Non-genetic heterogeneity--a mutation-independent driving force for the somatic evolution of tumours

Temporal variability and cell mechanics control robustness in mammalian embryogenesis

How living systems achieve precision in form and function despite their intrinsic stochasticity is a fundamental yet ongoing question in biology. We generated morphomaps of preimplantation embryogenesis in mouse, rabbit, and monkey embryos, and these morphomaps revealed that although blastomere divisions desynchronized passively, 8-cell embryos converged toward robust three-dimensional shapes. Using topological analysis and genetic perturbations, we found that embryos progressively changed their cellular connectivity to a preferred topology, which could be predicted by a physical model in which actomyosin contractility and noise facilitate topological transitions, lowering surface energy. This mechanism favored regular embryo packing and promoted a higher number of inner cells in the 16-cell embryo. Synchronized division reduced embryo packing and generated substantially more misallocated cells and fewer inner-cell-mass cells. These findings suggest that stochasticity in division timing contributes to robust patterning.

Cellular adaptation to cancer therapy along a resistance continuum

Advancements in precision oncology over the past decades have led to new therapeutic interventions, but the efficacy of such treatments is generally limited by an adaptive process that fosters drug resistance1. In addition to genetic mutations2, recent research has identified a role for non-genetic plasticity in transient drug tolerance3 and the acquisition of stable resistance4,5. However, the dynamics of cell-state transitions that occur in the adaptation to cancer therapies remain unknown and require a systems-level longitudinal framework. Here we demonstrate that resistance develops through trajectories of cell-state transitions accompanied by a progressive increase in cell fitness, which we denote as the 'resistance continuum'. This cellular adaptation involves a stepwise assembly of gene expression programmes and epigenetically reinforced cell states underpinned by phenotypic plasticity, adaptation to stress and metabolic reprogramming. Our results support the notion that epithelial-to-mesenchymal transition or stemness programmes-often considered a proxy for phenotypic plasticity-enable adaptation, rather than a full resistance mechanism. Through systematic genetic perturbations, we identify the acquisition of metabolic dependencies, exposing vulnerabilities that can potentially be exploited therapeutically. The concept of the resistance continuum highlights the dynamic nature of cellular adaptation and calls for complementary therapies directed at the mechanisms underlying adaptive cell-state transitions.

Single-cell RNA sequencing elucidated the landscape of breast cancer brain metastases and identified ILF2 as a potential therapeutic target

Distant metastasis remains the primary cause of morbidity in patients with breast cancer. Hence, the development of more efficacious strategies and the exploration of potential targets for patients with metastatic breast cancer are urgently needed. The data of six patients with breast cancer brain metastases (BCBrM) from two centres were collected, and a comprehensive landscape of the entire tumour ecosystem was generated through the utilisation of single-cell RNA sequencing. We utilised the Monocle2 and CellChat algorithms to investigate the interrelationships among each subcluster. In addition, multiple signatures were collected to evaluate key components of the subclusters through multi-omics methodologies. Finally, we elucidated common expression programs of malignant cells, and experiments were conducted in vitro and in vivo to determine the functions of interleukin enhancer-binding factor 2 (ILF2), which is a key gene in the metastasis module, in BCBrM progression. We found that subclusters in each major cell type exhibited diverse characteristics. Besides, our study indicated that ILF2 was specifically associated with BCBrM, and experimental validations further demonstrated that ILF2 deficiency hindered BCBrM progression. Our study offers novel perspectives on the heterogeneity of BCBrM and suggests that ILF2 could serve as a promising biomarker or therapeutic target for BCBrM.

Graphene-Based Photodynamic Therapy and Overcoming Cancer Resistance Mechanisms: A Comprehensive Review

Photodynamic therapy (PDT) is a non-invasive therapy that has made significant progress in treating different diseases, including cancer, by utilizing new nanotechnology products such as graphene and its derivatives. Graphene-based materials have large surface area and photothermal effects thereby making them suitable candidates for PDT or photo-active drug carriers. The remarkable photophysical properties of graphene derivates facilitate the efficient generation of reactive oxygen species (ROS) upon light irradiation, which destroys cancer cells. Surface functionalization of graphene and its materials can also enhance their biocompatibility and anticancer activity. The paper delves into the distinct roles played by graphene-based materials in PDT such as photosensitizers (PS) and drug carriers while at the same time considers how these materials could be used to circumvent cancer resistance. This will provide readers with an extensive discussion of various pathways contributing to PDT inefficiency. Consequently, this comprehensive review underscores the vital roles that graphene and its derivatives may play in emerging PDT strategies for cancer treatment and other medical purposes. With a better comprehension of the current state of research and the existing challenges, the integration of graphene-based materials in PDT holds great promise for developing targeted, effective, and personalized cancer treatments.

A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations

Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest in developing machine learning-based perturbation response prediction models to handle the non-linearity of perturbation data, but their interpretation in terms of molecular regulatory dynamics remains a challenge. Alternatively, for meaningful biological interpretation, logical network models such as Boolean networks are widely used in systems biology to represent intracellular molecular regulation. However, determining the appropriate regulatory logic of large-scale networks remains an obstacle due to the high-dimensional and discontinuous search space. To tackle these challenges, we present a scalable derivative-free optimizer trained by meta-reinforcement learning for Boolean network models. The logical network model optimized by the trained optimizer successfully predicts anti-cancer drug responses of cancer cell lines, while simultaneously providing insight into their underlying molecular regulatory mechanisms.

Decoding the interplay between genetic and non-genetic drivers of metastasis

Metastasis is a multistep process by which cancer cells break away from their original location and spread to distant organs, and is responsible for the vast majority of cancer-related deaths. Preventing early metastatic dissemination would revolutionize the ability to fight cancer. Unfortunately, the relatively poor understanding of the molecular underpinnings of metastasis has hampered the development of effective anti-metastatic drugs. Although it is now accepted that disseminating tumour cells need to acquire multiple competencies to face the many obstacles they encounter before reaching their metastatic site(s), whether these competencies are acquired through an accumulation of metastasis-specific genetic alterations and/or non-genetic events is often debated. Here we review a growing body of literature highlighting the importance of both genetic and non-genetic reprogramming events during the metastatic cascade, and discuss how genetic and non-genetic processes act in concert to confer metastatic competencies. We also describe how recent technological advances, and in particular the advent of single-cell multi-omics and barcoding approaches, will help to better elucidate the cross-talk between genetic and non-genetic mechanisms of metastasis and ultimately inform innovative paths for the early detection and interception of this lethal process.

