The conceptual foundation dates back to 1976, when Peter Nowell proposed that tumour cell populations evolve through acquired genetic lability, with stepwise selection of variant sublines driving progression. Half a century of bulk sequencing vindicated the model, revealing branched evolution, intratumoural heterogeneity and the selective sweeps that follow treatment. Yet bulk measurements average across millions of cells, obscuring which mutation resides in which cell and, crucially, what that mutation actually does to the cell carrying it. The review’s authors argue that closing this genotype-to-phenotype gap is now the central task of cancer evolutionary biology.

One striking motivation comes from healthy tissue. Landmark studies of normal skin, oesophagus, colon, endometrium, bladder and bronchial epithelium have shown that somatic mutations in canonical cancer driver genes are under pervasive positive selection in tissue that looks entirely normal under the microscope. In sun-exposed skin, roughly a quarter of cells carry cancer-associated mutations, and mutation burdens in some cells rival those of tumours. Clonal haematopoiesis, the age-related expansion of blood cells carrying mutations in genes such as DNMT3A, TET2 and JAK2, likewise demonstrates that genetic mosaicism is a feature of ordinary physiology and ageing, seeding the pre-malignant landscape from which frank cancers emerge.

Mapping this diversity, however, is only the first step. The review emphasises that understanding somatic clonal evolution requires defining the phenotypes that give mutated clones a fitness advantage, whether those phenotypes involve altered differentiation, survival, proliferation or interaction with the microenvironment. This is where genotype-aware single-cell multi-omics enters. Methods such as G&T-seq and its descendants physically split or barcode the genome and transcriptome of an individual cell, while genotyping-of-transcriptomes approaches recover expressed mutations directly from single-cell RNA-sequencing data. Targeted strategies enrich for known mutant loci, and chromatin-focused assays now co-capture mutations alongside single-cell accessibility profiles.

The biological payoffs have been substantial. In clonal haematopoiesis, single-cell multi-omics has shown that the effects of a mutation are often cell-state specific. DNMT3A R882 mutations, for example, were found to perturb early progenitor states through selective hypomethylation, a phenotype invisible to bulk assays. Splicing aberrations in haematopoietic clonal outgrowths display distinct cell-type-specific impacts, and maps linking genotypes to chromatin accessibility profiles reveal how individual mutations reshape regulatory landscapes in a lineage-dependent manner. In myeloproliferative neoplasms, clonally resolved analyses have traced how JAK2 and CALR mutations propagate through differentiation hierarchies, while work in acute myeloid leukaemia has connected RAS-mutant leukaemia stem cells to clinical resistance against the BCL-2 inhibitor venetoclax.

Beyond single time points, the review highlights the power of coupling phylogenetic reconstruction with phenotypic measurement. Endogenous marks such as somatic point mutations, copy-number alterations, mitochondrial DNA mutations, microsatellite shifts and stochastic epimutations each leave heritable traces that allow researchers to infer the ancestral relationships among single cells. Mitochondrial mutations in particular have enabled lineage tracing directly in human samples, and somatic epimutations have recently been used to chart the dynamics of blood ageing. Reconstructed single-cell phylogenies can then be time-calibrated, converting a branching diagram into a chronogram that estimates when a clone originated within a patient’s lifespan.

Such temporal mapping demands careful statistical treatment. The authors describe phylogenetic frameworks that quantify heritability and plasticity of cell states, decoupling genetic inheritance from non-genetic, environmentally driven transitions. Molecular clock models, whether strict or relaxed, permit inference of mutation rates and timing of clonal expansions, and phylodynamic approaches borrowed from pathogen genetics now illuminate how tumour population sizes fluctuate over the course of disease. Applied to colorectal cancer, these tools have revisited the Big Bang model of tumour growth, in which most subclonal diversity is generated in an early expansion rather than through later selective sweeps, and have documented phenotypic plasticity under genetic control during malignant progression.

Spatial context adds a further dimension. Multiclonal invasion patterns in breast tumours, spatially resolved copy-number maps in benign and malignant tissue, and spatial genomics of cancer clones all demonstrate that evolutionary dynamics are constrained by tumour architecture. Mechanical confinement has been shown to govern phenotypic plasticity in melanoma, and harsh microenvironments select for glycolytic phenotypes in early breast cancer. Integrating spatially resolved or lineage-resolved phenotypes with genotype maps is therefore revealing how selection operates not just on mutations but on the cell states and niches in which those mutations find themselves.

