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Microbe meets cancer

Posted in Bacterial Resistance, Biochemical pathways, Biological Networks, Gene Regulation and Evolution, Cancer - General, Cancer Informatics, Cell Biology, Signaling & Cell Circuits, Chemical Biology and its relations to Metabolic Disease, Clinical & Translational, Clinical Genomics, Curation, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Gastroenterology, Gene Regulation, Genome Biology, Inflammasome, Liver & Digestive Diseases Research, Metabolism, Methylation, Microbiologial genetics, Microbiology, Molecular Genetics & Pharmaceutical, Oxidative phosphorylation, Warburg effect, tagged Cancer immunology, Cancer immunotherapy, gut microbiome, gut microbiota, Immunotherapy, Microbiology, microbiome on April 10, 2016| Leave a Comment »

Microbe meets cancer

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Microbes Meet Cancer

Understanding cancer’s relationship with the human microbiome could transform immune-modulating therapies.

By Kate Yandell | April 1, 2016  http://www.the-scientist.com/?articles.view/articleNo/45616/title/Microbes-Meet-Cancer

 © ISTOCK.COM/KATEJA_FN; © ISTOCK.COM/FRANK RAMSPOTT  http://www.the-scientist.com/images/April2016/feature1.jpg

In 2013, two independent teams of scientists, one in Maryland and one in France, made a surprising observation: both germ-free mice and mice treated with a heavy dose of antibiotics responded poorly to a variety of cancer therapies typically effective in rodents. The Maryland team, led by Romina Goldszmidand Giorgio Trinchieri of the National Cancer Institute, showed that both an investigational immunotherapy and an approved platinum chemotherapy shrank a variety of implanted tumor types and improved survival to a far greater extent in mice with intact microbiomes.¹ The French group, led by INSERM’s Laurence Zitvogel, got similar results when testing the long-standing chemotherapeutic agent cyclophosphamide in cancer-implanted mice, as well as in mice genetically engineered to develop tumors of the lung.²

The findings incited a flurry of research and speculation about how gut microbes contribute to cancer cell death, even in tumors far from the gastrointestinal tract. The most logical link between the microbiome and cancer is the immune system. Resident microbes can either dial up inflammation or tamp it down, and can modulate immune cells’ vigilance for invaders. Not only does the immune system appear to be at the root of how the microbiome interacts with cancer therapies, it also appears to mediate how our bacteria, fungi, and viruses influence cancer development in the first place.

“We clearly see shifts in the [microbial] community that precede development of tumors,” says microbiologist and immunologist Patrick Schloss, who studies the influence of the microbiome on colon cancer at the University of Michigan.

But the relationship between the microbiome and cancer is complex: while some microbes promote cell proliferation, others appear to protect us against cancerous growth. And in some cases, the conditions that spur one cancer may have the opposite effect in another. “It’s become pretty obvious that the commensal microbiota affect inflammation and, through that or through other mechanisms, affect carcinogenesis,” says Trinchieri. “What we really need is to have a much better understanding of which species, which type of bug, is doing what and try to change the balance.”

Gut feeling

In the late 1970s, pathologist J. Robin Warren of Royal Perth Hospital in Western Australia began to notice that curved bacteria often appeared in stomach tissue biopsies taken from patients with chronic gastritis, an inflammation of the stomach lining that often precedes the development of stomach cancer. He and Barry J. Marshall, a trainee in internal medicine at the hospital, speculated that the bacterium, now called Helicobacter pylori, was somehow causing the gastritis.³ So committed was Marshall to demonstrating the microbe’s causal relationship to the inflammatory condition that he had his own stomach biopsied to show that it contained no H. pylori, then infected himself with the bacterium and documented his subsequent experience of gastritis.⁴ Scientists now accept that H. pylori, a common gut microbe that is present in about 50 percent of the world’s population, is responsible for many cases of gastritis and most stomach ulcers, and is a strong risk factor for stomach cancer.⁵ Marshall and Warren earned the 2005 Nobel Prize in Physiology or Medicine for their work.

H. pylori may be the most clear-cut example of a gut bacterium that influences cancer development, but it is likely not the only one. Researchers who study cancer in mice have long had anecdotal evidence that shifts in the microbiome influence the development of diverse tumor types. “You have a mouse model of carcinogenesis. It works beautifully,” says Trinchieri. “You move to another institution. It works completely differently,” likely because the animals’ microbiomes vary with environment.

IMMUNE INFLUENCE: In recent years, research has demonstrated that microbes living in and on the mammalian body can affect cancer risk, as well as responses to cancer treatment. Although the details of this microbe-cancer link remain unclear, investigators suspect that the microbiome’s ability to modulate inflammation and train immune cells to react to tumors is to blame.
See full infographic: WEB | PDF© AL GRANBERG

Around the turn of the 21st century, cancer researchers began to systematically experiment with the rodent microbiome, and soon had several lines of evidence linking certain gut microbes with a mouse’s risk of colon cancer. In 2001, for example, Shoichi Kado of the Yakult Central Institute for Microbiological Research in Japan and colleagues found that a strain of immunocompromised mice rapidly developed colon tumors, but that germ-free versions of these mice did not.⁶ That same year, an MIT-based group led by the late David Schauer demonstrated that infecting mice with the bacterium Citrobacter rodentium spurred colon tumor development.⁷ And in 2003, MIT’s Susan Erdman and her colleagues found that they could induce colon cancer in immunocompromised mice by infecting them with Helicobacter hepaticus, a relative of? H. pylori that commonly exists within the murine gut microbiome.⁸

More recent work has documented a similar link between colon cancer and the gut microbiome in humans. In 2014, a team led by Schloss sequenced 16S rRNA genes isolated from the stool of 90 people, some with colon cancer, some with precancerous adenomas, and still others with no disease.⁹ The researchers found that the feces of people with cancer tended to have an altered composition of bacteria, with an excess of the common mouth microbes Fusobacterium or Porphyromonas. A few months later, Peer Bork of the European Molecular Biology Laboratory performed metagenomic sequencing of stool samples from 156 people with or without colorectal cancer. Bork and his colleagues found they could predict the presence or absence of cancer using the relative abundance of 22 bacterial species, including Porphyromonas andFusobacterium.¹⁰ They could also use the method to predict colorectal cancer with about the same accuracy as a blood test, correctly identifying about 50 percent of cancers while yielding false positives less than 10 percent of the time. When the two tests were combined, they caught more than 70 percent of cancers.

Whether changes in the microbiota in colon cancer patients are harbingers of the disease or a consequence of tumor development remained unclear. “What comes first, the change in the microbiome or tumor development?” asks Schloss. To investigate this question, he and his colleagues treated mice with microbiome-altering antibiotics before administering a carcinogen and an inflammatory agent, then compared the outcomes in those animals and in mice that had received only the carcinogenic and inflammatory treatments, no antibiotics. The antibiotic-treated animals had significantly fewer and smaller colon tumors than the animals with an undisturbed microbiome, suggesting that resident bacteria were in some way promoting cancer development. And when the researchers transferred microbiota from healthy mice to antibiotic-treated or germ-free mice, the animals developed more tumors following carcinogen exposure. Sterile mice that received microbiota from mice already bearing malignancies developed the most tumors of all.¹¹

Most recently, Schloss and his colleagues showed that treating mice with seven unique combinations of antibiotics prior to exposing them to carcinogens yielded variable but predictable levels of tumor formation. The researchers determined that the number of tumors corresponded to the unique ways that each antibiotic cocktail modulated the microbiome.¹²

“We’ve kind of proven to ourselves, at least, that the microbiome is involved in colon cancer,” says Schloss, who hypothesizes that gut bacteria–driven inflammation is to blame for creating an environment that is hospitable to tumor development and growth. Gain or loss of certain components of the resident bacterial community could lead to the release of reactive oxygen species, damaging cells and their genetic material. Inflammation also involves increased release of growth factors and blood vessel proliferation, potentially supporting the growth of tumors. (See illustration above.)

Recent research has also yielded evidence that the gut microbiota impact the development of cancer in sites far removed from the intestinal tract, likely through similar immune-modulating mechanisms.

Systemic effects

In the mid-2000s, MIT’s Erdman began infecting a strain of mice predisposed to intestinal tumors withH. hepaticus and observing the subsequent development of colon cancer in some of the animals. To her surprise, one of the mice developed a mammary tumor. Then, more of the mice went on to develop mammary tumors. “This told us that something really interesting was going on,” Erdman recalls. Sure enough, she and her colleagues found that mice infected with H. hepaticus were more likely to develop mammary tumors than mice not exposed to the bacterium.¹³The researchers showed that systemic immune activation and inflammation could contribute to mammary tumors in other, less cancer-prone mouse models, as well as to the development of prostate cancer.

MICROBIAL STOWAWAYS: Bacteria of the human gut microbiome are intimately involved in cancer development and progression, thanks to their interactions with the immune system. Some microbes, such as Helicobacter pylori, increase the risk of cancer in their immediate vicinity (stomach), while others, such as some Bacteroides species, help protect against tumors by boosting T-cell infiltration.© EYE OF SCIENCE/SCIENCE SOURCE
http://www.the-scientist.com/images/April2016/immune_2.jpg

 

 

© DR. GARY GAUGLER/SCIENCE SOURCE  http://www.the-scientist.com/images/April2016/immune3.jpg

At the University of Chicago, Thomas Gajewski and his colleagues have taken a slightly different approach to studying the role of the microbiome in cancer development. By comparing Black 6 mice coming from different vendors—Taconic Biosciences (formerly Taconic Farms) and the Jackson Laboratory—Gajewski takes advantage of the fact that the animals’ different origins result in different gut microbiomes. “We deliberately stayed away from antibiotics, because we had a desire to model how intersubject heterogeneity [in cancer development] might be impacted by the commensals they happen to be colonized with,” says Gajewski in an email to The Scientist.

Last year, the researchers published the results of a study comparing the progression of melanoma tumors implanted under the mice’s skin, finding that tumors in the Taconic mice grew more aggressively than those in the Jackson mice. When the researchers housed the different types of mice together before their tumors were implanted, however, these differences disappeared. And transferring fecal material from the Jackson mice into the Taconic mice altered the latter’s tumor progression.¹⁴

Instead of promoting cancer, in these experiments the gut microbiome appeared to slow tumor growth. Specifically, the reduced tumor growth in the Jackson mice correlated with the presence of Bifidobacterium, which led to the greater buildup of T?cells in the Jackson mice’s tumors. Bifidobacteriaactivate dendritic cells, which present antigens from bacteria or cancer cells to T?cells, training them to hunt down and kill these invaders. Feeding Taconic mice bifidobacteria improved their response to the implanted melanoma cells.