Transient loss of Polycomb components induces an epigenetic cancer fate

Although cancer initiation and progression are generally associated with the accumulation of somatic mutations1,2, substantial epigenomic alterations underlie many aspects of tumorigenesis and cancer susceptibility3-6, suggesting that genetic mechanisms might not be the only drivers of malignant transformation7. However, whether purely non-genetic mechanisms are sufficient to initiate tumorigenesis irrespective of mutations has been unknown. Here, we show that a transient perturbation of transcriptional silencing mediated by Polycomb group proteins is sufficient to induce an irreversible switch to a cancer cell fate in Drosophila. This is linked to the irreversible derepression of genes that can drive tumorigenesis, including members of the JAK-STAT signalling pathway and zfh1, the fly homologue of the ZEB1 oncogene, whose aberrant activation is required for Polycomb perturbation-induced tumorigenesis. These data show that a reversible depletion of Polycomb proteins can induce cancer in the absence of driver mutations, suggesting that tumours can emerge through epigenetic dysregulation leading to inheritance of altered cell fates.

Contributions of transcriptional noise to leukaemia evolution: KAT2A as a case-study

Transcriptional noise is proposed to participate in cell fate changes, but contributions to mammalian cell differentiation systems, including cancer, remain associative. Cancer evolution is driven by genetic variability, with modulatory or contributory participation of epigenetic variants. Accumulation of epigenetic variants enhances transcriptional noise, which can facilitate cancer cell fate transitions. Acute myeloid leukaemia (AML) is an aggressive cancer with strong epigenetic dependencies, characterized by blocked differentiation. It constitutes an attractive model to probe links between transcriptional noise and malignant cell fate regulation. Gcn5/KAT2A is a classical epigenetic transcriptional noise regulator. Its loss increases transcriptional noise and modifies cell fates in stem and AML cells. By reviewing the analysis of KAT2A-depleted pre-leukaemia and leukaemia models, I discuss that the net result of transcriptional noise is diversification of cell fates secondary to alternative transcriptional programmes. Cellular diversification can enable or hinder AML progression, respectively, by differentiation of cell types responsive to mutations, or by maladaptation of leukaemia stem cells. KAT2A-dependent noise-responsive genes participate in ribosome biogenesis and KAT2A loss destabilizes translational activity. I discuss putative contributions of perturbed translation to AML biology, and propose KAT2A loss as a model for mechanistic integration of transcriptional and translational control of noise and fate decisions. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

Hsp70-Bim incoherent feedforward loop contributes to cell-fate heterogeneity and fractional killing

BACKGROUND AND PURPOSE

Although chemotherapeutics or molecular targeted drugs often elicit profound initial responses, fractional killing capable of driving acquired resistance can persist. Identifying stress-induced negative feedback or an incoherent feedforward loop (IFFL), which may contribute to fractional killing, is urgently needed.

EXPERIMENTAL APPROACH

Mathematical modelling was used to identify how and to what extent a recently reported Hsp70-Bim protein-protein interaction (PPI) contributes to the adaptation of the Bcl-2 network. Experimental validation was made by using a specific inhibitor of Hsp70-Bim PPI, S1g-2, as chemical tool. Bifurcation analysis and stochastic simulation were used for the theoretical study of the impact of Hsp70-Bim PPI on cell-fate heterogeneity and factional killing.

KEY RESULTS

The Hsp70-Bim-AKT circuit forms an IFFL that greatly contributes to the adaptation of the Bcl-2-regulated apoptosis network, thus leading to fractional killing. This adaptive programme enhances noise-induced cell-fate heterogeneity by shifting from a saddle-node to a saddle-collision transition scenario.

CONCLUSION AND IMPLICATIONS

Hsp70-Bim IFFL serves as a molecular pathway induced by DNA damaging drugs or tyrosine kinase inhibitors that enabled fractional killing, whereby acquired resistance emerges. A synergistic strategy is unveiled for overcoming fractional killing by suppressing Hsp70-Bim PPI.

Increased heterogeneity in expression of genes associated with cancer progression and drug resistance

Fluctuations in the number of regulatory molecules and differences in timings of molecular events can generate variation in gene expression among genetically identical cells in the same environmental condition. This variation, termed as expression noise, can create differences in metabolic state and cellular functions, leading to phenotypic heterogeneity. Expression noise and phenotypic heterogeneity have been recognized as important contributors to intra-tumor heterogeneity, and have been associated with cancer growth, progression, and therapy resistance. However, how expression noise changes with cancer progression in actual cancer patients has remained poorly explored. Such an analysis, through identification of genes with increasing expression noise, can provide valuable insights into generation of intra-tumor heterogeneity, and could have important implications for understanding immune-suppression, drug tolerance and therapy resistance. In this work, we performed a genome-wide identification of changes in gene expression noise with cancer progression using single-cell RNA-seq data of lung adenocarcinoma patients at different stages of cancer. We identified 37 genes in epithelial cells that showed an increasing noise trend with cancer progression, many of which were also associated with cancer growth, EMT and therapy resistance. We found that expression of several of these genes was positively associated with expression of mitochondrial genes, suggesting an important role of mitochondria in generation of heterogeneity. In addition, we uncovered substantial differences in sample-specific noise profiles which could have implications for personalized prognosis and treatment.

Cellular subpopulations identified using an ensemble average of multiple dielectrophoresis measurements

While the average value measurement approach can successfully analyze and predict the general behavior and biophysical properties of an isogenic cell population, it fails when significant differences among individual cells are generated in the population by intracellular changes such as the cell cycle, or different cellular responses to certain stimuli. Detecting such single-cell differences in a cell population has remained elusive. Here, we describe an easy-to-implement and generalizable platform that measures the dielectrophoretic cross-over frequency of individual cells by decreasing measurement noise with a stochastic method and computing ensemble average statistics. This platform enables multiple, real-time, label-free detection of individual cells with significant dielectric variations over time within an isogenic cell population. Using a stochastic method in combination with the platform, we distinguished cell subpopulations from a mixture of drug-untreated and -treated isogenic cells. Furthermore, we demonstrate that our platform can identify drug-treated isogenic cells with different recovery rates.

