Benign Meningiomas Develop Through a Four-Hit Mechanism

Benign Meningiomas Develop Through a Four-Hit Mechanism | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Benign Meningiomas Develop Through a Four-Hit Mechanism Alex Devarajan, Carina Seah, Jack Zhang, Vikram Vasan, Rui Feng, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5253027/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Nov, 2024 Read the published version in Journal of Neuro-Oncology → Version 1 posted 10 You are reading this latest preprint version Abstract Purpose : Meningiomas are central nervous system tumors whose incidence increases with age. Benign meningioma pathogenesis involves germline or somatic mutation of target genes, such as NF2 , leading to clonal expansion. We used an established cancer epidemiology model to investigate the number of rate-limiting steps sufficient for benign meningioma development. Methods : Incidence data was obtained from the Surveillance, Epidemiology and End Results Program (SEER) for nonmalignant meningioma from 2004-2020. Age-adjusted incidence rates per 100,000 person-years were divided into 5-year bands. This was repeated for vestibular schwannomas as a negative control. The Armitage-Doll methodology was applied. Mathematical solutions correcting for volatile tumor microenvironments were applied to fit higher-order models using polynomial regression when appropriate. A 75:25 training:test split was utilized for validation. Results : 222,509 cases of benign meningiomas were identified. We noted strong linear relationships between log-transformed incidence and age across the cohort and multiple subpopulations: male, white, black, Hispanic, Asian/Pacific Islander, and American Indian subpopulations all demonstrated R 2 =0.99. Slopes were between 3.1 and 3.4, suggesting a four-step process for benign meningioma development. Female patients exhibited nonlinear deviations, but the corrected model demonstrated R 2 =0.99 with a four-hit pathway. This model performed robustly on test data with R 2 =0.99. Vestibular schwannomas demonstrated a slope of 2.1 with R 2 =0.99, suggesting a separate three-step process. Conclusion : Four mutations are uniquely required for the development of benign meningiomas. Correcting for volatile tumor microenvironments reliably accounted for nonlinear deviations in behavior. Further studies are warranted to elucidate genomic findings suggestive of key mutations in this pathway. Funding : None. Genetic Models Incidence Meningioma Theoretical Model Tumor Microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Meningiomas are tumors of the central nervous system (CNS) originating from the meninges and are the most common non-malignant CNS tumors. 1 Their molecular pathogenesis remains unclear, though most occur sporadically and somatic mutational profiling has implicated a number of drivers. 2 Inactivating NF2 mutations are most common, but stable mutations in TRAF7 , KLF4 , AKT1 , and SMO without NF2 have been detected. 3 , 4 NF2 mutations are not deterministic of meningioma: germline NF2 mutations lead to meningioma in about 50% of cases. 5 NF2 and other driver mutations exhibit pleiotropy: in other tumors like mesothelioma, differential NF2 loss corresponds to distinct molecular subgroups. 6 NF2 mutations may co-occur with somatic mutations like SMARCB1 , which is associated with hereditary multiple meningiomas. 7 , 8 Meningioma incidence is higher in females, possibly due to responsive increases in progesterone receptor expression. Incidence is also increased in adults, even among individuals with necessary driver mutations. Benign meningiomas preferentially develop in specific meningeal locations. Despite advances, characterization of the mechanisms underlying meningioma development and progression is limited, but important for profiling and treatment. Computational modeling has long helped propose genomic hypotheses for neoplasia and malignant transformation. 9 The Armitage-Doll model describes a power relationship between cancer incidence and age and was used to propose the multistage hypothesis of carcinogenesis, where sequential accumulation of discrete mutations drives tumor development. 10 , 11 This model led to the two-hit hypothesis and has identified tumor suppressor genes of colorectal and lung carcinoma. 12 The Armitage-Doll model has been adapted for age-related diseases with cancer-like epidemiology, including Parkinson’s and amyotrophic lateral sclerosis. 13 – 15 Model expansions have furthered our understanding of diverse tumorigenesis pathways, including clonal expansion and tumor microenvironment effects. 9 , 11 , 16 Given the increasing number of identified mutations markers implicated in benign meningiomas, we sought to develop a mathematical framework to organize the role, timing, and function of known and unknown driver mutations. We further aimed to model potential environmental influences such as sex hormones and apply this model to predict meningioma development. Methods Ethics Approval This study was exempted from Institutional Review Board (IRB) review by the senior author’s IRB as the Surveillance, Epidemiology, and End Results (SEER) database utilized does not contain identifiable patient information. The SEER database only provides anonymized public-use data. 17 Data Sources and Selection Incidence data was obtained from SEER. The SEER program’s 22-registry database (SEER-22) was queried for cases of nonmalignant meningioma from 2004 to 2020. Meningiomas were defined per the International Classification of Diseases for Oncology, version 3 (ICD-O-3) morphology codes 9530–9539. Malignant behavior subclassifications or any histology suggestive of World Health Organization (WHO) 2021 Grades II or III were excluded. 18 Subclasses were excluded if the total number of cases was less than 16. All diagnoses were confirmed by microscopic examination through either positive histology, immunohistochemistry, genetic studies, or other unspecified methods. The primary site was limited to intracranial meningeal locations. Incidence rates per 100,000 person-years age-adjusted to the 2000 U.S. standard population were generated from 25 to 85 years of age in 5-year bands. Fitting of the data was constrained at older ages as the basic Armitage-Doll model does not account for reductions in incidence at old age, which can occur for a multitude of reasons and is seen in numerous cancer types. 19 After data inspection, 85 years was set as the cutoff age. All incidence data was calculated using the SEER*Stat software ( www.seer.cancer.gov/seerstat ) , version 8.4.1.2, based on the November 2022 submission to SEER-22. This was repeated for vestibular schwannoma incidence. Vestibular schwannomas were defined by ICD-O-3 morphology code 9560 (Neurilemoma) and primary site C72.4 (Acoustic nerve). After inspection, the data was constrained to 65 years of age for modeling. Armitage-Doll Methodology The Armitage-Doll model states: if the development of cancer requires multiple prior irreversible steps, and each step has a relatively low probability of occurring over a given time period, then a power relationship will naturally form between age and the incidence of the disease. 9 , 10 This model is defined as: $$\:f\left(t\right)=\alpha\:{t}^{m-1}$$ where f ( t ) is age-adjusted incidence of the meningioma, t is age, α is the product of the exposure risks for each step, and m is the number of slow stages or steps. 11 This gives the linear equation: $$\:log\left(f\right(t\left)\right)=(m-1)log\left(t\right)+c$$ where \(\:c=(m-1)log\left(\alpha\:\right)\) . Therefore, the log of age-adjusted incidence was regressed against the log of age by least squares regression. This linear regression was used to examine the fit of the Armitage-Doll model: the slope is equal to one less than the number of rate-limiting steps m , while the intercept c is equivalent to the combined probability of a given cell undergoing these mutational steps. Some assumptions must be met to satisfy the Armitage-Doll model. Cancer is assumed to be the result of several discrete changes, or mutations, with a low overall rate of occurrence in the general population. These changes are stable, or irreversible, and must proceed in a unique and specific order to lead to the development of cancer. This model was tested for goodness of fit (R 2 ) in the overall cohort and stratified by sex and ethnicity. Piecewise Regression for Nonlinear Relationships Since benign meningioma incidence in women may be influenced by menopausal status due to estrogen/progesterone receptor overexpression, women could be viewed as two distinct groups with varying steps to meningioma development. Assuming menopause is a fixed point where the regression slope changes, we applied a piecewise linear regression (“broken-stick” model) with unknown breakpoint t bp to model the relationship between log incidence and age in female patients. This regression model is: \(\:log\left(f\right(t\left)\right)=({m}_{1}-1)log\left(t\right)+{c}_{1}\) for t ≤ t bp \(\:=({m}_{2}-1)log\left(t\right)+{c}_{2}\) for t > t bp where \(\:{c}_{i}=({m}_{i}-1)log\left({\alpha\:}_{i}\right)\) for i ∈ 1, 2. 