Skip to main content
Download PDF
Download ePub

A comparative analysis of somatic mutational profiles according to HIV status among women with cervical intraepithelial neoplasia 3 (CIN3): a focus on hotspots in TP53, PIK3CA, PTEN, and EGFR

Infectious Agents and Cancer volume 20, Article number: 18 (2025) Cite this article

Abstract

Background

Despite the success of antiretroviral therapy in HIV treatment, cervical cancer remains a leading malignancy in HIV-infected women. Additionally, co-infection by HIV and HPV further accelerates cervical cancer development. There are limited studies on the role of host somatic variations in HIV infected and HIV-negative women with cervical cancer. Therefore, this study aimed to investigate and compare host somatic genetic variation in cervical biopsies obtained from HIV infected and HIV-negative women with cervical intraepithelial neoplasia 3 to understand the genomic landscape. The distribution of HPV types was also investigated between HIV infected and HIV-negative women.

Methods

The project used an age-matched case–control study utilizing archived cervical biopsies from 88 women (44 HIV infected, 44 HIV-negative) attending Groote Schuur Hospital Cancer Clinic between 2020 and 2022. HPV infection and type were confirmed using the Anyplex™ II HPV28 Detection kit. Six hotspot regions in the four commonly mutated genes (TP53, PIK3CA, PTEN, and EGFR) in cervical cancer were genotyped using PCR and Sanger Sequencing. Variant pathogenicity was assessed using SIFT, Polyphen-2, and ClinVar tools.

Results

The median age was 37 years (IQR: 34–41) for HIV infected women and 35 years (IQR:32- 43) for HIV-negative women. Significantly more HIV-negative women (51% vs. 12%) reported tobacco smoking (p < 0.0001), menstruation irregularities (74% vs. 35%; p = 0.005), and contraception usage (77% vs. 59%; p = 0.019), when compared to their HIV-infected counterparts. Common HPV types identified were HPV16 (n = 43/88, 49%), HPV35 (n = 12/88, 14%), and HPV58 (n = 10/88, 11%). A total of 232 genetic variants were reported. HIV infected women had a significantly higher (p = 0.0406) burden of pathogenic variants (31%) compared to the HIV-negative (15%). The spectrum of observed mutations included stop-gain, missense, synonymous, and intronic changes. Most of the stop gain mutations in TP53 and PIK3CA were reported among HIV infected women (n = 4/5), compared to HIV-negative women (n = 1/5). Damaging variants were more prevalent in women under 50 in both cohorts. We also report on rare HPV subtypes currently not included in the diagnostic HPV test kits in this cohort (HPV 82, 42, 43 and 53).

Conclusion

HIV-infection status and age appear to be risk factors for higher burden of pathogenic mutations in genes that predispose to cervical cancer. Mutation profiles in PIK3CA and TP53 genes could be biomarkers of cervical cancer progression but more studies are needed.

Introduction

Despite increasing screening and vaccination programs, cervical cancer remains a prevalent malignancy globally, especially in low and middle-income countries (LMICs) [1], with an estimated 604 127 new cases and 341 831 deaths worldwide in 2020 [1]. Cervical cancer is the fourth most diagnosed malignancy and cause of death in women globally [2]. In developing countries, cervical cancer is the second most diagnosed cancer and the cause of death of women after breast cancer [2]. In South Africa, cervical cancer is the second most diagnosed cancer in women, with an incidence rate of between 22.8 to 27 per 100 000 women, 5743 new cases, and 3027 deaths per annum [3].

Cervical cancer originates from precancerous lesions called cervical intraepithelial neoplasia (CIN), categorized into low-grade squamous intraepithelial lesion (LSIL) which is CIN1, and high-grade squamous intraepithelial lesions (HSIL) divided into CIN2 and CIN3 [4]. The main etiology of cervical cancer and its precursor CIN is infection with human papillomavirus (HPV). HPV is a common sexually transmitted infection affecting over half of sexually active individuals worldwide [5, 6]. There are over 200 HPV types, categorized as either high-risk HPV (HR-HPV) or low-risk HPV (LR-HPV) based on their cancer-inducing potential. HR-HPVs (e.g., HPV16 and HPV18), are associated with anogenital cancers, including cervical cancer, while LR-HPVs (e.g., HPV6 and HPV11), cause genital warts and low-grade cervical lesions [6]. Persistent infection with HR-HPV types causes the progression of CIN to invasive cervical cancer [7]. However, HR-HPV types alone are insufficient to drive cervical carcinogenesis [8]. Several risk factors influence the development of precancerous lesions to invasive cervical cancer. These include lifestyle, host genetics and being infected with multiple HR-HPV types [8]. These risk factors function as co-factors for cervical cancer by enhancing cancer development with HPV infection. The common risk factors for susceptibility to cervical cancer include early age of sexual debut, multiple sexual partners, tobacco smoking, long-term use of oral contraceptives, and immunosuppression [9, 10].

Human immunodeficiency virus (HIV) infection leads to immunosuppression [11]. The introduction of antiretroviral therapy (ART) has markedly changed the global epidemiology of HIV [11]. However, even in the presence of ART-controlled HIV, HIV-infected individuals still experience a high burden of premalignant and malignant lesions associated with HPV infection [12]. It has been shown that HIV infection increases the risk of incident and persistent HPV infection, rapid progression to HPV-associated lesions, and invasive cervical cancer [13]. Therefore, women infected with HPV and co-infected with HIV have a substantial risk of developing cervical cancer, estimated at six-fold compared to their HIV-negative counterparts [14]. Additionally, the morbidity of cervical cancer in HIV infected women is up to 8 times higher compared to among HIV-negative women [15, 16].

Several studies have examined the association between cervical cancer among women with HIV and HPV. However, very few have compared the host somatic differences in HIV infected and HIV-negative women. Thus, we considered it important to evaluate genes that encode proteins essential for controlling normal cell growth, proliferation, and apoptosis (i.e. tumour suppressor genes, oncogenes, and DNA repair genes), which have been implicated in cervical carcinogenesis. Mutations in these genes lead to dysregulation including aberrant gene overexpression.

