Multiplexed in situ analysis of protein expression to predict response to trastuzumab
Bibliographic record
Abstract
5028 HER-2 belongs to a family of four Type I tyrosine kinase receptors, including EGFR, HER-3, and HER-4, that homo- and hetero-dimerize to activate distinct cellular programs. In breast cancer, this receptor family also demonstrates cross-talk with the hormone receptors for estrogen (ER) and progesterone (PR). Trastuzumab is an antibody-based targeted therapy effective in less than half of the patients that over express HER-2. We hypothesize that assessment of other HER2 family members and related markers could more specifically classify patients and define responders to the therapy. In this study, we quantitatively assessed the expression of ER, PR, and the four HER family proteins in four subcellular compartments using automated quantitative analysis (AQUA), a novel, immunofluorescence-based quantitative method of measuring protein expression in situ. Multiplexing was tested for both prognostic and predictive value. We used unsupervised hierarchical clustering to assess prognostic value in the archival Yale breast cohort (n=656). Six classes were identified, including a small class enriched for HER-2 expression. Disease specific survival of this class was compared to other HER-2 classification methods by Kaplan Meier analysis. The separation of classes by survival was optimal when multiple markers were considered, as the median survival for the HER-2 group dropped from 98 months in the IHC 3+ group, to 55 months for AQUA HER-2 positive, to 43 months for multiplexed clustering positive. To assess predictive value, individual, pair-wise, and multivariate logistic models were constructed with the data from expression of the six proteins in a trastuzumab-treated, retrospectively collected cohort (n=152) from the British Columbia Cancer Agency. Prediction error estimates for dichotomized outcome (complete/partial response vs. stable/progressive disease) were assessed with leave-one-out cross-validation and the corresponding intervals with bootstrap resampling. Using continuous AQUA scores, both low estrogen receptor expression (p=0.027) and high HER-2/neu (p=0.0007) expression were significantly associated with favorable clinical response. A multivariate model including ER, HER-2, EGFR, and HER4 had a misclassification estimate of 31.8% (95% CI 18.9%-44.7%), which was superior to any one protein, traditional IHC, or FISH for prediction of trastuzumab response. In summary, using AQUA, we found that combined analysis of ER, PR, and HER family members improved prognostic classification, and we developed a multivariate logistic model that optimized prediction for trastuzumab response in the metastatic setting. As trastuzumab treatment is extended to patients in the adjuvant setting, development of predictive methods such as these will be valuable to more accurately identify patients that will respond favorably to treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".