Subtopic Deep Dive
Genomic Profiling of Meningioma Progression
Research Guide
What is Genomic Profiling of Meningioma Progression?
Genomic profiling of meningioma progression uses whole-exome sequencing, RNA-seq, and copy number analysis to detect driver mutations like NF2, TERT, AKT1, and subclonal evolution in recurrent or malignant meningiomas.
Studies identify PI3K, AKT1, SMO mutations at similar frequencies in meningiomas (Abedalthagafi et al., 2016, 281 citations). CDKN2A/B homozygous deletion links to early recurrence (Sievers et al., 2020, 222 citations). High-grade meningiomas show complex genomic landscapes from 134 tumor analyses (Bi et al., 2017, 199 citations). Over 10 key papers span 2002-2021.
Why It Matters
Genomic profiling reveals therapeutic targets like PI3K signaling for precision medicine in meningiomas lacking effective therapies (Abedalthagafi et al., 2016). CDKN2A/B deletions predict recurrence risk, guiding postoperative monitoring (Sievers et al., 2020). NDRG2 inactivation identifies aggressive cases for targeted interventions (Lusis et al., 2005). These insights address high recurrence in inoperable tumors, improving EANO management guidelines (Goldbrunner et al., 2021).
Key Research Challenges
Subclonal evolution detection
Sequencing recurrent meningiomas reveals branching evolution, complicating driver identification (Bi et al., 2017). Phylogenetic modeling requires multi-region sampling to track progression (Harmancı et al., 2017). Over 199 citations highlight need for longitudinal data.
Driver mutation validation
PI3K mutations match AKT1/SMO frequency but functional impacts vary (Abedalthagafi et al., 2016). Validating oncogenicity demands orthogonal assays beyond exome data (Lusis et al., 2005). 281 citations underscore assay standardization gaps.
Therapeutic translation barriers
NF2 loss and TERT alterations lack direct inhibitors despite prevalence (Choy et al., 2011). High-grade profiles show vulnerabilities unmet by trials (Bi et al., 2017). EANO guidelines note absent controlled studies (Goldbrunner et al., 2021).
Essential Papers
CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2011–2015
Quinn T. Ostrom, Haley Gittleman, Gabrielle Truitt et al. · 2018 · Neuro-Oncology · 2.2K citations
Breast (female only)Prostate (male only)
The WHO Classification of Tumors of the Nervous System
Paul Kleihues, David N. Louis, Bernd W. Scheithauer et al. · 2002 · Journal of Neuropathology & Experimental Neurology · 1.9K citations
The new World Health Organization (WHO) classification of nervous system tumors, published in 2000, emerged from a 1999 international consensus conference of neuropathologists. New entities include...
EANO guideline on the diagnosis and management of meningiomas
Roland Goldbrunner, Pantelis Stavrinou, Michael D. Jenkinson et al. · 2021 · Neuro-Oncology · 635 citations
Abstract Meningiomas are the most common intracranial tumors. Yet, only few controlled clinical trials have been conducted to guide clinical decision making, resulting in variations of management a...
Oncogenic PI3K mutations are as common as<i>AKT1</i>and<i>SMO</i>mutations in meningioma
Malak Abedalthagafi, Wenya Linda Bi, Ayal A. Aizer et al. · 2016 · Neuro-Oncology · 281 citations
This work identifies PI3K signaling as an important target for precision medicine trials in meningioma patients.
Efficacy and Tolerability of Temozolomide in an Alternating Weekly Regimen in Patients With Recurrent Glioma
Antje Wick, Jörg Felsberg, Joachim P. Steinbach et al. · 2007 · Journal of Clinical Oncology · 260 citations
Purpose Evaluation of toxicity and efficacy of an alternating weekly regimen of temozolomide administered 1 week on and 1 week off in patients with recurrent glioma. Patients and Methods Ninety adu...