Translationally, the authors argue that genotype-to-phenotype mapping can expose therapeutic vulnerabilities for the precision elimination of disease-propagating mutant cells. Because mutant phenotypes are often confined to specific cell states or lineages, vulnerabilities may exist that spare wild-type tissue. Single-cell analyses have identified drug-tolerant persister states, non-genetic determinants of clonal fitness, and epigenetically inherited plasticity that drives drug resistance through one-to-many genotype-to-phenotype relationships. In glioblastoma, recurring cellular states whose abundance is modulated by genetic aberrations suggest combination strategies; in IDH-mutant oligodendroglioma, mutant IDH inhibitors induce lineage differentiation detectable at single-cell resolution. Evolutionary steering, in which treatment is designed to guide tumours toward collateral sensitivities, becomes more tractable when clonal phenotypes can be read directly in patients.

The review closes with a sober assessment of remaining challenges. Whole-genome amplification artefacts, allelic dropout and tissue dissociation biases still limit sensitivity and fidelity, and artifacts in mitochondrial DNA analyses can misinform phylogenetic inference if uncorrected. Computational methods continue to mature, from probabilistic single-cell phylogeny inference that accounts for sequencing error to models that relax the infinite-sites assumption when back mutations and parallel evolution occur. Yet the trajectory is clear: by pairing genotype with phenotype in the same cell and embedding those pairs within time-calibrated phylogenies, researchers can now define, directly in primary human samples, the mechanisms underlying clonal expansion in both healthy and malignant tissues. For a disease that evolves to evade every therapy thrown at it, that may prove the most consequential lens oncology has yet acquired.

Subject of Research: Single-cell multi-omics mapping of genotype and phenotype co-evolution in clonal evolution of healthy and cancerous tissues

Article Title: A single-cell lens into the co-evolution of genotypes and phenotypes in cancer

Article References: Izzo, F., Prieto, T., Potenski, C., & Landau, D. A. (2026). A single-cell lens into the co-evolution of genotypes and phenotypes in cancer. Nature Reviews Cancer. https://doi.org/10.1038/s41568-026-00970-8

Image Credits: AI Generated

DOI: 10.1038/s41568-026-00970-8

Keywords: single-cell multi-omics, clonal evolution, genotype-phenotype mapping, tumour heterogeneity, clonal haematopoiesis, single-cell phylogenetics, lineage tracing, somatic mosaicism, phenotypic plasticity, cancer genomics, epigenetics, therapeutic vulnerabilities

The research team, led by investigators at Stanford University including Chew Chai, Jesse Gibson, Pengyang Li, Brennan D. McDonald, Anusri Pampari, Aman Patel, Anshul Kundaje, and Bo Wang, set out to address a fundamental question in evolutionary biology: what mechanisms define and maintain families of related cell types across deep evolutionary time? Cell types can be organized into related families based on their functional and molecular properties, yet the regulatory underpinnings that sustain these families across hundreds of millions of years of divergence have remained largely unknown. By integrating single-nucleus multi-omic sequencing data from three species of flatworms and comparing it with vertebrate data, the researchers were able to identify hundreds of sequence motifs that dictate chromatin accessibility and partition into distinct, conserved sets referred to as vocabularies.

The concept of motif vocabularies represents a significant conceptual advance in the field. Each vocabulary is associated with a specific cell type family, meaning that the short DNA sequences recognized by transcription factors are not randomly distributed across the genome but instead cluster into coherent sets that define broad categories of cellular identity. When the researchers examined the combinatorial relationships among these motifs, they found that the particular combinations preferred by individual cell types are largely species-specific. This means that while the building blocks, the individual motifs themselves, remain stable across vast evolutionary distances, the ways in which those blocks are assembled into functional regulatory programmes evolve rapidly and independently in each lineage.

To dissect this layered organization, the team employed ChromBPNet, a deep-learning architecture designed to model chromatin accessibility at base resolution while factoring out technical biases introduced by the Tn5 transposase used in ATAC-seq library preparation. Models trained on chromatin accessibility data from one species accurately predicted family-level chromatin accessibility in distantly related species, demonstrating that the vocabulary-level information encoded in DNA sequences is conserved in a functionally meaningful way. However, interpretability analyses of the model predictions revealed a striking pattern: the deep-learning models frequently relied on different motifs from the shared vocabularies to arrive at convergent predictions. In other words, two species might achieve the same regulatory outcome for a given cell type family, but they do so by drawing on different members of the same motif vocabulary rather than by using identical regulatory elements.

