Leveraging molecular datasets for biomarker-based clinical trial design in glioblastoma

Inaugural Results of the Individualized Screening Trial of Innovative Glioblastoma Therapy: A Phase II Platform Trial for Newly Diagnosed Glioblastoma Using Bayesian Adaptive Randomization

PURPOSE

The Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT) is a phase II platform trial that uses response adaptive randomization and genomic profiling to efficiently identify novel therapies for phase III testing. Three initial experimental arms (abemaciclib [a cyclin-dependent kinase [CDK]4/6 inhibitor], neratinib [an epidermal growth factor receptor [EGFR]/human epidermal growth factor receptor 2 inhibitor], and CC-115 [a deoxyribonucleic acid-dependent protein kinase/mammalian target of rapamycin inhibitor]) were simultaneously evaluated against a common control arm. We report the results for each arm and examine the feasibility and conduct of the adaptive platform design.

PATIENTS AND METHODS

Patients with newly diagnosed O6-methylguanine-DNA methyltransferase-unmethylated glioblastoma were eligible if they had tumor genotyping to identify prespecified biomarker subpopulations of dominant glioblastoma signaling pathways (EGFR, phosphatidylinositol 3-kinase, and CDK). Initial random assignment was 1:1:1:1 between control (radiation therapy and temozolomide) and the experimental arms. Subsequent Bayesian adaptive randomization was incorporated on the basis of biomarker-specific progression-free survival (PFS) data. The primary end point was overall survival (OS), and one-sided P values are reported. The trial is registered with ClinicalTrials.gov (identifier: NCT02977780).

RESULTS

Two hundred thirty-seven patients were treated (71 control; 73 abemaciclib; 81 neratinib; 12 CC-115) in years 2017-2021. Abemaciclib and neratinib were well tolerated, but CC-115 was associated with ≥ grade 3 treatment-related toxicity in 58% of patients. PFS was significantly longer with abemaciclib (hazard ratio [HR], 0.72; 95% CI, 0.49 to 1.06; one-sided P = .046) and neratinib (HR, 0.72; 95% CI, 0.50 to 1.02; one-sided P = .033) relative to the control arm but there was no PFS benefit with CC-115 (one-sided P = .523). None of the experimental therapies demonstrated a significant OS benefit (P > .05).

CONCLUSION

The INSIGhT design enabled efficient simultaneous testing of three experimental agents using a shared control arm and adaptive randomization. Two investigational arms had superior PFS compared with the control arm, but none demonstrated an OS benefit. The INSIGhT design may promote improved and more efficient therapeutic discovery in glioblastoma. New arms have been added to the trial.

Validation of Predictive Analyses for Interim Decisions in Clinical Trials

PURPOSE

Adaptive clinical trials use algorithms to predict, during the study, patient outcomes and final study results. These predictions trigger interim decisions, such as early discontinuation of the trial, and can change the course of the study. Poor selection of the Prediction Analyses and Interim Decisions (PAID) plan in an adaptive clinical trial can have negative consequences, including the risk of exposing patients to ineffective or toxic treatments.

METHODS

We present an approach that leverages data sets from completed trials to evaluate and compare candidate PAIDs using interpretable validation metrics. The goal is to determine whether and how to incorporate predictions into major interim decisions in a clinical trial. Candidate PAIDs can differ in several aspects, such as the prediction models used, timing of interim analyses, and potential use of external data sets. To illustrate our approach, we considered a randomized clinical trial in glioblastoma. The study design includes interim futility analyses on the basis of the predictive probability that the final analysis, at the completion of the study, will provide significant evidence of treatment effects. We examined various PAIDs with different levels of complexity to investigate if the use of biomarkers, external data, or novel algorithms improved interim decisions in the glioblastoma clinical trial.

RESULTS

Validation analyses on the basis of completed trials and electronic health records support the selection of algorithms, predictive models, and other aspects of PAIDs for use in adaptive clinical trials. By contrast, PAID evaluations on the basis of arbitrarily defined ad hoc simulation scenarios, which are not tailored to previous clinical data and experience, tend to overvalue complex prediction procedures and produce poor estimates of trial operating characteristics such as power and the number of enrolled patients.

