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: The R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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 upregulated 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.

Intestinal tuft cells assemble a cytoskeletal superstructure composed of co-aligned actin bundles and microtubules.

Background & Aims: All tissues consist of a distinct set of cell types, which collectively support organ function and homeostasis. Tuft cells are a rare epithelial cell type found in diverse epithelia, where they play important roles in sensing antigens and stimulating downstream immune responses. Exhibiting a unique polarized morphology, tuft cells are defined by an array of giant actin filament bundles that support ∼2 µm of apical membrane protrusion and extend over 7 µm towards the cell's perinuclear region. Despite their established roles in maintaining intestinal epithelial homeostasis, tuft cells remain understudied due to their rarity (e.g. ∼ 1% in the small intestinal epithelium). Details regarding the ultrastructural organization of the tuft cell cytoskeleton, the molecular components involved in building the array of giant actin bundles, and how these cytoskeletal structures support tuft cell biology remain unclear. Methods: To begin to answer these questions, we used advanced light and electron microscopy to perform quantitative morphometry of the small intestinal tuft cell cytoskeleton. Results: We found that tuft cell core bundles consist of actin filaments that are crosslinked in a parallel "barbed-end out" configuration. These polarized structures are also supported by a unique group of tuft cell enriched actin-binding proteins that are differentially localized along the giant core bundles. Furthermore, we found that tuft cell actin bundles are co-aligned with a highly ordered network of microtubules. Conclusions: Tuft cells assemble a cytoskeletal superstructure that is well positioned to serve as a track for subcellular transport along the apical-basolateral axis and in turn, support the dynamic sensing functions that are critical for intestinal epithelial homeostasis. SYNOPSIS: This research leveraged advanced light and electron microscopy to perform quantitative morphometry of the intestinal tuft cell cytoskeleton. Three-dimensional reconstructions of segmented image data revealed a co-aligned actin-microtubule superstructure that may play a fundamental role in tuft cell function.

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.

Cross-scale multi-instance learning for pathological image diagnosis.

Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). However, such processing is typically performed at a single scale (e.g., 20× magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale relationships into a single MIL network for pathological image diagnosis. The contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL) algorithm that integrates the multi-scale information and the inter-scale relationships is proposed; (2) A toy dataset with scale-specific morphological features is created and released to examine and visualize differential cross-scale attention; (3) Superior performance on both in-house and public datasets is demonstrated by our simple cross-scale MIL strategy. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.

Endocrine islet ß-cell subtypes with differential function are derived from biochemically distinct embryonic endocrine islet progenitors that are regulated by maternal nutrients.

Endocrine islet b cells comprise heterogenous cell subsets. Yet when/how these subsets are produced and how stable they are remain unknown. Addressing these questions is important for preventing/curing diabetes, because lower numbers of b cells with better secretory function is a high risk of this disease. Using combinatorial cell lineage tracing, scRNA-seq, and DNA methylation analysis, we show here that embryonic islet progenitors with distinct gene expression and DNA methylation produce b-cell subtypes of different function and viability in adult mice. The subtype with better function is enriched for genes involved in vesicular production/trafficking, stress response, and Ca2+-secretion coupling, which further correspond to differential DNA methylation in putative enhancers of these genes. Maternal overnutrition, a major diabetes risk factor, reduces the proportion of endocrine progenitors of the b-cell subtype with better-function via deregulating DNA methyl transferase 3a. Intriguingly, the gene signature that defines mouse b-cell subtypes can reliably divide human cells into two sub-populations while the proportion of b cells with better-function is reduced in diabetic donors. The implication of these results is that modulating DNA methylation in islet progenitors using maternal food supplements can be explored to improve b-cell function in the prevention and therapy of diabetes.

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.

A Specialized Epithelial Cell Type Regulating Mucosal Immunity and Driving Human Crohn's Disease.

Crohn's disease (CD) is a complex chronic inflammatory disorder that may affect any part of gastrointestinal tract with extra-intestinal manifestations and associated immune dysregulation. To characterize heterogeneity in CD, we profiled single-cell transcriptomics of 170 samples from 65 CD patients and 18 non-inflammatory bowel disease (IBD) controls in both the terminal ileum (TI) and ascending colon (AC). Analysis of 202,359 cells identified a novel epithelial cell type in both TI and AC, featuring high expression of LCN2, NOS2, and DUOX2, and thus is named LND. LND cells, confirmed by high-resolution in-situ RNA imaging, were rarely found in non-IBD controls, but expanded significantly in active CD. Compared to other epithelial cells, genes defining LND cells were enriched in antimicrobial response and immunoregulation. Moreover, multiplexed protein imaging demonstrated that LND cell abundance was associated with immune infiltration. Cross-talk between LND and immune cells was explored by ligand-receptor interactions and further evidenced by their spatial colocalization. LND cells showed significant enrichment of expression specificity of IBD/CD susceptibility genes, revealing its role in immunopathogenesis of CD. Investigating lineage relationships of epithelial cells detected two LND cell subpopulations with different origins and developmental potential, early and late LND. The ratio of the late to early LND cells was related to anti-TNF response. These findings emphasize the pathogenic role of the specialized LND cell type in both Crohn's ileitis and Crohn's colitis and identify novel biomarkers associated with disease activity and treatment response.

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.

MTGR1 is required to maintain small intestinal stem cell populations.

