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Innovative methods for the identification of predictive biomarker signatures in oncology: Application to bevacizumab.

ABSTRACT: Current methods for subgroup analyses of data collected from randomized clinical trials (RCTs) may lead to false-positives from multiple testing, lack power to detect moderate but clinically meaningful differences, or be too simplistic in characterizing patients who may benefit from treatment. Herein, we present a general procedure based on a set of newly developed statistical methods for the identification and evaluation of complex multivariate predictors of treatment effect. Furthermore, we implemented this procedure to identify a subgroup of patients who may receive the largest benefit from bevacizumab treatment using a panel of 10 biomarkers measured at baseline in patients enrolled on two RCTs investigating bevacizumab in metastatic breast cancer. Data were collected from patients with human epidermal growth factor receptor 2 (HER2)-negative (AVADO) and HER2-positive (AVEREL) metastatic breast cancer. We first developed a classification rule based on an estimated individual scoring system, using data from the AVADO study only. The classification rule takes into consideration a panel of biomarkers, including vascular endothelial growth factor (VEGF)-A. We then classified the patients in the independent AVEREL study into patient groups according to "promising" or "not-promising" treatment benefit based on this rule and conducted a statistical analysis within these subgroups to compute point estimates, confidence intervals, and p-values for treatment effect and its interaction. In the group with promising treatment benefit in the AVEREL study, the estimated hazard ratio of bevacizumab versus placebo for progression-free survival was 0.687 (95% confidence interval [CI]: 0.462-1.024, p = 0.065), while in the not-promising group the hazard ratio (HR) was 1.152 (95% CI: 0.526-2.524, p = 0.723). Using the median level of VEGF-A from the AVEREL study to divide the study population, then the HR becomes 0.711 (95% CI: 0.435-1.163, p = 0.174) in the promising group and 0.828 (95% CI: 0.496-1.380, p = 0.468) in the not-promising group. Similar results were obtained with the median VEGF-A levels from the AVADO study ("promising" group: HR = 0.709, 95%CI: 0.444-1.133, p = 0.151; "not-promising" group: HR = 0.851, 95% CI: 0.497-1.458, p = 0.556). Our analysis shows it is feasible to employ statistical methods for empirically constructing and validating a scoring system based on a panel of biomarkers. This scoring system can be used to estimate the treatment effect for individual patients and identify a subgroup of patients who may benefit from treatment. The proposed procedure can provide a general framework to organize many statistical methods (existing or to be developed) into a coherent set of analyses for the development of personalized medicines and has the potential of broad applications.

SUBMITTER: Delmar P

PROVIDER: S-EPMC5936698 | biostudies-other | 2017 Mar

REPOSITORIES: biostudies-other

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Innovative methods for the identification of predictive biomarker signatures in oncology: Application to bevacizumab.

Delmar Paul P Irl Cornelia C Tian Lu L

Contemporary clinical trials communications 20170119

Current methods for subgroup analyses of data collected from randomized clinical trials (RCTs) may lead to false-positives from multiple testing, lack power to detect moderate but clinically meaningful differences, or be too simplistic in characterizing patients who may benefit from treatment. Herein, we present a general procedure based on a set of newly developed statistical methods for the identification and evaluation of complex multivariate predictors of treatment effect. Furthermore, we im ...[more]

PMID: 29740627

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Project description:BackgroundDespite the extensive use of bevacizumab in a range of oncology indications, the US FDA revoked its approval for breast cancers, and multiple negative trials in several solid malignancies have been reported, so the need for predictive biomarkers has increased. The development of predictive biomarkers for anti-angiogenic bevacizumab therapy has long been pursued but without success.IntroductionHeat shock protein (HSP)-27 expression has recently been identified as a predictive biomarker for bevacizumab in treating metastatic melanoma. This study aimed to evaluate the cost effectiveness of HSP27 biomarker testing before administration of bevacizumab.MethodsA partitioned survival analysis model with three mutually exclusive health states (progression-free survival, progressed disease, and death) was developed using a Norwegian health system perspective. The proportion of patients in each state was calculated using the area under the Kaplan-Meier curve for progression-free and overall survival derived from trials of bevacizumab and dacarbazine. Three strategies were compared: (1) test-treat with HSP27 biomarker and bevacizumab, (2) treat-all with dacarbazine without HSP27 testing, (3) treat-all with bevacizumab without HSP27 testing. Quality-adjusted life-years (QALYs) and costs were calculated for each strategy and discounted at 4%. A lifetime horizon was applied. Uncertainty analyses were performed. Expected value of perfect information (EVPI) was estimated to assess the potential value of further research to generate more evidence.ResultsAlthough the test-treat strategy was cost effective compared with treat-all with dacarbazine, it was not cost effective compared with treat-all with bevacizumab without HSP27 testing. However, EVPI results showed very minimal or no value in conducting further research efforts to reduce uncertainties around current information.ConclusionThe results of this study suggested that testing for HSP27 expression before administering bevacizumab is not cost effective compared with treat-all with bevacizumab without testing. It indicates that HSP27 expression is not cost effective as a potential predictive biomarker for bevacizumab. This may not necessarily mean that HSP27 is a bad biomarker for bevacizumab, but it may mean that bevacizumab is much better than dacarbazine regardless of HSP27 expression, so patient stratification according to HSP27 status is meaningless. Or, indeed, it may imply that HSP27 is not sufficiently good at identifying the right patients for bevacizumab.

