Faculty Bibliography

The standard of care for managing early stage non-small cell lung cancer (NSCLC) is definitive surgical resection. Stereotactic body radiation therapy (SBRT) has become the standard treatment for patient who are medically inoperable, and it is increasingly being considered as an option in operable patients. With the growing use of screening thoracic CT scans for patients with a history of heavy smoking, as well as improved imaging capabilities, the discovery of small lung nodes has become a common dilemma. As a result, clinicians are increasingly faced with managing lung nodules in patients in whom diagnostic biopsy is not safe or feasible. Herein, we describe the scope of the problem, tools available for predicting the probability that a lung nodule is a malignancy, staging procedures, benefits of pathology-proven and empiric SBRT, considerations of safety based on location of the lesion of concern, and overall efficacy of SBRT.

OBJECTIVES:A malignant pleural effusion (MPE) is a common complication in non-small cell lung cancer (NSCLC) with important staging and prognostic information. Patients with MPEs are often candidates for advanced therapies, however, the current gold standard, cytological analysis of pleural fluid samples, has limited sensitivity. We aimed to demonstrate the feasibility of non-invasive enumeration and immunophenotyping of EpCAM-positive cells in pleural fluid samples for the diagnosis of a MPE in NSCLC patients. MATERIALS AND METHODS:Pleural fluid specimens were prospectively collected from patients with NSCLC and the CellSearch® technology was utilized for the enumeration of pleural EpCAM-positive cells (PECs) and determination of PD-L1 expression on PECs from pleural fluid samples. The diagnostic performance of the enumeration of single PECs and PEC clusters was assessed using receiver operating characteristic (ROC) curves. The Kaplan-Meier method and Cox proportional hazards model was used to assess the impact of PECs and PEC clusters on overall survival (OS). RESULTS:101 NSCLC patients were enrolled. The median number of PECs was significantly greater in the malignant (n = 84) versus non-malignant group (n = 17) (730 PECs/mL vs 1.0 PEC/mL, p < 0.001). The area under the ROC curve was 0.91. A cutoff value of 105 PECs/mL had a sensitivity and specificity of 73% and 100% for the diagnosis of a MPE, respectively. Among 69 patients with a pathology-confirmed MPE and tissue immunohistochemistry (IHC) results, 15 (22%) had greater than 50% PD-L1+ PECs. Overall concordance between tissue and PEC PD-L1 expression was 76%. Higher numbers of pleural effusion single PECs were associated with inferior overall survival (Cox adjusted HR 1.8, 95% CI: 1.02-3.05 p = 0.043). CONCLUSION:Non-invasive measurement of PECs in NSCLC patients, using an automated, clinically available approach, may improve the diagnostic accuracy of a MPE, allow for immunophenotyping of PECs, and provide prognostic information.

Estimation of common cost-effectiveness measures, including the incremental cost-effectiveness ratio and the net monetary benefit, is complicated by the need to account for informative censoring and inherent skewness of the data. In addition, since the two components of these measures, medical costs and survival are often collected from observational claims data, one must account for potential confounders. We propose a novel doubly robust, unbiased estimator for cost-effectiveness based on propensity scores that allow the incorporation of cost history and time-varying covariates. Further, we use an ensemble machine learning approach to obtain improved predictions from parametric and non-parametric cost and propensity score models. Our simulation studies demonstrate that the proposed doubly robust approach performs well even under mis-specification of either the propensity score model or the outcome model. We apply our approach to a cost-effectiveness analysis of two competing lung cancer surveillance procedures, CT vs. chest X-ray, using SEER-Medicare data.

