Survival outcomes for lung neuroendocrine tumors in California differ by sociodemographic factors.

Lung neuroendocrine tumors (NETs) have few known predictors of survival. We investigated associations of sociodemographic, clinicopathologic, and treatment factors with overall survival (OS) and lung cancer-specific survival (LCSS) for incident lung NET cases (typical or atypical histology) in the California Cancer Registry (CCR) from 1992 to 2019. OS was estimated with the Kaplan-Meier method and compared by sociodemographic and disease factors univariately with the log-rank test. We used sequential Cox proportional hazards regression for multivariable OS analysis. LCSS was estimated using Fine-Gray competing risks regression. There were 6038 lung NET diagnoses (5569 typical, 469 atypical carcinoid); most were women (70%) and non-Hispanic White (73%). In our multivariable model, sociodemographic factors were independently associated with OS, with better survival for women (hazard ratio (HR) 0.62, 95% confidence interval (CI) 0.57-0.68, P < 0.001), married (HR 0.76, 95% CI 0.70-0.84, P < 0.001), and residents of high socioeconomic status (SES) neighborhoods (HRQ5vsQ1 0.73, 95% CI 0.62-0.85, P < 0.001). Compared to cases with private insurance, OS was worse for cases with Medicare (HR 1.24, 95% CI 1.10-1.40, P < 0.001) or Medicaid/other public insurance (HR 1.45, 95% CI 1.24-1.68, P < 0.001). In our univariate model, non-Hispanic Black Californians had worse OS than other racial/ethnic groups, but differences attenuated after adjusting for stage at diagnosis. In our LCSS models, we found similar associations between sex and marital status on survival, but no differences in outcomes by SES or insurance. By race/ethnicity, American Indian cases had worse LCSS. In summary, beyond disease-related and treatment variables, sociodemographic factors were independently associated with survival in lung NETs.

Emerging Precision Medicine Approaches for Lung Neuroendocrine Tumors.

Well-differentiated lung neuroendocrine tumors (LNETs) are heterogeneous cancers that are increasing in incidence. Treatment options for LNETs have expanded in recent years, and our knowledge of the molecular subtypes has also advanced. Multidisciplinary teams have an established role in personalizing the best treatment for individual patients. Other precision medicine approaches for the treatment of LNETs have lagged behind those for non-small-cell lung cancer, with only rare actionable molecular alterations identified and few established predictive factors to guide therapy selection. However, as summarized in this review, there is increasing potential for personalized treatment of patients with LNETs. In particular, advances in radiotheragnostics may allow us to tailor the treatment of individual patients with NETs in the coming years. These advances may soon deliver the promise of more effective, less toxic treatments and better outcomes for patients with these increasingly common cancers.

Osimertinib-induced biventricular cardiomyopathy with abnormal cardiac MRI findings: a case report.

BACKGROUND: Osimertinib is a third-generation epidermal growth factor receptor (EGFR) inhibitor that is currently the first-line treatment for metastatic EGFR-mutated non-small-cell lung cancer (NSCLC) due to its favorable efficacy and tolerability profile compared to previous generations of EGFR inhibitors. However, it can cause uncommon, yet serious, cardiovascular adverse effects. CASE PRESENTATION: We present the case of a 63-year-old man with EGFR-mutated NSCLC treated with osimertinib who developed new-onset non-ischemic cardiomyopathy with biventricular dysfunction and heart failure in the context of an enlarging pericardial effusion. For the first time, we demonstrate cardiac MR imaging findings associated with osimertinib-associated cardiomyopathy, including focal late gadolinium enhancement and myocardial edema. The patient's biventricular function normalized after initiation of goal-directed medical therapy for heart failure and holding osimertinib. The patient was subsequently started on afatinib, a second-generation epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI), without recurrence of cardiomyopathy. CONCLUSIONS: This case highlights the need to better understand osimertinib-induced cardiotoxicity and strategies to optimize oncologic care in patients who develop severe cardiac toxicities from cancer therapy. It further underlines the importance of specialized multidisciplinary care of cancer patients who develop cardiotoxicities to optimize their oncologic outcomes.