CDKN2A/B homozygous deletion is associated with early recurrence in meningiomas
Philipp Sievers, Thomas Hielscher, Daniel Schrimpf et al. · 2020 · Acta Neuropathologica · 222 citations
Integrated genomic analyses of de novo pathways underlying atypical meningiomas
Akdes Serin Harmancı, Mark W. Youngblood, Victoria Clark et al. · 2017 · Nature Communications · 206 citations
Reading Guide
Foundational Papers
Start with Kleihues et al. (2002, 1949 citations) for WHO grading context, then Lusis et al. (2005, 199 citations) for NDRG2 in aggressive cases to ground progression genetics.
Recent Advances
Study Bi et al. (2017, 199 citations) for high-grade genomes and Sievers et al. (2020, 222 citations) for CDKN2A recurrence predictors as key advances.
Core Methods
Core techniques include whole-exome sequencing for mutations (Abedalthagafi et al., 2016), copy number analysis (Bi et al., 2017), and expression profiling (Lusis et al., 2005).
How PapersFlow Helps You Research Genomic Profiling of Meningioma Progression
Discover & Search
Research Agent uses searchPapers('genomic profiling meningioma progression NF2 TERT') to retrieve Bi et al. (2017) on high-grade landscapes, then citationGraph reveals 199 citing works on subclonality, and findSimilarPapers expands to Sievers et al. (2020) CDKN2A findings.
Analyze & Verify
Analysis Agent applies readPaperContent on Abedalthagafi et al. (2016) to extract PI3K mutation frequencies, verifyResponse with CoVe cross-checks against Goldbrunner et al. (2021) guidelines, and runPythonAnalysis processes copy number data via pandas for mutation burden stats with GRADE scoring for evidence strength.
Synthesize & Write
Synthesis Agent detects gaps in PI3K inhibitor trials from Harmancı et al. (2017) and Lusis et al. (2005), flags NF2 contradictions; Writing Agent uses latexEditText for progression models, latexSyncCitations integrates 10 papers, latexCompile generates review PDF, exportMermaid diagrams subclonal trees.
Use Cases
"Analyze mutation burdens in high-grade meningioma WES data from Bi 2017"
Research Agent → searchPapers → readPaperContent (Bi et al., 2017) → Analysis Agent → runPythonAnalysis (pandas/NumPy on copy number CSV) → matplotlib plots of driver frequencies output.
"Write LaTeX review on CDKN2A deletion in recurrent meningiomas"
Research Agent → citationGraph (Sievers 2020) → Synthesis Agent → gap detection → Writing Agent → latexEditText (intro/methods) → latexSyncCitations (10 papers) → latexCompile → formatted PDF with figures.
"Find code for meningioma phylogenetic reconstruction"
Research Agent → paperExtractUrls (Harmancı 2017) → paperFindGithubRepo → githubRepoInspect → Code Discovery workflow outputs Python scripts for subclonal evolution trees.
Automated Workflows
Deep Research workflow scans 50+ meningioma genomics papers via searchPapers chains, producing structured reports on NF2/TERT progression with GRADE grades. DeepScan's 7-step analysis verifies CDKN2A recurrence links (Sievers et al., 2020) with CoVe checkpoints and Python stats. Theorizer generates hypotheses linking PI3K mutations to temozolomide sensitivity from Abedalthagafi (2016) and Wick (2007).
Frequently Asked Questions
What defines genomic profiling of meningioma progression?
It applies whole-exome sequencing, RNA-seq, and copy number analysis to track NF2, TERT, AKT1 drivers and subclonal changes in recurrent/malignant cases (Bi et al., 2017).
What methods identify key drivers?
Integrated analyses use exome sequencing for PI3K/AKT1 mutations (Abedalthagafi et al., 2016) and homozygous deletion detection for CDKN2A/B (Sievers et al., 2020).
What are key papers?
Bi et al. (2017, 199 citations) maps high-grade landscapes; Sievers et al. (2020, 222 citations) links CDKN2A to recurrence; Lusis et al. (2005, 199 citations) identifies NDRG2 suppression.
What open problems remain?
Translating PI3K vulnerabilities to trials (Abedalthagafi et al., 2016); modeling multi-region evolution (Harmancı et al., 2017); developing NF2-targeted therapies (Choy et al., 2011).
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