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The bacterial makeup of human milk is influenced by the mode of breastfeeding, according to a new study. Although previously considered sterile, breast milk is now known to contain a low abundance of bacteria. While the complexities of how maternal microbiota influence the infant microbiota are still unknown, this complex community of bacteria in breast milk may help to establish the infant gut microbiota. Disruptions in this process could alter the infant microbiota, causing predisposition to chronic diseases such as allergies, asthma, and obesity. While it’s unclear how the breast milk microbiome develops, there are two theories describing its origins. One theory speculates that it originates in the maternal mammary gland, while the other theory suggests that it is due to retrograde inoculation by the infant’s oral microbiome.
To address this gap in knowledge scientists carried out bacterial gene sequencing on milk samples from 393 healthy mothers three to four months after giving birth. They used this information to examine how the milk microbiota composition is affected by maternal factors, early life events, breastfeeding practices, and other milk components. Among the many factors analyzed, the mode of breastfeeding (with or without a pump) was the only consistent factor directly associated with the milk microbiota composition. Specifically, indirect breastfeeding was associated with a higher abundance of potential opportunistic pathogens, such as Stenotrophomonas and Pseudomonadaceae. By contrast, direct breastfeeding without a pump was associated with microbes typically found in the mouth, as well as higher overall bacterial richness and diversity. Taken together, the findings suggest that direct breastfeeding facilitates the acquisition of oral microbiota from infants, whereas indirect breastfeeding leads to enrichment with environmental (pump-associated) bacteria.
The researchers argued that this study supports the theory that the breast milk microbiome is due to retrograde inoculation. Their findings indicate that the act of pumping and contact with the infant oral microbiome influences the milk microbiome, though they noted more research is needed. In future studies, the researchers will further explore the composition and function of the milk microbiota. In addition to bacteria, they will profile fungi in the milk samples. They also plan to investigate how the milk microbiota influences both the gut microbiota of infants and infant development and health. Specifically, their projects will examine the association of milk microbiota with infant growth, asthma, and allergies. This work could have important implications for microbiota-based strategies for early-life prevention of chronic conditions.
This is How Much Daily Fiber to Eat for Better Health – More appears better in meta-analysis — as in more than 30 g/day
by Ashley Lyles, Staff Writer, MedPage Today
In the systematic review, observational data showed a 15% to 30% decline in cardiovascular-related death, all-cause mortality, and incidence of stroke, coronary heart disease, type 2 diabetes, and colorectal cancer among people who consumed the most dietary fiber compared to those consuming the lowest amounts.
Whole grain intake yielded similar findings.
Risk reduction associated with a range of critical outcomes was greatest when daily intake of dietary fibre was between 25 g and 29 g. Dose-response curves suggested that higher intakes of dietary fibre could confer even greater benefit to protect against cardiovascular diseases, type 2 diabetes, and colorectal and breast cancer.
Eating more dietary fiber was linked with lower risk of disease and death, a meta-analysis showed.
According to observational studies, risk was reduced most for a range of critical outcomes from all-cause mortality to stroke when daily fiber consumption was between 25 grams and 29 grams, reported Jim Mann, PhD, of University of Otago in Dunedin, New Zealand, and colleagues in The Lancet.
By upping daily intake to 30 grams or more, people had even greater prevention of certain conditions: colorectal and breast cancer, type 2 diabetes, and cardiovascular diseases, according to dose-response curves the authors created.
Quantitative guidelines relating to dietary fiber have not been available, the researchers said. With the GRADE method, they determined that there was moderate and low-to-moderate certainty of evidence for the benefits of dietary fiber consumption and whole grain consumption, respectively.
Included in the systematic review were 58 clinical trials and 185 prospective studies for a total of 4,635 adult participants with 135 million person-years of information (one trial in children was included, but analyzed separately from adults). Trials and prospective studies assessing weight loss, supplement use, and participants with a chronic disease were excluded.
Food is digested by bathing in enzymes that break down its molecules. Those molecular fragments then pass through the gut wall and are absorbed in our intestines. But our bodies make a limited range of enzymes, so that we cannot break down many of the tough compounds in plants. The term “dietary fiber” refers to those indigestible molecules. These dietary fibers are indigestible only to us. The gut is coated with a layer of mucus, on which sits a carpet of hundreds of species of bacteria, part of the human microbiome. Some of these microbes carry the enzymes needed to break down various kinds of dietary fibers.
Scientists at the University of Gothenburg in Sweden are running experiments that are yielding some important new clues about fiber’s role in human health. Their research indicates that fiber doesn’t deliver many of its benefits directly to our bodies. Instead, the fiber we eat feeds billions of bacteria in our guts. Keeping them happy means our intestines and immune systems remain in good working order. The scientists have recently reported that the microbes are involved in the benefits obtained from the fruits-and-vegetables diet. Research proved that low fiber diet decreases the gut bacteria population by tenfold.
Along with changes to the microbiome there were also rapid changes observed in the experimental mice. Their intestines got smaller, and its mucus layer thinner. As a result, bacteria wound up much closer to the intestinal wall, and that encroachment triggered an immune reaction. After a few days on the low-fiber diet, mouse intestines developed chronic inflammation. After a few weeks, they started putting on fat and developing higher blood sugar levels. Inflammation can help fight infections, but if it becomes chronic, it can harm our bodies. Among other things, chronic inflammation may interfere with how the body uses the calories in food, storing more of it as fat rather than burning it for energy.
In a way fiber benefits human health is by giving, indirectly, another source of food. When bacteria finished harvesting the energy in the dietary fiber, they cast off the fragments as waste. That waste — in the form of short-chain fatty acids — is absorbed by intestinal cells, which use it as fuel. But the gut’s microbes do more than just make energy. They also send messages. Intestinal cells rely on chemical signals from the bacteria to work properly. The cells respond to the signals by multiplying and making a healthy supply of mucus. They also release bacteria-killing molecules. By generating these responses, gut bacteria help to maintain a peaceful coexistence with the immune system. They rest on the gut’s mucus layer at a safe distance from the intestinal wall. Any bacteria that wind up too close get wiped out by antimicrobial poisons.
A diet of fiber-rich foods, such as fruits and vegetables, reduces the risk of developing diabetes, heart disease and arthritis. Eating more fiber seems to lower people’s mortality rate, whatever be the cause. Researchers hope that they will learn more about how fiber influences the microbiome to use it as a way to treat disorders. Lowering inflammation with fiber may also help in the treatment of immune disorders such as inflammatory bowel disease. Fiber may also help reverse obesity. They found that fiber supplements helped obese people to lose weight. It’s possible that each type of fiber feeds a particular set of bacteria, which send their own important signals to our bodies.
The trillions of microbes in the human gut are known to aid the body in synthesizing key vitamins and other nutrients. But this new study suggests that things can sometimes be more adversarial.
Choline is a key nutrient in a range of metabolic processes, as well as the production of cell membranes. Researchers identified a strain of choline-metabolizing E. coli that, when transplanted into the guts of germ-free mice, consumed enough of the nutrient to create a choline deficiency in them, even when the animals consumed a choline-rich diet.
This new study indicate that choline-utilizing bacteria compete with the host for this nutrient, significantly impacting plasma and hepatic levels of methyl-donor metabolites and recapitulating biochemical signatures of choline deficiency. Mice harboring high levels of choline-consuming bacteria showed increased susceptibility to metabolic disease in the context of a high-fat diet.
DNA methylation is essential for normal development and has been linked to everything from aging to carcinogenesis. This study showed changes in DNA methylation across multiple tissues, not just in adult mice with a choline-consuming gut microbiota, but also in the pups of those animals while they developed in utero.
Bacterially induced reduction of methyl-donor availability influenced global DNA methylation patterns in both adult mice and their offspring and engendered behavioral alterations. This study reveal an underappreciated effect of bacterial choline metabolism on host metabolism, epigenetics, and behavior.
The choline-deficient mice with choline-consuming gut microbes also showed much higher rates of infanticide, and exhibited signs of anxiety, with some mice over-grooming themselves and their cage-mates, sometimes to the point of baldness.
Tests have also shown as many as 65 percent of healthy individuals carry genes that encode for the enzyme that metabolizes choline in their gut microbiomes. This work suggests that interpersonal differences in microbial metabolism should be considered when determining optimal nutrient intake requirements.
During pregnancy, the baby is mostly protected from harmful microorganisms by the amniotic sac, but recent research suggests the baby could be exposed to small quantities of microbes from the placenta, amniotic fluid, umbilical cord blood and fetal membranes. One theory is that any possible prenatal exposure could ‘pre-seed’ the infant microbiome. In other words, to set the right conditions for the ‘main seeding event’ for founding the infant microbiome.
When a mother gives birth vaginally and if she breastfeeds, she passes on colonies of essential microbes to her baby. This continues a chain of maternal heritage that stretches through female ancestry for thousands of generations, if all have been vaginally born and breastfed. This means a child’s microbiome, that is the trillions of microorganisms that live on and in him or her, will resemble the microbiome of his/her mother, the grandmother, the great-grandmother and so on, if all have been vaginally born and breastfed.
As soon as the mother’s waters break, suddenly the baby is exposed to a wave of the mother’s vaginal microbes that wash over the baby in the birth canal. They coat the baby’s skin, and enter the baby’s eyes, ears, nose and some are swallowed to be sent down into the gut. More microbes form of the mother’s gut microbes join the colonization through contact with the mother’s faecal matter. Many more microbes come from every breath, from every touch including skin-to-skin contact with the mother and of course, from breastfeeding.
With formula feeding, the baby won’t receive the 700 species of microbes found in breast milk. Inside breast milk, there are special sugars called human milk oligosaccharides (HMO’s) that are indigestible by the baby. These sugars are designed to feed the mother’s microbes newly arrived in the baby’s gut. By multiplying quickly, the ‘good’ bacteria crowd out any potentially harmful pathogens. These ‘good’ bacteria help train the baby’s naive immune system, teaching it to identify what is to be tolerated and what is pathogen to be attacked. This leads to the optimal training of the infant immune system resulting in a child’s best possible lifelong health.
With C-section birth and formula feeding, the baby is not likely to acquire the full complement of the mother’s vaginal, gut and breast milk microbes. Therefore, the baby’s microbiome is not likely to closely resemble the mother’s microbiome. A baby born by C-section is likely to have a different microbiome from its mother, its grandmother, its great-grandmother and so on. C-section breaks the chain of maternal heritage and this break can never be restored.
The long term effect of an altered microbiome for a child’s lifelong health is still to be proven, but many studies link C-section with a significantly increased risk for developing asthma, Type 1 diabetes, celiac disease and obesity. Scientists might not yet have all the answers, but the picture that is forming is that C-section and formula feeding could be significantly impacting the health of the next generation. Through the transgenerational aspect to birth, it could even be impacting the health of future generations.
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.
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.
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.”
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.”
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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_Kadohttp://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)
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
•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 TLR5R392X 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.
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.
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.
Body fluids from individuals with possible Creutzfeldt-Jakob disease (CJD) present distinctive safety challenges for clinical laboratories. Sporadic, iatrogenic, and familial CJD (known collectively as classic CJD), along with variant CJD, kuru, Gerstmann-Sträussler-Scheinker, and fatal familial insomnia, are prion diseases, also known as transmissible spongiform encephalopathies. Prion diseases affect the central nervous system, and from the onset of symptoms follow a typically rapid progressive neurological decline. While prion diseases are rare, it is not uncommon for the most prevalent form—sporadic CJD—to be included in the differential diagnosis of individuals presenting with rapid cognitive decline. Thus, laboratories may deal with a significant number of possible CJD cases, and should have protocols in place to process specimens, even if a confirmatory diagnosis of CJD is made in only a fraction of these cases.
The Lab’s Role in Diagnosis
Laboratory protocols for handling specimens from individuals with possible, probable, and definitive cases of CJD are important to ensure timely and appropriate patient management. When the differential includes CJD, an attempt should be made to rule-in or out other causes of rapid neurological decline. Laboratories should be prepared to process blood and cerebrospinal fluid (CSF) specimens in such cases for routine analyses.
Definitive diagnosis requires identification of prion aggregates in brain tissue, which can be achieved by immunohistochemistry, a Western blot for proteinase K-resistant prions, and/or by the presence of prion fibrils. Thus, confirmatory diagnosis is typically achieved at autopsy. A probable diagnosis of CJD is supported by elevated concentration of 14-3-3 protein in CSF (a non-specific marker of neurodegeneration), EEG, and MRI findings. Thus, the laboratory may be required to process and send CSF samples to a prion surveillance center for 14-3-3 testing, as well as blood samples for sequencing of the PRNP gene (in inherited cases).
Processing Biofluids
Laboratories should follow standard protective measures when working with biofluids potentially containing abnormally folded prions, such as donning standard personal protective equipment (PPE); avoiding or minimizing the use of sharps; using single-use disposable items; and processing specimens to minimize formation of aerosols and droplets. An additional safety consideration is the use of single-use disposal PPE; otherwise, re-usable items must be either cleaned using prion-specific decontamination methods, or destroyed.
Blood. In experimental models, infectivity has been detected in the blood; however, there have been no cases of secondary transmission of classical CJD via blood product transfusions in humans. As such, blood has been classified, on epidemiological evidence by the World Health Organization (WHO), as containing “no detectible infectivity,” which means it can be processed by routine methods. Similarly, except for CSF, all other body fluids contain no infectivity and can be processed following standard procedures.
In contrast to classic CJD, there have been four cases of suspected secondary transmission of variant CJD via transfused blood products in the United Kingdom. Variant CJD, the prion disease associated with mad cow disease, is unique in its distribution of prion aggregates outside of the central nervous system, including the lymph nodes, spleen, and tonsils. For regions where variant CJD is a concern, laboratories should consult their regulatory agencies for further guidance.
