples, providing the means to investigate nascent tumor development. Some ASTs can identify mutant clones in a spatially defined context; others enable integration of mutant data with analyses of gene expression, thereby elaborating immune, inflammatory, metabolic, and/or stromal microenvironmental impacts on CE. As a whole, these studies make it clear that a startlingly large fraction of cells in histologically normal tissues carry CDMs, CDMs may confer a context-specific selective advantage leading to CE, and only a small fraction of cells carrying CDMs eventually result in neoplasia. These observations were integrated with available literature regarding the mechanisms underlying clonal selection to interpret how measurements of CDMs and CE can be interpreted as biomarkers of cancer risk. Given the stochastic nature of carcinogenesis, the potential functional latency of driver mutations, the complexity of potential mutational and microenvironmental interactions, and involvement of other types of genetic and epigenetic changes, it is concluded that CDM-based measurements should be viewed as probabilistic rather than deterministic biomarkers. Increasing inter-sample variability in CDM levels (as a consequence of CE) may be interpretable as a shift away from normal tissue homeostasis and an indication of increased future cancer risk, a process that may reflect normal aging or carcinogen exposure. Consequently, analyses of variability in levels of CDMs have the potential to bolster existing approaches for carcinogenicity testing.

Tumor initiation and early tumorigenesis: molecular mechanisms and interventional targets

Tumorigenesis is a multistep process, with oncogenic mutations in a normal cell conferring clonal advantage as the initial event. However, despite pervasive somatic mutations and clonal expansion in normal tissues, their transformation into cancer remains a rare event, indicating the presence of additional driver events for progression to an irreversible, highly heterogeneous, and invasive lesion. Recently, researchers are emphasizing the mechanisms of environmental tumor risk factors and epigenetic alterations that are profoundly influencing early clonal expansion and malignant evolution, independently of inducing mutations. Additionally, clonal evolution in tumorigenesis reflects a multifaceted interplay between cell-intrinsic identities and various cell-extrinsic factors that exert selective pressures to either restrain uncontrolled proliferation or allow specific clones to progress into tumors. However, the mechanisms by which driver events induce both intrinsic cellular competency and remodel environmental stress to facilitate malignant transformation are not fully understood. In this review, we summarize the genetic, epigenetic, and external driver events, and their effects on the co-evolution of the transformed cells and their ecosystem during tumor initiation and early malignant evolution. A deeper understanding of the earliest molecular events holds promise for translational applications, predicting individuals at high-risk of tumor and developing strategies to intercept malignant transformation.

GammaGateR: semi-automated marker gating for single-cell multiplexed imaging

MOTIVATION

Multiplexed immunofluorescence (mIF) is an emerging assay for multichannel protein imaging that can decipher cell-level spatial features in tissues. However, existing automated cell phenotyping methods, such as clustering, face challenges in achieving consistency across experiments and often require subjective evaluation. As a result, mIF analyses often revert to marker gating based on manual thresholding of raw imaging data.

RESULTS

To address the need for an evaluable semi-automated algorithm, we developed GammaGateR, an R package for interactive marker gating designed specifically for segmented cell-level data from mIF images. Based on a novel closed-form gamma mixture model, GammaGateR provides estimates of marker-positive cell proportions and soft clustering of marker-positive cells. The model incorporates user-specified constraints that provide a consistent but slide-specific model fit. We compared GammaGateR against the newest unsupervised approach for annotating mIF data, employing two colon datasets and one ovarian cancer dataset for the evaluation. We showed that GammaGateR produces highly similar results to a silver standard established through manual annotation. Furthermore, we demonstrated its effectiveness in identifying biological signals, achieved by mapping known spatial interactions between CD68 and MUC5AC cells in the colon and by accurately predicting survival in ovarian cancer patients using the phenotype probabilities as input for machine learning methods. GammaGateR is a highly efficient tool that can improve the replicability of marker gating results, while reducing the time of manual segmentation.

AVAILABILITY AND IMPLEMENTATION

The R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR.

