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.

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.

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.

Dynamic Proteomic Profiling of Extra-Embryonic Endoderm Differentiation in Mouse Embryonic Stem Cells.

During mammalian preimplantation development, the cells of the blastocyst's inner cell mass differentiate into the epiblast and primitive endoderm lineages, which give rise to the fetus and extra-embryonic tissues, respectively. Extra-embryonic endoderm (XEN) differentiation can be modeled in vitro by induced expression of GATA transcription factors in mouse embryonic stem cells. Here, we use this GATA-inducible system to quantitatively monitor the dynamics of global proteomic changes during the early stages of this differentiation event and also investigate the fully differentiated phenotype, as represented by embryo-derived XEN cells. Using mass spectrometry-based quantitative proteomic profiling with multivariate data analysis tools, we reproducibly quantified 2,336 proteins across three biological replicates and have identified clusters of proteins characterized by distinct, dynamic temporal abundance profiles. We first used this approach to highlight novel marker candidates of the pluripotent state and XEN differentiation. Through functional annotation enrichment analysis, we have shown that the downregulation of chromatin-modifying enzymes, the reorganization of membrane trafficking machinery, and the breakdown of cell-cell adhesion are successive steps of the extra-embryonic differentiation process. Thus, applying a range of sophisticated clustering approaches to a time-resolved proteomic dataset has allowed the elucidation of complex biological processes which characterize stem cell differentiation and could establish a general paradigm for the investigation of these processes.

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 foundation for reliable spatial proteomics data analysis.

Quantitative mass-spectrometry-based spatial proteomics involves elaborate, expensive, and time-consuming experimental procedures, and considerable effort is invested in the generation of such data. Multiple research groups have described a variety of approaches for establishing high-quality proteome-wide datasets. However, data analysis is as critical as data production for reliable and insightful biological interpretation, and no consistent and robust solutions have been offered to the community so far. Here, we introduce the requirements for rigorous spatial proteomics data analysis, as well as the statistical machine learning methodologies needed to address them, including supervised and semi-supervised machine learning, clustering, and novelty detection. We present freely available software solutions that implement innovative state-of-the-art analysis pipelines and illustrate the use of these tools through several case studies involving multiple organisms, experimental designs, mass spectrometry platforms, and quantitation techniques. We also propose sound analysis strategies for identifying dynamic changes in subcellular localization by comparing and contrasting data describing different biological conditions. We conclude by discussing future needs and developments in spatial proteomics data analysis.

Human SIRT1 regulates DNA binding and stability of the Mcm10 DNA replication factor via deacetylation.

The eukaryotic DNA replication initiation factor Mcm10 is essential for both replisome assembly and function. Human Mcm10 has two DNA-binding domains, the conserved internal domain (ID) and the C-terminal domain (CTD), which is specific to metazoans. SIRT1 is a nicotinamide adenine dinucleotide (NAD)-dependent deacetylase that belongs to the sirtuin family. It is conserved from yeast to human and participates in cellular controls of metabolism, longevity, gene expression and genomic stability. Here we report that human Mcm10 is an acetylated protein regulated by SIRT1, which binds and deacetylates Mcm10 both in vivo and in vitro, and modulates Mcm10 stability and ability to bind DNA. Mcm10 and SIRT1 appear to act synergistically for DNA replication fork initiation. Furthermore, we show that the two DNA-binding domains of Mcm10 are modulated in distinct fashion by acetylation/deacetylation, suggesting an integrated regulation mechanism. Overall, our study highlights the importance of protein acetylation for DNA replication initiation and progression, and suggests that SIRT1 may mediate a crosstalk between cellular circuits controlling metabolism and DNA synthesis.

Molecular architecture of the DNA replication origin activation checkpoint.

Perturbation of DNA replication initiation arrests human cells in G1, pointing towards an origin activation checkpoint. We used RNAi against Cdc7 kinase to inhibit replication initiation and dissect this checkpoint in fibroblasts. We show that the checkpoint response is dependent on three axes coordinated through the transcription factor FoxO3a. In arrested cells, FoxO3a activates the ARF-∣Hdm2-∣p53 → p21 pathway and mediates p15(INK4B) upregulation; p53 in turn activates expression of the Wnt/ß-catenin signalling antagonist Dkk3, leading to Myc and cyclin D1 downregulation. The resulting loss of CDK activity inactivates the Rb-E2F pathway and overrides the G1-S transcriptional programme. Fibroblasts concomitantly depleted of Cdc7/FoxO3a, Cdc7/p15, Cdc7/p53 or Cdc7/Dkk3 can bypass the arrest and proceed into an abortive S phase followed by apoptosis. The lack of redundancy between the checkpoint axes and reliance on several tumour suppressor proteins commonly inactivated in human tumours provides a mechanistic basis for the cancer-cell-specific killing observed with emerging Cdc7 inhibitors.

