ASCL1-ERK1/2 Axis: ASCL1 restrains ERK1/2 via the dual specificity phosphatase DUSP6 to promote survival of a subset of neuroendocrine lung cancers.

The transcription factor achaete-scute complex homolog 1 (ASCL1) is a lineage oncogene that is central for the growth and survival of small cell lung cancers (SCLC) and neuroendocrine non-small cell lung cancers (NSCLC-NE) that express it. Targeting ASCL1, or its downstream pathways, remains a challenge. However, a potential clue to overcoming this challenage has been information that SCLC and NSCLC-NE that express ASCL1 exhibit extremely low ERK1/2 activity, and efforts to increase ERK1/2 activity lead to inhibition of SCLC growth and surival. Of course, this is in dramatic contrast to the majority of NSCLCs where high activity of the ERK pathway plays a major role in cancer pathogenesis. A major knowledge gap is defining the mechanism(s) underlying the low ERK1/2 activity in SCLC, determining if ERK1/2 activity and ASCL1 function are inter-related, and if manipulating ERK1/2 activity provides a new therapeutic strategy for SCLC. We first found that expression of ERK signaling and ASCL1 have an inverse relationship in NE lung cancers: knocking down ASCL1 in SCLCs and NE-NSCLCs increased active ERK1/2, while inhibition of residual SCLC/NSCLC-NE ERK1/2 activity with a MEK inhibitor increased ASCL1 expression. To determine the effects of ERK activity on expression of other genes, we obtained RNA-seq from ASCL1-expressing lung tumor cells treated with an ERK pathway MEK inhibitor and identified down-regulated genes (such as SPRY4, ETV5, DUSP6, SPRED1) that potentially could influence SCLC/NSCLC-NE tumor cell survival. This led us to discover that genes regulated by MEK inhibition suppress ERK activation and CHIP-seq demonstrated these are bound by ASCL1. In addition, SPRY4, DUSP6, SPRED1 are known suppressors of the ERK1/2 pathway, while ETV5 regulates DUSP6. Survival of NE lung tumors was inhibited by activation of ERK1/2 and a subset of ASCL1-high NE lung tumors expressed DUSP6. Because the dual specificity phosphatase 6 (DUSP6) is an ERK1/2-selective phosphatase that inactivates these kinases and has a pharmacologic inhibitor, we focused mechanistic studies on DUSP6. These studies showed: Inhibition of DUSP6 increased active ERK1/2, which accumulated in the nucleus; pharmacologic and genetic inhibition of DUSP6 affected proliferation and survival of ASCL1-high NE lung cancers; and that knockout of DUSP6 "cured" some SCLCs while in others resistance rapidly developed indicating a bypass mechanism was activated. Thus, our findings fill this knowledge gap and indicate that combined expression of ASCL1, DUSP6 and low phospho-ERK1/2 identify some neuroendocrine lung cancers for which DUSP6 may be a therapeutic target.

Development of Human Adrenocortical Adenoma (HAA1) Cell Line from Zona Reticularis.

The human adrenal cortex is composed of distinct zones that are the main source of steroid hormone production. The mechanism of adrenocortical cell differentiation into several functionally organized populations with distinctive identities remains poorly understood. Human adrenal disease has been difficult to study, in part due to the absence of cultured cell lines that faithfully represent adrenal cell precursors in the early stages of transformation. Here, Human Adrenocortical Adenoma (HAA1) cell line derived from a patient's macronodular adrenocortical hyperplasia and was treated with histone deacetylase inhibitors (HDACis) and gene expression was examined. We describe a patient-derived HAA1 cell line derived from the zona reticularis, the innermost zone of the adrenal cortex. The HAA1 cell line is unique in its ability to exit a latent state and respond with steroidogenic gene expression upon treatment with histone deacetylase inhibitors. The gene expression pattern of differentiated HAA1 cells partially recreates the roster of genes in the adrenal layer that they have been derived from. Gene ontology analysis of whole genome RNA-seq corroborated increased expression of steroidogenic genes upon HDAC inhibition. Surprisingly, HDACi treatment induced broad activation of the Tumor Necrosis Factor (TNF) alpha pathway. This novel cell line we developed will hopefully be instrumental in understanding the molecular and biochemical mechanisms controlling adrenocortical differentiation and steroidogenesis.

SRGN-Triggered Aggressive and Immunosuppressive Phenotype in a Subset of TTF-1-Negative Lung Adenocarcinomas.

