CAP TODAY Pathology/Laboratory Medicine/Laboratory Management
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 Andrés G. Madrigal, MD, PhD, molecular genetic pathology fellow, Department of Pathology, OHSU.
Pathogenic germline mutations in hereditary cancer risk genes: yield and utility of testing
December 2020—Next-generation sequencing-based mutation testing of various cancer types is clinically indicated and widely used to diagnose disease, inform potential therapeutic targets, prognosticate disease course, and monitor responses to targeted and nontargeted therapies. The genetic variants discovered by tumor-based next-generation sequencing (NGS) can be somatically acquired by the neoplastic cells or a fixed inherited component of the patient’s germline genome. Distinguishing the germline versus somatic status of tumor NGS-defined variants is of significant clinical importance not only for patient care but possibly for patients’ families. Because many cancers have a substantial inherited component, the discovery of a pathogenic germline mutation by tumor-based NGS may have substantial familial implications. For example, being aware of a cancer risk allele, such as BRCA1, can lead to the use of highly effective interventions to prevent or treat the related cancer in family members. Consensus guidelines recommend germline genetic testing only for those cancer patients who have a clinical presentation or family history suggestive of hereditary disease. To more broadly determine the diagnostic yield and clinical utility of germline testing in cancer patients, the authors conducted a retrospective cohort study of 2,023 cancer patients who underwent germline testing of hereditary cancer predisposition genes. The participants had previously undergone tumor sample sequencing. The reasons that they chose to undergo subsequent germline testing varied and included family history, suspected germline risk alleles discovered by tumor sequencing, and patient concern. Despite the preconceived perception that hereditary cancers are rather rare, pathogenic germline variants (PGVs) in cancer predisposition genes were detected in 617 (31 percent) of the study patients and were prevalent across an age spectrum of one to 85 years. Pathogenic germline variants were also prevalent across cancer types, including cancers known to be strongly associated with germline inheritance, such as breast and colorectal, as well as others, such as renal, lung, bladder, brain, and pancreas. Most of the pathogenic germline variants detected in the cancer patients were potentially clinically actionable per current management guidelines, published expert opinion, approved precision therapy labels, or clinical trial eligibility. About eight percent of these PGVs represented false-negative results that had not been reported in prior tumor-based sequencing, suggesting that tumor-based sequencing is not a particularly sensitive method for detecting germline mutations. In addition, only about 70 to 80 percent of the patients with PGVs would have met consensus criteria for germline follow-up testing, suggesting that these criteria may be too restrictive and, therefore, insensitive for detecting clinically-relevant inherited cancer risk. Furthermore, 11 percent of the patients had PGVs identified only after presenting with a second primary cancer that possibly could have been detected earlier or prevented, as 46 percent of these patients had pathogenic variants associated with known screening or risk-reduction guidelines. In summary, this study presents compelling evidence of a surprisingly high prevalence of hereditary cancer risk genes in most common cancer types and confirms that germline testing to detect these risk genes is underutilized and clinically and analytically insensitive in clinical practice. Based on these data, the current and perhaps overly restrictive guidelines for hereditary cancer germline testing may need to be reconsidered to improve the diagnostic yield for these heterogeneous inherited cancers, many of which have effective interventions for patients and their families.
Lincoln SE, Nussbaum RL, Kurian AW, et al. Yield and utility of germline testing following tumor sequencing in patients with cancer. JAMA Netw Open. 2020;3(10):e2019452. doi:10.1001/jamanetworkopen.2020.19452
Correspondence: Stephen E. Lincoln at steve.lincoln@me.com
Ability to predict severity of COVID-19 with modern omics tools
The COVID-19 pandemic has caused more than a million deaths worldwide, primarily due to SARS-CoV-2–associated acute respiratory distress syndrome (ARDS), but its clinical course is highly variable and unpredictable. Previous attempts to assess the heterogeneous factors associated with developing SARS-CoV-2–associated ARDS have implicated the genetics of the host response and other comorbidities as primary determinants of disease severity. In severe COVID-19 cases, the host’s inflammatory and coagulation pathways appear to be hyperactivated, and therapies targeting these pathologies have shown clinical benefit. A deeper understanding of the heterogeneity of COVID-19 host response will inform the identification of a variety of validated biomarkers for prognosticating disease severity and, therefore, assist with pandemic resource utilization. These virally-induced host responses will also likely inform the identification of relevant novel therapeutic targets to further reduce morbidity and mortality. Previous approaches to better understand the heterogeneity of SARS-CoV-2–induced host responses have focused on finding disease signatures through proteomics, lipidomics, or metabolomics. The authors of this study applied a concomitant multi-omic systems biology approach to a cohort of 102 SARS-CoV-2–infected patients who had well-characterized clinical outcomes and to 26 controls. The blood/plasma from these subjects was used for comprehensive high-resolution mass spectrometry and next-generation sequencing–based RNA-sequencing (RNA-seq) to quantify transcripts, proteins, metabolites, and lipids. These methods were combined with other omics techniques, including shotgun proteomics, discovery lipidomics, discovery metabolomics, and targeted metabolomics. The authors subsequently conducted a systems analysis using sophisticated machine-learning algorithms to assess the correlative associations between the quantitative biomolecules and COVID-19 status and severity. To quantify disease severity, they defined a novel outcome measurement of clinical disease burden that integrated length of hospitalization with mortality—namely hospital-free days at day 45. The authors found 2,537 leukocyte transcripts, 146 plasma proteins, 168 plasma lipids, 13 plasma metabolites, and 511 unidentified metabolites and lipids that were significantly associated with COVID-19 or clinical outcome status, or both. Two hundred and nineteen mapped molecular features were highly correlated with COVID-19 status and disease severity and included cellular pathways related to complement activation, dysregulated lipid transport, and neutrophil activation. In contrast to findings from the control group, findings from the COVID-19 phenotype included dysregulated platelet function, heightened acute-phase response, and endotheliopathy. The pronounced hypercoagulative signature induced by the virus was especially notable given the extensive set of approved therapeutics known to modulate various components of the inflammatory/hypercoagulation pathway, some of which can now be more rationally introduced into clinical trials. The analysis also revealed that circulating levels of pulmonary surfactant-associated protein B (SFTPB), which is known to be correlated with decreased lung function in smokers, may be a useful surrogate marker of lung deterioration in COVID-19 patients. Perhaps the most useful outcome of the authors’ research was that it led them to create a free Web-based tool (https://covid-omics.app) that the broader scientific community can use to explore the entire complex data set.