Tailoring Glioblastoma Treatment for Frail Populations: Insights from Genomic and Clinical Data

Tailoring Glioblastoma Treatment for Frail Populations: Insights from Genomic and Clinical Data | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Tailoring Glioblastoma Treatment for Frail Populations: Insights from Genomic and Clinical Data View ORCID Profile Masab Mansoor , Andrew Ibrahim , Kashif Ansari doi: https://doi.org/10.1101/2025.01.22.25320979 Masab Mansoor 1 Edward Via College of Osteopathic Medicine Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Masab Mansoor For correspondence: mmansoor{at}vcom.edu Andrew Ibrahim 2 Texas Tech University Health Science Center Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kashif Ansari 3 East Houston Medical Center, Department of Oncology Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Preview PDF Abstract Background/Objectives Glioblastoma is the most common primary malignant brain tumor in adults, with a particularly high incidence among individuals aged 65 and older. Older patients often experience worse outcomes due to limited treatment options, comorbidities, and frailty. This study investigates the impact of radiation therapy and genomic factors on survival outcomes in older glioblastoma patients, aiming to inform treatment strategies for this vulnerable population. Methods We analyzed clinical and genomic data from 109 glioblastoma patients aged 65 and older, obtained from The Cancer Genome Atlas (TCGA). Kaplan-Meier survival analysis was performed to assess the impact of radiation therapy on survival. Correlations between genomic features, including mutation count, tumor mutational burden, and aneuploidy score, and overall survival were examined. Descriptive statistics were used to summarize patient demographics and treatment patterns. Results Radiation therapy was associated with a higher mean survival (10.3 months) compared to patients who did not receive radiation (6.2 months). Genomic factors, such as mutation count and tumor mutational burden, showed weak negative correlations with survival. Despite the overall poor prognosis, radiation therapy appeared to modestly improve survival in this cohort. Conclusions Our findings highlight the potential benefits of radiation therapy for older glioblastoma patients, even in the context of frailty and comorbidities. Further research is needed to explore how genomic markers can inform personalized treatment strategies and improve outcomes in this population. Simple Summary Glioblastoma is an aggressive brain cancer that disproportionately affects older adults, a group often excluded from clinical trials. This study aims to examine how treatment approaches, such as radiation therapy and tumor characteristics, influence survival outcomes in glioblastoma patients aged 65 and older. By analyzing clinical and genomic data, we hope to identify factors that may improve treatment strategies and outcomes for this vulnerable population. Our findings could help guide healthcare professionals in making more personalized and effective treatment decisions for older patients, potentially improving their quality of life and survival. 1. Introduction Glioblastoma (GBM) stands as the most prevalent and aggressive primary malignant brain tumor in adults, with a median survival of approximately 12 to 15 months despite intensive treatment efforts [ 1 ]. The incidence of GBM notably increases with age, particularly affecting individuals over 65 years. This demographic shift is significant, as the aging population is expanding globally, leading to a higher prevalence of GBM among the elderly. Treating GBM in older adults presents unique challenges. Factors such as decreased functional status, comorbidities, and increased susceptibility to treatment-related toxicities often result in less aggressive therapeutic approaches for this group [ 2 ]. Consequently, survival outcomes in elderly patients are generally poorer compared to their younger counterparts [ 3 ]. Radiation therapy remains a cornerstone in GBM management [ 4 , 5 ]. However, its efficacy and tolerability in the elderly population are subjects of ongoing debate [ 6 ]. Some studies suggest that hypofractionated radiation therapy, which delivers higher doses over fewer sessions, may offer comparable survival benefits with reduced side effects. Yet, the optimal radiation regimen for older patients continues to be a matter of investigation [ 7 ]. In addition to treatment modalities, genomic factors such as mutation count and tumor mutational burden (TMB) have emerged as potential prognostic indicators in GBM [ 8 ]. Understanding the relationship between these molecular characteristics and patient outcomes could pave the way for personalized treatment strategies, particularly in the context of an aging patient population [ 9 ]. This study aims to evaluate the impact of radiation therapy and specific genomic features on survival outcomes in GBM patients aged 65 and older. By analyzing clinical and molecular data, we seek to identify factors that could inform tailored therapeutic approaches, ultimately improving the prognosis and quality of life for this vulnerable population. 2. Materials and Methods 2.1. Study Design and Data Source This retrospective cohort study analyzed clinical and genomic data from The Cancer Genome Atlas (TCGA) Glioblastoma Multiforme (GBM) dataset. The dataset was accessed via cBioPortal and included 109 patients aged 65 and older [ 10 - 12 ]. The study complied with TCGA data usage policies. 