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At least 19 recordsLinked to original sources
PubMed2025

Unraveling cellular dynamic changes in tumor evolution induced by long-term low dose-rate radiation.

BACKGROUND: In recent years, there has been a steady increase in professionals engaged in radioactive work. The biological impacts of long-term exposure to low dose-rate radiation remain elusive, as there is a dearth of systematic research in this field. METHODS: BEAS-2B cells were used to establish a cell model with continuous passaging after radiation exposure, which was subsequently subjected to in vivo tumorigenesis assays and in vitro malignant phenotype experiments. By scRNA-seq, we conducted copy number variation analysis, cell trajectory analysis, and cell communication analysis. Furthermore, we used FACS, molecular docking, multiplex immunohistochemistry, qRT-PCR, and co-immunoprecipitation to validate and further explore the molecular mechanisms driving tumor evolution. RESULTS: Long-term low dose-rate exposure is associated with a higher degree of malignancy, as evidenced by the induction of more CNV and EMT events, as well as the delayed activation of DNA repair pathways, which trigger increased genomic instability. The long-term low dose-rate specific ligand-receptor pair, ANGPTL4-SDC4, enhances cell malignancy by promoting angiogenesis in newly formed lung tumor cells. CONCLUSIONS: This study not only provides the first evidence and mechanistic explanation that long-term low dose-rate radiation leads to increased cellular malignancy but also offers valuable theoretical insights into the dynamic processes of early tumor evolution in lung cancer within the realm of tumor biology.

Humans↗
PubMed2026

Chromosome-scale genome remodeling in tumor evolution: Copy number alterations and structural variants as two sides of the same coin.

Chromosome-scale genomic rearrangements are a dominant force in tumor evolution. Copy-number alterations (CNAs) and structural variants (SVs) constitute two complementary axes of this process. Although detection technologies now deliver near-comprehensive catalogs, technical resolution has outpaced conceptual integration. In this review, we frame CNAs and SVs as inextricable facets of chromosomal aberrations. They reshape cancer genomes through altered gene dosage and three-dimensional regulatory rewiring. CNAs quantify the gene-dosage imbalance, yet arise through mechanistically distinct routes. Segmental CNAs typically require chromosomal breakage, and therefore often coincide with SV junctions. By contrast, whole-chromosome aneuploidy and whole-genome doubling (WGD) primarily reflect mitotic or cytokinetic failure and can occur without local breakpoints, while nevertheless reshaping the karyotypic landscape and seeding subsequent structural complexity. SVs, in turn, range from unbalanced events that alter copy number to ostensibly balanced exchanges that predominantly rewire regulatory architecture. Despite their diverse and sometimes catastrophic architectures, SVs are ultimately rooted in double-strand break formation and error-prone resolution. By integrating CNAs and SVs within a unified mechanistic and functional framework, we aim to convert catalogs into concepts and distill the organizing principles that govern tumor genome evolution.

Humans↗
PubMed2026

Tumor evolution shapes metastatic competence in colorectal cancer.

In this issue of Cancer Cell, Manca et al. investigate the genomic evolution and metastatic tropism of over 7,000 colorectal cancers. Integrating primary tumor location, genomic alterations, and clinical variables, they reveal how distinct patterns of tumor evolution shape metastatic competence and impact organ tropism, offering a framework to refine surveillance.

Humans↗
PubMed2025

SISTEM: simulation of tumor evolution, metastasis, and DNA-seq data under genotype-driven selection.

SUMMARY: SISTEM is a software package and mathematical framework for simulating tumor evolution and cell migrations at single-cell resolution. Unlike existing frameworks which simulate cancer cell populations under the neutral coalescent or using simple birth-death models, SISTEM simulates tumor populations under somatic clonal selection using an agent-based framework. SISTEM can generate mutation profiles, read counts, and DNA sequencing reads along with ground truth cell lineages and migration graphs under a number of easily customizable mutation and selection models. For improved realism, SISTEM allows for cell fitness to be driven by genomic events of various scales including single nucleotide variants, segmental gains and losses, whole-chromosomal and chromosome-arm aberrations, and whole-genome duplications. SISTEM also includes numerous migration models to simulate metastatic cancers, facilitating the exploration and evaluation of diverse migration patterns. AVAILABILITY AND IMPLEMENTATION: SISTEM is written in Python and is freely available open-source under GNU GPLv3 from: https://github.com/samsonweiner/sistem.

