Abstract LB-499: Malignancy-sustaining characteristics of cancer-associated plasma: role of systemic cytokines in maintaining tumor latency
Notice bibliographique
Résumé
Abstract BACKGROUND: While most studies investigate the biology of the active disease, the focus of our study was on the state of clinical remission, during which patients are considered healthy and tumor free. We demonstrate that the ‘disease-promoting latency’ factor(s) are sustained post therapy and likely contribute to tumor re-growth and drug-resistance that accompanies the relapse. We propose that therapies aimed at shrinking the tumor do not restore the homeostasis within the tissue; thus, tumor-associated stroma remains post therapy sustaining the production of pro-tumorigenic factors that ultimately induce a relapse. Multiple myeloma (MM) is an incurable bone marrow malignancy of B cell lineage that accounts for 20% of deaths from hematologic malignancies. Despite the development of potent new regimens, nearly all MM patients relapse and become refractory to treatment; thus the median survival rate remains 3-5 years. The goal of this study was to determine whether the balance of the systemic cytokines returns to normal during complete remission and whether we can identify a cytokine signature specific for various phases of the disease. METHODS: Utilizing multiplex technology of Luminex we measured the plasma levels of 25 cytokines in normal donors (n=177) and MM patients (n=54) either at diagnosis/relapse, treatment, or remission phases of MM. The cytokine levels were compared between normal donors and MM patients as well as between various phases of the MM, and discriminant analysis was used to create predictive classification of disease phase based on the levels of differentially expressed cytokines. RESULTS: Based on fold-change analysis, we identified 15 cytokines that were differentially expressed between normal donors and MM patients, whose profile was heavily skewed toward a pro-tumorigenic Th2 response. Moreover, chemokines (IL-8, eotaxin, MCP1) were also upregulated in plasma of patients. Significantly, the expression of cytokines in patients in clinical remission was maintained at the same levels as in patients with active disease and our predictive model placed all patients in remission in the same category as those at diagnosis/relapse. CONCLUSIONS: Our study demonstrates that remission is not a state of health, but tumor latency, and that once an organism has cancer, the system is primed for relapse and emergence of secondary neoplasms. Thus, the cytokines maintained in circulation will induce proliferation and confer drug resistance of the minimal residual disease, and chemokines will stimulate systemic dissemination of tumor cells leading to the colonization of secondary sites. We believe that such systemic alteration of tissue homeostasis is consistent for all cancers; thus our findings suggest the need to shift the paradigm of clinical intervention to include homeostasis-stabilizing interventions. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-499. doi:1538-7445.AM2012-LB-499
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 | 0,000 | 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) | 0,001 | 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 ».