Association Between Pre-Treatment HPV Genotype and Survival in Cervical Cancer: Insights From a Prospective Cohort

Association Between Pre-Treatment HPV Genotype and Survival in Cervical Cancer: Insights From a Prospective Cohort | 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 Association Between Pre-Treatment HPV Genotype and Survival in Cervical Cancer: Insights From a Prospective Cohort JORGE CEA GARCÍA, INMACULADA RODRÍGUEZ JIMÉNEZ, LAURA RÍOS-PENA, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6770268/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose : To assess the prevalence of human papillomavirus (HPV) genotypes in cervical cancer (CC) and evaluate their association with survival outcomes and treatment response. Methods : This was a prospective observational cohort study including 229 CC patients diagnosed between 2010 and 2019. HPV genotyping was performed using the HybriSpot24™ platform in 84 tumor samples. Patients were treated and followed in a tertiary referral hospital. Primary outcomes included overall survival (OS), disease-free survival (DFS), and treatment response. Group comparisons were conducted using Kaplan-Meier and Cox regression models. Statistical significance was set at p <0.05. Results : HPV DNA was detected in 91.67% of tumors, with HPV 16 being the most prevalent genotype (30.71%). HPV-positive patients had significantly longer OS than HPV-negative patients (difference: 26.1 months; 95% CI: 16.5–35.7; p <0.0001). No significant OS difference was observed between HPV 16 and HPV 18 (difference: 3.3 months; 95% CI: -7.6 to 14.1; p = 0.589). Patients with multiple HR-HPV infections had better OS (64.6 vs. 38.5 months; p = 0.047) and DFS (64.6 vs. 30.31 months; p = 0.017), particularly in early-stage disease. Conclusions : HPV positivity was associated with improved OS in CC. Multiple HR-HPV infections correlated with enhanced survival and treatment response, especially in early stages. These findings support the prognostic relevance of HPV genotyping in cervical cancer. HPV Genotype Coinfection Uterine Cervical Neoplasms Prognosis Survival Treatment Outcome Figures Figure 1 Figure 2 Figure 3 Introduction Cervical cancer (CC) remains one of the leading causes of cancer-related mortality among women worldwide, particularly in low- and middle-income countries. Despite notable advances in prevention through Pap smear screening, human papillomavirus (HPV) testing, and vaccination, CC continues to disproportionately affect women between the ages of 45 and 64. In 2020, CC ranked as the ninth most common cause of cancer-related death in our country, with a reported mortality rate of 3.6%¹. Specifically, in the region where this study was conducted, the crude mortality rate ranged from 2.35 to 2.71 per 1,000 women¹. Persistent infection with high-risk HPV (HR-HPV) genotypes is a prerequisite for the development of CC. While HPV 16 and 18 are the most prevalent oncogenic genotypes, other HR-HPV types, such as HPV 31 and 45, also play a role in carcinogenesis. However, the prognostic impact of specific HPV genotypes—particularly in cases of multiple genotype co-infections—remains unclear. Prior studies have reported conflicting results² ³, often due to variability in study design, sample size, duration of follow-up⁴, and whether CC was confirmed as the primary cause of death⁵ ⁶. The objective of this study was to determine the prevalence of HPV genotypes among patients with CC and to explore their association with survival outcomes and treatment response. Methods Study Design This was a prospective observational cohort study. Setting The study was conducted at a tertiary referral hospital. Participants were recruited between January 2010 and January 2019. HPV genotyping and molecular testing were carried out at the hospital’s pathology and molecular biology laboratories. Participants (a) Eligibility Criteria Inclusion criteria: Women aged 18 years or older Histological confirmation of invasive CC (any histologic subtype) Exclusion criteria: Prior history of preneoplastic cervical lesions or any other cancer Receipt of radiation or chemotherapy before initial diagnosis (b) Recruitment Among the 229 patients meeting inclusion criteria, 84 had available formalin-fixed, paraffin-embedded tumor tissue for HPV genotyping. Variables Outcomes: Primary outcomes: Overall Survival (OS): Time from diagnosis to death from any cause Disease-Free Survival (DFS): Time from treatment completion to recurrence or cancer-related death Treatment response: Assessed using radiological (RECIST v1.1), metabolic (PERCIST v1.0), and pathological criteria (SNAP01/SNAP02 classification Exposures: HPV infection status: HPV-positive or negative tumors HPV genotype: Specific HR genotypes detected, categorized as single or multiple infections Predictors and Confounders: HPV genotype type (e.g., HPV 16, HPV 18, other HR types) Age, FIGO stage, histological type, tumor size, depth of stromal invasion, lymphovascular space invasion, lymph node status, and treatment modality Effect Modifiers: Disease stage Histological subtype Diagnostic Criteria: CC confirmed histologically according to World Health Organization (WHO) classification International Federation of Gynecology and Obstetrics (FIGO) 2009 system used for staging HPV testing performed via HybriSpot24™ platform (LABTRONICS S.A.S., Bogotá, Colombia), a multiplex PCR and hybridization assay. Negative cases were further tested using L1, E6, and E7 gene-targeted PCR to prevent false negatives Measurement and data sources Measurement Consistency: All measurements followed national and international clinical guidelines, the Spanish Society of Gynecology and Obstetrics (SEGO), the Spanish Society of Medical Oncology (SEOM), and the Spanish Society of Radiation Oncology (SEOR). For radiological and metabolic responses, imaging interpretation was done by certified radiologists blinded to HPV status. Pathological response was reviewed by two experienced pathologists. Data Sources: Clinical radiological, metabolic, and pathological data: Retrieved from institutional cancer registries and electronic medical records. Patients were identified from historical clinical cohorts and here data were retrieved from and electronic health records. Bias Several strategies were implemented to minimize bias: Selection bias: Use of pre-defined inclusion/exclusion criteria across all patients Information bias: Blinded assessment of HPV status and response outcomes Misclassification bias: Use of L1, E6, and E7 gene PCR minimized the risk of false-negative HPV results Confounding: Controlled using multivariate Cox regression models with covariates selected based on univariate significance (p <0.05) Study Size The final sample of 229 patients was determined based on the total number of CC cases diagnosed during the 9-year study period. The subset of 84 patients for HPV genotyping was based on the availability of suitable tumor samples for molecular analysis. Power calculations were not pre-specified but post hoc analyses suggest sufficient power to detect clinically relevant differences in OS and DFS, particularly in early-stage disease subgroups. Quantitative Variables Quantitative variables (e.g., age, tumor diameter, survival times) were expressed as medians (M e ) with interquartile ranges (IQRs). Variables with skewed distributions were analyzed using non-parametric tests (Wilcoxon, Kruskal-Wallis) HPV genotype counts were grouped into categories: single vs. multiple infections, and specific genotypes (HPV 16, HPV 18, others) Variables associated with outcomes (p <0.05) were entered into multivariate models Statistical analysis (a) Statistical Methods and Confounding Control Data analysis was performed using R statistical software (R Foundation for Statistical Computing, Vienna, Austria; R Core Team [2017]). Continuous variables were summarized as M e ± IQR, and categorical variables as frequencies and percentages. To assess differences: Student's t-test was applied for normally distributed continuous variables. Wilcoxon rank-sum test was used for non-normal distributions. Chi-square (χ²) test, Z-test for proportions, and exact binomial tests were used for categorical comparisons, depending on sample size and expected frequency. Kruskal-Wallis test was used for comparisons involving more than two non-normally distributed groups. Spearman’s rank correlation coefficient (ρ) was used to assess associations between non-normally distributed continuous or ordinal variables. To control for confounding, we conducted: Univariate analyses to identify associations between exposures (e.g., HPV status or genotype) and outcomes (OS and DFS). Multivariable Cox proportional hazards regression, including variables with p <0.05 in univariate analysis as covariates. These included: Age at diagnosis FIGO stage Histological type Tumor diameter Depth of stromal invasion Lymphovascular space invasion Nodal status Treatment modality The odds ratios (ORs) and 95% confidence intervals (CIs) were reported. Proportional hazards assumptions were tested using Schoenfeld residuals. (b) Subgroup and Interaction Analyses Subgroup analyses were conducted to explore effect modification by: Disease stage: We stratified Kaplan-Meier survival curves and Cox regression by stage to evaluate whether the association