bims-gerecp 2026-02-01 papers

bims-gerecp

Biomed News

on Gene regulatory networks of epithelial cell plasticity

Issue of 2026–02–01
fifteen papers selected by
Xiao Qin, University of Oxford

CellRank: consistent and data view agnostic fate mapping for single-cell genomics.
Advances in Colorectal Cancer Cell Biology and Clonal Evolution.
Scalable and multiplexed recorders of gene regulation dynamics across weeks.
Cancer Reversion Therapy: Prospects, Progress and Future Directions.
Integrating artificial intelligence (AI) into colorectal cancer reporting.
Biomarkers for Early Detection and Monitoring of Colorectal Cancer.
An Integrative Roadmap for Advancing Colorectal Cancer Organoid.
Tumor immune organoids in immunotherapy resistance and drug screening.
Deriving genetic codes for molecular phenotypes from first principles.
Molecular mosaic of colorectal cancer: Why one classification system is no longer enough?
Canalization as a stabilizing principle of gene regulatory networks: a discrete dynamical systems perspective.
The language of cytokines: Decoding immune signals with information theory.
Molecular Mechanisms of Transcription Factors with Dual Activator and Repressor Functions.
ChromBERT: A foundation model for learning interpretable representations for context-specific transcriptional regulatory networks.
Single-cell differential abundance detection: A new angle on dissecting tumor heterogeneity.

Nat Protoc. 2026 Jan 29.

CellRank: consistent and data view agnostic fate mapping for single-cell genomics.

Philipp Weiler, Fabian J Theis.

Single-cell RNA sequencing quantifies biological samples at an unprecedented scale, allowing us to decipher biological differentiation dynamics such as normal development or disease progression. As conventional single-cell RNA sequencing experiments are destructive by nature, reconstructing cellular trajectories computationally is an essential aspect of analysis pipelines. To infer trajectories in a consistent and scalable manner, we have developed CellRank. In its first iteration, CellRank quantitatively recovered trajectories from RNA velocity estimates and transcriptomic similarity. Given these data views, CellRank constructed a cell-cell transition matrix, inducing a Markov chain to automatically infer terminal states and describe their lineage formation. However, CellRank did not enable incorporating complementary data views such as experimental time points, pseudotime or stemness potential. To facilitate these and future views, CellRank 2 generalizes CellRank's trajectory inference framework to multiview single-cell data, leading to a general and scalable framework for cellular fate mapping. Overall, the CellRank framework enables the consistent quantification of cellular fate, combining complementary views and analyzing lineage priming consistently. Here we provide detailed protocols on how to run exemplary CellRank analyses at scale and across different data views. Using CellRank requires basic apprehension and knowledge of single-cell omics data and the Python programming language.

DOI: https://doi.org/10.1038/s41596-025-01314-w
Int J Mol Sci. 2026 Jan 18. pii: 953. [Epub ahead of print]27(2):

Advances in Colorectal Cancer Cell Biology and Clonal Evolution.

Sopozme Toghey, Elizabeth J Harvey-Jones, Jonathan D Towler, Charlotte J H Hafkamp, Irene Y Chong.

  Colorectal cancer (CRC) develops through evolutionary processes involving genomic alterations, epigenetic regulation, and microenvironmental interactions. While traditionally explained by the stepwise accumulation of driver mutations, contemporary evidence supports a 'Big Bang' model in which many early-arising clones expand simultaneously to establish extensive heterogeneity. We reviewed recent studies employing spatially resolved multi-omic sequencing of tumour glands combined with computational modelling. These approaches enable high-resolution reconstruction of clonal architecture, transcriptional states, and chromatin accessibility. Findings show that although early clonal mutations shape tumour expansion, gene expression variability can be independent of genetic ancestry and instead reflects phenotypic plasticity driven by microenvironmental cues. Epigenomic analyses identified recurrent somatic chromatin accessibility alterations in promotors and enhancers of oncogenic pathways, frequently in the absence of DNA mutations, suggesting alternative mechanisms of gene regulation. Immune-focused studies demonstrated that early silencing of antigen-presenting genes and loss of neoantigens facilitate immune escape despite active surveillance. CRC is shaped by an interplay of genome, epigenome, and immune evolution, with non-genetic mechanisms and tumour plasticity emerging as important drivers of progression and therapeutic resistance.

Keywords:  cell biology clonal evolution; colorectal; transcriptional heterogeneity epigenetic regulation

DOI:  https://doi.org/10.3390/ijms27020953
Nature. 2026 Jan 26.