Multiple genes have been described as significantly mutated genes (SMG) in cancers by The Cancer Genome Atlas (TCGA) [17]. The SMGs in cervical cancer include tumour TP53, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), phosphatase and tensin homolog (PTEN), and epidermal growth factor receptor (EGFR) genes. These genes are also reported as significantly mutated in numerous studies [18, 19], including the TCGA. Genetic variation within the human host also plays a significant role in cancer progression and can be a potential insight into prognostic differences between the observed varying carcinogenesis rate in HIV infected women when compared to their HIV negative counterparts. This study therefore investigated host somatic genetic variations in TP53, PIK3CA, PTEN and EGFR in cervical biopsies obtained from HIV infected and HIV-negative women with histologically confirmed CIN3 with the aim to determine potential differences in genomic landscapes and HPV infection between HIV infected and HIV-negative women.

Methods

Study design and setting and ethics considerations

This was an age-matched case–control study where cases were women infected with HIV and controls were women who were not infected with HIV. Both cases and controls had histologically confirmed cervical intraepithelial neoplasia 3 (CIN3). The study employed archived cervical biopsies obtained from women who attended the Groote Schuur Hospital Cancer Clinic in Cape Town, South Africa between 2020 and 2022. Cervical biopsies were stored in the Division of Anatomical Pathology, Department of Pathology, Faculty of Health Sciences, University of Cape Town.

Study population and data collection

Eligible study participants were women 18 years or older, with histologically confirmed CIN3, and had biopsies collected prior to treatment. Women younger than 18 years, without histologically confirmed CIN3, nor archived cervical biopsies, and had been treated with chemotherapy or radiotherapy prior to tumour resection were excluded from the study. Demographic and clinical data were collected from patient’s medical records, including viral load and CD4 count.

DNA extraction and HPV typing

Deoxyribonucleic Acid (DNA) was extracted from 4 µm thick formalin-fixed paraffin embedded (FFPE) curls using the Zymo genomic DNA extraction FFPE kit (Zymo Research, California, USA), following the manufacturer's protocol. DNA concentration and purity were assessed using Nanodrop® ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, Massachusetts, United States). Gel electrophoresis using 1% agarose gel was used to assess the integrity of DNA. HPV genotyping was performed using the Anyplex™ II HPV28 Detection kit (Seegene, Seoul, South Korea), following the manufacturer's protocol. This kit tests for 19 high-risk HPV genotypes namely HPV type 16, 18, 26, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68, 69, 73, 82, and 9 low-risk HPV genotypes namely HPV type 6, 11, 40, 42, 43, 44, 54, 61, 70.

Gene selection and sequencing

This study reports alterations detected in the hotspot regions of selected genes (Table 1). To further support the selection of these genes, GeneMANIA (https://genemania.org/) a useful tool in determining the interactions between genes was used. Using GeneMANIA (Fig. 1), PIK3CA, TP53, PTEN and EGFR were selected as the most altered genes in cervical carcinogenesis pathways. Based on existing literature, somatic mutations in TP53 (exon 4), PIK3CA (exon 9 and 20), PTEN (exon 5), and EGFR (exon 19 and 20) in the 88 tumour samples were analyzed using PCR and Sanger sequencing.

Table 1 Literature guided selection of tumour hotspots
Full size table
Fig. 1
figure 1

A GeneMANIA gene–gene interaction network of the hotspot genes (TP53, PIK3CA, PTEN and EGFR). Different colors of the network nodes indicate different interactions which are physical interaction, co-expression, predicted, co-localization, common pathway, genetic interaction and shared protein domains

Full size image

The primers used were ordered from Inqaba Biotechnical Industries (Pretoria, South Africa) (Supplementary Table 1). Each PCR mix contained 1X Reaction Buffer (Promega Cooperation, Madison, USA), 0.4 mM deoxyribonucleoside triphosphate (dNTPs)(Kappa Biosystems, Cape Town, South Africa), 0.03U Go Taq Polymerase (Promega, USA), 1.5 mM magnesium chloride (Thermo Scientific, Waltham, USA), 0.4 µM forward and reverse primers,and the total reaction volume was made up to 25 μl using nuclease-free water (Thermo Scientific, Waltham, USA). PCR was carried on an Applied Biosystems SimpliAmp™ Thermal Cycler (Thermo Scientific, USA) and cycling conditions included an initial denaturation at 95 °C for 3 min followed by 35 cycles of denaturation at 95 °C for 30 s, annealing for 30 s at appropriate temperature and extension at 72 °C for 1 min, then final extension at 72 °C for 5 min. Sanger sequencing was performed using 5 X Big-dye Terminator v3.1 (Life Technologies, CA, USA), 1 X Big-dye Terminator buffer (Life Technologies, CA, USA) and 1 µM of either forward or reverse primer. The sequencing reaction was performed in an Applied Biosystems SimpliAmp™ Thermal Cycler under the following conditions: initial denaturation at 98 °C for 5 min, followed by 25 cycles of denaturation at 96 °C for 30 s, annealing at 50 °C for 15 s, extension at 60 °C for 4 min, then final extension at 60 °C for 4 min. Capillary electrophoresis was done on the 3130xl DNA Analyzer (Applied Biosystems, CA, USA).

Data analysis and pathogenicity testing

Data from the somatic gene sequencing using Sanger sequencing was analyzed using the DNASTAR Lasergene SeqMan Pro alignment tool (v16.0.0), Blastn and dbSNP Pathogenicity testing for missense variants was done using Sorting Intolerant from Tolerant (SIFT) (https://sift.bii.astar.edu.sg/www/Extended_SIFT_chr_coords_submit.html), Polymorphism Phenotyping v2 (PolyPhen2) (http://genetics.bwh.harvard.edu/pph2/bgi.shtml), and ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/).

Statistical analysis was performed using the STATA® SE-64 software program, version 15.0. Continuous data was reported as mean ± standard deviation (SD) or median ± interquartile range (IQR) depending on whether the data is normally distributed or not normally distributed. The Shapiro-Wilks test was used to test for the normality of the data. Chi-square test was used to compare the differences in sociodemographic and clinical data between HIV infected and HIV-negative women. The p value < 0.05 was considered statistically significant.