CONCLUSION

Validation analyses on the basis of completed trials and real world data support the selection of predictive models, interim analysis rules, and other aspects of PAIDs in future clinical trials.

Expanded analysis of high-grade astrocytoma with piloid features identifies an epigenetically and clinically distinct subtype associated with neurofibromatosis type 1

High-grade astrocytoma with piloid features (HGAP) is a recently recognized glioma type whose classification is dependent on its global epigenetic signature. HGAP is characterized by alterations in the mitogen-activated protein kinase (MAPK) pathway, often co-occurring with CDKN2A/B homozygous deletion and/or ATRX mutation. Experience with HGAP is limited and to better understand this tumor type, we evaluated an expanded cohort of patients (n = 144) with these tumors, as defined by DNA methylation array testing, with a subset additionally evaluated by next-generation sequencing (NGS). Among evaluable cases, we confirmed the high prevalence CDKN2A/B homozygous deletion, and/or ATRX mutations/loss in this tumor type, along with a subset showing NF1 alterations. Five of 93 (5.4%) cases sequenced harbored TP53 mutations and RNA fusion analysis identified a single tumor containing an NTRK2 gene fusion, neither of which have been previously reported in HGAP. Clustering analysis revealed the presence of three distinct HGAP subtypes (or groups = g) based on whole-genome DNA methylation patterns, which we provisionally designated as gNF1 (n = 18), g1 (n = 72), and g2 (n = 54) (median ages 43.5 years, 47 years, and 32 years, respectively). Subtype gNF1 is notable for enrichment with patients with Neurofibromatosis Type 1 (33.3%, p = 0.0008), confinement to the posterior fossa, hypermethylation in the NF1 enhancer region, a trend towards decreased progression-free survival (p = 0.0579), RNA processing pathway dysregulation, and elevated non-neoplastic glia and neuron cell content (p < 0.0001 and p < 0.0001, respectively). Overall, our expanded cohort broadens the genetic, epigenetic, and clinical phenotype of HGAP and provides evidence for distinct epigenetic subtypes in this tumor type.

Evaluating hematologic parameters in newly diagnosed and recurrent glioblastoma: Prognostic utility and clinical trial implications of myelosuppression

BACKGROUND

Glioblastoma (GBM) patients are treated with radiation therapy, chemotherapy, and corticosteroids, which can cause myelosuppression. To understand the relative prognostic utility of blood-based biomarkers in GBM and its implications for clinical trial design, we examined the incidence, predictors, and prognostic value of lymphopenia, neutrophil-to-lymphocyte ratio (NLR), and platelet count during chemoradiation (CRT) and recurrence.

METHODS

This cohort study included 764 newly diagnosed glioblastoma patients treated from 2005 to 2019 with blood counts prior to surgery, within 6 weeks of CRT, and at first recurrence available for automatic extraction from the medical record. Logistic regression was used to evaluate exposures and Kaplan-Meier was used to evaluate outcomes.

RESULTS

Among the cohort, median age was 60.3 years; 87% had Karnofsky performance status ≥ 70, 37.5% had gross total resection, and 90% received temozolomide (TMZ). During CRT, 37.8% (248/656) of patients developed grade 3 or higher lymphopenia. On multivariable analysis (MVA), high NLR during CRT remained an independent predictor for inferior survival (Adjusted Hazard Ratio [AHR] = 1.57, 95% CI = 1.14-2.15) and shorter progression-free survival (AHR = 1.42, 95% CI = 1.05-1.90). Steroid use was associated with lymphopenia (OR = 2.66,1.20-6.00) and high NLR (OR = 3.54,2.08-6.11). Female sex was associated with lymphopenia (OR = 2.33,1.03-5.33). At first recurrence, 28% of patients exhibited grade 3 or higher lymphopenia. High NLR at recurrence was associated with worse subsequent survival on MVA (AHR = 1.69, 95% CI = 1.25-2.27).

