P14.86 Predictive value of MGMT promoter (pMGMT) methylation status on pseudoprogression (PsP) and survival analysis in Glioblastoma (GBM) patients: a retrospective single institutional analysis

2021 ◽  
Vol 23 (Supplement_2) ◽  
pp. ii54-ii54
Author(s):  
V Interno’ ◽  
P De Santis ◽  
L Stucci ◽  
C Porta
Keyword(s):  
Survival Analysis ◽  
Brain Mri ◽  
Methylation Status ◽  
Response Assessment ◽  
Potential Effect ◽  
Primary Brain Tumor ◽  
Survival Outcome ◽  
Maximal Extension ◽  
Radiological Response ◽  
True Progression

Abstract BACKGROUND Glioblastoma is the most common and aggressive primary brain tumor. Conventional therapies, such as maximal extension of surgery followed by radiotherapy (RT) and chemotherapy with Temozolomide (TMZ) have not resulted in major improvements in terms of patients’ outcome, overall survival (OS) still remaining poor. In this context, radiological response assessment after radiotherapy remains challenging due to the potential effect of radionecrosis, often mimicking tumor progression. Differentiation between PsP and true progression is required to avoid further unnecessary surgeries, or the premature discontinuation of TMZ. It is known that pMGMT methylated patients respond better to chemotherapy than unmethylated counterpart, so, tumor cells necrosis can be enhanced in this setting. The aim of the study is to observe the correlation between pMGMT methylation status with the incidence of PsP in GBM patients at the first radiological evaluation after RT. MATERIALS AND METHODS Patients with histologically diagnosis of GBM from 2017 to 2021 and availability of pMGMT methylation status were enrolled. PsP was radiologically defined at first brain MRI after RT in case of increasing size of the enhancing component and of peritumoral oedema that remain stable or decrease after antioedema therapy, such as a clinical improvement was observed. RESULTS We analysed 55 GBM patients, 35 (64%) displayed pMGMT methylation whereas 20 (36%) resulted pMGMT unmethylated. PsP was evident in 29 patients (53%), all of them showed methylation of pMGMT. In our analysis, none of pMGMT unmethylated patients experienced PsP. Regarding survival outcome for pMGMT methylated patients, our analysis shows a mPFS of 8.7 (95% CI: 5–10) months versus 9.3 (95%CI: 4.6–12.3) months in methylated and unmethylated respectively (p=0.87). CONCLUSIONS Methylation status of pMGMT showed to be predictor of PsP in GBM patients. If validated, this information could be very useful to guide clinicians in differentiating PsP from true progression. To date, our survival analysis regarding PFS showed no statistical difference among methylated patients with respect to the presence or absence of PsP. Thus, PsP seems not to be a marker of responsiveness to common treatment. Further data are needed to validate our results.

scholarly journals Early Pseudoprogression following Chemoradiotherapy in Glioblastoma Patients: The Value of RANO Evaluation

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Paulo Linhares ◽  
Bruno Carvalho ◽  
Rita Figueiredo ◽  
Rui M. Reis ◽  
Rui Vaz
Keyword(s):  
Survival Data ◽  
Response Assessment ◽  
Rank Analysis ◽  
Rano Criteria ◽  
Macdonald Criteria ◽  
Kaplan Meier ◽  
Early Progression ◽  
Radiological Response ◽  
The Impact ◽  
True Progression

Introduction. The aim of this study was to determine the frequency of pseudoprogression in a cohort of glioblastoma (GBM) patients following radiotherapy/temozolomide (RT/TMZ) by comparing Macdonald criterial to Response Assessment in Neuro-Oncology (RANO) criteria. The impact on prognosis and survival analysis was also studied.Materials and Methods. All patients receiving RT/TMZ for newly diagnosed GBM from January 2005 to December 2009 were retrospectively evaluated, and demographic, clinical, radiographic, treatment, and survival data were reviewed. Updated RANO criteria were used for the evaluation of the pre-RT and post-RT MRI and compared to classic Macdonald criteria. Survival data was evaluated using the Kaplan-Meier and log-rank analysis.Results and Discussion. 70 patients were available for full radiological response assessment. Early progression was confirmed in 42 patients (60%) according to Macdonald criteria and 15 patients (21%) according to RANO criteria. Pseudoprogression was identified in 10 (23.8%) or 2 (13.3%) patients in Macdonald and RANO groups, respectively. Cumulative survival of pseudoprogression group was higher than that of true progression group and not statistically different from the non-progressive disease group.Conclusion. In this cohort, the frequency of pseudoprogression varied between 13% and 24%, being overdiagnosed by older Macdonald criteria, which emphasizes the importance of RANO criteria and new radiological biomarkers for correct response evaluation.

