Panel Comparative Analysis Tool: An Open-Source Comparative Analysis Tool for Next-Generation Sequencing Panel Target Regions.

Multigene next-generation sequencing (NGS) panels have become a routine diagnostic method in the contemporary practice of personalized medicine. To avoid inadequate test choice or interpretation, a detailed understanding of the precise panel target regions is required. However, the necessary bioinformatic expertise is not always available, and publicly accessible and easily interpretable analyses of target regions are scarce. To address this critical knowledge gap, we present the Panel Comparative Analysis Tool (PanelCAT), an open-source application to analyze, visualize, and compare NGS panel DNA target regions. PanelCAT uses Reference Sequence, ClinVar, and Catalogue of Somatic Mutations in Cancer mutation census databases to quantify the exon and mutation coverage of target regions and provides interactive graphical representations and search functions to inspect the results. We demonstrate the utility of PanelCAT by analyzing two large NGS panels (TruSight Oncology 500 and Human Pan Cancer Panel) to validate the advertised target genes, quantify targeted exons and mutations, and identify differences between panels. PanelCAT will enable institutions and researchers to catalog and visualize NGS panel target regions independent of the manufacturer, promote transparency of panel limitations, and share this information with employees and requisitioners.

Nfe2l2/NRF2 Deletion Attenuates Tumorigenesis and Increases Bacterial Diversity in a Mouse Model of Lynch Syndrome.

Lynch syndrome (LS) is the most prevalent heritable form of colorectal cancer. Its early onset and high lifetime risk for colorectal cancer emphasize the necessity for effective chemoprevention. NFE2L2 (NRF2) is often considered a potential druggable target, and many chemopreventive compounds induce NRF2. However, although NRF2 counteracts oxidative stress, it is also overexpressed in colorectal cancer and may promote tumorigenesis. In this study, we evaluated the role of NRF2 in the prevention of LS-associated neoplasia. We found increased levels of NRF2 in intestinal epithelia of mice with intestinal epithelium-specific Msh2 deletion (MSH2ΔIEC) compared with C57BL/6 (wild-type) mice, as well as an increase in downstream NRF2 targets NAD(P)H dehydrogenase (quinone 1) and glutamate-cysteine ligase catalytic subunit. Likewise, NRF2 levels were increased in human MSH2-deficient LS tumors compared with healthy human controls. In silico analysis of a publicly accessible RNA sequencing LS dataset also found an increase in downstream NRF2 targets. Upon crossing MSH2ΔIEC with Nrf2null (MSH2ΔIECNrf2null) mice, we unexpectedly found reduced tumorigenesis in MSH2ΔIECNrf2null mice compared with MSH2ΔIEC mice after 40 weeks, which occurred despite an increase in oxidative damage in MSH2ΔIECNrf2null mice. The loss of NRF2 impaired proliferation as seen by Ki67 intestinal staining and in organoid cultures. This was accompanied by diminished WNT/ß-catenin signaling, but apoptosis was unaffected. Microbial α-diversity increased over time with the loss of NRF2 based upon 16S rRNA gene amplicon sequencing of murine fecal samples. Altogether, we show that NRF2 protein levels are increased in MSH2 deficiency and associated neoplasia, but the loss of NRF2 attenuates tumorigenesis. Activation of NRF2 may not be a feasible strategy for chemoprevention in LS. Prevention Relevance: Patients with LS have an early onset and high lifetime risk for colorectal cancer. In this study, we show that NRF2 protein levels are increased in MSH2 deficiency and associated neoplasia, but the loss of NRF2 attenuates tumorigenesis. This suggests that NRF2 may not be a tumor suppressor in this specific context.

GIInger predicts homologous recombination deficiency and patient response to PARPi treatment from shallow genomic profiles.

Homologous recombination deficiency (HRD) is a predictive biomarker for poly(ADP-ribose) polymerase 1 inhibitor (PARPi) sensitivity. Routine HRD testing relies on identifying BRCA mutations, but additional HRD-positive patients can be identified by measuring genomic instability (GI), a consequence of HRD. However, the cost and complexity of available solutions hamper GI testing. We introduce a deep learning framework, GIInger, that identifies GI from HRD-induced scarring observed in low-pass whole-genome sequencing data. GIInger seamlessly integrates into standard BRCA testing workflows and yields reproducible results concordant with a reference method in a multisite study of 327 ovarian cancer samples. Applied to a BRCA wild-type enriched subgroup of 195 PAOLA-1 clinical trial patients, GIInger identified HRD-positive patients who experienced significantly extended progression-free survival when treated with PARPi. GIInger is, therefore, a cost-effective and easy-to-implement method for accurately stratifying patients with ovarian cancer for first-line PARPi treatment.