Publications by authors named "Adi Gazdar"

309 Publications

Cell-autonomous immune gene expression is repressed in pulmonary neuroendocrine cells and small cell lung cancer.

Commun Biol 2021 Mar 9;4(1):314. Epub 2021 Mar 9.

Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA.

Small cell lung cancer (SCLC) is classified as a high-grade neuroendocrine (NE) tumor, but a subset of SCLC has been termed "variant" due to the loss of NE characteristics. In this study, we computed NE scores for patient-derived SCLC cell lines and xenografts, as well as human tumors. We aligned NE properties with transcription factor-defined molecular subtypes. Then we investigated the different immune phenotypes associated with high and low NE scores. We found repression of immune response genes as a shared feature between classic SCLC and pulmonary neuroendocrine cells of the healthy lung. With loss of NE fate, variant SCLC tumors regain cell-autonomous immune gene expression and exhibit higher tumor-immune interactions. Pan-cancer analysis revealed this NE lineage-specific immune phenotype in other cancers. Additionally, we observed MHC I re-expression in SCLC upon development of chemoresistance. These findings may help guide the design of treatment regimens in SCLC.
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http://dx.doi.org/10.1038/s42003-021-01842-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7943563PMC
March 2021

Contribution of a Blood-Based Protein Biomarker Panel to the Classification of Indeterminate Pulmonary Nodules.

J Thorac Oncol 2021 02 31;16(2):228-236. Epub 2020 Oct 31.

McCombs Institute for the Early Detection and Treatment of Cancer, University of Texas MD Anderson Cancer Center, Houston, Texas.

Rationale: The workup and longitudinal monitoring for subjects presenting with pulmonary nodules is a pressing clinical problem. A blood-based biomarker panel potentially has utility for identifying subjects at higher risk for harboring a malignant nodule for whom additional workup would be indicated or subjects at reduced risk for whom imaging-based follow-up would be indicated.

Objectives: To assess whether a previously described four-protein biomarker panel, reported to improve assessment of lung cancer risk compared with a smoking-based lung cancer risk model, can provide discrimination between benign and malignant indeterminate pulmonary nodules.

Methods: A previously validated multiplex enzyme-linked immunoassay was performed on matched case and control samples from each cohort.

Measurements: The biomarker panel was tested in two case-control cohorts of patients presenting with indeterminate pulmonary nodules at the University of Pittsburgh Medical Center and the University of Texas Southwestern.

Main Results: In both cohorts, the biomarker panel resulted in improved prediction of lung cancer risk over a model on the basis of nodule size alone. Of particular note, the addition of the marker panel to nodule size greatly improved sensitivity at a high specificity in both cohorts.

Conclusions: A four-marker biomarker panel, previously validated to improve lung cancer risk prediction, was found to also have utility in distinguishing benign from malignant indeterminate pulmonary nodules. Its performance in improving sensitivity at a high specificity indicates potential utility of the marker panel in assessing likelihood of malignancy in otherwise indeterminate nodules.
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http://dx.doi.org/10.1016/j.jtho.2020.09.024DOI Listing
February 2021

Guanosine triphosphate links MYC-dependent metabolic and ribosome programs in small-cell lung cancer.

J Clin Invest 2021 Jan;131(1)

Children's Medical Center Research Institute.

MYC stimulates both metabolism and protein synthesis, but how cells coordinate these complementary programs is unknown. Previous work reported that, in a subset of small-cell lung cancer (SCLC) cell lines, MYC activates guanosine triphosphate (GTP) synthesis and results in sensitivity to inhibitors of the GTP synthesis enzyme inosine monophosphate dehydrogenase (IMPDH). Here, we demonstrated that primary MYChi human SCLC tumors also contained abundant guanosine nucleotides. We also found that elevated MYC in SCLCs with acquired chemoresistance rendered these otherwise recalcitrant tumors dependent on IMPDH. Unexpectedly, our data indicated that IMPDH linked the metabolic and protein synthesis outputs of oncogenic MYC. Coexpression analysis placed IMPDH within the MYC-driven ribosome program, and GTP depletion prevented RNA polymerase I (Pol I) from localizing to ribosomal DNA. Furthermore, the GTPases GPN1 and GPN3 were upregulated by MYC and directed Pol I to ribosomal DNA. Constitutively GTP-bound GPN1/3 mutants mitigated the effect of GTP depletion on Pol I, protecting chemoresistant SCLC cells from IMPDH inhibition. GTP therefore functioned as a metabolic gate tethering MYC-dependent ribosome biogenesis to nucleotide sufficiency through GPN1 and GPN3. IMPDH dependence is a targetable vulnerability in chemoresistant MYChi SCLC.
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http://dx.doi.org/10.1172/JCI139929DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773395PMC
January 2021

MAX Functions as a Tumor Suppressor and Rewires Metabolism in Small Cell Lung Cancer.