CONCLUSIONS

High NLR is associated with worse outcomes in newly diagnosed and recurrent glioblastoma. Appropriate eligibility criteria and accounting and reporting of blood-based biomarkers are important in the design and interpretation of newly diagnosed and recurrent glioblastoma trials.

Cost Matrix of Molecular Pathology in Glioma-Towards AI-Driven Rational Molecular Testing and Precision Care for the Future

Gliomas are the most common and aggressive primary brain tumors. Gliomas carry a poor prognosis because of the tumor's resistance to radiation and chemotherapy leading to nearly universal recurrence. Recent advances in large-scale genomic research have allowed for the development of more targeted therapies to treat glioma. While precision medicine can target specific molecular features in glioma, targeted therapies are often not feasible due to the lack of actionable markers and the high cost of molecular testing. This review summarizes the clinically relevant molecular features in glioma and the current cost of care for glioma patients, focusing on the molecular markers and meaningful clinical features that are linked to clinical outcomes and have a realistic possibility of being measured, which is a promising direction for precision medicine using artificial intelligence approaches.

The Next Frontier in Health Disparities—A Closer Look at Exploring Sex Differences in Glioma Data and Omics Analysis, from Bench to Bedside and Back

Sex differences are increasingly being explored and reported in oncology, and glioma is no exception. As potentially meaningful sex differences are uncovered, existing gender-derived disparities mirror data generated in retrospective and prospective trials, real-world large-scale data sets, and bench work involving animals and cell lines. The resulting disparities at the data level are wide-ranging, potentially resulting in both adverse outcomes and failure to identify and exploit therapeutic benefits. We set out to analyze the literature on women’s data disparities in glioma by exploring the origins of data in this area to understand the representation of women in study samples and omics analyses. Given the current emphasis on inclusive study design and research, we wanted to explore if sex bias continues to exist in present-day data sets and how sex differences in data may impact conclusions derived from large-scale data sets, omics, biospecimen analysis, novel interventions, and standard of care management.

Sp1 induced gene TIMP1 is related to immune cell infiltration in glioblastoma

Tumor immune microenvironment exerts a profound effect on the population of infiltrating immune cells. Tissue inhibitor of matrix metalloproteinase 1 (TIMP1) is frequently overexpressed in a variety of cells, particularly during inflammation and tissue injury. However, its function in cancer and immunity remains enigmatic. In this study, we find that TIMP1 is substantially up-regulated during tumorigenesis through analyzing cancer bioinformatics databases, which is further confirmed by IHC tissue microarrays of clinical samples. The TIMP1 level is significantly increased in lymphocytes infiltrating the tumors and correlated with cancer progression, particularly in GBM. Notably, we find that the transcriptional factor Sp1 binds to the promoter of TIMP1 and triggers its expression in GBM. Together, our findings suggest that the Sp1-TIMP1 axis can be a potent biomarker for evaluating immune cell infiltration at the tumor sites and therefore, the malignant progression of GBM.

High-grade glioma therapy: adding flexibility in trial design to improve patient outcomes

INTRODUCTION

Outcomes for patients with high grade gliomas have changed little over the past thirty years. This realization prompted renewed efforts to increase flexibility in the design and conduct of clinical brain tumor trials.

AREAS COVERED

This manuscript reviews the development of clinical trial methods, challenges and considerations of flexible clinical trial designs, approaches to improve identification and testing of active agents for high grade gliomas, and evaluation of their delivery to the central nervous system.

EXPERT OPINION

Flexibility can be introduced in clinical trials in several ways. Flexible designs tout smaller sample sizes, adaptive modifications, fewer control arms, and inclusion of multiple arms in one study. Unfortunately, modifications in study designs cannot address two challenges that are largely responsible for the lack of progress in treating high grade gliomas: 1) the identification of active pharmaceutical agents and 2) the delivery of these agents to brain tumor tissue in therapeutic concentrations. To improve the outcomes of patients with high grade gliomas efforts must be focused on the pre-clinical screening of drugs for activity, the ability of these agents to achieve therapeutic concentrations in non-enhancing tumors, and a willingness to introduce novel compounds in minimally pre-treated patient populations.

