Development and validation of a prognostic model for recurrent glioblastoma patients treated with bevacizumab and irinotecan

Abstract

Background Predictive markers and prognostic models are required in order to individualize treatment of recurrent glioblastoma (GBM) patients. Here, we sought to identify clinical factors able to predict response and survival in recurrent GBM patients treated with bevacizumab (BEV) and irinotecan. Material and methods A total of 219 recurrent GBM patients treated with BEV plus irinotecan according to a previously published treatment protocol were included in the initial population. Prognostic models were generated by means of multivariate logistic and Cox regression analysis. Results In multivariate analysis, corticosteroid use had a negative predictive impact on response at first evaluation (OR 0.45; 95% CI 0.22-0.93; p ¼ 0.03) and at best response (OR 0.51; 95% CI 0.26-1.02; p ¼ 0.056). Three significant (p50.05) prognostic factors associated with reduced progressionfree survival and overall survival (OS) were identified. These factors were included in the final model for OS, namely corticosteroid use (HR 1.70; 95% CI 1.18-2.45; p ¼ 0.004), neurocognitive deficit (HR 1.40; 95% CI 1.04-1.89; p ¼ 0.03) and multifocal disease (HR 1.56; 95% CI 1.15-2.11; p50.0001). Based on these results a prognostic index able to calculate the probability for OS at 6 and 12 months for the individual patient was established. The predictive value of the model for OS was validated in a separate patient cohort of 85 patients. Discussion and conclusion A prognostic model for OS was established and validated. This model can be used by physicians to risk stratify the individual patient and together with the patient decide whether to initiate BEV relapse treatment.

Angiotensinogen promoter methylation predicts bevacizumab treatment response of patients with recurrent glioblastoma

“…One explanatory factor is that DNA demethylation of the CEBP region in the AGT promoter may occur from the time of glioblastoma diagnosis to the time of glioblastoma recurrence as a result of continues AGT gene activation (Wang et al , 2014). Such stimulatory signals of AGT activation include the pro‐inflammatory cytokine interleukin‐6 and glucocorticosteroids (Wang et al , 2014), which both have been reported to predict poor outcome in bevacizumab‐treated cancer patients (Duerinck et al , 2015; Noonan et al , 2018; Urup et al , 2016a). In addition, the most prominent transcription factors known to induce angiotensinogen transcription are CEBP and STAT3 (Jain et al , 2007; Wang et al , 2014).…”

“…Bevacizumab in combination with chemotherapy has been shown to produce high response rates of approximately 30% in recurrent glioblastoma patients (Friedman et al , 2009; Urup et al , 2016a; Wick et al , 2017). Although this treatment has not proven active in the total population of recurrent glioblastoma patients (Taal et al , 2014; Wick et al , 2017), patients whom achieve response to bevacizumab combination therapy obtain clinical improvement and have prolonged survival (Henriksson et al , 2011; Huang et al , 2016; Jakobsen et al , 2018).…”

“…Although this treatment has not proven active in the total population of recurrent glioblastoma patients (Taal et al , 2014; Wick et al , 2017), patients whom achieve response to bevacizumab combination therapy obtain clinical improvement and have prolonged survival (Henriksson et al , 2011; Huang et al , 2016; Jakobsen et al , 2018). Especially, for the prognostic favorable group of recurrent glioblastoma patients (defined as baseline ECOG performance status ≤ 1, prednisolone ≤ 25 mg, and unifocal disease) we have based on retrospective data (Urup et al , 2016a) observed an impressive difference in median overall survival (OS) of 10 months between responding and nonresponding patients. This highlights the importance of identifying patients benefitting from bevacizumab combination treatment based on prognostic factors and predictive biomarkers for bevacizumab efficacy.…”

Survival prediction of glioblastoma patients—are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential

“…The utilized data sources were clinical parameters (n = 2) [19,32]; genomics (n = 2) [12,21]; MRI imaging (n = 4) [22,23,34,37]; combined clinical and genomics (n = 4) [13,16,27,28]; combined clinical and MRI imaging (n = 10) [14, 18, 20, 24, 25, 29-31, 35, 38]; combined clinical, MRI imaging, and genomics (n = 3) [15,26,36]; histopathology (n = 1) [33]; and combined clinical and pharmacokinetics (n = 1) [17]. Up to 2017, only two studies analyzed high-dimensional data sources (i.e., MRI or genomic information) in addition to clinical information [12,13].…”

“…Overall survival was modeled as a continuous (n = 7) [15,17,26,28,30,35,38], binary (n = 11) [16,19,22,24,25,27,29,31,34,37,38], or time-to-event outcome (n = 11) [12-14, 18, 20, 21, 23, 32, 33, 36, 38]. In studies that defined survival as a binary outcome, survival was dichotomized into short and long survival at 6 [19], 12 [27,38], and 18 months [37], more or less than 400 days [31], as well as the median [29] and mean [25] overall survival in the training cohort.…”