Spontaneous Fusion with Transformed Mesenchymal Stromal Cells Results in Complete Heterogeneity in Prostate Cancer Cells

Tumor cells gain advantages in growth and survival by acquiring genotypic and phenotypic heterogeneity. Interactions with bystander cells in the tumor microenvironment contribute to the progression of heterogeneity. We have shown that fusion between tumor and bystander cells is one form of interaction, and that tumor-bystander cell fusion has contrasting effects. By trapping fusion hybrids in the heterokaryon or synkaryon state, tumor-bystander cell fusion prevents the progression of heterogeneity. However, if trapping fails, fusion hybrids will resume replication to form derivative clones with diverse genomic makeups and behavioral phenotypes. To determine the characteristics of bystander cells that influence the fate of fusion hybrids, we co-cultured prostate mesenchymal stromal cell lines and their spontaneously transformed sublines with LNCaP as well as HPE-15 prostate cancer cells. Subclones derived from cancer-stromal fusion hybrids were examined for genotypic and phenotypic diversifications. Both stromal cell lines were capable of fusing with cancer cells, but only fusion hybrids with the transformed stromal subline generated large numbers of derivative subclones. Each subclone had distinct cell morphologies and growth behaviors and was detected with complete genomic hybridization. The health conditions of the bystander cell compartment play a crucial role in the progression of tumor cell heterogeneity.

An inflamed tumor cell subpopulation promotes chemotherapy resistance in triple negative breast cancer

Individual cancers are composed of heterogeneous tumor cells with distinct phenotypes and genotypes, with triple negative breast cancers (TNBC) demonstrating the most heterogeneity among breast cancer types. Variability in transcriptional phenotypes could meaningfully limit the efficacy of monotherapies and fuel drug resistance, although to an unknown extent. To determine if transcriptional differences between tumor cells lead to differential drug responses we performed single cell RNA-seq on cell line and PDX models of breast cancer revealing cell subpopulations in states associated with resistance to standard-of-care therapies. We found that TNBC models contained a subpopulation in an inflamed cellular state, often also present in human breast cancer samples. Inflamed cells display evidence of heightened cGAS/STING signaling which we demonstrate is sufficient to cause tumor cell resistance to chemotherapy. Accordingly, inflamed cells were enriched in human tumors taken after neoadjuvant chemotherapy and associated with early recurrence, highlighting the potential for diverse tumor cell states to promote drug resistance.

Non-genetic mechanisms of drug resistance in acute leukemias

Acute leukemia is characterized by clonal heterogeneity that contributes to poor drug responses in patients. Despite treatment advances, the occurrence of relapse remains a major barrier to achieving cures as current therapeutic approaches are inadequate to effectively prevent or overcome resistance. Given that only a few genetic mutations are associated with relapse in acute leukemia patients, there is a growing focus on 'non-genetic' mechanisms that affect the hallmarks of cancer to allow leukemic cells to survive post therapy. In this review, we provide an overview of the therapeutic landscape in acute leukemias. Importantly, we discuss non-genetic mechanisms exploited by leukemic cells to promote their survival after treatment. Last, we present current strategies to prevent or overcome drug resistance in this disease.

Transcriptional state dynamics lead to heterogeneity and adaptive tumor evolution in urothelial bladder carcinoma

Intra-tumor heterogeneity contributes to treatment failure and poor survival in urothelial bladder carcinoma (UBC). Analyzing transcriptome from a UBC cohort, we report that intra-tumor transcriptomic heterogeneity indicates co-existence of tumor cells in epithelial and mesenchymal-like transcriptional states and bi-directional transition between them occurs within and between tumor subclones. We model spontaneous and reversible transition between these partially heritable states in cell lines and characterize their population dynamics. SMAD3, KLF4 and PPARG emerge as key regulatory markers of the transcriptional dynamics. Nutrient limitation, as in the core of large tumors, and radiation treatment perturb the dynamics, initially selecting for a transiently resistant phenotype and then reconstituting heterogeneity and growth potential, driving adaptive evolution. Dominance of transcriptional states with low PPARG expression indicates an aggressive phenotype in UBC patients. We propose that phenotypic plasticity and dynamic, non-genetic intra-tumor heterogeneity modulate both the trajectory of disease progression and adaptive treatment response in UBC.

Comparing variants related to chronic diseases from genome-wide association study (GWAS) and the cancer genome atlas (TCGA)

BACKGROUND

Several genome-wide association studies (GWAS) have been performed to identify variants related to chronic diseases. Somatic variants in cancer tissues are associated with cancer development and prognosis. Expression quantitative trait loci (eQTL) and methylation QTL (mQTL) analyses were performed on chronic disease-related variants in TCGA dataset.

METHODS

MuTect2 calling variants for 33 cancers from TCGA and 296 GWAS variants provided by LocusZoom were used. At least one mutation was found in TCGA 22 cancers and LocusZoom 23 studies. Differentially expressed genes (DEGs) and differentially methylated regions (DMRs) from the three cancers (TCGA-COAD, TCGA-STAD, and TCGA-UCEC). Variants were mapped to the world map using population locations of the 1000 Genomes Project (1GP) populations. Decision tree analysis was performed on the discovered features and survival analysis was performed according to the cluster.

RESULTS

Based on the DEGs and DMRs with clinical data, the decision tree model classified seven and three nodes in TCGA-COAD and TCGA-STAD, respectively. A total of 11 variants were commonly detected from TCGA and LocusZoom, and eight variants were selected from the 1GP variants, and the distribution patterns were visualized on the world map.

CONCLUSIONS

Variants related to tumors and chronic diseases were selected, and their geological regional 1GP-based proportions are presented. The variant distribution patterns could provide clues for regional clinical trial designs and personalized medicine.

Adaptive DNA amplification of synthetic gene circuit opens a way to overcome cancer chemoresistance

Drug resistance continues to impede the success of cancer treatments, creating a need for experimental model systems that are broad, yet simple, to allow the identification of mechanisms and novel countermeasures applicable to many cancer types. To address these needs, we investigated a set of engineered mammalian cell lines with synthetic gene circuits integrated into their genome that evolved resistance to Puromycin. We identified DNA amplification as the mechanism underlying drug resistance in 4 out of 6 replicate populations. Triplex-forming oligonucleotide (TFO) treatment combined with Puromycin could efficiently suppress the growth of cell populations with DNA amplification. Similar observations in human cancer cell lines suggest that TFOs could be broadly applicable to mitigate drug resistance, one of the major difficulties in treating cancer.