Cancer Cell 2020 07 28;38(1):97-114.e7. Epub 2020 May 28.

Division of Human Biology, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N, Seattle, WA 98109, USA; Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA. Electronic address:

Small cell lung cancer (SCLC) is a highly aggressive and lethal neoplasm. To identify candidate tumor suppressors we applied CRISPR/Cas9 gene inactivation screens to a cellular model of early-stage SCLC. Among the top hits was MAX, the obligate heterodimerization partner for MYC family proteins that is mutated in human SCLC. Max deletion increases growth and transformation in cells and dramatically accelerates SCLC progression in an Rb1/Trp53-deleted mouse model. In contrast, deletion of Max abrogates tumorigenesis in MYCL-overexpressing SCLC. Max deletion in SCLC resulted in derepression of metabolic genes involved in serine and one-carbon metabolism. By increasing serine biosynthesis, Max-deleted cells exhibit resistance to serine depletion. Thus, Max loss results in metabolic rewiring and context-specific tumor suppression.
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http://dx.doi.org/10.1016/j.ccell.2020.04.016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7363581PMC
July 2020

SV40 and human mesothelioma.

Transl Lung Cancer Res 2020 Feb;9(Suppl 1):S47-S59

Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA.

Simian virus 40 (SV40) is a DNA tumor virus capable of infecting and transforming human mesothelial (HM) cells . Hamsters injected intracardially to expose most tissue types to SV40 preferentially develop mesotheliomas. In humans, asbestos is the main cause of mesothelioma, and asbestos and SV40 are co-carcinogens in transforming HM cells in tissue culture and in causing mesothelioma in hamsters. Laser microdissection experiments conducted in the laboratory of Adi Gazdar demonstrated that SV40 was present specifically in the malignant mesothelioma cells and not in nearby stromal cells. Further experiments demonstrated that SV40 remains episomal in HM cells and astrocytes because of the production of a long antisense RNA that represses viral capsid protein production. Thus, the potent SV40 oncoprotein, T-antigen (Tag), is expressed, but because the capsid proteins are not produced, the cells are not lysed and, instead, become transformed. Together this evidence suggests that SV40 may contribute to the development of mesotheliomas in humans. However, epidemiological evidence to support this hypothesis is lacking. This chapter also summarizes the introduction of SV40, a monkey virus, into the human population as an unrecognized contaminant of early poliovaccines. In addition to mesotheliomas, SV40 now is linked with brain cancers, osteosarcomas, and lymphomas in humans. Explanations are provided for the apparent geographic variations in SV40 prevalence and for controversies about the role of SV40 in human cancer.
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http://dx.doi.org/10.21037/tlcr.2020.02.03DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7082262PMC
February 2020

Correction: LCE: an open web portal to explore gene expression and clinical associations in lung cancer.

Oncogene 2020 Jan;39(3):718-719

Quantitative Biomedical Research Center, Department of Clinical Sciences, UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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http://dx.doi.org/10.1038/s41388-019-1000-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7608478PMC
January 2020

ClickGene: an open cloud-based platform for big pan-cancer data genome-wide association study, visualization and exploration.

BioData Min 2019 26;12:12. Epub 2019 Jun 26.

1School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072 China.

Tremendous amount of whole-genome sequencing data have been provided by large consortium projects such as TCGA (The Cancer Genome Atlas), COSMIC and so on, which creates incredible opportunities for functional gene research and cancer associated mechanism uncovering. While the existing web servers are valuable and widely used, many whole genome analysis functions urgently needed by experimental biologists are still not adequately addressed. A cloud-based platform, named CG (ClickGene), therefore, was developed for DIY analyzing of user's private in-house data or public genome data without any requirement of software installation or system configuration. CG platform provides key interactive and customized functions including Bee-swarm plot, linear regression analyses, Mountain plot, Directional Manhattan plot, Deflection plot and Volcano plot. Using these tools, global profiling or individual gene distributions for expression and copy number variation (CNV) analyses can be generated by only mouse button clicking. The easy accessibility of such comprehensive pan-cancer genome analysis greatly facilitates data mining in wide research areas, such as therapeutic discovery process. Therefore, it fills in the gaps between big cancer genomics data and the delivery of integrated knowledge to end-users, thus helping unleash the value of the current data resources. More importantly, unlike other R-based web platforms, Dubbo, a cloud distributed service governance framework for 'big data' stream global transferring, was used to develop CG platform. After being developed, CG is run on an independent cloud-server, which ensures its steady global accessibility. More than 2 years running history of CG proved that advanced plots for hundreds of whole-genome data can be created through it within seconds by end-users anytime and anywhere. CG is available at http://www.clickgenome.org/.
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http://dx.doi.org/10.1186/s13040-019-0202-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6595587PMC
June 2019

Comparison of four DLL3 antibodies performance in high grade neuroendocrine lung tumor samples and cell cultures.

Diagn Pathol 2019 May 20;14(1):47. Epub 2019 May 20.

