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Archive for the ‘Metastasis Process’ Category

A Mechanism of Cancer Metastasis

Larry H. Bernstein, MD, FCAP, Curator

LPBI

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Word Cloud By Danielle Smolyar

A Protein that Spreads Cancer

Nils Halberg at the University of Bergen has identified a protein that makes it possible for cancer cells to spread

http://www.technologynetworks.com/Proteomics/news.aspx?ID=190563

The cells inside a tumour differ a lot. While some remains “good” and do not cause trouble, others become aggressive and starts to spread to other organ sites. It is very hard to predict which cells become aggressive or not.

Nevertheless, by isolating these aggressive cancer cells in in vivotests on animals, Nils Halberg at the Department of Biomedicinet the University of Bergen (UiB) and the researchers Dr. Sohail Tavazoie and Dr. Caitlin Sengelaub at The Rockefeller University have discovered a certain protein (PITPNC1) that characterise aggressive cancer cells.

“We discovered that the aggressive cancer cells that are spreading in colon, breast, and skin cancer contained a much higher portion of the protein PITPNC1, than the non-aggressive cancer cells,” says researcher Nils Halberg of the CELLNET Group at the Department of Biomedicine at UiB.

“This means we can predict which of the cancer cells are getting aggressive and spread, at a much earlier stage than today.”

How cells penetrate tissue

The researcher also discovered that this protein, that characterizes the aggressive cancer cells, has got a very specific function in the process of spreading cancer.

The cancer cells spread from one place in the body to another, through the blood vessel. To get into the blood vessels, the cell needs to penetrate tissue, both when it leaves the tumour and when it is attaching to a new organ.

“The protein PITPNC1 regulates a process whereby the cancer cells are secreting molecules, which cut through a network of proteins outside the cells, like scissors. The cancer cell is then able to penetrate the tissue and set up a colonies at new organ sites,” Halberg explains.

Custom-made therapy

A tumour that is not spreading, is usually not dangerous for the patient if it is removed. The hard part in cancer therapy is when the tumour starts to spread. Guided by the new discoveries, supported by the Bergen Research Foundation´s (BFS) Recruitment Programme, Halberg hopes to contribute to a better treatment of cancer patients.

“If we get to the point where we can offer a custom-made therapy that targets the function of this protein, we might be able to stop it spreading,” says Nils Halberg.

RDGBB; RDGBB1; MRDGBbeta; RDGB-BETA; M-RDGB-beta

This gene encodes a member of the phosphatidylinositol transfer protein family. The encoded cytoplasmic protein plays a role in multiple processes including cell signaling and lipid metabolism by facilitating the transfer of phosphatidylinositol between membrane compartments. Alternatively spliced transcript variants encoding multiple isoforms have been observed for this gene, and a pseudogene of this gene is located on the long arm of chromosome 1. [provided by RefSeq, May 2012]

Phosphatidylinositol Transfer Protein, Cytoplasmic 1 (PITPNC1) Binds and Transfers Phosphatidic Acid*

Kathryn Garner‡ , Alan N. Hunt§ , Grielof Koster§ , Pentti Somerharju¶ , Emily Groves‡1, Michelle Li‡ , Padinjat Raghu , Roman Holic‡ , and Shamshad Cockcroft‡2
JBC Papers in Press, July 21, 2012,      http://dx.doi.org:/10.1074/jbc.M112.375840

Background: Phosphatidylinositol transfer protein, cytoplasmic 1 (PITPNC1) (alternative name, RdgB) promotes metastatic colonization and angiogenesis in humans.

Results: We demonstrate that RdgB is a phosphatidic acid (PA)- and phosphatidylinositol-binding protein and binds PA derived from the phospholipase D pathway.

Conclusion: RdgB is the first lipid-binding protein identified that can bind and transfer PA.

Significance: PA bound to RdgB is a likely effector downstream of phospholipase D

PITPNC1 Recruits RAB1B to the Golgi Network to Drive Malignant Secretion

Nils Halberg3,4,, Caitlin A. Sengelaub3, Kristina Navrazhina, Henrik Molina, Kunihiro Uryu, Sohail F. Tavazoie
Cancer Cell 14 Mar 2016; Volume 29, Issue 3:339–353   http://dx.doi.org/10.1016/j.ccell.2016.02.013
Highlights
  • PITPNC1 promotes metastasis by melanoma, breast cancer, and colon cancer cells
  • PITPNC1 recruits RAB1B to the Golgi compartment of the cell
  • Golgi localization of RAB1B enhances vesicular secretion via GOLPH3 recruitment

Summary

Enhanced secretion of tumorigenic effector proteins is a feature of malignant cells. The molecular mechanisms underlying this feature are poorly defined. We identify PITPNC1 as a gene amplified in a large fraction of human breast cancer and overexpressed in metastatic breast, melanoma, and colon cancers. Biochemical, molecular, and cell-biological studies reveal that PITPNC1 promotes malignant secretion by binding Golgi-resident PI4P and localizing RAB1B to the Golgi. RAB1B localization to the Golgi allows for the recruitment of GOLPH3, which facilitates Golgi extension and enhanced vesicular release. PITPNC1-mediated vesicular release drives metastasis by increasing the secretion of pro-invasive and pro-angiogenic mediators HTRA1, MMP1, FAM3C, PDGFA, and ADAM10. We establish PITPNC1 as a PI4P-binding protein that enhances vesicular secretion capacity in malignancy.

Cancerous Conduits

Metastatic cancer cells use nanotubes to manipulate blood vessels.

By Amanda B. Keener | April 1, 2016

http://www.the-scientist.com/?articles.view/articleNo/45579/title/Cancerous-Conduits

http://www.the-scientist.com/images/April2016/shortlit2.jpg

MAKING CONTACT: Breast cancer cells (white arrows) in culture deliver microRNAs to endothelial cells through filamentous nanotubes (yellow arrow).
LABORATORY OF SHILADITYA SENGUPTA

EDITOR’S CHOICE IN CELL & MOLECULAR BIOLOGY

The paper
Y. Conner et al., “Physical nanoscale conduit-mediated communication between tumour cells and the endothelium modulates endothelial phenotype,” Nat Commun, 6:8671, 2015.

