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Archive for the ‘Cancer Genomics’ Category

Liquid Biopsy Chip detects an array of metastatic cancer cell markers in blood – R&D @Worcester Polytechnic Institute,  Micro and Nanotechnology Lab

Reporters: Tilda Barliya, PhD and Aviva Lev-Ari, PhD, RN
bold face added by ALA

Static micro-array isolation, dynamic time series classification, capture and enumeration of spiked breast cancer cells in blood: the nanotube–CTC chip

Farhad Khosravi1, Patrick J Trainor2, Christopher Lambert3, Goetz Kloecker4, Eric Wickstrom5, Shesh N Rai2,6 and Balaji Panchapakesan1

Published 29 September 2016© 2016 IOP Publishing Ltd
Nanotechnology, Volume 27, Number 44

Nanotechnology (2016). DOI: 10.1088/0957-4484/27/44/44LT03

Researchers build liquid biopsy chip that detects metastatic cancer cells in blood: One blood sample can be tested for a comprehensive array of cancer cell markers.

“Imagine going to the doctor for your yearly physical,” he said. “You have blood drawn and that one can be tested for a comprehensive array of cancer cell markers. Cancers would be caught at their earliest stage and other stages of development, and doctors would have the necessary protein or genetic information from these captured to customize your treatment based on the specific markers for your cancer. This would really be a way to put your health in your own hands.”

[T]he WPI device is also highly effective in separating cancer cells from the other cells and material in the blood through differential settling.

“White blood cells, in particular, are a problem, because they are quite numerous in blood and they can be mistaken for cancer cells,” he said. “Our device uses what is called a passive leukocyte depletion strategy. Because of density differences, the tend to settle to the bottom of the wells (and this only happens in a narrow window), where they encounter the antibodies. The remainder of the blood contents stays at the top of the wells and can simply be washed away.”

In addition to capturing tumor cells, Panchapakesan says the chip will also latch on to tiny structures called exosomes, which are produced by cancers cells and carry the same markers. “These highly elusive 3-nanometer structures are too small to be captured with other types of liquid biopsy devices, such as microfluidics, due to shear forces that can potentially destroy them,” he noted. “Our chip is currently the only device that can potentially capture circulating tumor cells and exosomes directly on the chip, which should increase its ability to detect metastasis. This can be important because emerging evidence suggests that tiny proteins excreted with exosomes can drive reactions that may become major barriers to effective cancer drug delivery and treatment.”

The device developed by Panchapakesan’s team includes an array of tiny elements, each about a tenth of an inch (3 millimeters) across. Each element has a well, at the bottom of which are antibodies attached to carbon nanotubes. Each well holds a specific antibody that will bind selectively to one type of cancer cell type, based on genetic markers on its surface. By seeding elements with an assortment of antibodies, the device could be set up to capture several different cancer cells types using a single blood sample. In the lab, the researchers were able to fill a total of 170 wells using just under 0.3 fluid ounces (0.85 milliliter) of blood. Even with that small sample, they captured between one and a thousand cells per device, with a capture efficiency of between 62 and 100 percent.

The carbon nanotubes used in the device act as semiconductors. When a cancer cell binds to one of the attached antibodies, it creates an electrical signature that can be detected. These signals can be used to identify which of the elements in the array have captured cancer cells. Those individual arrays can then be removed and taken to a lab, where the captured cells can be stained and identified under a microscope. In the lab, the binding and electrical signature generation process took just a few minutes, suggesting the possibility of getting same-day results from a blood test using the chip, Panchapakesan says.

SOURCE

https://genesisnanotech.wordpress.com/2016/12/27/researchers-build-liquid-biopsy-chip-that-detects-metastatic-cancer-cells-in-blood-one-blood-sample-can-be-tested-for-a-comprehensive-array-of-cancer-cell-markers/

Balaji Panchapakesan – List of Recent Publications

 

Static micro-array isolation, dynamic time series classification, capture and enumeration of spiked breast cancer cells in blood: the nanotube–CTC chip

F Khosravi, PJ Trainor, C Lambert, G Kloecker, E Wickstrom, SN Rai, …
Nanotechnology 27 (44), 44LT03
  2016
A Thermoacoustic Model for High Aspect Ratio Nanostructures

MS Loeian, RW Cohn, B Panchapakesan
Actuators 5 (4), 23
  2016
Spatially Nonuniform Heating and the Nonlinear Transient Response of Elastomeric Photomechanical Actuators

RW Cohn, B Panchapakesan
Actuators 5 (2), 16
  2016
Ultraflexible nanostructures and implications for future nanorobots

RW Cohn, B Panchapakesan
SPIE Commercial+ Scientific Sensing and Imaging, 98590B-98590B-7
  2016
Label-free capture of breast cancer cells spiked in buffy coats using carbon nanotube antibody micro-arrays

F Khosravi, P Trainor, SN Rai, G Kloecker, E Wickstrom, …
Nanotechnology 27 (13), 13LT02
2 2016
Chromatic Mechanical Response in 2-D Layered Transition Metal Dichalcogenide (TMDs) based Nanocomposites

V Rahneshin, F Khosravi, DA Ziolkowska, JB Jasinski, B Panchapakesan
Scientific Reports 6
  2016
Classification of biosensor time series using dynamic time warping: applications in screening cancer cells with characteristic biomarkers

SN Rai, PJ Trainor, F Khosravi, G Kloecker, B Panchapakesan
Open access medical statistics 2016 (6), 21
1 2016
STIMULI-RESPONSIVE POLYMER COMPOSITES

J Loomis, B Panchapakesan
US Patent 20,150,361,241
  2015
MoS2 actuators: reversible mechanical responses of MoS2-polymer nanocomposites to photons

X Fan, F Khosravi, V Rahneshin, M Shanmugam, M Loeian, J Jasinski, …
Nanotechnology 26 (26), 261001
6 2015
Programmable Skins based on Core-Shell Microsphere/Nanotube/Polymer Composites

B Panchapakesan, C Onal, J Loomis
MRS Proceedings 1800, mrss15-2136299
  2015
Photothermal nanopositioners based on graphene nanocomposites

J Loomis, B Panchapakesan
SPIE NanoScience+ Engineering, 91700B-91700B-9
  2014
Nanotube liquid crystal elastomers: photomechanical response and flexible energy conversion of layered polymer composites

X Fan, BC King, J Loomis, EM Campo, J Hegseth, RW Cohn, E Terentjev, …
Nanotechnology 25 (35), 355501
6 2014
Vacuum filtration based formation of liquid crystal films of semiconducting carbon nanotubes and high performance transistor devices

B King, B Panchapakesan
Nanotechnology 25 (17), 175201
15 2014
2013 Index IEEE Nanotechnology Magazine Vol. 7

C Chen, H Chen, L Chen, C Chng, M Chua, C Chui, J Gao, V Gau, …
  2014
Nanotube Devices for Digital Profiling: A focus on cancer biomarkers and circulating tumor cells.

F Khosravi, B King, S Rai, G Kloecker, E Wickstrom, B Panchapakesan
IEEE Nanotechnology Magazine 7 (4), 20-26
4 2013
Nanotube devices for digital profiling of cancer biomarkers and circulating tumor cells

F Khosravi, B King, B Panchapakesan, S Rai, G Kloecker, E Wickstrom
The 7th IEEE International Conference on Nano/Molecular Medicine and …
1 2013
Graphene/elastomer composite-based photo-thermal nanopositioners

J Loomis, X Fan, F Khosravi, P Xu, M Fletcher, RW Cohn, …
Scientific reports 3
32 2013
Methods for fabricating polymer composites

B Panchapakesan
US Patent App. 13/889,121
1 2013
Stimuli-responsive transformation in carbon nanotube/expanding microsphere? polymer composites

J Loomis, P Xu, B Panchapakesan
Nanotechnology 24 (18), 185703
9 2013
Synergism in Binary (MWNT, SLG) Nano-carbons in Polymer Nano-composites: A Raman Study

P Xu, J Loomis, B King, B Panchapakesan
MRS Proceedings 1505, mrsf12-1505-w17-01
  2013
Show more

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Evaluating the Genetic Profiles of Tumor Cells circulating in the Bloodstream could transform Cancer Care: A Blood Test for managing Lung Cancer @Stanford University Medical School

Reporter: Aviva Lev-Ari, PhD, RN

 

A Legacy of Innovation @Stanford University Medical School

  1. 1967

    First synthesis of biologically active DNA in test tube

  2. 1968

    First adult human heart transplant in the United States

    Norman Shumway successfully transplants a heart into 54-year-old steelworker Mike Kasperak, who survives for 14 days.