CSF. Relative to highly infectious tissues of the brain, spinal cord, and eye, infectivity has been identified less often in CSF and is considered to have “low infectivity,” along with kidney, liver, and lung tissue. Since CSF can contain infectious material, WHO has recommended that analyses not be performed on automated equipment due to challenges associated with decontamination. Laboratories should perform a risk assessment of their CSF processes, and, if deemed necessary, consider using manual methods as an alternative to automated systems.
Decontamination
The infectious agent in prion disease is unlike any other infectious pathogen encountered in the laboratory; it is formed of misfolded and aggregated prion proteins. This aggregated proteinacious material forms the infectious unit, which is incredibly resilient to degradation. Moreover, in vitro studies have demonstrated that disrupting large aggregates into smaller aggregates increases cytotoxicity. Thus, if the aim is to abolish infectivity, all aggregates must be destroyed. Disinfectant procedures used for viral, bacterial, and fungal pathogens such as alcohol, boiling, formalin, dry heat (<300°C), autoclaving at 121°C for 15 minutes, and ionizing, ultraviolet, or microwave radiation, are either ineffective or variably effective against aggregated prions.
The only means to ensure no risk of residual infectious prions is to use disposable materials. This is not always practical, as, for instance, a biosafety cabinet cannot be discarded if there is a CSF spill in the hood. Fortunately, there are several protocols considered sufficient for decontamination. For surfaces and heat-sensitive instruments, such as a biosafety cabinet, WHO recommends flooding the surface with 2N NaOH or undiluted NaClO, letting stand for 1 hour, mopping up, and rinsing with water. If the surface cannot tolerate NaOH or NaClO, thorough cleaning will remove most infectivity by dilution. Laboratories may derive some additional benefit by using one of the partially effective methods discussed previously. Non-disposable heat-resistant items preferably should be immersed in 1N NaOH, heated in a gravity displacement autoclave at 121°C for 30 min, cleaned and rinsed in water, then sterilized by routine methods. WHO has outlined several alternate decontamination methods. Using disposable cover sheets is one simple solution to avoid contaminating work surfaces and associated lengthy decontamination procedures.
With standard PPE—augmented by a few additional safety measures and prion-specific decontamination procedures—laboratories can safely manage biofluid testing in cases of prion disease.
The Microscopic World Inside Us
Emerging Research Points to Microbiome’s Role in Health and Disease
Thousands of species of microbes—bacteria, viruses, fungi, and protozoa—inhabit every internal and external surface of the human body. Collectively, these microbes, known as the microbiome, outnumber the body’s human cells by about 10 to 1 and include more than 1,000 species of microorganisms and several million genes residing in the skin, respiratory system, urogenital, and gastrointestinal tracts. The microbiome’s complicated relationship with its human host is increasingly considered so crucial to health that researchers sometimes call it “the forgotten organ.”
Disturbances to the microbiome can arise from nutritional deficiencies, antibiotic use, and antiseptic modern life. Imbalances in the microbiome’s diverse microbial communities, which interact constantly with cells in the human body, may contribute to chronic health conditions, including diabetes, asthma and allergies, obesity and the metabolic syndrome, digestive disorders including irritable bowel syndrome (IBS), and autoimmune disorders like multiple sclerosis and rheumatoid arthritis, research shows.
While study of the microbiome is a growing research enterprise that has attracted enthusiastic media attention and venture capital, its findings are largely preliminary. But some laboratorians are already developing a greater appreciation for the microbiome’s contributions to human biochemistry and are considering a future in which they expect to measure changes in the microbiome to monitor disease and inform clinical practice.
Pivot Toward the Microbiome
Following the National Institutes of Health (NIH) Human Genome Project, many scientists noted the considerable genetic signal from microbes in the body and the existence of technology to analyze these microorganisms. That realization led NIH to establish the Human Microbiome Project in 2007, said Lita Proctor, PhD, its program director. In the project’s first phase, researchers studied healthy adults to produce a reference set of microbiomes and a resource of metagenomic sequences of bacteria in the airways, skin, oral cavities, and the gastrointestinal and vaginal tracts, plus a catalog of microbial genome sequences of reference strains. Researchers also evaluated specific diseases associated with disturbances in the microbiome, including gastrointestinal diseases such as Crohn’s disease, ulcerative colitis, IBS, and obesity, as well as urogenital conditions, those that involve the reproductive system, and skin diseases like eczema, psoriasis, and acne.
Phase 1 studies determined the composition of many parts of the microbiome, but did not define how that composition affects health or specific disease. The project’s second phase aims to “answer the question of what microbes actually do,” explained Proctor. Researchers are now examining properties of the microbiome including gene expression, protein, and human and microbial metabolite profiles in studies of pregnant women at risk for preterm birth, the gut hormones of patients at risk for IBS, and nasal microbiomes of patients at risk for type 2 diabetes.
Promising Lines of Research
Cystic fibrosis and microbiology investigator Michael Surette, PhD, sees promising microbiome research not just in terms of evidence of its effects on specific diseases, but also in what drives changes in the microbiome. Surette is Canada research chair in interdisciplinary microbiome research in the Farncombe Family Digestive Health Research Institute at McMaster University
in Hamilton, Ontario.
One type of study on factors driving microbiome change examines how alterations in composition and imbalances in individual patients relate to improving or worsening disease. “IBS, cystic fibrosis, and chronic obstructive pulmonary disease all have periods of instability or exacerbation,” he noted. Surette hopes that one day, tests will provide clinicians the ability to monitor changes in microbial composition over time and even predict when a patient’s condition is about to deteriorate. Monitoring perturbations to the gut microbiome might also help minimize collateral damage to the microbiome during aggressive antibiotic therapy for hospitalized patients, he added.
Monitoring changes to the microbiome also might be helpful for “culture negative” patients, who now may receive multiple, unsuccessful courses of different antibiotics that drive antibiotic resistance. Frustration with standard clinical biology diagnosis of lung infections in cystic fibrosis patients first sparked Surette’s investigations into the microbiome. He hopes that future tests involving the microbiome might also help asthma patients with neutrophilia, community-acquired pneumonia patients who harbor complex microbial lung communities lacking obvious pathogens, and hospitalized patients with pneumonia or sepsis. He envisions microbiome testing that would look for short-term changes indicating whether or not a drug is effective.
Companion Diagnostics
Daniel Peterson, MD, PhD, an assistant professor of pathology at Johns Hopkins University School of Medicine in Baltimore, believes the future of clinical testing involving the microbiome lies in companion diagnostics for novel treatments, and points to companies that are already developing and marketing tests that will require such assays.
Examples of microbiome-focused enterprises abound, including Genetic Analysis, based in Oslo, Norway, with its high-throughput test that uses 54 probes targeted to specific bacteria to measure intestinal gut flora imbalances in inflammatory bowel disease and irritable bowel syndrome patients. Paris, France-based Enterome is developing both novel drugs and companion diagnostics for microbiome-related diseases such as IBS and some metabolic diseases. Second Genome, based in South San Francisco, has developed an experimental drug, SGM-1019, that the company says blocks damaging activity of the microbiome in the intestine. Cambridge, Massachusetts-based Seres Therapeutics has received Food and Drug Administration orphan drug designation for SER-109, an oral therapeutic intended to correct microbial imbalances to prevent recurrent Clostridium difficile infection in adults.
One promising clinical use of the microbiome is fecal transplantation, which both prospective and retrospective studies have shown to be effective in patients with C. difficile infections who do not respond to front-line therapies, said James Versalovic, MD, PhD, director of Texas Children’s Hospital Microbiome Center and professor of pathology at Baylor College of Medicine in Houston. “Fecal transplants and other microbiome replacement strategies can radically change the composition of the microbiome in hours to days,” he explained.
But NIH’s Proctor discourages too much enthusiasm about fecal transplant. “Natural products like stool can have [side] effects,” she pointed out. “The [microbiome research] field needs to mature and we need to verify outcomes before anything becomes routine.”
Hurdles for Lab Testing
While he is hopeful that labs someday will use the microbiome to produce clinically useful information, Surette pointed to several problems that must be solved beforehand. First, molecular methods commonly used right now should be more quantitative and accurate. Additionally, research on the microbiome encompasses a wide variety of protocols, some of which are better at extracting particular types of bacteria and therefore can give biased views of communities living in the body. Also, tests may need to distinguish between dead and live microbes. Another hurdle is that labs using varied bioinfomatic methods may produce different results from the same sample, a problem that Surette sees as ripe for a solution from clinical laboratorians, who have expertise in standardizing robust protocols and in automating tests.
One way laboratorians can prepare for future, routine microbiome testing is to expand their notion of clinical chemistry to include both microbial and human biochemistry. “The line between microbiome science and clinical science is blurring,” said Versalovic. “When developing future assays to detect biochemical changes in disease states, we must consider the contributions of microbial metabolites and proteins and how to tailor tests to detect them.” In the future, clinical labs may test for uniquely microbial metabolites in various disease states, he predicted.
Automated Review of Mass Spectrometry Results
Can We Achieve Autoverification?
Author: Katherine Alexander and Andrea R. Terrell, PhD // Date: NOV.1.2015 // Source:Clinical Laboratory News
Paralleling the upswing in prescription drug misuse, clinical laboratories are receiving more requests for mass spectrometry (MS) testing as physicians rely on its specificity to monitor patient compliance with prescription regimens. However, as volume has increased, reimbursement has declined, forcing toxicology laboratories both to increase capacity and lower their operational costs—without sacrificing quality or turnaround time. Now, new solutions are available enabling laboratories to bring automation to MS testing and helping them with the growing demand for toxicology and other testing.
What is the typical MS workflow?
A typical workflow includes a long list of manual steps. By the time a sample is loaded onto the mass spectrometer, it has been collected, logged into the lab information management system (LIMS), and prepared for analysis using a variety of wet chemistry techniques.
Most commercial clinical laboratories receive enough samples for MS analysis to batch analyze those samples. A batch consists of a calibrator(s), quality control (QC) samples, and patient/donor samples. Historically, the method would be selected (i.e. “analysis of opiates”), sample identification information would be entered manually into the MS software, and the instrument would begin analyzing each sample. Upon successful completion of the batch, the MS operator would view all of the analytical data, ensure the QC results were acceptable, and review each patient/donor specimen, looking at characteristics such as peak shape, ion ratios, retention time, and calculated concentration.
The operator would then post acceptable results into the LIMS manually or through an interface, and unacceptable results would be rescheduled or dealt with according to lab-specific protocols. In our laboratory we perform a final certification step for quality assurance by reviewing all information about the batch again, prior to releasing results for final reporting through the LIMS.
What problems are associated with this workflow?
The workflow described above results in too many highly trained chemists performing manual data entry and reviewing perfectly acceptable analytical results. Lab managers would prefer that MS operators and certifying scientists focus on troubleshooting problem samples rather than reviewing mounds of good data. Not only is the current process inefficient, it is mundane work prone to user errors. This risks fatigue, disengagement, and complacency by our highly skilled scientists.
Importantly, manual processes also take time. In most clinical lab environments, turnaround time is critical for patient care and industry competitiveness. Lab directors and managers are looking for solutions to automate mundane, error-prone tasks to save time and costs, reduce staff burnout, and maintain high levels of quality.
How can software automate data transfer from MS systems to LIMS?
Automation is not a new concept in the clinical lab. Labs have automated processes in shipping and receiving, sample preparation, liquid handling, and data delivery to the end user. As more labs implement MS, companies have begun to develop special software to automate data analysis and review workflows.
In July 2011, AIT Labs incorporated ASCENT into our workflow, eliminating the initial manual peak review step. ASCENT is an algorithm-based peak picking and data review system designed specifically for chromatographic data. The software employs robust statistical and modeling approaches to the raw instrument data to present the true signal, which often can be obscured by noise or matrix components.
The system also uses an exponentially modified Gaussian (EMG) equation to apply a best-fit model to integrated peaks through what is often a noisy signal. In our experience, applying the EMG results in cleaner data from what might appear to be poor chromatography ultimately allows us to reduce the number of samples we might otherwise rerun.
How do you validate the quality of results?
We’ve developed a robust validation protocol to ensure that results are, at minimum, equivalent to results from our manual review. We begin by building the assay in ASCENT, entering assay-specific information from our internal standard operating procedure (SOP). Once the assay is configured, validation proceeds with parallel batch processing to compare results between software-reviewed data and staff-reviewed data. For new implementations we run eight to nine batches of 30–40 samples each; when we are modifying or upgrading an existing implementation we run a smaller number of batches. The parallel batches should contain multiple positive and negative results for all analytes in the method, preferably spanning the analytical measurement range of the assay.
The next step is to compare the results and calculate the percent difference between the data review methods. We require that two-thirds of the automated results fall within 20% of the manually reviewed result. In addition to validating patient sample correlation, we also test numerous quality assurance rules that should initiate a flag for further review.
What are the biggest challenges during implementation and continual improvement initiatives?
On the technological side, our largest hurdle was loading the sequence files into ASCENT. We had created an in-house mechanism for our chemists to upload the 96-well plate map for their batch into the MS software. We had some difficulty transferring this information to ASCENT, but once we resolved this issue, the technical workflow proceeded fairly smoothly.
The greater challenge was changing our employees’ mindset from one of fear that automation would displace them, to a realization that learning this new technology would actually make them more valuable. Automating a non-mechanical process can be a difficult concept for hands-on scientists, so managers must be patient and help their employees understand that this kind of technology leverages the best attributes of software and people to create a powerful partnership.
We recommend that labs considering automated data analysis engage staff in the validation and implementation to spread the workload and the knowledge. As is true with most technology, it is best not to rely on just one or two super users. We also found it critical to add supervisor level controls on data file manipulation, such as removing a sample that wasn’t run from the sequence table. This can prevent inadvertent deletion of a file, requiring reinjection of the entire batch!
What is the relationship of FGF-23 to heart failure?
