Diagnosing growth in low-grade gliomas with and without longitudinal volume measurements: A retrospective observational study.

BACKGROUND: Low-grade gliomas cause significant neurological morbidity by brain invasion. There is no universally accepted objective technique available for detection of enlargement of low-grade gliomas in the clinical setting; subjective evaluation by clinicians using visual comparison of longitudinal radiological studies is the gold standard. The aim of this study is to determine whether a computer-assisted diagnosis (CAD) method helps physicians detect earlier growth of low-grade gliomas. METHODS AND FINDINGS: We reviewed 165 patients diagnosed with grade 2 gliomas, seen at the University of Alabama at Birmingham clinics from 1 July 2017 to 14 May 2018. MRI scans were collected during the spring and summer of 2018. Fifty-six gliomas met the inclusion criteria, including 19 oligodendrogliomas, 26 astrocytomas, and 11 mixed gliomas in 30 males and 26 females with a mean age of 48 years and a range of follow-up of 150.2 months (difference between highest and lowest values). None received radiation therapy. We also studied 7 patients with an imaging abnormality without pathological diagnosis, who were clinically stable at the time of retrospective review (14 May 2018). This study compared growth detection by 7 physicians aided by the CAD method with retrospective clinical reports. The tumors of 63 patients (56 + 7) in 627 MRI scans were digitized, including 34 grade 2 gliomas with radiological progression and 22 radiologically stable grade 2 gliomas. The CAD method consisted of tumor segmentation, computing volumes, and pointing to growth by the online abrupt change-of-point method, which considers only past measurements. Independent scientists have evaluated the segmentation method. In 29 of the 34 patients with progression, the median time to growth detection was only 14 months for CAD compared to 44 months for current standard of care radiological evaluation (p < 0.001). Using CAD, accurate detection of tumor enlargement was possible with a median of only 57% change in the tumor volume as compared to a median of 174% change of volume necessary to diagnose tumor growth using standard of care clinical methods (p < 0.001). In the radiologically stable group, CAD facilitated growth detection in 13 out of 22 patients. CAD did not detect growth in the imaging abnormality group. The main limitation of this study was its retrospective design; nevertheless, the results depict the current state of a gold standard in clinical practice that allowed a significant increase in tumor volumes from baseline before detection. Such large increases in tumor volume would not be permitted in a prospective design. The number of glioma patients (n = 56) is a limitation; however, it is equivalent to the number of patients in phase II clinical trials. CONCLUSIONS: The current practice of visual comparison of longitudinal MRI scans is associated with significant delays in detecting growth of low-grade gliomas. Our findings support the idea that physicians aided by CAD detect growth at significantly smaller volumes than physicians using visual comparison alone. This study does not answer the questions whether to treat or not and which treatment modality is optimal. Nonetheless, early growth detection sets the stage for future clinical studies that address these questions and whether early therapeutic interventions prolong survival and improve quality of life.

Key rates for the grades and transformation ability of glioma: model simulations and clinical cases.

Tumor progression to higher grade is a fundamental property of cancer. The malignant advancement of the pathological features may either develop during the later stages of cancer growth (natural evolution) or it may necessitate new mutations or molecular events that alter the rates of growth, dispersion, or neovascularization (transformation). Here, we model the pathological and radiological features of grades 2-4 gliomas at the times of diagnosis and death and study grade development and the progression to higher grades. We perform a retrospective review of clinical cases based on model predictions. Simulations uncover two unusual patterns of glioma progression, which are supported by clinical cases: (1) some grades 2 and 3 gliomas lack the ability of progression to higher grades, and (2) grade 3 glioma may evolve to GBM in a few weeks. All 13 gliomas that recurred at the same grade carry either the IDH1-R132H or the ATRX mutation. All (five of five) grade 3 tumors are 1p/19q co-deleted, IDH1-R132H mutated and ATRX wt. Furthermore, three of seven grade 2 gliomas are both IDH1-R132H mutated and ATRX mutated. Simulations replicate the good prognosis of secondary GBM. The results support the hypothesis that constant rates of dispersion, proliferation, and angiogenesis prescribe either a natural evolution or the inability to progress to higher grades. Furthermore, the accrual of molecular events that change a tumor's ability to infiltrate, proliferate or neovascularize may transform the glioma either into a more aggressive tumor at the same grade or elevate its grade.

