Novel therapeutic targets for cancer metastasis
Bibliographic record
Abstract
Introduction: Metastatic cancers are extremely difficult to treat, and account for the vast majority of cancer-related deaths. The dissemination of tumor cells to distant sites is highly dynamic, asynchronous, and involves both tumor and host intrinsic factors. Effective therapeutic targets to block metastasis will need to disrupt key pathways that are required for multiple stages of metastasis.Areas covered: This review discusses the heterogeneity of cancers and metastasis, with an emphasis on motility as a key driver trait of metastasis. Recent metastatic cancer studies that identified either host or cancer cell intrinsic factors important for metastasis, using single gene-deficient animal models or 3D intravital imaging of avian embryo models, are also discussed. Potential metastatic blocking targets are listed as they relate to metastatic cancer therapy.Expert opinion: The development of metastatic disease is a complex interplay of genetic and epigenetic factors from the host and cancer cells acting in a patient-specific manner. Inhibiting key driver traits of metastasis should yield survival benefit at any stage of the disease, and we look forward to the next generation of personalized medicines for cancer therapy that target cancer cell motility for increased therapeutic efficacy.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".