Sepsis in the era of data-driven medicine: personalizing risks, diagnoses, treatments and prognoses.

Sepsis is a series of clinical syndromes caused by the immunological response to infection. The clinical evidence for sepsis could typically attribute to bacterial infection or bacterial endotoxins, but infections due to viruses, fungi or parasites could also lead to sepsis. Regardless of the etiology, rapid clinical deterioration, prolonged stay in intensive care units and high risk for mortality correlate with the incidence of sepsis. Despite its prevalence and morbidity, improvement in sepsis outcomes has remained limited. In this comprehensive review, we summarize the current landscape of risk estimation, diagnosis, treatment and prognosis strategies in the setting of sepsis and discuss future challenges. We argue that the advent of modern technologies such as in-depth molecular profiling, biomedical big data and machine intelligence methods will augment the treatment and prevention of sepsis. The volume, variety, veracity and velocity of heterogeneous data generated as part of healthcare delivery and recent advances in biotechnology-driven therapeutics and companion diagnostics may provide a new wave of approaches to identify the most at-risk sepsis patients and reduce the symptom burden in patients within shorter turnaround times. Developing novel therapies by leveraging modern drug discovery strategies including computational drug repositioning, cell and gene-therapy, clustered regularly interspaced short palindromic repeats -based genetic editing systems, immunotherapy, microbiome restoration, nanomaterial-based therapy and phage therapy may help to develop treatments to target sepsis. We also provide empirical evidence for potential new sepsis targets including FER and STARD3NL. Implementing data-driven methods that use real-time collection and analysis of clinical variables to trace, track and treat sepsis-related adverse outcomes will be key. Understanding the root and route of sepsis and its comorbid conditions that complicate treatment outcomes and lead to organ dysfunction may help to facilitate identification of most at-risk patients and prevent further deterioration. To conclude, leveraging the advances in precision medicine, biomedical data science and translational bioinformatics approaches may help to develop better strategies to diagnose and treat sepsis in the next decade.

Clinical Validation of a Targeted Next-Generation Sequencing Panel for Lymphoid Malignancies.

Lymphoid malignancies are a heterogeneous group of hematological disorders characterized by a diverse range of morphologic, immunophenotypic, and clinical features. Next-generation sequencing (NGS) is increasingly being applied to delineate the complex nature of these malignancies and identify high-value biomarkers with diagnostic, prognostic, or therapeutic benefit. However, there are various challenges in using NGS routinely to characterize lymphoid malignancies, including pre-analytic issues, such as sequencing DNA from formalin-fixed, paraffin-embedded tissue, and optimizing the bioinformatic workflow for accurate variant calling and filtering. This study reports the clinical validation of a custom capture-based NGS panel to test for molecular markers in a range of lymphoproliferative diseases and histiocytic neoplasms. The fully validated clinical assay represents an accurate and sensitive tool for detection of single-nucleotide variants and small insertion/deletion events to facilitate the characterization and management of patients with hematologic cancers specifically of lymphoid origin.

Variant Detection in 3' Exons of PMS2 Using Exome Sequencing Data.

PMS2 is one of the mismatch repair genes included in routine genetic testing for Lynch syndrome, colorectal, ovarian, and endometrial cancers. PMS2 is also included in the American College of Medical Genetics and Genomics (ACMG) secondary findings gene list in the context of clinical exome and genome sequencing. However, sequencing of PMS2 by short-read based next generation sequencing (NGS) technologies is complicated by the presence of the pseudogene PMS2CL and often supplemented by long-range based approaches such as long-range polymerase chain reaction (LR-PCR) or long-read based next generation sequencing, which increases the complexity and cost. Here, we described a bioinformatics homology triage workflow that can eliminate the need for long-read based testing for PMS2 for the vast majority of patients undergoing exome sequencing, thus simplifying PMS2 testing and reducing the associated cost.

Clinical Validation of Tagmentation-Based Genome Sequencing for Germline Disorders.

Genome sequencing (GS) is a powerful clinical tool used for the comprehensive diagnosis of germline disorders. GS library preparation typically involves mechanical DNA fragmentation, end repair, and bead-based library size selection followed by adapter ligation, which can require a large amount of input genomic DNA. Tagmentation using bead-linked transposomes can simplify the library preparation process and reduce the DNA input requirement. Here we describe the clinical validation of tagmentation-based PCR-free GS as a clinical test for rare germline disorders. Compared with the Genome-in-a-Bottle Consortium benchmark variant sets, GS had a recall >99.7% and a precision of 99.8% for single nucleotide variants and small insertion-deletions. GS also exhibited 100% sensitivity for clinically reported sequence variants and the copy number variants examined. Furthermore, GS detected mitochondrial sequence variants above 5% heteroplasmy and showed reliable detection of disease-relevant repeat expansions and SMN1 homozygous loss. Our results indicate that while lowering DNA input requirements and reducing library preparation time, GS enables uniform coverage across the genome as well as robust detection of various types of genetic alterations. With the advantage of comprehensive profiling of multiple types of genetic alterations, GS is positioned as an ideal first-tier diagnostic test for germline disorders.