Dataset Information

Prediction model for hyperprogressive disease in non-small cell lung cancer treated with immune checkpoint inhibitors.

ABSTRACT:

Background

Hyperprogressive disease (HPD) is a paradoxical acceleration of tumor growth after immune checkpoint inhibitor (ICI) treatment. This study aimed to identify the risk factors and to present a predictive model for HPD in patients treated with ICIs.

Methods

A total of 78 non-small cell lung cancer (NSCLC) cases, treated with at least two cycles of ICIs who underwent computed tomography (CT) for response assessment were recruited into the study from January 2016 to August 2019. HPD was defined by the following criteria: (i) time-to-treatment failure <2 months; (ii) a 50% increase in the sum of target lesion diameters; (iii) new development of at least two lesions in an already involved organ; (iv) appearance of a new organ lesion; and (v) a decrease in ECOG PS 2.

Results

Of the 78 total patients, 15 (19.2%) had HPD. The risk factors of HPD were age; primary lesion size; and metastases in the contralateral lung, pleura, liver, and bone in multivariable logistic regression (odds ratio [OR]; 0.9038, 1.6619, 28.5913, 23.8264, 14.5711, and 20.1533, respectively, all P-values < 0.05). By using these risk factors, we developed a prediction model for HPD and the area under the receiver operating characteristic curve of the model was 0.9556 (95% confidence interval [CI]: 0.9133-0.9978).

Conclusions

HPD is relatively common and associated with a grave clinical outcome, requiring a careful monitoring in lung cancer patients treated with ICIs. Moreover, risk factors such as age, size of tumor and number of various metastatic lesions should be taken into consideration before ICI administration.

Key points

SIGNIFICANT FINDINGS OF THE STUDY: Age, primary lesion size, and number of metastases are risk factors of HPD. HPD is strongly associated with poor prognosis. HPD during ICI use needs comprehensive monitoring.

What this study adds

This is the first study to develop a prediction model. The area under the curve of the prediction model for HPD was 0.9556.

SUBMITTER: Choi YJ

PROVIDER: S-EPMC7529559 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prediction model for hyperprogressive disease in non-small cell lung cancer treated with immune checkpoint inhibitors.

Choi Yong Jun YJ Kim Taehee T Kim Eun Young EY Lee Sang Hoon SH Kwon Do Sun DS Chang Yoon Soo YS

Thoracic cancer 20200811 10

<h4>Background</h4>Hyperprogressive disease (HPD) is a paradoxical acceleration of tumor growth after immune checkpoint inhibitor (ICI) treatment. This study aimed to identify the risk factors and to present a predictive model for HPD in patients treated with ICIs.<h4>Methods</h4>A total of 78 non-small cell lung cancer (NSCLC) cases, treated with at least two cycles of ICIs who underwent computed tomography (CT) for response assessment were recruited into the study from January 2016 to August 2 ...[more]

PMID: 32779394

Similar Datasets

Project description:BackgroundAlthough the thymus undergoes degeneration with the advancement of age, recent studies have continuously revealed that the thymus possesses the potential for regeneration and may reverse this aging trend. Furthermore, an increasing number of studies indicate an association between thymus function and immunotherapy. Considering that lung cancer patients typically undergo chest computed tomography (CT) scans during treatment, this provides convenient conditions for us to observe thymic remodeling through imaging data. Therefore, exploring the changes in the thymus on CT images is of great significance for understanding its relationship with the efficacy of immunotherapy in non-small cell lung cancer (NSCLC) patients. This study investigated the CT imaging characteristics of thymic density changes in patients with advanced NSCLC after immunotherapy. The primary objective was to determine whether changes in thymic density are predictors of response to immunotherapy in patients with NSCLC.MethodsA total of 412 patients with advanced NSCLC who underwent immunotherapy were included. Thymic density measurements were taken initially and after immunotherapy, with the annualized change calculated. Comprehensive analysis, including disease progression, survival, and subgroup assessments, was conducted. The primary outcome was overall survival (OS), and the secondary outcomes were progression-free survival (PFS), objective response rate (ORR) and disease control rate (DCR).ResultsThe annual change in density of the thymic region ranged from -108 to 108 HU after the initiation of ICIs. Patients were categorized into "loss" or "non-loss" groups (210 vs. 202) based on thymic density changes. Analysis of short-term progression of solid tumors revealed no statistically significant differences in ORR (P=0.55) and DCR (P=0.67) between the two groups. Throughout the entire follow-up period, 41 patients (19.5%) in the "loss" group and 64 patients (31.7%) in the "non-loss" group died. Thymic density reduction was not associated with PFS (P=0.08), but it was positively associated with increased OS (P=0.003). The results were consistent across subgroups.ConclusionsThymic density changes were observed in nearly all NSCLC patients undergoing immunotherapy, with decreased density associated with longer OS. These findings suggest a potential association between thymic density changes and immune efficacy in NSCLC immunotherapy.