Serum Markers of Methylation Status in Relation to Brain MRI Measures and Risk of Dementia

2018 ◽  
Author(s):  
Babak Hooshmand ◽  
Helga Refsum ◽  
A. David Smith ◽  
Grégoria Kalpouzos ◽  
Francesca Mangialasche ◽  
...  
Keyword(s):  
Brain Mri ◽  
Methylation Status ◽  
Serum Markers

scholarly journals Genome-wide analysis identifies critical DNA methylations within NTRKs genes in colorectal cancer

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Zijian Chen ◽  
Zenghong Huang ◽  
Yanxin Luo ◽  
Qi Zou ◽  
Liangliang Bai ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Dna Methylation ◽  
Promoter Methylation ◽  
Expression Profiles ◽  
Methylation Status ◽  
Gene Promoter ◽  
Normal Mucosa ◽  
Suppressor Gene ◽  
Survival Outcome ◽  
Prognostic Models

Abstract Background Neurotrophic tropomyosin receptor kinases (NTRKs) are a gene family function as oncogene or tumor suppressor gene in distinct cancers. We aimed to investigate the methylation and expression profiles and prognostic value of NTRKs gene in colorectal cancer (CRC). Methods An analysis of DNA methylation and expression profiles in CRC patients was performed to explore the critical methylations within NTRKs genes. The methylation marker was validated in a retrospectively collected cohort of 229 CRC patients and tested in other tumor types from TCGA. DNA methylation status was determined by quantitative methylation-specific PCR (QMSP). Results The profiles in six CRC cohorts showed that NTRKs gene promoter was more frequently methylated in CRC compared to normal mucosa, which was associated with suppressed gene expression. We identified a specific methylated region within NTRK3 promoter targeted by cg27034819 and cg11525479 that best predicted survival outcome in CRC. NTRK3 promoter methylation showed independently predictive value for survival outcome in the validation cohort (P = 0.004, HR 2.688, 95% CI [1.355, 5.333]). Based on this, a nomogram predicting survival outcome was developed with a C-index of 0.705. Furthermore, the addition of NTRK3 promoter methylation improved the performance of currently-used prognostic model (AIC: 516.49 vs 513.91; LR: 39.06 vs 43.64, P = 0.032). Finally, NTRK3 promoter methylation also predicted survival in other tumors, including pancreatic cancer, glioblastoma and stomach adenocarcinoma. Conclusions This study highlights the essential value of NTRK3 methylation in prognostic evaluation and the potential to improve current prognostic models in CRC and other tumors.

Elderly glioblastoma patients: Survival analysis according adjuvant therapy and tumor molecular analysis.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e14046-e14046
Author(s):  
Maria Angeles Vaz ◽  
Isaac Ceballos Lenza ◽  
Sonia Del Barco Berron ◽  
Maria Cruz Martin Soberón ◽  
Oscar Gallego Rubio ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Adjuvant Therapy ◽  
Surgical Resection ◽  
Promoter Methylation ◽  
Systemic Treatment ◽  
Methylation Status ◽  
Mgmt Promoter Methylation ◽  
Mgmt Promoter ◽  
Mgmt Promoter Status ◽  
Mgmt Promoter Methylation Status

