Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention and intervention.

Identifying risk protein targets and their therapeutic drugs is crucial for effective cancer prevention. Here, we conduct integrative and fine-mapping analyses of large genome-wide association studies data for breast, colorectal, lung, ovarian, pancreatic, and prostate cancers, and characterize 710 lead variants independently associated with cancer risk. Through mapping protein quantitative trait loci (pQTL) for these variants using plasma proteomics data from over 75,000 participants, we identify 365 proteins associated with cancer risk. Subsequent colocalization analysis identifies 101 proteins, including 74 not reported in previous studies. We further characterize 36 potential druggable proteins for cancers or other disease indications. Analyzing >3.5 million electronic health records, we uncover five drugs (Haloperidol, Trazodone, Tranexamic Acid, Haloperidol, and Captopril) associated with increased cancer risk and two drugs (Caffeine and Acetazolamide) linked to reduced colorectal cancer risk. This study offers novel insights into therapeutic drugs targeting risk proteins for cancer prevention and intervention.

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.

Interleukin 13 Promotes Maturation and Proliferation in Metaplastic Gastroids.

BACKGROUND & AIMS: Type 2 innate lymphoid cells (ILC2s) and interleukin-13 (IL-13) promote the onset of spasmolytic polypeptide-expressing metaplasia (SPEM) cells. However, little is known about molecular effects of IL-13 in SPEM cells. We now sought to establish a reliable organoid model, Meta1 gastroids, to model SPEM cells in vitro. We evaluated cellular and molecular effects of ILC2s and IL-13 on maturation and proliferation of SPEM cells. METHODS: We performed single-cell RNA sequencing to characterize Meta1 gastroids, which were derived from stomachs of Mist1-Kras transgenic mice that displayed pyloric metaplasia. Cell sorting was used to isolate activated ILC2s from stomachs of IL-13-tdTomato reporter mice treated with L635. Three-dimensional co-culture was used to determine the effects of ILC2s on Meta1 gastroids. Mouse normal or metaplastic (Meta1) and human metaplastic gastroids were cultured with IL-13 to evaluate cell responses. Air-Liquid Interface culture was performed to test long-term culture effects of IL-13. In silico analysis determined possible STAT6-binding sites in gene promoter regions. STAT6 inhibition was performed to corroborate STAT6 role in SPEM cells maturation. RESULTS: Meta1 gastroids showed the characteristics of SPEM cell lineages in vitro even after several passages. We demonstrated that co-culture with ILC2s or IL-13 treatment can induce phosphorylation of STAT6 in Meta1 and normal gastroids and promote the maturation and proliferation of SPEM cell lineages. IL-13 up-regulated expression of mucin-related proteins in human metaplastic gastroids. Inhibition of STAT6 blocked SPEM-related gene expression in Meta1 gastroids and maturation of SPEM in both normal and Meta1 gastroids. CONCLUSIONS: IL-13 promotes the maturation and proliferation of SPEM cells consistent with gastric mucosal regeneration.

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.

Intestinal Tuft Cells: Morphology, Function, and Implications for Human Health.

Tuft cells are a rare and morphologically distinct chemosensory cell type found throughout many organs, including the gastrointestinal tract. These cells were identified by their unique morphologies distinguished by large apical protrusions. Ultrastructural data have begun to describe the molecular underpinnings of their cytoskeletal features, and tuft cell-enriched cytoskeletal proteins have been identified, although the connection of tuft cell morphology to tuft cell functionality has not yet been established. Furthermore, tuft cells display variations in function and identity between and within tissues, leading to the delineation of distinct tuft cell populations. As a chemosensory cell type, they display receptors that are responsive to ligands specific for their environment. While many studies have demonstrated the tuft cell response to protists and helminths in the intestine, recent research has highlighted other roles of tuft cells as well as implicated tuft cells in other disease processes including inflammation, cancer, and viral infections. Here, we review the literature on the cytoskeletal structure of tuft cells. Additionally, we focus on new research discussing tuft cell lineage, ligand-receptor interactions, tuft cell tropism, and the role of tuft cells in intestinal disease. Finally, we discuss the implication of tuft cell-targeted therapies in human health and how the morphology of tuft cells may contribute to their functionality.

Effects of KRAS Genetic Interactions on Outcomes in Cancers of the Lung, Pancreas, and Colorectum.

