High-risk HPV genotypes in Zimbabwean women with cervical cancer: Comparative analyses between HIV-negative and HIV-positive women.

BACKGROUND: High-risk human papillomavirus HPV (HR-HPV) modifies cervical cancer risk in people living with HIV, yet African populations are under-represented. We aimed to compare the frequency, multiplicity and consanguinity of HR-HPVs in HIV-negative and HIV-positive Zimbabwean women.
METHODS: This was a cross-sectional study consisting of women with histologically confirmed cervical cancer attending Parirenyatwa Group of Hospitals in Harare, Zimbabwe. Information on HIV status was also collected for comparative analysis. Genomic DNA was extracted from 258 formalin fixed paraffin embedded tumour tissue samples, and analysed for 14 HR-HPV genotypes. Data was analysed using Graphpad Prism and STATA.
RESULTS: Forty-five percent of the cohort was HIV-positive, with a median age of 51 (IQR = 42-62) years. HR-HPV positivity was detected in 96% of biospecimens analysed. HPV16 (48%), was the most prevalent genotype, followed by HPV35 (26%), HPV18 (25%), HPV58 (11%) and HPV33 (10%), irrespective of HIV status. One third of the cohort harboured a single HPV infection, and HPV16 (41%), HPV18 (21%) and HPV35 (21%) were the most prevalent. HIV status did not influence the prevalence and rate of multiple HPV infections (p>0.05). We reported significant (p<0.05) consanguinity of HPV16/18 (OR = 0.3; 95% CI = 0.1-0.9), HPV16/33 (OR = 0.3; 95% CI = 0.1-1.0), HPV16/35 (OR = 3.3; 95% CI = 2.0-6.0), HPV35/51 (OR = 6.0; 95%CI = 1.8-15.0); HPV39/51 (OR = 6.4; 95% CI = 1.8-15), HPV31/52 (OR = 6.2; 95% CI = 1.8-15), HPV39/56 (OR = 11 95% CI = 8-12), HPV59/68 (OR = 8.2; 95% CI = 5.3-12.4), HPV66/68 (OR = 7; 95% CI = 2.4-13.5), independent of age and HIV status.
CONCLUSION: We found that HIV does not influence the frequency, multiplicity and consanguinity of HR-HPV in cervical cancer. For the first time, we report high prevalence of HPV35 among women with confirmed cervical cancer in Zimbabwe, providing additional evidence of HPV diversity in sub-Saharan Africa. The data obtained here probes the need for larger prospective studies to further elucidate HPV diversity and possibility of selective pressure on genotypes.

Introduction

Human papillomavirus (HPV) is a ubiquitous virus, which, under normal physiological conditions can be cleared within 24 months [1,2]. In the presence of favourable physiological and behavioural risk factors, persistent HPV infections lead to carcinogenesis in the cervix and other organs, especially in immunocompromised individuals [3-7]. Numerous HPV genotypes with phylogenetic relatedness have been discovered, yet these genotypes exhibit distinct pathogenic aptitude. High risk HPVs (HR-HPVs) such as 16, 18, 31, 33, 39, 45, 51, 52, 56, 58, 59 and 68 harbour high oncogenic potential, and so, have been influential in the development of HPV prophylactic vaccines [8,9]. The coverage for the HPV prophylactic vaccines is in its infancy in most developing countries, where the coverage is estimated to be more than 12-fold lower than developed countries [10]. As a matter of interest countries such as Zambia, Malawi and Zimbabwe, where the highest global cervical cancer cases are recorded, are still in the pilot and early implementation phases of the national HPV vaccination programs [11,12].

Evidences from diverse global HPV ethnogeography data patterns, for example, shows that HPV51 is predominantly observed in Caucasians, while HPV58 is skewed towards East Asia and Latin America [13-17]. An abundance of data also substantiates that Africa harbours significant heterogeneity between populations, and to be specific, persistent HPV66 is ubiquitous in Northern Africa, while genotypes such as HPV35 and 52 are reportedly abundant in sub-Saharan Africa (SSA) [18-23]. It is thought that the stark dissimilarities of HPV distribution and cervical cancer between SSA and the rest of the world are influenced by HIV co-infection, leading to the classification of cervical cancer as an AIDS-defining condition [5,24-29]. However, emerging data to this effect has been contraindicative. Of great relevance is the unabated cervical cancer burden even after the introduction of anti-retroviral therapies (ART), compared to other HIV-related cancers, that have since dwindled [30-39]. This data suggests other potential competing risk factors, which are yet to be defined.

Despite the strides made to understand the relationship between HPV, HIV and cervical cancer, there is still a gap in SSA. Particularly, in Zimbabwe, most studies have examined HPV in normal cervical cytology and precancerous lesions, leaving only a few studies that focus on cervical tumour tissue [5,7,17,40-43]. High prevalence of genotypes such as HPV16, 18, 31, 33, 45, 56 have been detected in tumour tissue non-synonymously [22,44-46]. There is very little data on the HPV genotype profile for cervical cancer in Zimbabwe. This study aimed to analyse the frequency and multiplicity of HR-HPV genotypes in HIV-negative and HIV-positive Zimbabwean women with histologically confirmed cervical cancer.

Materials and methods

Study design and setting

This was a cross-sectional study consisting of 258 women with histologically confirmed invasive cervical cancer diagnosis. This study recruited participants between July 2016 and January 2019 from an outpatient oncology facility, Parirenyatwa Group of Hospitals Radiotherapy and Chemotherapy Centre (RTC), in Harare, Zimbabwe. The biospecimens for analysis were collected at the pathology laboratory of diagnosis, namely Lancet Pathology and Parirenyatwa Group of Hospitals Pathology Department. Collected samples were processed as Formalin fixed paraffin embedded (FFPE) blocks.

Ethical approval

All human health research was in accordance with the Helsinki declaration. Ethical approval was obtained from University of Zimbabwe College of Health Sciences and Parirenyatwa Group of Hospitals Joint Research Ethics Committee (412/16), Medical Research Council of Zimbabwe (A/2153), University of Cape Town Research Ethics Committee and Research Council of Zimbabwe (No: 03351).

Study participants

The participants from the current study were nested in a pharmacogenomics of cervical cancer study, aiming to recruit women who were newly diagnosed with cervical cancer, and were eligible to receive radical therapy. Potential study participants were identified through the RTC hospital registry. To be considered eligible for this study, women had to be ≥18 years, with histologically confirmed diagnoses of invasive cervical cancer staged between 1b – 4A, and were earmarked for curative anti-cancer therapy. Individuals who were diagnosed with resectable (stage 1A) disease that did not require chemo/radio- therapies or advanced disease that required palliative care (stage 4B) were excluded from the study, because these individuals were referred for care outside of the RTC. Eligible participants were informed of the study verbally, and the women willing to enroll provided written consent. To maintain participant confidentiality, all participant data was de-identified and assigned study numbers. The clinical and demographic information for all recruited participants were abstracted onto a case report form. Demographic information such as age, residency, histopathological tumour characteristics, were collected from the patient folder in the RTC. In addition, behavioral, lifestyle factors and sexual history were collected by interviewing the study participants, namely history of alcohol, smoking, parity, age at sexual debut, number of sexual partners, history of circumcised partner, sexually transmitted infections were also collected. Data on HIV status was also collected for comparative analysis.

DNA extraction and HPV genotyping

A total of 5mg of the FFPE blocks were sectioned off in preparation for genetic analysis. Genomic DNA was isolated from the FFPE scrolls following a slightly modified manufacturers’ protocol from Zymo genomic DNA extraction FFPE kit (Zymo Research, California, USA). Paraffin was removed by adding a Xylene-based deparaffinization solution and incubating at 55°C for 30 minutes. The wax layer was then aspirated and the remaining biopsy tissue was digested overnight using 10mg/ml Proteinase K (Zymo Research, California, USA). The quantity and quality of DNA was assessed using a Nanodrop™ Spectrophotometer 2000/2000c (Nano-drop™, ThermoFisher, Denver, USA) and DNA integrity was evaluated by running a 1% agarose gel electrophoresis. The extracted DNA was stored at -80°C for further analysis.

Genotyping for high-risk HPV subtypes was undertaken using clinically validated Anyplex™ II HPV HR detection kit (Seegene, Seoul, South Korea). The Anyplex™ II HPV HR detection kit (Seegene, Seoul, South Korea) targets the HPV L1 gene (encoding the capsid) using primers provided in the Anyplex kit. This assay uses Dual Priming Oligonucleotides (DPO™) and tagging oligonucleotide cleavage and extension (TOCE™) technology which enables the distinguishing of 14 HR-HPVs in a single reaction, namely, HPV 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, 68. A total reaction of 20μl was used during genotyping, following the manufacturers’ protocol. Each reaction contained a human β-globin internal control, L1 primers, oligonucleotides and enzymes. A negative control, as well as 3 positive controls (DNA mixture of pathogen clones) were run concurrently with each test plate to confirm the validity of the amplification. The CFX96™ real-time thermocycler (Bio-Rad, Applied Biosystems, California, USA) was used under thermocycling conditions recommended in the Seegene protocol. Fluorescence was detected using the end point cyclic melting temperature analysis. Seegene Viewer v2.0 program (Seegene, Seoul, South Korea) was used to analyse and interpret fluorescence data. Each result was considered valid when the internal control was detected in the sample (IC+). Positive result (+) indicated HPV DNA presence, while negative result (-) indicated the absence of the viral DNA. All HPV negative results with a positive endogen internal control (IC+) were not re-analysed.

HPV genotype frequency data mining

Data on the HPV genotypes of African populations was extracted from the HPV Information Centre database [47]. The HPV Information Centre consolidates data from published literature and official reports published by the World Health Organisation, United Nations, The World Bank, International Agency for Research on Cancer’s Globocan and Cancer Incidence in Five Continents. We filtered the report for data on the African continent, and collected the data on the most frequently detected HPV genotypes in women with confirmed cervical cancer.

Statistical and data analysis

STATA v12.0 (StataCorp LLC, Station College, TX, USA) and Graphpad v8 (Prisma, San Diego, California) statistical software packages were used for data analysis. Demographic and clinical data were allocated into either continuous data or categorical data. Continuous data were expressed as mean ± standard deviation or median (inter-quartile range), while the categorical data was expressed as absolute or relative frequencies. For the comparison of the sociodemographic factors between HIV negative and HIV positive study participants, the two-sample Wilcoxon rank-sum test and Chi-squared test of independence was used. Correlation between the various HPV genotypes and HIV status was performed using Poisson’s and Logistic regression. The Chi-squared test of homogeneity was used to compare the frequency of HPV genotypes among African women using Fisher’s exact and Chi-squared values as test statistics. Univariate regression was used to determine an association between the established HPV risk factors with the HR-HPV genotype infections. Multivariate logistic regression was used to determine an association between the pairings of the HPV genotypes using age, and HIV status as predictors for the model. Further sensitivity analyses were performed for the co-segregating HPV genotypes, using multivariate regression models, and controlling for known HPV risk factors, namely, age, history of STI, parity, HIV status, age of sexual debut and tumour histology. We considered p<0.05 as statistically significant. For analysis, all individual HPV genotypes regardless of whether they occur as single or multiple infections were analysed and reported as standalone genotypes. None of the statistical analysis took into account the species from which the HPV genotypes are from.

Results

Sociodemographic features of the study participants

Sociodemographic and clinical characteristics of the study group are summarized in Table 1. Of the 258 cervical cancer patients recruited for this study, 45% (n = 116) were confirmed HIV positive. The median age (inter-quartile range) of the participants was 51 years (42–62). The overall median for age was significantly different (p = 0.001) between the HIV-positive and HIV-negative cervical cancer groups. Further analysis of the age frequency distribution showed that in HIV positive women, age was skewed to the younger women (<50 years) compared to women with HIV-negative status (Fig 1). Most of the sociodemographic risk factors were comparable between the groups, however, the behavioural risk factors, such as number of sexual partners (p = 0.001) and history of sexually transmitted infections (p = 0.010) differed between the HIV-negative and HIV-positive groups. In univariate regression analysis, none of the HPV-related risk factors (i.e., age, sexual debut, parity and STI history) were found to be significantly associated with any of the HR-HPV genotypes or multiple HR-HPV genotype infections (S1 Table).

Table 1

Sociodemographic and behavioural characteristics of the study participants.

	Combined	HIV- negative	HIV-positive	p-value
Total number of participants, N(freq)	258 (1.00)	142 (0.55)	116 (0.45)
Median Age (IQR)	51 (42–62)	56 (45–64)	46 (41–54)	0.001 a
Median Parity (IQR)	4 (3–6)	5 (4–7)	2 (3–5)	0.001 a
Median Sexual partner history (IQR)	1 (1–3)	1 (1–1)	2 (1–3)	0.001 a
Median Age at sexual debut (IQR)	17 (15–19)	17 (15–19)	17 (15–20)	0.525 a
Residency
Urban	137 (0.53)	77 (0.54)	60 (0.52)	Ref.
Peri-urban	48 (0.19)	27 (0.19)	21 (0.18)	0.438 b
Rural	73 (0.280)	38 (0.27)	35 (0.30)	0.392 b
Marital Status
Married	114 (0.44)	76 (0.54)	68 (0.59)	Ref.
Never married	9 (0.03)	3 (0.02)	6 (0.05)	0.418 b
Divorced/	51 (0.20)	25 (0.18)	22 (0.19)	0.961 b
Widow	84 (0.33)	38 (0.27)	22 (0.19)	0.167 b
Alcohol consumption
No	229 (0.89)	122 (0.86)	107 (0.92)	Ref.
Yes	29 (0.11)	16 (0.14)	6 (0.08)	0.080 b
Smoking History
No	246 (0.97)	132 (0.93)	114 (0.98)	Ref.
Yes	12 (0.03)	6 (0.07)	6 (0.02)	0.804 b
STI history
No	181 (0.70)	109 (0.77)	72 (0.62)	Ref.
Yes	77 (0.298)	33 (0.23)	44 (0.38)	0.010 b
Circumcised partner
No	232 (0.90)	123 (0.87)	108 (0.93)	Ref.
Yes	(0.10)	19 (0.13)	8 (0.07)	0.091b
Tumour Histology
Squamous Cell	207 (0.80)	115 (0.83)	92 (0.77)	Ref.
Adenocarcinoma	25 (0.10)	14 (0.10)	11 (0.09)	0.950b
Adenosquamous	11 (0.04)	2 (0.01)	9 (0.08)	0.017 b
Other*	15 (0.06)	8 (0.06)	7 (0.06)	0.900b

IQR = inter-quartile range;

= Wilcoxon rank sum test;

= Chi-squared test;

Other* = spindle cell carcinoma, papillary serous carcinoma, adenoid cystic, adenosarcoma, small cell carcinoma.

Fig 1

Age distribution of study cohort stratified by HIV status (n = 258).

HPV genotype prevalence

The 258 cervical cancer patients were screened for HR-HPV DNA, and 96% (n = 248) were positive, representing 14 HPV types (16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66 and 68) (Table 2) There was no difference in HPV positivity and HPV type between the HIV negative and positive groups. Distribution of HPV in this cohort stratified by HIV status is illustrated in Table 2. HPV16 was the most prevalent genotype detected in 48% (n = 123) of the participants, with high frequencies for the following genotypes; HPV35 (26.4%), HPV18 (25.2%), HPV58 (10.5%), HPV33 (9.7%), and HPV31 (6.6%).

Table 2

HR-HPV genotypes by HIV status (n = 258) and the HPV phylogenetic classification.

	Frequency (Proportion)
HPV genotype	All	HIV negative	HIV positive	IRR (95% CI)	p value
Negative	10 (0.04)	6 (0.02)	4 (0.02)	0.40 (0.22–1.00)	0.443
16a	123 (0.48)	66 (0.26)	57 (0.22)	0.97 (0.69–1.37)	0.881
18b	65 (0.25)	34 (0.13)	31 (0.12)	1.11 (0.78–1.57)	0.573
31a	17 (0.07)	8 (0.03)	9 (0.04)	1.06 (0.57–1.96)	0.854
33a	25 (0.10)	18 (0.07)	7 (0.03)	0.81 (0.49–1.34)	0.416
35a	68 (0.26)	40 (0.16)	28 (0.10)	0.90 (0.63–1.29)	0.574
39b	10 (0.04)	3 (0.01)	7 (0.03)	1.42 (0.67–3.05)	0.361
45b	14 (0.05)	8 (0.03)	6 (0.02)	1.05 (0.53–2.06)	0.892
51c	11 (0.04)	5 (0.02)	6 (0.02)	1.35 (0.75–2.45)	0.316
52a	9 (0.04)	5 (0.02)	4 (0.02)	0.88 (0.33–2.38)	0.799
56d	6 (0.02)	2 (0.0)	4 (0.02)	1.11 (0.41–2.99)	0.843
58a	23 (0.11)	9 (0.05)	14 (0.05)	1.28 (0.78–2.09)	0.334
59b	3 (0.01)	0 (0.00)	3 (0.01)	1.67 (0.53–5.24)	0.380
66d	1 (0.0)	0 (0.00)	1 (0.01)	1.1 (0.15–7.89)	0.922
68b	7 (0.03)	2 (0.01)	5 (0.02)	1.23 (0.51–3.01)	0.645

IRR = incidence rate ratio computed from the Poisson regression analysis.

a = Human papillomavirus alpha 9 species;

b = Human papillomavirus alpha 7 species;

c = Human papillomavirus alpha 5 species;

d = Human papillomavirus alpha 6 species.