The picture changes dramatically when the resolution of analysis shifts from the cell type family level to the individual cell type level. Models trained on chromatin accessibility data from a specific cell type within one species lost their predictive power when applied to the corresponding cell type in a distantly related species. This loss of cross-species transferability indicates that the regulatory syntax governing cell type-level identity, the precise arrangements and combinations of motifs that specify an individual cell type, evolves much more rapidly than the vocabulary-level constraints that define broader cell type families. The researchers refer to this hierarchical organization as a collective maintenance model, in which the identity of a cell type family is preserved not by any single conserved regulatory element but by the collective stability of a large pool of conserved regulatory factors.

This collective maintenance framework draws a compelling parallel to the concept of developmental homology in evolutionary biology. In homology, a character identity persists across species through conservation at the network level, even as the individual components of the network undergo extensive rewiring. The flatworm and vertebrate data suggest that cell type family identity operates under a similar logic: the vocabulary of sequence motifs defining a family is evolutionarily stable, yet the recombination of these motifs generates cell type-specific regulatory programmes that can differ substantially between species. This decoupling of family-level conservation from cell type-level innovation provides a mechanistic explanation for how new cell types can arise during evolution without disrupting the fundamental identities of existing cell type families.

The technical rigor underlying these conclusions is substantial. The researchers generated single-nucleus multi-omic sequencing data, capturing both gene expression and chromatin accessibility from the same individual nuclei, across three flatworm species: Schmidtea mediterranea, Schistosoma mansoni, and Macrostomum lignano. These species span a considerable range of evolutionary divergence within the flatworm phylum, providing a robust framework for comparative analysis. The team also extended their analysis to vertebrate systems by leveraging existing single-cell multi-omic data from mouse and zebrafish, two species separated by approximately 450 million years of evolution. The cross-species comparison between flatworms and vertebrates is particularly informative because these lineages diverged over 550 million years ago, representing one of the deepest evolutionary comparisons feasible with single-cell genomics.

Among the findings that emerge from this analysis is the observation that chromatin accessibility is conserved within cell type families even when the individual regulatory elements driving that accessibility are not. This means that the overall pattern of which parts of the genome are open and accessible in a given cell type family remains similar across species, but the specific DNA sequences responsible for opening those regions differ. The deep-learning models captured this distinction with remarkable fidelity, as they were able to predict family-level accessibility patterns across species using different combinations of motifs from the same conserved vocabulary. When the researchers examined neural cell types specifically, they documented extensive turnover in combinatorial motif usage, with individual neural cell types in different species employing different sets of motif pairs from the shared neural vocabulary to achieve their specific regulatory identities.

The implications of this work extend beyond basic evolutionary biology into the realm of biomedical research. Understanding that the vocabulary-level organization of regulatory motifs is conserved across species suggests that insights gained from model organisms about cell type family regulation may be more broadly transferable than previously appreciated, provided the analysis is conducted at the appropriate level of biological organization. Conversely, the rapid evolution of cell type-specific regulatory syntax means that extrapolating detailed regulatory mechanisms from one species to another requires caution, particularly for cell types that have undergone significant diversification. The collective maintenance model also raises intriguing questions about the evolutionary dynamics that maintain vocabulary stability while permitting combinatorial flexibility, and whether disruptions to vocabulary-level conservation might underlie certain developmental disorders or diseases.

As the field of single-cell genomics continues to expand the catalog of cell types across the tree of life, the framework developed in this study provides a conceptual scaffold for interpreting cross-species comparisons at multiple levels of resolution. The finding that conserved motif vocabularies constrain genome access while their flexible recombination drives cell type innovation bridges a persistent gap between the stability of cellular identities and the remarkable diversity of cell types observed across the animal kingdom. The research team has made all data and computational tools publicly available, including the single-cell multi-ome datasets deposited in the Sequence Read Archive, the ChromBPNet models shared through figshare, and the complete analysis code archived on GitHub and Zenodo, ensuring that the broader scientific community can build upon these findings to further unravel the regulatory architecture of cell type evolution.

Subject of Research: Evolutionary conservation of regulatory motif vocabularies governing chromatin accessibility and cell type family identity across flatworms and vertebrates

Article Title: Flexible use of conserved motifs constrains genome access in cell type evolution

Article References: Flexible use of conserved motifs constrains genome access in cell type evolution. (n.d.). https://doi.org/10.1038/s41559-026-03164-5

Image Credits: AI Generated

DOI: 10.1038/s41559-026-03164-5

Keywords: cell type evolution, chromatin accessibility, deep learning, single-cell multi-omics, motif vocabularies, regulatory syntax, flatworms, vertebrates, developmental homology, gene regulation, Nature Ecology & Evolution, transcription factors

TNBC remains a major clinical challenge due to its lack of hormone receptors or HER2 expression, which limits the effectiveness of targeted therapies. Stromal components, particularly CAFs, play a pivotal role in shaping tumor progression, immune evasion, and therapeutic resistance. However, heterogeneity within CAF populations and their multifunctional roles have historically obscured attempts to therapeutically exploit these cells.