Implementing targeted therapies in the treatment of glioblastoma: Previous shortcomings, future promises, and a multimodal strategy recommendation

The introduction of targeted therapies to the field of oncology has prolonged the survival of several tumor types. Despite extensive research and numerous trials, similar outcomes have unfortunately not been realized for glioblastoma. For more than 15 years, the standard treatment of glioblastoma has been unchanged. This review walks through the elements that have challenged the success of previous trials and highlight some future promises. Concurrently, this review describes how institutions, through a multimodal and comprehensive strategy with 4 essential components, may increase the probability of finding a meaningful role for targeted therapies in the treatment of glioblastoma. These components are (1) prudent trial designs, (2) considered drug and target selection, (3) harnessed real-world clinical and molecular evidence, and (4) incorporation of translational research.

Preoperative Magnetic Resonance Imaging Radiomics for Predicting Early Recurrence of Glioblastoma

Purpose

Early recurrence of glioblastoma after standard treatment makes patient care challenging. This study aimed to assess preoperative magnetic resonance imaging (MRI) radiomics for predicting early recurrence of glioblastoma.

Patients and Methods

A total of 122 patients (training cohort: n = 86; validation cohort: n = 36) with pathologically confirmed glioblastoma were included in this retrospective study. Preoperative brain MRI images were analyzed for both radiomics and the Visually Accessible Rembrandt Image (VASARI) features of glioblastoma. Models incorporating MRI radiomics, the VASARI parameters, and clinical variables were developed and presented in a nomogram. Performance was assessed based on calibration, discrimination, and clinical usefulness.

Results

The nomogram consisting of the radiomic signatures, the VASARI parameters, and blood urea nitrogen (BUN) values showed good discrimination between the patients with early recurrence and those with later recurrence, with an area under the curve of 0.85 (95% CI, 0.77-0.94) in the training cohort and 0.84 [95% CI, 0.71-0.97] in the validation cohort. Decision curve analysis demonstrated favorable clinical application of the nomogram.

Conclusion

This study showed the potential usefulness of preoperative brain MRI radiomics in predicting the early recurrence of glioblastoma, which should be helpful in personalized management of glioblastoma.

CircCDC45 promotes the malignant progression of glioblastoma by modulating the miR-485-5p/CSF-1 axis

BACKGROUND

Glioblastoma (GBM) is characterized by progressive growth and metastasis. Numerous studies claim that the deregulation of circular RNAs (circRNAs) is associated with cancer progression. However, the role of circRNAs in GBM is largely limited. The purpose of this study was to investigate the functions of circCDC45 in GBM and provide a feasible functional mechanism to support its role.

METHODS

The expression of circCDC45, miR-485-5p and colony-stimulating factor 1 (CSF-1) mRNA was examined using quantitative real-time polymerase chain reaction (qRT-PCR). Cell proliferation was assessed using cell counting kit - 8 (CCK-8) assay and colony formation assay. Cell migration and cell invasion were monitored using transwell assay. The protein levels of proliferation-related markers and CSF-1 were determined using western blot. The target relationship was predicted using bioinformatics tools and validated using dual-luciferase reporter assay and RNA immunoprecipitation (RIP) assay. Animal models were constructed to verify the role of circCDC45 in vivo.

RESULTS

The expression of circCDC45 and CSF-1 was elevated in GBM tissues and cells, while the expression of miR-485-5p was declined. Downregulation of circCDC45 or CSF-1 blocked GBM cell proliferation, invasion and migration as well as tumor growth in vivo. In mechanism, circCDC45 positively regulated the expression of CSF-1 by targeting miR-485-5p. Inhibition of miR-485-5p reversed the biological effects caused by circCDC45 downregulation in GBM cells.

CONCLUSION

CircCDC45 promoted the progression of GBM by mediating the miR-485-5p/CSF-1 axis, and circCDC45 might be a promising plasmatic biomarker for GBM diagnosis and treatment.