Branching Out
Harvard bioengineer Shiladitya Sengupta and his team were establishing a culture system to model the matrix and blood vessel networks that surround tumors when they found that human breast cancer cells spread out along blood vessel endothelial cells rather than form spheroid tumors as expected. Taking a closer look using scanning electron microscopy, they spied nanoscale filaments consisting of membrane and cytoskeletal components linking the two cell types.

Manipulative Metastases
These cancer cell–spawned nanotubes, the team discovered, could transfer a dye from cancer cells to endothelial cells both in culture and in a mouse model of breast cancer metastasis to the lungs.The cells also transferred microRNAs known to regulate endothelial cell adhesion and disassociation of tight junctions, which Sengupta speculates may help cancer cells slip in and out of blood vessels. This study is the first to suggest a role for nanotubes in metastasis.

Breaking the Chain
Sengupta’s team then used low doses of cytoskeleton-disrupting drugs to block nanotube formation. Emil Lou, an oncologist at the University of Minnesota who studies nanotubes in cancer and was not involved in the study, says this approach is a “good start,” though such drugs would not be used in human patients because they are not specific to nanotubes.

In the Details
Lou says the study emphasizes the importance of understanding interactions between tumors and their surrounding tissues on a molecular level. Going forward, Sengupta plans to study how the tubes are formed in melanoma as well as breast and ovarian cancers to try to identify other drug targets.

Physical nanoscale conduit-mediated communication between tumour cells and the endothelium modulates endothelial phenotype

Yamicia ConnorSarah TekleabShyama NandakumarCherelle Walls,….., Bruce ZetterElazer R. Edelman & Shiladitya Sengupta
Nature Communications6,Article number:8671
    
          doi:10.1038/ncomms9671

Metastasis is a major cause of mortality and remains a hurdle in the search for a cure for cancer. Not much is known about metastatic cancer cells and endothelial cross-talk, which occurs at multiple stages during metastasis. Here we report a dynamic regulation of the endothelium by cancer cells through the formation of nanoscale intercellular membrane bridges, which act as physical conduits for transfer of microRNAs. The communication between the tumour cell and the endothelium upregulates markers associated with pathological endothelium, which is reversed by pharmacological inhibition of these nanoscale conduits. These results lead us to define the notion of ‘metastatic hijack’: cancer cell-induced transformation of healthy endothelium into pathological endothelium via horizontal communication through the nanoscale conduits. Pharmacological perturbation of these nanoscale membrane bridges decreases metastatic foci in vivo. Targeting these nanoscale membrane bridges may potentially emerge as a new therapeutic opportunity in the management of metastatic cancer.

Metastasis is the culmination of a cascade of events, including invasion and intravasation of tumour cells, survival in circulation, extravasation and metastatic colonization4. Multiple studies have reported a dynamic interaction between the metastatic tumour cell and the target organ, mediated by cytokines4, 12 or by exosomes that can prime metastasis by creating a pre-metastatic niche13. Interestingly, the interactions between cancer cells and endothelium in the context of metastasis, which occurs during intravasation, circulation and extravasation, remains less studied. Cancer cell-secreted soluble factors can induce retraction of endothelial cells and the subsequent attachment and transmigration of tumour cells through the endothelial monolayers14, 15. Recently, studies indicate a more intricate communication between cancer cells and the endothelium. For example, a miRNA regulon was found to mediate endothelial recruitment and metastasis by cancer cells16. Similarly, exosome-mediated transfer of cancer-secreted miR-105 was recently reported to disrupt the endothelial barrier and promote metastasis17. We rationalized that a better understanding of cancer–endothelial intercellular communication, primarily during extravasation, could lead to novel strategies for inhibiting metastasis18.

Recently, nanoscale membrane bridges, such as tunnelling nanotubes (TNTs) and filopodias, have emerged as a novel mechanism of intercellular communication19. For example, specialized signalling filopodia or cytonemes were recently shown to transport morphogens during development20. Similarly, TNTs, which unlike filopodia have no contact with the substratum21, were shown to facilitate HIV-1 transmission between T cells, enable the spread of calcium-mediated signal between cells and transfer p-glycoproteins conferring multi-drug resistance between cancer cells22, 23, 24, 25. TNTs were also recently implicated in trafficking of mitochondria from endothelial to cancer cells and transfer miRNA between osteosarcoma cells and stromal murine osteoblast cells, and between smooth muscle cells and the endothelium26, 27, 28. However, whether similar intercellular nanostructure-mediated communication can be harnessed by cancer cells to modulate the endothelium is not known.

Here we report that metastatic cancer cells preferentially form nanoscale intercellular membrane bridges with endothelial cells. These nanoscale bridges act as physical conduits through which the cancer cells can horizontally transfer miRNA to the endothelium. We observe that the recipient endothelial cells present an miRNA profile that is distinct from non-recipient endothelial cells isolated from the same microenvironment. Furthermore, the co-cultures of cancer and endothelial cells upregulate markers associated with pathological endothelium, which is inhibited by pharmacological disruption of the nanoscale conduits. Additionally, the pharmacological inhibitors of these nanoscale conduits can decrease metastatic foci in vivo, which suggests that these nanoscale conduits may potentially emerge as new targets in the management of metastatic cancer.

