Current drug development and trial designs in neuro-oncology: report from the first American Society of Clinical Oncology and Society for Neuro-Oncology Clinical Trials Conference.

Successful drug development for people with cancers of the CNS has been challenging. There are multiple barriers to successful drug development including biological factors, rarity of the disease, and ineffective use of clinical trials. Based upon a series of presentations at the First Central Nervous System Clinical Trials Conference hosted by the American Society of Clinical Oncology and the Society for Neuro-Oncology, we provide an overview on drug development and novel trial designs in neuro-oncology. This Review discusses the challenges of therapeutic development in neuro-oncology and proposes strategies to improve the drug discovery process by enriching the pipeline of promising therapies, optimising trial design, incorporating biomarkers, using external data, and maximising efficacy and reproducibility of clinical trials.

Prognostic value of interim FDG-PET in diffuse large cell lymphoma: results from the CALGB 50303 Clinical Trial.

As part of a randomized, prospective clinical trial in large cell lymphoma, we conducted serial fluorodeoxyglucose positron emission tomography (FDG-PET) at baseline, after 2 cycles of chemotherapy (interim PET [i-PET]), and at end of treatment (EoT) to identify biomarkers of response that are predictive of remission and survival. Scans were interpreted in a core laboratory by 2 imaging experts, using the visual Deauville 5-point scale (5-PS), and by calculating percent change in FDG uptake (change in standardized uptake value [ΔSUV]). Visual scores of 1 through 3 and ΔSUV ≥66% were prospectively defined as negative. Of 524 patients enrolled in the parent trial, 169 agreed to enroll in the PET substudy and 158 were eligible for final analysis. In this selected population, all had FDG-avid disease at baseline; by 5-PS, 55 (35%) remained positive on i-PET and 28 (18%) on EoT PET. Median ΔSUV on i-PET was 86.2%. With a median follow-up of 5 years, ΔSUV, as continuous variable, was associated with progression-free survival (PFS) (hazard ratio [HR] = 0.99; 95% confidence interval [CI], 0.97-1.00; P = .02) and overall survival (OS) (HR, 0.98; 95% CI, 0.97-0.99; P = .03). ΔSUV ≥66% was predictive of OS (HR, 0.31; 95% CI, 0.11-0.85; P = .02) but not PFS (HR, 0.47; 95% CI, 0.19-1.13; P = .09). Visual 5-PS on i-PET did not predict outcome. ΔSUV, but not visual analysis, on i-PET predicted OS in DLBCL, although the low number of events limited the statistical analysis. These data may help guide future clinical trials using PET response-adapted therapy. This trial was registered at www.clinicaltrials.gov as #NCT00118209.

Leveraging external data in the design and analysis of clinical trials in neuro-oncology.

Integration of external control data, with patient-level information, in clinical trials has the potential to accelerate the development of new treatments in neuro-oncology by contextualising single-arm studies and improving decision making (eg, early stopping decisions). Based on a series of presentations at the 2020 Clinical Trials Think Tank hosted by the Society of Neuro-Oncology, we provide an overview on the use of external control data representative of the standard of care in the design and analysis of clinical trials. High-quality patient-level records, rigorous methods, and validation analyses are necessary to effectively leverage external data. We review study designs, statistical methods, risks, and potential distortions in using external data from completed trials and real-world data, as well as data sources, data sharing models, ongoing work, and applications in glioblastoma.

A clinical calculator to predict disease outcomes in women with hormone receptor-positive advanced breast cancer treated with first-line endocrine therapy.

PURPOSE: Endocrine therapy (ET) is an effective strategy to treat hormone receptor-positive, human epidermal growth factor receptor 2-negative (HR+/HER2-) advanced breast cancer (ABC) but nearly all patients eventually progress. Our goal was to develop and validate a web-based clinical calculator for predicting disease outcomes in women with HR+ABC who are candidates for receiving first-line single-agent ET. METHODS: The meta-database comprises 891 patient-level data from the control arms of five contemporary clinical trials where patients received first-line single-agent ET (either aromatase inhibitor or fulvestrant) for ABC. Risk models were constructed for predicting 24-months progression-free survival (PFS-24) and 24-months overall survival (OS-24). Final models were internally validated for calibration and discrimination using ten-fold cross-validation. RESULTS: Higher number of sites of metastases, measurable disease, younger age, lower body mass index, negative PR status, and prior endocrine therapy were associated with worse PFS. Final PFS and OS models were well-calibrated and associated with cross-validated time-dependent area under the curve (AUC) of 0.61 and 0.62, respectively. CONCLUSIONS: The proposed ABC calculator is internally valid and can accurately predict disease outcomes. It may be used to predict patient prognosis, aid planning of first-line treatment strategies, and facilitate risk stratification for future clinical trials in patients with HR+ABC. Future validation of the proposed models in independent patient cohorts is warranted.