The team deployed single-cell transcriptomic, epigenomic, and proteomic profiling simultaneously on isolated CAFs from TNBC patient samples. This integrative approach allowed unprecedented resolution in identifying distinct CAF subsets and unraveling their specific functional states. Notably, they characterized a unique myofibro-inflammatory program marked by concurrent activation of contractile myofibroblast features and inflammatory signaling pathways.

Functionally, this dual phenotype equips CAFs with the ability to remodel extracellular matrix and secrete pro-inflammatory cytokines, creating a tumor microenvironment that supports cancer cell invasion and suppresses effective anti-tumor immunity. These findings suggest that targeting either the contractile or inflammatory axes alone may be insufficient, emphasizing the need for combinatorial approaches.

Moreover, the study discovered epigenetic mechanisms driving this myofibro-inflammatory signature, offering deeper mechanistic understanding of how CAFs adopt and maintain these states in response to local cues. Modulation of these epigenetic regulators demonstrated potential in reprogramming CAFs toward less tumor-promoting phenotypes, opening new avenues for therapeutic intervention.

The integration of multi-omics data also enabled the identification of cell surface markers specific to pathogenic CAF subsets, laying the groundwork for future development of diagnostic tools and targeted delivery systems. This advancement could facilitate stratification of TNBC patients based on stromal composition and predict responses to stroma-targeted therapies.

This research not only provides a detailed atlas of CAF diversity within TNBC but also redefines the stromal dynamics as a critical component of tumor biology. It challenges the previous notion of CAFs as a uniform cell population and highlights their plasticity and complex role in tumor progression.

As clinical research moves toward more precise anti-cancer strategies, this study represents a significant leap, suggesting that disrupting the myofibro-inflammatory CAF program may synergize with current immunotherapies and chemotherapies to achieve durable remission in TNBC patients.

The publication challenges the existing paradigm and marks a promising step toward overcoming one of the deadliest breast cancer subtypes by illuminating the hidden choreography of the tumor microenvironment.

Subject of Research: Cancer-associated fibroblasts and their role in triple-negative breast cancer tumor microenvironment

Article Title: Single-cell multi-omics deciphers the myofibro-inflammatory program of cancer-associated fibroblasts in triple-negative breast cancer

Article References: Li, M., Lin, J., Yang, C. et al. Single-cell multi-omics deciphers the myofibro-inflammatory program of cancer-associated fibroblasts in triple-negative breast cancer. Cell Death Discov. (2026). https://doi.org/10.1038/s41420-026-03255-z

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41420-026-03255-z

One of the study’s pivotal innovations lies in its ability to untangle the diverse cellular landscape of ICC tumors. Leveraging the publicly available GSE201425 single-cell RNA sequencing dataset, the researchers embarked on a comprehensive investigation to reveal the identity and trajectories of cells implicated in ICC metastasis. This approach allowed them to capture the complex interplay of tumor epithelial cells and their microenvironmental context, which classical bulk sequencing could easily mask due to cellular averaging effects. By focusing at the single-cell level, the team could isolate and characterize rare malignant subpopulations critical for cancer dissemination.

Employing copy number variation (CNV) profiling and clonal evolution analysis, the researchers identified a subset of malignant epithelial cells distinctively associated with metastatic ICC lesions. These cells exhibited unique genetic alterations indicative of aggressive oncogenic behavior. Pseudotime trajectory analysis further illuminated the dynamic progression of epithelial cells, pinpointing a specific population—termed metastasis-associated epithelial cells (MAECs)—that appears to act as key drivers of ICC metastasis. This detailed mapping of cell state transitions unveils the stepwise evolution through which ICC cells acquire metastatic competence.

The investigation didn’t halt at cellular identification. The study meticulously screened for biomarker candidates uniquely enriched in MAECs, identifying MMP7, FXYD2, and PTHLH as top candidates tightly linked to metastatic activity. Each of these molecules has known implications in cancer biology: MMP7 is a metalloproteinase involved in extracellular matrix remodeling, FXYD2 modulates ion transport and cellular homeostasis, and PTHLH (parathyroid hormone-like hormone) influences cell proliferation and migration. Their co-expression in MAECs forms a distinctive molecular fingerprint of metastatic potential.