FOXC2 promotes vasculogenic mimicry and resistance to anti-angiogenic therapy.

Vasculogenic mimicry (VM) describes the formation of pseudo blood vessels constructed of tumor cells that have acquired endothelial-like properties. VM channels endow the tumor with a tumor-derived vascular system that directly connects to host blood vessels, and their presence is generally associated with poor patient prognosis. Here we show that the transcription factor, Foxc2, promotes VM in diverse solid tumor types by driving ectopic expression of endothelial genes in tumor cells, a process that is stimulated by hypoxia. VM-proficient tumors are resistant to anti-angiogenic therapy, and suppression of Foxc2 augments response. This work establishes co-option of an embryonic endothelial transcription factor by tumor cells as a key mechanism driving VM proclivity and motivates the search for VM-inhibitory agents that could form the basis of combination therapies with anti-angiogenics.

Pembrolizumab alone and pembrolizumab plus chemotherapy in previously treated, extrapulmonary poorly differentiated neuroendocrine carcinomas.

BACKGROUND: To date, single-agent immune checkpoint inhibitor (CPI) therapy has proven to be ineffective against biomarker-unselected extrapulmonary poorly differentiated neuroendocrine carcinomas (EP-PDNECs). The efficacy of CPI in combination with chemotherapy remains under investigation. METHODS: Patients with advanced, progressive EP-PDNECs were enrolled in a two-part study of pembrolizumab-based therapy. In Part A, patients received pembrolizumab alone. In Part B, patients received pembrolizumab plus chemotherapy. PRIMARY ENDPOINT: objective response rate (ORR). Secondary endpoints: safety, progression-free survival (PFS) and overall survival (OS). Tumours were profiled for programmed death-ligand 1 expression, microsatellite-high/mismatch repair deficient status, mutational burden (TMB), genomic correlates. Tumour growth rate was evaluated. RESULTS: Part A (N = 14): ORR (pembrolizumab alone) 7% (95% CI, 0.2-33.9%), median PFS 1.8 months (95% CI, 1.7-21.4), median OS 7.8 months (95% CI, 3.1-not reached); 14% of patients (N = 2) had grade 3/4 treatment-related adverse events (TRAEs). Part B (N = 22): ORR (pembrolizumab plus chemotherapy) 5% (95% CI, 0-22.8%), median PFS 2.0 months (95% CI, 1.9-3.4), median OS 4.8 months (95% CI, 4.1-8.2); 45% of patients (N = 10) had grade 3/4 TRAEs. The two patients with objective response had high-TMB tumours. DISCUSSION: Treatment with pembrolizumab alone and pembrolizumab plus chemotherapy was ineffective in advanced, progressive EP-PDNECs. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov NCT03136055.

Imaging and Molecular Annotation of Xenographs and Tumours (IMAXT): High throughput data and analysis infrastructure.

With the aim of producing a 3D representation of tumors, imaging and molecular annotation of xenografts and tumors (IMAXT) uses a large variety of modalities in order to acquire tumor samples and produce a map of every cell in the tumor and its host environment. With the large volume and variety of data produced in the project, we developed automatic data workflows and analysis pipelines. We introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located, instead of bringing data to their local computers. Here, we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumor models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT. The automatic pipelines are highly optimized and allow to obtain processed datasets several times quicker than current solutions narrowing the gap between data acquisition and scientific exploitation.

Spatiotemporal proteomic profiling of the pro-inflammatory response to lipopolysaccharide in the THP-1 human leukaemia cell line.

