Citations

Abstract Lung cancer is the leading cancer diagnosis worldwide and the number one cause of cancer deaths. Exposure to cigarette smoke, the primary risk factor in lung cancer, reduces epithelial barrier function and increases susceptibility to infections. Herein, we hypothesized that somatic mutations together with cigarette smoke create a dysbiotic microbiota that is associated with lung carcinogenesis. Using lung tissue from controls (n=33) or cancer cases (n=143), we conducted 16S rRNA gene sequencing (MiSeq), with RNA-seq data from lung cancer cases in The Cancer Genome Atlas (n=1112) serving as the validation cohort. We demonstrate a lower alpha diversity in normal lung as compared to non-tumor adjacent or tumor tissue, indicating a shift in the overall microbial community in lung cancer patients as compared to those without cancer. Lung cancer cases were classified by the relative abundance of two taxa, Variovorax and Streptococcus, with an increase in Variovorax abundance in tumors as compared to non-tumor adjacent paired lung tissue (FDR corrected P=0.013). The species of Variovorax was identified histologically, and also by two additional 16S rRNA strategies (Resphera Insight analysis and PacBio sequencing). A group of taxa were associated with squamous cell carcinoma (SCC), of which Acidovorax were enriched in smokers (P =0.0013). Further, these taxa, including Acidovorax, exhibited higher abundance among the subset of SCC cases with TP53 mutations, an association not seen in adenocarcinomas (AD). Therefore, we observed a microbiome-gene and a microbiome-exposure interaction in SCC lung cancer tissue. Together, these results open the door for future biomarker research that could be used to improve screening and direct mechanistic studies of lung cancer therapy. Citation Format: Leigh Greathouse, James White, Valery Bliskovsky, Ashley Vargas, Eric Polley, Elise Bowman, Mohammed Khan, Ana Robles, Brid Ryan, Amiran Dzutsev, Giorgio Trinchieri, Marbin Pineda, Sven Bilke, Paul Meltzer, Marina Walther-Antonio, Garth Ehrlich, Joshua Mell, Joshua Earl, Sergey Balashov, Archana Bhat, Alex Valm, Clayton Deming, Sean Conlan, Julia Oh, Julie Segre, Curtis Harris. Microbiome-TP53 gene interaction in human lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4925. doi:10.1158/1538-7445.AM2017-4925

Skeletal muscle mitochondrial protein synthesis is regulated in part by insulin. The development of insulin resistance with diet-induced obesity may therefore contribute to impairments to protein synthesis and decreased mitochondrial respiration. Yet the impact of diet-induced obesity and insulin resistance on mitochondrial energetics is controversial, with reports varying from decreases to increases in mitochondrial respiration. We investigated the impact of changes in insulin sensitivity on long-term rates of mitochondrial protein synthesis as a mechanism for changes to mitochondrial respiration in skeletal muscle. Insulin resistance was induced in C57BL/6J mice using 4 wk of a high-fat compared with a low-fat diet. For 8 additional weeks, diets were enriched with pioglitazone to restore insulin sensitivity compared with nonenriched control low-fat or high-fat diets. Skeletal muscle mitochondrial protein synthesis was measured using deuterium oxide labeling during weeks 10–12. High-resolution respirometry was performed using palmitoyl-l-carnitine, glutamate+malate, and glutamate+malate+succinate as substrates for mitochondria isolated from quadriceps. Mitochondrial protein synthesis and palmitoyl- l-carnitine oxidation were increased in mice consuming a high-fat diet, regardless of differences in insulin sensitivity with pioglitazone treatment. There was no effect of diet or pioglitazone treatment on ADP-stimulated respiration or H2O2 emission using glutamate+malate or glutamate+malate+succinate. The results demonstrate no impairments to mitochondrial protein synthesis or respiration following induction of insulin resistance. Instead, mitochondrial protein synthesis was increased with a high-fat diet and may contribute to remodeling of the mitochondria to increase lipid oxidation capacity. Mitochondrial adaptations with a high-fat diet appear driven by nutrient availability, not intrinsic defects that contribute to insulin resistance.

Abstract Rationale Usual interstitial pneumonia (UIP) is the histopathologic hallmark of idiopathic pulmonary fibrosis. Although UIP can be detected by high-resolution computed tomography of the chest, the results are frequently inconclusive, and pathology from transbronchial biopsy (TBB) has poor sensitivity. Surgical lung biopsy may be necessary for a definitive diagnosis. Objectives To develop a genomic classifier in tissue obtained by TBB that distinguishes UIP from non-UIP, trained against central pathology as the reference standard. Methods Exome enriched RNA sequencing was performed on 283 TBBs from 84 subjects. Machine learning was used to train an algorithm with high rule-in (specificity) performance using specimens from 53 subjects. Performance was evaluated by cross-validation and on an independent test set of specimens from 31 subjects. We explored the feasibility of a single molecular test per subject by combining multiple TBBs from upper and lower lobes. To address whether classifier accuracy depends upon adequate alveolar sampling, we tested for correlation between classifier accuracy and expression of alveolar-specific genes. Results The top-performing algorithm distinguishes UIP from non-UIP conditions in single TBB samples with an area under the receiver operator characteristic curve (AUC) of 0.86, with specificity of 86% (confidence interval = 71–95%) and sensitivity of 63% (confidence interval = 51–74%) (31 test subjects). Performance improves to an AUC of 0.92 when three to five TBB samples per subject are combined at the RNA level for testing. Although we observed a wide range of type I and II alveolar–specific gene expression in TBBs, expression of these transcripts did not correlate with classifier accuracy. Conclusions We demonstrate proof of principle that genomic analysis and machine learning improves the utility of TBB for the diagnosis of UIP, with greater sensitivity and specificity than pathology in TBB alone. Combining multiple individual subject samples results in increased test accuracy over single sample testing. This approach requires validation in an independent cohort of subjects before application in the clinic.

