Abstract 5622: Integrative cross-platform characterisation of mammographic screen-detected breast cancer
Notice bibliographique
Résumé
Abstract Mammography screening is widely used for earlier detection of breast cancer and has been shown to contribute to reduction of mortality and morbidity. Most screen-detected breast cancers are early stage, hormonal receptor-positive, HER2-negative (HR+ HER2-) breast cancers. The majority of HR+ HER2- cancers are assigned to molecular intrinsic subtypes of Luminal A and Luminal B, which generally harbour low recurrence risk, with Luminal B cases being more invasive but still less aggressive than HER2-enriched or Basal-like subtypes. However, some of these Luminal cancers later recur (5+ years after diagnosis) often as advanced and/or metastatic disease. We studied a cohort of screen-detected breast cancers (42 cases) at Sunnybrook Health Sciences Centre (Toronto, ON Canada) with integrated cross-platform radiomics, molecular and proteomic analysis in an attempt to better characterise these cancers and their proclivity for late recurrence. Utilising a Nanostring 200-gene assay, the molecular subtypes (PAM50 and MammaTyper-like) and a range of recapitulated clinical prognostic scores (50-Gene, 70-Gene and 21-Gene Risk) were determined for these cancers. While a majority of cases (29 out of 42) were subtyped as Luminal A cancers by PAM50, a fraction (12 out of 29) of these were either subtyped as Luminal B/HER2+ based on MammaTyper-like results, or measured as intermediate to high risk of recurrence based on the 70-Gene or 21-Gene Risk classification. Although discordant results from multi-parametric assays are not uncommon, we postulate that here, discordance could suggest presence of heterogeneous cellular or molecular elements with invasive phenotype that could lead to aggressive transformations in the long run. Differential expression analysis between Luminal A cases with consistent subtyping and low risk prognostication and those with discordant subtyping or prognostication results revealed a panel of genes with significant changes in expression. Further analysis of RNA expression of these genes via Receiver Operating Characteristic (ROC) curve demonstrated that some genes showed high Area Under the Curve (AUC) scores in the classification between the two groups of Luminal A breast cancers, further supporting the existence of distinct molecular phenotypes. Targeted sequencing with Oncomine Comprehensive Assay V3 did not reveal particular mutational patterns between the two Luminal A groups. Nevertheless, radiomic analysis of mammographic images of the cancer, as well as single cell phenotyping of the tumor microenvironment with protein multiplexing will be incorporated to further characterise elements that are phenotypically different, and potentially identify mechanistic drivers contributing to late recurrence in Luminal A cancers. Citation Format: Alison M. Cheung, Dan Wang, Kela Liu, Yutaka Amemiya, Elzbieta Slodkowska, James G. Mainprize, Jane Bayani, Arun Seth, John Bartlett, Martin J. Yaffe. Integrative cross-platform characterisation of mammographic screen-detected breast cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5622.
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 | 0,001 |
| 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 | 0,001 |
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 ».