Cellular and transcriptional impacts of Janus kinase and/or IFN-gamma inhibition in a mouse model of primary hemophagocytic lymphohistiocytosis

BackgroundPrimary hemophagocytic lymphohistiocytosis (pHLH) is an inherited inflammatory syndrome driven by the exuberant activation of interferon-gamma (IFNg)-producing CD8 T cells. Towards this end, ruxolitinib treatment or IFNg neutralization (aIFNg) lessens immunopathology in a model of pHLH in which perforin-deficient mice (Prf1–/–) are infected with Lymphocytic Choriomeningitis virus (LCMV). However, neither agent completely eradicates inflammation. Two studies combining ruxolitinib with aIFNg report conflicting results with one demonstrating improvement and the other worsening of disease manifestations. As these studies used differing doses of drugs and varying LCMV strains, it remained unclear whether combination therapy is safe and effective.MethodsWe previously showed that a ruxolitinib dose of 90 mg/kg lessens inflammation in Prf1–/– mice infected with LCMV-Armstrong. To determine whether this dose controls inflammation induced by a different LCMV strain, we administered ruxolitinib at 90mg/kg to Prf1–/– mice infected with LCMV-WE. To elucidate the impacts of single agent versus combination therapy, Prf1–/– animals were infected with LCMV, treated or not with ruxolitinib, aIFNg or both agents, and analyzed for disease features and the transcriptional impacts of therapy within purified CD8 T cells.ResultsRuxolitinib is well-tolerated and controls disease regardless of the viral strain used. aIFNg, administered alone or with ruxolitinib, is most effective at reversing anemia and reducing serum IFNg levels. In contrast, ruxolitinib appears better than aIFNg, and equally or more effective than combination therapy, at lessening immune cell expansion and cytokine production. Each treatment targets distinct gene expression pathways with aIFNg downregulating IFNg, IFNa, and IL-6-STAT3 pathways, and ruxolitinib downregulating IL-6-STAT3, glycolysis, and reactive oxygen species pathways. Unexpectedly, combination therapy is associated with upregulation of genes driving cell survival and proliferation.ConclusionsRuxolitinib is tolerated and curtails inflammation regardless of the inciting viral strain and whether it is given alone or in combination with aIFNg. When administered at the doses used in this study, the combination of ruxolitinb and aIFNg appears no better than treatment with either drug alone in lessening inflammation. Further studies are warranted to elucidate the optimal doses, schedules, and combinations of these agents for the treatment of patients with pHLH

Germline landscape of RPA1, RPA2 and RPA3 variants in pediatric malignancies: identification of RPA1 as a novel cancer predisposition candidate gene

Replication Protein A (RPA) is single-strand DNA binding protein that plays a key role in the replication and repair of DNA. RPA is a heterotrimer made of 3 subunits – RPA1, RPA2, and RPA3. Germline pathogenic variants affecting RPA1 were recently described in patients with Telomere Biology Disorders (TBD), also known as dyskeratosis congenita or short telomere syndrome. Premature telomere shortening is a hallmark of TBD and results in bone marrow failure and predisposition to hematologic malignancies. Building on the finding that somatic mutations in RPA subunit genes occur in ~1% of cancers, we hypothesized that germline RPA alterations might be enriched in human cancers. Because germline RPA1 mutations are linked to early onset TBD with predisposition to myelodysplastic syndromes, we interrogated pediatric cancer cohorts to define the prevalence and spectrum of rare/novel and putative damaging germline RPA1, RPA2, and RPA3 variants. In this study of 5,993 children with cancer, 75 (1.25%) harbored heterozygous rare (non-cancer population allele frequency (AF) < 0.1%) variants in the RPA heterotrimer genes, of which 51 cases (0.85%) had ultra-rare (AF < 0.005%) or novel variants. Compared with Genome Aggregation Database (gnomAD) non-cancer controls, there was significant enrichment of ultra-rare and novel RPA1, but not RPA2 or RPA3, germline variants in our cohort (adjusted p-value < 0.05). Taken together, these findings suggest that germline putative damaging variants affecting RPA1 are found in excess in children with cancer, warranting further investigation into the functional role of these variants in oncogenesis

Transient inhibition of the JAK/STAT pathway prevents B-ALL development in genetically predisposed mice

Preventing development of childhood B-cell acute lymphoblastic leukemia (B-ALL), a disease with devastating effects, is a longstanding and unsolved challenge. Heterozygous germline alterations in the PAX5 gene can lead to B-ALL upon accumulation of secondary mutations affecting the JAK/STAT signaling pathway. Preclinical studies have shown that this malignant transformation occurs only under immune stress such as exposure to infectious pathogens. Here we show in Pax5+/− mice that transient, early-life administration of clinically relevant doses of ruxolitinib, a JAK1/2 inhibitor, significantly mitigates the risk of B-ALL following exposure to infection; 1 of 29 animals treated with ruxolitinib developed B-ALL versus 8 of 34 untreated mice. Ruxolitinib treatment preferentially targeted Pax5+/− versus wild-type B-cell progenitors and exerted unique effects on the Pax5+/− B-cell progenitor transcriptional program. These findings provide the first in vivo evidence for a potential strategy to prevent B-ALL development.C. Cobaleda and C. Vicente-Dueñas labs are members of the EU COST Action LEGEND (CA16223). Research in C. Vicente-Dueñas group has been funded by Instituto de Salud Carlos III through the project " PI17/00167 and by a “Miguel Servet Grant” [CPII19/00024 - AES 2017-2020; co-funded by European Regional Development Fund (ERDF)/European Social Fund (ESF) "A way to make Europe"/"Investing in your future"]. J.J. Yang and K.E. Nichols receive funding from the American Lebanese Syrian Associated Charities (ALSAC) and R01CA241452 from the NCI. Research in ISG group is partially supported by FEDER and by SAF2015-64420-R MINECO/FEDER, UE, RTI2018-093314-B-I00 MCIU/AEI/FEDER, UE, 9659122185-122185-4-21 MCIU/AEI/FEDER, UE, by Junta de Castilla y León (UIC-017, CSI001U16, CSI234P18, and CSI144P20). M. Ramírez-Orellana and I. Sánchez-García have been supported by the Fundacion Unoentrecienmil (CUNINA project). C. Cobaleda, M. Ramírez-Orellana, and I. Sánchez-García have been supported by the Fundación Científica de la Asociación Española contra el Cáncer (PRYCO211305SANC). A. Casado-García (CSI067-18) and M. Isidro-Hernández (CSI021-19) are supported by FSE-Conserjería de Educación de la Junta de Castilla y León 2019 and 2020 (ESF, European Social Fund) fellowship, respectively. J. Raboso-Gallego is supported by a scholarship from University of Salamanca co-financed by Banco Santander and ESF. S. Alemán-Arteaga is supported by an Ayuda para Contratos predoctorales para la formación de doctores (PRE2019-088887)

