A subgroup of microRNAs defines PTEN-deficient, triple-negative breast cancer patients with poorest prognosis and alterations in RB1, MYC, and Wnt signaling
Bibliographic record
Abstract
BACKGROUND: Triple-negative breast cancer (TNBC) represents a heterogeneous group of ER- and HER2-negative tumors with poor clinical outcome. We recently reported that Pten-loss cooperates with low expression of microRNA-145 to induce aggressive TNBC-like lesions in mice. To systematically identify microRNAs that cooperate with PTEN-loss to induce aggressive human BC, we screened for miRNAs whose expression correlated with PTEN mRNA levels and determined the prognostic power of each PTEN-miRNA pair alone and in combination with other miRs. METHODS: Publically available data sets with mRNA, microRNA, genomics, and clinical outcome were interrogated to identify miRs that correlate with PTEN expression and predict poor clinical outcome. Alterations in genomic landscape and signaling pathways were identified in most aggressive TNBC subgroups. Connectivity mapping was used to predict response to therapy. RESULTS: In TNBC, PTEN loss cooperated with reduced expression of hsa-miR-4324, hsa-miR-125b, hsa-miR-381, hsa-miR-145, and has-miR136, all previously implicated in metastasis, to predict poor prognosis. A subgroup of TNBC patients with PTEN-low and reduced expression of four or five of these miRs exhibited the worst clinical outcome relative to other TNBCs (hazard ratio (HR) = 3.91; P < 0.0001), and this was validated on an independent cohort (HR = 4.42; P = 0.0003). The PTEN-low/miR-low subgroup showed distinct oncogenic alterations as well as TP53 mutation, high RB1-loss signature and high MYC, PI3K, and β-catenin signaling. This lethal subgroup almost completely overlapped with TNBC patients selected on the basis of Pten-low and RB1 signature loss or β-catenin signaling-high. Connectivity mapping predicted response to inhibitors of the PI3K pathway. CONCLUSIONS: This analysis identified microRNAs that define a subclass of highly lethal TNBCs that should be prioritized for aggressive therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".