Cytokine release syndrome after bronchoalveolar lavage.

BACKGROUND: Immunosuppressed bone marrow transplant patients with pulmonary infiltrates routinely undergo bronchoscopy with bronchoalveolar lavage (BAL) to investigate potential etiologies. Cytokine release syndrome after BAL is unreported in the literature in general and in this patient population. CASE PRESENTATION: We report on an allogeneic bone marrow transplant patient with non-infectious organizing pneumonia of the lungs who developed delayed and rapidly progressive shock and hypoxia post-procedure over the course of 12 h resulting in intensive care unit admission for supportive care. BAL was characterized by a marked lymphocytic, cytotoxic T cell infiltrate on pathology and flow cytometry without clear evidence of infection. The patient's clinical status improved quickly only after the initiation of high dose intravenous steroids and returned to baseline as an outpatient. CONCLUSION: The patient's clinical data and course suggest a cytotoxic T cell response from the lung and BAL as the etiology. With an increasing number of cellular therapies for cancer entering the clinic, the potential for unusual but morbid complications from routine bronchoscopy should be considered.

Protocol optimization of a targeted sequencing panel for genomic profiling of bronchoalveolar lavage fluid in lung cancer.

INTRODUCTION: We investigated a commercially available sequencing panel to study the effect of sequencing depth, variant calling strategy, and targeted sequencing region on identifying tumor-derived variants in cell-free bronchoalveolar lavage (cfBAL) DNA compared with plasma cfDNA. METHODS: Sequencing was performed at low or high coverage using two filtering algorithms to identify tumor variants on two panels targeting 77 and 197 genes respectively. RESULTS: One hundred and four sequencing files from 40 matched DNA samples of cfBAL, plasma, germline leukocytes, and archival tumor specimens in 10 patients with early-stage lung cancer were analyzed. By low-coverage sequencing, tumor-derived cfBAL variants were detected in 5/10 patients (50%) compared with 2/10 (20%) for plasma. High-coverage sequencing did not affect the number of tumor-derived variants detected in either biospecimen type. Accounting for germline mutations eliminated false-positive plasma calls regardless of coverage (0/10 patients with tumor-derived variants identified) and increased the number of cfBAL calls (5/10 patients with tumor-derived variants identified). These results were not affected by the number of targeted genes.

Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment.

The clinical management of patients with indeterminate pulmonary nodules is associated with unintended harm to patients and better methods are required to more precisely quantify lung cancer risk in this group. Here, we combine multiple noninvasive approaches to more accurately identify lung cancer in indeterminate pulmonary nodules. We analyzed 94 quantitative radiomic imaging features and 41 qualitative semantic imaging variables with molecular biomarkers from blood derived from an antibody-based microarray platform that determines protein, cancer-specific glycan, and autoantibody-antigen complex content with high sensitivity. From these datasets, we created a PSR (plasma, semantic, radiomic) risk prediction model comprising nine blood-based and imaging biomarkers with an area under the receiver operating curve (AUROC) of 0.964 that when tested in a second, independent cohort yielded an AUROC of 0.846. Incorporating known clinical risk factors (age, gender, and smoking pack years) for lung cancer into the PSR model improved the AUROC to 0.897 in the second cohort and was more accurate than a well-characterized clinical risk prediction model (AUROC = 0.802). Our findings support the use of a multi-omics approach to guide the clinical management of indeterminate pulmonary nodules.

Clinical Impact and Generalizability of a Computer-Assisted Diagnostic Tool to Risk-Stratify Lung Nodules With CT.

