Brain Cancer Patients Research Articles

Elevated Serum Lactate in Glioma Patients: Associated Factors.

IntroductionSerum lactate levels in brain cancer patients correlate with tumor malignancy grading, and serum lactate has been suggested as a potential biomarker and prognostic factor. The purpose of this study was to identify potential sources of elevated serum lactate in patients with brain gliomas by examining factors of importance for serum lactate production and clearance.MethodsIn this cross-sectional study, data were collected from 261 glioma patients who underwent surgery from March 2011 to June 2015. We recorded patient gender, age, blood serum measures of lactate, glucose, pH, hemoglobin and base excess, patient health status, medications, and tumor characteristics. Patients with elevated and normal serum lactate levels were compared, and we explored if there were correlations between the variables. The association of serum lactate with the measured variables was investigated by simple and multivariable linear regression models.Results and DiscussionPatients with elevated serum lactate had higher blood glucose, larger tumor volumes, and more tumor edema; more often needed pressor medication during surgery; and more often received corticosteroid treatment. The investigated variables were highly correlated. Multivariable linear regression indicated that gender, tumor volume, Charlson Comorbidity Index, hyperglycemia, and corticosteroid treatment were associated with serum lactate levels. Histopathology was not an independent factor. In conclusion, comorbidities, hyperglycemia, and presurgical corticosteroid treatment exhibited the strongest association with serum lactate in glioma patients.

Combinatorial therapeutic strategies for enhanced delivery of therapeutics to brain cancer cells through nanocarriers: current trends and future perspectives

Brain cancer is the most aggressive one among various cancers. It has a drastic impact on people's lives because of the failure in treatment efficacy of the currently employed strategies. Various strategies used to relieve pain in brain cancer patients and to prolong survival time include radiotherapy, chemotherapy, and surgery. Nevertheless, several inevitable limitations are accompanied by such treatments due to unsatisfactory curative effects. Generally, the treatment of cancers is very challenging due to many reasons including drugs’ intrinsic factors and physiological barriers. Blood-brain barrier (BBB) and blood-cerebrospinal fluid barrier (BCSFB) are the two additional hurdles in the way of therapeutic agents to brain tumors delivery. Combinatorial and targeted therapies specifically in cancer show a very promising role where nanocarriers’ based formulations are designed primarily to achieve tumor-specific drug release. A dual-targeting strategy is a versatile way of chemotherapeutics delivery to brain tumors that gets the aid of combined ligands and mediators that cross the BBB and reaches the target site efficiently. In contrast to single targeting where one receptor or mediator is targeted, the dual-targeting strategy is expected to produce a multiple-fold increase in therapeutic efficacy for cancer therapy, especially in brain tumors. In a nutshell, a dual-targeting strategy for brain tumors enhances the delivery efficiency of chemotherapeutic agents via penetration across the blood-brain barrier and enhances the targeting of tumor cells. This review article highlights the ongoing status of the brain tumor therapy enhanced by nanoparticle based delivery with the aid of dual-targeting strategies. The future perspectives in this regard have also been highlighted.

Neuro-oncology clinicians’ perspectives on factors affecting brain cancer patients’ access to medical assistance in dying: A qualitative study

In most jurisdictions where medical assistance in dying (MAiD) is legal, patients must have decision-making capacity. Brain cancer often damages the cognitive networks required to maintain decision-making capacity. Using qualitative methodology guided by a relational ethics conceptual framework, this study explored neuro-oncology clinicians’ perspectives on access to and eligibility for MAiD for patients diagnosed with brain cancer. We interviewed 24 neuro-oncology clinicians from 6 countries. Participants described the unique challenges facing brain cancer patients, potentially resulting in their inequitable access to MAiD. The findings highlight the importance of early end-of-life conversations, advance care planning, and access to end-of-life treatment options.

