Anti-TIGIT antibody improves PD-L1 blockade through myeloid and Treg cells.

Tiragolumab, an anti-TIGIT antibody with an active IgG1κ Fc, demonstrated improved outcomes in the phase 2 CITYSCAPE trial (ClinicalTrials.gov: NCT03563716 ) when combined with atezolizumab (anti-PD-L1) versus atezolizumab alone1. However, there remains little consensus on the mechanism(s) of response with this combination2. Here we find that a high baseline of intratumoural macrophages and regulatory T cells is associated with better outcomes in patients treated with atezolizumab plus tiragolumab but not with atezolizumab alone. Serum sample analysis revealed that macrophage activation is associated with a clinical benefit in patients who received the combination treatment. In mouse tumour models, tiragolumab surrogate antibodies inflamed tumour-associated macrophages, monocytes and dendritic cells through Fcγ receptors (FcγR), in turn driving anti-tumour CD8+ T cells from an exhausted effector-like state to a more memory-like state. These results reveal a mechanism of action through which TIGIT checkpoint inhibitors can remodel immunosuppressive tumour microenvironments, and suggest that FcγR engagement is an important consideration in anti-TIGIT antibody development.

Divarasib plus cetuximab in KRAS G12C-positive colorectal cancer: a phase 1b trial.

KRAS G12C mutation is prevalent in ~4% of colorectal cancer (CRC) and is associated with poor prognosis. Divarasib, a KRAS G12C inhibitor, has shown modest activity as a single agent in KRAS G12C-positive CRC at 400 mg. Epidermal growth factor receptor has been recognized as a major upstream activator of RAS-MAPK signaling, a proposed key mechanism of resistance to KRAS G12C inhibition in CRC. Here, we report on divarasib plus cetuximab (epidermal growth factor receptor inhibitor) in patients with KRAS G12C-positive CRC (n = 29) from arm C of an ongoing phase 1b trial. The primary objective was to evaluate safety. Secondary objectives included preliminary antitumor activity. The safety profile of this combination was consistent with those of single-agent divarasib and cetuximab. Treatment-related adverse events led to divarasib dose reductions in four patients (13.8%); there were no treatment withdrawals. The objective response rate was 62.5% (95% confidence interval: 40.6%, 81.2%) in KRAS G12C inhibitor-naive patients (n = 24). The median duration of response was 6.9 months. The median progression-free survival was 8.1 months (95% confidence interval: 5.5, 12.3). As an exploratory objective, we observed a decline in KRAS G12C variant allele frequency associated with response and identified acquired genomic alterations at disease progression that may be associated with resistance. The manageable safety profile and encouraging antitumor activity of divarasib plus cetuximab support the further investigation of this combination in KRAS G12C-positive CRC.ClinicalTrials.gov identifier: NCT04449874.

Single-Agent Divarasib (GDC-6036) in Solid Tumors with a KRAS G12C Mutation.

BACKGROUND: Divarasib (GDC-6036) is a covalent KRAS G12C inhibitor that was designed to have high potency and selectivity. METHODS: In a phase 1 study, we evaluated divarasib administered orally once daily (at doses ranging from 50 to 400 mg) in patients who had advanced or metastatic solid tumors that harbor a KRAS G12C mutation. The primary objective was an assessment of safety; pharmacokinetics, investigator-evaluated antitumor activity, and biomarkers of response and resistance were also assessed. RESULTS: A total of 137 patients (60 with non-small-cell lung cancer [NSCLC], 55 with colorectal cancer, and 22 with other solid tumors) received divarasib. No dose-limiting toxic effects or treatment-related deaths were reported. Treatment-related adverse events occurred in 127 patients (93%); grade 3 events occurred in 15 patients (11%) and a grade 4 event in 1 patient (1%). Treatment-related adverse events resulted in a dose reduction in 19 patients (14%) and discontinuation of treatment in 4 patients (3%). Among patients with NSCLC, a confirmed response was observed in 53.4% of patients (95% confidence interval [CI], 39.9 to 66.7), and the median progression-free survival was 13.1 months (95% CI, 8.8 to could not be estimated). Among patients with colorectal cancer, a confirmed response was observed in 29.1% of patients (95% CI, 17.6 to 42.9), and the median progression-free survival was 5.6 months (95% CI, 4.1 to 8.2). Responses were also observed in patients with other solid tumors. Serial assessment of circulating tumor DNA showed declines in KRAS G12C variant allele frequency associated with response and identified genomic alterations that may confer resistance to divarasib. CONCLUSIONS: Treatment with divarasib resulted in durable clinical responses across KRAS G12C-positive tumors, with mostly low-grade adverse events. (Funded by Genentech; ClinicalTrials.gov number, NCT04449874.).