Figure 1: Nanoscale structures physically connect metastatic cells and the endothelium

Nanoscale structures physically connect metastatic cells and the endothelium.

http://www.nature.com/ncomms/2015/151216/ncomms9671/images_article/ncomms9671-f1.jpg

(a) Representative image of MDA-MB-231 cancer cells exhibiting an invasive phenotype in the presence of preformed endothelial tubes in co-culture. (b) Representative image of a mammosphere typically formed by MDA-MB-231 cells when cultured on 3D tumour matrix in the absence of endothelial cells. MDA-MB-231 cells were loaded with CFSE. Actin was labelled with rhodamine phalloidin and nuclei were counterstained with DAPI. (c) A representative SEM of epithelial (EPI) MDA-MB-231 cells aligning on HUVEC (ENDO) tubules in the co-culture. Lower panel shows higher magnification. (d) SEM image reveals nanoscale membrane bridges connecting (nCs) metastatic breast cancer (EPI) cells and endothelial vessels (arrows). (e) A representative transmission electron micrograph shows intercellular connectivity through the nanoscale membrane bridge between MDA-MB-231 and an endothelial cell. (f) A cartoon represents the types of homotypic and heterotypic intercellular nanoscale connections that an epithelial cell may form in the presence of endothelial tubules. Highly metastatic (MDA-MB-468, MDA-MB-231 or MDA-MB-435) or low metastatic (MCF7 and SkBr3) cancer cells were co-cultured with the endothelial tubes. Normal HMECs were used as control. Graphs show percentage of total population of epithelial cells that exhibit either homotyptic (Epi–Epi) or heterotypic (Epi–Endo) nanoscale connections and (g) average number of nanoscale connections formed per cell. Quantification analysis was done on >300 cells of each cell type. Data shown are mean±s.e.m. (n=6 replicates per study, with 2–3 independent experiments). **P<0.01, ***P<0.001 (analysis of variance followed by Bonferroni’s post-hoctest).

The nanoscale bridges are composed of cytoskeletal elements

Figure 3: Structure and function of the heterotypic intercellular nanoscale membrane bridges.

a) Representative images show the heterotypic nanoscale membrane bridges are composed of both F-actin and α/β-tubulin cytoskeletal components. Co-cultures were stained with α/β-tubulin antibody (green) and phalloidin (purple) to label actin, and counterstained with DAPI (nuclear)+WGA (plasma membrane) (blue). Endothelial cells were labelled with DiL-Ac-LDL (red). (b) Mathematical modelling of the structure of the nanoscale connections. The physical properties of actin filaments necessitate microtubules for projections of certain length scales. The maximum projection length for a given minimum diameter at the buckling limit is plotted for actin-only nanoscale structures (purple line). This curve is overlaid with the experimental length and diameter measurements (red dots) from the observed thin projections measured in these studies. Projections containing only actin or projections containing both actin and tubulin can exist to the right of the curve (purple line). However, actin-only projections cannot exist to the left of the curve (green region). (c) The effect of incorporating tubulin in these projections. The maximum length is plotted against the minimum diameter for varying fractions of tubulin incorporated in the nanoscale projection. Addition of microtubules to the projections increases the overall flexural rigidity, shifting the curves left of the actin-only limit (purple line), thus allowing for longer and thinner nanoscale connections. However, owing to the larger radius of microtubules (4 × radius of actin filaments), there is an optimal fraction of tubulin (green line) that can be incorporated into the projection before the effect is reversed. (d) The optimal fraction of microtubules is about 6.6% (red dashed line) to maximize nanostructure flexural strength, while minimizing thickness. (e) Representative confocal image shows the presence of myosin V motor proteins within the intercellular nanostructure (inset shows higher magnification). Scale bar 10μm.

Nanoscale bridges act as conduits for communication

Figure 4: The nanoscale membrane bridges act as conduits for intercellular communication between cancer and endothelial cells.

(a) Confocal image of nanoscale membrane bridge-mediated transfer of cytoplasmic contents. CFSE (green)-loaded MDA-MB-231 cells were co-cultured with the Dil-Ac-LDL (red)-labelled HUVECs. Transfer of the CFSE dye was observed after 24-h co-culture. CFSE dye can be seen within HUVEC cells (yellow arrow). Tumour cells can form a nanobridge with a distal endothelial cell (EC1) than an endothelial cell (EC2) in close proximity. (b,c) Cartoon shows the experimental design, where dual cultures control for vesicle-mediated intercellular transfer. FACS plot show gating for sorting endothelial cells from the co-cultures using dual staining for DiI-Ac-LDL and PECAM-1, and then quantification for CFSE transfer in the isolated endothelial cells. (d) Graph shows quantification of FACS analysis, highlighting increased transfer of CFSE to endothelial cells in the co-culture. (N>100,000 events, n=36 replicates, 3 replicates per study). (e) Graph shows the temporal kinetics of nanoscale connection-mediated intercellular transfer of CFSE from MDA-MB-231 cells to the endothelium (n=2 studies, 3 replicates per study). (f) Effect of small molecule inhibitors of cytoskeletal components on membrane nanobridges. (g) Graphs show treatment with vehicle (control) or a low-dose combination of docetaxel and cytochalasin do not affect the exosome shedding (n=2 independent studies). (h,i) Graphs show the effect of pharmacological inhibitors on the formation of heterotypic and homotypic nanoscale bridges (arrows). (n=2 studies, 6 replicates per study). (j) Graph shows the effect of pharmacological inhibitors on intercellular transfer of CFSE to endothelial cells from cancer cells (n=10 studies, 3 replicates per study). Data shown are mean±s.e.m. (*P<0.05,**P<0.01, ****P<0.001, analysis of variance followed by Bonferroni’s post-hoc test).

Effect of pharmacological inhibition of nanoscale bridges

Nanobridges transfer miRNA from cancer cells to endothelium

Figure 5: The nanoscale membrane bridges act as conduits for intercellular transfer of miRNA between cancer and endothelial cells.

