Longitudinal Patterns in Clinical and Imaging Measurements Predict Residual Survival in Glioblastoma Patients

Association between the Anatomical Location of Glioblastoma and Its Evaluation with Clinical Considerations: A Systematic Review and Meta-Analysis

Background: Glioblastoma is a primary malignant brain tumor; it is aggressive with a high degree of malignancy and unfavorable prognosis and is the most common type of malignant brain tumor. Glioblastomas can be located in the brain, cerebellum, brainstem, and spinal cord, originating from glial cells, particularly astrocytes. Methods: The databases MEDLINE, Scopus, Web of Science, Google Scholar, and CINAHL were researched up to January 2024. Two authors independently performed the search, study selection, and data extraction. Methodological quality was evaluated with an assurance tool for anatomical studies (AQUA). The statistical mean, standard deviation, and difference of means calculated with the Student's t-test for presence between hemispheres and presence in the frontal and temporal lobes were analyzed. Results: A total of 123 studies met the established selection criteria, with a total of 6224 patients. In relation to the mean, GBM between hemispheres had a mean of 33.36 (SD 58.00) in the right hemisphere and a mean of 34.70 (SD 65.07) in the left hemisphere, due to the difference in averages between hemispheres. There were no statistically significant differences, p = 0.35. For the comparison between the presence of GBM in the frontal lobe and the temporal lobe, there was a mean in the frontal lobe of 23.23 (SD 40.03), while in the temporal lobe, the mean was 22.05 (SD 43.50), and for the difference in means between the frontal lobe and the temporal lobe, there was no statistically significant difference for the presence of GBM, p = 0.178. Conclusions: We believe that before a treatment, it will always be correct to know where the GBM is located and how it behaves clinically, in order to generate correct conservative or surgical treatment guidelines for each patient. We believe that more detailed studies are also needed to show why GBM is associated more with some regions than others, despite the brain structure being homologous to other regions in which GMB occurs less frequently, which is why knowing its predominant presence in brain regions is very important.

Potent in vivo efficacy of oral gallium maltolate in treatment-resistant glioblastoma

Background

Treatment-resistant glioblastoma (trGBM) is an aggressive brain tumor with a dismal prognosis, underscoring the need for better treatment options. Emerging data indicate that trGBM iron metabolism is an attractive therapeutic target. The novel iron mimetic, gallium maltolate (GaM), inhibits mitochondrial function via iron-dependent and -independent pathways.

Methods

In vitro irradiated adult GBM U-87 MG cells were tested for cell viability and allowed to reach confluence prior to stereotactic implantation into the right striatum of male and female athymic rats. Advanced MRI at 9.4T was carried out weekly starting two weeks after implantation. Daily oral GaM (50mg/kg) or vehicle were provided on tumor confirmation. Longitudinal MRI parameters were processed for enhancing tumor ROIs in OsiriX 8.5.1 (lite) with Imaging Biometrics Software (Imaging Biometrics LLC). Statistical analyses included Cox proportional hazards regression models, Kaplan-Meier survival plots, linear mixed model comparisons, and t-statistic for slopes comparison as indicator of tumor growth rate.

Results

In this study we demonstrate non-invasively, using longitudinal MRI surveillance, the potent antineoplastic effects of GaM in a novel rat xenograft model of trGBM, as evidenced by extended suppression of tumor growth (23.56 mm3/week untreated, 5.76 mm3/week treated, P < 0.001), a blunting of tumor perfusion, and a significant survival benefit (median overall survival: 30 days untreated, 56 days treated; P < 0.001). The therapeutic effect was confirmed histologically by the presence of abundant cytotoxic cellular swelling, a significant reduction in proliferation markers (P < 0.01), and vessel normalization characterized by prominent vessel pruning, loss of branching, and uniformity of vessel lumina. Xenograft tumors in the treatment group were further characterized by an absence of an invasive edge and a significant reduction in both, MIB-1% and mitotic index (P < 0.01 each). Transferrin receptor and ferroportin expression in GaM-treated tumors illustrated cellular iron deprivation. Additionally, treatment with GaM decreased the expression of pro-angiogenic markers (von Willebrand Factor and VEGF) and increased the expression of anti-angiogenic markers, such as Angiopoietin-2.

Conclusion

Monotherapy with the iron-mimetic GaM profoundly inhibits trGBM growth and significantly extends disease-specific survival in vivo.

