Subnetwork-based prognostic biomarkers exhibit performance and robustness superior to gene-based biomarkers in breast cancer
Bibliographic record
Abstract
Abstract Background Effective classification of cancer patients into groups with differential survival remains an important and unsolved challenge. Biomarkers have been developed based on mRNA abundance data, but their replicability and clinical utility is modest. Integrating functional information, such as pathway data, has been suggested to improve biomarker performance. To date, however, the advantages of subnetwork-based biomarkers have not been quantified. Results We deeply sampled the population of prognostic gene-based and subnetwork-based biomarkers in a breast cancer meta-dataset of 4,960 patients. Analysing the performance and robustness of 22,000,000 gene biomarkers and 6,250,000 subnetwork biomarkers across twenty different training:testing cohort partitions of the meta-dataset revealed that subnetwork biomarkers exhibit superior overall performance and higher concordance across partitions. We find evidence of an upper bound for optimal biomarker size of ∼200 genes or ∼100 subnetworks. Additionally, with both biomarker feature types, larger biomarkers tend to show less consistency in performance across partitions, suggestive of over-fitting. Finally, an evaluation of varying training cohort sizes quantifies the effects of training cohort size. Conclusions Many groups are developing techniques for exploiting network-based representations of biological pathways to characterize cancer and other diseases. By considering the distribution of gene- and subnetwork-based biomarkers, we show that pathway data improves performance and replicability, and that smaller biomarkers are more robust across patient cohorts. These insights may facilitate development of clinically useful biomarkers.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".