Rituximab Versus Mycophenolate in the Treatment of Recalcitrant Connective Tissue Disease-Associated Interstitial Lung Disease.

OBJECTIVE: Interstitial lung disease (ILD) is a major cause of morbidity and mortality in connective tissue diseases (CTDs). We aimed to assess the effect of rituximab ± mycophenolate mofetil (MMF) compared with MMF on pulmonary function and prednisone dosage in patients with CTD-related ILD (CTD-ILD). METHODS: This retrospective study included 83 patients from Stanford and Centre Hospitalier de l'Universite de Montreal. Fifteen patients received rituximab ± MMF (rituximab group), and 68 patients received MMF only (control group). RESULTS: Median ILD duration at the start of treatment was longer in the rituximab group at 47 months (range: 4-170) versus 6.5 months (range: 0-164) in controls. Forced vital capacity (FVC) decreased by 3.0% (range: 11%-21%) after treatment in the rituximab group, whereas it increased by 2.0% (range: 14%-25%) in the control group (p = 0.025). Diffusing capacity of carbon monoxide (DLCO) decreased by 3.0% (range: 10%-12%) after treatment in the rituximab group, whereas it increased by 4.5% (range: 30%-36%) in the control group (p = 0.046). Mixed model analysis controlling for ILD duration, baseline DLCO, systemic sclerosis, pulmonary hypertension, and prednisone use showed no significant difference in FVC or DLCO between groups at 6 months or 1 year. The average daily prednisone dose score decreased after treatment in the rituximab group, whereas it remained unchanged in the control group (p = 0.017). CONCLUSION: Rituximab ± MMF did not significantly change pulmonary function compared with MMF alone, but it did result in a relative decrease in average daily prednisone dose in a population with recalcitrant CTD-ILD.

Thoracic Radiologists' Versus Computer Scientists' Perspectives on the Future of Artificial Intelligence in Radiology.

BACKGROUND: There is intense interest and speculation in the application of artificial intelligence (AI) to radiology. The goals of this investigation were (1) to assess thoracic radiologists' perspectives on the role and expected impact of AI in radiology, and (2) to compare radiologists' perspectives with those of computer science (CS) experts working in the AI development. METHODS: An online survey was developed and distributed to chest radiologists and CS experts at leading academic centers and societies, comparing their expectations of AI's influence on radiologists' jobs, job satisfaction, salary, and role in society. RESULTS: A total of 95 radiologists and 45 computer scientists responded. Computer scientists reported having read more scientific journal articles on AI/machine learning in the past year than radiologists (mean [95% confidence interval]=17.1 [9.01-25.2] vs. 7.3 [4.7-9.9], P=0.0047). The impact of AI in radiology is expected to be high, with 57.8% and 73.3% of computer scientists and 31.6% and 61.1% of chest radiologists predicting radiologists' job will be dramatically different in 5 to 10 years, and 10 to 20 years, respectively. Although very few practitioners in both fields expect radiologists to become obsolete, with 0% expecting radiologist obsolescence in 5 years, in the long run, significantly more computer scientists (15.6%) predict radiologist obsolescence in 10 to 20 years, as compared with 3.2% of radiologists reporting the same (P=0.0128). Overall, both chest radiologists and computer scientists are optimistic about the future of AI in radiology, with large majorities expecting radiologists' job satisfaction to increase or stay the same (89.5% of radiologists vs. 86.7% of CS experts, P=0.7767), radiologists' salaries to increase or stay the same (83.2% of radiologists vs. 73.4% of CS experts, P=0.1827), and the role of radiologists in society to improve or stay the same (88.4% vs. 86.7%, P=0.7857). CONCLUSIONS: Thoracic radiologists and CS experts are generally positive on the impact of AI in radiology. However, a larger percentage, but still small minority, of computer scientists predict radiologist obsolescence in 10 to 20 years. As the future of AI in radiology unfolds, this study presents a historical timestamp of which group of experts' perceptions were closer to eventual reality.

Left Atrial Volume as a Biomarker of Atrial Fibrillation at Routine Chest CT: Deep Learning Approach.

PURPOSE: To test the performance of a deep learning (DL) model in predicting atrial fibrillation (AF) at routine nongated chest CT. MATERIALS AND METHODS: A retrospective derivation cohort (mean age, 64 years; 51% female) consisting of 500 consecutive patients who underwent routine chest CT served as the training set for a DL model that was used to measure left atrial volume. The model was then used to measure atrial size for a separate 500-patient validation cohort (mean age, 61 years; 46% female), in which the AF status was determined by performing a chart review. The performance of automated atrial size as a predictor of AF was evaluated by using a receiver operating characteristic analysis. RESULTS: There was good agreement between manual and model-generated segmentation maps by all measures of overlap and surface distance (mean Dice = 0.87, intersection over union = 0.77, Hausdorff distance = 4.36 mm, average symmetric surface distance = 0.96 mm), and agreement was slightly but significantly greater than that between human observers (mean Dice = 0.85 [automated] vs 0.84 [manual]; P = .004). Atrial volume was a good predictor of AF in the validation cohort (area under the receiver operating characteristic curve = 0.768) and was an independent predictor of AF, with an age-adjusted relative risk of 2.9. CONCLUSION: Left atrial volume is an independent predictor of the AF status as measured at routine nongated chest CT. Deep learning is a suitable tool for automated measurement.© RSNA, 2019See also the commentary by de Roos and Tao in this issue.

