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Neoplastic cellularity contributes to the analytic sensitivity of most present technologies for mutation detection, such that they underperform when stroma and inflammatory cells dilute a cancer specimen's variant fraction. Thus, tumor purity assessment by light microscopy is used to determine sample adequacy before sequencing and to interpret the significance of negative results and mutant allele fraction afterwards. However, pathologist estimates of tumor purity are imprecise and have limited reproducibility. With the advent of massively parallel sequencing, large amounts of molecular data can be analyzed by computational purity algorithms. We retrospectively compared tumor purity of 3 computational algorithms with neoplastic cellularity using hematoxylin and eosin light microscopy to determine which was best for clinical evaluation of molecular profiling. Data were analyzed from 881 cancer patients from a clinical trial cohort, LCCC1108 (UNCseq), whose tumors had targeted massively parallel sequencing. Concordance among algorithms was poor, and the specimens analyzed had high rates of algorithm failure partially due to variable tumor purity. Computational tumor purity estimates did not add value beyond the pathologist's estimate of neoplastic cellularity microscopy. To improve present methods, we propose a semiquantitative, clinically applicable strategy based on mutant allele fraction and copy number changes present within a given specimen, which when combined with the morphologic tumor purity estimate, guide the interpretation of next-generation sequencing results in cancer patients.

Human papillomaviruses (HPV) are oncogenic DNA viruses implicated in squamous cell carcinomas of several anatomic sites, as well as endocervical adenocarcinomas. Identification of HPV is an actionable finding in some carcinomas, potentially influencing tumor classification, prognosis, and management. We incorporated capture probes for oncogenic HPV strains 16 and 18 into a broader next-generation sequencing (NGS) panel designed to identify actionable mutations in solid malignancies. A total of 21 head and neck, genitourinary, and gynecologic squamous cell carcinomas and endocervical adenocarcinomas were sequenced as part of the UNCSeq project. Using p16 immunohistochemical results as the gold standard, we set a cutoff for proportion of aligned HPV reads that maximized performance of our NGS assay (92% sensitive, 100% specific for HPV). These results suggest that sequencing of oncogenic pathogens can be incorporated into targeted NGS panels, extending the clinical utility of genomic assays.

The anatomic pathology discipline is slowly moving toward a digital workflow, where pathologists will evaluate whole-slide images on a computer monitor rather than glass slides through a microscope. One of the driving factors in this workflow is computer-assisted scoring, which depends on appropriate selection of regions of interest. With advances in tissue pattern recognition techniques, a more precise region of the tissue can be evaluated, no longer bound by the pathologist's patience in manually outlining target tissue areas. Pathologists use entire tissues from which to determine a score in a region of interest when making manual immunohistochemistry assessments. Tissue pattern recognition theoretically offers this same advantage; however, error rates exist in any tissue pattern recognition program, and these error rates contribute to errors in the overall score. To provide a real-world example of tissue pattern recognition, 11 HER2-stained upper gastrointestinal malignancies with high heterogeneity were evaluated. HER2 scoring of gastric cancer was chosen due to its increasing importance in gastrointestinal disease. A method is introduced for quantifying the error rates of tissue pattern recognition. The trade-off between fully sampling tumor with a given tissue pattern recognition error rate versus randomly sampling a limited number of fields of view with higher target accuracy was modeled with a Monte-Carlo simulation. Under most scenarios, stereological methods of sampling-limited fields of view outperformed whole-slide tissue pattern recognition approaches for accurate immunohistochemistry analysis. The importance of educating pathologists in the use of statistical sampling is discussed, along with the emerging role of hybrid whole-tissue imaging and stereological approaches.