Defining triple‐negative breast cancer with neuroendocrine differentiation (<scp>TNBC‐NED</scp>)
Bibliographic record
Abstract
Primary breast neuroendocrine (NE) neoplasms are uncommon, and definitions harbor controversy. We retrospectively collected 73 triple-negative breast cancers (TNBC) and evaluated NE biomarker expression along with p53 aberrant staining (which correlates with TP53 gene mutation) and Rb protein loss by immunohistochemistry. In the study cohort, we found 11 (15%) cases of TNBC with neuroendocrine differentiation (TNBC-NED) showing positivity for one or more NE markers (synaptophysin/chromogranin/insulinoma-associated protein 1 [INSM1]). We also identified one separate small cell neuroendocrine carcinoma. Histologic types for these 11 TNBC-NED cases were as follows: 8 invasive ductal carcinoma (IDC) not otherwise specified (NOS), 2 IDC with apocrine features, 1 IDC with solid papillary features. INSM1 had the highest positivity and was seen in all 11 carcinomas. Seven (64%) cases showed p53 aberrant staining, 6 (55%) had Rb protein loss, while 6 (55%) had p53/Rb co-aberrant staining/protein loss. TNBC-NED was associated with Rb protein loss (p < 0.001), as well as p53/Rb co-aberrant staining/protein loss (p < 0.001). In 61 cases negative for NE markers, 37 (61%) showed p53 aberrant staining, while 5 (8%) had Rb protein loss. We also analyzed genomic and transcriptomic data from The Cancer Genome Atlas (TCGA) PanCancer Atlas of 171 basal/TNBC patients. Transcriptomic analysis revealed mRNA expression of RB1 to be correlated negatively with SYN1 mRNA expression (p = 0.0400) and INSM1 mRNA expression (p = 0.0106) in this cohort. We would like to highlight the importance of these findings. TNBC-NED is currently diagnosed as TNBC, and although it overlaps morphologically with TNBC without NED, the unique p53/Rb signature highlights a genetic overlap with NE carcinomas of the breast.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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".