BACKGROUND: KRAS is among the most commonly mutated oncogenes in cancer, and previous studies have shown associations with survival in many cancer contexts. Evidence from both clinical observations and mouse experiments further suggests that these associations are allele- and tissue-specific. These findings motivate using clinical data to understand gene interactions and clinical covariates within different alleles and tissues. METHODS: We analyze genomic and clinical data from the AACR Project GENIE Biopharma Collaborative for samples from lung, colorectal, and pancreatic cancers. For each of these cancer types, we report epidemiological associations for different KRAS alleles, apply principal component analysis (PCA) to discover groups of genes co-mutated with KRAS, and identify distinct clusters of patient profiles with implications for survival. RESULTS: KRAS mutations were associated with inferior survival in lung, colon, and pancreas, although the specific mutations implicated varied by disease. Tissue- and allele-specific associations with smoking, sex, age, and race were found. Tissue-specific genetic interactions with KRAS were identified by PCA, which were clustered to produce five, four, and two patient profiles in lung, colon, and pancreas. Membership in these profiles was associated with survival in all three cancer types. CONCLUSIONS: KRAS mutations have tissue- and allele-specific associations with inferior survival, clinical covariates, and genetic interactions. IMPACT: Our results provide greater insight into the tissue- and allele-specific associations with KRAS mutations and identify clusters of patients that are associated with survival and clinical attributes from combinations of genetic interactions with KRAS mutations.

Molecular cartography uncovers evolutionary and microenvironmental dynamics in sporadic colorectal tumors.

Colorectal cancer exhibits dynamic cellular and genetic heterogeneity during progression from precursor lesions toward malignancy. Analysis of spatial multi-omic data from 31 human colorectal specimens enabled phylogeographic mapping of tumor evolution that revealed individualized progression trajectories and accompanying microenvironmental and clonal alterations. Phylogeographic mapping ordered genetic events, classified tumors by their evolutionary dynamics, and placed clonal regions along global pseudotemporal progression trajectories encompassing the chromosomal instability (CIN+) and hypermutated (HM) pathways. Integrated single-cell and spatial transcriptomic data revealed recurring epithelial programs and infiltrating immune states along progression pseudotime. We discovered an immune exclusion signature (IEX), consisting of extracellular matrix regulators DDR1, TGFBI, PAK4, and DPEP1, that charts with CIN+ tumor progression, is associated with reduced cytotoxic cell infiltration, and shows prognostic value in independent cohorts. This spatial multi-omic atlas provides insights into colorectal tumor-microenvironment co-evolution, serving as a resource for stratification and targeted treatments.

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.

Oncogenic K-Ras suppresses global miRNA function.

K-Ras frequently acquires gain-of-function mutations (K-RasG12D being the most common) that trigger significant transcriptomic and proteomic changes to drive tumorigenesis. Nevertheless, oncogenic K-Ras-induced dysregulation of post-transcriptional regulators such as microRNAs (miRNAs) during oncogenesis is poorly understood. Here, we report that K-RasG12D promotes global suppression of miRNA activity, resulting in the upregulation of hundreds of targets. We constructed a comprehensive profile of physiological miRNA targets in mouse colonic epithelium and tumors expressing K-RasG12D using Halo-enhanced Argonaute pull-down. Combining this with parallel datasets of chromatin accessibility, transcriptome, and proteome, we uncovered that K-RasG12D suppressed the expression of Csnk1a1 and Csnk2a1, subsequently decreasing Ago2 phosphorylation at Ser825/829/832/835. Hypo-phosphorylated Ago2 increased binding to mRNAs while reducing its activity to repress miRNA targets. Our findings connect a potent regulatory mechanism of global miRNA activity to K-Ras in a pathophysiological context and provide a mechanistic link between oncogenic K-Ras and the post-transcriptional upregulation of miRNA targets.

Predicting Crohn's disease severity in the colon using mixed cell nucleus density from pseudo labels.

Crohn's disease (CD) is a debilitating inflammatory bowel disease with no known cure. Computational analysis of hematoxylin and eosin (H&E) stained colon biopsy whole slide images (WSIs) from CD patients provides the opportunity to discover unknown and complex relationships between tissue cellular features and disease severity. While there have been works using cell nuclei-derived features for predicting slide-level traits, this has not been performed on CD H&E WSIs for classifying normal tissue from CD patients vs active CD and assessing slide label-predictive performance while using both separate and combined information from pseudo-segmentation labels of nuclei from neutrophils, eosinophils, epithelial cells, lymphocytes, plasma cells, and connective cells. We used 413 WSIs of CD patient biopsies and calculated normalized histograms of nucleus density for the six cell classes for each WSI. We used a support vector machine to classify the truncated singular value decomposition representations of the normalized histograms as normal or active CD with four-fold cross-validation in rounds where nucleus types were first compared individually, the best was selected, and further types were added each round. We found that neutrophils were the most predictive individual nucleus type, with an AUC of 0.92 ± 0.0003 on the withheld test set. Adding information improved cross-validation performance for the first two rounds and on the withheld test set for the first three rounds, though performance metrics did not increase substantially beyond when neutrophils were used alone.