BACKGROUND:Lung nodules are a diagnostic challenge, with an estimated yearly incidence of 1.6 million in the United States. This study evaluated the accuracy of an integrated proteomic classifier in identifying benign nodules in patients with a pretest probability of cancer (pCA) ≤ 50%. METHODS:A prospective, multicenter observational trial of 685 patients with 8- to 30-mm lung nodules was conducted. Multiple reaction monitoring mass spectrometry was used to measure the relative abundance of two plasma proteins, LG3BP and C163A. Results were integrated with a clinical risk prediction model to identify likely benign nodules. Sensitivity, specificity, and negative predictive value were calculated. Estimates of potential changes in invasive testing had the integrated classifier results been available and acted on were made. RESULTS:A subgroup of 178 patients with a clinician-assessed pCA ≤ 50% had a 16% prevalence of lung cancer. The integrated classifier demonstrated a sensitivity of 97% (CI, 82-100), a specificity of 44% (CI, 36-52), and a negative predictive value of 98% (CI, 92-100) in distinguishing benign from malignant nodules. The classifier performed better than PET, validated lung nodule risk models, and physician cancer probability estimates (P < .001). If the integrated classifier results were used to direct care, 40% fewer procedures would be performed on benign nodules, and 3% of malignant nodules would be misclassified. CONCLUSIONS:When used in patients with lung nodules with a pCA ≤ 50%, the integrated classifier accurately identifies benign lung nodules with good performance characteristics. If used in clinical practice, invasive procedures could be reduced by diverting benign nodules to surveillance. TRIAL REGISTRY:ClinicalTrials.gov; No.: NCT01752114; URL: www.clinicaltrials.gov).

High mobility group box 1 (HMGB1) is a non-histone chromosomal protein, which can be secreted through a variety of pathways and bind to pattern recognition receptors to release pro-inflammatory cytokines. Previous studies have suggested that HMGB1 is upregulated in numerous inflammatory diseases and that it could be a biomarker for such diseases. However, these studies used immunoassay-based methods to analyze serum HMGB1. Autoantibodies to HMGB1 in serum are found in healthy control subjects as well as in patients with different diseases. HMGB1 also binds to haptoglobin, a highly abundant plasma protein. This means that antibodies used in immunoassays must compete with binding of HMGB1 to endogenous serum HMGB1 autoantibodies and haptoglobin. To overcome these potential problems, we developed and validated a specific and sensitive assay based on stable isotope dilution and immunopurification to quantify HMGB1 in plasma and serum using two-dimensional nano-ultra-high-performance liquid chromatography parallel reaction monitoring/high-resolution mass spectrometry. Using this assay, we found that serum HMGB1 in 24 healthy control subjects (6.0 ± 2.1 ng/mL) was above the mean concentration reported for 18 different diseases (5.4 ± 2.8 ng/mL) where the analyses were conducted with immunoassay methodology. In light of our finding, the role of HMGB1 in these diseases will have to be re-evaluated. The concentration of HMGB1 in citrated and EDTA-treated plasma from the same healthy control subjects was below the limit of detection of our assay (1 ng/mL), confirming that HMGB1 in serum arises when blood is allowed to clot. This means that future studies on the role of HMGB1 in vivo should be conducted on plasma rather than serum.

Timely follow-up for positive cancer screening results remains suboptimal, and the evidence base to inform decisions on optimizing the timeliness of diagnostic testing is unclear. This systematic review evaluated published studies regarding time to follow-up after a positive screening for breast, cervical, colorectal, and lung cancers. The quality of available evidence was very low or low across cancers, with potential attenuated or reversed associations from confounding by indication in most studies. Overall, evidence suggested that the risk for poorer cancer outcomes rises with longer wait times that vary within and across cancer types, which supports performing diagnostic testing as soon as feasible after the positive result, but evidence for specific time targets is limited. Within these limitations, we provide our opinion on cancer-specific recommendations for times to follow-up and how existing guidelines relate to the current evidence. Thresholds set should consider patient worry, potential for loss to follow-up with prolonged wait times, and available resources. Research is needed to better guide the timeliness of diagnostic follow-up, including considerations for patient preferences and existing barriers, while addressing methodological weaknesses. Research is also needed to identify effective interventions for reducing wait times for diagnostic testing, particularly in underserved or low-resource settings. CA Cancer J Clin 2018;68:199-216. © 2018 American Cancer Society.