OBJECTIVE: To evaluate whether an imaging classifier for radiology practice can improve lung nodule classification and follow-up. METHODS: A machine learning classifier was developed and trained using imaging data from the National Lung Screening Trial (NSLT) to produce a malignancy risk score (malignancy Similarity Index [mSI]) for individual lung nodules. In addition to NLST cohorts, external cohorts were developed from a tertiary referral lung cancer screening program data set and an external nonscreening data set of all nodules detected on CT. Performance of the mSI combined with Lung-RADS was compared with Lung-RADS alone and the Mayo and Brock risk calculators. RESULTS: We analyzed 963 subjects and 1,331 nodules across these cohorts. The mSI was comparable in accuracy (area under the curve = 0.89) to existing clinical risk models (area under the curve = 0.86-0.88) and independently predictive in the NLST cohort of 704 nodules. When compared with Lung-RADS, the mSI significantly increased sensitivity across all cohorts (25%-117%), with significant increases in specificity in the screening cohorts (17%-33%). When used in conjunction with Lung-RADS, use of mSI would result in earlier diagnoses and reduced follow-up across cohorts, including the potential for early diagnosis in 42% of malignant NLST nodules from prior-year CT scans. CONCLUSION: A computer-assisted diagnosis software improved risk classification from chest CTs of screening and incidentally detected lung nodules compared with Lung-RADS. mSI added predictive value independent of existing radiological and clinical variables. These results suggest the generalizability and potential clinical impact of a tool that is straightforward to implement in practice.

Predicting recurrence risks in lung cancer patients using multimodal radiomics and random survival forests.

Purpose: We developed a model integrating multimodal quantitative imaging features from tumor and nontumor regions, qualitative features, and clinical data to improve the risk stratification of patients with resectable non-small cell lung cancer (NSCLC). Approach: We retrospectively analyzed 135 patients [mean age, 69 years (43 to 87, range); 100 male patients and 35 female patients] with NSCLC who underwent upfront surgical resection between 2008 and 2012. The tumor and peritumoral regions on both preoperative CT and FDG PET-CT and the vertebral bodies L3 to L5 on FDG PET were segmented to assess the tumor and bone marrow uptake, respectively. Radiomic features were extracted and combined with clinical and CT qualitative features. A random survival forest model was developed using the top-performing features to predict the time to recurrence/progression in the training cohort ( n = 101 ), validated in the testing cohort ( n = 34 ) using the concordance, and compared with a stage-only model. Patients were stratified into high- and low-risks of recurrence/progression using Kaplan-Meier analysis. Results: The model, consisting of stage, three wavelet texture features, and three wavelet first-order features, achieved a concordance of 0.78 and 0.76 in the training and testing cohorts, respectively, significantly outperforming the baseline stage-only model results of 0.67 ( p < 0.005 ) and 0.60 ( p = 0.008 ), respectively. Patients at high- and low-risks of recurrence/progression were significantly stratified in both the training ( p < 0.005 ) and the testing ( p = 0.03 ) cohorts. Conclusions: Our radiomic model, consisting of stage and tumor, peritumoral, and bone marrow features from CT and FDG PET-CT significantly stratified patients into low- and high-risk of recurrence/progression.

Genomic Profiling of Bronchoalveolar Lavage Fluid in Lung Cancer.

Genomic profiling of bronchoalveolar lavage (BAL) samples may be useful for tumor profiling and diagnosis in the clinic. Here, we compared tumor-derived mutations detected in BAL samples from subjects with non-small cell lung cancer (NSCLC) to those detected in matched plasma samples. Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) was used to genotype DNA purified from BAL, plasma, and tumor samples from patients with NSCLC. The characteristics of cell-free DNA (cfDNA) isolated from BAL fluid were first characterized to optimize the technical approach. Somatic mutations identified in tumor were then compared with those identified in BAL and plasma, and the potential of BAL cfDNA analysis to distinguish lung cancer patients from risk-matched controls was explored. In total, 200 biofluid and tumor samples from 38 cases and 21 controls undergoing BAL for lung cancer evaluation were profiled. More tumor variants were identified in BAL cfDNA than plasma cfDNA in all stages (P < 0.001) and in stage I to II disease only. Four of 21 controls harbored low levels of cancer-associated driver mutations in BAL cfDNA [mean variant allele frequency (VAF) = 0.5%], suggesting the presence of somatic mutations in nonmalignant airway cells. Finally, using a Random Forest model with leave-one-out cross-validation, an exploratory BAL genomic classifier identified lung cancer with 69% sensitivity and 100% specificity in this cohort and detected more cancers than BAL cytology. Detecting tumor-derived mutations by targeted sequencing of BAL cfDNA is technically feasible and appears to be more sensitive than plasma profiling. Further studies are required to define optimal diagnostic applications and clinical utility. SIGNIFICANCE: Hybrid-capture, targeted deep sequencing of lung cancer mutational burden in cell-free BAL fluid identifies more tumor-derived mutations with increased allele frequencies compared with plasma cell-free DNA. See related commentary by Rolfo et al., p. 2826.

