Abstract P2-03-04: Characteristics and clinical differences of breast cancer patients with negative or low HER2 expression
Bibliographic record
Abstract
Abstract Background: High expression of HER2 receptor in Immunohistochemistry (IHC) sections or amplification of its gene in in situ hybridization (ISH) assays define a subset of breast cancers that have an aggressive natural history but respond to treatments blocking HER2. In contrast, patients with lower HER2 expression, not meeting the criteria for positivity, are currently treated similarly to completely HER2 negative patients. However, emerging data suggest that patients with low HER2 expression may derive benefit from newer antibody drug conjugates. Thus, this investigation sought to clarify the characteristics of HER2 low expressors and compare them with patients with no HER2 expression. Methods: We undertook a retrospective analysis of all breast cancer patients seen in our cancer center over a six-year period and classified as HER2 negative. Patients were categorized as HER2 negative when they had a score of 0 in IHC and as HER2 low if they had a score of 1+ or 2+ in IHC and no amplification by ISH. Characteristics of the patients and tumors in the two groups were compared. Results: A total of 391 HER2 negative and low patients have been included. Among them, 130 patients (33.2%) were HER2 negative (score 0 by IHC) and 261 patients (66.8%) were HER2 low (score 1+ and 2+ by IHC/ISH non-amplified). There were no differences in age, menopause status, mode of detection of cancer (clinical or screening) and clinical stage at diagnosis between the two groups. Patients in the HER2 low group had less commonly high-grade cancers than HER2 negative patients (25.35 versus 34.6%, x2 p=0.04).In addition, The HER2 low group had higher ER Histoscore (>240) in 88.1% of cases compared with 69.2% in HER2 negative group (x2 p<0.000). Similarly, a higher percentage of HER2 low cases than HER2 negative cases were expressing the progesterone receptor (PR, x2 p<0.000). HER2 low patients were rarely (3.8%) classified as triple negative, while this percentage was 21.5% in HER2 negative patients. Conclusion: HER2 negative staining (IHC score 0) is associated with averse tumor characteristics compared with clinically HER2 negative patients with low HER2 expression (score 1+ and 2+/ISH non-amplified). Citation Format: Ioannis Voutsadakis, Christopher Rosso. Characteristics and clinical differences of breast cancer patients with negative or low HER2 expression [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-03-04.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.004 | 0.001 |
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".