Michael Yang

Low PIAS3 Expression in Malignant Mesothelioma Is Associated with Increased STAT3 Activation and Poor Patient Survival

Purpose: Deregulation of STAT3 activation is a hallmark of many cancer cells, and the underlying mechanisms are subject to intense investigation. We examined the extent of PIAS3 expression in mesothelioma cells and human tumor samples and determined the functional effects of PIAS3 expression on STAT3 signaling. Experimental design: We evaluated the expression of PIAS3 in mesothelioma tumors from patients and correlated the expression levels with the course of the disease. We also measured the effects of enhanced PIAS3 activity on STAT3 signaling, cellular growth, and viability in cultured mesothelioma cells. Results: Gene expression databases revealed that mesotheliomas have the lowest levels of PIAS3 transcripts among solid tumors. PIAS3 expression in human mesothelioma tumors is significantly correlated with overall survival intervals (P = 0.058). The high expression of PIAS3 is predictive of a favorable prognosis and decreases the probability of death within one year after diagnosis by 44%. PIAS3 expression is functionally linked to STAT3 activation in mesothelioma cell lines. STAT3 downregulation with siRNA or enhanced expression of PIAS3 both inhibited mesothelioma cell growth and induced apoptosis. Mesothelioma cells are sensitive to curcumin and respond by the induction of PIAS3. Corroborative evidence has been obtained from STAT3 inhibition experiments. Exposure of the cells to a peptide derived from the PIAS3 protein that interferes with STAT3 function resulted in apoptosis induction and the inhibition of cell growth. Conclusion: These results suggest that PIAS3 protein expression impacts survival in patients with mesothelioma and that PIAS3 activation could become a therapeutic strategy. Clin Cancer Res; 20(19); 5124–32. ©2014 AACR.

RET Mutation and Expression in Small-Cell Lung Cancer

Background: There is growing interest in defining the somatic mutations associated with small-cell lung cancer (SCLC). Unfortunately, a serious blockade to genomic analyses of this disease is a limited access to tumors because surgery is rarely performed. We used our clinical/pathologic database of SCLC patients to determine the availability of biopsy specimens that could be used for genomic studies and to identify tumors for initial oncogene analysis. Methods: DNA was extracted from six tumors, three primary and three metastatic, and analyzed by SEQUENOM platform technology. Results: Primary-resected tumor tissue represents less than 3% of all diagnostic specimens in this disease, highlighting the limited access to tissue sufficient for comprehensive genomic analyses. We identified an activating M918T RET somatic mutation in a metastatic SCLC tumor specimen. Bioinformatic search identified RET mutations in other SCLC studies. Stable overexpression of both mutant M918T and wild-type RET in two SCLC cell lines, H1048 and SW1271, activated ERK signaling, MYC expression, and increased cell proliferation, particularly by mutant RET. Stable cells became sensitized to the RET tyrosine kinase inhibitors, vandetanib and ponatinib. Further analysis of RET mRNA expression in SCLC revealed wide variability in both cells and tumors, and SCLC cells demonstrated significantly higher RET expression compared with adenocarcinoma lung cells. Conclusions: Our data suggest that a subpopulation of SCLC patients may derive benefit from tyrosine kinase inhibitors targeting RET. Coupled with the presence of RET fusion proteins in non-small-cell lung cancer, our data indicate an emerging role for RET in SCLC.

PIAS3 expression in squamous cell lung cancer is low and predicts overall survival

Unlike lung adenocarcinoma, little progress has been made in the treatment of squamous cell lung carcinoma (SCC). The Cancer Genome Atlas (TCGA) has recently reported that receptor tyrosine kinase signaling pathways are altered in 26% of SCC tumors, validating the importance of downstream Signal Transducers and Activators of Transcription 3 (STAT3) activity as a prime therapeutic target in this cancer. In the present report we examine the status of an endogenous inhibitor of STAT3, called Protein Inhibitor of Activated STAT3 (PIAS3), in SCC and its potential role in this disease. We examine PIAS3 expression in SCC tumors and cell lines by immunohistochemistry of a tissue microarray and western blotting. PIAS3 mRNA expression and survival data are analyzed in the TCGA data set. SCC cell lines are treated with curcumin to regulate PIAS3 expression and cell growth. PIAS3 protein expression is decreased in a majority of lung SCC tumors and cell lines. Analysis of PIAS3 mRNA transcript levels demonstrated that low PIAS3 levels predicted poor survival; Cox regression analysis revealed a hazard ratio of 0.57 (95% CI: 0.37–0.87), indicating a decrease in the risk of death by 43% for every unit elevation in PIAS3 gene expression. Curcumin treatment increased endogenous PIAS3 expression and decreased cell growth and viability in Calu‐1 cells, a model of SCC. Our results implicate PIAS3 loss in the pathology of lung SCC and raise the therapeutic possibility of upregulating PIAS3 expression as a single target that can suppress signaling from the multiple receptor tyrosine kinase receptors found to be amplified in SCC.