Software↗
PubMed1977

Immune surveillance against virus-induced tumors and nonrejectability of spontaneous tumors: contrasting consequences of host versus tumor evolution.

Spontaneous tumours are defined as tumors that develop in the absence of all experimental interference. In contrast to the widely documented, strong rejection reactions against most virus-induced tumors, spontaneous tumors evoke little or no detectable rejection reaction in intact or preimmunized syngeneic hosts. The difference can be viewed in relation to the contrasting natural history of the two conditions. Spontaneous tumors evolve in several steps, as a fule. "Tumor progression" is a microevolutionary process at the level of the somatic tissue where successive clonal variants replace each other. Each new variant gains the upper hand due to its greater independence of some restricting host mechanism. Independence of immune restrictions must be part of this process. Host selection for immune resistance apparently plays no major role here, presumably because most of the naturally occurring tumors arise after the host has passed the peak of its reproductive period. Protection against the oncogenic effects of ubiquitous tumor viruses is, on the other hand, the result of host selection for immune mechanisms favoring prompt rejection of virus-transformed cells. This is neither synonymous with nor related to protection against the viral infection per se, which is frequently successful and usually quite harmless. A certain relationship can be perceived between the degree of viral ubiquity and the strength of immune protection against the corresponding tumor cells. Natural selection for host recognition of commonly occurring, virally induced changes in neoplastic cell membranes can be surmised to occur, at least in part, by the fixation of appropriate immune responsiveness (Ir) genes. The role of Ir genes for tumor recognition can be approached by the genetic analysis of the F1 hybrid resistance effect. Unresponsiveness to spontaneous tumors may be overcome by target-cell modification, e.g., by chemical coupling, somatic cell hybridization, or viral "xenogenization".

Animals↗
PubMed2026

Competing subclones and fitness diversity shape tumor evolution across cancer types.

MOTIVATION: Intratumor heterogeneity arises from ongoing somatic evolution and complicates cancer diagnosis, prognosis, and treatment. Reconstructing evolutionary dynamics typically requires spatiotemporal samples, which are often unavailable in clinical settings. Computational approaches that can infer tumor evolutionary history from single-timepoint bulk sequencing data remain limited. RESULTS: We present estimating evolutionary events through single-timepoint sequencing (TEATIME), a novel computational framework that models tumors as mixtures of two competing cell populations: an ancestral clone with baseline fitness and a derived subclone with elevated fitness. Using cross-sectional bulk sequencing data, TEATIME estimates mutation rates, timing of subclone emergence, relative fitness, and number of generations of growth. To quantify intratumor fitness asymmetries, we introduce a novel metric-fitness diversity-which captures the imbalance between competing cell populations and serves as a measure of functional intratumor heterogeneity. Applying TEATIME to 33 tumor types from The Cancer Genome Atlas, we revealed divergent as well as convergent evolutionary patterns. Notably, we found that immune-hot microenvironments constraint subclonal expansion and limit fitness diversity. Moreover, we detected temporal dependencies in mutation acquisition, where early driver mutations in ancestral clones epistatically shape the fitness landscape, predisposing specific subclones to selective advantages. These findings underscore the importance of intratumor competition and tumor-microenvironment interactions in shaping evolutionary trajectories, driving intratumor heterogeneity. Lastly, we demonstrate that TEATIME-derived evolutionary parameters and fitness diversity offer novel prognostic insights across multiple cancer types. AVAILABILITY AND IMPLEMENTATION: R implementation of TEATIME is available on GitHub (https://github.com/liliulab/TEATIME) and Zenodo (https://zenodo.org/records/17422174).

