Challenges & recommendations for identification of human epidermal growth factor receptor -2 (HER2)-low metastatic breast cancer in India: Expert opinion statement
Bibliographic record
Abstract
Results from a recent Phase 3 clinical trial (DESTINY-Breast04) established the efficacy of the antibody-drug conjugate, trastuzumab deruxtecan (T-DXd) in patients with metastatic breast cancer with immunohistochemistry (IHC) score 1+ or 2+ and without in situ hybridization amplification, defining a new category of metastatic breast cancer known as human epidermal growth factor receptor 2 (HER2)-low. Across studies of patients with primary or metastatic breast cancer, approximately 50 per cent have HER2-low tumours, thereby emphasizing the importance of accurately identifying these tumours. Results from the DESTINY-Breast06 trial further corroborated the DESTINY-Breast04 results showing T-DXd as a new standard of care for patients with HER2-low and HER2-ultralow (defined as IHC score 0 with membrane staining) metastatic breast cancer following one or more lines of hormone therapy. Differentiating between HER2 IHC scores 0 and 1+ shows significant interobserver variability, possibly because the low scores did not have any clinical relevance until now. To establish a standardized approach in scoring of HER2-low tumours in India, a panel of experts comprising histopathologists, molecular pathologists, and clinicians met to discuss guidelines on HER2 testing. The identification of HER2-low expression depends on multiple methodological pre-analytical and analytical variables, including sample handling, fixation, processing, antigen clones, staining methodology, and substrates. The panel also focused on the challenges in the interpretation of HER2-low status. Challenges in the pre-analytical and analytical phases could be addressed by rigorous quality control procedures and training the pathologists. In the post-analytical phase, the subjective mode of HER2 assessment and HER2 intratumoural heterogeneity in HER2-low breast cancer are factors that influence HER2-low assessment. The panel recommended robust standard operating procedures to overcome these challenges. The central point of discussion was to implement clear guidelines, careful supervision of pre-analytical and analytical issues, and specialized training for accurate HER2 testing that would help select patients eligible for novel therapies.
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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 | 0.023 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.016 |
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".