     

  3. 1973

    First expression of a foreign gene implanted in bacteria by recombinant DNA methods

  4. 1981

    First successful human combined heart/lung transplant in the world (fourth attempted worldwide)

  5. 1984

    Isolation of a gene coding for part of the T-cell receptor, a key to the immune system’s function

  6. 1988

    Isolation of pure hematopoietic stem cells from mice

  7. 2002

    First use of gene expression profiling to predict cancer outcomes

  8. 2007

    Application and expansion of optogenetics, a technique to control brain cell activity with light

SOURCE

Evaluating the Genetic Profiles of Tumor Cells circulating in the Bloodstream could transform Cancer Care: A Blood Test for managing Lung Cancer @Stanford University Medical School

The approach that the team developed could be used to look at mutations in three or four genes, and it requires no more than 2 milliliters of blood — about half a teaspoon. The test can be completed in about five hours, the researcher said, and costs less than $30. For comparison, a single state-of-the art biopsy of lung tissue with DNA sequencing costs about $18,000 and takes as long as three weeks to furnish results. Johnson & Johnson’s CellSearch — another blood test, already approved by the FDA — costs about $900 and takes a week to deliver results.

The researchers created a system for isolating circulating tumor cells from the blood of cancer patients and reading a handful of genes from inside each tumor cell. Thus, they were able to obtain genetic information about the original cancer tumor that resides deep in the lungs without doing a biopsy, which can be dangerous for the patient.

“We are trying to make minimally invasive technology that allows us to continuously monitor one person’s health over time,” said radiology instructor Seung-min Park, PhD, a lead author of the new study, which was published online Dec. 12 in the Proceedings of the National Academy of Sciences. Park shares lead authorship of the study with former Stanford graduate students Dawson Wong, PhD, and Chin Chun Ooi.

A MagSifter chip, shown here fastened to an acrylic holder, can purify circulating tumor cells from the blood of cancer patients.

The MagSifter is an electromagnetic sieve that can be turned on and off. When the MagSifter is on, it pulls the nanoparticle-labeled CTCs from the blood sample and allows the rest of the blood to flow through the sifter. The CTCs pulled from the blood are then deposited into a flat array of tiny wells, each large enough for only one cell. Now the tumor cells are ready for genetic analysis. Each flat of 25,600 wells looks like a miniature muffin tin, with room for a lot of tiny muffins.

SOURCE

http://med.stanford.edu/news/all-news/2016/12/blood-test-could-provide-cheaper-way-to-evaluate-lung-tumors.html

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Reduction in Mortality in Breast Cancer Patients: Outcome of Drug Interactions

 Reporter: Aviva Lev-Ari, PhD, RN

Drug interactions using gene-expression data: common molecular pathways that might account for drug pairs with apparent synergistic effects, searching for drug-protein interactions in the twoXAR company’s database.
“This is a holistic look at the data — EHR, gene expression, protein targets of drugs — all in one analysis,”

Study reveals drug interactions that may reduce mortality in breast cancer patients

Stanford researchers found that certain drug combinations were associated with lower mortality rates among breast cancer patients, pointing to potential drug targets and new ways of thinking about known diseases.

DEC 9 2016

Nigam Shah

Nigam Shah

Patient health records revealed two drug combinations that may reduce mortality rates in breast cancer patients, according to a study led by researchers at the Stanford University School of Medicine.

The drugs involved were commonly used noncancer drugs that turned out to be associated with a longer average survival rate in breast cancer patients.

The study was published online Dec. 9 in the Journal of the American Medical Informatics Association. The lead author is Stanford postdoctoral scholar Yen Low, PhD. The senior author is Nigam Shah, MBBS, PhD, associate professor of medicine and of biomedical data science.

“So we ran the analysis, and we found a few drug combinations that seemed to associate with better survival,” said Shah.

‘How do we know it’s true?’

Specifically, there were three pairs of drug types. The two combinations in Red are impplicated with improved survivability.

  • anti-inflammatory drugs, such as aspirin or naproxen, and blood-lipid modifiers, such as statins;
  • lipid modifiers and drugs such as fluticasone used to treat asthma like conditions; and
  • anti-inflammatories and anti cancer hormone antagonists — typically, drugs that suppress the synthesis of estrogen.

SOURCE

http://med.stanford.edu/news/all-news/2016/12/study-reveals-drug-interactions-that-may-reduce-mortality.html

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Li -Fraumeni Syndrome and Pancreatic Cancer

Curator: Marzan Khan, B.Sc.

Li-Fraumeni syndrome (LFS) is a condition that makes individuals prone to developing a wide variety of cancers that occur early on in life, the most common types being- soft tissue sarcoma, osteosarcoma, breast cancer, brain tumors, adrenocortical carcinoma (ACC), and leukemia. (1) Pancreatic cancer is minimally associated with the condition. (2) A survey found the presence of pancreatic cancer in only 1% of 475 tumor samples collected from 91 families who were carriers of p53 mutations, with half of them having LFS. The incidence of breast cancer amongst them was the highest -24%. (2) Pancreatic carcinoma in LFS patients usually occurs in the later stages of life. (3)

The underlying cause of LFS is germline mutations in TP53 gene on chromosome 17p, that encodes the transcription factor p53, crucial in cell cycle regulation and the repair of damaged and/or abnormal cells. (4) In the majority of cases, this mutation is obtained by inheritance. (5) De-novo germline mutations in p53 occur in 7%-20% of the cases. (5)

A person showing symptoms of any type of cancer at an early age or having first or second-degree relatives with cancer are at risk of developing LFS. (5) That is why tracing family history is an important part of diagnosis in LFS patients. Genetic testing can confirm mutations present in the gene, however, there are controversial ethical issues regarding their use, particularly in children and fetuses.

In patients with LFS, it is important to control the manifestations of the disease. They should be monitored closely so that any new cancers that arise are diagnosed and treated during the early stages. (6) Patients are also at risk of developing radiation-induced second and third primary tumors. (6) Therefore, radiation and alkylating agents should be used minimally (6) People at risk can be cautioned to avoid exposure to carcinogens such as sunlight, cigarette smoke, and alcohol consumption. (5) Therapeutic approaches that are aimed at restoring wild-type p53 by gene therapy as well as reactivating non-functional p53 by the use of small-molecule drugs are currently being investigated in many cancers. (7) Unlike radiation therapy, these small-molecule drugs are non-toxic to healthy cells, thus eliminating the risk of forming new tumors.