Diagnostic and Research Center for Molecular BioMedicine, Diagnostic and Research Institute of Pathology, Medical University of Graz, Neue Stiftingtalstrasse 6, 8010, Graz, Austria.

Background: Small cell lung cancer (SCLC) is usually diagnosed in the advanced stage. It has a very poor prognosis, with no advancements in therapy in the last few decades. A recent phase 1 clinical study, using an antibody-drug conjugate directed against DLL3, showed promising results. A prerequisite for this therapy is an immunohistochemical test for DLL3 expression. The antibody used in the clinical trial was bound to a specific platform, which is not available in all pathology laboratories. In this study, the expression of DLL3 was analyzed using different DLL3 antibodies in high-grade neuroendocrine tumors of the lung and cell cultures. Additionally, correlation of DLL3 expression with Rb1 loss and TP53 mutation was evaluated.

Methods: The study cohort consisted of surgically resected cases, 24 SCLC and 29 large cell neuroendocrine carcinoma (LCNEC), from which tissue microarrays (TMAs) were constructed. The validation cohort included 46 SCLC samples, mostly small biopsies. Additionally, well-characterized SCLC cell lines were used. Immunohistochemical analysis was performed using four different DLL3 antibodies, as well as TP53 and Rb1 antibodies. Expression was evaluated microscopically and manually scored.

Results: The comparison of all DLL3 antibodies showed poor results for the overall agreement, as well as positive and negative agreement. Differences were observed regardless of the applied cut-off values and the tumor type. The antibody used in the clinical trial was the only which always positively stained the tumor cells obtained from cell cultures with known DLL3 expression and was negative on cells that did not express DLL3. There was no correlation between p53 and DLL3 expression in SCLC and LCNEC. RB1 loss in SCLC showed statistical significant correlation with the DLL3 positivity (p = 0.037), while no correlation was found in LCNEC.

Conclusion: The DLL3 antibody used in the clinical trial demonstrated superiority in the detection of DLL3 expression. Cell cultures, which can be used for DLL3 antibodies as positive and negative probes, were established. Evidence of DLL3 expression in high proportions of patients with LCNEC might provide basis for studies of new therapy options in this group of patients.
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http://dx.doi.org/10.1186/s13000-019-0827-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6528329PMC
May 2019

Development and Validation of a Pathology Image Analysis-based Predictive Model for Lung Adenocarcinoma Prognosis - A Multi-cohort Study.

Sci Rep 2019 05 3;9(1):6886. Epub 2019 May 3.

Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, 75390, USA.

Prediction of disease prognosis is essential for improving cancer patient care. Previously, we have demonstrated the feasibility of using quantitative morphological features of tumor pathology images to predict the prognosis of lung cancer patients in a single cohort. In this study, we developed and validated a pathology image-based predictive model for the prognosis of lung adenocarcinoma (ADC) patients across multiple independent cohorts. Using quantitative pathology image analysis, we extracted morphological features from H&E stained sections of formalin fixed paraffin embedded (FFPE) tumor tissues. A prediction model for patient prognosis was developed using tumor tissue pathology images from a cohort of 91 stage I lung ADC patients from the Chinese Academy of Medical Sciences (CAMS), and validated in ADC patients from the National Lung Screening Trial (NLST), and the UT Special Program of Research Excellence (SPORE) cohort. The morphological features that are associated with patient survival in the training dataset from the CAMS cohort were used to develop a prognostic model, which was independently validated in both the NLST (n = 185) and the SPORE (n = 111) cohorts. The association between predicted risk and overall survival was significant for both the NLST (Hazard Ratio (HR) = 2.20, pv = 0.01) and the SPORE cohorts (HR = 2.15 and pv = 0.044), respectively, after adjusting for key clinical variables. Furthermore, the model also predicted the prognosis of patients with stage I ADC in both the NLST (n = 123, pv = 0.0089) and SPORE (n = 68, pv = 0.032) cohorts. The results indicate that the pathology image-based model predicts the prognosis of ADC patients across independent cohorts.
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http://dx.doi.org/10.1038/s41598-019-42845-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6499884PMC
May 2019

Molecular subtypes of small cell lung cancer: a synthesis of human and mouse model data.

Nat Rev Cancer 2019 05;19(5):289-297

University of Texas Southwestern Medical Center, Dallas, TX, USA.

Small cell lung cancer (SCLC) is an exceptionally lethal malignancy for which more effective therapies are urgently needed. Several lines of evidence, from SCLC primary human tumours, patient-derived xenografts, cancer cell lines and genetically engineered mouse models, appear to be converging on a new model of SCLC subtypes defined by differential expression of four key transcription regulators: achaete-scute homologue 1 (ASCL1; also known as ASH1), neurogenic differentiation factor 1 (NeuroD1), yes-associated protein 1 (YAP1) and POU class 2 homeobox 3 (POU2F3). In this Perspectives article, we review and synthesize these recent lines of evidence and propose a working nomenclature for SCLC subtypes defined by relative expression of these four factors. Defining the unique therapeutic vulnerabilities of these subtypes of SCLC should help to focus and accelerate therapeutic research, leading to rationally targeted approaches that may ultimately improve clinical outcomes for patients with this disease.
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http://dx.doi.org/10.1038/s41568-019-0133-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6538259PMC
May 2019

Small cell lung cancers made from scratch.