Scalable and multiplexed recorders of gene regulation dynamics across weeks.

Lirong Zheng, Dongqing Shi, Yixiao Yan, Bingxin Zhou, Jormay Lim, Yongjie Hou, Bobae An, Jason K Adhinarta, Michael Lin, BumJin Ko, William C Joesten, Mehul Gautam, Elie D M Huez, Eung Chang Kim, Emily G Klyder, Boxuan Chang, Sethuramasundaram Pitchiaya, Michael T Roberts, Denise J Cai, Edward S Boyden, Donglai Wei, Pietro Liò, Changyang Linghu.

Gene expression is dynamically regulated by gene regulatory networks comprising multiple regulatory components to mediate cellular functions1. An ideal tool for analyzing these processes would track multiple-component dynamics with both spatiotemporal resolution and scalability within the same cells, a capability not yet achieved. Here, we present CytoTape, a genetically encoded, modular protein tape recorder for multiplexed and spatiotemporally scalable recording of gene regulation dynamics continuously for up to three weeks, physiologically compatible, with single-cell, minutes-scale resolution. CytoTape employs a flexible, thread-like, elongating intracellular protein self-assembly engineered via computationally assisted rational design, built on earlier XRI technology2. We demonstrated its utility across multiple mammalian cell types, achieving simultaneous recording of five transcription factor activities and gene transcriptional activities. CytoTape reveals that divergent transcriptional trajectories correlate with transcriptional history and signal integration, and that distinct immediate early genes (IEGs) exhibit complex temporal correlations within single cells. We further extended CytoTape into CytoTape-vivo for scalable, spatiotemporally resolved single-cell recording in the living brain, enabling simultaneous weeks-long recording of doxycycline- and IEG promoter-dependent gene expression histories across up to 14,123 neurons spanning multiple brain regions per mouse. Together, the CytoTape toolkit establishes a versatile platform for scalable and multiplexed analysis of cell physiological processes in vitro and in vivo.

DOI: https://doi.org/10.1038/s41586-026-10156-9
Curr Issues Mol Biol. 2025 Dec 15. pii: 1049. [Epub ahead of print]47(12):

Cancer Reversion Therapy: Prospects, Progress and Future Directions.

Emmanuel O Oisakede, David B Olawade, Oluwakemi Jumoke Bello, Claret Chinenyenwa Analikwu, Eghosasere Egbon, Oluwaseun Fapohunda, Stergios Boussios.

  Cancer reversion therapy represents a paradigm shift in oncology, focusing on reprogramming malignant cells to a non-malignant state rather than destroying them. This narrative review synthesizes current evidence, emerging technologies, and future directions in this promising field. Cancer reversion is founded on key biological observations: somatic cell reprogramming, spontaneous cancer regression, and microenvironmental influences on malignant behavior. Current approaches include epigenetic reprogramming using HDAC inhibitors and DNA methyltransferase inhibitors; microenvironmental modulation through extracellular matrix manipulation and vascular normalization; differentiation therapy exemplified by all-trans retinoic acid in acute promyelocytic leukemia; and targeting oncogene addiction as demonstrated in BCR-ABL-driven leukemias. Emerging technologies accelerating progress include single-cell analyses that reveal cancer heterogeneity and cellular state transitions; CRISPR-based approaches enabling precise genetic and epigenetic manipulation; patient-derived organoids that model tumor complexity; and artificial intelligence applications that identify novel reversion-inducing agents. Critical evaluation reveals that many reported "reversion" phenomena represent stimulus-dependent plasticity or transient growth arrest rather than stable phenotypic normalization. True cancer reversion requires durable, heritable phenotypic changes that persist after treatment withdrawal, with evidence of epigenetic consolidation and functional restoration. Despite promising advances, significant challenges remain: cancer cell plasticity facilitating therapeutic escape, difficulties in establishing stable reversion states, delivery challenges for solid tumors, and the need for combination approaches to address tumor heterogeneity. Future directions include integrated multi-omics analyses to comprehensively map cellular state transitions, studies of natural regression phenomena to identify reversion mechanisms, advanced nanodelivery systems for targeted therapy, and synthetic biology approaches creating intelligent therapeutic systems. By redirecting rather than destroying cancer cells, reversion therapy offers the potential for reduced toxicity and resistance, potentially transforming cancer from a deadly disease to a manageable condition.