HPV mono-infections

About one-third (n = 85) of the study cohort were harbouring a single HR-HPV infection. The prevalence and distribution of HR-HPV mono infections is illustrated in Fig 2. In this group of women with confirmed cervical cancer, the most commonly reported mono-infections were HPV16 (41%), HPV18 (21%) and HPV35 (21%). This study did not observe any HPV mono-infections arising from HPV genotypes 56, 59 and 66. Comparative analysis between HIV-negative and HIV-positive women showed that there were no statistically significant differences in the prevalence HR-HPV mono-infections (p = 0.400).

Fig 2

The HPV mono infection distribution in women with confirmed cervical cancer (n = 85).

Multiple HR-HPV infections

Majority (67%) of the cohort exhibited multiple HPV infections. Of the 14 HR-HPV genotypes analysed in this study, 63% (n = 163) harboured multiple (>2) HPV genotypes (Table 3). Fifty nine percent of the study participants exhibited infection with 2 HR-HPVs, while 19% were infected with 3 HR-HPVs. The most common multiple infections included HPV16 and 35 (21%), followed by HPV16/18 and HPV16/58. The highest number of multiple HPV serovariants per individual reported was 6, in 3.7% of study subjects (Table 3). There was no difference in the number of multiple infections between HIV positive and HIV negative cervical cancer patients. Distribution of multiple HPV genotypes stratified by HIV status are illustrated in S2 Table.

Table 3

Multiple HR-HPV infections, stratified by HIV status (n = 163).

Number of HPVs found together	Frequency (Proportion)
	All	HIV negative	HIV positive	OR (95% CI)	p-value
2	94 (0.59)	45 (0.28)	50 (0.31)	1.65 (0.46–5.98)	0.443
3	31 (0.19)	16 (0.10)	15 (0.09)	2.10 (0.55–8.01)	0.277
4	19 (0.12)	11 (0.07)	8 (0.05)	0.80 (0.16–4.02)	0.782
5	12 (0.08)	6 (0.04)	6 (0.04)	1.75 (0.08–36.28)	0.718
6	6 (0.04)	4 (0.03)	2 (0.01)	1.2 (0.34–4.41)	0.756

Furthermore, the incidence of multiple-type HPV infections was assessed to determine if specific HPV genotypes tend to co-segregate, or if the multiple-type infections occur as a result of chance. HIV status was not a significant contributor to the co-segregation patterns. We used multivariate logistic regression, using the HPV genotypes as predictors, while controlling for age and HIV status, as illustrated below (Fig 3). We detected statistically significant associations (p<0.05) between HPV16 and 33 (OR = 0.3; 95% CI = 0.1–1.0), HPV16 and 35 (OR = 3.3; 95% CI = 2.0–6.0), HPV16 and 18 (OR = 0.3 95% CI = 0.1–0.9), HPV35 and 51 (OR = 6.0; 95%CI = 1.8–15.0), HPV39 and 51 (OR = 6.4; 95% CI = 1.8–15), HPV31 and 52 (OR = 6.2; 95% CI = 1.8–15), HPV39 and 56 (OR = 11 95% CI = 8–12), HPV59 and 68 (OR = 8.2; 95% CI = 5.3–12.4), and HPV66 with 68 (OR = 7; 95% CI = 2.4–13.5). The respective odds ratios and 95% confidence intervals for all significant HPV genotypes are shown in Fig 4. In order to ensure that lack of significance observed was not a result of low study power, a sensitivity appraisal was conducted, using multivariate regression analyses controlling for age, history of sexually transmitted infections, parity, HIV status, age at sexual debut and tumour histology. In this sensitivity analyses, all the co-segregation patterns remained statistically significant except HPV16/33 (OR = 0.4; 95% CI = 0.2–1.9; p = 0.05), HPV35/52 (OR = 0.5; 95% CI = 0.1–4.3; p = 0.50) (S3 Table).

Fig 3

Heatmap of co-segregation of any two HPV types among Zimbabweans.

Fig 4

Odds ratios and 95% confidence intervals (95% CI) for co-occurrence of high-risk HPV infections among Zimbabweans.

* Indicates co-segregation of HPV genotypes in the same phylogenetic clades.

Comparing the distribution of different HPV types in women from populations across Africa

The prevalence of the various HPV genotypes in the Zimbabwean study cohort was compared to the most common HPV genotypes on other African women across the continent, using data obtained from HPV Information Centre database [47] and summarized into Table 4. In comparison to the rest of the African continent data, our study reported significantly higher HPV35 (p<0.001), HPV51 (p = 0.043), HPV58 (p = 0.010) and HPV39 (p = 0.043). Across the different African regions, most of the HPV genotypes were comparable (p>0.05), and HPV16 was the most prevalent genotype. However, in North Africa (p = 0.047), the frequency of HPV16 is significantly higher, while HPV45 (p = 0.011) and HPV59 (p = 0.005) are significantly higher in West Africa compared to the other African regions. Additionally, the HPV information centre database also showed that HPV26 was one of the top 10 genotypes detected in cervical cancer tissue in Southerrn Africa, and HPV53 in Northern and Southern Africa.

Table 4

Comparison of the frequencies of the most common HPV genotypes reported across Africa.

	Frequency in %
	Our Study*	East Africa	West Africa	North Africa	Southern Africa	P1	P2	P3	P4
HPV16	48	50	36	62	48	0.777	0.086	0.047	0.999
HPV18	25	18	20	17	15	0.228	0.397	0.165	0.077
HPV31	7	2	9	3	3	0.088	0.088	0.602	0.194
HPV33	10	6	3	3	7	0.279	0.297	0.053	0.047
HPV35	26	4	4	3	6	<0.001	<0.001	<0.001	<0.001
HPV45	5	9	16	9	6	0.268	0.011	0.268	0.756
HPV51	4	3	0	0	0	0.700	0.043	0.043	0.043
HPV52	4	4	2	3	2	0.999	0.407	0.700	0.407
HPV56	2	0	2	0	0	0.155	0.999	0.155	0.155
HPV58	11	3	2	3	1	0.027	0.010	0.027	0.029
HPV59	1	0	10	0	0	0.316	0.005	0.316	0.316
HPV66	0	0	0	2	0	-	-	0.155	-
HPV39	4	1	0	0	0	0.174	0.043	0.043	0.043
HPV53	NA	0	0	2	1	-	-	-	-
HPV26	NA	0	0	0	2	-	-	-	-

*Reference group for comparative analaysis;

- = p-value cannot be computed; NA = not assayed in our study population; East Africa v our study data;

2 = West Africa v our study data;

3 = North Africa v our study data;

4 = Southern Africa v our study data.

Discussion

There is a plethora of studies that have analyzed HPV genotyping profiles in different populations across the globe, and what is apparent is that HPV16 and 18 are highly prevalent in cervical cancer. The global HPV prevalence reports HPV16 (55%) as the highest followed by HPV18 (14%), which is supported by our data, with 48% and 25%, respectively. However, in stark contrast to the global data, we report here a >13-fold higher frequency (26%) of HPV35 among Zimbabwean women with cervical cancer, compared to Caucasians (~2%) and Asian (~1%) cervical cancer patients [48,49]. Across the African continent, there is heterogeneity in the frequency of HPV35, ranging from as low as 6% in South Africa, Burkina Faso (9%), Nigeria (9%), Tanzania (15%), Mozambique (17%), and up to the comparable 24% observed among Malawian women [18,28,50-58]. It is important to note, an earlier study among Zimbabwean women [22] which showed a frequency of 11% for HPV35. This discrepancy could be parenthetic to methodological differences in HPV genotyping. Specifically, our study utilised the multiplex PCR method, while Mudini et al. used PGMY09/11 PCR and dot plot hybridization. Although both methods yield highly concordant findings, the PGMY09/11 HPV detection method has been associated with a higher rate of false negatives compared multiplex PCR [59-61].