Undifferentiated intestinal stem cells (ISCs), particularly those marked by Lgr5, are crucial for maintaining homeostasis and resolving injury. Lgr5+ cells in the crypt base constantly divide, pushing daughter cells upward along the crypt axis, where they differentiate into a variety of specialized cell types. This process requires coordinated execution of complex transcriptional programs, which allow for the maintenance of undifferentiated stem cells while permitting differentiation of the wide array of intestinal cells necessary for homeostasis. Thus, disrupting these programs may negatively impact homeostasis and response to injury. Previously, members of the myeloid translocation gene (MTG) family have been identified as transcriptional co-repressors that regulate stem cell maintenance and differentiation programs in multiple organ systems, including the intestine. One MTG family member, myeloid translocation gene related 1 (MTGR1), has been recognized as a crucial regulator of secretory cell differentiation and response to injury. However, whether MTGR1 contributes to the function of ISCs has not yet been examined. Here, using Mtgr1-/- mice, we have assessed the effects of MTGR1 loss on ISC biology and differentiation programs. Interestingly, loss of MTGR1 increased the total number of cells expressing Lgr5, the canonical marker of cycling ISCs, suggesting higher overall stem cell numbers. However, expanded transcriptomic analyses revealed MTGR1 loss may instead promote stem cell differentiation into transit-amplifying cells at the expense of cycling ISC populations. Furthermore, ex vivo intestinal organoids established from Mtgr1 null were found nearly completely unable to survive and expand, likely due to aberrant ISC differentiation, suggesting that Mtgr1 null ISCs were functionally deficient as compared to WT ISCs. Together, these results identify a novel role for MTGR1 in ISC function and suggest that MTGR1 is required to maintain the undifferentiated state.

Topological-Preserving Membrane Skeleton Segmentation in Multiplex Immunofluorescence Imaging.

Multiplex immunofluorescence (MxIF) is an emerging imaging technology whose downstream molecular analytics highly rely upon the effectiveness of cell segmentation. In practice, multiple membrane markers (e.g., NaKATPase, PanCK and ß-catenin) are employed to stain membranes for different cell types, so as to achieve a more comprehensive cell segmentation since no single marker fits all cell types. However, prevalent watershed-based image processing might yield inferior capability for modeling complicated relationships between markers. For example, some markers can be misleading due to questionable stain quality. In this paper, we propose a deep learning based membrane segmentation method to aggregate complementary information that is uniquely provided by large scale MxIF markers. We aim to segment tubular membrane structure in MxIF data using global (membrane markers z-stack projection image) and local (separate individual markers) information to maximize topology preservation with deep learning. Specifically, we investigate the feasibility of four SOTA 2D deep networks and four volumetric-based loss functions. We conducted a comprehensive ablation study to assess the sensitivity of the proposed method with various combinations of input channels. Beyond using adjusted rand index (ARI) as the evaluation metric, which was inspired by the clDice, we propose a novel volumetric metric that is specific for skeletal structure, denoted as clDiceSKEL. In total, 80 membrane MxIF images were manually traced for 5-fold cross-validation. Our model outperforms the baseline with a 20.2% and 41.3% increase in clDiceSKEL and ARI performance, which is significant (p<0.05) using the Wilcoxon signed rank test. Our work explores a promising direction for advancing MxIF imaging cell segmentation with deep learning membrane segmentation. Tools are available at https://github.com/MASILab/MxIF_Membrane_Segmentation.

A contamination focused approach for optimizing the single-cell RNA-seq experiment.

Droplet-based single-cell RNA-seq (scRNA-seq) data are plagued by ambient contaminations caused by nucleic acid material released by dead and dying cells. This material is mixed into the buffer and is co-encapsulated with cells, leading to a lower signal-to-noise ratio. Although there exist computational methods to remove ambient contaminations post-hoc, the reliability of algorithms in generating high-quality data from low-quality sources remains uncertain. Here, we assess data quality before data filtering by a set of quantitative, contamination-based metrics that assess data quality more effectively than standard metrics. Through a series of controlled experiments, we report improvements that can minimize ambient contamination outside of tissue dissociation, via cell fixation, improved cell loading, microfluidic dilution, and nuclei versus cell preparation; many of these parameters are inaccessible on commercial platforms. We provide end-users with insights on factors that can guide their decision-making regarding optimizations that minimize ambient contamination, and metrics to assess data quality.

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.

Influence of Cell-type Ratio on Spatially Resolved Single-cell Transcriptomes using the Tangram Algorithm: Based on Implementation on Single-Cell and MxIF Data.

The Tangram algorithm is a benchmarking method of aligning single-cell (sc/snRNA-seq) data to various forms of spatial data collected from the same region. With this data alignment, the annotation of the single-cell data can be projected to spatial data. However, the cell composition (cell-type ratio) of the single-cell data and spatial data might be different because of heterogeneous cell distribution. Whether the Tangram algorithm can be adapted when the two data have different cell-type ratios has not been discussed in previous works. In our practical application that maps the cell-type classification results of single-cell data to the Multiplex immunofluorescence (MxIF) spatial data, cell-type ratios were different, though they were sampled from adjacent areas. In this work, both simulation and empirical validation were conducted to quantitatively explore the impact of the mismatched cell-type ratio on the Tangram mapping in different situations. Results show that the cell-type difference has a negative influence on classification accuracy.

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.