Oncofetal reprogramming in tumor development and progression: novel insights into cancer therapy

Emerging evidence indicates that cancer cells can mimic characteristics of embryonic development, promoting their development and progression. Cancer cells share features with embryonic development, characterized by robust proliferation and differentiation regulated by signaling pathways such as Wnt, Notch, hedgehog, and Hippo signaling. In certain phase, these cells also mimic embryonic diapause and fertilized egg implantation to evade treatments or immune elimination and promote metastasis. Additionally, the upregulation of ATP-binding cassette (ABC) transporters, including multidrug resistance protein 1 (MDR1), multidrug resistance-associated protein 1 (MRP1), and breast cancer-resistant protein (BCRP), in drug-resistant cancer cells, analogous to their role in placental development, may facilitate chemotherapy efflux, further resulting in treatment resistance. In this review, we concentrate on the underlying mechanisms that contribute to tumor development and progression from the perspective of embryonic development, encompassing the dysregulation of developmental signaling pathways, the emergence of dormant cancer cells, immune microenvironment remodeling, and the hyperactivation of ABC transporters. Furthermore, we synthesize and emphasize the connections between cancer hallmarks and embryonic development, offering novel insights for the development of innovative cancer treatment strategies.

Unveiling the novel immune and molecular signatures of ovarian cancer: insights and innovations from single-cell sequencing

Ovarian cancer is a highly heterogeneous and lethal malignancy with limited treatment options. Over the past decade, single-cell sequencing has emerged as an advanced biological technology capable of decoding the landscape of ovarian cancer at the single-cell resolution. It operates at the level of genes, transcriptomes, proteins, epigenomes, and metabolisms, providing detailed information that is distinct from bulk sequencing methods, which only offer average data for specific lesions. Single-cell sequencing technology provides detailed insights into the immune and molecular mechanisms underlying tumor occurrence, development, drug resistance, and immune escape. These insights can guide the development of innovative diagnostic markers, therapeutic strategies, and prognostic indicators. Overall, this review provides a comprehensive summary of the diverse applications of single-cell sequencing in ovarian cancer. It encompasses the identification and characterization of novel cell subpopulations, the elucidation of tumor heterogeneity, the investigation of the tumor microenvironment, the analysis of mechanisms underlying metastasis, and the integration of innovative approaches such as organoid models and multi-omics analysis.

PI3K signaling through a biochemical systems lens

Following 3 decades of extensive research into PI3K signaling, it is now evidently clear that the underlying network does not equate to a simple ON/OFF switch. This is best illustrated by the multifaceted nature of the many diseases associated with aberrant PI3K signaling, including common cancers, metabolic disease, and rare developmental disorders. However, we are still far from a complete understanding of the fundamental control principles that govern the numerous phenotypic outputs that are elicited by activation of this well-characterized biochemical signaling network, downstream of an equally diverse set of extrinsic inputs. At its core, this is a question on the role of PI3K signaling in cellular information processing and decision making. Here, we review the determinants of accurate encoding and decoding of growth factor signals and discuss outstanding questions in the PI3K signal relay network. We emphasize the importance of quantitative biochemistry, in close integration with advances in single-cell time-resolved signaling measurements and mathematical modeling.

Deciphering a global source of non-genetic heterogeneity in cancer cells

Cell-to-cell variability within a clonal population, also known as non-genetic heterogeneity, has created significant challenges for intervening with diseases such as cancer. While non-genetic heterogeneity can arise from the variability in the expression of specific genes, it remains largely unclear whether and how clonal cells could be heterogeneous in the expression of the entire transcriptome. Here, we showed that gene transcriptional activity is globally modulated in individual cancer cells, leading to non-genetic heterogeneity in the global transcription rate. Such heterogeneity contributes to cell-to-cell variability in transcriptome size and displays both dynamic and static characteristics, with the global transcription rate temporally modulated in a cell-cycle-coupled manner and the time-averaged rate being distinct between cells and heritable across generations. Additional evidence indicated the role of ATP metabolism in this heterogeneity, and suggested its implication in intrinsic cancer drug tolerance. Collectively, our work shed light on the mode, mechanism, and implication of a global but often hidden source of non-genetic heterogeneity.

Quantitative systems-based prediction of antimicrobial resistance evolution

Predicting evolution is a fundamental problem in biology with practical implications for treating antimicrobial resistance, which is a complex system-level phenomenon. In this perspective article, we explore the limits of predicting antimicrobial resistance evolution, quantitatively define the predictability and repeatability of microevolutionary processes, and speculate on how these quantities vary across temporal, biological, and complexity scales. The opportunities and challenges for predicting antimicrobial resistance in the context of systems biology are also discussed. Based on recent research, we conclude that the evolution of antimicrobial resistance can be predicted using a systems biology approach integrating quantitative models with multiscale data from microbial evolution experiments.

Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics

Phenotypic plasticity of cancer cells can lead to complex cell state dynamics during tumor progression and acquired resistance. Highly plastic stem-like states may be inherently drug-resistant. Moreover, cell state dynamics in response to therapy allow a tumor to evade treatment. In both scenarios, quantifying plasticity is essential for identifying high-plasticity states or elucidating transition paths between states. Currently, methods to quantify plasticity tend to focus on 1) quantification of quasi-potential based on the underlying gene regulatory network dynamics of the system; or 2) inference of cell potency based on trajectory inference or lineage tracing in single-cell dynamics. Here, we explore both of these approaches and associated computational tools. We then discuss implications of each approach to plasticity metrics, and relevance to cancer treatment strategies.

Noncoding RNA circuitry in melanoma onset, plasticity, and therapeutic response

Melanoma, the cancer of the melanocyte, is the deadliest form of skin cancer with an aggressive nature, propensity to metastasize and tendency to resist therapeutic intervention. Studies have identified that the re-emergence of developmental pathways in melanoma contributes to melanoma onset, plasticity, and therapeutic response. Notably, it is well known that noncoding RNAs play a critical role in the development and stress response of tissues. In this review, we focus on the noncoding RNAs, including microRNAs, long non-coding RNAs, circular RNAs, and other small RNAs, for their functions in developmental mechanisms and plasticity, which drive onset, progression, therapeutic response and resistance in melanoma. Going forward, elucidation of noncoding RNA-mediated mechanisms may provide insights that accelerate development of novel melanoma therapies.