Representative confocal images show the transfer of Cy3-labelled miRNA from MDA-MB-231 cells (EPI) to endothelial cells (ENDO) at (a) 24h and (b) 36h of co-culture. Alexa Fluor 488-Ac-LDL (green)-labelled endothelial cells were co-cultured with Cy3-labelled miRNA-transfected MDA-MB-231. Co-cultures were counterstained with phalloidin (purple) and DAPI+WGA (blue). A 3D visualization shows the localization of miRNA within the nanoscale connections (white arrows), which act as conduits for horizontal transfer of miRNAs to endothelial cells. (c) Schema shows quantification of Cy3-labelled control miRNA and Cy3-labelled miR132 transfer between cancer cell and endothelium using flow cytometry. Endothelial cell populations were isolated from the co-cultures and percentage of miRNA+ve cells was determined. Dual cultures in Boyden chambers were included as controls. (d) Graph shows the effect of pharmacological disruption of nanoscale conduits on miRNA transfer. (e) Schema shows experimental design for reverse transcriptase–PCR-based detection of transferred miR-132 in endothelial cells under different experimental conditions. MDA-MB-231 cells transfected with miR-132 and α-miR-132 were co-cultured with endothelial tubes. FACS-isolated endothelial cell populations were analysed for the expression of miR-132. (f) Graph shows miR-132+ve cell populations (solid red) show 5 × increase compared with miR-132−ve populations (solid blue) (P<0.0001), whereas anti-miR-132+ve cells (striped red) show 26 × decrease in miR-132 expression (P<0.0001) compared with α-miR-132−ve cells (striped blue). Direct transfection of miR-132 (black) and α-miR-132 (light blue) in endothelial cells is used as positive and negative controls, respectively. Upregulation of miR-132 from baseline was observed in dual culture (solid green), which could be inhibited with anti-miR-132 (striped green). MiR-132 levels are increased compared with dual only in those cells that are positive for intercellular transfer. Fold change was determined compared with endothelial cell transfection with control miRNA (grey). (g) FACS analysis shows nanoscale bridges-mediated transfer of miRNAs leads to changes in p120RasGAP and pAkt (S473) expression downstream of the miR-132 pathway in endothelial cell populations isolated from co-cultures. (h) Graphs show p120RasGAP expression is decreased in the miR-132+ve cell populations and increased in the α-miR-132+ve cell populations, while further downstream miR-132 positively regulates pAkt expression. Data shown are mean±s.e.m. (N=2–5 independent studies, with 3 replicates per study,*P<0.05, **P<0.01, ***P<0.001, ****P<0.0001, analysis of variance followed by Bonferroni’s post-hoctest).

Nanobridge-mediated transfer alters endogenous miRNA profile

Figure 6: Cancer cell–endothelial intercellular transfer alters the endogenous miRNA profile and phenotype of recipient endothelial cells.

The complexity of regulatory tumour parenchyma–endothelial communication is increasingly being unravelled7, 50. The altered phenotypic behaviour of the metastatic cancer cells in the presence of endothelial cells observed in this study, instead of forming classical mammospheres, is consistent with the emerging paradigm of modulatory tumour parenchyma–stroma communication and the creation of a pre-metastatic niche. Indeed, a recent study proposed the concept of the formation of a pre-metastatic niche mediated via metastatic cell-secreted exosomes, leading to vascular leakiness at the pre-metastatic sites13. Here we demonstrate that cancer cells form nanoscale membrane bridges, which can act as conduits for horizontal transfer of miRNA from the cancer cells to the endothelium, switching the latter to a pathological phenotype. Our findings reveal that the ability to form the nanoscale conduits with endothelial cells correlates with the metastatic potential of the cancer cell, and that the pharmacological perturbation of these nanoscale connections can lead to a reduction in the metastatic burden in experimental metastasis models. Together, our studies shed new insights into the tumour parenchyma–endothelial communication, adding depth to the emerging paradigm of the ability of a cancer cell to ‘hijack’ a physiological stromal cell for self-gain13.

Indeed, exosomes have emerged as an extensively studied mechanism of horizontal intercellular transfer of information51. However, a key distinction exists between the exosome-mediated versus the nanoscale membrane bridge-mediated intercellular communication. Although the former is stochastic, that is, it is unlikely the cancer cell has control over which cell will be targeted by a secreted exosome, the communication via nanoscale membrane bridges is deterministic, that is, the cancer cell can connect to a specific endothelial cell, which could be further away than the most proximal endothelial cell.

Although the aim of this study was to study the nanoscale membrane bridges as a mode of horizontal transfer of miRNAs from the metastatic cancer cells to the endothelium, and not to characterize a specific miRNA that are implicated in metastasis, many of the miRNAs, which were differentially regulated in the recipient endothelial cells, have previously been shown to regulate metastasis (Supplementary Discussion).

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Antibiotics that target mitochondria effectively eradicate cancer stem cells, across multiple tumor types: Treating cancer like an infectious disease

Reporter: Aviva Lev-Ari, PhD, RN

 

 

 

 

 

 

 

Antibiotics that target mitochondria effectively eradicate cancer stem cells, across multiple tumor types: Treating cancer like an infectious disease.

 

A group of scientists has proposed a new strategy for the treatment of early cancerous lesions and advanced metastatic disease, via the selective targeting of cancer stem cells (CSCs), a.k.a., tumor-initiating cells (TICs). They searched for a global phenotypic characteristic that was highly conserved among cancer stem cells, across multiple tumor types, to provide a mutation-independent approach to cancer therapy. This would allow doctors to target cancer stem cells specifically, effectively treating cancer as a single disease of “stemness”, independently of the tumor tissue type.

Using this approach, the researchers identified a conserved phenotypic weak point – a strict dependence on mitochondrial biogenesis for the clonal expansion and survival of cancer stem cells. Interestingly, several classes of FDA-approved antibiotics inhibit mitochondrial biogenesis as a known “side-effect”, which could be harnessed instead as a “therapeutic effect”.

Based on this analysis, they were able to show that 4-to-5 different classes of FDA-approved drugs can be used to eradicate cancer stem cells, in 12 different cancer cell lines, across 8 different tumor types (breast, DCIS, ovarian, prostate, lung, pancreatic, melanoma, and glioblastoma (brain)). These five classes of mitochondrially-targeted antibiotics include: erythromycins, tetracyclines,  glycylcyclines, an anti-parasitic drug, and chloramphenicol. Within their work, functional data are presented for one antibiotic in each drug class: azithromycin, doxycycline, tigecycline, pyrvinium pamoate, as well as chloramphenicol, as proof-of-concept. Importantly, many of these drugs are non-toxic for normal cells, likely reducing the side effects of anti-cancer therapy.