A Heart failure (HF) is an increasingly common syndrome associated with high morbidity, elevated hospital readmission rates, and high mortality. Improving diagnosis, prognosis, and treatment of HF requires a better understanding of its different sub-phenotypes. As researchers gained a comprehensive understanding of neurohormonal activation—one of the hallmarks of HF—they discovered several biomarkers, including natriuretic peptides, which now are playing an important role in sub-phenotyping HF and in driving more personalized management of this chronic condition.
Like the natriuretic peptides, fibroblast growth factor 23 (FGF-23) could become important in risk-stratifying and managing HF patients. Produced by osteocytes, FGF-23 is a key regulator of phosphorus homeostasis. It binds to renal and parathyroid FGF-Klotho receptor heterodimers, resulting in phosphate excretion, decreased 1-α-hydroxylation of 25-hydroxyvitamin D, and decreased parathyroid hormone (PTH) secretion. The relationship to PTH is important because impaired homeostasis of cations and decreased glomerular filtration rate might contribute to the rise of FGF-23. The amino-terminal portion of FGF-23 (amino acids 1-24) serves as a signal peptide allowing secretion into the blood, and the carboxyl-terminal portion (aa 180-251) participates in its biological action.
How might FGF-23 improve HF risk assessment?
Studies have shown that FGF-23 is related to the risk of cardiovascular diseases and mortality. It was first demonstrated that FGF-23 levels were independently associated with left ventricular mass index and hypertrophy as well as mortality in patients with chronic kidney disease (CKD). FGF-23 also has been associated with left ventricular dysfunction and atrial fibrillation in coronary artery disease subjects, even in the absence of impaired renal function.
FGF-23 and FGF receptors are both expressed in the myocardium. It is possible that FGF-23 has direct effects on the heart and participates in the physiopathology of cardiovascular diseases and HF. Experiments have shown that for in vitro cultured rat cardiomyocytes, FGF-23 stimulates pathological hypertrophy by activating the calcineurin-NFAT pathway—and in wild-type mice—the intra-myocardial or intravenous injection of FGF-23 resulted in left ventricular hypertrophy. As such, FGF-23 appears to be a potential stimulus of myocardial hypertrophy, and increased levels may contribute to the worsening of heart failure and long-term cardiovascular death.
Researchers have documented that HF patients have elevated FGF-23 circulating levels. They have also found a significant correlation between plasma levels of FGF-23 and B-type natriuretic peptide, a biomarker related to ventricular stretch and cardiac hypertrophy, in patients with left ventricular hypertrophy. As such, measuring FGF-23 levels might be a useful tool to predict long-term adverse cardiovascular events in HF patients.
Interestingly, researchers have documented a significant relationship between FGF-23 and PTH in both CKD and HF patients. As PTH stimulates FGF-23 expression, it could be that in HF patients, increased PTH levels increase the bone expression of FGF-23, which enhances its effects on the heart.
The Past, Present, and Future of Western Blotting in the Clinical Laboratory
Much of the discussion about Western blotting centers around its performance as a biological research tool. This isn’t surprising. Since its introduction in the late 1970s, the Western blot has been adopted by biology labs of virtually every stripe, and become one of the most widely used techniques in the research armamentarium. However, Western blotting has also been employed in clinical laboratories to aid in the diagnosis of various diseases and disorders—an equally important and valuable application. Yet there has been relatively little discussion of its use in this context, or of how advances in Western blotting might affect its future clinical use.
Highlighting the clinical value of Western blotting, Stanley Naides, MD, medical director of Immunology at Quest Diagnostics observed that, “Western blotting has been a very powerful tool in the laboratory and for clinical diagnosis. It’s one of many various methods that the laboratorian brings to aid the clinician in the diagnosis of disease, and the selection and monitoring of therapy.” Indeed, Western blotting has been used at one time or the other to aid in the diagnosis of infectious diseases including hepatitis C (HCV), HIV, Lyme disease, and syphilis, as well as autoimmune disorders such as paraneoplastic disease and myositis conditions.
However, Naides was quick to point out that the choice of assays to use clinically is based on their demonstrated sensitivity and performance, and that the search for something better is never-ending. “We’re constantly looking for methods that improve detection of our target [protein],” Naides said. “There have been a number of instances where we’ve moved away from Western blotting because another method proves to be more sensitive.” But this search can also lead back to Western blotting. “We’ve gone away from other methods because there’s been a Western blot that’s been developed that’s more sensitive and specific. There’s that constant movement between methods as new tests are developed.”
In recent years, this quest has been leading clinical laboratories away from Western blotting toward more sensitive and specific diagnostic assays, at least for some diseases. Using confirmatory diagnosis of HCV infection as an example, Sai Patibandla, PhD, director of the immunoassay group at Siemens Healthcare Diagnostics, explained that movement away from Western blotting for confirmatory diagnosis of HCV infection began with a technical modification called Recombinant Immunoblotting Assay (RIBA). RIBA streamlines the conventional Western blot protocol by spotting recombinant antigen onto strips which are used to screen patient samples for antibodies against HCV. This approach eliminates the need to separate proteins and transfer them onto a membrane.
The RIBA HCV assay was initially manufactured by Chiron Corporation (acquired by Novartics Vaccines and Diagnostics in 2006). It received Food and Drug Administration (FDA) approval in 1999, and was marketed as Chiron RIBA HCV 3.0 Strip Immunoblot Assay. Patibandla explained that, at the time, the Chiron assay “…was the only FDA-approved confirmatory testing for HCV.” In 2013 the assay was discontinued and withdrawn from the market due to reports that it was producing false-positive results.
Since then, clinical laboratories have continued to move away from Western blot-based assays for confirmation of HCV in favor of the more sensitive technique of nucleic acid testing (NAT). “The migration is toward NAT for confirmation of HCV [diagnosis]. We don’t use immunoblots anymore. We don’t even have a blot now to confirm HCV,” Patibandla said.
Confirming HIV infection has followed a similar path. Indeed, in 2014 the Centers for Disease Control and Prevention issued updated recommendations for HIV testing that, in part, replaced Western blotting with NAT. This change was in response to the recognition that the HIV-1 Western blot assay was producing false-negative or indeterminable results early in the course of HIV infection.
At this juncture it is difficult to predict if this trend away from Western blotting in clinical laboratories will continue. One thing that is certain, however, is that clinicians and laboratorians are infinitely pragmatic, and will eagerly replace current techniques with ones shown to be more sensitive, specific, and effective. This raises the question of whether any of the many efforts currently underway to improve Western blotting will produce an assay that exceeds the sensitivity of currently employed techniques such as NAT.
Some of the most exciting and groundbreaking work in this area is being done by Amy Herr, PhD, a professor of bioengineering at University of California, Berkeley. Herr’s group has taken on some of the most challenging limitations of Western blotting, and is developing techniques that could revolutionize the assay. For example, the Western blot is semi-quantitative at best. This weakness dramatically limits the types of answers it can provide about changes in protein concentrations under various conditions.
To make Western blotting more quantitative, Herr’s group is, among other things, identifying losses of protein sample mass during the assay protocol. About this, Herr explains that the conventional Western blot is an “open system” that involves lots of handling of assay materials, buffers, and reagents that makes it difficult to account for protein losses. Or, as Kevin Lowitz, a senior product manager at Thermo Fisher Scientific, described it, “Western blot is a [simple] technique, but a really laborious one, and there are just so many steps and so many opportunities to mess it up.”
Herr’s approach is to reduce the open aspects of Western blot. “We’ve been developing these more closed systems that allow us at each stage of the assay to account for [protein mass] losses. We can’t do this exactly for every target of interest, but it gives us a really good handle [on protein mass losses],” she said. One of the major mechanisms Herr’s lab is using to accomplish this is to secure proteins to the blot matrix with covalent bonding rather than with the much weaker hydrophobic interactions that typically keep the proteins in place on the membrane.
Herr’s group also has been developing microfluidic platforms that allow Western blotting to be done on single cells, “In our system we’re doing thousands of independent Westerns on single cells in four hours. And, hopefully, we’ll cut that down to one hour over the next couple years.”
Other exciting modifications that stand to dramatically increase the sensitivity, quantitation, and through-put of Western blotting also are being developed and explored. For example, the use of capillary electrophoresis—in which proteins are conveyed through a small electrolyte-filled tube and separated according to size and charge before being dropped onto a blotting membrane—dramatically reduces the amount of protein required for Western blot analysis, and thereby allows Westerns to be run on proteins from rare cells or for which quantities of sample are extremely limited.
Jillian Silva, PhD, an associate specialist at the University of California, San Francisco Helen Diller Family Comprehensive Cancer Center, explained that advances in detection are also extending the capabilities of Western blotting. “With the advent of fluorescence detection we have a way to quantitate Westerns, and it is now more quantitative than it’s ever been,” said Silva.
Whether or not these advances produce an assay that is adopted by clinical laboratories remains to be seen. The emphasis on Western blotting as a research rather than a clinical tool may bias advances in favor of the needs and priorities of researchers rather than clinicians, and as Patibandla pointed out, “In the research world Western blotting has a certain purpose. [Researchers] are always coming up with new things, and are trying to nail down new proteins, so you cannot take Western blotting away.” In contrast, she suggested that for now, clinical uses of Western blotting remain “limited.”
Adapting Next Generation Technologies to Clinical Molecular Oncology Service
Next generation technologies (NGT) deliver huge improvements in cost efficiency, accuracy, robustness, and in the amount of information they provide. Microarrays, high-throughput sequencing platforms, digital droplet PCR, and other technologies all offer unique combinations of desirable performance.
As stronger evidence of genetic testing’s clinical utility influences patterns of patient care, demand for NGT testing is increasing. This presents several challenges to clinical laboratories, including increased urgency, clinical importance, and breadth of application in molecular oncology, as well as more integration of genetic tests into synoptic reporting. Laboratories need to add NGT-based protocols while still providing old tests, and the pace of change is increasing.What follows is one viewpoint on the major challenges in adopting NGTs into diagnostic molecular oncology service.
Choosing a Platform
Instrument selection is a critical decision that has to align with intended test applications, sequencing chemistries, and analytical software. Although multiple platforms are available, a mainstream standard has not emerged. Depending on their goals, laboratories might set up NGTs for improved accuracy of mutation detection, massively higher sequencing capacity per test, massively more targets combined in one test (multiplexing), greater range in sequencing read length, much lower cost per base pair assessed, and economy of specimen volume.
When high-throughput instruments first made their appearance, laboratories paid more attention to the accuracy of base-reading: Less accurate sequencing meant more data cleaning and resequencing (1). Now, new instrument designs have narrowed the differences, and test chemistry can have a comparatively large impact on analytical accuracy (Figure 1). The robustness of technical performance can also vary significantly depending upon specimen type. For example, LifeTechnologies’ sequencing platforms appear to be comparatively more tolerant of low DNA quality and concentration, which is an important consideration for fixed and processed tissues.
Sequence pile-ups of the same target sequence (2 large genes), all performed on the same analytical instrument. Results from 4 different chemistries, as designed and supplied by reagent manufacturers prior to optimization in the laboratory. Red lines represent limits of exons. Height of blue columns proportional to depth of coverage. In this case, the intent of the test design was to provide high depth of coverage so that reflex Sanger sequencing would not be necessary. Courtesy B. Sadikovic, U. of Western Ontario.
In addition, batching, robotics, workload volume patterns, maintenance contracts, software licenses, and platform lifetime affect the cost per analyte and per specimen considerably. Royalties and reagent contracts also factor into the cost of operating NGT: In some applications, fees for intellectual property can represent more than 50% of the bench cost of performing a given test, and increase substantially without warning.
Laboratories must also deal with the problem of obsolescence. Investing in a new platform brings the angst of knowing that better machines and chemistries are just around the corner. Laboratories are buying bigger pieces of equipment with shorter service lives. Before NGTs, major instruments could confidently be expected to remain current for at least 6 to 8 years. Now, a major instrument is obsolete much sooner, often within 2 to 3 years. This means that keeping it in service might cost more than investing in a new platform. Lease-purchase arrangements help mitigate year-to-year fluctuations in capital equipment costs, and maximize the value of old equipment at resale.
One Size Still Does Not Fit All
Laboratories face numerous technical considerations to optimize sequencing protocols, but the test has to be matched to the performance criteria needed for the clinical indication (2). For example, measuring response to treatment depends first upon the diagnostic recognition of mutation(s) in the tumor clone; the marker(s) then have to be quantifiable and indicative of tumor volume throughout the course of disease (Table 1).
As a result, diagnostic tests need to cover many different potential mutations, yet accurately identify any clinically relevant mutations actually present. On the other hand, tests for residual disease need to provide standardized, sensitive, and accurate quantification of a selected marker mutation against the normal background. A diagnostic panel might need 1% to 3% sensitivity across many different mutations. But quantifying early response to induction—and later assessment of minimal residual disease—needs a test that is reliably accurate to the 10-4 or 10-5 range for a specific analyte.
Covering all types of mutations in one diagnostic test is not yet possible. For example, subtyping of acute myeloid leukemia is both old school (karyotype, fluorescent in situ hybridization, and/or PCR-based or array-based testing for fusion rearrangements, deletions, and segmental gains) and new school (NGT-based panel testing for molecular mutations).
Chemistries that cover both structural variants and copy number variants are not yet in general use, but the advantages of NGTs compared to traditional methods are becoming clearer, such as in colorectal cancer (3). Researchers are also using cell-free DNA (cfDNA) to quantify residual disease and detect resistance mutations (4). Once a clinically significant clone is identified, enrichment techniques help enable extremely sensitive quantification of residual disease (5).
Validation and Quality Assurance
Beyond choosing a platform, two distinct challenges arise in bringing NGTs into the lab. The first is assembling the resources for validation and quality assurance. The second is keeping tests up-to-date as new analytes are needed. Even if a given test chemistry has the flexibility to add analytes without revalidating the entire panel, keeping up with clinical advances is a constant priority.
