Multiplex Spatial Proteomic Analysis of HER2–Positive Breast Tumors Reveals Unique Molecular and Immunologic Features Associated With Treatment Response
Bibliographic record
Abstract
PURPOSE Predictive biomarkers to better tailor therapy for patients with early-stage human epidermal growth factor 2 (HER2)-positive breast cancer are a priority. We hypothesized that HER2 and immune-based biomarkers would be predictive of pathologic complete response (pCR) to preoperative trastuzumab/pertuzumab (HP). MATERIALS AND METHODS Patients with stage II/III, estrogen receptor (ER)-negative, HER2-positive breast cancer received neoadjuvant HP in the TBCRC026 clinical trial. The pCR after receiving HP alone was 22% (18/83). Tumor biopsies were performed at baseline. Secondary correlative objectives were to determine the relationship between HER2-based biomarkers and immune processes with pCR. NanoString code sets BC360 and IO360 were used to compare differential gene expression in baseline tumors that underwent pCR versus no pCR. NanoString GeoMx digital spatial profiling was used to assess immune protein abundance in intraepithelial and stromal segments. Stromal tumor-infiltrating lymphocytes and Ki67 were evaluated by hematoxylin and eosin and immunohistochemistry, respectively. RESULTS Intraepithelial HER2 protein abundance was significantly associated with pCR ( P = .001). Low HER2 abundance tumors were primarily basal-like, and essentially all (19/20) failed to achieve pCR. High HER2 abundance tumors that achieved pCR (14/51) exhibited a high degree of immune cell activity, whereas high HER2 abundance tumors that failed to achieve pCR tumors (37/51) were enriched for M-phase processes and epidermal growth factor receptor signaling. Baseline Ki67 was significantly higher in nonresponders ( P = .04). CONCLUSION ER-negative, HER2-positive breast cancer has unique molecular and immunologic features that may predict pCR after neoadjuvant HP. Validation of these potential biomarkers and composite biomarker analyses may guide design of future clinical trials.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".