Objective To describe an expanded phenotypic spectrum and longitudinal outcome in 256 LGI1‐IgG–seropositive and/or CASPR2‐IgG–seropositive patients. Methods Patients were identified through service neural autoantibody evaluation. Ninety‐five had longitudinal follow‐up (7–456 months; median = 35). Results Among 3,910 patients tested, 196 were LGI1‐IgG positive, 51 were CASPR2‐IgG positive, and 9 were dual positive. Cerebrospinal fluid testing was less sensitive than serum testing, detecting only 24 of 38 (63%) LGI1‐IgG–positive and 5 of 6 (83%) CASPR2‐IgG–positive patients. LGI1‐IgG–positive specimens had higher voltage‐gated potassium channel–IgG immunoprecipitation values (0.33nmol/l, range = 0.02–5.14) than CASPR2‐IgG–positive specimens (0.10nmol/l, range = 0.00–0.45, p < 0.001). Of patients presenting with pain or peripheral nervous system (PNS) manifestations, 39% were LGI1‐IgG seropositive (7% had solely neuropathy or pain). Multivariate analysis identified age as the only significant predictor of central nervous system (CNS) versus PNS involvement (>50 years; odds ratio = 15, p < 0.001). Paroxysmal dizziness spells (PDS), a unique LGI1‐IgG accompaniment (14% of patients), frequently delayed the diagnosis. T2‐mesiotemporal hyperintensity was more common in LGI1‐IgG–positive (41%) than in CASPR2‐IgG–positive patients ( p = 0.033). T1‐bright basal ganglia were confined to LGI1‐IgG–positive patients with faciobrachial–dystonic seizures (9 of 39, 31%). Cancer was found in 44% of LGI1‐IgG/CASPR2‐IgG dual seropositive patients (one‐third thymoma). Response to initial immunotherapy was favorable in 97%; mean modified Rankin score was 3 (range = 1–5) at onset and 1.74 (range = 0–6) at last follow‐up, with 9% having severe refractory disability, 20% being asymptomatic, 28% receiving immunotherapy, and 58% receiving antiepileptic medication. Interpretation Older age is a strong predictor of CNS involvement in patients seropositive for CASPR2‐IgG or LGI1‐IgG. Pain, peripheral manifestations, and stereotypic paroxysmal dizziness spells are common with LGI1‐IgG. Response to initial immunotherapy is often favorable, but some patients remain severely disabled, requiring long‐term immunotherapy and/or antiepileptic medications. Ann Neurol 2017;82:79–92

Summary Objective To validate predictive models for neural antibody positivity and immunotherapy response in epilepsy. Methods We conducted a retrospective study of epilepsy cases at Mayo Clinic (Rochester‐ MN ; Scottsdale‐ AZ , and Jacksonville‐ FL ) in whom autoimmune encephalopathy/epilepsy/dementia autoantibody testing profiles were requested (06/30/2014‐06/30/2016). An Antibody Prevalence in Epilepsy ( APE ) score, based on clinical characteristics, was assigned to each patient. Among patients who received immunotherapy, a Response to Immunotherapy in Epilepsy ( RITE ) score was assigned. Favorable seizure outcome was defined as >50% reduction of seizure frequency at the first follow‐up . Results Serum and cerebrospinal fluid (CSF) from 1,736 patients were sent to the Mayo Clinic Neuroimmunology Laboratory for neural autoantibody evaluation. Three hundred eighty‐seven of these patients met the diagnostic criteria for epilepsy. Central nervous system (CNS)–specific antibodies were detected in 44 patients. Certain clinical features such as new‐onset epilepsy, autonomic dysfunction, viral prodrome, faciobrachial dystonic seizures/oral dyskinesia, inflammatory CSF profile, and mesial temporal magnetic resonance imaging (MRI) abnormalities had a significant association with positive antibody results. A significantly higher proportion of antibody‐positive patients had an APE score ≥4 (97.7% vs. 21.6%, p < 0.01). Sensitivity and specificity of an APE score ≥4 to predict presence of specific neural auto‐antibody were 97.7% and 77.9%, respectively. In the subset of patients who received immunotherapy (77), autonomic dysfunction, faciobrachial dystonic seizures/oral dyskinesia, early initiation of immunotherapy, and presence of antibodies targeting plasma membrane proteins (cell‐surface antigens) were associated with favorable seizure outcome. Sensitivity and specificity of a RITE score ≥7 to predict favorable seizure outcome were 87.5% and 83.8%, respectively. Significance APE and RITE scores can aid diagnosis, treatment, and prognostication of autoimmune epilepsy. A PowerPoint slide summarizing this article is available for download in the Supporting Information section here .