CD30 Is a Potential Therapeutic Target in Malignant Mesothelioma

CD30 is a cytokine receptor belonging to the TNF superfamily (TNFRSF8) that acts as a regulator of apoptosis. The presence of CD30 antigen is important in the diagnosis of Hodgkin disease and anaplastic large cell lymphoma. There have been sporadic reports of CD30 expression in nonlymphoid tumors, including malignant mesothelioma. Given the remarkable success of brentuximab vedotin, an antibody–drug conjugate directed against CD30 antigen, in lymphoid malignancies, we undertook a study to examine the incidence of CD30 in mesothelioma and to investigate the ability to target CD30 antigen in mesothelioma. Mesothelioma tumor specimens (N = 83) were examined for CD30 expression by IHC. Positive CD30 expression was noted in 13 mesothelioma specimens, primarily those of epithelial histology. There was no significant correlation of CD30 positivity with tumor grade, stage, or survival. Examination of four mesothelioma cell lines (H28, H2052, H2452, and 211H) for CD30 expression by both FACS analysis and confocal microscopy showed that CD30 antigen localized to the cell membrane. Brentuximab vedotin treatment of cultured mesothelioma cells produced a dose-dependent decrease in cell growth and viability at clinically relevant concentrations. Our studies validate the presence of CD30 antigen in a subgroup of epithelial-type mesothelioma tumors and indicate that selected mesothelioma patients may derive benefit from brentuximab vedotin treatment. Mol Cancer Ther; 14(3); 740–6. ©2015 AACR.

RICTOR amplification identifies a subgroup in small cell lung cancer and predicts response to drugs targeting mTOR

Small cell lung cancer (SCLC) is an aggressive cancer that represents ~15% of all lung cancers. Currently there are no targeted therapies to treat SCLC. Our genomic analysis of a metastatic SCLC cohort identified recurrent RICTOR amplification. Here, we examine the translational potential of this observation. RICTOR was the most frequently amplified gene observed (~14% patients), and co-amplified with FGF10 and IL7R on chromosome 5p13. RICTOR copy number variation correlated with RICTOR protein expression in SCLC cells. In parallel, cells with RICTOR copy number (CN) gain showed increased sensitivity to three mTOR inhibitors, AZD8055, AZD2014 and INK128 in cell growth assays, with AZD2014 demonstrating the best inhibition of downstream signaling. SCLC cells with RICTOR CN gain also migrated more rapidly in chemotaxis and scratch wound assays and were again more sensitive to mTOR inhibitors. The overall survival in SCLC patients with RICTOR amplification was significantly decreased (p = 0.021). Taken together, our results suggest that SCLC patients with RICTOR amplification may constitute a clinically important subgroup because of their potential response to mTORC1/2 inhibitors.

An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT

Purpose Distinguishing between benign granulmoas and adenocarcinomas is confounded by their similar visual appearance on routine CT scans. Unfortunately, owing to the inability to discriminate these lesions radigraphically, many patients with benign granulomas are subjected to unnecessary surgical wedge resections and biopsies for pathologic confirmation of cancer presence or absence. This suggests the need for improved computerized characterization of these nodules in order to distinguish between these two classes of lesions on CT scans. While there has been substantial interest in the use of textural analysis for radiomic characterization of lung nodules, relatively less work has been done in shape based characterization of lung nodules, particularly with respect to granulmoas and adenocarcinomas. The primary goal of this study is to evaluate the role of 3D shape features for discrimination of benign granulomas from malignant adenocarcinomas on lung CT images. Towards this end we present an integrated framework for segmentation, feature characterization and classification of these nodules on CT. Methods The nodule segmentation method starts with separation of lung regions from the surrounding lung anatomy. Next, the lung CT scans are projected into and represented in a three dimensional spectral embedding (SE) space, allowing for better determination of the boundaries of the nodule. This then enables the application of a gradient vector flow active contour (SEGvAC) model for nodule boundary extraction. A set of 24 shape features from both 2D slices and 3D surface of the segmented nodules are extracted, including features pertaining to the angularity, spiculation, elongation and nodule compactness. A feature selection scheme, PCA‐VIP, is employed to identify the most discriminating set of features to distinguish granulmoas from adenocarcinomas within a learning set of 82 patients. The features thus identified were then combined with a support vector machine classifier and independently validated on a distinct test set comprising 67 patients. The performance of the classifier for both of the training and validation cohorts was evaluated by the area under receiver characteristic curve (ROC). Results We used 82 and 67 studies from two different institutions respectively for training and independent validation of the model and the shape features. The Dice coefficient between automatically segmented nodules by SEGvAC and the manual delineations by expert radiologists (readers) was 0.84± 0.04 whereas inter‐reader segmentation agreement was 0.79± 0.12. We also identified a set of consistent features (Roughness, Convexity and Spherecity) that were found to be strongly correlated across both manual and automated nodule segmentations (R > 0.80, p < 0.0001) and capture the marginal smoothness and 3D compactness of the nodules. On the independent validation set of 67 studies our classifier yielded a ROC AUC of 0.72 and 0.64 for manually‐ and automatically segmented nodules respectively. On a subset of 20 studies, the AUCs for the two expert radiologists and 1 pulmonologist were found to be 0.82, 0.68 and 0.58 respectively. Conclusions The major finding of this study was that certain shape features appear to differentially express between granulomas and adenocarcinomas and thus computer extracted shape cues could be used to distinguish these radiographically similar pathologies.