Proteomics in immunity and herpes simplex encephalitis.

The genetic theory of infectious diseases has proposed that susceptibility to life-threatening infectious diseases in childhood, occurring in the course of primary infection, results mostly from individually rare but collectively diverse single-gene variants. Recent evidence of an ever-expanding spectrum of genes involved in susceptibility to infectious disease indicates that the paradigm has important implications for diagnosis and treatment. One such pathology is childhood herpes simplex encephalitis, which shows a pattern of rare but diverse disease-disposing genetic variants. The present report shows how proteomics can help to understand susceptibility to childhood herpes simplex encephalitis and other viral infections, suggests that proteomics may have a particularly important role to play, emphasizes that variation over the population is a critical issue for proteomics and notes some new challenges for proteomics and related bioinformatics tools in the context of rare but diverse genetic defects.

The proteome of Toll-like receptor 3-stimulated human immortalized fibroblasts: implications for susceptibility to herpes simplex virus encephalitis.

BACKGROUND: Inborn errors in Toll-like receptor 3 (TLR3)-IFN type I and III pathways have been implicated in susceptibility to herpes simplex virus encephalitis (HSE) in children, but most patients studied do not carry mutations in any of the genes presently associated with HSE susceptibility. Moreover, many patients do not display any TLR3-IFN-related fibroblastic phenotype. OBJECTIVE: To study other signaling pathways downstream of TLR3 and/or other independent pathways that may contribute to HSE susceptibility. METHODS: We used the stable isotope labeling of amino acids in cell culture proteomics methodology to measure changes in the human immortalized fibroblast proteome after TLR3 activation. RESULTS: Cells from healthy controls were compared with cells from a patient with a known genetic etiology of HSE (UNC-93B-/-) and also to cells from an HSE patient with an unknown gene defect. Consistent with known variation in susceptibility of individuals to viral infections, substantial variation in the response level of different healthy controls was observed, but common functional networks could be identified, including upregulation of superoxide dismutase 2. The 2 patients with HSE studied show clear differences in functional response networks when compared with healthy controls and also when compared with each other. CONCLUSIONS: The present study delineates a number of novel proteins, TLR3-related pathways, and cellular phenotypes that may help elucidate the genetic basis of childhood HSE. Furthermore, our results reveal superoxide dismutase 2 as a potential therapeutic target for amelioration of the neurologic sequelae caused by HSE.

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.

Subcellular proteomics reveals a role for nucleo-cytoplasmic trafficking at the DNA replication origin activation checkpoint.

Depletion of DNA replication initiation factors such as CDC7 kinase triggers the origin activation checkpoint in healthy cells and leads to a protective cell cycle arrest at the G1 phase of the mitotic cell division cycle. This protective mechanism is thought to be defective in cancer cells. To investigate how this checkpoint is activated and maintained in healthy cells, we conducted a quantitative SILAC analysis of the nuclear- and cytoplasmic-enriched compartments of CDC7-depleted fibroblasts and compared them to a total cell lysate preparation. Substantial changes in total abundance and/or subcellular location were detected for 124 proteins, including many essential proteins associated with DNA replication/cell cycle. Similar changes in protein abundance and subcellular distribution were observed for various metabolic processes, including oxidative stress, iron metabolism, protein translation and the tricarboxylic acid cycle. This is accompanied by reduced abundance of two karyopherin proteins, suggestive of reduced nuclear import. We propose that altered nucleo-cytoplasmic trafficking plays a key role in the regulation of cell cycle arrest. The results increase understanding of the mechanisms underlying maintenance of the DNA replication origin activation checkpoint and are consistent with our proposal that cell cycle arrest is an actively maintained process that appears to be distributed over various subcellular locations.

Race and gender variation in response to evoked inflammation.