Due to their throughput and multiplexing capacities, NGT platforms typically require considerable upfront investment to adopt, and training staff to perform testing takes even more time. Proper validation is harder to document: Assembling positive controls, documenting test performance criteria, developing quality assurance protocols, and conducting proficiency testing are all demanding. Labs meet these challenges in different ways. Laboratory-developed tests (LDTs) allow self-determined choice in design, innovation, and control of the test protocol, but can be very expensive to set up.
Food and Drug Administration (FDA)-approved methods are attractive but not always an option. More FDA-approved methods will be marketed, but FDA approval itself brings other trade-offs. There is a cost premium compared to LDTs, and the test methodologies are locked down and not modifiable. This is particularly frustrating for NGTs, which have the specific attraction of extensive multiplexing capacity and accommodating new analytes.
IT and the Evolution of Molecular Oncology Reporting Standards
The options for information technology (IT) pipelines for NGTs are improving rapidly. At the same time, recent studies still show significant inconsistencies and lack of reproducibility when it comes to interpreting variants in array comparative genomic hybridization, panel testing, tumor expression profiling, and tumor genome sequencing. It can be difficult to duplicate published performances in clinical studies because of a lack of sufficient information about the protocol (chemistry) and software. Building bioinformatics capacity is a key requirement, yet skilled people are in short supply and the qualifications needed to work as a bioinformatician in a clinical service are not yet clearly defined.
Tumor biology brings another level of complexity. Bioinformatic analysis must distinguish tumor-specific variants from genomic variants. Sequencing of paired normal tissue is often performed as a control, but virtual normal controls may have intriguing advantages (6). One of the biggest challenges is to reproducibly interpret the clinical significance of interactions between different mutations, even with commonly known, well-defined mutations (7). For multiple analyte panels, such as predictive testing for breast cancer, only the performance of the whole panel in a population of patients can be compared; individual patients may be scored into different risk categories by different tests, all for the same test indication.
In large scale sequencing of tumor genomes, which types of mutations are most informative in detecting, quantifying, and predicting the behavior of the tumor over time? The amount and complexity of mutation varies considerably across different tumor types, and while some mutations are more common, stable, and clinically informative than others, the utility of a given tumor marker varies in different clinical situations. And, for a given tumor, treatment effect and metastasis leads to retesting for changes in drug sensitivities.
These complexities mean that IT must be designed into the process from the beginning. Like robotics, IT represents a major ancillary decision. One approach many labs choose is licensed technologies with shared databases that are updated in real time. These are attractive, despite their cost and licensing fees. New tests that incorporate proprietary IT with NGT platforms link the genetic signatures of tumors to clinically significant considerations like tumor classification, recommended methodologies for monitoring response, predicted drug sensitivities, eligible clinical trials, and prognostic classifications. In-house development of such solutions will be difficult, so licensing platforms from commercial partners is more likely to be the norm.
The Commercial Value of Health Records and Test Data
The future of cancer management likely rests on large-scale databases that link hereditary and somatic tumor testing with clinical outcomes. Multiple centers have such large studies underway, and data extraction and analysis is providing increasingly refined interpretations of clinical significance.
Extracting health outcomes to correlate with molecular test results is commercially valuable, as the pharmaceutical, insurance, and healthcare sectors focus on companion diagnostics, precision medicine, and evidence-based health technology assessment. Laboratories that can develop tests based on large-scale integration of test results to clinical utility will have an advantage.
NGTs do offer opportunities for net reductions in the cost of healthcare. But the lag between availability of a test and peer-evaluated demonstration of clinical utility can be considerable. Technical developments arise faster than evidence of clinical utility. For example, immunohistochemistry, estrogen receptor/progesterone receptor status, HER2/neu, and histology are still the major pathological criteria for prognostic evaluation of breast cancer at diagnosis, even though multiple analyte tumor profiling has been described for more than 15 years. Healthcare systems need a more concerted assessment of clinical utility if they are to take advantage of the promises of NGTs in cancer care.
Disruptive Advances
Without a doubt, “disruptive” is an appropriate buzzword in molecular oncology, and new technical advances are about to change how, where, and for whom testing is performed.
• Predictive Testing
Besides cost per analyte, one of the drivers for taking up new technologies is that they enable multiplexing many more analytes with less biopsy material. Single-analyte sequential testing for epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase, and other targets on small biopsies is not sustainable when many more analytes are needed, and even now, a significant proportion of test requests cannot be completed due to lack of suitable biopsy material. Large panels incorporating all the mutations needed to cover multiple tumor types are replacing individual tests in companion diagnostics.
• Cell-Free Tumor DNA
Challenges of cfDNA include standardizing the collection and processing methodologies, timing sampling to minimize the effect of therapeutic toxicity on analytical accuracy, and identifying the most informative sample (DNA, RNA, or protein). But for more and more tumor types, it will be possible to differentiate benign versus malignant lesions, perform molecular subtyping, predict response, monitor treatment, or screen for early detection—all without a surgical biopsy.
cfDNA technologies can also be integrated into core laboratory instrumentation. For example, blood-based EGFR analysis for lung cancer is being developed on the Roche cobas 4800 platform, which will be a significant change from the current standard of testing based upon single tests of DNA extracted from formalin-fixed, paraffin-embedded sections selected by a pathologist (8).
• Whole Genome and Whole Exome Sequencing
Whole genome and whole exome tumor sequencing approaches provide a wealth of biologically important information, and will replace individual or multiple gene test panels as the technical cost of sequencing declines and interpretive accuracy improves (9). Laboratories can apply informatics selectively or broadly to extract much more information at relatively little increase in cost, and the interpretation of individual analytes will be improved by the context of the whole sequence.
• Minimal Residual Disease Testing
Massive resequencing and enrichment techniques can be used to detect minimal residual disease, and will provide an alternative to flow cytometry as costs decline. The challenge is to develop robust analytical platforms that can reliably produce results in a high proportion of patients with a given tumor type, despite using post-treatment specimens with therapy-induced degradation, and a very low proportion of target (tumor) sequence to benign background sequence.
The tumor markers should remain informative for the burden of disease despite clonal evolution over the course of multiple samples taken during progression of the clinical course and treatment. Quantification needs to be accurate and sensitive down to the 10-5 range, and cost competitive with flow cytometry.
• Point-of-Care Test Methodologies
Small, rapid, cheap, and single use point-of-care (POC) sequencing devices are coming. Some can multiplex with analytical times as short as 20 minutes. Accurate and timely testing will be possible in places like pharmacies, oncology clinics, patient service centers, and outreach programs. Whether physicians will trust and act on POC results alone, or will require confirmation by traditional laboratory-based testing, remains to be seen. However, in the simplest type of application, such as a patient known to have a particular mutation, the advantages of POC-based testing to quantify residual tumor burden are clear.
Conclusion
Molecular oncology is moving rapidly from an esoteric niche of diagnostics to a mainstream, required component of integrated clinical laboratory services. While NGTs are markedly reducing the cost per analyte and per specimen, and will certainly broaden the scope and volume of testing performed, the resources required to choose, install, and validate these new technologies are daunting for smaller labs. More rapid obsolescence and increased regulatory scrutiny for LDTs also present significant challenges. Aligning test capacity with approved clinical indications will require careful and constant attention to ensure competitiveness.
References
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3. Haley L, Tseng LH, Zheng G, et al. Performance characteristics of next-generation sequencing in clinical mutation detection of colorectal cancers. [Epub ahead of print] Modern Pathol July 31, 2015 as doi:10.1038/modpathol.2015.86.
4. Butler TM, Johnson-Camacho K, Peto M, et al. Exome sequencing of cell-free DNA from metastatic cancer patients identifies clinically actionable mutations distinct from primary disease. PLoS One 2015;10:e0136407.
5. Castellanos-Rizaldos E, Milbury CA, Guha M, et al. COLD-PCR enriches low-level variant DNA sequences and increases the sensitivity of genetic testing. Methods Mol Biol 2014;1102:623–39.
6. Hiltemann S, Jenster G, Trapman J, et al. Discriminating somatic and germline mutations in tumor DNA samples without matching normals. Genome Res 2015;25:1382–90.
7. Lammers PE, Lovly CM, Horn L. A patient with metastatic lung adenocarcinoma harboring concurrent EGFR L858R, EGFR germline T790M, and PIK3CA mutations: The challenge of interpreting results of comprehensive mutational testing in lung cancer. J Natl Compr Canc Netw 2015;12:6–11.
8. Weber B, Meldgaard P, Hager H, et al. Detection of EGFR mutations in plasma and biopsies from non-small cell lung cancer patients by allele-specific PCR assays. BMC Cancer 2014;14:294.
9. Vogelstein B, Papadopoulos N, Velculescu VE, et al. Cancer genome landscapes. Science 2013;339:1546–58.
10. Heitzer E, Auer M, Gasch C, et al. Complex tumor genomes inferred from single circulating tumor cells by array-CGH and next-generation sequencing. Cancer Res 2013;73:2965–75.
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Background: To date, antiangiogenic therapy has failed to improve overall survival in cancer patients when used in the adjuvant setting (local-regional disease with no detectable systemic metastasis). The presence of lymph node metastases worsens prognosis, however their reliance on angiogenesis for growth has not been reported.
Methods: Here, we introduce a novel chronic lymph node window (CLNW) model to facilitate new discoveries in the growth and spread of lymph node metastases. We use the CLNW in multiple models of spontaneous lymphatic metastases in mice to study the vasculature of metastatic lymph nodes (n = 9–12). We further test our results in patient samples (n = 20 colon cancer patients; n = 20 head and neck cancer patients). Finally, we test the ability of antiangiogenic therapy to inhibit metastatic growth in the CLNW. All statistical tests were two-sided.
Results: Using the CLNW, we reveal the surprising lack of sprouting angiogenesis during metastatic growth, despite the presence of hypoxia in some lesions. Treatment with two different antiangiogenic therapies showed no effect on the growth or vascular density of lymph node metastases (day 10: untreated mean = 1.2%, 95% confidence interval [CI] = 0.7% to 1.7%; control mean = 0.7%, 95% CI = 0.1% to 1.3%; DC101 mean = 0.4%, 95% CI = 0.0% to 3.3%; sunitinib mean = 0.5%, 95% CI = 0.0% to 1.0%, analysis of variance P = .34). We confirmed these findings in clinical specimens, including the lack of reduction in blood vessel density in lymph node metastases in patients treated with bevacizumab (no bevacizumab group mean = 257 vessels/mm2, 95% CI = 149 to 365 vessels/mm2; bevacizumab group mean = 327 vessels/mm2, 95% CI = 140 to 514 vessels/mm2, P = .78).
Conclusion: We provide preclinical and clinical evidence that sprouting angiogenesis does not occur during the growth of lymph node metastases, and thus reveals a new mechanism of treatment resistance to antiangiogenic therapy in adjuvant settings. The targets of clinically approved angiogenesis inhibitors are not active during early cancer progression in the lymph node, suggesting that inhibitors of sprouting angiogenesis as a class will not be effective in treating lymph node metastases.
Introduction
Although antiangiogenic therapy is standard of care for several advanced (metastatic) cancers, all phase III clinical trials of antiangiogenic therapy to date have failed in the adjuvant setting.[1–4] The presence of lymph node metastases—the most common form of cancer dissemination—dictates treatment decisions,[5,6] however their reliance on angiogenesis for growth has not been reported. Furthermore, observations from preclinical and clinical studies suggest that lymph node metastases and primary tumors can respond differently to the same therapeutic regimen.[7–9] The clinical relevance of lymph node metastases has been the subject of debate for many years. Some argue that the presence of lymph node metastasis only demonstrates the ability of the cancer to metastasize and that disease in the lymph node is inconsequential.[10,11] The strong predictive power of lymph node metastases has led others to hypothesize that cancer cells in the lymph node can exit and spread to distant metastatic sites.[12,13] These advocates argue disease in lymph nodes needs to be treated in order to prevent distant metastasis and ultimately eradicate disease from the patient.[14,15] Likely the answer lies in between, depending where on the spectrum of progression to distant metastasis the cancer is diagnosed.[16]These issues highlight our fundamental lack of understanding of the biology of how metastatic cancer cells grow in a lymph node and affect the overall prognosis for the patient, limiting our ability to discover effective adjuvant therapy to treat lymph node metastases.
We and others have previously shown that antiangiogenic therapy did not stop the seeding or growth of lymph node metastases,[9,17,18] but no mechanism of failure has been determined. Nonsprouting angiogenesis mechanisms to sustain tumor growth, such as vessel co-option and intussusception, have been implicated in the growth of lung, liver, and brain metastases[19] and are thought to play a role in resistance to antiangiogenic therapy.[20] Based on these findings, we hypothesized that early growth of lymph node metastases is not dependent on sprouting angiogenesis.
Although reports show reduced vascular density in lymph node metastases compared with corresponding primary tumors and surrounding normal lymph node,[17,21,22] these data do not describe the degree of angiogenesis or whether the vessels are functional. Here, we introduce a novel model to longitudinally image the formation and growth of metastatic tumors in lymph nodes and reveal the surprising lack of sprouting angiogenesis, despite the presence of hypoxia in some lesions. Treatment with two different therapies designed to target sprouting angiogenesis showed no effect on the growth or vascular density of lymph node metastases in our models. These data are corroborated in clinical specimens and further add to mechanisms for the failure of antiangiogenic treatments in adjuvant settings.[1–4,20]
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Intravital Multiphoton Microscopy
Intravital multiphoton microscopy was carried out as described previously on a custom-built multiphoton microscope.[25] Details of the imaging equipment, imaging protocols, and image analysis can be found in the Supplementary Methods http://jnci.oxfordjournals.org/content/107/9/djv155/suppl/DC1 (available online).
….
Longitudinal Imaging of the Formation of Spontaneous Lymph Node Metastases Using a Novel Chronic Lymph Node Window
Holding back our understanding of the biology of lymph node metastasis is our inability to longitudinally monitor spontaneous lymph node metastases. Inspired by pioneering intravital microscopy of the lymph node,[30–35] we developed a chronic lymph node window (CLNW)—a modification of the mammary fat pad chamber[23,24]—to create a CLNW that allows intravital imaging for up to 14 days with minimal morphological, cellular or biochemical changes in the inguinal lymph node (Figure 1, A and B; Supplementary Figure 1,http://jnci.oxfordjournals.org/content/107/9/djv155/suppl/DC1 available online).