A clinical calculator to predict disease outcomes in women with triple-negative breast cancer.

PURPOSE: Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, characterized by substantial risks of early disease recurrence and mortality. We constructed and validated clinical calculators for predicting recurrence-free survival (RFS) and overall survival (OS) for TNBC. METHODS: Data from 605 women with centrally confirmed TNBC who underwent primary breast cancer surgery at Mayo Clinic during 1985-2012 were used to train risk models. Variables included age, menopausal status, tumor size, nodal status, Nottingham grade, surgery type, adjuvant radiation therapy, adjuvant chemotherapy, Ki67, stromal tumor-infiltrating lymphocytes (sTIL) score, and neutrophil-to-lymphocyte ratio (NLR). Final models were internally validated for calibration and discrimination using ten-fold cross-validation and compared with their base-model counterparts which include only tumor size and nodal status. Independent external validation was performed using data from 478 patients diagnosed with stage II/III invasive TNBC during 1986-1992 in the British Columbia Breast Cancer Outcomes Unit database. RESULTS: Final RFS and OS models were well calibrated and associated with C-indices of 0.72 and 0.73, as compared with 0.64 and 0.62 of the base models (p < 0.001). In external validation, the discriminant ability of the final models was comparable to the base models (C-index: 0.59-0.61). The RFS model demonstrated greater accuracy than the base model both overall and within patient subgroups, but the advantages of the OS model were less profound. CONCLUSIONS: This TNBC clinical calculator can be used to predict patient outcomes and may aid physician's communication with TNBC patients regarding their long-term disease outlook and planning treatment strategies.

Randomized trial of ofatumumab and bendamustine versus ofatumumab, bendamustine, and bortezomib in previously untreated patients with high-risk follicular lymphoma: CALGB 50904 (Alliance).

BACKGROUND: This multicenter, randomized phase 2 trial evaluated complete responses (CRs), efficacy, and safety with ofatumumab and bendamustine and with ofatumumab, bendamustine, and bortezomib in patients with untreated, high-risk follicular lymphoma (FL). METHODS: Patients with grade 1 to 3a FL and either a Follicular Lymphoma International Prognostic Index (FLIPI) score of 2 with 1 lymph node >6 cm or an FLIPI score of 3 to 5 were randomized to arm A (ofatumumab, bendamustine, and maintenance ofatumumab) or to arm B (ofatumumab, bendamustine, bortezomib, and maintenance ofatumumab and bortezomib). RESULTS: One hundred twenty-eight patients (66 in arm A and 62 in arm B) received treatment. The median age was 61 years, and 61% had disease >6 cm; 29% had an FLIPI score of 2, and 71% had an FLIPI score of 3 to 5. In arm A, 86% completed induction, and 64% completed maintenance. In arm B, 66% and 52% completed induction and maintenance, respectively. Dose modifications were required in 65% and 89% in arms A and B, respectively. Clinically significant grade 3 to 4 toxicities included neutropenia (A, 36%; B, 31%), nausea/vomiting (A, 0%; B, 8%), diarrhea (A, 5%; B, 11%), and sensory neuropathy (A, 0%; B, 5%). The estimated CR rates were 62% (95% confidence interval [CI], 50%-74%) and 60% (95% CI, 47%-72%) in arms A and B, respectively (P = .68). With a median follow-up of 3.3 years, the estimated 2-year progression-free survival (PFS) and overall survival (OS) rates were 80% and 97%, respectively, for arm A and 76% and 91%, respectively, for arm B. CONCLUSIONS: The CR rates, PFS, and OS were not improved with the addition of bortezomib to ofatumumab and bendamustine in patients with high-risk FL. Although grade 3 to 4 toxicities were similar, more patients treated with bortezomib required dose modifications and early discontinuation.