Statistical inference of the rates of cell proliferation and phenotypic switching in cancer

Recent evidence suggests that nongenetic (epigenetic) mechanisms play an important role at all stages of cancer evolution. In many cancers, these mechanisms have been observed to induce dynamic switching between two or more cell states, which commonly show differential responses to drug treatments. To understand how these cancers evolve over time, and how they respond to treatment, we need to understand the state-dependent rates of cell proliferation and phenotypic switching. In this work, we propose a rigorous statistical framework for estimating these parameters, using data from commonly performed cell line experiments, where phenotypes are sorted and expanded in culture. The framework explicitly models the stochastic dynamics of cell division, cell death and phenotypic switching, and it provides likelihood-based confidence intervals for the model parameters. The input data can be either the fraction of cells or the number of cells in each state at one or more time points. Through a combination of theoretical analysis and numerical simulations, we show that when cell fraction data is used, the rates of switching may be the only parameters that can be estimated accurately. On the other hand, using cell number data enables accurate estimation of the net division rate for each phenotype, and it can even enable estimation of the state-dependent rates of cell division and cell death. We conclude by applying our framework to a publicly available dataset.

Nonmonotone invasion landscape by noise-aware control of metastasis activator levels

A major pharmacological assumption is that lowering disease-promoting protein levels is generally beneficial. For example, inhibiting metastasis activator BACH1 is proposed to decrease cancer metastases. Testing such assumptions requires approaches to measure disease phenotypes while precisely adjusting disease-promoting protein levels. Here we developed a two-step strategy to integrate protein-level tuning, noise-aware synthetic gene circuits into a well-defined human genomic safe harbor locus. Unexpectedly, engineered MDA-MB-231 metastatic human breast cancer cells become more, then less and then more invasive as we tune BACH1 levels up, irrespective of the native BACH1. BACH1 expression shifts in invading cells, and expression of BACH1's transcriptional targets confirm BACH1's nonmonotone phenotypic and regulatory effects. Thus, chemical inhibition of BACH1 could have unwanted effects on invasion. Additionally, BACH1's expression variability aids invasion at high BACH1 expression. Overall, precisely engineered, noise-aware protein-level control is necessary and important to unravel disease effects of genes to improve clinical drug efficacy.

Cellular barcoding tracks heterogeneous clones through selective pressures and phenotypic transitions

Genomic DNA barcoding has emerged as a sensitive and flexible tool to measure the fates of clonal subpopulations within a heterogeneous cancer cell population. Coupling cellular barcoding with single-cell transcriptomics permits the longitudinal analysis of molecular mechanisms with detailed clone-level resolution. Numerous recent studies have employed these tools to track clonal cell states in cancer progression and treatment response. With these new technologies comes the opportunity to examine longstanding questions about the origins and contributions of tumor cell heterogeneity and the roles of selection and phenotypic plasticity in disease progression and treatment.

Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise

Introduction: Despite continued technological improvements, measurement errors always reduce or distort the information that any real experiment can provide to quantify cellular dynamics. This problem is particularly serious for cell signaling studies to quantify heterogeneity in single-cell gene regulation, where important RNA and protein copy numbers are themselves subject to the inherently random fluctuations of biochemical reactions. Until now, it has not been clear how measurement noise should be managed in addition to other experiment design variables (e.g., sampling size, measurement times, or perturbation levels) to ensure that collected data will provide useful insights on signaling or gene expression mechanisms of interest. Methods: We propose a computational framework that takes explicit consideration of measurement errors to analyze single-cell observations, and we derive Fisher Information Matrix (FIM)-based criteria to quantify the information value of distorted experiments. Results and Discussion: We apply this framework to analyze multiple models in the context of simulated and experimental single-cell data for a reporter gene controlled by an HIV promoter. We show that the proposed approach quantitatively predicts how different types of measurement distortions affect the accuracy and precision of model identification, and we demonstrate that the effects of these distortions can be mitigated through explicit consideration during model inference. We conclude that this reformulation of the FIM could be used effectively to design single-cell experiments to optimally harvest fluctuation information while mitigating the effects of image distortion.

BdLT-Seq as a barcode decay-based method to unravel lineage-linked transcriptome plasticity

Cell plasticity is a core biological process underlying a myriad of molecular and cellular events taking place throughout organismal development and evolution. It has been postulated that cellular systems thrive to balance the organization of meta-stable states underlying this phenomenon, thereby maintaining a degree of populational homeostasis compatible with an ever-changing environment and, thus, life. Notably, albeit circumstantial evidence has been gathered in favour of the latter conceptual framework, a direct observation of meta-state dynamics and the biological consequences of such a process in generating non-genetic clonal diversity and divergent phenotypic output remains largely unexplored. To fill this void, here we develop a lineage-tracing technology termed Barcode decay Lineage Tracing-Seq. BdLT-Seq is based on episome-encoded molecular identifiers that, supported by the dynamic decay of the tracing information upon cell division, ascribe directionality to a cell lineage tree whilst directly coupling non-genetic molecular features to phenotypes in comparable genomic landscapes. We show that cell transcriptome states are both inherited, and dynamically reshaped following constrained rules encoded within the cell lineage in basal growth conditions, upon oncogene activation and throughout the process of reversible resistance to therapeutic cues thus adjusting phenotypic output leading to intra-clonal non-genetic diversity.

Constructing 3D In Vitro Models of Heterocellular Solid Tumors and Stromal Tissues Using Extrusion-Based Bioprinting

Malignant tumor tissues exhibit inter- and intratumoral heterogeneities, aberrant development, dynamic stromal composition, diverse tissue phenotypes, and cell populations growing within localized mechanical stresses in hypoxic conditions. Experimental tumor models employing engineered systems that isolate and study these complex variables using in vitro techniques are under development as complementary methods to preclinical in vivo models. Here, advances in extrusion bioprinting as an enabling technology to recreate the three-dimensional tumor milieu and its complex heterogeneous characteristics are reviewed. Extrusion bioprinting allows for the deposition of multiple materials, or selected cell types and concentrations, into models based upon physiological features of the tumor. This affords the creation of complex samples with representative extracellular or stromal compositions that replicate the biology of patient tissue. Biomaterial engineering of printable materials that replicate specific features of the tumor microenvironment offer experimental reproducibility, throughput, and physiological relevance compared to animal models. In this review, we describe the potential of extrusion-based bioprinting to recreate the tumor microenvironment within in vitro models.

