PD-L1 immune checkpoint inhibition in combination with radiation across different bladder cancer molecular subtypes and influences on immune memory
Notice bibliographique
Résumé
Bladder cancer is the 5th most prevalent cancer in Canada. 30% of the cases are muscle-invasive (MIBC). MIBC is a highly lethal disease; the patient survival rate has not improved over the last decades, and treatments option is limited. The standard gold treatment is radical cystectomy; however, more than 40% of the patients are unfit to undergo surgery. Hence, new strategies to improve treatments for MIBC are imperative. Radiotherapy (RT) is an alternative treatment and allows bladder preservation. Although it maintains the quality of life, half of the patients will develop metastasis. RT can enhance tumor antigen presentation, but it can also induce a higher expression of programmed-cell-death1(PD-1) on T cells and programmed-cell-death-ligand1(PD-L1) on cancer cells. This interaction causes T cell exhaustion and impairs the immune response against cancer. The effect can be prevented by the addition of anti-PD-L1 molecules, which will result in an increase of infiltrating T cells. Thus, combining immunotherapy with RT may boost the systemic immune response leading to improving oncological outcomes of bladder preserving strategies. Our lab has previously shown an abscopal response when treated with the combined treatment, which proved the efficacy of the combination. However, immunological memory is required for durable response, which could prevent metastasis and recurrence. We hypothesize that the combination of radiotherapy and immunotherapy will generate the development of immunological memory, which is needed for long-lasting responses. To analyze the different responses to the combinational treatment, we used different murine bladder cancer cell lines and characterized the tumor microenvironment and the immune memory profile. MB49 represents the basal molecular subtype and a hot tumor model. UPPL represents the luminal molecular subtype and a cold tumor model. The baseline characterization of the immune response was compared between the two molecular subtypes of bladder cancer. MB49 had a higher T cell infiltration compared to UPPL. UPPL had a higher infiltration of neutrophils. The induction of memory was assessed by comparing the memory populations across treatment groups: control, RT, anti-PD-L1 and RT + anti-PD-L1. Following treatment, the tumors were excised via surgery to create a tumor-free, tumor antigen encountered mice model. Mice deemed tumor-free after 5 weeks without tumor growth in the right flank were re-challenge with a second cancer cell injection in the opposite flank. This tumor re-challenge growth kinetics determined the memory response. The combined treatment was expected to produce a larger and effective pool of memory T cells compared to other treatment groups by generating long-term immune memory, which would correlate with either tumor rejection or growth delay in the re-challenge. A distinct immune profile was expected in response to combination therapy across the molecular subtypes. Findings from this study will have a very important clinical relevance as it provides evidence for a more sustained clinical response using combination therapy across different molecular subtypes. Importantly, the potential of eliminating micrometastatic disease using augmented systemic effects of the combined approach is highly promising
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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) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».