Association Between Human Herpesvirus-6 and Primary Brain Tumors: A Systematic Review and Meta-Analysis

Aim: The present study aimed to find out the prevalence and any possible association between human herpesvirus (HHV-6) and primary brain tumors. Materials & methods: The systematic literature search was performed by finding related articles from major databases. Analysis was performed by comprehensive meta-analysis software. Results: A total of 13 (25 datasets) articles were included in the study, seven (15 datasets) of which were case/control and the rest (10 datasets) were cross-sectional studies. The pooled prevalence of HHV-6 among primary brain cancer patients was 29% (95% CI: 24–33%; I2 = 97.89%). An association was found between HHV-6 and primary brain cancer (summary odds ratio: 3.77% [95% CI: 2.59–5.49%; I2 = 19.0%]). Conclusion: Our analysis demonstrated that HHV-6 is associated with primary brain cancer. Reactivation of the virus could be triggered by the tumor, therefore, we cannot be certain that the virus appeared before the cancer development.

High-Dimensional Gaussian Graphical Regression Models with Covariates

Though Gaussian graphical models have been widely used in many scientific fields, relatively limited progress has been made to link graph structures to external covariates. We propose a Gaussian graphical regression model, which regresses both the mean and the precision matrix of a Gaussian graphical model on covariates. In the context of co-expression quantitative trait locus (QTL) studies, our method can determine how genetic variants and clinical conditions modulate the subject-level network structures, and recover both the population-level and subject-level gene networks. Our framework encourages sparsity of covariate effects on both the mean and the precision matrix. In particular for the precision matrix, we stipulate simultaneous sparsity, that is, group sparsity and element-wise sparsity, on effective covariates and their effects on network edges, respectively. We establish variable selection consistency first under the case with known mean parameters and then a more challenging case with unknown means depending on external covariates, and establish in both cases the convergence rates and the selection consistency of the estimated precision parameters. The utility and efficacy of our proposed method is demonstrated through simulation studies and an application to a co-expression QTL study with brain cancer patients. Supplementary materials for this article are available online.

WHAT ELEMENTS CONTRIBUTE TO IMPROVING BRAIN CANCER TREATMENTS?

Brian cancer is a serious disease that has recently been discovered in an increasing number of people. It isnt a simple problem to solve, and it involves several procedures to overcome it. Brain cancer is also the 10th biggest cause of mortality for both men and women, according to Cancer.Net. In the coming years, it is also expected that 18,600 persons (10,500 men and 8,100 women) will die from primary malignant brain and CNS tumors. Moreover, people with a malignant brain or CNS tumor have a 36 percent 5-year survival rate. Although there are numerous therapies for brain cancer, such as chemotherapy and radiation, the death rate remains high, and those treatments arent the most comfortable. As a result, complementary therapies and other supplements are utilized to promote the health and well-being of brain cancer patients. Workout, diet, acupuncture, aromatherapy, talking therapy, and music are some of the various treatments discussed in this article. The primary goal of this article is to learn more about the aspects that can help improve and enhance the success of brain cancer treatment. Furthermore, to investigate and research the advantages of each aspect, as well as how it might be used in future medical treatments.

Conformity and Homogeneity Indices for Brain Cancer Patients Using 3DCRT Technique

Background: 3D conformal radiotherapy 3DCRT technique are used to treat patients with brain cancer. Goal: this study aims to compare conformity and homogeneity indices for eight patients of brain cancer by using three-dimension conformal radiotherapy (3DCRT) technique on planning target volume (PTV) located in brain. Methods: Comparative study of Conformity and Homogeneity indices for ten brain cancer patients during period from October 2019 to March 2020 that carried out at ZCC (Zhianawa Cancer Center) in Sulaimany-Iraq. 3DECRT technique was applied to get the heist dosage to the target volume (tumor) and lowest dose to the healthy structures around the tumor. By using Linear Accelerator machine- LINAC, Elekta synergy type with 6MV photon beam. SPSS-version 22 was applied for analyzing and caring out the data measurements. Results: It has been observed from the results, in both techniques the highest conformity and homogeneity indices have the acceptable values, in this study with 3D-CRT plan technique showed that the mean conformity and homogeneity indices were (0.1999 ± 0.03, 0.9457 ± 0.05) respectively. The mean dose values for organs at risk spinal cord and brain stem were (41.98 ± 2.23, 26.01 ± 16.62) respectively, for ten right and left parotid glands were (23.52 ± 3.99, 23.34 ± 3.93) respectively. In both plans, the average dose values for all organs at risk mentioned above were less than tolerance radiation doses. Conclusion: This study also supports that indices of homogeneity and conformity are important tools for improving the long-term quality of life of brain cancer patients using 3DCRT technology.