Moreover, a high HPV genotype frequency in the general population is not an adequate proxy for carcinogenic potential. For example, in Latin America, HPV35 is as ubiquitous as in SSA, but is widespread in precancerous lesions and not in cancer [62]. Novel genetic variations in the coding and non-coding regions of HPV35 have been discovered, especially in African women, and women of African ancestry, which are not present in Caucasian, Asian and Hispanic populations [23,63]. These genetic polymorphisms are thought to account for the distinct HPV35 virulence and oncogenic potential observed in Africans, versus Latin America. However, not much whole HPV35 sequencing has been reported on African cervical tumour samples, thus, there is a potential to discover even more variants in different African populations.

While it has been previously thought that HPV16 and a few other genotypes exhibited higher innate potential to evade the host immune system, and other genotypes required a suppressed immune system to flourish e.g. HPV35 [64,65], new data seem to show that infection with HPV35 can also be correlated with persistence and unresolvable precancerous lesions [13,23,28,53,66-68]. For example, and contrary to earlier expectations, studies from SSA show that HPV35 is one of the most ubiquitious genotypes, irrespective of HIV status, further confirming our observation in the present study [19,20,22,57,69]. Our observations agree with reports from other studies evaluating HPV prevalence, distribution and multiplicity in Zimbabwean women, notwithstanding cervical cytology status [22,63]. It is equally vital to include in the equation immune reconstitution resulting from increased access to ART in Zimbabwe, which could be contributing to the homogeneity of the HPV genotype distribution and observed multiplicity in our study, and other Zimbabwean studies.

Surprisingly, the introduction of ART has not been met with sizeable decline of cervical cancer, compared to other HIV-related malignancies such as Kaposi’s Sarcoma and Non-Hodgkins’ Lymphoma, which are now rarely detected [70,71]. To ascertain the role of immune reconstitution in HPV persistence and cervical cancer, immunological factors (which were not evaluated in the current study) such as HIV viral load, HPV viral load, CD4+ count and duration of ART need to be investigated [53,72]. There is, however, evidence in other African populations to suggest that only a small fraction of women with HIV/HPV are likely to develop invasive cervical cancer without influence from CD4+ count, thus pointing to an increased role of the other competing risk factors and the need to interrogate them as potential HPV persistence drivers, including host genetics [73-82].

Persistence of HPV and the presence of multiple HPV infections are both phenomena closely correlated with HIV-induced immunosuppression [83-88]. Additionally, other sexual behavioural characteristics such as history of STIs, higher number of sexual partners are also key contributors for HPV multiplicity [89-91]. Although our study reports high multiple HPV infection rate, we found no relationship between sexual behavioral factors such as HIV and STI history, and risk of harbouring multiple HPV genotypes. Not many studies have sought to understand the patterns by which multiple HPV genotypes co-segregate and interact to induce carcinogenesis. Some studies highlight that multiple infections occur as random events, while other studies allude to competitive or co-operative consanguinity of specific HPV genotypes [59,92-94]. Aggregation of HPV genotypes with phylogenetic relatedness has been observed at different degrees, and HPV 16, 18, 58 and 66 have been shown to occur mostly as single infections, in Americans and Latin Americans, while HPV35 and 45 were more likely to present as multiple infections [59,92-94]. On the contrary, our study reports no statistically significant differences in the HPV genotype distribution for single and multiple infections for any of the HPV genotypes. Furthermore, we report type-specific HPV clustering of genotypes from the same phylogenetic clades, namely, HPV16/33 and HPV16/35; while the rest of the co-segregation we reported here was of genotypes that are of diverse phylogenetic clades. Previous data suggests that multiple infection with genotypes of diverse HPV phylogenetic clades is directly correlated with no history of the HPV prophylactic vaccine, ascribed to the punitive host induced cross-protection, compared to the vaccine induced response [59,92,95-98]. In Zimbabwe, the HPV vaccination program is still in early implementation phase, therefore, most women at reproductive ages and above are reliant on host immunology prevention. This is similar in most of Africa, where there is limited access and coverage of the HPV vaccine, even though the burden of cervical cancer is disproportionately high here, and the populations are exposed to other dissonant evolutionary selective pressures which collectively exacerbate the risk of HPV persistence [99,100].

Analysis of the HPV genotypes detected across Africa, by region confirms distinct HPV ethnogeographical patterns. Of great interest is the probable high-risk genotype, HPV26, which was observed only in cervical cancer tissue from Southern Africa. Because HPV26 is not a definitive HR-HPV, it is often overlooked, so its burden in Africa may be under-reported, and consequently its contribution to cervical cancer. HPV51, HPV56 and HPV59 were seemingly exclusively observed in East and West African women. It is important to note that these genotypes are not covered by current HPV vaccines. As of 01 August, 2021, the most robust vaccine, Gardasil-9, targets HPV 6, 11, 16, 18, 31, 33, 45, 52 and 58, for which the estimated cross-protective potential translates to cloistering <70% of HPV-related cancers [8,101-103]. Even so, longitudinal data illustrates diminished cross-protective immunogenicity and reactogenicity over time, leaving some ethnogeographical groups exposed and susceptible to HPV infection [23,53,54,104-107]. Given the significant HPV diversity, and the genetic variation in Africa it may be of great benefit to conduct large-scale, longitudinal HPV genotyping studies in women from different African countries, such as the African Collaborative Center for Microbiome and Genomics Research (ACCME) study in Nigeria [107] so as to understand the impact of HPV, HIV and related host genetic factors for potential utility as biomarkers for cervical cancer in Africa. This large-scale empirical data would be key to determine which genotypes characteristically resolve, or progress to cervical cancer along with HIV in African settings and are fundamental towards developing a comprehensive and Africa-specific HPV vaccine.

Additionally, no HR-HPV infection detected in about 4% of the cohort irrespective of HIV status. This could have resulted from the fact that this particular sub-group were harbouring an HPV subtype that was not characterised by the assay kit used in this study. For example, HPV6 and 11 are established to play a causative role in genital warts, or HPV26 which we found in the comparative analysis to be idiosyncratic to Southern Africa [17,108]. Future studies should characterise for HPV using broader spectrum kits, simultaneously detecting low- and high- risk HPV subtypes. It is also possible that this group harbours HPV-negative cervical tumours, which are known to be quite rare and occurring in <5% of cases. A previous study conducted by Kjetland and collaborators (2010) reported schistosomiasis-induced squamous intraepithelial neoplasia with no HPV on Zimbabwean women [109].

In conclusion, our study analysed HR-HPV sub-types in Zimbabwean women, and presents data which will add to HPV diversity in SSA. This study is the first to describe high frequency of HPV35, comparable to other HR-HPVs such as HPV18 in Zimbabwean women with cervical cancer. Furthermore, this study is also the first to report multiple type-specific HPV subtypes in African populations, providing empirical evidence of high consanguinity of HR-HPV genotypes despite HIV status. The data obtained here is fundamental towards determining the efficacy of the commercially available prophylactic vaccines in SSA populations that harbour disparate viral genome profiles and are subject to evolutionary pressures.

Risk factors of HPV in association with the various HPV genotypes.

(PDF)

Click here for additional data file.

The number of individuals harbouring specific HPV genotypes stratified by HIV status.

(PDF)

Click here for additional data file.

Sensitivity analysis for HPV co-segregation.

(PDF)

Click here for additional data file.

19 Mar 2021

PONE-D-21-04071

High-Risk HPV genotypes in Zimbabwean women with cervical cancer: Comparative Analyses between HIV-negative and HIV-positive women.

PLOS ONE

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Reviewer #1: This cross-sectional study on HPV types in 258 cases of cervical cancer in Zimbabwe, categorized by HIV status, supports earlier results, which have shown that differences in HPV type distribution diminishes with the severity of the cervical lesions. This study also brings more data to the growing body of knowledge regarding the increased proportion of HPV35 in women from sub-Saharan Africa/ancestry.

1. Throughout the text “despite” is often used when it should be “irrespective of” or “regardless of” for example third line in result-section of abstract.

2. Methods: I would suggest using regression analyses for Table 2, 3 and 4 rather than Chi2. Either Poisson regression for prevalence ratios or Logistic regression for odds ratios.

3. Results: I suggest categorizing by HIV status in Figure 2.

4. Results: For better understanding of Figure 3A and Figure 3B it might be better if the absolute numbers of certain genotype combinations were presented. I lack an explanation as to how to assess the odds ratios (eg OR increased at 3.3 for the most common combination 16/35 but decreased at OR 0.3 for the second most common combination 16/18. Is 16 reference?).

