2020 January
Working to Improve Immunotherapy for Lung Cancer
Posted on by Dr. Francis Collins
For those who track cancer statistics, this year started off on a positive note with word that lung cancer deaths continue to decline in the United States [1]. While there’s plenty of credit to go around for that encouraging news—and continued reduction in smoking is a big factor—some of this progress likely can be ascribed to a type of immunotherapy, called PD-1 inhibitors. This revolutionary approach has dramatically changed the treatment landscape for the most common type of lung cancer, non-small cell lung cancer (NSCLC).
PD-1 inhibitors, which have only been available for about five years, prime one component of a patient’s own immune system, called T cells, to seek and destroy malignant cells in the lungs. Unfortunately, however, only about 20 percent of people with NSCLC respond to PD-1 inhibitors. So, many researchers, including the team of A. McGarry Houghton, Fred Hutchinson Cancer Research Center, Seattle, are working hard to extend the benefits of immunotherapy to more cancer patients.
The team’s latest paper, published in JCI Insight [2], reveals that one culprit behind a poor response to immunotherapy may be the immune system’s own first responders: neutrophils. Billions of neutrophils circulate throughout the body to track down abnormalities, such as harmful bacteria and malignant cells. They also contact other parts of the immune system, including T cells, if help is needed to eliminate the health threat.
In their study, the Houghton team, led by Julia Kargl, combined several lab techniques to take a rigorous, unbiased look at the immune cell profiles of tumor samples from dozens of NSCLC patients who received PD-1 inhibitors as a frontline treatment. The micrographs above show tumor samples from two of these patients.
In the image on the left, large swaths of T cells (light blue) have infiltrated the cancer cells (white specks). Interestingly, other immune cells, including neutrophils (magenta), are sparse.
In contrast, in the image on the right, T cells (light blue) are sparse. Instead, the tumor teems with other types of immune cells, including macrophages (red), two types of monocytes (yellow, green), and, most significantly, lots of neutrophils (magenta). These cells arise from myeloid progenitor cells in the bone marrow, while T cells arise from the marrow’s lymphoid progenitor cell.
Though the immune profiles of some tumor samples were tough to classify, the researchers found that most fit neatly into two subgroups: tumors showing active levels of T cell infiltration (like the image on the left) or those with large numbers of myeloid immune cells, especially neutrophils (like the image on the right). This dichotomy then served as a reliable predictor of treatment outcome. In the tumor samples with majority T cells, the PD-1 inhibitor worked to varying degrees. But in the tumor samples with predominantly neutrophil infiltration, the treatment failed.
Houghton’s team has previously found that many cancers, including NSCLC, actively recruit neutrophils, turning them into zombie-like helpers that falsely signal other immune cells, like T cells, to stay away. Based on this information, Houghton and colleagues used a mouse model of lung cancer to explore a possible way to increase the success rate of PD-1 immunotherapy.
In their mouse experiments, the researchers found that when PD-1 was combined with an existing drug that inhibits neutrophils, lung tumors infiltrated with neutrophils were converted into tumors infiltrated by T cells. The tumors treated with the combination treatment also expressed genes associated with an active immunotherapy response.
This year, January brought encouraging news about decreasing deaths from lung cancer. But with ongoing basic research, like this study, to tease out the mechanisms underlying the success and failure of immunotherapy, future months may bring even better news.
References:
[1] Cancer statistics, 2020. Siegel RL, Miller KD, Jemal A. CA Cancer J Clin. 2020 Jan;70(1):7-30.
[2] Neutrophil content predicts lymphocyte depletion and anti-PD1 treatment failure in NSCLC. Kargl J, Zhu X, Zhang H, Yang GHY, Friesen TJ, Shipley M, Maeda DY, Zebala JA, McKay-Fleisch J, Meredith G, Mashadi-Hossein A, Baik C, Pierce RH, Redman MW, Thompson JC, Albelda SM, Bolouri H, Houghton AM. JCI Insight. 2019 Dec 19;4(24).
[3] Neutrophils dominate the immune cell composition in non-small cell lung cancer. Kargl J, Busch SE, Yang GH, Kim KH, Hanke ML, Metz HE, Hubbard JJ, Lee SM, Madtes DK, McIntosh MW, Houghton AM. Nat Commun. 2017 Feb 1;8:14381.
