Analytical Validation of a Telomerase Reverse Transcriptase (TERT) Promoter Mutation Assay.

CONTEXT: TERT promoter mutated thyroid cancers are associated with a decreased rate of disease free and disease specific survival. High quality analytical validation of a diagnostic test promotes confidence in the results which inform clinical decision making. OBJECTIVE: To demonstrate the analytical validation of the Afirma TERT promoter mutation assay. METHODS: TERT promoter C228T and C250T variant detection in genomic DNA (gDNA) was analyzed by assessing variable DNA input and the limit of detection (LOD) of variant allele frequency (VAF). The negative and positive percent agreement (NPA and PPA) of the Afirma TERT test was examined against a reference primer pair as was the analytical specificity from potential interfering substances (RNA and blood gDNA). Further, the intra-run, inter-run and inter-laboratory reproducibility of the assay were tested. RESULTS: The Afirma TERT test is tolerant to variation in DNA input amount (7-13 ng) and can detect expected positive TERT promoter variants down to 5% VAF LOD at 7ng DNA input with > 95% sensitivity. Both NPA and PPA were 100% against the reference primer pair. The test remains accurate in presence of 20% RNA or 80% blood gDNA for an average patient sample that typically has 30% VAF. The test also demonstrated a 100% confirmation rate when compared with an external NGS-based reference assay executed in a non-Veracyte laboratory. CONCLUSION: The analytical robustness and reproducibility of the Afirma TERT test support its routine clinical use among thyroid nodules with indeterminate cytology that are Afirma GSC suspicious or among Bethesda V/VI nodules.

A Nasal Swab Classifier to Evaluate the Probability of Lung Cancer in Patients With Pulmonary Nodules.

BACKGROUND: Accurate assessment of the probability of lung cancer (pCA) is critical in patients with pulmonary nodules (PNs) to help guide decision-making. We sought to validate a clinical-genomic classifier developed using whole-transcriptome sequencing of nasal epithelial cells from patients with a PN ≤ 30 mm who smoke or have previously smoked. RESEARCH QUESTION: Can the pCA in individuals with a PN and a history of smoking be predicted by a classifier that uses clinical factors and genomic data from nasal epithelial cells obtained by cytologic brushing? STUDY DESIGN AND METHODS: Machine learning was used to train a classifier using genomic and clinical features on 1,120 patients with PNs labeled as benign or malignant established by a final diagnosis or a minimum of 12 months of radiographic surveillance. The classifier was designed to yield low-, intermediate-, and high-risk categories. The classifier was validated in an independent set of 312 patients, including 63 patients with a prior history of cancer (other than lung cancer), comparing the classifier prediction with the known clinical outcome. RESULTS: In the primary validation set, sensitivity and specificity for low-risk classification were 96% and 42%, whereas sensitivity and specificity for high-risk classification was 58% and 90%, respectively. Sensitivity was similar across stages of non-small cell lung cancer, independent of subtype. Performance compared favorably with clinical-only risk models. Analysis of 63 patients with prior cancer showed similar performance as did subanalyses of patients with light vs heavy smoking burden and those eligible for lung cancer screening vs those who were not. INTERPRETATION: The nasal classifier provides an accurate assessment of pCA in individuals with a PN ≤ 30 mm who smoke or have previously smoked. Classifier-guided decision-making could lead to fewer diagnostic procedures in patients without cancer and more timely treatment in patients with lung cancer.

Preoperative Identification of Medullary Thyroid Carcinoma (MTC): Clinical Validation of the Afirma MTC RNA-Sequencing Classifier.

Background: Cytopathological evaluation of thyroid fine-needle aspiration biopsy (FNAB) specimens can fail to raise preoperative suspicion of medullary thyroid carcinoma (MTC). The Afirma RNA-sequencing MTC classifier identifies MTC among FNA samples that are cytologically indeterminate, suspicious, or malignant (Bethesda categories III-VI). In this study we report the development and clinical performance of this MTC classifier. Methods: Algorithm training was performed with a set of 483 FNAB specimens (21 MTC and 462 non-MTC). A support vector machine classifier was developed using 108 differentially expressed genes, which includes the 5 genes in the prior Afirma microarray-based MTC cassette. Results: The final MTC classifier was blindly tested on 211 preoperative FNAB specimens with subsequent surgical pathology, including 21 MTC and 190 non-MTC specimens from benign and malignant thyroid nodules independent from those used in training. The classifier had 100% sensitivity (21/21 MTC FNAB specimens correctly called positive; 95% confidence interval [CI] = 83.9-100%) and 100% specificity (190/190 non-MTC FNAs correctly called negative; CI = 98.1-100%). All positive samples had pathological confirmation of MTC, while all negative samples were negative for MTC on surgical pathology. Conclusions: The RNA-sequencing MTC classifier accurately identified MTC from preoperative thyroid nodule FNAB specimens in an independent validation cohort. This identification may facilitate an MTC-specific preoperative evaluation and resulting treatment.

