Molecular mediators of breast cancer bone metastasis
Notice bibliographique
Résumé
Breast cancer is the most frequently diagnosed and the second leading cause of cancer deaths in Canadian women. The most devastating and deadly feature of the disease is the emergence of metastases. Breast cancer most commonly metastasizes to bone, often leading to a significantly decreased quality of life in affected patients. Despite progress in understanding the underlying molecular biology of breast tumors that relapse to bone, to date there are no therapies capable of curing the disease. Hence, it is essential to gain a more in-depth knowledge of the molecular mechanisms that underlie the emergence and growth of breast cancer skeletal metastases. Consequently, it was attempted to: 1) examine the efficacy of targeting a known pathway important for breast cancer metastasis to bone, 2) identify novel mediators of this process and 3) develop a stratification tool capable of identifying patients with breast cancer that possesses a high likelihood of spreading to bone. Transforming growth factor-beta (TGF-β) signaling is a potent modulator of the invasive and metastatic behavior of breast cancer cells. The work in this thesis demonstrates that expression of a TGF-β ligand trap, which neutralizes TGF-β1 and TGF-β3 in breast cancer cells, diminished their outgrowth in bone and reduced the severity of osteolytic lesion formation. It is further shown that a reduction or loss of host-derived TGF-β1 reduced the incidence of breast tumor outgrowth in the skeleton. Moreover, tumor cells capable of growing within the bone of a TGF-β1 deficient host up-regulated expression of all three TGF-β isoforms within the tumor cells themselves, effectively bypassing the host-deficiency. Next, a gene discovery approach was undertaken to identify novel candidate mediators of breast cancer skeletal metastasis. Invasive breast epithelium was selectively isolated by laser capture microdissection (LCM) performed on bone metastases and primary tumors from patients displaying breast cancer with subsequent recurrence to the skeleton. In this search, ABCC5 was found to be overexpressed in osseous metastases compared to primary mammary tumors metastatic to bone. Furthermore, this protein was detected at substantially higher levels in human and mouse breast cancer cells, which metastasize to bone in animal models. Importantly, removal of this protein from these cells resulted in their decreased ability to induce osteolytic bone lesions, which was correlated with a decreased recruitment of osteoclasts, cells responsible for the bone resorption process. Finally, the molecular changes occurring within the primary breast tumor were investigated in an attempt to identify a prognostic bone metastatic signature. Gene expression profiling was performed on estrogen receptor (ER)-positive primary breast tumors metastastatic to bone and breast cancers, which spread to soft tissue. A 25-gene signature was derived from the top 100 differentially expressed probes and was found to be capable of discriminating breast tumors metastatic to bone from cancers recurring to visceral sites in an independent gene expression dataset.
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 | 0,000 | 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 | 0,001 |
| 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 ».