bims-gerecp 2025-08-24 papers

Issue of 2025–08–24
seven papers selected by
Xiao Qin, University of Oxford

bioRxiv. 2025 Aug 13. pii: 2025.08.11.669722. [Epub ahead of print]

Colorectal cancer heterogeneity co-evolves with tumor architecture to determine disease outcome.

iCAN

Intratumoral heterogeneity, originating from genetic, epigenetic, and phenotypic cellular diversity, is pervasive in cancer. As these heterogeneous states employ diverse mechanisms to promote tumor progression, metastasis, and therapy resistance, emergence of cancer heterogeneity is one of the most significant barriers to curative treatment. Here we leverage deep learning approaches to develop a high-throughput image-analysis paradigm with subcellular resolution that quantifies and predicts colorectal cancer (CRC) patient outcome based on tissue architecture and nuclear morphology. We further combine this approach with spatial transcriptomics, multiplex immunohistochemistry, and patient-derived organoids to uncover a dynamic, co-evolutionary relationship between tumor architecture and cell states. We identify clinically relevant architectural interfaces in CRC tissue that diversify cellular identities by favoring distinct cancer stem cell states and thus promote evolution of tumor heterogeneity. Specifically, tissue fragmentation and associated compressive forces promote loss of classic stem cell signature and acquisition of fetal/regenerative stem cell states, which initially emerges as a hybrid state with features of epithelial-to-mesenchyme (EMT) transition. Additional tumor stroma communication then diversifies these states into distinct stem cell and EMT states at the invasive margins of tumors. Reciprocally, Wnt signaling state of tumor cells tunes their responsiveness to tissue architecture. Collectively, this work uncovers a feedback loop between cell states and tissue architecture that drives cancer heterogeneity and cell state diversification. Machine learning-based analyses harness this co-dependency, independently of mutational status or cancer stage, to predict patient outcome.

DOI: https://doi.org/10.1101/2025.08.11.669722

Nature. 2025 Aug;644(8077): 829-832

Beyond AlphaFold: how AI is decoding the grammar of the genome.

Jeffrey M Perkel.

Keywords: Genomics; Machine learning; Synthetic biology; Technology

DOI: https://doi.org/10.1038/d41586-025-02621-8

Cancer Res. 2025 Aug 18.

Generations in Flux: Tracking the Birth of Cellular Heterogeneity in Cancer.

Aaron McKenna.

Intratumor heterogeneity is a hallmark of cancer, enabling subpopulations of cells to evade therapy, adapt to immune attack, and thrive in diverse microenvironments. While retrospective genomic and epigenomic analyses have mapped the large-scale histories of tumor evolution, they cannot capture the rapid, dynamic changes in cell state that occur as individual cells divide. Panagopoulos and colleagues recently developed a cellular platform to monitor the role of transient replication stress in real time, tracking sister cells as they divide and replicate. The authors use these techniques to show that daughter and granddaughter cells can inherit very different states, often leading to further cellular instability. This work broadens our understanding of how diverse cell states arise from oncogenic stress and how cellular heterogeneity emerges.

DOI: https://doi.org/10.1158/0008-5472.CAN-25-3601

Front Oncol. 2025 ;15 1624467

Cancer complexity: why we need a novel cancer research strategy.

Anders Bredberg.

Keywords: cancer complexity; cancer treatment; chaos theory; research strategies; tinkering

DOI: https://doi.org/10.3389/fonc.2025.1624467

Comput Struct Biotechnol J. 2025 ;27 3556-3564

A subset of Polycomb-targeted transcription factor genes become hypermethylated yet upregulated in colorectal cancer.

Min-Kyeong Kwon, Goeun Park, Dayoung Go, Donghyun Park, Sridhar Hannenhalli, Sun Shim Choi.