Results

Clinical and demographic characteristics

Eighty-eight samples (n = 88) were retrieved for this study consisting of tumours derived from 44 HIV infected women (case group) and 44 HIV-negative women (control group). Clinical and demographic characteristics were available for 82 women (Table 2). Clinical files for the remaining 6 participants could not be located hence, were excluded from the clinical and demographic analyses. HIV-negative women showed higher rates for smoking history (20 HIV-negative versus 5 HIV infected; p < 0.0001), menstruation irregularity (26 HIV-negative versus 15 HIV infected; p = 0.005), and use of contraception (30 HIV-negative versus 24 HIV infected; p = 0.019) when compared to their HIV infected counterparts. The HIV infected women had controlled HIV viral load with 44% with non-detectable viral load, 37% having < 200 copies/ml, and only 12% (n = 5) had viral loads greater than 1000 copies/ml. Twenty-three percent (n = 10) had CD4 count greater than 500 cells/mm3, and 8 individuals had CD4 < 200 cells/mm3. These findings show that the study participants infected with HIV had satisfactory virological and immunological response to treatment.

Table 2 Demographic and clinical characteristics of HIV infected and HIV-negative women with CIN3
Full size table

Overall prevalence of HPV infection

The 88 participants with CIN3 were screened for HPV and 87 were successfully genotyped with one sample failing. We report on 11 HR-HPV types (16, 31, 33, 35, 39, 45, 51, 52, 53, 58 and 82) and 4 LR-HPV types (6, 11, 42 and 43) (Fig. 2A). HPV16 was the most prevalent type (n = 43/87, 49%), followed by HPV35 (n = 12/87, 14%) and HPV58 (n = 10/87, 11%). HIV infected women exhibited significantly higher rates of HPV co-infections compared to HIV-negative women (p = 0.002) (Fig. 2B). HR-HPV18 was not detected in this study cohort.

Fig. 2
figure 2

HPV genotypes. A Distribution of HPV genotypes identified in the study cohort. B Distribution of HPV genotypes in women with confirmed CIN3 stratified by HIV status, showing mono-infections and co-infections. The co-infections were predominantly limited to HIV infected women

Full size image

Overall variants identified in a cohort

Genotyping identified four types of mutations: stop-gain, missense, synonymous and intronic. A total of 232 variants were identified (Fig. 3). Overall, HIV infected women had less mutation burden (n = 105) compared to HIV-negative women (n = 127), however, they had more damaging mutations, which includes both missense and stop gain variants. Stop gain variants were limited to TP53 and PIK3CA with three identified in TP53 and two in PIK3CA. Four (80%) of the five stop-gain variants were identified in HIV infected women. TP53 had a higher frequency of missense and stop-gain mutations than all other genes (Fig. 3). PIK3CA presented with all four types of mutations, most of which were missense mutations. PTEN had no stop-gain nor intronic mutations, while EGFR had no stop-gain mutations but exhibited a higher frequency of intronic mutations.

Fig. 3
figure 3

Distribution of variants according to HIV status among the women. Each oncoprint box represents a mutation, and different colors represent different mutation types, while each row represents a gene and exon. The key shows the total number of variants identified for HIV infected and HIV-negative participants for each mutation type and the description of different colors in the oncoprint

Full size image

Genomic landscapes of pathogenic variants

Missense variants were further analyzed to determine whether the amino acid substitution was damaging to the protein function. Sorting Intolerant from Tolerant (SIFT), Polymorphism Phenotyping v2 (Polyphen-2), and ClinVar tools were used to test for the pathogenicity of the missense variants. Variants were categorized based on their reported damaging status in SIFT or PolyPhen2 or ClinVar. Those reported as damaging in one tool were classified as probably damaging, while those reported by at least two tools were classified with high confidence as most likely damaging. Variants not reported as damaging in any tool were classified as unlikely damaging.

The pathogenicity analysis results, including stop-gain variants, are presented in (Supplementary Tables 2 and 3). After pathogenicity testing, 48 variants were identified as pathogenic. HIV-negative women had 25 variants, while HIV-negative women had 23 variants, (p = 0.4321). However, a higher mutation burden of stop-gain variants was identified in HIV infected women. Additionally, HIV infected women (n = 17) had more most likely damaging variants compared to HIV-negative women (n = 9) (p = 0.0406) (Fig. 4). Three genes, TP53, PIK3CA and EGFR presented with damaging variants. Most likely damaging variants were most common in PIK3CA among HIV infected women when compared to HIV-negative women (p = 0.0071).

Fig. 4
figure 4

Overall number of pathogenic variants. A total of 48 pathogenic variants were identified in the cohort. The probably damaging mutations were reported as pathogenic on one pathogenicity testing tool, while most likely damaging variants were reported pathogenic in two or more testing tools. Mostly likely damaging variants were more prone on the HIV infected women

Full size image

Genomic landscapes of pathogenic variants versus age

Most-likely damaging variants were further stratified based on HIV status and age (Fig. 5). Age was divided into < 40 years old and ≥ 40 years old. Four stop-gain variants were identified in HIV infected women, and three of those were among women younger than 40 years, while for HIV-negative women one stop-gain was identified also in a woman younger than 40 years.

Fig. 5
figure 5

Overall number of pathogenic variants per age group (< 40 years old and ≥ 40 years old) per HIV status. Showing A HIV infected, B HIV-negative. The boxes with asterisks represent the most likely damaging variants, while those without asterisks are probably damaging

Full size image

Discussion

The interaction of HIV infection and HPV on host somatic genetic variation which leads to CIN3 remains to be fully elucidated. The median age observed in this study cohort was 36 (IQR: 33–43), which is lower than the median age reported in other studies conducted in women with cervical cancer. A study by Taku and colleagues [20], reported a median age of 40 (IQR: 33–48). While another study by Kuguyo and colleagues [21], reported a median age of 51 (IQR: 42–62). Other studies have shown that participants with cervical cancer tend to be relatively young, with the median age at diagnosis of 47 years; however, almost 50% of cases diagnosed in this study were under the age of 35 years [22]. This means that in our cohort, there seems to be an earlier onset of cervical cancer whose molecular drivers need to be decoded and understood.

