Molecular diagnostic testing of diffuse gliomas in the real-life setting: A practical approach

IDH wild-type lower-grade gliomas with glioblastoma molecular features: a systematic review and meta-analysis

The WHO 2021 classification defines IDH wild type (IDHw) histologically lower-grade glioma (hLGG) as molecular glioblastoma (mGBM) if TERT promoter mutation (pTERTm), EGFR amplification or chromosome seven gain and ten loss aberrations are indicated. We systematically reviewed articles of IDHw hLGGs studies (49 studies, N = 3748) and meta-analyzed mGBM prevalence and overall survival (OS) according to the PRISMA statement. mGBM rates in IDHw hLGG were significantly lower in Asian regions (43.7%, 95% confidence interval [CI: 35.8-52.0]) when compared to non-Asian regions (65.0%, [CI: 52.9-75.4]) (P = 0.005) and were significantly lower in fresh-frozen specimen when compared to formalin-fixed paraffin-embedded samples (P = 0.015). IDHw hLGGs without pTERTm rarely expressed other molecular markers in Asian studies when compared to non-Asian studies. Patients with mGBM had significantly longer OS times when compared to histological GBM (hGBM) (pooled hazard ratio (pHR) 0.824, [CI: 0.694-0.98], P = 0.03)). In patients with mGBM, histological grade was a significant prognostic factor (pHR 1.633, [CI: 1.09-2.447], P = 0.018), as was age (P = 0.001) and surgical extent (P = 0.018). Although bias risk across studies was moderate, mGBM with grade II histology showed better OS rates when compared to hGBM.

Molecular Biomarker Testing for the Diagnosis of Diffuse Gliomas

CONTEXT.—

The diagnosis and clinical management of patients with diffuse gliomas (DGs) have evolved rapidly over the past decade with the emergence of molecular biomarkers that are used to classify, stratify risk, and predict treatment response for optimal clinical care.

OBJECTIVE.—

To develop evidence-based recommendations for informing molecular biomarker testing for pediatric and adult patients with DGs and provide guidance for appropriate laboratory test and biomarker selection for optimal diagnosis, risk stratification, and prediction.

DESIGN.—

The College of American Pathologists convened an expert panel to perform a systematic review of the literature and develop recommendations. A systematic review of literature was conducted to address the overarching question, "What ancillary tests are needed to classify DGs and sufficiently inform the clinical management of patients?" Recommendations were derived from quality of evidence, open comment feedback, and expert panel consensus.

RESULTS.—

Thirteen recommendations and 3 good practice statements were established to guide pathologists and treating physicians on the most appropriate methods and molecular biomarkers to include in laboratory testing to inform clinical management of patients with DGs.

CONCLUSIONS.—

Evidence-based incorporation of laboratory results from molecular biomarker testing into integrated diagnoses of DGs provides reproducible and clinically meaningful information for patient management.

Diagnostic test accuracy and cost-effectiveness of tests for codeletion of chromosomal arms 1p and 19q in people with glioma

BACKGROUND

Complete deletion of both the short arm of chromosome 1 (1p) and the long arm of chromosome 19 (19q), known as 1p/19q codeletion, is a mutation that can occur in gliomas. It occurs in a type of glioma known as oligodendroglioma and its higher grade counterpart known as anaplastic oligodendroglioma. Detection of 1p/19q codeletion in gliomas is important because, together with another mutation in an enzyme known as isocitrate dehydrogenase, it is needed to make the diagnosis of an oligodendroglioma. Presence of 1p/19q codeletion also informs patient prognosis and prediction of the best drug treatment. The main two tests in use are fluorescent in situ hybridisation (FISH) and polymerase chain reaction (PCR)-based loss of heterozygosity (LOH) assays (also known as PCR-based short tandem repeat or microsatellite analysis). Many other tests are available. None of the tests is perfect, although PCR-based LOH is expected to have very high sensitivity.

OBJECTIVES

To estimate the sensitivity and specificity and cost-effectiveness of different deoxyribonucleic acid (DNA)-based techniques for determining 1p/19q codeletion status in glioma.

SEARCH METHODS

We searched MEDLINE, Embase and BIOSIS up to July 2019. There were no restrictions based on language or date of publication. We sought economic evaluation studies from the results of this search and using the National Health Service Economic Evaluation Database.

SELECTION CRITERIA

We included cross-sectional studies in adults with glioma or any subtype of glioma, presenting raw data or cross-tabulations of two or more DNA-based tests for 1p/19q codeletion. We also sought economic evaluations of these tests.

DATA COLLECTION AND ANALYSIS

We followed procedures outlined in the Cochrane Handbook for Diagnostic Test Accuracy Reviews. Two review authors independently screened titles/abstracts/full texts, performed data extraction, and undertook applicability and risk of bias assessments using QUADAS-2. Meta-analyses used the hierarchical summary ROC model to estimate and compare test accuracy. We used FISH and PCR-based LOH as alternate reference standards to examine how tests compared with those in common use, and conducted a latent class analysis comparing FISH and PCR-based LOH. We constructed an economic model to evaluate cost-effectiveness.