Criteria for the use of omics-based predictors in clinical trials.

The US National Cancer Institute (NCI), in collaboration with scientists representing multiple areas of expertise relevant to 'omics'-based test development, has developed a checklist of criteria that can be used to determine the readiness of omics-based tests for guiding patient care in clinical trials. The checklist criteria cover issues relating to specimens, assays, mathematical modelling, clinical trial design, and ethical, legal and regulatory aspects. Funding bodies and journals are encouraged to consider the checklist, which they may find useful for assessing study quality and evidence strength. The checklist will be used to evaluate proposals for NCI-sponsored clinical trials in which omics tests will be used to guide therapy.

Impact of histopathology, tumor-infiltrating lymphocytes, and adjuvant chemotherapy on prognosis of triple-negative breast cancer.

BACKGROUND: Given its high recurrence risk, guidelines recommend systemic therapy for most patients with early-stage triple-negative breast cancer (TNBC). While some clinicopathologic factors and tumor-infiltrating lymphocytes (TILs) are known to be prognostic in patients receiving chemotherapy, their prognostic implications in systemically untreated patients remain unknown. METHODS: From a cohort of 9982 women with surgically treated non-metastatic breast cancer, all patients with clinically reported ER-negative/borderline (≤10%) disease were selected for central assessment of ER/PR/HER2, histopathology, Ki-67, and TILs. The impact of these parameters on invasive disease-free survival (IDFS) and overall survival (OS) was assessed using Cox proportional hazards models. RESULTS: Six hundred five patients met the criteria for TNBC (ER/PR < 1% and HER2 negative). Most were T1-2 (95%), N0-1 (86%), grade 3 (88%), and had a Ki-67 >15% (75%). Histologically, 70% were invasive carcinoma of no special type, 16% medullary, 8% metaplastic, and 6% apocrine. The median stromal TIL content was 20%. Four hundred twenty-three (70%) patients received adjuvant chemotherapy. Median OS follow-up was 10.6 years. On multivariate analysis, only higher nodal stage, lower TILs, and the absence of adjuvant chemotherapy were associated with worse IDFS and OS. Among systemically untreated patients (n = 182), the 5-year IDFS was 69.9% (95% CI 60.7-80.5) [T1a: 82.5% (95% CI 62.8-100), T1b: 67.5% (95% CI 51.9-87.8) and T1c: 67.3% (95% CI 54.9-82.6)], compared to 77.8% (95% CI 68.3-83.6) for systemically treated T1N0. Nodal stage and TILs remained strongly associated with outcomes. CONCLUSIONS: In early-stage TNBC, nodal involvement, TILs, and receipt of adjuvant chemotherapy were independently associated with IDFS and OS. In systemically untreated TNBC, TILs remained prognostic and the risk of recurrence or death was substantial, even for T1N0 disease.

Power estimation in biomarker studies where events are already observed.

Background The clinical utility of a new biomarker should ideally be established in a prospective randomized clinical trial. However, such trials are not always practical. As such, it is common for investigators to identify promising biomarkers using archived specimens and clinical data collected from previously completed therapeutic trials. Simon et al. defined such biomarker studies as prospective-retrospective studies and proposed specific conditions to satisfy for such evaluations to be more than hypothesis generating. One condition they proposed is that archived tissues must be available on a sufficiently large number of patients from the pivotal trials to ensure adequately powered analyses. Purpose The purpose of this article is to provide a new perspective on how to estimate power for assessing the prognostic and predictive values of a single binary biomarker in prospective-retrospective biomarker studies. Computer programs are provided to facilitate the use of these methods in practice. Methods The proposed methods utilize additional information that becomes available during the course of the treatment trial including sample size, accrual time, additional follow-up time, and the observed number of events at time of biomarker analysis. These methods involve solving for the exponential hazard rates that give rise to the event numbers that are consistent with those observed while satisfying other design parameter constraints. Conclusion Simon et al. proposed a new paradigm for biomarker design, conduct, analysis, and evaluation in prospective-retrospective studies that offers a more efficient alternative than fully prospective biomarker studies. As a general rule, they suggest that samples from at least two-thirds of the patients be available for analysis. In this article, I expand on this issue and provide a methodological tool useful for estimating study power in prospective-retrospective biomarker studies. It is my hope that these incremental efforts to improve the quality and statistical rigor in biomarker studies will hasten the introduction of useful tumor biomarkers into clinical practice.

