Abstract 1773P
Background
The pathologic complete response rate to cisplatin-based neoadjuvant chemotherapy (NAC) in patients with muscle-invasive bladder cancer (MIBC) is roughly 30%, highlighting the need for biomarkers to drive patient selection in order to minimize treatment-related morbidity and optimize oncologic outcomes. We aimed to develop an artificial intelligence-based platform leveraging routine pre-treatment histopathology specimens to predict NAC response.
Methods
Using the Cancer Genome Atlas bladder cancer dataset, 59 patients with MIBC who received gemcitabine and cisplatin (GC) NAC from were identified. A deep learning-based algorithm was employed to segment nuclei from pre-NAC, hematoxylin and eosin-stained histopathology specimens and subsequently to extract quantitative, cell-type-specific features falling in one of three categories: nuclear geometry, cellular spatial arrangement, and tissue heterogeneity. These features were subsequently correlated with cancer-specific survival (CSS) utilizing a multivariable Cox proportional hazards (CPH) model in order to construct a signature associated with NAC response. Lastly, univariate CPH regression was conducted to identify the features most associated with NAC response.
Results
The multivariable CPH model incorporating features from all three aforementioned categories was predictive of NAC response with a concordance index of 0.70 [95% CI 0.53-0.87]. Kaplan-Meier analysis demonstrated the model was able to separate the cohort robustly with a statistically significant hazard ratio of 0.265 [95% CI 0.07, 0.98] (p=0.03) for predicted responders as compared to predicted non-responders. Features most correlated with CSS were those related to tissue heterogeneity and diversity of nuclear axis measurements, and increased heterogeneity was associated with worse CSS.
Conclusions
An artificial intelligence-based platform utilizing routine pre-treatment histopathology specimens may help identify patients most likely to respond to cisplatin-based NAC.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
Valar Labs Inc.
Funding
Valar Labs Inc.
Disclosure
H. Bhambhvani, E. Tiu, V. Krishna, V. Nimgaonkar, R. Krishnan, O. O'Donoghue, A. Joshi: Financial Interests, Personal, Full or part-time Employment: Valar Labs Inc. V. Krishna, D. Vrabac: Financial Interests, Personal, Leadership Role: Valar Labs Inc. All other authors have declared no conflicts of interest.