13 The slopes were calculated to determine if any significant mutational differences in meningioma pathogenesis existed between premenopausal and postmenopausal women. The breakpoint t bp was determined by the intersection of the two best-fit regressions. Bayesian information criteria (BIC) were calculated to generate Bayes factors (BF) to assess whether the broken-stick model better fit the incidence data than the Armitage-Doll model. Polynomial Expansion and Regression for Perturbative Correction to the Armitage-Doll Model Slight non-linearities in a cohort may suggest time-related changes in the meningioma microenvironment, for example from fluctuating hormone levels. However, these deviations may lack the significance or uniformity to fit a broken-stick model or linear relationships. The inclusion of a changing microenvironment, potentially accelerating early cancer development, violates the Armitage-Doll model’s assumption that cancer arises through a specific sequence of discrete changes. Because the meningioma microenvironment is likely to change with time, we considered a perturbative correction derived by Webster 2019 to allow for slow changes to the microenvironment over time with respect to the original model. 11 This perturbative correction for a volatile tumor microenvironment is: $$\:f\left(t\right)\:=\:{a}_{0}\frac{{t}^{m-1}}{\varGamma\:\left(m\right)}+{\sum\:}_{j=1}^{m}{a}_{j}\frac{\varGamma\:(m-j+2)}{\varGamma\:(2m-j+1)}{t}^{2m-j}$$ where j is the theoretical at-risk mutational step that could lead to the early development of cancer, and a 0 or a j is the product of exposure risks at step 0 and j respectively. This solution generated higher-order models which were fitted by polynomial regression. The polynomial expansion was limited to the two highest-order terms to prevent overfitting of the curve. This microenvironment correction model was trained and validated on a 75:25 train-test split of the SEER data, with groupings assigned at random. Results Armitage-Doll Modeling of Benign Meningiomas 222,509 cases of benign meningiomas were identified from SEER. The logarithmic relationship between age and age-adjusted incidence of benign meningiomas is depicted in Fig. 1 a. The log of age is plotted against the log of age-adjusted incidence in Fig. 1 b. The slope ( m − 1) is 3.2, implying a four-hit process for the development of benign meningiomas in the American population with a high degree of correlation (R 2 = 0.99). To determine whether this four-hit mechanism persisted in different subpopulations, incidence data was plotted for all male (Fig. 2 a) and female (Fig. 2 b) patients. Both male and female patients demonstrated a four-hit mechanism with a high degree of correlation (R 2 = 0.99). This four-hit mechanism was persistently seen when analyzing different racial and ethnic groups within the cohort (Fig. 2 c-g). The slopes are summarized in Table 1 , with all cohorts demonstrating the four-hit mechanism. Table 1 Summary of Slope m − 1 and Number of Steps m for All Populations Population Slope # of steps m All Benign Meningiomas 3.21 4 Male 3.39 4 Female 3.06 4 White 3.11 4 Black 3.17 4 Hispanic 3.47 4 API 3.45 4 American Indian/Alaskan Native 2.98 4 Broken Stick Modeling of Linear Relationship Further inspection of the female subpopulation data suggested a nonlinear relationship which may be violating the Armitage-Doll model assumptions ( Online Resource S1 ). We first tested a changing slope, suggestive of distinct subgroups with different tumorigenesis mechanisms, using a broken-stick model. Linear regressions were plotted in Fig. 3 . The pre-breakpoint segment had a slope of 3.74 (CI 3.49–3.98), while the post-breakpoint segment has a slope of 2.68 (CI 2.51–2.84). The unknown breakpoint of best fit t bp was 3.72, or 41.22 years of age. There was very strong evidence for a lack of superiority of the broken-stick models compared to the linear Armitage-Doll model (BF = 0.00001). This suggested that another solution was needed. Application of a Perturbative Correction for Volatile Tumor Microenvironment in Female Patients As the broken-stick model did not outperform the Armitage-Doll model for female patients, we explored whether the cell microenvironment nonrandomly influenced tumorigenesis in women. We considered a perturbative correction to allow for gradual microenvironmental change over time within the Armitage-Doll framework. Using m = 4 as a starting value from the original linear solution, the correction yielded a seventh-order polynomial expansion. After taking only the two highest-order terms of the correction, the model is plotted in Fig. 4 a with a high degree of correlation to the training set, composed of 75% of the SEER data (R 2 = 0.99). The model performed robustly when validated on the test dataset (Fig. 4 b), composed of the remaining 25% of patients in the SEER data (R 2 = 0.99). Testing of the Four-Hit Hypothesis in Other NF2-Positive CNS Tumors As NF2 is the most frequently mutated gene in benign meningiomas and drives other CNS tumors, we aimed to confirm the four-hit mechanism's specificity to meningiomas rather than all NF2 -mutated tumors. Therefore, SEER was queried for vestibular schwannomas. Given the Armitage-Doll model’s limitations, incidence data was truncated at ages 25–65 and plotted. The untransformed incidence data and Armitage-Doll model applied to the truncated log-transformed dataset is presented in Online Resource S2a-b . The linear regression (R 2 = 0.99) yielded a slope of 2.1, indicating three steps for vestibular schwannoma development versus four steps for meningiomas. This suggested distinct mutational processes despite sharing NF2 as a driver mutation. Discussion Meningioma research has focused on identifying mutations, but it runs into a familiar problem: it is difficult to recognize how many “driver” mutations we need to look for in an individual meningioma. A unifying mechanism characterizing the number, timing, and role of these mutations has yet to be developed. In this study, we applied a foundational cancer modeling framework to benign meningiomas using a large national cancer database, providing the first computational evidence of a four-hit mutational pathway for their development. This pathway holds across sex and race, with our model explaining subtle deviations in female patients via a perturbative correction for a variable microenvironment. The model performed robustly, suggesting that microenvironmental exposure dynamics, rather than a different mechanism, underlie the female preponderance for benign meningiomas. Finally, we confirmed that this mechanism is unique to benign meningiomas, as NF2 -positive vestibular schwannomas demonstrated a separate three-step pathway. Knowing the number of expected driver mutations or phenotypes simplifies genome interpretation and advances our understanding of meningioma progression. This framework organizes current meningioma genomic findings, can aid in identifying other key germline or somatic mutations, and suggests the timing and function of these mutations relative to others. Studies over the past decade have begun to offer genomic evidence of a four-step pathway for benign meningioma development. A 2011 study of multiple meningioma patients describes a four-hit mechanism of biallelic NF2 loss and SMARCB1 haploinsufficiency. 8 Other cohorts have reported similar findings and described location preferences suggestive of somatic mosaicism. 20 Further studies have suggested an additional LZTR1 loss-of-function mutation leading to haploinsufficiency. 7 Genomic research has confirmed the importance of mutation order. NF2 inactivation is already an “early” event in meningioma tumorigenesis. 21 A 2017 study found that the timing of SMARCB1 inactivation relative to NF2 inactivation determined schwannoma versus rhabdoid tumor development. 22 SMARCB1 is part of the SWI / SNF chromatin remodeling complex: these genes have been previously implicated, with one study describing 76.9% of SMARCB1 mutations co-occurring with NF2 mutations in their cohort. 