SELENOP modifies sporadic colorectal carcinogenesis and WNT signaling activity through LRP5/6 interactions.

Although selenium deficiency correlates with colorectal cancer (CRC) risk, the roles of the selenium-rich antioxidant selenoprotein P (SELENOP) in CRC remain unclear. In this study, we defined SELENOP's contributions to sporadic CRC. In human single-cell cRNA-Seq (scRNA-Seq) data sets, we discovered that SELENOP expression rose as normal colon stem cells transformed into adenomas that progressed into carcinomas. We next examined the effects of Selenop KO in a mouse adenoma model that involved conditional, intestinal epithelium-specific deletion of the tumor suppressor adenomatous polyposis coli (Apc) and found that Selenop KO decreased colon tumor incidence and size. We mechanistically interrogated SELENOP-driven phenotypes in tumor organoids as well as in CRC and noncancer cell lines. Selenop-KO tumor organoids demonstrated defects in organoid formation and decreases in WNT target gene expression, which could be reversed by SELENOP restoration. Moreover, SELENOP increased canonical WNT signaling activity in noncancer and CRC cell lines. In defining the mechanism of action of SELENOP, we mapped protein-protein interactions between SELENOP and the WNT coreceptors low-density lipoprotein receptor-related proteins 5 and 6 (LRP5/6). Last, we confirmed that SELENOP-LRP5/6 interactions contributed to the effects of SELENOP on WNT activity. Overall, our results position SELENOP as a modulator of the WNT signaling pathway in sporadic CRC.

Advances in Mapping Tumor Progression from Precancer Atlases.

Tissue profiling technologies present opportunities for understanding transition from precancerous lesions to malignancy, which may impact risk stratification, prevention, and even cancer treatment. A human precancer atlas building effort is ongoing to tackle the significant challenge of decoding the heterogeneity among cells, specimens, and patients. Here, we discuss the findings resulting from atlases built across precancer types, including those found in colon, breast, lung, stomach, cervix, and skin, using bulk, single-cell, and spatial profiling strategies. We highlight two main themes that emerge across precancer types: the ordering of molecular events that occur during tumor progression and the fluctuation of microenvironmental response during precancer progression. We further highlight the key challenges of data integration across large cohorts of patients, and the need for computational tools to reliably annotate and quality control high-volume, high-dimensional data.

Multiplexed 3D atlas of state transitions and immune interaction in colorectal cancer.

Advanced solid cancers are complex assemblies of tumor, immune, and stromal cells characterized by high intratumoral variation. We use highly multiplexed tissue imaging, 3D reconstruction, spatial statistics, and machine learning to identify cell types and states underlying morphological features of known diagnostic and prognostic significance in colorectal cancer. Quantitation of these features in high-plex marker space reveals recurrent transitions from one tumor morphology to the next, some of which are coincident with long-range gradients in the expression of oncogenes and epigenetic regulators. At the tumor invasive margin, where tumor, normal, and immune cells compete, T cell suppression involves multiple cell types and 3D imaging shows that seemingly localized 2D features such as tertiary lymphoid structures are commonly interconnected and have graded molecular properties. Thus, while cancer genetics emphasizes the importance of discrete changes in tumor state, whole-specimen imaging reveals large-scale morphological and molecular gradients analogous to those in developing tissues.

Matrix stiffness enhances cancer-macrophage interactions and M2-like macrophage accumulation in the breast tumor microenvironment.