A case series of morbid COPD exacerbations during immune checkpoint inhibitor therapy in cancer patients.

BACKGROUND: Immune checkpoint inhibitor therapy is rapidly becoming front line adjuvant or primary therapy in a number of solid cancer types. Since many of these cancers are a result of tobacco smoking, a large number of these patients will have underlying comorbid conditions attributed to smoking such as Chronic Obstructive Pulmonary Disease (COPD). The effect of immune checkpoint inhibitor therapy on COPD is not well documented, and COPD exacerbations are not currently considered a pulmonary associated immune checkpoint inhibitor toxicity in current guidelines. CASE PRESENTATION: We describe and summarize here a series of patients with prolonged and severe COPD exacerbations upon the initiation of immune checkpoint inhibitor therapy for cancers of the skin and lung without radiographic evidence of pneumonitis. CONCLUSIONS: COPD exacerbation from immune checkpoint inhibitor is not reported in the literature and is associated with prolonged and severe episodes without radiographic evidence of pneumonitis. Awareness of this potential morbid toxicity and research efforts to understand its etiology are required.

A multi-scale integrated analysis identifies KRT8 as a pan-cancer early biomarker.

An early biomarker would transform our ability to screen and treat patients with cancer. The large amount of multi-scale molecular data in public repositories from various cancers provide unprecedented opportunities to find such a biomarker. However, despite identification of numerous molecular biomarkers using these public data, fewer than 1% have proven robust enough to translate into clinical practice. One of the most important factors affecting the successful translation to clinical practice is lack of real-world patient population heterogeneity in the discovery process. Almost all biomarker studies analyze only a single cohort of patients with the same cancer using a single modality. Recent studies in other diseases have demonstrated the advantage of leveraging biological and technical heterogeneity across multiple independent cohorts to identify robust disease biomarkers. Here we analyzed 17149 samples from patients with one of 23 cancers that were profiled using either DNA methylation, bulk and single-cell gene expression, or protein expression in tumor and serum. First, we analyzed DNA methylation profiles of 9855 samples across 23 cancers from The Cancer Genome Atlas (TCGA). We then examined the gene expression profile of the most significantly hypomethylated gene, KRT8, in 6781 samples from 57 independent microarray datasets from NCBI GEO. KRT8 was significantly over-expressed across cancers except colon cancer (summary effect size=1.05; p < 0.0001). Further, single-cell RNAseq analysis of 7447 single cells from lung tumors showed that genes that significantly correlated with KRT8 (p < 0.05) were involved in p53-related pathways. Immunohistochemistry in tumor biopsies from 294 patients with lung cancer showed that high protein expression of KRT8 is a prognostic marker of poor survival (HR = 1.73, p = 0.01). Finally, detectable KRT8 in serum as measured by ELISA distinguished patients with pancreatic cancer from healthy controls with an AUROC=0.94. In summary, our analysis demonstrates that KRT8 is (1) differentially expressed in several cancers across all molecular modalities and (2) may be useful as a biomarker to identify patients that should be further tested for cancer.

A Comparison of the PanCan Model and Lung-RADS to Assess Cancer Probability Among People With Screening-Detected, Solid Lung Nodules.