23 Though many mutations have been described overall, mutations in grouped targets like the SWI/SNF complex produce functionally similar end-products. Future modeling strategies may incorporate redundancies to permit multiple solutions and account for this diversity. This analysis should not be used to suggest that a maximum of four driver genes or mutations are implicated in the development of benign meningiomas. Instead, the model suggests that four mutations are sufficient for the development of benign meningiomas. Meningiomas are not quiescent and continue to evolve and mutate independently or in response to radical microenvironment changes, such as exposure to stressors related to surgical resection or radiotherapy. 24 Meningiomas may develop mutations which confer selective growth advantages and promote recurrence or malignant progression. We expect additional mutations to continue occurring heterogeneously within the tissue, which is reflected in genomic studies documenting this heterogeneity. As germline mutations in early tumorigenesis continue to be discovered, research has increasingly focused on somatic mutations which may predispose patients to meningiomas. Many initial germline mutations are high-penetrance mutations with associated phenotypes, like neurofibromatosis 2. 3 Despite the presence of these germline mutations, benign meningiomas often only develop in adulthood. This model demonstrates that meningiomas require four sequential mutations: a single high-penetrance driver is insufficient without accumulating downstream somatic “hits”. Genetic studies have highlighted clonal expansion of mosaic tissue underlying benign meningiomas, as NF2 -mutated meningiomas demonstrate higher chromosome instability and accumulate more oncogenic mutations than their non- NF2 counterparts. 21 Some studies of paired primary and recurrent meningiomas have combined all NF2 /22q-mutated benign meningiomas as one group which displays increased cytogenetic abnormalities on presentation. 25 Benign meningiomas may exhibit saltatory growth patterns with intermittent periods of quiescence not explained solely by genetics. 5 Though the original Armitage-Doll model focused on genetic mutations as “steps”, epigenetic changes such as differential gene expression and DNA methylation may also contribute. Methylation and single-cell transcriptomic studies have proposed subgroups of meningiomas based on clinical behavior and integrated analysis of molecular markers. 26 Nassiri et al. described a subset of NF2 wild-type benign meningiomas which demonstrated silencing of NF2 expression not associated with methylation changes in the gene. These meningiomas functionally acted similar to NF2 -mutated meningiomas: if the expressed gene product is what we define as a “step”, then epigenetic silencing of the gene would be functionally equivalent to a loss-of-function mutation and mathematically lead to the same result. For example, the second most common benign meningioma mutation TRAF7 has been hypothesized to act on a common pathway with NF2 . 27 Redundancies within the four-hit pathway can be incorporated to model differing mechanisms for the same “step”. Further studies are necessary to evaluate whether higher-level control of expression can be modeled. The role of hormones in benign meningioma development has been hypothesized. Meta-analyses have agreed that cumulatively increased exposure to female sex hormones may increase meningioma risk in women, but the underlying mechanisms are unclear. 28 We attempted to model the potential effects of hormones as outside influences in female patients by either considering that two separate tumorigenic mechanisms existed or that one mechanism existed which could be relaxed to some of the original assumptions. First, we treated pre- and postmenopausal women as two distinct populations with different tumorigenic mechanisms by allowing for a changing slope at the average age of menopause. The broken-stick model tested whether pre- and postmenopausal women had two separate mechanisms, but the confidence intervals indicated that a four-step process was feasible in both populations and the breakpoint was inconsistent with the expected age for perimenopause (45 to 50 years). 29 Additionally, the Bayes factor calculated for model selection favored the simpler Armitage-Doll. Second, we developed a model influenced by environmental exposure dynamics to reflect subtle changes in the microenvironment of precancerous cells. Modeling failure rates in complex systems using a multi-stage model like Armitage-Doll required a solution accounting for potentially interdependent sequential and non-sequential steps. Within a normal multistep framework, we assume that lower sex hormone exposure or other risk factors reduces the chance of accumulating a step without changing the mechanism itself. This corrected model allows for conditional changes to the preexisting pathway or tumor growth rate without proposing a completely new process. It is important to note that the nature of these exposures cannot be discerned from computational data alone, and other microenvironmental exposures like ionizing radiation may affect tumorigenesis in female patients. 30 Clearly identifying these steps and understanding their interactions in patients is a future aim. This study has a few limitations. While we mathematically demonstrate how a changing microenvironment over time impacts benign meningioma tumorigenesis and suggest hormone exposure as a key factor, the exact effects remain undetermined and other environmental exposures may contribute. The results are generated using population-level data, and therefore cannot provide information about the number of steps any given individual patient has already undertaken or will need to undergo. The model estimates the development of a primary benign meningioma but cannot predict subsequent mutations or local recurrences. Lastly, the model is inherently simple and requires multiple assumptions. More complex models, such as accounting for multiple pathways or probabilistic changes like mutations that increase chromosomal instability, should be considered. We showed that adding complexity like the microenvironment correction does not necessarily alter the conclusions of the simplified Armitage-Doll model, but this cannot be definitively stated for all complex models without further derivation. Conclusion We present a clonal expansion model for benign meningioma development, applying this framework to large-scale patient data to characterize a four-hit oncogenic mechanism. This model was predictive across patient subsets by sex and race. The mechanism is specific to benign meningiomas, as vestibular schwannomas—a CNS tumor with a shared driver mutation—follow a three-hit mechanism. Deviations in female patients were accurately predicted by adjusting for tumor microenvironment volatility. This model provides a unified genetic framework for meningioma pathogenesis consistent with existing molecular evidence. Future molecular research on benign meningiomas may leverage this framework to understand the timing, propensity, and function of new markers. Declarations Funding : No specific funding was received for this work. Conflict of Interest : None declared. Authorship : Conceptualization: AD, CS Methodology: AD Data curation: AD Formal data analysis and investigation: AD, CS Original draft preparation: AD, CS, JYZ, RKS Review and editing of the manuscript: AD, CS, JYZ, VV, RF, EKC, TS, JB, RKS Project supervision: TS, JB, RKS Previous Presentations : This project was presented as a podium presentation at the North American Skull Base Society 2024 Annual Meeting. Author Contribution Conceptualization: ADMethodology: ADData curation: ADFormal data analysis and investigation: AD, CSOriginal draft preparation: AD, CS, JYZ, RKSReview and editing of the manuscript: AD, CS, JYZ, VV, RF, EKC, TS, JB, RKSProject supervision: TS, JB, RKS Acknowledgement The authors would like to acknowledge the contributions of Dr. Anthony J. Webster, who graciously reviewed the expansion performed to account for the changing meningioma microenvironment using his perturbative correction to the original Armitage-Doll model. 