“One hypothesis going into the experiments was that we might identify immune-suppressive bacteria, or commensals that shift the immune response towards a character that was unfavorable for tumor control,” says Gajewski.  “But in fact, we found that even a single type of bacteria could boost the antitumor immune response.”

http://www.the-scientist.com/images/April2016/immune4.jpg

 

Drug interactions

Ideally, the immune system should recognize cancer as invasive and nip tumor growth in the bud. But cancer cells display “self” molecules that can inhibit immune attack. A new type of immunotherapy, dubbed checkpoint inhibition or blockade, spurs the immune system to attack cancer by blocking either the tumor cells’ surface molecules or the receptors on T?cells that bind to them.

CANCER THERAPY AND THE MICROBIOME In addition to influencing the development and progression of cancer by regulating inflammation and other immune pathways, resident gut bacteria appear to influence the effectiveness of many cancer therapies that are intended to work in concert with host immunity to eliminate tumors. Some cancer drugs, such as oxaliplatin chemotherapy and CpG-oligonucleotide immunotherapy, work by boosting inflammation. If the microbiome is altered in such a way that inflammation is reduced, these therapeutic agents are less effective. Cancer-cell surface proteins bind to receptors on T cells to prevent them from killing cancer cells. Checkpoint inhibitors that block this binding of activated T cells to cancer cells are influenced by members of the microbiota that mediate these same cell interactions. Cyclophosphamide chemotherapy disrupts the gut epithelial barrier, causing the gut to leak certain bacteria. Bacteria gather in lymphoid tissue just outside the gut and spur generation of T helper 1 and T helper 17 cells that migrate to the tumor and kill it.

As part of their comparison of Jackson and Taconic mice, Gajewski and his colleagues decided to test a type of investigational checkpoint inhibitor that targets PD-L1, a ligand found in high quantities on the surface of multiple types of cancer cells. Monoclonal antibodies that bind to PD-L1 block the PD-1 receptors on T?cells from doing so, allowing an immune response to proceed against the tumor cells. While treating Taconic mice with PD-L1–targeting antibodies did improve their tumor responses, they did even better when that treatment was combined with fecal transfers from Jackson mice, indicating that the microbiome and the immunotherapy can work together to take down cancer. And when the researchers combined the anti-PD-L1 therapy with a bifidobacteria-enriched diet, the mice’s tumors virtually disappeared.¹⁴

Gajewski’s group is now surveying the gut microbiota in humans undergoing therapy with checkpoint inhibitors to better understand which bacterial species are linked to positive outcomes. The researchers are also devising a clinical trial in which they will give Bifidobacterium supplements to cancer patients being treated with the approved anti-PD-1 therapy pembrolizumab (Keytruda), which targets the immune receptor PD-1 on T?cells, instead of the cancer-cell ligand PD-L1.

Meanwhile, Zitvogel’s group at INSERM is investigating interactions between the microbiome and another class of checkpoint inhibitors called CTLA-4 inhibitors, which includes the breakthrough melanoma treatment ipilimumab (Yervoy). The researchers found that tumors in antibiotic-treated and germ-free mice had poorer responses to a CTLA-4–targeting antibody compared with mice harboring unaltered microbiomes.¹⁵ Particular Bacteroides species were associated with T-cell infiltration of tumors, and feedingBacteroides fragilis to antibiotic-treated or germ-free mice improved the animals’ responses to the immunotherapy. As an added bonus, treatment with these “immunogenic” Bacteroides species decreased signs of colitis, an intestinal inflammatory condition that is a dangerous side effect in patients using checkpoint inhibitors. Moreover, Zitvogel and her colleagues showed that human metastatic melanoma patients treated with ipilimumab tended to have elevated levels of B. fragilis in their microbiomes. Mice transplanted with feces from patients who showed particularly strong B. fragilis gains did better on anti-CTLA-4 treatment than did mice transplanted with feces from patients with normal levels of B. fragilis.

“There are bugs that allow the therapy to work, and at the same time, they protect against colitis,” says Trinchieri. “That is very exciting, because not only [can] we do something to improve the therapy, but we can also, at the same time, try to reduce the side effect.”

And these checkpoint inhibitors aren’t the only cancer therapies whose effects are modulated by the microbiome. Trinchieri has also found that an immunotherapy that combines antibodies against interleukin-10 receptors with CpG oligonucleotides is more effective in mice with unaltered microbiomes.¹He and his NCI colleague Goldszmid further found that the platinum chemotherapy oxaliplatin (Eloxatin) was more effective in mice with intact microbiomes, and Zitvogel’s group has shown that the chemotherapeutic agent cyclophosphamide is dependent on the microbiota for its proper function.

Although the mechanisms by which the microbiome influences the effectiveness of such therapies remains incompletely understood, researchers once again speculate that the immune system is the key link. Cyclophosphamide, for example, spurs the body to generate two types of T?helper cells, T?helper 1 cells and a subtype of T?helper 17 cells referred to as “pathogenic,” both of which destroy tumor cells. Zitvogel and her colleagues found that, in mice with unaltered microbiomes, treatment with cyclophosphamide works by disrupting the intestinal mucosa, allowing bacteria to escape into the lymphoid tissues just outside the gut. There, the bacteria spur the body to generate T?helper 1 and T?helper 17 cells, which translocate to the tumor. When the researchers transferred the “pathogenic” T?helper 17 cells into antibiotic-treated mice, the mice’s response to chemotherapy was partly restored.

Microbiome modification

As the link between the microbiome and cancer becomes clearer, researchers are thinking about how they can manipulate a patient’s resident microbial communities to improve their prognosis and treatment outcomes. “Once you figure out exactly what is happening at the molecular level, if there is something promising there, I would be shocked if people don’t then go in and try to modulate the microbiome, either by using pharmaceuticals or using probiotics,” says Michael Burns, a postdoc in the lab of University of Minnesota genomicist Ran Blekhman.

Even if researchers succeed in identifying specific, beneficial alterations to the microbiome, however, molding the microbiome is not simple. “It’s a messy, complicated system that we don’t understand,” says Schloss.

So far, studies of the gut microbiome and colon cancer have turned up few consistent differences between cancer patients and healthy controls. And the few bacterial groups that have repeatedly shown up are not present in every cancer patient. “We should move away from saying, ‘This is a causal species of bacteria,’” says Blekhman. “It’s more the function of a community instead of just a single bacterium.”

But the study of the microbiome in cancer is young. If simply adding one type of microbe into a person’s gut is not enough, researchers may learn how to dose people with patient-specific combinations of microbes or antibiotics. In February 2016, a team based in Finland and China showed that a probiotic mixture dubbed Prohep could reduce liver tumor size by 40 percent in mice, likely by promoting an anti-inflammatory environment in the gut.¹⁶

“If it is true that, in humans, we can alter the course of the disease by modulating the composition of the microbiota,” says José Conejo-Garcia of the Wistar Institute in Philadelphia, “that’s going to be very impactful.”

Kate Yandell has been a freelance writer living Philadelphia, Pennsylvania. In February she became an associate editor at Cancer Today.

GENETIC CONNECTION The microbiome doesn’t act in isolation; a patient’s genetic background can also greatly influence response to therapy. Last year, for example, the Wistar Institute’s José Garcia-Conejo and Melanie Rutkowski, now an assistant professor at the University of Virginia, showed that a dominant polymorphism of the gene for the innate immune protein toll-like receptor 5 (TLR5) influences clinical outcomes in cancer patients by changing how the patients’ immune cells interact with their gut microbes (Cancer Cell, 27:27-40, 2015). More than 7 percent of people carry a specific mutation in TLR5 that prevents them from mounting a full immune response when exposed to bacterial flagellin. Analyzing both genetic and survival data from the Cancer Genome Atlas, Conejo-Garcia, Rutkowski, and their colleagues found that estrogen receptor–positive breast cancer patients who carry the TLR5 mutation, called the R392X polymorphism, have worse outcomes than patients without the mutation. Among patients with ovarian cancer, on the other hand, those with the TLR5 mutation were more likely to live at least six years after diagnosis than patients who don’t carry the mutation. Investigating the mutation’s contradictory effects, the researchers found that mice with normal TLR5produce higher levels of the cytokine interleukin 6 (IL-6) than those carrying the mutant version, which have higher levels of a different cytokine called interleukin 17 (IL-17). But when the researchers knocked out the animals’ microbiomes, these differences in cytokine production disappeared, as did the differences in cancer progression between mutant and wild-type animals. “The effectiveness of depleting specific populations or modulating the composition of the microbiome is going to affect very differently people who are TLR5-positive or TLR5-negative,” says Conejo-Garcia. And Rutkowski speculates that many more polymorphisms linked to cancer prognosis may act via microbiome–immune system interactions. “I think that our paper is just the tip of the iceberg.”

References

1. N. Iida et al., “Commensal bacteria control cancer response to therapy by modulating the tumor microenvironment,” Science, 342:967-70, 2013.
2. S. Viaud et al., “The intestinal microbiota modulates the anticancer immune effects of cyclophosphamide,” Science, 342:971-76, 2013.
3. J.R. Warren, B. Marshall, “Unidentified curved bacilli on gastric epithelium in active chronic gastritis,”Lancet, 321:1273-75, 1983.
4. B.J. Marshall et al., “Attempt to fulfil Koch’s postulates for pyloric Campylobacter,” Med J Aust, 142:436-39, 1985.
5. J. Parsonnet et al., “Helicobacter pylori infection and the risk of gastric carcinoma,” N Engl J Med, 325:1127-31, 1991.
6. S. Kado et al., “Intestinal microflora are necessary for development of spontaneous adenocarcinoma of the large intestine in T-cell receptor β chain and p53 double-knockout mice,” Cancer Res, 61:2395-98, 2001.
7. J.V. Newman et al., “Bacterial infection promotes colon tumorigenesis in ApcMin/+ mice,” J Infect Dis, 184:227-30, 2001.
8. S.E. Erdman et al., “CD4+ CD25+ regulatory T lymphocytes inhibit microbially induced colon cancer in Rag2-deficient mice,” Am J Pathol, 162:691-702, 2003.
9. J.P. Zackular et al., “The human gut microbiome as a screening tool for colorectal cancer,” Cancer Prev Res, 7:1112-21, 2014.
10. G. Zeller et al., “Potential of fecal microbiota for early-stage detection of colorectal cancer,” Mol Syst Biol, 10:766, 2014.
11. J.P. Zackular et al., “The gut microbiome modulates colon tumorigenesis,” mBio, 4:e00692-13, 2013.
12. J.P. Zackular et al., “Manipulation of the gut microbiota reveals role in colon tumorigenesis,”mSphere, doi:10.1128/mSphere.00001-15, 2015.
13. V.P. Rao et al., “Innate immune inflammatory response against enteric bacteria Helicobacter hepaticus induces mammary adenocarcinoma in mice,” Cancer Res, 66:7395, 2006.
14. A. Sivan et al., “Commensal Bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy,” Science, 350:1084-89, 2015.
15. M. Vétizou et al., “Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota,”Science, 350:1079-84, 2015.

