Immune-related adverse events predict the therapeutic efficacy of pembrolizumab in urothelial cancer patients

2019 ◽  
Vol 116 ◽  
pp. 114-115 ◽  
Author(s):  
Taketo Kawai ◽  
Yusuke Sato ◽  
Katsuhiro Makino ◽  
Yuta Yamada ◽  
Akira Nomiya ◽  
...  
Keyword(s):  
Adverse Events ◽  
Cancer Patients ◽  
Therapeutic Efficacy ◽  
Urothelial Cancer

scholarly journals Immune-related adverse events (irAEs) predict therapeutic efficacy of an anti-PD-1 antibody in cancer patients

2018 ◽  
Vol 29 ◽  
pp. viii435
Author(s):  
J. Rogado ◽  
N. Romero Laorden ◽  
J.M. Sanchez Torres ◽  
A.I. Ballesteros Garcia ◽  
V.E. Pacheco-Barcia ◽  
...  
Keyword(s):  
Adverse Events ◽  
Cancer Patients ◽  
Therapeutic Efficacy

Immune-related adverse events predict the therapeutic efficacy of anti–PD-1 antibodies in cancer patients

2019 ◽  
Vol 109 ◽  
pp. 21-27 ◽  
Author(s):  
J. Rogado ◽  
J.M. Sánchez-Torres ◽  
N. Romero-Laorden ◽  
A.I. Ballesteros ◽  
V. Pacheco-Barcia ◽  
...  
Keyword(s):  
Adverse Events ◽  
Cancer Patients ◽  
Therapeutic Efficacy

Presence of constipation predicts the therapeutic efficacy of pembrolizumab in metastatic urothelial cancer patients

2020 ◽  
Vol 27 (12) ◽  
pp. 1116-1123
Author(s):  
Satoshi Inoue ◽  
Naoto Sassa ◽  
Takashi Kato ◽  
Yushi Yamauchi ◽  
Tsuyoki Hirabayashi ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Therapeutic Efficacy ◽  
Urothelial Cancer ◽  
Metastatic Urothelial Cancer

Comparison of efficacy and safety of checkpoint inhibitors in patients with genitourinary cancers aged below and above 75 years.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e16101-e16101
Author(s):  
Gerald Bastian Schulz ◽  
Bernadett Szabados ◽  
Annabel Spek ◽  
Michael D. Staehler ◽  
Christian Stief ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Adverse Events ◽  
Cell Carcinoma ◽  
Cancer Patients ◽  
Renal Cell ◽  
Urothelial Cancer ◽  
Checkpoint Inhibitors ◽  
The Elderly ◽  
Genitourinary Cancers ◽  
Grade 3

e16101 Background: Checkpoint-inhibitors have recently been introduced in the treatment of patients with genitourinary cancers. However, the use in elderly patients is controversial due to putative age-associated changes including the dysregulation of the immune system. We sought to investigate the safety and efficacy of immunotherapy in patients younger and older than 75 years of age. Methods: We conducted a retrospective review of patients with renal cell carcinoma and urothelial cancer treated with different immunotherapeutic agents between August 2015 and September 2018 at a high-volume single institution. Eligible patients received at least one cycle of single agent or a combination of checkpoint inhibitors as first or following treatment line. Immune-related adverse events (irAE) were graded using the NCI CTCAE v 4.0. Clinicopathological parameters including gender, cancer entity, ECOG, adverse events, comorbidities and response to treatment were stratified by age ≥ 75 vs. < 75 years and tested with a Pearson's chi-squared test. Additionally, we evaluated the impact of irAE on oncological outcome using the log-rank test. Results: 79 patients were identified, of those 27 (34.2%) were 75 years and older (15 renal cell carcinoma and 12 urothelial cancer patients) and 52 (65.8%) were younger than 75 years (39 renal cell carcinoma and 13 urothelial cancer patients). 2 complete responses were achieved in the elderly group and 6 in the younger group (p = 0.56). Disease control rate (stable disease, partial and complete response) was 48,1% in the elderly group and 53.8% in the younger group (p = 0.631). We observed a total of 30 irAEs (18 grade 1-2 and 12 grade 3-4), with an even distribution among the groups (≥75 years: 1 grade 4 AE; < 75 years: 12 grade 3-4 AEs). Except for ECOG ≥2 (p = 0.009) and ≥2 comorbidities (p < 0.001), there was no difference when groups were stratified by age. Both disease control rate and irAE did not differ between age subgroups. Occurrence of irAE showed no impact on oncological survival. Conclusions: The study demonstrates that patients over 75 years of age with renal cell and urothelial cancer treated with checkpoint-inhibitors respond with a good toxicity profile and an efficacy comparable with the younger population. irAE seem to have no impact on prognosis.

scholarly journals Olaparib dose re‐escalation in ovarian cancer patients who experienced severe and/or uncommon adverse events: A case series

2021 ◽  
Vol 17 (S3) ◽  
pp. 3-11
Author(s):  
Siew‐Fei Ngu ◽  
Ka‐Yu Tse ◽  
Mandy M. Y. Chu ◽  
Hextan Y. S. Ngan ◽  
Karen K. L. Chan
Keyword(s):  
Ovarian Cancer ◽  
Adverse Events ◽  
Cancer Patients ◽  
Case Series

scholarly journals Electronic patient-reported outcomes and machine learning in predicting immune-related adverse events of immune checkpoint inhibitor therapies

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sanna Iivanainen ◽  
Jussi Ekstrom ◽  
Henri Virtanen ◽  
Vesa V. Kataja ◽  
Jussi P. Koivunen
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Adverse Events ◽  
Cancer Patients ◽  
Immune Checkpoint ◽  
Patient Reported Outcomes ◽  
Prediction Models ◽  
Accuracy Score ◽  
Patient Reported ◽  
Auc Value

Abstract Background Immune-checkpoint inhibitors (ICIs) have introduced novel immune-related adverse events (irAEs), arising from various organ systems without strong timely dependency on therapy dosing. Early detection of irAEs could result in improved toxicity profile and quality of life. Symptom data collected by electronic (e) patient-reported outcomes (PRO) could be used as an input for machine learning (ML) based prediction models for the early detection of irAEs. Methods The utilized dataset consisted of two data sources. The first dataset consisted of 820 completed symptom questionnaires from 34 ICI treated advanced cancer patients, including 18 monitored symptoms collected using the Kaiku Health digital platform. The second dataset included prospectively collected irAE data, Common Terminology Criteria for Adverse Events (CTCAE) class, and the severity of 26 irAEs. The ML models were built using extreme gradient boosting algorithms. The first model was trained to detect the presence and the second the onset of irAEs. Results The model trained to predict the presence of irAEs had an excellent performance based on four metrics: accuracy score 0.97, Area Under the Curve (AUC) value 0.99, F1-score 0.94 and Matthew’s correlation coefficient (MCC) 0.92. The prediction of the irAE onset was more difficult with accuracy score 0.96, AUC value 0.93, F1-score 0.66 and MCC 0.64 but the model performance was still at a good level. Conclusion The current study suggests that ML based prediction models, using ePRO data as an input, can predict the presence and onset of irAEs with a high accuracy, indicating that ePRO follow-up with ML algorithms could facilitate the detection of irAEs in ICI-treated cancer patients. The results should be validated with a larger dataset. Trial registration Clinical Trials Register (NCT3928938), registration date the 26th of April, 2019

Sign in / Sign up

Export Citation Format

Share Document