Project description:Predictive biomarkers are needed to triage patients to the best therapy. We prospectively planned examination of sequential blood, biopsy, and functional imaging with which to confirm the mechanism and to identify potential predictive biomarkers in a phase Ib clinical trial expansion of patients with solid tumors receiving sorafenib/bevacizumab. The maximally tolerated doses of sorafenib at 200 mg twice daily with bevacizumab at 5 mg/kg every other week were given to biopsiable patients. Patients were randomized to receive either sorafenib or bevacizumab monotherapy for the first 28-day cycle with the second drug added with cycle 2. Biopsies, dynamic contrast-enhanced MRI, and fluorodeoxyglucose-proton emission tomography were done pre-therapy and at 2 and 6 weeks (2 weeks into combination therapy). Tumor and serum proteomics, Ras/Raf mutational analysis, and functional imaging results were examined individually and across the dataset to identify potential changes predictive of response to therapy and those that confirm the biochemical drug mechanism(s). Therapy with sorafenib/bevacizumab resulted in clinical benefit in 45% of this mixed solid tumor group. ERK activation and microvessel density were decreased with monotherapy treatment with sorafenib or bevacizumab, respectively; whereas a decreased signal over the group of total AKT, phospho(p)-VEGF receptor2, p-endothelial nitric-oxide synthase, b-RAF, and cleaved poly(ADP-ribose) polymerase was associated with earlier progression of disease. Tumor metabolic activity decreased in those patients with clinical benefits lasting longer than 4 months, and activity increased with progression of disease. Cleavage of caspase 3 and poly(ADP-ribose) polymerase was increased, and Ki67 expression decreased in patients with prolonged clinical benefits, consistent with decreased proliferation and increased apoptosis. The conglomerate analysis, incorporating pharmacodynamic and tumor biochemistry, demonstrated sorafenib/bevacizumab-targeted vascular activity in the tumor. Results suggest potential biomarkers for which changes, as a group, during early therapeutic exposure may predict clinical benefit.

Project description:Combining bevacizumab with frontline chemotherapy statistically significantly improved progression-free survival (PFS) but not overall survival (OS) in the phase III GOG-0218 trial. Evaluation of candidate biomarkers was an exploratory objective. Patients with stage III (incompletely resected) or IV ovarian cancer were randomly assigned to receive six chemotherapy cycles with placebo or bevacizumab followed by single-agent placebo or bevacizumab. Five candidate tumor biomarkers were assessed by immunohistochemistry. The biomarker-evaluable population was categorized into high or low biomarker-expressing subgroups using median and quartile cutoffs. Associations between biomarker expression and efficacy were analyzed. All statistical tests were two-sided. The biomarker-evaluable population (n = 980) comprising 78.5% of the intent-to-treat population had representative baseline characteristics and efficacy outcomes. Neither prognostic nor predictive associations were seen for vascular endothelial growth factor (VEGF) receptor-2, neuropilin-1, or MET. Higher microvessel density (MVD; measured by CD31) showed predictive value for PFS (hazard ratio [HR] for bevacizumab vs placebo = 0.40, 95% confidence interval [CI] = 0.29 to 0.54, vs 0.80, 95% CI = 0.59 to 1.07, for high vs low MVD, respectively, P interaction = .003) and OS (HR = 0.67, 95% CI = 0.51 to 0.88, vs 1.10, 95% CI = 0.84 to 1.44, P interaction = .02). Tumor VEGF-A was not predictive for PFS but showed potential predictive value for OS using a third-quartile cutoff for high VEGF-A expression. These retrospective tumor biomarker analyses suggest a positive association between density of vascular endothelial cells (the predominant cell type expressing VEGF receptors) and tumor VEGF-A levels and magnitude of bevacizumab effect in ovarian cancer. The potential predictive value of MVD (CD31) and tumor VEGF-A is consistent with a mechanism of action driven by VEGF-A signaling blockade.

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Innovative methods for the identification of predictive biomarker signatures in oncology: Application to bevacizumab.

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Innovative methods for the identification of predictive biomarker signatures in oncology: Application to bevacizumab.

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