between HPV genotype (single vs. multiple infections) and outcomes differed by stage. Histological subtype: Stratified analyses assessed whether HPV genotype associations with treatment response or survival varied by histology. We also evaluated potential interactions by including interaction terms (e.g., HPV genotype × disease stage) in multivariable models, though none reached statistical significance. (c) Missing Data Handling Missing data were minimal. For key variables like HPV genotype, histological subtype, and clinical stage: We performed complete-case analysis. Sensitivity analyses confirmed no significant differences in baseline characteristics between cases with complete vs. incomplete data, supporting the assumption that data were missing at random (MAR). No imputation methods were applied due to the low rate of missing data (<5%). (d) Loss to Follow-Up Patients were censored in survival analysis if: They were lost to follow-up, They died from non-cancer-related causes, or They were alive at the end of the study period. Censoring was handled appropriately within the Kaplan-Meier and Cox regression frameworks. The proportion of censored cases was reported and did not differ significantly by HPV status or genotype group, minimizing risk of attrition bias. (e) Sensitivity Analyses We conducted sensitivity analyses to test the robustness of our findings: Repeating Cox regression excluding non-cancer-related deaths (i.e., cause-specific survival). Re-analyzing the data using only patients with pathologically confirmed recurrence rather than radiological progression. Excluding rare histological subtypes (e.g., clear cell, neuroendocrine) to test if results were driven by outliers. Ethical approval The research project was approved by the Research Ethics Committee of our institution on April 23, 2018 in accordance with the requirements of Spanish Law 14/2007, dated July 3rd, on biomedical research, and the Declaration of Helsinki (1964). A research assistant obtained written informed consent from each subject for participation and publication of their data. In case of deceased subjects and the impossibility to obtain informed consent, ethical approval was obtained from the Ethics Committee to proceed with the use of posthumous data for research purposes. Results Participants (a) Study Progression A total of 229 patients diagnosed with CC were initially screened for eligibility. After applying inclusion and exclusion criteria, 84 patients were selected for HPV genotyping and molecular analysis. Of these, 77 patients had sufficient tumor tissue for testing, while 7 patients were excluded due to insufficient sample availability. All 77 patients completed the required follow-up assessments, which were integrated into the study's final data analysis. (b) Non-Participation Reasons Out of the 229 patients initially screened, 145 were excluded for various reasons: 30 had a prior history of preneoplastic cervical lesions, 50 had other cancers, and 65 had received previous treatments such as radiation or chemotherapy. Additionally, 19 patients were excluded due to incomplete clinical data or missing tumor tissue for HPV analysis. Descriptive Data (a) Characteristics of Study Participants The M e age at diagnosis was 50 years [range (R)=22–88]. The histological subtypes included: squamous cell carcinoma [74.63%, sample size (n)=50], adenocarcinoma (17.91%, n=12), adenosquamous carcinoma (4.48%, n=3), clear cell adenocarcinoma (1.49%, n=1), and neuroendocrine carcinoma (1.49%, n=1). HPV testing revealed HR-HPV in 83.58% of patients, with the predominant infections being HPV 16 (30.71%), HPV 18 (9.09%), and multiple HR-HPV genotypes (14.29%). (b) Follow-up Time The median follow-up duration was 41 months (IQR=57.5 months; R=1–264 months). Outcome Data (a) Outcome Events During the follow-up period, 81 patients (96.43%) died, with 5 deaths unrelated to CC (6.17%). Among the patients who died from CC, 69 were HPV-positive (89.61%) and 7 were HPV-negative (100%). (b) Summary Measures The OS rate was significantly higher in patients infected with multiple HPV genotypes compared with those with a single HPV genotype (M e OS = 64.6 months vs. 38.5 months, p=0.047). DFS was also significantly longer for patients with multiple HR-HPV genotypes (median DFS=58.4 months vs. 36.13 months, p=0.027), with early-stage disease showing the most pronounced difference (64.6 months vs. 30.31 months, p=0.017). Main Results (a) Unadjusted and Adjusted Estimates Unadjusted ORs for OS between HPV-positive and HPV-negative patients showed significant differences (p <0.0001). Multivariate analysis adjusting for age, FIGO stage, tumor size, and histological type revealed that patients with multiple HR genotypes had a significantly better DFS compared to those with single genotypes (OR=0.56, 95% CI: 0.31–0.92). (b) Categorization of Continuous Variables Patients younger than 50 years at diagnosis had significantly better survival outcomes (OR= 0.75, 95% CI: 0.52–1.09). Tumor size >4 cm was associated with a worse prognosis (OR=1.22, 95% CI: 1.05–1.42). (c) Translating Estimates into Absolute Risk For a 5-year period, the absolute risk of death was 15% for HPV-positive patients with multiple HR genotypes, compared with 35% for those with a single HPV genotype, demonstrating the clinical relevance of genotype-specific differences in survival. Other Analyses (a) Subgroup and Interaction Analyses Subgroup analysis by histological type revealed that the survival benefit of multiple HR genotypes was more pronounced in patients with squamous cell carcinoma compared withthose with adenocarcinoma. No significant interaction was found between HPV genotype and treatment modality (p=0.78). (b) Sensitivity Analyses Sensitivity analyses were conducted by excluding non-cancer-related deaths to assess cause-specific survival, which confirmed the robustness of the observed survival differences. Repeating the Cox regression excluding patients with rare histological types (clear cell and neuroendocrine carcinoma) did not change the overall survival results, supporting the generalizability of the findings. Discussion Key Results The findings demonstrated that most of the patients with CC tested positive for HR-HPV, with HPV 16 being the most prevalent genotype. A noteworthy observation was that patients with multiple HR-HPV infections had significantly longer OS and DFS compared with those with a single HPV genotype, particularly in early stages of the disease. This may reflect broader immune activation or differences in viral oncogenic synergy. While no significant survival differences were found between patients infected with HPV 16 and those with HPV 18, the results provide insights into the potential implications of HPV genotyping on prognosis in CC. Strenghts and Limitations A key strength of this study was its prospective nature and the use of validated methods to determine HR-HPV status. We also adjusted for cancer-specific mortality to minimize misclassification. This approach is susceptible to biases, including selection bias and information bias. Furthermore, the sample size with multiple HR-HPV infections was relatively small (only 14.29% of the study population), which may limit the statistical power of some of the analyses, particularly when examining the impact of co-infections on prognosis. Additionally, although we adjusted for known confounders, residual confounding could still influence the results, particularly in a cohort with such variability in tumor characteristics and treatment responses. Another limitation concerns the follow-up duration. While the median follow-up time of 41 months provides some insight into long-term survival, a longer follow-up would be beneficial to more accurately assess late recurrence and survival in this patient population. Additionally, the classification of HPV infection as a singular or multiple genotype infection could introduce a degree of misclassification, particularly if the viral load or tumor tissue sample quality was suboptimal for detecting less prevalent genotypes. Finally, the study was limited to a specific geographical region, which may affect the external validity or generalisability of our findings to other populations, particularly in regions with different HPV prevalence or vaccination rates. Interpretation The results of this study offer a cautious but promising interpretation regarding the prognostic value of multiple HR-HPV genotypes in patients with CC. Our findings suggest that while single infections with HR-HPV types such as HPV 16 or HPV 18 are most common, contributing to 72.4% of all HPV DNA-positive cases, while HPV 45 and HPV 31 account for less than 15% of CCs. 7 Co-infection with multiple HPV genotypes is relatively common in CC, typically varying between 10 and 20%, 8,9 which is accordance with our findings. Multiple HR-HPV genotypes have been associated with adenosquamous histology in CC, 10 which is a more aggressive form of the disease with lower radiosensitivity and nearly 5-fold higher of treatment failure compared with single infection (57% vs. 12%). 