Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
Kyle M. Rose , Aram Vosoughi , Gustavo Borjas , Heather L Huelster , Philippe E. Spiess , Anders E. Berglund , Wade J. Sexton , Anirudh Joshi , Nagi B. Kumar , Roger Li
Background: Low-grade noninvasive (LGTa) bladder cancer is a relatively quiescent but heterogenous malignancy, characterized by downstream recurrences requiring repeated transurethral resections and frequent surveillance. Investigations to elucidate drivers of recurrence have been sparse, but will help risk-stratify patients with LGTa and allow augmentation of follow up protocols. Methods: Patients with LGTa index tumors were stratified by those with no downstream recurrences (nonrecurrent) vs. those with later recurrences (recurrent). RNA sequencing identified differentially expressed genes (DEGs), deconvoluted for cell-type using xCell. Pathologic analysis was performed by a genitourinary pathologist, then a deep-learning artificial intelligence (AI) platform was leveraged to correlate recurrence risk and recurrence-free survival (RFS) based on deep-learning algorithm of segmented nuclei. Results: Thirty index bladder tumors/patients were identified, 18 (60%) of which had later recurrence (Table). There were 238 DEGs recognized, with recurrent tumors expressing signatures for epithelial mesenchymal transition, myogenesis, TNFα signaling via NFκB, and angiogenesis. Recurrent tumors also demonstrated a higher tissue micoenvironment, stroma, and cancer-associated fibroblast score. Pathologic TME analysis validated these findings, with recurrent tumors demonstrating a higher frequency of inverted growth pattern and a higher median stroma percentage. Finally, the AI-derived signature was predictive of recurrence and risk-stratified the cohort (HR= 5.43 [95% CI 1.1-26.76]) for predicting high vs. low risk of recurrence. Patients in the high risk group had a 87.5% recurrence rate while those in the low risk group had a 28.5% recurrence rate (p<0.01). Conclusions: Using a multi-disciplinary approach, we identified key signatures in recurrent LGTa bladder cancer. Characterization of these factors is a critical first step in the risk-stratification of LGTa tumors, and may allow risk-stratification of surveillance protocols and identification of possible targets for chemoprevention trials.
Clinicopathologic data and pathologic analysis of tumor microenvironment in patients with non-recurrent vs. recurrent LGTa cancer. | |||
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Demographics | Non-recurrent LGTa n= 12 | Recurrent LGTa n= 17 | p-value |
Age (years)* | 67.0 (63.5-72.5) | 68.0 (60.0-75.0) | 0.97 |
Gender Female Male | 3 8 | 5 12 | 0.22 |
Smoking History Yes No | 2 9 | 5 4 | 0.20 |
Tumor Size (mm)* | 12.0 (11.0-30.5) | 14.0 (12.0-19.0) | 0.53 |
No. Recurrences* Months to Recurrence | 4 (2-10) 9.0 (6.0-17.0) | NA NA | |
Follow up (months)* | 71.0 (48.0-77.5) | 54.0 (49.0-62.0) | 0.86 |
Tumor Microenvironment Analysis§ | |||
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Focal Cytologic and Architectural Atypia Suggestive of HG | 2 | 3 | 0.97 |
Inverted Growth Pattern | 1 | 5 | 0.04 |
Median Stroma % | 20% | 50% | <0.01 |
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