Search bioRxiv⌕ Search

subject

cancer biology

cancer biology: explore 38 source-linked works published from 2026 to 2026, with original documents and citations.

This collection is a preview while coverage and quality are evaluated.

Search within this collection

Coverage and selection

Includes records with this source-supplied label or an explicit phrase match in their metadata. Matches indicate a mention, not proof that a paper uses a method or tests a material. Source versions are consolidated by DOI.

Sources: biorxiv. Collection updated 2026-09-17. Counts describe this index, not the complete source archives.

Spatial Mapping of the Lung Cancer Ecosystem Reveals Distinct Patterns of Intratumoral and Internodular Heterogeneity

The spatial organization of malignant and non-malignant cells within the tumor microenvironment (TME) critically influences tumor evolution and therapeutic response. However, the architecture of micro-niches remains incompletely understood. Leveraging Xenium-based spatial transcriptomics, we comprehensively mapped the spatial ecosystem of an orthotopic murine lung cancer model, identifying distinct spatial domains that form unique, organized cellular neighborhoods. These domains cluster into three major communities: (1) non-tumoral regions that recapitulate canonical normal lung structures; (2) a heterogeneous peri-tumoral region composed of spatial domains characterized by mesenchymal remodeling, active immune checkpoint signaling, and immunosuppressive myeloid populations; and (3) intra-tumoral regions that reveal marked tumor nodule heterogeneity, with unique tumor-specific domains exhibiting hallmark cancer pathways. Furthermore, our analytic approach was applicable to human lung cancer tissue. Notably, spatial domain analysis allowed us to resolve tumor nodules into multiple biologically distinct subtypes, defined by domain composition, hallmark cancer programs, and intercellular communication patterns within the TME.

cancer biology↗

A replicated patient-specific component of tumour telomere length across two pan-cancer cohorts

Bulk telomere length measured from tumour sequencing is routinely interpreted as a property of the cancer cells. However, a tumour specimen is a mixture, and the patient who supplies it has a telomere length of their own. Here I re-analyse published pan-cancer telomere estimates and ask how much of a tumour's telomere length is patient-specific. A calibration step comes first. Whole-genome and low-pass estimates recover the known cross-sectional attrition of leukocyte telomeres with age, at 26.6 bp per year in blood normals, whereas whole-exome estimates do not. After adjustment for cancer type, sequencing centre and sex, the exome slope is minus 0.6 bp per year. In 684 blood-normal aliquots sequenced by both assays, the whole-genome estimate declines at 38.9 bp per year, whereas the exome estimate from the same DNA shows no detectable decline. The difference between assays is 41.5 bp per year, with P = 3 x 10^-10. Because exome data constitute 78.6% of the original resource, downstream analyses use only whole-genome and low-pass libraries. Within those data, tumour telomere length tracks the patient's matched-normal telomere length. The Spearman correlation is 0.395 in TCGA, with positive associations in 22 of 23 cancer types. This finding replicates in PCAWG using a different telomere estimator, with a correlation of 0.472 and positive associations in all 24 histologies examined. Adjustment for cancer type, sequencing centre and library type leaves a regression coefficient of 0.385. The association is also stable after adjustment for age, sex, tumour purity, leukocyte fraction, ploidy, sequencing coverage and continental ancestry, with coefficients ranging from 0.406 to 0.429. Pure normal-cell admixture is rejected as the sole explanation. Under a two-compartment mixture model, the coefficient for host telomere length is expected to equal 1 and the host-by-purity interaction to equal minus 1. These restrictions are jointly rejected with P = 0.001. Tumour purity, leukocyte fraction and age each explain only about 1 to 3% of within-cohort variance and do not alter the cross-cancer ranking. By contrast, the between-cohort coefficient is not directly interpretable. Its apparent near one-to-one relationship with tissue-associated telomere length depends strongly on which tissue supplies the matched-normal reference and on the statistical spread of that predictor, falling to 0.44 when organ-matched solid tissue is used. Bulk tumour telomere length is therefore a composite phenotype containing a replicated patient-specific component. Telomere biomarker studies should include matched-normal telomere length as a covariate rather than treating tumour telomere length as exclusively tumour-intrinsic.

cancer biology↗
Compare source metadata on this page

These are bibliographic comparisons, not experimental rankings. Follow the original document for methods and conditions.