Mutational Landscape of TP53 Across Cancer Types

Mutational Landscape of TP53 Across Cancer Types | bioRxiv /* */ /* */ <!-- <!-- /*! * 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-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Confirmatory Results Mutational Landscape of TP53 Across Cancer Types View ORCID Profile Maria Abran doi: https://doi.org/10.1101/2025.08.12.669884 Maria Abran ^1 Department of Medical Biology and Genetics, Near East University , Cyprus Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Maria Abran For correspondence: mariahussain033{at}gmail.com Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract The TP53 gene is the most frequently mutated tumor suppressor in human cancers, but its mutation patterns vary across tumor types. Using data from cBioPortal (downloaded 9 August 2025), we analyzed 4,245 TP53 mutation events in 10,967 unique tumor samples from 32 cancer studies, covering 27 cancer types. Mutations were classified into missense, nonsense, frameshift, in-frame indels, splice-site variants, and fusions, and cancer types were grouped for comparative analysis. Missense mutations were most common, followed by nonsense and frameshift variants. Female reproductive and gastrointestinal cancers had the highest mutation burdens, while central nervous system tumors showed a higher proportion of missense events. These findings highlight both the ubiquity and cancer type–specific patterns of TP53 alterations, providing a reference for future functional and clinical studies. 2. Introduction The TP53 gene, often called the “guardian of the genome,” is a tumor suppressor gene that helps prevent cancer by controlling cell division, repairing DNA damage, and triggering cell death when needed. [ 1 ] It is located on chromosome 17 and is one of the most frequently mutated genes in many cancer types, contributing to tumor development and progression. Mutation in TP53 can disable these protective functions, allowing cells to grow uncontrollably and form tumors. Different mutation types, such as missense, nonsense, frameshift, and splice site mutations, can variably affect the gene’s function. [ 2 ], [ 3 ] Moreover, the distribution and prevalence of these mutation types can differ across cancer types due to distinct biological contexts and mutational processes. Understanding the mutational landscape of TP53 can provide insights into cancer biology and potentially inform therapeutic strategies. This study aims to characterize the mutational landscape of TP53 by analyzing the distribution of mutation types across various grouped cancer types. Using publicly available cancer genomics data, the study seeks to identify patterns and differences in TP53 mutation profiles, enhancing our understanding of its role in diverse cancers. 3. Methods This study employed a bioinformatics approach to analyze TP53 mutation profiles across multiple cancer types using publicly available genomic datasets. The methodology was designed to ensure reproducibility and clarity, encompassing four main stages: data retrieval, preprocessing, cancer type grouping, and statistical visualization. Mutation data were sourced from the TCGA Pan-Cancer Atlas via the cBioPortal platform, curated for accuracy, and systematically categorized into defined mutation classes. To facilitate meaningful interpretation, cancer types were consolidated into broader groups, and descriptive statistics were applied to determine the distribution and frequency of mutation types. Visual representations were generated to highlight key trends and inter-group differences. 3.1 Data Source Mutation data for the TP53 gene were obtained from cBioPortal [ 4 ] for Cancer Genomics ( https://www.cbioportal.org/ ) by querying the TCGA Pan-Cancer Atlas studies. On 9 August 2025, TP53 was searched in the TCGA section, and the “Mutations” tab was selected to retrieve all available somatic mutation data. The complete output, including study identifiers, cancer type annotations, and mutation details, was downloaded in tab-separated values (TSV) format directly from cBioPortal . 3.2 Data Preprocessing The raw data were imported into Microsoft Excel for preprocessing. Non-essential columns were removed, and the dataset was reformatted for clarity. Multiple mutation records corresponding to the same sample were merged. Cancer type labels were consolidated into broader categories to simplify analysis. Mutation events were classified into six categories: missense, nonsense, frameshift (insertions and deletions combined), in-frame (insertions and deletions combined), splice-site (including splice region) mutations, and gene fusions. 