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Cite Share Download PDF Status: Published Journal Publication published 25 Nov, 2024 Read the published version in Journal of Neuro-Oncology → Version 1 posted Editorial decision: Revision requested 30 Oct, 2024 Reviews received at journal 23 Oct, 2024 Reviews received at journal 19 Oct, 2024 Reviewers agreed at journal 18 Oct, 2024 Reviewers agreed at journal 18 Oct, 2024 Reviewers agreed at journal 14 Oct, 2024 Reviewers invited by journal 14 Oct, 2024 Editor assigned by journal 14 Oct, 2024 Submission checks completed at journal 14 Oct, 2024 First submitted to journal 12 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5253027","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":372181763,"identity":"414ed6d5-7566-4ff5-be49-71e20516df3e","order_by":0,"name":"Alex Devarajan","email":"data:image/png;base64,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","orcid":"","institution":"Icahn School of Medicine at Mount Sinai","correspondingAuthor":true,"prefix":"","firstName":"Alex","middleName":"","lastName":"Devarajan","suffix":""},{"id":372181764,"identity":"819da439-8e56-419a-9897-f75e30b403c9","order_by":1,"name":"Carina Seah","email":"","orcid":"","institution":"Icahn School of Medicine at Mount Sinai","correspondingAuthor":false,"prefix":"","firstName":"Carina","middleName":"","lastName":"Seah","suffix":""},{"id":372181765,"identity":"be2ebb8a-1e47-433f-9dd4-76dd8f7e7675","order_by":2,"name":"Jack Zhang","email":"","orcid":"","institution":"Icahn School of Medicine at Mount Sinai","correspondingAuthor":false,"prefix":"","firstName":"Jack","middleName":"","lastName":"Zhang","suffix":""},{"id":372181766,"identity":"4790156c-c6dc-4dca-beca-3a7319e5edb2","order_by":3,"name":"Vikram Vasan","email":"","orcid":"","institution":"Icahn School of Medicine at Mount Sinai","correspondingAuthor":false,"prefix":"","firstName":"Vikram","middleName":"","lastName":"Vasan","suffix":""},{"id":372181768,"identity":"4070d248-9ac4-411b-97d4-9e41d6d9f435","order_by":4,"name":"Rui Feng","email":"","orcid":"","institution":"Icahn School of Medicine at Mount Sinai","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Feng","suffix":""},{"id":372181769,"identity":"a8ac585d-4dc8-434d-92c0-07c0c1b3beb1","order_by":5,"name":"Emily Chapman","email":"","orcid":"","institution":"Icahn School of Medicine at Mount Sinai","correspondingAuthor":false,"prefix":"","firstName":"Emily","middleName":"","lastName":"Chapman","suffix":""},{"id":372181771,"identity":"0612575e-5759-4060-a374-ecbd25e81f07","order_by":6,"name":"Tomoyoshi Shigematsu","email":"","orcid":"","institution":"Icahn School of Medicine at Mount Sinai","correspondingAuthor":false,"prefix":"","firstName":"Tomoyoshi","middleName":"","lastName":"Shigematsu","suffix":""},{"id":372181772,"identity":"fae3602d-3e6d-4767-bdc3-03b1c18883ee","order_by":7,"name":"Joshua Bederson","email":"","orcid":"","institution":"Icahn School of Medicine at Mount Sinai","correspondingAuthor":false,"prefix":"","firstName":"Joshua","middleName":"","lastName":"Bederson","suffix":""},{"id":372181773,"identity":"c40f0fc2-c120-4154-bbb6-af93147e0f39","order_by":8,"name":"Raj Shrivastava","email":"","orcid":"","institution":"Icahn School of Medicine at Mount Sinai","correspondingAuthor":false,"prefix":"","firstName":"Raj","middleName":"","lastName":"Shrivastava","suffix":""}],"badges":[],"createdAt":"2024-10-12 20:23:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5253027/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5253027/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11060-024-04877-y","type":"published","date":"2024-11-25T15:58:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68538782,"identity":"fc8d4c57-6e20-460d-b6a0-58ea1139061b","added_by":"auto","created_at":"2024-11-08 10:31:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3840889,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea-b\u003c/strong\u003e Patient age in years is plotted against the age-adjusted incidence rate per 100,000 patients for all identified patients with benign meningiomas (\u003cstrong\u003ea\u003c/strong\u003e) in the SEER database, demonstrating a logarithmic relationship. When the data is log-transformed and regression analysis is performed, a linear relationship is demonstrated (\u003cstrong\u003eb\u003c/strong\u003e) with R\u003csup\u003e2\u003c/sup\u003e = 0.99 and a slope of 3.21, suggesting that the number of steps \u003cem\u003em\u003c/em\u003e is equal to four.\u003c/p\u003e","description":"","filename":"4HitFig1.tif.png","url":"https://assets-eu.researchsquare.com/files/rs-5253027/v1/6a09c16837192d6439f38167.png"},{"id":68538778,"identity":"f812ee59-11f6-447b-b538-023b8a22bef7","added_by":"auto","created_at":"2024-11-08 10:31:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":11819426,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea-g\u003c/strong\u003e The Armitage-Doll methodology is applied to subsets of the benign meningioma cohort. Male (\u003cstrong\u003ea\u003c/strong\u003e) and female (\u003cstrong\u003eb\u003c/strong\u003e) patients are plotted to investigate sex-related differences in the pathologic mechanism. White (\u003cstrong\u003ec\u003c/strong\u003e), black (\u003cstrong\u003ed\u003c/strong\u003e), Native American (\u003cstrong\u003ee\u003c/strong\u003e), Asian/Pacific Islander (\u003cstrong\u003ef\u003c/strong\u003e), and Hispanic (\u003cstrong\u003eg\u003c/strong\u003e) patients are plotted to investigate race-related differences in the mechanism. All subgroups demonstrate slopes suggestive of a four-hit mechanism akin to that seen in the overall cohort.\u003c/p\u003e","description":"","filename":"4HitFig2.tif.png","url":"https://assets-eu.researchsquare.com/files/rs-5253027/v1/d73c7b7359b5f7b04a5dd961.png"},{"id":68538781,"identity":"422d0e01-60cf-44eb-82a6-50a931985761","added_by":"auto","created_at":"2024-11-08 10:31:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":153134,"visible":true,"origin":"","legend":"\u003cp\u003eTo investigate whether pre- and postmenopausal female patients comprised two distinct populations with fundamentally different mechanisms to develop benign meningioma, a broken-stick model was evaluated by introducing an unknown breakpoint \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003ebp\u003c/em\u003e\u003c/sub\u003e. The slope and 95% confidence interval of the pre-breakpoint segment is 3.74 (3.49, 3.98), and the slope and 95% confidence interval of the post-breakpoint segment is 2.68 (2.51, 2.84). The breakpoint \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003ebp\u003c/em\u003e\u003c/sub\u003e was identified at t = 3.72, or 41.22 years of age. The Bayes factor, comparing the simple linear model and the broken-stick model, provided strong evidence in favor of the simple linear model (BF = 0.00001).\u003c/p\u003e","description":"","filename":"4HitFig3.tiff.png","url":"https://assets-eu.researchsquare.com/files/rs-5253027/v1/66cbf43dc24995250cfa5677.png"},{"id":68538783,"identity":"17f74691-dfc7-4fd4-8c6b-3eb6bb935c4c","added_by":"auto","created_at":"2024-11-08 10:31:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4442096,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea-b\u003c/strong\u003e To investigate whether the deviations from linear behavior in female patients could be explained by differential exposure to hormones or other factors in the microenvironment, a perturbative correction was introduced into the original model. When the number of steps \u003cem\u003em\u003c/em\u003e was set to 4 as a starting value, the proposed solution (\u003cstrong\u003ea\u003c/strong\u003e) predicted age-adjusted incidence as a function of age in 75% of the overall SEER data with a high degree of correlation (R\u003csup\u003e2\u003c/sup\u003e = 0.99). Only the two highest-order terms of the polynomial expansion were included to avoid overfitting. When tested with the remaining 25% of the data (\u003cstrong\u003eb\u003c/strong\u003e), the model performed robustly in predicting age-adjusted incidence (R\u003csup\u003e2\u003c/sup\u003e = 0.99).\u003c/p\u003e","description":"","filename":"4HitFig4.tif.png","url":"https://assets-eu.researchsquare.com/files/rs-5253027/v1/08ade14bc6dd036a912e8548.png"},{"id":70389540,"identity":"e86968ae-8bfc-4dff-9dbf-62fb3da98916","added_by":"auto","created_at":"2024-12-02 17:28:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16444902,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5253027/v1/a4db0f90-2dc4-496f-af98-ac491a49f528.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Benign Meningiomas Develop Through a Four-Hit Mechanism","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMeningiomas are tumors of the central nervous system (CNS) originating from the meninges and are the most common non-malignant CNS tumors.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Their molecular pathogenesis remains unclear, though most occur sporadically and somatic mutational profiling has implicated a number of drivers.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Inactivating \u003cem\u003eNF2\u003c/em\u003e mutations are most common, but stable mutations in \u003cem\u003eTRAF7\u003c/em\u003e, \u003cem\u003eKLF4\u003c/em\u003e, \u003cem\u003eAKT1\u003c/em\u003e, and \u003cem\u003eSMO\u003c/em\u003e without \u003cem\u003eNF2\u003c/em\u003e have been detected.