J Exp Med 2019 03 13;216(3):476-478. Epub 2019 Feb 13.

Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, TX

In this issue of , Chen et al. (https://doi.org/10.1084/jem.20181155) describe a new approach for the transformation of human pluripotent embryonic stem cells (hESCs) into neuroendocrine (NE) tumors of the lung closely resembling human small cell lung cancer (SCLC). Another recent study uses a different method to transform fully differentiated normal human cells into high-grade NE tumors (Park et al. 2018. https://doi.org/10.1126/science.aat5749). These approaches and their models provide important new resources for developing diagnostic, preventative, and therapeutic approaches for high-grade NE tumors.
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http://dx.doi.org/10.1084/jem.20182216DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6400528PMC
March 2019

LCE: an open web portal to explore gene expression and clinical associations in lung cancer.

Oncogene 2019 04 7;38(14):2551-2564. Epub 2018 Dec 7.

Quantitative Biomedical Research Center, Department of Clinical Sciences, UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.

We constructed a lung cancer-specific database housing expression data and clinical data from over 6700 patients in 56 studies. Expression data from 23 genome-wide platforms were carefully processed and quality controlled, whereas clinical data were standardized and rigorously curated. Empowered by this lung cancer database, we created an open access web resource-the Lung Cancer Explorer (LCE), which enables researchers and clinicians to explore these data and perform analyses. Users can perform meta-analyses on LCE to gain a quick overview of the results on tumor vs non-malignant tissue (normal) differential gene expression and expression-survival association. Individual dataset-based survival analysis, comparative analysis, and correlation analysis are also provided with flexible options to allow for customized analyses from the user.
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http://dx.doi.org/10.1038/s41388-018-0588-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6477796PMC
April 2019

A quantitative method for assessing smoke associated molecular damage in lung cancers.

Transl Lung Cancer Res 2018 Aug;7(4):439-449

Hamon Center for Therapeutic Oncology Research, UT Southwestern Medical Center, Dallas, TX, USA.

Background: While tobacco exposure is the cause of the vast majority of lung cancers, an important percentage arise in lifetime never smokers. Documenting the precise extent of tobacco induced molecular changes may be of importance. Also, the contribution of environmental tobacco smoke (ETS) is difficult to assess.

Methods: We developed and validated a quantitative method to assess the extent of tobacco related molecular damage by combing the most characteristic changes associated with tobacco smoke, the tumor mutation burden (TMB) and type of molecular changes present in lung cancers. Using maximum entropy (MaxEnt) as a classifier, we developed a F score. F score values >0 were considered to show evidence of tobacco related molecular damage, while values ≤0 were considered to lack evidence of tobacco related molecular damage. Compared to the stated patient tobacco exposure histories, the F scores had sensitivity, specificity and accuracy values of 85-87%. Using this method, we analyzed public data sets of lung adenocarcinoma (LUAD), lung squamous cell (LUSC) and small cell lung cancer (SCLC).

Results: Less than 10% of LUSCs and SCLCs had negative F scores, while 27% to 35% of LUADs had positive scores. The F score showed a highly significant downward trend when LUADs were subdivided into the following categories: ever, reformed ≤15 years, reformed >15 years and never smokers. Most of the examined bronchial carcinoids (a lung cancer type not associated with smoke exposure) had negative F scores. In addition, most LUADs with EGFR mutations had negative F scores, while almost all with KRAS mutations had positive scores.

Conclusions: We have established and validated a quantitative assay that will be of use in assessing the presence and degree of smoke associated molecular damage in lung cancers arising in ever and never smokers.
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http://dx.doi.org/10.21037/tlcr.2018.07.01DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6131178PMC
August 2018

Preface: lung cancer in never smokers.

Authors:
Adi F Gazdar

Transl Lung Cancer Res 2018 Aug;7(4):437-438

UT Southwestern Medical Center, Dallas, TX, USA.

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http://dx.doi.org/10.21037/tlcr.2018.06.06DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6131179PMC
August 2018

Loss Drives Small Cell Lung Cancer and Increases Sensitivity to HDAC Inhibition.

Cancer Discov 2018 11 4;8(11):1422-1437. Epub 2018 Sep 4.

Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington.

