Comparison of immunohistochemistry and DNA sequencing for the detection of IDH1 mutations in gliomas

Rapid detection of IDH mutations in gliomas by intraoperative mass spectrometry

The development and performance of two mass spectrometry (MS) workflows for the intraoperative diagnosis of isocitrate dehydrogenase (IDH) mutations in glioma is implemented by independent teams at Mayo Clinic, Jacksonville, and Huashan Hospital, Shanghai. The infiltrative nature of gliomas makes rapid diagnosis necessary to guide the extent of surgical resection of central nervous system (CNS) tumors. The combination of tissue biopsy and MS analysis used here satisfies this requirement. The key feature of both described methods is the use of tandem MS to measure the oncometabolite 2-hydroxyglutarate (2HG) relative to endogenous glutamate (Glu) to characterize the presence of mutant tumor. The experiments i) provide IDH mutation status for individual patients and ii) demonstrate a strong correlation of 2HG signals with tumor infiltration. The measured ratio of 2HG to Glu correlates with IDH-mutant (IDH-mut) glioma (P < 0.0001) in the tumor core data of both teams. Despite using different ionization methods and different mass spectrometers, comparable performance in determining IDH mutations from core tumor biopsies was achieved with sensitivities, specificities, and accuracies all at 100%. None of the 31 patients at Mayo Clinic or the 74 patients at Huashan Hospital were misclassified when analyzing tumor core biopsies. Robustness of the methodology was evaluated by postoperative re-examination of samples. Both teams noted the presence of high concentrations of 2HG at surgical margins, supporting future use of intraoperative MS to monitor for clean surgical margins. The power of MS diagnostics is shown in resolving contradictory clinical features, e.g., in distinguishing gliosis from IDH-mut glioma.

Prognostic Value of Immunohistochemical Expression of MTAP and AKIP1 in IDH1 Mutant Astrocytoma

BACKGROUND

Definite treatment for glioma is not exist, and with increased drug resistance, more effort should be paid to identify new prognostic biomarkers and molecular targets for therapy for glioma patients.

AIM

The current study aimed to evaluate the immunohistochemical (IHC) expression of MTAP and A-Kinase Interacting Protein 1 (AKIP1) in astrocytoma and to investigate their association with the clinicopathological characters of these cases.

METHODS

Totally 66 cases of astrocytoma patients involved in this study. Cases underwent tumor resection and tissue sections were stained with MTAP, AKIP1 and IDH1 by IHC and evaluated in different grades of astrocytoma and their association with survival and response to therapy was investigated.

RESULTS

High AKIP1 expression was positively correlated with treatment resistance and progressive disease. Positive IDH and retained MTAP expressions had shown better treatment response rather than negative IDH and lost MTAP. High AKIP, negative IDH and loss of MTAP expressions were significantly associated with poor survival outcome.

CONCLUSION

Irrespective to grade and IDH status, the loss of MTAP immunoreactivity and high AKIP1 expression are predictive factors in astrocytoma, and they may be used as a biomarker for guiding astrocytoma management and prognosis surveillance.

Clinical application of a highly sensitive digital PCR assay to detect a small fraction of IDH1 R132H-mutant alleles in diffuse gliomas

The current World Health Organization classification of diffuse astrocytic and oligodendroglial tumors requires the examination of isocitrate dehydrogenase 1 (IDH1) or IDH2 mutations. Conventional analysis tools, including Sanger DNA sequencing or pyrosequencing, fail in detecting these variants of low frequency owing to their limited sensitivity. Digital polymerase chain reaction (dPCR) is a recently developed, highly sensitive, and precise quantitative rare variant assay. This study aimed to establish a robust limit of quantitation of the dPCR assay to detect a small fraction of IDH1 R132H mutation. The dPCR assays with serially diluted IDH1 R132H constructs detected 0.05% or more of mutant IDH1 R132H in samples containing mutant DNA. The measured target/total value of the experiments was proportional to the dilution factors and was almost equal to the actual frequencies of the mutant alleles. Based on the average target/total values, together with a twofold standard deviation of the normal DNA, a limit of quantitation of 0.25% was set to secure a safe margin to judge the mutation status of the IDH1 R132H dPCR assay. In clinical settings, detecting IDH1 R132H using dPCR assays can validate ambiguous immunohistochemistry results even when conventional DNA sequencing cannot detect the mutation and assure diagnostic quality.

Comparison of Polymerase Chain Reaction-Restriction Fragment Length Polymorphism, Immunohistochemistry, and DNA Sequencing for the Detection of IDH1 Mutations in Gliomas

BACKGROUND

IDH1 mutation shows diagnostic, prognostic, and predictive value in gliomas. Direct Sanger sequencing is considered the gold standard to detect IDH1 mutation. However, this technology is not available in most neuropathological centers in developing countries such as Indonesia. Immunohistochemistry (IHC) and polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) have also been used to detect IDH1 mutation. This study aimed to compare DNA sequencing, IHC, and PCR-RFLP in detecting IDH1 mutations in gliomas.