Keywords:  cancer reversion; cellular reprogramming; differentiation therapy; epigenetic regulation; tumor microenvironment

DOI:  https://doi.org/10.3390/cimb47121049
J Pathol. 2026 Jan 26.

Integrating artificial intelligence (AI) into colorectal cancer reporting.

Konstantin Bräutigam, Ann-Marie Baker, Viktor H Koelzer, Jakob N Kather, Trevor A Graham.

  Artificial intelligence (AI) and deep learning (DL) are transforming cancer research and clinical care, with histopathology playing a central role in this transformation. In colorectal cancer (CRC), the second leading cause of cancer mortality world-wide, multimodal and vision-language models (VLMs) hold particular promise for enhancing the standardisation of histopathology reporting, the understanding of disease biology, and the discovery of novel prognostic indicators. Despite the availability of guidelines and reporting templates for essential prognostic indicators, variability remains in how key features such as TNM staging or tumour deposits are assessed and reported in routine clinical practice. AI-based tools have the potential to support refined extraction of established and extended features directly from whole-slide images. In parallel, recent studies have shown that DL models applied to pathology slides and associated AI-based biomarkers can outperform traditional histopathological prognostic indicators and uncover novel parameters, including tumour-adipocyte interactions, tumour-stroma ratio, and immune cell patterns at the invasive margin. Here, we review recent advances in both domains: AI-assisted standardisation of CRC pathology reporting and AI-driven identification of novel prognostic biomarkers. We highlight the need to refine and standardise CRC reporting practices and propose that a harmonised approach combining established pathology features with AI-derived prognostic indicators could refine risk assessment and improve outcomes for CRC patients. © 2026 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Keywords:  artificial intelligence; cancer reporting; colorectal cancer; deep learning; evolution; prediction; prognosis

DOI:  https://doi.org/10.1002/path.70029
Gastroenterol Clin North Am. 2026 Mar;pii: S0889-8553(25)00068-8. [Epub ahead of print]55(1): 143-164

Biomarkers for Early Detection and Monitoring of Colorectal Cancer.

Sara K Char, Harshabad Singh, Kimmie Ng.

  Colorectal cancer (CRC) is a major global health burden. Although colonoscopy remains the gold standard for screening, less invasive biomarker-based stool and blood tests including FIT, multi-target stool DNA/RNA assays, and blood-based biomarker tests are emerging with promising diagnostic performance. In the post-treatment setting of localized CRC, ctDNA has emerged as a powerful tool, with studies showing it can predict recurrence earlier than imaging or traditional tumor biomarkers. ctDNA testing is used for genomic profiling of CRC to guide therapy decisions. Ongoing research aims to refine ctDNA-based surveillance and integrate it with other biomarkers to transform CRC monitoring and treatment.

Keywords:  Colorectal cancer; Early detection; Molecular residual disease; ctDNA

DOI:  https://doi.org/10.1016/j.gtc.2025.10.002
Biomedicines. 2026 Jan 22. pii: 248. [Epub ahead of print]14(1):

An Integrative Roadmap for Advancing Colorectal Cancer Organoid.

Youqing Zhu, Ke He, Zhi Shi.

  Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Compared with traditional two-dimensional (2D) models, patient-derived CRC organoids more faithfully preserve the genomic, transcriptomic, and architectural features of primary tumors, making them a powerful intermediate platform bridging basic discovery and clinical translation. Over the past several years, organoid systems have rapidly expanded beyond conventional epithelial-only cultures toward increasingly complex architectures, including immune-organoid co-culture models and mini-colon systems that enable long-term, spatially resolved tracking of tumor evolution. These advanced platforms, combined with high-throughput technologies and clustered regularly interspaced short palindromic repeats (CRISPR)-based functional genomics, have substantially enhanced our ability to dissect CRC mechanisms, identify therapeutic vulnerabilities, and evaluate drug responses in a physiologically relevant context. However, current models still face critical limitations, such as the lack of systemic physiology (e.g., gut-liver or gut-brain axes), limited standardization across platforms, and the need for large-scale, prospective clinical validation. These gaps highlight an urgent need for next-generation platforms and computational frameworks. The development of high-throughput multi-omics, CRISPR-based perturbation, drug screening technologies, and artificial intelligence-driven predictive approaches will offer a promising avenue to address these challenges, accelerating mechanistic studies of CRC, enabling personalized therapy, and facilitating clinical translation. In this perspective, we propose a roadmap for CRC organoid research centered on two major technical pillars: advanced organoid platforms, including immune co-culture and mini-colon systems, and mechanistic investigations leveraging multi-omics and CRISPR-based functional genomics. We then discuss translational applications, such as high-throughput drug screening, and highlight emerging computational and translational strategies that may support future clinical validation and precision medicine.