Project description:BackgroundLung cancer is the second most common cancer worldwide, with non-small-cell lung cancer (NSCLC) accounting for the majority of cases. Patients with NSCLC have achieved great survival benefits from immunotherapies targeting immune checkpoints. Glucocorticoids (GCs) are frequently used for palliation of cancer-associated symptoms, as supportive care for non-cancer-associated symptoms, and for management of immune-related adverse events (irAEs). The aim of this study was to clarify the safety and prognostic significance of glucocorticoid use in advanced patients with NSCLC treated with immune checkpoint inhibitors (ICIs).MethodsThe study searched publications from PubMed, Embase, Cochrane Library, Web of Science, China Biology Medicine disc, Chinese National Knowledge Infrastructure, Wanfang Data, and Chinese Science and Technology Journal Database up to March 1st, 2022, and conducted a meta-analysis to assess the effects of glucocorticoid use on overall survival (OS) and progression-free survival (PFS) in NSCLC patients treated with ICIs through the available data. The study calculated the pooled hazard ratios (HRs) and 95% confidence intervals (CIs).ResultsThis study included data from 25 literatures that were mainly retrospective, with 8713 patients included. Patients taking GCs had a higher risk for tumor progression and death compared with those not taking GCs (PFS: HR = 1.57, 95% CI: 1.33-1.86, P <0.001; OS: HR = 1.63, 95% CI: 1.41-1.88, P <0.001). GCs used for cancer-associated symptoms caused an obviously negative effect on both PFS and OS (PFS: HR = 1.74, 95% CI: 1.32-2.29, P <0.001; OS: HR = 1.76, 95% CI: 1.52-2.04, P <0.001). However, GCs used for irAEs management did not negatively affect prognosis (PFS: HR = 0.68, 95% CI: 0.46-1.00, P = 0.050; OS: HR = 0.53, 95% CI: 0.34-0.83, P = 0.005), and GCs used for non-cancer-associated indications had no effect on prognosis (PFS: HR = 0.92, 95%CI: 0.63-1.32, P = 0.640; OS: HR = 0.91, 95% CI: 0.59-1.41, P = 0.680).ConclusionsIn advanced NSCLC patients treated with ICIs, the use of GCs for palliation of cancer-associated symptoms may result in a worse PFS and OS, indicating that they increase the risk of tumor progression and death. But, in NSCLC patients treated with ICIs, the use of GCs for the management of irAEs may be safe, and the use of GCs for the treatment of non-cancer-associated symptoms may not affect the ICIs' survival benefits. Therefore, it is necessary to be careful and evaluate indications rationally before administering GCs in individualized clinical management.

Dataset Information

Prediction model for hyperprogressive disease in non-small cell lung cancer treated with immune checkpoint inhibitors.

Background

Methods

Results

Conclusions

Key points

What this study adds

Publications

Prediction model for hyperprogressive disease in non-small cell lung cancer treated with immune checkpoint inhibitors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets