Risk stratification of cervical lesions using capture sequencing and machine learning method based on HPV and human integrated genomic profiles

Accurate detection of copy number aberrations in FFPE samples using the mFAST-SeqS approach

BACKGROUND

Shallow whole genome sequencing (Shallow-seq) is used to determine the copy number aberrations (CNA) in tissue samples and circulating tumor DNA. However, costs of NGS and challenges of small biopsies ask for an alternative to the untargeted NGS approaches. The mFAST-SeqS approach, relying on LINE-1 repeat amplification, showed a good correlation with Shallow-seq to detect CNA in blood samples. In the present study, we evaluated whether mFAST-SeqS is suitable to assess CNA in small formalin-fixed paraffin-embedded (FFPE) tissue specimens, using vulva and anal HPV-related lesions.

METHODS

Seventy-two FFPE samples, including 36 control samples (19 vulva;17 anal) for threshold setting and 36 samples (24 vulva; 12 anal) for clinical evaluation, were analyzed by mFAST-SeqS. CNA in vulva and anal lesions were determined by calculating genome-wide and chromosome arm-specific z-scores in comparison with the respective control samples. Sixteen samples were also analyzed with the conventional Shallow-seq approach.

RESULTS

Genome-wide z-scores increased with the severity of disease, with highest values being found in cancers. In vulva samples median and inter quartile ranges [IQR] were 1[0-2] in normal tissues (n = 4), 3[1-7] in premalignant lesions (n = 9) and 21[13-48] in cancers (n = 10). In anal samples, median [IQR] were 0[0-1] in normal tissues (n = 4), 14[6-38] in premalignant lesions (n = 4) and 18[9-31] in cancers (n = 4). At threshold 4, all controls were CNA negative, while 8/13 premalignant lesions and 12/14 cancers were CNA positive. CNA captured by mFAST-SeqS were mostly also found by Shallow-seq.

CONCLUSION

mFAST-SeqS is easy to perform, requires less DNA and less sequencing reads reducing costs, thereby providing a good alternative for Shallow-seq to determine CNA in small FFPE samples.

Molecular aspects of cervical cancer: a pathogenesis update

Cervical cancer (CC) is a significant health problem, especially in low-income countries. Functional studies on the human papillomavirus have generated essential advances in the knowledge of CC. However, many unanswered questions remain. This mini-review discusses the latest results on CC pathogenesis, HPV oncogenesis, and molecular changes identified through next-generation technologies. Interestingly, the percentage of samples with HPV genome integrations correlates with the degree of the cervical lesions, suggesting a role in the development of CC. Also, new functions have been described for the viral oncoproteins E5, E6, and E7, resulting in the acquisition and maintenance of cancer hallmarks, including proliferation, immune response evasion, apoptosis, and genomic instability. Remarkably, E5 oncoprotein affects signaling pathways involved in the expression of interferon-induced genes and EGFR-induced proliferation, while E6 and E7 oncoproteins regulate the DNA damage repair and cell cycle continuity pathways. Furthermore, next-generation technologies provide vast amounts of information, increasing our knowledge of changes in the genome, transcriptome, proteome, metabolome, and epigenome in CC. These studies have identified novel molecular traits associated with disease susceptibility, degree of progression, treatment response, and survival as potential biomarkers and therapeutic targets.

Coinfections in human papillomavirus associated cancers and prophylactic recommendations

The Human Papillomavirus (HPV) infection is responsible for more than 80% of reported cervical cancer and other virus-associated tumours. Although this global threat can be controlled using effective vaccination strategies, a growing perturbation of HPV infection is an emerging coinfection likely to increase the severity of the infection in humans. Moreover, these coinfections prolong the HPV infections, thereby risking the chances for oncogenic progression. The present review consolidated the clinically significant microbial coinfections/co-presence associated with HPV and their underlying molecular mechanisms. We discussed the gaps and concerns associated with demography, present vaccination strategies, and other prophylactic limitations. We concluded our review by highlighting the potential clinical as well as emerging computational intervention measures to kerb down HPV-associated severities.