11,12 In our study, 90.7% of patients with a single HR genotype achieved a complete response, as did all patients with multiple infections. Multiple HR-HPV infections may be associated with improved survival in early-stage cervical cancer, possibly reflecting earlier diagnosis and enhanced treatment efficacy. This aligns with some studies that suggest multiple HR-HPV genotypes could lead to broader immune targeting, enhancing the body's ability to control the tumor and possibly influencing the response to treatment. However, these findings might be influenced by sample size and require further validation. Some studies have reported a more favorable prognosis for HPV 16 12-14 and worse outcomes for HPV 18. 4,15 The lack of significant differences in OS between HPV 16 and HPV 18 patients, as well as the absence of strong associations between specific genotypes and survival, suggests that other factors—such as tumor characteristics, 2,16,17 treatment modalities, 18,19 and host immune responses—may play a more significant role in influencing survival outcomes. In line with the growing body of literature on HPV and CC, our findings contribute to the understanding of how multiple HR-HPV infections might influence cancer progression. Previous studies have shown inconsistent results regarding the prognostic impact of specific HPV genotypes, with some studies 4,15 suggesting worse outcomes for HPV 18-positive CCs compared with HPV 16-positive ones, and others reporting no significant differences. 14,15 Our study adds to this discourse by highlighting that multiple HR-HPV infections, rather than a single genotype, may be a more important determinant of prognosis, particularly in the context of early-stage disease. A variable proportion of CCs test negative for HR-HPV, 15,20 ranging from 8% to 19%, 11,21 which is consistent with our findings and it could be due to factors like low viral load, 20 sample variability, or misclassification. 14,20-22 Moreover, the study contributes to the ongoing debate about HPV-negative CC, 23 which was associated with poorer outcomes in our cohort, similarly to other studies. 2,3,6,14,17,22,24-29 This finding reinforces the importance of HR-HPV testing in CC diagnosis and prognosis, as HR-HPV negativity is likely to indicate a distinct and more aggressive tumor biology, becoming less susceptible to immune control. 30 The implications of our findings are multifaceted. Clinically, the results suggest that HPV genotyping could help identify patients at higher risk of poor outcomes, particularly those with multiple HR-HPV genotypes. This could have important consequences for treatment strategies, especially in early-stage disease where surgical interventions are common. Additionally, the findings emphasize the importance of considering HPV status in prognosis models for CC, particularly in the context of treatment planning and follow-up strategies. Generalisability The generalisability of our results is a critical consideration, especially given the limitations in sample size and regional variability. While the study was conducted in limited geographic area, and primarily reflects the HPV genotypes and clinical outcomes observed in this population, similar studies conducted in different geographical regions could yield different results, especially in areas where HPV vaccination rates or HPV genotype distributions differ. Furthermore, while the study included a diverse cohort in terms of histological subtypes, the relatively small number of patients with adenocarcinoma and other rare forms of CC limits the ability to generalize findings to all histological subtypes of CC. In terms of treatment, the study population may not reflect the full spectrum of treatment approaches used globally. The use of different treatment regimens, the role of adjuvant therapy, and variations in radiotherapy or chemotherapy regimens could influence survival outcomes. Thus, while the findings are relevant to understanding the relationship between HPV status and prognosis in the studied region, further studies in different cohorts, including international populations, are needed to assess whether these results hold true across various clinical settings. Conclusion In conclusion, our study highlights the complex role of HR-HPV genotypes in the prognosis of CC. While the presence of multiple HR-HPV infections was associated with better survival outcomes, particularly in early-stage disease, HPV genotyping alone does not appear to be a strong predictor of survival. Our findings suggest that further research is needed to clarify the specific role of multiple HR-HPV infections in CC progression and to explore the underlying mechanisms that might explain the observed survival benefits. Future studies should focus on larger, multi-center cohorts, and long-term follow-up to better understand the clinical implications of HPV co-infection and its potential as a therapeutic target. Furthermore, HPV vaccination programs could play a crucial role in reducing the burden of cervical cancer, and understanding the nuances of HPV genotyping could help refine early detection and treatment strategies for better patient outcomes. Abbreviations CC, cervical cancer CI, confidence interval DFS, disease-free survival FIGO, International Federation of Gynecology and Obstetrics HPV, human papillomavirus HR, high-risk IQR, interquartile range MAR, missing at random M e , median MFS, metastasis-free survival N, sample size OR, odds ratio OS, overall survival PERCIST, PET response criteria in solid tumors PCR, polymerase chain reaction R, range RECIST, response evaluation criteria in solid tumors SE, standard error SEGO, Spanish Society of Gynecology and Obstetrics SEOM, Spanish Society of Medical Oncology SEOR, Spanish Society of Radiation Oncology SNAP, studio neo-adjuvante portio WHO, World Health Organization Declarations Ethical Approval: This study was approved by the Research Ethics Committee of Virgen Macarena and Virgen del Rocío University Hospitals (Approval Date: April 23, 2018; Code: JCG-CCI-2018-01). A research assistant obtained written informed consent from all participants, ensuring that they fully understood the study and their rights. Participants also provided consent for the publication of their data. In case of deceased subjects and the impossibility to obtain informed consent, ethical approval was obtained from the Ethics Committee to proceed with the use of posthumous data for research purposes. Conflict of Interest: The authors declare no conflict of interest. Funding: This research received no external funding. Author Contributions: J.C.G. : Investigation, Conceptualization, Methodology, Writing—Original Draft Preparation, Visualization, Writing—Review and Editing. L.R.P. : Methodology, Software, Data Curation, Formal Analysis, Validation, Writing—Review and Editing, Supervision. M.C.R.R. : Supervision. F.M.M. : Resources, Supervision. I.R.J. : Resources, Supervision. 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N Engl J Med. 2010 Jul 1;363(1):24-35. doi: 10.1056/NEJMoa0912217 Table Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files TableS1.docx Table S1 Comparison of the demographic and clinical characteristics of the patients with CC regarding HPV status and genotyping a Abbreviations CC, cervical cancer; FIGO, International Federation of Gynecology and Obstetrics; HPV, human papillomavirus; HR, high-risk; IQR, interquartile range; LVI, lymphovascular invasion; M e , median; MPALN, metastatic para-aortic lymph node; MPLN, metastatic pelvic lymph node; MTD, maximum tumor diameter; n, sample size; NA, not available; PI, parametrial invasion; R, range TableS2.docx Table S2 Comparison of clinicopathological variables among VPH groups a Abbreviations FIGO, International Federation of Gynecology and Obstetrics; HPV, human papillomavirus; LVI, lymphovascular invasion; MTD, maximum tumor diameter; PI, parametrial invasion; SI, stromal invasion TableS3.docx Table S3 HPV genotypes regarding histological types of CC a Abbreviations HR, high-risk; n, sample size Table1.docx Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-6770268","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467182188,"identity":"9ee61508-94b2-4b96-a77b-aa44e26a7a0d","order_by":0,"name":"JORGE CEA GARCÍA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYBACxgYILWcApgwsiNdibMDADNIiQbxtiRvAWhiI0MLc3vuA4UeFXfp29v6jG34USDDwt3cn4HdYz3EDxp4zybk7ew6z3ewBOkzizNkN+LXMSGNgZmxjzt1wI5ntBg9Qi4FELjFa/tWnGwC13PxDvJaGwwkgLbeJs6XnGAgfN9xw5rDZbRkDCR6CfjFsb2Ng+FFTLW9wvPHZzTd/bOT423sJaGlgYP+BLMCDVzkIyBNUMQpGwSgYBaMAAEllQl0Zx7a4AAAAAElFTkSuQmCC","orcid":"","institution":"Hospital Universitario Virgen Macarena","correspondingAuthor":true,"prefix":"","firstName":"JORGE","middleName":"CEA","lastName":"GARCÍA","suffix":""},{"id":467182189,"identity":"ff1e8d8b-e3dc-467a-a360-0eeaf4ce85f2","order_by":1,"name":"INMACULADA RODRÍGUEZ JIMÉNEZ","email":"","orcid":"","institution":"Hospital Universitario Virgen Macarena","correspondingAuthor":false,"prefix":"","firstName":"INMACULADA","middleName":"RODRÍGUEZ","lastName":"JIMÉNEZ","suffix":""},{"id":467182190,"identity":"8b903120-21b2-4806-85d3-88357258d70d","order_by":2,"name":"LAURA RÍOS-PENA","email":"","orcid":"","institution":"Universidad Loyola Andalucía","correspondingAuthor":false,"prefix":"","firstName":"LAURA","middleName":"","lastName":"RÍOS-PENA","suffix":""},{"id":467182191,"identity":"477cbaab-a903-453a-ad95-869d33b887fb","order_by":3,"name":"M. CARMEN RUBIO RODRÍGUEZ","email":"","orcid":"","institution":"Hospital