Artificial intelligence‐driven consensus gene signatures for improving bladder cancer clinical outcomes identified by multi‐center integration analysis

Abstract

To accurately predict the prognosis and further improve the clinical outcomes of bladder cancer (BLCA), we leveraged large-scale data to develop and validate a robust signature consisting of small gene sets. Ten machine-learning algorithms were enrolled and subsequently transformed into 76 combinations, which were further performed on eight independent cohorts (n = 1218). We ultimately determined a consensus artificial intelligence-derived gene signature (AIGS) with the best performance among 76 model types. In this model, patients with high AIGS showed a higher risk of mortality, recurrence, and disease progression. AIGS is not only independent of traditional clinical traits [(e.g., American Joint Committee on Cancer (AJCC) stage)] and molecular features (e.g., TP53 mutation) but also demonstrated superior performance to these variables. Comparisons with 58 published signatures also indicated that AIGS possessed the best performance. Additionally, the combination of AIGS and AJCC stage could achieve better performance. Patients with low AIGS scores were sensitive to immunotherapy, whereas patients with high AIGS scores might benefit from seven potential therapeutics: BRD-K45681478, 1S,3R-RSL-3, RITA, U-0126, temsirolimus, MRS-1220, and LY2784544. Additionally, some mutations (TP53 and RB1), copy number variations (7p11.2), and a methylation-driven target were characterized by AIGS-related multi-omics alterations. Overall, AIGS provides an attractive platform to optimize decision-making and surveillance protocol for individual BLCA patients.

SurvivalML: an integrative platform for the discovery and exploration of prognostic models in multi-center cancer cohorts

“…The copyright holder for this preprint this version posted October 26, 2022. ; https://doi.org/10.1101/2022.10.25.513678 doi: bioRxiv preprint possessed stronger generalization capabilities than most survival signatures, as previously reported 3,23,[26][27][28] . Here, the optimal model was generalized boosted regression modeling (GBM), which achieved a mean C-index of 0.685 across 15 independent datasets (Figure 3B).…”

“…As mentioned above, the optimal model with the highest mean C-index across all datasets was considered the optimal one. Indeed, the model developed by this module possessed stronger generalization capabilities than most survival signatures, as previously reported 3,23,[26][27][28] . Here, the optimal model was generalized boosted regression modeling (GBM), which achieved a mean C-index of 0.685 across 15 independent datasets (Figure 3B).…”

Applications of artificial intelligence in urologic oncology

“…While prognosis and outcome prediction using ML algorithms, such as those based on a support vector machine [ 26 44 46 55 57 ], have been widely investigated in the past decade, computer vision technologies like CNN are now rapidly advancing and the application of DL algorithms in various medical image analyses is being extensively studied [ 79 ].…”

“…Each study’s inclusion and exclusion criteria were highly heterogeneous, but they all focused on urological cancer. Finally, 58 studies on urological cancers (prostate cancer: 21 [ 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 ], bladder cancer: 20 [ 5 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 ], kidney cancer: 17 [ 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 ]) were identified as shown in Fig. 1 .…”

The Present and Future of Artificial Intelligence in Urological Cancer

“…The AIGs model with the best performance was finally selected. Patients with high AIGS had a worse prognosis than those with low AIGS and a correspondingly increased risk of disease progression [73]. Similar to Xu's study, Wang et al used seven ML algorithms to develop a prediction model for 5-year OS in bladder cancer patients to select the best ML method.…”