Reference-free RNA profiling predicts triple negative breast cancer chemoresistance to neoadjuvant treatment
Bibliographic record
Abstract
Triple negative breast cancer (TNBC) is the most aggressive breast cancer (BC) and often affects young women. TNBCs are highly heterogeneous and do not benefit from personalized medicine at localized stages. Most TNBC patients undergo neoadjuvant chemotherapy (NAC) before surgery. In case of chemoresistance with residual tumor after NAC, survival is poor despite execution of complete tumor resection. There is currently no clinically useful biomarker to predict TNBC chemoresistance to NAC that would enable targeted therapeutic intensification. We analyzed here a unique cohort of 106 TNBC tumors before NAC, including 58 chemoresistant and 48 chemosensitive cases, from 2 independent hospitals. Using machine learning under a nested cross-validation design, we obtained two transcriptomic signatures respectively generated from standard differential gene expression analysis and reference-free analysis of differential fragments of transcripts, without any annotation bias. This approach resulted in accurate signatures of TNBC chemoresistance to NAC. Gene ontology analyses of reference-free signatures highlighted DNA repair, replication, and metabolism, in agreement with current knowledge of TNBC resistance biology. In summary, these results show the potential of a reference-free generated transcriptomic signature as predictive biomarker of early TNBC chemoresistance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".