There are several factors that influence susceptibility to cervical cancer. Here we report significant differences between HIV infected and HIV-negative women in smoking history (p < 0.0001), menstruation irregularity (p = 0.005), and use of contraception (p = 0.019). Smoking history is a risk factor for various cancers, including cervical cancer [23]. Smoking has been independently shown to accelerate the risk of developing cervical cancer, and individuals who smoke are said to have four times higher risk of developing cervical cancer compared to nonsmokers, even after adjusting for other risk factors such as sexual behavior and history of infections [24]. These findings suggest that smoking drives cervical cancer by impairing the immune system and damaging the cervical epithelium through increased modification of DNA in the cervical epithelium [25].

In this study, we report of more smokers among HIV-negative women compared to their HIV infected counterparts, an observation probably driven by adjusted behavior among the HIV infected group. Among HIV-negative women irregular menstruation patterns were pronounced as was use of contraception. Irregular menstruation and the use of contraceptives have been associated with the development of cervical cancer [26]. This may suggest that contraceptives affect hormonal imbalances, which may lead to irregular menstruation, thereby promoting the development of aberrant cervical cells further increasing [27]. In other studies, women using hormonal contraceptives have been reported to have an increased chance of engaging in unprotected sex, placing them at a higher risk of HPV infection, a major etiology of cervical cancer [28]. In the current study, a higher incidence of multiple HPV infections was observed among HIV infected women using contraception. However, no instances of multiple HPV infections were found in HIV-negative women using contraceptives.

Cervical cancer is considered an AIDS-defining illness [29], indicating an increased risk among HIV infected women compared to their HIV-negative counterparts. A study by Bosch and colleagues [30], revealed a two to 22-fold increased risk of cervical cancer in HIV infected women compared to HIV-negative women. Similarly, Stelzle and co-workers [14] reported a six-fold increase in cervical cancer in HIV infected women compared to HIV-negative women. This increased risk is due to factors such as recurring oncogenic HPV infection, higher viral loads and decreased CD4 cell counts [31]. A higher viral load in HIV infected women is associated with increased risk of cervical dysplasia and progression to invasive cervical cancer [32], which is a result of immune suppression that leads to failure to fight off AIDS-defining illness or opportunistic diseases. Thus, it is important to monitor and manage viral load in HIV infected women to facilitate early detection and prevention of cervical cancer. In this study there was insufficient data to categorize HIV-infected individuals based on viral load (< 200 HIV copies/ml) and CD4 count (> 200 cells/mm3) due to a small sample size and inadequate medical history of the CD4 count. Out of 44 HIV infected individuals, 14 of them did not have the CD4 count data. Therefore, categorization by both viral load and CD4 count was not possible. However, each component was looked at independently. Out of 44 HIV-infected participants 36 had viral load < 200 HIV copies/ml, and eight had viral load > 200 HIV copies/ml. Of the eight participants that had viral load > 200 HIV copies/ml, one patient had one pathogenic variant in EGFR gene. A lower CD4 count has been linked with cervical cancer in women living with HIV [33]. Konopnicki and colleagues [34], showed that a CD4 count higher than 500 cells/μl for 18 months among HIV infected women were significantly associated with reduced risk of HPV infection, which is a driver of cervical cancer development. In this study 30 participants had CD4 count information, and eight had CD4 < 200 cells/mm3, while 22 had CD4 > 200 cells/mm3. Of the eight participants that had that CD4 < 200 cells/mm3, one patient had one pathogenic variant in EGFR gene. These findings suggest that identified pathogenic variants might not necessarily be linked to viral load or CD4 count, as only one pathogenic variant was found in one individual in the immunocompromised cohort.

Distribution of HPV types

This study identified prevalence and distribution of HR-HPV and LR-HPV types among HIV infected and HIV-negative women with CIN3. Seventy-four (84.09%) of women enrolled in this study were positive for HPV DNA. Thirteen (14.77%) of the women were negative for HPV DNA and one sample (1.14%) failed HPV genotyping. The women that did not have HPV DNA could be due to the choice of kit used which only targeted 28 HPV types. HPV16 was the most prevalent in the study cohort, which aligns with the previous study by Stoler and colleagues [35]. The reported high prevalence of both HPV 35 and 58 is similar to observations earlier among South African participants with CIN3 [36]. We also report a significantly higher rate of HPV-coinfection in HIV infected women compared to HIV-negative women (p value = 0.002), further confirming the observations by Mcharo and colleagues [37], who reported multiple HPV infections in HIV infected women compared to HIV-negative women (p = 0.0006). These findings suggest that HIV infected women may be more susceptible to multiple strains of HPV due to the compromised immunity [38, 39]. Thus, the distribution of HPV subtypes appears to be affected by local demographics, which is important in healthcare decision-making, particularly, in the selection of HPV vaccines. In addition, we report on four HPV subtypes HPV 82, 42, 43 and 53 that are ordinarily not detected using commercial HPV DNA diagnostic test kits such as BD Onclarity™, Abbot Alinity™, and Roche cobas ®. All four HPV types (82, 42, 43 and 53) were found in multiple infections, while HPV82 was also reported as a mono-infection. Our study therefore implores wider HPV screening that covers more HPV subtypes, for proper stratification of participants and management.

Genomic landscapes

The main etiological factor for cervical cancer is HPV [40]. Additionally, oncogenic mutations can also trigger neoplastic transformation, leading to abnormal protein or changing the protein's expression level coded by the mutated gene [19]. In this study genomic profiles of four frequently mutated genes, TP53, PIK3CA, PTEN, and EGFR in cervical cancer were evaluated, comparing results between HIV infected and HIV-negative women with CIN3. Of the 232 mutations found in the cohort, 38% were found in PIK3CA, 31% in EGFR, 29% in TP53 and 2% in PTEN. Similarly, a study by Sharmin and co-workers, found a high frequency of mutation in EGFR and PIK3CA, respectively [41]. However, a study by Wright and colleagues, found a lower EGFR mutation frequency compared to the current study (3.8% versus 31%), and a lower mutation frequency for PIK3CA compared to our study (31.3% versus 38%) [42]. PIK3CA frequency of mutations in the current study was also higher than that identified in a study by Spaans and colleagues [43], (38% versus 20%). TP53 also had a higher frequency of mutations when compared to a study by Wang and colleagues, (29% versus 12%), while PTEN had a lower frequency of mutations in this study (2% versus 16%) [44].

