Computational identification of preneoplastic cells displaying high stemness and risk of cancer progression

The burgeoning spatial multi-omics in human gastrointestinal cancers

The development and progression of diseases in multicellular organisms unfold within the intricate three-dimensional body environment. Thus, to comprehensively understand the molecular mechanisms governing individual development and disease progression, precise acquisition of biological data, including genome, transcriptome, proteome, metabolome, and epigenome, with single-cell resolution and spatial information within the body's three-dimensional context, is essential. This foundational information serves as the basis for deciphering cellular and molecular mechanisms. Although single-cell multi-omics technology can provide biological information such as genome, transcriptome, proteome, metabolome, and epigenome with single-cell resolution, the sample preparation process leads to the loss of spatial information. Spatial multi-omics technology, however, facilitates the characterization of biological data, such as genome, transcriptome, proteome, metabolome, and epigenome in tissue samples, while retaining their spatial context. Consequently, these techniques significantly enhance our understanding of individual development and disease pathology. Currently, spatial multi-omics technology has played a vital role in elucidating various processes in tumor biology, including tumor occurrence, development, and metastasis, particularly in the realms of tumor immunity and the heterogeneity of the tumor microenvironment. Therefore, this article provides a comprehensive overview of spatial transcriptomics, spatial proteomics, and spatial metabolomics-related technologies and their application in research concerning esophageal cancer, gastric cancer, and colorectal cancer. The objective is to foster the research and implementation of spatial multi-omics technology in digestive tumor diseases. This review will provide new technical insights for molecular biology researchers.

Computational single-cell methods for predicting cancer risk

Despite recent biotechnological breakthroughs, cancer risk prediction remains a formidable computational and experimental challenge. Addressing it is critical in order to improve prevention, early detection and survival rates. Here, I briefly summarize some key emerging theoretical and computational challenges as well as recent computational advances that promise to help realize the goals of cancer-risk prediction. The focus is on computational strategies based on single-cell data, in particular on bottom-up network modeling approaches that aim to estimate cancer stemness and dedifferentiation at single-cell resolution from a systems-biological perspective. I will describe two promising methods, a tissue and cell-lineage independent one based on the concept of diffusion network entropy, and a tissue and cell-lineage specific one that uses transcription factor regulons. Application of these tools to single-cell and single-nucleus RNA-seq data from stages prior to invasive cancer reveal that they can successfully delineate the heterogeneous inter-cellular cancer-risk landscape, identifying those cells that are more likely to turn cancerous. Bottom-up systems biological modeling of single-cell omic data is a novel computational analysis paradigm that promises to facilitate the development of preventive, early detection and cancer-risk prediction strategies.

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.

Alarmin S100A8 imparts chemoresistance of esophageal cancer by reprogramming cancer-associated fibroblasts

Chemotherapy remains the first-line treatment for advanced esophageal cancer. However, durable benefits are achieved by only a limited subset of individuals due to the elusive chemoresistance. Here, we utilize patient-derived xenografts (PDXs) from esophageal squamous-cell carcinoma to investigate chemoresistance mechanisms in preclinical settings. We observe that activated cancer-associated fibroblasts (CAFs) are enriched in the tumor microenvironment of PDXs resistant to chemotherapy. Mechanistically, we reveal that cancer-cell-derived S100A8 triggers the intracellular RhoA-ROCK-MLC2-MRTF-A pathway by binding to the CD147 receptor of CAFs, inducing CAF polarization and leading to chemoresistance. Therapeutically, we demonstrate that blocking the S100A8-CD147 pathway can improve chemotherapy efficiency. Prognostically, we found the S100A8 levels in peripheral blood can serve as an indicator of chemotherapy responsiveness. Collectively, our study offers a comprehensive understanding of the molecular mechanisms underlying chemoresistance in esophageal cancer and highlights the potential value of S100A8 in the clinical management of esophageal cancer.

