Abstract P2-10-34: Development and validation of ClinicoMolecular Triad Classification (CMTC), a platform for breast cancer (BC) prognostic and predictive gene signature portfolios.
Notice bibliographique
Résumé
Abstract Background: Numerous gene signatures have claimed prognostic significance in BCs. Each of these gene signatures was designed to answer a specific clinical or biological question, often by dichotomizing the targeted populations into a good and a bad risk group. None of these gene signatures on its own has sufficient degree of complexity to fully characterize this very heterogenous group of diseases, and hence lacks the flexibility to personalize treatments. To exploit the full potential of the genomic approach, we developed an 803-gene molecular classification, termed ClinicoMolecular Triad Classification (CMTC) that categorized BCs into 3 clinical treatment groups (triad) that can serve as a basic framework to guide management. CMTC also provide a detailed “portfolio” of 14 other gene signatures and 19 oncogenic pathways to allow further customization of the treatments. The ability to get CMTC portfolio results at the time of initial diagnosis offers the unique advantage of early treatment planning, including the use of pre-operative chemotherapy to improve breast conservation in selected patients. This study aimed to validate the CMTC classification using an independent BC cohort. Study design/ results: RNA from fine needle aspirates were collected in a prospective BC cohort (n = 340) between 2008 and 2010 at Princess Margaret Hospital and Mount Sinai Hospital, Toronto, we included all newly diagnosed BC patients going for surgery who consented to join the study. DNA microarray analyses were carried out using genome-wide Illumina Human Ref-8 version 3 Beadarrays, which contained >24K oligonucleotide probes. After excluding tumors with low RNA yield (n = 8, success rate 97%), non-invasive cancers (n = 27), insufficient follow-up data (n = 21), CMTC divided the remaining 284 BCs into 3 similar sized groups (triad). At a median follow-up of 32 months (range 6.3–52 months), the short-term recurrence was significantly worse (p = 0.0048) in the poor prognostic groups. This result was similar to using an independent external validation cohort (n = 2100) with long-term follow-up reported before, CMTC outperformed all other gene signatures in predicting prognosis and treatment response. Discussion/conclusion: This prospective validation cohort study demonstrated reproducibility of CMTC in classifying BCs into the three major treatment groups and its prognostic significance. CMTC can be used as a platform to personalize treatments: CMTC-1 BCs (ER+, low proliferation) in general can be treated with surgery and tamoxifen alone. CMTC-2 tumours (ER+, high proliferation) will require additional treatments, including chemotherapy, in addition to tamoxifen; other biologics can be prescribed based on the activities of additional oncogenic pathways. Neo-adjuvant chemotherapy should be considered for CMTC-3 tumours (triple negative and HER2+) with addition of trastuzumab in those that show activation of the HER2 pathway. CMTC portfolio is being further developed into a genomic platform to guide personalized BC treatments.. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P2-10-34.
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 | 0,005 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».