When looking at the frequencies of mutations stratified by HIV status, PIK3CA mutations were more frequent in HIV infected women (53% compared to 47% HIV-negative). This finding contradicts a previous study that identified a higher proportion of PIK3CA mutations in HIV-negative women compared to HIV infected women (45% compared to 29% HIV infected), resulting in a 1.3 times higher expression of PIK3CA in HIV-negative women [45]. Conversely, higher mutation frequencies for TP53, EGFR, and PTEN were observed in HIV-negative women (TP53: 41% versus. 59%, EGFR: 41% versus. 59%, PTEN: 20% versus. 80%), respectively. It is important to compare the somatic profiles of HIV infected and HIV-negative women with CIN3 to better understand how cancer progresses between these two groups. Currently, there is limited research in this area.

Genomic landscapes of Pathogenic variants

HIV infected women had a lower mutation burden (n = 105), compared to their HIV-negative counterparts (n = 127), mutation burden refers to the total number of genetic mutations present in a tumour, regardless of whether such mutations have detrimental effects [46]. A more nuanced approach considers the burden of damaging mutations. Thus, the identified missense variants were tested for pathogenicity using SIFT, Polyphen-2 and ClinVar.

A total of 48 pathogenic variants in the entire cohort (Sup. Table 2 and 3), were reported to be pathogenic There were significantly more (31% versus 15%) likely damaging mutations among the HIV infected women than among the HIV-negative women, respectively (p = 0.0406). The higher burden of most-likely damaging variants in HIV infected women potentially indicate increased susceptibility to CIN3 and cervical cancer development compared to HIV-negative women. This is supported by a study by Mpata and Nkosi that reported HIV infected women being 2–10 times more likely to develop precancerous lesions [47]. These pathogenic mutations may be drivers of carcinogenesis through alteration functions of the proteins concerned.

Overall, the most likely damaging variants were significantly more prevalent in PIK3CA among HIV infected women (n = 13) compared to HIV-negative (n = 3) (p = 0.0071). PIK3CA is a significantly mutated gene in cervical cancer reported in multiple studies, with a frequency of 5.7–35.7% [48]. The majority of the PIK3CA most likely damaging variants were found on exon 20 which is a kinase domain responsible for the phosphorylation of phosphatidylinositol-4,5-bisphosphate (PIP2) into phosphatidylinositol-3,4,5-trisphosphate (PIP3) [49]. The mutations found in this region can result in neoplastic transformation, which results in cancer progression by altering the normal activation of PI3K/AKT/mTOR pathway [50]. The increase in PIK3CA kinase activity results in the overactivation of PI3K/AKT/mTOR signaling pathway. The overexpression of mutant PIK3CA proteins is believed to result in tumour progression and resistance to standard therapy [51, 52]. Thus, understanding the function and mutations of PIK3CA is important to determine its effectiveness as a target of molecular targeted therapy or as a predictive biomarker in both gene therapy and radiation therapy [53]. PTEN and TP53 are among the genes that are significantly mutated in cervical cancer, and they are either mutated or functionally inactive in cervical cancer [54, 55]. PTEN did not have any most likely damaging variants for HIV infected and HIV-negative women. There was no significant difference in the number of TP53 most likely damaging variants between HIV infected and HIV-negative women. Abnormal activation of EGFR (mutations/amplification/overexpression) has been reported in different human cancers, including cervical cancer [56]. In the present study, EGFR had most likely damaging variants present in both HIV infected and HIV-negative women; however, there was no statistical significance. Overall, in the current study, we report a higher number of PIK3CA pathogenic mutations in CIN3 compared to other sequenced genes (TP53, PTEN and EGFR).

Pathogenic variants were mostly identified in women younger than 40 years old (Fig. 5). The reason for more pathogenic variants in younger women may include changes in sexual behavior and the burden of associated sexually transmitted infections, though the exact causes remain unclear [57, 58]. All the stop-gain variants were identified in a younger cohort of women for both HIV infected and HIV-negative women. This study cohort was virally suppressed and have good CD4 count showing that the progression to CIN3 at such a young age of virtually immunocompetent HIV infected women in this population needs to be further investigated. Something is driving the rapid progression that has not yet been identified. More multidisciplinary and concerted efforts are required.

Some of the limitations of this study were that it focused on a small set of genes and that our genetic characterization did not cover each gene from start to end but focused on hotspot regions. Future studies should therefore explore a broader genomic landscape between HIV infected and HIV-negative women and seek to cover entire gene regions. Differential expression studies are necessary for the understanding of the impact of the pathogenic variants on the protein level.

Conclusion

In this study, we were particularly interested in the hotspot regions of four significantly mutated genes in cervical cancer because these regions exhibit a higher frequency of mutations than other regions within the target genes. The results indicate that HIV infected women have a higher number of pathogenic variants compared to HIV-negative women. These variants include a higher mutation burden of stop-gain variants in HIV infected women. These preliminary results indicate that HIV infection potentially has an impact on the genetic profiles, resulting in increased genetic alterations that may be the cause of rapid progression to invasive cervical cancer in these women. Concerning HIV status, HPV and host genetics, our study highlights possible interaction between HIV and HPV on the occurrence of CIN3 in younger women and postulates host genetics (mutation burden) as a possible enabler. Understanding this interaction helps come up with molecular-based interventions that may enhance improved treatment and management of cervical cancer in HIV infected women.

Data availability

The supplementary files contain most of the data generated and analyzed during the current study. Other details on data for this study are available from the corresponding author on reasonable request.