So far, PRIMA-1 has proven to be quite effective at correcting non-functional p53. (8) PRIMA-1 is changed to its methylated form, PRIMA-1MET   that forms covalent adducts to thiol groups in the mutated protein and modifies them. (8) As a result, p53 regains its ability to destroy malignant cells. (8) A research study also found that PRIMA-1 induces apoptosis and increases the sensitivity of pancreatic cancer cells to various chemotherapeutic agents. (9)

  1. Magali Olivier, David E. Goldgar, Nayanta Sodha, Hiroko Ohgaki, Paul Kleihues, Pierre Hainaut and Rosalind A. Eeles. Li-Fraumeni and Related Syndromes. Cancer Res October 15 2003 63 (20) 6643-6650 http://cancerres.aacrjournals.org/content/63/20/6643.abstract
  2. Kleihues P, Schauble B, zur Hausen H, et al. Tumors associated with p53 germline mutations: a synopsis of 91 families. Am J Pathol 1997; 150:1-13 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1858532/
  3. John P. Neoptolemos, Raul Urrutia, James L. Abbruzzese, Markus W. Buchler. Pancreatic Cancer. 2010.1st ed, pp-6, 2010, Springer, Verlag, New York
  4. Mishra B and Patel RR. Gene Therapy for Treatment of Pancreatic Cancer. Austin Therapeutics. 2014;1(1): 10. https://books.google.ca/books?id=NmBB5ZoKkk4C&pg=PA6&lpg=PA6&dq=connection+between+li+fraumeni+and+Pancreatic+cancer&source=bl&ots=H0iCeaPP0N&sig=pqJT1tPMR6C-NIig3S_NkFKFsD0&hl=en&sa=X&ved=0ahUKEwi4nLrgzuPQAhUUIWMKHS3wBoc4ChDoAQhNMAg#v=onepage&q=connection%20between%20li%20fraumeni%20and%20Pancreatic%20cancer&f=false
  5. Schneider K, Zelley K, Nichols KE, et al. Li-Fraumeni Syndrome. 1999 Jan 19 [Updated 2013 Apr 11]. In: Pagon RA, Adam MP, Ardinger HH, et al., editors. GeneReviews® [Internet]. Seattle (WA): University of Washington, Seattle; 1993-2016. https://www.ncbi.nlm.nih.gov/pubmed/20301488
  6. Elisa Becze BA, ELS, 2011 Mar 1. An introduction to Li-Fraumeni Syndrome, Five-Minute-In-Service. http://connect.ons.org/columns/five-minute-in-service/an-introduction-to-li-fraumeni-syndrome
  7. Sorrell, A. D., Espenschied, C. R., Culver, J. O., & Weitzel, J. N. (2013).TP53Testing and Li-Fraumeni Syndrome: Current Status of Clinical Applications and Future Directions. Molecular Diagnosis & Therapy17(1), 31–47. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3627545/
  8. Emily J. Lewis. PRIMA-1 as a cancer therapy restoring mutant p53: a reviewBioscience Horizons (2015) 8: hzv006 http://biohorizons.oxfordjournals.org/content/8/hzv006.full
  9. Izetti, Patricia, Agnes Hautefeuille, Ana Lucia Abujamra, Caroline Brunetto de Farias, Juliana Giacomazzi, Bárbara Alemar, Guido Lenz, et al. ‘PRIMA-1, a Mutant p53 Reactivator, Induces Apoptosis and Enhances Chemotherapeutic Cytotoxicity in Pancreatic Cancer Cell Lines’. Investigational New Drugs 32, no. 5 (October 2014): 783–94. https://www.ncbi.nlm.nih.gov/pubmed/24838627

Izetti, Patricia, Agnes Hautefeuille, Ana Lucia Abujamra, Caroline Brunetto de Farias, Juliana Giacomazzi, Bárbara Alemar, Guido Lenz, et al. ‘PRIMA-1, a Mutant p53 Reactivator, Induces Apoptosis and Enhances Chemotherapeutic Cytotoxicity in Pancreatic Cancer Cell Lines’. Investigational New Drugs 32, no. 5 (October 2014): 783–94

Other related articles published in this Online Scientific Journal include the following:

p53 mutation – Li-Fraumeni Syndrome – Likelihood of Genetic or Hereditary conditions playing a role in Intergenerational incidence of Cancer

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/12/01/p53-mutation-li-fraumeni-syndrome-likelihood-of-genetic-or-hereditary-conditions-playing-a-role-in-intergenerational-incidence-of-cancer/

Pancreatic Cancer: Articles of Note @PharmaceuticalIntelligence.com

Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/05/26/pancreatic-cancer-articles-of-note-pharmaceuticalintelligence-com/

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Cancer-free after immunotherapy treatment: Treating advanced colon cancer – targeting KRAS gene mutation by tumor-infiltrating lymphocytes (TILs) and Killer T-cells (NK)

Reporter: Aviva Lev-Ari, PhD, RN

 

Based on

SOURCE

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Inactivation of an enzyme needed to metabolize glucose by Vitamic C deprives tumor cells of energy

Reporter: Aviva Lev-Ari, PhD, RN

 

 

Vitamin C did kill cultured colon cancer cells with BRAF or KRAS mutations by raising free radical levels, which in turn inactivate an enzyme needed to metabolize glucose, depriving the cells of energy.

 

Vitamin C selectively kills KRAS and BRAF mutant colorectal cancer cells by targeting GAPDH

Glucose Deprivation Contributes to the Development of KRAS Pathway Mutations in Tumor Cells

A few years ago, Jihye Yun, then a graduate student at Johns Hopkins University in Baltimore, Maryland, found that colon cancer cells whose growth is driven by mutations in the gene KRAS or a less commonly mutated gene,BRAF, make unusually large amounts of a protein that transports glucose across the cell membrane. The transporter, GLUT1, supplies the cells with the high levels of glucose they need to survive. GLUT1 also transports the oxidized form of vitamin C, dehydroascorbic acid (DHA), into the cell, bad news for cancer cells, because Yun found that DHA can deplete a cell’s supply of a chemical that sops up free radicals. Because free radicals can harm a cell in various ways, the finding suggested “a vulnerability” if the cells were flooded with DHA, says Lewis Cantley at Weill Cornell Medicine in New York City, where Yun is now a postdoc.

Cantley’s lab and collaborators found that large doses of vitamin C did indeed kill cultured colon cancer cells with BRAF or KRAS mutations by raising free radical levels, which in turn inactivate an enzyme needed to metabolize glucose, depriving the cells of energy. Then they gave daily high dose injections—equivalent to a person eating 300 oranges—to mice engineered to develop KRAS-driven colon tumors. The mice developed fewer and smaller colon tumors compared with control mice.

SOURCE
http://www.sciencemag.org/news/2015/11/vitamin-c-kills-tumor-cells-hard-treat-mutation

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A Mechanism of Cancer Metastasis

Larry H. Bernstein, MD, FCAP, Curator

LPBI

Screen Shot 2021-07-19 at 6.11.06 PM

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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miRNA Therapeutic Promise

Curator: Larry H. Bernstein, MD, FCAP

 

MicroRNA Expression Could Be Key to Leukemia Treatment

http://www.genengnews.com/gen-news-highlights/microrna-expression-could-be-key-to-leukemia-treatment/81252662/

MicroRNA Expression Could Be Key to Leukemia Treatment

Generalized gene regulation mechanisms of miRNAs. [NIH]

 

Increasingly, cancer researchers are discovering novel biological pathways that regulate the expression of various genes that are often strongly associated with tumorigenesis. These new molecular mechanisms represent important potential therapeutic targets for aggressive and difficult-to-treat cancers. In particular, microRNAs (miRNAs)—small, noncoding genetic material that regulates gene expression—have steadily become implicated in the progression of some cancers.

Now, researchers at the University of Cincinnati (UC) have found a particular signaling route for a microRNA, miR-22, that they believe leads to targets for acute myeloid leukemia (AML), the most common type of fast-growing cancer of the blood and bone marrow.

The findings from this study were published recently in Nature Communications in an article entitled “miR-22 Has a Potent Anti-Tumour Role with Therapeutic Potential in Acute Myeloid Leukaemia.”

Structure of mi-22 miccroRNA. [Ppgardne at el., via Wikimedia Commons]

Increasingly, cancer researchers are discovering novel biological pathways that regulate the expression of various genes that are often strongly associated with tumorigenesis. These new molecular mechanisms represent important potential therapeutic targets for aggressive and difficult-to-treat cancers. In particular, microRNAs (miRNAs)—small, noncoding genetic material that regulates gene expression—have steadily become implicated in the progression of some cancers.

Now, researchers at the University of Cincinnati (UC) have found a particular signaling route for a microRNA, miR-22, that they believe leads to targets for acute myeloid leukemia (AML), the most common type of fast-growing cancer of the blood and bone marrow.

The findings from this study were published recently in Nature Communications in an article entitled “miR-22 Has a Potent Anti-Tumour Role with Therapeutic Potential in Acute Myeloid Leukaemia.”

