brain cancer
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.
NIH Family Members Giving Back: Diane Baker
Posted on by Dr. Francis Collins
Caption: My wife Diane inspired me and my staff to volunteer to make dinner for patients and their families at The Children’s Inn at NIH.
Credit: NIH Record
My blog usually celebrates biomedical advances made possible by NIH-supported research. But every August, I like to try something different and highlight an aspect of the scientific world that might not make headlines. This year, I’d like to take a moment to pay tribute to just a few of the many NIH family members around the country who, without pay or fanfare, freely give of themselves to make a difference in their communities.
I’d like to start by recognizing my wife Diane Baker, a genetic counselor who has always found time during her busy career to volunteer. When I was first being considered as NIH director, we had lots of kitchen table discussions about what it might mean for us as a couple. We decided to approach the position as a partnership. Diane immediately embraced the NIH community and, true to her giving spirit, now contributes to some wonderful charities that lend a welcome hand to patients and their loved ones who come to the NIH Clinical Center here in Bethesda, MD.
Precision Oncology: Epigenetic Patterns Predict Glioblastoma Outcomes
Posted on by Dr. Francis Collins
Caption: Oncologists review a close-up image of a brain tumor (green dot).
Credit: National Cancer Institute
Scientists have spent much time and energy mapping the many DNA misspellings that can transform healthy cells into cancerous ones. But recently it has become increasingly clear that changes to the DNA sequence itself are not the only culprits. Cancer can also be driven by epigenetic changes to DNA—modifications to chemical marks on the genome don’t alter the sequence of the DNA molecule, but act to influence gene activity. A prime example of this can been seen in glioblastoma, a rare and deadly form of brain cancer that strikes about 12,000 Americans each year.
In fact, an NIH-funded research team recently published in Nature Communications the most complete portrait to date of the epigenetic patterns characteristic of the glioblastoma genome [1]. Among their findings were patterns associated with how long patients survived after the cancer was detected. While far more research is needed, the findings highlight the potential of epigenetic information to help doctors devise more precise ways of diagnosing, treating, and perhaps even preventing glioblastoma and many other forms of cancer.
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