406 Imaging mass cytometry detects true dynamic range of low-abundance T cell exhaustion biomarkers in human cancers
Bibliographic record
Abstract
Background Detecting clinically relevant biomarkers in cancer tissues provides key insights into the unique tumor characteristics of patients, allowing for more personalized and effective immunotherapies. Immunohistochemistry (IHC) is the gold-standard technique for biomarker detection and is widely used by pathologists to score low-abundance biomarkers (LABs) in tissues. Limitations related to plexity, quantitation and false signal detection are frequently observed using IHC and variability due to multiple signal amplification steps and pigment mistaken for true signal can misinform pathologists about LAB expression.Methods Imaging Mass Cytometry™ (IMC™) technology is a multiplexed imaging technique that incorporates quantitative assessment of 40-plus biomarkers simultaneously on the same slide. We strove to determine whether IMC can be used for pathological evaluation of LABs and provides additional key biological insights for clinical evaluation offered through multiplexed analysis. We performed a comparison of IHC and IMC technology to detect clinically relevant LABs (PD-1, PD-L1, CTLA-4 and LAG-3) on human tumor tissue microarray and whole tissue samples. For IMC technology, we detected single cells using the Human Immuno-Oncology IMC Panel, which offers cell phenotyping of tumor and immune cell subtypes and their functional states. 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. We conducted quantitative image analysis to detect LAB expression on single cells.Results Our results demonstrate that while IMC and IHC are similar in detecting LABs, IMC technology can accomplish this without signal amplification ( figures 1 and 2), offering an opportunity to evaluate LABs in their true dynamic signal ranges. Analysis of IHC and IMC data further demonstrated the equivalent performance of both platforms, with IMC offering quantitative evaluation of signal intensities in addition to multiplexed analysis. Single-cell analysis using IMC data provided insights about LAB expression on specific immune and tumor cells. While IHC is semi-quantitative and cannot reliably determine the high abundance of a target, IMC technology offers improved signal quantitation as it displays the complete dynamic range of signal.Conclusions Clinical assessment of tissues using IMC technology offers an advantage over traditional IHC methods by providing biomarker expression with fully intact dynamic range and multiplexing capabilities. The ability of IMC to provide high-dimensional spatially resolved data makes it a powerful tool for clinical and translational applications and shows that it is poised to significantly contribute to biomarker detection and therapeutic development.For Research Use Only. Not for use in diagnostic procedures.Abstract 406 Figure 1Comparative evaluation of PD-1 detection in normal human tonsil using IHC and IMC approaches.Images from serial sections of normal human tonsils processed with immunohistochemistry (left) and Imaging Mass Cytometry technology (middle, right) demonstrate the equivalent performance of IHC and IMC to detect the presence of PD-1Abstract 406 Figure 2Comparative evaluation of PD-L1 detection in lung squamous cell cancer using IMC and IHC approaches. Images from serial sections of lung squamous cell carcinoma processed with immunohistochemistry (left) and Imaging Mass Cytometry technology (middle, right) demonstrate the equivalent performance of IHC and IMC to detect the presence of PD-L1
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".