Abstract A026: Single-cell landscape deciphering cancer Single-cell landscape deciphering cancer cell-of-origin and cellular heterogeneity in malignant transformation of 13 major tissues
Bibliographic record
Abstract
Abstract Deciphering disease progression and the sophisticated tumor ecosystems is imperative for exploring tumorigenesis mechanisms and developing novel prevention strategies. Here, we dissected heterogeneous tissue microenvironments during malignant transitions by leveraging data from 4,972,145 single cells in 1396 samples from 62 datasets spanning 13 major tissue types. Within transitional stem-like subpopulations highly enriched in precancerous lesions and cancers, we identified 30 recurring cellular states, including hypoxia and epithelial senescence, revealing a high degree of plasticity in epithelial stem cells. By characterizing the dynamics of stem-cell crosstalk with the microenvironment along the pseudotime axis, we uncovered distinct roles of ANXA1 at different stages of tumor development. ANXA1 expression levels in stem cells were decreased from the healthy to the precancerous stages, which promoted inflammatory responses by recruiting neutrophils and regulating monocyte differentiation towards M1 macrophages. In contrast, during malignant progression, upregulated ANXA1 fostered M2 macrophage polarization and cancer-associated fibroblast transformation. Our spatiotemporal analysis further provided insights into mechanisms responsible for immunosuppression. Collectively, this study provided a systematic view of cancer origins, and suggested that restoring and maintaining the balance of inflammation and their mediators (e.g., AnxA1/FPRs signaling) may represent a novel approach to control the evolution of precancerous lesions and mitigate the risk for cancer development. Citation Format: Ruihan Luo. Single-cell landscape deciphering cancer cell-of-origin and cellular heterogeneity in malignant transformation of 13 major tissues [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr A026.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".