Neoplasms↗
PubMed2025

Spatial-Temporal Diversity of Extrachromosomal DNA Shapes Urothelial Carcinoma Evolution and Tumor-Immune Microenvironment.

Extrachromosomal DNA (ecDNA) presents a promising target for cancer therapy; however, its spatial-temporal diversity and influence on tumor evolution and the immune microenvironment remain largely unclear. We apply computational methods to analyze ecDNA from whole-genome sequencing data of 595 urothelial carcinoma (UC) patients. We demonstrate that ecDNA drives clonal evolution through structural rearrangements during malignant transformation and recurrence of UC. This supports a model wherein tumors evolve via the selective expansion of ecDNA-bearing cells. Through multi-regional sampling of tumors, we demonstrate that ecDNA contributes to the evolution of multifocality and increased intratumoral heterogeneity. EcDNA is present in 36% of UC tumors and correlates with an immunosuppressive phenotype and poor prognosis. Single-cell RNA sequencing analyses reveal that ecDNA+ malignant cells exhibit diminished expression of major histocompatibility complex class I molecules, enabling them to evade T-cell immunity. Finally, we show that sequencing of urinary sediment-derived DNA has excellent specificity in detecting ecDNA.

Journal Article↗
PubMed2026

Evolution of tumor subclones and T-cell dynamics underlie variable ibrutinib responses in Waldenström macroglobulinemia.

To elucidate the molecular basis underlying differential responses and resistance to ibrutinib in Waldenström macroglobulinemia (WM), we conducted a prospective phase 2 trial of ibrutinib monotherapy in treatment-naïve patients. A total of 74 sequential bone marrow (BM) aspirates from 17 patients, collected from baseline through 48 treatment cycles, were profiled using single-cell multiomics. BM cells were segregated primarily into B-cell/plasma cell and T-cell compartments. Longitudinal clonal tracking of malignant B cells/plasma cells identified 3 distinct evolutionary patterns: evolution (early clone contraction with late clone expansion and increasing genomic complexity), devolution (early clone expansion with late clone contraction and genomic simplification), and no evolution (stable clonal architecture). The evolution pattern was strongly associated with disease progression, whereas devolution correlated with durable clinical response. Transcriptomic profiling of resistant clones enabled development and validation of the Waldenström ibrutinib prediction (WIP) score, which predicted treatment response at baseline. Within the WIP signature, LYN emerged as a key regulator; LYN knockdown or inhibition significantly increased WM cell sensitivity to ibrutinib, suggesting a rational combination strategy. In parallel, GZMB+ CD8+ effector-memory T cells expanded after treatment in patients with progressive disease and coexisted with tumor evolution. These cells exhibited persistently impaired cytotoxic programs (eg, GNLY), a dedifferentiated memory-like state, elevated PDCD1 expression, and reduced T-cell receptor diversity. Together, this study provides, to our knowledge, the first single-cell framework of tumor clonal evolution and T-cell dysfunction under ibrutinib in WM, introduces the WIP score as a predictive biomarker for treatment response, and identifies actionable tumor-intrinsic and immune mechanisms driving resistance. This trial was registered at www.ClinicalTrials.gov as NCT02604511.

Aged↗
PubMed2025

Bayesian inference of fitness landscapes via tree-structured branching processes.

MOTIVATION: The complex dynamics of cancer evolution, driven by mutation and selection, underlies the molecular heterogeneity observed in tumors. The evolutionary histories of tumors of different patients can be encoded as mutation trees and reconstructed in high resolution from single-cell sequencing data, offering crucial insights for studying fitness effects of and epistasis among mutations. Existing models, however, either fail to separate mutation and selection or neglect the evolutionary histories encoded by the tumor phylogenetic trees. RESULTS: We introduce FiTree, a tree-structured multi-type branching process model with epistatic fitness parameterization and a Bayesian inference scheme to learn fitness landscapes from single-cell tumor mutation trees. Through simulations, we demonstrate that FiTree outperforms state-of-the-art methods in inferring the fitness landscape underlying tumor evolution. Applying FiTree to a single-cell acute myeloid leukemia dataset, we identify epistatic fitness effects consistent with known biological findings and quantify uncertainty in predicting future mutational events. The new model unifies probabilistic graphical models of cancer progression with population genetics, offering a principled framework for understanding tumor evolution and informing therapeutic strategies. AVAILABILITY AND IMPLEMENTATION: The Python package FiTree and the analysis workflows are available at https://github.com/cbg-ethz/FiTree.