Automated Robust Image Segmentation: Level Set Method Using Nonnegative Matrix Factorization with Application to Brain MRI.

We address the problem of fully automated region discovery and robust image segmentation by devising a new deformable model based on the level set method (LSM) and the probabilistic nonnegative matrix factorization (NMF). We describe the use of NMF to calculate the number of distinct regions in the image and to derive the local distribution of the regions, which is incorporated into the energy functional of the LSM. The results demonstrate that our NMF-LSM method is superior to other approaches when applied to synthetic binary and gray-scale images and to clinical magnetic resonance images (MRI) of the human brain with and without a malignant brain tumor, glioblastoma multiforme. In particular, the NMF-LSM method is fully automated, highly accurate, less sensitive to the initial selection of the contour(s) or initial conditions, more robust to noise and model parameters, and able to detect as small distinct regions as desired. These advantages stem from the fact that the proposed method relies on histogram information instead of intensity values and does not introduce nuisance model parameters. These properties provide a general approach for automated robust region discovery and segmentation in heterogeneous images. Compared with the retrospective radiological diagnoses of two patients with non-enhancing grade 2 and 3 oligodendroglioma, the NMF-LSM detects earlier progression times and appears suitable for monitoring tumor response. The NMF-LSM method fills an important need of automated segmentation of clinical MRI.

Prolonged treatment with bevacizumab is associated with brain atrophy: a pilot study in patients with high-grade gliomas.

Bevacizumab is widely used for treatment of high-grade gliomas and other malignancies. Because bevacizumab has been shown to be associated with neurocognitive decline, this study is designed to investigate whether prolonged treatment with bevacizumab is also associated with brain atrophy. We identified 12 high-grade glioma patients who received bevacizumab for 12 months at the first recurrence and 13 matched controls and blindly compared the volumes of the contralateral hemispheres and contralateral ventricle in these two groups at baseline and after 12 ± 2 months of the baseline scan by two independent analyses. The volumes of the contralateral hemispheres and ventricles did not differ significantly between the two groups at baseline. Whereas, in the control group the volumes of the contralateral hemisphere changed subtly from baseline to follow-up (p = 0.23), in the bevacizumab-treated group the volumes significantly decreased from baseline to follow-up (p = 0.03). There was significant increase in the contralateral ventricle volume from base line to follow-up scans in both the control group (p = 0.01) and in the bevacizumab group (p = 0.005). Both the absolute and the percentage changes of contralateral hemisphere volumes and contralateral ventricular volumes between the two patient groups were statistically significant (p < 0.05). Results of this study demonstrate prolonged treatment with bevacizumab is associated with atrophy of the contralateral brain hemisphere.

Case 212: chronic lymphocytic inflammation with pontine perivascular enhancement responsive to steroids.

History A previously healthy 23-year-old white man presented to the emergency department of our hospital with a 2-month history of dysarthria, progressively worsening vertigo, and difficulty walking. A diagnosis of retinitis pigementosa was made in this patient's childhood. He did not have any history of congenital syphilis. He did not have a history of nausea or vomiting, fever, weight loss, headache, photophobia, seizure, extremity weakness, or sensory disturbance. Physical examination revealed dysarthria, dysmetria, and ataxia. Kernig and Brudzinski signs were absent, and pathergy test results were negative. Laboratory evaluation revealed normal complete and differential blood counts and normal serum chemistry, including a normal serum angiotensin-converting enzyme level. Analysis of his serum was negative for antinuclear antibody (or ANA), cytoplasmic antineutrophil cvtoplasmic antibody (or cANCA), Sjögren syndrome antigens A and B (SS-A and SS-B, respectively), antitissue transglutaminase and antiendomysial antibodies, and paraneoplastic profile. Serum analysis was also negative for human immunodeficiency virus type 1 and type 2 RNA, Venereal Disease Research Laboratory (VDRL) test, rapid plasma regain (RPR), and fluorescent treponemal antibody absorption. Cerebrospinal fluid (CSF) analysis revealed clear fluid, a normal glucose level (64 mg/dL [3.6 mmol/L]; normal range, 40-70 mg/dL [2.2-3.9 mmol/L]), an elevated protein level (97 mg/dL; normal range, 12-60 mg/dL), and an elevated white blood cell count (7/mm(3) [0.007 ×10(9)/L] in tube 1 and 17/mm(3) [0.017 × 10(9)/L] in tube 2) with 84% lymphocytes. CSF immunoglobulin G level was elevated (30.1 mg/dL; normal, <5.9 mg/dL); however, there were no oligoclonal bands. Gram staining, acid-fast staining, and lactic acid, cryptococcal antigen, histoplasma antigen, herpes simplex virus polymerase chain reaction, VDRL, and RPR test results for CSF were negative. CSF did not grow any bacteria, fungus, or acid-fast bacillus at culture. CSF flow cytometry did not reveal a monoclonal lymphoid population. Initial imaging included brain magnetic resonance (MR) imaging. Computed tomography (CT) images of the chest, abdomen, and pelvis were normal (not shown). The patient's clinical symptoms and imaging findings responded to treatment with a high dose of oral steroids. However, the patient's symptoms exhibited clinical and radiologic progression after several attempts to taper the steroid dose.