The role of intratumor heterogeneity is becoming increasingly apparent in part due to expansion in single cell technologies. Clinically, tumor heterogeneity poses several obstacles to effective cancer therapy dealing with biomarker variability and treatment responses. Matrix stiffening is known to occur during tumor progression and contribute to pathogenesis in several cancer hallmarks, including tumor angiogenesis and metastasis. However, the effects of matrix stiffening on intratumor heterogeneity have not been thoroughly studied. In this study, we applied single-cell RNA sequencing to investigate the differences in the transcriptional landscapes between stiff and compliant MMTV-PyMT mouse mammary tumors. We found similar compositions of cancer and stromal subpopulations in compliant and stiff tumors but differential intercellular communication and a significantly higher concentration of tumor-promoting, M2-like macrophages in the stiffer tumor microenvironments. Interestingly, we found that cancer cells seeded on stiffer substrates recruited more macrophages. Furthermore, elevated matrix stiffness increased Colony Stimulating Factor 1 (CSF-1) expression in breast cancer cells and reduction of CSF-1 expression on stiffer substrates reduced macrophage recruitment. Thus, our results demonstrate that tissue phenotypes were conserved between stiff and compliant tumors but matrix stiffening altered cell-cell interactions which may be responsible for shifting the phenotypic balance of macrophages residing in the tumor microenvironment towards a pro-tumor progression M2 phenotype. STATEMENT OF SIGNIFICANCE: Cells within tumors are highly heterogeneous, posing challenges with treatment and recurrence. While increased tissue stiffness can promote several hallmarks of cancer, its effects on tumor heterogeneity are unclear. We used single-cell RNA sequencing to investigate the differences in the transcriptional landscapes between stiff and compliant MMTV-PyMT mouse mammary tumors. We found similar compositions of cancer and stromal subpopulations in compliant and stiff tumors but differential intercellular communication and a significantly higher concentration of tumor-promoting, M2-like macrophages in the stiffer tumor microenvironments. Using a biomaterial-based platform, we found that cancer cells seeded on stiffer substrates recruited more macrophages, supporting our in vivo findings. Together, our results demonstrate a key role of matrix stiffness in affecting cell-cell communication and macrophage recruitment.

Temporal recording of mammalian development and precancer.

Key to understanding many biological phenomena is knowing the temporal ordering of cellular events, which often require continuous direct observations [1, 2]. An alternative solution involves the utilization of irreversible genetic changes, such as naturally occurring mutations, to create indelible markers that enables retrospective temporal ordering [3-8]. Using NSC-seq, a newly designed and validated multi-purpose single-cell CRISPR platform, we developed a molecular clock approach to record the timing of cellular events and clonality in vivo , while incorporating assigned cell state and lineage information. Using this approach, we uncovered precise timing of tissue-specific cell expansion during murine embryonic development and identified new intestinal epithelial progenitor states by their unique genetic histories. NSC-seq analysis of murine adenomas and single-cell multi-omic profiling of human precancers as part of the Human Tumor Atlas Network (HTAN), including 116 scRNA-seq datasets and clonal analysis of 418 human polyps, demonstrated the occurrence of polyancestral initiation in 15-30% of colonic precancers, revealing their origins from multiple normal founders. Thus, our multimodal framework augments existing single-cell analyses and lays the foundation for in vivo multimodal recording, enabling the tracking of lineage and temporal events during development and tumorigenesis.

Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images.

Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training. Unfortunately, many prior anomaly detection methods were optimized for a specific "known" abnormality (e.g., brain tumor, bone fraction, cell types). Moreover, even though only the normal images were used in the training process, the abnormal images were often employed during the validation process (e.g., epoch selection, hyper-parameter tuning), which might leak the supposed "unknown" abnormality unintentionally. In this study, we investigated these two essential aspects regarding universal anomaly detection in medical images by (1) comparing various anomaly detection methods across four medical datasets, (2) investigating the inevitable but often neglected issues on how to unbiasedly select the optimal anomaly detection model during the validation phase using only normal images, and (3) proposing a simple decision-level ensemble method to leverage the advantage of different kinds of anomaly detection without knowing the abnormality. The results of our experiments indicate that none of the evaluated methods consistently achieved the best performance across all datasets. Our proposed method enhanced the robustness of performance in general (average AUC 0.956).

Archetype tasks link intratumoral heterogeneity to plasticity and cancer hallmarks in small cell lung cancer.

Small cell lung cancer (SCLC) tumors comprise heterogeneous mixtures of cell states, categorized into neuroendocrine (NE) and non-neuroendocrine (non-NE) transcriptional subtypes. NE to non-NE state transitions, fueled by plasticity, likely underlie adaptability to treatment and dismal survival rates. Here, we apply an archetypal analysis to model plasticity by recasting SCLC phenotypic heterogeneity through multi-task evolutionary theory. Cell line and tumor transcriptomics data fit well in a five-dimensional convex polytope whose vertices optimize tasks reminiscent of pulmonary NE cells, the SCLC normal counterparts. These tasks, supported by knowledge and experimental data, include proliferation, slithering, metabolism, secretion, and injury repair, reflecting cancer hallmarks. SCLC subtypes, either at the population or single-cell level, can be positioned in archetypal space by bulk or single-cell transcriptomics, respectively, and characterized as task specialists or multi-task generalists by the distance from archetype vertex signatures. In the archetype space, modeling single-cell plasticity as a Markovian process along an underlying state manifold indicates that task trade-offs, in response to microenvironmental perturbations or treatment, may drive cell plasticity. Stifling phenotypic transitions and plasticity may provide new targets for much-needed translational advances in SCLC. A record of this paper's Transparent Peer Review process is included in the supplemental information.