2.2. Patient Selection Patients with a confirmed diagnosis of glioblastoma and available clinical and genomic data were included. Exclusion criteria included incomplete survival data or age below 65 years. 2.3. Clinical Variables Clinical data included age at diagnosis, sex, overall survival (months), and radiation therapy status (Yes/No). Radiation therapy information was extracted to analyze its association with survival outcomes. 2.4. Genomic Analysis Key genomic variables analyzed included mutation count, tumor mutational burden (TMB), aneuploidy score, and fraction genome altered. These variables were correla ed with overall survival to identify potential prognostic markers. 2.5. Statistical Analysis Kaplan-Meier survival analysis was used to evaluate the impact of radiation therapy on overall survival. Correlations between genomic variables and survival were assessed using Pearson’s correlation coefficient. Descriptive statistics were calculated for demographic and clinical characteristics. 2.6. Ethical Considerations This study utilized publicly available de-identified data and did not require additional ethical approval. Data were handled in compliance with TCGA policies. 2.7. Data Availability The dataset used in this study is publicly available through cBioPortal ( https://www.cbioportal.org/ ). No new datasets were generated during this study. 3. Results This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn. 3.1. Patient Characteristics The study cohort included 109 patients aged 65 and older, with a mean diagnosis age of 73 years (range: 65–89). Males comprised 57% of the cohort, while 43% were female. Radiation therapy was administered to 64% of patients, while the remaining 36% did not receive radiation. 3.2. Survival Outcomes Patients receiving radiation therapy demonstrated a mean overall survival of 10.3 months compared to 6.2 months in those who did not receive radiation ( Figure 1 ). Kaplan-Meier survival analysis revealed a statistically significant improvement in survival associated with radiation therapy (p < 0.05). Download figure Open in new tab Figure 1. Kaplan-Meier Survival Curve. Kaplan-Meier survival curves comparing overall survival in glioblastoma patients aged 65 and older based on radiation therapy status. Patients receiving radiation therapy exhibited a higher survival probability over time compared to those who did not receive radiation therapy. 3.3. Genomic Correlates of Survival Mutation count: r = -0.11 ( Figure 2 ) Tumor mutational burden (TMB): r = -0.11 Aneuploidy score: r = -0.03 Fraction genome altered: r = -0.07 3.4. Download figure Open in new tab Figure 2. Correlation of Mutation Count with Overall Survival. Scatter plot illustrating the relationship between mutation count and overall survival in glioblastoma patients aged 65 and older. The weak negative correlation (r = -0.11) suggests that higher mutation counts may be associated with slightly reduced survival. 3.4. Genomic Correlates of Survival Males demonstrated a slightly higher mean survival (8.5 months) compared to females (7.8 months), though this difference was not statistically significant. Patients with a high mutation count (≥65 mutations) exhibited a mean survival of 7.5 months compared to 9.0 months in those with lower mutation counts. 4. Discussion This study highlights the importance of tailoring treatment approaches for glioblastoma (GBM) in older adults. Our findings demonstrate that radiation therapy provides a survival benefit for patients aged 65 and older, even in the context of advanced age and potential frailty. These results align with previous studies emphasizing the role of radiation therapy in improving outcomes for elderly GBM patients [ 13 , 14 ]. The observed survival benefit with radiation therapy underscores the need for careful consideration of treatment regimens in this population. While hypofractionated radiation protocols have been suggested as an effective alternative to standard regimens, further studies are needed to identify the optimal approach that balances efficacy and tolerability [ 15 , 16 ]. Our analysis of genomic factors revealed weak negative correlations between mutation count, tumor mutational burden (TMB), and survival. These findings are consistent with previous studies indicating that high mutation burdens may reflect tumor aggressiveness rather than therapeutic responsiveness [ 17 ]. However, key prognostic markers such as IDH1 mutations and MGMT promoter methylation, which are associated with improved survival, were not available in this dataset [ 18 ]. Future studies should incorporate these molecular markers to better stratify risk and inform treatment decisions. The limitations of this study include its retrospective design and reliance on publicly available data, which may not capture all relevant clinical variables, such as performance status or comorbidities. Additionally, the relatively small sample size limits the generalizability of our findings. Prospective studies with larger cohorts are warranted to validate these results and explore additional factors influencing survival in elderly GBM patients. 