Abstract B037: Single-cell analysis reveals immune characteristics linked to poor prognosis in <i>Fusobacterium nucleatum</i>-infected colorectal cancer
Bibliographic record
Abstract
Abstract Fusobacterium nucleatum (Fn) is commonly detected in colorectal cancer (CRC) and worsens patient survival. Fn appears to play a role in colorectal cancer carcinogenesis through suppression of the antitumor immune response. Aiming to uncover the underlying mechanisms, we collected 42 samples of surgically removed colon tissues from patients newly diagnosed with colon cancer. To analyze the bacterial community composition within these tissues, we utilized amplicon sequencing, targeting the V4 variable region of the 16S rRNA. We performed single-cell RNA sequencing (scRNA-seq) analysis of tumor-infiltrating immune cells from Fn-infected [Fn (+)] and Fn-uninfected [Fn (−)] patients. By utilizing gene expression signature derived from scRNA-seq data and bulk transcriptome profiles of the TCGA cohort, we identified immune cell types associated with Fn infection in CRC. Using RNA velocity and cell-cell interaction analysis of single-cell transcriptome data, we unraveled immune cell subtypes modulated by Fn infection. Furthermore, trajectory-based differential expression analysis and single-cell gene network analysis shed light on how the intratumor immune system is disturbed by Fn infection. A novel gene signature had a poor prognostic impact in patients with Fn-infected tumors, compared with Fn-uninfected patients in the TCGA cohort. Overall, this study identified a novel potential Fn-related immune evasion mechanism, beyond suppression of T cell-mediated immune response. Novel gene signatures with a poor prognostic impact can be used for patient stratification and developing targeted strategies in patients with Fn infection. Citation Format: Ilseok Choi, Kyung-A Kim, Yoon Dae Han, Sang Cheol Kim, Han Sang Kim, Insuk Lee. Single-cell analysis reveals immune characteristics linked to poor prognosis in Fusobacterium nucleatum-infected colorectal cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr B037.
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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 | 0.001 | 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) | 0.001 | 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".