Specific human papillomavirus genotypes are associated with most ano‐genital carcinomas and a large subset of oro‐pharyngeal carcinomas. Human papillomavirus DNA is thus a tumour marker that can be detected in the blood of patients for clinical monitoring. However, data concerning circulating human papillomavirus DNA in cervical cancer patients has provided little clinical value, due to insufficient sensitivity of the assays used for the detection of small sized tumours. Here we took advantage of the sensitive droplet digital PCR method to identify circulating human papillomavirus DNA in patients with human papillomavirus‐associated carcinomas.A series of 70 serum specimens, taken at the time of diagnosis, between 2002 and 2013, were retrospectively analyzed in patients with human papillomavirus‐16 or human papillomavirus‐18‐associated carcinomas, composed of 47 cases from the uterine cervix, 15 from the anal canal and 8 from the oro‐pharynx. As negative controls, 18 serum samples from women with human papillomavirus‐16‐associated high‐grade cervical intraepithelial neoplasia were also analyzed. Serum samples were stored at −80°C (27 cases) or at −20°C (43 cases). DNA was isolated from 200 µl of serum or plasma and droplet digital PCR was performed using human papillomavirus‐16 E7 and human papillomavirus‐18 E7 specific primers.Circulating human papillomavirus DNA was detected in 61/70 (87%) serum samples from patients with carcinoma and in no serum from patients with cervical intraepithelial neoplasia. The positivity rate increased to 93% when using only serum stored at −80°C. Importantly, the two patients with microinvasive carcinomas in this series were positive. Quantitative evaluation showed that circulating viral DNA levels in cervical cancer patients were related to the clinical stage and tumour size, ranging from 55 ± 85 copies/ml (stage I) to 1774 ± 3676 copies/ml (stage IV).Circulating human papillomavirus DNA is present in patients with human papillomavirus‐associated invasive cancers even at sub‐clinical stages and its level is related to tumour dynamics. Droplet digital PCR is a promising method for circulating human papillomavirus DNA detection and quantification. No positivity was found in patients with human papillomavirus‐associated high grade cervical intraepithelial neoplasia.

IntroductionIn most cases of cervical cancers, HPV DNA is integrated into the genome of carcinoma cells. This mutational insertion constitutes a highly specific molecular marker of tumor DNA for every patient. Circulating tumor DNA (ctDNA) is an emerging marker of tumor dynamics which detection requires specific molecular motif. To determine whether the sequence of the cell-viral junction could be used in clinical practice as a specific marker of ctDNA, we analyzed a series of cervical cancer patient serums.Methods and FindingsSerum specimens of 16 patients diagnosed with HPV16/18-associated cervical cancer, and for which the viral integration locus had been previously localized, were analyzed. Sequential serum specimens, taken at different times during the course of the disease, were also available for two of these cases. ctDNA was found in 11 out of 13 patients with tumor size greater than 20 mm at diagnosis, and analysis of sequential serum specimens showed that ctDNA concentration in patients serum was related to tumor dynamics.ConclusionsWe report that HPV mutational insertion constitutes a highly specific molecular marker of ctDNA in HPV-associated tumor patients. Using this original approach, ctDNA was detected in most cervical cancer patients over stage I and ctDNA concentration was found to reflect tumor burden. In addition to its potential prognostic and predictive value, HPV mutation insertion is likely to constitute a new molecular surrogate of minimal residual disease and of subclinical relapse in HPV-associated tumor. This is of major importance in the perspective of specific anti-HPV therapy.

The advances in diagnostic and treatment technology are responsible for a remarkable transformation in the internal medicine concept with the establishment of a new idea of personalized medicine. Inter- and intra-patient tumor heterogeneity and the clinical outcome and/or treatment's toxicity's complexity, justify the effort to develop predictive models from decision support systems. However, the number of evaluated variables coming from multiple disciplines: oncology, computer science, bioinformatics, statistics, genomics, imaging, among others could be very large thus making traditional statistical analysis difficult to exploit. Automated data-mining processes and machine learning approaches can be a solution to organize the massive amount of data, trying to unravel important interaction. The purpose of this paper is to describe the strategy to collect and analyze data properly for decision support and introduce the concept of an 'umbrella protocol' within the framework of 'rapid learning healthcare'.

Cancer treatment is facing major evolution since the advent of targeted therapies. Building genetic profiles could predict sensitivity or resistance to these therapies and highlight disease-specific abnormalities, supporting personalized patient care. In the context of biomedical research and clinical diagnosis, our laboratory has developed an oncogenic panel comprised of 226 genes and a dedicated bioinformatic pipeline to explore somatic mutations in cervical carcinomas, using high-throughput sequencing. Twenty-nine tumors were sequenced for exons within 226 genes. The automated pipeline used includes a database and a filtration system dedicated to identifying mutations of interest and excluding false positive and germline mutations. One-hundred and seventy-six total mutational events were found among the 29 tumors. Our cervical tumor mutational landscape shows that most mutations are found in PIK3CA (E545K, E542K) and KRAS (G12D, G13D) and others in FBXW7 (R465C, R505G, R479Q). Mutations have also been found in ALK (V1149L, A1266T) and EGFR (T259M). These results showed that 48% of patients display at least one deleterious mutation in genes that have been already targeted by the Food and Drug Administration approved therapies. Considering deleterious mutations, 59% of patients could be eligible for clinical trials. Sequencing hundreds of genes in a clinical context has become feasible, in terms of time and cost. In the near future, such an analysis could be a part of a battery of examinations along the diagnosis and treatment of cancer, helping to detect sensitivity or resistance to targeted therapies and allow advancements towards personalized oncology.

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