Protein localisation and translocation between intracellular compartments underlie almost all physiological processes. The hyperLOPIT proteomics platform combines mass spectrometry with state-of-the-art machine learning to map the subcellular location of thousands of proteins simultaneously. We combine global proteome analysis with hyperLOPIT in a fully Bayesian framework to elucidate spatiotemporal proteomic changes during a lipopolysaccharide (LPS)-induced inflammatory response. We report a highly dynamic proteome in terms of both protein abundance and subcellular localisation, with alterations in the interferon response, endo-lysosomal system, plasma membrane reorganisation and cell migration. Proteins not previously associated with an LPS response were found to relocalise upon stimulation, the functional consequences of which are still unclear. By quantifying proteome-wide uncertainty through Bayesian modelling, a necessary role for protein relocalisation and the importance of taking a holistic overview of the LPS-driven immune response has been revealed. The data are showcased as an interactive application freely available for the scientific community.

A direct role for SNX9 in the biogenesis of filopodia.

Filopodia are finger-like actin-rich protrusions that extend from the cell surface and are important for cell-cell communication and pathogen internalization. The small size and transient nature of filopodia combined with shared usage of actin regulators within cells confounds attempts to identify filopodial proteins. Here, we used phage display phenotypic screening to isolate antibodies that alter the actin morphology of filopodia-like structures (FLS) in vitro. We found that all of the antibodies that cause shorter FLS interact with SNX9, an actin regulator that binds phosphoinositides during endocytosis and at invadopodia. In cells, we discover SNX9 at specialized filopodia in Xenopus development and that SNX9 is an endogenous component of filopodia that are hijacked by Chlamydia entry. We show the use of antibody technology to identify proteins used in filopodia-like structures, and a role for SNX9 in filopodia.

Periprocedural Management of Patients Undergoing Liver Resection or Embolotherapy for Neuroendocrine Tumor Metastases.

OBJECTIVES: The objective of this study was to describe the periprocedural management of patients with well-differentiated neuroendocrine tumors with hepatic metastases who underwent liver-directed procedures. METHODS: We performed a retrospective review of patients with metastatic neuroendocrine tumors who underwent liver resection, ablation, or embolotherapy at a single center from 2012 to 2016. The primary outcome was occurrence of documented carcinoid crisis (CC) or hemodynamic instability (HDI), defined as 10 minutes or more of systolic blood pressure less than 80 or greater than 180 mm Hg, or pulse greater than 120 beats per minute. RESULTS: We identified 75 patients who underwent liver resection/ablation (n = 38) or embolotherapy (n = 37). Twenty-four patients (32%) experienced CC or HDI (CC/HDI); CC occurred in 3 patients. No clinicopathologic or procedural factors, including procedure type, octreotide or long-acting somatostatin analog use, and history of carcinoid syndrome, were associated with CC/HDI. Grades 2 to 4 complications were reported in 42% of patients who experienced CC/HDI versus in 16% of patients who did not experience CC/HDI (P < 0.05). CONCLUSIONS: A significant portion of patients developed CC/HDI, and these patients were more likely to develop severe postprocedural complications. Periprocedural octreotide use was not associated with lower CC/HDI occurrence, but continued use is advised given its safety profile until additional studies definitively demonstrate lack of benefit.

Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics.

The study of protein localisation has greatly benefited from high-throughput methods utilising cellular fractionation and proteomic profiling. Hyperplexed Localisation of Organelle Proteins by Isotope Tagging (hyperLOPIT) is a well-established method in this area. It achieves high-resolution separation of organelles and subcellular compartments but is relatively time- and resource-intensive. As a simpler alternative, we here develop Localisation of Organelle Proteins by Isotope Tagging after Differential ultraCentrifugation (LOPIT-DC) and compare this method to the density gradient-based hyperLOPIT approach. We confirm that high-resolution maps can be obtained using differential centrifugation down to the suborganellar and protein complex level. HyperLOPIT and LOPIT-DC yield highly similar results, facilitating the identification of isoform-specific localisations and high-confidence localisation assignment for proteins in suborganellar structures, protein complexes and signalling pathways. By combining both approaches, we present a comprehensive high-resolution dataset of human protein localisations and deliver a flexible set of protocols for subcellular proteomics.