e14046 Background: Glioblastoma (GBM) grade IV represents the most frequent and aggressive primary brain tumor. Despite complete surgical resection, GBM infiltrative potential leads to local recurrence rates of around 100%. Standard treatment with adjuvant chemotherapy (CT) and radiotherapy (RT) according Stupp regimen aims to reduce relapse and improve survival, but toxicities associated with these therapies represent a problem in elderly unfit population. O6-Methylguanine-DNA methyltransferase (MGMT) promoter methylation status has been recognized as a predictive factor of response to alkylating agents as temozolomide. We aimed to compare overall survival (OS) results in elderly GBM patients according with MGMT promoter status and systemic treatment after surgery. Methods: We performed a database from the information available from RETSINE (Registro Nacional Español de Tumores de Sistema Nervioso Central). We selected ≥ 65 years GBM diagnosed patients. Relevant information was tumor MGMT promoter methylation status and adjuvant CT and/or RT after resection. Kaplan- Meier analysis was performed. Selected outcome was OS and 95% confidence intervals (CI) and p value < 0.05 were used as measures of statistical significance. Results: We identified 400 eligible GBM patients diagnosed ≥ 65 years (male = 232- 58%; female = 168-42% ). According tumor MGMT status: 125 (31.3%) methylated tumors, 115 (28.7%) non methylated and 160 unknown MGMT status. Included population median age was 72 years (65-88 years). Median global population OS was 7.93 months (IC95% 6.84-9.02). Survival analysis showed better OS for methylated tumors group, median OS 7.33 (IC 95%4.1-10.56) vs. unmethylated OS 7.06 (IC95% 4.9-9.1) (p = 0.021). Survival analysis in methylated patients showed improved OS in patients treated with RT + CT vs. no adjuvant therapy. Median OS for methylated patients treated with CT + RT was 11.46m (IC95%7.6-15.9) vs 9.6 months with only RT(IC95%3.67-7.26) and 2.1m with no treatment (IC95%2.03-3.76) p = 0,00. Unmethylated patients median OS was 9.36m (IC95%3.67-7.26) for RT-CT, 5.4 m (IC95%2.37-8.42) for RT only and 2.76 (IC95% 1.37-4.15) for no treatment p = 0.00. Conclusions: Elderly GBM patients have similar treatment options than young patients and comprise surgical resection, RT and alkylating CT with temozolomide. Comorbidities and performance status have relevant implications in elderly population treatment decisions. The MGMT promoter status has been described as a prognostic and predictive marker of response to temozolomide. In our series both methylated and unmethylated patients can benefit with systemic treatment.

GLIA-Deep: Glioblastoma Image Analysis Using Deep Learning Convolutional Neural Networks to Accurately Classify Gene Methylation and Predict Drug Effectiveness

2020 ◽  
Author(s):  
Viraj Mehta
Keyword(s):  
Neural Networks ◽  
Dna Repair ◽  
Precision Medicine ◽  
Survival Time ◽  
Convolutional Neural Networks ◽  
Alkylating Agent ◽  
Brain Mri ◽  
Methylation Status ◽  
Chemotherapy Drugs ◽  
Mri Scans

Glioblastoma multiforme is a deadly brain cancer with a median patient survival time of 18-24 months, despite aggressive treatments. This limited success is due to a combination of aggressive tumor behavior, genetic heterogeneity of the disease within a single patient&rsquo;s tumor, resistance to therapy, and lack of precision medicine treatments. A single specimen using a biopsy cannot be used for complete assessment of the tumor&rsquo;s microenvironment, making personalized care limited and challenging. Temozolomide (TMZ) is a commercially approved alkylating agent used to treat glioblastoma, but around 50% of temozolomide-treated patients do not respond to it due to the over-expression of O6-methylguanine methyltransferase (MGMT). MGMT is a DNA repair enzyme that rescues tumor cells from alkylating agent-induced damage, leading to resistance to chemotherapy drugs. Epigenetic silencing of the MGMT gene by promoter methylation results in decreased MGMT protein expression, reduced DNA repair activity, increased sensitivity to TMZ, and longer survival time. Thus, it is paramount that clinicians determine the methylation status of patients to provide personalized chemotherapy drugs. However, current methods for determining this via invasive biopsies or manually curated features from brain MRI (Magnetic Resonance Imaging) scans are time- and cost- intensive, and have a very low accuracy. Authors present a novel approach of using convolutional neural networks to predict methylation status and recommend patient-specific treatments via an analysis of brain MRI scans. The authors have developed an AI platform, GLIA-Deep, using a U-Net architecture and a ResNet-50 architecture trained on genomic data from TCGA (The Cancer Genome Atlas through the National Cancer Institute) and brain MRI scans from TCIA (The Cancer Imaging Archive). GLIA-Deep performs tumor region identification and determines MGMT methylation status with 90% accuracy in less than 5 seconds, a real-time analysis that eliminates huge time and cost investments of invasive biopsies. Using computational modeling, the analysis further recommends microRNAs that modulate MGMT gene expression by translational repression to make glioma cells TMZ sensitive, thereby improving the survival of glioblastoma patients with unmethylated MGMT. GLIA-Deep is a completely integrated, end-to-end, cost-effective and time-efficient platform that advances precision medicine by recommending personalized therapies from an analysis of individual MRI scans to improving glioblastoma treatment options.