Keywords:  CRC organoid; drug screening; mini colon; multi-omics; organoid-immune co-culture; personalized treatment

DOI:  https://doi.org/10.3390/biomedicines14010248
Drug Resist Updat. 2026 Jan 23. pii: S1368-7646(26)00011-7. [Epub ahead of print]85 101360

Tumor immune organoids in immunotherapy resistance and drug screening.

Minghui Zhang, Lijun Li, Haihong Pu, Yan Wang, Xiaoxin Zhang, Xiaotian Guo, Yanbin Zhao, Qingwei Meng, Mingzhu Yin.

  Tumor organoids represent a transformative tool in cancer research, as they retain the genetic and phenotypic features of parental tumors and accurately recapitulate their heterogeneity. However, one of the limitations of tumor organoids lies in the lack of immune and stromal cells in the tumor microenvironment (TME). To address this challenge, tumor immune organoids have been developed, which contain complex immune and stromal compartments beyond preserving tumor architecture. Tumor immune organoids show great potential for studying personalized immunotherapy responses and mechanisms of immunotherapy resistance. However, integrating the models into clinical practice remains challenging. In this Review, we outline currently available and rapidly evolving tumor immune organoids that recapitulate the TME and immunotherapy effects. These tumor immune organoids can be established by co-culturing traditional tumor organoids with stromal and immune cells, as well as preserving the TME using microfluidic and air-liquid interface (ALI) culture technologies. Additionally, we delineate the applications of tumor immune organoids for unravelling tumor-intrinsic and -extrinsic immunotherapy resistance mechanisms, predicting immunotherapy efficacy, and facilitating novel drug screening. Finally, we highlight the current challenges of organoid culture technology that need to be addressed for its broader applications, both in basic and translational cancer research. This review provides a theoretical foundation for future research on the application of tumor immune organoids to investigate immunotherapy resistance mechanisms and develop personalized immunotherapies. With continuous advancements, tumor immune organoids are expected to play an increasingly indispensable role in cancer immunotherapy, providing patients with more effective and tailored treatment options.

Keywords:  Immunotherapy; Personalized treatment; Resistance; Tumor immune organoids; Tumor microenvironment

DOI:  https://doi.org/10.1016/j.drup.2026.101360
bioRxiv. 2026 Jan 10. pii: 2022.08.15.503769. [Epub ahead of print]

Deriving genetic codes for molecular phenotypes from first principles.

Salil S Bhate, Anna Seigal, Juan Caicedo.

The genetic code is a formal principle that determines which proteins an organism can produce from only its genome sequence, without mechanistic modeling. Whether similar formal principles govern the relationship between genome sequence and phenotype across scales - from molecules to cells to tissues - is unknown. Here, we show that a single formal principle - structural correspondence - underlies the relationship between phenotype and genome sequence across scales. We represent phenotypes and the genome as graphs and find mappings between them using structure preservation as the sole constraint. Combinatorial richness in phenotypes more tightly constrains which mappings preserve that structure. Thus, phenotypic structure predicts genetic associations independently of covariation with genotype. This principle rediscovers the amino acid code without prior knowledge of translation or coding sequences, using just one protein and genome sequence as input. We benchmark this principle: applied to phenotypes at the cell, tissue and organ scales, the mappings correctly predict established associations and are driven by transcription factor motifs. Applied to cancer tissue images, we find regulators of spatial gene expression in immune cells. We thus offer a first-principles framework to relate genome sequence with phenotypic structure and guide mechanistic discovery across scales.

DOI: https://doi.org/10.1101/2022.08.15.503769
World J Gastrointest Oncol. 2026 Jan 15. 18(1): 114502

Molecular mosaic of colorectal cancer: Why one classification system is no longer enough?

Sunita Ahlawat, Sumanta Das.