Based on these results, the researchers propose to treat cancer like an infectious disease, by repurposing FDA-approved antibiotics for anti-cancer therapy, across multiple tumor types. These drug classes should also be considered for prevention studies, specifically focused on the prevention of tumor recurrence and distant metastasis.

Recent clinical trials with doxycycline and azithromycin (intended to target cancer-associated infections, but not cancer cells) have already shown positive therapeutic effects in cancer patients, although their ability to eradicate cancer stem cells was not yet evaluated and fully appreciated.

Source: www.impactjournals.com

See on Scoop.itCardiovascular Disease: PHARMACO-THERAPY

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Impairment of vascularization of the surface covering epithelium induces ischemia and promotes malignization: a new hypothesis of a possible mechanism of cancer pathogenesis – Online First – Springer

Reporter: Aviva Lev-Ari, PhD, RN

Impairment of vascularization of the surface covering epithelium induces ischemia and promotes malignization: … http://t.co/752c9CuBzz

Source: link.springer.com

See on Scoop.itCardiovascular and vascular imaging

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The Human Proteome Map Completed

Reporter and Curator: Larry H. Bernstein, MD, FCAP

UPDATED 6/02/2024

The genetic, pharmacogenomic, and immune landscapes associated with protein expression across human cancers.

Source: Chen C, Liu Y, Li Q, Zhang Z, Luo M, Liu Y, Han L. The Genetic, Pharmacogenomic, and Immune Landscapes Associated with Protein Expression across Human Cancers. Cancer Res. 2023 Nov 15;83(22):3673-3680. doi: 10.1158/0008-5472.CAN-23-0758. PMID: 37548539; PMCID: PMC10843800.

Abstract

Proteomics is a powerful approach that can rapidly enhance our understanding of cancer development. Detailed characterization of the genetic, pharmacogenomic, and immune landscape in relation to protein expression in cancer patients could provide new insights into the functional roles of proteins in cancer. By taking advantage of the genotype data from The Cancer Genome Atlas (TCGA) and protein expression data from The Cancer Proteome Atlas (TCPA), we characterized the effects of genetic variants on protein expression across 31 cancer types and identified approximately 100,000 protein quantitative trait loci (pQTL). Among these, over 8000 pQTL were associated with patient overall survival. Furthermore, characterization of the impact of protein expression on more than 350 imputed anticancer drug responses in patients revealed nearly 230,000 significant associations. In addition, approximately 21,000 significant associations were identified between protein expression and immune cell abundance. Finally, a user-friendly data portal, GPIP (https://hanlaboratory.com/GPIP), was developed featuring multiple modules that enable researchers to explore, visualize, and browse multidimensional data. This detailed analysis reveals the associations between the proteomic landscape and genetic variation, patient outcome, the immune microenvironment, and drug response across cancer types, providing a resource that may offer valuable clinical insights and encourage further functional investigations of proteins in cancer.

Introduction

Functional proteomics is a powerful approach that helps us understand cancer pathophysiology and identify potential therapeutic strategies (). Functional protein analysis using reverse-phase protein arrays (RPPA) has already proven highly effective in studying large numbers of TCGA samples, especially when integrated with genomic, transcriptomic, and clinical information (). Previous works demonstrated that a QTL mapping approach is effective to understand the genetic basis of multiple molecular features in human diseases (). Identifying the sequence determinants of protein levels (pQTLs) may guide the search for causal genes and facilitate understanding the underlying mechanisms of human diseases. However, it remains challenging to further understand the functional roles of protein expression in cancers. For example, it is unclear whether proteins are associated with drug response and/or immune features in patients. In this study, we systematically investigated the effects of genetic variants on protein expression and characterized the impact of protein expression on imputed drug responses and immune cell abundances from different sources (Fig. 1). To facilitate broad access of these data for the biomedical research community, we developed a user-friendly database, GPIP (https://hanlaboratory.com/GPIP). We expect this study to have a significant clinical impact on the future development of protein-based targeted therapies.

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Impact of genetic variants on protein expression.

A Workflow of GPIP to identify pQTLs and survival-associated pQTLs. B The number of pQTLs identified for each cancer type. C Association between CYCLINB1 protein expression level and rs12576855 in LUAD patients. D Association between CYCLINB1 protein expression level and rs2722796 in LGG patients. E The number of survival-associated pQTLs identified for each cancer type. F Kaplan–Meier plot showing the association between rs10918659 (pQTL of HER2_pY1248) genotypes and overall survival times of STAD patients. G Kaplan–Meier plot showing the association between rs13158796 (pQTL of HER2_pY1248) genotypes and overall survival times of STAD patients.

Identification of protein–drug associations

To investigate potential associations between protein expression and drug response, we calculated the Spearman rank correlation between protein expression data and drug response from DrVAEN and cancerRxTissue. These two datasets employed distinct predictive models that integrated omics data from CCLE and drug response data from GDSC to predict drug response in TCGA samples (Fig. 2A) (,). Association with |Rs| > 0.3 and FDR < 0.05 were considered as significant associations in each cancer type.

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Exploring the pharmacogenomics of protein in human cancer.

A Workflow of GPIP to identify Drug-associated proteins. B The number of protein-drug response pairs identified from DrVAEN (left) and cancerRxTissue (right) for each cancer type. C Visualization of the associations between proteins and drugs (DrVAEN) within and across different cancer signaling pathways. Blue links represent associations within a single pathway, while orange links represent associations cross pathways. D Enrichment analysis of drug target pathways among significant protein-drug response pairs. The color represents the log2 (odds ratio) of Fisher’s exact test. The size represents the FDR value.

Identification of protein–immune cell associations

To examine the relationship between protein expression and immune cell abundance, we utilized Spearman rank correlation coefficient to calculate the associations between protein expression data and immune cell abundance data from TIMER, CIBERSORT, ImmuneCellAI, and ImmuneCellGSVA (Fig. 3). These datasets utilized different methods to evaluate immune cell abundance by leveraging immune gene signatures as a proxy (). We considered correlations with |Rs| > 0.3 and FDR < 0.05 as significant associations.

