Molecular stratification within triple-negative breast cancer subtypes
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) has been subdivided into six distinct subgroups: basal-like 1 (BL1), basal-like 2 (BL2), mesenchymal (M), mesenchymal stem-like (MSL), immunomodulatory (IM), and luminal androgen receptor (LAR). We recently identified a subgroup of TNBC with loss of the tumor suppressor PTEN and five specific microRNAs that exhibits exceedingly poor clinical outcome and contains TP53 mutation, RB1 loss and high MYC and WNT signalling. Here, show that these PTEN-low/miRNA-low lesions cluster with BL1 TNBC. These tumors exhibited high RhoA signalling and were significantly stratified on the basis of PTEN-low/RhoA-signalling-high with hazard ratios (HRs) of 8.2 (P = 0.0009) and 4.87 (P = 0.033) in training and test cohorts, respectively. For BL2 TNBC, we identified AKT1 copy gain/high mRNA expression as surrogate for poor prognosis (HR = 3.9; P = 0.02 and HR = 6.1; P = 0.0032). In IM, programmed cell death 1 (PD1) was elevated and predictive of poor prognosis (HR = 5.3; P = 0.01 and HR = 3.5; P < 0.004). Additional alterations, albeit without prognostic power, characterized each subtype including high E2F2 and TGFβ signalling and CXCL8 expression in BL2, high IFNα and IFNγ signalling and CTLA4 expression in IM, and high EGFR signalling in MSL, and may be targeted for therapy. This study identified PTEN-low/RhoA-signalling-high, and high AKT1 and PD1 expression as potent prognostications for BL1, BL2 and IM subtypes with survival differences of over 14, 2.75 and 10.5 years, respectively. This intrinsic heterogeneity could be exploited to prioritize patients for precision medicine.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".