Using multiphoton microscopy in the CLNW, we were able to serially image various stages of the growth of spontaneous metastasis in the lymph node from murine SCCVII squamous cell carcinoma[36,37]transduced with green fluorescence protein (SCCVII-GFP) (Figure 1C). Initially, cancer cells remain in or near the subcapsular sinus as individual cells (Figure 1C). Later, small aggregates of a few cancer cells form near the subcapsular sinus, which then grow into metastatic lesions that invade deeper into the lymph node (Figure 1C). This sequence was also observed in syngeneic MCa-P0008 breast cancer and B16F10 melanoma cells lines (Supplementary Figure 2,http://jnci.oxfordjournals.org/content/107/9/djv155/suppl/DC1 available online).
Recent genomic studies suggest that metastatic cells within lymph nodes consist of multiple clones.[38,39]To investigate this concept, we transduced SCCVII and SCCVII-GFP cells with a red fluorescence protein (DsRed), producing three different colors of cells (red, green, and red+green) that were mixed in equal proportions to form primary tumors. Single cells of multiple colors disseminated from the multicolor primary tumor and grew in the subcapsular sinus (Figure 1D). The metastatic lesions that subsequently formed contained all three colors with great spatial heterogeneity (Figure 1D), suggesting that lymph node metastases form from multiple cells. These findings were reproduced when using an equal mix of 4T1-DsRed and 4T1-GFP mammary carcinoma cells implanted in the mammary fat pad. In contrast, more than 80% of detected lung metastases from these 4T1 tumors were single color (Figure 1E).
The Role of the Existing Lymph Node Vascular Supply in Supporting the Growth of Lymph Node Metastases
Next, we directly measured for the first time whether angiogenesis is occurring in lymph node metastases by using intravital multiphoton microscopy to make longitudinal measurements in our CLNW. In early stages, metastatic cells resided in the lymph node sinus, away from blood vessels (Figure 2A). These metastatic tumor cells eventually invaded the lymph node cortex, growing closer to functional lymph node blood vessels and presumably utilizing the nutrient supply of these pre-existing vessels (Figure 2A). We found that the tumor cells started to access host lymph node blood vessels when they invaded approximately 50 to 100 μm into the cortex (Figure 2, B and C). Although the tumor invaded deeper into the node (day 6 mean depth = 43 μm, 95% CI = 24 to 61 μm; day 12 mean depth = 131 μm, 95% CI = 71 to 191 μm, P = .01), blood vessels did not invade toward the surface of the lymph node (day 6 mean depth = 52 μm, 95% CI = 49 to 55 μm; day 20 mean depth = 58 μm, 95% CI = 41 to 75 μm,P = .38), as would be expected for tumor-induced sprouting angiogenesis. These data provide the first direct evidence of the lack of sprouting angiogenesis during the growth of metastatic lesions in the lymph node.
Figure 2.
Intravital imaging of lymph node metastases and the native lymph node vasculature. A) Representative time course of images from a single metastatic lymph node, showing cancer cells (SCCVII, green) and blood vessels (TRITC-dextran, red) at three different depths in tissue. The image was created using multiphoton microscopy, and second harmonic generation was used to highlight fibrillar collagen (blue) in the lymph node capsule. The images are created from maximum intensity projections of 25 μm of tissue from inside the lymph node. In day 40 images, the red signal is background signal from the accumulation of TRITC-dextran as a result of the five intravenous injections over the course of the metastatic growth. Yellow arrows identify individual cancer cells. Yellow circles identify areas in which many cancer cells are found in the subcapsular sinus. White arrows identify blood vessels in the metastatic lesion. Purple, green and light blue arrows identify features in the lymph node vasculature that can be used to identify the same region in the mouse over the multiday experiment. White line marks edge of lymph node. Scale bars = 100 μm. B) A vertical image reconstruction showing the tumor cells (SCCVII, green) initially growing above the blood vessels (red). C) Measurements of the maximum depth of tumor cell invasion (SCCVII) and the minimum depth of blood vessels. Data are presented as mean ± 95% confidence interval.
Immunofluorescent staining for CD31 (Figure 3A) showed that the vessel density in lymph nodes with micrometastases from SCCVII tumors (Figure 3B) and macrometastases (lesions greater than 500 microns in one dimension) from 4T1 tumors (Figures 3E; Supplementary Figure 3A,http://jnci.oxfordjournals.org/content/107/9/djv155/suppl/DC1 available online) were not increased compared with those of control (from naïve mice with no tumor implantation) and contralateral nodes. The vessel density inside metastatic lesions was lower than the surrounding lymph node tissue (vessel density: SCCVII: metastatic lesion = 1.0%, 95% CI = 0.0% to 2.0%; nontumor area = 7.0%, 95% CI = 1.0% to 13.0%, P = .04; 4T1: metastatic lesion = 4.0%, 95% CI = 1.0% to 7.0%; nontumor area = 10.0%, 95% CI = 5.0% to 15.0%, P = .04) (Figure 3, C and F). To indicate sprouting angiogenesis, Ki67—a marker of cell proliferation—showed no difference in endothelial cell proliferation in micrometastatic lymph nodes (SCCVII) (Figure 3D; Supplementary Figure 4,http://jnci.oxfordjournals.org/content/107/9/djv155/suppl/DC1 available online) and a reduction in endothelial cell proliferation in macrometastatic lymph nodes (4T1) (Figure 3G; Supplementary Figure 3B, http://jnci.oxfordjournals.org/content/107/9/djv155/suppl/DC1 available online) in comparison with control and contralateral nodes. Vessel density in the metastatic lesions was not related to lesion size (Supplementary Figure 3C, http://jnci.oxfordjournals.org/content/107/9/djv155/suppl/DC1 available online). These data further indicate that sprouting angiogenesis is not induced in the lymph node at this stage of cancer progression.
Figure 3.
Immunohistochemical analysis of lymph node blood vessels and metastases. A) Representative sections of control (from non–tumor bearing mice), contralateral, and tumor-draining lymph nodes with micrometastases (SCCVII, green). Vessels were stained with CD31 (red) and nuclei with DAPI (blue).Scale bars = 300 μm. B) Quantification of CD31+ area per lymph node area in control, contralateral, and micrometastatic lymph nodes. C) In micrometastatic lymph nodes, quantification of CD31+ area per tissue area comparing tumor areas with nontumor areas. D) Costaining for CD105 and Ki67 measured blood vessel proliferation in micrometastatic lymph nodes. E) Using a different tumor model (4T1) that formed macrometastasis in the lymph node (greater than 500 μm in one direction), we measured CD31+ area in micrometastatic or macrometastatic lymph nodes, compared with control or contralateral nodes. F) The vascular area of macrometastatic lesions was measured in tumor areas and nontumor lymph node tissue. G) Costaining for CD31 and Ki67 measured blood vessel proliferation in macrometastatic lymph nodes. Data are presented as mean ± 95% confidence interval. Statistical significance was tested by one-way analysis of variance with Tukey’s Honestly Significant Difference post hoc test (B, D, E, G) or two-tailed paired Student’s t test (C, F).
In contrast, LYVE-1 staining for lymphatic vessels showed an increase in lymphatic vascular area (vessel density: SCCVII: control = 5.0%, 95% CI = 3.0% to 7.0%; contralateral = 8.0%, 95% CI = 6.0% to 10.0%; metastatic = 10.0%, 95% CI = 6.0% to 14.0%; control vs metastatic P = .03; 4T1: control = 5.0%, 95% CI = 2.0% to 8.0%; contralateral = 9.0%, 95% CI = 6.0% to 12.0%; nonmetastatic tumor draining = 22.0%, 95% CI = 18.0% to 26.0%; metastatic = 4.0%, 95% CI = 1.0% to 7.0%; control vs nonmetastatic tumor draining P < .001) and proliferating lymphatic endothelial cells in draining lymph nodes from SCCVII and 4T1 tumors (Supplementary Figures 5 and 6,http://jnci.oxfordjournals.org/content/107/9/djv155/suppl/DC1 available online), consistent with previous reports.[40–43] Interestingly, the lymphatic vascular area was greater in the contralateral and nonmetastatic tumor-draining lymph nodes of 4T1-bearing mice compared with lymph nodes with macrometastatic lesions (P < .001) (Supplementary Figure 6,http://jnci.oxfordjournals.org/content/107/9/djv155/suppl/DC1 available online), suggesting that the presence of cancer cells causes the lymphatic vasculature to regress. When compared with lymph nodes from tumor-naïve animals, contralateral lymph nodes show greater lymphatic vascular density (SCCVII: P = .04; 4T1: P < .001), suggesting that contralateral lymph nodes are also affected by the presence of the primary tumor, as others have reported.[44]
Although lesions growing in the subcapsular sinus of the lymph node showed markers for hypoxia (Figure 4, A–D), sprouting angiogenesis was not induced in these lesions and they remained avascular. Metastatic lesions that invaded the lymph node parenchyma where functional nodal blood vessels reside had only focally heterogeneous areas positive for hypoxia markers (Figure 4, A, C, and E). These data suggest that growing metastatic lesions can utilize the existing lymph node vasculature in order to meet their metabolic demand. Whether this demand or hypoxia drives cancer cell invasion of the lymph node remains unknown.
Figure 4.
Hypoxia in lymph node metastases. A) Representative images of pimonidazole staining for hypoxia (green) and perfused lectin staining for functional blood vessels (red) in lymph node metastases from 4T1 mammary carcinoma (cytokeratin, blue). The top panels show a lesion in the subcapsular sinus that is hypoxic and has no perfused blood vessels in the lesion. The bottom panels show a lesion in the parenchyma of the lymph node with perfused blood vessels and no hypoxia. Dashed line shows edge of the lymph node. Scale bars = 100 μm. B) Higher magnification of pimonidazole staining in metastatic lymph node showing colocalization of cytokeratin and pimonidazole. Contralateral lymph node is non–tumor bearing. Dashed line shows edge of the lymph node. Scale bars = 50 μm. C) Quantification of pimonidazole and perfused vessel staining in metastatic lesions in the subcapsular sinus and lymph node parenchyma. Data are presented as mean ± 95% confidence interval. Statistical significance was tested by two-tailed unpaired Student’s t test. D and E) Staining for CAIX, a marker of the cellular response to hypoxia, and CD31-positive blood vessels shows similar results to pimonidazole staining. Dashed line shows the outline of the metastatic lesions. Scale bars = 636 μm.
Hypoxia generally induces the production of vascular endothelial growth factor (VEGF). However, VEGF levels in control, contralateral, and metastatic lymph nodes were not different (4T1: control = 0.3 pg VEGF/mg protein, 95% CI = 0.2 to 0.4 pg VEGF/mg protein; contralateral = 0.4 pg VEGF/mg protein, 95% CI = 0.3 to 0.5 pg VEGF/mg protein; metastatic = 0.5 pg VEGF/mg protein, 95% CI = 0.2 to 0.8 pg VEGF/mg protein; Figure 5A; SCCVII: control = 0.4 pg VEGF/mg protein, 95% CI = 0.3 to 0.5 pg VEGF/mg protein; contralateral = 0.4 pg VEGF/mg protein, 95% CI = 0.3 to 0.5 pg VEGF/mg protein; metastatic = 0.4 pg VEGF/mg protein, 95% CI = 0.3 to 0.5 pg VEGF/mg protein; Figure 5B; and E0771: control = 0.3 pg VEGF/mg protein, 95% CI = 0.2 to 0.4 pg VEGF/mg protein; contralateral = 0.4 pg VEGF/mg protein, 95% CI = 0.3 to 0.5 pg VEGF/mg protein; metastatic = 0.4 pg VEGF/mg protein, 95% CI = 0.3 to 0.5 pg VEGF/mg protein; Figure 5C; all P values > .05 for each ANOVA containing these three lymph nodes types). Furthermore, levels of VEGF-C and VEGF-D were lower in metastatic and nonmetastatic tumor draining lymph nodes when compared with naïve lymph nodes (Supplementary Figure 6, C and D, http://jnci.oxfordjournals.org/content/107/9/djv155/suppl/DC1 available online). Next, we screened for transcriptional changes in sprouting angiogenesis-related genes in lymph nodes with metastasis when compared with naïve lymph nodes. No pro-angiogenesis related genes were upregulated in metastatic lymph nodes, but thrombospondin-1 (Thbs-1) and TIMP-1—both of which are antiangiogenic—were upregulated (Figure 5D). We confirmed no change in Vegf levels (control = 0.24 VEGF/GAPDH, 95% CI = 0.06 to 0.42 VEGF/GAPDH; metastatic = 0.16 VEGF/GAPDH, 95% CI = 0.04 to 0.28 VEGF/GAPDH, P = .37) and the elevation in Thbs-1 in lymph node metastasis by quantitative polymerase chain reaction (qPCR) (control = 0.10 THBS-1/GAPDH, 95% CI = 0.05 to 0.15 THBS-1/GAPDH; metastatic = 0.38 THBS-1/GAPDH, 95% CI = 0.23 to 0.53 THBS-1/GAPDH; P = .001) (Figure 5E). Thrombospondin-1 (TSP-1) was specifically located surrounding the blood vessels of control, contralateral, and metastatic lymph nodes (Figure 5F), further defining the nonangiogenic phenotype associated with these vessels. Taken together, these data describe an environment lacking prosprouting angiogenesis stimuli and abundant in antiangiogenesis molecules, suggesting metastatic lesions in the lymph node do not induce nor rely upon sprouting angiogenesis during their early growth.
Figure 5.