Transcriptomic profiles and 5-year results from the randomized CLL14 study of venetoclax plus obinutuzumab versus chlorambucil plus obinutuzumab in chronic lymphocytic leukemia.

Data on long-term outcomes and biological drivers associated with depth of remission after BCL2 inhibition by venetoclax in the treatment of chronic lymphocytic leukemia (CLL) are limited. In this open-label parallel-group phase-3 study, 432 patients with previously untreated CLL were randomized (1:1) to receive either 1-year venetoclax-obinutuzumab (Ven-Obi, 216 patients) or chlorambucil-Obi (Clb-Obi, 216 patients) therapy (NCT02242942). The primary endpoint was investigator-assessed progression-free survival (PFS); secondary endpoints included minimal residual disease (MRD) and overall survival. RNA sequencing of CD19-enriched blood was conducted for exploratory post-hoc analyses. After a median follow-up of 65.4 months, PFS is significantly superior for Ven-Obi compared to Clb-Obi (Hazard ratio [HR] 0.35 [95% CI 0.26-0.46], p < 0.0001). At 5 years after randomization, the estimated PFS rate is 62.6% after Ven-Obi and 27.0% after Clb-Obi. In both arms, MRD status at the end of therapy is associated with longer PFS. MRD + ( ≥ 10-4) status is associated with increased expression of multi-drug resistance gene ABCB1 (MDR1), whereas MRD6 (< 10-6) is associated with BCL2L11 (BIM) expression. Inflammatory response pathways are enriched in MRD+ patient solely in the Ven-Obi arm. These data indicate sustained long-term efficacy of fixed-duration Ven-Obi in patients with previously untreated CLL. The distinct transcriptomic profile of MRD+ status suggests possible biological vulnerabilities.

TRAIL Score: A Simple Model to Predict Immunochemotherapy Tolerability in Patients With Diffuse Large B-Cell Lymphoma.

PURPOSE: Rituximab with cyclophosphamide, doxorubicin, vincristine, and prednisolone (R-CHOP) represents the standard of care for first-line treatment of diffuse large B-cell lymphoma (DLBCL). However, many patients are unable to tolerate R-CHOP and have inferior outcomes. This study aimed to develop a practical tool to help physicians identify patients with newly diagnosed DLBCL unlikely to tolerate a full course of R-CHOP. METHODS: We developed a predictive model (Tolerability of R-CHOP in Aggressive Lymphoma [TRAIL]) on the basis of a training data set from the phase III GOYA trial (obinutuzumab with CHOP v R-CHOP in 1L DLBCL) using a composite binary end point, identifying patients who prematurely stopped or required reductions of R-CHOP. Candidate predictive variables were selected on the basis of known baseline characteristics that contribute to patient frailty, comorbidity, and/or chemotherapy toxicity. TRAIL was developed using an iterative trial-and-error modeling process to fit a logistic regression model. The final model was evaluated for robustness using a GOYA holdout data set and the phase III MAIN (R-CHOP with or without bevacizumab in 1L DLBCL) R-CHOP-21 data set as external validation. RESULTS: TRAIL includes four simple predictors available in the routine clinical setting: Charlson Comorbidity Index, presence of cardiovascular disease or diabetes, serum albumin, and creatinine clearance. Model generalization performance estimated by the area under the curve was around or above 0.70 across GOYA training, GOYA holdout, and MAIN data sets. Classifying patients into low-, intermediate- and high-risk categories, the proportion of patients experiencing a tolerability event was 3.3%, 12.4%, and 32.9%, respectively, in GOYA holdout, and 9.7%, 9.7%, and 34.2%, respectively, in MAIN. CONCLUSION: TRAIL may be useful as a clinical decision support tool for treatment decisions in patients with DLBCL who may not tolerate standard chemoimmunotherapies.