“MicroRNAs make up a class of small, noncoding internal RNAs that control a gene’s job, or expression, by directing their target messaging RNAs, or mRNAs, to inhibit or stop. Cellular organisms use mRNA to convey genetic information,” explained senior study author Jianjun Chen, Ph.D., associate professor in the department of cancer biology at the UC College of Medicine. “Previous research has shown that microRNA miR-22 is linked to breast cancer and other blood disorders which sometimes turn into AML, but we found in this study that it could be an essential anti-tumor gatekeeper in AML when it is down-regulated, meaning its function is minimized.”

AML—most common type of acute leukemia—arises when the bone marrow begins to make blasts, cells that have not yet completely matured. These blast cells typically develop into white blood cells; however, in AML the cells do not develop and are unable to aid in warding off infections. In the current study, the UC team describes how altering the expression of miR-22 affected AML pathogenesis.

“When we forced miR-22 expression, we saw difficulty in leukemia cells developing, growing, and thriving. miR-22 targets multiple cancer-causing genes (CRTC1, FLT3, and MYCBP) and blocks certain pathways (CREB and MYC),” Dr. Chen noted. “The downregulation, or decreased output, of miR-22 in AML, is caused by the loss of the number of DNA being copied and/or stopping their expression through a pathway called TET1/GFI1/EZH2/SIN3A. Also, nanoparticles carrying miR-22 DNA oligonucleotides (short nucleic acid molecules) prevented leukemia advancement.”

The investigators conducted the study using bone marrow transplant samples and animal models. The researchers showed that the ten-eleven translocation proteins (TET1/2/3) in mammals helped to control genetic expression in normal developmental processes. This was in sharp contrast to mutations that cause function loss and tumor-slowing with TET2, which has been observed previously in blood and stem cell cancers.

“We recently reported that TET1 plays an essential cancer generating role in certain AML where it activates expression of homeobox genes, which are a large family of similar genes that direct the formation of many body structures during early embryonic development,” remarked Dr. Chen. “However, it is unknown whether TET1 can also function as a repressor for cellular function in cancer, and its role in microRNA expression has rarely been studied.”

Dr. Chen added that these findings are important in targeting a cancer that is both common and fatal, stating that “the majority of patients with ALM usually don’t survive longer than 5 years, even with chemotherapy, which is why the development of new effective therapies based on the underlying mechanisms of the disease is so important.”

“Our study uncovers a previously unappreciated signaling pathway (TET1/GFI1/EZH2/SIN3A/miR-22/CREB-MYC) and provides new insights into genetic mechanisms causing and progressing AML and also highlights the clinical potential of miR-22-based AML therapy. More research on this pathway and ways to target it are necessary,” Dr. Chen concluded.

 

miR-22 has a potent anti-tumour role with therapeutic potential in acute myeloid leukaemia

Xi JiangChao HuStephen ArnovitzJason BugnoMiao YuZhixiang ZuoPing Chen, et al.
Nature Communications 26 Apr 2016; 7(11452).    http://dx.doi.org:/doi:10.1038/ncomms11452

MicroRNAs are subject to precise regulation and have key roles in tumorigenesis. In contrast to the oncogenic role of miR-22 reported in myelodysplastic syndrome (MDS) and breast cancer, here we show that miR-22 is an essential anti-tumour gatekeeper in de novo acute myeloid leukaemia (AML) where it is significantly downregulated. Forced expression of miR-22 significantly suppresses leukaemic cell viability and growth in vitro, and substantially inhibits leukaemia development and maintenance in vivo. Mechanistically, miR-22 targets multiple oncogenes, including CRTC1, FLT3 and MYCBP, and thus represses the CREB and MYC pathways. The downregulation of miR-22 in AML is caused by TET1/GFI1/EZH2/SIN3A-mediated epigenetic repression and/or DNA copy-number loss. Furthermore, nanoparticles carrying miR-22 oligos significantly inhibit leukaemia progression in vivo. Together, our study uncovers a TET1/GFI1/EZH2/SIN3A/miR-22/CREB-MYC signalling circuit and thereby provides insights into epigenetic/genetic mechanisms underlying the pathogenesis of AML, and also highlights the clinical potential of miR-22-based AML therapy.

 

As one of the most common and fatal forms of hematopoietic malignancies, acute myeloid leukaemia (AML) is frequently associated with diverse chromosome translocations (for example t(11q23)/MLL-rearrangements, t(15;17)/PML-RARA and t(8;21)/AML1-ETO) and molecular abnormalities (for example, internal tandem duplications of FLT3 (FLT3-ITD) and mutations in nucleophosmin (NPM1c+))1. Despite intensive chemotherapies, the majority of patients with AML fail to survive longer than 5 years2, 3. Thus, development of effective therapeutic strategies based on a better understanding of the molecular mechanisms underlying the pathogenesis of AML is urgently needed.

MicroRNAs (miRNAs) are a class of small, non-coding RNAs that post-transcriptionally regulate gene expression4. Individual miRNAs may play distinct roles in cancers originating from different tissues or even from different lineages of hematopoietic cells4. It is unclear whether a single miRNA can play distinct roles between malignancies originating from the same hematopoietic lineage, such as de novo AML and myelodysplastic syndrome (MDS). Although around 30% of MDS cases transform to AML, the genetic and epigenetic landscapes of MDS or MDS-derived AML are largely different from those of de novo AML5, 6. MDS and MDS-derived AML are more responsive to hypomethylating agents than de novo AML7. The molecular mechanisms underlying the distinct pathogenesis and drug response between MDS (or MDS-derived AML) and de novo AML remain unclear.

The ten-eleven translocation (Tet1/2/3) proteins play critical transcriptional regulatory roles in normal developmental processes as activators or repressors8, 9, 10. In contrast to the frequent loss-of-function mutations and tumour-suppressor role of TET2 observed in hematopoietic malignancies11, 12, 13, we recently reported that TET1 plays an essential oncogenic role in MLL-rearranged AML where it activates expression of homeobox genes14. However, it is unknown whether TET1 can also function as a transcriptional repressor in cancer. Moreover, Tet1-mediated regulation of miRNA expression has rarely been studied10.

In the present study, we demonstrate that miR-22, an oncogenic miRNA reported in breast cancer and MDS15, 16, is significantly downregulated in most cases of de novo AML due to TET1/GFI1/EZH2/SIN3A-mediated epigenetic repression and/or DNA copy-number loss. miR-22 functions as an essential anti-tumour gatekeeper in various AML and holds great therapeutic potential to treat AML.

 

The downregulation of miR-22 in de novo AML

Through Exiqon miRNA array profiling, we previously identified a set of miRNAs, such as miR-150, miR-148a, miR-29a, miR-29b, miR-184, miR-342, miR-423 and miR-22, which are significantly downregulated in AML compared with normal controls17. Here we showed that among all the above miRNAs, miR-150 and especially miR-22 exhibited the most significant and consistent inhibitory effect on MLL-AF9-induced cell immortalization in colony-forming/replating assays (CFA) (Supplementary Fig. 1a). In contrast to the reported upregulation of miR-22 in MDS16, our original microarray data17 (Fig. 1a,b) and new quantitative PCR-independent validation data (Supplementary Fig. 1b) demonstrated a significant and global downregulation of miR-22 in de novo AML relative to normal controls. Notably, miR-22 is significantly downregulated in AML samples (P<0.05) compared with all three sub-populations of normal control cells, that is, normal CD34+ hematopoietic stem/progenitor cells (HSPCs), CD33+ myeloid progenitor cells, or mononuclear cells (MNCs) (Fig. 1a). Expression of miR-22 is significantly downregulated in all or the majority of individual subsets of AML samples than in the normal CD33+ or CD34+ cell samples (Fig. 1b).

Figure 1: miR-22 inhibits AML cell transformation and leukemogenesis.

miR-22 inhibits AML cell transformation and leukemogenesis.