Molecular signature of quiescent lymph node vasculature. A-C) Levels of vascular endothelial growth factor (VEGF) protein were measured in metastatic lymph nodes containing 4T1 (A), SCCVII (B), or E0771 (C) and compared with control and contralateral lymph nodes. D) Quantitative polymerase chain reaction (qPCR) transcriptional array for angiogenesis-related genes compared the transcriptional profile of a diaeresis lymph node to a tumor-bearing lymph node. Differentially transcribed genes were defined as having more than a four-fold change and a P value under .01 when comparing metastatic lymph nodes to diaeresis lymph nodes. E) Confirmation of the qPCR transcriptional array for the Vegf and Thbs1 genes. *P < .05. F) Dual immunofluorescence staining for CD31 (red) and TSP-1 (green) showed distinctive TSP-1 staining surrounding the blood vessels in diaeresis, contralateral, and metastatic lymph nodes. Scale bars = 100μm. Data are presented as mean ± 95% confidence interval. Statistical significance was tested by one-way analysis of variance with Tukey’s Honestly Significant Difference post hoc test (A, B, and C) and two-tailed unpaired Student’s t test (E).
Blood Vessel Density in Metastatic Lymph Nodes From Colon Cancer and Head and Neck Cancer Patients
To confirm these findings in clinical specimens in a cancer where angiogenesis inhibitors have shown efficacy, we stained lymph nodes from 20 colon cancer patients with lymphatic metastasis for CD31 (Figure 6A; Supplementary Figure 7A, http://jnci.oxfordjournals.org/content/107/9/djv155/suppl/DC1available online). These patients did not have metastases on initial staging and went directly for surgical resection with no prior cancer-directed treatments (eg, chemotherapy, radiation therapy). We found that blood vessel densities in metastatic lymph nodes and large metastatic lesions where lymph node tissue was completely replaced with tumor cells were on average lower than those of tumor-negative lymph nodes (nonmetastatic- = 220 blood vessels/mm2, 95% CI = 172 to 268 blood vessels/mm2; metastatic = 135 blood vessels/mm2, 95% CI = 113 to 157 blood vessels/mm2; lymph node replaced by cancer = 104 blood vessels/mm2, 95% CI = 75 to 133 blood vessels/mm2; comparisons of either group of tumor-bearing to nonmetastatic lymph nodes: P < .001) (Figure 6, B and C). Furthermore, the vessel density inside metastatic lesions was statistically significantly lower than in the remaining lymph node tissue (metastatic lesion = 148 blood vessels/mm2, 95% CI = 124 to 172 blood vessels/mm2; nontumor area = 115 blood vessels/mm2, 95% CI = 95 to 135 blood vessels/mm2, P = .03) (Figure 6, D and E). Accordingly, TSP-1 staining was also found to associate with lymph node blood vessels and to surround the gland-like structures formed by the cancer cells (Supplementary Figure 7B,http://jnci.oxfordjournals.org/content/107/9/djv155/suppl/DC1 available online), further suggesting that these vessels were not undergoing sprouting angiogenesis. Finally, the density of CD31-positive vessels was not dependent on the lesion size in the section, showing that vessel densities of macrometastases (clinically classified as lesions larger than 2mm in one direction[45]]) are the same as in micrometastases (Figure 6F). Blood vessel density and TSP-1 staining in specimens from head and neck cancer patients were similar to those from colon cancer patients (Supplementary Figure 7, C–G,http://jnci.oxfordjournals.org/content/107/9/djv155/suppl/DC1 available online). Taken together, these data from two different patient populations support the concept that the growth of metastatic lesions in the lymph nodes is not dependent upon sprouting angiogenesis.
Figure 6.
Vascular density in metastatic lymph nodes from colon cancer patients. A) Representative images of nonmetastatic (n = 19) and metastatic (n = 39) lymph nodes as well as lymph node tumors in which no normal lymph node tissue remained (n = 9). The sections were stained with CD31 (brown) to identify blood vessels. Scale bars = 200 μm. Images of whole lymph node sections can be found in Supplementary Figure 7 (available online). B) The number of vessels per area as determined by CD31 staining was measured in metastatic lymph nodes and in lymph node tumors in which no normal lymph node tissue remained and compared with nonmetastatic lymph nodes. C) The fraction of lymph node area composed of CD31-positive vessels was similarly measured in metastatic lymph nodes and in lymph node tumors in which no normal lymph node tissue remained and compared with nonmetastatic lymph nodes. *P value was determined by Tukey’s Honestly Significant Difference post hoc test of analysis of variance model. D and E) Within a metastatic lymph node, vascular density (D) and vessel area fraction (E) were measured in the tumor and the nontumor area. * P value was determined by paired Student’s t test.F) Vessel density was not dependent on the lesion size. Data are presented as mean ± 95% confidence interval throughout figure.
Growth of Lymph Node Metastases With Antiangiogenic Treatment
To directly measure the response of lymph node metastases to antiangiogenic therapy in the CLNW, we began treatment when micrometastases were between 100 and 125 μm in diameter (5–10×10–3 mm3)—the stage when we found blood vessels surrounding lymph node metastases—with either a monoclonal VEGF receptor (VEGFR)-2–blocking antibody (DC101, ImClone Systems) or the pan-VEGFR small-molecule tyrosine kinase inhibitor sunitinib. We chose agents with differential mechanisms of VEGF pathway inhibition—monoclonal antibody vs tyrosine kinase inhibitor (TKI)—to understand whether our findings were agent specific. Measuring lymph node blood vessels using the CLNW and longitudinal multiphoton microscopy, the growth of lymph node metastases (Figure 7, A–C) and functional blood vessel volume density remained at similar levels during treatment with either DC101 or sunitinib when compared with untreated controls (vessel density: day 10: untreated = 1.2%, 95% CI = 0.7% to 1.7%; control = 0.7%, 95% CI = 0.1% to 1.3%; DC101 = 0.4%, 95% CI = 0.0% to 3.3%; sunitinib = 0.5%, 95% CI = 0.0% to 1.0%; ANOVA P = .34) (Figure 7D). These direct measurements, supported by previous endpoint studies,[9,17] suggest that inhibitors of sprouting angiogenesis as a class of drugs will not be effective in inhibiting the early phase of lymph node metastasis. In contrast, sunitinib—a pan-VEGF receptor TKI—reduced the elevated lymphatic vessel density found in early metastatic lymph nodes compared with PBS control (Supplementary Figure 8, http://jnci.oxfordjournals.org/content/107/9/djv155/suppl/DC1 available online).
Figure 7.
Antiangiogenic therapy in the early growth of lymph node metastases. A) Representative intravital multiphoton microscopy images of spontaneous lymph node metastases treated with vehicle control, sunitinib, or the blocking monoclonal anti–VEGFR-2 antibody DC101. Tumor cells are shown in green and blood vessels in red. Scale bars = 200 μm. B) Primary tumors were of equal size at the time treatment began, when the lymph node micrometastases were 5–10×10–3 mm3. C) The growth rate of the metastatic tumor in the lymph node was measured during antiangiogenesis therapy. D) The vessel density in metastatic lesions in the lymph node was measured during antiangiogenesis therapy. Biological replicates: untreated n = 15 (C), 12 (D), control (IgG = 2, PBS = 4) n = 6, sunitinib = 6, DC101 = 5. Data are presented as mean ± 95% confidence interval. Statistical significance was tested by one-way analysis of variance with Tukey’s Honestly Significant Difference post hoc test (B and C) and two-tailed unpaired Student’s t test (D and E).
Blood Vessel Density of Lymph Node Metastasis From Patients Treated With Bevacizumab
Finally, we identified rectal cancer patients that received neoadjuvant chemoradiation and bevacizumab and a comparator cohort of rectal cancer patients who received only neoadjuvant chemoradiation, as previously described.[46,47] Despite downstaging of the primary tumor after neoadjuvant therapy, lymph node metastases were often found at the time of surgery and pathological evaluation. Comparing lymph node metastases from 10 patients in each group, we found no difference in the vessel density in lymph node metastases (no bevacizumab group mean = 257 vessels/mm2, 95% CI = 149 to 365 vessels/mm2; bevacizumab group mean = 327 vessels/mm2, 95% CI = 140 to 514 vessels/mm2, P = .78) (Figure 8, A and B). The vascular density in the tumor lesions specifically was also not different between the groups (no bevacizumab group mean = 307 blood vessels/mm2, 95% CI = 186 to 428 vessels/mm2; bevacizumab group mean = 318 blood vessels/mm2, 95% CI = 118 to 518 vessels/mm2, P = .60) (Figure 8, C and D). Metastatic lymph nodes showed lower vascular density than nonmetastatic nodes after neoadjuvant therapy (Figure 8, A and B), independent of whether bevacizumab was used. Finally, lymphatic vessel density was not different in metastatic and nonmetastatic lymph nodes when comparing patients who received bevacizumab to those who did not (Supplementary Figure 8, D and E, http://jnci.oxfordjournals.org/content/107/9/djv155/suppl/DC1 available online). These data provide the first clinical evidence for the lack of response of lymph node metastasis to antiangiogenic therapy.
Figure 8.
Vascular density in lymph node metastases in rectal cancer patients treated with bevacizumab. The number of CD31+ vessels per area (A) and the fraction of lymph node area composed of CD31+ vessels(B) were measured in nonmetastatic and metastatic lymph nodes in colorectal cancer (CRC) patients that received neoadjuvant chemoradiation (No Bev.) or neoadjuvant chemoradiation with bevacizumab (Bev.).P value was determined by two-tailed unpaired Student’s t test. C and D) Within the tumor area of metastatic lymph nodes, we measured vascular density (C) and vessel area fraction (D) in rectal cancer patients that received neoadjuvant chemoradiation (No Bev.) or neoadjuvant chemoradiation with bevacizumab (Bev.). P value was determined by two-tailed unpaired Student’s t test. Data are presented as mean ± 95% confidence interval.
Discussion
The main concept driving antiangiogenic therapy has been the hypothesis that tumors depend on new blood vessel growth. A critical observation made by longitudinal intravital microscopy in the CLNW is that metastatic lesions did not induce sprouting angiogenesis as they grew, in spite of the presence of hypoxia. Lesions that invaded into the blood vessel–rich lymph node parenchyma showed reduced hypoxia, suggesting that cancer cells survive in the lymph node by utilizing the existing lymph node vascular supply. The lack of VEGF, VEGF-C, and VEGF-D, along with the presence of TSP-1 surrounding lymph node blood vessels, provides a mechanism behind the lack of sprouting angiogenesis observed in lymph node metastases. A limitation of the use of longitudinal intravital microscopy is the limited imaging depth of 300 μm by multiphoton microscopy in the CLNW. To balance this, we used histological techniques, which allow full lymph node depth to be characterized but are limited in their ability to monitor the kinetic changes occurring as metastatic lesions grow in the lymph node. Using these complimentary techniques allowed better characterization of the growth of lymphatic metastases.
Our data show lymph node lymphangiogenesis is an early event in the natural history of cancer progression, in agreement with previous studies.[40,41,43] However, decreased lymphatic vessel density was found in macrometastatic lymph nodes, suggesting that the presence of the cancer cells in the lymph node causes lymphatic vessel regression. Furthermore, bevacizumab did not statistically significantly affect the lymphatic vasculature in patients. These data suggest that late intervention with antiangiogenic or antilymphangiogenic therapies after lymphatic vessel regression has begun in patients will show no effect on lymph node lymphatic vessels.
In patients, the observation that large metastatic lesions do not exhibit increased vascular density relative to those with micrometastases further suggests that sprouting angiogenesis is not required to sustain the growth of lymph node metastases. A limitation of our data is that we estimated lesion size based on the two-dimensional area available in the histological sections, so we are likely underestimating the size of the lesion. An additional limitation of our study is that we cannot rule out the contribution from different modes of new blood vessel formation in lymph node metastasis such as vasculogenesis, intussusception, vessel co-option, vascular mimicry, and tumor cell differentiation into endothelial cells.[20] The mechanisms of these alternative processes are not clearly defined, although VEGF and endothelial proliferation have been shown to contribute to these processes.[48–51] Our preclinical and clinical data, however, show that inhibitors targeting primarily sprouting angiogenesis will not inhibit the growth of metastases in the lymph node.
Predicted by recent genomic data,[38,39,52] we provide direct evidence that lymph node metastasis forms from multiple cells that disseminate from the primary tumor and suggest a fundamental difference in their formation compared with hematogenous metastases. Cancer cells that invade lymphatic vessels travel to the draining lymph node where they enter in locations defined by afferent lymphatic vessels. As such, lymph node metastasis can be reinforced by the continual arrival of new cells as they gain a foothold in their new microenvironment, leading to the spatially heterogeneous lesions imaged here and the genetically heterogeneous lesions documented previously.[38,39,52,53] In contrast, cells that metastasize through the blood spread out to different locations in an organ by the branching vasculature, leading to a higher probability of individually homogenous lesions. One can thus speculate that targeting a single genetic trait, unless ubiquitous in the primary tumor, may not be effective in eradicating lymph node metastases and any subsequent spread to distant sites.[39]
Using multiple spontaneous metastasis models, we show the first direct evidence that sprouting angiogenesis is not required in lymph node lesions during early metastatic growth. The lack of sprouting angiogenesis in lymph node metastases suggests an additional explanation for the poor outcomes of antiangiogenic therapy in adjuvant settings. As the lymph node is able to metabolically support rapid cellular expansion during an active immune response, it seems the existing vasculature of the lymph node is also able to support the growth of a nascent metastasis. Thus, the mechanisms of angiogenesis and the targets of clinically approved drugs are not active during this early step in cancer progression, suggesting that inhibitors of sprouting angiogenesis as a class will not be effective in treating lymph node metastases. Our novel preclinical models provide opportunities to uncover strategies to better control and eradicate disease in lymph nodes in metastatic cancer patients.
Gene Mutation Signals Poor Prognosis for Pancreatic Tumors
NASHVILLE, Tennessee — For patients with pancreatic neuroendocrine tumors, the presence of recently identified mutations in two key genes is a prognostic factor for poor outcome, researchers report.
