Abstract PS5-46: Introduction and clinical validation of metrology standards for immunohistochemistry (IHC); New tool for standardization of estrogen receptor (ER) IHC assay in breast cancer
Bibliographic record
Abstract
Abstract Traceability of measurement to a higher order reference standard is a foundation of laboratory testing. There is as yet no method for creating reference standards for cellular proteins in situ in an analogous fashion as for soluble analytes. At present, IHC laboratories produce results for breast cancer hormone receptors without connection to a reference standard. Not surprisingly, high rates of testing variation as well as discrepancies among IHC laboratories have been reported. To address this need, we developed a system of measurement traceability using a linked fluorescein tag for creating reference standards for any cellular analyte and, as a first test, validate it for estrogen receptor (ER) testing. In this study, the newly developed ER standard defines and compares the thresholds separating “high positive”, “low positive”, and “negative” tests according to updated ASCO/CAP guidelines as detected by clinical IHC laboratories in a national external quality assessment survey. This reference standard utilizes NIST Standard Reference Material (SRM) 1934 as a universal IHC standard. We calculated ER concentration based on a linked fluorescence measurement traceable to NIST SRM 1934 as each ER is linked to a single fluorescein, and fluorescein concentration equals ER concentration. Each laboratory’s lowest detected ER concentration (i.e. “limit of detection”, LOD) was compared to their results with 80 tumor samples enriched for triple negative breast cases. For the Canadian Immunohistochemistry Quality Control (CIQC) ER proficiency testing run, calibrator sets with peptides for the SP1, EP1, and 6F11 epitopes were created. The various concentrations were pipetted onto histology slides used by CIQC to place its 80-case breast cancer tissue microarray. These slides were stained by participating laboratories using their routine ER IHC protocols and returned to the CIQC. For the purpose of this study, Histology Score and ASCO/CAP categorical scoring recommendation was used for the readout. Results with SP1 clone are reported here because it was employed by overwhelming majority of laboratories. A total of 3,038 readouts were included in the analysis. Most IHC laboratories had a LOD between 10,000 – 25,000 molecules ER per microbead. Highly sensitive ER assays (low LODs) detected more positive cases while those with poorly sensitive assay detected fewer. The LOD correlated with the percent positive cases (R2= -.767, p < 0.0001, Spearman Correlation) as well as with cumulative individual laboratory Histology Score (R2= -.612, p < 0.0001, Spearman Correlation). The tumor samples accounting for this difference were principally “low positive” (ASCO/CAP classification) while ER-high positive tumors were less affected. Although the concept of a non-quantitative IHC LOD has been introduced with critical assay performance controls (iCAPCs, e.g. germinal center cells in the tonsil for ER), there were no tools until now to actually measure LOD for ER IHC testing. With the calibrators introduced here, it is now possible to measure the LOD for ER IHC assays. Furthermore, our data show that ER low positive and some cases of ER high positive breast cancers are highly affected by the variability of analytic sensitivity between clinical IHC laboratories. These data argue for the urgent need to standardize testing to a defined analytic sensitivity range, which is now possible for the first time. Beyond inter-laboratory standardization, known measured LOD also enables: i) methodology transfer from clinical trials to clinical laboratories; ii) determination of optimal analytical sensitivity to produce assays with highest clinical and analytical validity, and iii) daily monitoring of analytical sensitivity for each tested IHC slide. Citation Format: Emina Emilia Torlakovic, Seshi R. Sompuram, Kodela Vani, Lili Wang, Anika K Schaedle, Paul C. DeRose, Steven A. Bogen. Introduction and clinical validation of metrology standards for immunohistochemistry (IHC); New tool for standardization of estrogen receptor (ER) IHC assay in breast cancer [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS5-46.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.040 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".