brain cancer
Rice-Sized Device Tests Brain Tumor’s Drug Responses During Surgery
Posted on by Lawrence Tabak, D.D.S., Ph.D.
Scientists have made remarkable progress in understanding the underlying changes that make cancer grow and have applied this knowledge to develop and guide targeted treatment approaches to vastly improve outcomes for people with many cancer types. And yet treatment progress for people with brain tumors known as gliomas—including the most aggressive glioblastomas—has remained slow. One reason is that doctors lack tests that reliably predict which among many therapeutic options will work best for a given tumor.
Now an NIH-funded team has developed a miniature device with the potential to change this for the approximately 25,000 people diagnosed with brain cancers in the U.S. each year [1]. When implanted into cancerous brain tissue during surgery, the rice-sized drug-releasing device can simultaneously conduct experiments to measure a tumor’s response to more than a dozen drugs or drug combinations. What’s more, a small clinical trial reported in Science Translational Medicine offers the first evidence in people with gliomas that these devices can safely offer unprecedented insight into tumor-specific drug responses [2].
These latest findings come from a Brigham and Women’s Hospital, Boston, team led by Pierpaolo Peruzzi and Oliver Jonas. They recognized that drug-screening studies conducted in cells or tissue samples in the lab too often failed to match what happens in people with gliomas undergoing cancer treatment. Wide variation within individual brain tumors also makes it hard to predict a tumor’s likely response to various treatment options.
It led them to an intriguing idea: Why not test various therapeutic options in each patient’s tumor? To do it, they developed a device, about six millimeters long, that can be inserted into a brain tumor during surgery to deliver tiny doses of up to 20 drugs. Doctors can then remove and examine the drug-exposed cancerous tissue in the laboratory to determine each treatment’s effects. The data can then be used to guide subsequent treatment decisions, according to the researchers.
In the current study, the researchers tested their device on six study volunteers undergoing brain surgery to remove a glioma tumor. For each volunteer, the device was implanted into the tumor and remained in place for about two to three hours while surgeons worked to remove most of the tumor. Next, the device was taken out along with the last piece of a tumor at the end of the surgery for further study of drug responses.
Importantly, none of the study participants experienced any adverse effects from the device. Using the devices, the researchers collected valuable data, including how a tumor’s response changed with varying drug concentrations or how each treatment led to molecular changes in the cancerous cells.
More research is needed to better understand how use of such a device might change treatment and patient outcomes in the longer term. The researchers note that it would take more than a couple of hours to determine how treatments produce less immediate changes, such as immune responses. As such, they’re now conducting a follow-up trial to test a possible two-stage procedure, in which their device is inserted first using minimally invasive surgery 72 hours prior to a planned surgery, allowing longer exposure of tumor tissue to drugs prior to a tumor’s surgical removal.
Many questions remain as they continue to optimize this approach. However, it’s clear that such a device gives new meaning to personalized cancer treatment, with great potential to improve outcomes for people living with hard-to-treat gliomas.
References:
[1] National Cancer Institute Surveillance, Epidemiology, and End Results Program. Cancer Stat Facts: Brain and Other Nervous System Cancer.
[2] Peruzzi P et al. Intratumoral drug-releasing microdevices allow in situ high-throughput pharmaco phenotyping in patients with gliomas. Science Translational Medicine DOI: 10.1126/scitranslmed.adi0069 (2023).
Links:
Brain Tumors – Patient Version (National Cancer Institute/NIH)
Pierpaolo Peruzzi (Brigham and Women’s Hospital, Boston, MA)
Jonas Lab (Brigham and Women’s Hospital, Boston, MA)
NIH Support: National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Institute of Neurological Disorders and Stroke
Basic Researchers Discover Possible Target for Treating Brain Cancer
Posted on by Lawrence Tabak, D.D.S., Ph.D.
Over the years, cancer researchers have uncovered many of the tricks that tumors use to fuel their growth and evade detection by the body’s immune system. More tricks await discovery, and finding them will be key in learning to target the right treatments to the right cancers.
Recently, a team of researchers demonstrated in lab studies a surprising trick pulled off by cells from a common form of brain cancer called glioblastoma. The researchers found that glioblastoma cells steal mitochondria, the power plants of our cells, from other cells in the central nervous system [1].
