Development, Validation, and Implementation of an Augmented Multiwell, Multitarget Quantitative PCR for the Analysis of Human Papillomavirus Genotyping through Software Automation, Data Science, and Artificial Intelligence.

The value of human papillomavirus (HPV) testing for cervical cancer screening is well established; however, its use as a primary screening option or as a reflex test after atypical cytology results is now gaining wider acceptance. The importance of full genotyping and viral load determination has been demonstrated to enhance the clinical understanding of the viral infection progression during follow-up or after treatment, thereby providing clinicians with supplementary tools for optimized patient management. We developed a new analysis method for the RIATOL quantitative PCR assay, and validated and implemented it in the laboratory of clinical molecular pathology at Algemeen Medisch Laboratorium (AML), under national accreditation and following the International Organization for Standardization guidelines. This study presents the successful validation of a high-throughput, multitarget HPV analysis method, with enhanced accuracy on both qualitative and quantitative end results. This is achieved by software standardization and automation of PCR curve analysis and interpretation, using data science and artificial intelligence. Moreover, the user-centric functionality of the platform was demonstrated to enhance both staff training and routine analysis workflows, thereby saving time and laboratory personnel resources. Overall, the integration of the FastFinder plugin semi-automatic analysis algorithm with the RIATOL real-time quantitative PCR assay proved to be a remarkable advancement in high-throughput HPV quantification, with demonstrated capability to provide highly accurate clinical-grade results and to reduce manual variability and analysis time.

Validation and Application of a Custom-Designed Targeted Next-Generation Sequencing Panel for the Diagnostic Mutational Profiling of Solid Tumors.

The inevitable switch from standard molecular methods to next-generation sequencing for the molecular profiling of tumors is challenging for most diagnostic laboratories. However, fixed validation criteria for diagnostic accreditation are not in place because of the great variability in methods and aims. Here, we describe the validation of a custom panel of hotspots in 24 genes for the detection of somatic mutations in non-small cell lung carcinoma, colorectal carcinoma and malignant melanoma starting from FFPE sections, using 14, 36 and 5 cases, respectively. The targeted hotspots were selected for their present or future clinical relevance in solid tumor types. The target regions were enriched with the TruSeq approach starting from limited amounts of DNA. Cost effective sequencing of 12 pooled libraries was done using a micro flow cell on the MiSeq and subsequent data analysis with MiSeqReporter and VariantStudio. The entire workflow was diagnostically validated showing a robust performance with maximal sensitivity and specificity using as thresholds a variant allele frequency >5% and a minimal amplicon coverage of 300. We implemented this method through the analysis of 150 routine diagnostic samples and identified clinically relevant mutations in 16 genes including KRAS (32%), TP53 (32%), BRAF (12%), APC (11%), EGFR (8%) and NRAS (5%). Importantly, the highest success rate was obtained when using also the low quality DNA samples. In conclusion, we provide a workflow for the validation of targeted NGS by a custom-designed pan-solid tumor panel in a molecular diagnostic lab and demonstrate its robustness in a clinical setting.