HPV genotyping by L1 amplicon sequencing of archived invasive cervical cancer samples: a pilot study

Evaluation of Seegene Anyplex II Performance for Detection of Human Papillomavirus Genotypes in Formalin-Fixed, Paraffin-Embedded Cervical Cancer Specimens

CONTEXT.—

Detection of human papillomavirus (HPV) in formalin-fixed, paraffin-embedded (FFPE) tissues may identify the cause of lesions and has value for the development of new diagnostic assays and epidemiologic studies. Seegene Anyplex II assays are widely used for HPV screening, but their performance using FFPE samples has not been fully explored.

OBJECTIVE.—

To validate Anyplex II HPV HR Detection (Anyplex II, Seegene) using FFPE samples.

DESIGN.—

We used 248 stored DNA extracts from cervical cancer FFPE samples collected during 2005-2015 that tested HPV positive using the RHA kit HPV SPF10-LiPA25, v1 (SPF10, Labo Biomedical Products) HPV genotyping assay, manufacturer-validated for FFPE samples.

RESULTS.—

Of the selected 248 samples, 243 were used in our analysis. Consistent with SPF10 genotyping results, Anyplex II detected all 12 oncogenic types and had an overall HPV detection rate of 86.4% (210 of 243 samples). Anyplex II and SPF10 showed very high agreement for the detection of the 2 most important oncogenic genotypes: HPV 16 (219 of 226; 96.9%; 95% CI, 93.7-98.75) and HPV 18 (221 of 226; 97.8%; 95% CI, 94.9-99.3).

CONCLUSIONS.—

Overall results showed that both platforms produced comparable HPV genotyping results, indicating the suitability of Anyplex II for FFPE samples. The Anyplex II assay has the added convenience of being an efficient, single-well semiquantitative polymerase chain reaction assay. Further optimization of Anyplex II may enhance its performance using FFPE samples by improving the detection limit.

Critical Differential Expression Assessment for Individual Bulk RNA-Seq Projects

Finding the right balance of quality and quantity can be important, and it is essential that project quality does not drop below the level where important main conclusions are missed or misstated. We use knock-out and over-expression studies as a simplification to test recovery of a known causal gene in RNA-Seq cell line experiments. When single-end RNA-Seq reads are aligned with STAR and quantified with htseq-count, we found potential value in testing the use of the Generalized Linear Model (GLM) implementation of edgeR with robust dispersion estimation more frequently for either single-variate or multi-variate 2-group comparisons (with the possibility of defining criteria less stringent than |fold-change| > 1.5 and FDR < 0.05). When considering a limited number of patient sample comparisons with larger sample size, there might be some decreased variability between methods (except for DESeq1). However, at the same time, the ranking of the gene identified using immunohistochemistry (for ER/PR/HER2 in breast cancer samples from The Cancer Genome Atlas) showed as possible shift in performance compared to the cell line comparisons, potentially highlighting utility for standard statistical tests and/or limma-based analysis with larger sample sizes. If this continues to be true in additional studies and comparisons, then that could be consistent with the possibility that it may be important to allocate time for potential methods troubleshooting for genomics projects. Analysis of public data presented in this study does not consider all experimental designs, and presentation of downstream analysis is limited. So, any estimate from this simplification would be an underestimation of the true need for some methods testing for every project. Additionally, this set of independent cell line experiments has a limitation in being able to determine the frequency of missing a highly important gene if the problem is rare (such as 10% or lower). For example, if there was an assumption that only one method can be tested for "initial" analysis, then it is not completely clear to the extent that using edgeR-robust might perform better than DESeq2 in the cell line experiments. Importantly, we do not wish to cause undue concern, and we believe that it should often be possible to define a gene expression differential expression workflow that is suitable for some purposes for many samples. Nevertheless, at the same time, we provide a variety of measures that we believe emphasize the need to critically assess every individual project and maximize confidence in published results.