bioRxiv Science⌕ Search

bioRxiv · 10.1101/2025.05.07.652660

Non-Intestinal Microbial Signatures in Stool as Predictors of Cancer Immunotherapy Outcome

Abstract

Despite the recognized role of the gut microbiome in modulating immune checkpoint inhibitor (ICI) efficacy, the ecological principles governing this relationship remain elusive. Moving beyond cataloging specific bacteria, we investigated whether general ecosystem properties determine clinical outcome. Through genome-resolved metagenomic analysis, we constructed a comprehensive catalog from 951 stool metagenomes and subsequently analyzed a curated subset of 624 samples from 11 multi-cancer cohorts. Our catalog comprises 3,816 non-redundant metagenome-assembled genomes (MAGs) and reveals key ecological determinants of ICI response. We found that clinical benefit is associated with an ecosystem dominated by prevalent, autochthonous taxa. A taxons prevalence in the population positively correlated with its association with positive outcome. Functionally, responder-associated microbes were enriched in genomic capacity for complex carbohydrate metabolism (including specialized mucin degradation) and amino acid biosynthesis. In contrast, non-response was characterized by enrichment of low-prevalence, exogenous (oral and food-derived) bacteria and a functional shift toward nucleoside metabolism, indicative of a dysbiotic state focused on replication. A log-ratio biomarker capturing this ecological shift provided generalizable predictive value across independent cohorts (mean AUC = 0.67 {+/-} 0.13). Our results support an ecological interpretation of the "Anna Karenina principle" in microbiomes: response is linked to a stable, functionally coherent microbial community, whereas non-response represents a destabilized state with high individual variability. This reframes the search for biomarkers from individual taxa to the assessment of ecosystem stability and metabolic competence, providing a foundation for microbiome-targeted strategies to improve cancer immunotherapy outcomes. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/652660v2_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@f0c9edorg.highwire.dtl.DTLVardef@591c6eorg.highwire.dtl.DTLVardef@59863forg.highwire.dtl.DTLVardef@17c934d_HPS_FORMAT_FIGEXP M_FIG C_FIG

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kanaeva, V. A., Olekhnovich, E. I.. 2025-05-11. Non-Intestinal Microbial Signatures in Stool as Predictors of Cancer Immunotherapy Outcome. https://doi.org/10.1101/2025.05.07.652660

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Senescence-associated KRAS upregulation in peripheral T cells links to premature coronary artery disease

Aims: Premature coronary artery disease (PCAD) lacks specific molecular drivers, and the role of immunosenescence is unclear. We investigated whether aging-related gene dysregulation in T cells contributes to PCAD. Methods: We combined bulk transcriptomics of PBMCs from 12 PCAD patients and 21 controls, single-cell RNA sequencing of PBMCs and human atherosclerotic plaques, weighted gene co-expression network analysis, gene perturbation network analysis, and molecular docking. Results: KRAS was identified as a hub gene intersecting PCAD-associated genes and aging-related genes. Single-cell analysis showed KRAS upregulation predominantly in effector CD8+ T cells, which exhibited the highest senescence scores that were further elevated in disease. Network perturbation of KRAS strongly impacted the cell killing pathway. KRAS-high effector CD8+ T cells were detected in coronary and carotid plaques, displaying enhanced cytotoxicity, exhaustion, and senescence features. Additionally, a candidate small molecule was computationally predicted to bind inactive KRAS. Conclusions: Elevated KRAS expression in senescent, cytotoxic CD8+ T cells is associated with PCAD, bridging immunosenescence and premature atherosclerosis. This finding provides a novel biomarker candidate and potential therapeutic entry point, awaiting further functional validation.

bioinformatics↗

Targeted finetuning enables co-folding models to learn ligand-induced protein conformational states

Advances in protein structure prediction have enabled all-atom protein-ligand co-folding models that predict bound conformations directly from sequence and small-molecule structure. However, these models often fail to generalize to novel binding sites or alternative protein conformational states, limiting their utility for chemical biology and drug discovery. Here we show this limitation reflects training data bias rather than architectural constraints and can be overcome through targeted finetuning. Using ten previously unseen X-ray structures of Werner (WRN) helicase from a drug discovery program, we finetune Boltz-1 to learn both an allosteric binding site and a large conformational change locking the enzyme in an inactive state, while preserving accuracy on the ATP-bound state. The finetuned model generalizes to different chemical series and transfers the conformational logic across RecQ-family helicases in a binding-site sequence-dependent manner. This approach provides a blueprint for adapting foundation models as new structural and mechanistic data emerge, enabling co-folding networks to capture ligand-induced conformational switches and binding poses absent from their training data but central to biological regulation and therapeutic intervention.

bioinformatics↗

Benchmarking single-cell foundation models for aging biology

Single cell foundation models (scFMs) provide representations of cellular states, but their utility across biological questions in aging research remains unclear. We established a benchmark of cellular representations for aging research, evaluating ten general-purpose scFMs, three aging-specific models and conventional methods across five biological questions using more than 2.5 million single cell transcriptomes. Using frozen pretrained representations, Geneformer performed best among scFMs for chronological age prediction and age pseudotime concordance, although 2,000 highly variable genes achieved higher mean performance. Several scFMs captured positive molecular age shifts across three disease contexts, consistent with reported aging-associated changes. SCimilarity performed well for rare cellular state identification across out-of-distribution datasets, exceeding aging specific models and conventional baselines. At the gene level, scGPT showed the highest recovery of reference TF target interactions, including aging-related regulatory hubs. Overall, scFMs supported diverse aging analyses, but performance depended on the biological question, highlighting their utility for rare cellular state identification and regulatory analysis.

bioinformatics↗