Faculty Bibliography

INTRODUCTION:Pemetrexed inhibits folate-dependent enzymes involved in pyrimidine and purine synthesis. Previous studies of genetic variation in these enzymes as predictors of pemetrexed efficacy have yielded inconsistent results. We investigated whether red blood cell (RBC) total folate, a phenotypic rather than genotypic, marker of cellular folate status was associated with the response to pemetrexed-based chemotherapy in advanced nonsquamous non-small-cell lung cancer (NSCLC). MATERIALS AND METHODS:We conducted a prospective cohort study of patients with stage IV nonsquamous NSCLC receiving first-line chemotherapy containing pemetrexed. The pretreatment RBC total folate level was quantified using liquid chromatography mass spectrometry. We then compared the objective response rate (ORR) between patients with RBC total folate concentrations greater than and less than an optimal cutoff value determined from the receiver operating characteristic curve. A logistic regression model was used to adjust for age, sex, and the use of bevacizumab. RESULTS:The ORR was 62% (32 of 52 patients). Receiver operating characteristic analysis was used to establish that a RBC total folate cutoff value of 364.6 nM optimally discriminated between pemetrexed responders and nonresponders. Patients with RBC total folate < 364.5 nM had an ORR of 27% compared with 71% for patients with RBC total folate > 364.5 nM (P = .01). This difference persisted after adjusting for age, sex, and the use of bevacizumab (odds ratio, 0.07; 95% confidence interval, 0.01-0.57; P = .01). CONCLUSION:A low pretreatment RBC total folate was associated with an inferior response to pemetrexed-based chemotherapy in stage IV nonsquamous NSCLC. Larger, multicenter studies are needed to validate RBC total folate as a predictive marker of pemetrexed response.

BACKGROUND:The annual incidence of pulmonary nodules is estimated at 1.57 million. Guidelines recommend using an initial assessment of nodule probability of malignancy (pCA). A previous study found that despite this recommendation, physicians did not follow guidelines. METHODS:Physician assessments (N = 337) and two previously validated risk model assessments of pretest probability of cancer were evaluated for performance in 337 patients with pulmonary nodules based on final diagnosis and compared. Physician-assessed pCA was categorized into low, intermediate, and high risk, and the next test ordered was evaluated. RESULTS:The prevalence of malignancy was 47% (n = 158) at 1 year. Physician-assessed pCA performed better than nodule prediction calculators (area under the curve, 0.85 vs 0.75; P < .001 and .78; P = .0001). Physicians did not follow indicated guidelines when selecting the next test in 61% of cases (n = 205). Despite recommendations for serial CT imaging in those with low pCA, 52% (n = 13) were managed more aggressively with PET imaging or biopsy; 12% (n = 3) underwent biopsy procedures for benign disease. Alternatively, in the high-risk category, the majority (n = 103 [75%]) were managed more conservatively. Stratified by diagnosis, 92% (n = 22) with benign disease underwent more conservative management with CT imaging (20%), PET scanning (15%), or biopsy (8%), although three had surgery (8%). CONCLUSIONS:Physician assessment as a means for predicting malignancy in pulmonary nodules is more accurate than previously validated nodule prediction calculators. Despite the accuracy of clinical intuition, physicians did not follow guideline-based recommendations when selecting the next diagnostic test. To provide optimal patient care, focus in the areas of guideline refinement, implementation, and dissemination is needed.

OBJECTIVES:Efficient use of nivolumab in non-small-cell lung cancer (NSCLC) has been limited by the lack of a definitive predictive biomarker. In patients with metastatic melanoma treated with ipilimumab, a pretreatment neutrophil-to-lymphocyte ratio (NLR)<5 has been associated with improved survival. This retrospective cohort study aimed to determine whether the pretreatment NLR was associated with outcomes in NSCLC patients treated with nivolumab. METHODS:We reviewed the medical records of all patients with previously treated advanced NSCLC who received nivolumab between March 2015 and March 2016 outside of a clinical trial at the University of Pennsylvania. Patients were dichotomized according to pretreatment NLR<5 vs. ≥5. Multivariable logistic regression and Cox proportional hazards models were used to assess the impact of pretreatment NLR on overall survival (OS), progression-free survival (PFS), and overall response rate (ORR). RESULTS:175 patients were treated. Median age was 68 (range, 33-88); 54% were female. Twenty-five percent of patients had an Eastern Cooperative Oncology Group Performance Status (ECOG PS) ≥2; 46% had received ≥2 prior systemic therapies. In multivariate analyses, pretreatment neutrophil-to-lymphocyte ratio (NLR) ≥5 was independently associated with inferior OS (median 5.5 vs. 8.4 months; HR 2.07, 95% CI 1.3-3.3; p=0.002) and inferior PFS (median 1.9 vs. 2.8 months; HR 1.43, 95% CI 1.02-2.0; p=0.04). CONCLUSIONS:In a cohort of patients with NSCLC treated with nivolumab in routine practice, pretreatment NLR≥5 was associated with inferior outcomes. It is unclear whether this marker is predictive or prognostic. Prospective studies are warranted to determine the utility of NLR in the context of other biomarkers of programmed death-1 (PD-1) therapy.

