Abstract PO-002: Revealing tumour spatial heterogeneity in breast cancer and the impact on clinical management
Notice bibliographique
Résumé
Abstract Many cancers are highly complex mixtures of many sub-populations of cells, also influenced by their microenvironment. This heterogeneity explains, in part, why the majority of current therapeutic approaches for cancer work best when multiple agents are combined. Therefore, in an era of targeted therapeutics it becomes critical to understand the complexity of tumours both at diagnosis and over the course of therapy, including measures of heterogeneity. Here we present genomic, transcriptomic and in situ proteomic profiling of a cohort of breast cancer (BCa) lumpectomies with associated imaging data to reveal the extent of heterogeneity in pathologically defined unifocal and multifocal cancers. Integration of molecular profiling, phylogenetic analyses and radiomics has the potential to significantly improve BCa clinical management and stratification to targeted therapies that are already available in the clinic. In this study, lumpectomies were processed as whole mounts with serial blocks reviewed. Tissue cores were taken from at least three different regions throughout the lumpectomy for nucleic acid extraction and tissue microarray (TMA) construction, focusing on morphologic/histological differences in addition to the spatial orientation of the sampled region within the lumpectomy. Targeted sequencing using the Oncomine Comprehensive Assay v3 (OCAv3); transcriptional profiling using the NanoString Breast Cancer 360 Panel; and in situ profiling by multiplex fluorescence immunohistochemistry (MxIF) performed (see abstract Cheung et al). From this cohort of 60 patients, we present a subset of patients demonstrating integration of the molecular profiling to reveal the phylogenetic relationship between the multiple samplings and the potential impact on clinical decision making in BCa. We identified differences in the molecular subtypes between the different sample regions from the same unifocal cancer as well as differences in the predicted responses to anti-PDL1 therapy by transcriptional profiling. Targeted sequencing of driver mutations suggested the likelihood of an ancestral tumour cell giving rise to the lesions in pathologically defined multifocal cancers; however it was evident that genes and pathways found to be aberrant in these different lesions from the same cancer could impact the response to standard BCa treatment, or the selection of targeted therapies. In situ proteomics demonstrated differences in the expression of standard BCa markers ER, PgR, HER2 and Ki67, in addition to immune markers in the tumour and its microenvironment. While there are clinically validated transcriptional risk tests available for BCa, we have demonstrated that transcriptomics or genomics alone is insufficient for a rational stratification of patients to currently available targeted therapies, therefore, supporting the need for an integrative approach. Citation Format: Jane Bayani, Quang M. Trinh, Mary Anne Quintayo, Cheryl Crozier, Megan Hopkins, Jingping Qiao, Alison Cheung, James G Mainprize, Quaid Morris, Melanie Spears, Martin J. Yaffe, Lincoln D. Stein, John M.S. Bartlett. Revealing tumour spatial heterogeneity in breast cancer and the impact on clinical management [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-002.
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 | 0,000 | 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 ».