Unsupervised pseudo CT generation using heterogenous multicentric CT/MR images and CycleGAN: Dosimetric assessment for 3D conformal radiotherapy

PurposeAbsorbed dose calculation in magnetic resonance-guided radiation therapy (MRgRT) is commonly based on pseudo CT (pCT) images. This study investigated the feasibility of unsupervised pCT generation from MRI using a cycle generative adversarial network (CycleGAN) and a heterogenous multicentric dataset. A dosimetric analysis in three-dimensional conformal radiotherapy (3DCRT) planning was also performed. Material and methodsOverall, 87 T1-weighted and 102 T2-weighted MR images alongside with their corresponding computed tomography (CT) images of brain cancer patients from multiple centers were used. Initially, images underwent a number of preprocessing steps, including rigid registration, novel CT Masker, N4 bias field correction, resampling, resizing, and rescaling. To overcome the gradient vanishing problem, residual blocks and mean squared error (MSE) loss function were utilized in the generator and in both networks (generator and discriminator), respectively. The CycleGAN was trained and validated using 70 T1 and 80 T2 randomly selected patients in an unsupervised manner. The remaining patients were used as a holdout test set to report final evaluation metrics. The generated pCTs were validated in the context of 3DCRT. ResultsThe CycleGAN model using masked T2 images achieved better performance with a mean absolute error (MAE) of 61.87 ± 22.58 HU, peak signal to noise ratio (PSNR) of 27.05 ± 2.25 (dB), and structural similarity index metric (SSIM) of 0.84 ± 0.05 on the test dataset. T1-weighted MR images used for dosimetric assessment revealed a gamma index of 3%, 3 mm, 2%, 2 mm and 1%, 1 mm with acceptance criteria of 98.96% ± 1.1%, 95% ± 3.68%, 90.1% ± 6.05%, respectively. The DVH differences between CTs and pCTs were within 2%. ConclusionsA promising pCT generation model capable of handling heterogenous multicenteric datasets was proposed. All MR sequences performed competitively with no significant difference in pCT generation. The proposed CT Masker proved promising in improving the model accuracy and robustness. There was no significant difference between using T1-weighted and T2-weighted MR images for pCT generation.

Neuro-Oncology Patients as Human Research Subjects: Ethical Considerations for Cognitive and Behavioral Testing for Research Purposes.

Simple SummaryPrevious publications have elaborated on the exposure of ethical issues surrounding the enrollment and neurological testing of brain cancer patients into clinical studies. Existing literature has been tailored to provide insight on how to overcome ethical challenges for clinical team members but not for the research component that runs in parallel. The aim of this paper is to highlight the obstacles that researchers encounter when obtaining informed consent and administering language, cognitive or behavioral tasks for the sole purpose of research. Researchers should be encouraged to practice their best judgment and effectively communicate the purpose of the study while emphasizing the voluntary participation of neurologically impaired cancer patients. The solutions proposed in this paper can serve as future reference and a guide on maintaining a transparent balance between research and clinical testing for both researchers and clinical team members in the neuro-oncology field.Language, cognition, and behavioral testing have become a fundamental component of standard clinical care for brain cancer patients. Many existing publications have identified and addressed potential ethical issues that are present in the biomedical setting mostly centering around the enrollment of vulnerable populations for therapeutic clinical trials. Well-established guides and publications have served as useful tools for clinicians; however, little has been published for researchers who share the same stage but administer tests and collect valuable data solely for non-therapeutic investigational purposes derived from voluntary patient participation. Obtaining informed consent and administering language, cognition, and behavioral tasks for the sole purpose of research involving cancer patients that exhibit motor speech difficulties and cognitive impairments has its own hardships. Researchers may encounter patients who experience emotional responses during tasks that challenge their existing impairments. Patients may have difficulty differentiating between clinical testing and research testing due to similarity of task design and their physician’s dual role as a principal investigator in the study. It is important for researchers to practice the proposed methods emphasized in this article to maintain the overall well-being of patients while simultaneously fulfilling the purpose of the study in a research setting.

