From forest and agro‐ecosystems to the microecosystems of the human body: what can landscape ecology tell us about tumor growth, metastasis, and treatment options?
Bibliographic record
Abstract
Cancer is now understood to be a process that follows Darwinian evolution. Heterogeneous populations of cancerous cells that make up the tumor inhabit the tissue 'microenvironment', where ecological interactions analogous to predation and competition for resources drive the somatic evolution of cancer. The tumor microenvironment plays a crucial role in the tumor genesis, development, and metastasis processes, as it creates the microenvironmental selection forces that ultimately determine the cellular characteristics that result in the greatest fitness. Here, we explore and offer new insights into the spatial aspects of tumor-microenvironment interactions through the application of landscape ecology theory to tumor growth and metastasis within the tissue microhabitat. We argue that small tissue microhabitats in combination with the spatial distribution of resources within these habitats could be important selective forces driving tumor invasiveness. We also contend that the compositional and configurational heterogeneity of components in the tissue microhabitat do not only influence resource availability and functional connectivity but also play a crucial role in facilitating metastasis and may serve to explain, at least in part, tissue tropism in certain cancers. This novel work provides a compelling argument for the necessity of taking into account the structure of the tissue microhabitat when investigating tumor progression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 0.000 |
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 teacher head, 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".