……..

 

Th17 Cell Induction by Adhesion of Microbes to Intestinal Epithelial Cells

Koji Atarashi · Takeshi Tanoue · Minoru Ando ·Nobuhiko Kamada ·….. · Yoshinori Umesaki · Kenya Honda ·

ABSTRACT: Intestinal Th17 cells are induced and accumulate in response to colonization with a subgroup of intestinal microbes such as segmented filamentous bacteria (SFB) and certain extracellular pathogens. Here, we show that adhesion of microbes to intestinal epithelial cells (ECs) is a critical cue for Th17 induction. Upon monocolonization of germ-free mice or rats with SFB indigenous to mice (M-SFB) or rats (R-SFB), M-SFB and R-SFB showed host-specific adhesion to small intestinal ECs, accompanied by host-specific induction of Th17 cells. Citrobacter rodentium and Escherichia coli O157 triggered similar Th17 responses, whereas adhesion-defective mutants of these microbes failed to do so. Moreover, a mixture of 20 bacterial strains, which were selected and isolated from fecal samples of a patient with ulcerative colitis on the basis of their ability to cause a robust induction of Th17 cells in the mouse colon, also exhibited EC-adhesive characteristics….. · Sep 2015 · Cell 163(2)    https://www.researchgate.net/profile/Shoichi_Kado     http://dx.doi.org:/10.1016/j.cell.2015.08.058

MIT Faculty Newsletter     Vol. XXII No. 2  Nov / Dec 2009  Memorial Resolution for David B. Schauer

We regretfully acknowledge the sudden death of our friend and colleague, Dr. David Schauer, on June 7, 2009 – one day after his 48th birthday. David Schauer was known not only for his keen scientific mind, but also as a friend whose empathy and compassion touched countless individuals.

David’s research was supported on a continuous basis by the National Institutes of Health throughout his career at MIT. His studies on microbial pathogenesis of gastrointestinal pathogenic bacteria, particularly Citrobacter rodentium, a murine model of enteropathogenic Escherichia coli, and enterohepatic helicobacter are widely known and respected.

David’s research provided important insights into the molecular mechanisms evoked by enteric pathogens and how the infections caused by these bacteria perturb the gastrointestinal barrier, elicit inflammation, and produce clinically relevant disease.

Susan Erdman is a Principal Research Scientist and Assistant Director in the Division of Comparative Medicine at MIT.

• Invited Speaker. National Cancer Institute (NCI) Mucosal, Immunosurveillance, Inflammation and Cancer Workshop.  April, 2005.
• Invited reviewer. National Cancer Institute (NCI) RFA-CA-06-014 Tumor Microenvironment Network.  August, 2006.
• Invited speaker. Keystone Symposium on Mechanisms Linking Inflammation and Cancer, February, 2007
• Alumni Fellow, College of Veterinary Medicine, Mississippi State University, 2007
• Invited Speaker. National Cancer Institute (NCI) Changing Concepts in Cancer Etiology: The Role of the Human Microbiome.  November, 2009.
• Invited speaker. Keystone Symposium on Role of Inflammation in Oncogenesis, February, 2010
• Invited reviewer. National Cancer Institute (NCI) Tumor Microenvironment Network.  June, 2011
• Invited Speaker, National Institute of Health (NIH) Human Microbiome Science Conference: Vision for the Future. Wound healing to longevity: Microbe-induced immune proficiency for human health. July, 2013.
• Invited speaker, Wellcome Trust, Human Host-Microbiome Interactions Conference, April 2014.
• Invited speaker, Van Andel Institute, Origins of Cancer Conference, July 2014

Peer Bork

• Current position: senior group leader (bioinformatics) at the EMBL (Heidelberg);
  joint coordinator of the EMBL Structural and Computational Biology unit;
  visiting group leader at the MDC (Berlin-Buch)
• Advisor: Current and former member of various SABs and evaluation panels (academia + industry)
  Cofounder of LION bioscience AG (now SYGNIS Pharma AG), Cellzome AG, Anadys Pharmaceuticals Inc. and biobyte solutions GmbH
• Scientific interests: prediction of protein function at different spatial scales,
  protein and genome evolution (iTOL), protein domain analysis,
  comparative genome and proteome analysis, bridging genotype and phenotype
• Some Projects: genome annotation,
  comparative analysis of protein modules (SMART),
  biochemical pathways and networks (STRING),
  networks of chemicals and proteins (STITCH)
  bioinformatics applications in molecular medicine
  comparative metagenomics
• List of group publications

Microbially Driven TLR5-Dependent Signaling Governs Distal Malignant Progression through Tumor-Promoting Inflammation

Melanie R. Rutkowski, Tom L. Stephen, Nikolaos Svoronos, …., Julia Tchou,  Gabriel A. Rabinovich, Jose R. Conejo-Garcia
Cancer cell    12 Jan 2015; Volume 27, Issue 1, p27–40  http://dx.doi.org/10.1016/j.ccell.2014.11.009

http://www.cell.com/cms/attachment/2023542033/2043769432/fx1.jpg

• •TLR5-dependent IL-6 mobilizes MDSCs that drive galectin-1 production by γδ T cells
• •IL-17 drives malignant progression in IL-6-unresponsive tumors
• •TLR5-dependent differences in tumor growth are abrogated upon microbiota depletion
• •A common dominant TLR5 polymorphism influences the outcome of human cancers

The dominant TLR5^R392X polymorphism abrogates flagellin responses in >7% of humans. We report that TLR5-dependent commensal bacteria drive malignant progression at extramucosal locations by increasing systemic IL-6, which drives mobilization of myeloid-derived suppressor cells (MDSCs). Mechanistically, expanded granulocytic MDSCs cause γδ lymphocytes in TLR5-responsive tumors to secrete galectin-1, dampening antitumor immunity and accelerating malignant progression. In contrast, IL-17 is consistently upregulated in TLR5-unresponsive tumor-bearing mice but only accelerates malignant progression in IL-6-unresponsive tumors. Importantly, depletion of commensal bacteria abrogates TLR5-dependent differences in tumor growth. Contrasting differences in inflammatory cytokines and malignant evolution are recapitulated in TLR5-responsive/unresponsive ovarian and breast cancer patients. Therefore, inflammation, antitumor immunity, and the clinical outcome of cancer patients are influenced by a common TLR5 polymorphism.

see also… Immune Influence

In recent years, research has demonstrated that microbes living in and on the mammalian body can affect cancer risk, as well as responses to cancer treatment.

By Kate Yandell | April 1, 2016

http://www.the-scientist.com/?articles.view/articleNo/45644/title/Immune-Influence

Although the details of this microbe-cancer link remain unclear, investigators suspect that the microbiome’s ability to modulate inflammation and train immune cells to react to tumors is to blame. Here are some of the hypotheses that have come out of recent research in rodents for how gut bacteria shape immunity and influence cancer.

HOW THE MICROBIOME PROMOTES CANCER

Gut bacteria can dial up inflammation locally in the colon, as well as in other parts of the body, leading to the release of reactive oxygen species, which damage cells and DNA, and of growth factors that spur tumor growth and blood vessel formation.

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http://www.the-scientist.com/images/April2016/ImmuneInfluence2_310px1.jpg

Helicobacter pylori can cause inflammation and high cell turnover in the stomach wall, which may lead to cancerous growth.

HOW THE MICROBIOME STEMS CANCER

Gut bacteria can also produce factors that lower inflammation and slow tumor growth.

Some gut bacteria (e.g., Bifidobacterium) appear to activate dendritic cells, which present cancer-cell antigens to T cells that in turn kill the cancer cells.

http://www.the-scientist.com/images/April2016/ImmuneInfluence3_310px1.jpg

http://www.the-scientist.com/images/April2016/ImmuneInfluence4_310px1.jpg

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Tracking protein expression

Posted in Cell Biology, Curation, Gene Regulation, Proteins, Proteomics, Transcriptomics, tagged GFAP; body fluid biomarker, Glial fibrillary acidic protein, Intermediate filaments, Neurodegeneration; Autoimmune astrocytopathies, protein expression in single cells on March 8, 2016| Leave a Comment »

Tracking protein expression

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Protein Counting in Single Cancer Cells

Stephanie M. Schubert, Stephanie R. Walter, Mael Manesse, and David R. Walt^*
Analytical Chemistry  Anal. Chem., 2016, 88 (5), pp 2952–2957   http://dx.doi.org:/10.1021/acs.analchem.6b00146

 

 

 

The cell is the basic unit of biology and protein expression drives cellular function. Tracking protein expression in single cells enables the study of cellular pathways and behavior, but requires methodologies sensitive enough to detect low numbers of protein molecules with a wide dynamic range to distinguish unique cells and quantify population distributions. This study presents an ultrasensitive and automated approach for quantifying phenotypic responses with single cell resolution using single molecule array (Simoa) technology. We demonstrate how prostate specific antigen (PSA) expression varies over several orders of magnitude between single prostate cancer cells, and how PSA expression shifts with genetic drift. Single cell Simoa intduces a straightforward process that is capable of detecting both high and low protein expression levels. This technique could be useful for understanding fundamental biology and may eventually enable both earlier disease detection and targeted therapy.

 

Quanterix’s proprietary Simoa™ technology (named for single molecule array) is based upon the isolation of individual immunocomplexes on paramagnetic beads using standard ELISA reagents. The main difference between Simoa and conventional immunoassays lies in the ability to trap single molecules in femtoliter-sized wells, allowing for a “digital” readout of each individual bead to determine if it is bound to the target analyte or not.

The digital nature of the technique allows an average of 1000x sensitivity increase over conventional assays with CVs <10%.

 

 

A. Single protein molecules are captured and labeled on beads using standard ELISA reagents.

 

B. Tens of thousands of beads – with or without immunoconjugate – are mixed with enzyme substrate and loaded into individual femtoliter-sized wells.

 

C. The microwells are sealed with oil.

 

D. Fluorophore concentration in the small sample volume of wells containing the target analyte rapidly reach detectable limits using conventional fluorescence imaging and can be digitally counted.