MAIN RESULTS

We included 53 studies examining: PCR-based LOH, FISH, single nucleotide polymorphism (SNP) array, next-generation sequencing (NGS), comparative genomic hybridisation (CGH), array comparative genomic hybridisation (aCGH), multiplex-ligation-dependent probe amplification (MLPA), real-time PCR, chromogenic in situ hybridisation (CISH), mass spectrometry (MS), restriction fragment length polymorphism (RFLP) analysis, G-banding, methylation array and NanoString. Risk of bias was low for only one study; most gave us concerns about how patients were selected or about missing data. We had applicability concerns about many of the studies because only patients with specific subtypes of glioma were included. 1520 participants contributed to analyses using FISH as the reference, 1304 participants to analyses involving PCR-based LOH as the reference and 262 participants to analyses of comparisons between methods from studies not including FISH or PCR-based LOH. Most evidence was available for comparison of FISH with PCR-based LOH (15 studies, 915 participants): PCR-based LOH detected 94% of FISH-determined codeletions (95% credible interval (CrI) 83% to 98%) and FISH detected 91% of codeletions determined by PCR-based LOH (CrI 78% to 97%). Of tumours determined not to have a deletion by FISH, 94% (CrI 87% to 98%) had a deletion detected by PCR-based LOH, and of those determined not to have a deletion by PCR-based LOH, 96% (CrI 90% to 99%) had a deletion detected by FISH. The latent class analysis suggested that PCR-based LOH may be slightly more accurate than FISH. Most other techniques appeared to have high sensitivity (i.e. produced few false-negative results) for detection of 1p/19q codeletion when either FISH or PCR-based LOH was considered as the reference standard, although there was limited evidence. There was some indication of differences in specificity (false-positive rate) with some techniques. Both NGS and SNP array had high specificity when considered against FISH as the reference standard (NGS: 6 studies, 243 participants; SNP: 6 studies, 111 participants), although we rated certainty in the evidence as low or very low. NGS and SNP array also had high specificity when PCR-based LOH was considered the reference standard, although with much more uncertainty as these results were based on fewer studies (just one study with 49 participants for NGS and two studies with 33 participants for SNP array). G-banding had low sensitivity and specificity when PCR-based LOH was the reference standard. Although MS had very high sensitivity and specificity when both FISH and PCR-based LOH were considered the reference standard, these results were based on only one study with a small number of participants. Real-time PCR also showed high specificity with FISH as a reference standard, although there were only two studies including 40 participants. We found no relevant economic evaluations. Our economic model using FISH as the reference standard suggested that the resource-optimising test depends on which measure of diagnostic accuracy is most important. With FISH as the reference standard, MLPA is likely to be cost-effective if society was willing to pay GBP 1000 or less for a true positive detected. However, as the value placed on a true positive increased, CISH was most cost-effective. Findings differed when the outcome measure changed to either true negative detected or correct diagnosis. When PCR-based LOH was used as the reference standard, MLPA was likely to be cost-effective for all measures of diagnostic accuracy at lower threshold values for willingness to pay. However, as the threshold values increased, none of the tests were clearly more likely to be considered cost-effective.

AUTHORS' CONCLUSIONS

In our review, most techniques (except G-banding) appeared to have good sensitivity (few false negatives) for detection of 1p/19q codeletions in glioma against both FISH and PCR-based LOH as a reference standard. However, we judged the certainty of the evidence low or very low for all the tests. There are possible differences in specificity, with both NGS and SNP array having high specificity (fewer false positives) for 1p/19q codeletion when considered against FISH as the reference standard. The economic analysis should be interpreted with caution due to the small number of studies.

Deep cross-view co-regularized representation learning for glioma subtype identification

The new subtypes of diffuse gliomas are recognized by the World Health Organization (WHO) on the basis of genotypes, e.g., isocitrate dehydrogenase and chromosome arms 1p/19q, in addition to the histologic phenotype. Glioma subtype identification can provide valid guidances for both risk-benefit assessment and clinical decision. The feature representations of gliomas in magnetic resonance imaging (MRI) have been prevalent for revealing underlying subtype status. However, since gliomas are highly heterogeneous tumors with quite variable imaging phenotypes, learning discriminative feature representations in MRI for gliomas remains challenging. In this paper, we propose a deep cross-view co-regularized representation learning framework for glioma subtype identification, in which view representation learning and multiple constraints are integrated into a unified paradigm. Specifically, we first learn latent view-specific representations based on cross-view images generated from MRI via a bi-directional mapping connecting original imaging space and latent space, and view-correlated regularizer and output-consistent regularizer in the latent space are employed to explore view correlation and derive view consistency, respectively. We further learn view-sharable representations which can explore complementary information of multiple views by projecting the view-specific representations into a holistically shared space and enhancing via adversary learning strategy. Finally, the view-specific and view-sharable representations are incorporated for identifying glioma subtype. Experimental results on multi-site datasets demonstrate the proposed method outperforms several state-of-the-art methods in detection of glioma subtype status.

The impact of adjuvant radiotherapy on molecular prognostic markers in gliomas

Purpose

Changes in MGMT promoter methylation, IDH1 and IDH2 mutation, and 1p/19q co-deletion status in gliomas between first and subsequent resections and their associated clinical factors are poorly described. In this study, we assayed these biomarkers in the clinical setting.

Patients and methods

We used multiplex ligation-dependent probe amplification to measure MGMT promoter methylation, IDH mutation status, and 1p/19q co-deletion in 45 paired tumor samples from patients undergoing resection and subsequent re-resections for gliomas.

Results

Molecular changes were present in 20 patients (44%). At least one molecular characteristic changed over time in 89% of patients with primary grade III tumors. Gliomas with IDH wild-type and/or non-co-deleted were stable, but IDH1/2 mutation and/or co-deletion were sometimes lost at the time of recurrence. In a multivariate analysis, adjuvant radiotherapy alone was independently associated (P=0.02) with changes in molecular profile.

Conclusion

Molecular biomarkers change in gliomas during the course of the disease, most often MGMT methylation status. These changes in genetic profiles are related to adjuvant treatment with radiotherapy alone, which might be important for individualized treatment planning over the disease course.