Clinical Validation of the Vitro HPV Screening Assay for Its Use in Primary Cervical Cancer Screening.

Many scientific societies have issued guidelines to introduce population-based cervical cancer screening with HPV testing. The Vitro HPV Screening assay is a fully automatic multiplex real-time PCR test targeting the L1 GP5+/GP6+ region of HPV genome. The assay detects 14 high risk (HR) HPV genotypes, identifying individual HPV16 and HPV18 genotypes, and the HPV-positive samples for the other 12 HR HPV types are subsequently genotyped with the HPV Direct Flow Chip test. Following international guidelines, the aim of this study was to validate the clinical accuracy of the Vitro HPV Screening test on ThinPrep-collected samples for its use as primary cervical cancer screening, using as comparator the validated cobas® 4800 HPV test. The non-inferiority analysis showed that the clinical sensitivity and specificity of the Vitro HPV Screening assay for a diagnosis of cervical intraepithelial neoplasia of grade 2 or worse (CIN2+) were not inferior to those of cobas® 4800 HPV (p = 0.0049 and p < 0.001 respectively). The assay has demonstrated a high intra- and inter-laboratory reproducibility, also among the individual genotypes. The Vitro HPV Screening assay is valid for cervical cancer screening and it provides genotyping information on HPV-positive samples without further sample processing in a fully automated workflow.

Novel Endometrial Cancer Models Using Sensitive Metastasis Tracing for CXCR4-Targeted Therapy in Advanced Disease.

Advanced endometrial cancer (EC) lacks therapy, thus, there is a need for novel treatment targets. CXCR4 overexpression is associated with a poor prognosis in several cancers, whereas its inhibition prevents metastases. We assessed CXCR4 expression in EC in women by using IHC. Orthotopic models were generated with transendometrial implantation of CXCR4-transduced EC cells. After in vitro evaluation of the CXCR4-targeted T22-GFP-H6 nanocarrier, subcutaneous EC models were used to study its uptake in tumor and normal organs. Of the women, 91% overexpressed CXCR4, making them candidates for CXCR4-targeted therapies. Thus, we developed CXCR4+ EC mouse models to improve metastagenesis compared to current models and to use them to develop novel CXCR4-targeted therapies for unresponsive EC. It showed enhanced dissemination, especially in the lungs and liver, and displayed 100% metastasis penetrance at all clinically relevant sites with anti-hVimentin IHC, improving detection sensitivity. Regarding the CXCR4-targeted nanocarrier, 60% accumulated in the SC tumor; therefore, selectively targeting CXCR4+ cancer cells, without toxicity in non-tumor organs. Our CXCR4+ EC models will allow testing of novel CXCR4-targeted drugs and development of nanomedicines derived from T22-GFP-H6 to deliver drugs to CXCR4+ cells in advanced EC. This novel approach provides a therapeutic option for women with metastatic, high risk or recurrent EC that have a dismal prognosis and lack effective therapies.

A Straightforward HPV16 Lineage Classification Based on Machine Learning.

Human Papillomavirus (HPV) is the causal agent of 5% of cancers worldwide and the main cause of cervical cancer and it is also associated with a significant percentage of oropharyngeal and anogenital cancers. More than 60% of cervical cancers are caused by HPV16 genotype, which has been classified into lineages (A, B, C, and D). Lineages are related to the progression of cervical cancer and the current method to assess lineages is by building a Maximum Likelihood Tree (MLT); which is slow, it cannot assess poor sequenced samples, and annotation is done manually. In this study, we have developed a new model to assess HPV16 lineage using machine learning tools. A total of 645 HPV16 genomes were analyzed using Genome-Wide Association Study (GWAS), which identified 56 lineage-specific Single Nucleotide Polymorphisms (SNPs). From the SNPs found, training-test models were constructed using different algorithms such as Random Forest (RF), Support Vector Machine (SVM), and K-nearest neighbor (KNN). A distinct set of HPV16 sequences (n = 1,028), whose lineage was previously determined by MLT, was used for validation. The RF-based model allowed a precise assignment of HPV16 lineage, showing an accuracy of 99.5% in the known lineage samples. Moreover, the RF model could assess lineage to 273 samples that MLT could not determine. In terms of computer consuming time, the RF-based model was almost 40 times faster than MLT. Having a fast and efficient method for assigning HPV16 lineages, could facilitate the implementation of lineage classification as a triage or prognostic marker in the clinical setting.