A multi-variable predictive warning model for cervical cancer using clinical and SNPs data

Introduction

Cervical cancer is the fourth most common cancer among female worldwide. Early detection and intervention are essential. This study aims to construct an early predictive warning model for cervical cancer and precancerous lesions utilizing clinical data and simple nucleotide polymorphisms (SNPs).

Methods

Clinical data and germline SNPs were collected from 472 participants. Univariate logistic regression, least absolute shrinkage selection operator (LASSO), and stepwise regression were performed to screen variables. Logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), extreme gradient boosting(XGBoost) and neural network(NN) were applied to establish models. The receiver operating characteristic (ROC) curve was used to compare the models' efficiencies. The performance of models was validated using decision curve analysis (DCA).

Results

The LR model, which included 6 SNPs and 2 clinical variables as independent risk factors for cervical carcinogenesis, was ultimately chosen as the most optimal model. The DCA showed that the LR model had a good clinical application.

Discussion

The predictive model effectively foresees cervical cancer risk using clinical and SNP data, aiding in planning timely interventions. It provides a transparent tool for refining clinical decisions in cervical cancer management.

Artificial intelligence and visual inspection in cervical cancer screening

INTRODUCTION

Visual inspection with acetic acid is limited by subjectivity and a lack of skilled human resource. A decision support system based on artificial intelligence could address these limitations. We conducted a diagnostic study to assess the diagnostic performance using visual inspection with acetic acid under magnification of healthcare workers, experts, and an artificial intelligence algorithm.

METHODS

A total of 22 healthcare workers, 9 gynecologists/experts in visual inspection with acetic acid, and the algorithm assessed a set of 83 images from existing datasets with expert consensus as the reference. Their diagnostic performance was determined by analyzing sensitivity, specificity, and area under the curve, and intra- and inter-observer agreement was measured using Fleiss kappa values.

RESULTS

Sensitivity, specificity, and area under the curve were, respectively, 80.4%, 80.5%, and 0.80 (95% CI 0.70 to 0.90) for the healthcare workers, 81.6%, 93.5%, and 0.93 (95% CI 0.87 to 1.00) for the experts, and 80.0%, 83.3%, and 0.84 (95% CI 0.75 to 0.93) for the algorithm. Kappa values for the healthcare workers, experts, and algorithm were 0.45, 0.68, and 0.63, respectively.

CONCLUSION

This study enabled simultaneous assessment and demonstrated that expert consensus can be an alternative to histopathology to establish a reference standard for further training of healthcare workers and the artificial intelligence algorithm to improve diagnostic accuracy.

The Significance of Human Papillomavirus Receptors Related Genetic Variants in Cervical Cancer Screening

To investigate the relationship between single nucleotide polymorphisms (SNPs) in human papillomavirus (HPV) receptor associated genes and HPV susceptibility and clinical outcomes in Chinese women, from October 2016 to March 2020, a total of 3,066 women were recruited for a 3-year prospective population-based cervical cancer screening clinical trial. The primary endpoint was histological cervical intraepithelial neoplasia 2 and worse (CIN2+). Twenty-nine SNPs of HPV receptor associated genes on women with available cytology residual samples at baseline were detected using MALDI-TOF MS. Eligible data were available for 2,938 women. Rs16894821 (GG versus AA, OR =1.71 [1.08 to 2.69]) and rs724236 (TT versus AA, OR = 1.73 [1.14 to 2.62]) in SDC2 were significantly related to the HPV susceptibility. And rs2575712 (TT versus GG, OR = 2.78 [1.22 to 6.36]) in SDC2 was associated with increased HPV 16/18 susceptibility. Four SNPs (rs1047057 and rs10510097 in FGFR2 gene, rs2575735 in SDC2 gene, and rs878949 in HSPG2 gene) were significantly associated with persistent HPV infection. Importantly, the genotypes of rs16894821 under recessive model (GG versus AA/AG, OR = 2.40 [1.12 to 5.15]) in SDC2 and rs11199993 under dominant model (GC/CC versus GG, OR = 1.64 [1.01 to 2.68]) in FGFR2 were significantly associated with the disease progression. Finally, SNPs showed comparable efficacy in detecting CIN2+ for the women infected with non-HPV16/18 compared with cervical cytology (sensitivity: 0.51 [0.36 to 0.66] versus 0.44 [0.30 to 0.60], specificity: 0.96 [0.96 to 0.97] versus 0.98 [0.97 to 0.99], positive predictive value: 0.23 [0.15 to 0.33] versus 0.33 [0.22 to 0.47], and negative predictive value: 0.99 [0.98 to 0.99] versus 0.99 [0.98 to 0.99]). SNPs in HPV receptor related genes may influence HPV susceptibilities and clinical outcomes in Chinese women. IMPORTANCE Virus receptors are known to mediate virus attachment and further lead to virus infection of the host cells. In the current study, we investigated the relationship between single nucleotide polymorphisms (SNPs) in human papillomavirus (HPV) receptor associated genes and HPV susceptibility and clinical outcomes in Chinese women, and to explore the new triaging strategy for non-16/18 high-risk HPV infection.

Optical imaging for early detection of cervical cancer: state of the art and perspectives

Significance

Cervical cancer is one of the major causes of death in females worldwide. HPV infection is the key cause of uncontrolled cell growth leading to cervical cancer. About 90% of cervical cancer is preventable because of the slow progression of the disease, giving a window of about 10 years for the precancerous lesion to be recognized and treated.

Aim

The present challenges for cervical cancer diagnosis are interobserver variation in clinicians' interpretation of visual inspection with acetic acid/visual inspection with Lugol's iodine, cost of cytology-based screening, and lack of skilled clinicians. The optical modalities can assist in qualitatively and quantitatively analyzing the tissue to differentiate between cancerous and surrounding normal tissues.

Approach

This work is on the recent advances in optical techniques for cervical cancer diagnosis, which promise to overcome the above-listed challenges faced by present screening techniques.

Results

The optical modalities provide substantial measurable information in addition to the conventional colposcopy and Pap smear test to clinically aid the diagnosis.

Conclusions

Recent optical modalities on fluorescence, multispectral imaging, polarization-sensitive imaging, microendoscopy, Raman spectroscopy, especially with the portable design and assisted by artificial intelligence, have a significant scope in the diagnosis of premalignant cervical cancer in future.

Genomic alterations related to HPV infection status in a cohort of Chinese prostate cancer patients

BACKGROUND

Human papillomavirus (HPV) has been proposed as a potential pathogenetic organism involved in prostate cancer (PCa), but the association between HPV infection and relevant genomic changes in PCa is poorly understood.

METHODS

To evaluate the relationship between HPV genotypes and genomic alterations in PCa, HPV capture sequencing of DNA isolated from 59 Han Chinese PCa patients was performed using an Illumina HiSeq2500. Additionally, whole-exome sequencing of DNA from these 59 PCa tissue samples and matched normal tissues was carried out using the BGI DNBSEQ platform. HPV infection status and genotyping were determined, and the genetic disparities between HPV-positive and HPV-negative PCa were evaluated.

RESULTS

The presence of the high-risk HPV genome was identified in 16.9% of our cohort, and HPV16 was the most frequent genotype detected. The overall mutational burden in HPV-positive and HPV-negative PCa was similar, with an average of 2.68/Mb versus 2.58/Mb, respectively, in the targeted whole-exome region. HPV-negative tumors showed a mutational spectrum concordant with published PCa analyses with enrichment for mutations in SPOP, FOXA1, and MED12. HPV-positive tumors showed more mutations in KMT2C, KMT2D and ERCC2. Copy number alterations per sample were comparable between the two groups. However, the significantly amplified or deleted regions of the two groups only partially overlapped. We identified amplifications in oncogenes, including FCGR2B and CCND1, and deletions of tumor suppressors, such as CCNC and RB1, only in HPV-negative tumors. HPV-positive tumors showed unique deletions of tumor suppressors such as NTRK1 and JAK1.

CONCLUSIONS

The genomic mutational landscape of PCa differs based on HPV infection status. This work adds evidence for the direct involvement of HPV in PCa etiology. Different genomic features render HPV-positive PCa a unique subpopulation that might benefit from virus-targeted therapy.

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

OBJECTIVE

The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions.

MATERIALS AND METHODS

Comprehensive searches were performed on three databases: Medline, Web of Science Core Collection (Indexes = SCI-EXPANDED, SSCI, A & HCI Timespan) and Scopus to find papers published until July 2022. Articles that applied any AI technique for the prediction, screening, and diagnosis of cervical cancer were included in the review. No time restriction was applied. Articles were searched, screened, incorporated, and analyzed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines.

RESULTS

The primary search yielded 2538 articles. After screening and evaluation of eligibility, 117 studies were incorporated in the review. AI techniques were found to play a significant role in screening systems for pre-cancerous and cancerous cervical lesions. The accuracy of the algorithms in predicting cervical cancer varied from 70% to 100%. AI techniques make a distinction between cancerous and normal Pap smears with 80-100% accuracy. AI is expected to serve as a practical tool for doctors in making accurate clinical diagnoses. The reported sensitivity and specificity of AI in colposcopy for the detection of CIN2+ were 71.9-98.22% and 51.8-96.2%, respectively.

CONCLUSION

The present review highlights the acceptable performance of AI systems in the prediction, screening, or detection of cervical cancer and pre-cancerous lesions, especially when faced with a paucity of specialized centers or medical resources. In combination with human evaluation, AI could serve as a helpful tool in the interpretation of cervical smears or images.

Earth Mover’s Distance-Based Tool for Rapid Screening of Cervical Cancer Using Cervigrams

Cervical cancer is a major public health challenge that can be cured with early diagnosis and timely treatment. This challenge formed the rationale behind our design and development of an intelligent and robust image analysis and diagnostic tool/scale, namely “OM—The OncoMeter”, for which we used R (version-3.6.3) and Linux (Ubuntu-20.04) to tag and triage patients in order of their disease severity. The socio-demographic profiles and cervigrams of 398 patients evaluated at OPDs of Batra Hospital & Medical Research Centre, New Delhi, India, and Delhi State Cancer Institute (East), New Delhi, India, were acquired during the course of this study. Tested on 398 India-specific women’s cervigrams, the scale yielded significant achievements, with 80.15% accuracy, a sensitivity of 84.79%, and a specificity of 66.66%. The statistical analysis of sociodemographic profiles showed significant associations of age, education, annual income, occupation, and menstrual health with the health of the cervix, where a p-value less than (<) 0.05 was considered statistically significant. The deployment of cervical cancer screening tools such as “OM—The OncoMeter” in live clinical settings of resource-limited healthcare infrastructure will facilitate early diagnosis in a non-invasive manner, leading to a timely clinical intervention for infected patients upon detection even during primary healthcare (PHC).

Artificial Intelligence in Cervical Cancer Screening and Diagnosis

Cervical cancer remains a leading cause of cancer death in women, seriously threatening their physical and mental health. It is an easily preventable cancer with early screening and diagnosis. Although technical advancements have significantly improved the early diagnosis of cervical cancer, accurate diagnosis remains difficult owing to various factors. In recent years, artificial intelligence (AI)-based medical diagnostic applications have been on the rise and have excellent applicability in the screening and diagnosis of cervical cancer. Their benefits include reduced time consumption, reduced need for professional and technical personnel, and no bias owing to subjective factors. We, thus, aimed to discuss how AI can be used in cervical cancer screening and diagnosis, particularly to improve the accuracy of early diagnosis. The application and challenges of using AI in the diagnosis and treatment of cervical cancer are also discussed.

Feasibility and Accuracy of Menstrual Blood Testing for High-risk Human Papillomavirus Detection With Capture Sequencing

IMPORTANCE

High-risk human papillomavirus (hrHPV) persistent infection is the major etiology of cervical precancer and cancer. Noninvasive self-sampling HPV testing is a promising alternative cervical cancer screening for avoiding stigma and improving patient willingness to participate.

OBJECTIVE

To investigate the feasibility and accuracy of menstrual blood (MB) hrHPV capture sequencing in hrHPV detection.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study collected 137 sanitary pads from 120 women who were premenopausal and had hrHPV as detected by cervical HPV GenoArray testing. Patients were recruited from September 1, 2020, to April 1, 2021, at Central Hospital of Wuhan, China. Target capture sequencing was performed to determine hrHPV genotypes in MB. Sanger sequencing was performed as the criterion standard for detecting hrHPV genotypes among enrolled women. Data were analyzed from April 1 through June 1, 2021.

