Categorization of cancer through genomic complexity could guide research and management strategies
Bibliographic record
Abstract
Cancer management decisions are currently informed by cancer type and clinical stage, as well as age, health condition, and individual patient needs. Cancer is a genetic disease and recent genomic studies have revealed the genomic landscape of multiple tumour types. This has led to readily available catalogues of genomic features for many cancers and efforts to incorporate such information into treatment decisions. From this has evolved the concept that mutation-based taxonomies may supersede the current cell of origin-based categorization of neoplasia. Unfortunately, genomic features as clinically actionable information may not be directly transferable between tumour types, due to the importance of cellular and genomic context. However, we believe that high-level views of different genomic landscapes could broadly inform research study design and treatment strategies. Herein, we use ovarian and bone cancer as examples to propose a genomic complexity-based categorization for cancer. In addition to informing clinical study design, we describe how this categorization scheme could impact (i) improvement of accuracy of histological diagnoses, (ii) stratification of patients for targeted therapies, (iii) research study design, and (iv) personalized treatment strategies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".