Clinical validation and utility of Percepta GSC for the evaluation of lung cancer.

The Percepta Genomic Sequencing Classifier (GSC) was developed to up-classify as well as down-classify the risk of malignancy for lung lesions when bronchoscopy is non-diagnostic. We evaluated the performance of Percepta GSC in risk re-classification of indeterminate lung lesions. This multicenter study included individuals who currently or formerly smoked undergoing bronchoscopy for suspected lung cancer from the AEGIS I/ II cohorts and the Percepta Registry. The classifier was measured in normal-appearing bronchial epithelium from bronchial brushings. The sensitivity, specificity, and predictive values were calculated using predefined thresholds. The ability of the classifier to decrease unnecessary invasive procedures was estimated. A set of 412 patients were included in the validation (prevalence of malignancy was 39.6%). Overall, 29% of intermediate-risk lung lesions were down-classified to low-risk with a 91.0% negative predictive value (NPV) and 12.2% of intermediate-risk lesions were up-classified to high-risk with a 65.4% positive predictive value (PPV). In addition, 54.5% of low-risk lesions were down-classified to very low risk with >99% NPV and 27.3% of high-risk lesions were up-classified to very high risk with a 91.5% PPV. If the classifier results were used in nodule management, 50% of patients with benign lesions and 29% of patients with malignant lesions undergoing additional invasive procedures could have avoided these procedures. The Percepta GSC is highly accurate as both a rule-out and rule-in test. This high accuracy of risk re-classification may lead to improved management of lung lesions.

Intra-operative neurophysiological mapping to identify distorted functional anatomy of the 4th ventricle in a 5-month-old infant.

BACKGROUND: Neurophysiological brainstem mapping techniques facilitate the intra-operative localisation of cranial nerve nuclei amidst distorted anatomy. Neurophysiological recording in young infants can be limited due to immature myelination and synaptogenesis, as well as an increased sensitivity to anaesthetic agents. CASE REPORT: A 5-month-old boy was diagnosed with a cystic brainstem lesion located dorsally within the pons and upper medulla. An open surgical biopsy was undertaken via a posterior fossa craniotomy, revealing a grossly distorted fourth ventricular floor. Intra-operative neurophysiological mapping produced oculomotor, facial, glossopharyngeal and vagal muscle responses allowing a deviated functional midline to be identified. Direct stimulation was used to identify an area in the floor of the fourth ventricle eliciting no cranial nerve responses and allow safe entry into the tumour cavity and biopsy. Transcranial motor evoked responses (TcMEPs), short-latency somatosensory evoked potentials (SSEPs) and brainstem auditory evoked potentials (BAEPs) were all successfully recorded throughout the procedure, despite the use of halogenated gaseous anaesthesia. CONCLUSIONS: We describe the use of brainstem mapping techniques for identification of a distorted midline on the floor of the 4th ventricle in an infant, with reproducible recordings of intra-operative TcMEPs, SSEPs and BAEPs.

Maximizing Small Biopsy Patient Samples: Unified RNA-Seq Platform Assessment of over 120,000 Patient Biopsies.

Despite its wide-ranging benefits, whole-transcriptome or RNA exome profiling is challenging to implement in a clinical diagnostic setting. The Unified Assay is a comprehensive workflow wherein exome-enriched RNA-sequencing (RNA-Seq) assays are performed on clinical samples and analyzed by a series of advanced machine learning-based classifiers. Gene expression signatures and rare and/or novel genomic events, including fusions, mitochondrial variants, and loss of heterozygosity were assessed using RNA-Seq data generated from 120,313 clinical samples across three clinical indications (thyroid cancer, lung cancer, and interstitial lung disease). Since its implementation, the data derived from the Unified Assay have allowed significantly more patients to avoid unnecessary diagnostic surgery and have played an important role in guiding follow-up decisions regarding treatment. Collectively, data from the Unified Assay show the utility of RNA-Seq and RNA expression signatures in the clinical laboratory, and their importance to the future of precision medicine.

Analytical validation of the Percepta genomic sequencing classifier; an RNA next generation sequencing assay for the assessment of Lung Cancer risk of suspicious pulmonary nodules.