Systematical identification of key genes and regulatory genetic variants associated with prognosis of esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) stands as a highly lethal malignancy characterized by pronounced recurrence and metastasis, resulting in a bleak 5-year survival rate. Despite extensive investigations, encompassing genome-wide association studies, the identification of robust prognostic markers has remained elusive. In this study, leveraging four independent data sets comprising 404 ESCC patients, we conducted a systematic analysis to unveil pivotal genes influencing overall survival. our meta-analysis identified 278 genes significantly associated with ESCC prognosis. Further exploration of the prognostic landscape involved an examination of expression quantitative trait loci for these genes, leading to the identification of six tag single nucleotide polymorphisms predictive of overall survival in a cohort of 904 ESCC patients. Notably, functional annotation spotlighted rs11227223, residing in the enhancer region of nuclear paraspeckle assembly transcript 1 (NEAT1), as a crucial variant likely exerting a substantive biological role. Through a series of biochemistry experiments, we conclusively demonstrated that the rs11227223-T allele, indicative of a poorer prognosis, augmented NEAT1 expression. Our results underscore the substantive role of NEAT1 and its regulatory variant in prognostic predictions for ESCC. This comprehensive analysis not only advances our comprehension of ESCC prognosis but also unveils a potential avenue for targeted interventions, offering promise for enhanced clinical outcomes.

An improved epigenetic counter to track mitotic age in normal and precancerous tissues

The cumulative number of stem cell divisions in a tissue, known as mitotic age, is thought to be a major determinant of cancer-risk. Somatic mutational and DNA methylation (DNAm) clocks are promising tools to molecularly track mitotic age, yet their relationship is underexplored and their potential for cancer risk prediction in normal tissues remains to be demonstrated. Here we build and validate an improved pan-tissue DNAm counter of total mitotic age called stemTOC. We demonstrate that stemTOC's mitotic age proxy increases with the tumor cell-of-origin fraction in each of 15 cancer-types, in precancerous lesions, and in normal tissues exposed to major cancer risk factors. Extensive benchmarking against 6 other mitotic counters shows that stemTOC compares favorably, specially in the preinvasive and normal-tissue contexts. By cross-correlating stemTOC to two clock-like somatic mutational signatures, we confirm the mitotic-like nature of only one of these. Our data points towards DNAm as a promising molecular substrate for detecting mitotic-age increases in normal tissues and precancerous lesions, and hence for developing cancer-risk prediction strategies.

Jag1/2 maintain esophageal homeostasis and suppress foregut tumorigenesis by restricting the basal progenitor cell pool

Basal progenitor cells are crucial for maintaining foregut (the esophagus and forestomach) homeostasis. When their function is dysregulated, it can promote inflammation and tumorigenesis. However, the mechanisms underlying these processes remain largely unclear. Here, we employ genetic mouse models to reveal that Jag1/2 regulate esophageal homeostasis and foregut tumorigenesis by modulating the function of basal progenitor cells. Deletion of Jag1/2 in mice disrupts esophageal and forestomach epithelial homeostasis. Mechanistically, Jag1/2 deficiency impairs activation of Notch signaling, leading to reduced squamous epithelial differentiation and expansion of basal progenitor cells. Moreover, Jag1/2 deficiency exacerbates the deoxycholic acid (DCA)-induced squamous epithelial injury and accelerates the initiation of squamous cell carcinoma (SCC) in the forestomach. Importantly, expression levels of JAG1/2 are lower in the early stages of human esophageal squamous cell carcinoma (ESCC) carcinogenesis. Collectively, our study demonstrates that Jag1/2 are important for maintaining esophageal and forestomach homeostasis and the onset of foregut SCC.

On epigenetic stochasticity, entropy and cancer risk

Epigenetic changes are known to accrue in normal cells as a result of ageing and cumulative exposure to cancer risk factors. Increasing evidence points towards age-related epigenetic changes being acquired in a quasi-stochastic manner, and that they may play a causal role in cancer development. Here, I describe the quasi-stochastic nature of DNA methylation (DNAm) changes in ageing cells as well as in normal cells at risk of neoplastic transformation, discussing the implications of this stochasticity for developing cancer risk prediction strategies, and in particular, how it may require a conceptual paradigm shift in how we select cancer risk markers. I also describe the mounting evidence that a significant proportion of DNAm changes in ageing and cancer development are related to cell proliferation, reflecting tissue-turnover and the opportunity this offers for predicting cancer risk via the development of epigenetic mitotic-like clocks. Finally, I describe how age-associated DNAm changes may be causally implicated in cancer development via an irreversible suppression of tissue-specific transcription factors that increases epigenetic and transcriptomic entropy, promoting a more plastic yet aberrant cancer stem-cell state. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

Single-cell analysis identified POSTN+ cells associated with the aggressive phenotype and risk of esophageal squamous cell carcinoma

