Comparative analysis of immunohistochemistry and imaging mass cytometry technologies for detection of clinical biomarkers in cancer tissues.
Bibliographic record
Abstract
e14628 Background: Detecting clinically relevant biomarkers in cancer tissues provides key insights into the unique tumor characteristics of patients, allowing for more personalized and effective therapies. Clinical biomarkers such as PD-1 and PD-L1 are associated with an immune-suppressive tumor microenvironment (TME), whereas HER2 is associated with metastasis and recurrence in multiple types of cancers. Immunohistochemistry (IHC) is the gold-standard technique for biomarker detection and is widely used by pathologists to grade tissue expression. Limitations related to plexity, quantitation and false signal detection are frequently observed using IHC, and day-to-day variability due to signal amplification and false positive signal can misinform pathologists about biomarker expression. Imaging Mass Cytometry (IMC) technology is a multiplexed spatial imaging technique that incorporates stoichiometric and quantitative assessment of 40-plus biomarkers simultaneously on a single slide and offers a large dynamic range of signal detection. We strove to determine whether IMC based spatial proteomics can be used for pathological evaluation of PD-1, PD-L1 and HER2 and provide key biological insights for clinical and translational studies. Methods: We stained serial sections of tissues using the same antibody clone and generated IHC and IMC data, which was assessed by a board-certified pathologist. For IMC technology, we detected single cells using the Human Immuno-Oncology IMC Panel, which highlights individual tumor, immune and stromal components of the TME. We conducted quantitative image analysis to detect expression of relevant biomarkers on cells and found enriched cellular neighborhoods associated with various pro- and antitumor processes. Results: Our analysis demonstrated that IMC technology and IHC similarly detected PD-1 and PD-L1. However, IMC accomplished it without signal amplification. For HER2, IMC technology and IHC provided comparable data. However, IMC detected the true dynamic range of signal intensities. Quantitative comparison of IMC technology combined with single-cell spatial proteomic analysis resolved the location of PD-1-, PD-L1- and HER2-expressing cells relative to other immune, stromal and tumor cell populations and offered additional biological insights into disease mechanisms. Conclusions: Clinical assessment of tissues using IMC technology offers an advantage over IHC by providing true biological context through multiplexing capabilities. High-dimensional spatially resolved data offered by IMC technology has the potential to expand our understanding of disease mechanisms of cancers and expedite development of personalized therapies for cancer patients in the clinic. For Research Use Only. Not for use in diagnostic procedures.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".