Bayes Theorem↗
PubMed2026

Metabolic convergence of diabetes and prostate cancer: from dysglycemia to tumor microenvironment reprogramming.

The relationship between diabetes mellitus and prostate cancer (PC) represents one of the most intriguing paradoxes in cancer epidemiology, with diabetic individuals exhibiting a reduced incidence of PC yet poorer prognosis following diagnosis. This apparent contradiction underscores the need for an integrated understanding of how systemic metabolic dysfunction influences prostate carcinogenesis and disease progression. The present review critically synthesizes contemporary epidemiological, mechanistic, and translational evidence to establish metabolic convergence as a unifying framework linking diabetes-associated metabolic abnormalities with PC biology. Current evidence indicates that chronic dysglycemia, hyperinsulinemia, insulin resistance, and endocrine perturbations orchestrate interconnected intracellular signaling networks involving PI3K-AKT-mTOR, AMPK, AGE-RAGE signaling, oxidative stress, mitochondrial dysfunction, and epigenetic reprogramming, collectively driving metabolic adaptation and tumor evolution. Beyond tumor-intrinsic mechanisms, diabetes profoundly remodels the prostate tumor microenvironment through alterations in stromal metabolism, cancer-associated fibroblast activation, adipocyte-tumor crosstalk, extracellular matrix (ECM) remodeling, hypoxic adaptation, and vascular dysfunction, while simultaneously promoting immunometabolic reprogramming characterized by macrophage polarization, T-cell dysfunction, immune checkpoint activation, and immune evasion. The review further examines the bidirectional interactions between antidiabetic therapies and PC treatment, critically evaluating the translational potential of metformin and emerging glucose-lowering agents within the context of precision metabolic therapeutics. Finally, future directions encompassing biomarker-guided patient stratification, longitudinal metabolic profiling, multi-omics integration, artificial intelligence, and clinically relevant mechanistic validation are discussed as essential components of next-generation precision oncology. Collectively, this review reframes diabetes as an active metabolic determinant of PC rather than a coincidental comorbidity and highlights metabolism-centered precision strategies as promising avenues for improving risk stratification, therapeutic decision-making, and clinical outcomes in diabetes-associated PC.

Humans↗
PubMed2024

Stochastic modeling of single-cell gene expression adaptation reveals non-genomic contribution to evolution of tumor subclones.

Cancer progression is an evolutionary process driven by the selection of cells adapted to gain growth advantage. We present a formal study on the adaptation of gene expression in subclonal evolution. We model evolutionary changes in gene expression as stochastic Ornstein-Uhlenbeck processes, jointly leveraging the evolutionary history of subclones and single-cell expression data. Applying our model to sublines derived from single cells of a mouse melanoma revealed that sublines with distinct phenotypes are underlined by different patterns of gene expression adaptation, indicating non-genetic mechanisms of cancer evolution. Sublines previously observed to be resistant to anti-CTLA4 treatment showed adaptive expression of genes related to invasion and non-canonical Wnt signaling, whereas sublines that responded to treatment showed adaptive expression of genes related to proliferation and canonical Wnt signaling. Our results suggest that clonal phenotypes emerge as the result of specific adaptivity patterns of gene expression. A record of this paper's transparent peer review process is included in the supplemental information.

Animals↗
PubMed2026

Interpreting cancer genetics through a two-step "evolutionary cascade hypothesis": bridging neutral and selective perspectives.