Deep learning-based convolutional neural network for intramodality brain MRI synthesis.

PurposeThe existence of multicontrast magnetic resonance (MR) images increases the level of clinical information available for the diagnosis and treatment of brain cancer patients. However, acquiring the complete set of multicontrast MR images is not always practically feasible. In this study, we developed a state‐of‐the‐art deep learning convolutional neural network (CNN) for image‐to‐image translation across three standards MRI contrasts for the brain.MethodsBRATS’2018 MRI dataset of 477 patients clinically diagnosed with glioma brain cancer was used in this study, with each patient having T1‐weighted (T1), T2‐weighted (T2), and FLAIR contrasts. It was randomly split into 64%, 16%, and 20% as training, validation, and test set, respectively. We developed a U‐Net model to learn the nonlinear mapping of a source image contrast to a target image contrast across three MRI contrasts. The model was trained and validated with 2D paired MR images using a mean‐squared error (MSE) cost function, Adam optimizer with 0.001 learning rate, and 120 epochs with a batch size of 32. The generated synthetic‐MR images were evaluated against the ground‐truth images by computing the MSE, mean absolute error (MAE), peak signal‐to‐noise ratio (PSNR), and structural similarity index (SSIM).ResultsThe generated synthetic‐MR images with our model were nearly indistinguishable from the real images on the testing dataset for all translations, except synthetic FLAIR images had slightly lower quality and exhibited loss of details. The range of average PSNR, MSE, MAE, and SSIM values over the six translations were 29.44–33.25 dB, 0.0005–0.0012, 0.0086–0.0149, and 0.932–0.946, respectively. Our results were as good as the best‐reported results by other deep learning models on BRATS datasets.ConclusionsOur U‐Net model exhibited that it can accurately perform image‐to‐image translation across brain MRI contrasts. It could hold great promise for clinical use for improved clinical decision‐making and better diagnosis of brain cancer patients due to the availability of multicontrast MRIs. This approach may be clinically relevant and setting a significant step to efficiently fill a gap of absent MR sequences without additional scanning.

Prevalence of anxiety and depression in people with different types of cancer or haematologic malignancies: a cross-sectional study.

Cancer patients often present with psychological symptoms that affect their quality of life, physical health outcomes and survival. Two of the most frequent psychiatric comorbidities are anxiety and depression. However, the prevalence of these disorders among cancer patients remains unclear, as studies frequently report varying rates. In the present study, we aimed to provide robust point estimates for the prevalence of anxiety and depression for both a mixed cancer sample and for 13 cancer types separately, considering confounding variables. In a sample of 7509 cancer outpatients (51.4% female), we used the Hospital Anxiety and Depression Scale to assess rates of anxiety and depression. Applying ordinal logistic regression models, we compared the prevalence of anxiety and depression between different cancer types, controlling for age and gender. About one third of our sample showed symptoms of anxiety (35.2%) or depression (27.9%), and every sixth patient had a very likely psychiatric condition, with women being more frequently affected. Elderly patients more often showed signs of depression. The prevalence of anxiety and depression was significantly higher in lung and brain cancer patients, than in other cancer patients. Lowest depression rates were found in breast cancer patients. The prevalence of anxiety and depression is high in cancer patients. Type of cancer is an important predictor for anxiety and depressive symptoms, with lung and brain cancer patients being highly burdened. Considering a personalised medicine approach, physicians should take into account the high prevalence of psychiatric comorbidities and include psychiatric consultations in the treatment plan.

Segmentation of Organs and Tumor within Brain Magnetic Resonance Images Using K-Nearest Neighbor Classification.

