Preanalytics and Precision Pathology: Pathology Practices to Ensure Molecular Integrity of Cancer Patient Biospecimens for Precision Medicine

Biospecimens acquired during routine medical practice are the primary sources of molecular information about patients and their diseases that underlies precision medicine and translational research. In cancer care, molecular analysis of biospecimens is especially common because it often determines treatment choices and may be used to monitor therapy in real time. However, patient specimens are collected, handled, and processed according to routine clinical procedures during which they are subjected to factors that may alter their molecular quality and composition. Such artefactual alteration may skew data from molecular analyses, render analysis data uninterpretable, or even preclude analysis altogether if the integrity of a specimen is severely compromised. As a result, patient care and safety may be affected, and medical research dependent on patient samples may be compromised. Despite these issues, there is currently no requirement to control or record preanalytical variables in clinical practice with the single exception of breast cancer tissue handled according to the guideline jointly developed by the American Society of Clinical Oncology and College of American Pathologists (CAP) and enforced through the CAP Laboratory Accreditation Program. Recognizing the importance of molecular data derived from patient specimens, the CAP Personalized Healthcare Committee established the Preanalytics for Precision Medicine Project Team to develop a basic set of evidence-based recommendations for key preanalytics for tissue and blood specimens. If used for biospecimens from patients, these preanalytical recommendations would ensure the fitness of those specimens for molecular analysis and help to assure the quality and reliability of the analysis data.

Euclidean Distance Analysis Enables Nucleotide Skew Analysis in Viral Genomes

Nucleotide skew analysis is a versatile method to study the nucleotide composition of RNA/DNA molecules, in particular to reveal characteristic sequence signatures. For instance, skew analysis of the nucleotide bias of several viral RNA genomes indicated that it is enriched in the unpaired, single-stranded genome regions, thus creating an even more striking virus-specific signature. The comparison of skew graphs for many virus isolates or families is difficult, time-consuming, and nonquantitative. Here, we present a procedure for a more simple identification of similarities and dissimilarities between nucleotide skew data of coronavirus, flavivirus, picornavirus, and HIV-1 RNA genomes. Window and step sizes were normalized to correct for differences in length of the viral genome. Cumulative skew data are converted into pairwise Euclidean distance matrices, which can be presented as neighbor-joining trees. We present skew value trees for the four virus families and show that closely related viruses are placed in small clusters. Importantly, the skew value trees are similar to the trees constructed by a “classical” model of evolutionary nucleotide substitution. Thus, we conclude that the simple calculation of Euclidean distances between nucleotide skew data allows an easy and quantitative comparison of characteristic sequence signatures of virus genomes. These results indicate that the Euclidean distance analysis of nucleotide skew data forms a nice addition to the virology toolbox.

When t-tests or Wilcoxon-Mann-Whitney tests won't do

t-Tests are widely used by researchers to compare the average values of a numeric outcome between two groups. If there are doubts about the suitability of the data for the requirements of a t-test, most notably the distribution being non-normal, the Wilcoxon-Mann-Whitney test may be used instead. However, although often applied, both tests may be invalid when discrete and/or extremely skew data are analyzed. In medicine, extremely skewed data having an excess of zeroes are often observed, representing a numeric outcome that does not occur for a large percentage of cases (so is often zero) but which also sometimes takes relatively large values. For data such as this, application of the t-test or Wilcoxon-Mann-Whitney test could lead researchers to draw incorrect conclusions. A valid alternative is regression modeling to quantify the characteristics of the data. The increased availability of software has simplified the application of these more complex statistical analyses and hence facilitates researchers to use them. In this article, we illustrate the methodology applied to a comparison of cyst counts taken from control and steroid-treated fetal mouse kidneys.