Dissociation and inDrops microfluidic encapsulation of human gut tissues for single-cell atlasing studies.

In droplet-based single-cell RNA-sequencing (scRNA-seq) experiments, cells, along with some of their surrounding buffer and ambient material, are encapsulated into droplets for mRNA capture and barcoding. This protocol details the steps for human gut tissue dissociation using cold active protease, and subsequent isolation of single epithelial cells, with enrichment of viability through washes. Next, the steps for encapsulation on the inDrops scRNA-seq platform are described. This procedure has been demonstrated to be applicable to polyps, cancers, and inflamed tissues. For complete details on the use and execution of this protocol, please refer to Chen et al. (2021).

Human Colon Cancer-Derived Clostridioides difficile Strains Drive Colonic Tumorigenesis in Mice.

Defining the complex role of the microbiome in colorectal cancer and the discovery of novel, protumorigenic microbes are areas of active investigation. In the present study, culturing and reassociation experiments revealed that toxigenic strains of Clostridioides difficile drove the tumorigenic phenotype of a subset of colorectal cancer patient-derived mucosal slurries in germ-free ApcMin/+ mice. Tumorigenesis was dependent on the C. difficile toxin TcdB and was associated with induction of Wnt signaling, reactive oxygen species, and protumorigenic mucosal immune responses marked by the infiltration of activated myeloid cells and IL17-producing lymphoid and innate lymphoid cell subsets. These findings suggest that chronic colonization with toxigenic C. difficile is a potential driver of colorectal cancer in patients. SIGNIFICANCE: Colorectal cancer is a leading cause of cancer and cancer-related deaths worldwide, with a multifactorial etiology that likely includes procarcinogenic bacteria. Using human colon cancer specimens, culturing, and murine models, we demonstrate that chronic infection with the enteric pathogen C. difficile is a previously unrecognized contributor to colonic tumorigenesis. See related commentary by Jain and Dudeja, p. 1838. This article is highlighted in the In This Issue feature, p. 1825.

Dysplastic Stem Cell Plasticity Functions as a Driving Force for Neoplastic Transformation of Precancerous Gastric Mucosa.

BACKGROUND & AIMS: Dysplasia carries a high risk of cancer development; however, the cellular mechanisms for dysplasia evolution to cancer are obscure. We have previously identified 2 putative dysplastic stem cell (DSC) populations, CD44v6neg/CD133+/CD166+ (double positive [DP]) and CD44v6+/CD133+/CD166+ (triple positive [TP]), which may contribute to cellular heterogeneity of gastric dysplasia. Here, we investigated functional roles and cell plasticity of noncancerous Trop2+/CD133+/CD166+ DSCs initially developed in the transition from precancerous metaplasia to dysplasia in the stomach. METHODS: Dysplastic organoids established from active Kras-induced mouse stomachs were used for transcriptome analysis, in vitro differentiation, and in vivo tumorigenicity assessments of DSCs. Cell heterogeneity and genetic alterations during clonal evolution of DSCs were examined by next-generation sequencing. Tissue microarrays were used to identify DSCs in human dysplasia. We additionally evaluated the effect of casein kinase 1 alpha (CK1α) regulation on the DSC activities using both mouse and human dysplastic organoids. RESULTS: We identified a high similarity of molecular profiles between DP- and TP-DSCs, but more dynamic activities of DP-DSCs in differentiation and survival for maintaining dysplastic cell lineages through Wnt ligand-independent CK1α/ß-catenin signaling. Xenograft studies demonstrated that the DP-DSCs clonally evolve toward multiple types of gastric adenocarcinomas and promote cancer cell heterogeneity by acquiring additional genetic mutations and recruiting the tumor microenvironment. Last, growth and survival of both mouse and human dysplastic organoids were controlled by targeting CK1α. CONCLUSIONS: These findings indicate that the DSCs are de novo gastric cancer-initiating cells responsible for neoplastic transformation and a promising target for intervention in early induction of gastric cancer.