BACKGROUND: Approximately 20% of lung adenocarcinoma (LUAD) is negative for the lineage-specific oncogene Thyroid transcription factor 1 (TTF-1) and exhibits worse clinical outcome with a low frequency of actionable genomic alterations. To identify molecular features associated with TTF-1-negative LUAD, we compared the transcriptomic and proteomic profiles of LUAD cell lines. SRGN , a chondroitin sulfate proteoglycan Serglycin, was identified as a markedly overexpressed gene in TTF-1-negative LUAD. We therefore investigated the roles and regulation of SRGN in TTF-1-negative LUAD. METHODS: Proteomic and metabolomic analyses of 41 LUAD cell lines were done using mass spectrometry. The function of SRGN was investigated in 3 TTF-1-negative and 4 TTF-1-positive LUAD cell lines and in a syngeneic mouse model (n = 5 to 8 mice per group). Expression of SRGN was evaluated in 94 and 105 surgically resected LUAD tumor specimens using immunohistochemistry. All statistical tests were 2-sided. RESULTS: SRGN was markedly overexpressed at mRNA and protein levels in TTF-1-negative LUAD cell lines (P < .001 for both mRNA and protein levels). Expression of SRGN in LUAD tumor tissue was associated with poor outcome (hazard ratio = 4.22, 95% confidence interval = 1.12 to 15.86, likelihood ratio test, P = .03), and with higher expression of Programmed cell death 1 ligand 1 (PD-L1) in tumor cells and higher infiltration of Programmed cell death protein 1-positive lymphocytes. SRGN regulated expression of PD-L1 as well as proinflammatory cytokines, including Interleukin-6, Interleukin-8, and C-X-C motif chemokine 1 in LUAD cell lines; increased migratory and invasive properties of LUAD cells and fibroblasts; and enhanced angiogenesis. SRGN was induced by DNA demethylation resulting from Nicotinamide N-methyltransferase-mediated impairment of methionine metabolism. CONCLUSIONS: Our findings suggest that SRGN plays a pivotal role in tumor-stromal interaction and reprogramming into an aggressive and immunosuppressive tumor microenvironment in TTF-1-negative LUAD.

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

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.

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

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.

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

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.

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

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.

SV40 and human mesothelioma.

Simian virus 40 (SV40) is a DNA tumor virus capable of infecting and transforming human mesothelial (HM) cells in vitro. 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.

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

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A biobank of small cell lung cancer CDX models elucidates inter- and intratumoral phenotypic heterogeneity.

Although small cell lung cancer (SCLC) is treated as a homogeneous disease, biopsies and preclinical models reveal heterogeneity in transcriptomes and morphology. SCLC subtypes were recently defined by neuroendocrine transcription factor (NETF) expression. Circulating-tumor-cell-derived explant models (CDX) recapitulate donor patients' tumor morphology, diagnostic NE marker expression and chemotherapy responses. We describe a biobank of 38 CDX models, including six CDX pairs generated pretreatment and at disease progression revealing complex intra- and intertumoral heterogeneity. Transcriptomic analysis confirmed three of four previously described subtypes based on ASCL1, NEUROD1 and POU2F3 expression and identified a previously unreported subtype based on another NETF, ATOH1. We document evolution during disease progression exemplified by altered MYC and NOTCH gene expression, increased 'variant' cell morphology, and metastasis without strong evidence of epithelial to mesenchymal transition. This CDX biobank provides a research resource to facilitate SCLC personalized medicine.

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

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/.

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

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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

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.

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

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.

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

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.

Small cell lung cancers made from scratch.

In this issue of JEM, 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. Science 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.

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

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.

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

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.

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

CREBBP, 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 Crebbp inactivation in an autochthonous mouse model. Extending these observations beyond the lung, broad Crebbp deletion in mouse neuroendocrine cells cooperated with Rb1/Trp53 loss to promote neuroendocrine thyroid and pituitary carcinomas. Gene expression analyses showed that Crebbp loss results in reduced expression of tight junction and cell adhesion genes, including Cdh1, across neuroendocrine tumor types, whereas suppression of Cdh1 promoted transformation in SCLC. CDH1 and other adhesion genes exhibited reduced histone acetylation with Crebbp inactivation. Treatment with the histone deacetylase (HDAC) inhibitor Pracinostat increased histone acetylation and restored CDH1 expression. In addition, a subset of Rb1/Trp53/Crebbp-deficient SCLC exhibited exceptional responses to Pracinostat in vivo Thus, CREBBP acts as a potent tumor suppressor in SCLC, and inactivation of CREBBP enhances responses to a targeted therapy.Significance: 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 Crebbp-deleted tumors. These data provide a rationale for selectively treating CREBBP-mutant SCLC with HDAC inhibitors. Cancer Discov; 8(11); 1422-37. ©2018 AACR. This article is highlighted in the In This Issue feature, p. 1333.