AIM/OBJECTIVE:We compared three statistical methods in selecting a panel of serum lipid biomarkers for mesothelioma and asbestos exposure. MATERIALS & METHODS/METHODS:Serum samples from mesothelioma, asbestos-exposed subjects and controls (40 per group) were analyzed. Three variable selection methods were considered: top-ranked predictors from univariate model, stepwise and least absolute shrinkage and selection operator. Crossed-validated area under the receiver operating characteristic curve was used to compare the prediction performance. RESULTS:Lipids with high crossed-validated area under the curve were identified. Lipid with mass-to-charge ratio of 372.31 was selected by all three methods comparing mesothelioma versus control. Lipids with mass-to-charge ratio of 1464.80 and 329.21 were selected by two models for asbestos exposure versus control. CONCLUSION/CONCLUSIONS:Different methods selected a similar set of serum lipids. Combining candidate biomarkers can improve prediction.

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.

Cancer treatment decisions are complex and may be challenging for patients, as multiple treatment options can often be reasonably considered. As a result, decisional support tools have been developed to assist patients in the decision-making process. A commonly used intervention to facilitate shared decision-making is a decision aid, which provides evidence-based outcomes information and guides patients towards choosing the treatment option that best aligns with their preferences and values. To ensure high quality, systematic frameworks and standards have been proposed for the development of an optimal aid for decision making. Studies have examined the impact of these tools on facilitating treatment decisions and improving decision-related outcomes. In radiation oncology, randomized controlled trials have demonstrated that decision aids have the potential to improve patient outcomes, including increased knowledge about treatment options and decreased decisional conflict with decision-making. This article provides an overview of the shared-decision making process and summarizes the development, validation, and implementation of decision aids as patient educational tools in radiation oncology. Finally, this article reviews the findings from decision aid studies in radiation oncology and offers various strategies to effectively implement shared decision-making into clinical practice.

The past century has witnessed a transformative shift in lung cancer from a rare reportable disease to the leading cause of cancer death among men and women worldwide. This historic shift reflects the increase in tobacco consumption worldwide, spurring public health efforts over the past several decades directed at tobacco cessation and control. Although most lung cancers are still diagnosed at a late stage, there have been significant advances in screening high-risk smokers, diagnostic modalities, and chemopreventive approaches. Improvements in surgery and radiation are advancing our ability to manage early-stage disease, particularly among patients considered unfit for traditional open resection. Arguably, the most dramatic progress has occurred on the therapeutic side, with the development of targeted and immune-based therapy over the past decade. This article reviews the major shifts in the lung cancer landscape over the past 100 years. Although many ongoing clinical challenges remain, this review will also highlight emerging molecular and imaging-based approaches that represent opportunities to transform the prevention, early detection, and treatment of lung cancer in the years ahead.

At least seven studies have suggested that microRNA levels in whole blood can be diagnostic for lung cancer. We conducted a large bi-institutional study to validate this. Qiagen® PAXgene™ Blood miRNA System was used to collect blood and extract RNA from it for 85 pathologic stage I-IV non-small cell lung cancer (NSCLC) cases and 76 clinically-relevant controls who had a benign pulmonary mass, or a high risk of developing lung cancer because of a history of cigarette smoking or age >60 years. Cases and controls were similar for age, gender, race, and blood hemoglobin and leukocyte but not platelet levels (0.23 and 0.26 million/μl, respectively; t test P = 0.01). Exiqon® MiRCURY™ microarrays were used to quantify microRNAs in RNA isolates. Quantification was also performed using Taqman™ microRNA reverse transcription (RT)-PCR assays for five microRNAs whose lung cancer-diagnostic potential had been suggested in seven published studies. Of the 1,941 human mature microRNAs detectable with the microarray platform, 598 (31%) were identified as expressed and reliably quantified among the study's subjects. However, none of the microRNAs was differentially expressed between cases and controls (P >0.05 at false discovery rate <5% in test using empirical Bayes-moderated t statistics). In classification analyses with leave-one-out internal cross-validation, cases and controls could be identified by microRNA expression with 47% and 50% accuracy with support vector machines and top-scoring pair methods, respectively. Cases and controls did not differ for RT-PCR-based measurements of any of the five microRNAs whose biomarker potential had been suggested by seven previous studies. Additionally, no difference for microRNA expression was noticed in microarray-based microRNA profiles of whole blood of 12 stage IA-IIIB NSCLC cases before and three-four weeks after tumor resection. These findings show that whole blood microRNA expression profiles lack diagnostic value for high-risk screening of NSCLC, though such value may exist for selective sub-groups of NSCLC and control populations.