Cancer as Competing Clones: Theoretical and Experimental Cellular Sociology>Systems Biology
Bibliographic record
Abstract
B30 The pre-invasive neoplastic development process is one of competing normal and molecularly evolving clonal populations. These altered competing cells are recapitulating their internal molecular dynamics and interactions at the physical level through increased growth, decreased apoptosis, resource request signaling and usage, as well as interacting with the host immune system and surrounding stroma. An in depth understanding of all these interactions is key to understanding and altering the neoplastic process for therapeutic (chemoprevention for pre-neoplastic tissue and chemotherapy for invasive cancer) purposes. >We are developing a two prong approach to this problem; 1) a computational 3D static and dynamic model of tissue epithelium and stroma (based on physical and molecular interactions) and 2) an automated system for multi-colour FISH of tissue sections for the identification of genetically related clones in excised tissue samples. >Our current mathematical model is based on quantitative data from the nuclear phenotypes of the cells, molecular characteristics from quantitative IHC and quantitative tissue architectural information (spatial arrangement of cells in tissue) from more than 10,000 of lung, cervix and oral sections. >The automated system for multi-colour FISH is designed to scan up to 6 FISH labels automatically across an entire tissue section and analyze the results cell by cell and then by clonal neighbourhood. Several versions of clonal neigbourhood have been tested. These are derived from the Voronoi tessellation of the imaged tissue based upon the spatial arrangement of the individual cells and their FISH attributes. Initial testing of this system showed clonal populations of cells resistant to lung cancer chemotherapy based upon a specific gene copy number alteration profile, derived form array CGH data.
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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 | 0.002 | 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 | 0.000 | 0.001 |
| 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".