Purpose:To fully exploit the benefits of magnetic resonance imaging (MRI) for radiotherapy, it is desirable to develop segmentation methods to delineate patients' MRI images fast and accurately. The purpose of this work is to develop a semi-automatic method to segment organs and tumor within the brain on standard T1- and T2-weighted MRI images.Methods and Materials:Twelve brain cancer patients were retrospectively included in this study, and a simple rigid registration was used to align all the images to the same spatial coordinates. Regions of interest were created for organs and tumor segmentations. The K-nearest neighbor (KNN) classification algorithm was used to characterize the knowledge of previous segmentations using 15 image features (T1 and T2 image intensity, 4 Gabor filtered images, 6 image gradients, and 3 Cartesian coordinates), and the trained models were used to predict organ and tumor contours. Dice similarity coefficient (DSC), normalized surface dice, sensitivity, specificity, and Hausdorff distance were used to evaluate the performance of segmentations.Results:Our semi-automatic segmentations matched with the ground truths closely. The mean DSC value was between 0.49 (optical chiasm) and 0.89 (right eye) for organ segmentations and was 0.87 for tumor segmentation. Overall performance of our method is comparable or superior to the previous work, and the accuracy of our semi-automatic segmentation is generally better for large volume objects.Conclusion:The proposed KNN method can accurately segment organs and tumor using standard brain MRI images, provides fast and accurate image processing and planning tools, and paves the way for clinical implementation of MRI-guided radiotherapy and adaptive radiotherapy.

Estimating Radiotherapy-Induced Secondary Cancer Risk Arising from Brain Irradiation at High Energy: A Monte Carlo Study

Background: The present study aims to determine the whole-body out-of-field photon dose equivalents of high-energy conventional radiation therapy treatment. Also, it is tried to estimate the probability of fatal secondary cancer risk for the susceptible organs using a Monte Carlo (MC) code. Materials and methods: An Monte Carlo N-Particle eXtended (MCNPX)-based model of 18-MV Medical Linear Accelerator (LINAC) was created to calculate the out-of-field photon dose equivalent at the locations of fascinating organs in the mathematical female Medical Internal Radiation Dosimetry (MIRD) phantom. Then, the secondary malignancies risk was estimated based on out-of-field doses and radiation risk coefficients according to the National Council of Radiation Protection and Measurements (NCRP). Results: The average photon equivalent dose in out-of-field organs was about 3.25 mSv/Gy, ranging from 0.23 to 37.2 mSv/Gy, respectively, for the organs far from the Planning Target Volume (PTV) (Eyes) and those close to the treatment field (rectum). The entire secondary cancer risk for the 60 Gy prescribed dose to isocenter was obtained as 2.9987%. Here, the maximum doses among off-field organs were related to stomach (0.0805%), lung (0.0601%), and thyroid (0.0404%). Conclusion: Regarding the estimated values for the probability of secondary cancer risk, it is suggested to perform a long-term follow-up of brain cancer patients regarding the prevalence of thyroid, stomach, and lung cancer after completing the treatment course.

TMOD-28. MYC GENERATES AGGRESSIVE MEDULLOBLASTOMA BY HSP90 PATHWAY ACTIVATION AND ARF SILENCING

Abstract Medulloblastoma, the most common malignant pediatric brain tumor, often shows amplification or overexpression of the MYC transcription factor and arises in the presence of a functional p53 tumor suppressor protein. To elucidate the mechanism behind this inexplicable tumor development we generated an inducible, immunocompetent transgenic mouse model of MYC-expressing medulloblastoma. Aggressive tumors developed clonally in the presence of an unaltered p53 gene that molecularly resembled Group 3 medulloblastoma. Compared to MYCN-expressing medulloblastoma driven from the same promoter, we instead discovered pronounced and MIZ1-independent silencing of the ARF suppressor, which was also suppressed in MYC-amplified as compared to MYCN-amplified human medulloblastoma. While MYCN-driven tumor malignancy was more sensitive to ARF depletion, it dramatically increased metastatic spread of MYC-driven tumors. DNMT inhibition could restore ARF levels in MYC-expressing tumors but did not show any therapeutic advantage in tumors in vivo. Bioinformatics analysis further showed a strong correlation of the HSP90 pathway with MYC in human Group 3 MB and in the MYC-driven mouse model. The HSP90 inhibitor Onalespib showed significant selectivity for targeting MYC-driven as compared to MYCN-driven tumors. The drug promoted ARF restoration and increased the survival in our animal model which suggests that it could be potentially used in the treatment of MYC-driven ARF-silenced brain cancer patients.