Abbreviations

ART:

Antiretroviral therapy

HIV:

Human immunodeficiency virus

AIDS:

Acquired immunodeficiency syndrome

HPV:

Human papillomavirus

LMICs:

Low-income and middle-income countries

CIN:

Cervical intraepithelial neoplasia

LSIL:

Low-grade squamous intraepithelial lesion

HSIL:

High-grade squamous intraepithelial lesion

HR-HPV:

High-risk HPV

LR-HPV:

Low-risk HPV

SMG:

Significantly mutated genes

TCGA:

The cancer genome atlas

TP53 :

Tumour protein 53

PIK3CA :

Phosphatidylinositol-4,5- bisphosphate 3-kinase, catalytic subunit alpha

RBD:

Ras Binding domain

PI3K/Akt/mTOR:

Phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin

PIP2:

Phosphatidylinositol-4,5-bis-phosphate

PIP3:

Phosphatidylinositol-3,4,5-triphosphate

PTEN :

Phosphatase and tensin homolog

EGFR :

Epidermal growth factor receptor

DNA:

Deoxyribonucleic acid

HREC:

Human research ethics committee

FFPE:

Formalin-fixed paraffin-embedded

PCR:

Polymerase chain reaction

IARC:

International agency research on cancer

FastAP:

FastAp thermosensitive alkaline phosphatase

ExoI:

Exonuclease I

dNTP:

Deoxynucleotide triphosphate

EDTA:

Ethylenediaminetetraacetic acid

SIFT:

Sorting intolerant from tolerant

PolyPhen2:

Polymorphism phenotyping v2

SD:

Standard deviation

IQR:

Interquartile range

References

  1. Singh D, Vignat J, Lorenzoni V, Eslahi M, Ginsburg O, Lauby-Secretan B, et al. Global estimates of incidence and mortality of cervical cancer in 2020: a baseline analysis of the WHO Global Cervical Cancer Elimination Initiative. Lancet Glob Health. 2023;11(2):e197–206.

    Article  CAS  PubMed  Google Scholar 

  2. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424.

    Article  PubMed  Google Scholar 

  3. WHO. Cervical cancer 2021 [Available from: https://www.westerncape.gov.za/general-publication/cervical-cancer].

  4. Yousefi Z, Aria H, Ghaedrahmati F, Bakhtiari T, Azizi M, Bastan R, et al. An update on human papilloma virus vaccines: history, types, protection, and efficacy. Front Immunol. 2022;12: 805695.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Hellner K, Dorrell L. Recent advances in understanding and preventing human papillomavirus-related disease. F1000Res. 2017;6.

  6. Castro-Munoz LJ, Manzo-Merino J, Munoz-Bello JO, Olmedo-Nieva L, Cedro-Tanda A, Alfaro-Ruiz LA, et al. The Human Papillomavirus (HPV) E1 protein regulates the expression of cellular genes involved in immune response. Sci Rep. 2019;9(1):13620.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Castellsague X. Natural history and epidemiology of HPV infection and cervical cancer. Gynecol Oncol. 2008;110(3 Suppl 2):S4-7.

    Article  PubMed  Google Scholar 

  8. de Freitas AC, Gurgel AP, Chagas BS, Coimbra EC, do Amaral CM. Susceptibility to cervical cancer: an overview. Gynecol Oncol. 2012;126(2):304–11.

    Article  PubMed  Google Scholar 

  9. Collins S, Rollason TP, Young LS, Woodman CB. Cigarette smoking is an independent risk factor for cervical intraepithelial neoplasia in young women: a longitudinal study. Eur J Cancer. 2010;46(2):405–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ribeiro AA, Costa MC, Alves RR, Villa LL, Saddi VA, Carneiro MA, et al. HPV infection and cervical neoplasia: associated risk factors. Infect Agent Cancer. 2015;10:16.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Wilkins T. HIV-1-epidemiology-pathophysiology-and-transmission. Nurs Times. 2020;116(7):39.

    Google Scholar 

  12. Brickman C, Palefsky JM. Human papillomavirus in the HIV-infected host: epidemiology and pathogenesis in the antiretroviral era. Curr HIV/AIDS Rep. 2015;12(1):6–15.

    Article  PubMed  Google Scholar 

  13. Mbulawa ZZ, Marais DJ, Johnson LF, Coetzee D, Williamson AL. Impact of human immunodeficiency virus on the natural history of human papillomavirus genital infection in South African men and women. J Infect Dis. 2012;206(1):15–27.

    Article  PubMed  Google Scholar 

  14. Stelzle D, Tanaka LF, Lee KK, Ibrahim Khalil A, Baussano I, Shah ASV, et al. Estimates of the global burden of cervical cancer associated with HIV. Lancet Glob Health. 2021;9(2):e161–9.

    Article  CAS  PubMed  Google Scholar 

  15. Denny LA, Franceschi S, de Sanjose S, Heard I, Moscicki AB, Palefsky J. Human papillomavirus, human immunodeficiency virus and immunosuppression. Vaccine. 2012;30(Suppl 5):F168–74.

    Article  CAS  PubMed  Google Scholar 

  16. Chaturvedi AK, Madeleine MM, Biggar RJ, Engels EA. Risk of human papillomavirus-associated cancers among persons with AIDS. J Natl Cancer Inst. 2009;101(16):1120–30.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Frumovitz M, Sun CC, Schover LR, Munsell MF, Jhingran A, Wharton JT, et al. Quality of life and sexual functioning in cervical cancer survivors. J Clin Oncol. 2005;23(30):7428–36.

    Article  PubMed  Google Scholar 

  18. Wang L, Zhou Y, Cao C, Lin S, Zhi W, Zhang D, et al. The exon 12-containing LHX6 isoforms promote cervical cancer cell proliferation by regulating the MAPK signaling pathway. Cancer Med. 2022;11(19):3657–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Sharmin S, Zohura FT, Islam MS, Shimonty A, Khan MA, Parveen R, et al. Mutational profiles of marker genes of cervical carcinoma in Bangladeshi patients. BMC Cancer. 2021;21(1):289.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Taku O, Mbulawa ZZA, Phohlo K, Garcia-Jardon M, Businge CB, Williamson AL. Distribution of human papillomavirus (HPV) genotypes in HIV-negative and HIV-positive women with cervical intraepithelial lesions in the Eastern Cape Province, South Africa. Viruses. 2021;13(2).