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e \u003cem\u003eNF2\u003c/em\u003e mutations are not deterministic of meningioma: germline \u003cem\u003eNF2\u003c/em\u003e mutations lead to meningioma in about 50% of cases.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e \u003cem\u003eNF2\u003c/em\u003e and other driver mutations exhibit pleiotropy: in other tumors like mesothelioma, differential \u003cem\u003eNF2\u003c/em\u003e loss corresponds to distinct molecular subgroups.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e \u003cem\u003eNF2\u003c/em\u003e mutations may co-occur with somatic mutations like \u003cem\u003eSMARCB1\u003c/em\u003e, which is associated with hereditary multiple meningiomas.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Meningioma incidence is higher in females, possibly due to responsive increases in progesterone receptor expression. Incidence is also increased in adults, even among individuals with necessary driver mutations. Benign meningiomas preferentially develop in specific meningeal locations. Despite advances, characterization of the mechanisms underlying meningioma development and progression is limited, but important for profiling and treatment.\u003c/p\u003e \u003cp\u003eComputational modeling has long helped propose genomic hypotheses for neoplasia and malignant transformation.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e The Armitage-Doll model describes a power relationship between cancer incidence and age and was used to propose the multistage hypothesis of carcinogenesis, where sequential accumulation of discrete mutations drives tumor development.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e This model led to the two-hit hypothesis and has identified tumor suppressor genes of colorectal and lung carcinoma.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e The Armitage-Doll model has been adapted for age-related diseases with cancer-like epidemiology, including Parkinson\u0026rsquo;s and amyotrophic lateral sclerosis.\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Model expansions have furthered our understanding of diverse tumorigenesis pathways, including clonal expansion and tumor microenvironment effects.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eGiven the increasing number of identified mutations markers implicated in benign meningiomas, we sought to develop a mathematical framework to organize the role, timing, and function of known and unknown driver mutations. We further aimed to model potential environmental influences such as sex hormones and apply this model to predict meningioma development.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEthics Approval\u003c/h2\u003e \u003cp\u003eThis study was exempted from Institutional Review Board (IRB) review by the senior author\u0026rsquo;s IRB as the Surveillance, Epidemiology, and End Results (SEER) database utilized does not contain identifiable patient information. The SEER database only provides anonymized public-use data.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Sources and Selection\u003c/h3\u003e\n\u003cp\u003eIncidence data was obtained from SEER. The SEER program\u0026rsquo;s 22-registry database (SEER-22) was queried for cases of nonmalignant meningioma from 2004 to 2020. Meningiomas were defined per the International Classification of Diseases for Oncology, version 3 (ICD-O-3) morphology codes 9530\u0026ndash;9539. Malignant behavior subclassifications or any histology suggestive of World Health Organization (WHO) 2021 Grades II or III were excluded.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Subclasses were excluded if the total number of cases was less than 16. All diagnoses were confirmed by microscopic examination through either positive histology, immunohistochemistry, genetic studies, or other unspecified methods. The primary site was limited to intracranial meningeal locations.\u003c/p\u003e \u003cp\u003eIncidence rates per 100,000 person-years age-adjusted to the 2000 U.S. standard population were generated from 25 to 85 years of age in 5-year bands. Fitting of the data was constrained at older ages as the basic Armitage-Doll model does not account for reductions in incidence at old age, which can occur for a multitude of reasons and is seen in numerous cancer types.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e After data inspection, 85 years was set as the cutoff age. All incidence data was calculated using the SEER*Stat software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.seer.cancer.gov/seerstat\" target=\"_blank\"\u003ewww.seer.cancer.gov/seerstat\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.seer.cancer.gov/seerstat\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, version 8.4.1.2, based on the November 2022 submission to SEER-22.\u003c/p\u003e \u003cp\u003eThis was repeated for vestibular schwannoma incidence. Vestibular schwannomas were defined by ICD-O-3 morphology code 9560 (Neurilemoma) and primary site C72.4 (Acoustic nerve). After inspection, the data was constrained to 65 years of age for modeling.\u003c/p\u003e\n\u003ch3\u003eArmitage-Doll Methodology\u003c/h3\u003e\n\u003cp\u003eThe Armitage-Doll model states: if the development of cancer requires multiple prior irreversible steps, and each step has a relatively low probability of occurring over a given time period, then a power relationship will naturally form between age and the incidence of the disease.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e This model is defined as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:f\\left(t\\right)=\\alpha\\:{t}^{m-1}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ef\u003c/em\u003e(\u003cem\u003et\u003c/em\u003e) is age-adjusted incidence of the meningioma, \u003cem\u003et\u003c/em\u003e is age, \u003cem\u003eα\u003c/em\u003e is the product of the exposure risks for each step, and \u003cem\u003em\u003c/em\u003e is the number of slow stages or steps.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e This gives the linear equation:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:log\\left(f\\right(t\\left)\\right)=(m-1)log\\left(t\\right)+c$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:c=(m-1)log\\left(\\alpha\\:\\right)\\)\u003c/span\u003e\u003c/span\u003e. Therefore, the log of age-adjusted incidence was regressed against the log of age by least squares regression. This linear regression was used to examine the fit of the Armitage-Doll model: the slope is equal to one less than the number of rate-limiting steps \u003cem\u003em\u003c/em\u003e, while the intercept \u003cem\u003ec\u003c/em\u003e is equivalent to the combined probability of a given cell undergoing these mutational steps.\u003c/p\u003e \u003cp\u003eSome assumptions must be met to satisfy the Armitage-Doll model. Cancer is assumed to be the result of several discrete changes, or mutations, with a low overall rate of occurrence in the general population. These changes are stable, or irreversible, and must proceed in a unique and specific order to lead to the development of cancer. This model was tested for goodness of fit (R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) in the overall cohort and stratified by sex and ethnicity.\u003c/p\u003e\n\u003ch3\u003ePiecewise Regression for Nonlinear Relationships\u003c/h3\u003e\n\u003cp\u003eSince benign meningioma incidence in women may be influenced by menopausal status due to estrogen/progesterone receptor overexpression, women could be viewed as two distinct groups with varying steps to meningioma development. Assuming menopause is a fixed point where the regression slope changes, we applied a piecewise linear regression (\u0026ldquo;broken-stick\u0026rdquo; model) with unknown breakpoint \u003cem\u003et\u003c/em\u003e\u003csub\u003ebp\u003c/sub\u003e to model the relationship between log incidence and age in female patients. This regression model is:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:log\\left(f\\right(t\\left)\\right)=({m}_{1}-1)log\\left(t\\right)+{c}_{1}\\)\u003c/span\u003e \u003c/span\u003e for \u003cem\u003et\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;\u003cem\u003et\u003c/em\u003e\u003csub\u003ebp\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:=({m}_{2}-1)log\\left(t\\right)+{c}_{2}\\)\u003c/span\u003e \u003c/span\u003e for \u003cem\u003et\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003et\u003c/em\u003e\u003csub\u003ebp\u003c/sub\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{c}_{i}=({m}_{i}-1)log\\left({\\alpha\\:}_{i}\\right)\\)\u003c/span\u003e\u003c/span\u003e for \u003cem\u003ei\u003c/em\u003e \u0026isin; 1, 2.