  Colorectal cancer (CRC) is one of the most molecularly heterogeneous malignancies, with complexity that extends far beyond traditional histopathological classifications. The consensus molecular subtypes (CMS) established in 2015 brought a marked advancement in the taxonomy of CRC, consolidating six classification systems into four novel subtypes, which focus on vital gene expression patterns and clinical and prognostic outcomes. However, nearly a decade of clinical experience with CMS classification has revealed fundamental limitations that underscore the inadequacy of any single classification system for capturing the full spectrum of CRC biology. The inherent challenges of the current paradigm are multifaceted. In the CMS classification, mixed phenotypes that remain unclassifiable constitute 13% of CRC cases. This reflects the remarkable heterogeneity that CRC shows. The tumor budding regions reflect the molecular shift due to CMS 2 to CMS 4 switching, causing further heterogeneity. Moreover, the reliance on bulk RNA sequencing fails to capture the spatial organization of molecular signatures within tumors and the critical contributions of the tumor microenvironment. Recent technological advances in spatial transcriptomics, single-cell RNA sequencing, and multi-omic integration have revealed the limitations of transcriptome-only classifications. The emergence of CRC intrinsic subtypes that attempt to remove microenvironmental contributions, pathway-derived subtypes, and stem cell-based classifications demonstrates the field's recognition that multiple complementary classification systems are necessary. These newer molecular subtypes are not discrete categories but biological continua, thus highlighting that the vast molecular landscape is a tapestry of interlinked features, not rigid subtypes. Multiple technical hurdles cause difficulty in implementing the clinical translation of these newer molecular subtypes, including gene signature complexity, platform-dependent variations, and the difficulty of getting and preserving fresh frozen tissue. CMS 4 shows a poor prognostic outcome among the CMS subtypes, while CMS 1 is associated with poor survival in metastatic cases. However, the predictive value for definitive therapy remains subdued. Looking forward, the integration of artificial intelligence, liquid biopsy approaches, and real-time molecular monitoring promises to enable dynamic, multi-dimensional tumor characterization. The temporal and spatial complexity can only be captured by complementary molecular taxonomies rather than a single, unified system of CRC classification. Such an approach recognizes that different clinical questions - prognosis, treatment selection, resistance prediction - may require different molecular lenses, each optimized for specific clinical applications. This editorial advocates for a revolutionary change from pursuing a single "best" classification system toward a diverse approach that welcomes the molecular mosaic of CRC. Only through such comprehensive molecular characterization can we hope to achieve the promise of precision oncology for the diverse spectrum of patients with CRC.

Keywords:  Clinical translation; Colorectal cancer intrinsic subtypes; Consensus molecular subtypes; Heterogeneity; Pathway-derived subtypes

DOI:  https://doi.org/10.4251/wjgo.v18.i1.114502
NPJ Syst Biol Appl. 2026 Jan 25.

Canalization as a stabilizing principle of gene regulatory networks: a discrete dynamical systems perspective.

Claus Kadelka.

Gene regulatory networks exhibit remarkable stability, maintaining functional phenotypes despite genetic and environmental perturbations. Discrete dynamical models, such as Boolean networks, provide systems biologists with a tractable framework to explore the mathematical underpinnings of this robustness. A key mechanism conferring stability is canalization. This perspective synthesizes historical insights, formal definitions of canalization in discrete dynamical models, quantitative measures of stability, and emerging challenges at the interface of theory and experiment.

DOI: https://doi.org/10.1038/s41540-026-00655-w
Biochim Biophys Acta Mol Cell Res. 2026 Jan 22. pii: S0167-4889(26)00015-7. [Epub ahead of print] 120119

The language of cytokines: Decoding immune signals with information theory.

Sophie Streuber, Emelie Rieneck, Fred Schaper, Anna Dittrich.

  Communication between cells is fundamental for maintaining and restoring homeostasis in multicellular organisms under both physiological and pathological conditions. A variety of mechanisms for encoding, transmitting, and decoding information have evolved. Information theory, originally developed in engineering, is increasingly being applied to dissect how cells process and exchange signals. Yet, biological systems exhibit distinctive properties that pose conceptual and quantitative challenges not encountered in technical systems. In this review, we examine how cellular networks manage and often exploit the intrinsic heterogeneity of cell populations. We discuss how individual cells and cell populations sense cytokine stimulus strength and specificity, and how regulatory proteins shape not only signalling dynamics but also the capacity and robustness of information transmission. From an information theoretical perspective, health can be viewed as a state of efficient and reliable cellular communication, whereas disease reflects the loss or distortion of robust cellular communication. We conclude that information theory offers an intuitive framework for biologists seeking to unravel the principles of cytokine signalling.