NIMG-42. MP-MRI-BASED TUMOR PROBABILITY MAPS TRAINED USING AUTOPSY TISSUE SAMPLES AS GROUND TRUTH NON-INVASIVELY PREDICT INFILTRATIVE TUMOR BEYOND THE CONTRAST ENHANCING REGION

Abstract Infiltrative glioma beyond contrast enhancement on MRI is often difficult to identify with conventional imaging. In this study, we use large-format autopsy samples aligned to multi-parametric MRI to test the hypothesis that radio-pathomic machine learning models are able to accurately identify areas of infiltrative tumor beyond the contrast enhancing region. At autopsy, 140 tissue samples from 62 brain cancer patients were collected from brain slices sectioned to align with the patients’ last clinical MRI prior to death. Cell, extra-cellular fluid (ECF), and cytoplasm densities were computed from digitized, hematoxylin and eosin-stained samples, and a subset of 20 slides from 9 patients were annotated for tumor presence by a pathologist-trained technician. In-house custom software was used to align the tissue samples to the patients’ last clinical imaging, which included pre- and post-contrast T1, FLAIR, and ADC images. Bagging random forest models were then trained to predict cellularity, ECF, and cytoplasm density using 5-by-5 voxel tiles from each MRI as input. A 2/3-1/3 train-test split was used to validate model generalizability. A naïve Bayes classifier was trained to predict tumor class using cellularity, ECF, and cytoplasm segmentations within the annotation data set, again using a 2/3-1/3 train-test split to validate performance. The random forest models each accurately predicted cellularity, ECF, and cytoplasm density within the test data set, with root-mean-squared error values for each falling within one standard deviation of the ground truth. The histology-based tumor prediction model accurately predicted tumor, with a test set ROC AUC of 0.86. When using whole brain cellularity, ECF, and cytoplasm predictions from the random forest models as inputs for the naïve Bayes classifier, tumor probability maps identified regions of infiltrative tumor beyond contrast enhancement. Our results suggest that radio-pathomic maps of tumor probability accurately identify regions of infiltrative tumor beyond currently accepted MRI signatures.

Urgent elective pathway service reconfiguration facilitates increased use of surgical adjuncts, improvement in survival trends, and reduced hospital stay for glioblastoma patients

Abstract Aims Glioblastomas (GB) are the most common and aggressive of intrinsic brain tumours. Median survival with maximal therapy is reported to be 14.6 months. Service reconfiguration at the Queen Elizabeth Hospital Birmingham (QEHB) has transformed the service for high grade brain cancer patients, including GB, from a predominantly emergency pathway based system to one of planned urgent-elective admissions consisting of: A. Patient-focused, consultant-led, research orientated “one stop shop” model of integrated outpatient neurosurgical oncology clinic B. Standardisation of urgent elective pathways C. Incorporation of neuro-surgical intra-operative adjuncts (neuro-monitoring, 5-ALA) into routine surgical practice for oncology. Using this model, we have reduced hospital length of stay (with associated financial savings), improved extent of resection and achieved a trend towards increased survival. Method We retrospectively identified patients with primary histological diagnoses of GB (WHO grade IV), who underwent surgery over a six year period, from 01/01/2014 to 31/12/2019, from the QEHB pathology database. Data was collected for demographics, surgical and oncological therapy, use of intra-operative adjuncts, emergency and elective admission status, year of admission, length of stay (LOS), and extent of resection (EOR) on first post-operative MRI scan from hospital databases. Survival was analysed using the Kaplan-Meier method and independent-samples median testing for survival. Proportion of patients undergoing resective surgery and admission status was calculated by year. Overall median survival was calculated and subgroup comparisons made of patients by: age, admission status, year of admission, biopsy or resection, oncology treatment. Hospital length of stay was calculated for patients by surgical procedure, admission pathways and compared across the year. Financial data taken from averages of inpatient episode costs were used to estimate cost savings. Results 610 patients underwent primary procedures for GB, of which 64 were still alive at time of analysis (02/02/2021). Median overall survival time was 9.53 months, this was greatest in patients who underwent resection with completion of Stupp protocol: 28.67 months (n=114). From 2014 to 2019, there has been an increase in elective admission rates (28.1% to 90.3%, p<0.001) and increased proportion of resective surgery (68.4% to 81.9%, p<0.001). There is a trend of improved survival from 2014 to 2019 (median 7.95 and 11.08 months, χ2=9.249, p=0.002). Increasing use of intra-operative adjuvants improved EOR (χ2 =31.064, p<0.001). Through improved urgent-elective admission rates, hospital length of stay has decreased by five days for craniotomies and six days for biopsies. Cost analysis of three cases demonstrated that reducing the LOS by one night alone result in an average cost saving of approximately £750 per patient per night. Conclusion Switching to a system of planned and urgent elective based admission, with standardisation of neuro-oncology patient pathways, increased use of intra-operative adjuncts, earlier oncology multidisciplinary input and outpatient review, has improved the extent of GB resection, led to shorter length of hospital stay associated with significant financial savings and achieved a trend towards increased overall survival.