, encoding an acetyltransferase, is among the most frequently mutated genes in small cell lung cancer (SCLC), a deadly neuroendocrine tumor type. We report acceleration of SCLC upon inactivation in an autochthonous mouse model. Extending these observations beyond the lung, broad deletion in mouse neuroendocrine cells cooperated with loss to promote neuroendocrine thyroid and pituitary carcinomas. Gene expression analyses showed that loss results in reduced expression of tight junction and cell adhesion genes, including , across neuroendocrine tumor types, whereas suppression of promoted transformation in SCLC. and other adhesion genes exhibited reduced histone acetylation with inactivation. Treatment with the histone deacetylase (HDAC) inhibitor Pracinostat increased histone acetylation and restored CDH1 expression. In addition, a subset of -deficient SCLC exhibited exceptional responses to Pracinostat Thus, CREBBP acts as a potent tumor suppressor in SCLC, and inactivation of CREBBP enhances responses to a targeted therapy. Our findings demonstrate that CREBBP loss in SCLC reduces histone acetylation and transcription of cellular adhesion genes, while driving tumorigenesis. These effects can be partially restored by HDAC inhibition, which exhibited enhanced effectiveness in -deleted tumors. These data provide a rationale for selectively treating -mutant SCLC with HDAC inhibitors. .
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http://dx.doi.org/10.1158/2159-8290.CD-18-0385DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6294438PMC
November 2018

Inosine Monophosphate Dehydrogenase Dependence in a Subset of Small Cell Lung Cancers.

Cell Metab 2018 09 28;28(3):369-382.e5. Epub 2018 Jun 28.

Children's Medical Center Research Institute, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; Department of Pediatrics and Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA. Electronic address:

Small cell lung cancer (SCLC) is a rapidly lethal disease with few therapeutic options. We studied metabolic heterogeneity in SCLC to identify subtype-selective vulnerabilities. Metabolomics in SCLC cell lines identified two groups correlating with high or low expression of the Achaete-scute homolog-1 (ASCL1) transcription factor (ASCL1 and ASCL1), a lineage oncogene. Guanosine nucleotides were elevated in ASCL1 cells and tumors from genetically engineered mice. ASCL1 tumors abundantly express the guanosine biosynthetic enzymes inosine monophosphate dehydrogenase-1 and -2 (IMPDH1 and IMPDH2). These enzymes are transcriptional targets of MYC, which is selectively overexpressed in ASCL1 SCLC. IMPDH inhibition reduced RNA polymerase I-dependent expression of pre-ribosomal RNA and potently suppressed ASCL1 cell growth in culture, selectively reduced growth of ASCL1 xenografts, and combined with chemotherapy to improve survival in genetic mouse models of ASCL1/MYC SCLC. The data define an SCLC subtype-selective vulnerability related to dependence on de novo guanosine nucleotide synthesis.
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http://dx.doi.org/10.1016/j.cmet.2018.06.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6125205PMC
September 2018

Retraction notice to "Sun exposure related methylation in malignant and non-malignant skin lesions" [Cancer Letters 245/1-2 (2007) 112-120].

Cancer Lett 2018 Sep;432:272

Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center at Dallas, 6000 Harry Hines Boulevard, Dallas, Texas75390-8593, USA; Departments of Pathology, University of Texas Southwestern Medical Center, Dallas, TX75390, USA.

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http://dx.doi.org/10.1016/j.canlet.2018.07.010DOI Listing
September 2018

Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome.

Sci Rep 2018 Jul 10;8(1):10393. Epub 2018 Jul 10.

Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, 75390, USA.

Pathology images capture tumor histomorphological details in high resolution. However, manual detection and characterization of tumor regions in pathology images is labor intensive and subjective. Using a deep convolutional neural network (CNN), we developed an automated tumor region recognition system for lung cancer pathology images. From the identified tumor regions, we extracted 22 well-defined shape and boundary features and found that 15 of them were significantly associated with patient survival outcome in lung adenocarcinoma patients from the National Lung Screening Trial. A tumor region shape-based prognostic model was developed and validated in an independent patient cohort (n = 389). The predicted high-risk group had significantly worse survival than the low-risk group (p value = 0.0029). Predicted risk group serves as an independent prognostic factor (high-risk vs. low-risk, hazard ratio = 2.25, 95% CI 1.34-3.77, p value = 0.0022) after adjusting for age, gender, smoking status, and stage. This study provides new insights into the relationship between tumor shape and patient prognosis.
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http://dx.doi.org/10.1038/s41598-018-27707-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6039531PMC
July 2018

A Bayesian hidden Potts mixture model for analyzing lung cancer pathology images.

Biostatistics 2019 10;20(4):565-581

Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, TX, USA.