An international study to increase concordance in Ki67 scoring.

Although an important biomarker in breast cancer, Ki67 lacks scoring standardization, which has limited its clinical use. Our previous study found variability when laboratories used their own scoring methods on centrally stained tissue microarray slides. In this current study, 16 laboratories from eight countries calibrated to a specific Ki67 scoring method and then scored 50 centrally MIB-1 stained tissue microarray cases. Simple instructions prescribed scoring pattern and staining thresholds for determination of the percentage of stained tumor cells. To calibrate, laboratories scored 18 'training' and 'test' web-based images. Software tracked object selection and scoring. Success for the calibration was prespecified as Root Mean Square Error of scores compared with reference <0.6 and Maximum Absolute Deviation from reference <1.0 (log2-transformed data). Prespecified success criteria for tissue microarray scoring required intraclass correlation significantly >0.70 but aiming for observed intraclass correlation ≥0.90. Laboratory performance showed non-significant but promising trends of improvement through the calibration exercise (mean Root Mean Square Error decreased from 0.6 to 0.4, Maximum Absolute Deviation from 1.6 to 0.9; paired t-test: P=0.07 for Root Mean Square Error, 0.06 for Maximum Absolute Deviation). For tissue microarray scoring, the intraclass correlation estimate was 0.94 (95% credible interval: 0.90-0.97), markedly and significantly >0.70, the prespecified minimum target for success. Some discrepancies persisted, including around clinically relevant cutoffs. After calibrating to a common scoring method via a web-based tool, laboratories can achieve high inter-laboratory reproducibility in Ki67 scoring on centrally stained tissue microarray slides. Although these data are potentially encouraging, suggesting that it may be possible to standardize scoring of Ki67 among pathology laboratories, clinically important discrepancies persist. Before this biomarker could be recommended for clinical use, future research will need to extend this approach to biopsies and whole sections, account for staining variability, and link to outcomes.

Two-stage adaptive cutoff design for building and validating a prognostic biomarker signature.

Cancer biomarkers are frequently evaluated using archived specimens collected from previously conducted therapeutic trials. Routine collection and banking of high quality specimens is an expensive and time-consuming process. Therefore, care should be taken to preserve these precious resources. Here, we propose a novel two-stage adaptive cutoff design that affords the possibility to stop the biomarker study early if an evaluation of the model performance is unsatisfactory at an early stage, thereby allowing one to preserve the remaining specimens for future research. In addition, our design integrates important elements necessary to meet statistical rigor and practical demands for developing and validating a prognostic biomarker signature, including maintaining strict separation between the datasets used to build and evaluate the model and producing a locked-down signature to facilitate future validation. We conduct simulation studies to evaluate the operating characteristics of the proposed design. We show that under the null hypothesis when the model performance is deemed undesirable, the proposed design maintains type I error at the nominal level, has high probabilities of terminating the study early, and results in substantial savings in specimens. Under the alternative hypothesis, power is generally high when the total sample size and the targeted degree of improvement in prediction accuracy are reasonably large. We illustrate the use of the procedure with a dataset in patients with diffuse large-B-cell lymphoma. The practical aspects of the proposed designs are discussed. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

Comparison of confidence interval methods for an intra-class correlation coefficient (ICC).