E. The percentage of beads containing labelled immunocomplexes can be computed at low concentration because they follow a Poisson distribution; at higher concentrations the intensity of the aggregate signal provides an analog measurement.

 

 

Clin Chem Lab Med. 2015 Oct 23. pii: /j/cclm.ahead-of-print/cclm-2015-0733/cclm-2015-0733.xml. http://dx.doi.org:/10.1515/cclm-2015-0733. [Epub ahead of print]

Assessing the commutability of reference material formats for the harmonization of amyloid beta measurements.

Bjerke M, Andreasson U, Kuhlmann J, Portelius E, Pannee J, Lewczuk P, Umek RM, Vanmechelen E, Vanderstichele H, Stoops E,Lewis J, Vandijck M, Kostanjevecki V, Jeromin A, Salamone SJ, Schmidt O, Matzen A, Madin K, Eichenlaub U, Bittner T, Shaw LM,Zegers I, Zetterberg H, Blennow K.

The cerebrospinal fluid (CSF) amyloid-β (Aβ42) peptide is an important biomarker for Alzheimer’s disease (AD). Variability in measured Aβ42 concentrations at different laboratories may be overcome by standardization and establishing traceability to a reference system. Candidate certified reference materials (CRMs) are validated herein for this purpose.

METHODS:

Commutability of 16 candidate CRM formats was assessed across five CSF Aβ42 immunoassays and one mass spectrometry (MS) method in a set of 48 individual clinical CSF samples. Promising candidate CRM formats (neat CSF and CSF spiked with Aβ42) were identified and subjected to validation across eight (Elecsys, EUROIMMUN, IBL, INNO-BIA AlzBio3, INNOTEST, MSD, Simoa, and Saladax) immunoassays and the MS method in 32 individual CSF samples. Commutability was evaluated by Passing-Bablok regression and the candidate CRM termed commutable when found within the prediction interval (PI). The relative distance to the regression line was assessed.

RESULTS:

The neat CSF candidate CRM format was commutable for almost all method comparisons, except for the Simoa/MSD, Simoa/MS and MS/IBL where it was found just outside the 95% PI. However, the neat CSF was found within 5% relative distance to the regression line for MS/IBL, between 5% and 10% for Simoa/MS and between 10% and 15% for Simoa/MSD comparisons.

CONCLUSIONS:

The neat CSF candidate CRM format was commutable for 33 of 36 method comparisons, only one comparison more than expected given the 95% PI acceptance limit. We conclude that the neat CSF candidate CRM can be used for value assignment of the kit calibrators for the different Aβ42 methods.

 

 

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  Persistence of Aβ seeds in APP null mouse brain

• Lan Ye, Sarah K Fritschi, Juliane Schelle, Ulrike Obermüller, Karoline Degenhardt, …, Frank Baumann, Matthias Staufenbiel & Mathias Jucker

Nature Neuroscience18, 1559–1561(2015)    http://dx.doi.org:/10.1038/nn.4117

Cerebral β-amyloidosis is induced by inoculation of Aβ seeds into APP transgenic mice, but not into App^−/− (APP null) mice. We found that brain extracts from APP null mice that had been inoculated with Aβ seeds up to 6 months previously still induced β-amyloidosis in APP transgenic hosts following secondary transmission. Thus, Aβ seeds can persist in the brain for months, and they regain propagative and pathogenic activity in the presence of host Aβ.

Ultra-sensitive bead-based Simoa technology detects residual human Aβ in hippocampal extracts of inoculated App^−/− mice.

Aβ seed-containing APP23 brain extract or seed-negative WT mouse brain extract was injected into the hippocampus of App^−/− mice. The injected hippocampi were isolated 1, 30, 60, or 180 dpi (see Figures 1 and 2).

 

Induced amyloid lesions are partly congophilic and surrounded by activated microglia and dystrophic boutons.

(a) Congo red-positive amyloid deposits induced in the dentate gyrus were surrounded by Iba1-positive microglia (black). (b) Congo red-positive plaque with surrounding hypertrophic microglial cell bodies and processes at higher magnification

 

Glial fibrillary acidic protein is a body fluid biomarker for glial pathology in human disease

• Axel Petzold^{a, b,}

Brain Research  Volume 1600, 10 March 2015, Pages 17–31    doi:10.1016/j.brainres.2014.12.027

Highlights

• Reviewing 45 years of Glial fibrillary acidic protein (Gfap).

•Gfap discovered in multiple sclerosis brain tissue.

•From Gfap genetics to post-translational modifications.

•Ninety-nine ways to quantify Gfap and related immune phenomena.

•Emergence of Gfap as a body fluid biomarker in human disease.

This review on the role of glial fibrillary acidic protein (GFAP) as a biomarker for astroglial pathology in neurological diseases provides background to protein synthesis, assembly, function and degeneration. Qualitative and quantitative analytical techniques for the investigation of human tissue and biological fluid samples are discussed including partial lack of parallelism and multiplexing capabilities. Pathological implications are reviewed in view of immunocytochemical, cell-culture and genetic findings. Particular emphasis is given to neurodegeneration related to autoimmune astrocytopathies and to genetic gain of function mutations. The current literature on body fluid levels of GFAP in human disease is summarised and illustrated by disease specific meta-analyses. In addition to the role of GFAP as a diagnostic biomarker for chronic disease, there are important data on the prognostic value for acute conditions. The published evidence permits to classify the dominant GFAP signatures in biological fluids. This classification may serve as a template for supporting diagnostic criteria of autoimmune astrocytopathies, monitoring disease progression in toxic gain of function mutations, clinical treatment trials (secondary outcome and toxicity biomarker) and provide prognostic information in neurocritical care if used within well defined time-frames.

 

 

 

CSF and Plasma Amyloid-b Temporal Profiles and Relationships with Neurological Status and Mortality after Severe Traumatic Brain Injury

http://www.quanterix.com/literature/publications/neurology/item/482-csf-and-plasma-amyloid-b-temporal-profiles-and-relationships-with-neurological-status-and-mortality-after-severe-traumatic-brain-injury

by Stefania Mondello, Andras Burk, Pal Barzo, Jeff Randall, Gail Provuncher, David Hanlon, David Wilson, Firas Kobeissy & Andreas Jeromin

 

The role of amyloid-b (Ab) neuropathology and its significant changes in biofluids after traumatic braininjury (TBI) is still debated. We used ultrasensitive digital  ELISA approach to assess amyloid-b1-42 (Ab42) concentrations and time-course in cerebrospinal fluid (CSF) and in plasma of patients with severe TBI and

investigated their relationship to injury characteristics, neurological status and clinical outcome. We found decreased CSF Ab42 levels in TBI patients acutely after injury with lower levels in patients who died 6 months post-injury than in survivors. Conversely, plasma Ab42 levels were significantly increased in TBI

with lower levels in patients who survived. A trend analysis showed that both CSF and plasma Ab42 levels strongly correlated with mortality. A positive correlation between changes in CSF Ab42 concentrations and neurological status as assessed by Glasgow Coma Scale (GCS) was identified. Our results suggest that determination of Ab42 may be valuable to obtain prognostic information in patients with severe TBI as well as in monitoring the response of the brain to injury.

Plasma tau levels in Alzheimer’s disease

Henrik Zetterberg, David Wilson, Ulf Andreasson, Lennart Minthon, Kaj Blennow, Jeffrey Randall and Oskar Hansson

Alzheimer’s Research & Therapy 2013; 5:9   http://dx.doi.org:/10.1186/alzrt163

Efforts to find reliable blood biomarkers for Alzheimer’s disease (AD) in a highly warranted clinical laboratory test have met with little success. There is no clear change in plasma ß-amyloid in AD, and assays for the axonal injury marker tau have been hampered by a lack of analytical sensitivity for accurate measurement in blood samples[1]. Here, the results of a novel ultra-sensitive assay for tau in peripheral blood are reported. Publication: Alzheimer’s Research & Therapy 2013, 5:9.

Efforts to find reliable blood biomarkers for Alzheimer’s disease (AD) in a highly warranted clinical laboratory test have met with little success. There is no clear change in plasma β-amyloid in AD, and assays for the axonal injury marker tau have been hampered by a lack of analytical sensitivity for accurate measurement in blood samples [1]. Here, the results of a novel ultra-sensitive assay for tau in peripheral blood are reported.

We have developed an ultra-sensitive assay for tau in peripheral blood [2]. In brief, the assay is based on digital array technology [3] and uses the Tau5 monoclonal antibody for capture (Covance, Princeton, NJ, USA) and HT7 and BT2 monoclonal antibodies for detection (Pierce, now part of Thermo Fisher Scientific Inc., Waltham, MA, USA). This combination reacts with both normal and phosphorylated tau with epitopes in the mid-region of the molecule, making the assay sensitive to all known tau isoforms. The calibrator was recombinant tau 381 (EMD Millipore Corporation, Billerica, MA, USA). To minimize matrix effects, all samples were diluted 1:4 in phosphate-buffered saline with 2% bovine serum albumin diluent prior to assay. The limit of detection of the assay, which requires 30 μL of plasma, is 0.02 pg/mL [2], which is more than 1,000-fold more sensitive than conventional immunoassays.

Here, we assess the association of plasma tau levels with AD in a cross-sectional study of 54 patients with AD dementia [4], 75 patients with mild cognitive impairment (MCI) [5], and 25 cognitively normal controls (Table 1). All participants were recruited at the specialized memory clinic at Skåne University Hospital in Malmö, Sweden, and underwent extensive clinical evaluation, including cerebrospinal fluid (CSF) sampling by lumbar puncture, in addition to venipuncture and collection of blood in ethylenediaminetetraacetic acid (EDTA) tubes for plasma preparation by centrifugation within 15 minutes from sampling. Plasma samples were aliquoted into cryo tubes and stored at -80°C pending analysis, which was performed on one occasion by using one batch of reagents with an average coefficient of variation of 9.7% for triplicate measurements of each sample. The patients with MCI were cognitively stable for an average of 101 months (n = 36) or developed AD dementia (n = 35) or other types of dementias – vascular dementia (n = 3) and semantic dementia (n = 1) – during follow-up. The study was approved by the regional ethics committee at Lund University and complied with the Declaration of Helsinki. Informed consent was obtained from all study participants.