  21. Kuguyo O, Dube Mandishora RS, Thomford NE, Makunike-Mutasa R, Nhachi CFB, Matimba A, et al. High-risk HPV genotypes in Zimbabwean women with cervical cancer: Comparative analyses between HIV-negative and HIV-positive women. PLoS ONE. 2021;16(9): e0257324.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Cohen PA, Jhingran A, Oaknin A, Denny L. Cervical cancer. Lancet. 2019;393(10167):169–82.

    Article  PubMed  Google Scholar 

  23. Banura C, Mirembe FM, Katahoire AR, Namujju PB, Mbidde EK. Universal routine HPV vaccination for young girls in Uganda: a review of opportunities and potential obstacles. Infect Agent Cancer. 2012;7(1):24.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Winkelstein W Jr. Smoking and cervical cancer–current status: a review. Am J Epidemiol. 1990;131(6):945–57 discussion 58-60.

    Article  PubMed  Google Scholar 

  25. Simons AM, Phillips DH, Coleman DV. Damage to DNA in cervical epithelium related to smoking tobacco. BMJ. 1993;306(6890):1444–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Munoz N, Castellsague X, de Gonzalez AB, Gissmann L. Chapter 1: HPV in the etiology of human cancer. Vaccine. 2006;24(Suppl 3):1–10.

    Article  Google Scholar 

  27. Syahadat DS, Eviyulianti NM, Rau MJ, Mantao E, Krisnasari S. The risks of sexual and reproductive activity on the occurrence of cervical cancer in central Sulawesi Province: case study of patients of Undata Hospital. J Health Nutr Res. 2022;1(3):171–7.

    Article  Google Scholar 

  28. McFarlane-Anderson N, Bazuaye PE, Jackson MD, Smikle M, Fletcher HM. Cervical dysplasia and cancer and the use of hormonal contraceptives in Jamaican women. BMC Womens Health. 2008;8:9.

    Article  PubMed  PubMed Central  Google Scholar 

  29. WHO. Consolidated guidelines on the use of antiretroviral drugs for treating and preventing HIV infection: recommendations for a public health approach, 2nd ed 2016 [Available from: https://www.who.int/publications/i/item/9789241549684].

  30. Bosch FX, Broker TR, Forman D, Moscicki AB, Gillison ML, Doorbar J, et al. Comprehensive control of human papillomavirus infections and related diseases. Vaccine. 2013;31(Suppl 7):H1-31.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Ports KA, Haffejee F, Mosavel M, Rameshbabu A. Integrating cervical cancer prevention initiatives with HIV care in resource-constrained settings: a formative study in Durban, South Africa. Glob Public Health. 2015;10(10):1238–51.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Abraham AG, Strickler HD, D’Souza G. Invasive cervical cancer risk among HIV-infected women is a function of CD4 count and screening. Jaids-J Acq Imm Def. 2013;63(5):E163.

    Google Scholar 

  33. Kelly H, Weiss HA, Benavente Y, de Sanjose S, Mayaud P, et al. Association of antiretroviral therapy with high-risk human papillomavirus, cervical intraepithelial neoplasia, and invasive cervical cancer in women living with HIV: a systematic review and meta-analysis. Lancet HIV. 2018;5(1):e45–58.

    Article  PubMed  Google Scholar 

  34. Konopnicki D, Wit SD, Clumeck N. HPV and HIV coinnfection- complex interaction resulting in epidemiology, clinical and therapeutic implications. Futur Virol. 2013;8(9):903–15.

    Article  CAS  Google Scholar 

  35. Stoler MH, Wright TC Jr, Parvu V, Yanson K, Cooper CK, Andrews J. Stratified risk of high-grade cervical disease using onclarity HPV extended genotyping in women, >/=25 years of age, with NILM cytology. Gynecol Oncol. 2019;153(1):26–33.

    Article  PubMed  Google Scholar 

  36. Mbulawa ZZA, Phohlo K, Garcia-Jardon M, Williamson AL, Businge CB. High human papillomavirus (HPV)-35 prevalence among South African women with cervical intraepithelial neoplasia warrants attention. PLoS ONE. 2022;17(3): e0264498.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. McHaro R, Lennemann T, France J, Torres L, Gari M, Mbuya W, et al. HPV type distribution in HIV positive and negative women with or without cervical dysplasia or cancer in East Africa. Front Oncol. 2021;11: 763717.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Adebamowo SN, Olawande O, Famooto A, Dareng EO, Offiong R, Adebamowo CA, et al. Persistent low-risk and high-risk human papillomavirus infections of the uterine cervix in HIV-negative and HIV-positive women. Front Public Health. 2017;5:178.

    Article  PubMed  PubMed Central  Google Scholar 

  39. de Oliveira GR, Vieira VC, Avila EC, Finger-Jardim F, Caldeira TD, Gatti FA, et al. Human papillomavirus type distribution and HPV16 intratype diversity in southern Brazil in women with and without cervical lesions. Mem Inst Oswaldo Cruz. 2017;112(7):492–8.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Snijders PJ, Steenbergen RD, Heideman DA, Meijer CJ. HPV-mediated cervical carcinogenesis: concepts and clinical implications. J Pathol. 2006;208(2):152–64.

    Article  CAS  PubMed  Google Scholar 

  41. Wang J, Lam D, Yang J, Hu L. Discovery of mobocertinib, a new irreversible tyrosine kinase inhibitor indicated for the treatment of non-small-cell lung cancer harboring EGFR exon 20 insertion mutations. Med Chem Res. 2022;31(10):1647–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Wright AA, Howitt BE, Myers AP, Dahlberg SE, Palescandolo E, Van Hummelen P, et al. Oncogenic mutations in cervical cancer: genomic differences between adenocarcinomas and squamous cell carcinomas of the cervix. Cancer. 2013;119(21):3776–83.