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Exploring the immune landscapes of protein in human cancer.

A Workflow of GPIP to identify Immune cell-associated proteins. B The number of protein-drug response pairs identified from ImmuneCellsGSVA (purple), ImmuCellAI (yellow), TIMER (red) and CIBERSORT (green) for each cancer type. C The top 10 proteins with the highest number of significantly associated immune cell types in HNSC. The color represents the Rs between protein expression and immune cell abundance (ImmuneCellGSVA). The size represents the FDR value. D Association between PREX1expression and impute MDSC abundance in HNSC patients.

Database construction

GPIP was developed using Python Flask-RESTful API frameworks (https://flask-restful.readthedocs.io/), AngularJS (https://angularjs.org), and Bootstrap (https://getbootstrap.com/). The database for GPIP was implemented using the NoSQL database program MongoDB (https://www.mongodb.com/). The user-friendly interface of the GPIP web application was served through the Apache HTTP Server, allowing users to access the database and perform queries and analysis through a web browser.

Data availability

All results generated in this study can be found in GPIP database, (https://hanlaboratory.com/GPIP). Publicly available data generated by others were used by the authors in this study: The genotype data and clinical data were obtained from The Cancer Genome Atlas (TCGA) data portal at https://tcga-data.nci.nih.gov/tcga/. The reverse-phase protein array (RPPA) protein expression data was obtained from The Cancer Proteome Atlas (TCPA) data portal at https://www.tcpaportal.org/. The imputed pharmacogenomic data were obtained from DrVAEN at https://bioinfo.uth.edu/drvaen/ and cancerRxTissue at https://manticore.niehs.nih.gov/cancerRxTissue/. The immune-cell infiltration data were obtained from Tumor Immune Estimation Resource (TIMER) at http://timer.cistrome.org/, Immune Cell Abundance Identifier (ImmuCellAI) at http://bioinfo.life.hust.edu.cn/ImmuCellAI/, and CIBERSORT at https://cibersort.stanford.edu/.

A comprehensive data portal

We developed a user-friendly data portal, GPIP (https://hanlaboratory.com/GPIP), to facilitate visualizing, searching, and browsing of our results by the biomedical research community (Fig. 4A). GPIP contains four main modules: Protein-QTLs, Surivial-QTLs, Drug Response, and Immune Infiltration (Fig. 4B). Querying can be easily performed by selecting cancer type, protein, drug, immune cell abundance, or entering the SNP ID of interest (Fig. 4C). For example, in the Protein-QTLs and Survival-QTLs modules, users can search for pQTLs by selecting a cancer type (e.g., LUAD) and entering a protein name (e.g., CYCLINB1) or an SNP ID (e.g., rs12576855). In the Drug Response module, users can search for protein-drug response associations by selecting a data source for imputed drug response (e.g., DrVAEN) and selecting an anticancer drug (e.g., Talazoparib) or a protein (e.g., PARP1). In the Immune Infiltration module, users can search for protein-immune infiltration pairs by selecting a data source for imputed immune cell abundance (e.g., ImmuneCellsGSVA), and selecting an immune cell type (e.g., Activated B cell) or a protein (e.g., PDL1). In addition, on the bottom of the main page, we developed a cancer type module where users can click on a specific cancer type (e.g., BLCA) to search for related information across all 4 modules (Fig. 4D). The search results for each module included a table to list related information accordingly (Fig. 4E). A “Details” button for each result item was clicked for generating a box plot in protein-QTLs module (Fig. 4F), a Kaplan–Meier plot in Survival-QTLs module (Fig. 4G) and a scatter plot in Drug Response and Immune Infiltration modules, respectively (Fig. 4H,I).I). Our database provides a valuable resource for cancer research and will be of great interest to the research community.

An external file that holds a picture, illustration, etc.
Object name is nihms-1924390-f0004.jpg
Content and interface of GPIP.

A GPIP homepage and browser bar. B The four main modules of GPIP. C Search boxes in the pQTLs module. D Search boxes in the cancer type-specific search module. E An example of resulting list in the pQTL module. F An example of boxplot for the pQTLs module result. G An example of Kaplan–Meier plot for the Survival protein-QTLs module result. H An example of scatter plot for the Drug Response module result. I An example of scatter plot for the Immune Infiltration module result.

Discussion

Proteomics plays a crucial role in identifying potential therapeutic strategies and understanding cancer pathophysiology (). In this study, we investigated the effects of genetic variants on protein expression and characterized the impact of protein expression on imputed drug responses and immune cell abundances across human cancers. We also developed the user-friendly data portal, GPIP, to provide access to these results. Our study provides a comprehensive analysis of protein expression in different cancer types and their association with drug response and immune cell abundance.

Identifying genetic variants associated with cancer has revolutionized our understanding of the disease and holds promise for improved diagnosis and treatment. In GPIP, we identified ~100,000 pQTLs across 31 cancer types and 8.8% of them were found to be associated with patient survival (Fig. 1). These genetic variants hold significant promise for unraveling the underlying biological mechanisms of disease progression and response to treatments. For example, a survival-associated pQTL may help to identify a genetic variant that controls the expression of a protein crucial for tumor growth or immune response, thus impacting patient survival. Our results suggest that pQTLs have the potential to serve as prognostic biomarkers and aid in the development of precision medicine.

Despite the promising implications, it is crucial to consider potential limitations of pQTL identification. One limitation is the small number of tumor samples in rare cancers, which limits statistical power and the detection of significant pQTLs. For example, only 8 proteins with pQTLs were found in CHOL, likely due to the small sample size (Table S1). Additionally, we observed that some cancer types with large sample sizes identified only a small number of pQTLs (e.g., BRAC), possibly due to the data quality of protein abundance. Tumors originating from different tissues may have variations in protein extraction quality or protein measurement accuracy (). Furthermore, cancer type heterogeneity can impact pQTL identification, as tumors from different tissues exhibit distinct protein expression profiles and genetic landscapes. Addressing these limitations is necessary to ensure valid and reliable results.