Survival analysis in patients with newly diagnosed primary glioblastoma multiforme using pre- and post-treatment peritumoral perfusion imaging parameters.

The objective of this study was to evaluate if peritumoral (PT) perfusion parameters obtained from dynamic susceptibility weighted contrast enhanced perfusion MRI can predict overall survival (OS) and progression free survival (PFS) in patients with newly diagnosed glioblastoma multiforme (GBM). Twenty-eight newly diagnosed GBM patients, who were treated with resection followed by concurrent chemoradiation and adjuvant chemotherapy, were included in this study. Evaluated perfusion parameters were pre- and post-treatment PT relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF). Proportional hazard analysis was used to assess the relationship OS, PFS and perfusion parameters. Kaplan-Meier survival estimates and log-rank test were used to characterize and compare the patient groups with high and low perfusion parameter values in terms of OS and PFS. Pretreatment PT rCBV and rCBF were not associated with OS and PFS whereas there was statistically significant association of both posttreatment PT rCBV and rCBF with OS and posttreatment rCBV with PFS (association of PFS and posttreatment rCBF was not statistically significant). Neither the Kaplan-Meier survival estimates nor the log-rank test demonstrated any differences in OS between high and low pretreatment PT rCBV values and rCBF values; however, high and low post-treatment PT rCBV and rCBF values did demonstrate statistically significant difference in OS and PFS. Our study found posttreatment, not pretreatment, PT perfusion parameters can be used to predict OS and PFS in patients with newly diagnosed GBM.

Expression of PRMT5 correlates with malignant grade in gliomas and plays a pivotal role in tumor growth in vitro.

Protein arginine methyltransferase 5 (PRMT5) catalyzes the formation of ω-NG,N'G-symmetric dimethylarginine residues on histones as well as other proteins. These modifications play an important role in cell differentiation and tumor cell growth. However, the role of PRMT5 in human glioma cells has not been characterized. In this study, we assessed protein expression profiles of PRMT5 in control brain, WHO grade II astrocytomas, anaplastic astrocytomas, and glioblastoma multiforme (GBM) by immunohistochemistry. PRMT5 was low in glial cells in control brain tissues and low grade astrocytomas. Its expression increased in parallel with malignant progression, and was highly expressed in GBM. Knockdown of PRMT5 by small hairpin RNA caused alterations of p-ERK1/2 and significantly repressed the clonogenic potential and viability of glioma cells. These findings indicate that PRMT5 is a marker of malignant progression in glioma tumors and plays a pivotal role in tumor growth.

A multilayer grow-or-go model for GBM: effects of invasive cells and anti-angiogenesis on growth.

The recent use of anti-angiogenesis (AA) drugs for the treatment of glioblastoma multiforme (GBM) has uncovered unusual tumor responses. Here, we derive a new mathematical model that takes into account the ability of proliferative cells to become invasive under hypoxic conditions; model simulations generate the multilayer structure of GBM, namely proliferation, brain invasion, and necrosis. The model is able to replicate and justify the clinical observation of rebound growth when AA therapy is discontinued in some patients. The model is interrogated to derive fundamental insights int cancer biology and on the clinical and biological effects of AA drugs. Invasive cells promote tumor growth, which in the long run exceeds the effects of angiogenesis alone. Furthermore, AA drugs increase the fraction of invasive cells in the tumor, which explain progression by fluid-attenuated inversion recovery (FLAIR) signal and the rebound tumor growth when AA is discontinued.

