Whole Exome Sequencing of Cell-Free DNA for Early Lung Cancer: A Pilot Study to Differentiate Benign From Malignant CT-Detected Pulmonary Lesions

Introduction: Indeterminate pulmonary lesions (IPL) detected by CT pose a significant clinical challenge, frequently necessitating long-term surveillance or biopsy for diagnosis. In this pilot investigation, we performed whole exome sequencing (WES) of plasma cell free (cfDNA) and matched germline DNA in patients with CT-detected pulmonary lesions to determine the feasibility of somatic cfDNA mutations to differentiate benign from malignant pulmonary nodules.Methods: 33 patients with a CT-detected pulmonary lesions were retrospectively enrolled (n = 16 with a benign nodule, n = 17 with a malignant nodule). Following isolation and amplification of plasma cfDNA and matched peripheral blood mononuclear cells (PBMC) from patient blood samples, WES of cfDNA and PBMC DNA was performed. After genomic alignment and filtering, we looked for lung-cancer associated driver mutations and next identified high-confidence somatic variants in both groups.Results: Somatic cfDNA mutations were observed in both groups, with the cancer group demonstrating more variants than the benign group (1083 ± 476 versus 553 ± 519, p < 0.0046). By selecting variants present in >2 cancer patients and not the benign group, we accurately identified 82% (14/17) of cancer patients.Conclusions: This study suggests a potential role for cfDNA for the early identification of lung cancer in patients with CT-detected pulmonary lesions. Importantly, a substantial number of somatic variants in healthy patients with benign pulmonary nodules were also found. Such “benign” variants, while largely unexplored to date, have widespread relevance to all liquid biopsies if cfDNA is to be used accurately for cancer detection

Climate Change and Local Public Health in the United States: Preparedness, Programs and Perceptions of Local Public Health Department Directors

While climate change is inherently a global problem, its public health impacts will be experienced most acutely at the local and regional level, with some jurisdictions likely to be more burdened than others. The public health infrastructure in the U.S. is organized largely as an interlocking set of public agencies at the federal, state and local level, with lead responsibility for each city or county often residing at the local level. To understand how directors of local public health departments view and are responding to climate change as a public health issue, we conducted a telephone survey with 133 randomly selected local health department directors, representing a 61% response rate. A majority of respondents perceived climate change to be a problem in their jurisdiction, a problem they viewed as likely to become more common or severe over the next 20 years. Only a small minority of respondents, however, had yet made climate change adaptation or prevention a top priority for their health department. This discrepancy between problem recognition and programmatic responses may be due, in part, to several factors: most respondents felt personnel in their health department–and other key stakeholders in their community–had a lack of knowledge about climate change; relatively few respondents felt their own health department, their state health department, or the Centers for Disease Control and Prevention had the necessary expertise to help them create an effective mitigation or adaptation plan for their jurisdiction; and most respondents felt that their health department needed additional funding, staff and staff training to respond effectively to climate change. These data make clear that climate change adaptation and prevention are not currently major activities at most health departments, and that most, if not all, local health departments will require assistance in making this transition. We conclude by making the case that, through their words and actions, local health departments and their staff can and should play a role in alerting members of their community about the prospect of public health impacts from climate change in their jurisdiction

Preliminary Evaluation of Biplane Correlation (BCI) Stereographic Imaging for Lung Nodule Detection

A biplane correlation (BCI) imaging system obtains images that can be viewed in stereo, thereby minimizing overlapping structures. This study investigated whether using stereoscopic visualization provides superior lung nodule detection compared to standard postero-anterior (PA) image display. Images were acquired at two oblique views of ±3° as well as at a standard PA position from 60 patients. Images were processed using optimal parameters and displayed on a stereoscopic display. The PA image was viewed in the standard format, while the oblique views were paired to provide a stereoscopic view of the subject. A preliminary observer study was performed with four radiologists who viewed and scored the PA image then viewed and scored the BCI stereoscopic image. The BCI stereoscopic viewing of lung nodules resulted in 71 % sensitivity and 0.31 positive predictive value (PPV) index compared to PA results of 86 % sensitivity and 0.26 PPV index. The sensitivity for lung nodule detection with the BCI stereoscopic system was reduced by 15 %; however, the total number of false positives reported was reduced by 35 % resulting in an improved PPV index of 20 %. The preliminary results indicate observer dependency in terms of relative advantage of either system in the detection of lung nodules, but overall equivalency of the two methods with promising potential for BCI as an adjunct diagnostic technique

Upregulation of complement proteins in lung cancer cells mediates tumor progression

IntroductionIn vivo, cancer cells respond to signals from the tumor microenvironment resulting in changes in expression of proteins that promote tumor progression and suppress anti-tumor immunity. This study employed an orthotopic immunocompetent model of lung cancer to define pathways that are altered in cancer cells recovered from tumors compared to cells grown in culture.MethodsStudies used four murine cell lines implanted into the lungs of syngeneic mice. Cancer cells were recovered using FACS, and transcriptional changes compared to cells grown in culture were determined by RNA-seq.ResultsChanges in interferon response, antigen presentation and cytokine signaling were observed in all tumors. In addition, we observed induction of the complement pathway. We previously demonstrated that activation of complement is critical for tumor progression in this model. Complement can play both a pro-tumorigenic role through production of anaphylatoxins, and an anti-tumorigenic role by promoting complement-mediated cell killing of cancer cells. While complement proteins are produced by the liver, expression of complement proteins by cancer cells has been described. Silencing cancer cell-specific C3 inhibited tumor growth In vivo. We hypothesized that induction of complement regulatory proteins was critical for blocking the anti-tumor effects of complement activation. Silencing complement regulatory proteins also inhibited tumor growth, with different regulatory proteins acting in a cell-specific manner.DiscussionBased on these data we propose that localized induction of complement in cancer cells is a common feature of lung tumors that promotes tumor progression, with induction of complement regulatory proteins protecting cells from complement mediated-cell killing

Identification of Potential Prognostic Biomarkers in Patients with Untreated, Advanced Pancreatic Cancer from a Phase III Trial (CALGB 80303)

Patients with advanced stage adenocarcinoma of the pancreas have a poor prognosis. The identification of prognostic and/or predictive biomarkers may help stratify patients so that therapy can be individualized

Pacific Symposium on Biocomputing 11:279-290(2006) FINDING DIAGNOSTIC BIOMARKERS IN PROTEOMIC SPECTRA

In seeking to find diagnostic biomarkers in proteomic spectra, two significant problems arise. First, not only is there noise in the measured intensity at each m/z value, but there is also noise in the measured m/z value itself. Second, the potential for overfitting is severe: it is easy to find features in the spectra that accurately discriminate disease states but have no biological meaning. We address these problems by developing and testing a series of steps for pre-processing proteomic spectra and extracting putatively meaningful features before presentation to feature selection and classification algorithms. These steps include an HMM-based latent spectrum extraction algorithm for fusing the information from multiple replicate spectra obtained from a single tissue sample, a simple algorithm for baseline correction based on a segmented convex hull, a peak identification and quantification algorithm, and a peak registration algorithm to align peaks from multiple tissue samples into common peak registers. We apply these steps to MALDI spectral data collected from normal and tumor lung tissue samples, and then compare the performance of feature selection with FDR followed by classification with an SVM, versus joint feature selection and classification with Bayesian sparse multinomial logistic regression (SMLR). The SMLR approach outperformed FDR+SVM, but both were effective in achieving good diagnostic accuracy with a small number of features. Some of the selected features have previously been investigated as clinical markers for lung cancer diagnosis; some of the remaining features are excellent candidates for further research. 1 Introduction an