Prognostic Indicators for H3K27M-Mutant Diffuse Midline Glioma: A Population-Based Retrospective Surveillance, Epidemiology, and End Results Database Analysis

Adhikari, Srijan; Bhutada, Abhishek S; Ladner, Liliana; Cuoco, Joshua A.; Entwistle, John J.; Marvin, Eric A.; Rogers, Cara M.

doi:10.1016/j.wneu.2023.07.001

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…In this study, tumor size, tumor location, enhancement, radiotherapy, and chemotherapy were also significant prognostic factors. Previous studies have found that tumor location [30,31], radiotherapy [12,31], and chemotherapy [32] are significant prognostic factors for H3K27M-DMG. In the future, larger cohort studies are needed to explore whether tumor size and enhancement are significant prognostic factors for H3K27M-DMG.…”

Section: Discussionmentioning

confidence: 98%

Deep Learning for the Prediction of the Survival of Midline Diffuse Glioma with an H3K27M Alteration

Huang,

Chen,

Zhang

et al. 2023

Brain Sciences

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Background: The prognosis of diffuse midline glioma (DMG) patients with H3K27M (H3K27M-DMG) alterations is poor; however, a model that encourages accurate prediction of prognosis for such lesions on an individual basis remains elusive. We aimed to construct an H3K27M-DMG survival model based on DeepSurv to predict patient prognosis. Methods: Patients recruited from a single center were used for model training, and patients recruited from another center were used for external validation. Univariate and multivariate Cox regression analyses were used to select features. Four machine learning models were constructed, and the consistency index (C-index) and integrated Brier score (IBS) were calculated. We used the receiver operating characteristic curve (ROC) and area under the receiver operating characteristic (AUC) curve to assess the accuracy of predicting 6-month, 12-month, 18-month and 24-month survival rates. A heatmap of feature importance was used to explain the results of the four models. Results: We recruited 113 patients in the training set and 23 patients in the test set. We included tumor size, tumor location, Karnofsky Performance Scale (KPS) score, enhancement, radiotherapy, and chemotherapy for model training. The accuracy of DeepSurv prediction is highest among the four models, with C-indexes of 0.862 and 0.811 in the training and external test sets, respectively. The DeepSurv model had the highest AUC values at 6 months, 12 months, 18 months and 24 months, which were 0.970 (0.919–1), 0.950 (0.877–1), 0.939 (0.845–1), and 0.875 (0.690–1), respectively. We designed an interactive interface to more intuitively display the survival probability prediction results provided by the DeepSurv model. Conclusion: The DeepSurv model outperforms traditional machine learning models in terms of prediction accuracy and robustness, and it can also provide personalized treatment recommendations for patients. The DeepSurv model may provide decision-making assistance for patients in formulating treatment plans in the future.

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Section: Discussionmentioning

confidence: 98%

Deep Learning for the Prediction of the Survival of Midline Diffuse Glioma with an H3K27M Alteration

Huang,

Chen,

Zhang

et al. 2023

Brain Sciences

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“…Nomograms represent the future of prognostic survival data modeling, gaining popularity due to their ability to simplify complex predictive survival models into a numerical point-based system for estimating event probabilities, such as overall survival (OS) [28]. These tools provide a robust and practical means for clinicians to stratify the risk of survival in patients with rare tumors like CPTs.…”

Section: Nomogrammentioning

confidence: 99%

Prognostic Factors and Nomogram for Choroid Plexus Tumors: A Population-Based Retrospective Surveillance, Epidemiology, and End Results Database Analysis

