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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↗
PubMed1990

Parallel karyotypic evolution and tumor progression in uterine leiomyoma.

Cytogenetic evidence of clonal evolution was detected in five uterine leiomyomas. In two tumors, two clones were found, the third tumor had four, the fourth had nine, and the fifth had 12 clones. The first tumor had trisomy 12 as the primary anomaly and a sideline that also contained a del(7)(q21q31). Both clones of the second tumor had three structural changes in common but differed by the presence in the more advanced clone of an inv(7)(q31q34). Two cytogenetically unrelated pairs of clones were seen in the third tumor. One clone had a stemline of 46 and an r(1); a sideline had developed through duplication of this clone. The other pair had a del(7)(q21q31) in common. The last two tumors both had t(12;14)(q14-15;q23-24) as the primary abnormality. They also had a high frequency of telomeric associations that involved certain chromosome arms only. One of the secondary changes in the fourth tumor was a del(7)(q21q31); the principal secondary change in the fifth case was a ring chromosome 1 of variable size in the different clones. The analysis of these five uterine leiomyomas and the collation of the results with previously obtained data lead us to conclude that del(7)(q21q31) is secondary to t(12;14) and + 12 in this tumor type, and that ring formation involving chromosome 1 material, often with duplication of segments, is a common phenomenon during clonal evolution. The fact that the tumors were classified as cellular and had an increased mitotic rate indicates a parallel development between histologically detectable tumor progression and cytogenetically recognizable clonal evolution in uterine leiomyomas.

Chromosome Aberrations↗
PubMed1991

Genetic events in breast cancer and their clinical correlates.

The great heterogeneity of clinical breast cancer probably reflects heterogeneity of the mechanisms that are involved in its genesis and in disease progression. Genetic events that may be critical for tumor etiology, may determine tumor characteristics, and may contribute to tumor evolution should differ among individuals and among individual tumors and may distinguish subgroups with varying prognoses. We approach this problem from two viewpoints: one is based on population studies and the other on genetic events at the cellular level. 1. Population-based questions: What susceptibility factors are associated with individuals and groups who develop tumors? Inheritance patterns, familial aggregates, and associations with other tumors and other genetic diseases have been described for breast cancer. Bilateral and multifocal tumors may be examples of inherited predisposition, and their clinical and biological characteristics should be informative. Are there associations with tumor identifiers, such as histologic type and other tumor markers, for any of the "increased susceptibility" states? 2. Tumor-derived information: What types and frequencies of genetic alterations are found; do they relate to stage of tumor evolution, to morphologic classification, or to clinical evidence of comparative malignancy? Genetic evaluation depends upon cytogenetic and cytometric methods at the cellular level, and on detection of mutation, gene deletion, or amplification at the molecular level. We examine whether observed genetic alterations suggest mechanistic bases for morphologic distinctions among breast cancers and whether they assist in defining clinical phenotypes or steps in tumor progression. The general conclusions are that breast cancer comprises a complex set of neoplasms, that there is as yet little evidence to favor a final common pathway for its origin, and that a wide range of biological perturbations underlie its clinical course in the individual patient. An understanding of the basic mechanisms, their variety, and which of them apply in individual cases, is necessary as a rational basis for classification and for the development of strategies to interfere with tumor development in high-risk individuals.

Breast Neoplasms↗
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↗
PubMed1990

Chromosomal evolution and tumor progression in a myxoid liposarcoma.

A myxoid liposarcoma showed macroscopic, histologic, and cytogenetic heterogeneity. In one of three myxoid nodules and in the surrounding lipoma-like tumor tissue, the translocation t(12;16)(q13;p11), known to be specific for myxoid liposarcoma, was found as the sole chromosomal abnormality. In the other two nodules, additional rearrangements involving chromosomes 1, 12, and 16 were found. These aberrations were probably secondary to the primary t(12;16), and are cytogenetic evidence of clonal evolution. The complex chromosome aberrations were present in those tumor parts that had more malignant histology, indicating that the acquisition of secondary chromosomal aberrations parallels the histologic manifestations of tumor progression.

Chromosome Aberrations↗
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↗