Epidemiology of Kaposi’s sarcoma in sub-Saharan Africa

Motlhale, Melitah; Sitas, Freddy; Bradshaw, Debbie; Chen, Wenlong Carl; Singini, Mwiza Gideon; Villiers, Chantal Babb de; Lewis, Cathryn M.; Muchengeti, Mazvita; Waterboer, Tim; Mathew, Christopher G.; Newton, Robert; Singh, Elvira

doi:10.1016/j.canep.2022.102167

Cited by 20 publications

(15 citation statements)

References 41 publications

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“…Our findings highlight the high seroprevalence of antibodies against KSHV and the striking association between KSHV, HIV and KS found in African settings. ART rollout coincides with a decline in KSHV seroprevalence, in keeping with a decline in KS incidence after ART rollout 35 . By contrast we did not observe any declines in HIV prevalence over the same period.…”

Section: Discussionsupporting

confidence: 77%

“…ART rollout coincides with a decline in KSHV seroprevalence, in keeping with a decline in KS incidence after ART rollout. 35 By contrast we did not observe any declines in HIV prevalence over the same period. Our observed higher KS risks in KSHV and HIV seropositive females cannot explain the higher population male to female KS incidence ratios.…”

Section: Discussioncontrasting

confidence: 68%

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Kaposi sarcoma‐associated herpesvirus, HIV‐1 and Kaposi sarcoma risk in black South Africans diagnosed with cancer during antiretroviral treatment rollout

Motlhale

Muchengeti

Bradshaw

et al. 2023

Intl Journal of Cancer

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Kaposi sarcoma‐associated herpesvirus (KSHV) causes Kaposi sarcoma (KS). The risk of KS is amplified in HIV‐immunosuppressed individuals and antiretroviral therapy (ART) reduces KS incidence. Reliable data on the relationship between these factors are lacking in Africa. We used questionnaires and serum from 7886 black South Africans (18‐74 years) with incident cancer, recruited between 1995 and 2016. ART rollout started in 2004. We measured associations between KS, HIV‐1 and KSHV before and after ART rollout. We measured seropositivity to HIV‐1, KSHV latency‐associated nuclear antigen (LANA) and glycoprotein (K8.1) and calculated case‐control‐adjusted odds ratios (ORadj) and 95% confidence intervals (CI) in relation to KS and KSHV infection, before (1995‐2004), early (2005‐2009) and late (2010‐2016) ART rollout periods. KSHV seropositivity among 1237 KS cases was 98%. Among 6649 controls, KSHV seropositivity was higher in males (ORadj = 1.4 [95%CI 1.23‐1.52]), in persons with HIV, (ORadj = 4.2 [95%CI 3.74‐4.73]) and lower in high school leavers (ORadj = 0.7 [95%CI 0.59‐0.83]). KSHV seropositivity declined over the three ART rollout periods (37%, 28% and 28%, Ptrend < .001) coinciding with increases in high school leavers over the same periods (46%, 58% and 67%, Ptrend < .001). HIV‐1 seroprevalence increased from 10% in the pre‐ART period to 22% in the late ART period (Ptrend < .001). Compared to HIV‐1 and KSHV seronegatives, KSHV seropositives yielded an OR for KS of 26 (95%CI 11‐62) in HIV‐1 seronegative participants and an OR of 2501 (95%CI 1083‐5776) in HIV‐1 seropositive participants. HIV‐1 increases the risk of KS in those infected with KSHV by 100‐fold. Declines in KSHV seroprevalence coincide with ART rollout and with improvements in educational standards and general hygiene.

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Section: Discussionsupporting

confidence: 77%

Section: Discussioncontrasting

confidence: 68%

Kaposi sarcoma‐associated herpesvirus, HIV‐1 and Kaposi sarcoma risk in black South Africans diagnosed with cancer during antiretroviral treatment rollout

Motlhale

Muchengeti

Bradshaw

et al. 2023

Intl Journal of Cancer

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“…Thus, it also provides further evidence that malaria seems to be reactivating KSHV replication, possibly thereby contributing to the increased incidence of endemic KS in the region. Though the average decline in KS incidence is largely associated with a reduction in HIV/AIDS incidence and the rollout of antiretroviral therapy [ 38 ], the impacts of effective malaria control programs within sub-Saharan Africa on this downward trend cannot be ignored.…”

Section: Discussionmentioning

confidence: 99%

Distinctive Kaposi Sarcoma-Associated Herpesvirus Serological Profile during Acute Plasmodium falciparum Malaria Episodes