Correlation between in vitro binding activity of sweeteners to cloned human sweet taste receptor and sensory evaluation.

The human sweet taste receptor is a TAS1R2/TAS1R3 heterodimer. To investigate the correlation between the in vitro affinity of sweeteners with stably expressed human sweet taste receptor in HEK-293 cells and human sensory evaluation, the receptor-ligand activity of bulk (sucrose, D-fructose, and allulose) and high-intensity sweeteners (saccharin, rebaudioside A, rebaudioside M, and neohesperidin dihydrochalcone) was compared by analyzing the Ca2+ release. The relative potency of the sweeteners was identified over a wide concentration range for EC50s. Relative to sucrose, bulk sweeteners showed similar concentration ranges and potency, whereas high-intensity sweeteners exhibited lower concentration ranges and higher potency. The log of the calculated EC50 of each sweetener relative to sucrose by the in vitro affinity assay was positively correlated (r = 0.9943) with the molar relative sweetness reported in the previous literatures. These results suggested a good correlation between the in vitro activity assay of sweeteners and human sensory evaluation.

Utility of a Molecular Classifier as a Complement to High-Resolution Computed Tomography to Identify Usual Interstitial Pneumonia.

Rationale: Usual interstitial pneumonia (UIP) is the defining morphology of idiopathic pulmonary fibrosis (IPF). Guidelines for IPF diagnosis conditionally recommend surgical lung biopsy for histopathology diagnosis of UIP when radiology and clinical context are not definitive. A "molecular diagnosis of UIP" in transbronchial lung biopsy, the Envisia Genomic Classifier, accurately predicted histopathologic UIP.Objectives: We evaluated the combined accuracy of the Envisia Genomic Classifier and local radiology in the detection of UIP pattern.Methods: Ninety-six patients who had diagnostic lung pathology as well as a transbronchial lung biopsy for molecular testing with Envisia Genomic Classifier were included in this analysis. The classifier results were scored against reference pathology. UIP identified on high-resolution computed tomography (HRCT) as documented by features in local radiologists' reports was compared with histopathology.Measurements and Main Results: In 96 patients, the Envisia Classifier achieved a specificity of 92.1% (confidence interval [CI],78.6-98.3%) and a sensitivity of 60.3% (CI, 46.6-73.0%) for histology-proven UIP pattern. Local radiologists identified UIP in 18 of 53 patients with UIP histopathology, with a sensitivity of 34.0% (CI, 21.5-48.3%) and a specificity of 96.9% (CI, 83.8-100%). In conjunction with HRCT patterns of UIP, the Envisia Classifier results identified 24 additional patients with UIP (sensitivity 79.2%; specificity 90.6%).Conclusions: In 96 patients with suspected interstitial lung disease, the Envisia Genomic Classifier identified UIP regardless of HRCT pattern. These results suggest that recognition of a UIP pattern by the Envisia Genomic Classifier combined with HRCT and clinical factors in a multidisciplinary discussion may assist clinicians in making an interstitial lung disease (especially IPF) diagnosis without the need for a surgical lung biopsy.

Impact of the Percepta Genomic Classifier on Clinical Management Decisions in a Multicenter Prospective Study.