Optimal control of bioproduction in the presence of population heterogeneity

Cell-to-cell variability, born of stochastic chemical kinetics, persists even in large isogenic populations. In the study of single-cell dynamics this is typically accounted for. However, on the population level this source of heterogeneity is often sidelined to avoid the inevitable complexity it introduces. The homogeneous models used instead are more tractable but risk disagreeing with their heterogeneous counterparts and may thus lead to severely suboptimal control of bioproduction. In this work, we introduce a comprehensive mathematical framework for solving bioproduction optimal control problems in the presence of heterogeneity. We study population-level models in which such heterogeneity is retained, and propose order-reduction approximation techniques. The reduced-order models take forms typical of homogeneous bioproduction models, making them a useful benchmark by which to study the importance of heterogeneity. Moreover, the derivation from the heterogeneous setting sheds light on parameter selection in ways a direct homogeneous outlook cannot, and reveals the source of approximation error. With view to optimally controlling bioproduction in microbial communities, we ask the question: when does optimising the reduced-order models produce strategies that work well in the presence of population heterogeneity? We show that, in some cases, homogeneous approximations provide remarkably accurate surrogate models. Nevertheless, we also demonstrate that this is not uniformly true: overlooking the heterogeneity can lead to significantly suboptimal control strategies. In these cases, the heterogeneous tools and perspective are crucial to optimise bioproduction.

Distinct but interchangeable subpopulations of colorectal cancer cells with different growth fates and drug sensitivity

Dynamic changes in cell properties lead to intratumor heterogeneity; however, the mechanisms of nongenetic cellular plasticity remain elusive. When the fate of each cell from colorectal cancer organoids was tracked through a clonogenic growth assay, the cells showed a wide range of growth ability even within the clonal organoids, consisting of distinct subpopulations; the cells generating large spheroids and the cells generating small spheroids. The cells from the small spheroids generated only small spheroids (S-pattern), while the cells from the large spheroids generated both small and large spheroids (D-pattern), both of which were tumorigenic. Transition from the S-pattern to the D-pattern occurred by various extrinsic triggers, in which Notch signaling and Musashi-1 played a key role. The S-pattern spheroids were resistant to chemotherapy and transited to the D-pattern upon drug treatment through Notch signaling. As the transition is linked to the drug resistance, it can be a therapeutic target.

The role of mitosis in generating fitness heterogeneity

Cancer cells have heterogeneous fitness, and this heterogeneity stems from genetic and epigenetic sources. Here, we sought to assess the contribution of asymmetric mitosis (AM) and time on the variability of fitness in sister cells. Around one quarter of sisters had differences in fitness, assessed as the intermitotic time (IMT), from 330 to 510 min. Phenotypes related to fitness, such as ERK activity (herein referring to ERK1 and ERK2, also known as MAPK3 and MAPK1, respectively), DNA damage and nuclear morphological phenotypes were also asymmetric at mitosis or turned asymmetric over the course of the cell cycle. The ERK activity of mother cell was found to influence the ERK activity and the IMT of the daughter cells, and cells with ERK asymmetry at mitosis produced more offspring with AMs, suggesting heritability of the AM phenotype for ERK activity. Our findings demonstrate how variabilities in sister cells can be generated, contributing to the phenotype heterogeneities in tumor cells.

Modeling Cellular Signaling Variability Based on Single-Cell Data: The TGFβ-SMAD Signaling Pathway

Nongenetic heterogeneity is key to cellular decisions, as even genetically identical cells respond in very different ways to the same external stimulus, e.g., during cell differentiation or therapeutic treatment of disease. Strong heterogeneity is typically already observed at the level of signaling pathways that are the first sensors of external inputs and transmit information to the nucleus where decisions are made. Since heterogeneity arises from random fluctuations of cellular components, mathematical models are required to fully describe the phenomenon and to understand the dynamics of heterogeneous cell populations. Here, we review the experimental and theoretical literature on cellular signaling heterogeneity, with special focus on the TGFβ/SMAD signaling pathway.

Revising immune cell coordination: Origins and importance of single-cell variation

Moving from the optimalization of single-cell technologies to the interpretation of the multi-complex single-cell data, the field of immunoengineering is granted with numerous important insights into the coordination of immune cell activation and how to modulate it for therapeutic purposes. However, insights come with additional follow-up questions that challenge our perception on how immune responses are generated and fine-tuned to fight a wide array of pathogens in ever-changing and often unpredictable microenvironments. Are immune responses really either being tightly regulated by molecular determinants, or highly flexible attributed to stochasticity? What exactly makes up the basic rules by which single cells cooperate to establish tissue-level immunity? Taking the type I IFN system and its newest insights as a main example throughout this review, we revise the basic concepts of (single) immune cell coordination, redefine the concepts of noise, stochasticity and determinism, and highlight the importance of single-cell variation in immunology and beyond.

A lineage tree-based hidden Markov model quantifies cellular heterogeneity and plasticity

Individual cells can assume a variety of molecular and phenotypic states and recent studies indicate that cells can rapidly adapt in response to therapeutic stress. Such phenotypic plasticity may confer resistance, but also presents opportunities to identify molecular programs that could be targeted for therapeutic benefit. Approaches to quantify tumor-drug responses typically focus on snapshot, population-level measurements. While informative, these methods lack lineage and temporal information, which are particularly critical for understanding dynamic processes such as cell state switching. As new technologies have become available to measure lineage relationships, modeling approaches will be needed to identify the forms of cell-to-cell heterogeneity present in these data. Here we apply a lineage tree-based adaptation of a hidden Markov model that employs single cell lineages as input to learn the characteristic patterns of phenotypic heterogeneity and state transitions. In benchmarking studies, we demonstrated that the model successfully classifies cells within experimentally-tractable dataset sizes. As an application, we analyzed experimental measurements in cancer and non-cancer cell populations under various treatments. We find evidence of multiple phenotypically distinct states, with considerable heterogeneity and unique drug responses. In total, this framework allows for the flexible modeling of single cell heterogeneity across lineages to quantify, understand, and control cell state switching.

Artificial intelligence-based preoperative prediction system for diagnosis and prognosis in epithelial ovarian cancer: A multicenter study

Background

Ovarian cancer (OC) is the most lethal gynecological malignancy, with limited early screening methods and poor prognosis. Artificial intelligence technology has made a great breakthrough in cancer diagnosis.