scholarly journals Chemotherapy response assessment in stage IV melanoma patients—comparison of 18F-FDG-PET/CT, CT, brain MRI, and tumormarker S-100B

2008 ◽  
Vol 35 (10) ◽  
pp. 1786-1795 ◽  
Author(s):  
Klaus Strobel ◽  
Reinhard Dummer ◽  
Hans C. Steinert ◽  
Katrin Baumann Conzett ◽  
Karin Schad ◽  
...  
Keyword(s):  
Brain Mri ◽  
Stage Iv ◽  
Response Assessment ◽  
Chemotherapy Response ◽  
Pet Ct ◽  
Melanoma Patients ◽  
18F Fdg ◽  
Ct Brain ◽  
Fdg Pet Ct ◽  
Stage Iv Melanoma

scholarly journals A novel, reproducible, and objective method for volumetric magnetic resonance imaging assessment of enhancing glioblastoma

2014 ◽  
Vol 121 (3) ◽  
pp. 536-542 ◽  
Author(s):  
Charles W. Kanaly ◽  
Ankit I. Mehta ◽  
Dale Ding ◽  
Jenny K. Hoang ◽  
Peter G. Kranz ◽  
...  
Keyword(s):  
Interobserver Variability ◽  
Intraclass Correlation ◽  
Evaluation Criteria ◽  
Correlation Coefficients ◽  
Response Assessment ◽  
Volumetric Method ◽  
Kappa Statistics ◽  
Macdonald Criteria ◽  
Automated Method ◽  
Radiological Response

Object Robust methodology that allows objective, automated, and observer-independent measurements of brain tumor volume, especially after resection, is lacking. Thus, determination of tumor response and progression in neurooncology is unreliable. The objective of this study was to determine if a semi-automated volumetric method for quantifying enhancing tissue would perform with high reproducibility and low interobserver variability. Methods Fifty-seven MR images from 13 patients with glioblastoma were assessed using our method, by 2 neuroradiologists, 1 neurosurgeon, 1 neurosurgical resident, 1 nurse practitioner, and 1 medical student. The 2 neuroradiologists also performed traditional 1-dimensional (1D) and 2-dimensional (2D) measurements. Intraclass correlation coefficients (ICCs) assessed interobserver variability between measurements. Radiological response was determined using Response Evaluation Criteria In Solid Tumors (RECIST) guidelines and Macdonald criteria. Kappa statistics described interobserver variability of volumetric radiological response determinations. Results There was strong agreement for 1D (RECIST) and 2D (Macdonald) measurements between neuroradiologists (ICC = 0.42 and 0.61, respectively), but the agreement using the authors' novel automated approach was significantly stronger (ICC = 0.97). The volumetric method had the strongest agreement with regard to radiological response (κ = 0.96) when compared with 2D (κ = 0.54) or 1D (κ = 0.46) methods. Despite diverse levels of experience of the users of the volumetric method, measurements using the volumetric program remained remarkably consistent in all users (0.94). Conclusions Interobserver variability using this new semi-automated method is less than the variability with traditional methods of tumor measurement. This new method is objective, quick, and highly reproducible among operators with varying levels of expertise. This approach should be further evaluated as a potential standard for response assessment based on contrast enhancement in brain tumors.