Synchronous primary lung adenocarcinomas harboring distinct MET Exon 14 splice site mutations.

When a patient is found to have multiple lung tumors, distinguishing whether they represent metastatic nodules or separate primary cancers is crucial for staging and therapy. We report the case of a 79-year-old patient with two surgically resected synchronous left upper lobe adenocarcinomas initially pathologically staged as T3 (IIB), indicating adjuvant chemotherapy should be recommended. However, the tumors appeared radiographically distinct, so next-generation sequencing was performed on each nodule. Each tumor harbored a different mesenchymal-to-epithelial transition (MET) exon 14 skipping mutation, an emerging targetable mutation, suggestive of distinct clonality. While the in frame protein deletion was the same in each tumor, the nucleotide base substitutions were different. Thus, the patient was down-staged to having two separate IA tumors, spared of adjuvant chemotherapy, and routine surveillance was recommended. This case highlights the utility of using molecular analysis in diagnosing and treating multifocal lung tumors, and the process of convergent molecular evolution toward a common oncogenic driver mutation. This is the first case of multiple synchronous lung tumors each harboring a distinct MET exon 14 splice site mutation.

Prognostic Value of Pretreatment FDG-PET Parameters in High-dose Image-guided Radiotherapy for Oligometastatic Non-Small-cell Lung Cancer.

BACKGROUND: Emerging data support aggressive local treatment of oligometastatic non-small-cell lung cancer (NSCLC) patients. We sought to determine whether the metabolic burden of disease found by fluorodeoxyglucose positron emission tomography at the time of high-dose radiotherapy (RT) for oligometastatic NSCLC can serve as a prognostic biomarker. MATERIALS AND METHODS: We conducted a retrospective cohort study of 67 RT treatment courses in 55 patients with oligometastatic NSCLC who had undergone high-dose RT to all sites of active disease at our institution. The metabolic tumor volume, total lesion glycolysis (TLG), and maximum standardized uptake value of all lesions were measured on the pretreatment fluorodeoxyglucose positron emission tomography scans. Cox regression analysis was used to assess the influence of imaging and clinical factors on overall survival (OS). RESULTS: On univariate analysis, a greater metabolic tumor volume and TLG were predictive of shorter OS (hazard ratio of death, 2.42 and 2.14, respectively; P = .009 and P = .004, respectively). The effects remained significant on multivariate analysis. Neither the maximum standardized uptake value nor the number of lesions was significantly associated with OS. Patients within the highest quartile of TLG values (> 86.8 units) had a shorter median OS than those within the lower 3 quartiles (12.4 vs. 30.1 months; log-rank P = .014). CONCLUSION: The metabolic tumor burden was prognostic of OS and might help to better select oligometastatic NSCLC patients for locally ablative therapy.

Frameshift mutagenesis and microsatellite instability induced by human alkyladenine DNA glycosylase.

Human alkyladenine DNA glycosylase (hAAG) excises alkylated purines, hypoxanthine, and etheno bases from DNA to form abasic (AP) sites. Surprisingly, elevated expression of hAAG increases spontaneous frameshift mutagenesis. By random mutagenesis of eight active site residues, we isolated hAAG-Y127I/H136L double mutant that induces even higher rates of frameshift mutation than does the wild-type hAAG; the Y127I mutation accounts for the majority of the hAAG-Y127I/H136L-induced mutator phenotype. The hAAG-Y127I/H136L and hAAG-Y127I mutants increased the rate of spontaneous frameshifts by up to 120-fold in S. cerevisiae and also induced high rates of microsatellite instability (MSI) in human cells. hAAG and its mutants bind DNA containing one and two base-pair loops with significant affinity, thus shielding them from mismatch repair; the strength of such binding correlates with their ability to induce the mutator phenotype. This study provides important insights into the mechanism of hAAG-induced genomic instability.

Substrate binding pocket residues of human alkyladenine-DNA glycosylase critical for methylating agent survival.