Why would cancer cells do this? How do they pull it off? The researchers don’t have all the answers yet. But glioblastoma arises from abnormal astrocytes, a particular type of the glial cell, a common cell in the brain and spinal cord. It seems from their initial work that stealing mitochondria from neighboring normal cells help these transformed glioblastoma cells to ramp up their growth. This trick might also help to explain why glioblastoma is one of the most aggressive forms of primary brain cancer, with limited treatment options.
In the new study, published in the journal Nature Cancer, a team co-led by Justin Lathia, Lerner Research Institute, Cleveland Clinic, OH, and Hrvoje Miletic, University of Bergen, Norway, had noticed some earlier studies suggesting that glioblastoma cells might steal mitochondria. They wanted to take a closer look.
This very notion highlights an emerging and much more dynamic view of mitochondria. Scientists used to think that mitochondria—which can number in the thousands within a single cell—generally just stayed put. But recent research has established that mitochondria can move around within a cell. They sometimes also get passed from one cell to another.
It also turns out that the intercellular movement of mitochondria has many implications for health. For instance, the transfer of mitochondria helps to rescue damaged tissues in the central nervous system, heart, and respiratory system. But, in other circumstances, this process may possibly come to the rescue of cancer cells.
While Lathia, Miletic, and team knew that mitochondrial transfer was possible, they didn’t know how relevant or dangerous it might be in brain cancers. To find out, they studied mice implanted with glioblastoma tumors from other mice or people with glioblastoma. This mouse model also had been modified to allow the researchers to trace the movement of mitochondria.
Their studies show that healthy cells often transfer some of their mitochondria to glioblastoma cells. They also determined that those mitochondria often came from healthy astrocytes, a process that had been seen before in the recovery from a stroke.
But the transfer process isn’t easy. It requires that a cell expend a lot of energy to form actin filaments that contract to pull the mitochondria along. They also found that the process depends on growth-associated protein 43 (GAP43), suggesting that future treatments aimed at this protein might help to thwart the process.
Their studies also show that, after acquiring extra mitochondria, glioblastoma cells shift into higher gear. The cancerous cells begin burning more energy as their metabolic pathways show increased activity. These changes allow for more rapid and aggressive growth. Overall, the findings show that this interaction between healthy and cancerous cells may partly explain why glioblastomas are so often hard to beat.
While more study is needed to confirm the role of this process in people with glioblastoma, the findings are an important reminder that treatment advances in oncology may come not only from study of the cancer itself but also by carefully considering the larger context and environments in which tumors grow. The hope is that these intriguing new findings will one day lead to new treatment options for the approximately 13,000 people in the U.S. alone who are diagnosed with glioblastoma each year [2].
References:
[1] GAP43-dependent mitochondria transfer from astrocytes enhances glioblastoma tumorigenicity. Watson DC, Bayik D, Storevik S, Moreino SS, Hjelmeland AB, Hossain JA, Miletic H, Lathia JD et al. Nat Cancer. 2023 May 11. [Published online ahead of print.]
[2] CBTRUS statistical report: Primary brain and other central nervous system tumors diagnosed in the United States in 2011-2015. Ostrom QT, Gittleman H, Truitt G, Boscia A, Kruchko C, Barnholtz-Sloan JS. 2018 Oct 1, Neuro Oncol., p. 20(suppl_4):iv1-iv86.
Links:
Glioblastoma (National Center for Advancing Translational Sciences/NIH)
Brain Tumors (National Cancer Institute/NIH)
Justin Lathia Lab (Cleveland Clinic, OH)
Hrvoje Miletic (University of Bergen, Norway)
NIH Support: National Institute of Neurological Disorders and Stroke; National Center for Advancing Translational Sciences; National Cancer Institute; National Institute of Allergy and Infectious Diseases
A Global Look at Cancer Genomes
Posted on by Dr. Francis Collins
Cancer is a disease of the genome. It can be driven by many different types of DNA misspellings and rearrangements, which can cause cells to grow uncontrollably. While the first oncogenes with the potential to cause cancer were discovered more than 35 years ago, it’s been a long slog to catalog the universe of these potential DNA contributors to malignancy, let alone explore how they might inform diagnosis and treatment. So, I’m thrilled that an international team has completed the most comprehensive study to date of the entire genomes—the complete sets of DNA—of 38 different types of cancer.
Among the team’s most important discoveries is that the vast majority of tumors—about 95 percent—contained at least one identifiable spelling change in their genomes that appeared to drive the cancer [1]. That’s significantly higher than the level of “driver mutations” found in past studies that analyzed only a tumor’s exome, the small fraction of the genome that codes for proteins. Because many cancer drugs are designed to target specific proteins affected by driver mutations, the new findings indicate it may be worthwhile, perhaps even life-saving in many cases, to sequence the entire tumor genomes of a great many more people with cancer.