BACKGROUND: DNA mutations are the fundamental engines of cancer, driving its initiation and progression. The forces that fuel malignancy are also the architects of evolution, shaping life through genetic variations. Mutations, in fact, can emerge naturally from endogenous processes, such as oxidative DNA damage or errors in replication, as well as induced by external factors, including cosmic radiation and chemical carcinogens. MAIN BODY: A key question in cancer research is whether tumor evolution is primarily governed by selective bottlenecks, neutral evolution, or dynamic genetic plasticity. In this work, we examine cancer as a disease driven by evolutionary processes rooted in fundamental biological requirements, including sustained proliferation and nutrient utilization. We hypothesize that the accumulation of mutations activates an evolutionary switch, enabling tumor cells to acquire an enhanced capacity for survival, adaptation, and growth at rates far exceeding typical evolutionary timescales. We propose the "evolutionary cascade hypothesis," a unifying framework that integrates these models into a coherent sequence. At its core lies the failure of DNA repair mechanisms, representing a critical transition in cancer progression. This shift marks the transition from an initial non-Darwinian, neutral phase to a Darwinian, more deterministic phase. CONCLUSIONS: As predictive models of tumor evolution advance through genomic big data and artificial intelligence-driven analysis, the future of cancer treatment may extend beyond targeting individual mutations to disrupting the underlying evolutionary mechanisms that sustain malignancy. This paradigm shift could redefine therapeutic strategies and ultimately improve patient outcomes.

Humans↗
PubMed2026

highSpaClone enables copy number alteration inference and tumor subclone analysis for high-resolution spatial transcriptomics.

High-resolution spatially resolved transcriptomics (SRT) offers unprecedented opportunities to investigate tumor heterogeneity but poses substantial computational and analytical challenges. Here, we present highSpaClone, a computational framework for copy number alteration (CNA) inference and tumor subclone identification from high-resolution SRT data across multiple spatial scales. By integrating spatial constraints into CNA estimation and clonal clustering, highSpaClone enables neighboring spatial locations to share information, thereby improving the robustness of genomic signals and the accuracy of subclone delineation. Across multiple Xenium and Visium HD datasets, highSpaClone revealed unique transcriptional programs, clonal evolutionary trajectories, and distinct tumor-microenvironment interactions. Furthermore, in human colorectal cancer samples, highSpaClone detected CNA events in histologically normal epithelial regions, highlighting early genomic alterations associated with field cancerization. These findings establish highSpaClone as a scalable framework for studying clonal architecture and tumor evolution.

CP: cancer biology↗
PubMed2026

Translational case series comparing next-generation sequencing profiles of primary breast cancer and brain metastases.

BACKGROUND: Breast cancer (BC) is a heterogeneous disease, and its molecular and immunohistochemical (IHC) profiles may change over time, particularly under therapeutic pressure. IHC discordance between primary tumors and BC brain metastases (BCBM) has been reported, yet its biological and clinical significance remains incompletely defined. Genomic profiling using next-generation sequencing (NGS) may provide additional insight into tumor evolution and clonal selection, although data from paired BC and BCBM are limited. METHODS: This translational case series included six patients randomly selected from an institutional cohort of BC patients who underwent neurosurgical resection of BCBM. IHC reassessment (ER, PR, and HER2) and NGS profiling using targeted panels were performed. RESULTS: Three of the six cases presented with IHC discordance, mainly loss of HR expression and gain of HER2 in BCBM. Genomic profiling identified 23 mutations in primary tumors compared with four in BCBM. BRCA1/2 variants predominated in primary tumors (21/23, 91%), most predicted to result in loss-of-function alterations. One mutation (PIK3CA/N345K) was shared between primary and metastatic tissues within the same patient. Overall survival ranged from 28 to 146 months. CONCLUSION: This paired analysis demonstrates immunophenotypic and genomic divergence between BC and BCBM, supporting the concept of dynamic tumor evolution. Receptor conversion and emergence or loss of actionable genomic alterations highlight the potential value of repeat molecular assessment in advanced stages. Although limited by a small sample size, retrospective design, and absence of matched germline testing, these findings reinforce the importance of integrating biomarker reevaluation into the management of selected patients.