Objective response rate targets for recurrent glioblastoma clinical trials based on the historic association between objective response rate and median overall survival

Durable objective response rate (ORR) remains a meaningful endpoint in recurrent cancer; however, the target ORR for single-arm recurrent glioblastoma trials has not been based on historic information or tied to patient outcomes. The current study reviewed 68 treatment arms comprising 4793 patients in past trials in recurrent glioblastoma in order to judiciously define target ORRs for use in recurrent glioblastoma trials. ORR was estimated at 6.1% [95% CI 4.23; 8.76%] for cytotoxic chemothera + pies (ORR = 7.59% for lomustine, 7.57% for temozolomide, 0.64% for irinotecan, and 5.32% for other agents), 3.37% for biologic agents, 7.97% for (select) immunotherapies, and 26.8% for anti-angiogenic agents. ORRs were significantly correlated with median overall survival (mOS) across chemotherapy (R2= 0.4078, P < .0001), biologics (R2= 0.4003, P = .0003), and immunotherapy trials (R2= 0.8994, P < .0001), but not anti-angiogenic agents (R2= 0, P = .8937). Pooling data from chemotherapy, biologics, and immunotherapy trials, a meta-analysis indicated a strong correlation between ORR and mOS (R2= 0.3900, P < .0001; mOS [weeks] = 1.4xORR + 24.8). Assuming an ineffective cytotoxic (control) therapy has ORR = 7.6%, the average ORR for lomustine and temozolomide trials, a sample size of ≥40 patients with target ORR>25% is needed to demonstrate statistical significance compared to control with a high level of confidence (P < .01) and adequate power (>80%). Given this historic data and potential biases in patient selection, we recommend that well-controlled, single-arm phase II studies in recurrent glioblastoma should have a target ORR >25% (which translates to a median OS of approximately 15 months) and a sample size of ≥40 patients, in order to convincingly demonstrate antitumor activity. Crucially, this response needs to have sufficient durability, which was not addressed in the current study.

Change in volumetric tumor growth rate after cytotoxic therapy is predictive of overall survival in recurrent glioblastoma

Background

Alterations in tumor growth rate (TGR) in recurrent glioblastoma (rGBM) after treatment may be useful for identifying therapeutic activity. The aim of this study was to assess the impact of volumetric TGR alterations on overall survival (OS) in rGBM treated with chemotherapy with or without radiation therapy (RT).

Methods

Sixty-one rGBM patients treated with chemotherapy with or without concomitant radiation therapy (RT) at 1st or 2nd recurrence were retrospectively examined. Pre- and post-treatment contrast enhancing volumes were computed. Patients were considered "responders" if they reached progression-free survival at 6 months (PFS6) and showed a decrease in TGR after treatment and "non-responders" if they didn't reach PFS6 or if TGR increased.

Results

Stratification by PFS6 and based on TGR resulted in significant differences in OS both for all patients and for patients without RT (P < 0.05). A decrease of TGR (P = 0.009), smaller baseline tumor volume (P = 0.02), O6-methylguanine-DNA methyltransferase promoter methylation (P = 0.048) and fewer number of recurrences (P = 0.048) were significantly associated with longer OS after controlling for age, sex and concomitant RT.

Conclusion

A decrease in TGR in patients with PFS6, along with smaller baseline tumor volume, were associated with a significantly longer OS in rGBM treated with chemotherapy with or without radiation. Importantly, all patients that exhibited PFS6 also showed a measurable decrease in TGR.

Radiographic Response Assessment Strategies for Early-Phase Brain Trials in Complex Tumor Types and Drug Combinations: from Digital "Flipbooks" to Control Systems Theory

There is an urgent need for drug development in brain tumors. While current radiographic response assessment provides instructions for identifying large treatment effects in simple high- and low-grade gliomas, there remains a void of strategies to evaluate complex or difficult to measure tumors or tumors of mixed grade with enhancing and non-enhancing components. Furthermore, most patients exhibit some period of alteration in tumor growth after starting a new therapy, but simple response categorization (e.g., stable disease, progressive disease) fails to provide any meaningful insight into the depth or degree of potential "subclinical" therapeutic response. We propose a creative solution to these issues based on a tiered strategy meant to increase confidence in identifying therapeutic effects even in the most challenging tumor types, while also providing a framework for complex evaluation of combination and sequential treatment schemes. Specifically, we demonstrate the utility of digital "flipbooks" to quickly identify subtle changes in complex tumors. We show how a modified Levin criteria can be used to quantify the degree of visual changes, while establishing estimates of the association between tumor volume and visual inspection. Lastly, we introduce the concept of quantifying therapeutic response using control systems theory. We propose measuring changes in volume (proportional), the area under the volume vs. time curve (integral) and changes in growth rates (derivative) to utilize a "PID" controller model of single or combination therapeutic activity.