(a,b) Exiqon microRNA profiling assay showed that miR-22 is significantly (P<0.05) downregulated in the entire set of AML set (n=85) (a) or each individual subset (b), relative to normal controls. The expression data were log(2) transformed and mean-centred. Mean±s.e.m. values were shown. (c) Comparison of effects of in-house miR-22, miR-22_Song16 and miR-22 mutant (miR-22mut; see the mutation sequence at the top) on MLL-AF9-induced colony forming. CFAs were performed using mouse BM progenitor (Lin) cells transduced with MSCV-neo+MSCV-PIG (Ctrl), MSCV-neo-MLL-AF9+MSCV-PIG (MLL-AF9), or MSCV-neo-MLL-AF9+MSCV-PIG-miR-22/miR-22_Song/miR-22mut. (d) Effects of miR-22 on the colony forming induced by multiple fusion genes. CFA was performed using wild-type BM progenitor cells co-transduced with MSCV-neo-MLL-AF9 (MA9), -MLL-AF10 (MA10), -PML-RARA (PR) or –AML1-ETO9a(AE9a)19, together with MSCV-PIG (Ctrl) or MSCV-PIG-miR-22 (+miR-22), as well as miR-22−/− BM progenitors co-transduced with individual fusion genes and MSCV-PIG. Colony counts (mean±s.d.) of the second round of plating are shown. *P<0.05; **P<0.01. (e,f) Effect of miR-22 on MLL-AF9-induced primary leukemogenesis. Kaplan–Meier curves are shown for six cohorts of transplanted mice including MSCVneo+MSCV-PIG (Ctrl; n=5), MSCVneo+MSCV-PIG-miR-22 (miR-22; n=5), MSCVneo-MLL-AF9+MSCV-PIG (MA9; n=8), MSCVneo-MLL-AF9+MSCV-PIG-miR-150 (MA9+miR-150, n=6), MSCVneo-MLL-AF9+MSCV-PIG-miR-22 (MA9+miR-22; n=10) and MSCVneo-MLL-AF9+MSCV-PIG-miR-22mutant (MA9+miR-22mut; n=5) (e); Wright–Giemsa stained PB and bone marrow (BM), and hematoxylin and eosin (H&E) stained spleen and liver of the primary BMT recipient mice at the end point are shown (f). (g) Effect of miR-22 on MLL-AF10-induced primary leukemogenesis. Kaplan–Meier curves are shown for two cohorts of transplanted mice including MSCVneo-MLL-AF10+MSCV-PIG (MA10; n=5) and MSCVneo-MLL-AF10+MSCV-PIG-miR-22 (MA10+miR-22; n=5). (h) miR-22 knockout promotes AE9a-induced leukemogenesis. Kaplan–Meier curves are shown for mice transplanted with wild-type or miR-22−/− BM progenitor cells transduced MSCV-PIG-AE9a (n=5 for each group). The P values were generated by t-test (ad) or log-rank test (e,g,h).

To rule out the possibility that the inhibitory effect of miR-22 shown in Supplementary Fig. 1a was due to a non-specific effect of our miR-22 construct, we included the MSCV-PIG-miR-22 construct from Song et al.16 in a repeated CFA. Both miR-22 constructs dramatically inhibited MLL-AF9-induced colony formation (Fig. 1c). As the ‘seed’ sequences at the 5′ end of individual miRNAs are essential for the miRNA-target binding18, we also mutated the 6-bases ‘seed’ sequence of miR-22 and found that the miR-22 mutant did not inhibit colony formation anymore (Fig. 1c). In human AML cells, forced expression of miR-22, but not miR-22 mutant, significantly inhibited cell viability and growth/proliferation, while promoting apoptosis (Supplementary Fig. 1c,d).

Furthermore, as miR-22 is globally downregulated in all major types of AML (Fig. 1b), we also investigated the role of miR-22 in colony formation induced by other oncogenic fusion genes, including MLL-AF10/t(10;11), PML-RARA/t(15;17) and AML1-ETO9a/t(8;21) (ref. 19). As expected, forced expression of miR-22 significantly inhibited colony formation induced by all individual oncogenic fusions; conversely, miR-22 knockout20 significantly enhanced colony forming (Fig. 1d). These results suggest that miR-22 likely plays a broad anti-tumour role in AML.

In accordance with the potential anti-tumour function of miR-22 in AML, miR-22 was expressed at a significantly higher level (P<0.05) in human normal CD33+ myeloid progenitor cells than in more immature CD34+ HSPCs or MNC cells (a mixed population containing both primitive progenitors and committed cells) (Fig. 1a,b), implying that miR-22 is upregulated during normal myelopoiesis. Similarly, we showed that miR-22 was also expressed at a significantly higher level in mouse normal bone marrow (BM) myeloid (Gr-1+/Mac-1+) cells, relative to lineage negative (Lin) progenitor cells, long-term hematopoietic stem cells (LT-HSCs), short-term HSCs (ST-HSCs), and committed progenitors (CPs) (Supplementary Fig. 1e), further suggesting that miR-22 is upregulated in normal myelopoiesis.

The anti-tumour effect of miR-22 in the pathogenesis of AML

Through bone marrow transplantation (BMT) assays, we showed that forced expression of miR-22 (but not miR-22 mutant) dramatically blocked MLL-AF9 (MA9)-mediated leukemogenesis in primary BMT recipient mice, with a more potent inhibitory effect than miR-150 (Fig. 1e;Supplementary Fig. 2a). All MA9+miR-22 mice exhibited normal morphologies in peripheral blood (PB), BM, spleen and liver tissues (Fig. 1f), with a substantially reduced c-Kit+ blast cell population in BM (Supplementary Fig. 2b). Forced expression of miR-22 also almost completely inhibited leukemogenesis induced by MLL-AF10 (Fig. 1g; Supplementary Fig. 2a). Conversely, miR-22 knockout significantly promoted AML1-ETO9a (AE9a)-induced AML (Fig. 1h). Thus, the repression of miR-22 is critical for the development of primary AML. Notably, forced expression of miR-22 inMLL-AF9 and MLL-AF10 leukaemia mouse models caused only a 2–3-fold increase in miR-22 expression level (Supplementary Fig. 2a), in a degree comparable to the difference in miR-22 expression levels between human AML samples and normal controls (Fig. 1a), suggesting that a 2–3-fold change in miR-22 expression level appears to be able to exert significant physiological or pathological effects.

To examine whether the maintenance of AML is also dependent on the repression of miR-22, we performed secondary BMT assays. Forced expression of miR-22 remarkably inhibited progression of MLL-AF9-, AE9a– or FLT3-ITD/NPM1c+-induced AML in secondary recipient mice (Fig. 2a–d), resulting in largely normal morphologies in PB, BM, spleen and liver tissues (Fig. 2b;Supplementary Fig. 2c). Collectively, our findings demonstrate that miR-22 is a pivotal anti-tumour gatekeeper in both development and maintenance of various AML.

Figure 2: Effect of miR-22 on the maintenance of AML in vivo.

Effect of miR-22 on the maintenance of AML in vivo.

(a,b) Effect of miR-22 on the maintenance of MLL-AF9-induced AML in secondary BMT recipient mice. The secondary BMT recipients were transplanted with BM blast cells from the primary MLL-AF9 AML mice retrovirally transduced with MSCV-PIG+MSCVneo (MA9-AML+Ctrl; n=7) or MSCV-PIG+MSCVneo-miR-22 (MA9-AML+miR-22; n=10). Kaplan–Meier curves (a) and Wright–Giemsa or H&E-stained PB, BM, spleen and liver (b) of the secondary leukaemic mice are shown. (c,d) Effect of miR-22 on the maintenance/progression of AML1-ETO9a (AE9a)-induced AML (c) or FLT3-ITD/NPM1c+-induced AML (d) in secondary BMT recipient mice (n=5 for each group). Kaplan–Meier curves and P values (log-rank test) are shown.

 

Identification of critical target genes of miR-22 in AML

To identify potential targets of miR-22 in AML, we performed a series of data analysis. Analysis of In-house_81S (ref. 21) and TCGA_177S (ref. 22) data sets revealed a total of 999 genes exhibiting significant inverse correlations with miR-22 in expression. Of them, 137 genes, including 21 potential targets of miR-22 as predicted by TargetScan18 (Supplementary Table 1), were significantly upregulated in both human and mouse AML compared with normal controls as detected in two additional in-house data sets14, 23. Among the 21 potential targets, CRTC1, ETV6and FLT3 are known oncogenes24, 25, 26, 27, 28, 29. We then focused on these three genes, along with MYCBP that encodes the MYC-binding protein and is an experimentally validated target of miR-22 (ref. 30) although due to a technical issue it was not shown in the 21-gene list (Supplementary Table 1), for further studies.

