Molecular pathology selected abstracts

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Editors: Donna E. Hansel, MD, PhD, chair of pathology, Oregon Health and Science University, Portland; Richard D. Press, MD, PhD, professor and director of molecular pathology, OHSU; James Solomon, MD, PhD, assistant professor, Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York; Sounak Gupta, MBBS, PhD, senior associate consultant, Mayo Clinic, Rochester, Minn.; Tauangtham Anekpuritanang, MD, molecular pathology fellow, Department of Pathology, OHSU; Fei Yang, MD, assistant professor, Department of Pathology, OHSU; and Andres Madrigal, MD, molecular genetic pathology fellow, Department of Pathology, OHSU.

Analysis of transcript deleterious variants in Mendelian disorders

August 2020—Patients and families suspected of having Mendelian diseases, syndromes thought to be caused by a single mutated gene, often undergo comprehensive next-generation sequencing to determine the underlying pathogenic variant. Whole exome sequencing, in which the coding regions of all genes are analyzed, is usually the preferred testing method. However, this identifies the diagnostic pathogenic variant in only 25 to 52 percent of cases. Whole genome sequencing appears to confer only marginal benefit over whole exome sequencing, presumably because the pathogenic variants likely are not missed by whole exome sequencing but may be misinterpreted as nondiagnostic. Among the variants that are difficult to interpret are those that affect RNA by directly influencing transcription, or altering normal splicing, or by mediating their effects through chromatin. These diagnostic variants can be located deep in intronic or untranslated regions. The authors proposed that comprehensively analyzing transcripts by whole transcriptomic RNA sequencing could be a promising ancillary diagnostic test to whole exome sequencing. It would improve variant interpretation, especially for cryptic splice-altering variants, the function of which is not easily predicted. To analyze the benefit of RNA-based diagnostics, the authors examined a cohort of 5,647 families with suspected Mendelian phenotypes and assessed how many of the phenotypes were caused by variants that affect transcription, or transcript-deleterious variants (TDVs). They found that in 2,438 of the families examined, whole exome sequencing identified likely causal variants, 1,807 of which were unique. Of these unique variants, 272 (15 percent) were TDVs. However, this initial overall estimate was likely biased because these were the cases with causal variants that were easily identified by whole exome sequencing and interpreted as pathogenic. To get an unbiased estimate, the authors focused on 157 patients for whom they could map the recessive Mendelian phenotype to a single locus. They thoroughly analyzed sequencing data at these specific loci to identify the underlying diagnostic variant. In this subset of cases, TDVs accounted for 18.9 percent of causative variants. Interestingly, the authors discovered that only two percent of these diagnostic variants would not be covered by whole exome sequencing, as they were located more than 50 base pairs from the nearest exon, which again suggests issues in interpretation rather than detection. The authors reanalyzed 155 cases for which clinical whole exome sequencing did not reveal a likely causal variant and identified TDVs in 21 of them. Therefore, they concluded that the hypothetical diagnostic yield of RNA sequencing would be 13.5 percent in cases with negative whole exome sequencing results. Probably the greatest enhancement in diagnostic yield was for noncanonical splice site intronic variants located more than three base pairs from the exons, as these are the most difficult to interpret, especially using in silico prediction tools. Overall, this study highlights the potential diagnostic benefit of RNA sequencing in identifying and interpreting pathogenic variants that cause Mendelian disease.

Maddirevula S, Kuwahara H, Ewida N, et al. Analysis of transcript-deleterious variants in Mendelian disorders: implications for RNA-based diagnostics. Genome Biol. 2020. doi:10.1186/s13059-020-02053-9.

Correspondence: Dr. Xin Gao at xin.gao@kaust.edu.sa and Dr. Fowzan Alkuraya at falkuraya@kfshrc.edu.sa

Genome-wide cell-free DNA mutational integration for cancer monitoring

Oncologists increasingly are using liquid biopsies to monitor cancer patients for residual disease. These noninvasive tests require only a vial of blood for isolating plasma and extracting cell-free DNA (cfDNA). CfDNA originates from many sources in the body, including healthy tissue and tumors, if the latter are present. In many of the tests used in the clinical setting, the cfDNA undergoes targeted sequencing to identify mutations that are characteristic of a patient’s tumor. The presence of these mutations signifies residual disease. However, a significant impediment to applying this method to patients with low disease burden, as would be the case after surgery or chemotherapy, is that the quantity of the cfDNA that comes from the tumor is very low. It is often less than 0.1 percent of the total cfDNA collected, which makes it extremely difficult to detect. In a recent study, the authors demonstrated that using ultra-deep next-generation sequencing to target mutations does not reach appropriate diagnostic sensitivity, especially when there is limited tumor cfDNA. Even under the ideal conditions of exhaustive sequencing, perfect cfDNA recovery, and no sequencing errors, the specific targeted mutations are not present in certain situations. The authors hypothesized that increasing the breadth of sequencing and, thereby, examining a greater number of somatic mutation sites could overcome this limitation. To this end, they developed MRDetect, a method in which low-coverage whole genome sequencing of cfDNA (approximately 35× depth) is used to examine mutations across the entire genome and compare them to mutations present in a tumor sample. Single nucleotide variants and copy number alterations contributed to an integrated genomewide mutational signal applicable to many tumor types. Results showed that the assay was extremely sensitive, detecting fractions of circulating tumor DNA as low as one in 100,000 molecules in in silico simulations. The authors then validated MRDetect in clinical cohorts of lung adenocarcinoma, colorectal cancer, and melanoma using control samples with no known malignancy to account for sequencing noise. In these cohorts, MRDetect was able to sensitively and specifically identify residual disease. In the lung cancer cohort, for example, the area under the receiver operating characteristic curve was 0.86 in the preoperative setting. In addition, detectable circulating tumor DNA postoperatively was significantly associated with early disease recurrence. None of the patients with undetectable circulating tumor DNA had recurrence after a median follow-up of 18 months. It should be noted that although the assay is very sensitive for the presence of circulating tumor DNA overall, its sensitivity for individual variants is limited. Therefore, important therapeutic drivers may be missed. Nonetheless, MRDetect holds great promise for identifying patients with residual disease to optimize treatment strategies.