Universitario HM Sanchinarro","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"CARMEN RUBIO","lastName":"RODRÍGUEZ","suffix":""},{"id":467182192,"identity":"c6b71e9d-707b-427e-b11c-545e90db084e","order_by":4,"name":"FRANSCISCO MÁRQUEZ MARAVER","email":"","orcid":"","institution":"Hospital Universitario Virgen Macarena","correspondingAuthor":false,"prefix":"","firstName":"FRANSCISCO","middleName":"MÁRQUEZ","lastName":"MARAVER","suffix":""}],"badges":[],"createdAt":"2025-05-28 17:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6770268/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6770268/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84821823,"identity":"c2fe499e-de3c-43f7-90ad-c92b30963719","added_by":"auto","created_at":"2025-06-17 16:14:59","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":125464,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Kaplan-Meier OS curves and life curves between patients diagnosed with HPV-related (upper image) and unrelated (lower image) CC\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eAbbreviations\u003c/p\u003e\n\u003cp\u003eCI, confidence interval; SE, standard error\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6770268/v1/66ecb5cef47e06084d5878e9.jpg"},{"id":84819334,"identity":"dcf75eee-ed84-4cba-b25b-de221f553e4b","added_by":"auto","created_at":"2025-06-17 15:58:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":123829,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Kaplan-Meier OS curves and life curves between patients diagnosed with HPV 16 (upper image) and 18 (lower image)-related CC\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eAbbreviations\u003c/p\u003e\n\u003cp\u003eCI, confidence interval; SE, standard error\u003c/p\u003e","description":"","filename":"Figura2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6770268/v1/39ae9d3010d5426c549d0932.jpg"},{"id":84819336,"identity":"5dd0da6e-cfe5-4702-bf74-c8da1d469c0e","added_by":"auto","created_at":"2025-06-17 15:58:59","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":123017,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Kaplan-Meier OS curves and life curves between patients diagnosed with single and multiple high-risk HPV-related CC\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eAbbreviations\u003c/p\u003e\n\u003cp\u003eCI, confidence interval; SE, standard error\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6770268/v1/33e36baa2b6aa6155ab6d15e.jpg"},{"id":86879709,"identity":"690d94ee-960f-4542-b5d0-12ec0edbeff4","added_by":"auto","created_at":"2025-07-16 16:08:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1171550,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6770268/v1/f6d4a221-7a79-4d58-ab27-8acd6c6cab08.pdf"},{"id":84820307,"identity":"ac964cc7-fdb5-4582-b3e4-255b52d4763f","added_by":"auto","created_at":"2025-06-17 16:06:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":49346,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1\u003c/strong\u003e Comparison of the demographic and clinical characteristics of the patients with CC regarding HPV status and genotyping\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eAbbreviations\u003c/p\u003e\n\u003cp\u003eCC, cervical cancer; FIGO, International Federation of Gynecology and Obstetrics; HPV, human papillomavirus; HR, high-risk; IQR, interquartile range; LVI, lymphovascular invasion; M\u003csub\u003ee\u003c/sub\u003e, median; MPALN, metastatic para-aortic lymph node; MPLN, metastatic pelvic lymph node; MTD, maximum tumor diameter; n, sample size; NA, not available; PI, parametrial invasion; R, range\u003c/p\u003e","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6770268/v1/619d5366290b348df2374cef.docx"},{"id":84819335,"identity":"902df4aa-0001-472c-ada5-4d43f0634555","added_by":"auto","created_at":"2025-06-17 15:58:59","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":30099,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S2\u003c/strong\u003e Comparison of clinicopathological variables among VPH groups\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eAbbreviations\u003c/p\u003e\n\u003cp\u003eFIGO, International Federation of Gynecology and Obstetrics; HPV, human papillomavirus; LVI, lymphovascular invasion; MTD, maximum tumor diameter; PI, parametrial invasion; SI, stromal invasion\u003c/p\u003e","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6770268/v1/9260d1982108849d94a6fe8c.docx"},{"id":84819339,"identity":"f73b0bef-d059-4e31-b837-fc026760f368","added_by":"auto","created_at":"2025-06-17 15:58:59","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":38675,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S3\u003c/strong\u003e HPV genotypes regarding histological types of CC\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eAbbreviations\u003c/p\u003e\n\u003cp\u003eHR, high-risk; n, sample size\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"TableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6770268/v1/93e8ffb27693027b003b2053.docx"},{"id":84819337,"identity":"3c31f675-2b13-4995-85b8-7f41fe75442c","added_by":"auto","created_at":"2025-06-17 15:58:59","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":21282,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6770268/v1/ba82b77e998e1fd6945a4978.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAssociation Between Pre-Treatment HPV Genotype and Survival in Cervical Cancer: Insights From a Prospective Cohort\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical cancer (CC) remains one of the leading causes of cancer-related mortality among women worldwide, particularly in low- and middle-income countries. Despite notable advances in prevention through Pap smear screening, human papillomavirus (HPV) testing, and vaccination, CC continues to disproportionately affect women between the ages of 45 and 64. In 2020, CC ranked as the ninth most common cause of cancer-related death in our country, with a reported mortality rate of 3.6%\u0026sup1;. Specifically, in the region where this study was conducted, the crude mortality rate ranged from 2.35 to 2.71 per 1,000 women\u0026sup1;.\u003c/p\u003e \u003cp\u003ePersistent infection with high-risk HPV (HR-HPV) genotypes is a prerequisite for the development of CC. While HPV 16 and 18 are the most prevalent oncogenic genotypes, other HR-HPV types, such as HPV 31 and 45, also play a role in carcinogenesis. However, the prognostic impact of specific HPV genotypes\u0026mdash;particularly in cases of multiple genotype co-infections\u0026mdash;remains unclear. Prior studies have reported conflicting results\u0026sup2; \u0026sup3;, often due to variability in study design, sample size, duration of follow-up⁴, and whether CC was confirmed as the primary cause of death⁵ ⁶.\u003c/p\u003e \u003cp\u003eThe objective of this study was to determine the prevalence of HPV genotypes among patients with CC and to explore their association with survival outcomes and treatment response.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003eStudy Design\u003c/h3\u003e\n\u003cp\u003eThis was a prospective observational cohort study.\u003c/p\u003e\n\u003ch3\u003eSetting\u003c/h3\u003e\n\u003cp\u003eThe study was conducted at a tertiary referral hospital. Participants were recruited between January 2010 and January 2019. HPV genotyping and molecular testing were carried out at the hospital\u0026rsquo;s pathology and molecular biology laboratories.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003e(a) Eligibility Criteria \u0026nbsp;\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eInclusion criteria:\u003cul type=\"circle\"\u003e\n \u003cli\u003eWomen aged 18 years or older\u003c/li\u003e\n \u003cli\u003eHistological confirmation of invasive CC (any histologic subtype)\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003eExclusion criteria:\u003cul type=\"circle\"\u003e\n \u003cli\u003ePrior history of preneoplastic cervical lesions or any other cancer\u003c/li\u003e\n \u003cli\u003eReceipt of radiation or chemotherapy before initial diagnosis\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e(b) Recruitment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong the 229 patients meeting inclusion criteria, 84 had available formalin-fixed, paraffin-embedded tumor tissue for HPV genotyping.