scholarly journals Pitfalls in the radiological response assessment of immunotherapy

2018 ◽  
Vol 11 (2) ◽  
pp. 138-143 ◽  
Author(s):  
Lucian Beer ◽  
Maximilian Hochmair ◽  
Helmut Prosch
Keyword(s):  
Response Assessment ◽  
Radiological Response

scholarly journals Artificial intelligence for early prediction of treatment response in glioblastoma

2021 ◽  
Vol 23 (Supplement_4) ◽  
pp. iv1-iv1
Author(s):  
Markand Patel ◽  
Jinfeng Zhan ◽  
Kal Natarajan ◽  
Robert Flintham ◽  
Nigel Davies ◽  
...  
Keyword(s):  
Machine Learning ◽  
Treatment Response ◽  
Promoter Methylation ◽  
Clinical Characteristics ◽  
Methylation Status ◽  
Response Assessment ◽  
Mgmt Promoter Methylation ◽  
Mgmt Promoter ◽  
Treatment Response Assessment ◽  
Mgmt Promoter Methylation Status

Abstract Aims Treatment response assessment in glioblastoma is challenging. Patients routinely undergo conventional magnetic resonance imaging (MRI), but it has a low diagnostic accuracy for distinguishing between true progression (tPD) and pseudoprogression (psPD) in the early post-chemoradiotherapy time period due to similar imaging appearances. The aim of this study was to use artificial intelligence (AI) on imaging data, clinical characteristics and molecular information within machine learning models, to distinguish between and predict early tPD from psPD in patients with glioblastoma. Method The study involved retrospective analysis of patients with newly-diagnosed glioblastoma over a 3.5 year period (n=340), undergoing surgery and standard chemoradiotherapy treatment, with an increase in contrast-enhancing disease on the baseline MRI study 4-6 weeks post-chemoradiotherapy. Studies had contrast-enhanced T1-weighted imaging (CE-T1WI), T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences, acquired at 1.5 Tesla with 6-months follow-up to determine the reference standard outcome. 76 patients (mean age 55 years, range 18-76 years, 39% female, 46 tPD, 30 psPD) were included. Machine learning models utilised information from clinical characteristics (age, gender, resection extent, performance status), O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and 307 quantitative imaging features; extracted from baseline study CE-T1WI/ADC and T2WI sequences using semi-automatically segmented enhancing disease and perilesional oedema masks respectively. Feature selection was performed within bootstrapped cross-validated recursive feature elimination with a random forest algorithm and Naïve Bayes five-fold cross-validation to validate the final model. Results Treatment response assessment based on the standard-of-care reports by clinical neuroradiologists showed an accuracy of 33% (sensitivity/specificity 52%/3%) to distinguish between tPD and psPD from the early post-treatment MRI study at 4-6 weeks. Machine learning-based models based on clinical and molecular features alone demonstrated an AUC of 0.66 and models using radiomic features alone from the early post-treatment MRI demonstrated an AUC of 0.46-0.69 depending on the feature and mask subset. A combined clinico-radiomic model utilising top common features demonstrated an AUC of 0.80 and an accuracy of 74% (sensitivity/specificity 78%/67%). The features in the final model were age, MGMT promoter methylation status, two shape-based features from the enhancing disease mask (elongation and sphericity), three radiomic features from the enhancing disease mask on ADC (kurtosis, correlation, contrast) and one radiomic feature from the perilesional oedema mask on T2WI (dependence entropy). Conclusion Current standard-of-care glioblastoma treatment response assessment imaging has limitations. In this study, the use of AI through a machine learning-based approach incorporating clinical characteristics and MGMT promoter methylation status with quantitative radiomic features from standard MRI sequences at early 4-6 weeks post-treatment imaging showed the best model performance and a higher accuracy to distinguish between tPD and psPD for early prediction of glioblastoma treatment response.

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