Digital pathology imaging of tumor tissues, which captures histological details in high resolution, is fast becoming a routine clinical procedure. Recent developments in deep-learning methods have enabled the identification, characterization, and classification of individual cells from pathology images analysis at a large scale. This creates new opportunities to study the spatial patterns of and interactions among different types of cells. Reliable statistical approaches to modeling such spatial patterns and interactions can provide insight into tumor progression and shed light on the biological mechanisms of cancer. In this article, we consider the problem of modeling a pathology image with irregular locations of three different types of cells: lymphocyte, stromal, and tumor cells. We propose a novel Bayesian hierarchical model, which incorporates a hidden Potts model to project the irregularly distributed cells to a square lattice and a Markov random field prior model to identify regions in a heterogeneous pathology image. The model allows us to quantify the interactions between different types of cells, some of which are clinically meaningful. We use Markov chain Monte Carlo sampling techniques, combined with a double Metropolis-Hastings algorithm, in order to simulate samples approximately from a distribution with an intractable normalizing constant. The proposed model was applied to the pathology images of $205$ lung cancer patients from the National Lung Screening trial, and the results show that the interaction strength between tumor and stromal cells predicts patient prognosis (P = $0.005$). This statistical methodology provides a new perspective for understanding the role of cell-cell interactions in cancer progression.
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http://dx.doi.org/10.1093/biostatistics/kxy019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6797059PMC
October 2019

Main bronchus location is a predictor for metastasis and prognosis in lung adenocarcinoma: A large cohort analysis.

Lung Cancer 2018 06 19;120:22-26. Epub 2018 Mar 19.

Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA; Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, 75390, USA; Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, 75390, USA. Electronic address:

Objectives: In the literature, inconsistent associations between the primary locations of lung adenocarcinomas (ADCs) with patient prognosis have been reported, due to varying definitions for central and peripheral locations. In this study, we investigated the clinical characteristics and prognoses of ADCs located in the main bronchus.

Methods: A total of 397,189 lung ADCs registered from 2004 to 2013 in the National Cancer Database (NCDB) were extracted and divided into main bronchus-located ADCs (2.5%, N = 10,111) and non-main bronchus ADCs (97.5%, N = 387,078). The ADCs located in the main bronchus and those not in the main bronchus were compared in terms of patient prognosis, lymph node involvement, distant metastases and other clinical features, including rate of curative-intent resection, histologic grade, and stage.

Results: ADCs located in the main bronchus had significantly worse patient survival than those in the non-main bronchus, both for all patients (HR = 1.82, 95% CI 1.78-1.86) and for those undergoing curative-intent resection (HR = 2.49, 95% CI 2.23-2.78). Furthermore, ADCs located in the main bronchus had a significantly higher rate of lymph node involvement and distant metastasis than those not in the main bronchus, when stratified by tumor size (trend test, p < e). Multivariate analysis of overall survival showed that main bronchus location is a prognostic factor (HR = 1.15, 95% CI 1.08-1.23) independent of other clinical factors.

Conclusions: Main bronchus location is an independent predictor for metastasis and worse outcomes irrespective of stage and treatment. Tumor primary location might be considered in prognostication and treatment planning.
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http://dx.doi.org/10.1016/j.lungcan.2018.03.011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7678407PMC
June 2018

Chemistry-First Approach for Nomination of Personalized Treatment in Lung Cancer.

Cell 2018 05 19;173(4):864-878.e29. Epub 2018 Apr 19.

Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX 77030, USA.

Diversity in the genetic lesions that cause cancer is extreme. In consequence, a pressing challenge is the development of drugs that target patient-specific disease mechanisms. To address this challenge, we employed a chemistry-first discovery paradigm for de novo identification of druggable targets linked to robust patient selection hypotheses. In particular, a 200,000 compound diversity-oriented chemical library was profiled across a heavily annotated test-bed of >100 cellular models representative of the diverse and characteristic somatic lesions for lung cancer. This approach led to the delineation of 171 chemical-genetic associations, shedding light on the targetability of mechanistic vulnerabilities corresponding to a range of oncogenotypes present in patient populations lacking effective therapy. Chemically addressable addictions to ciliogenesis in TTC21B mutants and GLUT8-dependent serine biosynthesis in KRAS/KEAP1 double mutants are prominent examples. These observations indicate a wealth of actionable opportunities within the complex molecular etiology of cancer.
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http://dx.doi.org/10.1016/j.cell.2018.03.028DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5935540PMC
May 2018

Dysregulation of fibulin-5 and matrix metalloproteases in epithelial ovarian cancer.

Oncotarget 2018 Mar 14;9(18):14251-14267. Epub 2018 Feb 14.

Department of Obstetrics and Gynecology, Green Center for Reproductive Biology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.

