Abstract 6262: Changes in the spatial architecture of the tumor microenvironment associated with treatment resistance in muscle invasive bladder cancer
Bibliographic record
Abstract
Abstract Approximately one-third of bladder cancer (BC) patients are diagnosed with muscle-invasive bladder cancer (MIBC). Despite recent successes in clinical trials with neo-adjuvant therapies such as checkpoint inhibitors and chemotherapy, clinical implementation is hindered by a lack of biomarkers to guide therapy decisions, as not all patients respond to certain treatments. While the prognostic value of immune biomarkers in BC has been studied in the tumor microenvironment (TME), previous studies only evaluate single therapies, lack comparison to untreated groups, and fail to examine post-treatment residual tissue, limiting cross-therapy comparisons. Most studies have also relied solely on immune cell densities, ignoring spatial immune interactions critical for anti-tumor responses. Measuring spatial relationships (SRs) between immune cells in the TME provides a mathematical description of proximity and adjacency, which is more biologically relevant. Associations between SRs, prognosis, and treatment response have been reported across cancer types. For MIBC, analyzing SRs may improve tumor immunology insights and reveal treatment-response biomarkers. We analyzed four MIBC cohorts using multiplex immunofluorescence (mIF, three panels with six markers): 49 pre-NAC, 90 pre-CPI (pembrolizumab), 41 pre-NAC and CPI (nivolumab), and 70 untreated, plus corresponding residual tissue (N=26, 13, 20, and 35). A univariate logistic regression model identified spatial immune features associated with treatment resistance, examining metrics like first nearest neighbor (1NN) distances (e.g., median, Weibull distribution shape/scale) and frequency of cellular triads (interaction patterns). Model coefficients were exponentiated into interpretable odds ratios (ORs) for resistance associations. Multiple immune features were linked to NAC resistance. For example, pre-treatment, a higher Weibull shape parameter for CD4+ T cells and Tregs was associated with resistance to NAC (OR = 0.22, 95% CI [0.05, 0.67], p = 0.02). Similarly, increased pre-treatment abundance of triads involving B cells, CD4+ T cells, and Tregs was associated with NAC resistance (OR = 0.77, 95% CI [0.55, 0.94], p = 0.04). Importantly, these associations were NAC-specific, as no negative prognostic associations were observed in untreated patients (Weibull shape: OR = 2.22, 95% CI [0.71, 8.2], p = 0.19; triads: OR = 0.99, 95% CI [0.97, 1.01], p = 0.33). These findings highlight the potential of spatial immune metrics to refine biomarker identification and improve personalized treatment strategies in MIBC. Citation Format: Nikolay Alabi, Nicolas Zheng, Joshua Scurll, Jussi Nikkola, Alberti Contreras-Sanz, Morgan Roberts, Ali Bashashati, Peter Black. Changes in the spatial architecture of the tumor microenvironment associated with treatment resistance in muscle invasive bladder cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6262.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".