Identifying Site-specific Metastasis Genes and Functions
Bibliographic record
Abstract
Metastasis is a multistep and multifunctional biological cascade that is the final and most life-threatening stage of cancer progression. Understanding the biological underpinnings of this complex process is of extreme clinical relevance and requires unbiased and comprehensive biological scrutiny. In recent years, we have utilized a xenograft model of breast cancer metastasis to discover genes that mediate organ-specific patterns of metastatic colonization. Examination of transcriptomic data from cohorts of primary breast cancers revealed a subset of site-specific metastasis genes that are selected for early in tumor progression. High expression of these genes predicts the propensity for lung metastasis independently of several classic markers of poor prognosis. These genes fulfill dual functions-enhanced primary tumorigenicity and augmented organ-specific metastatic activity. Other metastasis genes fulfill functions specialized for the microenvironment of the metastatic site and are consequently not selected for in primary tumors. These findings improve our understanding of metastatic progression, facilitate the interpretation of primary tumor gene expression data, and open several important possibilities for future clinical application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
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".