<i>Candida albicans</i> infiltrates colon and rectal cancers causing therapeutic resistance and decreased survival
Bibliographic record
Abstract
Abstract The microbiome is increasingly recognized as a modifier of cancer progression and therapy response, yet the role of intratumoral fungi remains poorly defined. Here, we identify Candida albicans colonization within human colorectal tumors as a predictor of reduced survival and impaired radiation response. Leveraging the Oncology Research Information Exchange Network (ORIEN) cohort, we show that high intratumoral Candida burden is associated with decreased survival across multiple gastrointestinal cancers, with the strongest treatment-specific effect in rectal cancer patients receiving radiotherapy. This observation was validated in independent rectal cancer cohorts using RNA sequencing and quantitative PCR. In immune-competent murine colorectal cancer models, oral gavage of C. albicans resulted in intratumoral colonization, accelerated tumor growth, and radiation resistance, effects not observed with Saccharomyces cerevisiae or PBS controls. Colonized tumors exhibited increased hypoxia, altered metabolic and transcriptional programs, and distinct expression of genes linked to cytokine signaling and cell survival. Hypoxia conditioned C. albicans secreted metabolites that directly conferred radiation resistance to colorectal cancer cells in vitro , implicating a cancer cell intrinsic mechanism independent of immune signaling. Untargeted metabolomics revealed enrichment of nucleosides and lipid oxidation intermediates under hypoxia, suggesting that C. albicans metabolites may provide substrates facilitating tumor recovery after irradiation. These findings establish C. albicans as a causal modifier of tumor biology and radiation response, highlighting intratumoral fungi as future potential therapeutic targets. Modulating fungal colonization or metabolism may improve radiotherapy outcomes and broaden our understanding of interactions between microbes and tumors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".