Abstract P2-06-11: Ramifications of HER2/ER/PR Guidelines from ASCO/CAP for Translational Cancer Research Using a Cohort from a Tertiary Care Centre in Ontario
Bibliographic record
Abstract
Abstract Background: A transdisciplinary team from basic science, pathology, clinical and biostatistics was assembled to establish a framework with which to take novel laboratory biomarkers and targets to clinical validation. Human epidermal growth factor receptor (HER2), estrogen (ER) and progesterone (PR) receptor are of important prognostic and predictive value and drivers of systemic therapy for breast cancer (BC). As a first step, the current ASCO/CAP guidelines were used to re-assign centrally reviewed tumour specimens and compare to the clinically assigned scores for ER/PR and HER2. Methods: With REB approval, a cohort of 62 cases of non-metastatic invasive BC with banked tumour specimens was assembled between 2005 and 2007. Clinico-pathological information for each case was retrospectively obtained from the medical file and entered into an anonymized database. Full section slides were originally stained by routine immunohistochemistry (IHC). Categorical clinical scores for ER/PR (negative-neg/weak/positive-pos) were compared to the continuous scores assigned in a blinded fashion using ASCO/CAP criteria (% pos/H-score). Categorical clinical scores obtained with duplicate IHC antibody staining of full sections for HER2 (neg/equivocal-eq/pos) were compared to those obtained from IHC assessments of triplicate 6mm cores in a tissue microarray (TMA) that were assigned to be neg/eq/pos using ASCO/CAP criteria. A senior breast pathologist adjudicated discordant specimens. Exact Fisher tests were used to compare the two sets of categorical assessments. Results: Mean age was 43.5 years, (range 29-49). The majority of the cohort (59.7%) had N0 disease and received adjuvant chemotherapy (74.2%); 72.6% of the cohort was alive at the time of this analysis. Score means and ranges of ER/PR are displayed below. Two of 16 clinically ER neg cases (12.5%) were rescored as pos and 0/43 clinically ER pos cases were rescored as neg, P<0.0001. Two of 13 clinically PR neg cases (15.4%) were rescored as pos and 4/46 clinically PR pos cases (8.7%) were rescored as neg, (P<0.0001). HER2 status was reassessed for 51 cases, 41 of which (80%) had concordant scores (P<0.0001). Thirty-nine (76%) cases were classified as HER2 neg on TMA, 7 of which (18%) were eq on routine IHC and neg by fluorescence in situ hybridization. In routine IHC, 15.7% of tumours were eq. Four TMA cases were eq (7.8%%); with routine IHC, one of these was neg, one eq, and two were pos. Eight patients were HER2 pos in both assessments. ER/PR scores Conclusions: Systemic therapy recommendations could be impacted in a small but substantive number of cases by the methodology used for biomarker assessment and scoring, particularly near threshold values. This study illustrates that the scoring criteria used may be an important contributor to variability in correlative biomarker studies. Consideration should be given to routine systematic reassessment with continuous scoring for biomarker data proposed for use in correlative science studies. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P2-06-11.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".