5. Conclusions This study emphasizes the importance of radiation therapy in extending survival for older glioblastoma (GBM) patients, even in the context of advanced age and frailty. While our findings suggest a modest survival benefit from radiation therapy, the weak correlations between genomic factors and survival highlight the complexity of GBM biology in elderly populations. Further research is needed to integrate clinical and genomic data into personalized treatment strategies, particularly for frail patients who may be unable to tolerate aggressive therapies. Expanding prospective studies and incorporating emerging molecular markers will be critical in improving outcomes for this vulnerable group. Author Contributions Both authors contributed equally to this research. Funding This research received no external funding. Institutional Review Board Statement Ethical review and approval were waived for this study due to only using retrospective, publicly available, deidentified data. Informed Consent Statement Patient consent was waived because this research solely uses retrospective, publicly available, deidentified data. Data Availability Statement Data obtained from https://www.cbioportal.org . Conflicts of Interest The authors declare no conflicts of interest. Footnotes andrew.Ibrahim{at}ttuhsc.edu kansari{at}ehoftexas.com References 1. ↵ Arvold ND , Reardon DA . Treatment options and outcomes for glioblastoma in the elderly patient . CIA . 2014 ; 9 : 357 – 367 . doi: 10.2147/CIA.S44259 OpenUrl CrossRef 2. ↵ Ladomersky E , Scholtens DM , Kocherginsky M , et al. The Coincidence Between Increasing Age, Immunosuppression, and the Incidence of Patients With Glioblastoma . Front Pharmacol . 2019 ; 10 . doi: 10.3389/fphar.2019.00200 OpenUrl CrossRef 3. ↵ Laperriere N , Weller M , Stupp R , et al. Optimal management of elderly patients with glioblastoma . Cancer Treatment Reviews . 2013 ; 39 ( 4 ): 350 – 357 . doi: 10.1016/j.ctrv.2012.05.008 OpenUrl CrossRef PubMed Web of Science 4. ↵ Lopci E , Mansi L Desideri I , Nardone V , Morelli I , Gagliardi F , Minniti G. Radiation Oncology in Glioblastoma (GBM ). In: Lopci E , Mansi L , eds. Advanced Imaging and Therapy in Neuro-Oncology . Springer Nature Switzerland ; 2024 : 101 – 136 . doi: 10.1007/978-3-031-59341-3_7 OpenUrl CrossRef 5. ↵ Corso CD , Bindra RS , Mehta MP . The role of radiation in treating glioblastoma: here to stay . J Neurooncol . 2017 ; 134 ( 3 ): 479 – 485 . doi: 10.1007/s11060-016-2348-x OpenUrl CrossRef PubMed 6. ↵ Hau E , Shen H , Clark C , Graham PH , Koh ES , L. McDonald K. The evolving roles and controversies of radiotherapy in the treatment of glioblastoma . Journal of Medical Radiation Sciences . 2016 ; 63 ( 2 ): 114 – 123 . doi: 10.1002/jmrs.149 OpenUrl CrossRef PubMed 7. ↵ Management of glioblastoma in older adults - UpToDate . Accessed January 22, 2025 . https://www.uptodate.com/contents/management-of-glioblastoma-in-older-adults?utm_source=chatgpt.com 8. ↵ Wang L , Ge J , Lan Y , et al. Tumor mutational burden is associated with poor outcomes in diffuse glioma . BMC Cancer . 2020 ; 20 ( 1 ): 213 . doi: 10.1186/s12885-020-6658-1 OpenUrl CrossRef PubMed 9. ↵ Hwang M , Jiang Y. Personalization in digital health interventions for older adults with cancer: A scoping review . Journal of Geriatric Oncology . 2023 ; 14 ( 8 ): 101652 . doi: 10.1016/j.jgo.2023.101652 OpenUrl CrossRef PubMed 10. ↵ Cerami et al. The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data . Cancer Discovery . May 2012 2; 401. PubMed. 11. Gao et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal . Sci. Signal . 6 , pl1 ( 2013 ). PubMed. 12. ↵ de Bruijn et al. Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal . Cancer Res ( 2023 ). PubMed. 13. ↵ Jordan JT , Gerstner ER , Batchelor TT , Cahill DP , Plotkin SR . Glioblastoma care in the elderly . Cancer . 2016 ; 122 ( 2 ): 189 – 197 . doi: 10.1002/cncr.29742 OpenUrl CrossRef PubMed 14. ↵ Yovino S , Grossman SA . Treatment of Glioblastoma in “Elderly” Patients . Curr Treat Options in Oncol . 2011 ; 12 ( 3 ): 253 – 262 . doi: 10.1007/s11864-011-0158-0 OpenUrl CrossRef PubMed 15. ↵ Kaul D , Florange J , Badakhshi H , et al. Accelerated hyperfractionation plus temozolomide in glioblastoma . Radiat Oncol . 2016 ; 11 ( 1 ): 70 . doi: 10.1186/s13014-016-0645-3 OpenUrl CrossRef PubMed 16. ↵ Yu VY , Nguyen D , O’Connor D , et al. Treating Glioblastoma Multiforme (GBM) with super hyperfractionated radiation therapy: Implication of temporal dose fractionation optimization including cancer stem cell dynamics . PLOS ONE . 2021 ; 16 ( 2 ): e0245676 . doi: 10.1371/journal.pone.0245676 OpenUrl CrossRef PubMed 17. ↵ Jang B , Yoon D , Lee JY , et al. Integrative multi-omics characterization reveals sex differences in glioblastoma . Biol Sex Differ . 2024 ; 15 ( 1 ): 23 . doi: 10.1186/s13293-024-00601-7 OpenUrl CrossRef PubMed 18. ↵ Mansouri A , Hachem LD , Mansouri S , et al. MGMT promoter methylation status testing to guide therapy for glioblastoma: refining the approach based on emerging evidence and current challenges . Neuro Oncol . 2019 ; 21 ( 2 ): 167 – 178 . doi: 10.1093/neuonc/noy132 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted January 24, 2025. Download PDF Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Tailoring Glioblastoma Treatment for Frail Populations: Insights from Genomic and Clinical Data Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. 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