Tumors are intricate and heterogeneous systems characterized by mosaic cancer cell populations with diverse expression profiles. Leveraging single-cell technologies, we employed the Scissor algorithm to delineate an epithelial subpopulation associated with the aggressive phenotype in esophageal squamous cell carcinoma (ESCC). This identified subpopulation exhibited elevated expression of genes involved in critical pathways, such as epithelial-mesenchymal transition and PI3K-Akt. Key signature genes within this subpopulation, namely CAV1, COL3A1, COL6A1, POSTN, and TAGLN, demonstrated significant upregulation concomitant with both tumorigenesis and tumor progression across independent single-cell datasets. Furthermore, we selected 1,450 expression quantitative trait loci of the top 62 signature genes of this cell subpopulation to investigate their potential in predicting ESCC risk. The results showed that the POSTN loci were predominantly associated with ESCC susceptibility. Through functional annotation and replication analyses, we identified that the rs1028728 in the POSTN promoter was significantly associated with increased ESCC risk in 7,049 ESCC cases and 8,063 controls (odds ratio = 1.29, 95% confidence interval: 1.18-1.42, p = 4.03 × 10-8). Subsequent biochemical experiments showed that the rs1028728[T] allele enhanced POSTN expression by affecting the binding of PRRX1 in the POSTN promoter. In summary, our meticulous single-cell analysis delineates an invasive epithelial subpopulation in ESCC, with POSTN emerging as an important marker for the aggressive phenotype. These findings offer more insights into potential strategies for the prevention and intervention of ESCC, enriching our understanding of this complex cancer landscape.

HMGA1 drives chemoresistance in esophageal squamous cell carcinoma by suppressing ferroptosis

Chemotherapy is a primary treatment for esophageal squamous cell carcinoma (ESCC). Resistance to chemotherapeutic drugs is an important hurdle to effective treatment. Understanding the mechanisms underlying chemotherapy resistance in ESCC is an unmet medical need to improve the survival of ESCC. Herein, we demonstrate that ferroptosis triggered by inhibiting high mobility group AT-hook 1 (HMGA1) may provide a novel opportunity to gain an effective therapeutic strategy against chemoresistance in ESCC. HMGA1 is upregulated in ESCC and works as a key driver for cisplatin (DDP) resistance in ESCC by repressing ferroptosis. Inhibition of HMGA1 enhances the sensitivity of ESCC to ferroptosis. With a transcriptome analysis and following-up assays, we demonstrated that HMGA1 upregulates the expression of solute carrier family 7 member 11 (SLC7A11), a key transporter maintaining intracellular glutathione homeostasis and inhibiting the accumulation of malondialdehyde (MDA), thereby suppressing cell ferroptosis. HMGA1 acts as a chromatin remodeling factor promoting the binding of activating transcription factor 4 (ATF4) to the promoter of SLC7A11, and hence enhancing the transcription of SLC7A11 and maintaining the redox balance. We characterized that the enhanced chemosensitivity of ESCC is primarily attributed to the increased susceptibility of ferroptosis resulting from the depletion of HMGA1. Moreover, we utilized syngeneic allograft tumor models and genetically engineered mice of HMGA1 to induce ESCC and validated that depletion of HMGA1 promotes ferroptosis and restores the sensitivity of ESCC to DDP, and hence enhances the therapeutic efficacy. Our finding uncovers a critical role of HMGA1 in the repression of ferroptosis and thus in the establishment of DDP resistance in ESCC, highlighting HMGA1-based rewiring strategies as potential approaches to overcome ESCC chemotherapy resistance. Schematic depicting that HMGA1 maintains intracellular redox homeostasis against ferroptosis by assisting ATF4 to activate SLC7A11 transcription, resulting in ESCC resistance to chemotherapy.

Aberrant epithelial cell interaction promotes esophageal squamous-cell carcinoma development and progression

Epithelial-mesenchymal transition (EMT) and proliferation play important roles in epithelial cancer formation and progression, but what molecules and how they trigger EMT is largely unknown. Here we performed spatial transcriptomic and functional analyses on samples of multistage esophageal squamous-cell carcinoma (ESCC) from mice and humans to decipher these critical issues. By investigating spatiotemporal gene expression patterns and cell-cell interactions, we demonstrated that the aberrant epithelial cell interaction via EFNB1-EPHB4 triggers EMT and cell cycle mediated by downstream SRC/ERK/AKT signaling. The aberrant epithelial cell interaction occurs within the basal layer at early precancerous lesions, which expands to the whole epithelial layer and strengthens along the cancer development and progression. Functional analysis revealed that the aberrant EFNB1-EPHB4 interaction is caused by overexpressed ΔNP63 due to TP53 mutation, the culprit in human ESCC tumorigenesis. Our results shed new light on the role of TP53-TP63/ΔNP63-EFNB1-EPHB4 axis in EMT and cell proliferation in epithelial cancer formation.