DNA methylation is a key epigenetic regulator often disrupted in cancer, yet how promoter methylation dynamics translate into transcriptional changes during cancer progression remains incompletely understood. Here, we employed targeted bisulfite sequencing and RNA-seq on paired tumor and non-tumor tissues from 80 Korean colorectal cancer (CRC) patients to map promoter methylation and gene expression dynamics. Promoters with high baseline methylation in non-tumor tissues tended to become hypomethylated in tumors, while those with low baseline methylation underwent partial hypermethylation. However, these changes did not consistently correlate with gene silencing or activation. Strikingly, promoters marked by Polycomb (PcG⁺) in non-tumor tissue were prone to hypermethylation yet often remained transcriptionally active in tumors, a paradox most prominent in transcription factor (TF) genes. In contrast, hypermethylation in PcG⁻ promoters was more consistently associated with transcriptional repression. Our findings suggest that epigenetic plasticity at PcG⁺ TF gene promoters can override the typically repressive effects of DNA methylation, potentially enabling tumors to maintain or enhance the expression of key regulatory genes. This highlights the importance of PcG occupancy in shaping the functional consequences of methylation changes during colorectal tumorigenesis, warranting deeper investigation into how these epigenetic adaptations drive cancer progression.

Keywords: Chromatin; Colorectal Cancer; DNA Methylation; Polycomb; Transcriptional regulation

DOI: https://doi.org/10.1016/j.csbj.2025.07.057

Trends Immunol. 2025 Aug 15. pii: S1471-4906(25)00179-6. [Epub ahead of print]

Molecular mimicry as a driver of T cell-mediated tumour immunity.

Jamie Rossjohn, Luigi Nezi, Julianne S Walz, Maria Tagliamonte, Luigi Buonaguro.

Recently, a large pool of antigens derived from viral and bacterial microorganisms showing molecular mimicry with tumour-cell-expressed antigens was identified. These antigens can be presented by MHC molecules and elicit T cells that are crossreactive with microbial antigens and tumour-cell-associated antigens. In the setting of metastatic melanoma, such T cells can contribute to the response induced by immune checkpoint blockade therapy. Here, the current understanding of molecular mimicry in T cell-mediated tumour immunity and how this might be exploited for developing new preventive and therapeutic approaches for cancer is described. In particular, the literature on the concept and evidence of molecular mimicry in cancer is reviewed, covering the whole translational spectrum, from the antigen discovery strategy to the clinical evaluation.

Keywords: cancer vaccines; cross-reactive T cells; microbial-associated antigens; molecular mimicry; tumor-associated antigens

DOI: https://doi.org/10.1016/j.it.2025.07.012

Nat Genet. 2025 Aug 18.

Tracing the evolution of single-cell 3D genomes in Kras-driven cancers.

Miao Liu, Shengyan Jin, Sherry S Agabiti, Tyler B Jensen, Tianqi Yang, Jonathan S D Radda, Christian F Ruiz, Gabriel Baldissera, Moein Rajaei, Fang-Yong Li, Jeffrey P Townsend, Mandar Deepak Muzumdar, Siyuan Wang.

Although three-dimensional (3D) genome structures are altered in cancer, it remains unclear how these changes evolve and diversify during cancer progression. Leveraging genome-wide chromatin tracing to visualize 3D genome folding directly in tissues, we generated 3D genome cancer atlases of oncogenic Kras-driven mouse lung adenocarcinoma (LUAD) and pancreatic ductal adenocarcinoma. Here we define nonmonotonic, stage-specific alterations in 3D genome compaction, heterogeneity and compartmentalization as cancers progress from normal to preinvasive and ultimately to invasive tumors, discovering a potential structural bottleneck in early tumor progression. Remarkably, 3D genome architectures distinguish morphologic cancer states in single cells, despite considerable cell-to-cell heterogeneity. Analyses of genome compartmentalization changes not only showed that compartment-associated genes are more homogeneously regulated but also elucidated prognostic and dependency genes in LUAD, as well as an unexpected role for Rnf2 in 3D genome regulation. Our results highlight the power of single-cell 3D genome mapping to identify diagnostic, prognostic and therapeutic biomarkers in cancer.

DOI: https://doi.org/10.1038/s41588-025-02297-w