To translate these insights into a clinically actionable framework, the researchers constructed a Metastasis Index (Met-Index) based on one-class logistic regression, integrating expression patterns of the identified biomarkers. Validation using bulk RNA-seq data from TCGA-CHOL revealed the Met-Index as a robust stratifier of patient risk. Patients exhibiting a high Met-Index faced significantly poorer overall survival and progression-free survival rates, underscoring the index’s prognostic value. This tool could empower clinicians to identify high-risk patients early and tailor aggressive treatment strategies accordingly.

Validation extended beyond computational models. Multiplex immunofluorescence staining of 34 clinical ICC specimens confirmed elevated expression of MMP7, FXYD2, and PTHLH in metastatic tumors compared to their non-metastatic counterparts. Importantly, elevated biomarker levels correlated with adverse clinicopathological parameters, reinforcing their relevance as indicators of metastatic aggressiveness. This multi-modal verification strengthens the credibility of these markers as both diagnostic and therapeutic targets.

Functional assays in the HuCCT1 cholangiocarcinoma cell line provided direct evidence of the biomarkers’ roles in tumor biology. siRNA-mediated silencing of MMP7, FXYD2, and PTHLH significantly curtailed cell proliferation while impeding migration capabilities, hallmark characteristics of metastatic phenotypes. These in vitro results spotlight these molecules as potential targets for therapeutically halting ICC progression, opening doors to novel drug development avenues.

This study’s approach epitomizes the power of integrative single-cell multi-omics in oncology. By combining genetic, transcriptional, and spatial data, the research constructs a holistic model of metastasis, moving beyond mere association toward mechanistic understanding. It also exemplifies how computational modeling and experimental validation can coalesce to produce clinically translatable outcomes that may revolutionize patient management protocols.

ICC has long been hampered by late diagnosis and scant prognostic biomarkers, leading to treatment failures and dismal survival rates. The revelation of MAECs and their defining molecular traits offers a targeted pathway for early intervention. Tailoring therapies to inhibit these metastasis-initiating cells could significantly curtail disease dissemination, ultimately improving patient prognosis and quality of life.

Moreover, the Met-Index developed here presents an elegant and statistically sound method for quantifying metastasis risk from existing bulk transcriptomic data, facilitating broader clinical deployment. This index could potentially be integrated into routine diagnostic pipelines, guiding patient stratification and personalized treatment decisions, particularly in settings where single-cell sequencing may not be readily available.

The study invites further exploration into how the tumor microenvironment interacts with MAECs, possibly influencing metastatic capabilities or therapeutic resistance. Additionally, translating these findings into in vivo models and clinical trials will be imperative for validating therapeutic targeting of MMP7, FXYD2, and PTHLH. Such future investigations could pave the way for innovative combination therapies that neutralize metastatic pathways in ICC.

In conclusion, this landmark investigation articulates a detailed map of ICC metastasis at an unprecedented resolution. The identification and characterization of MAECs as a discrete metastatic subpopulation, together with the novel Met-Index, represents a major leap forward in understanding and managing this formidable malignancy. This integrative research underscores the transformative potential of single-cell multi-omics approaches in oncology and sets a new standard for biomarker discovery and prognostic modeling.

As interest in precision oncology escalates, studies like this exemplify the synthesis of advanced technologies and computational prowess needed to combat complex cancers. The journey toward conquering ICC metastasis is far from over, but armed with these new molecular insights and diagnostic tools, the future offers hope for more effective management and improved survival of affected patients. The continued unraveling of cancer’s cellular heterogeneity will undeniably fuel the next generation of targeted therapies and prognostic innovations.

The oncology community awaits with anticipation how these findings will reshape ICC treatment paradigms and inspire similar multi-omics investigations across other challenging cancer types. This study is a testament to the power of collaborative, interdisciplinary science in decoding and defeating cancer’s most lethal traits.

Subject of Research: Intrahepatic cholangiocarcinoma (ICC) metastasis drivers and prognostic biomarkers

Article Title: Single-cell multi-omics analysis reveals drivers of intrahepatic cholangiocarcinoma metastasis

Article References:
Zhang, Z., Dou, H., Zhao, S. et al. Single-cell multi-omics analysis reveals drivers of intrahepatic cholangiocarcinoma metastasis. BMC Cancer (2025). https://doi.org/10.1186/s12885-025-15253-y

Image Credits: Scienmag.com

DOI: https://doi.org/10.1186/s12885-025-15253-y