Keywords:  Channel capacity; Cytokine; Information theory; Mutual information; Signal transduction; Single cells

DOI:  https://doi.org/10.1016/j.bbamcr.2026.120119
Mol Cell Biol. 2026 Jan 28. 1-9

Molecular Mechanisms of Transcription Factors with Dual Activator and Repressor Functions.

Jinhong Dong, Michael J Guertin.

  Transcription factors (TFs) are traditionally classified as activators or repressors, yet some can perform both roles. We highlight well-supported examples of dual activator/repressor functions and review the mechanisms that explain how duality arises. These examples reveal that transcriptional duality arises from three recurring mechanisms: positional effects, cofactor exchange, and regulatory switches. Even within these recurring mechanisms, the precise molecular details diverge, with regulatory outcomes dictated by differences in TF positioning, cofactor availability, modification state, and ligand binding. We propose that future work should move beyond descriptive labels of context specificity and instead focus on elucidating the precise molecular mechanisms by which TFs function to elicit opposing regulatory effects.

Keywords:  Transcription factors; activator; bifunctional; context specific; repressor

DOI:  https://doi.org/10.1080/10985549.2026.2619741
Cell Genom. 2026 Jan 26. pii: S2666-979X(25)00386-6. [Epub ahead of print] 101130

ChromBERT: A foundation model for learning interpretable representations for context-specific transcriptional regulatory networks.

Zhaowei Yu, Dongxu Yang, Qianqian Chen, Yuxuan Zhang, Zhanhao Li, Yucheng Wang, Chenfei Wang, Yong Zhang.

  Gene expression is shaped by transcriptional regulatory networks (TRNs), where transcription regulators interact within regulatory elements in a context-specific manner. Deciphering context-specific TRNs has long been constrained by the severe sparsity of cell-type-specific chromatin immunoprecipitation sequencing (ChIP-seq) profiles. Here, we present ChromBERT, a foundation model pre-trained on large-scale human ChIP-seq datasets covering ∼1,000 transcription regulators. ChromBERT learns the genome-wide syntax of regulatory cooperation and generates interpretable TRN representations. After prompt-enhanced fine-tuning, it outperforms existing methods for imputing unseen cistromes. Moreover, lightweight fine-tuning on cell-type-specific downstream tasks adapts the TRN representations to capture regulatory effects and dynamics within any given cellular context. The resulting context-specific representations can then be interpreted to infer regulatory roles of transcription regulators underlying these cell-type-specific regulatory outcomes without requiring additional ChIP-seq experiments. By overcoming the limitations of sparse transcription regulator data, ChromBERT significantly enhances our ability to model and interpret transcriptional regulation across a wide range of biological contexts.

Keywords:  foundation model; genomics; interpretable representation; regulatory networks; transcription regulation

DOI:  https://doi.org/10.1016/j.xgen.2025.101130
World J Clin Oncol. 2026 Jan 24. 17(1): 113244

Single-cell differential abundance detection: A new angle on dissecting tumor heterogeneity.

Ying-Xue Xiao, Jing Sun, Ling-Ling Xie, Yue Zou, Tong Li, You-Jin Hao, Bo Li.

  Tumor heterogeneity is one of the central challenges in oncology, contributing to treatment resistance and disease recurrence. Bulk RNA sequencing has advanced understanding of tumor biology, yet its averaging effect conceals cell type-specific alterations. Single-cell RNA sequencing overcomes this limitation by capturing gene expression and cellular phenotypes with high-resolution, thereby illuminating tumor composition and the surrounding microenvironment. Within this framework, differential abundance (DA) detection has emerged as a powerful strategy to quantify shifts in cell population proportions across conditions. Unlike differential gene expression, DA highlights compositional changes in cellular ecosystems, offering a structural perspective on tumor dynamics. This review introduces the main categories of DA methods in single-cell RNA sequencing analysis, outlining their modeling strategies, assumptions, and representative applications in oncology. We also discuss key challenges, including reliance on clustering quality and batch correction. By linking methodological principles with biological insight, this review clarifies the role of DA detection in single-cell oncology and provides a conceptual framework for integrating compositional analysis into efforts to understand tumor evolution, treatment response, and disease stratification.

Keywords:  Cellular composition; Differential abundance detection; Immune remodeling; Precision oncology; Single-cell RNA sequencing; Tumor heterogeneity; Tumor microenvironment

DOI:  https://doi.org/10.5306/wjco.v17.i1.113244