BACKGROUND: The intraclass correlation coefficient (ICC) is widely used in biomedical research to assess the reproducibility of measurements between raters, labs, technicians, or devices. For example, in an inter-rater reliability study, a high ICC value means that noise variability (between-raters and within-raters) is small relative to variability from patient to patient. A confidence interval or Bayesian credible interval for the ICC is a commonly reported summary. Such intervals can be constructed employing either frequentist or Bayesian methodologies. METHODS: This study examines the performance of three different methods for constructing an interval in a two-way, crossed, random effects model without interaction: the Generalized Confidence Interval method (GCI), the Modified Large Sample method (MLS), and a Bayesian method based on a noninformative prior distribution (NIB). Guidance is provided on interval construction method selection based on study design, sample size, and normality of the data. We compare the coverage probabilities and widths of the different interval methods. RESULTS: We show that, for the two-way, crossed, random effects model without interaction, care is needed in interval method selection because the interval estimates do not always have properties that the user expects. While different methods generally perform well when there are a large number of levels of each factor, large differences between the methods emerge when the number of one or more factors is limited. In addition, all methods are shown to lack robustness to certain hard-to-detect violations of normality when the sample size is limited. CONCLUSIONS: Decision rules and software programs for interval construction are provided for practical implementation in the two-way, crossed, random effects model without interaction. All interval methods perform similarly when the data are normal and there are sufficient numbers of levels of each factor. The MLS and GCI methods outperform the NIB when one of the factors has a limited number of levels and the data are normally distributed or nearly normally distributed. None of the methods work well if the number of levels of a factor are limited and data are markedly non-normal. The software programs are implemented in the popular R language.

Leveraging external control data in the design and analysis of neuro-oncology trials: Pearls and perils.

BACKGROUND: Randomized controlled trials have been the gold standard for evaluating medical treatments for many decades but they are often criticized for requiring large sample sizes. Given the urgent need for better therapies for glioblastoma, it has been argued that data collected from patients treated with the standard regimen can provide high-quality external control data to supplement or replace concurrent control arm in future glioblastoma trials. METHODS: In this article, we provide an in-depth appraisal of the use of external control data in the context of neuro-oncology trials. We describe several clinical trial designs with particular attention to how external information is utilized and address common fallacies that may lead to inappropriate adoptions of external control data. RESULTS: Using 2 completed glioblastoma trials, we illustrate the use of an assessment tool that lays out a blueprint for assembling a high-quality external control data set. Using statistical simulations, we draw caution from scenarios where these approaches can fall short on controlling the type I error rate. CONCLUSIONS: While this approach may hold promise in generating informative data in certain settings, this sense of optimism should be tampered with a healthy dose of skepticism due to a myriad of design and analysis challenges articulated in this review. Importantly, careful planning is key to its successful implementation.

Criteria for the use of omics-based predictors in clinical trials: explanation and elaboration.

High-throughput 'omics' technologies that generate molecular profiles for biospecimens have been extensively used in preclinical studies to reveal molecular subtypes and elucidate the biological mechanisms of disease, and in retrospective studies on clinical specimens to develop mathematical models to predict clinical endpoints. Nevertheless, the translation of these technologies into clinical tests that are useful for guiding management decisions for patients has been relatively slow. It can be difficult to determine when the body of evidence for an omics-based test is sufficiently comprehensive and reliable to support claims that it is ready for clinical use, or even that it is ready for definitive evaluation in a clinical trial in which it may be used to direct patient therapy. Reasons for this difficulty include the exploratory and retrospective nature of many of these studies, the complexity of these assays and their application to clinical specimens, and the many potential pitfalls inherent in the development of mathematical predictor models from the very high-dimensional data generated by these omics technologies. Here we present a checklist of criteria to consider when evaluating the body of evidence supporting the clinical use of a predictor to guide patient therapy. Included are issues pertaining to specimen and assay requirements, the soundness of the process for developing predictor models, expectations regarding clinical study design and conduct, and attention to regulatory, ethical, and legal issues. The proposed checklist should serve as a useful guide to investigators preparing proposals for studies involving the use of omics-based tests. The US National Cancer Institute plans to refer to these guidelines for review of proposals for studies involving omics tests, and it is hoped that other sponsors will adopt the checklist as well.

Development of omics-based clinical tests for prognosis and therapy selection: the challenge of achieving statistical robustness and clinical utility.