Tau levels in plasma were significantly higher in AD patients compared with both controls and MCI patients (Figure 1a). MCI patients who developed AD during follow-up had tau levels similar to those of patients with stable MCI and cognitively normal controls (Figure 1b). There was no correlation between tau levels in plasma and CSF in any diagnostic group (Figure 1c).

https://static-content.springer.com/image/art%3A10.1186%2Falzrt163/MediaObjects/13195_2013_Article_139_Fig1_HTML.jpg

Figure 1

Elevated tau levels in plasma from patients with Alzheimer’s disease (AD). (a) Plasma levels of tau are elevated in patients with AD compared with cognitively normal controls and patients with mild cognitive impairment (MCI). (b) MCI patients who developed AD (MCI-AD) during follow-up had baseline tau levels similar to those of patients with stable MCI (SMCI). (c) There was no correlation between tau levels in plasma and cerebrospinal fluid (CSF) in any diagnostic group. Thin horizontal lines in panels (a) and (b) indicate medians. A nonparametric Kruskal-Wallis test followed by Mann-Whitney was performed to test for statistical significance. Spearman’s rank correlation coefficient was used to assess the relationship between plasma and CSF tau concentrations in panel (c), where open circles, gray squares, and black triangles represent AD, MCI, and controls, respectively.

The results of this study have several important implications. First, plasma tau levels are elevated in AD but with overlapping ranges across diagnostic groups. This overlap diminishes the utility of plasma tau as a diagnostic test. However, further studies are needed to evaluate plasma tau as a first-in-line screening tool (for example, in the primary care setting and perhaps together with other markers in a biomarker panel). Second, normal plasma tau levels in the MCI stage of AD suggest that plasma tau is a late marker, requiring substantial axonal injury before increasing to abnormal levels. In this context, other neurodegenerative diseases (for example, Creutzfeldt-Jakob disease) as well as acute conditions (for example, stroke and brain trauma) should be tested. Third, the lack of correlation of tau levels in plasma and CSF suggests that steady-state concentrations of tau in these two body fluids are differentially regulated. In our earlier study of patients with hypoxic brain injury following cardiac arrest, tau was rapidly (within 24 hours) cleared from blood in patients with good neurological outcome [2], indicating potent clearance mechanisms for this marker in the bloodstream. This may obscure any correlation with CSF tau levels, which stay elevated for weeks following an acute neurological insult [6].

Researchers Use CRISPR-based Method to Track RNA In Vivo

Mar 17, 2016   https://www.genomeweb.com/cell-biology-research/researchers-use-crispr-based-method-track-rna-vivo

A research team led by researchers from the University of California has modified the CRISPR/Cas9 system to demonstrate the ability to track specific RNA sequences and processes in vivo.

As described in a paper published today in Cell, the investigators were able to use their system to visualize specific RNA molecules accumulating in stress granules — dense aggregations of proteins and RNA that form in the cytosol in response to cellular stress and have been linked to neurodegenerative disorders such as amyotrophic lateral sclerosis.

They also found that they could use Cas9 to target an mRNA without altering mRNA abundance or the amount of translated protein.

“We are just beginning to see the implications of genome engineering using the CRISPR technology, but many diseases, including cancer and autism, are linked to problems with another fundamental biological molecule: RNA,” Gene Yeo, senior study author and an associate professor at the University of California, San Diego, said in a statement.

The researchers began their project based on a modification attempted in the lab of co-author Jennifer Doudna from the University of California, Berkeley. Inthat study, the researchers found that it was possible to design a protospacer adjacent motif (PAM) as part of an oligonucleotide (PAMmer) which binds to the single-stranded RNA, allowing Cas9 to efficiently recognize and cleave RNA rather than DNA (RCas9). The researchers determined that with a few further modifications, they could use this method to not only recognize RNA instead of DNA but actually track its movements through cells.

Previously, researchers have attempted to use molecular beacons to track RNA sequences, however, these are limited to imaging applications and are difficult to deliver into cells. Researchers have also attempted to use aptamers to enable RNA tracking in living cells, but these are limited in the number of RNA sequences that they can recognize.

CRISPR/Cas9, however, has thus far proved extremely useful in the genome engineering field and the research team thought that it would be an ideal base to create a better RNA tracking tool.

To prove their concept, the team tested whether a dead Cas9 (dCas9) that was tagged with the fluorescent protein mCherry and contained a nuclear localization signal could be co-exported from the nucleus with a messenger RNA in the presence of a single-guide RNA (sgRNA) and PAMmer designed to recognize that specific mRNA.

The experiment succeeded and the researchers were also able to observe accumulation of ACTB, CCNA2, and TFRC mRNAs in RNA granules that correlated with fluorescence in situ hybridization visualization using image analysis software.

Once they had established that their method was effective, the researchers showed that they could use the sgRNA and PAMmer targeting sequences to track mRNA trafficking to stress granules.

The researchers demonstrated that they could take time-resolved measurements of ACTB mRNA trafficking to stress granules over a period of 30 minutes. They noted in the paper that RCas9 was capable of measuring the association of CCNA2 and TFRC mRNA trafficking to stress granules, as well.

Based on their results, the investigators believe they have established RCas9 as a means to track RNA in living cells in a programmable manner that doesn’t require genetically encoded tags.

“One potential application of this technique is to track RNA transport in diseased neurons over time in order to identify the molecular features of these diseases and support the developments of therapies,” David Nelles, first author on the study and a researcher at the University of California, San Diego, said in a statement. “Just as CRISPR-Cas9 is making genetic engineering accessible to any scientists with access to basic equipment, RNA-targeted Cas9 may support countless other efforts for studying the role of RNA processing in disease or for identifying drugs that reverse defects in RNA processing.”

 

Programmable RNA Tracking in Live Cells with CRISPR/Cas9

David A. Nelles, Mark Y. Fang, Mitchell R. O’Connell, Jia L. Xu, Sebastian J. Markmiller, Jennifer A. Doudna, Gene W. Yeo

Publication stage: In Press Corrected Proof

http://dx.doi.org/10.1016/j.cell.2016.02.054     Article Info

http://www.cell.com/cms/attachment/2050893021/2059121637/fx1.jpg

Clustered regularly-interspaced short palindromic repeats (CRISPRs) form the basis of adaptive immune systems in bacteria and archaea by encoding CRISPR RNAs that guide CRISPR-associated (Cas) nucleases to invading genetic material (Wiedenheft et al., 2012). Cas9 from the type II CRISPR system ofS. pyogenes has been repurposed for genome engineering in eukaryotic organisms (Hwang et al., 2013, Li et al., 2013a, Mali et al., 2013, Nakayama et al., 2013, Sander and Joung, 2014, Yang et al., 2014) and is rapidly proving to be an efficient means of DNA targeting for other applications such as gene expression modulation (Qi et al., 2013) and imaging (Chen et al., 2013). Cas9 and its associated single-guide RNA (sgRNA) require two critical features to target DNA: a short DNA sequence of the form 5′-NGG-3′ (where “N” = any nucleotide) known as the protospacer adjacent motif (PAM) and an adjacent sequence on the opposite DNA strand that is antisense to the sgRNA. By supporting DNA recognition with specificity determined entirely by a short spacer sequence within the sgRNA, CRISPR/Cas9 provides uniquely flexible and accessible manipulation of the genome. Manipulating cellular RNA content, in contrast, remains problematic. Whereas there exist robust means of attenuating gene expression via RNAi and antisense oligonucleotides, other critical aspects of post-transcriptional gene expression regulation such as subcellular trafficking, alternative splicing or polyadenylation, and spatiotemporally restricted translation are difficult to measure in living cells and are largely intractable.

Analogous to the assembly of zinc finger nucleases (Urnov et al., 2010) and transcription activator-like effector nucleases (TALEN) to recognize specific DNA sequences, efforts to recognize specific RNA sequences have focused on engineered RNA-binding domains. Pumilio and FBF homology (PUF) proteins carry well-defined modules capable of recognizing a single base each and have supported successful targeting of a handful of transcripts for imaging and other manipulations (Filipovska et al., 2011, Ozawa et al., 2007, Wang et al., 2009). PUF proteins can be fused to arbitrary effector domains to alter or tag target RNAs, but PUFs must be redesigned and validated for each RNA target and can only recognize eight contiguous bases, which does not allow unique discrimination in the transcriptome. Molecular beacons are self-quenched synthetic oligonucleotides that fluoresce upon binding to target RNAs and allow RNA detection without construction of a target-specific protein (Sokol et al., 1998). But molecular beacons must be microinjected to avoid the generation of excessive background signal associated with endosome-trapped probes and are limited to imaging applications. An alternative approach to recognition of RNA substrates is to introduce RNA aptamers into target RNAs, enabling specific and strong association of cognate aptamer-binding proteins such as the MS2 coat protein (Fouts et al., 1997). This approach has enabled tracking of RNA localization in living cells over time with high sensitivity (Bertrand et al., 1998) but relies upon laborious genetic manipulation of the target RNA and is not suitable for recognition of arbitrary RNA sequences. Furthermore, insertion of exogenous aptamer sequence has the potential to interfere with endogenous RNA functions. Analogous to CRISPR/Cas9-based recognition of DNA, programmable RNA recognition based on nucleic acid specificity alone without the need for genetic manipulation or libraries of RNA-binding proteins would greatly expand researchers’ ability to modify the mammalian transcriptome and enable transcriptome engineering.

Although the CRISPR/Cas9 system has evolved to recognize double-stranded DNA, recent in vitro work has demonstrated that programmable targeting of RNAs with Cas9 is possible by providing the PAM as part of an oligonucleotide (PAMmer) that hybridizes to the target RNA (O’Connell et al., 2014). By taking advantage of the Cas9 target search mechanism that relies on PAM sequences (Sternberg et al., 2014), a mismatched PAM sequence in the PAMmer/RNA hybrid allows exclusive targeting of RNA and not the encoding DNA. The high affinity and specificity of RNA recognition by Cas9 in cell-free extracts and the success of genome targeting with Cas9 indicate the potential of CRISPR/Cas9 to support programmable RNA targeting in living cells.

