Spatial molecular analyses reveal key features associated with response to KN026 in advanced HER2-positive breast cancer
Bibliographic record
Abstract
BACKGROUND: KN026 is a novel bispecific HER2-targeting antibody for HER2-positive recurrent or metastatic breast cancer that showed prolonged median PFS and lessened distinction of PFS regarding the HR subgroup in our phase II clinical trial, compared with PUFFIN study of first-line trastuzumab combined with pertuzumab therapy. A more detailed discovery of its peculiarity is needed for optimal application of KN026 treatment. METHODS: We performed whole-transcriptome sequencing of digital spatial profiling (DSP) on 8 pre/post-treatment tumor samples. Mechanistic explorations were conducted by plasmid transfection, co-culture, CCK8 proliferation assay and flow cytometry. RESULTS: Compared to the tumor regions with non-objective response (OR), those with OR had high expression of CALML5, TFAP2B, and ERBB2, and relatively low expression of ESR1 in tumor cells at baseline. The expression of ESR1 had a tentative association with PI3K/AKT and NOTCH signaling pathways which were downstream or interactive pathways of HER2 target and also acted as interactive pathways of ER-mediated signaling. The co-expression of ERBB2 and CDK12 emerged as a distinctive signature of OR. KN026 treatment also reshaped intratumoral activated T- and B-cell subtypes in hot-tumor regions, regardless of myeloid-derived cells. CONCLUSIONS: Both HER2 and ESR1 are determinant of KN026 efficacy in advanced HER2-positive breast cancer, implying the potential of KN026 combined with endocrine therapy in HER2- and ER-positive breast cancer.
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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 | 0.000 |
| 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) | 0.001 | 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".