Utilizing cohort-level and individual networks to predict best response in patients with metastatic triple negative breast cancer
Bibliographic record
Abstract
Given the highly aggressive and heterogeneous nature of metastatic triple-negative breast cancer, molecular subtypes have been evaluated for their utility in patient stratification and therapeutic selection. Leveraging both our unique longitudinal multimodal analysis of serial tumor biopsies, as well as existing public reference cohorts, we refined clinically relevant molecular subtypes through de-novo network-based approaches. A plasma/B-cell related co-expression module emerged as a robust predictor of clinical response. Refinements of this module were significantly associated with pathological complete response and survival in the CALGB and METABRIC cohorts, as well as dramatically improving the call rate in a CLIA setting. We explored patient-specific networks to monitor individual adaptive responses to therapy, allowing for dynamic adjustments in treatment strategies. Our work supports the shift from traditional molecular subtyping towards a more integrated view that includes the tumor microenvironment and immune landscape in a network-based context.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".