A Feasibility Study Evaluating the Use of Cell-free DNA Analysis in Laboratory Brain Cancer Investigations

Abstract Aims Circulating tumour DNA (ctDNA), shed from solid cancers in to the plasma, represents an exciting analyte for diagnosis and monitoring of disease in cancer patients. However, its use in glioma brain cancer patients represents a challenge, due to reduced permeability of the blood brain barrier. This pilot study sought to investigate the practical aspects and clinical utility of using cell-free DNA (cfDNA) in glioma tests in a NHS diagnostic laboratory. Firstly, we investigated the potential of ctDNA as a proxy for the brain cancer biopsy; where cfDNA analysis was compared to the paired FFPE brain specimen for relevant glioma genetic biomarkers. Secondly, ctDNA constitutes a portion of the overall cfDNA and there is evidence cfDNA metrics per se may also be of value as prognostic tools and surrogates of tumour burden. Additionally, we investigated a potential role for cfDNA metrics in prognostic impact; linking cfDNA concentrations to clinical outcome measures. Method 10ml peripheral blood was collected in specialist preservative tubes and cfDNA isolated using an extraction kit (Qiagen MinElute ccfDNA kit). cfDNA concentration and purity was assessed using chip-based automated electrophoresis. Where relevant (12/39 cases), cfDNA samples were run though laboratory tests of IDH variant detection, 1p19q co-deletion assessment and MGMT promoter methylation analysis. Results were compared with ‘standard of care’ brain biopsy tests. A potential correlate of cfDNA concentration and clinical outcomes data were assessed in a sub-cohort of glioblastoma patients (n=32). The cohort was divided in to 2 groups – high cfDNA vs. low cfDNA - based on whether a subject’s extracted sample cfDNA concentration fell above or below the mean. Comparison of overall survival in months between subjects was checked for normal distribution using the Shapiro-Wilk t-test. The test of equity of survival distributions for the high cfDNA vs. low cfDNA was then analysed as a Kaplan-Meier curve. Results The protocol delivered cfDNA of high purity, averaging 91%, within the plasma nucleic acid fraction, however the cfDNA concentrations (mean ≈1ng µl-1) fell below the conventional limit of detection of the laboratory tests. In spite of the low concentration, cfDNA samples did generate test PCR amplicon; however results reflected the germline DNA profile rather than the new somatic changes of the tumour. The cfDNA analysis did not pick up the tumour biomarkers seen in the paired tumour biopsy sample. In a second part of the study, cfDNA concentrations for the glioblastoma cohort were assessed in the context of their clinical outcomes data. The data showed a correlate where high cfDNA concentration in the extracted sample was independently associated with inferior outcome in terms of overall survival, with Log Rank significance p=0.014 (Figure 1). Conclusion The cfDNA yields from a 10ml blood sample were consistently too low to meet the limit of detection requirements of the standard laboratory neuropathology genetic tests and glioma tumour profile could not be picked up against the germline background. Thus, in spite of the considerable advantages to glioma plasma molecular testing, using cfDNA as a proxy for a brain biopsy would currently not be possible in our routine diagnostic environment. However, within the limitations of the pilot project testing strategy, the data showed an interesting correlate where high cfDNA concentration was independently associated with inferior outcome in terms of overall survival for glioblastoma patients. Given the simplicity of obtaining this quantifiable metric, there are grounds for further investigations as to its utility; not only with survival outcomes, but also potential correlation with the clinical assessment of tumour burden, blood brain barrier integrity and disease pseudoprogression.