DDR1-targeted therapies: current limitations and future potential

Discoidin domain receptor (DDR)-1 has a crucial role in regulating vital processes, including cell differentiation, proliferation, adhesion, migration, invasion, and matrix remodeling. Overexpression or activation of DDR1 in various pathological scenarios makes it a potential therapeutic target for the treatment of cancer, fibrosis, atherosclerosis, and neuropsychiatric, psychiatric, and neurodegenerative disorders. In this review, we summarize current therapeutic approaches targeting DDR1 from a medicinal chemistry perspective. Furthermore, we analyze factors other than issues of low selectivity and risk of resistance, contributing to the infrequent success of DDR1 inhibitors. The complex interplay between DDR1 and the extracellular matrix (ECM) necessitates additional validation, given that DDR1 might exhibit complex and synergistic interactions with other signaling molecules during ECM regulation. The mechanisms involved in DDR1 regulation in cancer and inflammation-related diseases also remain unknown.

Association of preoperative and recurrent serum carcinoembryonic antigen and outcome of colorectal cancer patients with metastatic relapse

Background

Seldom have the associations of preoperative CEA (p-CEA) and recurrent CEA (r-CEA) levels as well as changes in p-CEA and r-CEA with survival in patients with stage I-III colorectal cancer (CRC) who have experienced metastatic relapse, been thoroughly examined.

Methods

241 consecutive patients with stage I-III CRC who experienced metastatic relapse at Fudan University Shanghai Cancer Center (FUSCC) between January 2008 and January 2016 were investigated. The influence of p-CEA, r-CEA and CEA alteration on the overall survival (OS) and relapse-to-death survival (RDS) was evaluated. The restricted cubic spline regression model was employed to explore the optimal cut-off value of CEA.

Results

All 241 patients were categorized into four groups built on their CEA alteration patterns as follows: A, patients presenting elevated p-CEA levels but normal r-CEA levels (P-N); B, patients displaying normal levels of both p-CEA and r-CEA (N-N); C, patients exhibiting elevated levels of both p-CEA and r-CEA (P-P); D, patients with normal p-CEA levels but elevated r-CEA levels (N-P). The correlation between p-CEA and OS (P = 0.3266) and RDS (P = 0.2263) was insignificant. However, r-CEA exhibited a significant association with both OS (P = 0.0005) and RDS (P = 0.0002). Group A demonstrated the longest OS and RDS, whereas group D exhibited the poorest OS and RDS outcomes. For both OS and RDS, the CEA alteration groups served as an independent prognostic indicator. The optimal cut-off threshold for CEA was determined to be 5.1 ng/ml via the restricted cubic spline regression model.

Conclusion

r-CEA has a stronger correlation with OS and RDS in individuals with stage I-III CRC who have experienced metastatic relapse.The change between p-CEA and r-CEA could further indicate post-relapse survival, thereby facilitating the assessment of mortality risk stratification in stage I-III CRC patients experiencing metastatic relapse.

Advancing Cancer Interception

Rapid advances in technology and therapeutics, along with better methods to discern who is at risk for cancer by genetic testing and other means, has enabled the development of cancer interception. Targeted therapies and "immuno-interception" may eliminate premalignant lesions and require clinical trial and treatment paradigms altogether distinct from current approaches.

The Hallmarks of Precancer

Research on precancers, as defined as at-risk tissues and early lesions, is of high significance given the effectiveness of early intervention. We discuss the need for risk stratification to prevent overtreatment, an emphasis on the role of genetic and epigenetic aging when considering risk, and the importance of integrating macroenvironmental risk factors with molecules and cells in lesions and at-risk normal tissues for developing effective intervention and health policy strategies.

Seq-Scope Protocol: Repurposing Illumina Sequencing Flow Cells for High-Resolution Spatial Transcriptomics