Links:
Non-Small Cell Lung Cancer Treatment (PDQ®)–Patient Version (National Cancer Institute/NIH)
Spotlight on McGarry Houghton (Fred Hutchinson Cancer Research Center, Seattle)
Houghton Lab (Fred Hutchinson Cancer Research Center)
NIH Support: National Cancer Institute
After Opioid Overdose, Most Young People Aren’t Getting Addiction Treatment
Posted on by Dr. Francis Collins
Drug overdoses continue to take far too many lives, driven primarily by the opioid crisis (though other drugs, such as methamphetamine and cocaine, are also major concerns). While NIH’s Helping to End Addiction Long-term (HEAL) Initiative is taking steps to address this terrible crisis, new findings serve as another wake-up call that young people battling opioid addiction need a lot more assistance to get back on the right track.
In a study of more than 3,600 individuals, aged 13-22, who survived an opioid overdose, an NIH-funded team found that only about one-third received any kind of follow-up addiction treatment [1]. Even more troubling, less than 2 percent of these young people received the gold standard approach of medication treatment.
The findings reported in JAMA Pediatrics come from Rachel Alinsky, an adolescent medicine and addiction medicine fellow at Johns Hopkins Children’s Center, Baltimore. She saw first-hand the devastating toll that opioids are taking on our youth.
Alinsky also knew that nationally more than 4,000 fatal opioid overdoses occurred in people between the ages of 15 and 24 in 2016 [2]. Likewise, rates of nonfatal opioid overdoses for teens and young adults also have been escalating, leading to more than 7,000 hospitalizations and about 28,000 emergency department visits in 2015 alone [3].
In the latest study, Alinsky wanted to find out whether young people who overdose receive timely treatment to help prevent another life-threatening emergency. According to our best evidence-based guidelines, timely treatment for youth with an opioid addiction should include medication, ideally along with behavioral interventions.
That’s because opioid addiction rewires the brain—will power alone is simply not sufficient to achieve and sustain recovery. After one overdose, the risk of dying from another one rises dramatically. So, it is critical to get those who survived an overdose into effective treatment right away.
Alinsky and her team dove into the best-available dataset, consisting of data on more than 4 million mostly low-income adolescents and young adults who’d been enrolled in Medicaid for at least six months in 16 states. The sample included 3,606 individuals who’d been seen by a doctor and diagnosed with opioid poisoning. A little over half of them were female; most were non-Hispanic whites.
Heroin accounted for about a quarter of those overdoses. The rest involved other opioids, most often prescription painkillers. However, the researchers note that some overdoses attributed to heroin might have been caused by the powerful synthetic opioid fentanyl. The use of fentanyl, often mixed with heroin, was on the rise in the study’s final years, but it was rarely included in drug tests at the time.
Less than 20 percent of young people in the sample received a diagnosis of opioid use disorder, or a problematic pattern of opioid use resulting in impairment or distress. What’s more, in the month following an overdose, few received the current standard for addiction treatment, which should include behavioral therapy and treatment with one of three drugs: buprenorphine, naltrexone, or methadone.
Drilling a little deeper into the study’s findings:
• 68.9 percent did not receive addiction treatment of any kind.
• 29.3 percent received behavioral health services alone.
• Only 1.9 percent received one of three approved medications for opioid use disorder.
It’s been estimated previously that teens and young adults are one-tenth as likely as adults 25 years and older to get the recommended treatment for opioid use disorder [4]. How can that be? The researchers suggest that one factor might be inexperience among pediatricians in diagnosing and treating opioid addiction. They also note that, even when the problem is recognized, doctors sometimes struggle to take the next step and connect young people with addiction treatment facilities that are equipped to provide the needed treatment to adolescents.
As this new study shows, interventions designed to link teens and young adults with the needed recovery treatment and care are desperately needed. As we continue to move forward in tackling this terrible crisis through the NIH’s HEAL Initiative and other efforts, finding ways to overcome such systemic barriers and best engage our youth in treatment, including medication, will be essential.