MAIN OUTCOMES AND MEASURES

Complete concordance, incomplete concordance, and discordance of MB hrHPV capture sequencing and conventional HPV testing were defined according to genotype overlapping levels. Concordance of the 2 detection methods and comparative power of MB hrHPV capture sequencing during different menstrual cycle days (MCDs) were the main outcomes.

RESULTS

A total of 120 enrolled women with hrHPV (mean [SD; range] age, 33.9 [6.9; 20.0 -52.0] years) provided 137 sanitary pads. The overall concordance rate of MB hrHPV capture sequencing and cervical HPV testing was 92.7% (95% CI, 88.3%-97.1%), with a κ value of 0.763 (P < .001). Among 24 samples with incomplete concordance or discordant results, 11 samples with additional hrHPV genotypes (45.8%), 5 true-negative samples (20.8%), and the correct hrHPV genotypes of 2 samples (8.3%) were correctly identified by MB hrHPV capture sequencing. MB hrHPV detection of hrHPV was equivalent on different MCDs, with an MB hrHPV-positive rate of 27 of 28 patients (96.4%) for MCD 1, 52 of 57 patients (91.2%) for MCD 2, 27 of 28 patients for MCD 3, 4 of 4 patients (100%) for MCD 4, and 3 of 3 patients (100%) for MCD 5 (P = .76). The sensitivity of the MB hrHPV capture sequencing was 97.7% (95% CI, 95.0%-100%).

CONCLUSIONS AND RELEVANCE

These findings suggest that MB hrHPV capture sequencing is a feasible and accurate self-collected approach for cervical cancer screening. This study found that this method is associated with superior performance in identification of HPV genotypes and true-negative events compared with cervical HPV testing.

SearcHPV: A novel approach to identify and assemble human papillomavirus-host genomic integration events in cancer

BACKGROUND

Human papillomavirus (HPV) is a well-established driver of malignant transformation at a number of sites, including head and neck, cervical, vulvar, anorectal, and penile squamous cell carcinomas; however, the impact of HPV integration into the host human genome on this process remains largely unresolved. This is due to the technical challenge of identifying HPV integration sites, which includes limitations of existing informatics approaches to discovering viral-host breakpoints from low-read-coverage sequencing data.

METHODS

To overcome this limitation, the authors developed SearcHPV, a new HPV detection pipeline based on targeted capture technology, and applied the algorithm to targeted capture data. They performed an integrated analysis of SearcHPV-defined breakpoints with genome-wide linked-read sequencing to identify potential HPV-related structural variations.

RESULTS

Through an analysis of HPV+ models, the authors showed that SearcHPV detected HPV-host integration sites with a higher sensitivity and specificity than 2 other commonly used HPV detection callers. SearcHPV uncovered HPV integration sites adjacent to known cancer-related genes, including TP63, MYC, and TRAF2, and near regions of large structural variation. The authors further validated the junction contig assembly feature of SearcHPV, which helped to accurately identify viral-host junction breakpoint sequences. They found that viral integration occurred through a variety of DNA repair mechanisms, including nonhomologous end joining, alternative end joining, and microhomology-mediated repair.

CONCLUSIONS

In summary, SearcHPV is a new optimized tool for the accurate detection of HPV-human integration sites from targeted capture DNA sequencing data.

Application of artificial intelligence in gynecologic malignancies: A review

With the development of machine learning and deep learning models, artificial intelligence is now being applied to the field of medicine. In oncology, the use of artificial intelligence for the diagnostic evaluation of medical images such as radiographic images, omics analysis using genome data, and clinical information has been increasing in recent years. There have been increasing numbers of reports on the use of artificial intelligence in the field of gynecologic malignancies, and we introduce and review these studies. For cervical and endometrial cancers, the evaluation of medical images, such as colposcopy, hysteroscopy, and magnetic resonance images, using artificial intelligence is frequently reported. In ovarian cancer, many reports combine the assessment of medical images with the multi-omics analysis of clinical and genomic data using artificial intelligence. However, few study results can be implemented in clinical practice, and further research is needed in the future.

