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9. Treatment patterns and survival outcomes of patients admitted to the intensive care unit due to immune-related adverse events of immune checkpoint inhibitors

Author

Lin, Lishi, Houwink, Aletta P. I., van Dieren, Jolanda M., Wolthuis, Esther K., van Thienen, Johannes V., van der Heijden, Michiel S., Haanen, John B. A. G., Beijnen, Jos H., Huitema, Alwin D. R., Lin, Lishi, Houwink, Aletta P. I., van Dieren, Jolanda M., Wolthuis, Esther K., van Thienen, Johannes V., van der Heijden, Michiel S., Haanen, John B. A. G., Beijnen, Jos H., and Huitema, Alwin D. R.

Abstract

IntroductionSevere immune-related adverse events (irAEs) due to immune checkpoint inhibitors (ICIs) can lead to admission to the intensive care unit (ICU). In this retrospective study, we determined the incidence, treatment patterns and survival outcomes of this patient population at a comprehensive cancer center.MethodsAll patients admitted to the ICU due to irAEs from ICI treatment between January 2015 and July 2022 were included. Descriptive statistics were reported on patient characteristics and treatment patterns during hospital admission. Overall survival (OS) from the time of ICU discharge to death was estimated using the Kaplan-Meier method.ResultsOver the study period, 5561 patients received at least one ICI administration, of which 32 patients (0.6%) were admitted to the ICU due to irAEs. Twenty patients were treated with anti-PD-1 plus anti-CTLA-4 treatment, whereas 12 patients were treated with ICI monotherapy. The type of irAEs were de novo diabetes-related ketoacidosis (n = 8), immune-related gastrointestinal toxicity (n = 8), myocarditis or myositis (n = 10), nephritis (n = 3), pneumonitis (n = 2), and myelitis (n = 1). The median duration of ICU admission was 3 days (interquartile range: 2-6 days). Three patients died during ICU admission. The median OS of the patients who were discharged from the ICU was 18 months (95% confidence interval, 5.0-NA).ConclusionThe incidence of irAEs leading to ICU admission in patients treated with ICI was low in this study. ICU mortality due to irAEs was low and a subset of this patient population even had long-term survival.

Published
2024

10. Impact of bariatric surgery on oral anticancer drugs: an analysis of real-world data

Author

Apotheek O&O&O, MS Medische Oncologie, Cancer, Lau, Cedric, Mohmaed Ali, Ma Ida, Lin, Lishi, van Balen, Dorieke E M, Jacobs, Bart A W, Nuijen, Bastiaan, Smeenk, Robert M, Steeghs, Neeltje, Huitema, Alwin D R, Apotheek O&O&O, MS Medische Oncologie, Cancer, Lau, Cedric, Mohmaed Ali, Ma Ida, Lin, Lishi, van Balen, Dorieke E M, Jacobs, Bart A W, Nuijen, Bastiaan, Smeenk, Robert M, Steeghs, Neeltje, and Huitema, Alwin D R

Published
2024

11. Are novel oral oncolytics underdosed in obese patients?

Author

Apotheek O&O&O, Cancer, Lin, Lishi, van der Meer, Ellen K O, Steeghs, Neeltje, Beijnen, Jos H, Huitema, Alwin D R, Apotheek O&O&O, Cancer, Lin, Lishi, van der Meer, Ellen K O, Steeghs, Neeltje, Beijnen, Jos H, and Huitema, Alwin D R

Published
2024

12. Treatment patterns and survival outcomes of patients admitted to the intensive care unit due to immune-related adverse events of immune checkpoint inhibitors

Author

Apotheek O&O&O, Cancer, Lin, Lishi, Houwink, Aletta P.I., van Dieren, Jolanda M., Wolthuis, Esther K., van Thienen, Johannes V., van der Heijden, Michiel S., Haanen, John B.A.G., Beijnen, Jos H., Huitema, Alwin D.R., Apotheek O&O&O, Cancer, Lin, Lishi, Houwink, Aletta P.I., van Dieren, Jolanda M., Wolthuis, Esther K., van Thienen, Johannes V., van der Heijden, Michiel S., Haanen, John B.A.G., Beijnen, Jos H., and Huitema, Alwin D.R.

Published
2024

14. Clinical Relevance of High Plasma Trough Levels of the Kinase Inhibitors Crizotinib, Alectinib, Osimertinib, Dabrafenib, and Trametinib in NSCLC Patients

Author

Zorgeenheid Vaatchirurgie Medisch, MS Medische Oncologie, Apotheek O&O&O, Cancer, Lin, Lishi, Barkman, Hannerieke J., Smit, Egbert F., De Langen, Adrianus J., Steeghs, Neeltje, Beijnen, Jos H., Huitema, Alwin D.R., Zorgeenheid Vaatchirurgie Medisch, MS Medische Oncologie, Apotheek O&O&O, Cancer, Lin, Lishi, Barkman, Hannerieke J., Smit, Egbert F., De Langen, Adrianus J., Steeghs, Neeltje, Beijnen, Jos H., and Huitema, Alwin D.R.