Oluoch

Forconi

Oduor

et al. 2023

IJMS

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The seroprevalence of Kaposi sarcoma-associated herpesvirus (KSHV) and the incidence of endemic Kaposi sarcoma (KS) overlap with regions of malaria endemicity in sub-Saharan Africa. Multiple studies have shown an increased risk of KSHV seroconversion in children from high malaria compared to low malaria regions; however, the impact of acute episodes of Plasmodium falciparum (P. falciparum) malaria on KSHV’s biphasic life cycle and lytic reactivation has not been determined. Here, we examined KSHV serological profiles and viral loads in 134 children with acute malaria and 221 healthy children from high malaria regions in Kisumu, as well as 77 healthy children from low malaria regions in Nandi. We assayed KSHV, Epstein–Barr virus (EBV), and P. falciparum malaria antibody responses in these three by multiplexed Luminex assay. We confirmed that KSHV seroprevalence was significantly associated with malaria endemicity (OR = 1.95, 1.18–3.24 95% CI, p = 0.01) with 71–77% seropositivity in high-malaria (Kisumu) compared to 28% in low-malaria (Nandi) regions. Furthermore, KSHV serological profiles during acute malaria episodes were distinct from age-matched non-malaria-infected children from the same region. Paired IgG levels also varied after malaria treatment, with significantly higher anti-ORF59 at day 0 but elevated ORF38, ORF73, and K8.1 at day 3. Acute malaria episodes is characterized by perturbation of KSHV latency in seropositive children, providing further evidence that malaria endemicity contributes to the observed increase in endemic KS incidence in sub-Saharan Africa.

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“…Regarding the cancer types for which outcomes were modeled using ML algorithms, breast cancer and head and neck cancer (mostly oral cancer) were the most common in line with the majority of LLMICs represented in this review ( 1 ). However, outcomes for some cancers uniformly common in many developing countries and have infectious causes related to poverty and awareness such as Kaposi’s sarcoma, Burkitt’s lymphoma, and gastric cancer have not been considered ( 1 , 183 – 185 ). Hopefully, with an increase in cancer ML-based model development among more LLMICs in the future, outcomes for these specific malignancies will be further explored for indigenous patients.…”

Section: Discussionmentioning

confidence: 99%

Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review

Adeoye

Akinshipo²,

Koohi‐Moghadam³

et al. 2022

Front. Oncol.

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BackgroundThe impact and utility of machine learning (ML)-based prediction tools for cancer outcomes including assistive diagnosis, risk stratification, and adjunctive decision-making have been largely described and realized in the high income and upper-middle-income countries. However, statistical projections have estimated higher cancer incidence and mortality risks in low and lower-middle-income countries (LLMICs). Therefore, this review aimed to evaluate the utilization, model construction methods, and degree of implementation of ML-based models for cancer outcomes in LLMICs.MethodsPubMed/Medline, Scopus, and Web of Science databases were searched and articles describing the use of ML-based models for cancer among local populations in LLMICs between 2002 and 2022 were included. A total of 140 articles from 22,516 citations that met the eligibility criteria were included in this study.ResultsML-based models from LLMICs were often based on traditional ML algorithms than deep or deep hybrid learning. We found that the construction of ML-based models was skewed to particular LLMICs such as India, Iran, Pakistan, and Egypt with a paucity of applications in sub-Saharan Africa. Moreover, models for breast, head and neck, and brain cancer outcomes were frequently explored. Many models were deemed suboptimal according to the Prediction model Risk of Bias Assessment tool (PROBAST) due to sample size constraints and technical flaws in ML modeling even though their performance accuracy ranged from 0.65 to 1.00. While the development and internal validation were described for all models included (n=137), only 4.4% (6/137) have been validated in independent cohorts and 0.7% (1/137) have been assessed for clinical impact and efficacy.ConclusionOverall, the application of ML for modeling cancer outcomes in LLMICs is increasing. However, model development is largely unsatisfactory. We recommend model retraining using larger sample sizes, intensified external validation practices, and increased impact assessment studies using randomized controlled trial designsSystematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=308345, identifier CRD42022308345.

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Epidemiology of Kaposi’s sarcoma in sub-Saharan Africa

Cited by 20 publications

References 41 publications

Kaposi sarcoma‐associated herpesvirus, HIV‐1 and Kaposi sarcoma risk in black South Africans diagnosed with cancer during antiretroviral treatment rollout

Kaposi sarcoma‐associated herpesvirus, HIV‐1 and Kaposi sarcoma risk in black South Africans diagnosed with cancer during antiretroviral treatment rollout

Distinctive Kaposi Sarcoma-Associated Herpesvirus Serological Profile during Acute Plasmodium falciparum Malaria Episodes

Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review

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