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e The slopes were calculated to determine if any significant mutational differences in meningioma pathogenesis existed between premenopausal and postmenopausal women. The breakpoint \u003cem\u003et\u003c/em\u003e\u003csub\u003ebp\u003c/sub\u003e was determined by the intersection of the two best-fit regressions. Bayesian information criteria (BIC) were calculated to generate Bayes factors (BF) to assess whether the broken-stick model better fit the incidence data than the Armitage-Doll model.\u003c/p\u003e\n\u003ch3\u003ePolynomial Expansion and Regression for Perturbative Correction to the Armitage-Doll Model\u003c/h3\u003e\n\u003cp\u003eSlight non-linearities in a cohort may suggest time-related changes in the meningioma microenvironment, for example from fluctuating hormone levels. However, these deviations may lack the significance or uniformity to fit a broken-stick model or linear relationships. The inclusion of a changing microenvironment, potentially accelerating early cancer development, violates the Armitage-Doll model\u0026rsquo;s assumption that cancer arises through a specific sequence of discrete changes. Because the meningioma microenvironment is likely to change with time, we considered a perturbative correction derived by Webster 2019 to allow for slow changes to the microenvironment over time with respect to the original model.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e This perturbative correction for a volatile tumor microenvironment is:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:f\\left(t\\right)\\:=\\:{a}_{0}\\frac{{t}^{m-1}}{\\varGamma\\:\\left(m\\right)}+{\\sum\\:}_{j=1}^{m}{a}_{j}\\frac{\\varGamma\\:(m-j+2)}{\\varGamma\\:(2m-j+1)}{t}^{2m-j}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ej\u003c/em\u003e is the theoretical at-risk mutational step that could lead to the early development of cancer, and \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e or \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e is the product of exposure risks at step \u003cem\u003e0\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e respectively. This solution generated higher-order models which were fitted by polynomial regression. The polynomial expansion was limited to the two highest-order terms to prevent overfitting of the curve. This microenvironment correction model was trained and validated on a 75:25 train-test split of the SEER data, with groupings assigned at random.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eArmitage-Doll Modeling of Benign Meningiomas\u003c/h2\u003e \u003cp\u003e222,509 cases of benign meningiomas were identified from SEER. The logarithmic relationship between age and age-adjusted incidence of benign meningiomas is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea. The log of age is plotted against the log of age-adjusted incidence in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb. The slope (\u003cem\u003em\u003c/em\u003e \u0026minus;\u0026thinsp;1) is 3.2, implying a four-hit process for the development of benign meningiomas in the American population with a high degree of correlation (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.99).\u003c/p\u003e \u003cp\u003eTo determine whether this four-hit mechanism persisted in different subpopulations, incidence data was plotted for all male (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) and female (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) patients. Both male and female patients demonstrated a four-hit mechanism with a high degree of correlation (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.99). This four-hit mechanism was persistently seen when analyzing different racial and ethnic groups within the cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec-g). The slopes are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, with all cohorts demonstrating the four-hit mechanism.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Slope \u003cem\u003em\u003c/em\u003e \u0026minus;\u0026thinsp;1 and Number of Steps \u003cem\u003em\u003c/em\u003e for All Populations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e# of steps \u003cem\u003em\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAll Benign Meningiomas\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhite\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlack\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHispanic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAPI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAmerican Indian/Alaskan Native\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBroken Stick Modeling of Linear Relationship\u003c/h3\u003e\n\u003cp\u003eFurther inspection of the female subpopulation data suggested a nonlinear relationship which may be violating the Armitage-Doll model assumptions (\u003cb\u003eOnline Resource S1\u003c/b\u003e). We first tested a changing slope, suggestive of distinct subgroups with different tumorigenesis mechanisms, using a broken-stick model. Linear regressions were plotted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The pre-breakpoint segment had a slope of 3.74 (CI 3.49\u0026ndash;3.98), while the post-breakpoint segment has a slope of 2.68 (CI 2.51\u0026ndash;2.84). The unknown breakpoint of best fit \u003cem\u003et\u003c/em\u003e\u003csub\u003ebp\u003c/sub\u003e was 3.72, or 41.22 years of age. There was very strong evidence for a lack of superiority of the broken-stick models compared to the linear Armitage-Doll model (BF\u0026thinsp;=\u0026thinsp;0.00001). This suggested that another solution was needed.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eApplication of a Perturbative Correction for Volatile Tumor Microenvironment in Female Patients\u003c/h2\u003e \u003cp\u003eAs the broken-stick model did not outperform the Armitage-Doll model for female patients, we explored whether the cell microenvironment nonrandomly influenced tumorigenesis in women. We considered a perturbative correction to allow for gradual microenvironmental change over time within the Armitage-Doll framework. Using \u003cem\u003em\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4 as a starting value from the original linear solution, the correction yielded a seventh-order polynomial expansion. After taking only the two highest-order terms of the correction, the model is plotted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea with a high degree of correlation to the training set, composed of 75% of the SEER data (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.99). The model performed robustly when validated on the test dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), composed of the remaining 25% of patients in the SEER data (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.99).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTesting of the Four-Hit Hypothesis in Other NF2-Positive CNS Tumors\u003c/h2\u003e \u003cp\u003eAs \u003cem\u003eNF2\u003c/em\u003e is the most frequently mutated gene in benign meningiomas and drives other CNS tumors, we aimed to confirm the four-hit mechanism's specificity to meningiomas rather than all \u003cem\u003eNF2\u003c/em\u003e-mutated tumors. Therefore, SEER was queried for vestibular schwannomas. Given the Armitage-Doll model\u0026rsquo;s limitations, incidence data was truncated at ages 25\u0026ndash;65 and plotted. The untransformed incidence data and Armitage-Doll model applied to the truncated log-transformed dataset is presented in \u003cb\u003eOnline Resource S2a-b\u003c/b\u003e. The linear regression (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.99) yielded a slope of 2.1, indicating three steps for vestibular schwannoma development versus four steps for meningiomas. This suggested distinct mutational processes despite sharing \u003cem\u003eNF2\u003c/em\u003e as a driver mutation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMeningioma research has focused on identifying mutations, but it runs into a familiar problem: it is difficult to recognize how many \u0026ldquo;driver\u0026rdquo; mutations we need to look for in an individual meningioma. A unifying mechanism characterizing the number, timing, and role of these mutations has yet to be developed. In this study, we applied a foundational cancer modeling framework to benign meningiomas using a large national cancer database, providing the first computational evidence of a four-hit mutational pathway for their development. This pathway holds across sex and race, with our model explaining subtle deviations in female patients via a perturbative correction for a variable microenvironment. The model performed robustly, suggesting that microenvironmental exposure dynamics, rather than a different mechanism, underlie the female preponderance for benign meningiomas. Finally, we confirmed that this mechanism is unique to benign meningiomas, as \u003cem\u003eNF2\u003c/em\u003e-positive vestibular schwannomas demonstrated a separate three-step pathway. Knowing the number of expected driver mutations or phenotypes simplifies genome interpretation and advances our understanding of meningioma progression. This framework organizes current meningioma genomic findings, can aid in identifying other key germline or somatic mutations, and suggests the timing and function of these mutations relative to others.