Robust AI-Driven Segmentation of Glioblastoma T1c and FLAIR MRI Series and the Low Variability of the MRIMath© Smart Manual Contouring Platform.

Patients diagnosed with glioblastoma multiforme (GBM) continue to face a dire prognosis. Developing accurate and efficient contouring methods is crucial, as they can significantly advance both clinical practice and research. This study evaluates the AI models developed by MRIMath© for GBM T1c and fluid attenuation inversion recovery (FLAIR) images by comparing their contours to those of three neuro-radiologists using a smart manual contouring platform. The mean overall Sørensen-Dice Similarity Coefficient metric score (DSC) for the post-contrast T1 (T1c) AI was 95%, with a 95% confidence interval (CI) of 93% to 96%, closely aligning with the radiologists' scores. For true positive T1c images, AI segmentation achieved a mean DSC of 81% compared to radiologists' ranging from 80% to 86%. Sensitivity and specificity for T1c AI were 91.6% and 97.5%, respectively. The FLAIR AI exhibited a mean DSC of 90% with a 95% CI interval of 87% to 92%, comparable to the radiologists' scores. It also achieved a mean DSC of 78% for true positive FLAIR slices versus radiologists' scores of 75% to 83% and recorded a median sensitivity and specificity of 92.1% and 96.1%, respectively. The T1C and FLAIR AI models produced mean Hausdorff distances (<5 mm), volume measurements, kappa scores, and Bland-Altman differences that align closely with those measured by radiologists. Moreover, the inter-user variability between radiologists using the smart manual contouring platform was under 5% for T1c and under 10% for FLAIR images. These results underscore the MRIMath© platform's low inter-user variability and the high accuracy of its T1c and FLAIR AI models.

Evaluation of RANO Criteria for the Assessment of Tumor Progression for Lower-Grade Gliomas.

PURPOSE: The Response Assessment in Neuro-Oncology (RANO) criteria for lower-grade gliomas (LGGs) define tumor progression as ≥25% change in the T2/FLAIR signal area based on an operator's discretion of the perpendicular diameter of the largest tumor cross-section. Potential sources of error include acquisition inconsistency of 2D slices, operator selection variabilities in both representative tumor cross-section and measurement line locations, and the inability to quantify infiltrative tumor margins and satellite lesions. Our goal was to assess the accuracy and reproducibility of RANO in LG. MATERIALS AND METHODS: A total of 651 FLAIR MRIs from 63 participants with LGGs were retrospectively analyzed by three blinded attending physicians and three blinded resident trainees using RANO criteria, 2D visual assessment, and computer-assisted 3D volumetric assessment. RESULTS: RANO product measurements had poor-to-moderate inter-operator reproducibility (r2 = 0.28-0.82; coefficient of variance (CV) = 44-110%; mean percent difference (diff) = 0.4-46.8%) and moderate-to-excellent intra-operator reproducibility (r2 = 0.71-0.88; CV = 31-58%; diff = 0.3-23.9%). When compared to 2D visual ground truth, the accuracy of RANO compared to previous and baseline scans was 66.7% and 65.1%, with an area under the ROC curve (AUC) of 0.67 and 0.66, respectively. When comparing to volumetric ground truth, the accuracy of RANO compared to previous and baseline scans was 21.0% and 56.5%, with an AUC of 0.39 and 0.55, respectively. The median time delay at diagnosis was greater for false negative cases than for false positive cases for the RANO assessment compared to previous (2.05 > 0.50 years, p = 0.003) and baseline scans (1.08 > 0.50 years, p = 0.02). CONCLUSION: RANO-based assessment of LGGs has moderate reproducibility and poor accuracy when compared to either visual or volumetric ground truths.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn).

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.

Mathematical model of the Drosophila circadian clock: loop regulation and transcriptional integration.

