Understanding the Bone in Cancer Metastasis
Bibliographic record
Abstract
Unfortunately, once cancer spreads to the bone, it is rarely cured and is associated with a wide range of morbidities including pain, increased risk of fracture, and hypercalcemia. This fact has driven experts in the fields of bone and cancer biology to study the bone, and has revealed that there is a great deal that each can teach the other. The complexity of the bone was first described in 1889 when Stephen Paget proposed that tumor cells have a proclivity for certain organs, where they "seed" into a friendly "soil" and eventually grow into metastatic lesions. Dr. Paget went on to argue that although many study the "seed" it would be paramount to understand the "soil." Since this original work, significant advances have been made not only in understanding the cell-autonomous mechanisms that drive metastasis, but also alterations which drive changes to the "soil" that allow a tumor cell to thrive. Indeed, it is now clear that the "soil" in different metastatic sites is unique, and thus the mechanisms that allow tumor cells to remain in a dormant or growing state are specific to the organ in question. In the bone, our knowledge of the components that contribute to this fertile "soil" continues to expand, but our understanding of how they impact tumor growth in the bone remains in its infancy. Indeed, we now appreciate that the endosteal niche likely contributes to tumor cell dormancy, and that osteoclasts, osteocytes, and adipocytes can impact tumor cell growth. Here, we discuss the bone microenvironment and how it impacts cancer cell seeding, dormancy, and growth. © 2018 American Society for Bone and Mineral Research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".