BACKGROUND: The Pan-Canadian Early Detection of Lung Cancer (PanCan) risk model and the Lung CT Screening Reporting & Data System (Lung-RADS) estimate cancer probability for screening-detected nodules. The accuracy and agreement of these models require further study. RESEARCH QUESTION: What is the performance of the PanCan model and Lung-RADS to estimate the probability of cancer in screening-detected solid nodules? STUDY DESIGN AND METHODS: We analyzed data for newly identified, solid nodules detected on any screening round in the low-dose CT arm of the National Lung Screening Trial to assign a PanCan risk and Lung-RADS score. We compared PanCan risk with the corresponding Lung-RADS category according to the expected prevalence of cancer and examined accuracy using logistic regression and between-test agreement. We also analyzed baseline screen-detected nodules only, high (defined as ≥ 5% probability of cancer) vs low-risk nodules, "risk-gap" nodules with a 3% to 5% PanCan probability and no equivalent Lung-RADS category, and procedure use by model. RESULTS: Participants with solid nodules (6,956) had a calculable PanCan risk and Lung-RADS score. PanCan accuracy by cancer probabilities < 1%, 1% to 2%, 5% to 15%, and > 15% was similar to corresponding Lung-RADS categories 2, 3, 4A, and 4B for any solid nodule (area under the curve, 0.84 vs 0.84; P = .95) and for nodules identified at baseline (area under the curve, 0.85 vs 0.84; P = .17). When dichotomized by high/low risk, PanCan and Lung-RADS were discordant (P < .001). Participants with risk-gap nodules (n = 543) were distributed across Lung-RADS categories 2 through 4; 41 (8%) had invasive procedures with 23 (4%) having unnecessary invasive procedure use for solid, benign nodules. INTERPRETATION: PanCan and Lung-RADS had similar overall accuracy for assessing cancer in screening-detected, solid lung nodules with evidence of discordance by subgroup. The existence of Lung-RADS category 4 nodules with a ≥ 3% to 5% PanCan risk may result in unnecessary procedures.

Differences in airway microbiome and metabolome of single lung transplant recipients.

BACKGROUND: Recent studies suggest that alterations in lung microbiome are associated with occurrence of chronic lung diseases and transplant rejection. To investigate the host-microbiome interactions, we characterized the airway microbiome and metabolome of the allograft (transplanted lung) and native lung of single lung transplant recipients. METHODS: BAL was collected from the allograft and native lungs of SLTs and healthy controls. 16S rRNA microbiome analysis was performed on BAL bacterial pellets and supernatant used for metabolome, cytokines and acetylated proline-glycine-proline (Ac-PGP) measurement by liquid chromatography-high-resolution mass spectrometry. RESULTS: In our cohort, the allograft airway microbiome was distinct with a significantly higher bacterial burden and relative abundance of genera Acinetobacter & Pseudomonas. Likewise, the expression of the pro-inflammatory cytokine VEGF and the neutrophil chemoattractant matrikine Ac-PGP in the allograft was significantly higher. Airway metabolome distinguished the native lung from the allografts and an increased concentration of sphingosine-like metabolites that negatively correlated with abundance of bacteria from phyla Proteobacteria. CONCLUSIONS: Allograft lungs have a distinct microbiome signature, a higher bacterial biomass and an increased Ac-PGP compared to the native lungs in SLTs compared to the native lungs in SLTs. Airway metabolome distinguishes the allografts from native lungs and is associated with distinct microbial communities, suggesting a functional relationship between the local microbiome and metabolome.

A human lung tumor microenvironment interactome identifies clinically relevant cell-type cross-talk.

BACKGROUND: Tumors comprise a complex microenvironment of interacting malignant and stromal cell types. Much of our understanding of the tumor microenvironment comes from in vitro studies isolating the interactions between malignant cells and a single stromal cell type, often along a single pathway. RESULT: To develop a deeper understanding of the interactions between cells within human lung tumors, we perform RNA-seq profiling of flow-sorted malignant cells, endothelial cells, immune cells, fibroblasts, and bulk cells from freshly resected human primary non-small-cell lung tumors. We map the cell-specific differential expression of prognostically associated secreted factors and cell surface genes, and computationally reconstruct cross-talk between these cell types to generate a novel resource called the Lung Tumor Microenvironment Interactome (LTMI). Using this resource, we identify and validate a prognostically unfavorable influence of Gremlin-1 production by fibroblasts on proliferation of malignant lung adenocarcinoma cells. We also find a prognostically favorable association between infiltration of mast cells and less aggressive tumor cell behavior. CONCLUSION: These results illustrate the utility of the LTMI as a resource for generating hypotheses concerning tumor-microenvironment interactions that may have prognostic and therapeutic relevance.

