Abstract 4246: Comparison of pathology versus IHC-based ovarian carcinoma histology assignment using gene expression, DNA methylation, and clinical outcome data
Notice bibliographique
Résumé
Abstract Background: Epithelial ovarian cancer (EOC) is composed of five major histologic types: 1) high-grade serous carcinoma (HGSC), accounting for most cases (∼70%); and the rarer 2) clear cell, 3) endometrioid, 4) mucinous, and 5) low- grade serous carcinoma (LGSC). As EOC risk factors are histology specific, as are the site of precursor lesions, accurate subtyping is critical to understanding EOC prognostic factors and etiology. Our aim was to compare two currently employed histology assignment strategies using gene expression, DNA methylation, and clinical outcome data Methods: Histology assignment based on: a) pathologist, and b) an integrated pathologist and IHC prediction algorithm (pathIHC) (using ARID1A, CDKN2A, DKK1, HNF1B, MDM2, PGR, TP53, TFF3, VIM, and WT1 staining patterns), was compared for tumors of all histologies at the Mayo Clinic, Rochester. The 500 most variable probes from Illumina Methylation450 BeadChips (N = 259) and Agilent 4×44K expression arrays (N = 245) were used to perform unsupervised hierarchical clustering. Fisher's Exact test was used to test the association of clusters with histology. Cox proportional hazards regression analysis was used to test the association of clusters and histology with time to progression (TTP) and time to death (TTD). Results: Eighty-two percent of tumors were concordant between histology assignment strategies. Clustering based on methylation data produced two distinct clusters. PathIHC produced more homogenous clusters (p-value = 2.8×10-16) than pathology alone (p = 1.3×10-9). Cluster M1, characterized by high levels of methylation across almost all probes, was enriched for the rarer EOC subtypes; cluster M2, characterized by moderate levels of methylation across 50%-75% of probes, was predominantly HGSC. These patterns were observed irrespective of histology assignment strategy; however, when using histology by pathologist, cluster M2 had more endometrioid and LGSC tumors interspersed with HGSC tumors. Clustering based on expression data also produced two clusters. PathIHC produced slightly more homogenous clusters (p = 9.0×10-11, pathology alone, p = 2.3×10-10). Cluster E1 was enriched for the rarer EOC subtypes; cluster E2 was primarily serous lineage tumours (HGSC and LGSC), particularly when using histology by pathIHC. Considering only clinical outcomes, histologies by pathology were more significantly different in terms of TTP (p = 1.1×10-6) and TTD (p = 1.7×10-3) than histologies by pathIHC (TTP, p = 5.4×10-6; TTD, p = 5.7×10-3). Conclusions: Histology by pathIHC produced more homogeneous clusters in both the methylation and expression data; thus, molecular data supports this strategy. Differences in clinical outcomes (TTP and TTD) between histology groups were more pronounced when using assignment by pathologist; thus, clinical data supports this strategy. Citation Format: M A. Earp, S J. Winham, S M. Armasu, B L. Fridley, M C. Larson, Z C. Fogarty, K R. Kalli, C Wang, G L. Keeney, J M. Cunningham, S Ramus, M Kobel, E L. Goode. Comparison of pathology versus IHC-based ovarian carcinoma histology assignment using gene expression, DNA methylation, and clinical outcome data. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4246. doi:10.1158/1538-7445.AM2015-4246
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 | 0,004 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».