5. Results: It says 58% having 2 HR-HPVs in the text but 59% in Table 3.

6. Supplementary Table 1. Suggest adding HIV as risk factor

Reviewer #2: In this hospital-based cross-sectional study, the authors examine the distribution of high-risk HPV in HIV+ and HIV- women in Zimbabwe. The study reported high prevalent of HPV 35 among women confirmed with cervical cancer, and HIV status was not associated with frequency, multiplicity and consanguinity of HR-HPV in cervical cancer. The study is well-designed and performed, and also provides important information on high-risk HPV distribution in African women.

Several aspects could be further clarified:

1. It would be informative to provide a brief description of the HPV vaccination program and uptake in Zimbabwe early in the introduction.

2. Could the authors clarify the reasons of only including cervical cancer diagnosed at stage 1b-4.

3. It seems the histopathological characteristics are available for all cases included in this study. Such information should be included in the descriptive analysis.

4. Maybe only relevant covariables to the current study are needed to be described. Some of the information collected through the project but not used for the study might not be necessary to mention, such as weight, height, comorbidities and treatment etc.

5. I figure no significant association was detected in the univariate regression between HPV-related risk factors (i.e., age, sexual debut, parity and STI history) and any HR-HPV genotypes might be due to limited power, when the HR-HPV types were stratified by each type. However, those are known factors that associated with HPV infection. Could the authors provide estimates (figure 3b) additionally adjusting for those factors and histology at least as sensitivity analysis when examine the co-occurrence of HPV types. Besides, please clarify what is the reference group for the ORs.

6. A legend explaining how the HR-HPV types were classified (ie. hierarchical classification) especially for those cases with multiple types of HPV infection in table 1 could be informative.

7. In table 4, could the authors clarify the data source for the HPV types in each region of Africa if they are from external studies?

8. Could the authors provide a detailed descriptive table as supplement the distribution of HR-HPV types by HIV status for all included women (including the specific types for multiple infections)?

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Reviewer #1: No

Reviewer #2: No

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10 May 2021

Response to reviewer comments: PONE-D-21-04071

28 April 2021

Dear Professor, Joakim Dilner,

Thank you for reviewing our manuscript PONE-D-21-04071- entitled “High-Risk HPV genotypes in Zimbabwean women with cervical cancer: Comparative Analyses between HIV-negative and HIV-positive women” and providing us with constructive criticism on how to improve the statistical analysis and the scientific soundness of the work. Below are our responses to each of the reviewers’ and editor’s comments.

Reviewer #1:

1. Throughout the text “despite” is often used when it should be “irrespective of” or “regardless of” for example third line in result-section of abstract

Thank you for pointing this out. The authors have edited the document accordingly, and replaced the word “despite” with other terminologies

2. Methods: I would suggest using regression analyses for Table 2, 3 and 4 rather than Chi2. Either Poisson regression for prevalence ratios or Logistic regression for odds ratios.

Thank you for this suggestion. The re-analysis for Tables 2 and 3 was performed, as per the reviewer’s recommendations. Data generated for Table 2 was tested using Poisson’s regression, while Table 3 was analysed using logistic regression. However, Table 4 compares absolute frequencies for the HPV genotypes as recorded by an established database, the HPV Information Centre (https://hpvcentre.net) and so the best analyses to compare these frequencies is using the Chi2 statistical methods.

Table 2: HR-HPV genotypes by HIV status (n=258) and the HPV phylogenetic classification.

Frequency (Proportion)

HPV genotype All HIV negative HIV positive IRR (95% CI) p value

Negative 10 (0.04) 6 (0.02) 4 (0.02) 0.40 (0.22 – 1.00) 0.443

16a 123 (0.48) 66 (0.26) 57 (0.22) 0.97 (0.69 - 1.37) 0.881

18b 65 (0.25) 34 (0.13) 31 (0.12) 1.11 (0.78 – 1.57) 0.573

31a 17 (0.07) 8 (0.03) 9 (0.04) 1.06 (0.57 – 1.96) 0.854

33a 25 (0.10) 18 (0.07) 7 (0.03) 0.81 (0.49 – 1.34) 0.416

35a 68 (0.26) 40 (0.16) 28 (0.10) 0.90 (0.63 – 1.29) 0.574

39b 10 (0.04) 3 (0.01) 7 (0.03) 1.42 (0.67 – 3.05) 0.361

45b 14 (0.05) 8 (0.03) 6 (0.02) 1.05 (0.53 – 2.06) 0.892

51c 11 (0.04) 5 (0.02) 6 (0.02) 1.35 (0.75 – 2.45) 0.316

52a 9 (0.04) 5 (0.02) 4 (0.02) 0.88 (0.33 – 2.38) 0.799

56d 6 (0.02) 2 (0.0) 4 (0.02) 1.11 (0.41 – 2.99) 0.843

58a 23 (0.11) 9 (0.05) 14 (0.05) 1.28 (0.78 – 2.09) 0.334

59b 3 (0.01) 0 (0.00) 3 (0.01) 1.67 (0.53 – 5.24) 0.380

66d 1 (0.0) 0 (0.00) 1 (0.01) 1.1 (0.15 – 7.89) 0.922

68b 7 (0.03) 2 (0.01) 5 (0.02) 1.23 (0.51 – 3.01) 0.645

a= Human papillomavirus alpha 9 species; b= Human papillomavirus alpha 7 species; c= Human papillomavirus alpha 5 species; d= Human papillomavirus alpha 6 species.

Table 3: Multiple HR-HPV infections, stratified by HIV status (n=163).

Number of HPVs found together Frequency (Proportion)

All HIV negative HIV positive OR (95% CI) p-value

2 94 (0.59) 45 (0.28) 50 (0.31) 1.65 (0.46 – 5.98) 0.443

3 31 (0.19) 16 (0.10) 15 (0.09) 2.10 (0.55 –8.01) 0.277

4 19 (0.12) 11 (0.07) 8 (0.05) 0.80 (0.16 – 4.02) 0.782

5 12 (0.08) 6 (0.04) 6 (0.04) 1.75 (0.08- 36.28) 0.718

6 6 (0.04) 4 (0.03) 2 (0.01) 1.2 (0.34 – 4.41) 0.756

3. Results: I suggest categorizing by HIV status in Figure 2.

The authors appreciate this recommendation and have presented the data as recommended, please see below.

Figure 2: The HPV mono infection distribution in women with confirmed cervical cancer (n=85).

4. Results: For better understanding of Figure 3A and Figure 3B it might be better if the absolute numbers of certain genotype combinations were presented. I lack an explanation as to how to assess the odds ratios (eg OR increased at 3.3 for the most common combination 16/35 but decreased at OR 0.3 for the second most common combination 16/18. Is 16 reference?).

Thank you for indicating this, a supplementary table was added to this effect (Supplementary Table 2). Logistic regression analysis was performed to estimate the co-occurrence by adding the two specific HPV genotypes as the predictor and co-variate. For example, where HPV16/18 are analysed, it indicates that HPV16 is the predictor, and HPV18 is the covariate. In the comparative analysis, the reference is the whole dataset, less the HPV genotypes being analysed for co-occurrence.

Supplementary Table 2: HPV co-occurrence regression analysis, without HIV status, and with HIV status as a co-variate.