Hypothetical generalized framework for a new imaging endpoint of therapeutic activity in early phase clinical trials in brain tumors

Imaging response assessment is a cornerstone of patient care and drug development in oncology. Clinicians/clinical researchers rely on tumor imaging to estimate the impact of new treatments and guide decision making for patients and candidate therapies. This is important in brain cancer, where associations between tumor size/growth and emerging neurological deficits are strong. Accurately measuring the impact of a new therapy on tumor growth early in clinical development, where patient numbers are small, would be valuable for decision making regarding late-stage development activation. Current attempts to measure the impact of a new therapy have limited influence on clinical development, as determination of progression, stability or response does not currently account for individual tumor growth kinetics prior to the initiation of experimental therapies. Therefore, we posit that imaging-based response assessment, often used as a tool for estimating clinical effect, is incomplete as it does not adequately account for growth trajectories or biological characteristics of tumors prior to the introduction of an investigational agent. Here, we propose modifications to the existing framework for evaluating imaging assessment in primary brain tumors that will provide a more reliable understanding of treatment effects. Measuring tumor growth trajectories prior to a given intervention may allow us to more confidently conclude whether there is an anti-tumor effect. This updated approach to imaging-based tumor response assessment is intended to improve our ability to select candidate therapies for later-stage development, including those that may not meet currently sought thresholds for "response" and ultimately lead to identification of effective treatments.

Therapeutic Response Assessment of High-Grade Gliomas During Early-Phase Drug Development in the Era of Molecular and Immunotherapies

ABSTRACT

Several new therapeutic strategies have emerged over the past decades to address unmet clinical needs in high-grade gliomas, including targeted molecular agents and various forms of immunotherapy. Each of these strategies requires addressing fundamental questions, depending on the stage of drug development, including ensuring drug penetration into the brain, engagement of the drug with the desired target, biologic effects downstream from the target including metabolic and/or physiologic changes, and identifying evidence of clinical activity that could be expanded upon to increase the likelihood of a meaningful survival benefit. The current review article highlights these strategies and outlines how imaging technology can be used for therapeutic response evaluation in both targeted and immunotherapies in early phases of drug development in high-grade gliomas.

Modified Needleman-Wunsch algorithm for clinical pathway clustering

Clinical pathways are used to guide clinicians to provide a standardised delivery of care. Because of their standardisation, the aim of clinical pathways is to reduce variation in both care process and patient outcomes. When learning clinical pathways from data through data mining, it is common practice to represent each patient pathway as a string corresponding to their movements through activities. Clustering techniques are popular methods for pathway mining, and therefore this paper focuses on distance metrics applied to string data for k-medoids clustering. The two main aims are to firstly, develop a technique that seamlessly integrates expert information with data and secondly, to develop a string distance metric for the purpose of process data. The overall goal was to allow for more meaningful clustering results to be found by adding context into the string similarity calculation. Eight common distance metrics and their applicability are discussed. These distance metrics prove to give an arbitrary distance, without consideration for context, and each produce different results. As a result, this paper describes the development of a new distance metric, the modified Needleman-Wunsch algorithm, that allows for expert interaction with the calculation by assigning groupings and rankings to activities, which provide context to the strings. This algorithm has been developed in partnership with UK's National Health Service (NHS) with the focus on a lung cancer pathway, however the handling of the data and algorithm allows for application to any disease type. This method is contained within Sim.Pro.Flow, a publicly available decision support tool.

Early Mortality of Brain Cancer Patients and its Connection to Cytomegalovirus Reactivation During Radiochemotherapy

PURPOSE

If routine diagnostics are inconclusive, neurologic deterioration and death of patients with brain cancer are attributed to tumor or therapy. Therefore, diagnosing symptoms of encephalopathy caused by human cytomegalovirus (HCMV) reactivation remains uncommon. We investigated the role of HCMV reactivation in neurologic decline and clinical outcome after the start of radiochemotherapy.

EXPERIMENTAL DESIGN

HCMV analyses and extended MRI studies including additional independent retrospective neuroradiologic evaluation were performed at predetermined intervals and in case of sudden neurologic decline for 118 adult patients: 63 histologically proven high-grade gliomas, 55 with brain metastases. Immunophenotyping from simultaneously taken whole-blood samples was carried out to detect immune cells serving as prognostic marker for HCMV-associated complications. Symptomatic viremia and overall survival (OS) were the endpoints.