BACKGROUND: The Percepta genomic classifier has been clinically validated as a complement to bronchoscopy for lung nodule evaluation. RESEARCH QUESTION: The goal of this study was to examine the impact on clinical management decisions of the Percepta result in patients with low- and intermediate-risk lung nodules. STUDY DESIGN AND METHODS: A prospective "real world" registry was instituted across 35 US centers to observe physician management of pulmonary nodules following a nondiagnostic bronchoscopy. To assess the impact on management decisions of the Percepta genomic classifier, a subset of patients was analyzed who had an inconclusive bronchoscopy for a pulmonary nodule, a Percepta result, and an adjudicated lung diagnosis with at least 1 year of follow-up. In this cohort, change in the decision to pursue additional invasive procedures following Percepta results was assessed. RESULTS: A total of 283 patients met the study eligibility criteria. In patients with a low/intermediate risk of malignancy for whom the clinician had designated a plan for a subsequent invasive procedure, a negative Percepta result down-classified the risk of malignancy in 34.3% of cases. Of these down-classified patients, 73.9% had a change in their management plan from an invasive procedure to surveillance, and the majority avoided a procedure up to 12 months following the initial evaluation. In patients with confirmed lung cancers, the time to diagnosis was not significantly delayed when comparing Percepta down-classified patients vs patients who were not down-classified (P = .58). INTERPRETATION: The down-classification of nodule malignancy risk with the Percepta test decreased additional invasive procedures without a delay in time to diagnosis among those with lung cancer.

Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts.

BACKGROUND: Bronchoscopy for suspected lung cancer has low diagnostic sensitivity, rendering many inconclusive results. The Bronchial Genomic Classifier (BGC) was developed to help with patient management by identifying those with low risk of lung cancer when bronchoscopy is inconclusive. The BGC was trained and validated on patients in the Airway Epithelial Gene Expression in the Diagnosis of Lung Cancer (AEGIS) trials. A modern patient cohort, the BGC Registry, showed differences in key clinical factors from the AEGIS cohorts, with less smoking history, smaller nodules and older age. Additionally, we discovered interfering factors (inhaled medication and sample collection timing) that impacted gene expressions and potentially disguised genomic cancer signals. METHODS: In this study, we leveraged multiple cohorts and next generation sequencing technology to develop a robust Genomic Sequencing Classifier (GSC). To address demographic composition shift and interfering factors, we synergized three algorithmic strategies: 1) ensemble of clinical dominant and genomic dominant models; 2) development of hierarchical regression models where the main effects from clinical variables were regressed out prior to the genomic impact being fitted in the model; and 3) targeted placement of genomic and clinical interaction terms to stabilize the effect of interfering factors. The final GSC model uses 1232 genes and four clinical covariates - age, pack-years, inhaled medication use, and specimen collection timing. RESULTS: In the validation set (N = 412), the GSC down-classified low and intermediate pre-test risk subjects to very low and low post-test risk with a specificity of 45% (95% CI 37-53%) and a sensitivity of 91% (95%CI 81-97%), resulting in a negative predictive value of 95% (95% CI 89-98%). Twelve percent of intermediate pre-test risk subjects were up-classified to high post-test risk with a positive predictive value of 65% (95%CI 44-82%), and 27% of high pre-test risk subjects were up-classified to very high post-test risk with a positive predictive value of 91% (95% CI 78-97%). CONCLUSIONS: The GSC overcame the impact of interfering factors and achieved consistent performance across multiple cohorts. It demonstrated diagnostic accuracy in both down- and up-classification of cancer risk, providing physicians actionable information for many patients with inconclusive bronchoscopy.

An Evaluation of Diagnostic Yield From Bronchoscopy: The Impact of Clinical/Radiographic Factors, Procedure Type, and Degree of Suspicion for Cancer.