BACKGROUND: Bronchoscopy is a common procedure used for evaluation of suspicious lung nodules, but the low diagnostic sensitivity of bronchoscopy often results in inconclusive results and delays in treatment. Percepta Genomic Sequencing Classifier (GSC) was developed to assist with patient management in cases where bronchoscopy is inconclusive. Studies have shown that exposure to tobacco smoke alters gene expression in airway epithelial cells in a way that indicates an increased risk of developing lung cancer. Percepta GSC leverages this idea of a molecular "field of injury" from smoking and was developed using RNA sequencing data generated from lung bronchial brushings of the upper airway. A Percepta GSC score is calculated from an ensemble of machine learning algorithms utilizing clinical and genomic features and is used to refine a patient's risk stratification. METHODS: The objective of the analysis described and reported here is to validate the analytical performance of Percepta GSC. Analytical performance studies characterized the sensitivity of Percepta GSC test results to input RNA quantity, the potentially interfering agents of blood and genomic DNA, and the reproducibility of test results within and between processing runs and between laboratories. RESULTS: Varying the amount of input RNA into the assay across a nominal range had no significant impact on Percepta GSC classifier results. Bronchial brushing RNA contaminated with up to 10% genomic DNA by nucleic acid mass also showed no significant difference on classifier results. The addition of blood RNA, a potential contaminant in the bronchial brushing sample, caused no change to classifier results at up to 11% contamination by RNA proportion. Percepta GSC scores were reproducible between runs, within runs, and between laboratories, varying within less than 4% of the total score range (standard deviation of 0.169 for scores on 4.57 scale). CONCLUSIONS: The analytical sensitivity, analytical specificity, and reproducibility of Percepta GSC laboratory results were successfully demonstrated under conditions of expected day to day variation in testing. Percepta GSC test results are analytically robust and suitable for routine clinical use.

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.

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.

Analysis of the aetiology of epilepsy in 3,216 adult patients attending a tertiary referral center enabled by an electronic patient record.

PURPOSE: The aim of this study was to review the causes of the epilepsies in our institution, an adult tertiary referral center for neurology and neurosurgery in Dublin, Ireland. Data was obtained from a bespoke epilepsy electronic patient record (EPR). METHODS: Predetermined search parameters of well-established broad categories of epilepsy aetiology were used to identify patients with a diagnosis of epilepsy attending Beaumont Hospital, Dublin. There were 3216 patients that met the inclusion criteria for this study. We included living patients with epilepsy attending our institution. We then excluded patients with a diagnosis of pure non-epileptic attack disorder and patients found to have idiopathic generalised epilepsy (IGE) (n = 382) from our final cohort. We excluded IGE due to the complex polygenic basis underlying this patient group. RESULTS: An aetiology was identified in 54.3 % (n = 1747) of the total number of patients studied. Of the symptomatic epilepsies, 41.08 % (n = 1321) were acquired and 13.3 % (n = 426) were predominantly of genetic or developmental aetiology. The most common causes of the acquired epilepsies were hippocampal sclerosis (n = 380; 28.75 %), cerebral tumor (n = 279; 21.06 %), traumatic brain injury (n = 248; 18.77 %), stroke and cerebrovascular disease (n = 151; 11.43 %) and perinatal causes (n = 138; 10.45 %). The leading causes in the genetic / developmental category included cavernous haemangiomas (n = 62, 22.22 %), arteriovenous malformations (n = 59; 21.15 %) and cortical dysplasia (n = 55; 19.71 %). The aetiology of a patient's epilepsy was undetermined in 45.68 % (n = 1469) of individuals. CONCLUSION: This study emphasizes the clinical utility of the ILAE's 2017 revised classification of the epilepsies and highlights the evolving dynamic nature of attributing causality in epilepsy. This is the largest single centre analysis of the aetiology of the epilepsies described in the literature. It is also the first large scale study examining aetiology utilising a bespoke electronic patient record in epilepsy.

Analytical and Clinical Validation of Expressed Variants and Fusions From the Whole Transcriptome of Thyroid FNA Samples.