Integrating genomic features for non-invasive early lung cancer detection.

Radiologic screening of high-risk adults reduces lung-cancer-related mortality1,2; however, a small minority of eligible individuals undergo such screening in the United States3,4. The availability of blood-based tests could increase screening uptake. Here we introduce improvements to cancer personalized profiling by deep sequencing (CAPP-Seq)5, a method for the analysis of circulating tumour DNA (ctDNA), to better facilitate screening applications. We show that, although levels are very low in early-stage lung cancers, ctDNA is present prior to treatment in most patients and its presence is strongly prognostic. We also find that the majority of somatic mutations in the cell-free DNA (cfDNA) of patients with lung cancer and of risk-matched controls reflect clonal haematopoiesis and are non-recurrent. Compared with tumour-derived mutations, clonal haematopoiesis mutations occur on longer cfDNA fragments and lack mutational signatures that are associated with tobacco smoking. Integrating these findings with other molecular features, we develop and prospectively validate a machine-learning method termed 'lung cancer likelihood in plasma' (Lung-CLiP), which can robustly discriminate early-stage lung cancer patients from risk-matched controls. This approach achieves performance similar to that of tumour-informed ctDNA detection and enables tuning of assay specificity in order to facilitate distinct clinical applications. Our findings establish the potential of cfDNA for lung cancer screening and highlight the importance of risk-matching cases and controls in cfDNA-based screening studies.

Bone Marrow and Tumor Radiomics at 18F-FDG PET/CT: Impact on Outcome Prediction in Non-Small Cell Lung Cancer.

Background Primary tumor maximum standardized uptake value is a prognostic marker for non-small cell lung cancer. In the setting of malignancy, bone marrow activity from fluorine 18-fluorodeoxyglucose (FDG) PET may be informative for clinical risk stratification. Purpose To determine whether integrating FDG PET radiomic features of the primary tumor, tumor penumbra, and bone marrow identifies lung cancer disease-free survival more accurately than clinical features alone. Materials and Methods Patients were retrospectively analyzed from two distinct cohorts collected between 2008 and 2016. Each tumor, its surrounding penumbra, and bone marrow from the L3-L5 vertebral bodies was contoured on pretreatment FDG PET/CT images. There were 156 bone marrow and 512 tumor and penumbra radiomic features computed from the PET series. Randomized sparse Cox regression by least absolute shrinkage and selection operator identified features that predicted disease-free survival in the training cohort. Cox proportional hazards models were built and locked in the training cohort, then evaluated in an independent cohort for temporal validation. Results There were 227 patients analyzed; 136 for training (mean age, 69 years ± 9 [standard deviation]; 101 men) and 91 for temporal validation (mean age, 72 years ± 10; 91 men). The top clinical model included stage; adding tumor region features alone improved outcome prediction (log likelihood, -158 vs -152; P = .007). Adding bone marrow features continued to improve performance (log likelihood, -158 vs -145; P = .001). The top model integrated stage, two bone marrow texture features, one tumor with penumbra texture feature, and two penumbra texture features (concordance, 0.78; 95% confidence interval: 0.70, 0.85; P < .001). This fully integrated model was a predictor of poor outcome in the independent cohort (concordance, 0.72; 95% confidence interval: 0.64, 0.80; P < .001) and a binary score stratified patients into high and low risk of poor outcome (P < .001). Conclusion A model that includes pretreatment fluorine 18-fluorodeoxyglucose PET texture features from the primary tumor, tumor penumbra, and bone marrow predicts disease-free survival of patients with non-small cell lung cancer more accurately than clinical features alone. © RSNA, 2019 Online supplemental material is available for this article.

[18F] FDG Positron Emission Tomography (PET) Tumor and Penumbra Imaging Features Predict Recurrence in Non-Small Cell Lung Cancer.