To assess the potential of Cas9 as a programmable RNA-binding protein in live cells, we used a modified sgRNA scaffold with improved expression and Cas9 association (Chen et al., 2013) with a stabilized PAMmer oligonucleotide that does not form a substrate for RNase H. We measured the degree of nuclear export of a nuclear localization signal-tagged nuclease-deficient Cas9-GFP fusion and demonstrate that the sgRNA alone is sufficient to promote nuclear export of Cas9 without influencing the abundance of the targeted mRNA or encoded protein. In order to evaluate whether RNA-targeting Cas9 (RCas9) signal patterns correspond with an established untagged RNA-labeling method, we compared distributions of RCas9 and fluorescence in situ hybridization (FISH) targeting ACTB mRNA. We observed high correlation among FISH and RCas9 signal that was dependent on the presence of a PAMmer, indicating the importance of the PAM for efficient RNA targeting. RNA trafficking and subcellular localization are critical to gene expression regulation and reaction to stimuli such as cellular stress. To address whether RCas9 allows tracking of RNA to oxidative stress-induced RNA/protein accumulations called stress granules, we measured ACTB, TFRC, and CCNA2 mRNA association with stress granules in cells subjected to sodium arsenite. Finally, we demonstrated the ability of RCas9 to track trafficking of ACTB mRNA to stress granules over time in living cells. This work establishes the ability of RCas9 to bind RNA in live cells and sets the foundation for manipulation of the transcriptome in addition to the genome by CRISPR/Cas9.

http://www.cell.com/cms/attachment/2050893021/2059121638/gr1.jpg

Figure 1

Targeting mRNA in Living Cells with RCas9

(A) Components required for RNA-targeting Cas9 (RCas9) recognition of mRNA include a nuclear localization signal-tagged nuclease-inactive Cas9 fused to a fluorescent protein such as GFP, a modified sgRNA with expression driven by the U6 polymerase III promoter, and a PAMmer composed of DNA and 2′-O-methyl RNA bases with a phosphodiester backbone. The sgRNA and PAMmer are antisense to adjacent regions of the target mRNA whose encoding DNA does not carry a PAM sequence. After formation of the RCas9/mRNA complex in the nucleus, the complex is exported to the cytoplasm.

(B) RCas9 nuclear co-export with GAPDH mRNA. The RCas9 system was delivered to U2OS cells with a sgRNA and PAMmer targeting the 3′ UTR of GAPDH or sgRNA and PAMmer targeting a sequence from λ bacteriophage that should not be present in human cells (“N/A”). Cellular nuclei are outlined with a dashed white line. Scale bars represent 5 microns.

(C) Fraction of cells with cytoplasmic RCas9 signal. Mean values ± SD (n = 50).

(D) A plasmid carrying the Renilla luciferase open reading frame with a β-globin 3′ UTR containing a target site for RCas9 and MS2 aptamer. A PEST protein degradation signal was appended to luciferase to reveal any translational effects of RCas9 binding to the mRNA.

(E) RNA immunoprecipitation of EGFP after transient transfection of the RCas9 system in HEK293T cells targeting the luciferase mRNA compared to non-targeting sgRNA and PAMmer or EGFP alone. Mean values ± SD (n = 3).

(F and G) Renilla luciferase mRNA (F) and protein (G) abundances were compared among the targeting and non-targeting conditions. Mean values ± SD (n = 4).

p values are calculated by Student’s t test, and one, two, and three asterisks represent p values less than 0.05, 0.01, and 0.001, respectively. See also Figure S1.

View Large Image | View Hi-Res Image | Download PowerPoint Slide

Highlights

• •RNA-targeting Cas9 (RCas9) enabled recognition of endogenous, unmodified mRNAs
• •RCas9 did not influence mRNA abundance or amount of translated protein
• •Subcellular distribution of RCas9 was highly correlated with RNA-FISH
• •RCas9 revealed trafficking of mRNAs to stress granules in live cells

Summary

RNA-programmed genome editing using CRISPR/Cas9 from Streptococcus pyogenes has enabled rapid and accessible alteration of specific genomic loci in many organisms. A flexible means to target RNA would allow alteration and imaging of endogenous RNA transcripts analogous to CRISPR/Cas-based genomic tools, but most RNA targeting methods rely on incorporation of exogenous tags. Here, we demonstrate that nuclease-inactive S. pyogenesCRISPR/Cas9 can bind RNA in a nucleic-acid-programmed manner and allow endogenous RNA tracking in living cells. We show that nuclear-localized RNA-targeting Cas9 (RCas9) is exported to the cytoplasm only in the presence of sgRNAs targeting mRNA and observe accumulation ofACTB, CCNA2, and TFRC mRNAs in RNA granules that correlate with fluorescence in situ hybridization. We also demonstrate time-resolved measurements of ACTB mRNA trafficking to stress granules. Our results establish RCas9 as a means to track RNA in living cells in a programmable manner without genetically encoded tags.

Correlation of RNA-Targeting Cas9 Signal Distributions with an Established Untagged RNA Localization Measurement

 Tracking RNA Trafficking to Stress Granules over Time

Effective RNA recognition by Cas9 in living cells while avoiding perturbation of the target transcript relies on careful design of the PAMmer and delivery of Cas9 and its cognate guide RNA to the appropriate cellular compartments. Binding of Cas9 to nucleic acids requires two critical features: a PAM DNA sequence and an adjacent spacer sequence antisense to the Cas9-associated sgRNA. By separating the PAM and sgRNA target among two molecules (the PAMmer oligonucleotide and the target mRNA) that only associate in the presence of a target mRNA, RCas9 allows recognition of RNA while avoiding the encoding DNA. To avoid unwanted degradation of the target RNA, the PAMmer is composed of a mixed 2′OMe RNA and DNA that does not form a substrate for RNase H. Further, the sgRNA features a modified scaffold that removes partial transcription termination sequences and a modified structure that promotes association with Cas9 (Chen et al., 2013). Other CRISPR/Cas systems have demonstrated RNA binding in bacteria (Hale et al., 2009, Sampson et al., 2013) or eukaryotes (Price et al., 2015), although these systems cannot discriminate RNA from DNA targets, feature RNA-targeting rules that remain unclear, or rely on large protein complexes that may be difficult to reconstitute in mammalian cells.

In this work, we demonstrate RCas9-based recognition of GAPDH, ACTB,CCNA2, and TFRC mRNAs in live cells. Because the U6-driven sgRNA is largely restricted to the nucleus, the NLS-tagged dCas9 allows association with its sgRNA and subsequent interaction with the target mRNA before nuclear co-export with the target mRNA. As an initial experiment, we evaluated the potential of RNA recognition with Cas9 by targeting GAPDH mRNA and evaluating degree of nuclear export of dCas9-mCherry (Figure 1B). Robust cytoplasmic localization of dCas9-mCherry in the presence of a sgRNA-targeting GAPDH mRNA compared to nuclear retention in the presence of a non-targeting sgRNA indicated that Cas9 association with the mRNA was sufficiently stable to support co-export from the nucleus.

RCas9 as an RNA-imaging reagent requires that RNA recognition by RCas9 does not interfere with normal RNA metabolism. Here, we show that RCas9 binding within the 3′ UTR of Renilla luciferase does not affect its mRNA abundance and translation (Figures 1F and 1G). The utility of RCas9 for imaging and other applications hinges on the recognition of endogenous transcripts, so we evaluated the influence of RCas9 targeting on GAPDH and ACTB mRNAs and observed no significant differences among the mRNA and protein abundances by western blot analysis and qRT-PCR (Figure S1). These results indicate that RCas9 targeting these 3′ UTRs does not perturb the levels of mRNA or encoded protein.

We also evaluated the ability of RCas9 to reveal RNA localization by comparing RCas9 signal patterns to FISH. We utilized a FISH probe set composed of tens of singly labeled probes targeting ACTB mRNA and compared FISH signal distributions to a single dCas9-GFP/sgRNA/PAMmer that recognizes the ACTBmRNA. Our findings indicate that the sgRNA primarily determines the degree of overlap among the FISH and RCas9 signals whereas the PAMmer plays a significant but secondary role. Importantly, in contrast to other untagged RNA localization determination methods such as FISH and molecular beacons, RCas9 is compatible with tracking untagged RNA localization in living cells and can be delivered rapidly to cells using established transfection methods. We also note that the distribution of ACTB mRNA was visualized using a single EGFP tag per transcript, and higher-sensitivity RNA tracking or single endogenous RNA molecule visualization may be possible in the future with RCas9 targeting multiple sites in a transcript or with a multiply tagged dCas9 protein.

Stress granules are translationally silent mRNA and protein accumulations that form in response to cellular stress and are increasingly thought to be involved with neurodegeneration (Li et al., 2013b). There are limited means that can track the movement of endogenous RNA to these structures in live cells (Bertrand et al., 1998). In addition to ACTB mRNA, we demonstrate that RCas9 is capable of measuring association of CCNA2 and TFRC mRNA trafficking to stress granules (Figure 3A). Upon stress induction with sodium arsenite, we observed that 50%, 39%, and 23% of stress granules featured overlapping RCas9 foci when targeting ACTB, TFRC, and CCNA2 mRNAs, respectively (Figure 3C). This result correlates with the expression levels of these transcripts (Figure S3) asACTB is expressed about 8 and 11 times more highly than CCNA2 and TFRC, respectively. We also observed that RCas9 is capable of tracking RNA localization over time as ACTB mRNA is trafficked to stress granules over a period of 30 min (Figure 3B). We noted a dependence of RCas9 signal accumulation in stress granules on stressor concentration (Figure 3D). This approach for live-cell RNA tracking stands in contrast to molecular beacons and aptamer-based RNA-tracking methods, which suffer from delivery issues and/or require alteration of the target RNA sequence via incorporation of RNA tags.

Future applications of RCas9 could allow the measurement or alteration of RNA splicing via recruitment of split fluorescent proteins or splicing factors adjacent to alternatively spliced exons. Further, the nucleic-acid-programmable nature of RCas9 lends itself to multiplexed targeting (Cong et al., 2013) and the use of Cas9 proteins that bind orthogonal sgRNAs (Esvelt et al., 2013) could support distinct activities on multiple target RNAs simultaneously. It is possible that the simple RNA targeting afforded by RCas9 could support the development of sensors that recognize specific healthy or disease-related gene expression patterns and reprogram cell behavior via alteration of gene expression or concatenation of enzymes on a target RNA (Delebecque et al., 2011, Sachdeva et al., 2014). Efforts toward Cas9 delivery in vivo are underway (Dow et al., 2015,Swiech et al., 2015, Zuris et al., 2015), and these efforts combined with existing oligonucleotide chemistries (Bennett and Swayze, 2010) could support in vivo delivery of the RCas9 system for targeted modulation of many features of RNA processing in living organisms.

RNA is subject to processing steps that include alternative splicing, nuclear export, subcellular transport, and base or backbone modifications that work in concert to regulate gene expression. The development of a programmable means of RNA recognition in order to measure and manipulate these processes has been sought after in biotechnology for decades. This work is, to our knowledge, the first demonstration of nucleic-acid-programmed RNA recognition in living cells with CRISPR/Cas9. By relying upon a sgRNA and PAMmer to determine target specificity, RCas9 supports versatile and unambiguous RNA recognition analogous to DNA recognition afforded by CRISPR/Cas9. The diverse applications supported by DNA-targeted CRISPR/Cas9 range from directed cleavage, imaging, transcription modulation, and targeted methylation, indicating the utility of both the native nucleolytic activity of Cas9 as well as the range of activities supported by Cas9-fused effectors. In addition to providing a flexible means to track this RNA in live cells, future developments of RCas9 could include effectors that modulate a variety of RNA-processing steps with applications in synthetic biology and disease modeling or treatment.