Protein expression levels in tumors can impact response of cancer cells to therapeutic drugs due to their role as targets of drug action, with alterations in expression potentially modifying drug sensitivity or resistance. In GPIP, we utilized the imputed drug response and protein expression data in TCGA patients to identify the potential associations between protein expression and drug response (Fig. 2). Our results revealed that certain proteins were significantly associated with drug sensitivity or resistance, suggesting that protein expression levels could potentially be used as biomarkers to predict drug response in cancer patients. Recent studies have shown that the impact of genetic variants on drug response can be mediated through protein-protein interaction (PPI) networks (,). Integrating genetic variants and PPI to further understand the associations between protein expression and drug response may provide further insights.

The protein expression level in tumors is crucial in the context of tumor immune microenvironment and immunotherapy, as it might impact immune cell abundance and response, and potentially improve the efficacy of immunotherapy. In GPIP, we examined the association between protein expression levels and imputed immune cell abundance across multiple cancer types. Our study identified ~21,000 significant correlations between proteins and immune cell types, highlighting the potential role of protein expression levels in shaping the tumor immune microenvironment (Fig. 3). Our results offer a promising avenue for future research to understand the interplay between protein expression and the tumor immune microenvironment, leading to personalized immunotherapy strategies and better treatment outcomes for cancer patients.

In summary, GPIP is a comprehensive and multifaceted data platform designed to aid functional and clinical research on protein in cancer patients. As more relevant datasets become available, we will continually update GPIP to ensure its relevance and usefulness to the research community.

Significance:

Comprehensive characterization of the relationship between protein expression and the genetic, pharmacogenomic, and immune landscape of tumors across cancer types provides a foundation for investigating the role of protein expression in cancer development and treatment.

Researchers Produce First Map of Human Proteome, and Reveal New
Significance in The Human Proteome

HAHNE, TECHNISCHE UNIVERSITÄT MÜNCHENTwo international teams have
independently produced the first drafts of the human proteome. These curated
catalogs of the proteins expressed in most non-diseased human tissues and
organs can be used as a baseline to better understand changes that occur in
disease states. Their findings were published today (May 29) in Nature.

Both teams uncovered new complexities of the human genome, identifying novel
proteins from regions of the genome previously thought to be non-coding.

“the real breakthrough with these two projects is the comprehensive coverage of
more than 80 percent of the expected human proteome” said Hanno Steen, director
of proteomics at Boston Children’s Hospital, who was not involved in the work.

The human proteome map provides a catalog of proteins expressed in nondiseased tissues and organs to use as baseline in understanding changes that occur in disease

Given the growing importance of proteins in medical laboratory testing,

Experts are comparing this to the first complete map of the human genome

  • and this information provides for rapid advances
  • in understanding transcriptomics and metabolomics

Map of Human Proteome Expected to Advance Medical Science

“Housekeeping genes” that are expressed in all tissues and cell types

  • have been thought to be involved in basic cellular functions.

Two teams developing a Human Proteome Map

  • detected proteins encoded by 2,350 genes
  • across all human cells and tissues.

The corresponding housekeeping proteins comprised
about 75% of total protein mass.

  •  histones,
  • ribosomal proteins,
  • metabolic enzymes, and
  • cytoskeletal proteins

The two international teams produced

  • the first drafts of the human protoeome,
  • a catalog of proteins expressed in most
  • nondiseased human issues and organs.

The evidence suggests there is translation from DNA regions

  • that were not thought to be translated—including
  • more than 400 translated long, intergenic non-coding RNAs (lincRNAs)—
    found by the Küster team—and
  • 193 new proteins—uncovered by the Pandey team.

This proteome map can be used as a baseline to understand

  • changes that occur in the disease state

These studies are part of the Human Proteome Project,

  1. an international effort by the Human Proteome Organization
  2. to revolutionize our understanding of the human proteome
  3. by coordinating research at laboratories around the world directed
  4. at mapping the entire human proteome.

This new information about the human proteome

  • is expected to trigger rapid advances in medical science
  • and a better understanding of the underlying causes of human diseases.

One Study Team Was at Johns Hopkins University

  • In one study, which was headed by Ahilesh Pandey, M.D.,
    at Johns Hopkins University in Baltimore,
  • and colleague Harsha Gowda, Ph.D.,
    of the Institute of Bioinformatics in Bangalore, India,
  • the research team used an advanced form of mass spectrometry to analyze proteins
  • to create the human proteome map,

according to a report published in NIH Research Matters.

The research team examined

  1. 30 normal human tissue and cell types:
  2. 17 adult tissues,
  3. 7 fetal tissue and
  4. 6 blood cell types.

Samples from three people per tissue type

  • were processed through several steps.

The protein fragments, or peptides, were analyzed on

The amino acid sequences were

  • then compared to known sequences.

Their results were published in the May 28, 2014, issue of Nature.

The resulting draft map of the human proteome map includes

  • proteins encoded by more than 17,000 genes,
  • noted the Research Matters article.

Among these are hundreds of proteins from regions

  • previously thought to be non-coding.

This study also provided a new understanding of

  • how genes are expressed.

For example, almost 200 genes begin in locations

  • other than those predicted based on genetic sequence.

“The fact that 193 of the proteins came from DNA sequences

  • predicted to be non-coding means that
  • we don’t fully understand how cells read DNA,
  • since the sequences code for proteins

This study also produced the Human Proteome Map,

  • an interactive online portal.

This can be accessed at this link.

The study data will soon be accessible through

German’s ProteomicsDB Analyzed a Mix of Available and New Tissue Data

The other study was conducted by a team lead by  Bernhard Küster
of the Technische Universität München in Germany.

Küster and his colleagues created a

This database contains 92% of the

  • estimated 19,629 human proteins,

noted The Scientist article.

Küster’s team also used mass spectrometry

  • to analyze human tissue samples.