Eukaryotic circadian clocks include interconnected positive and negative feedback loops. The clock-cycle dimer (CLK-CYC) and its homolog, CLK-BMAL1, are key transcriptional activators of central components of the Drosophila and mammalian circadian networks, respectively. In Drosophila, negative loops include period-timeless and vrille; positive loops include par domain protein 1. Clockwork orange (CWO) is a recently discovered negative transcription factor with unusual effects on period, timeless, vrille, and par domain protein 1. To understand the actions of this protein, we introduced a new system of ordinary differential equations to model regulatory networks. The model is faithful in the sense that it replicates biological observations. CWO loop actions elevate CLK-CYC; the transcription of direct targets responds by integrating opposing signals from CWO and CLK-CYC. Loop regulation and integration of opposite transcriptional signals appear to be central mechanisms as they also explain paradoxical effects of period gain-of-function and null mutations.

Approximate kernel reconstruction for time-varying networks.

BACKGROUND: Most existing algorithms for modeling and analyzing molecular networks assume a static or time-invariant network topology. Such view, however, does not render the temporal evolution of the underlying biological process as molecular networks are typically "re-wired" over time in response to cellular development and environmental changes. In our previous work, we formulated the inference of time-varying or dynamic networks as a tracking problem, where the target state is the ensemble of edges in the network. We used the Kalman filter to track the network topology over time. Unfortunately, the output of the Kalman filter does not reflect known properties of molecular networks, such as sparsity. RESULTS: To address the problem of inferring sparse time-varying networks from a set of under-sampled measurements, we propose the Approximate Kernel RecONstruction (AKRON) Kalman filter. AKRON supersedes the Lasso regularization by starting from the Lasso-Kalman inferred network and judiciously searching the space for a sparser solution. We derive theoretical bounds for the optimality of AKRON. We evaluate our approach against the Lasso-Kalman filter on synthetic data. The results show that not only does AKRON-Kalman provide better reconstruction errors, but it is also better at identifying if edges exist within a network. Furthermore, we perform a real-world benchmark on the lifecycle (embryonic, larval, pupal, and adult stages) of the Drosophila Melanogaster. CONCLUSIONS: We show that the networks inferred by the AKRON-Kalman filter are sparse and can detect more known gene-to-gene interactions for the Drosophila melanogaster than the Lasso-Kalman filter. Finally, all of the code reported in this contribution will be publicly available.

Inception Modules Enhance Brain Tumor Segmentation.

Magnetic resonance images of brain tumors are routinely used in neuro-oncology clinics for diagnosis, treatment planning, and post-treatment tumor surveillance. Currently, physicians spend considerable time manually delineating different structures of the brain. Spatial and structural variations, as well as intensity inhomogeneity across images, make the problem of computer-assisted segmentation very challenging. We propose a new image segmentation framework for tumor delineation that benefits from two state-of-the-art machine learning architectures in computer vision, i.e., Inception modules and U-Net image segmentation architecture. Furthermore, our framework includes two learning regimes, i.e., learning to segment intra-tumoral structures (necrotic and non-enhancing tumor core, peritumoral edema, and enhancing tumor) or learning to segment glioma sub-regions (whole tumor, tumor core, and enhancing tumor). These learning regimes are incorporated into a newly proposed loss function which is based on the Dice similarity coefficient (DSC). In our experiments, we quantified the impact of introducing the Inception modules in the U-Net architecture, as well as, changing the objective function for the learning algorithm from segmenting the intra-tumoral structures to glioma sub-regions. We found that incorporating Inception modules significantly improved the segmentation performance (p < 0.001) for all glioma sub-regions. Moreover, in architectures with Inception modules, the models trained with the learning objective of segmenting the intra-tumoral structures outperformed the models trained with the objective of segmenting the glioma sub-regions for the whole tumor (p < 0.001). The improved performance is linked to multiscale features extracted by newly introduced Inception module and the modified loss function based on the DSC.

Single Cell Mathematical Model Successfully Replicates Key Features of GBM: Go-Or-Grow Is Not Necessary.

Glioblastoma (GBM) is a malignant brain tumor that continues to be associated with neurological morbidity and poor survival times. Brain invasion is a fundamental property of malignant glioma cells. The Go-or-Grow (GoG) phenotype proposes that cancer cell motility and proliferation are mutually exclusive. Here, we construct and apply a single glioma cell mathematical model that includes motility and angiogenesis and lacks the GoG phenotype. Simulations replicate key features of GBM including its multilayer structure (i.e.edema, enhancement, and necrosis), its progression patterns associated with bevacizumab treatment, and replicate the survival times of GBM treated or untreated with bevacizumab. These results suggest that the GoG phenotype is not a necessary property for the formation of the multilayer structure, recurrence patterns, and the poor survival times of patients diagnosed with GBM.