As expected, all four genes were significantly downregulated in expression by ectopic expression of miR-22 in human MONOMAC-6/t(9;11) cells (Fig. 3a). The coincidence of downregulation of those genes and upregulation of miR-22 was also observed in mouse MLL-ENL-ERtm cells, a leukaemic cell line with an inducible MLL-ENL derivative31, when MLL-ENL was depleted by 4-hydroxy-tamoxifen (4-OHT) withdrawal (Fig. 3b; Supplementary Fig. 3a). While MLL-AF9 remarkably promoted expression of those four genes in mouse BM progenitor cells, co-expressed miR-22 reversed the upregulation (Fig. 3c). In leukaemia BM blast cells of mice with MLL-AF9-induced AML, the expression of Crtc1, Flt3 and Mycbp, but not Etv6, was significantly downregulated by co-expressed miR-22 (but not by miR-22 mutant) (Fig. 3d). Because miR-22-mediated downregulation of Etv6 could be observed only in the in vitro models (Fig. 3a–c), but not in the in vivo model (Fig. 3d), which was probably due to the difference between in vitro and in vivo microenvironments, we decided to focus on the three target genes (that is, Crtc1, Flt3 and Mycbp) that showed consistent patterns between in vitro and in vivo for further studies. The repression of Crtc1, Flt3 and Mycbpwas also found in leukaemia BM cells of mice with AE9a or FLT3-ITD/NPM1c+-induced AML (Fig. 3e,f). As Mycbp is already a known target of miR-22 (ref. 30), here we further confirmed that FLT3and CRTC1 are also direct targets of miR-22 (Fig. 3g,h). The downregulation of CRTC1, FLT3 and MYCBP by miR-22 at the protein level was confirmed in both human and mouse leukaemic cells (Supplementary Fig. 3b,c). Overexpression of miR-22 had no significant influence on the level of leukaemia fusion genes (Supplementary Fig. 3d).

Figure 3: miR-22 targets multiple oncogenes.

miR-22 targets multiple oncogenes.

(a) Downregulation of CRTC1, FLT3, MYCBP and ETV6 by forced expression of miR-22 in MONOMAC-6 cells. Expression of these genes was detected 48h post transfection of MSCV-PIG (Ctrl) or MSCV-PIG-miR-22 (miR-22). (b) Crtc1, Flt3, Mycbp and Etv6 levels in MLL-ENL-ERtm cells after withdrawal of 4-OHT for 0, 7 or 10 days. (c) Expression levels of Crtc1, Flt3, Mycbp and Etv6 in mouse BM progenitor cells retrovirally transduced with MSCV-PIG+MSCV-neo (Ctrl), MSCV-PIG-miR-22+MSCV-neo (miR-22), MSCV-PIG+MSCV-neo-MLL-AF9 (MLL-AF9) or MSCV-PIG-miR-22+MSCV-neo-MLL-AF9 (MLL-AF9+miR-22). (d) Expression levels of Crtc1, Flt3, Mycbp and Etv6 in BM blast cells of leukaemic mice transplanted with MLL-AF9, MLL-AF9+miR-22 or MLL-AF9+miR-22mut primary leukaemic cells. (e,f) Expression levels of Crtc1, Flt3 and Mycbp in BM blast cells of leukaemic mice transplanted with MSCV-PIG or MSCV-PIG-miR-22-retrovirally transduced AE9a (e) or FLT3-ITD/NPM1c+ (f) primary leukaemic cells. (g) Putative miR-22 target sites and mutants in the 3′UTRs of CRTC1 (upper panel) and FLT3(lower panel). (h) Effects of miR-22 on luciferase activity of the reporter gene bearing wild type or mutant 3′UTRs of CRTC1 or FLT3 in HEK293T cells. The mean±s.d. values from three replicates are shown.*P<0.05, t-test.

Co-expression of the coding region (CDS) of each of the three target genes (that is, CRTC1, FLT3and MYCBP) largely reversed the effects of miR-22 on cell viability, apoptosis and proliferation (Fig. 4a–e). More importantly, in vivo BMT assays showed that co-expressing CRTC1, FLT3 orMYCBP largely rescued the inhibitory effect of miR-22 on leukemogenesis (Fig. 4f,g;Supplementary Fig. 3e). Our data thus suggest that CRTC1, FLT3 and MYCBP are functionally important targets of miR-22 in AML.

Figure 4: Multiple onocgenes are functionally important targets of miR-22 in AML.

Multiple onocgenes are functionally important targets of miR-22 in AML.

(a,b) Relative viability (a) and apoptosis (b) levels of MONOMAC-6 cells transfected with MSCV-PIG-CRTC1, -FLT3 or –MYCBP alone, or together with MSCVneo-miR-22. Values were detected 48h post transfection. (c–e) Rescue effects of CRTC1 (c), FLT3 (d) and MYCBP (e) on the inhibition of MONOMAC-6 growth mediated by miR-22. Cell counts at the indicated time points are shown. Mean±s.d. values are shown. *P<0.05, t-test. (f) In vivo rescue effects of CRTC1, FLT3 and MYCBP on the inhibition of MLL-AF9-induced leukemogenesis mediated by miR-22. The secondary recipients were transplanted with BM blast cells of the primary MLL-AF9 leukaemic mice retrovirally transduced with MSCVneo+MSCV-PIG (MA9-AML+Ctrl; n=7), MSCVneo-miR-22+MSCV-PIG (MA9-AML+miR-22; n=10), MSCVneo-miR-22+MSCV-PIG-CRTC1 (MA9-AML+miR-22+CRTC1; n=5), MSCVneo-miR-22+MSCV-PIG-FLT3 (MA9-AML+miR-22+FLT3; n=6) or MSCVneo-miR-22+MSCV-PIG-MYCBP (MA9-AML+miR-22+MYCBP; n=6). Kaplan–Meier curves for all the five groups of transplanted mice are shown. MA9-AML+Ctrl versus MA9-AML+miR-22, P<0.001 (log-rank test); MA9-AML+Ctrl versus any other groups,P>0.05 (log-rank test). (g) Wright–Giemsa stained PB and BM, and H&E stained spleen and liver of the secondary leukaemic mice.

miR-22 represses both CREB and MYC signalling pathways

DNA copy-number loss of miR-22 gene locus in AML

Expression of miR-22 is epigenetically repressed in AML

 

Figure 5: Transcriptional correlation between miR-22 and TET1.

http://www.nature.com/ncomms/2016/160426/ncomms11452/images_article/ncomms11452-f5.jpg

(a) Correlation between the expression levels of miR-22 and TET1 in three independent AML patient databases. All expression data were log(2) transformed; the data in In-house_81S were also mean-centred. The correlation coefficient (r) and P values were detected by ‘Pearson Correlation’, and the correlation regression lines were drawn with the ‘linear regression’ algorithm. (b) Expression of pri-, pre- and mature miR-22, and Tet1/2/3 in colony-forming cells of wild-type mouse BM progenitors retrovirally transduced with MSCVneo (Ctrl), MSCVneo-MLL-AF9 (MLL-AF9), MSCVneo-MLL-AF10 (MLL-AF10) or MSCVneo-AE9a (AE9a), or of FLT3-ITD/NPM1c+ mouse BM progenitors transduced with MSCVneo (FLT3-ITD+/NPM1c+). (c) Expression of miR-22 and Tet1/2/3 in MLL-ENL-ERtm cells. Expression levels were detected at the indicated time points post 4-OHT withdrawal. (d) Effect of miR-22 overexpression onTet1 expression in colony-forming cells with MLL-AF9, AE9a or FLT3-ITD/NPM1c+. (e) Expression ofTet1 in BM progenitor cells of 6-weeks old miR-22−/− or wild-type mice. (f) Effect of miR-22 overexpression on TET1 expression in THP-1 and KOCL-48 AML cells 48h post transfection. (g) Expression of pri-, pre- and mature miR-22 in BM progenitor cells of 6-weeks old Tet1−/− or wild-type mice. Mean±s.d. values are shown. *P<0.05, t-test.