Toward the Next Generation of High-Grade Glioma Clinical Trials in the Era of Precision Medicine

ABSTRACT

In the era of precision medicine, there is a desire to harness our improved understanding of genomic and molecular underpinnings of gliomas to develop therapies that can be tailored to individual patients and tumors. With the rapid development of novel therapies, there has been a growing need to develop smart clinical trials that are designed to efficiently test promising agents, identify therapies likely to benefit patients, and discard ineffective therapies. We review clinical trial design in gliomas and developments designed to address the unique challenges of precision medicine. To provide an overview of this topic, we examine considerations for endpoints and response assessment, biomarkers, and novel clinical trial designs such as adaptive platform trials in the testing of new therapies for glioma patients.

Association of plasma cell-free DNA with survival in patients with IDH wild-type glioblastoma

BACKGROUND

We aimed to determine whether plasma cell-free DNA (cfDNA) concentration is associated with survival in patients with isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM).

METHODS

Pre-operative and post-chemoradiotherapy blood samples were prospectively collected from patients with newly diagnosed IDH wild-type GBM. Patients underwent surgical resection or biopsy and received adjuvant radiotherapy with concomitant temozolomide. Cell-free DNA (cfDNA) was isolated from plasma and quantified using SYBR Green-based q polymerase chain reaction (qPCR).

RESULTS

Sixty-two patients were enrolled and categorized into high vs. low cfDNA groups relative to the pre-operative median value (25.2 ng/mL, range 5.7-153.0 ng/mL). High pre-operative cfDNA concentration was associated with inferior PFS (median progression-free survival (PFS), 3.4 vs. 7.7 months; log-rank P = .004; hazard ratio [HR], 2.19; 95% CI, 1.26-3.81) and overall survival (OS) (median OS, 8.0 vs. 13.9 months; log-rank P = .01; HR, 2.43; 95% CI, 1.19-4.95). After adjusting for risk factors, including O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status, pre-operative cfDNA remained independently associated with PFS (HR, 2.70; 95% CI, 1.50-4.83; P = .001) and OS (HR, 2.65; 95% CI, 1.25-5.59; P = .01). Post-hoc analysis of change in cfDNA post-chemoradiotherapy compared to pre-surgery (n = 24) showed increasing cfDNA concentration was associated with worse PFS (median, 2.7 vs. 6.0 months; log-rank P = .003; HR, 4.92; 95% CI, 1.53-15.84) and OS (median, 3.9 vs. 19.4 months; log-rank P < .001; HR, 7.77; 95% CI, 2.17-27.76).

CONCLUSIONS

cfDNA concentration is a promising prognostic biomarker for patients with IDH wild-type GBM. Plasma cfDNA can be obtained noninvasively and may enable more accurate estimates of survival and effective clinical trial stratification.

The FDA NIH Biomarkers, EndpointS, and other Tools (BEST) resource in neuro-oncology

In early 2016, the FDA and the National Institutes of Health (NIH) published the first version of the glossary included in the Biomarkers, EndpointS, and other Tools (BEST) resource.1 The BEST glossary was constructed to harmonize and clarify terms used in translational science and medical product development and to provide a common language used for communication by those agencies. It is considered a "living" document that will be updated in the future. This review will discuss the main biomarker and clinical outcome categories contained in the BEST glossary as they apply to neuro-oncology, as well as the overlapping and hierarchical relationships among them.

Copy number profiling across glioblastoma populations has implications for clinical trial design

Background

Copy number alterations form prognostic molecular subtypes of glioblastoma with clear differences in median overall survival. In this study, we leverage molecular data from several glioblastoma cohorts to define the distribution of copy number subtypes across random cohorts as well as cohorts with selection biases for patients with inherently better outcome.

Methods

Copy number subtype frequency was established for 4 glioblastoma patient cohorts. Two randomly selected cohorts include The Cancer Genome Atlas (TCGA) and the German Glioma Network (GGN). Two more selective cohorts include the phase II trial ARTE in elderly patients with newly diagnosed glioblastoma and a multi-institutional cohort focused on paired resected initial/recurrent glioblastoma. The paired initial/recurrent cohort also had exome data available, which allowed for evaluation of multidimensional scaling analysis.