Spatial transcriptomics (ST) technologies represent a significant advance in gene expression studies, aiming to profile the entire transcriptome from a single histological slide. These techniques are designed to overcome the constraints faced by traditional methods such as immunostaining and RNA in situ hybridization, which are capable of analyzing only a few target genes simultaneously. However, the application of ST in histopathological analysis is also limited by several factors, including low resolution, a limited range of genes, scalability issues, high cost, and the need for sophisticated equipment and complex methodologies. Seq-Scope-a recently developed novel technology-repurposes the Illumina sequencing platform for high-resolution, high-content spatial transcriptome analysis, thereby overcoming these limitations. Here we provide a detailed step-by-step protocol to implement Seq-Scope with an Illumina NovaSeq 6000 sequencing flow cell that allows for the profiling of multiple tissue sections in an area of 7 mm × 7 mm or larger. In addition to detailing how to prepare a frozen tissue section for both histological imaging and sequencing library preparation, we provide comprehensive instructions and a streamlined computational pipeline to integrate histological and transcriptomic data for high-resolution spatial analysis. This includes the use of conventional software tools for single cell and spatial analysis, as well as our recently developed segmentation-free method for analyzing spatial data at submicrometer resolution. Given its adaptability across various biological tissues, Seq-Scope establishes itself as an invaluable tool for researchers in molecular biology and histology.

Graph neural network model GGDisnet for identifying genes in gastrointestinal cancer and single-cell analysis

Gastrointestinal cancer, a highly prevalent form of cancer, has been the subject of extensive research resulting in the identification of numerous pathogenic genes. However, validation and exploration of these findings often require traditional biological experiments, which are time-consuming and limit the ability to make extensive assessments promptly. To address this challenge, this paper introduces GGDisnet, a novel model for identifying genes associated with gastrointestinal cancer. GGDisnet efficiently screens human genes, providing a set of genes with a high correlation to gastrointestinal cancer for reference. Comparative analysis with other models demonstrates GGDisnet's superior performance. Furthermore, we conducted enrichment and single-cell analyses based on GGDisnet-predicted genes, offering valuable clinical insights.

New clinical trial design in precision medicine: discovery, development and direction

In the era of precision medicine, it has been increasingly recognized that individuals with a certain disease are complex and different from each other. Due to the underestimation of the significant heterogeneity across participants in traditional "one-size-fits-all" trials, patient-centered trials that could provide optimal therapy customization to individuals with specific biomarkers were developed including the basket, umbrella, and platform trial designs under the master protocol framework. In recent years, the successive FDA approval of indications based on biomarker-guided master protocol designs has demonstrated that these new clinical trials are ushering in tremendous opportunities. Despite the rapid increase in the number of basket, umbrella, and platform trials, the current clinical and research understanding of these new trial designs, as compared with traditional trial designs, remains limited. The majority of the research focuses on methodologies, and there is a lack of in-depth insight concerning the underlying biological logic of these new clinical trial designs. Therefore, we provide this comprehensive review of the discovery and development of basket, umbrella, and platform trials and their underlying logic from the perspective of precision medicine. Meanwhile, we discuss future directions on the potential development of these new clinical design in view of the "Precision Pro", "Dynamic Precision", and "Intelligent Precision". This review would assist trial-related researchers to enhance the innovation and feasibility of clinical trial designs by expounding the underlying logic, which be essential to accelerate the progression of precision medicine.

Spatially Resolved Multiomics: Data Analysis from Monoomics to Multiomics

Spatial monoomics has been recognized as a powerful tool for exploring life sciences. Recently, spatial multiomics has advanced considerably, which could contribute to clarifying many biological issues. Spatial monoomics techniques in epigenomics, genomics, transcriptomics, proteomics, and metabolomics can enhance our understanding of biological functions and cellular identities by simultaneously measuring tissue structures and biomolecule levels. Spatial monoomics technology has evolved from monoomics to spatial multiomics. Moreover, the spatial resolution, high-throughput detection capability, capture efficiency, and compatibility with various sample types of omics technology have considerably advanced. Despite the technological advances in this field, data analysis frameworks have stagnated. Current challenges include incomplete spatial monoomics data analysis pipeline, overly complex data analysis tasks, and few established spatial multiomics data analysis strategies. In this review, we systematically summarize recent developments of various spatial monoomics techniques and improvements in related data analysis pipeline. On the basis of the spatial multiomics technology, we propose a data integration strategy with cross-platform, cross-slice, and cross-modality. We summarize the potential applications of spatial monoomics technology, aiming to provide researchers and clinicians with a better understanding of how such applications have advanced. Spatial multiomics technology is expected to substantially impact biology and precision medicine through measurements of cellular tissue structures and the extraction of biomolecular features.