Prognostic Role of Systemic Inflammation Indexes in Metastatic Urothelial Carcinoma Treated With Immunotherapy

Dionese, Michele; Basso, Umberto; Pierantoni, Francesco; Lai, Eleonora; Cavasin, Nicolò; Erbetta, Elisa; Jubran, Salim; Bonomi, Giorgio; Bimbatti, Davide; Maruzzo, Marco

doi:10.2144/fsoa-2023-0049

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…[10][11][12][13][14][15] Other studies used more practical data but either used small cohorts, data that were not human-verified, did not consider multiple toxicities, were limited in drug or disease setting, or did not address both toxicity and efficacy/ effectiveness. [16][17][18] Thus, there is a need for comprehensive tools to accurately predict irAEs alongside effectiveness from routinely collected data.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data

Lippenszky,

Mittendorf,

Kiss

et al. 2024

JCO Clin Cancer Inform

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PURPOSE Although immune checkpoint inhibitors (ICIs) have improved outcomes in certain patients with cancer, they can also cause life-threatening immunotoxicities. Predicting immunotoxicity risks alongside response could provide a personalized risk-benefit profile, inform therapeutic decision making, and improve clinical trial cohort selection. We aimed to build a machine learning (ML) framework using routine electronic health record (EHR) data to predict hepatitis, colitis, pneumonitis, and 1-year overall survival. METHODS Real-world EHR data of more than 2,200 patients treated with ICI through December 31, 2018, were used to develop predictive models. Using a prediction time point of ICI initiation, a 1-year prediction time window was applied to create binary labels for the four outcomes for each patient. Feature engineering involved aggregating laboratory measurements over appropriate time windows (60-365 days). Patients were randomly partitioned into training (80%) and test (20%) sets. Random forest classifiers were developed using a rigorous model development framework. RESULTS The patient cohort had a median age of 63 years and was 61.8% male. Patients predominantly had melanoma (37.8%), lung cancer (27.3%), or genitourinary cancer (16.4%). They were treated with PD-1 (60.4%), PD-L1 (9.0%), and CTLA-4 (19.7%) ICIs. Our models demonstrate reasonably strong performance, with AUCs of 0.739, 0.729, 0.755, and 0.752 for the pneumonitis, hepatitis, colitis, and 1-year overall survival models, respectively. Each model relies on an outcome-specific feature set, though some features are shared among models. CONCLUSION To our knowledge, this is the first ML solution that assesses individual ICI risk-benefit profiles based predominantly on routine structured EHR data. As such, use of our ML solution will not require additional data collection or documentation in the clinic.

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Section: Introductionmentioning

confidence: 99%

Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data

Lippenszky,

Mittendorf,

Kiss

et al. 2024

JCO Clin Cancer Inform

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Prognostic risk score and index including the platelet-to-lymphocyte ratio and lactate dehydrogenase in patients with metastatic or unresectable urothelial carcinoma treated with immune checkpoint inhibitors

Kobayashi,

Sakano,

Matsumoto

et al. 2024

Japanese Journal of Clinical Oncology

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Background Avelumab and pembrolizumab are administered after platinum-based chemotherapy for the treatment of metastatic urothelial carcinoma. We explored the prognostic factors and risk scores for predicting the outcomes of metastatic or unresectable urothelial carcinoma at the start of treatment with immune checkpoint inhibitors. Methods This retrospective study included patients with metastatic or unresectable urothelial carcinoma treated with avelumab or pembrolizumab after platinum-based chemotherapy between January 2017 and December 2022. Prognostic factors, including patient and tumor characteristics and blood data at the initiation of immune checkpoint inhibitor therapy, were examined. Results This study included 36 and 207 patients treated with avelumab and pembrolizumab, respectively, for metastatic or unresectable urothelial carcinoma. Eastern Cooperative Oncology Group performance status, presence of visceral metastases, platelet-to-lymphocyte ratio and lactate dehydrogenase levels were independent prognostic factors for predicting overall survival. The median overall survival of patients in the risk-score model was 58.5 months (score zero), 27.9 months (one), 13.1 months (two) and 3.9 months (three or higher). The C-index for overall survival was 0.718 for the newly developed risk score compared with 0.679 for the Bellmunt score and 0.703 for the Bellmunt-C-reactive protein score. Additionally, the C-index for overall survival using the immune prognostic index derived from lactate dehydrogenase and the platelet-to-lymphocyte ratio was 0.646 compared with 0.615 for the Lung Immune Prognostic Index. Conclusions A risk score that includes the platelet-to-lymphocyte ratio and lactate dehydrogenase may serve as a useful model for predicting prognosis following the initiation of immune checkpoint inhibitors in patients with metastatic or unresectable urothelial carcinoma.

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Monocyte-Related Markers as Predictors of Immune Checkpoint Inhibitor Efficacy and Immune-Related Adverse Events: A Systematic Review and Meta-Analysis

Ezdoglian,

Tsang-A-Sjoe,

Khodadust

et al. 2024

Preprint

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Prognostic Role of Systemic Inflammation Indexes in Metastatic Urothelial Carcinoma Treated With Immunotherapy

Cited by 3 publications

References 26 publications

Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data

Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data

Prognostic risk score and index including the platelet-to-lymphocyte ratio and lactate dehydrogenase in patients with metastatic or unresectable urothelial carcinoma treated with immune checkpoint inhibitors

Monocyte-Related Markers as Predictors of Immune Checkpoint Inhibitor Efficacy and Immune-Related Adverse Events: A Systematic Review and Meta-Analysis

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