\u003c/p\u003e \u003cp\u003eStudies over the past decade have begun to offer genomic evidence of a four-step pathway for benign meningioma development. A 2011 study of multiple meningioma patients describes a four-hit mechanism of biallelic \u003cem\u003eNF2\u003c/em\u003e loss and \u003cem\u003eSMARCB1\u003c/em\u003e haploinsufficiency.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Other cohorts have reported similar findings and described location preferences suggestive of somatic mosaicism.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Further studies have suggested an additional \u003cem\u003eLZTR1\u003c/em\u003e loss-of-function mutation leading to haploinsufficiency.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Genomic research has confirmed the importance of mutation order. \u003cem\u003eNF2\u003c/em\u003e inactivation is already an \u0026ldquo;early\u0026rdquo; event in meningioma tumorigenesis.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e A 2017 study found that the timing of \u003cem\u003eSMARCB1\u003c/em\u003e inactivation relative to \u003cem\u003eNF2\u003c/em\u003e inactivation determined schwannoma versus rhabdoid tumor development.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e \u003cem\u003eSMARCB1\u003c/em\u003e is part of the \u003cem\u003eSWI\u003c/em\u003e/\u003cem\u003eSNF\u003c/em\u003e chromatin remodeling complex: these genes have been previously implicated, with one study describing 76.9% of \u003cem\u003eSMARCB1\u003c/em\u003e mutations co-occurring with \u003cem\u003eNF2\u003c/em\u003e mutations in their cohort.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Though many mutations have been described overall, mutations in grouped targets like the \u003cem\u003eSWI/SNF\u003c/em\u003e complex produce functionally similar end-products. Future modeling strategies may incorporate redundancies to permit multiple solutions and account for this diversity.\u003c/p\u003e \u003cp\u003eThis analysis should not be used to suggest that a maximum of four driver genes or mutations are implicated in the development of benign meningiomas. Instead, the model suggests that four mutations are sufficient for the development of benign meningiomas. Meningiomas are not quiescent and continue to evolve and mutate independently or in response to radical microenvironment changes, such as exposure to stressors related to surgical resection or radiotherapy.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Meningiomas may develop mutations which confer selective growth advantages and promote recurrence or malignant progression. We expect additional mutations to continue occurring heterogeneously within the tissue, which is reflected in genomic studies documenting this heterogeneity.\u003c/p\u003e \u003cp\u003eAs germline mutations in early tumorigenesis continue to be discovered, research has increasingly focused on somatic mutations which may predispose patients to meningiomas. Many initial germline mutations are high-penetrance mutations with associated phenotypes, like neurofibromatosis 2.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Despite the presence of these germline mutations, benign meningiomas often only develop in adulthood. This model demonstrates that meningiomas require four sequential mutations: a single high-penetrance driver is insufficient without accumulating downstream somatic \u0026ldquo;hits\u0026rdquo;. Genetic studies have highlighted clonal expansion of mosaic tissue underlying benign meningiomas, as \u003cem\u003eNF2\u003c/em\u003e-mutated meningiomas demonstrate higher chromosome instability and accumulate more oncogenic mutations than their non-\u003cem\u003eNF2\u003c/em\u003e counterparts.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e Some studies of paired primary and recurrent meningiomas have combined all \u003cem\u003eNF2\u003c/em\u003e/22q-mutated benign meningiomas as one group which displays increased cytogenetic abnormalities on presentation.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBenign meningiomas may exhibit saltatory growth patterns with intermittent periods of quiescence not explained solely by genetics.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Though the original Armitage-Doll model focused on genetic mutations as \u0026ldquo;steps\u0026rdquo;, epigenetic changes such as differential gene expression and DNA methylation may also contribute. Methylation and single-cell transcriptomic studies have proposed subgroups of meningiomas based on clinical behavior and integrated analysis of molecular markers.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Nassiri et al. described a subset of \u003cem\u003eNF2\u003c/em\u003e wild-type benign meningiomas which demonstrated silencing of \u003cem\u003eNF2\u003c/em\u003e expression not associated with methylation changes in the gene. These meningiomas functionally acted similar to \u003cem\u003eNF2\u003c/em\u003e-mutated meningiomas: if the expressed gene product is what we define as a \u0026ldquo;step\u0026rdquo;, then epigenetic silencing of the gene would be functionally equivalent to a loss-of-function mutation and mathematically lead to the same result. For example, the second most common benign meningioma mutation \u003cem\u003eTRAF7\u003c/em\u003e has been hypothesized to act on a common pathway with \u003cem\u003eNF2\u003c/em\u003e.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Redundancies within the four-hit pathway can be incorporated to model differing mechanisms for the same \u0026ldquo;step\u0026rdquo;. Further studies are necessary to evaluate whether higher-level control of expression can be modeled.\u003c/p\u003e \u003cp\u003eThe role of hormones in benign meningioma development has been hypothesized. Meta-analyses have agreed that cumulatively increased exposure to female sex hormones may increase meningioma risk in women, but the underlying mechanisms are unclear.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e We attempted to model the potential effects of hormones as outside influences in female patients by either considering that two separate tumorigenic mechanisms existed or that one mechanism existed which could be relaxed to some of the original assumptions. First, we treated pre- and postmenopausal women as two distinct populations with different tumorigenic mechanisms by allowing for a changing slope at the average age of menopause. The broken-stick model tested whether pre- and postmenopausal women had two separate mechanisms, but the confidence intervals indicated that a four-step process was feasible in both populations and the breakpoint was inconsistent with the expected age for perimenopause (45 to 50 years).\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Additionally, the Bayes factor calculated for model selection favored the simpler Armitage-Doll.\u003c/p\u003e \u003cp\u003eSecond, we developed a model influenced by environmental exposure dynamics to reflect subtle changes in the microenvironment of precancerous cells. Modeling failure rates in complex systems using a multi-stage model like Armitage-Doll required a solution accounting for potentially interdependent sequential and non-sequential steps. Within a normal multistep framework, we assume that lower sex hormone exposure or other risk factors reduces the chance of accumulating a step without changing the mechanism itself. This corrected model allows for conditional changes to the preexisting pathway or tumor growth rate without proposing a completely new process. It is important to note that the nature of these exposures cannot be discerned from computational data alone, and other microenvironmental exposures like ionizing radiation may affect tumorigenesis in female patients.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Clearly identifying these steps and understanding their interactions in patients is a future aim.