References:
[1] Receipt of addiction treatment after opioid overdose among Medicaid-enrolled adolescents and young adults. Alinsky RH, Zima BT, Rodean J, Matson PA, Larochelle MR, Adger H Jr, Bagley SM, Hadland SE. JAMA Pediatr. 2020 Jan 6:e195183.
[2] Overdose death rates. National Institute on Drug Abuse, NIH.
[3] 2018 annual surveillance drug-related risks and outcomes—United States: surveillance special report. Centers for Disease Control and Prevention.
[4] Medication-assisted treatment for adolescents in specialty treatment for opioid use disorder. Feder KA, Krawczyk N, Saloner B. J Adolesc Health. 2017 Jun;60(6):747-750.
Links:
Opioid Overdose Crisis (National Institute on Drug Abuse/NIH)
Opioid Overdose (Centers for Disease Control and Prevention, Atlanta)
Decisions in Recovery: Treatment for Opioid Use Disorder (Substance Abuse and Mental Health Services Administration, Rockville, MD)
Rachel Alinsky (Johns Hopkins University Children’s Center, Baltimore)
Helping to End Addiction Long-term (HEAL) Initiative (NIH)
NIH Support: Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute on Drug Abuse
New Chief Executive Officer for All of Us
Posted on by Dr. Francis Collins
Could A Gut-Brain Connection Help Explain Autism?
Posted on by Dr. Francis Collins
You might think nutrient-sensing cells in the human gastrointestinal (GI) tract would have no connection whatsoever to autism spectrum disorder (ASD). But if Diego Bohórquez’s “big idea” is correct, these GI cells, called neuropods, could one day help to provide a direct link into understanding and treating some aspects of autism and other brain disorders.
Bohórquez, a researcher at Duke University, Durham, NC, recently discovered that cells in the intestine, previously known for their hormone-releasing ability, form extensions similar to neurons. He also found that those extensions connect to nerve fibers in the gut, which relay signals to the vagus nerve and onward to the brain. In fact, he found that those signals reach the brain in milliseconds [1].
Bohórquez has dedicated his lab to studying this direct, high-speed hookup between gut and brain and its impact on nutrient sensing, eating, and other essential behaviors. Now, with support from a 2019 NIH Director’s New Innovator Award, he will also explore the potential for treating autism and other brain disorders with drugs that act on the gut.
Bohórquez became interested in autism and its possible link to the gut-brain connection after a chance encounter with Geraldine Dawson, director of the Duke Center for Autism and Brain Development. Dawson mentioned that autism typically affects multiple organ systems.
With further reading, he discovered that kids with autism frequently cope with GI issues, including bowel inflammation, abdominal pain, constipation, and/or diarrhea [2]. They often also show unusual food-related behaviors, such as being extremely picky eaters. But his curiosity was especially piqued by evidence that certain gut microbes can influence abnormal behaviors in mice that model autism.
With his New Innovator Award, Bohórquez will study neuropods and the gut-brain connection in a mouse model of autism. Using the tools of optogenetics, which make it possible to activate cells with light, he’ll also see whether autism-like symptoms in mice can be altered or alleviated by controlling neuropods in the gut. Those symptoms include anxiety, repetitive behaviors, and lack of interest in interacting with other mice. He’ll also explore changes in the animals’ eating habits.
In another line of study, he will take advantage of intestinal tissue samples collected from people with autism. He’ll use those tissues to grow and then examine miniature intestinal “organoids,” looking for possible evidence that those from people with autism are different from others.
For the millions of people now living with autism, no truly effective drug therapies are available to help to manage the condition and its many behavioral and bodily symptoms. Bohórquez hopes one day to change that with drugs that act safely on the gut. In the meantime, he and his fellow “GASTRONAUTS” look forward to making some important and fascinating discoveries in the relatively uncharted territory where the gut meets the brain.
References:
[1] A gut-brain neural circuit for nutrient sensory transduction. Kaelberer MM, Buchanan KL, Klein ME, Barth BB, Montoya MM, Shen X, Bohórquez DV. Science. 2018 Sep 21;361(6408).