Mathematical algorithm for discovering states of expression from direct genetic comparison by microarrays.

Highly specific direct genome-scale expression discovery from two biological samples facilitates functional discovery of molecular systems. Here, expression data from cDNA arrays are ranked and curve-fitted. The algorithm uses filters based on the derivatives (slopes) of the curve fits. The rules are set to (i) filter the largest number of artifactual ratios from same-to-same datasets and (ii) maximize discovery from direct comparisons of different samples. The unsupervised discovery is optimized without lowering specificity. The false discovery rates are significantly lower than other methods. The discovered states of genetic expression facilitate functional discovery and are validated by real-time RT-PCR. Better quality improves sensitivity.

Computational Trials: Unraveling Motility Phenotypes, Progression Patterns, and Treatment Options for Glioblastoma Multiforme.

Glioblastoma multiforme is a malignant brain tumor with poor prognosis and high morbidity due to its invasiveness. Hypoxia-driven motility and concentration-driven motility are two mechanisms of glioblastoma multiforme invasion in the brain. The use of anti-angiogenic drugs has uncovered new progression patterns of glioblastoma multiforme associated with significant differences in overall survival. Here, we apply a mathematical model of glioblastoma multiforme growth and invasion in humans and design computational trials using agents that target angiogenesis, tumor replication rates, or motility. The findings link highly-dispersive, moderately-dispersive, and hypoxia-driven tumors to the patterns observed in glioblastoma multiforme treated by anti-angiogenesis, consisting of progression by Expanding FLAIR, Expanding FLAIR + Necrosis, and Expanding Necrosis, respectively. Furthermore, replication rate-reducing strategies (e.g. Tumor Treating Fields) appear to be effective in highly-dispersive and moderately-dispersive tumors but not in hypoxia-driven tumors. The latter may respond to motility-reducing agents. In a population computational trial, with all three phenotypes, a correlation was observed between the efficacy of the rate-reducing agent and the prolongation of overall survival times. This research highlights the potential applications of computational trials and supports new hypotheses on glioblastoma multiforme phenotypes and treatment options.

Mathematical modeling of noise and discovery of genetic expression classes in gliomas.

The microarray array experimental system generates noisy data that require validation by other experimental methods for measuring gene expression. Here we present an algebraic modeling of noise that extracts expression measurements true to a high degree of confidence. This work profiles the expression of 19 200 cDNAs in 35 human gliomas; the experiments are designed to generate four replicate spots/gene with switching of probes. The validity of the extracted measurements is confirmed by: (1) cluster analysis that generates a molecular classification differentiating glioblastoma from lower-grade tumors and radiation necrosis; (2) By what other investigators have reported in gliomas using paradigms for assaying molecular expression other than gene profiling; and (3) Real-time RT-PCR. The results yield a genetic analysis of gliomas and identify classes of genetic expression that link novel genes to the biology of gliomas.

Microarrays: applications and pitfalls.

Microarrays are simple assays that measure the relative expression levels of tens of thousands of genes. Excitement about their importance and potential contributions to biology and medicine has been intense. Nonetheless, recent insights into the limitations and pitfalls of microarrays have led to caution about data interpretation. Microarrays are very useful but they are also very misleading; better data analysis tools are needed to improve accuracy.

Genomic discovery reveals a molecular system for resistance to oxidative and endoplasmic reticulum stress in cultured glioma.

Oxygen is required for respiration and the energetic processes that enable aerobic life. Reactive oxygen species and free radicals, by-products of oxygen use, cause DNA damage and induce endoplasmic reticulum (ER) stress and apoptosis. However, rapidly multiplying cancer cells are resistant to ER and oxidative stress-induced apoptosis. The present article reports the results of highly specific genome-scale expression discovery used to identify genes differentially expressed in cultured glioma cells vs normal brain tissue. The discovered states of expression reveal a cohesive molecular system that protects rapidly growing glioma cells from ER and oxidative stress-induced apoptosis.