BACKGROUND: Bronchoscopy is commonly used to evaluate suspicious lung lesions. The yield is likely dependent on patient, radiographic, and bronchoscopic factors. Few studies have assessed these factors simultaneously while also including the preprocedure physician-assessed probability of cancer (pCA) when assessing yield. METHODS: This study is a secondary data analysis from a prospective multicenter trial. Diagnostic yield of standard bronchoscopy with biopsy ± fluoroscopy, endobronchial ultrasound with transbronchial needle aspiration (EBUS-TBNA), electromagnetic navigation, and combination bronchoscopies was assessed. Definitions for diagnostic and nondiagnostic bronchoscopies were rigorously predefined. The association of diagnostic yield with individual variables was examined by using univariate and multivariate logistic regression analyses where appropriate. RESULTS: A total of 687 patients were included from 28 sites. Overall diagnostic yield was 69%; 80% for EBUS, 55% for bronchoscopy with biopsy ± fluoroscopy, 57% for electromagnetic navigation, and 74% for combination procedures (P < .001). Patients with larger, central lesions with adenopathy were significantly more likely to undergo a diagnostic bronchoscopy. Patients with pCA < 10% and 10% to 60% had lower yields (44% and 42%, respectively), whereas pCA > 60% yielded a positive result in 77% (P < .001). In multivariate logistic regression, the use of EBUS-TBNA, larger sized lesions, and central location were significantly associated with a diagnostic bronchoscopy. Seventeen percent of those with a malignant diagnosis and 28% of those with a benign diagnosis required secondary procedures to establish a diagnosis. CONCLUSIONS: This study is the first to assess the yield of bronchoscopy according to physician-assessed pCA in a large, prospective multicenter trial. The yield of bronchoscopy varied greatly according to physician suspicion that cancer is present, the patients' clinical/radiographic features, and the type of procedure performed. Of the procedures performed, EBUS-TBNA was the most likely to provide a diagnosis.

Analytical Verification Performance of Afirma Genomic Sequencing Classifier in the Diagnosis of Cytologically Indeterminate Thyroid Nodules.

Background: Fine needle aspiration (FNA) cytology, a diagnostic test central to thyroid nodule management, may yield indeterminate results in up to 30% of cases. The Afirma® Genomic Sequencing Classifier (GSC) was developed and clinically validated to utilize genomic material obtained during the FNA to accurately identify benign nodules among those deemed cytologically indeterminate so that diagnostic surgery can be avoided. A key question for diagnostic tests is their robustness under different perturbations that may occur in the lab. Herein, we describe the analytical performance of the Afirma GSC. Results: We examined the analytical sensitivity of the Afirma GSC to varied input RNA amounts and the limit of detection of malignant signals with heterogenous samples mixed with adjacent normal or benign tissues. We also evaluated the analytical specificity from potential interfering substances such as blood and genomic DNA. Further, the inter-laboratory, intra-run, and inter-run reproducibility of the assay were examined. Analytical sensitivity analysis showed that Afirma GSC calls are tolerant to variation in RNA input amount (5-30 ng), and up to 75% dilution of malignant FNA material. Analytical specificity studies demonstrated Afirma GSC remains accurate in presence of up to 75% blood or 30% genomic DNA. The Afirma GSC results are highly reproducible across different operators, runs, reagent lots, and laboratories. Conclusion: The analytical robustness and reproducibility of the Afirma GSC test support its routine clinical use among thyroid nodules with indeterminant FNA cytology.

Use of a molecular classifier to identify usual interstitial pneumonia in conventional transbronchial lung biopsy samples: a prospective validation study.