[2] Association of maternal report of infant and toddler gastrointestinal symptoms with autism: evidence from a prospective birth cohort. Bresnahan M, Hornig M, Schultz AF, Gunnes N, Hirtz D, Lie KK, Magnus P, Reichborn-Kjennerud T, Roth C, Schjølberg S, Stoltenberg C, Surén P, Susser E, Lipkin WI. JAMA Psychiatry. 2015 May;72(5):466-474.
Links:
Autism Spectrum Disorder (National Institute of Mental Health/NIH)
Bohórquez Lab (Duke University, Durham, NC)
Bohórquez Project Information (NIH RePORTER)
NIH Director’s New Innovator Award (Common Fund)
NIH Support: Common Fund; National Institute of Mental Health
Insurance Status Helps Explain Racial Disparities in Cancer Diagnosis
Posted on by Dr. Francis Collins
Women have the best odds of surviving breast cancer if their disease is caught at an early stage, when treatments are most likely to succeed. Major strides have been made in the early detection of breast cancer in recent years. But not all populations have benefited equally, with racial and ethnic minorities still more likely to be diagnosed with later-stage breast cancer than non-Hispanic whites. Given that recent observance of Martin Luther King Day, I thought that it would be particularly appropriate to address a leading example of health disparities.
A new NIH-funded study of more than 175,000 U.S. women diagnosed with breast cancer from 2010-2016 has found that nearly half of the troubling disparity in breast cancer detection can be traced to lack of adequate health insurance. The findings suggest that improving insurance coverage may help to increase early detection and thereby reduce the disproportionate number of breast cancer deaths among minority women.
Naomi Ko, Boston University School of Medicine, has had a long interest in understanding the cancer disparities she witnesses first-hand in her work as a medical oncologist. For the study published in JAMA Oncology, she teamed up with epidemiologist Gregory Calip, University of Illinois Cancer Center, Chicago [1]. Their goal was to get beyond documenting disparities in breast cancer and take advantage of available data to begin to get at why such disparities exist and what to do about them.
Disparities in breast cancer outcomes surely stem from a complicated mix of factors, including socioeconomic factors, culture, diet, stress, environment, and biology. Ko and Calip focused their attention on insurance, thinking of it as a factor that society can collectively modify.
Many earlier studies had shown a link between insurance and cancer outcomes [2]. It also stood to reason that broad differences among racial and ethnic minorities in their access to adequate insurance might drive some of the observed cancer disparities. But, Ko and Calip asked, just how big a factor was it?
To find out, they looked to the NIH’s Surveillance Epidemiology, and End Results (SEER) Program, run by the National Cancer Institute. The SEER Program is an authoritative source of information on cancer incidence and survival in the United States.
The researchers focused their attention on 177,075 women of various races and ethnicities, ages 40 to 64. All had been diagnosed with invasive stage I to III breast cancer between 2010 and 2016.
The researchers found that a higher proportion of women receiving Medicaid or who were uninsured received a diagnosis of advanced stage III breast cancer compared with women with health insurance. Black, American Indian, Alaskan Native, and Hispanic women also had higher odds of receiving a late-stage diagnosis.
Overall, their sophisticated statistical analyses traced up to 47 percent of the racial/ethnic differences in the risk of locally advanced disease to differences in health insurance. Such late-stage diagnoses and the more extensive treatment regimens that go with them are clearly devastating for women with breast cancer and their families. But, the researchers note, they’re also costly for society, due to lost productivity and escalating treatment costs by stage of breast cancer.
These researchers surely aren’t alone in recognizing the benefit of early detection. Last week, an independent panel convened by NIH called for enhanced research to assess and explore how to reduce health disparities that lead to unequal access to health care and clinical services that help prevent disease.
References:
[1] Association of Insurance Status and Racial Disparities With the Detection of Early-Stage Breast Cancer. Ko NY, Hong S, Winn RA, Calip GS. JAMA Oncol. 2020 Jan 9.
[2] The relation between health insurance coverage and clinical outcomes among women with breast cancer. Ayanian JZ, Kohler BA, Abe T, Epstein AM. N Engl J Med. 1993 Jul 29;329(5):326-31.
[3] Cancer Stat Facts: Female Breast Cancer. National Cancer Institute Surveillance, Epidemiology, and End Results Program.