Composite Genotypes N (proportion) OR (95% CI)a pa OR (95% CI)b pb

16/18 44 (0.17) 0.3 (0.2- 0.5) <0.001 0.3 (0.2- 0.5) <0.001

16/31 14 (0.05) 0.9 (0.4-2.2) 0.824 1 (0.4- 2.1) 0.773

16/33 18 (0.07) 0.3 (0.2 – 0.6) 0.001 0.3 (0.2-0.7) 0.001

16/35 83 (0.32) 3.3 (1.9 – 5.7) <0.001 3.7 (2.1-6.4) <0.001

16/39 6 (0.02) 0.7 (0.2 – 2.3) 0.555 0.6 (0.2 – 2.9) 0.358

16/45 14 (0.05) 1.7 (0.6 – 4.8) 0.325 1.7 (0.6 – 5.0) 0.331

16/51 15 (0.06) 1.8 (0.6 – 5.2) 0.257 1.6 (0.6 – 4.6) 0.386

16/52 5 (0.02) 0.6 (0.2 – 2.0) 0.391 0.6 (0.2 – 2.2) 0.426

16/56 7 (0.03) 4.2 (0.5 – 34.8) 0.181 4.3 (0.5 – 36.1) 0.182

16/58 23 (0.09) 1.4 (0.6 – 3.2) 0.410 1.4 (0.6 – 3.2) 0.410

16/59 1 (0.00) 0.2 (0.0 – 1.9) 0.154 0.1 (0.0 – 1.3) 0.083

18/31 5 (0.02) 0.6 (0.21 – 1.6) 0.285 0.5 (0.2 – 1.5) 0.250

18/33 12 (0.05) 0.7 (0.4 – 1.5) 0.422 0.8 (0.4 – 1.7) 0.567

18/35 26 (0.10) 0.6 (0.4 – 1.0) 0.068 0.6 (0.4 – 1.1) 0.089

18/39 1 (0.00) 0.2 (0.0 – 1.6) 0.135 0.2 (0.0- 1.3) 0.088

18/45 3 (0.01) 0.4 (0.1 – 1.3) 0.134 0.4 (0.1 – 1.3) 0.116

18/51 7 (0.03) 1.2 (0.4 – 3.0) 0.753 1.0 (0.4 – 2.7) 0.998

18/52 3 (0.01) 0.9 (0.2 – 3.6) 0.899 1.0 (0.2 – 4.0) 0.961

18/56 4 (0.02) 2.2 (0.5 – 8.9) 0.275 2.2 (0.5 – 9.3) 0.291

18/58 11 (0.04) 1.1 (0.5 – 2.5) 0.745 1.0 (0.5 – 2.2) 0.979

18/59 2 (0.01) 2.2 (0.3 – 15.6) 0.443 1.7 (0.2 – 12.5) 0.621

18/68 1 (0.00) 0.3 (0.0 – 2.1) 0.207 0.2 (0.0 – 1.8) 0.164

31/33 1 (0.00) 0.3 (0.0 – 2.1) 0.210 0.3 (0.0 – 1.9) 0.183

31/35 7 (0.03) 0.9 (0.3 – 2.2) 0.751 0.9 (0.4 – 2.3) 0.823

31/45 3 (0.01) 0.3 (0.0 - 1.9 ) 0.183 2.5 (0.7 -9.7) 0.169

31/51 2 (0.01) 1.4 (0.3 – 6.6) 0.647 1.2 (0.3 – 5.8) 0.784

31/52 3 (0.01) 6.0 (1.4 – 25.1) 0.014 6.8 (1.6 – 29.2) 0.010

31/56 1 (0.00) 1.8 (0.2 – 15.5) 0.582 1.8 (0.2 – 15.4) 0.608

31/58 1 (0.00) 0.4 (0.0 – 2.9) 0.348 0.3 (0.0 – 2.6) 0.291

31/68 1 (0.00) 1.6 (0.2 – 13.3) 0.668 1.5 (0.2 – 12.4) 0.734

33/35 8 (0.03) 0.4 (1.2 - 0.9) 0.019 0.4 (0.2 -0.9) 0.024

33/39 1(0.00) 0.6 (0.1 – 4.7) 0.610 0.5 (0.1 – 3.9) 0.494

33/45 4 (0.02) 1.6 (0.5 – 5.2) 0.404 1.6 (0.5 – 5.3) 0.418

33/51 2 (0.01) 0.6 (0.1 – 2.9) 0.560 0.5 (0.1 – 2.5) 0.432

33/52 1 (0.00) 0.6 (0.1 – 5.3) 0.685 0.7 (0.0 – 5.6) 0.720

33/58 5 (0.02) 1.1 (0.4 – 3.0) 0.845 1.0 (0.4 – 2.7) 0.977

33/68 3 (0.01) 2.9 (0.7 – 12.6) 0.150 3.1 (0.7 – 12.9) 0.120

35/39 2 (0.01) 0.4 (0.1 – 2.0) 0.288 0.3 (0.1 -1.7) 0.185

35/45 5 (0.02) 0.4 (0.1 – 2.0) 0.288 0.7 (0.2 -2.0) 0.462

35/51 13 (0.05) 4.1 (1.6 – 10.6) 0.004 4.0 (1.4 – 10.0) 0.008

35/52 2 (0.01) 0.5 (0.1 – 2.3) 0.369 0.5 (0.1 – 2.5) 0.394

35/56 3 (0.01) 1.2 (0.3 – 5.1) 0.805 1.1 (0.3 – 5.1) 0.851

35/58 12 (0.05) 1.2 (0.6 – 2.6) 0.608 1.1 (0.5 – 2.4) 0.832

35/66 1 (0.00) 2 (0.1 – 32.3) 0.625 2.0 (0.1 – 35.0) 0.639

39/45 1 (0.00) 1.6 (0.2 – 12.9) 0.677 1.5 (0.2 -12.9) 0.690

39/51 3 (0.01) 6.2 (1.5-25.7) 0.011 5.5 (1.3 – 23.1) 0.019

39/56 2 (0.01) 11 1.9 -62) 0.007 10.9 (2.0 - 64.3) 0.008

39/58 1 (0.00) 0.9 (0.1 -7.0) 0.894 1 (0.1 – 6.3) 0.810

39/59 1 (0.00) 9.9 (0.9 - 103) 0.056 7.8 (0.7 – 84.6) 0.090

39/68 1 (0.00) 3.4 (0.4 – 30.1) 0.279 3.6 (0.5 – 30.0) 0.280

45/52 1 (0.00) 1.9 (0.2 – 15.9) 0.569 1.9 (0.2 – 15.9) 0.569

45/56 1 (0.00) 2.2 (0.2 – 19.3) 0.479 2.2 (0.2 – 19.3) 0.479

45/58 1 (0.00) 0.4 (0.0 – 3.2) 0.400 0.4 (0.0 – 3.2) 0.400

45/59 1 (0.00) 4.2 (0.4 – 43.8) 0.229 4.2 (0.4 – 43.8) 0.229

45/68 1 (0.00) 2.0 (0.2 - 16.6) 0.533 1.8 (0.2 – 15.6) 0.591

51/52 1 (0.00) 1.6 (0.2 – 13.7) 0.643 1.4 (0.2 – 12.0) 0.740

51/56 2 (0.01) 5.3 (1.0 – 28.0) 0.051 5.3 (1.0 – 28.9) 0.056

51/58 2 (0.01) 1.0 (0.2 – 4.4) 0.965 0.9 (0.2 – 3.9) 0.847

58/68 2 (0.01) 2.3 (1.0 – 11.2) 0.300 2.4 (0.5- 12.5) 0.296

59/68 2 (0.01) 42 (5.3 – 349.4) 0.001 35.4 (4.2 - 301) 0.001

66/68 1 (0.00) 37 (2.2 – 565.6) 0.013 40 (2-766.4) 0.014

a- analyses with no consideration of HIV status; b- analyses with HIV status as a co-variate

5. Results: It says 58% having 2 HR-HPVs in the text but 59% in Table 3.

Thank you for this observation, this statement has been clarified based on re-analysis of the data, and the actual value is 59%.

6. Supplementary Table 1. Suggest adding HIV as risk factor

The authors appreciate this recommendation and have presented the data as recommended.

Supplementary Table 1: Risk factors of HPV in association with the various HPV genotypes