Fibulin 5 (FBLN5) is an extracellular matrix glycoprotein that suppresses matrix metalloprotease 9 (MMP-9), angiogenesis and epithelial cell motility. Here, we investigated the regulation and function of in epithelial ovarian cancer (EOC). FBLN5 mRNA was down-regulated 5-fold in EOC relative to benign ovary. Not surprisingly, mRNA and enzyme activity were increased significantly, and inversely correlated with gene expression. FBLN5 degradation products of 52.8 and 41.3 kDa were increased substantially in EOC. We identified two candidate proteases (serine elastase and MMP-7, but not MMP-9) that cleave FBLN5. MMP-7, but not neutrophil elastase, gene expression was increased dramatically in EOC. Recombinant FBLN5 significantly inhibited adhesion of EOC cells to both laminin and collagen I. Finally, using immunohistochemistry, we found immunoreactive FBLN5 within tumor macrophages throughout human EOC tumors. This work indicates that FBLN5 is degraded in EOC most likely by proteases enriched in macrophages of the tumor microenvironment. Proteolysis of FBLN5 serves as a mechanism to promote cell adhesion and local metastasis of ovarian cancer cells. Promotion of a stable ECM with intact FBLN5 in the tumor matrix may serve as a novel therapeutic adjunct to prevent spread of ovarian cancer.
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http://dx.doi.org/10.18632/oncotarget.24484DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5865667PMC
March 2018

Small cell lung cancer tumors and preclinical models display heterogeneity of neuroendocrine phenotypes.

Transl Lung Cancer Res 2018 Feb;7(1):32-49

Hamon Center for Therapeutic Oncology Research, UT Southwestern Medical Center, Dallas, TX, USA.

Background: Small cell lung cancer (SCLC) is a deadly, high grade neuroendocrine (NE) tumor without recognized morphologic heterogeneity. However, over 30 years ago we described a SCLC subtype with "variant" morphology which did not express some NE markers and exhibited more aggressive growth.

Methods: To quantitate NE properties of SCLCs, we developed a 50-gene expression-based NE score that could be applied to human SCLC tumors and cell lines, and genetically engineered mouse (GEM) models. We identified high and low NE subtypes of SCLC in all of our sample types, and characterized their properties.

Results: We found that 16% of human SCLC tumors and 10% of SCLC cell lines were of the low NE subtype, as well as cell lines from the GEM model. High NE SCLC lines grew as non-adherent floating aggregates or spheroids while Low NE lines had morphologic features of the variant subtype and grew as loosely attached cells. While the high NE subtype expressed one of the NE lineage master transcription factors or , together with , the entire range of NE markers, and lacked expression of the neuronal and NE repressor REST, the low NE subtype had lost expression of most NE markers, , and and expressed . The low NE subtype had undergone epithelial mesenchymal transition (EMT) and had activated the Notch, Hippo and TGFβ pathways and MYC oncogene . Importantly, the high and low NE group of SCLC lines had similar gene expression profiles as their SCLC tumor counterparts.

Conclusions: SCLC tumors and cell lines can exhibit distinct inter-tumor heterogeneity with respect to expression of NE features. Loss of NE expression results in major alterations in morphology, growth characteristics, and molecular properties. These findings have major clinical implications as the two subtypes are predicted to have very different responses to targeted therapies.
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http://dx.doi.org/10.21037/tlcr.2018.02.02DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5835590PMC
February 2018

Morphologic and Other Forms of Heterogeneity in Small Cell Lung Cancer: What Can We Learn from Them?

Authors:
Adi F Gazdar

J Thorac Oncol 2018 02;13(2):148-150

Hamon Center for Therapeutic Oncology Research and Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas. Electronic address:

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http://dx.doi.org/10.1016/j.jtho.2017.11.004DOI Listing
February 2018

The Epithelial Sodium Channel (αENaC) Is a Downstream Therapeutic Target of ASCL1 in Pulmonary Neuroendocrine Tumors.

Transl Oncol 2018 Apr 2;11(2):292-299. Epub 2018 Feb 2.

Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, Texas. Electronic address:

Small cell lung cancer (SCLC) is an aggressive neuroendocrine carcinoma, designated as a recalcitrant cancer by the National Cancer Institute, in urgent need of new rational therapeutic targets. Previous studies have determined that the basic helix-loop-helix transcription factor achaete-scute homolog 1 (ASCL1) is essential for the survival and progression of a fraction of pulmonary neuroendocrine cancer cells, which include both SCLC and a subset of non-SCLC. Previously, to understand how ASCL1 initiates tumorigenesis in pulmonary neuroendocrine cancer and identify the transcriptional targets of ASCL1, whole-genome RNA-sequencing analysis combined with chromatin immunoprecipitation-sequencing was performed with a series of lung cancer cell lines. From this analysis, we discovered that the gene SCNN1A, which encodes the alpha subunit of the epithelial sodium channel (αENaC), is highly correlated with ASCL1 expression in SCLC. The product of the SCNN1A gene ENaC can be pharmacologically inhibited with amiloride, a drug that has been used clinically for close to 50 years. Amiloride inhibited growth of ASCL1-dependent SCLC more strongly than ASCL1-independent SCLC in vitro and slowed growth of ASCL1-driven SCLC in xenografts. We conclude that SCNN1A/αENaC is a direct transcriptional target of the neuroendocrine lung cancer lineage oncogene ASCL1 that can be pharmacologically targeted with antitumor effects.
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http://dx.doi.org/10.1016/j.tranon.2018.01.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5884185PMC
April 2018

Small-cell lung cancer: what we know, what we need to know and the path forward.