\u003c/p\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cp\u003eOutcomes:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003ePrimary outcomes:\u003cul type=\"circle\"\u003e\n \u003cli\u003eOverall Survival (OS): Time from diagnosis to death from any cause\u003c/li\u003e\n \u003cli\u003eDisease-Free Survival (DFS): Time from treatment completion to recurrence or cancer-related death\u003c/li\u003e\n \u003cli\u003eTreatment response: Assessed using radiological (RECIST v1.1), metabolic (PERCIST v1.0), and pathological criteria (SNAP01/SNAP02 classification\u0026nbsp;\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eExposures:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eHPV infection status: HPV-positive or negative tumors\u003c/li\u003e\n \u003cli\u003eHPV genotype: Specific HR genotypes detected, categorized as single or multiple infections\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePredictors and Confounders:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eHPV genotype type (e.g., HPV 16, HPV 18, other HR types)\u003c/li\u003e\n \u003cli\u003eAge, FIGO stage, histological type, tumor size, depth of stromal invasion, lymphovascular space invasion, lymph node status, and treatment modality\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eEffect Modifiers:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eDisease stage\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHistological subtype\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDiagnostic Criteria:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eCC confirmed histologically according to\u0026nbsp;World Health Organization\u0026nbsp;(WHO) classification\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n \u003cli\u003eInternational Federation of Gynecology and Obstetrics (FIGO) 2009 system used for staging\u003csup\u003e\u0026nbsp;\u0026nbsp;\u003c/sup\u003e\u003c/li\u003e\n \u003cli\u003eHPV testing performed via HybriSpot24\u0026trade; platform (LABTRONICS S.A.S., Bogot\u0026aacute;, Colombia), a multiplex PCR and hybridization assay. Negative cases were further tested using L1, E6, and E7 gene-targeted PCR to prevent false negatives\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eMeasurement and data sources \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMeasurement Consistency:\u003cbr\u003e\u0026nbsp;All measurements followed national and international clinical guidelines, the Spanish Society of Gynecology and Obstetrics (SEGO), the Spanish Society of Medical Oncology (SEOM), and the Spanish Society of Radiation Oncology (SEOR). For radiological and metabolic responses, imaging interpretation was done by certified radiologists blinded to HPV status. Pathological response was reviewed by two experienced pathologists.\u003c/p\u003e\n\u003cp\u003eData Sources:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eClinical radiological, metabolic, and pathological data: Retrieved from institutional cancer registries and electronic medical records. Patients were identified from historical clinical cohorts and here data were retrieved from and electronic health records.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003eBias\u003c/h3\u003e\n\u003cp\u003eSeveral strategies were implemented to minimize bias:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eSelection bias: Use of pre-defined inclusion/exclusion criteria across all patients\u003c/li\u003e\n \u003cli\u003eInformation bias: Blinded assessment of HPV status and response outcomes\u003c/li\u003e\n \u003cli\u003eMisclassification bias: Use of L1, E6, and E7 gene PCR minimized the risk of false-negative HPV results\u003c/li\u003e\n \u003cli\u003eConfounding: Controlled using multivariate Cox regression models with covariates selected based on univariate significance (p \u0026lt;0.05)\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003eStudy Size\u003c/h3\u003e\n\u003cp\u003eThe final sample of 229 patients was determined based on the total number of CC cases diagnosed during the 9-year study period. The subset of 84 patients for HPV genotyping was based on the availability of suitable tumor samples for molecular analysis. Power calculations were not pre-specified but post hoc analyses suggest sufficient power to detect clinically relevant differences in OS and DFS, particularly in early-stage disease subgroups.\u003c/p\u003e\n\u003ch3\u003eQuantitative Variables\u003c/h3\u003e\n\u003cp\u003eQuantitative variables (e.g., age, tumor diameter, survival times) were expressed as medians (M\u003csub\u003ee\u003c/sub\u003e) with interquartile ranges (IQRs).\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eVariables with skewed distributions were analyzed using non-parametric tests (Wilcoxon, Kruskal-Wallis)\u003c/li\u003e\n \u003cli\u003eHPV genotype counts were grouped into categories: single vs. multiple infections, and specific genotypes (HPV 16, HPV 18, others)\u003c/li\u003e\n \u003cli\u003eVariables associated with outcomes (p \u0026lt;0.05) were entered into multivariate models\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003eStatistical analysis\u003c/h3\u003e\n\u003ch4\u003e\u0026nbsp;(a) Statistical Methods and Confounding Control\u003c/h4\u003e\n\u003cp\u003eData analysis was performed using R statistical software (R Foundation for Statistical Computing, Vienna, Austria; R Core Team [2017]). Continuous variables were summarized as M\u003csub\u003ee\u003c/sub\u003e \u0026plusmn; IQR, and categorical variables as frequencies and percentages.\u003c/p\u003e\n\u003cp\u003eTo assess differences:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eStudent\u0026apos;s t-test\u0026nbsp;was applied for normally distributed continuous variables.\u003c/li\u003e\n \u003cli\u003eWilcoxon rank-sum test was used for non-normal distributions.\u003c/li\u003e\n \u003cli\u003eChi-square (\u0026chi;\u0026sup2;) test, Z-test for proportions, and exact binomial tests were used for categorical comparisons, depending on sample size and expected frequency.\u003c/li\u003e\n \u003cli\u003eKruskal-Wallis test\u0026nbsp;was used for comparisons involving more than two non-normally distributed groups.\u003c/li\u003e\n \u003cli\u003eSpearman\u0026rsquo;s rank correlation coefficient (\u0026rho;) was used to assess associations between non-normally distributed continuous or ordinal variables.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTo control for confounding, we conducted:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eUnivariate analyses\u0026nbsp;to identify associations between exposures (e.g., HPV status or genotype) and outcomes (OS and DFS).\u003c/li\u003e\n \u003cli\u003eMultivariable Cox proportional hazards regression, including variables with p \u0026lt;0.05 in univariate analysis as covariates. These included:\u003cul type=\"circle\"\u003e\n \u003cli\u003eAge at diagnosis\u003c/li\u003e\n \u003cli\u003eFIGO stage\u003c/li\u003e\n \u003cli\u003eHistological type\u003c/li\u003e\n \u003cli\u003eTumor diameter\u003c/li\u003e\n \u003cli\u003eDepth of stromal invasion\u003c/li\u003e\n \u003cli\u003eLymphovascular space invasion\u003c/li\u003e\n \u003cli\u003eNodal status\u003c/li\u003e\n \u003cli\u003eTreatment modality\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe odds ratios (ORs) and 95% confidence intervals (CIs) were reported. Proportional hazards assumptions were tested using Schoenfeld residuals.\u003c/p\u003e\n\u003ch4\u003e(b) Subgroup and Interaction Analyses\u003c/h4\u003e\n\u003cp\u003eSubgroup analyses were conducted to explore effect modification by:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eDisease stage: We stratified Kaplan-Meier survival curves and Cox regression by stage to evaluate whether the association between HPV genotype (single vs. multiple infections) and outcomes differed by stage.\u003c/li\u003e\n \u003cli\u003eHistological subtype: Stratified analyses assessed whether HPV genotype associations with treatment response or survival varied by histology.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe also evaluated potential interactions by including interaction terms (e.g., HPV genotype \u0026times; disease stage) in multivariable models, though none reached statistical significance.\u003c/p\u003e\n\u003ch4\u003e(c) Missing Data Handling\u003c/h4\u003e\n\u003cp\u003eMissing data were minimal. For key variables like HPV genotype, histological subtype, and clinical stage:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eWe performed complete-case analysis.\u003c/li\u003e\n \u003cli\u003eSensitivity analyses confirmed no significant differences in baseline characteristics between cases with complete vs. incomplete data, supporting the assumption that data were missing at random (MAR).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNo imputation methods were applied due to the low rate of missing data (\u0026lt;5%).\u003c/p\u003e\n\u003ch4\u003e(d) Loss to Follow-Up\u003c/h4\u003e\n\u003cp\u003ePatients were censored in survival analysis if:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eThey were lost to follow-up,\u003c/li\u003e\n \u003cli\u003eThey died from non-cancer-related causes, or\u003c/li\u003e\n \u003cli\u003eThey were alive at the end of the study period.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCensoring was handled appropriately within the Kaplan-Meier and Cox regression frameworks. The proportion of censored cases was reported and did not differ significantly by HPV status or genotype group, minimizing risk of attrition bias.\u003c/p\u003e\n\u003ch4\u003e(e) Sensitivity Analyses\u003c/h4\u003e\n\u003cp\u003eWe conducted sensitivity analyses to test the robustness of our findings:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eRepeating Cox regression excluding non-cancer-related deaths (i.e., cause-specific survival).\u003c/li\u003e\n \u003cli\u003eRe-analyzing the data using only patients with pathologically confirmed recurrence rather than radiological progression.\u003c/li\u003e\n \u003cli\u003eExcluding rare histological subtypes (e.g., clear cell, neuroendocrine) to test if results were driven by outliers.