Links:
Cancer Disparities (National Cancer Institute/NIH)
Breast Cancer (National Cancer Institute/NIH)
Naomi Ko (Boston University)
Gregory Calip (University of Illinois Cancer Center, Chicago)
NIH Support: National Center for Advancing Translational Sciences; National Cancer Institute; National Institute on Minority Health and Health Disparities
NIH HEAL Investigator Meeting
Posted on by Dr. Francis Collins
Time Well Spent in North Carolina
Posted on by Dr. Francis Collins
A Special Honor from Washingtonian Magazine
Posted on by Dr. Francis Collins
A Real-Time Look at Value-Based Decision Making
Posted on by Dr. Francis Collins
All of us make many decisions every day. For most things, such as which jacket to wear or where to grab a cup of coffee, there’s usually no right answer, so we often decide using values rooted in our past experiences. Now, neuroscientists have identified the part of the mammalian brain that stores information essential to such value-based decision making.
Researchers zeroed in on this particular brain region, known as the retrosplenial cortex (RSC), by analyzing movies—including the clip shown about 32 seconds into this video—that captured in real time what goes on in the brains of mice as they make decisions. Each white circle is a neuron, and the flickers of light reflect their activity: the brighter the light, the more active the neuron at that point in time.
All told, the NIH-funded team, led by Ryoma Hattori and Takaki Komiyama, University of California at San Diego, La Jolla, made recordings of more than 45,000 neurons across six regions of the mouse brain [1]. Neural activity isn’t usually visible. But, in this case, researchers used mice that had been genetically engineered so that their neurons, when activated, expressed a protein that glowed.
Their system was also set up to encourage the mice to make value-based decisions, including choosing between two drinking tubes, each with a different probability of delivering water. During this decision-making process, the RSC proved to be the region of the brain where neurons persistently lit up, reflecting how the mouse evaluated one option over the other.
The new discovery, described in the journal Cell, comes as something of a surprise to neuroscientists because the RSC hadn’t previously been implicated in value-based decisions. To gather additional evidence, the researchers turned to optogenetics, a technique that enabled them to use light to inactivate neurons in the RSC’s of living animals. These studies confirmed that, with the RSC turned off, the mice couldn’t retrieve value information based on past experience.
The researchers note that the RSC is heavily interconnected with other key brain regions, including those involved in learning, memory, and controlling movement. This indicates that the RSC may be well situated to serve as a hub for storing value information, allowing it to be accessed and acted upon when it is needed.
The findings are yet another amazing example of how advances coming out of the NIH-led Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative are revolutionizing our understanding of the brain. In the future, the team hopes to learn more about how the RSC stores this information and sends it to other parts of the brain. They note that it will also be important to explore how activity in this brain area may be altered in schizophrenia, dementia, substance abuse, and other conditions that may affect decision-making abilities. It will also be interesting to see how this develops during childhood and adolescence.
Reference:
[1] Area-Specificity and Plasticity of History-Dependent Value Coding During Learning. Hattori R, Danskin B, Babic Z, Mlynaryk N, Komiyama T. Cell. 2019 Jun 13;177(7):1858-1872.e15.
Links:
Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative (NIH)
Komiyama Lab (UCSD, La Jolla)
NIH Support: National Institute of Neurological Disorders and Stroke; National Eye Institute; National Institute on Deafness and Other Communication Disorders
Artificial Intelligence Speeds Brain Tumor Diagnosis
Posted on by Dr. Francis Collins
Credit: Joe Hallisy, Michigan Medicine, Ann Arbor
Computers are now being trained to “see” the patterns of disease often hidden in our cells and tissues. Now comes word of yet another remarkable use of computer-generated artificial intelligence (AI): swiftly providing neurosurgeons with valuable, real-time information about what type of brain tumor is present, while the patient is still on the operating table.
This latest advance comes from an NIH-funded clinical trial of 278 patients undergoing brain surgery. The researchers found they could take a small tumor biopsy during surgery, feed it into a trained computer in the operating room, and receive a diagnosis that rivals the accuracy of an expert pathologist.