\u003c/p\u003e \u003cp\u003eThis study has a few limitations. While we mathematically demonstrate how a changing microenvironment over time impacts benign meningioma tumorigenesis and suggest hormone exposure as a key factor, the exact effects remain undetermined and other environmental exposures may contribute. The results are generated using population-level data, and therefore cannot provide information about the number of steps any given individual patient has already undertaken or will need to undergo. The model estimates the development of a primary benign meningioma but cannot predict subsequent mutations or local recurrences. Lastly, the model is inherently simple and requires multiple assumptions. More complex models, such as accounting for multiple pathways or probabilistic changes like mutations that increase chromosomal instability, should be considered. We showed that adding complexity like the microenvironment correction does not necessarily alter the conclusions of the simplified Armitage-Doll model, but this cannot be definitively stated for all complex models without further derivation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe present a clonal expansion model for benign meningioma development, applying this framework to large-scale patient data to characterize a four-hit oncogenic mechanism. This model was predictive across patient subsets by sex and race. The mechanism is specific to benign meningiomas, as vestibular schwannomas\u0026mdash;a CNS tumor with a shared driver mutation\u0026mdash;follow a three-hit mechanism. Deviations in female patients were accurately predicted by adjusting for tumor microenvironment volatility. This model provides a unified genetic framework for meningioma pathogenesis consistent with existing molecular evidence. Future molecular research on benign meningiomas may leverage this framework to understand the timing, propensity, and function of new markers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: No specific funding was received for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e: None declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eConceptualization: AD, CS\u003c/p\u003e\n\u003cp\u003eMethodology: AD\u003c/p\u003e\n\u003cp\u003eData curation: AD\u003c/p\u003e\n\u003cp\u003eFormal data analysis and investigation: AD, CS\u003c/p\u003e\n\u003cp\u003eOriginal draft preparation: AD, CS, JYZ, RKS\u003c/p\u003e\n\u003cp\u003eReview and editing of the manuscript: AD, CS, JYZ, VV, RF, EKC, TS, JB, RKS\u003c/p\u003e\n\u003cp\u003eProject supervision: TS, JB, RKS\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrevious Presentations\u003c/strong\u003e: This project was presented as a podium presentation at the North American Skull Base Society 2024 Annual Meeting.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: ADMethodology: ADData curation: ADFormal data analysis and investigation: AD, CSOriginal draft preparation: AD, CS, JYZ, RKSReview and editing of the manuscript: AD, CS, JYZ, VV, RF, EKC, TS, JB, RKSProject supervision: TS, JB, RKS\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to acknowledge the contributions of Dr. Anthony J. Webster, who graciously reviewed the expansion performed to account for the changing meningioma microenvironment using his perturbative correction to the original Armitage-Doll model.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll epidemiological data used was obtained from the SEER database as described in the Methods section.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOstrom QT, Gittleman H, Truitt G, Boscia A, Kruchko C, Barnholtz-Sloan JS (2018) CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2011\u0026ndash;2015. 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J Neurooncol 161(2):309\u0026ndash;316\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuro-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"neon","sideBox":"Learn more about [Journal of Neuro-Oncology](https://www.springer.com/journal/11060)","snPcode":"11060","submissionUrl":"https://submission.nature.com/new-submission/11060/3","title":"Journal of Neuro-Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Genetic Models, Incidence, Meningioma, Theoretical Model, Tumor Microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-5253027/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5253027/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e: Meningiomas are central nervous system tumors whose incidence increases with age. Benign meningioma pathogenesis involves germline or somatic mutation of target genes, such as \u003cem\u003eNF2\u003c/em\u003e, leading to clonal expansion. We used an established cancer epidemiology model to investigate the number of rate-limiting steps sufficient for benign meningioma development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Incidence data was obtained from the Surveillance, Epidemiology and End Results Program (SEER) for nonmalignant meningioma from 2004-2020. Age-adjusted incidence rates per 100,000 person-years were divided into 5-year bands. This was repeated for vestibular schwannomas as a negative control. The Armitage-Doll methodology was applied. Mathematical solutions correcting for volatile tumor microenvironments were applied to fit higher-order models using polynomial regression when appropriate. A 75:25 training:test split was utilized for validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e:\u003cstrong\u003e \u003c/strong\u003e222,509 cases of benign meningiomas were identified. We noted strong linear relationships between log-transformed incidence and age across the cohort and multiple subpopulations: male, white, black, Hispanic, Asian/Pacific Islander, and American Indian subpopulations all demonstrated R\u003csup\u003e2\u003c/sup\u003e=0.99. Slopes were between 3.1 and 3.4, suggesting a four-step process for benign meningioma development. Female patients exhibited nonlinear deviations, but the corrected model demonstrated R\u003csup\u003e2\u003c/sup\u003e=0.99 with a four-hit pathway. This model performed robustly on test data with R\u003csup\u003e2\u003c/sup\u003e=0.99. Vestibular schwannomas demonstrated a slope of 2.1 with R\u003csup\u003e2\u003c/sup\u003e=0.99, suggesting a separate three-step process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Four mutations are uniquely required for the development of benign meningiomas. Correcting for volatile tumor microenvironments reliably accounted for nonlinear deviations in behavior. Further studies are warranted to elucidate genomic findings suggestive of key mutations in this pathway.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: None.\u003c/p\u003e","manuscriptTitle":"Benign Meningiomas Develop Through a Four-Hit Mechanism","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-08 10:31:45","doi":"10.21203/rs.3.rs-5253027/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-30T10:33:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-23T18:13:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-19T10:37:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274784498765322104210308563277357046662","date":"2024-10-19T00:37:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99375233269088605526345991480680699709","date":"2024-10-18T06:56:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"183873398435141794604604472367839904823","date":"2024-10-14T12:50:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-14T12:34:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-14T07:21:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-14T07:20:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Neuro-Oncology","date":"2024-10-12T20:14:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuro-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"neon","sideBox":"Learn more about [Journal of Neuro-Oncology](https://www.springer.com/journal/11060)","snPcode":"11060","submissionUrl":"https://submission.nature.com/new-submission/11060/3","title":"Journal of Neuro-Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"20afb8a7-1681-47f3-a9cb-8c7032a4a1c2","owner":[],"postedDate":"November 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-02T17:24:00+00:00","versionOfRecord":{"articleIdentity":"rs-5253027","link":"https://doi.org/10.1007/s11060-024-04877-y","journal":{"identity":"journal-of-neuro-oncology","isVorOnly":false,"title":"Journal of Neuro-Oncology"},"publishedOn":"2024-11-25 15:58:30","publishedOnDateReadable":"November 25th, 2024"},"versionCreatedAt":"2024-11-08 10:31:45","video":"","vorDoi":"10.1007/s11060-024-04877-y","vorDoiUrl":"https://doi.org/10.1007/s11060-024-04877-y","workflowStages":[]},"version":"v1","identity":"rs-5253027","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5253027","identity":"rs-5253027","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}