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research project was approved by the Research Ethics Committee of our institution on April 23, 2018 in accordance with the requirements of Spanish Law 14/2007, dated July 3rd, on biomedical research, and the Declaration of Helsinki (1964). A research assistant obtained written informed consent from each subject for participation and publication of their data. In case of deceased subjects and the impossibility to obtain informed consent, ethical approval was obtained from the Ethics Committee to proceed with the use of posthumous data for research purposes.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a) Study Progression\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;A total of 229 patients diagnosed with CC were initially screened for eligibility. After applying inclusion and exclusion criteria, 84 patients were selected for HPV genotyping and molecular analysis. Of these, 77 patients had sufficient tumor tissue for testing, while 7 patients were excluded due to insufficient sample availability. All 77 patients completed the required follow-up assessments, which were integrated into the study\u0026apos;s final data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b) Non-Participation Reasons\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Out of the 229 patients initially screened, 145 were excluded for various reasons: 30 had a prior history of preneoplastic cervical lesions, 50 had other cancers, and 65 had received previous treatments such as radiation or chemotherapy. Additionally, 19 patients were excluded due to incomplete clinical data or missing tumor tissue for HPV analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescriptive Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a) Characteristics of Study Participants\u003c/strong\u003e\u003cbr\u003eThe M\u003csub\u003ee\u003c/sub\u003e age at diagnosis was 50 years [range (R)=22\u0026ndash;88]. The histological subtypes included: squamous cell carcinoma [74.63%, sample size (n)=50], adenocarcinoma (17.91%, n=12), adenosquamous carcinoma (4.48%, n=3), clear cell adenocarcinoma (1.49%, n=1), and neuroendocrine carcinoma (1.49%, n=1). HPV testing revealed HR-HPV in 83.58% of patients, with the predominant infections being HPV 16 (30.71%), HPV 18 (9.09%), and\u0026nbsp;multiple HR-HPV genotypes (14.29%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b) Follow-up Time\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The median follow-up duration was 41 months (IQR=57.5 months; R=1\u0026ndash;264 months).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a) Outcome Events\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003eDuring the follow-up period, 81 patients (96.43%) died, with 5 deaths unrelated to CC (6.17%). Among the patients who died from CC, 69 were HPV-positive (89.61%) and 7 were HPV-negative (100%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b) Summary Measures\u003c/strong\u003e\u003cbr\u003eThe OS rate was significantly higher in patients infected with multiple HPV genotypes compared with those with a single HPV genotype (M\u003csub\u003ee\u003c/sub\u003e OS = 64.6 months vs. 38.5 months, p=0.047). DFS was also significantly longer for patients with multiple HR-HPV genotypes (median DFS=58.4 months vs. 36.13 months, p=0.027), with early-stage disease showing the most pronounced difference (64.6 months vs. 30.31 months, p=0.017).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMain Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a) Unadjusted and Adjusted Estimates\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Unadjusted ORs for OS between HPV-positive and HPV-negative patients showed significant differences (p \u0026lt;0.0001). Multivariate analysis adjusting for age, FIGO stage, tumor size, and histological type revealed that patients with multiple HR genotypes had a significantly better DFS compared to those with single genotypes (OR=0.56, 95% CI: 0.31\u0026ndash;0.92).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b) Categorization of Continuous Variables\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003ePatients younger than 50 years at diagnosis had significantly better survival outcomes (OR= 0.75, 95% CI: 0.52\u0026ndash;1.09). Tumor size \u0026gt;4 cm was associated with a worse prognosis (OR=1.22, 95% CI: 1.05\u0026ndash;1.42).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(c) Translating Estimates into Absolute Risk\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;For a 5-year period, the absolute risk of death was 15% for HPV-positive patients with multiple HR genotypes, compared with 35% for those with a single HPV genotype, demonstrating the clinical relevance of genotype-specific differences in survival.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOther Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a) Subgroup and Interaction Analyses\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Subgroup analysis by histological type revealed that the survival benefit of multiple HR genotypes was more pronounced in patients with squamous cell carcinoma compared withthose with adenocarcinoma. No significant interaction was found between HPV genotype and treatment modality (p=0.78).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b) Sensitivity Analyses\u003c/strong\u003e\u003cbr\u003e Sensitivity analyses were conducted by excluding non-cancer-related deaths to assess cause-specific survival, which confirmed the robustness of the observed survival differences. Repeating the Cox regression excluding patients with rare histological types (clear cell and neuroendocrine carcinoma) did not change the overall survival results, supporting the generalizability of the findings.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eKey Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings demonstrated that most of the patients with CC tested positive for HR-HPV, with HPV 16 being the most prevalent genotype. A noteworthy observation was that patients with multiple HR-HPV infections had significantly longer OS and DFS compared with those with a single HPV genotype, particularly in early stages of the disease. This may reflect broader immune activation or differences in viral oncogenic synergy. While no significant survival differences were found between patients infected with HPV 16 and those with HPV 18, the results provide insights into the potential implications of HPV genotyping on prognosis in CC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrenghts and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA key strength of this study was its prospective nature and the use of validated methods to determine HR-HPV status. We also adjusted for cancer-specific mortality to minimize misclassification. This approach is susceptible to biases, including selection bias and information bias. Furthermore, the sample size with multiple HR-HPV infections was relatively small (only 14.29% of the study population), which may limit the statistical power of some of the analyses, particularly when examining the impact of co-infections on prognosis. Additionally, although we adjusted for known confounders, residual confounding could still influence the results, particularly in a cohort with such variability in tumor characteristics and treatment responses.\u003c/p\u003e\n\u003cp\u003eAnother limitation concerns the follow-up duration. While the median follow-up time of 41 months provides some insight into long-term survival, a longer follow-up would be beneficial to more accurately assess late recurrence and survival in this patient population. Additionally, the classification of HPV infection as a singular or multiple genotype infection could introduce a degree of misclassification, particularly if the viral load or tumor tissue sample quality was suboptimal for detecting less prevalent genotypes. Finally, the study was limited to a specific geographical region, which may affect the external validity or generalisability of our findings to other populations, particularly in regions with different HPV prevalence or vaccination rates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of this study offer a cautious but promising interpretation regarding the prognostic value of multiple HR-HPV genotypes in patients with CC. Our findings suggest that while single infections with HR-HPV types such as HPV 16 or HPV 18 are most common,\u0026nbsp;contributing to 72.4% of all HPV DNA-positive cases, while HPV 45 and HPV 31 account for less than 15% of CCs.\u003csup\u003e7\u003c/sup\u003e Co-infection with multiple HPV genotypes is relatively common in CC, typically varying between 10 and 20%,\u003csup\u003e8,9\u003c/sup\u003e which is accordance with our findings. Multiple HR-HPV genotypes have been associated with adenosquamous histology in CC,\u003csup\u003e10\u003c/sup\u003e which is a more aggressive form of the disease with lower radiosensitivity and nearly 5-fold higher of treatment failure compared with single infection (57% vs. 12%).\u003csup\u003e11,12\u003c/sup\u003e \u0026nbsp;In our study, 90.7% of patients with a single HR genotype achieved a complete response, as did all patients with multiple infections. Multiple HR-HPV infections may be associated with improved survival in early-stage cervical cancer, possibly reflecting earlier diagnosis and enhanced treatment efficacy. This aligns with some studies that suggest multiple HR-HPV genotypes could lead to broader immune targeting, enhancing the body\u0026apos;s ability to control the tumor and possibly influencing the response to treatment. However, these findings might be influenced by sample size and require further validation. Some studies have reported a more favorable prognosis for HPV 16\u003csup\u003e12-14\u003c/sup\u003e and worse outcomes for HPV 18.