Human alkyladenine-DNA glycosylase (AAG) initiates base excision repair (BER) of alkylated and deaminated bases in DNA. Here, we assessed the mutability of the AAG substrate binding pocket, and the essentiality of individual binding pocket amino acids for survival of methylation damage. We used oligonucleotide-directed mutagenesis to randomize 19 amino acids, 8 of which interact with substrate bases, and created more than 4.5 million variants. We expressed the mutant AAGs in repair-deficient Escherichia coli and selected for protection against the cytotoxicity of either methylmethane sulfonate (MMS) or methyl-lexitropsin (Me-lex), an agent that produces 3-methyladenine as the predominant base lesion. Sequence analysis of 116 methylation-resistant mutants revealed no substitutions for highly conserved Tyr(127)and His(136). In contrast, one mutation, L180F, was greatly enriched in both the MMS- and Me-lex-resistant libraries. Expression of the L180F single mutant conferred 4.4-fold enhanced survival at the high dose of MMS used for selection. The homogeneous L180F mutant enzyme exhibited 2.2-fold reduced excision of 3-methyladenine and 7.3-fold reduced excision of 7-methylguanine from methylated calf thymus DNA. Decreased excision of methylated bases by the mutant glycosylase could promote survival at high MMS concentrations, where the capacity of downstream enzymes to process toxic BER intermediates may be saturated. The mutant also displayed 6.6- and 3.0-fold reduced excision of 1,N(6)-ethenoadenine and hypoxanthine from oligonucleotide substrates, respectively, and a 1.7-fold increase in binding to abasic site-containing DNA. Our work provides in vivo evidence for the substrate binding mechanism deduced from crystal structures, illuminates the function of Leu(180) in wild-type human AAG, and is consistent with a role for balanced expression of BER enzymes in damage survival.

A dual-fluorescence reporter system for high-throughput clone characterization and selection by cell sorting.

Molecular biology critically depends upon the isolation of desired DNA sequences. Flow cytometry, with its capacity to interrogate and sort more than 50,000 cells/s, shows great potential to expedite clone characterization and isolation. Intrinsic heterogeneity of protein expression levels in cells limits the utility of single fluorescent reporters for cell-sorting. Here, we report a novel dual-fluorescence strategy that overcomes the inherent limitations of single reporter systems by controlling for expression variability. We demonstrate a dual-reporter system using the green fluorescent protein (GFP) gene fused to the Discosoma red fluorescent protein (DsRed) gene. The system reports the successful insertion of foreign DNA with the loss of DsRed fluorescence and the maintenance of GFP fluorescence. Single cells containing inserts are readily recognized by their altered ratios of green to red fluorescence and separated using a high-speed cell-sorter for further processing. This novel reporter system and vector were successfully validated by shotgun library construction, cloned sequence isolation, PCR amplification and DNA sequencing of cloned inserts from bacteria after cell-sorting. This simple, robust system can also be adapted for diverse biosensor assays and is amenable to miniaturization. We demonstrated that dual-fluorescence reporting coupled with high-speed cell-sorting provides a more efficient alternative to traditional methods of clone isolation.

Protein tolerance to random amino acid change.

Mutagenesis of protein-encoding sequences occurs ubiquitously; it enables evolution, accumulates during aging, and is associated with disease. Many biotechnological methods exploit random mutations to evolve novel proteins. To quantitate protein tolerance to random change, it is vital to understand the probability that a random amino acid replacement will lead to a protein's functional inactivation. We define this probability as the "x factor." Here, we develop a broadly applicable approach to calculate x factors and demonstrate this method using the human DNA repair enzyme 3-methyladenine DNA glycosylase (AAG). Three gene-wide mutagenesis libraries were created, each with 10(5) diversity and averaging 2.2, 4.6, and 6.2 random amino acid changes per mutant. After determining the percentage of functional mutants in each library using high-stringency selection (>19,000-fold), the x factor was found to be 34% +/- 6%. Remarkably, reanalysis of data from studies of diverse proteins reveals similar inactivation probabilities. To delineate the nature of tolerated amino acid substitutions, we sequenced 244 surviving AAG mutants. The 920 tolerated substitutions were characterized by substitutability index and mapped onto the AAG primary, secondary, and known tertiary structures. Evolutionarily conserved residues show low substitutability indices. In AAG, beta strands are on average less substitutable than alpha helices; and surface loops that are not involved in DNA binding are the most substitutable. Our results are relevant to such diverse topics as applied molecular evolution, the rate of introduction of deleterious alleles into genomes in evolutionary history, and organisms' tolerance of mutational burden.

Tumbling down a different pathway to genetic instability.

Ulcerative colitis (UC), a chronic inflammatory condition associated with a predisposition to colon cancer, is frequently characterized by DNA damage in the form of microsatellite instability (MSI). A new report links inflammation in UC with increases in the DNA repair enzymes 3-methyladenine DNA glycosylase and apurinic/apyrimidinic endonuclease, and, paradoxically, with increased MSI. These findings may represent a novel mechanism contributing to MSI in chronic inflammation.

Endogenous mutagenesis and cancer.

Mutations in DNA accrue relentlessly, largely via stochastic processes. Random changes accumulate, eventually disabling genetic components which result in the formation of the cancer phenotype. Given the infrequency of measured nucleotide changes and the requirement for several mutations to occur in the same cell, it has been postulated that the rate of mutation must become elevated early in the course of evolution of the cancer. Recently, large scale sequencing of tumor DNA has sought to directly measure random mutations. We discuss the implications of these findings and the factors that must be considered in order for fruitful determination of whether a mutator phenotype is a necessary precursor for cancer.