BACKGROUND: In the appropriate clinical setting, the diagnosis of idiopathic pulmonary fibrosis (IPF) requires a pattern of usual interstitial pneumonia to be present on high-resolution chest CT (HRCT) or surgical lung biopsy. A molecular usual interstitial pneumonia signature can be identified by a machine learning algorithm in less-invasive transbronchial lung biopsy samples. We report prospective findings for the clinical validity and utility of this molecular test. METHODS: We prospectively recruited 237 patients for this study from those enrolled in the Bronchial Sample Collection for a Novel Genomic Test (BRAVE) study in 29 US and European sites. Patients were undergoing evaluation for interstitial lung disease and had had samples obtained by clinically indicated surgical or transbronchial biopsy or cryobiopsy for pathology. Histopathological diagnoses were made by experienced pathologists. Available HRCT scans were reviewed centrally. Three to five transbronchial lung biopsy samples were collected from all patients specifically for this study, pooled by patient, and extracted for transcriptomic sequencing. After exclusions, diagnostic histopathology and RNA sequence data from 90 patients were used to train a machine learning algorithm (Envisia Genomic Classifier, Veracyte, San Francisco, CA, USA) to identify a usual interstitial pneumonia pattern. The primary study endpoint was validation of the classifier in 49 patients by comparison with diagnostic histopathology. To assess clinical utility, we compared the agreement and confidence level of diagnosis made by central multidisciplinary teams based on anonymised clinical information and radiology results plus either molecular classifier or histopathology results. FINDINGS: The classifier identified usual interstitial pneumonia in transbronchial lung biopsy samples from 49 patients with 88% specificity (95% CI 70-98) and 70% sensitivity (47-87). Among 42 of these patients who had possible or inconsistent usual interstitial pneumonia on HRCT, the classifier showed 81% positive predictive value (95% CI 54-96) for underlying biopsy-proven usual interstitial pneumonia. In the clinical utility analysis, we found 86% agreement (95% CI 78-92) between clinical diagnoses using classifier results and those using histopathology data. Diagnostic confidence was improved by the molecular classifier results compared with histopathology results in 18 with IPF diagnoses (proportion of diagnoses that were confident or provisional with high confidence 89% vs 56%, p=0·0339) and in all 48 patients with non-diagnostic pathology or non-classifiable fibrosis histopathology (63% vs 42%, p=0·0412). INTERPRETATION: The molecular test provided an objective method to aid clinicians and multidisciplinary teams in ascertaining a diagnosis of IPF, particularly for patients without a clear radiological diagnosis, in samples that can be obtained by a less invasive method. Further prospective clinical validation and utility studies are planned. FUNDING: Veracyte.

Identification of usual interstitial pneumonia pattern using RNA-Seq and machine learning: challenges and solutions.

BACKGROUND: We developed a classifier using RNA sequencing data that identifies the usual interstitial pneumonia (UIP) pattern for the diagnosis of idiopathic pulmonary fibrosis. We addressed significant challenges, including limited sample size, biological and technical sample heterogeneity, and reagent and assay batch effects. RESULTS: We identified inter- and intra-patient heterogeneity, particularly within the non-UIP group. The models classified UIP on transbronchial biopsy samples with a receiver-operating characteristic area under the curve of ~ 0.9 in cross-validation. Using in silico mixed samples in training, we prospectively defined a decision boundary to optimize specificity at ≥85%. The penalized logistic regression model showed greater reproducibility across technical replicates and was chosen as the final model. The final model showed sensitivity of 70% and specificity of 88% in the test set. CONCLUSIONS: We demonstrated that the suggested methodologies appropriately addressed challenges of the sample size, disease heterogeneity and technical batch effects and developed a highly accurate and robust classifier leveraging RNA sequencing for the classification of UIP.

Analytical performance of Envisia: a genomic classifier for usual interstitial pneumonia.