\u003csup\u003e4,15\u003c/sup\u003e The lack of significant differences in OS between HPV 16 and HPV 18 patients, as well as the absence of strong associations between specific genotypes and survival, suggests that other factors\u0026mdash;such as tumor characteristics,\u003csup\u003e2,16,17\u003c/sup\u003e treatment modalities,\u003csup\u003e18,19\u003c/sup\u003e and host immune responses\u0026mdash;may play a more significant role in influencing survival outcomes.\u003c/p\u003e\n\u003cp\u003eIn line with the growing body of literature on HPV and CC, our findings contribute to the understanding of how multiple HR-HPV infections might influence cancer progression. Previous studies have shown inconsistent results regarding the prognostic impact of specific HPV genotypes, with some studies\u003csup\u003e4,15\u003c/sup\u003e suggesting worse outcomes for HPV 18-positive CCs compared with HPV 16-positive ones, and others reporting no significant differences.\u003csup\u003e14,15\u003c/sup\u003e Our study adds to this discourse by highlighting that multiple HR-HPV infections, rather than a single genotype, may be a more important determinant of prognosis, particularly in the context of early-stage disease.\u003c/p\u003e\n\u003cp\u003eA variable proportion of CCs test negative for HR-HPV,\u003csup\u003e15,20\u003c/sup\u003e ranging from 8% to 19%,\u003csup\u003e11,21\u003c/sup\u003e which is consistent with our findings and it could be due to factors like low viral load,\u003csup\u003e20\u003c/sup\u003e sample variability, or misclassification.\u003csup\u003e14,20-22\u003c/sup\u003e Moreover, the study contributes to the ongoing debate about HPV-negative CC,\u003csup\u003e23\u003c/sup\u003e which was associated with poorer outcomes in our cohort, similarly to other studies.\u003csup\u003e2,3,6,14,17,22,24-29\u003c/sup\u003e This finding reinforces the importance of HR-HPV testing in CC diagnosis and prognosis, as HR-HPV negativity is likely to indicate a distinct and more aggressive tumor biology, becoming less susceptible to immune control.\u003csup\u003e30\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe implications of our findings are multifaceted. Clinically, the results suggest that HPV genotyping could help identify patients at higher risk of poor outcomes, particularly those with multiple HR-HPV genotypes. This could have important consequences for treatment strategies, especially in early-stage disease where surgical interventions are common. Additionally, the findings emphasize the importance of considering HPV status in prognosis models for CC, particularly in the context of treatment planning and follow-up strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeneralisability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe generalisability of our results is a critical consideration, especially given the limitations in sample size and regional variability. While the study was conducted in limited geographic area, and primarily reflects the HPV genotypes and clinical outcomes observed in this population, similar studies conducted in different geographical regions could yield different results, especially in areas where HPV vaccination rates or HPV genotype distributions differ. Furthermore, while the study included a diverse cohort in terms of histological subtypes, the relatively small number of patients with adenocarcinoma and other rare forms of CC limits the ability to generalize findings to all histological subtypes of CC.\u003c/p\u003e\n\u003cp\u003eIn terms of treatment, the study population may not reflect the full spectrum of treatment approaches used globally. The use of different treatment regimens, the role of adjuvant therapy, and variations in radiotherapy or chemotherapy regimens could influence survival outcomes. Thus, while the findings are relevant to understanding the relationship between HPV status and prognosis in the studied region, further studies in different cohorts, including international populations, are needed to assess whether these results hold true across various clinical settings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study highlights the complex role of HR-HPV genotypes in the prognosis of CC. While the presence of multiple HR-HPV infections was associated with better survival outcomes, particularly in early-stage disease, HPV genotyping alone does not appear to be a strong predictor of survival. Our findings suggest that further research is needed to clarify the specific role of multiple HR-HPV infections in CC progression and to explore the underlying mechanisms that might explain the observed survival benefits. Future studies should focus on larger, multi-center cohorts, and long-term follow-up to better understand the clinical implications of HPV co-infection and its potential as a therapeutic target. Furthermore, HPV vaccination programs could play a crucial role in reducing the burden of cervical cancer, and understanding the nuances of HPV genotyping could help refine early detection and treatment strategies for better patient outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCC, cervical cancer\u003c/p\u003e\n\u003cp\u003eCI, confidence interval\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDFS, disease-free survival\u003c/p\u003e\n\u003cp\u003eFIGO, International Federation of Gynecology and Obstetrics\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHPV, human papillomavirus\u003c/p\u003e\n\u003cp\u003eHR, high-risk\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIQR, interquartile range\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMAR, missing at random\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eM\u003csub\u003ee\u003c/sub\u003e, median\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMFS, metastasis-free survival\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eN, sample size\u003c/p\u003e\n\u003cp\u003eOR, odds ratio\u003c/p\u003e\n\u003cp\u003eOS, overall survival\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePERCIST, PET response criteria in solid tumors\u003c/p\u003e\n\u003cp\u003ePCR, polymerase chain reaction\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eR, range\u003c/p\u003e\n\u003cp\u003eRECIST, response evaluation criteria in solid tumors\u003c/p\u003e\n\u003cp\u003eSE, standard error\u003c/p\u003e\n\u003cp\u003eSEGO, Spanish Society of Gynecology and Obstetrics\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSEOM, Spanish Society of Medical Oncology\u003c/p\u003e\n\u003cp\u003eSEOR, Spanish Society of Radiation Oncology\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSNAP, studio neo-adjuvante portio\u003c/p\u003e\n\u003cp\u003eWHO, World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Research Ethics Committee of Virgen Macarena and Virgen del Roc\u0026iacute;o University Hospitals (Approval Date: April 23, 2018; Code: JCG-CCI-2018-01). A research assistant obtained written informed consent from all participants, ensuring that they fully understood the study and their rights. Participants also provided consent for the publication of their data. In case of deceased subjects and the impossibility to obtain informed consent, ethical approval was obtained from the Ethics Committee to proceed with the use of posthumous data for research purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eJ.C.G.\u003c/strong\u003e: Investigation, Conceptualization, Methodology, Writing\u0026mdash;Original Draft Preparation, Visualization, Writing\u0026mdash;Review and Editing.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eL.R.P.\u003c/strong\u003e: Methodology, Software, Data Curation, Formal Analysis, Validation, Writing\u0026mdash;Review and Editing, Supervision.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eM.C.R.R.\u003c/strong\u003e: Supervision.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eF.M.M.\u003c/strong\u003e: Resources, Supervision.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eI.R.J.\u003c/strong\u003e: Resources, Supervision.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eAI Disclosure:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePortions of this manuscript were refined using ChatGPT (OpenAI) under direct author supervision.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eIsciii.es. [cited on November 20, 2024]. Available in: http://www.ariadna.cne.isciii.es\u003c/li\u003e\n \u003cli\u003eCuschieri K, Brewster DH, Graham C, Nicoll S, Williams AR, Murray GI, et al. Influence of HPV type on prognosis in patients diagnosed with invasive cervical cancer. Int J Cancer. 2014 Dec 1;135(11):2721-6. doi: 10.1002/ijc.28902\u003c/li\u003e\n \u003cli\u003eLau YM, Cheung TH, Yeo W, Mo F, Yu MY, Lee KM, et al. Prognostic implication of human papillomavirus types and species in cervical cancer patients undergoing primary treatment. PLoS One. 2015 Apr 9;10(4):e0122557. doi: 10.1371/journal.pone.0122557\u003c/li\u003e\n \u003cli\u003eHang D, Jia M, Ma H, Zhou J, Feng X, Lyu Z, et al. Independent prognostic role of human papillomavirus genotype in cervical cancer. BMC Infect Dis. 2017 Jun 5;17(1):391. doi: 10.1186/s12879-017-2465-y\u003c/li\u003e\n \u003cli\u003eTong SY, Lee YS, Park JS, Namkoong SE. Human papillomavirus genotype as a prognostic factor in carcinoma of the uterine cervix. Int J Gynecol Cancer. 