PD25-07 PROTEOMIC PROFILING OF MUSCLE INVASIVE BLADDER CANCER TREATED WITH NEOADJUVANT CHEMOTHERAPY REVEALS UNIQUE BIOLOGIC CLUSTERS WITH CLINICAL RELEVANCE
Bibliographic record
Abstract
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 ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".