406 Imaging mass cytometry detects true dynamic range of low-abundance T cell exhaustion biomarkers in human cancers
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».