Study Unlocks Multiple Functions of CRISPR/Cas9 by Varying Guide RNAs

https://www.genomeweb.com/genetic-research/study-unlocks-multiple-functions-crisprcas9-varying-guide-rnas

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Breast Cancer Extratumor Microenvironment has Effect on Progression

Posted in Biochemical pathways, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Informatics, Cell Biology, Signaling & Cell Circuits, Clinical & Translational, Clinical Diagnostics, Computational Biology/Systems and Bioinformatics, Curation, Cytoskeleton, Diagnostics and Lab Tests, Disease Biology, Gene Regulation, Genome Biology, Genomic Endocrinology, Human Immune System in Health and in Disease, Lipid metabolism, Metabolism, Metabolomics, Micronutrients, Mutagenesis, tagged breast cancer, extratunot microenvironment, metastasis on February 20, 2016| Leave a Comment »

Breast Cancer Extratumor Microenvironment has Effect on Progression

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Tumor Microenvironment Diversity Predicts Breast Cancer Outcomes

GEN News Highlights   Feb 17, 2016   http://www.genengnews.com/gen-news-highlights/tumor-microenvironment-diversity-predicts-breast-cancer-outcomes/81252378/

 

Intratumor heterogeneity, it is known, can complicate cancer treatments. Now it appears the same may be true of tumor microenvironment heterogeneity. According to a new study from the Institute of Cancer Research (ICR), London, breast cancers that develop within an “ecologically diverse” breast cancer microenvironment are particularly likely to progress and lead to death.

The study took an unusual approach: It combined ecological scoring methods with genome-wide profiling data. This approach, besides showing clinical utility in the evaluation of breast cancer outcomes, demonstrated that even so contextual a discipline as genomics can benefit from being placed within a larger context. In this case, the context is essentially Darwinian, albeit at a small scale.

Natural selection is typically studied at the level of ecosystems consisting of animals and plants. In the current study, however, it was assessed at the level of the tumor microenvironment, which consists of cancer cells, immune system lymphocytes, and stromal cells.

The ICR scientists, led by Yinyin Yuan, Ph.D., presented their work February 16 in the journal PLoS Medicine, in an article entitled “Microenvironmental Heterogeneity Parallels Breast Cancer Progression: A Histology–Genomic Integration Analysis.” The article describes how the scientists developed a tumor ecosystem diversity index (EDI), a scoring system that indicates the degree of microenvironmental heterogeneity along three spatial dimensions in solid tumors. EDI scores take account of “fully automated histology image analysis coupled with statistical measures commonly used in ecology.”

“[EDI] was compared with disease-specific survival, key mutations, genome-wide copy number, and expression profiling data in a retrospective study of 510 breast cancer patients as a test set and 516 breast cancer patients as an independent validation set,” wrote the authors. “In high-grade (grade 3) breast cancers, we uncovered a striking link between high microenvironmental heterogeneity measured by EDI and a poor prognosis that cannot be explained by tumor size, genomics, or any other data types.”

By using the EDI, the ICR team was able to identify several particularly aggressive subgroups of breast cancer. In fact, the EDI was a stronger predictor of survival than many established markers for the disease.

The ICR researchers also looked at the EDI in addition to genetic factors. For example, the researchers found that the prognostic value of EDI was enhanced with the addition of TP53 mutation status. By integrating EDI data and genome-wide profiling data, the researchers identified losses of specific genes on 4p14 and 5q13 that were enriched in grade 3 tumors. These tumors, which showed high microenvironmental diversity, substratified patients into poor prognostic groups.

“Our findings show that mathematical models of ecological diversity can spot more aggressive cancers,” said Dr. Yuan. “By analyzing images of the environment around a tumor based on Darwinian natural selection principles, we can predict survival in some breast cancer types even more effectively than many of the measures used now in the clinic.

“In the future, we hope that by combining cell diversity scores with other factors that influence cancer survival, such as genetics and tumor size, we will be able to tell apart patients with more or less aggressive disease so we can identify those who might need different types of treatment.”

“This ingenious study…teaches us a valuable lesson,” added Paul Workman, Ph.D., chief executive of the ICR. “[We] should always remember that cancer cells are not developing and growing in isolation, but are part of a complex ecosystem that involves normal human cells, too. By better understanding these ecosystems, we aim to create new ways to diagnose, monitor and treat cancer.”

 

Microenvironmental Heterogeneity Parallels Breast Cancer Progression: A Histology–Genomic Integration Analysis

Rachael Natrajan, Heba Sailem, Faraz K. Mardakheh, Mar Arias Garcia, Christopher J. Tape, Mitch Dowsett, Chris Bakal, Yinyin Yuan    

 

    Feb 16, 2016   http://dx.doi.org:/10.1371/journal.pmed.1001961

 

Background

The intra-tumor diversity of cancer cells is under intense investigation; however, little is known about the heterogeneity of the tumor microenvironment that is key to cancer progression and evolution. We aimed to assess the degree of microenvironmental heterogeneity in breast cancer and correlate this with genomic and clinical parameters.

Methods and Findings

We developed a quantitative measure of microenvironmental heterogeneity along three spatial dimensions (3-D) in solid tumors, termed the tumor ecosystem diversity index (EDI), using fully automated histology image analysis coupled with statistical measures commonly used in ecology. This measure was compared with disease-specific survival, key mutations, genome-wide copy number, and expression profiling data in a retrospective study of 510 breast cancer patients as a test set and 516 breast cancer patients as an independent validation set. In high-grade (grade 3) breast cancers, we uncovered a striking link between high microenvironmental heterogeneity measured by EDI and a poor prognosis that cannot be explained by tumor size, genomics, or any other data types. However, this association was not observed in low-grade (grade 1 and 2) breast cancers. The prognostic value of EDI was superior to known prognostic factors and was enhanced with the addition of TP53 mutation status (multivariate analysis test set, p = 9 × 10⁻⁴, hazard ratio = 1.47, 95% CI 1.17–1.84; validation set, p = 0.0011, hazard ratio = 1.78, 95% CI 1.26–2.52). Integration with genome-wide profiling data identified losses of specific genes on 4p14 and 5q13 that were enriched in grade 3 tumors with high microenvironmental diversity that also substratified patients into poor prognostic groups. Limitations of this study include the number of cell types included in the model, that EDI has prognostic value only in grade 3 tumors, and that our spatial heterogeneity measure was dependent on spatial scale and tumor size.

Conclusions

To our knowledge, this is the first study to couple unbiased measures of microenvironmental heterogeneity with genomic alterations to predict breast cancer clinical outcome. We propose a clinically relevant role of microenvironmental heterogeneity for advanced breast tumors, and highlight that ecological statistics can be translated into medical advances for identifying a new type of biomarker and, furthermore, for understanding the synergistic interplay of microenvironmental heterogeneity with genomic alterations in cancer cells.

Background

The human body contains millions of cells, all of which grow, divide, and die in an orderly fashion to build tissues during early life and to replace worn-out or dying cells and repair injuries during adult life. Sometimes, however, normal cells acquire genetic changes (mutations) that allow them to divide uncontrollably and to move around the body (metastasize), resulting in cancer. Because any cell in the body can acquire the mutations needed for cancer development, there are many types of cancer. For example, breast cancer, the most common cancer in women, begins when the cells in the breast that normally make milk become altered. Moreover, different types of cancer progress and evolve differently—some cancers grow quickly and kill their “host” soon after diagnosis, whereas others can be successfully treated with drugs, surgery, or radiotherapy. The behavior of individual cancers depends both on the characteristics of the cancer cells within the tumor and on the interactions between the cancer cells and the normal stromal cells (the connective tissue cells of organs) and other cells (for example, immune cells) that surround and feed cancer cells (the tumor microenvironment).

Why Was This Study Done?

Although recent studies have highlighted the importance of the tumor microenvironment for disease-related outcomes, little is known about how the heterogeneity of the tumor microenvironment—the diversity of non-cancer cells within the tumor—affects outcomes. Mathematical modeling suggests that tumors with heterogeneous and homogeneous microenvironments have different growth patterns and that heterogeneous microenvironments are more likely to be associated with aggressive cancers than homogenous microenvironments. However, the lack of methods to quantify the spatial variability and cellular composition across solid tumors has prevented confirmation of these predictions. Here, the researchers develop a computational system for quantifying microenvironmental heterogeneity in breast cancer based on tumor morphology (shape and form) in histological sections (tissue samples taken from tumors that are examined microscopically). They then use this system to analyze the associations between clinical outcomes, molecular changes, and microenvironmental heterogeneity in breast cancer.

What Did the Researchers Do and Find?

The researchers used automated image analysis and statistical analysis to develop the ecosystem diversity index (EDI), a numerical measure of microenvironmental heterogeneity in solid tumors. They compared the EDI with prognosis (likely outcome), key mutations, genome-wide copy number (tumor cells often contain abnormal numbers of copies of specific genes), and expression profiling data (the expression of several key proteins is altered in tumors) in a test set of 510 samples from patients with breast cancer and in a validation set of 516 additional samples. Among high-grade breast cancers (grade 3 cancers; the grade of a cancer indicates what the cells look like; high-grade breast cancers have a poor prognosis), but not among low-grade breast cancers (grades 1 and 2), a high EDI (high microenvironmental heterogeneity) was associated with a poor prognosis. Specifically, patients with grade 3 tumors and a high EDI had a ten-year disease-specific survival rate of 51%, whereas the remaining patients with grade 3 tumors had a ten-year survival rate of 70%. Notably, the combination of a high EDI with specific DNA alterations—mutations in a gene called TP53 and loss of genes on Chromosomes 4p14 and 5q13—improved the accuracy of prognosis among patients with grade 3 breast cancer and stratified them into subgroups with disease-specific five-year survival rates of 35%, 9%, and 32%, respectively.

What Do These Findings Mean?

These findings establish a method for measuring the spatial heterogeneity of the microenvironment of solid tumors and suggest that the measurement of tumor microenvironmental heterogeneity can be coupled with information about genomic alterations to provide an accurate way to predict outcomes among patients with high-grade breast cancer. The association between EDI, specific genomic alterations, and outcomes needs to be confirmed in additional patients. However, these findings suggest that microenvironmental heterogeneity might provide an additional biomarker to help clinicians identify those patients with advanced breast cancer who have a particularly bad prognosis. The ability to identify these patients is important because it will help clinicians target aggressive treatments to individuals with a poor prognosis and avoid the overtreatment of patients whose prognosis is more favorable. Finally, and more generally, these findings describe a new way to investigate the interactions between the tumor microenvironment and genomic alterations in cancer cells.

