Metabolic Plasticity as a Determinant of Tumor Growth and Metastasis
Bibliographic record
Abstract
Cancer cells must adapt their metabolism to meet the energetic and biosynthetic demands that accompany rapid growth of the primary tumor and colonization of distinct metastatic sites. Different stages of the metastatic cascade can also present distinct metabolic challenges to disseminating cancer cells. However, little is known regarding how changes in cellular metabolism, both within the cancer cell and the metastatic microenvironment, alter the ability of tumor cells to colonize and grow in distinct secondary sites. This review examines the concept of metabolic heterogeneity within the primary tumor, and how cancer cells are metabolically coupled with other cancer cells that comprise the tumor and cells within the tumor stroma. We examine how metabolic strategies, which are engaged by cancer cells in the primary site, change during the metastatic process. Finally, we discuss the metabolic adaptations that occur as cancer cells colonize foreign metastatic microenvironments and how cancer cells influence the metabolism of stromal cells at sites of metastasis. Through a discussion of these topics, it is clear that plasticity in tumor metabolic programs, which allows cancer cells to adapt and grow in hostile microenvironments, is emerging as an important variable that may change clinical approaches to managing metastatic disease. Cancer Res; 76(18); 5201-8. ©2016 AACR.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".