Reciprocal expression of INSM1 and YAP1 defines subgroups in small cell lung cancer

The majority of small cell lung cancer (SCLC) patients demonstrate initial chemo-sensitivity, whereas a distinct subgroup of SCLC patients, termed chemo-refractory, do not respond to treatment. There is little understanding of how to distinguish these patients prior to disease treatment. Here we used gene expression profiling to stratify SCLC into subgroups and characterized a molecular phenotype that may identify, in part, chemo-refractive SCLC patients. Two subgroups of SCLC were identified in both cell lines and tumors by the reciprocal expression of two genes; INSM1, a neuroendocrine transcription factor, and YAP1, a key mediator of the Hippo pathway. The great majority of tumors expressed INSM1, which was prognostic for increased progression-free survival and associated with chemo-sensitivity in cell lines. YAP1 is expressed in a minority of SCLC tumors and was shown in cell lines to be downstream of the retinoblastoma protein (RB1) and associated with decreased drug sensitivity. RB1 expression in SCLC cell lines sensitizes them to CDK4/6 inhibitors. Wild-type RB1 mutation status, used as a surrogate marker of YAP1 expression, was prognostic for decreased patient survival and increased chemo-refractory tumor response. Thus, the reciprocal expression of INSM1 and YAP1 appears to stratify SCLC into distinct subgroups and may be useful, along with RB1 mutation status, to identify chemo-refractory SCLC patients.

Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography

Abstract. Differentiation between benign and malignant nodules is a problem encountered by radiologists when visualizing computed tomography (CT) scans. Adenocarcinomas and granulomas have a characteristic spiculated appearance and may be fluorodeoxyglucose avid, making them difficult to distinguish for human readers. In this retrospective study, we aimed to evaluate whether a combination of radiomic texture and shape features from noncontrast CT scans can enable discrimination between granulomas and adenocarcinomas. Our study is composed of CT scans of 195 patients from two institutions, one cohort for training (N  =  139) and the other (N  =  56) for independent validation. A set of 645 three-dimensional texture and 24 shape features were extracted from CT scans in the training cohort. Feature selection was employed to identify the most informative features using this set. The top ranked features were also assessed in terms of their stability and reproducibility across the training and testing cohorts and between scans of different slice thickness. Three different classifiers were constructed using the top ranked features identified from the training set. These classifiers were then validated on the test set and the best classifier (support vector machine) yielded an area under the receiver operating characteristic curve of 77.8%.

Prognostic potential of neutrophil-to-lymphocyte ratio and lymphocyte nadir in stage III non-small-cell lung cancer

AIM Studies have shown increased pretreatment neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios to be predictive of survival in various cancers. Our aim was to evaluate the prognostic role of such inflammatory markers in non-small-cell lung cancer (NSCLC). METHODS One hundred and sixty-three patients with stage III NSCLC who received definitive treatment were included. Survival analysis was performed using Kaplan-Meier method. Hazard ratios for overall and recurrence-free survival were estimated using Cox proportional hazards model. RESULTS Both neutrophil-to-lymphocyte >Q75 (4.5) and lymphocyte nadir values <Q25 (0.25) and their unified values were associated with 90% increased overall mortality risk (p = 0.040) and a nonsignificant 50% decreased recurrence-free survival risk. CONCLUSION Our exploratory analysis showed markers of systemic inflammation predicted survival outcomes in advanced NSCLC. Future prospective data analyses are needed to confirm this potential.

Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas

Adenocarcinomas and active granulomas can both have a spiculated appearance on computed tomography (CT) and both are often fluorodeoxyglucose (FDG) avid on positron emission tomography (PET) scan, making them difficult to distinguish. Consequently, patients with benign granulomas are often subjected to invasive surgical biopsies or resections. In this study, quantitative vessel tortuosity (QVT), a novel CT imaging biomarker to distinguish between benign granulomas and adenocarcinomas on routine non-contrast lung CT scans is introduced. Our study comprised of CT scans of 290 patients from two different institutions, one cohort for training (N = 145) and the other (N = 145) for independent validation. In conjunction with a machine learning classifier, the top informative and stable QVT features yielded an area under receiver operating characteristic curve (ROC AUC) of 0.85 in the independent validation set. On the same cohort, the corresponding AUCs for two human experts including a radiologist and a pulmonologist were found to be 0.61 and 0.60, respectively. QVT features also outperformed well known shape and textural radiomic features which had a maximum AUC of 0.73 (p-value = 0.002), as well as features learned using a convolutional neural network AUC = 0.76 (p-value = 0.028). Our results suggest that QVT features could potentially serve as a non-invasive imaging biomarker to distinguish granulomas from adenocarcinomas on non-contrast CT scans.