Results

Smaller selective glioblastoma cohorts are enriched for copy number subtypes that are associated with better survival, reflecting the selection of patients who do well enough to enter a clinical trial or who are deemed well enough to undergo resection at recurrence. Adding exome data to copy number data provides additional data reflective of outcome.

Conclusions

The overall outcome for diffuse glioma patients is predicted by DNA structure at initial tumor resection. Molecular signature shifts across glioblastoma populations reflect the inherent bias of patient selection toward longer survival in clinical trials. Therefore it may be important to include molecular profiling, including copy number, when enrolling patients for clinical trials in order to balance arms and extrapolate relevance to the general glioblastoma population.

TP53 Pathway Alterations Drive Radioresistance in Diffuse Intrinsic Pontine Gliomas (DIPG)

PURPOSE

Diffuse intrinsic pontine gliomas (DIPG) are the most severe pediatric brain tumors. Although accepted as the standard therapeutic, radiotherapy is only efficient transiently and not even in every patient. The goal of the study was to identify the underlying molecular determinants of response to radiotherapy in DIPG.

EXPERIMENTAL DESIGN

We assessed in vitro response to ionizing radiations in 13 different DIPG cellular models derived from treatment-naïve stereotactic biopsies reflecting the genotype variability encountered in patients at diagnosis and correlated it to their principal molecular alterations. Clinical and radiologic response to radiotherapy of a large cohort of 73 DIPG was analyzed according to their genotype. Using a kinome-wide synthetic lethality RNAi screen, we further identified target genes that can sensitize DIPG cells to ionizing radiations.

RESULTS

We uncover TP53 mutation as the main driver of increased radioresistance and validated this finding in four isogenic pairs of TP53^WT DIPG cells with or without TP53 knockdown. In an integrated clinical, radiological, and molecular study, we show that TP53^MUT DIPG patients respond less to irradiation, relapse earlier after radiotherapy, and have a worse prognosis than their TP53^WT counterparts. Finally, a kinome-wide synthetic lethality RNAi screen identifies CHK1 as a potential target, whose inhibition increases response to radiation specifically in TP53^MUT cells.

CONCLUSIONS

Here, we demonstrate that TP53 mutations are driving DIPG radioresistance both in patients and corresponding cellular models. We suggest alternative treatment strategies to mitigate radioresistance with CHK1 inhibitors. These findings will allow to consequently refine radiotherapy schedules in DIPG.

Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT): A Bayesian Adaptive Platform Trial to Develop Precision Medicines for Patients With Glioblastoma

PURPOSE

Adequately prioritizing the numerous therapies and biomarkers available in late-stage testing for patients with glioblastoma (GBM) requires an efficient clinical testing platform. We developed and implemented INSIGhT (Individualized Screening Trial of Innovative Glioblastoma Therapy) as a novel adaptive platform trial (APT) to develop precision medicine approaches in GBM.

METHODS

INSIGhT compares experimental arms with a common control of standard concurrent temozolomide and radiation therapy followed by adjuvant temozolomide. The primary end point is overall survival. Patients with newly diagnosed unmethylated GBM who are IDH R132H mutation negative and with genomic data available for biomarker grouping are eligible. At the initiation of INSIGhT, three experimental arms (neratinib, abemaciclib, and CC-115), each with a proposed genomic biomarker, are tested simultaneously. Initial randomization is equal across arms. As the trial progresses, randomization probabilities adapt on the basis of accumulating results using Bayesian estimation of the biomarker-specific probability of treatment impact on progression-free survival. Treatment arms may drop because of low probability of treatment impact on overall survival, and new arms may be added. Detailed information on the statistical model and randomization algorithm is provided to stimulate discussion on trial design choices more generally and provide an example for other investigators developing APTs.

CONCLUSION

INSIGhT (NCT02977780) is an ongoing novel biomarker-based, Bayesian APT for patients with newly diagnosed unmethylated GBM. Our goal is to dramatically shorten trial execution timelines while increasing scientific power of results and biomarker discovery using adaptive randomization. We anticipate that trial execution efficiency will also be improved by using the APT format, which allows for the collaborative addition of new experimental arms while retaining the overall trial structure.