BACKGROUND: Many articles have been published in biomedical journals reporting on the development of prognostic and therapy-guiding biomarkers or predictors developed from high-dimensional data generated by omics technologies. Few of these tests have advanced to routine clinical use. PURPOSE: We discuss statistical issues in the development and evaluation of prognostic and therapy-guiding biomarkers and omics-based tests. METHODS: Concepts relevant to the development and evaluation of prognostic and therapy-guiding clinical tests are illustrated through discussion and examples. Some differences between statistical approaches for test evaluation and therapy evaluation are explained. The additional complexities introduced in the evaluation of omics-based tests are highlighted. RESULTS: Distinctions are made between clinical validity of a test and clinical utility. To establish clinical utility for prognostic tests, it is explained why absolute risk should be evaluated in addition to relative risk measures. The critical role of an appropriate control group is emphasized for evaluation of therapy-guiding tests. Common pitfalls in the development and evaluation of tests generated from high-dimensional omics data such as model overfitting and inappropriate methods for test performance evaluation are explained, and proper approaches are suggested. LIMITATIONS: The cited references do not comprise an exhaustive list of useful references on this topic, and a systematic review of the literature was not performed. Instead, a few key points were highlighted and illustrated with examples drawn from the oncology literature. CONCLUSIONS: Approaches for the development and statistical evaluation of clinical tests useful for predicting prognosis and selecting therapy differ from standard approaches for therapy evaluation. Proper evaluation requires an understanding of the clinical setting and what information is likely to influence clinical decisions. Specialized expertise relevant to building mathematical predictor models from high-dimensional data is helpful to avoid common pitfalls in the development and evaluation of omics-based tests.

Molecular Profiling in Neuro-Oncology: Where We Are, Where We're Heading, and How We Ensure Everyone Can Come Along.

Advances in molecular profiling have led to improved understanding of glioma heterogeneity. Results have been used to inform diagnostic classification and targeted treatment strategies. Validation of these tests is necessary in the development of biomarkers that can aid in treatment decision, allowing for personalized medicine in neuro-oncologic diseases. Although not all populations have benefitted equally from awareness of and access to testing, opportunities arise regarding incorporating this testing into the standard of care for patients with glioma.

Abnormalities on baseline chest imaging are risk factors for immune checkpoint inhibitor associated pneumonitis.

BACKGROUND: Chronic lung disease is a proposed risk factor for immune checkpoint inhibitor pneumonitis (ICI-pneumonitis); however, data is sparse regarding the impact of pre-existing lung disease and baseline chest imaging abnormalities on the risk of developing ICI-pneumonitis. METHODS: We conducted a retrospective cohort study of patients with ICI treatment for cancer from 2015 to 2019. ICI-pneumonitis was determined by the treating physician with corroboration via an independent physician review and exclusion of alternative etiologies. Controls were patients treated with ICI without a diagnosis of ICI-pneumonitis. Fisher's exact tests, Student's t-tests, and logistic regression were used for statistical analysis. RESULTS: We analyzed 45 cases of ICI-pneumonitis and 135 controls. Patients with abnormal baseline chest CT imaging (emphysema; bronchiectasis; reticular, ground glass and/or consolidative opacities) had increased risk for ICI-pneumonitis (OR 3.41, 95%CI: 1.68-6.87, p = 0.001). Patients with gastroesophageal reflux disease (GERD) (OR 3.83, 95%CI: 1.90-7.70, p = < 0.0001) also had increased risk for ICI-pneumonitis. On multivariable logistic regression, patients with abnormal baseline chest imaging and/or GERD remained at increased risk for ICI-pneumonitis. Eighteen percent of all patients (32/180) had abnormal baseline chest CT consistent with chronic lung disease without a documented diagnosis. CONCLUSION: Patients with baseline chest CT abnormalities and GERD were at increased risk for developing ICI-pneumonitis. The large proportion of patients with baseline radiographic abnormalities without a clinical diagnosis of chronic lung disease highlights the importance of multidisciplinary evaluation prior to ICI initiation.

NRG/RTOG 0837: Randomized, phase II, double-blind, placebo-controlled trial of chemoradiation with or without cediranib in newly diagnosed glioblastoma.

