Mammostrat as an immunohistochemical multigene assay for prediction of early relapse risk in the TEAM pathology study.
Bibliographic record
Abstract
516 Background: Some postmenopausal patients with hormone sensitive early breast cancer remain at high risk of relapse despite endocrine therapy, and might benefit additionally from adjuvant chemotherapy. The challenge is to prospectively identify such patients. The Mammostrat test uses five immunohistochemical markers to stratify patients regarding recurrence risk, and may inform treatment decisions. We tested the efficacy of this panel in the TEAM trial. Methods: Pathology blocks from 4598 TEAM patients were collected and TMAs constructed. The cohort was 47% node positive and 36% were also treated with adjuvant chemotherapy. Triplicate 0.6mm 2 TMA cores were stained and positivity for p53, HTF9C, CEACAM5, NDRG1, SLC7A5 assessed. Cases were assigned a Mammostrat risk score, and distant relapse free (DRFS) and disease free survival (DFS) analysed. Results: In multivariate regression analyses, corrected for conventional clinicopathological markers, Mammostrat provided significant additional information on DRFS after endocrine therapy in ER positive node negative patients (N=1226) not receiving chemotherapy (p=0.004). Further analyses in all patients not exposed to chemotherapy, irrespective of nodal status (N=2559) and in the entire cohort (N=3837) showed Mammostrat scores provide additional information on DRFS in these groups (p=0.001 and p<0.0001 respectively; multivariate analyses). No differences were seen between the two endocrine treatment regimens. Conclusions: The Mammostrat score predicts DRFS for both exemestane and tamoxifen-exemestane treated patients irrespective of nodal status and chemotherapy. The ability of this test to provide additional outcome data following treatment provides further evidence for its’ utility in risk stratification of ER positive postmenopausal breast cancer patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".