A Bayesian mixture modelling approach for spatial proteomics.

Analysis of the spatial sub-cellular distribution of proteins is of vital importance to fully understand context specific protein function. Some proteins can be found with a single location within a cell, but up to half of proteins may reside in multiple locations, can dynamically re-localise, or reside within an unknown functional compartment. These considerations lead to uncertainty in associating a protein to a single location. Currently, mass spectrometry (MS) based spatial proteomics relies on supervised machine learning algorithms to assign proteins to sub-cellular locations based on common gradient profiles. However, such methods fail to quantify uncertainty associated with sub-cellular class assignment. Here we reformulate the framework on which we perform statistical analysis. We propose a Bayesian generative classifier based on Gaussian mixture models to assign proteins probabilistically to sub-cellular niches, thus proteins have a probability distribution over sub-cellular locations, with Bayesian computation performed using the expectation-maximisation (EM) algorithm, as well as Markov-chain Monte-Carlo (MCMC). Our methodology allows proteome-wide uncertainty quantification, thus adding a further layer to the analysis of spatial proteomics. Our framework is flexible, allowing many different systems to be analysed and reveals new modelling opportunities for spatial proteomics. We find our methods perform competitively with current state-of-the art machine learning methods, whilst simultaneously providing more information. We highlight several examples where classification based on the support vector machine is unable to make any conclusions, while uncertainty quantification using our approach provides biologically intriguing results. To our knowledge this is the first Bayesian model of MS-based spatial proteomics data.

A subcellular map of the human proteome.

Resolving the spatial distribution of the human proteome at a subcellular level can greatly increase our understanding of human biology and disease. Here we present a comprehensive image-based map of subcellular protein distribution, the Cell Atlas, built by integrating transcriptomics and antibody-based immunofluorescence microscopy with validation by mass spectrometry. Mapping the in situ localization of 12,003 human proteins at a single-cell level to 30 subcellular structures enabled the definition of the proteomes of 13 major organelles. Exploration of the proteomes revealed single-cell variations in abundance or spatial distribution and localization of about half of the proteins to multiple compartments. This subcellular map can be used to refine existing protein-protein interaction networks and provides an important resource to deconvolute the highly complex architecture of the human cell.

Using hyperLOPIT to perform high-resolution mapping of the spatial proteome.

The organization of eukaryotic cells into distinct subcompartments is vital for all functional processes, and aberrant protein localization is a hallmark of many diseases. Microscopy methods, although powerful, are usually low-throughput and dependent on the availability of fluorescent fusion proteins or highly specific and sensitive antibodies. One method that provides a global picture of the cell is localization of organelle proteins by isotope tagging (LOPIT), which combines biochemical cell fractionation using density gradient ultracentrifugation with multiplexed quantitative proteomics mass spectrometry, allowing simultaneous determination of the steady-state distribution of hundreds of proteins within organelles. Proteins are assigned to organelles based on the similarity of their gradient distribution to those of well-annotated organelle marker proteins. We have substantially re-developed our original LOPIT protocol (published by Nature Protocols in 2006) to enable the subcellular localization of thousands of proteins per experiment (hyperLOPIT), including spatial resolution at the suborganelle and large protein complex level. This Protocol Extension article integrates all elements of the hyperLOPIT pipeline, including an additional enrichment strategy for chromatin, extended multiplexing capacity of isobaric mass tags, state-of-the-art mass spectrometry methods and multivariate machine-learning approaches for analysis of spatial proteomics data. We have also created an open-source infrastructure to support analysis of quantitative mass-spectrometry-based spatial proteomics data (http://bioconductor.org/packages/pRoloc) and an accompanying interactive visualization framework (http://www. bioconductor.org/packages/pRolocGUI). The procedure we outline here is applicable to any cell culture system and requires ∼1 week to complete sample preparation steps, ∼2 d for mass spectrometry data acquisition and 1-2 d for data analysis and downstream informatics.