BACKGROUND: Race- and gender-variation in innate immunity may contribute to demographic differences in inflammatory and cardiometabolic disease; yet their influence on dynamic responses during inflammatory stress is poorly understood. Our objective was to examine race and gender influence on the response to experimental endotoxemia. METHODS: The Genetics of Evoked Responses to Niacin and Endotoxemia (GENE) study was designed to investigate regulation of inflammatory and metabolic responses during low-grade endotoxemia (LPS 1 ng/kg intravenously) in healthy individuals (median age 24, IQR=7) of European (EA; n=193, 47% female) and African ancestry (AA; n=101, 59% female). RESULTS: Baseline clinical, metabolic, and inflammatory biomarkers by race and gender were consistent with epidemiological literature; pre-LPS cytokines (e.g. median (IQR) IL-6, 2.7 (2) vs.2.1 (2) pg/ml, P=0.001) were higher in AA than EA. In contrast, acute cytokine responses during endotoxemia were lower in AA than EA (e.g. median (IQR) peak IL-1RA, 30 (38) vs.43 (45) ng/ml P=0.002) as was the induction of hepatic acute-phase proteins (e.g. median (IQR) peak CRP 12.9 (9) vs.17.4 (12) mg/L P=0.005). Further, baseline levels of cytokines were only weakly correlated with peak inflammatory responses (all r(s) <0.2) both in AA and in EA. There were less pronounced and less consistent differences in the response by gender, with males having a higher AUC for CRP response compared to females (median (IQR) AUC: 185 (112) vs. 155 (118), P=0.02). CONCLUSIONS: We observed lower levels of evoked inflammation in response to endotoxin in AA compared with EA, despite similar or higher baseline levels of inflammatory markers in AA. Our data also suggest that levels of inflammatory biomarkers measured in epidemiological settings might not predict the degree of acute stress-response or risk of diseases characterized by activation of innate immunity. TRIAL REGISTRATION: FDA clinicaltrials.gov registration number NCT00953667.

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.

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.

Quantitative proteomics reveals a "poised quiescence" cellular state after triggering the DNA replication origin activation checkpoint.

An origin activation checkpoint has recently been discovered in the G1 phase of the mitotic cell cycle, which can be triggered by loss of DNA replication initiation factors such as the Cdc7 kinase. Insufficient levels of Cdc7 activate cell cycle arrest in normal cells, whereas cancer cells appear to lack this checkpoint response, do not arrest, and proceed with an abortive S phase, leading to cell death. The differential response between normal and tumor cells at this checkpoint has led to widespread interest in the development of pharmacological Cdc7 inhibitors as novel anticancer agents. We have used RNAi against Cdc7 in combination with SILAC-based high resolution MS proteomics to investigate the cellular mechanisms underlying the maintenance of the origin activation checkpoint in normal human diploid fibroblasts. Bioinformatics analysis identified clear changes in wide-ranging biological processes including altered cellular energetic flux, moderate stress response, reduced proliferative capacity, and a spatially distributed response across the mitochondria, lysosomes, and the cell surface. These results provide a quantitative overview of the processes involved in maintenance of the arrested state, show that this phenotype involves active rather than passive cellular adaptation, and highlight a diverse set of proteins responsible for cell cycle arrest and ultimately for promotion of cellular survival. We propose that the Cdc7-depleted proteome maintains cellular arrest by initiating a dynamic quiescence-like response and that the complexities of this phenotype will have important implications for the continued development of promising Cdc7-targeted cancer therapies.

Quantitative organelle proteomics of MCF-7 breast cancer cells reveals multiple subcellular locations for proteins in cellular functional processes.

We have combined sucrose density gradient subcellular fractionation with quantitative, tandem-mass-spectrometry-based shotgun proteomics to investigate spatial distributions of proteins in MCF-7 breast cancer cells. Emphasis was placed on four major organellar compartments: cytosol, plasma membrane, endoplasmic reticulum, and mitochondrion. Two-thousand one-hundred eighty-four proteins were securely identified. Four-hundred eighty-one proteins (22.0% of total proteins identified) were found in unique sucrose gradient fractions, suggesting they may have unique subcellular locations. 454 proteins (20.8%) were found to be ubiquitously distributed. The remaining 1249 proteins (57.2%) were consistent with intermediate distribution over multiple, but not all, subcellular locations. Ninety-four proteins implicated in breast cancer and 478 other proteins which share the same five major cellular biological processes with a majority of the breast cancer proteins were observed in 334 and 1223 subcellular locations, respectively. The data obtained is used to evaluate the possibility of defining more exact sets of subcellular organelles, the completeness of current descriptions of spatial distribution of cellular proteins, the importance of multiple subcellular locations for proteins in functional processes, the subcellular distribution of proteins related to breast cancer, and the possibility of using these methods for dynamic spatio/temporal studies of function/regulation in MCF-7 breast cancer cells.