Bhutada,

Adhikari,

Cuoco

et al. 2024

Cancers

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Background: Choroid plexus tumors (CPTs) are rare neoplasms found in the central nervous system, comprising 1% of all brain tumors. These tumors include choroid plexus papilloma (CPP), atypical choroid plexus papilloma (aCPP), and choroid plexus carcinoma (CPC). Although gross total resection for choroid plexus papillomas (CPPs) is associated with long-term survival, there is a scarcity of prospective data concerning the role and sequence of neoadjuvant therapy in treating aCPP and CPC. Methods: From the years 2000 to 2019, 679 patients with CPT were identified from the Surveillance, Epidemiology, and End Result (SEER) database. Among these patients, 456 patients had CPP, 75 patients had aCPP, and 142 patients had CPC. Univariate and multivariable Cox proportional hazard models were run to identify variables that had a significant impact on the primary endpoint of overall survival (OS). A predictive nomogram was built for patients with CPC to predict 5-year and 10-year survival probability. Results: Histology was a significant predictor of OS, with 5-year OS rates of 90, 79, and 61% for CPP, aCPP, and CPC, respectively. Older age and African American race were prognostic for worse OS for patients with CPP. Older age was also associated with reduced OS for patients with aCPP. American Indian/Alaskan Native race was linked to poorer OS for patients with CPC. Overall, treatment with gross total resection or subtotal resection had no difference in OS in patients with CPP or aCPP. Meanwhile, in patients with CPC, gross total resection (GTR) was associated with significantly better OS than subtotal resection (STR) only. However, there is no difference in OS between patients that receive GTR and patients that receive STR with adjuvant therapy. The nomogram for CPC considers types of treatments received. It demonstrates acceptable accuracy in estimating survival probability at 5-year and 10-year intervals, with a C-index of 0.608 (95% CI of 0.446 to 0.77). Conclusions: This is the largest study on CPT to date and highlights the optimal treatment strategies for these rare tumors. Overall, there is no difference in OS with GTR vs. STR in CPP or aCPP. Furthermore, OS is equivalent for CPC with GTR and STR plus adjuvant therapy.

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“…As such, histones are associated with epigenetic regulation of the genome, and it is not surprising that mutations/alterations are implicated in cancer development and progression. Indeed, histone mutations are common in tumors affecting children and adolescents and are associated with poorer overall survival (OS) [1][2][3]. Histone 3 (H3) is the most frequently mutated histone in cancer, initially identified in pediatric high-grade glioma in 2012 [4,5].…”

Section: Introductionmentioning

confidence: 99%

From Pediatric to Adult Brain Cancer: Exploring Histone H3 Mutations in Australian Brain Cancer Patients

Grebstad Tune,

Sareen,

Powter

et al. 2023

Biomedicines

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Genetic histone variants have been implicated in cancer development and progression. Mutations affecting the histone 3 (H3) family, H3.1 (encoded by HIST1H3B and HIST1H3C) and H3.3 (encoded by H3F3A), are mainly associated with pediatric brain cancers. While considered poor prognostic brain cancer biomarkers in children, more recent studies have reported H3 alterations in adult brain cancer as well. Here, we established reliable droplet digital PCR based assays to detect three histone mutations (H3.3-K27M, H3.3-G34R, and H3.1-K27M) primarily linked to childhood brain cancer. We demonstrate the utility of our assays for sensitively detecting these mutations in cell-free DNA released from cultured diffuse intrinsic pontine glioma (DIPG) cells and in the cerebral spinal fluid of a pediatric patient with DIPG. We further screened tumor tissue DNA from 89 adult patients with glioma and 1 with diffuse hemispheric glioma from Southwestern Sydney, Australia, an ethnically diverse region, for these three mutations. No histone mutations were detected in adult glioma tissue, while H3.3-G34R presence was confirmed in the diffuse hemispheric glioma patient.

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Prognostic Indicators for H3K27M-Mutant Diffuse Midline Glioma: A Population-Based Retrospective Surveillance, Epidemiology, and End Results Database Analysis

Cited by 4 publications

References 19 publications

Deep Learning for the Prediction of the Survival of Midline Diffuse Glioma with an H3K27M Alteration

Deep Learning for the Prediction of the Survival of Midline Diffuse Glioma with an H3K27M Alteration

Prognostic Factors and Nomogram for Choroid Plexus Tumors: A Population-Based Retrospective Surveillance, Epidemiology, and End Results Database Analysis

From Pediatric to Adult Brain Cancer: Exploring Histone H3 Mutations in Australian Brain Cancer Patients

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