Introduction: The Afirma® Xpression Atlas (XA) detects gene variants and fusions in thyroid nodule FNA samples from a curated panel of 511 genes using whole-transcriptome RNA-sequencing. Its intended use is among cytologically indeterminate nodules that are Afirma GSC suspicious, Bethesda V/VI nodules, or known thyroid metastases. Here we report its analytical and clinical validation. Methods: DNA and RNA were purified from the same sample across 943 blinded FNAs and compared by multiple methodologies, including whole-transcriptome RNA-seq, targeted RNA-seq, and targeted DNA-seq. An additional 695 blinded FNAs were used to define performance for fusions between whole-transcriptome RNA-seq and targeted RNA-seq. We quantified the reproducibility of the whole-transcriptome RNA-seq assay across laboratories and reagent lots. Finally, variants and fusions were compared to histopathology results. Results: Of variants detected in DNA at 5 or 20% variant allele frequency, 74 and 88% were also detected by XA, respectively. XA variant detection was 89% when compared to an alternative RNA-based detection method. Low levels of expression of the DNA allele carrying the variant, compared with the wild-type allele, was found in some variants not detected by XA. 82% of gene fusions detected in a targeted RNA fusion assay were detected by XA. Conversely, nearly all variants or fusions detected by XA were confirmed by an alternative method. Analytical validation studies demonstrated high intra-plate reproducibility (89%-94%), inter-plate reproducibility (86-91%), and inter-lab accuracy (90%). Multiple variants and fusions previously described across the spectrum of thyroid cancers were identified by XA, including some with approved or investigational targeted therapies. Among 190 Bethesda III/IV nodules, the sensitivity of XA as a standalone test was 49%. Conclusion: When the Afirma Genomic Sequencing Classifier (GSC) is used first among Bethesda III/IV nodules as a rule-out test, XA supplements genomic insight among those that are GSC suspicious. Our data clinically and analytically validate XA for use among GSC suspicious, or Bethesda V/VI nodules. Genomic information provided by XA may inform clinical decision-making with precision medicine insights across a broad range of FNA sample types encountered in the care of patients with thyroid nodules and thyroid cancer.

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.

Identification of Hürthle cell cancers: solving a clinical challenge with genomic sequencing and a trio of machine learning algorithms.

BACKGROUND: Identification of Hürthle cell cancers by non-operative fine-needle aspiration biopsy (FNAB) of thyroid nodules is challenging. Resultingly, non-cancerous Hürthle lesions were conventionally distinguished from Hürthle cell cancers by histopathological examination of tissue following surgical resection. Reliance on histopathological evaluation requires patients to undergo surgery to obtain a diagnosis despite most being non-cancerous. It is highly desirable to avoid surgery and to provide accurate classification of benignity versus malignancy from FNAB preoperatively. In our first-generation algorithm, Gene Expression Classifier (GEC), we achieved this goal by using machine learning (ML) on gene expression features. The classifier is sensitive, but not specific due in part to the presence of non-neoplastic benign Hürthle cells in many FNAB. RESULTS: We sought to overcome this low-specificity limitation by expanding the feature set for ML using next-generation whole transcriptome RNA sequencing and called the improved algorithm the Genomic Sequencing Classifier (GSC). The Hürthle identification leverages mitochondrial expression and we developed novel feature extraction mechanisms to measure chromosomal and genomic level loss-of-heterozygosity (LOH) for the algorithm. Additionally, we developed a multi-layered system of cascading classifiers to sequentially triage Hürthle cell-containing FNAB, including: 1. presence of Hürthle cells, 2. presence of neoplastic Hürthle cells, and 3. presence of benign Hürthle cells. The final Hürthle cell Index utilizes 1048 nuclear and mitochondrial genes; and Hürthle cell Neoplasm Index leverages LOH features as well as 2041 genes. Both indices are Support Vector Machine (SVM) based. The third classifier, the GSC Benign/Suspicious classifier, utilizes 1115 core genes and is an ensemble classifier incorporating 12 individual models. CONCLUSIONS: The accurate algorithmic depiction of this complex biological system among Hürthle subtypes results in a dramatic improvement of classification performance; specificity among Hürthle cell neoplasms increases from 11.8% with the GEC to 58.8% with the GSC, while maintaining the same sensitivity of 89%.

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.

A propensity-score-matched analysis of laparoscopic vs open surgery for rectal cancer in a population-based study.

AIM: The oncological risk/benefit trade-off for laparoscopy in rectal cancer is controversial. Our aim was to compare laparoscopic vs open surgery for resection of rectal cancer, using unselected data from the public healthcare system of Catalonia (Spain). METHODS: This was a multicentre retrospective cohort study of all patients who had surgery with curative intent for primary rectal cancer at Catalonian public hospitals from 2011 to 2012. We obtained follow-up data for up to 5 years. To minimize the differences between the two groups, we performed propensity score matching on baseline patient characteristics. We used multivariate Cox proportional hazards regression analyses to assess locoregional relapse at 2 years and death at 2 and 5 years. RESULTS: Of 1513 patients with Stage I-III rectal cancer, 933 (61.7%) had laparoscopy (conversion rate 13.2%). After applying our propensity score matching strategy (2:1), 842 laparoscopy patients were matched to 517 open surgery patients. Multivariate Cox analysis of death at 2 years [hazard ratio (HR) 0.65, 95% CI 0.48, 0.87; P = 0.004] and 5 years (HR 0.61, 95% CI 0.5, 0.75; P < 0.001) and of local relapse at 2 years (HR 0.44, 95% CI 0.27, 0.72; P = 0.001) showed laparoscopy to be an independent protective factor compared with open surgery. CONCLUSIONS: Laparoscopy results in lower locoregional relapse and long-term mortality in rectal cancer in unselected patients with all-risk groups included. Studies using long-term follow-up of cohorts and unselected data can provide information on clinically relevant outcomes to supplement randomized controlled trials.