Purpose

We aim to develop a specific interpretable machine learning (ML) prediction model for the diagnosis and prognosis of epithelial ovarian cancer (EOC) based on a variety of biomarkers.

Methods

A total of 521 patients with EOC and 144 patients with benign gynecological diseases were enrolled including derivation datasets and an external validation cohort. The predicted information was acquired by 9 supervised ML methods, through 34 parameters. Behind predicted reasons for the best ML were improved by using the SHapley Additive exPlanations (SHAP) algorithm. In addition, the prognosis of EOC was analyzed by unsupervised clustering and Kaplan–Meier (KM) survival analysis.

Results

ML technology was superior to conventional logistic regression in predicting EOC diagnosis and XGBoost performed best in the external validation datasets. The AUC values of distinguishing EOC and benign disease patients, determining pathological type, grade and clinical stage were 0.958 (0.926-0.989), 0.792 (0.701-0.8834), 0.819 (0.687-0.950) and 0.68 (0.573-0.788) respectively. For negative CA-125 EOC patients, the AUC performance of XGBoost model was 0.835(0.763-0.907). We used unsupervised cluster analysis to identify EOC subgroups with significantly poor overall survival (p-value <0.0001) and recurrence-free survival (p-value <0.0001).

Conclusions

Based on the preoperative characteristics, we proved that ML algorithm can provide an acceptable diagnosis and prognosis prediction model for EOC patients. Meanwhile, SHAP analysis can improve the interpretability of ML models and contribute to precision medicine.

Unraveling phenotypic plasticity and evolution in small cell lung cancer

Malignant cell populations in a tumor often exist in distinct phenotypic states. Deciphering tumor heterogeneity requires determining how many such unique states exist and what the biological traits associated with each are. Archetype analysis of SCLC transcriptomes reveals key phenotypic states in SCLC tumors and their patterns of evolution.

Non-genetic resistance facilitates survival while hindering the evolution of drug resistance due to intraspecific competition

Rising rates of resistance to antimicrobial drugs threaten the effective treatment of infections across the globe. Drug resistance has been established to emerge from non-genetic mechanisms as well as from genetic mechanisms. However, it is still unclear how non-genetic resistance affects the evolution of genetic drug resistance. We develop deterministic and stochastic population models that incorporate resource competition to quantitatively investigate the transition from non-genetic to genetic resistance during the exposure to static and cidal drugs. We find that non-genetic resistance facilitates the survival of cell populations during drug treatment while hindering the development of genetic resistance due to competition between the non-genetically and genetically resistant subpopulations. Non-genetic resistance in the presence of subpopulation competition increases the fixation times of drug resistance mutations, while increasing the probability of mutation before population extinction during cidal drug treatment. Intense intraspecific competition during drug treatment leads to extinction of susceptible and non-genetically resistant subpopulations. Alternating between drug and no drug conditions results in oscillatory population dynamics, increased resistance mutation fixation timescales, and reduced population survival. These findings advance our fundamental understanding of the evolution of resistance and may guide novel treatment strategies for patients with drug-resistant infections.

A model for the intrinsic limit of cancer therapy: Duality of treatment-induced cell death and treatment-induced stemness

Intratumor cellular heterogeneity and non-genetic cell plasticity in tumors pose a recently recognized challenge to cancer treatment. Because of the dispersion of initial cell states within a clonal tumor cell population, a perturbation imparted by a cytocidal drug only kills a fraction of cells. Due to dynamic instability of cellular states the cells not killed are pushed by the treatment into a variety of functional states, including a "stem-like state" that confers resistance to treatment and regenerative capacity. This immanent stress-induced stemness competes against cell death in response to the same perturbation and may explain the near-inevitable recurrence after any treatment. This double-edged-sword mechanism of treatment complements the selection of preexisting resistant cells in explaining post-treatment progression. Unlike selection, the induction of a resistant state has not been systematically analyzed as an immanent cause of relapse. Here, we present a generic elementary model and analytical examination of this intrinsic limitation to therapy. We show how the relative proclivity towards cell death versus transition into a stem-like state, as a function of drug dose, establishes either a window of opportunity for containing tumors or the inevitability of progression following therapy. The model considers measurable cell behaviors independent of specific molecular pathways and provides a new theoretical framework for optimizing therapy dosing and scheduling as cancer treatment paradigms move from "maximal tolerated dose," which may promote therapy induced-stemness, to repeated "minimally effective doses" (as in adaptive therapies), which contain the tumor and avoid therapy-induced progression.

Scales of Cancer Evolution: Selfish Genome or Cooperating Cells?

Simple Summary

Cancer continuously evolves its ability to survive in time-varying microenvironment, which results, regarding the therapeutic context, in its therapeutic resistance. As it is accepted that the development of resistance is the direct consequence of intratumour heterogeneity, its evolutionary etiology is intensively studied. Models of carinogenesis are often assessed accordingly to how well they fit into the evolutionary scenario. In the paper, the relevant observations and concepts in cancer research, such as intratumour heterogeneity, cell plasticity, and Markov cell state dynamics, are reviewed and integrated into an evolutionary model. The possibility that the interaction between cancer cells can be interpreted as cooperation is proposed.

Abstract

The exploitation of the evolutionary modus operandi of cancer to steer its progression towards drug sensitive cancer cells is a challenging research topic. Integrating evolutionary principles into cancer therapy requires properly identified selection level, the relevant timescale, and the respective fitness of the principal selection unit on that timescale. Interpretation of some features of cancer progression, such as increased heterogeneity of isogenic cancer cells, is difficult from the most straightforward evolutionary view with the cancer cell as the principal selection unit. In the paper, the relation between the two levels of intratumour heterogeneity, genetic, due to genetic instability, and non-genetic, due to phenotypic plasticity, is reviewed and the evolutionary role of the latter is outlined. In analogy to the evolutionary optimization in a changing environment, the cell state dynamics in cancer clones are interpreted as the risk diversifying strategy bet hedging, optimizing the balance between the exploitation and exploration of the cell state space.