We identified computational imaging features on 18F-fluorodeoxyglucose positron emission tomography (PET) that predict recurrence/progression in non-small cell lung cancer (NSCLC). We retrospectively identified 291 patients with NSCLC from 2 prospectively acquired cohorts (training, n = 145; validation, n = 146). We contoured the metabolic tumor volume (MTV) on all pretreatment PET images and added a 3-dimensional penumbra region that extended outward 1 cm from the tumor surface. We generated 512 radiomics features, selected 435 features based on robustness to contour variations, and then applied randomized sparse regression (LASSO) to identify features that predicted time to recurrence in the training cohort. We built Cox proportional hazards models in the training cohort and independently evaluated the models in the validation cohort. Two features including stage and a MTV plus penumbra texture feature were selected by LASSO. Both features were significant univariate predictors, with stage being the best predictor (hazard ratio [HR] = 2.15 [95% confidence interval (CI): 1.56-2.95], P < .001). However, adding the MTV plus penumbra texture feature to stage significantly improved prediction (P = .006). This multivariate model was a significant predictor of time to recurrence in the training cohort (concordance = 0.74 [95% CI: 0.66-0.81], P < .001) that was validated in a separate validation cohort (concordance = 0.74 [95% CI: 0.67-0.81], P < .001). A combined radiomics and clinical model improved NSCLC recurrence prediction. FDG PET radiomic features may be useful biomarkers for lung cancer prognosis and add clinical utility for risk stratification.

A Feasibility Study of Single-inhalation, Single-energy Xenon-enhanced CT for High-resolution Imaging of Regional Lung Ventilation in Humans.

RATIONALE AND OBJECTIVES: The objective of this study was to assess the feasibility of single-inhalation xenon-enhanced computed tomography (XeCT) to provide clinically practical, high-resolution pulmonary ventilation imaging to clinics with access to only a single-energy computed tomography scanner, and to reduce the subject's overall exposure to xenon by utilizing a higher (70%) concentration for a much shorter time than has been employed in prior studies. MATERIALS AND METHODS: We conducted an institutional review board-approved prospective feasibility study of XeCT for 15 patients undergoing thoracic radiotherapy. For XeCT, we acquired two breath-hold single-energy computed tomography images of the entire lung with a single inhalation each of 100% oxygen and a mixture of 70% xenon and 30% oxygen, respectively. A video biofeedback system for coached patient breathing was used to achieve reproducible breath holds. We assessed the technical success of XeCT acquisition and side effects. We then used deformable image registration to align the breath-hold images with each other to accurately subtract them, producing a map of lung xenon distribution. Additionally, we acquired ventilation single-photon emission computed tomography-computed tomography (V-SPECT-CT) images for 11 of the 15 patients. For a comparative analysis, we partitioned each lung into 12 sectors, calculated the xenon concentration from the Hounsfield unit enhancement in each sector, and then correlated this with the corresponding V-SPECT-CT counts. RESULTS: XeCT scans were tolerated well overall, with a mild (grade 1) dizziness as the only side effect in 5 of the 15 patients. Technical failures in five patients occurred because of inaccurate breathing synchronization with xenon gas delivery, leaving seven patients analyzable for XeCT and single-photon emission computed tomography correlation. Sector-wise correlations were strong (Spearman coefficient >0.75, Pearson coefficient >0.65, P value <.002) for two patients for whom ventilation deficits were visibly pronounced in both scans. Correlations were nonsignificant for the remaining five who had more homogeneous XeCT ventilation maps, as well as strong V-SPECT-CT imaging artifacts attributable to airway deposition of the aerosolized imaging agent. Qualitatively, XeCT demonstrated higher resolution and no central airway deposition artifacts compared to V-SPECT-CT. CONCLUSIONS: In this pilot study, single-breath XeCT ventilation imaging was generally feasible for patients undergoing thoracic radiotherapy, using an imaging protocol that is clinically practical and potentially widely available. In the future, the xenon delivery failures can be addressed by straightforward technical improvements to the patient biofeedback coaching system.

An Analysis of Lung Cancer Screening Beliefs and Practice Patterns for Community Providers Compared to Academic Providers.