Traditionally, sending out a biopsy to an expert pathologist and getting back a diagnosis optimally takes about 40 minutes. But the computer can do it in the operating room on average in under 3 minutes. The time saved helps to inform surgeons how to proceed with their delicate surgery and make immediate and potentially life-saving treatment decisions to assist their patients.
As reported in Nature Medicine, researchers led by Daniel Orringer, NYU Langone Health, New York, and Todd Hollon, University of Michigan, Ann Arbor, took advantage of AI and another technological advance called stimulated Raman histology (SRH). The latter is an emerging clinical imaging technique that makes it possible to generate detailed images of a tissue sample without the usual processing steps.
The SRH technique starts off by bouncing laser light rapidly through a tissue sample. This light enables a nearby fiberoptic microscope to capture the cellular and structural details within the sample. Remarkably, it does so by picking up on subtle differences in the way lipids, proteins, and nucleic acids vibrate when exposed to the light.
Then, using a virtual coloring program, the microscope quickly pieces together and colors in the fine structural details, pixel by pixel. The result: a high-resolution, detailed image that you might expect from a pathology lab, minus the staining of cells, mounting of slides, and the other time-consuming processing procedures.
To interpret the SRH images, the researchers turned to computers and machine learning. To teach a computer, it must be fed large datasets of examples in order to learn how to perform a given task. In this case, they used a special class of machine learning called deep neural networks, or deep learning. It’s inspired by the way neural networks in the human brain process information.
In deep learning, computers look for patterns in large collections of data. As they begin to recognize complex relationships, some connections in the network are strengthened while others are weakened. The finished network is typically composed of multiple information-processing layers, which operate on the data to return a result, in this case a brain tumor diagnosis.
The team trained the computer to classify tissues samples into one of 13 categories commonly found in a brain tumor sample. Those categories included the most common brain tumors: malignant glioma, lymphoma, metastatic tumors, and meningioma. The training was based on more than 2.5 million labeled images representing samples from 415 patients.
Next, they put the machine to the test. The researchers split each of 278 brain tissue samples into two specimens. One was sent to a conventional pathology lab for prepping and diagnosis. The other was imaged with SRH, and then the trained machine made a diagnosis.
Overall, the machine’s performance was quite impressive, returning the right answer about 95 percent of the time. That’s compared to an accuracy of 94 percent for conventional pathology.
Interestingly, the machine made a correct diagnosis in all 17 cases that a pathologist got wrong. Likewise, the pathologist got the right answer in all 14 cases in which the machine slipped up.
The findings show that the combination of SRH and AI can be used to make real-time predictions of a patient’s brain tumor diagnosis to inform surgical decision-making. That may be especially important in places where expert neuropathologists are hard to find.
Ultimately, the researchers suggest that AI may yield even more useful information about a tumor’s underlying molecular alterations, adding ever greater precision to the diagnosis. Similar approaches are also likely to work in supplying timely information to surgeons operating on patients with other cancers too, including cancers of the skin and breast. The research team has made a brief video to give you a more detailed look at the new automated tissue-to-diagnosis pipeline.
Reference:
[1] Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Hollon TC, Pandian B, Adapa AR, Urias E, Save AV, Khalsa SSS, Eichberg DG, D’Amico RS, Farooq ZU, Lewis S, Petridis PD, Marie T, Shah AH, Garton HJL, Maher CO, Heth JA, McKean EL, Sullivan SE, Hervey-Jumper SL, Patil PG, Thompson BG, Sagher O, McKhann GM 2nd, Komotar RJ, Ivan ME, Snuderl M, Otten ML, Johnson TD, Sisti MB, Bruce JN, Muraszko KM, Trautman J, Freudiger CW, Canoll P, Lee H, Camelo-Piragua S, Orringer DA. Nat Med. 2020 Jan 6.
Links:
Video: Artificial Intelligence: Collecting Data to Maximize Potential (NIH)
New Imaging Technique Allows Quick, Automated Analysis of Brain Tumor Tissue During Surgery (National Institute of Biomedical Imaging and Bioengineering/NIH)
Daniel Orringer (NYU Langone, Perlmutter Cancer Center, New York City)
Todd Hollon (University of Michigan, Ann Arbor)
NIH Support: National Cancer Institute; National Institute of Biomedical Imaging and Bioengineering
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