Hub gene identification and prognostic model construction for isocitrate dehydrogenase mutation in glioma

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We identified ten hub genes which were driving IDH status in GBM and LGG.

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We constructed a prognostic model for IDH-mutant patients.

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Our findings have important clinical implications for accurate treatment in glioma.

Our study attempted to identify hub genes related to isocitrate dehydrogenase (IDH) mutation in glioma and develop a prognostic model for IDH-mutant glioma patients. In a first step, ten hub genes significantly associated with the IDH status were identified by weighted gene coexpression analysis (WGCNA). The functional enrichment analysis demonstrated that the most enriched terms of these hub genes were cadherin binding and glutathione metabolism. Three of these hub genes were significantly linked with the survival of glioma patients. 328 samples of IDH-mutant glioma were separated into two datasets: a training set (N = 228) and a test set (N = 100). Based on the training set, we identified two IDH-mutant subtypes with significantly different pathological features by using consensus clustering. A 31 gene-signature was identified by the least absolute shrinkage and selection operator (LASSO) algorithm and used for establishing a differential prognostic model for IDH-mutant patients. In addition, the test set was employed for validating the prognostic model, and the model was proven to be of high value in classifying prognostic information of samples. The functional annotation revealed that the genes related to the model were mainly enriched in nuclear division, DNA replication, and cell cycle. Collectively, this study provided novel insights into the molecular mechanism of IDH mutation in glioma, and constructed a prognostic model which can be effective for predicting prognosis of glioma patients with IDH-mutation, which might promote the development of IDH target agents in glioma therapies and contribute to accurate prognostication and management in IDH-mutant glioma patients.

Association of human papillomavirus integration with better patient outcomes in oropharyngeal squamous cell carcinoma

BACKGROUND

The molecular drivers of human papillomavirus-related head and neck squamous cell carcinoma (HPV + HNSCC) are not entirely understood. This study evaluated the relationship between HPV integration, expression of E6/E7, and patient outcomes in p16+ HNSCCs.

METHODS

HPV type was determined by HPV PCR-MassArray, and integration was called using detection of integrated papillomavirus sequences polymerase chain reaction (PCR). We investigated whether fusion transcripts were produced by reverse transcriptase polymerase chain reaction (RT-PCR). E6/E7 expression was assessed by quantitative RT-PCR. We assessed if there was a relationship between integration and E6/E7 expression, clinical variables, or patient outcomes.

RESULTS

Most samples demonstrated HPV integration, which sometimes resulted in a fusion transcript. HPV integration was positively correlated with age at diagnosis and E6/E7 expression. There was a significant difference in survival between patients with vs without integration.

CONCLUSIONS

Contrary to previous reports, HPV integration was associated with improved patient survival. Therefore, HPV integration may act as a molecular marker of good prognosis.

Alternations in mitochondrial genome in carcinogenesis of HPV positive cervix

OBJECTIVE

It is well known that mitochondrial dysfunctions are involved in tumorigenesis. A special interest of scientists is mitochondrial ND1 gene (mtND1). Recently detected mutations in the mtND1 can disrupt the normal activity of complex I and affect oxidative phosphorylation, thus leading to increase reactive oxygen species production. This study was undertaken to determine the alternations in the mtND1 and evaluate their association with development of precancerous lesions and cervical cancer.

METHODS

In the study 29 cervical cancer, 28 low grade squamous intraepithelial lesion (L-SIL) and 30 high grade squamous intraepithelial lesion (H-SIL) HPV positive fragments tissue were screened for the presence of mtND1 mutations.

RESULTS

Our study showed that mutations in the mtND1 gene were detected in patients with precancerous stage, as well as cervical cancer. We have identified 12 point mutations in 116 analyzed precancerous and cancer samples HPV positive. Most detected missense mutations were previously described, except one (p. M156K) with Grantham value 95. The lower expression of mRNA ND1 was detected in cervical cancer cases and in all samples in which mtND1 mutations were identified. Our analyses revealed that level of ROS production was higher in cervical cancer tissues and all cases characterized by mtND1 mutations.

CONCLUSIONS

We hypothesize that mutations in MT-ND1 observed in H-SIL and cancer could activate cervical carcinogenesis by increased ROS production.