Odds ratio 95% CI P value

HPV16

Age 1.0 0.9 – 1.0 0.686

Sexual debut 0.8 0.6 - 1.2 0.304

Parity 1.7 0.8 – 3.6 0.146

STI history 0.7 0.1 – 7.7 0.751

HIV 1.0 0.6 – 1.5 0.839

HPV18

Age 12.5 -13 – 38 0.333

Sexual debut -6.7 -22 – 9 0.398

Parity 1.3 0.5 – 3.5 0.627

STI history 0.2 -0.0 – 0.09 0.489

HIV 1.2 0.8 – 1.9 0.446

HPV31

Age 1.0 1.0 – 1.1 0.808

Sexual debut 0.9 0.7 – 1.1 0.340

Parity 0.8 0.6 – 1.0 0.063

STI history 2.3 0.8 – 6.3 0.117

HIV 0.8 0.3 – 2.3 0.664

HPV33

Age 1.0 0.9 – 1.0 0.391

Sexual debut 1.0 0.8 – 1.2 0.746

Parity 1.0 0.8 – 1.3 0.668

STI history 1.2 0.5 – 3.0 0.684

HIV 0.4 0.2 – 1.1 0.079

HPV35

Age 1.2 0.9 – 1.5 0.341

Sexual debut 0.7 0.3 – 1.4 0.312

Parity 1.0 -4.0 – 6.0 0.683

STI history 0.0 -0.0 – 0.10 0.576

HIV 0.8 0.5 – 1.3 0.447

HPV39

Age 1.0 1.0 – 1.1 0.307

Sexual debut 1.2 0.9 – 1.4 0.148

Parity 1.0 0.7 – 1.3 0.761

STI history 1.4 0.4 – 5.1 0.649

HIV 2.1 0.5 – 8.8 0.295

HPV45

Age 1.1 0.82 – 1.44 0.551

Sexual debut 0.9 0.60 – 1.24 0.414

Parity 1.2 0.45 – 3.39 0.681

STI history 0.3 -0.76 – 1.33 0.566

HIV 1.1 0.4 – 2.8 0.854

HPV51

Age 1.1 0.88 -1.31 0.480

Sexual debut 0.7 0.25 – 1.7 0.374

Parity 1.1 0.36 – 3.54 0.834

STI history 1.0 -0.24 – 0.52 0.424

HIV 1.9 0.7 - 4.7 0.179

HPV52

Age 1.1 0.85 – 1.52 0.382

Sexual debut 0.9 0.50 – 1.50 0.579

Parity 0.5 0.09 – 3.18 0.487

STI history 0.3 -0.21 – 0.71 0.242

HIV 0.8 0.2 – 2.9 0.731

HPV58

Age 1.0 1.0 – 1.1 0.209

Sexual debut 1.0 0.9 – 1.2 0.800

Parity 1.0 0.8 – 1.2 0.661

STI history 0.8 0.3 – 2.0 0.573

HIV 2.5 1.0 – 6.2 0.051

HPV58

Age 1.1 0.86 – 1.44 0.419

Sexual debut 0.7 0.25 – 1.67 0.374

Parity 1.1 0.44 – 2.79 0.822

STI history 0.0 -0.14 – 0.23 0.611

HIV 1.6 0.8 – 3.4 0.194

HPV59

Age 0.1 -0.08 – 0.24 0.198

Sexual debut 1.0 0.88 – 1.22 0.650

Parity -0.8 -2.53 – 1.03 0.272

STI history 0.7 -0.70 – 2.03 0.219

HIV 3.7 0.4 – 35.72 0.262

HPV66a

Age 1.1 0.81 – 1.57 0.473

Sexual debut 1.0 0.81 – 1.21 0.959

Parity 1.0 -0.31 – 0.54 0.369

HIV 1.2 0.1 – 19.47 0.894

HPV68

Age 1.1 0.83 – 1.39 0.594

Sexual debut 1.1 0.91 – 1.29 0.382

Parity 1.2 0.49 – 2.94 0.685

STI history 0.3 -0.61 – 1.28 0.420

HIV 1.5 0.40 – 5.79 0.535

Number of HPV infections

Age 1.0 0.95 – 1.04 0.802

Sexual debut 0.9 0.82 – 1.01 0.085

Parity 1.0 0.76 – 1.20 0.690

STI history 0.5 0.14 – 1.62 0.239

HIV 1.5 0.42 – 5.13 0.545

Note: STI, sexually transmitted infection; CI, confidence interval; a – no individual with HPV genotype had STI history

Reviewer #2:

1. It would be informative to provide a brief description of the HPV vaccination program and uptake in Zimbabwe early in the introduction.

The coverage for the HPV prophylactic vaccines is in its infancy in most developing countries, where the coverage is estimated to be more than 12-fold lower than developed countries [10]. As a matter of interest countries such as Zambia, Malawi and Zimbabwe, where the highest global cervical cancer cases are recorded, are still in the pilot and early implementation phases of the national HPV vaccination programs [11-12].

2. Could the authors clarify the reasons of only including cervical cancer diagnosed at stage 1b-4.

Thank you for noting this, we have clarified the selection criteria for our cohort. Our study recruited individuals from an outpatient radiotherapy and chemotherapy treatment facility, therefore, only patients seeking this type of care attend the clinics. Consequently, this excludes patients receiving surgical interventions only, or are receiving palliative care, because these individuals receive care from other oncological units in the hospital. The methods describe this as:

The participants from the current study were nested in a pharmacogenomics of cervical cancer study, aiming to recruit women who were newly diagnosed with cervical cancer, and were eligible to receive radical therapy. Potential study participants were identified through the RTC hospital registry. To be considered eligible for this study, women had to be ≥18 years, with histologically confirmed diagnoses of invasive cervical cancer staged between 1b – 4A, and were earmarked for curative anti-cancer therapy. Individuals who were diagnosed with resectable (stage 1A) disease that did not require chemo/radio- therapies, or with advanced disease that required palliative care (stage 4B) were excluded from the study, because these individuals were referred for care outside of the RTC.

3. It seems the histopathological characteristics are available for all cases included in this study. Such information should be included in the descriptive analysis

This descriptive data is now included in the demographics table (Table 1) as illustrated below.

Tumour Histology Combined n(prop) HIV – n(prop) HIV + n(prop) P

Squamous Cell 207 (0.80) 115 (0.83) 92 (0.77) Ref.

Adenocarcinoma 25 (0.10) 14 (0.10) 11 (0.09) 0.950b

Adenosquamous 11 (0.04) 2 (0.01) 9 (0.08) 0.017 b

Other* 15 (0.06) 8 (0.06) 7 (0.06) 0.900b

4. Maybe only relevant covariables to the current study are needed to be described. Some of the information collected through the project but not used for the study might not be necessary to mention, such as weight, height, comorbidities and treatment etc.

Thank you for this observation. All variables that are not further analysed in this study have been deleted. The amended statement reads:

Demographic information such as age, residency, histopathological tumour characteristics, were collected from the patient folder in the RTC. In addition, behavioral and lifestyle factors were collected by interviewing the study participants, namely history of alcohol, smoking and parity. Sexual history information including age at sexual debut, number of sexual partners, history of circumcised partner, sexually transmitted infections were also collected. Data on HIV status was also collected for comparative analysis.

5. I figure no significant association was detected in the univariate regression between HPV-related risk factors (i.e., age, sexual debut, parity and STI history) and any HR-HPV genotypes might be due to limited power, when the HR-HPV types were stratified by each type. However, those are known factors that associated with HPV infection. Could the authors provide estimates (figure 3b) additionally adjusting for those factors and histology at least as sensitivity analysis when examine the co-occurrence of HPV types. Besides, please clarify what is the reference group for the ORs.

As observed by the reviewer, these analyses had not been performed due to a lack of association detected in the univariate analysis. To ensure that these findings were not influenced by limited power of study, multivariable regression was performed using the HPV co-occurrences as predictors, and adjusting for the known HPV risk factors- age, sexual debut, parity, STI history and HIV status, and the findings were tabulated into Supplementary Table 3. Although there were no definitive indicators determined in this sensitivity analyses, we did observe a tendency towards of the HPV16/18 co-occurrence to be influences by age HIV status and age of sexual debut. A larger cohort would be needed to confirm the validity of the potential trend observed here.

Supplementary Table 3. Multivariate regression analysis of co-occurring HPV genotypes with risk factors.

Co-variates OR (95% CI) P

HPV16/18

Age 1.0 (1.0 – 1.1) 0.684

HIV 2.3 (0.9 -5.6) 0.065

STI history 0.5 (0.2 – 1.3) 0.159

Parity 1.1 (0.9 – 1.4) 0.274

Age of sexual debut 1.1 (1.0 – 1.3) 0.076

HPV16/33

Age 1.0 (0.9 – 1.0) 0.547

HIV 0.4 (0.1 – 1.5) 0.174

STI history 1.0 (0.2 – 3.9) 0.956

Parity 1.0 (0.7 – 1.3) 0.821

Age of sexual debut 1.0 (0.8 – 1.2) 0.834

HPV16/35

Age 1.0 (1.0 – 1.1) 0.152

HIV 0.8 (0.4 – 1.6) 0.573

STI history 1.1 (0.7 – 2.2) 0.769

Parity 1.0 (0.9 – 1.2) 0.985

Age of sexual debut 1.0 (0.9 – 1.2) 0.532

HPV35/51

Age 1.0 (1.0 – 1.1) 0.282

HIV 1.0 (0.2 – 4.4) 0.999

STI history 3.0 (0.8 – 12.2) 0.115

Parity 0.9 (0.7 – 1.3) 0.670

Age of sexual debut 1.0 (0.8 – 1.3) 0.856

HPV35/52

Age 1.0 (0.8 – 1.3) 0.930

HIV NA NA

STI history NA NA

Parity 0.9 (0.3 – 3.4) 0.933

Age of sexual debut 1.5 (0.7 – 3.6) 0.332

HPV39/51

Age 1.0 (0.8 – 1.2) 0.827

HIV NA NA

STI history NA NA

Parity 1.2 (0.7 – 2.0) 0.602

Age of sexual debut 1.0 (0.5 – 1.9) 0.984

HPV39/56

Age 1.2 (1.0 – 1.4) 0.108

HIV NA NA

STI history NA NA

Parity 1.2 (0.7 – 2.3) 0.520

Age of sexual debut 1.2 (0.7 – 2.3) 0.582

6. A legend explaining how the HR-HPV types were classified (ie. hierarchical classification) especially for those cases with multiple types of HPV infection in table 1 could be informative.