Influence of age on variation in patterns of care in patients with rectal cancer in Catalonia (Spain).

BACKGROUND: Population ageing is a relevant concern in people diagnosed with rectal cancer. This study evaluates the adherence to clinical practice guidelines (CPGs) and patient outcomes in rectal cancer, with a particular focus on variation according to age. METHODS: This is a multicentre retrospective cohort study of all patients surgically treated for the first time for primary rectal cancer with curative intent in public hospitals in Catalonia during two study periods: first, with data from 2005 to 2007, and then with data from 2011 to 2012. We obtained the study data through a comprehensive review of patients' clinical records. Information on diagnosis, treatment and follow-up was collected and then compared with the relevant CPGs. We then performed a descriptive analysis of the categorical variables followed by a univariate and multivariate logistic regression analysis to calculate the odds ratio. Finally, we calculated the recurrence and death rates per person-year at 2 years' follow-up. RESULTS: In total, 3770 people were included in this study. The participants aged 70 and under were more likely to receive neoadjuvant therapy than those aged over 80 (2005/2007, 63 vs 17.4% p < 0.001; 2011/2012, 72 vs 41.1% p < 0.001); however, in the second study period there was less imbalance between the two groups in this regard, showing improved administration of neoadjuvant therapy in the elderly. The quality of surgery was suboptimal in a larger proportion of the elderly population, and the choice of surgical technique was dependent to some extent on the age of the participant. Recurrence and survival rates were significantly better in the second study period. CONCLUSIONS: Compared to younger patients, the most elderly were less likely to receive the recommended adjuvant treatment, laparoscopy and total mesorectal excision. However, we observed improved adherence to the recommended treatment regimen in the most elderly participants, resulting in decreased recurrence and increased survival rates in this population.

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.

Performance of a Genomic Sequencing Classifier for the Preoperative Diagnosis of Cytologically Indeterminate Thyroid Nodules.

Importance: Use of next-generation sequencing of RNA and machine learning algorithms can classify the risk of malignancy in cytologically indeterminate thyroid nodules to limit unnecessary diagnostic surgery. Objective: To measure the performance of a genomic sequencing classifier for cytologically indeterminate thyroid nodules. Design, Setting, and Participants: A blinded validation study was conducted on a set of cytologically indeterminate thyroid nodules collected by fine-needle aspiration biopsy between June 2009 and December 2010 from 49 academic and community centers in the United States. All patients underwent surgery without genomic information and were assigned a histopathology diagnosis by an expert panel blinded to all genomic information. There were 210 potentially eligible thyroid biopsy samples with Bethesda III or IV indeterminate cytopathology that constituted a cohort previously used to validate the gene expression classifier. Of these, 191 samples (91.0%) had adequate residual RNA for validation of the genomic sequencing classifier. Algorithm development and independent validation occurred between August 2016 and May 2017. Exposures: Thyroid nodule surgical histopathology diagnosis by an expert panel blinded to all genomic data. Main Outcomes and Measures: The primary end point was measurement of genomic sequencing classifier sensitivity, specificity, and negative and positive predictive values in biopsies from Bethesda III and IV nodules. The secondary end point was measurement of classifier performance in biopsies from Bethesda II, V, and VI nodules. Results: Of the 183 included patients, 142 (77.6%) were women, and the mean (range) age was 51.7 (22.0-85.0) years. The genomic sequencing classifier had a sensitivity of 91% (95% CI, 79-98) and a specificity of 68% (95% CI, 60-76). At 24% cancer prevalence, the negative predictive value was 96% (95% CI, 90-99) and the positive predictive value was 47% (95% CI, 36-58). Conclusions and Relevance: The genomic sequencing classifier demonstrates high sensitivity and accuracy for identifying benign nodules. Its 36% increase in specificity compared with the gene expression classifier potentially increases the number of patients with benign nodules who can safely avoid unnecessary diagnostic surgery.