Additional Information

This list of resources contains links that can be accessed when viewing the PDF on a device or via the online version of the article at http://dx.doi.org/10.1371/journal.pmed.1001961.

• The US National Cancer Institute provides comprehensive information about cancer and its development (in English and Spanish), including detailed information about breast cancer and an online booklet for patients
• Cancer Research UK, a not-for-profit organization, provides information about cancer, including detailed information about breast cancer and a science blog on the tumor microenvironment
• Breast Cancer Now is a not-for-profit organization that provides up-to-date information about breast cancer (in English and Spanish)
• The UK National Health Service Choices website has information and personal stories about breast cancer; the not-for-profit organization Healthtalkonline also provides personal stories about dealing with breast cancer
• Wikipedia has a page about the tumor microenvironment (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)

Citation: Natrajan R, Sailem H, Mardakheh FK, Arias Garcia M, Tape CJ, Dowsett M, et al. (2016) Microenvironmental Heterogeneity Parallels Breast Cancer Progression: A Histology–Genomic Integration Analysis.
PLoS Med 13(2): e1001961.     http://dx.doi.org:/10.1371/journal.pmed.1001961

http://journals.plos.org/plosmedicine/article/figure/image?size=large&id=info:doi/10.1371/journal.pmed.1001961.g001

Fig 1. In silico tumor dissection pipeline for quantifying spatial diversity in the tumor ecosystem. (A) Flow diagram depicting the overall study design. (B) Schematic of our pipeline for quantifying spatial diversity in pathological samples. H&E sections are morphologically classified and divided into regions to be spatially scored. The number of clusters k in the regional scores is indicative of the number of sub-populations of cell types in the tumor regions. (C) Examples of tumor regions with low and high diversity scores using the Shannon diversity index, accounting for cancer cells (outlined in green), lymphocytes (blue), and stromal cells (red). Cell classification is automated by image analysis. (D) The 3-D landscape of cell diversity scores on an example H&E section; the x- and y-axes are the geometric axes of the image, and the z-axis is cell diversity computed on a region-by-region basis. (E) The distribution of regional scores in a tumor from the METABRIC study with two regional clusters identified using Gaussian mixture clustering (grey shading: histogram; dashed black line: density; solid black lines: mixture components/clusters).

http://journals.plos.org/plosmedicine/article/figure/image?size=medium&id=info:doi/10.1371/journal.pmed.1001961.g002

Fig 2. Application of EDI to 1,026 breast tumors from the METABRIC study. (A) The frequencies of EDI scores in breast tumors. (B) H&E staining, distribution of classified cells (green: cancer; blue: lymphocyte; red: stromal cells), and the heatmap of regional diversity scores for a tumor with the highest EDI score (EDI = 5). (C) Representative regions from each of the clusters k1–k5 are shown in a tumor with EDI = 5, with cluster k1 having the lowest diversity score and k5 the highest. By mapping regional clusters to the H&E image, we can begin to interpret these clusters with different cell diversity. We observed predominantly cancer cells in k1, increasingly more stromal cells and ductal in situ carcinoma cells (DCIS) in k2, and a vessel in k3. Cluster k4 features extensive stromal lymphocytes between ductal in situ carcinoma components, while k5 shows tumor-infiltrating lymphocytes (TIL) associated with invasive carcinoma cells.

Fig 3. Reproducibility, stability, and independence of the EDI-high group in 507 grade 3 breast tumors.

http://journals.plos.org/plosmedicine/article/figure/image?size=large&id=info:doi/10.1371/journal.pmed.1001961.g003

Fig 3. Reproducibility, stability, and independence of the EDI-high group in 507 grade 3 breast tumors. (A) Kaplan–Meier curves of disease-specific survival to illustrate the prognosis of EDI-high samples compared to other grade 3 samples in two independent patient cohorts. Shown below the graph are the number of patients (the number of disease-specific events) per group for EDI-low (grey) and EDI-high (red). (B) Agreement of the EDI subtyping between 100% data and resampling with progressively fewer tumor regions in 200 repeats. (C) Distribution of known subtypes in grade 3 tumors stratified by EDI; asterisks mark subtypes enriched in the EDI-high group. (D) Kaplan–Meier curves illustrating the duration of disease-specific survival according to tumor size (left) and improvement of stratification with the addition of EDI information (right).

Accumulating evidence suggests that the interactions of cancer cells and stromal cells within their microenvironment govern disease progression, metastasis, and, ultimately, the evolution of therapeutic resistance [1–3]. Recent reports have highlighted the significance of the contribution of stromal gene expression and morphological structure as powerful prognostic determinants for a number of tumor types, emphasizing the importance of the tumor microenvironment in disease-related outcomes [4–7]. In breast cancer, a number of studies have demonstrated the prognostic correlation of individual cell types, including the immune cell infiltrate that predicts response to therapy [8–10], and the high percentage of tumor stroma that predicts poor prognosis in triple-negative disease but good prognosis in estrogen receptor (ER)–positive disease [11,12]. Nevertheless, different types of cells coexist with varying degrees of heterogeneity within a tumor. This fundamental feature of human tumors and the combinatorial effects of cell types have been largely ignored, and the collective implications for clinical outcome remain elusive. Consistent observations from mathematical models have highlighted that tumors with diverse microenvironments show growth patterns dramatically different from those of tumors with homogeneous environments [13] and are more likely to be associated with aggressive cancer phenotypes [2] that select for cell migration and eventual metastasis by allowing cancer cells to evolve more rapidly [14]. These observations highlight the need to understand the collective physiological characteristics and heterogeneity of tumor microenvironments. However, there is a lack of methods to quantify the high spatial variability and diverse cellular composition across different solid tumors. Moreover, the interplay of genomic alterations in cancer cells and microenvironmental heterogeneity and its subsequent role in treatment response have not been explored. Our aims were (i) to develop a computational system for quantifying microenvironmental heterogeneity based on tumor morphology in routine histological sections, (ii) to define the clinical implications of microenvironmental heterogeneity, and (iii) to integrate this histologybased index with RNA gene expression and DNA copy number profiling data to identify molecular changes associated with microenvironmental heterogeneity.

The Ecosystem Diversity Index To characterize the tumor ecosystem based on cell compositions, we developed a new index to be used in conjunction with our image analysis tool [16]. First, we used our automated morphological classification method [16] to identify and classify cells into cancer, lymphocyte, or stromal cell classes in H&E sections (Fig 1B). We next divided sections into smaller spatial regions and quantified the diversity of the tumor ecosystem in a tumor region j using the Shannon diversity index: dj ¼ Sm i pi logpi ; ð1Þ where m is the number of cell types and pi is the proportion of the ith cell type (Fig 1B and 1C). A high value of the Shannon diversity index dj reports a heterogeneous environment populated by many cell types, whilst a low value indicates a homogeneous environment (Fig 1C). Compared to other methods such as the Simpson index, the Shannon diversity index accounts for rare species and, hence, is less dominated by main species [17]. Subsequently, we derived the ecosystem diversity index (EDI) by applying unsupervised clustering that identifies the optimum number of clusters in the dataset in an unbiased manner, in order to group tumor regions and quantify the degree of spatial heterogeneity. Let D = d1,d2,…,dn be the Shannon index for n regions in a tumor. We used Gaussian mixture models to fit data D: D SK k¼1okNðmk; s2 kÞ: ð2Þ where μk, ,s2 k, and ωk are the mean, variance, and weight of a Gaussian distribution k, and K is the number of clusters. The Bayesian information criterion was then used to select the best number of clusters K [18]. We used K = 1–5 as the range of K to avoid small EDI groups (S1 Text). The final value of K thus is a measurement of heterogeneity and the score of EDI for a tumor.

Fig 5. The relationship between ecological heterogeneity and cancer genomic aberrations in 507 grade 3 tumors. (A) Genome-wide copy number aberrations in grade 3 breast tumors and genomic coordinates of genes with copy number aberrations enriched in the EDI-high group. Lengths of black lines denote level of enrichment significance with copy number gains (above the horizontal line) or losses (below the horizontal line). (B) Kaplan–Meier curves illustrating the duration of disease-specific survival in grade 3 breast cancer patients according to copy number loss of the 4p14 region (left) and the EDI-high group with additional information of 4p14 copy number loss (right). (C) Kaplan–Meier curves illustrating the duration of disease-specific survival according to copy number loss of the 5q13 region (left) and the EDI-high group with additional information of 5q13 copy number loss (right).

http://dx.doi.org:/10.1371/journal.pmed.1001961.g005

This study has a number of limitations. The motivation for our computational development was to use a data-driven model and measure the degree of spatial heterogeneity in tumor pathological specimens. In this model, only three major cell types in breast tumors were considered. Further sub-classification of the different types of stromal and immune cells by immunohistochemistry may add additional discriminatory value to our model. For dissecting spatial heterogeneity, we chose to use square regions with equal sizes. We found that EDI was correlated with the size of the region chosen for calculation of the Shannon diversity index, and as such the spatial heterogeneity is scale dependent. This phenomenon has been well described in a number of studies in ecology that show that a scale needs to be chosen that is appropriate for the ecological process under study [38,39], further highlighting the analogy between tumor studies and ecology. Similar to the recent observation that breast cancer subclonal heterogeneity is correlated with tumor size [35], we also found an association between microenvironmental heterogeneity and tumor size; hence, EDI may have more limited value in smaller tumors. However, small tumors were present in the EDI-high group, and addition of EDI within tumors grouped by size further stratified their prognosis. We found that EDI was prognostic only in grade 3 tumors in our study, which could be a limitation, given the possible discordance in grading between pathologists.

The identification of additional biomarkers in subgroups of patients that identify them as high risk is important for patient management and to avoid overtreatment for low-risk patients. We envision that the use of our measure of microenvironment heterogeneity, together with key genomic alterations, will enable the diagnosis of patients at very high risk of relapse and facilitate the enrollment of these patients into additional clinical trials for novel therapies or treatment intensification. Our novel computational approach provides a fully automated tool that is relatively easy to implement. Integration of this measure with genomic profiling provides additional prognostic information independent of known clinical parameters. The results of this study highlight the possibility of a grade-3-specific prognostic tool that may aid in further classification of high-grade breast cancer patients beyond standard assays such as ER and HER2 status.

 

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