“We found loss of nuclear expression in about 23% of the tumors that we studied, and this loss of expression was associated with worse tumors from the outset,” lead investigator Michelle Heayn, MD, a second-year pathology resident at the University of Pittsburgh Medical Center, told Medscape Medical News.
Pancreatic tumors with neuroendocrine histology frequently respond to chemotherapy and have a more favorable prognosis than the more common pancreatic adenocarcinomas. However, the mutations are associated with worse disease-free and disease-specific survival.
The results of the study were presented here at the College of American Pathologists 2015 Meeting.
The mutations — in the alpha-thalassemia mental retardation syndrome X-linked gene (ATRX) and the death-domain-associated protein gene (DAXX) — cause loss of expression of the proteins coded by ATRX and DAXX, Dr Heayn explained.
We found loss of nuclear expression in about 23% of the tumors that we studied.
To test whether these mutations had any prognostic significance, Dr Heayn and her colleagues used immunolabeling in surgically resected pancreatic neuroendocrine tumors from 303 patients. They then correlated the findings with patient demographics, pathologic features, disease-free survival, and disease-specific survival. Follow-up ranged from 1.6 to 18.8 years.
Of the 303 tumors, 69 (23%) had mutations in one or both genes. Tumors with a gene mutation had a larger mean diameter than tumors with intact gene expression (5.0 vs 2.4 cm), as well as a significantly higher histologic grade, more lymphovascular and perineural invasion, a more advanced T stage, greater lymph node involvement, more synchronous metastases, and more frequent disease recurrence (P < .01 for all comparisons).
In addition, the mutations were associated with shorter mean disease-free survival (5.6 vs 17.2 years;P < .01) and shorter mean disease-specific survival (12.5 vs 17.7 years; P = .01).
On multivariate analysis that controlled for patient and tumor factors, the mutations were a significant predictor of shorter disease-free survival (P < .01), independent of tumor size, stage, histology, lymphovascular or perineural invasion, and lymph node status.
Dr Heayn and her colleagues are currently exploring whether there is an association between metastatic pancreatic cancer and these genetic mutations.
Metastatic Pancreatic Cancer
Patients with these mutations in their tumors should be followed more closely for recurrence or disease progression, Dr Heayn said. And in this subset of patients, there is the possibility of new targeted therapies.
These findings are very important, said Safia Salaria, MD, from the Vanderbilt University Medical Center in Nashville.
“There is so much heterogeneity in these tumors, and currently we are just using clinicopathologic features and the WHO-recommended Ki-67 labelling and white count,” she told Medscape Medical News.
“If we have something that can be an adjunct to that — immunohistochemistry to determine the loss of these genes — it’s definitely going to be something that will help us, especially in low-grade tumors,” she explained.
Staining for the expression of the genes could also help pathologists identify patients who are at higher risk for disease recurrence or metastasis but don’t have metastases at the time of primary resection, Dr Salaria said.
Microbiome May Predict Colon Cancer Tumor Mutational Status
BALTIMORE — Analysis of the microbiome surrounding colon cancer tumors could be used as a noninvasive screening test that is more sensitive and specific than fecal occult blood testing, according to the results of a new study.
“This is something that could be critical in colon cancer, because each tumor may have a different mutational landscape with different genes mutated, and that might have an effect on the microbiome,” said Ran Blekhman, PhD, from the University of Minnesota in Minneapolis.
The results of the study were presented here at the American Society of Human Genetics 2015.
Dr Blekhman and his colleagues looked at the genetic differences between healthy colon cells and tumor cells from adults with colorectal cancer, and found that specific tumor mutations are associated with the presence of specific bacteria in the gut.
For example, in people with an APC gene mutation, there is a strong association between familial adenomatous polyposis, a hereditary cancer syndrome, and an abundance of Fusobacterium, said Dr Blekhman.
He pointed out that his lab is the first to analyze the correlation between specific tumor mutations and the composition of the tumor microbiome.
More Mutations, More Diversity
The investigators used whole-exome sequencing to assess the protein-coding regions of tumors and microbiome profiling to characterize the microbiota in tumor biopsy specimens and normal colon tissue samples from 44 adults with colon cancer.
They found that the more mutations, the more varied the bacterial species in the tumor microbiome.
And for certain genes, there was a correlation between somatic mutations and changes in the abundance of specific microbes.
Other evidence of the correlation between bacteria and tumor was seen at the pathway level.
Loss-of-function mutations were detected in tumor glucose transport pathways and were strongly correlated with higher levels of energy utilization in the microbiome, said Dr Blekhman. This suggests that the tumor and the bacteria in its neighborhood are competing for bodily resources.
The investigators created a risk index that evaluated the correlation between microbes and each of several known tumor driver mutations. The index was able to accurately predict the presence of a loss-of-function mutation in ZFN717, a gene encoding for a zinc finger nuclease, part of a family of enzymes involved in DNA repair.
These findings suggest that it is possible to genetically classify tumors from fecal samples alone. Theoretically, this means that manipulation of the tumor microenvironment could be used to prevent or treat colon cancer, Dr Blekhman explained.
This study addresses, in part, the problem of “hidden heritability,” said Chris Gunter, PhD, from Emory University School of Medicine in Atlanta.
“If you look at cancer-sequencing studies now, they identify something like 10 possible driver mutations. We have not yet managed to predict what all the drivers and passengers will be,” she told Medscape Medical News.
“If this type of work can help us narrow down the list, that should add to our understanding of how cancer develops,” she said.
Natural Drug Target Discovery and Translational Medicine in Human Microbiome
Author and Curator: Demet Sag, PhD
Remember Ecology 101, simple description of ecosystem includes both living, biotic, and non-living, abiotic, that response to differentiation based on external and internal factors. Hence, biodiversity changes since living systems are open systems and always try to reach stability. Both soil and human body are rich in microbial life against ever changing conditions. Previously, discovery of marine microorganisms for treatment of complex diseases especially cancer and drug discovery for pharmaceutical applications was discussed. (http://pharmaceuticalintelligence.com/2014/03/20/without-the-past-no-future-but-learn-and-move-genomics-of-microorganisms-to-translational-medicine/)
Here, the focus will be given to clinical drug discovery based on how lactose intolerance and human microbiome related to treat cancer patients or other diseases. In sum, creating clinical relevance with human microbiome require knowledge of both of the worlds to make best of it to solve complex diseases naturally.
The huge undertake as a roadmap to biomedical research originated by NIH under The Human Microbiome Project (HMP) (http://nihroadmap.nih.gov) with 250 healthy individuals as a starting point. Recent developments opened the doors to pursue us to understand how human microbiome reflects on metabolism, drug interactions and numerous diseases. Finally, association between clinical states and microbiome are improving with advanced algorithms, bioinformatics and genomics. In classical reading tests questions finding the simile between two groups of words can well relate how microbiome- human and soil-earth relates. Both are rich in microbial life with quite changing characters to survive through commensal living.
Thus, it is also good to talk about how we can synthesize existing info on interactions between soil microorganisms and decomposers for human diseases and human microbiome. Epidemiology of living organisms is diverse but they all share common interest. In soil, for example, radioactively contaminated soil can’t support plant growth well so Nitrosomonas may support to bring the life to soil through supplying nitrogen. And others can be added to bring a favorable enriched soil.
In human microbiome nutrition-diseases interacts in such a harmony with genetic make up (the information received at time of birth germline- or acquired later in life due to mutations by various reasons). For example, the simplest example is lactose intolerance and the other is development of diabetes. Generally, it is described as If person is missing a gene to metabolize lactose (sugar) this person become Lactose intolerant yet this can be gained before birth or after. The fix is easy since avoiding certain food groups i.e. milk products.
Yet, this is not that simple!
In human microbiome, the rich gastrointestinal (GI) tract contains many organisms and one of the most important ones is Enterococci that are often simply described as lactic-acid–producing bacteria—by under- appreciation of their power of microbial physiology and outcomes as well as their ubiquitous nature of enterococci. Schleifer & Kilpper-Bälz, 1984 also reported that the Group D streptococci, such as Streptococcus faecalis and Streptococcus faecium, were included in the new genus called Enterococcus.
The importance of this genius, consists of 37 species, coming from their spectrum of habitats that include the gastrointestinal microbiota of nearly every animal phylum and flexibility with ability to widely colonize, intrinsic resistance to many inhabitable conditions even though they don’t have spores but they can survive against desiccation and can persist for months on dried surfaces. Furthermore, they can tolerate extreme conditions such as pH changes, ionizing radiation, osmotic and oxidative stresses, high heavy metal concentrations, and antibiotics.
There is a double sword application as these organisms used as probiotics to improve immune system of the host. If it is human to prevent contaminated food related diseases or in animals prevent transmitting them to the consumers. Thus, E. faecium and E. faecalis strains are used as probiotics and are ingested in high numbers, generally in the form of pharmaceutical preparations to treat diarrhea, antibiotic-associated diarrhea or irritable bowel syndrome, to lower cholesterol levels or to improve host immunity.
When it comes to human body within each system specific organs may create distinct values. For example the pH values of GI tract vary and during diseases since pH levels are not at at correct levels. As a result, due to mal-absorption of nutrients and elements such as food, vitamins and minerals body can’t heal itself. This changing microbial genomics on the surface of GI reflects on general health. Entrococcus family among the other GI’s natural flora has the microbial physiology adopt these various pH conditions well.
Our body has its own standards to function, such as pH, temperature, oxygen etc these are basics so that enzymatic reactions may happen to metabolize,synthesizing (making) or catalyzing (breaking) what we eat. The pH is the measure of hydrogen-ion concentration in solution. For example, human blood has a narrow pH (7.35 – 7.45 ) and below or above this range means symptoms and disease yet if blood pH moves to much below 6.8 or above 7.8, cells stop functioning and the patient dies since the ideal pH for blood is 7.4. This value is unified. On the other hand, the pH in the human digestive tract or GI changes tremendously to adopt and carry on its function, the pH of saliva (6.5 – 7.5), upper portion of the stomach (4.0 – 6.5) where “predigestion” occurs, the lower portion of the stomach is secreting hydrochloric acid (HCI) and pepsin until it reaches a pH between 1.5 – 4.0; duodenum, small intestine, (7.0 – 8.5) where 90% of the absorption of nutrients is taken in by the body while the waste products are passed out through the colon (pH 4.0 – 7.0).
Why is pH important and how related to anything?
Development and presence of cancer always require an acid pH and lack of oxygen. Thus, prevention of these two factors may be the key for treatment of cancer as it progress the acidity increases such that the level raises even up to 1000 more than normal levels.
Mainly, due to Warburg effect body opt to get its energy from fermentation of glucose and produce lactic acid that decreases the body pH from 7.3 down to 7 then to 6.5 in advanced stages of cancer. Furthermore, during metastases this level even reaches to 6.0 and even 5.7 where body can’t fight back with the disease. (Warburg effect is well explained previously by Dr. Larry Berstein (www.linkedin.com/pub/larry-bernstein/38/94b/3aa).
How to bypass the lack of oxygen naturally?
One of the many solution can be a natural solution. The nature made the hemoglobin carrying bacteria, Vitreoscilla hemoglobin (VHb), which is first described by Dale Webster in 1966. The gram negative and obligate aerobic bacterium, Vitreoscillasynthesizes elevated quantities of a homodimeric hemoglobin (VHb) under hypoxic growth conditions. The main role is likely the binding of oxygen at low concentrations and its direct delivery to the terminal respiratory oxidase(s) such as cytochrome o. Then, after 1986 with detailed description of the molecule other hemoglobins and flavohemoglobins were identified in a variety of microbes, indicating the widespread occurrence of Hb-like proteins. Currently, it is the most studied bacterial hemoglobin with application potentials in biotechnology.
It is a plausible solution to integrate Vitroscilla and Enterobacter powers for cancer detection and treatment naturally with body’s own microbiome.
However, there are many microbial organisms and differ person to person based on gender, age, background, genetic make-up, food intake, habits, location etc. The huge undertake as a roadmap to biomedical research originated by NIH under The Human Microbiome Project (HMP) (http://nihroadmap.nih.gov) with 250 healthy individuals as a starting point.
There were three goals in the agenda of The Human Microbiome Project (HMP) simply:
1. Utilize advanced high throughput technology,
2. Identify any association between microbiome and disease/health stages,
3. Initiate scientific studies to collect more data.
In sum, creating clinical relevance with human microbiome require knowledge of both of the worlds to make best of it to solve complex diseases naturally.
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What is the Future for Genomics in Clinical Medicine?
Author and Curator: Larry H Bernstein, MD, FCAP
Introduction
This is the last in a series of articles looking at the past and future of the genome revolution. It is a revolution indeed that has had a beginning with the first phase discovery leading to the Watson-Crick model, the second phase leading to the completion of the Human Genome Project, a third phase in elaboration of ENCODE. But we are entering a fourth phase, not so designated, except that it leads to designing a path to the patient clinical experience.
What is most remarkable on this journey, which has little to show in treatment results at this time, is that the boundary between metabolism and genomics is breaking down. The reality is that we are a magnificent “magical” experience in evolutionary time, functioning in a bioenvironment, put rogether like a truly complex machine, and with interacting parts. What are those parts – organelles, a genetic message that may be constrained and it may be modified based on chemical structure, feedback, crosstalk, and signaling pathways. This brings in diet as a source of essential nutrients, exercise as a method for delay of structural loss (not in excess), stress oxidation, repair mechanisms, and an entirely unexpected impact of this knowledge on pharmacotherapy. I illustrate this with some very new observations.
Gutenberg Redone
The first is a recent talk on how genomic medicine has constructed a novel version of the “printing press”, that led us out of the dark ages.
In our series The Creative Destruction of Medicine, I’m trying to get into critical aspects of how we can Schumpeter or reboot the future of healthcare by leveraging the big innovations that are occurring in the digital world, including digital medicine.