http://www.nature.com/ncomms/2016/160426/ncomms11452/images_article/ncomms11452-f6.jpg

(a) Tet1 targets miR-22 promoter region (−1,100/+55bp), as detected by luciferase reporter assay 48h post transfection in HEK293T cells. (b) Expression of TET1/2/3, EZH2, SIN3A, GFI1 and miR-22 in THP-1 cells 72h post treatment with 1μM ATRA or DMSO control. (c) Co-immunoprecipitation assay showing the binding of endogenous GFI1 and TET1 in THP1 cells. (d) ChIP-qPCR analyses of the promoter region of miR-22 in THP-1 cells 72h post treatment with 1μM ATRA or DMSO. Upper panel: PCR site on the CpG-enriched region of miR-22 gene locus. Note: miR-22 is coded within the second exon of a long non-coding RNA (MIR22HG), which represents the primary transcript of miR-22. Lower panels: enrichment of MLL-N terminal (for both wild-type MLL and MLL-fusion proteins), MLL-C terminal (for wild-type MLL), TET1, EZH2, SIN3A, GFI1, H3K27me3, H3K4me3 or RNA pol II at miR-22 promoter region. (e) Expression levels of TET1, EZH2, SIN3A and miR-22 in GFI1 knockdown cells. (f) ChIP-qPCR analyses of the promoter region of miR-22 in THP-1 cells transduced with GFI1 shRNA or control shRNA. Enrichment of GFI1, TET1, EZH2 and SIN3A are shown. (g) Effects of knockdown of TET1, EZH2 and/orSIN3A on miR-22 expression. The expression level of miR-22 was detected in THP-1 cells 72h post transfection with siRNAs targeting TET1, EZH2 and/or SIN3A. Mean±s.d. values are shown. *P<0.05;**P<0.01 (t-test). (h) Schematic model of the regulatory pathway involving miR-22 in AML and ATRA treatment.

 

The miR-22-associated regulatory circuit in AML

         Restoration of miR-22 expression and function to treat AML

 

Figure 7: Therapeutic effect of miR-22-nanoparticles in treating AML.

http://www.nature.com/ncomms/2016/160426/ncomms11452/images_article/ncomms11452-f7.jpg

(a,b) Primary leukaemia BM cells bearing MLL-AF9 (a) or AE9a (b) were transplanted into sublethally irradiated secondary recipient mice. After the onset of secondary AML (usually 10 days post transplantation), the recipient mice were treated with PBS control, or 0.5mgkg−1 miR-22 or miR-22 mutant RNA oligos formulated with G7 PAMAM dendrimer nanoparticles, i.v., every other day, until the PBS-treated control group all died of leukaemia. (c) NSGS mice49 were transplanted with MV4;11/t(4;11) AML cells. Five days post transplantation, these mice started to be treated with PBS control, miR-22 or miR-22 mutant nanoparticles at the same dose as described above. Kaplan–Meier curves are shown; the drug administration period and frequency were indicated with yellow arrows. The P values were detected by log-rank test. (d) Wright–Giemsa stained PB and BM, and H&E stained spleen and liver of the MLL-AF9-secondary leukaemic mice treated with PBS control, miR-22 or miR-22 mutant nanoparticles.

We then tested the miR-22 nanoparticles in a xeno-transplantation model49. Similarly, the nanoparticles carrying miR-22 oligos, but not miR-22 mutant, significantly delayed AML progression induced by human MV4;11/t(4;11) cells (Fig. 7c). The miR-22-nanoparticle administration also resulted in less aggressive leukaemic pathological phenotypes in the recipient mice (Supplementary Fig. 6e). Thus, our studies demonstrated the therapeutic potential of using miR-22-based nanoparticles to treat AML.

 

It remains poorly understood how TET proteins mediate gene regulation in cancer. Here we show that in de novo AML, it is TET1, but not TET2 (a reported direct target of miR-22 in MDS and breast cancer15, 16), that inversely correlates with miR-22 in expression and negatively regulates miR-22 at the transcriptional level. Likely together with GFI1, TET1 recruits polycomb cofactors (for example, EZH2/SIN3A) to the miR-22 promoter, leading to a significant increase in H3K27me3 occupancy and decrease in RNA pol II occupancy at that region, and thereby resulting in miR-22 repression in AML cells; such a repression can be abrogated by ATRA treatment. Thus, our study uncovers a novel epigenetic regulation mechanism in leukaemia involving the cooperation between TET1/GFI1 and polycomb factors.

Besides GFI1, it was reported that LSD1 is also a binding partner of TET1 (ref. 50). Interestingly, LSD1 is known as a common binding partner shared by TET1 and GFI1, and mediates the effect of GFI1 on hematopoietic differentiation51, 52. Thus, it is possible that LSD1 might also participate in the transcriptional repression of miR-22 as a component of the GFI1/TET1 repression complex.

We previously reported that TET1 cooperates with MLL fusions in positively regulating their oncogenic co-targets in MLL-rearranged AML14. Here we show that TET1 can also function as a transcriptional repressor (of a miRNA) in cancer. The requirement of TET1-mediated regulation on expression of its positive (for example, HOXA/MEIS1/PBX3)14 or negative (for example, miR-22) downstream effectors in leukemogenesis likely explains the rareness of TET1 mutations in AML53, and highlights its potent oncogenic role in leukaemia.

The aberrant activation of both CREB and MYC signalling pathways has been shown in AML24, 25,26, 54, 55, but the underlying molecular mechanisms remain elusive. Our data suggest that the activation of these two signalling pathways in AML can be attributed, at least in part, to the repression of miR-22, which in turn, results in the de-repression of CRTC1 (CREB pathway), FLT3and MYCBP (MYC pathway), and leads to the upregulation of oncogenic downstream targets (for example, CDK6, HOXA7, BMI1, FASN and HMGA1) and downregulation of tumour-suppressor downstream targets (for example, RGS2).

In summary, we uncover a TET1/GFI1/EZH2/SIN3A⊣miR-22⊣CREB-MYC signalling circuit in de novo AML, in which miR-22 functions as a pivotal anti-tumour gate-keeper, distinct from its oncogenic role reported in MDS or MDS-derived AML16. Thus, our study together with the study of Song et al.16 highlight the complexity and functional importance of miR-22-associated gene regulation and signalling pathways in hematopoietic malignancies, and may provide novel insights into the genetic/epigenetic differences between de novo AML and MDS.

Our findings also highlight the possibility of using miR-22-based therapy to treat AML patients. Our proof-of-concept studies demonstrate that the nanoparticles carrying miR-22 oligos significantly inhibit AML progression and prolong survival of leukaemic mice in both BMT and xeno-transplantation models. Notably, miRNA-based nanoparticles have already entered clinical trials56. It would be important, in the future, to further test the combination of miR-22-carrying nanoparticles (or small-molecule compounds that can induce endogenous expression of miR-22) with standard chemotherapy agents (cytosine arabinoside and anthracycline), or with the emerging small molecule inhibitors against MYC and/or CREB pathway effectors, to achieve optimal anti-leukaemia effect with minimal side effects. Overall, our results suggest that restoration of miR-22 expression/function (for example, using miR-22-carrying nanoparticles or small-molecule compounds) holds great therapeutic potential to treat AML, especially those resistant to current therapies.