RESULTS

Twenty-four percent (28/118) of all patients (12/44 glioblastoma, 3/13 anaplastic astrocytoma; 8/31 non-small cell lung cancer (NSCLC), 13/24 other brain metastases) developed HCMV-viremia during or within 4 weeks after radiotherapy; 21 of 28 patients experienced concurrent major neurologic decline, reversible by antiviral treatment. Identified by immunophenotyping, pretherapeutically low basophil counts predicted a high-risk for HCMV-associated encephalopathy (glioblastoma: P = 0.002, NSCLC: P = 0.02). Median OS was substantially reduced after HCMV-associated encephalopathy without MRI signs of tumor progression [glioblastoma: 99 vs. 570 days (calculated 1-year OS: 22% vs. 69%; P = 0.01) and NSCLC: 47 vs. 219 days (calculated 1-year OS: 0% vs. 32%; P = 0.02)].

CONCLUSIONS

For patients with brain cancer, HCMV reactivation after the start of radiochemotherapy is a frequent risk for cognitively detrimental but treatable encephalopathy and premature death. Routinely performed HCMV diagnostics, assessing basophil counts and study-based anti-viral regimens, are necessary to combat this hidden threat.See related commentary by Lawler et al., p. 3077.

Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods

BACKGROUND

Clinical prediction models (CPMs) predict the risk of health outcomes for individual patients. The majority of existing CPMs only harness cross-sectional patient information. Incorporating repeated measurements, such as those stored in electronic health records, into CPMs may provide an opportunity to enhance their performance. However, the number and complexity of methodological approaches available could make it difficult for researchers to explore this opportunity. Our objective was to review the literature and summarise existing approaches for harnessing repeated measurements of predictor variables in CPMs, primarily to make this field more accessible for applied researchers.

METHODS

MEDLINE, Embase and Web of Science were searched for articles reporting the development of a multivariable CPM for individual-level prediction of future binary or time-to-event outcomes and modelling repeated measurements of at least one predictor. Information was extracted on the following: the methodology used, its specific aim, reported advantages and limitations, and software available to apply the method.

RESULTS

The search revealed 217 relevant articles. Seven methodological frameworks were identified: time-dependent covariate modelling, generalised estimating equations, landmark analysis, two-stage modelling, joint-modelling, trajectory classification and machine learning. Each of these frameworks satisfies at least one of three aims: to better represent the predictor-outcome relationship over time, to infer a covariate value at a pre-specified time and to account for the effect of covariate change.

CONCLUSIONS

The applicability of identified methods depends on the motivation for including longitudinal information and the method's compatibility with the clinical context and available patient data, for both model development and risk estimation in practice.

An Inflammatory Landscape for Preoperative Neurologic Deficits in Glioblastoma

Introduction: Patients with glioblastoma (GBM), one of the most aggressive forms of primary brain tumors, exhibit a wide range of neurologic signs, ranging from headaches to neurologic deficits and cognitive impairment, at first clinical presentation. While such variability is attributed to inter-individual differences in increased intracranial pressure, tumor infiltration, and vascular compromise, a direct association with disease stage, tumor size and location, edema, and necrotic cell death has yet to be established. The lack of specificity of neurologic symptoms often confounds the diagnosis of GBM. It also limits clinicians' ability to elect treatment regimens that not only prolong survival but also promote symptom management and high quality of life. Methods: To decipher the heterogeneous presentation of neurologic symptoms in GBM, we investigated differences in the molecular makeup of tumors from patients with and without preoperative neurologic deficits. We used the Ivy GAP (Ivy Glioblastoma Atlas Project) database to integrate RNA sequencing data from histologically defined GBM tumor compartments and neurologic examination records for 41 patients. We investigated the association of neurologic deficits with various tumor and patient attributes. We then performed differential gene expression and co-expression network analysis to identify a transcriptional signature specific to neurologic deficits in GBM. Using functional enrichment analysis, we finally provided a comprehensive and detailed characterization of involved pathways and gene interactions. Results: An exploratory investigation of the association of tumor and patient variables with the early development of neurologic deficits in GBM revealed a lack of robust and consistent clinicopathologic prognostic factors. We detected significant differences in the expression of 728 genes (FDR-adjusted p-value ≤ 0.05 and relative fold-change ≥ 1.5), unique to the cellular tumor (CT) anatomical compartment, between neurologic deficit groups. Upregulated differentially expressed genes in CT were enriched for mesenchymal subtype-predictive genes. Applying a systems approach, we then identified co-expressed gene sets that correlated with neurological deficit manifestation (FDR-adjusted p-value < 0.1). Collectively, these findings uncovered significantly enriched immune activation, oxidative stress response, and cytokine-mediated proinflammatory processes. Conclusion: Our study posits that inflammatory processes, as well as a mesenchymal tumor subtype, are implicated in the pathophysiology of preoperative neurologic deficits in GBM.