This team’s approach differed from Johns Hopkins’ in that

  • it compiled about 60% of the information
  • in the ProteomicsDB database
  1. by using existing raw mass spec (MS) data
  2. from databases and colleagues’ contributions.

To fill data gaps, the Küster lab generated its own
MS data after analyzing

  1. 60 human tissues,
  2. 13 body fluids, and
  3. 147 cancer cell lines.

High-resolution public data

  • was selected and computationally processed
  • for strict quality

The database for ProteomicsDB is

  • public and searchable.

It can be accessed at this link.

German Study Added New Insights to Transcription Process

Comparing the ratio of protein to mRNA levels for every protein globally,

  • the Küster lab found that the translation rate
  • is a constant feature of each mRNA transcript. 

The proteomics community has viewed

  • transcriptome and proteome data as two sides of a coin.

But this analysis shows that at least, at steady state,

  • once the ratio for an mRNA/protein pair has been calculated,
  1. protein levels can be determined
  2. just from specific mRNA levels.

Proteomics researchers in Toronto maintaining ionic balance and in Boston commented on the
importance of the findings, even a “new paradigm” because of

  • the fixed ratio of protein to mRNA

This is quite in keeping with what we have been learning

  • with respect to homeostasis.

In 2003, the Human Genome Project created a

  • draft map of the human genome—
  • all the genes in the human body.

Genomics has since driven many advances in medical science.

This was a progress from the classic discovery of Watson and Crick –

  • the classical dogma holds that
  • DNA makes RNA makes protein.
  • no constraints are place on this

But the cell is functioning in contact with other cells,

  • immersed in interstitial fluid
  • maintaining cationic and anionic balance
  • and mitochondrial energy balance and ubiquitin systems interact
  • and protein interacts with the chromatin and transcriptional RNA

So the restriction that has been discovered has credence,

  • the classical diagram has to be redrawn

Deeper Knowledge of Proteome to Improve Diagnostics and Therapeutics

In the two projects is:

  • the comprehensive coverage of more than 80% of
  • the expected human proteome,

These studies indicate that to get to

  • a deep level of proteome coverage,
  • many different tissue types must be probed.

the  studies are  complimentary.

  1. The Hopkins group provided a survey of human proteins from a single source, which allows for easy comparisons within their data.
  2. The ProteomeDB effort connected new information with existing data

A deeper knowledge of the human proteome could help

  • fill the gap between genomes and phenotypes.

As this occurs, it has the potential to transform

  • the way diagnostics and therapeutics are developed,
  •  enhancing overall biomedical research and healthcare,

it was noted in a report presented to scientific leaders at a NIH workshop

  • on advances in proteomics and its applications.

Having completed a draft map of the human proteome—
the set of all proteins in the human body

  • It opens another window to cell function.

It has been ASSUMED –

  • genes control the most basic functions of the cell,
  • including what proteins to make and when.
  • but we have assumed for too much in assigning
    full control to the genome

Researchers have identified more than 20,000 protein- coding genes.

However, scientific understanding of the proteome has

  • lagged behind that of the genome,
  • partly because of the proteome’s complexities.

The relationship between genes and proteins isn’t a simple matter of

  • one gene coding for one protein.

Stretches of DNA can be read and translated

  • into proteins in different ways.

Proteins are also more difficult to sequence than genes.

The importance of these latest studies to pathologists and Ph.D.s working

  • in molecular diagnostics laboratories is that
  • this information will expedite further research into the human proteome.

Such research is expected to lead to

  • novel methods of diagnosis and complex
  • “multi-analyte” clinical laboratory tests that
  • look for multiple proteins in a single assay.

“The prevalent view was that information transfer was from genome to transcriptome to proteome.
What these efforts show is that it’s a two-way road— proteomics can be used to annotate the genome.
The importance is that, using these datasets, we can improve the annotation of the genome and the
algorithms that predict transcription and translation,” said Steen. “The genomics field can now hugely
benefit from proteomics data.”

Wilhelm et al., “Mass-spectrometry- based draft of the human proteome,”
Nature,  http://dx.doi.doi:/10.1038/nature13319, 2014

M.S. Kim et al. “A draft map of the human proteome,”
Nature,  http://dx.doi.org:/10.1038/nature13302, 2014.

Tags

proteomicsnoncoding RNAhuman researchhuman proteome projecthuman genetics and genomics

http://www.the-scientist.com/?articles.view/articleNo/40083/title/Human-Proteome-Mapped/

 

__Patricia Kirk

__by Harrison Wein, Ph.D.

__by Anna Azvolinsky

Related Information:

Revealing The Human Proteome

Human Proteome Mapped

The human proteome – a scientific opportunity for transforming diagnostics, therapeutics, and healthcare

Reference: A draft map of the human proteome.
Kim MS, Pinto SM, Getnet D, Nirujogi RS, Manda SS, Donahue CA, Gowda H, Pandey A.
Nature. 2014 May 29;509(7502):575-81. http://dx.doi.org:/10.1038/nature13302. PMID: 24870542

Funding: NIH’s National Institute of General Medical Sciences (NIGMS), National Cancer Institute (NCI),
and National Heart, Lung, and Blood Institute (NHLBI); the Sol Goldman Pancreatic Cancer Research Center;
India’s Council of Scientific and Industrial Research; and Wellcome Trust/DBT India Alliance.

http://nihprod.cit.nih.gov/researchmatters/june2014/06092014proteome.htm

 

 

 

 

 

 

 

 

 

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Preclinical study identifies cancer cell proteins – known as Src’ master’ proto-oncogene that regulates stress-induced ovarian cancer metastasis | MD Anderson Cancer Center

Reporter: Aviva Lev-Ari, PhD, RN

 

 

 

 

Scientists have discovered the signaling pathway whereby a master regulator of cancer cell proteins – known as Src – leads to ovarian cancer progression when exposed to stress hormones.

Source: www.mdanderson.org

See on Scoop.itCardiovascular Disease: PHARMACO-THERAPY

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