 

MicroRNAs: A Gene Silencing Mechanism with Therapeutic Implications  

Wed, July 13, 2016   The New York Academy of Sciences    Presented by the Biochemical Pharmacology Discussion Group
http://www.nyas.org/Events/Detail.aspx?cid=787a5d77-8354-4df7-92d5-91db18b2ce49

MicroRNAs (miRNAs) are single-stranded RNAs about 22 nucleotides in length that repress the expression of specific proteins by annealing to complementary sequences in the 3′ untranslated regions (UTRs) of target mRNAs. Apart from their posttranscriptional expression, or silencing, miRNAs may also direct mRNA destabilization and cleavage. Moreover, rather than targeting a single disease-associated protein target as many small molecule drugs and antibodies do, each miRNA may serve to repress the expression of numerous proteins involved in the pathogenesis and progression of various diseases and could therefore potentially interfere with multiple disease-promoting signal transduction pathways. Because aberrant expression of miRNAs has been implicated in numerous disease states, miRNA-based therapies have sparked much interest for the treatment of a variety of diseases. The objective of this symposium is to bring together investigators who have led the field in describing what miRNAs do and their potential in treating diseases, as well as those who are translating these findings into promising drug candidates, some of which have already advanced into early stage clinical trials.

Call for Poster Abstracts

Abstract submissions are invited for a poster session. For complete submission instructions, please send an email to miRNA@nyas.org with the words “Abstract Information” in the subject line. The deadline for abstract submission is May 13, 2016.

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Cancer initiatives

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Updated 4/12/2019

AACR 2016: Biden Calls for Overhauling Cancer Research Incentives

http://www.genengnews.com/gen-news-highlights/aacr-2016-biden-calls-for-overhauling-cancer-research-incentives/81252636/

 

The first priority cited by the vice president was data sharing. Biden defended the concept as essential to advancing the process of cancer research and countered a January 21 New England Journal of Medicine editorial in which editor-in-chief Jeffrey Drazen, M.D., contended that data sharing could breed data “parasites.”

Four days later, Dr. Drazen clarified NEJM’s position by adding that with “appropriate systems” in place, “we will require a commitment from authors to make available the data that underlie the reported results of their work within 6 months after we publish them.”

Other priorities Biden said should serve as the basis of new incentives:

  • Involve patients in clinical trial design—Raising awareness of trials, and allowing patients to participate in how they are designed and conducted, could help address the difficulty of recruiting patients for studies. Only 4% of cancer patients are involved in a trial, he said.
  • “Let scientists do science”—Biden contrasted unfavorably NIH’s roughly 1-year process for decisions on grants to that of the Prostate Cancer Foundation, which limits grant applications to 10 pages and decides on those funding requests within 30 days: “Why is it that it takes multiple submissions and more than a year to get an answer from us?” Biden said.
  • Encourage grants from younger researchers—Biden decried the current professional system under which younger researchers are sidetracked for years doing administrative work in labs before they can pursue their own research grants: “It’s like asking Derek Jeter to take several years off to sell bonds to build Yankee Stadium,” the VP quipped.
  • Measure progress by outcomes—Rather than the quantity of research papers generated by grants, Biden said, “what you propose and how it affects patients, it seems to me, should be the basis of whether you continue to get the grant.”
  • Promote open-access publication of results—Biden criticized academic publishing’s reliance on paid-subscription journals that block content behind paywalls and which own data for up to a year. He contrasted that system with the Bill and Melinda Gates Foundation’s stipulation that the research it funds be published in an open-access journal and be freely available once published.
  • Reward verification—Research that verifies results through replication should be encouraged, Biden said, which acknowledging that few people now get such funding.

Biden recalled how following Beau’s diagnosis with cancer, he and his wife Jill Biden, Ed.D., who introduced the VP at the AACR event, “had access to the best doctors in the world.”

“The more we talked to them, the more we understood that we are on the cusp of a real inflection point in the fight against cancer.”

Updated 4/12/2019

Pediatric Cancer Initiatives

Data Sharing for Pediatric Cancers: President Trump Announces Pledge to Fight Childhood Cancer Will Involve Genomic Data Sharing Effort

In the journal Science, Drs. Olena Morozova Vaske ( and David Haussler University of California, Santa Cruz) recently wrote an editorial entitled “Data Sharing for Pediatric Cancers“, in which they discuss the implications of President Trump’s intentions to increase funding for pediatric cancers with a corresponding effort for genomic data sharing.  Also discussed is the current efforts on pediatric genomic data sharing as well as some opinions on coordinating these efforts on a world-wide scale to benefit the patients, researchers, and clinicians.

The article is found below as it is a very good read on the state of data sharing in the pediatric cancer field and offers some very good insights in designing such a worldwide system to handle this data sharing, including allowing patients governance over their own data.

Last month, in a conference call held by the U.S. Department of Health and Human Services and National Institutes of Health (NIH), it was revealed that a large focus of President Trump’s pledge to fund childhood cancer research will be genomic data sharing. Although the United States has only 5% of the world’s pediatric cancer cases, it has disproportionately more resources and access to genomic information compared to low-income countries. We hope that the spotlight on genomic data sharing in the United States will galvanize the world’s pediatric cancer community to elevate genomic data sharing to a level where its full potential can finally be realized.

Pediatric cancers are rare, affecting 50 to 200 children per million a year worldwide. Thus, with 16 different major types and many subtypes, no cancer center encounters large cohorts of patients with the same diagnosis. To advance their understanding of particular cancer subtypes, pediatric oncologists must have access to data from similar cases at other centers. Because subtypes of pediatric cancer are rare, assembling large cohorts is a limiting factor in clinical trials as well. Here, too, data sharing is the first critical step.

Typically, pediatric cancers don’t have the number of mutations that make immunotherapies effective, and only a few subtypes have recurrent mutations that can be used to develop gene-targeted therapies. However, the abnormal expression level of genes gives a vivid picture of genetic misregulation, and just sharing this information would be a huge step forward. Using gene expression and mutation data, analysis of genetic misregulation in different pediatric cancer subtypes could point the way to new treatments.

A major challenge in genomic data sharing is the patient’s young age, which frequently precludes an opportunity for informed consent. Compounding this, the rarity of subtypes requires the aggregation of patients from multiple jurisdictions, raising barriers to assembling large representative data sets. A greater percentage of children than adults with cancer participate in research studies, and children often participate in multiple studies. However, this means that data collected on individual children may be found at multiple institutions, creating difficulties if there are no standards for data sharing.

To enable effective sharing of genomic and clinical data, the Global Alliance for Genomics and Health has developed the Key Implications for Data Sharing (KIDS) framework for pediatric genomics. The recommendations include involving children in the data-sharing decision-making process and imposing an ethical obligation on data generators to provide children and parents with the opportunity to share genomic and clinical information with researchers. Although KIDS guidelines are not legally binding, they could inform policy development worldwide.

To advance the sharing culture, along with the NIH, pediatric cancer foundations such as the St. Baldrick’s Foundation and Alex’s Lemonade Stand Foundation have incorporated genomic data-sharing requirements into their grants processes. Researchers and clinicians around the world have created dozens of pediatric cancer genomic databases and portals, but pulling these together into a larger network is problematic, especially for patients with data at more than one institution, as patient identifiers are stripped from shared data. However, initiatives like the Children’s Oncology Group’s Project Every Child and the European Network for Cancer Research in Children and Adolescents’ Unified Patient Identity may resolve this issue.

We urge the creators of pediatric cancer genomic resources to collaborate and build a real-time federated data-sharing system, and hope that the new U.S. initiative will inspire other countries to link databases rather than just create new siloed regional resources. The great advances in information technology and life sciences in the last decades have given us a new opportunity to save our children from the scourge of cancer. We must resolve to use them.

Source: Olena Morozova Vaske and David Haussler.  Science; 363(6432): 1125 (2019). Data sharing for pediatric cancers. 

NIH-NCI Initiative: International collaboration to create new cancer models to accelerate research

LIVE 1:45 pm – 3:10 pm 4/25/2016 Forum Opening, A War or Moonshot: Where Do We Stand? Creating a Disruptive Cancer Pipeline @2016 World Medical Innovation Forum: CANCER, April 25-27, 2016, Westin Hotel, Boston

Will President Obama’ s Cancer Immunotherapy Colloquium (dubbed Moonshot) mean Government is Fully Behind the War on Cancer or have we heard this before?

Exome Aggregation Consortium (ExAC), generated the largest catalogue so far of variation in human protein-coding regions: Sequence data of 60,000 people, NOW is a publicly accessible database

Healthcare conglomeration to access Big Data and lower costs

 

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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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