New Insights in Coagulation and Fibrinolysis in Patients with Primary Brain Cancer: A Systematic Review.

Patients with primary brain tumors have a high incidence of thrombosis and hemorrhage. The underlying mechanism is believed to be derangement of their hemostatic system. To get nearer a clarification of this, we aimed to systematically review the existing literature regarding primary and secondary hemostasis as well as fibrinolysis in patients with primary brain tumor. The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The databases PubMed, Embase, and Web of Science were searched on December 15, 2020, without time restrictions. Studies were included if they evaluated at least one blood coagulation and/or fibrinolysis parameter in patients with primary brain cancer. In total, 26 articles including 3,288 patients were included. Overall, increased activity of secondary hemostasis was observed as increased prothrombin fragment 1 + 2 and endogenous thrombin generation levels were found in glioma patients compared with controls. Furthermore, data showed a state of hypofibrinolysis with increased plasminogen activator inhibitor 1 and prolonged clot lysis time in glioma patients. In contrast, no consistent increase in the primary hemostasis was identified; however, data suggested that increased sP-selectin could be a biomarker of increased venous thromboembolism risk and that increased platelet count may be prognostic for survival. Lastly, data indicated that fibrinogen and D-dimer could hold prognostic value. In conclusion, this review indicates that an increased activity of secondary hemostasis and impaired fibrinolysis could be important players in the pathogeneses behind the high risk of thromboembolisms observed in brain cancer patients. Thus, long-term thromboprophylaxis may be beneficial and additional studies addressing this issue are wanted.

Recent Advances in Liquid Biopsy of Brain Cancers.

Brain cancers are among the top causes of death worldwide. Although, the survival rates vary widely depending on the type of the tumor, early diagnosis could generally benefit in better prognosis outcomes of the brain cancer patients. Conventionally, neuroimaging and biopsy are the most widely used approaches in diagnosis, subtyping, and prognosis monitoring of brain cancers, while emerging liquid biopsy assays using peripheral blood or cerebrospinal fluid have demonstrated many favorable characteristics in this task, especially due to their minimally invasive and easiness in sampling nature. Here, we review the recent studies in the liquid biopsy of brain cancers. We discuss the methodologies and performances of various assays on diagnosis, tumor subtyping, relapse prediction as well as prognosis monitoring in brain cancers, which approaches have made a big step toward clinical benefits of brain cancer patients.

What matters for people with brain cancer? Selecting clinical quality indicators for an Australian Brain Cancer Registry.

The goal of a clinical quality registry is to deliver immediate gains in survival and quality of life by delivering timely feedback to practitioners, thereby ensuring every patient receives the best existing treatment. We are developing an Australian Brain Cancer Registry (ABCR) to identify, describe, and measure the impact of the variation and gaps in brain cancer care from the time of diagnosis to the end of life. To determine a set of clinical quality indicators (CQIs) for the ABCR, a database and internet search were used to identify relevant guidelines, which were then assessed for quality using the AGREE II Global Rating Scale. Potential indicators were extracted from 21 clinical guidelines, ranked using a modified Delphi process completed in 2 rounds by a panel of experts and other stakeholders, and refined by a multidisciplinary Working Group. Nineteen key quality reporting domains were chosen, specified by 57 CQIs detailing the specific inclusion and outcome characteristics to be reported. The selected CQIs will form the basis for the ABCR, provide a framework for achievable data collection, and specify best practices for patients and health care providers, with a view to improving care for brain cancer patients. To our knowledge, the systematic and comprehensive approach we have taken is a world first in selecting the reporting specifications for a brain cancer clinical registry.