Thank you for this recommendation. In our analysis, HPVs were only analysed at a genotype level, due to the limited coverage of the HPV genotyping assay used to test for HPV DNA. We have clarified this in the methods section of the manuscript, and have also included a figure legend in the results for reference to phylogenetic clades in the discussion section.

Methods section:

For analysis all individual HPV genotypes regardless of whether they occur as single or multiple infections were analysed were reported as standalone genotypes. None of the statistical analysis taking into account the species from which the HPV genotypes are from.

Results section:

a= Human papillomavirus alpha 9 species; b= Human papillomavirus alpha 7 species; c= Human papillomavirus alpha 5 species; d= Human papillomavirus alpha 6 species.

7. In table 4, could the authors clarify the data source for the HPV types in each region of Africa if they are from external studies?

Thank you for this comment. We have clarified in the results section where we obtained the data from. The prevalence of the various HPV genotypes in the Zimbabwean study cohort was compared to the most common HPV genotypes on other African women across the continent, using data obtained from HPV Information Centre database (https://hpvcentre.net).

8. Could the authors provide a detailed descriptive table as supplement the distribution of HR-HPV types by HIV status for all included women (including the specific types for multiple infections)?

The additional data for the HPV multiple infections by HIV status was collated into a supplementary table 2. Further inclusion of more than 2 HR-HPVs

Submitted filename: PLOS Repsonses to reviewers_14Apr2021.docx

Click here for additional data file.

12 Aug 2021

PONE-D-21-04071R1

High-Risk HPV genotypes in Zimbabwean women with cervical cancer: Comparative Analyses between HIV-negative and HIV-positive women.

PLOS ONE

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Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #2: No

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Reviewer #2: Thank you for the extensive work from the authors in addressing the comments, and manuscript has been substantially improved. However, there are a couple responses to the comments could be further clarified:

In the response to comment point 5, the author has already performed a multivariate regression analysis, using the HPV genotypes as predictors, while controlling for age and HIV status, and the ORs for significant HPV genotypes were illustrated in fig 3b. Therefore, the sensitivity analysis is expecting to be repeating exact the same regression model by additionally control for factors of sexual debut, parity, STI history and histology and presented the ORs as a supplementary figure or table instead of presenting ORs for each of the covariate as shown in the current supplementary table 3.

I appreciate the work done by the authors for supplementary table 2, however, what would be informative to address the comments point 8 is to provide a simple frequency table showing the distribution of hrHPV types, including both single type infection and multiple types of infection by HIV status (HIV positive and HIV negative). Considering there are a proportion of women had more than 2 types of HPV infection (table 2), showing the actual HPV types by HIV status could be interesting to the readership.

**********

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24 Aug 2021

Response to Reviewer Comments: PONE-D-21-04071R1

24 August 2021,

Dear Professor Graciela Andrei

Thank you for reviewing our manuscript entitled “High-Risk HPV genotypes in Zimbabwean women with cervical cancer: Comparative Analyses between HIV-negative and HIV-positive women” and providing us with constructive criticism on how to improve the statistical analysis and the scientific soundness of the work. Below are our responses to each of the reviewers’ and editor’s comments;

Reviewer #2: Thank you for the extensive work from the authors in addressing the comments, and manuscript has been substantially improved. However, there are a couple responses to the comments could be further clarified. In the response to comment point 5, the author has already performed a multivariate regression analysis, using the HPV genotypes as predictors, while controlling for age and HIV status, and the ORs for significant HPV genotypes were illustrated in fig 3b. Therefore, the sensitivity analysis is expecting to be repeating exact the same regression model by additionally control for factors of sexual debut, parity, STI history and histology and presented the ORs as a supplementary figure or table instead of presenting ORs for each of the covariate as shown in the current supplementary table 3.

Thank you for further clarifying the expectations of the reviewer, which the authors may have misunderstood in the previous peer review process. The authors have done the best they can to address this comment. To this effect, S3 Table was added, consisting of multivariate analysis for the co-segregation of the HPV genotypes, taking into consideration the proposed covariates, namely, age, history of STI, parity, HIV status, age of sexual debut, tumour histology, as suggested. The comment was addressed in the manuscript to read:

In order to ensure that lack of significance observed was not a result of low study power, a sensitivity appraisal was conducted, using multivariate regression analyses controlling for age, history of sexually transmitted infections, parity, HIV status, age at sexual debut and tumour histology. In this sensitivity analyses, all the co-segregation patterns remained statistically significant except HPV16/33 (OR=0.4; 95% CI=0.2-1.9; p=0.05), HPV35/52 (OR=0.5; 95% CI=0.1-4.3; p=0.50) (S3 Table).

Table S3. Sensitivity analysis for HPV co-segregation.

HPV genotypes* OR (95% CI) P

16/18 0.3 (0.1-0.5) <0.01

16/33 0.4 (0.2-1.0) 0.05

16/35 3.6 (1.8-7.2) <0.01

35/51 8.8 (2.3-34.3) <0.01

35/52 0.5 (0.1-4.3) 0.50

39/51 4.2 (0.7-25.9) 0.12

39/56 9.5 (1.1-82.9) 0.04

*multivariate analysis for the co-segregation of HPV genotypes, taking into consideration age, history of STI, parity, HIV status, age of sexual debut, tumour histology.

I appreciate the work done by the authors for supplementary table 2, however, what would be informative to address the comments point 8 is to provide a simple frequency table showing the distribution of hrHPV types, including both single type infection and multiple types of infection by HIV status (HIV positive and HIV negative). Considering there are a proportion of women had more than 2 types of HPV infection (table 2), showing the actual HPV types by HIV status could be interesting to the readership.

Thank you for indicating which analyses and data would be more interesting to the readers. The comments were received well and taken into consideration. The authors have amended the table to report on absolute frequencies, by HIV status, as shown below. This tabulation was favoured as it clearly represents the number of individuals with specific HPV genotypes depending on HIV status.

S2 Table. The number of individuals harbouring specific HPV genotypes stratified by HIV status.

Number of HPVs (n)

HPV genotype HIV status 1

2 3 4 5 6

16 - 19 18 5 1 0 0

+ 15 13 3 1 1 0

18 - 13 12 1 0 1 1

+ 6 13 1 0 0 0

31 - 1 0 1 2 0 0

+ 1 0 0 0 0 1

33 - 2 0 1 2 0 0

+ 2 0 0 0 0 0

35 - 15 15 3 0 1 0

+ 3 10 3 0 1 1

39 - 0 2 0 1 0 0

+ 0 1 0 0 0 0

45 - 1 0 2 3 0 0

+ 0 0 1 1 0 0

51 - 0 0 1 0 1 0

+ 1 1 1 1 1 1

52 - 1 2 1 0 0 0

+ 0 3 1 0 0 1

56 - 0 0 0 1 0 0

+ 0 0 1 0 1 0

58 - 0 2 1 1 1 0

+ 0 2 0 0 1 0

59 - 0 0 0 2 0 0

+ 2 0 1 1 0 0

66 - 0 0 0 1 0 0

+ 0 0 1 0 0 0

68 - 2 0 1 0 1 0

+ 1 0 1 1 2 1

TOTAL 85 94 31 19 12 6

Submitted filename: PONE-D-21-04071R1-responses to reviewers 24082021.docx

Click here for additional data file.

31 Aug 2021

High-Risk HPV genotypes in Zimbabwean women with cervical cancer: Comparative Analyses between HIV-negative and HIV-positive women.

PONE-D-21-04071R2

Dear Dr. Dandara,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Graciela Andrei

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PLOS ONE

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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Reviewer #2: (No Response)

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Reviewer #2: (No Response)

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Reviewer #1: (No Response)

Reviewer #2: (No Response)

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16 Sep 2021

PONE-D-21-04071R2

High-Risk HPV genotypes in Zimbabwean women with cervical cancer: Comparative Analyses between HIV-negative and HIV-positive women.

Dear Dr. Dandara:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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