Genetic and Epigenetic Features of Bilateral Wilms Tumor Predisposition in Patients From the Children’s Oncology Group AREN18B5-Q

Developing synchronous bilateral Wilms tumor suggests an underlying (epi)genetic predisposition. Here, we evaluate this predisposition in 68 patients using whole exome or genome sequencing (n = 85 tumors from 61 patients with matched germline blood DNA), RNA-seq (n = 99 tumors), and DNA methylation analysis (n = 61 peripheral blood, n = 29 non-diseased kidney, n = 99 tumors). We determine the predominant events for bilateral Wilms tumor predisposition: 1)pre-zygotic germline genetic variants readily detectable in blood DNA [WT1 (14.8%), NYNRIN (6.6%), TRIM28 (5%), and BRCA-related genes (5%)] or 2)post-zygotic epigenetic hypermethylation at 11p15.5 H19/ICR1 that may require analysis of multiple tissue types for diagnosis. Of 99 total tumor specimens, 16 (16.1%) have 11p15.5 normal retention of imprinting, 25 (25.2%) have 11p15.5 copy neutral loss of heterozygosity, and 58 (58.6%) have 11p15.5 H19/ICR1 epigenetic hypermethylation (loss of imprinting). Here, we ascertain the epigenetic and genetic modes of bilateral Wilms tumor predisposition

Analytical protocol to identify local ancestry-associated molecular features in cancer

People of different ancestries vary in cancer risk and outcome, and their molecular differences may indicate sources of these variations. Determining the "local" ancestry composition at each genetic locus across ancestry-admixed populations can suggest causal associations. We present a protocol to identify local ancestry and detect the associated molecular changes, using data from the Cancer Genome Atlas. This workflow can be applied to cancer cohorts with matched tumor and normal data from admixed patients to examine germline contributions to cancer. For complete details on the use and execution of this protocol, please refer to Carrot-Zhang et al. (2020)

Cloning MicroRNAs with Somatic Mutations in Pediatric Acute Lymphoblastic Leukemia (pRL-CMV-6X-CXCR4)

The laboratory of Dr. Plon at Baylor College of Medicine focuses on deciphering the genetic basis of pediatric cancers. The most common childhood cancer, acute lymphoblastic leukemia (ALL), is a type of blood cancer and according to National Cancer Institute, there are approximately 41 cases per 1 million children in the US. In addition to studying mutations that alter protein-coding regions, the lab also focuses on identifying mutations in microRNAs that can lead to pediatric ALL. miRNAs are short (~22 bases) non-coding RNAs that regulate gene expression. Limited studies have described role of miRNA in human diseases but mutations has been shown to interrupt this pathway which could lead to diseases. DNA sequencing, is used in research labs for the identification of cancer mutations. This project was completed with contributions from Sharon Plon from the Molecular and Human Genetics, Texas Children’s Cancer Center.Biology and Biochemistry, Department ofHonors Colleg

Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines

Abstract Background The American College of Medical Genetics and American College of Pathologists (ACMG/AMP) variant classification guidelines for clinical reporting are widely used in diagnostic laboratories for variant interpretation. The ACMG/AMP guidelines recommend complete concordance of predictions among all in silico algorithms used without specifying the number or types of algorithms. The subjective nature of this recommendation contributes to discordance of variant classification among clinical laboratories and prevents definitive classification of variants. Results Using 14,819 benign or pathogenic missense variants from the ClinVar database, we compared performance of 25 algorithms across datasets differing in distinct biological and technical variables. There was wide variability in concordance among different combinations of algorithms with particularly low concordance for benign variants. We also identify a previously unreported source of error in variant interpretation (false concordance) where concordant in silico predictions are opposite to the evidence provided by other sources. We identified recently developed algorithms with high predictive power and robust to variables such as disease mechanism, gene constraint, and mode of inheritance, although poorer performing algorithms are more frequently used based on review of the clinical genetics literature (2011–2017). Conclusions Our analyses identify algorithms with high performance characteristics independent of underlying disease mechanisms. We describe combinations of algorithms with increased concordance that should improve in silico algorithm usage during assessment of clinically relevant variants using the ACMG/AMP guidelines

Additional file 2: Figure S1. of Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines

Concordance among predictions of 18 algorithms for 8386 variants in ClinVar for which predictions were available from all 18 algorithms. Figure S2. Variability in performance of algorithms shown in each panel across all analyzed datasets. Figure S3. Performance analysis of algorithms for the indicated datasets. (PPTX 87 kb