Nat Rev Cancer 2017 Nov 10;17(12):765. Epub 2017 Nov 10.

This corrects the article DOI: 10.1038/nrc.2017.87.
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http://dx.doi.org/10.1038/nrc.2017.106DOI Listing
November 2017

Small-cell lung cancer: what we know, what we need to know and the path forward.

Nat Rev Cancer 2017 12 27;17(12):725-737. Epub 2017 Oct 27.

Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, Texas 75230-8593, USA.

Small-cell lung cancer (SCLC) is a deadly tumour accounting for approximately 15% of lung cancers and is pathologically, molecularly, biologically and clinically very different from other lung cancers. While the majority of tumours express a neuroendocrine programme (integrating neural and endocrine properties), an important subset of tumours have low or absent expression of this programme. The probable initiating molecular events are inactivation of TP53 and RB1, as well as frequent disruption of several signalling networks, including Notch signalling. SCLC, when diagnosed, is usually widely metastatic and initially responds to cytotoxic therapy but nearly always rapidly relapses with resistance to further therapies. There were no important therapeutic clinical advances for 30 years, leading SCLC to be designated a 'recalcitrant cancer'. Scientific studies are hampered by a lack of tissue availability. However, over the past 5 years, there has been a worldwide resurgence of studies on SCLC, including comprehensive molecular analyses, the development of relevant genetically engineered mouse models and the establishment of patient-derived xenografts. These studies have led to the discovery of new potential therapeutic vulnerabilities for SCLC and therefore to new clinical trials. Thus, while the past has been bleak, the future offers greater promise.
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http://dx.doi.org/10.1038/nrc.2017.87DOI Listing
December 2017

Meta-analysis approaches to combine multiple gene set enrichment studies.

Stat Med 2018 02 19;37(4):659-672. Epub 2017 Oct 19.

Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX 75235, USA.

In the field of gene set enrichment analysis (GSEA), meta-analysis has been used to integrate information from multiple studies to present a reliable summarization of the expanding volume of individual biomedical research, as well as improve the power of detecting essential gene sets involved in complex human diseases. However, existing methods, Meta-Analysis for Pathway Enrichment (MAPE), may be subject to power loss because of (1) using gross summary statistics for combining end results from component studies and (2) using enrichment scores whose distributions depend on the set sizes. In this paper, we adapt meta-analysis approaches recently developed for genome-wide association studies, which are based on fixed effect and random effects (RE) models, to integrate multiple GSEA studies. We further develop a mixed strategy via adaptive testing for choosing RE versus FE models to achieve greater statistical efficiency as well as flexibility. In addition, a size-adjusted enrichment score based on a one-sided Kolmogorov-Smirnov statistic is proposed to formally account for varying set sizes when testing multiple gene sets. Our methods tend to have much better performance than the MAPE methods and can be applied to both discrete and continuous phenotypes. Specifically, the performance of the adaptive testing method seems to be the most stable in general situations.
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http://dx.doi.org/10.1002/sim.7540DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5771852PMC
February 2018

Evaluation of the 7 and 8 editions of the AJCC/UICC TNM staging systems for lung cancer in a large North American cohort.

Oncotarget 2017 Sep 24;8(40):66784-66795. Epub 2017 May 24.

Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA.

Purpose: The new 8 American Joint Committee on Cancer (AJCC)/International Union for Cancer Control (UICC) lung cancer staging system was developed and internally validated using the International Association for the Study of Lung Cancer (IASLC) database, but external validation is needed. The goal of this study is to validate the discriminatory ability and prognostic performance of this new staging system in a larger, independent non-small cell lung cancer (NSCLC) cohort with greater emphasis on North American patients.

Methods: A total of 858,909 NSCLC cases with one malignant primary tumor collected from 2004 to 2013 in the National Cancer Database (NCDB) were analyzed. The primary coding guidelines of the Collaborative Staging Manual and Coding Instructions for the new 8 edition AJCC/UICC lung cancer staging system was used to define the new T, M and TNM stages for all patients in the database. Kaplan-Meier curves, Cox regression models and time-dependent receiver operating characteristics were used to compare the discriminatory ability and prognostic performance of the 7 and the revised 8 T, M categories and overall stages.

Results: We demonstrated that the 8 staging system provides better discriminatory ability than the 7 staging system and predicts prognosis for NSCLC patients using the NCDB. There were significant survival differences between adjacent groups defined by both clinical staging and pathologic staging systems. These staging parameters were significantly associated with survival after adjusting for other factors.

Conclusions: The updated T, M, and overall TNM stage of the 8 staging system show improvement compared to the 7 edition in discriminatory ability between adjacent subgroups and are independent predictors for prognosis.
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http://dx.doi.org/10.18632/oncotarget.18158DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5620136PMC
September 2017