METHODS

Research subjects were recruited from Dr. Sardjito Hospital. Genomic DNA was extracted from fresh or formalin-fixed paraffin-embedded samples of tumor tissue. DNA sequencing, PCR-RFLP and IHC were performed to detect IDH1 mutation. Sensitivity, specificity, and accuracy of PCR-RFLP and IHC were calculated by comparing them to DNA sequencing as the gold standard.

RESULTS

Among 61 recruited patients, 13 (21.3%) of them carried a mutation in codon 132 of the IDH1 gene, as shown by DNA sequencing. PCR-RFLP and DNA sequencing have a concordance value of 100%. Meanwhile, the concordance value between IDH1 R132H IHC and DNA sequencing was 96.7%. The sensitivity, specificity, positive predictive values, negative predictive values, and accuracy for PCR-RFLP were all 100%. On the other hand, the sensitivity, specificity, and accuracy of IHC were 92.3%, 97.9%, and 96.7%, respectively.

CONCLUSION

This study showed that both PCR-RFLP and IHC have high accuracy in detecting IDH1 mutation. We recommend a combination of PCR-RFLP and IHC to detect IDH1 mutation in resource-limited settings.<br />.

A Preliminary Study on Machine Learning-Based Evaluation of Static and Dynamic FET-PET for the Detection of Pseudoprogression in Patients with IDH-Wildtype Glioblastoma

Simple Summary

Pseudoprogression detection in glioblastoma patients remains a challenging task. Although pseudoprogression has only a moderate prevalence of 10–30% following first-line treatment of glioblastoma patients, it bears critical implications for affected patients. Non-invasive techniques, such as amino acid PET imaging using the tracer O-(2-[¹⁸F]-fluoroethyl)-L-tyrosine (FET), expose features that have been shown to provide useful information to distinguish tumor progression from pseudoprogression. The usefulness of FET-PET in IDH-wildtype glioblastoma exclusively, however, has not been investigated so far. Recently, machine learning (ML) algorithms have been shown to offer great potential particularly when multiparametric data is available. In this preliminary study, a Linear Discriminant Analysis-based ML algorithm was deployed in a cohort of newly diagnosed IDH-wildtype glioblastoma patients (n = 44) and demonstrated a significantly better diagnostic performance than conventional ROC analysis. This preliminary study is the first to assess the performance of ML in FET-PET for diagnosing pseudoprogression exclusively in IDH-wildtype glioblastoma and demonstrates its potential.

Abstract

Pseudoprogression (PSP) detection in glioblastoma remains challenging and has important clinical implications. We investigated the potential of machine learning (ML) in improving the performance of PET using O-(2-[¹⁸F]-fluoroethyl)-L-tyrosine (FET) for differentiation of tumor progression from PSP in IDH-wildtype glioblastoma. We retrospectively evaluated the PET data of patients with newly diagnosed IDH-wildtype glioblastoma following chemoradiation. Contrast-enhanced MRI suspected PSP/TP and all patients underwent subsequently an additional dynamic FET-PET scan. The modified Response Assessment in Neuro-Oncology (RANO) criteria served to diagnose PSP. We trained a Linear Discriminant Analysis (LDA)-based classifier using FET-PET derived features on a hold-out validation set. The results of the ML model were compared with a conventional FET-PET analysis using the receiver-operating-characteristic (ROC) curve. Of the 44 patients included in this preliminary study, 14 patients were diagnosed with PSP. The mean (TBRmean) and maximum tumor-to-brain ratios (TBRmax) were significantly higher in the TP group as compared to the PSP group (p = 0.014 and p = 0.033, respectively). The area under the ROC curve (AUC) for TBRmax and TBRmean was 0.68 and 0.74, respectively. Using the LDA-based algorithm, the AUC (0.93) was significantly higher than the AUC for TBRmax. This preliminary study shows that in IDH-wildtype glioblastoma, ML-based PSP detection leads to better diagnostic performance.

RNA editing-based classification of diffuse gliomas: predicting isocitrate dehydrogenase mutation and chromosome 1p/19q codeletion

BACKGROUND

Accurate classification of diffuse gliomas, the most common tumors of the central nervous system in adults, is important for appropriate treatment. However, detection of isocitrate dehydrogenase (IDH) mutation and chromosome1p/19q codeletion, biomarkers to classify gliomas, is time- and cost-intensive and diagnostic discordance remains an issue. Adenosine to inosine (A-to-I) RNA editing has emerged as a novel cancer prognostic marker, but its value for glioma classification remains largely unexplored. We aim to (1) unravel the relationship between RNA editing and IDH mutation and 1p/19q codeletion and (2) predict IDH mutation and 1p/19q codeletion status using machine learning algorithms.