We have this big thing about evidence-based medicine and, of course, the sanctimonious randomized, placebo-controlled clinical trial. Well, that’s great if one can do that, but often we’re talking about needing thousands, if not tens of thousands, of patients for these types of clinical trials. And things are changing so fast with respect to medicine and, for example, genomically guided interventions that it’s going to become increasingly difficult to justify these very large clinical trials.
For example, there was a drug trial for melanoma and the mutation of BRAF, which is the gene that is found in about 60% of people with malignant melanoma. When that trial was done, there was a placebo control, and there was a big ethical charge asking whether it is justifiable to have a body count. This was a matched drug for the biology underpinning metastatic melanoma, which is essentially a fatal condition within 1 year, and researchers were giving some individuals a placebo.
The next observation is a progression of what he have already learned. The genome has a role is cellular regulation that we could not have dreamed of 25 years ago, or less. The role is far more than just the translation of a message from DNA to RNA to construction of proteins, lipoproteins, cellular and organelle structures, and more than a regulation of glycosidic and glycolytic pathways, and under the influence of endocrine and apocrine interactions. Despite what we have learned, the strength of inter-molecular interactions, strong and weak chemical bonds, essential for 3-D folding, we know little about the importance of trace metals that have key roles in catalysis and because of their orbital structures, are essential for organic-inorganic interplay. This will not be coming soon because we know almost nothing about the intracellular, interstitial, and intrvesicular distributions and how they affect the metabolic – truly metabolic events.
I shall however, use some new information that gives real cause for joy.
Reprogramming Alters Cells’ Fate
Kathy Liszewski
Gordon Conference Report: June 21, 2012;32(11)
New and emerging strategies were showcased at Gordon Conference’s recent “Reprogramming Cell Fate” meeting. For example, cutting-edge studies described how only a handful of key transcription factors were needed to entirely reprogram cells.
M. Azim Surani, Ph.D., Marshall-Walton professor at the Gurdon Institute, University of Cambridge, U.K., is examining cellular reprogramming in a mouse model. Epiblast stem cells are derived from the early-stage embryonic stage after implantation of blastocysts, about six days into development, and retain the potential to undergo reversion to embryonic stem cells (ESCs) or to PGCs.” They report two critical steps both of which are needed for exploring epigenetic reprogramming. “Although there are two X chromosomes in females, the inactivation of one is necessary for cell differentiation. Only after epigenetic reprogramming of the X chromosome can pluripotency be acquired. Pluripotent stem cells can generate any fetal or adult cell type but are not capable of developing into a complete organism.”
The second read-out is the activation of Oct4, a key transcription factor involved in ESC development. The expression of Oct4 in epiSCs requires its proximal enhancer. Dr. Surani said that their cell-based system demonstrates how a systematic analysis can be performed to analyze how other key genes contribute to the many-faceted events involved in reprogramming the germline.
Reprogramming Expressway
A number of other recent studies have shown the importance of Oct4 for self-renewal of undifferentiated ESCs. It is sufficient to induce pluripotency in neural tissues and somatic cells, among others. The expression of Oct4 must be tightly regulated to control cellular differentiation. But, Oct4 is much more than a simple regulator of pluripotency, according to Hans R. Schöler, Ph.D., professor in the department of cell and developmental biology at the Max Planck Institute for Molecular Biomedicine.
Oct4 has a critical role in committing pluripotent cells into the somatic cellular pathway. When embryonic stem cells overexpress Oct4, they undergo rapid differentiation and then lose their ability for pluripotency. Other studies have shown that Oct4 expression in somatic cells reprograms them for transformation into a particular germ cell layer and also gives rise to induced pluripotent stem cells (iPSCs) under specific culture conditions.
Oct4 is the gatekeeper into and out of the reprogramming expressway. By modifying experimental conditions, Oct4 plus additional factors can induce formation of iPSCs, epiblast stem cells, neural cells, or cardiac cells. Dr. Schöler suggests that Oct4 a potentially key factor not only for inducing iPSCs but also for transdifferention. “Therapeutic applications might eventually focus less on pluripotency and more on multipotency, especially if one can dedifferentiate cells within the same lineage. Although fibroblasts are from a different germ layer, we recently showed that adding a cocktail of transcription factors induces mouse fibroblasts to directly acquire a neural stem cell identity.
Stem cell diagram illustrates a human fetus stem cell and possible uses on the circulatory, nervous, and immune systems. (Photo credit: Wikipedia)
Transforming growth factor beta (TGF-β) is a secreted protein that controls proliferation, cellular differentiation, and other functions in most cells. http://en.wikipedia.org/wiki/TGFbeta (Photo credit: Wikipedia)
Pioneer Transcription Factors
Pioneer transcription factors take the lead in facilitating cellular reprogramming and responses to environmental cues. Multicellular organisms consist of functionally distinct cellular types produced by differential activation of gene expression. They seek out and bind specific regulatory sequences in DNA. Even though DNA is coated with and condensed into a thick fiber of chromatin. The pioneer factor, discovered by Prof. KS Zaret at UPenn SOM in 1996, he says, endows the competence for gene activity, being among the first transcription factors to engage and pry open the target sites in chromatin.
FoxA factors, expressed in the foregut endoderm of the mouse,are necessary for induction of the liver program. They found that nearly one-third of the DNA sites bound by FoxA in the adult liver occur near silent genes
A Nontranscriptional Role for HIF-1α as a Direct Inhibitor of DNA Replication
Many of the cellular responses to reduced O2 availability are mediated through the transcriptional activity of hypoxia-inducible factor 1 (HIF-1). We report a role for the isolated HIF-1α subunit as an inhibitor of DNA replication, and this role was independent of HIF-1β and transcriptional regulation. In response to hypoxia, HIF-1α bound to Cdc6, a protein that is essential for loading of the mini-chromosome maintenance (MCM) complex (which has DNA helicase activity) onto DNA, and promoted the interaction between Cdc6 and the MCM complex. The binding of HIF-1α to the complex decreased phosphorylation and activation of the MCM complex by the kinase Cdc7. As a result, HIF-1α inhibited firing of replication origins, decreased DNA replication, and induced cell cycle arrest in various cell types. To whom correspondence should be addressed. E-mail: gsemenza@jhmi.edu
Citation: M. E. Hubbi, Kshitiz, D. M. Gilkes, S. Rey, C. C. Wong, W. Luo, D.-H. Kim, C. V. Dang, A. Levchenko, G. L. Semenza, A Nontranscriptional Role for HIF-1α as a Direct Inhibitor of DNA Replication. Sci. Signal. 6, ra10 (2013).
Identification of a Candidate Therapeutic Autophagy-inducing Peptide
Beth Levine and colleagues have constructed a cell-permeable peptide derived from part of an autophagy protein called beclin 1. This peptide is a potent inducer of autophagy in mammalian cells and in vivo in mice and was effective in the clearance of several viruses including chikungunya virus, West Nile virus and HIV-1.
Could this small autophagy-inducing peptide may be effective in the prevention and treatment of human diseases?
PR-Set7 Is a Nucleosome-Specific Methyltransferase that Modifies Lysine 20 of
Histone H4 and Is Associated with Silent Chromatin
We have purified a human histone H4 lysine 20methyl-transferase and cloned the encoding gene, PR/SET07. A mutation in Drosophila pr-set7 is lethal: second in-star larval death coincides with the loss of H4 lysine 20 methylation, indicating a fundamental role for PR-Set7 in development. Transcriptionally competent regions lack H4 lysine 20 methylation, but the modification coincided with condensed chromosomal regions polytene chromosomes, including chromocenter euchromatic arms. The Drosophila male X chromosome, which is hyperacetylated at H4 lysine 16, has significantly decreased levels of lysine 20 methylation compared to that of females. In vitro, methylation of lysine 20 and acetylation of lysine 16 on the H4 tail are competitive. Taken together, these results support the hypothesis that methylation of H4 lysine 20 maintains silent chromatin, in part, by precluding neighboring acetylation on the H4 tail.
Next-Generation Sequencing vs. Microarrays
Shawn C. Baker, Ph.D., CSO of BlueSEQ
GEN Feb 2013
With recent advancements and a radical decline in sequencing costs, the popularity of next generation sequencing (NGS) has skyrocketed. As costs become less prohibitive and methods become simpler and more widespread, researchers are choosing NGS over microarrays for more of their genomic applications. The immense number of journal articles citing NGS technologies it looks like NGS is no longer just for the early adopters. Once thought of as cost prohibitive and technically out of reach, NGS has become a mainstream option for many laboratories, allowing researchers to generate more complete and scientifically accurate data than previously possible with microarrays.
Gene Expression
Researchers have been eager to use NGS for gene expression experiments for a detailed look at the transcriptome. Arrays suffer from fundamental ‘design bias’ —they only return results from those regions for which probes have been designed. The various RNA-Seq methods cover all aspects of the transcriptome without any a priori knowledge of it, allowing for the analysis of such things as novel transcripts, splice junctions and noncoding RNAs. Despite NGS advancements, expression arrays are still cheaper and easier when processing large numbers of samples (e.g., hundreds to thousands).
Methylation
While NGS unquestionably provides a more complete picture of the methylome, whole genome methods are still quite expensive. To reduce costs and increase throughput, some researchers are using targeted methods, which only look at a portion of the methylome. Because details of exactly how methylation impacts the genome and transcriptome are still being investigated, many researchers find a combination of NGS for discovery and microarrays for rapid profiling.
Diagnostics
They are interested in ease of use, consistent results, and regulatory approval, which microarrays offer. With NGS, there’s always the possibility of revealing something new and unexpected. Clinicians aren’t prepared for the extra information NGS offers. But the power and potential cost savings of NGS-based diagnostics is alluring, leading to their cautious adoption for certain tests such as non-invasive prenatal testing. Cytogenetics
Perhaps the application that has made the least progress in transitioning to NGS is cytogenetics. Researchers and clinicians, who are used to using older technologies such as karyotyping, are just now starting to embrace microarrays. NGS has the potential to offer even higher resolution and a more comprehensive view of the genome, but it currently comes at a substantially higher price due to the greater sequencing depth. While dropping prices and maturing technology are causing NGS to make headway in becoming the technology of choice for a wide range of applications, the transition away from microarrays is a long and varied one. Different applications have different requirements, so researchers need to carefully weigh their options when making the choice to switch to a new technology or platform. Regardless of which technology they choose, genomic researchers have never had more options.
Sequencing Hones In on Targets
Greg Crowther, Ph.D.
GEN Feb 2013
Cliff Han, PhD, team leader at the Joint Genome Institute in the Los Alamo National Lab, was one of a number of scientists who made presentations regarding target enrichment at the “Sequencing, Finishing, and Analysis in the Future” (SFAF) conference in Santa Fe, which was co-sponsored by the Los Alamos National Laboratory and DOE Joint Genome Institute. One of the main challenges is that of target enrichment: the selective sequencing of genomic or transcriptomic regions. The polymerase chain reaction (PCR) can be considered the original target-enrichment technique and continues to be useful in contexts such as genome finishing. “One target set is the unique gaps—the gaps in the unique sequence regions. Another is to enrich the repetitive sequences…ribosomal RNA regions, which together are about 5 kb or 6 kb.” The unique-sequence gaps targeted for PCR with 40-nucleotide primers complementary to sequences adjacent to the gaps, did not yield the several-hundred-fold enrichment expected based on previously published work. “We got a maximum of 70-fold enrichment and generally in the dozens of fold of enrichment,” noted Dr. Han.
“We enrich the genome, put the enriched fragments onto the Pacific Biosciences sequencer, and sequence the repeats,” continued Dr. Han. “In many parts of the sequence there will be a unique sequence anchored at one or both ends of it, and that will help us to link these scaffolds together.” This work, while promising, will remain unpublished for now, as the Joint Genome Institute has shifted its resources to other projects.
At the SFAF conference Dr. Jones focused on going beyond basic target enrichment and described new tools for more efficient NGS research. “Hybridization methods are flexible and have multiple stop-start sites, and you can capture very large sizes, but they require library prep,” said Jennifer Carter Jones, Ph.D., a genomics field applications scientist at Agilent. “With PCR-based methods, you have to design PCR primers and you’re doing multiplexed PCR, so it’s limited in the size that you can target. But the workflow is quick because there’s no library preparation; you’re just doing PCR.” She discussed Agilent’s recently acquired HaloPlex technology, a hybrid system that includes both a hybridization step and a PCR step. Because no library preparation is required, sequencing results can be obtained in about six hours, making it suitable for clinical uses. However, the hybridization step allows capture of targets of up to 5 megabases—longer than purely PCR-based methods can deliver. The Agilent talk also provided details on the applications of SureSelect, the company’s hybridization technology, to Methyl-Seq and RNA-Seq research. With this technology, 120-mer baits hybridize to targets, then are pulled down with streptavidin-coated magnetic beads.
These are selections from the SFAF conference, which is expected to be a boost to work on the microbiome, and lead to infectious disease therapeutic approaches.
Summary
We have finished a breathtaking ride through the genomic universe in several sessions. This has been a thorough review of genomic structure and function in cellular regulation. The items that have been discussed and can be studied in detail include:
the classical model of the DNA structure
the role of ubiquitinylation in managing cellular function and in autophagy, mitophagy, macrophagy, and protein degradation
the nature of the tight folding of the chromatin in the nucleus
intramolecular bonds and short distance hydrophobic and hydrophilic interactions
trace metals in molecular structure
nuclear to membrane interactions
the importance of the Human Genome Project followed by Encode
the Fractal nature of chromosome structure
the oligomeric formation of short sequences and single nucletide polymorphisms (SNPs)and the potential to identify drug targets
Enzymatic components of gene regulation (ligase, kinases, phosphatases)
Methods of computational analysis in genomics
Methods of sequencing that have become more accurate and are dropping in cost
Chromatin remodeling
Triplex and quadruplex models not possible to construct at the time of Watson-Crick
sequencing errors
propagation of errors
oxidative stress and its expected and unintended effects
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