The latest findings, detailed in an impressive collection of 23 papers published in Nature and its affiliated journals, come from the international Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. Also known as the Pan-Cancer Project for short, it builds on earlier efforts to characterize the genomes of many cancer types, including NIH’s The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC).
In these latest studies, a team including more than 1,300 researchers from around the world analyzed the complete genomes of more than 2,600 cancer samples. Those samples included tumors of the brain, skin, esophagus, liver, and more, along with matched healthy cells taken from the same individuals.
In each of the resulting new studies, teams of researchers dug deep into various aspects of the cancer DNA findings to make a series of important inferences and discoveries. Here are a few intriguing highlights:
• The average cancer genome was found to contain not just one driver mutation, but four or five.
• About 13 percent of those driver mutations were found in so-called non-coding DNA, portions of the genome that don’t code for proteins [2].
• The mutations arose within about 100 different molecular processes, as indicated by their unique patterns or “mutational signatures.” [3,4].
• Some of those signatures are associated with known cancer causes, including aberrant DNA repair and exposure to known carcinogens, such as tobacco smoke or UV light. Interestingly, many others are as-yet unexplained, suggesting there’s more to learn with potentially important implications for cancer prevention and drug development.
• A comprehensive analysis of 47 million genetic changes pieced together the chronology of cancer-causing mutations. This work revealed that many driver mutations occur years, if not decades, prior to a cancer’s diagnosis, a discovery with potentially important implications for early cancer detection [5].
The findings represent a big step toward cataloging all the major cancer-causing mutations with important implications for the future of precision cancer care. And yet, the fact that the drivers in 5 percent of cancers continue to remain mysterious (though they do have RNA abnormalities) comes as a reminder that there’s still a lot more work to do. The challenging next steps include connecting the cancer genome data to treatments and building meaningful predictors of patient outcomes.
To help in these endeavors, the Pan-Cancer Project has made all of its data and analytic tools available to the research community. As researchers at NIH and around the world continue to detail the diverse genetic drivers of cancer and the molecular processes that contribute to them, there is hope that these findings and others will ultimately vanquish, or at least rein in, this Emperor of All Maladies.
References:
[1] Pan-Cancer analysis of whole genomes. ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Nature. 2020 Feb;578(7793):82-93.
[2] Analyses of non-coding somatic drivers in 2,658 cancer whole genomes. Rheinbay E et al; PCAWG Consortium. Nature. 2020 Feb;578(7793):102-111.
[3] The repertoire of mutational signatures in human cancer. Alexandrov LB et al; PCAWG Consortium. Nature. 2020 Feb;578(7793):94-101.
[4] Patterns of somatic structural variation in human cancer genomes. Li Y et al; PCAWG Consortium. Nature. 2020 Feb;578(7793):112-121.
[5] The evolutionary history of 2,658 cancers. Gerstung M, Jolly C, Leshchiner I, Dentro SC et al; PCAWG Consortium. Nature. 2020 Feb;578(7793):122-128.
Links:
The Genetics of Cancer (National Cancer Institute/NIH)
Precision Medicine in Cancer Treatment (NCI)
The Cancer Genome Atlas Program (NIH)
NCI and the Precision Medicine Initiative (NCI)
NIH Support: National Cancer Institute, National Human Genome Research Institute
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
Creative Minds: A New Way to Look at Cancer
Posted on by Dr. Francis Collins
Bradley Bernstein
Inside our cells, strands of DNA wrap around spool-like histone proteins to form a DNA-histone complex called chromatin. Bradley Bernstein, a pathologist at Massachusetts General Hospital, Harvard University, and Broad Institute, has always been fascinated by this process. What interests him is the fact that an approximately 6-foot-long strand of DNA can be folded and packed into orderly chromatin structures inside a cell nucleus that’s just 0.0002 inch wide.
Bernstein’s fascination with DNA packaging led to the recent major discovery that, when chromatin misfolds in brain cells, it can activate a gene associated with the cancer glioma [1]. This suggested a new cancer-causing mechanism that does not require specific DNA mutations. Now, with a 2016 NIH Director’s Pioneer Award, Bernstein is taking a closer look at how misfolded and unstable chromatin can drive tumor formation, and what that means for treating cancer.
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