BACKGROUND:The benefit of invasive mediastinal nodal staging (IMNS) in addition to positron emission tomography-computed tomography (PET/CT) is undefined for early stage non-small cell lung cancer (NSCLC). This multi-institutional investigation aimed to evaluate outcomes and patterns of failure in patients staged with PET/CT with or without additional IMNS. METHODS:Two academic centers assessed all consecutive patients staged with PET/CT for early-stage, primary lung NSCLC (cT1-2aN0M0) treated with SBRT. Local recurrence-free survival (LRFS), nodal recurrence-free survival (NRFS), distant metastasis-free survival (DMFS), and overall survival (OS) were calculated using Kaplan-Meier methodology. Univariate and multivariate Cox proportional hazards modeling addressed factors associated with outcomes. RESULTS:Overall, 180 patients (199 lesions) were staged with PET/CT alone and 56 patients (58 lesions) underwent additional IMNS. Among patients receiving IMNS, 52 (93%) underwent EBUS and 4 (7%) underwent mediastinoscopy. At a median follow-up of 33.5 months (range, 1.9-80.9 months), there was no significant difference in LRFS (37 vs. 47 months, p=0.309), NRFS (34 vs. 42 months p=0.370), DMFS (36 vs. 47 months, p=0.234) or OS (37 vs. 47 months, p=0.236) between patients undergoing PET/CT-only versus PET/CT+IMNS staging, respectively. Receipt of IMNS did not correlate with any outcome on either univariate or multivariate analysis (p>0.05). Patterns of failure in both groups were similar, including crude isolated nodal failure rates (8% PET/CT-only versus 14% PET+IMNS group, p=0.202). CONCLUSIONS:Patients undergoing IMNS had similar survival and patterns of recurrence as those receiving PET/CT alone. Further study, ideally prospectively, is needed to determine which subgroups might benefit from IMNS.

BACKGROUND:Targeted therapy for patients with lung and colon cancer based on tumor molecular profiles is an important cancer treatment strategy, but the impact of gene mutation tests on cancer treatment and outcomes in large populations is not clear. In this study, we assessed the accuracy of an algorithm to identify tumor mutation testing in administrative claims data during a period before test-specific Current Procedural Terminology codes were available. MATERIALS AND METHODS:We used Pennsylvania Cancer Registry data to select patients with lung or colon cancer diagnosed between 2007 and 2011 who were treated at the University of Pennsylvania Health System, and we obtained their administrative claims. A combination of Current Procedural Terminology laboratory codes (stacking codes) was used to identify potential tumor mutation testing in the claims data. Patients' electronic medical records were then searched to determine whether tumor mutation testing actually had been performed. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. RESULTS:An algorithm using stacking codes had moderate sensitivity (86% for lung cancer and 81% for colon cancer) and high specificity (98% for lung cancer and 96% for colon cancer). Sensitivity and specificity did not vary significantly during 2007-2011. In patients with lung cancer, PPV was 98% and NPV was 92%. In patients with colon cancer, PPV was 96% and NPV was 83%. CONCLUSIONS:An algorithm using stacking codes can identify tumor mutation testing in administrative claims data among patients with lung and colon cancer with a high degree of accuracy.

Gene-by-environment (G × E) interactions are important in explaining the missing heritability and understanding the causation of complex diseases, but a single, moderately sized study often has limited statistical power to detect such interactions. With the increasing need for integrating data and reporting results from multiple collaborative studies or sites, debate over choice between mega- versus meta-analysis continues. In principle, data from different sites can be integrated at the individual level into a "mega" data set, which can be fit by a joint "mega-analysis." Alternatively, analyses can be done at each site, and results across sites can be combined through a "meta-analysis" procedure without integrating individual level data across sites. Although mega-analysis has been advocated in several recent initiatives, meta-analysis has the advantages of simplicity and feasibility, and has recently led to several important findings in identifying main genetic effects. In this paper, we conducted empirical and simulation studies, using data from a G × E study of lung cancer, to compare the mega- and meta-analyses in four commonly used G × E analyses under the scenario that the number of studies is small and sample sizes of individual studies are relatively large. We compared the two data integration approaches in the context of fixed effect models and random effects models separately. Our investigations provide valuable insights in understanding the differences between mega- and meta-analyses in practice of combining small number of studies in identifying G × E interactions.