Despite guidelines recommending annual low-dose computed tomography (LDCT) screening for lung cancer, uptake remains low due to the perceived complexity of initiating and maintaining a clinical program-problems that likely magnify in underserved populations. We conducted a survey of community providers at Federally Qualified Health Centers (FQHCs) in Santa Clara County, California, to evaluate provider-related factors that affect adherence. We then compared these findings to academic providers' (APs) LDCT screening knowledge, behaviors, and attitudes at an academic referral center in the same county. The 4 FQHCs enrolled care for 80 000 patients largely of minority descent and insured by Medi-Cal. Of the 75 FQHC providers (FQHCPs), 36 (48%) completed the survey. Of the 36 providers, 8 (22%) knew screening criteria. Fifteen (42%) FQHCPs discussed LDCT screening with patients. Compared to 36 APs, FQHCPs were more concerned about harms, false positives, discussion time, patient apathy, insurance coverage, and a lack of expertise for screening and follow-up. Yet, more FQHCPs thought screening was effective (27 [75%] of 36) compared to APs ( P = .0003). In conclusion, provider knowledge gaps are greater and barriers are different for community clinics caring for underserved populations compared to their academic counterparts, but practical and scalable solutions exist to enhance adoption.

The Exosome Total Isolation Chip.

Circulating tumor-derived extracellular vesicles (EVs) have emerged as a promising source for identifying cancer biomarkers for early cancer detection. However, the clinical utility of EVs has thus far been limited by the fact that most EV isolation methods are tedious, nonstandardized, and require bulky instrumentation such as ultracentrifugation (UC). Here, we report a size-based EV isolation tool called ExoTIC (exosome total isolation chip), which is simple, easy-to-use, modular, and facilitates high-yield and high-purity EV isolation from biofluids. ExoTIC achieves an EV yield ∼4-1000-fold higher than that with UC, and EV-derived protein and microRNA levels are well-correlated between the two methods. Moreover, we demonstrate that ExoTIC is a modular platform that can sort a heterogeneous population of cancer cell line EVs based on size. Further, we utilize ExoTIC to isolate EVs from cancer patient clinical samples, including plasma, urine, and lavage, demonstrating the device's broad applicability to cancers and other diseases. Finally, the ability of ExoTIC to efficiently isolate EVs from small sample volumes opens up avenues for preclinical studies in small animal tumor models and for point-of-care EV-based clinical testing from fingerprick quantities (10-100 µL) of blood.

Pulmonary function after lung tumor stereotactic ablative radiotherapy depends on regional ventilation within irradiated lung.

PURPOSE: To determine if regional ventilation within irradiated lung volume predicts change in pulmonary function test (PFT) measurements after stereotactic ablative radiotherapy (SABR) of lung tumors. METHODS: We retrospectively identified 27 patients treated from 2007 to 2014 at our institution who received: (1) SABR without prior thoracic radiation; (2) pre-treatment 4-dimensional computed tomography (4-D CT) imaging; (3) pre- and post-SABR PFTs <15months from treatment. We defined the ventilation ratio (VR20BED3) as the quotient of mean ventilation (mean Jacobian-based per-voxel volume change on deformably registered inhale/exhale 4-D CT phases) within the 20Gy biologically effective dose (α/ß=3Gy) isodose volume and that of the total lung volume (TLV). RESULTS: Most patients had moderate to very severe COPD by GOLD criteria (n=19, 70.1%). Higher VR20BED3 significantly predicted worse change in Forced Expiratory Volume/s normalized by baseline value (ΔFEV1/FEV1pre, p=0.04); n=7 had VR20BED3>1 (high regional ventilation) and worse ΔFEV1/FEV1pre (median=-0.16, range=-0.230 to -0.20). Five had VR20BED3<1 (low regional ventilation) and improved ΔFEV1/FEV1pre (median=0.13, range=0.07 to 0.20). In a multivariable linear model, increasing VR20BED3 and time to post-SABR PFT predicted decreasing ΔFEV1/FEV1pre (R2=0.25, p=0.03). CONCLUSIONS: After SABR to high versus low functioning lung regions, we found worsened or improved global pulmonary function, respectively. If pre-SABR VR20BED3 is validated as a predictor of eventual post-SABR PFT in larger studies, it may be used for individualized treatment planning to preserve or even improve pulmonary function after SABR.