Complicated Case Presentation: Management of Pancreatic Neuroendocrine Tumors in Multiple Endocrine Neoplasia Type 1.

Multiple endocrine neoplasia type 1 (MEN1) is an inherited predisposition to tumors of the parathyroid glands, anterior pituitary, and pancreatic islet cells. In this review, we discuss the clinical case of a 45-year-old woman with MEN1 that was presented at the 2015 North American Neuroendocrine Tumor Society Symposium. In our review of this patient's complicated clinical course and subsequent operative management, we highlight controversies in the diagnosis and management of pancreatic neuroendocrine tumors in MEN1. In particular, this case illustrates the lack of consensus regarding the optimal biochemical and radiologic screening for pancreatic neuroendocrine tumors and absence of guidelines about the appropriate surgical approach for treatment. We review these controversies and discuss possible approaches to management.

Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics.

Sub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis.

Selective association of electrocardiographic abnormalities with insulin sensitivity and beta-cell function in type 2 diabetes mellitus: a cross-sectional analysis.

BACKGROUND: We investigated the association of electrocardiographic (ECG) abnormalities with markers of insulin resistance and pancreatic beta-cell dysfunction in a cross-sectional study of type 2 diabetes patients. METHODS: Electrocardiographic criteria were evaluated in the Penn Diabetes Heart Study participants (n = 1671; 64% male; 61% Caucasian), including a sub-sample (n = 710) that underwent oral glucose tolerance testing. The Matsuda Insulin Sensitivity Index and homeostasis model assessment of insulin resistance (HOMA-IR) estimated insulin sensitivity; Insulinogenic Index and homeostasis model assessment of beta-cell function assessed beta-cell function. Multivariable regression modelling was used to analyse associations of ECG changes with these indices. RESULTS: In unadjusted analyses, subjects in the highest quartile of Matsuda index had the lowest prevalence of Q-waves (6.3% versus 15.3%, p = 0.005). In adjusted models, an inverse association was seen between Q-waves and log Matsuda index [one standard deviation increase; OR = 0.59 (95% CI 0.43-0.87 p = 0.001)]. In the full Penn Diabetes Heart Study, there was a direct association between Q-waves and HOMA-IR [one standard deviation increase; OR = 1.43 (95% CI 1.13-1.81, p = 0.003)]. In adjusted models, left ventricular hypertrophy also was inversely associated with Matsuda index and directly with HOMA-IR. Higher Insulinogenic Index scores were associated with a lower prevalence of nonspecific ST changes [OR = 0.78 (95% CI 0.62-0.98, p = 0.032)]. CONCLUSIONS: In type 2 diabetic patients, both oral glucose tolerance testing-derived and HOMA-derived measures of insulin resistance were associated with pathologic Q-waves and left ventricular hypertrophy on ECGs. Copyright © 2016 John Wiley & Sons, Ltd.

A draft map of the mouse pluripotent stem cell spatial proteome.

Knowledge of the subcellular distribution of proteins is vital for understanding cellular mechanisms. Capturing the subcellular proteome in a single experiment has proven challenging, with studies focusing on specific compartments or assigning proteins to subcellular niches with low resolution and/or accuracy. Here we introduce hyperLOPIT, a method that couples extensive fractionation, quantitative high-resolution accurate mass spectrometry with multivariate data analysis. We apply hyperLOPIT to a pluripotent stem cell population whose subcellular proteome has not been extensively studied. We provide localization data on over 5,000 proteins with unprecedented spatial resolution to reveal the organization of organelles, sub-organellar compartments, protein complexes, functional networks and steady-state dynamics of proteins and unexpected subcellular locations. The method paves the way for characterizing the impact of post-transcriptional and post-translational modification on protein location and studies involving proteome-level locational changes on cellular perturbation. An interactive open-source resource is presented that enables exploration of these data.