DASES: a database of alternative splicing for esophageal squamous cell carcinoma

Esophageal carcinoma ranks as the sixth leading cause of cancer-related mortality globally, with esophageal squamous cell carcinoma (ESCC) being particularly prevalent among Asian populations. Alternative splicing (AS) plays a pivotal role in ESCC development and progression by generating diverse transcript isoforms. However, the current landscape lacks a specialized database focusing on alternative splicing events (ASEs) derived from a large number of ESCC cases. Additionally, most existing AS databases overlook the contribution of long non-coding RNAs (lncRNAs) in ESCC molecular mechanisms, predominantly focusing on mRNA-based ASE identification. To address these limitations, we deployed DASES (http://www.hxdsjzx.cn/DASES). Employing a combination of publicly available and in-house ESCC RNA-seq datasets, our extensive analysis of 346 samples, with 93% being paired tumor and adjacent non-tumor tissues, led to the identification of 257 novel lncRNAs in esophageal squamous cell carcinoma. Leveraging a paired comparison of tumor and adjacent normal tissues, DASES identified 59,094 ASEs that may be associated with ESCC. DASES fills a critical gap by providing comprehensive insights into ASEs in ESCC, encompassing lncRNAs and mRNA, thus facilitating a deeper understanding of ESCC molecular mechanisms and serving as a valuable resource for ESCC research communities.

Cell-attribute aware community detection improves differential abundance testing from single-cell RNA-Seq data

Variations of cell-type proportions within tissues could be informative of biological aging and disease risk. Single-cell RNA-sequencing offers the opportunity to detect such differential abundance patterns, yet this task can be statistically challenging due to the noise in single-cell data, inter-sample variability and because such patterns are often of small effect size. Here we present a differential abundance testing paradigm called ELVAR that uses cell attribute aware clustering when inferring differentially enriched communities within the single-cell manifold. Using simulated and real single-cell and single-nucleus RNA-Seq datasets, we benchmark ELVAR against an analogous algorithm that uses Louvain for clustering, as well as local neighborhood-based methods, demonstrating that ELVAR improves the sensitivity to detect cell-type composition shifts in relation to aging, precancerous states and Covid-19 phenotypes. In effect, leveraging cell attribute information when inferring cell communities can denoise single-cell data, avoid the need for batch correction and help retrieve more robust cell states for subsequent differential abundance testing. ELVAR is available as an open-source R-package.

Epithelial cells activate fibroblasts to promote esophageal cancer development

Esophageal squamous-cell carcinoma (ESCC) develops through multistage epithelial cancer formation, i.e., from normal epithelium, low- and high-grade intraepithelial neoplasia to invasive carcinoma. However, how the precancerous lesions progress to carcinoma remains elusive. Here, we report a comprehensive single-cell RNA sequencing and spatial transcriptomic study of 79 multistage esophageal lesions from 29 patients with ESCC. We reveal a gradual and significant loss of ANXA1 expression in epithelial cells due to its transcription factor KLF4 suppression along the lesion progression. We demonstrate that ANXA1 is a ligand to formyl peptide receptor type 2 (FPR2) on fibroblasts that maintain fibroblast homeostasis. Loss of ANXA1 leads to uncontrolled transformation of normal fibroblasts into cancer-associated fibroblasts (CAFs), which can be enhanced by secreted TGF-β from malignant epithelial cells. Given the role of CAFs in cancer, our study underscores ANXA1/FPR2 signaling as an important crosstalk mechanism between epithelial cells and fibroblasts in promoting ESCC.

Distance covariance entropy reveals primed states and bifurcation dynamics in single-cell RNA-Seq data

Cell-fate transitions are fundamental to development and differentiation. Studying them with single-cell omic data is important to advance our understanding of the cell-fate commitment process, yet this remains challenging. Here we present a computational method called DICE, which analyzes the entropy of expression covariation patterns and which is applicable to static and dynamically changing cell populations. Using only single-cell RNA-Seq data, DICE is able to predict multipotent primed states and their regulatory factors, which we subsequently validate with single-cell epigenomic data. DICE reveals that primed states are often defined by epigenetic regulators or pioneer factors alongside lineage-specific transcription factors. In developmental time course single-cell RNA-Seq datasets, DICE can pinpoint the timing of bifurcations more precisely than lineage-trajectory inference algorithms or competing variance-based methods. In summary, by studying the dynamic changes of expression covariation entropy, DICE can help elucidate primed states and bifurcation dynamics without the need for single-cell epigenomic data.