PD25-07 PROTEOMIC PROFILING OF MUSCLE INVASIVE BLADDER CANCER TREATED WITH NEOADJUVANT CHEMOTHERAPY REVEALS UNIQUE BIOLOGIC CLUSTERS WITH CLINICAL RELEVANCE
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Résumé
You have accessJournal of UrologyCME1 Apr 2023PD25-07 PROTEOMIC PROFILING OF MUSCLE INVASIVE BLADDER CANCER TREATED WITH NEOADJUVANT CHEMOTHERAPY REVEALS UNIQUE BIOLOGIC CLUSTERS WITH CLINICAL RELEVANCE Alberto Contreras-Sanz, Moritz J. Reike, Gian L. Negri, Htoo Zarni Oo, Sandra Spencer Miko, Karina Nielsen, Morgan E. Roberts, Joshua Scurll, Kenichiro Ikeda, Gang Wang, Roland Seiler, Gregg B. Morin, and Peter C. Black Alberto Contreras-SanzAlberto Contreras-Sanz More articles by this author , Moritz J. ReikeMoritz J. Reike More articles by this author , Gian L. NegriGian L. Negri More articles by this author , Htoo Zarni OoHtoo Zarni Oo More articles by this author , Sandra Spencer MikoSandra Spencer Miko More articles by this author , Karina NielsenKarina Nielsen More articles by this author , Morgan E. RobertsMorgan E. Roberts More articles by this author , Joshua ScurllJoshua Scurll More articles by this author , Kenichiro IkedaKenichiro Ikeda More articles by this author , Gang WangGang Wang More articles by this author , Roland SeilerRoland Seiler More articles by this author , Gregg B. MorinGregg B. Morin More articles by this author , and Peter C. BlackPeter C. Black More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003303.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Neoadjuvant chemotherapy (NAC) followed by radical cystectomy (RC) is recommended for muscle invasive bladder cancer (MIBC). However, only ∼40% of patients show an objective response. While DNA alterations and RNA classifiers may predict response to NAC in retrospective studies, the proteome has not been evaluated in this context. Here we profile the MIBC proteome in a NAC-treated cohort to identify markers of response and to elucidate mechanisms of resistance to NAC. METHODS: Pre-NAC tissue was included from 107 MIBC patients who received NAC followed by RC. Residual tumor (≥ypT1N0-3M0-1) in the RC specimen was present in 62% of patients after NAC, and was profiled for 55 of those patients (51%). Multiregional sampling was conducted in 37/107 pre-NAC and 15/55 post-NAC samples. Benign ureter was used as control. SP3-Clinical Tissue Proteomics (SP3-CTP) and bioinformatic analysis using formalin-fixed paraffin-embedded tissue (FFPE) were performed. Immunohistochemistry (IHC) validation was conducted on matched tissues. RESULTS: We quantified 9769 proteins across all samples. Unsupervised clustering of pre NAC tissue established 4 clusters based on biology and survival outcomes, but no difference in response by pathologic stage. Clusters were confirmed by IHC, and consisted of: Cluster 1 (CC1), with high metabolic activity and a luminal profile; Cluster 2 (CC2) with high nuclear activity; Cluster 3 (CC3) with high immune infiltration and basal profile; and Cluster (CC4) with high immune infiltration and stromal signature. CC3 showed worse survival outcomes (p<0.01). Multivariable analysis identified novel favorable (MAPK9 and MTIF3) and unfavorable (DVL2 and NES) biomarkers. Matched analysis of pre- and post-NAC tissue showed markers (AZGP1 and ORM1) and pathways indicative of NAC resistance. In post-NAC (i.e. resistant) tumors we identified 2 clusters: Post-NAC Cluster 1 was enriched for nuclear processes and had worse outcomes, whereas Post-NAC Cluster 2 was enriched for immune pathways. Multiregional analysis of pre- and post-NAC matched tumors revealed clonal heterogeneity suggestive of important chemoresistance mechanisms. CONCLUSIONS: Here we describe 4 pre-NAC and 2 post-NAC proteomic clusters with distinct biology and survival outcomes, alongside novel prognostic biomarkers. Future work includes IHC validation of clusters in larger independent MIBC cohorts. A non-NAC cohort using pre-RC biopsy tissue will be used to confirm the prognostic vs. predictive relevance of these findings. Source of Funding: Bladder Cancer Canada, Deutsche Forschungsgemeinschaft © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e731 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Alberto Contreras-Sanz More articles by this author Moritz J. Reike More articles by this author Gian L. Negri More articles by this author Htoo Zarni Oo More articles by this author Sandra Spencer Miko More articles by this author Karina Nielsen More articles by this author Morgan E. Roberts More articles by this author Joshua Scurll More articles by this author Kenichiro Ikeda More articles by this author Gang Wang More articles by this author Roland Seiler More articles by this author Gregg B. Morin More articles by this author Peter C. Black More articles by this author Expand All Advertisement PDF downloadLoading ...
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
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 ».