Humans↗
PubMed2025

Sustained NF-κB activation allows mutant alveolar stem cells to co-opt a regeneration program for tumor initiation.

Disruptions to regulatory signals governing stem cell fate open the pathway to tumorigenesis. To determine how these programs become destabilized, we fate-map thousands of murine wild-type and KrasG12D-mutant alveolar type II (AT2) stem cells in vivo and find evidence for two independent AT2 subpopulations marked by distinct tumorigenic capacities. By combining clonal analyses with single-cell transcriptomics, we unveil striking parallels between lung regeneration and tumorigenesis that implicate Il1r1 as a common activator of AT2 reprogramming. We show that tumor evolution proceeds through the acquisition of lineage infidelity and reversible transitions between mutant states, which, in turn, modulate wild-type AT2 dynamics. Finally, we discover how sustained nuclear factor κB (NF-κB) activation sets tumorigenesis apart from regeneration, allowing mutant cells to subvert differentiation in favor of tumor growth.

Animals↗
PubMed2025

Extrachromosomal DNA-Driven Oncogene Dosage Heterogeneity Promotes Rapid Adaptation to Therapy in MYCN-Amplified Cancers.

UNLABELLED: Extrachromosomal DNA (ecDNA) amplification enhances intercellular oncogene dosage variability and accelerates tumor evolution by violating foundational principles of genetic inheritance through its asymmetric mitotic segregation. Spotlighting high-risk neuroblastoma, we demonstrate how ecDNA amplification undermines the clinical efficacy of current therapies in cancers with extrachromosomal MYCN amplification. Integrating theoretical models of oncogene copy number-dependent fitness with single-cell ecDNA quantification and phenotype analyses, we reveal that ecDNA copy-number heterogeneity drives phenotypic diversity and determines treatment sensitivity through mechanisms unattainable by chromosomal oncogene amplification. We demonstrate that ecDNA copy number directly influences cell fate decisions in cancer cell lines, patient-derived xenografts, and primary neuroblastomas, illustrating how extrachromosomal oncogene dosage-driven phenotypic diversity offers a strong evolutionary advantage under therapeutic pressure. Furthermore, we identify senescent cells with reduced ecDNA copy numbers as a source of treatment resistance in neuroblastomas and outline a strategy for their targeted elimination to improve the treatment of MYCN-amplified cancers. SIGNIFICANCE: ecDNA-driven tumor genome evolution provides a major challenge to curative cancer therapies. We demonstrate that ecDNA copy-number dynamics drives treatment resistance by promoting oncogene dosage-dependent phenotypic heterogeneity in MYCN-amplified cancers. Exploiting phenotype-specific vulnerabilities of ecDNA cells, therefore, presents a powerful strategy to overcome treatment resistance. See related commentary by Korsah, p. 1979.

Humans↗
PubMed2026

The mechanism of malignant progression in extramammary Paget's disease (EMPD): hallmarks of EMPD.

The pathogenesis of cancer is characterized by the acceleration of tumor growth, inhibition of tumor suppression, genetic and epigenetic alteration, lubricative transformation and tumor microenvironment. Extramammary Paget's disease (EMPD) is a rare skin cancer that originates from apocrine glands in genital and axillary area. Although the pathogenesis of EMPD is still poorly understood, increasing evidence reveals that the mechanism of EMPD progression is regulated by the acquired ability of EMPD cells and tumor microenvironment. HER2/PI3K/AKT signaling and hormone receptor pathways are activated. Whereas tumor mutation burden is low, numerous driver genes such as ERBB2 and PIK3CA are detected. Tumor evolution in EMPD is characterized by high genetic intratumor heterogeneity with shared background factors. Tumor microenvironment in EMPD promotes immune evasion through the reduction of reduced CD4 + and CD8 + T cells and the increase of Treg cells and CD163 + macrophages. Enhanced Warburg effect and S. aureus contribute to the suppression of antitumor immunity. This review focuses on the mechanism of malignant progression in EMPD (hallmarks of EMPD).

Genome mutation↗