Background: A randomized, phase II, placebo-controlled, and blinded clinical trial (NCT01062425) was conducted to determine the efficacy of cediranib, an oral pan-vascular endothelial growth factor receptor tyrosine kinase inhibitor, versus placebo in combination with radiation and temozolomide in newly diagnosed glioblastoma. Methods: Patients with newly diagnosed glioblastoma were randomly assigned 2:1 to receive (1) cediranib (20 mg) in combination with radiation and temozolomide; (2) placebo in combination with radiation and temozolomide. The primary endpoint was 6-month progression-free survival (PFS) based on blinded, independent radiographic assessment of postcontrast T1-weighted and noncontrast T2-weighted MRI brain scans and was tested using a 1-sided Z test for 2 proportions. Adverse events (AEs) were evaluated per CTCAE version 4. Results: One hundred and fifty-eight patients were randomized, out of which 9 were ineligible and 12 were not evaluable for the primary endpoint, leaving 137 eligible and evaluable. 6-month PFS was 46.6% in the cediranib arm versus 24.5% in the placebo arm (P = .005). There was no significant difference in overall survival between the 2 arms. There was more grade ≥ 3 AEs in the cediranib arm than in the placebo arm (P = .02). Conclusions: This study met its primary endpoint of prolongation of 6-month PFS with cediranib in combination with radiation and temozolomide versus placebo in combination with radiation and temozolomide. There was no difference in overall survival between the 2 arms.

Targeted gene expression profiling predicts meningioma outcomes and radiotherapy responses.

Surgery is the mainstay of treatment for meningioma, the most common primary intracranial tumor, but improvements in meningioma risk stratification are needed and indications for postoperative radiotherapy are controversial. Here we develop a targeted gene expression biomarker that predicts meningioma outcomes and radiotherapy responses. Using a discovery cohort of 173 meningiomas, we developed a 34-gene expression risk score and performed clinical and analytical validation of this biomarker on independent meningiomas from 12 institutions across 3 continents (N = 1,856), including 103 meningiomas from a prospective clinical trial. The gene expression biomarker improved discrimination of outcomes compared with all other systems tested (N = 9) in the clinical validation cohort for local recurrence (5-year area under the curve (AUC) 0.81) and overall survival (5-year AUC 0.80). The increase in AUC compared with the standard of care, World Health Organization 2021 grade, was 0.11 for local recurrence (95% confidence interval 0.07 to 0.17, P < 0.001). The gene expression biomarker identified meningiomas benefiting from postoperative radiotherapy (hazard ratio 0.54, 95% confidence interval 0.37 to 0.78, P = 0.0001) and suggested postoperative management could be refined for 29.8% of patients. In sum, our results identify a targeted gene expression biomarker that improves discrimination of meningioma outcomes, including prediction of postoperative radiotherapy responses.

Targeted gene expression profiling predicts meningioma outcomes and radiotherapy responses.

Background: Surgery is the mainstay of treatment for meningioma, the most common primary intracranial tumor, but improvements in meningioma risk stratification are needed and current indications for postoperative radiotherapy are controversial. Recent studies have proposed prognostic meningioma classification systems using DNA methylation profiling, copy number variants, DNA sequencing, RNA sequencing, histology, or integrated models based on multiple combined features. Targeted gene expression profiling has generated robust biomarkers integrating multiple molecular features for other cancers, but is understudied for meningiomas. Methods: Targeted gene expression profiling was performed on 173 meningiomas and an optimized gene expression biomarker (34 genes) and risk score (0 to 1) was developed to predict clinical outcomes. Clinical and analytical validation was performed on independent meningiomas from 12 institutions across 3 continents (N = 1856), including 103 meningiomas from a prospective clinical trial. Gene expression biomarker performance was compared to 9 other classification systems. Results: The gene expression biomarker improved discrimination of postoperative meningioma outcomes compared to all other classification systems tested in the independent clinical validation cohort for local recurrence (5-year area under the curve [AUC] 0.81) and overall survival (5-year AUC 0.80). The increase in area under the curve compared to the current standard of care, World Health Organization 2021 grade, was 0.11 for local recurrence (95% confidence interval [CI] 0.07-0.17, P < 0.001). The gene expression biomarker identified meningiomas benefiting from postoperative radiotherapy (hazard ratio 0.54, 95% CI 0.37-0.78, P = 0.0001) and re-classified up to 52.0% meningiomas compared to conventional clinical criteria, suggesting postoperative management could be refined for 29.8% of patients. Conclusions: A targeted gene expression biomarker improves discrimination of meningioma outcomes compared to recent classification systems and predicts postoperative radiotherapy responses.