RESULTS

By characterizing genome-wide A-to-I RNA editing signatures of 638 gliomas, we found that tumors without IDH mutation exhibited higher total editing level compared with those carrying it (Kolmogorov-Smirnov test, p < 0.0001). When tumor grade was considered, however, only grade IV tumors without IDH mutation exhibited higher total editing level. According to 10-fold cross-validation, support vector machines (SVM) outperformed random forest and AdaBoost (DeLong test, p < 0.05). The area under the receiver operating characteristic curve (AUC) of SVM in predicting IDH mutation and 1p/19q codeletion were 0.989 and 0.990, respectively. After performing feature selection, AUCs of SVM and AdaBoost in predicting IDH mutation were higher than that of random forest (0.985 and 0.983 vs. 0.977; DeLong test, p < 0.05), but AUCs of the three algorithms in predicting 1p/19q codeletion were similar (0.976-0.982). Furthermore, 67% of the six continuously misclassified samples by our 1p/19q codeletion prediction models were misclassifications in the original labelling after inspection of 1p/19q status and/or pathology report, highlighting the accuracy and clinical utility of our models.

CONCLUSIONS

The study represents the first genome-wide analysis of glioma editome and identifies RNA editing as a novel prognostic biomarker for glioma. Our prediction models provide standardized, accurate, reproducible and objective classification of gliomas. Our models are not only useful in clinical decision-making, but also able to identify editing events that have the potential to serve as biomarkers and therapeutic targets in glioma management and treatment.

Updates in prognostic markers for gliomas

Gliomas are the most common primary malignant brain tumor in adults. The traditional classification of gliomas has been based on histologic features and tumor grade. The advent of sophisticated molecular diagnostic techniques has led to a deeper understanding of genomic drivers implicated in gliomagenesis, some of which have important prognostic implications. These advances have led to an extensive revision of the World Health Organization classification of diffuse gliomas to include molecular markers such as isocitrate dehydrogenase mutation, 1p/19q codeletion, and histone mutations as integral components of brain tumor classification. Here, we report a comprehensive analysis of molecular prognostic factors for patients with gliomas, including those mentioned above, but also extending to others such as telomerase reverse transcriptase promoter mutations, O6-methylguanine-DNA methyltransferase promoter methylation, glioma cytosine-phosphate-guanine island methylator phenotype DNA methylation, and epidermal growth factor receptor alterations.

IDH2 is a novel diagnostic and prognostic serum biomarker for non-small-cell lung cancer

Lung cancer is the most common leading cause of cancer‐related death worldwide. Late diagnosis contributes to a high mortality rate and poor survival of this cancer. In our previous study, we found that IDH2 polymorphism rs11540478 is a risk factor for lung cancer. Here, we examined IDH2 protein expression in culture medium in which two non‐small‐cell lung cancer (NSCLC) cell lines, H460 and A549, were growing. We found that the IDH2 protein was elevated in the culture supernatant fraction in a time‐ and cell number‐dependent manner. Next, we used ELISA methods to examine IDH2 protein level in serum from patients with NSCLC and healthy controls. We found that IDH2 protein levels in serum could distinguish NSCLC patients from healthy controls with an AUC (area under the curve) of 0.83 (95% confidence interval = 0.79–0.88). The IDH2 level was decreased in serum from NSCLC postsurgical patients compared with the paired presurgical serum. High serum IDH2 levels appear to correlate with poor survival in patients with NSCLC. These results suggest that IDH2 levels in the serum could be a new effective biomarker for the diagnosis and prognosis of NSCLC.

Impact of IDH1 mutation status on outcome in clinical trials for recurrent glioblastoma

IDH1 mutated glioblastoma (GB) has a better prognosis than IDH1 wildtype GB. However, it remains unknown whether patients (pts) with IDH1 mutated GB have a higher 6-month progression free survival (PFS6) or radiographic response (RR) rate on clinical trials for recurrence. Retrospective review of GB pts at MDACC between 2006 and 2012 identified 330 patients in recurrent GB trials. 93 patients (28 %) had either PFS6 or a complete/partial RR per RANO criteria. 49/93 (53 %) patients with PFS6 or a complete/partial RR had tumor tissue for IDH1 testing. A matched cohort of 49 patients on recurrent GB clinical trials that failed to achieve PFS6 or RR (also with tissue for IDH1 testing) was identified for comparison. IDH1 status was obtained in 92/98 (94 %) patients of which 17 (18 %) had an IDH1 mutation. PFS6 was seen in 26/49 (53 %) patients. IDH status was unknown in two of these patients. 5/24 (21 %) were IDH1 mutated compared to 5/24 (21 %) of their matched cohort without PFS6. RR was found in 47/49 (94 %) patients. IDH status was unknown in four of these patients. IDH1 mutation was present in 7/43 (16 %) patients with RR compared to 10/43 (23 %) in the matched cohort without RR (p = 0.48). Median OS for trials at first recurrence was 9.8 months for IDH1 wildtype GB vs. 19.32 months for IDH1 mutated GB (p = 0.14). IDH1 mutation status was not predictive of PFS6 or RR in recurrent GB trials for this data set. However, further examination in larger randomized prospective studies is needed.