Published
2024

16. Clinical Relevance of High Plasma Trough Levels of the Kinase Inhibitors Crizotinib, Alectinib, Osimertinib, Dabrafenib, and Trametinib in NSCLC Patients

Author

Afd Pharmacoepi & Clinical Pharmacology, Pharmacoepidemiology and Clinical Pharmacology, Lin, Lishi, Barkman, HJ, Smit, EF, de Langen, AJ, Steeghs, N, Beijnen, JH, Huitema, ADR, Afd Pharmacoepi & Clinical Pharmacology, Pharmacoepidemiology and Clinical Pharmacology, Lin, Lishi, Barkman, HJ, Smit, EF, de Langen, AJ, Steeghs, N, Beijnen, JH, and Huitema, ADR

Published
2024

19. Longitudinal nonlinear mixed effects modeling of EGFR mutations in ctDNA as predictor of disease progression in treatment of EGFR ‐mutant non‐small cell lung cancer

Author

Janssen, Julie M., primary, Verheijen, Remy B., additional, van Duijl, Tirsa T., additional, Lin, Lishi, additional, van den Heuvel, Michel M., additional, Beijnen, Jos H., additional, Steeghs, Neeltje, additional, van den Broek, Daan, additional, Huitema, Alwin D. R., additional, and Dorlo, Thomas P. C., additional

Published
2022
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20. Longitudinal nonlinear mixed effects modeling of EGFR mutations in ctDNA as predictor of disease progression in treatment of EGFR-mutant non-small cell lung cancer

Author

Apotheek O&O&O, Cancer, Janssen, Julie M., Verheijen, Remy B., van Duijl, Tirsa T., Lin, Lishi, van den Heuvel, Michel M., Beijnen, Jos H., Steeghs, Neeltje, van den Broek, Daan, Huitema, Alwin D.R., Dorlo, Thomas P.C., Apotheek O&O&O, Cancer, Janssen, Julie M., Verheijen, Remy B., van Duijl, Tirsa T., Lin, Lishi, van den Heuvel, Michel M., Beijnen, Jos H., Steeghs, Neeltje, van den Broek, Daan, Huitema, Alwin D.R., and Dorlo, Thomas P.C.

Published
2022

21. Longitudinal nonlinear mixed effects modeling of EGFR mutations in ctDNA as predictor of disease progression in treatment of EGFR-mutant non-small cell lung cancer.

Author

Janssen, Julie M, Verheijen, Remy B, van Duijl, Tirsa T, Lin, Lishi, van den Heuvel, Michel M, Beijnen, Jos H, Steeghs, Neeltje, van den Broek, Daan, Huitema, Alwin D R, Dorlo, Thomas P. C., Janssen, Julie M, Verheijen, Remy B, van Duijl, Tirsa T, Lin, Lishi, van den Heuvel, Michel M, Beijnen, Jos H, Steeghs, Neeltje, van den Broek, Daan, Huitema, Alwin D R, and Dorlo, Thomas P. C.

Abstract

Correlations between increasing concentrations of circulating tumor DNA (ctDNA) in plasma and disease progression have been shown. A nonlinear mixed effects model to describe the dynamics of epidermal growth factor receptor (EGFR) ctDNA data from patients with non-small cell lung cancer (NSCLC) combined with a parametric survival model were developed to evaluate the ability of these modeling techniques to describe ctDNA data. Repeated ctDNA measurements on L858R, exon19del, and T790M mutants were available from 54 patients with EGFR mutated NSCLC treated with erlotinib or gefitinib. Different dynamic models were tested to describe the longitudinal ctDNA concentrations of the driver and resistance mutations. Subsequently, a parametric time-to-event model for progression-free survival (PFS) was developed. Predicted L858R, exon19del, and T790M concentrations were used to evaluate their value as predictor for disease progression. The ctDNA dynamics were best described by a model consisting of a zero-order increase and first-order elimination (19.7/day, 95% confidence interval [CI] 14.9-23.6/day) of ctDNA concentrations. In addition, time-dependent development of resistance (5.0 × 10-4 , 95% CI 2.0 × 10-4 -7.0 × 10-4 /day) was included in the final model. Relative change in L858R and exon19del concentrations from baseline was identified as most significant predictor of disease progression (p = 0.001). The dynamic model for L858R, exon19del, and T790M concentrations in ctDNA and time-to-event model adequately described the observed concentrations and PFS data in our clinical cohort. In addition, it was shown that nonlinear mixed effects modeling is a valuable method for the analysis of longitudinal and heterogeneous biomarker datasets obtained from clinical practice.

Published
2022
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