3.3 Grouping of Cancer Types To improve clarity in visualization and interpretation, the 27 cancer types were grouped into ten broader categories based on tissue origin and biological similarity. These groups include Genitourinary Cancers, Gastrointestinal Cancers, Female Reproductive Cancers, Central Nervous System Cancers, Hematologic Cancers, Skin & Melanocytic Cancers, Endocrine Cancers, Thoracic Cancers, Head & Neck Cancers, and Sarcomas. The full mapping of original cancer types to grouped categories, along with the corresponding number of TP53 mutation events, is provided in Table 1 . View this table: View inline View popup Table 1. Distribution of TP53 Mutations Across Cancer Categories The table presents the number of TP53 mutations identified in various grouped cancer categories. Each category includes specific cancer types as classified in cBioPortal. The data highlight variation in TP53 mutation frequency across cancer types, with the highest counts observed in female reproductive cancers and gastrointestinal cancers. 3.4 Data Analysis and Visualization Mutation counts were aggregated by mutation type within each cancer group. Frequencies and percentages were calculated for both overall and group-specific distributions. Data visualization was performed using Microsoft Excel and online charting tools to generate two primary figures: one summarizing the overall mutation type distribution and another showing mutation frequencies across the defined cancer groups. 4. Results Mutation data for TP53 were obtained from a combined dataset of 10,967 tumor samples across 32 studies available on cBioPortal . In total, 4,245 TP53 mutations were identified and analyzed, classified by mutation type and cancer group. The analysis covered 27 cancer types grouped into ten biologically relevant categories. 4.1 Overall Mutation Type Distribution Overall, missense mutations were the most frequent, accounting for 64.3% (n = 2,731) of all TP53 mutations, followed by nonsense mutations (12.9%, n = 546), frameshift mutations (12.3%, n = 522), splice-site mutations (7.9%, n = 335), in-frame mutations (2.1%, n = 91), and fusion events (0.5%, n = 20). Download figure Open in new tab Figure 1. Pie chart showing the distribution of 4,245 TP53 mutation types across 10,967 tumor samples from 32 studies. Missense mutations dominate, followed by nonsense, frameshift, splice-site, in-frame mutations, and fusion events. 4.2 Mutation Distribution by Cancer Group The mutation landscape of TP53 varied across cancer groups. Female reproductive cancers showed the highest overall burden (n = 1,039), predominantly missense (64.4%, n = 669), followed by frameshift (14.6%, n = 152) and nonsense mutations (11.5%, n = 119). Gastrointestinal cancers (n = 963) displayed a similar missense predominance (63.9%, n = 615) but a comparatively higher frameshift proportion (13.4%, n = 129). Thoracic cancers (n = 762) had 63.2% missense mutations (n = 481) and the highest splice mutation frequency (10.6%, n = 81). In head and neck cancers (n = 436), missense mutations accounted for 57.8% (n = 252), with frameshift mutations representing 14.7% (n = 64). Genitourinary cancers (n = 346) had a slightly lower missense share (67.9%, n = 235) and a balanced distribution of other mutation types. CNS cancers (n = 469) had the highest proportion of missense mutations among all groups (76.5%, n = 359), despite a moderate mutation count. Hematologic cancers exhibited the fewest TP53 mutations overall (n = 25), with 56.0% missense (n = 14) and 16.0% frameshift variants (n = 4). Sarcoma (n = 99), skin & melanocytic cancers (n = 83), and endocrine & neuroendocrine cancers (n = 23) presented smaller mutation counts but retained the missense-dominant pattern (51.5%, n = 51; 56.6%, n = 47; and 34.8%, n = 8, respectively). Download figure Open in new tab Figure 2: Proportional distribution of cancer types across six mutation categories: Frame Shift, Fusion, Inframe, Missense, Nonsense, and Splice mutations. Each stacked bar shows the percentage contribution of different cancer types, color-coded for Endocrine & Neuroendocrine, Hematologic, Skin & Melanocytic, Gastrointestinal, Thoracic, Female Reproductive, Central Nervous System (CNS), Genitourinary, Sarcoma, and Head & Neck cancers. The chart illustrates variation in cancer prevalence by mutation profile. 4.3 Notable Observations TP53 mutations were predominantly missense across all cancer groups, peaking in central nervous system cancers (76.5%). Female reproductive cancers had the highest mutation burden with notable frameshift (14.6%) and nonsense (11.5%) rates. Gastrointestinal cancers also showed elevated frameshift mutations (13.4%), while thoracic cancers had the most splice-site mutations (10.6%). Hematologic cancers had the fewest TP53 mutations, suggesting rarity in this group. Smaller cancer types followed similar missense-dominant patterns. 5. Discussion In this multi-study analysis of 10,967 tumor samples from 32 cancer studies, TP53 mutations were found to be widely distributed across multiple cancer types, with marked variability in frequency. The highest mutation prevalence was observed in head and neck cancer, non-small cell lung cancer, and esophagogastric cancer, whereas certain malignancies such as leukemia and adrenocortical carcinoma showed relatively low mutation rates. These findings are consistent with the established role of TP53 as one of the most frequently mutated tumor suppressor genes across human cancers, particularly in malignancies linked to environmental carcinogens or genomic instability. Our results broadly align with previous large-scale datasets such as TCGA and ICGC, which also report high TP53 mutation rates in smoking-associated cancers and tumors with high mutational burden. However, the relatively low mutation frequencies in some cancers may reflect biological differences in tumorigenesis, alternative driver mutations, or the influence of study-specific sampling strategies. The biological and clinical implications of these patterns are significant. High TP53 mutation frequencies may contribute to aggressive tumor behavior, resistance to therapy, and poor prognosis.