BACKGROUND: Clinical guidelines specify that diagnosis of interstitial pulmonary fibrosis (IPF) requires identification of usual interstitial pneumonia (UIP) pattern. While UIP can be identified by high resolution CT of the chest, the results are often inconclusive, making surgical lung biopsy necessary to reach a definitive diagnosis (Raghu et al., Am J Respir Crit Care Med 183(6):788-824, 2011). The Envisia genomic classifier differentiates UIP from non-UIP pathology in transbronchial biopsies (TBB), potentially allowing patients to avoid an invasive procedure (Brown et al., Am J Respir Crit Care Med 195:A6792, 2017). To ensure patient safety and efficacy, a laboratory developed test (LDT) must meet strict regulatory requirements for accuracy, reproducibility and robustness. The analytical characteristics of the Envisia test are assessed and reported here. METHODS: The Envisia test utilizes total RNA extracted from TBB samples to perform Next Generation RNA Sequencing. The gene count data from 190 genes are then input to the Envisia genomic classifier, a machine learning algorithm, to output either a UIP or non-UIP classification result. We characterized the stability of RNA in TBBs during collection and shipment, and evaluated input RNA mass and proportions on the limit of detection of UIP. We evaluated potentially interfering substances such as blood and genomic DNA. Intra-run, inter-run, and inter-laboratory reproducibility of test results were also characterized. RESULTS: RNA content within TBBs preserved in RNAprotect is stable for up to 14 days with no detectable change in RNA quality. The Envisia test is tolerant to variation in RNA input (5 to 30 ng), with no impact on classifier results. The Envisia test can tolerate dilution of non-UIP and UIP classification signals at the RNA level by up to 60% and 20%, respectively. Analytical specificity studies utilizing UIP and non-UIP samples mixed with genomic DNA (up to 30% relative input) demonstrated no impact to classifier results. The Envisia test tolerates up to 22% of blood contamination, well beyond the level observed in TBBs. The test is reproducible from RNA extraction through to Envisia test result (standard deviation of 0.20 for Envisia classification scores on > 7-unit scale). CONCLUSIONS: The Envisia test demonstrates the robust analytical performance required of an LDT. Envisia can be used to inform the diagnoses of patients with suspected IPF.

Usual Interstitial Pneumonia Can Be Detected in Transbronchial Biopsies Using Machine Learning.

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.

A Poisson Log-Normal Model for Constructing Gene Covariation Network Using RNA-seq Data.

Constructing expression networks using transcriptomic data is an effective approach for studying gene regulation. A popular approach for constructing such a network is based on the Gaussian graphical model (GGM), in which an edge between a pair of genes indicates that the expression levels of these two genes are conditionally dependent, given the expression levels of all other genes. However, GGMs are not appropriate for non-Gaussian data, such as those generated in RNA-seq experiments. We propose a novel statistical framework that maximizes a penalized likelihood, in which the observed count data follow a Poisson log-normal distribution. To overcome the computational challenges, we use Laplace's method to approximate the likelihood and its gradients, and apply the alternating directions method of multipliers to find the penalized maximum likelihood estimates. The proposed method is evaluated and compared with GGMs using both simulated and real RNA-seq data. The proposed method shows improved performance in detecting edges that represent covarying pairs of genes, particularly for edges connecting low-abundant genes and edges around regulatory hubs.

Genome-wide survey in African Americans demonstrates potential epistasis of fitness in the human genome.

The role played by epistasis between alleles at unlinked loci in shaping population fitness has been debated for many years and the existing evidence has been mainly accumulated from model organisms. In model organisms, fitness epistasis can be systematically inferred by detecting nonindependence of genotypic values between loci in a population and confirmed through examining the number of offspring produced in two-locus genotype groups. No systematic study has been conducted to detect epistasis of fitness in humans owing to experimental constraints. In this study, we developed a novel method to detect fitness epistasis by testing the correlation between local ancestries on different chromosomes in an admixed population. We inferred local ancestry across the genome in 16,252 unrelated African Americans and systematically examined the pairwise correlations between the genomic regions on different chromosomes. Our analysis revealed a pair of genomic regions on chromosomes 4 and 6 that show significant local ancestry correlation (P-value = 4.01 × 10-8 ) that can be potentially attributed to fitness epistasis. However, we also observed substantial local ancestry correlation that cannot be explained by systemic ancestry inference bias. To our knowledge, this study is the first to systematically examine evidence of fitness epistasis across the human genome.