    Article  CAS  PubMed  Google Scholar 

  43. Spaans VM, Trietsch MD, Peters AA, Osse M, Ter Haar N, Fleuren GJ, et al. Precise classification of cervical carcinomas combined with somatic mutation profiling contributes to predicting disease outcome. PLoS ONE. 2015;10(7): e0133670.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Wang M, Fan W, Ye M, Tian C, Zhao L, Wang J, et al. Molecular profiles and tumor mutational burden analysis in Chinese patients with gynecologic cancers. Sci Rep. 2018;8(1):8990.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Gagliardi A, Porter VL, Zong Z, Bowlby R, Titmuss E, Namirembe C, et al. Analysis of Ugandan cervical carcinomas identifies human papillomavirus clade-specific epigenome and transcriptome landscapes. Nat Genet. 2020;52(8):800–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Yarchoan M, Albacker LA, Hopkins AC, Montesion M, Murugesan K, Vithayathil TT, et al. PD-L1 expression and tumor mutational burden are independent biomarkers in most cancers. JCI Insight. 2019;4(6).

  47. Mpata PC, Nkosi ZZ. Experiences of cervical cancer screening in HIV-positive women in Zimbabwe. Curationis. 2021;44(1):e1–7.

    Article  PubMed  Google Scholar 

  48. Verlaat W, Snijders PJ, van Moorsel MI, Bleeker M, Rozendaal L, Sie D, et al. Somatic mutation in PIK3CA is a late event in cervical carcinogenesis. J Pathol Clin Res. 2015;1(4):207–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Ma YY, Wei SJ, Lin YC, Lung JC, Chang TC, Whang-Peng J, et al. PIK3CA as an oncogene in cervical cancer. Oncogene. 2000;19(23):2739–44.

    Article  CAS  PubMed  Google Scholar 

  50. Chung TK, Van Hummelen P, Chan PK, Cheung TH, Yim SF, Yu MY, et al. Genomic aberrations in cervical adenocarcinomas in Hong Kong Chinese women. Int J Cancer. 2015;137(4):776–83.

    Article  CAS  PubMed  Google Scholar 

  51. McCubrey JA, Steelman LS, Abrams SL, Lee JT, Chang F, Bertrand FE, et al. Roles of the RAF/MEK/ERK and PI3K/PTEN/AKT pathways in malignant transformation and drug resistance. Adv Enzyme Regul. 2006;46:249–79.

    Article  CAS  PubMed  Google Scholar 

  52. Sarris EG, Saif MW, Syrigos KN. The biological role of PI3K pathway in lung cancer. Pharmaceuticals (Basel). 2012;5(11):1236–64.

    Article  CAS  PubMed  Google Scholar 

  53. Mohseni M, Park BH. PIK3CA and KRAS mutations predict for response to everolimus therapy: now that’s RAD001. J Clin Invest. 2010;120(8):2655–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Tommasino M, Accardi R, Caldeira S, Dong W, Malanchi I, Smet A, et al. The role of TP53 in cervical carcinogenesis. Hum Mutat. 2003;21(3):307–12.

    Article  CAS  PubMed  Google Scholar 

  55. Cheung TH, Lo KW, Yim SF, Chan LK, Heung MS, Chan CS, et al. Epigenetic and genetic alternation of PTEN in cervical neoplasm. Gynecol Oncol. 2004;93(3):621–7.

    Article  CAS  PubMed  Google Scholar 

  56. Kato S, Okamura R, Mareboina M, Lee S, Goodman A, Patel SP, et al. Revisiting epidermal growth factor receptor (EGFR) amplification as a target for anti-EGFR therapy: Analysis of cell-free circulating tumor DNA in patients with advanced malignancies. JCO Precis Oncol. 2019;3.

  57. Castanon A, Leung VM, Landy R, Lim AW, Sasieni P. Characteristics and screening history of women diagnosed with cervical cancer aged 20–29 years. Br J Cancer. 2013;109(1):35–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Foley G, Alston R, Geraci M, Brabin L, Kitchener H, Birch J. Increasing rates of cervical cancer in young women in England: an analysis of national data 1982–2006. Br J Cancer. 2011;105(1):177–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We acknowledge the study participants, and everyone who took part in the success of this project.

Funding

No funding. The project was carried out in the Platform for Pharmacogenomics Research and Translation group, an SAMRC extramural research unit.

Author information

Authors and Affiliations

  1. Pharmacogenomics and Drug Metabolism Group, Division of Human Genetics, Department of Pathology and Institute of Infectious Diseases and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa

    Nosipho Mabizela, Nyarai Soko & Collet Dandara

  2. Platform for Pharmacogenomics Research and Translation (PREMED) Unit, South Africa Medical Research Council, Cape Town, South Africa

    Nosipho Mabizela, Nyarai Soko & Collet Dandara

  3. Department of Pharmaceutical Technology, School of Allied Health Sciences, Harare Institute of Technology, Harare, Zimbabwe

    Nyarai Soko

  4. South African Medical Research Council South African Medical Research Council, Gynaecological Cancer Research Centre (SAMRC GCRC), University of Cape Town, Cape Town, South Africa

    Hue-Tsi Wu

  5. Pathcare, Pathcare House, Cape Town, South Africa

    Hue-Tsi Wu

  6. Division of Anatomical Pathology, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa

    Richard Naidoo

Authors
  1. Nosipho Mabizela
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Nyarai Soko
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Hue-Tsi Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Richard Naidoo
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Collet Dandara
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

C.D., and N.S., conceptulaised the project, N.M., led the project experimentation, wrote the first draft. N.S., H.-T.W., C.D., and R.N., supervised the lead author (N.M.), commented on the draft manuscript. All authors reviewed the manuscript.

Corresponding author

Correspondence to Collet Dandara.

Ethics declarations

Ethics approval and consent to participate

Ethical approval was sought from the Human Research Ethics Committee (HREC) of the Faculty of Health Sciences, University of Cape Town (HREC Ref:560/2022).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mabizela, N., Soko, N., Wu, HT. et al. A comparative analysis of somatic mutational profiles according to HIV status among women with cervical intraepithelial neoplasia 3 (CIN3): a focus on hotspots in TP53, PIK3CA, PTEN, and EGFR. Infect Agents Cancer 20, 18 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13027-025-00647-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13027-025-00647-1

Keywords

Infectious Agents and Cancer

ISSN: 1750-9378

Contact us