Collective behavior and nongenetic inheritance allow bacterial populations to adapt to changing environments

Collective behaviors require coordination among a group of individuals. As a result, individuals that are too phenotypically different from the rest of the group can be left out, reducing heterogeneity, but increasing coordination. If individuals also reproduce, the offspring can have different phenotypes from their parent(s). This raises the question of how these two opposing processes-loss of diversity by collective behaviors and generation of it through growth and inheritance-dynamically shape the phenotypic composition of an isogenic population. We examine this question theoretically using collective migration of chemotactic bacteria as a model system, where cells of different swimming phenotypes are better suited to navigate in different environments. We find that the differential loss of phenotypes caused by collective migration is environment-dependent. With cell growth, this differential loss enables migrating populations to dynamically adapt their phenotype compositions to the environment, enhancing migration through multiple environments. Which phenotypes are produced upon cell division depends on the level of nongenetic inheritance, and higher inheritance leads to larger composition adaptation and faster migration at steady state. However, this comes at the cost of slower responses to new environments. Due to this trade-off, there is an optimal level of inheritance that maximizes migration speed through changing environments, which enables a diverse population to outperform a nondiverse one. Growing populations might generally leverage the selection-like effects provided by collective behaviors to dynamically shape their own phenotype compositions, without mutations.

Revisiting moment-closure methods with heterogeneous multiscale population models

Stochastic chemical kinetics at the single-cell level give rise to heterogeneous populations of cells even when all individuals are genetically identical. This heterogeneity can lead to nonuniform behaviour within populations, including different growth characteristics, cell-fate dynamics, and response to stimuli. Ultimately, these diverse behaviours lead to intricate population dynamics that are inherently multiscale: the population composition evolves based on population-level processes that interact with stochastically distributed single-cell states. Therefore, descriptions that account for this heterogeneity are essential to accurately model and control chemical processes. However, for real-world systems such models are computationally expensive to simulate, which can make optimisation problems, such as optimal control or parameter inference, prohibitively challenging. Here, we consider a class of multiscale population models that incorporate population-level mechanisms while remaining faithful to the underlying stochasticity at the single-cell level and the interplay between these two scales. To address the complexity, we study an order-reduction approximations based on the distribution moments. Since previous moment-closure work has focused on the single-cell kinetics, extending these techniques to populations models prompts us to revisit old observations as well as tackle new challenges. In this extended multiscale context, we encounter the previously established observation that the simplest closure techniques can lead to non-physical system trajectories. Despite their poor performance in some systems, we provide an example where these simple closures outperform more sophisticated closure methods in accurately, efficiently, and robustly solving the problem of optimal control of bioproduction in a microbial consortium model.

Single-Cell RNA-seq Reveals a Developmental Hierarchy Super-Imposed Over Subclonal Evolution in the Cellular Ecosystem of Prostate Cancer

Prostate cancer (PCa) is a complex disease. An ongoing accumulation of mutations results in increased genetic diversity, with the tumor acquiring distinct subclones. However, non-genetic intra-tumoral heterogeneity, the cellular differentiation state and the interplay between subclonal evolution and transcriptional heterogeneity are poorly understood. Here, the authors perform single-cell RNA sequencing from 14 untreated PCa patients. They create an extensive cell atlas of the PCa patients and mapped developmental states onto tumor subclonal evolution. They identify distinct subclones across PCa patients and then stratify tumor cells into four transcriptional subtypes, EMT-like (subtype 0), luminal A-like (subtype 1), luminal B/C-like (subtype 2), and basal-like (subtype 3). These subtypes are hierarchically organized into stem cell-like and differentiated status. Strikingly, multiple subclones within a single primary tumor present with distinct combinations of preferential subtypes. In addition, subclones show different communication strengths with other cell types within the tumor ecosystem, which may modulate the distinct transcriptional subtypes of the subclones. Notably, by integrating TCGA data, they discover that both tumor cell transcriptional heterogeneity and cellular ecosystem diversity correlate with features of a poor prognosis. Collectively, their study provides the analysis of subclonal and transcriptional heterogeneity and its implication for patient prognosis.

Can aggressive cancers be identified by the "aggressiveness" of their chromatin?

Phenotypic plasticity is a crucial feature of aggressive cancer, providing the means for cancer progression. Stochastic changes in tumor cell transcriptional programs increase the chances of survival under any condition. I hypothesize that unstable chromatin permits stochastic transitions between transcriptional programs in aggressive cancers and supports non-genetic heterogeneity of tumor cells as a basis for their adaptability. I present a mechanistic model for unstable chromatin which includes destabilized nucleosomes, mobile chromatin fibers and random enhancer-promoter contacts, resulting in stochastic transcription. I suggest potential markers for "unsettled" chromatin in tumors associated with poor prognosis. Although many of the characteristics of unstable chromatin have been described, they were mostly used to explain changes in the transcription of individual genes. I discuss approaches to evaluate the role of unstable chromatin in non-genetic tumor cell heterogeneity and suggest using the degree of chromatin instability and transcriptional noise in tumor cells to predict cancer aggressiveness.

Diagnosis of Serosal Invasion in Gastric Adenocarcinoma by Dual-Energy CT Radiomics: Focusing on Localized Gastric Wall and Peritumoral Radiomics Features

Objectives

To build a radiomics model and combined model based on dual-energy CT (DECT) for diagnosing serosal invasion in gastric adenocarcinoma.

Materials and methods

231 gastric adenocarcinoma patients were enrolled and randomly divided into a training (n = 132), testing (n = 58), and independent validation (n = 41) cohort. Radiomics features were extracted from the rectangular ROI of the 120-kV equivalent mixed images and iodine map (IM) images in the venous phase of DECT, which was manually delineated perpendicularly to the gastric wall in the deepest location of tumor infiltration, including the peritumoral adipose tissue within 5 mm outside the serosa. The random forest algorithm was used for radiomics model construction. Traditional features were collected by two radiologists. Univariate and multivariate logistic regression was used to construct the clinical model and combined model. The diagnostic efficacy of the models was evaluated using ROC curve analysis and compared using the Delong's test. The calibration curves were used to evaluate the calibration performance of the combined model.

Results

Both the radiomics model and combined model showed high efficacy in diagnosing serosal invasion in the training, testing and independent validation cohort, with AUC of 0.90, 0.90, and 0.85 for radiomics model; 0.93, 0.93, and 0.89 for combined model. The combined model outperformed the clinical model (AUC: 0.76, 0.76 and 0.81).

Conclusion

The radiomics model and combined model constructed based on tumoral and peritumoral radiomics features derived from DECT showed high diagnostic efficacy for serosal invasion in gastric adenocarcinoma.