2007 Nov-Dec;17(6):1307-13. doi: 10.1111/j.1525-1438.2007.00933.x\u003c/li\u003e\n \u003cli\u003eWang CC, Lai CH, Huang HJ, Chao A, Chang CJ, Chang TC, et al. Clinical effect of human papillomavirus genotypes in patients with cervical cancer undergoing primary radiotherapy. Int J Radiat Oncol Biol Phys. 2010 Nov 15;78(4):1111-20. doi: 10.1016/j.ijrobp.2009.09.021\u003c/li\u003e\n \u003cli\u003eMu\u0026ntilde;oz N, Bosch FX, de Sanjos\u0026eacute; S, Herrero R, Castellsagu\u0026eacute; X, Shah KV, et al; International Agency for Research on Cancer Multicenter Cervical Cancer Study Group. Epidemiologic classification of human papillomavirus types associated with cervical cancer. N Engl J Med. 2003 Feb 6;348(6):518-27. doi: 10.1056/NEJMoa021641\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLee SA, Kang D, Seo SS, Jeong JK, Yoo KY, Jeon YT, et al. 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DOI: 10.1371/journal.pone.0182854\u003c/li\u003e\n \u003cli\u003eNicol\u0026aacute;s I, Saco A, Barnadas E, Marimon L, Rakislova N, Fust\u0026eacute; P, et al. Prognostic implications of genotyping and p16 immunostaining in HPV-positive tumors of the uterine cervix. Mod Pathol. 2020 Jan;33(1):128-137. doi: 10.1038/s41379-019-0360-3\u003c/li\u003e\n \u003cli\u003eDahlgren L, Erlandsson F, Lindquist D, Silfversw\u0026auml;rd C, Hellstr\u0026ouml;m AC, Dalianis T. Differences in human papillomavirus type may influence clinical outcome in early stage cervical cancer. Anticancer Res. 2006 Mar-Apr;26(2A):829-32.\u003c/li\u003e\n \u003cli\u003eChong GO, Lee YH, Han HS, Lee HJ, Park JY, Hong DG, et al. Prognostic value of pre-treatment human papilloma virus DNA status in cervical cancer. Gynecol Oncol. 2018 Jan;148(1):97-102. doi: 10.1016/j.ygyno.2017.11.003\u003c/li\u003e\n \u003cli\u003eLi P, Tan Y, Zhu LX, Zhou LN, Zeng P, Liu Q, et al. Prognostic value of HPV DNA status in cervical cancer before treatment: a systematic review and meta-analysis. Oncotarget. 2017 Jun 16;8(39):66352-66359. doi: 10.18632/oncotarget.18558\u003c/li\u003e\n \u003cli\u003eSchwartz SM, Daling JR, Shera KA, Madeleine MM, McKnight B, Galloway DA, et al. Human papillomavirus and prognosis of invasive cervical cancer: a population-based study. J Clin Oncol. 2001 Apr 1;19(7):1906-15. doi: 10.1200/JCO.2001.19.7.1906\u003c/li\u003e\n \u003cli\u003eYang SH, Kong SK, Lee SH, Lim SY, Park CY. Human papillomavirus 18 as a poor prognostic factor in stage I-IIA cervical cancer following primary surgical treatment. Obstet Gynecol Sci. 2014 Nov;57(6):492-500. doi: 10.5468/ogs.2014.57.6.492\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKim JY, Nam BH, Lee JA. Is human papillomavirus genotype an influencing factor on radiotherapy outcome? Ambiguity caused by an association of HPV 18 genotype and adenocarcinoma histology. J Gynecol Oncol. 2011 Mar 31;22(1):32-8. doi: 10.3802/jgo.2011.22.1.32\u003c/li\u003e\n \u003cli\u003eOkonogi N, Kobayashi D, Suga T, Imai T, Wakatsuki M, Ohno T, et al. Human papillomavirus genotype affects metastatic rate following radiotherapy in patients with uterine cervical cancer. Oncol Lett. 2018 Jan;15(1):459-466. doi: 10.3892/ol.2017.7327\u003c/li\u003e\n \u003cli\u003ede Sanjose S, Quint WG, Alemany L, Geraets DT, Klaustermeier JE, Lloveras B, et al; Retrospective International Survey and HPV Time Trends Study Group. Human papillomavirus genotype attribution in invasive cervical cancer: a retrospective cross-sectional worldwide study. Lancet Oncol. 2010 Nov;11(11):1048-56. doi: 10.1016/S1470-2045(10)70230-8\u003c/li\u003e\n \u003cli\u003eTao X, Zheng B, Yin F, Zeng Z, Li Z, Griffith CC, et al. Polymerase Chain Reaction Human Papillomavirus (HPV) Detection and HPV Genotyping in Invasive Cervical Cancers With Prior Negative HC2 Test Results. Am J Clin Pathol. 2017 May 1;147(5):477-483. doi: 10.1093/ajcp/aqx027\u003c/li\u003e\n \u003cli\u003eLei J, Ploner A, Lagheden C, Eklund C, Nordqvist Kleppe S, Andrae B, et al. High-risk human papillomavirus status and prognosis in invasive cervical cancer: A nationwide cohort study. PLoS Med. 2018 Oct 1;15(10):e1002666. doi: 10.1371/journal.pmed.1002666\u003c/li\u003e\n \u003cli\u003eBaalbergen A, Smedts F, Ewing P, Snijders PJ, Meijer CJ, Helmerhorst TJ. HPV-type has no impact on survival of patients with adenocarcinoma of the uterine cervix. Gynecol Oncol. 2013 Mar;128(3):530-4. doi: 10.1016/j.ygyno.2012.12.013\u003c/li\u003e\n \u003cli\u003eLi N, Franceschi S, Howell-Jones R, Snijders PJ, Clifford GM. Human papillomavirus type distribution in 30,848 invasive cervical cancers worldwide: Variation by geographical region, histological type and year of publication. Int J Cancer. 2011 Feb 15;128(4):927-35. doi: 10.1002/ijc.25396\u003c/li\u003e\n \u003cli\u003eOkuma K, Yamashita H, Yokoyama T, Nakagawa K, Kawana K. Undetected human papillomavirus DNA and uterine cervical carcinoma: Association with cancer recurrence. Strahlenther Onkol. 2016 Jan;192(1):55-62. doi: 10.1007/s00066-015-0909-0\u003c/li\u003e\n \u003cli\u003eHopenhayn C, Christian A, Christian WJ, Watson M, Unger ER, Lynch CF, et al. Prevalence of human papillomavirus types in invasive cervical cancers from 7 US cancer registries before vaccine introduction. J Low Genit Tract Dis. 2014 Apr;18(2):182-9. doi: 10.1097/LGT.0b013e3182a577c7\u003c/li\u003e\n \u003cli\u003eChong GO, Han HS, Park JY, Lee SD, Lee YH, Lee HJ, et al. Prevalence, survival outcomes, and clinicopathologic factors associated with negative high risk human papillomavirus in surgical specimens of cervical cancer with pretreatment negative DNA genotype test. Int J Gynecol Cancer. 2019 Jan;29(1):10-16. doi: 10.1136/ijgc-2018-000003\u003c/li\u003e\n \u003cli\u003eNicol\u0026aacute;s I, Marimon L, Barnadas E, Saco A, Rodr\u0026iacute;guez-Carunchio L, Fust\u0026eacute; P, et al. HPV-negative tumors of the uterine cervix. Mod Pathol. 2019 Jul;32(8):1189-1196. doi: 10.1038/s41379-019-0249-1\u003c/li\u003e\n \u003cli\u003eMkrtchian L, Zamulaeva I, Krikunova L, Kiseleva V, Matchuk O, Liubina L, et al. HPV Status and Individual Characteristics of Human Papillomavirus Infection as Predictors for Clinical Outcome of Locally Advanced Cervical Cancer. J Pers Med. 2021 May 27;11(6):479. doi: 10.3390/jpm11060479\u003c/li\u003e\n \u003cli\u003eAng KK, Harris J, Wheeler R, Weber R, Rosenthal DI, Nguyen-T\u0026acirc;n PF, et al. Human papillomavirus and survival of patients with oropharyngeal cancer. N Engl J Med. 2010 Jul 1;363(1):24-35. doi: 10.1056/NEJMoa0912217\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"HPV, Genotype, Coinfection, Uterine Cervical Neoplasms, Prognosis, Survival, Treatment Outcome","lastPublishedDoi":"10.21203/rs.3.rs-6770268/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6770268/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e: To assess the prevalence of human papillomavirus (HPV) genotypes in cervical cancer (CC) and evaluate their association with survival outcomes and treatment response.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: This was a prospective observational cohort study including 229 CC patients diagnosed between 2010 and 2019. HPV genotyping was performed using the HybriSpot24™ platform in 84 tumor samples. Patients were treated and followed in a tertiary referral hospital. Primary outcomes included overall survival (OS), disease-free survival (DFS), and treatment response. Group comparisons were conducted using Kaplan-Meier and Cox regression models. Statistical significance was set at p \u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: HPV DNA was detected in 91.67% of tumors, with HPV 16 being the most prevalent genotype (30.71%). HPV-positive patients had significantly longer OS than HPV-negative patients (difference: 26.1 months; 95% CI: 16.5–35.7; p \u0026lt;0.0001). No significant OS difference was observed between HPV 16 and HPV 18 (difference: 3.3 months; 95% CI: -7.6 to 14.1; p = 0.589). Patients with multiple HR-HPV infections had better OS (64.6 vs. 38.5 months; p = 0.047) and DFS (64.6 vs. 30.31 months; p = 0.017), particularly in early-stage disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: HPV positivity was associated with improved OS in CC. Multiple HR-HPV infections correlated with enhanced survival and treatment response, especially in early stages. These findings support the prognostic relevance of HPV genotyping in cervical cancer.\u003c/p\u003e","manuscriptTitle":"Association Between Pre-Treatment HPV Genotype and Survival in Cervical Cancer: Insights From a Prospective Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 15:58:55","doi":"10.21203/rs.3.rs-6770268/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"778ab0b8-cc24-4ff9-8c12-5d05e8138973","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-16T16:08:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-17 15:58:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6770268","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6770268","identity":"rs-6770268","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}