[ 5 ] Conversely, tumors with lower TP53 mutation rates may rely on other oncogenic pathways, which could influence biomarker-driven treatment approaches. [ 6 ]Understanding these differences can guide the design of cancer-type– specific therapeutic strategies, including the development of drugs that target p53 restoration or exploit vulnerabilities caused by TP53 loss. [ 2 ] This study benefits from a large, diverse dataset, allowing broad comparisons across cancer types. However, limitations include the absence of detailed clinical outcomes, lack of treatment history, and heterogeneity among the contributing studies. Additionally, this analysis was restricted to mutation frequency and distribution; functional consequences of specific variants were not explored. Future work could expand this analysis by integrating TP53 mutation status with clinical data, co-mutation patterns, and pathway enrichment analysis. Such integrative approaches may yield deeper insights into the role of TP53 mutations in tumor progression and therapy resistance, ultimately aiding precision oncology efforts. 6. Conclusion This analysis highlights the variable distribution of TP53 mutations across different cancer types, with particularly high frequencies in head and neck, non-small cell lung, and esophagogastric cancers. These patterns reinforce the critical role of TP53 as a central tumor suppressor and suggest that its mutation burden may influence cancer behavior and therapeutic response. While this study provides valuable insights using publicly available genomic data, further research integrating clinical and functional information is needed to fully understand the prognostic and therapeutic implications of TP53 mutations. Footnotes https://github.com/Mariaabran/Mutational-Landscape-of-TP53-Across-Cancer-Types/commit/69086a3428845a472b0eef4dfba9479cd378467d References [1]. ↵ P. Baliakas and T. Soussi , “ The TP53 tumor suppressor gene: From molecular biology to clinical investigations .,” J Intern Med , vol. 298 , no. 2 , pp. 78 – 96 , Aug . 2025 , doi: 10.1111/joim.20106 . OpenUrl CrossRef PubMed [2]. ↵ X. Chen et al. , “ Mutant p53 in cancer: from molecular mechanism to therapeutic modulation ,” Cell Death Dis , vol. 13 , no. 11 , p. 974 , Nov . 2022 , doi: 10.1038/s41419-022-05408-1 . OpenUrl CrossRef PubMed [3]. ↵ G. Zhu et al. , “ Mutant p53 in Cancer Progression and Targeted Therapies ,” Front Oncol , vol. 10 , Nov . 2020 , doi: 10.3389/fonc.2020.595187 . OpenUrl CrossRef PubMed [4]. ↵ E. Cerami et al. , “ The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data ,” Cancer Discov , vol. 2 , no. 5 , pp. 401 – 404 , May 2012 , doi: 10.1158/2159-8290.CD-12-0095 . OpenUrl Abstract / FREE Full Text [5]. ↵ F. Xu et al. , “ A TP53 -associated gene signature for prediction of prognosis and therapeutic responses in lung squamous cell carcinoma ,” Oncoimmunology , vol. 9 , no. 1 , Jan . 2020 , doi: 10.1080/2162402X.2020.1731943 . OpenUrl CrossRef [6]. ↵ V. C. A. Caponio et al. , “ Computational analysis of TP53 mutational landscape unveils key prognostic signatures and distinct pathobiological pathways in head and neck squamous cell cancer ,” Br J Cancer , vol. 123 , no. 8 , pp. 1302 – 1314 , Oct . 2020 , doi: 10.1038/s41416-020-0984-6 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted August 15, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about bioRxiv. 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 Mutational Landscape of TP53 Across Cancer Types Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Mutational Landscape of TP53 Across Cancer Types Maria Abran bioRxiv 2025.08.12.669884; doi: https://doi.org/10.1101/2025.08.12.669884 Share This Article: Copy Citation Tools Mutational Landscape of TP53 Across Cancer Types Maria Abran bioRxiv 2025.08.12.669884; doi: https://doi.org/10.1101/2025.08.12.669884 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genetics Subject Areas All Articles Animal Behavior and Cognition (7636) Biochemistry (17697) Bioengineering (13896) Bioinformatics (41959) Biophysics (21457) Cancer Biology (18597) Cell Biology (25522) Clinical Trials (138) Developmental Biology (13381) Ecology (19904) Epidemiology (2067) Evolutionary Biology (24323) Genetics (15612) Genomics (22511) Immunology (17738) Microbiology (40401) Molecular Biology (17184) Neuroscience (88624) Paleontology (667) Pathology (2833) Pharmacology and Toxicology (4825) Physiology (7644) Plant Biology (15158) Scientific Communication and Education (2046) Synthetic Biology (4296) Systems Biology (9825) Zoology (2271)