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NCI Support for Basic Science Paves Way for Kidney Cancer Drug Belzutifan

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Belzutifan, Shrinking kidney cancer. woman with superimposed kidney tumor. Arrows suggest shrinking

There’s exciting news for people with von Hippel-Lindau (VHL) disease, a rare genetic disorder that can lead to cancerous and non-cancerous tumors in multiple organs, including the brain, spinal cord, kidney, and pancreas. In August 2021, the U.S. Food and Drug Administration (FDA) approved belzutifan (Welireg), a new drug that has been shown in a clinical trial led by National Cancer Institute (NCI) researchers to shrink some tumors associated with VHL disease [1], which is caused by inherited mutations in the VHL tumor suppressor gene.

As exciting as this news is, relatively few people have this rare disease. The greater public health implication of this advancement is for people with sporadic, or non-inherited, clear cell kidney cancer, which is by far the most common subtype of kidney cancer, with more than 70,000 cases and about 14,000 deaths per year. Most cases of sporadic clear cell kidney cancer are caused by spontaneous mutations in the VHL gene.

This advancement is also a great story of how decades of support for basic science through NCI’s scientists in the NIH Intramural Research Program and its grantees through extramural research funding has led to direct patient benefit. And it’s a reminder that we never know where basic science discoveries might lead.

Belzutifan works by disrupting the process by which the loss of VHL in a tumor turns on a series of molecular processes. These processes involve the hypoxia-inducible factor (HIF) transcription factor and one of its subunits, HIF-2α, that lead to tumor formation.

The unraveling of the complex relationship among VHL, the HIF pathway, and cancer progression began in 1984, when Bert Zbar, Laboratory of Immunobiology, NCI-Frederick; and Marston Linehan, NCI’s Urologic Oncology Branch, set out to find the gene responsible for clear cell kidney cancer. At the time, there were no effective treatments for advanced kidney cancer, and 80 percent of patients died within two years.

Zbar and Linehan started by studying patients with sporadic clear cell kidney cancer, but then turned their focus to investigations of people affected with VHL disease, which predisposes a person to developing clear cell kidney cancer. By studying the patients and the genetic patterns of tumors collected from these patients, the researchers hypothesized that they could find genes responsible for kidney cancer.

Linehan established a clinical program at NIH to study and manage VHL patients, which facilitated the genetic studies. It took nearly a decade, but, in 1993, Linehan, Zbar, and Michael Lerman, NCI-Frederick, identified the VHL gene, which is mutated in people with VHL disease. They soon discovered that tumors from patients with sporadic clear cell kidney cancer also have mutations in this gene.

Subsequently, with NCI support, William G. Kaelin Jr., Dana-Farber Cancer Institute, Boston, discovered that VHL is a tumor suppressor gene that, when inactivated, leads to the accumulation of HIF.

Another NCI grantee, Gregg L. Semenza, Johns Hopkins School of Medicine, Baltimore, identified HIF as a transcription factor. And Peter Ratcliffe, University of Oxford, United Kingdom, discovered that HIF plays a role in blood vessel development and tumor growth.

Kaelin and Ratcliffe simultaneously showed that the VHL protein tags a subunit of HIF for destruction when oxygen levels are high. These results collectively answered a very old question in cell biology: How do cells sense the intracellular level of oxygen?

Subsequent studies by Kaelin, with NCI’s Richard Klausner and Linehan, revealed the critical role of HIF in promoting the growth of clear cell kidney cancer. This work ultimately focused on one member of the HIF family, the HIF-2α subunit, as the key mediator of clear cell kidney cancer growth.

The fundamental work of Kaelin, Semenza, and Ratcliffe earned them the 2019 Nobel Prize in Physiology or Medicine. It also paved the way for drug discovery efforts that target numerous points in the pathway leading to clear cell kidney cancer, including directly targeting the transcriptional activity of HIF-2α with belzutifan.

Clinical trials of belzutifan, including several supported by NCI, demonstrated potent anti-cancer activity in VHL-associated kidney cancer, as well as other VHL-associated tumors, leading to the aforementioned recent FDA approval. This is an important development for patients with VHL disease, providing a first-in-class therapy that is effective and well-tolerated.

We believe this is only the beginning for belzutifan’s use in patients with cancer. A number of trials are now studying the effectiveness of belzutifan for sporadic clear cell kidney cancer. A phase 3 trial is ongoing, for example, to look at the effectiveness of belzutifan in treating people with advanced kidney cancer. And promising results from a phase 2 study show that belzutifan, in combination with cabozantinib, a widely used agent to treat kidney cancer, shrinks tumors in patients previously treated for metastatic clear cell kidney cancer [2].

This is a great scientific story. It shows how studies of familial cancer and basic cell biology lead to effective new therapies that can directly benefit patients. I’m proud that NCI’s support for basic science, both intramurally and extramurally, is making possible many of the discoveries leading to more effective treatments for people with cancer.

References:

[1] Belzutifan for Renal Cell Carcinoma in von Hippel-Lindau Disease. Jonasch E, Donskov F, Iliopoulos O, Rathmell WK, Narayan VK, Maughan BL, Oudard S, Else T, Maranchie JK, Welsh SJ, Thamake S, Park EK, Perini RF, Linehan WM, Srinivasan R; MK-6482-004 Investigators. N Engl J Med. 2021 Nov 25;385(22):2036-2046.

[2] Phase 2 study of the oral hypoxia-inducible factor 2α (HIF-2α) inhibitor MK-6482 in combination with cabozantinib in patients with advanced clear cell renal cell carcinoma (ccRCC). Choueiri TK et al. J Clin Oncol. 2021 Feb 20;39(6_suppl): 272-272.

Links:
Von Hippel-Lindau Disease (Genetic and Rare Diseases Information Center/National Center for Advancing Translational Sciences/NIH)

Clear Cell Renal Cell Carcinoma (National Cancer Institute/NIH)

Belzutifan Approved to Treat Tumors Linked to Inherited Disorder VHL, Cancer Currents Blog, National Cancer Institute, September 21, 2021.

The Long Road to Understanding Kidney Cancer (Intramural Research Program/NIH)

[Note: Acting NIH Director Lawrence Tabak has asked the heads of NIH’s institutes and centers to contribute occasional guest posts to the blog as a way to highlight some of the cool science that they support and conduct. This is the first in the series of NIH institute and center guest posts that will run until a new permanent NIH director is in place.]


How One Change to The Coronavirus Spike Influences Infectivity

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electron micrograph of COVID-19 viruses
Caption: Spike proteins (blue) crown SARS-CoV-2, the virus that causes COVID-19. Once the virus enters humans, the spike protein is decorated with sugars that attach to some of its amino acids, forming O-glycans. Loss of key O-glycans may facilitate viral spread to human cells. Credit: National Institute of Allergy and Infectious Diseases, NIH

Since joining NIH, I’ve held a number of different leadership positions. But there is one position that thankfully has remained constant for me: lab chief. I run my own research laboratory at NIH’s National Institute of Dental and Craniofacial Research (NIDCR).

My lab studies a biochemical process called O-glycosylation. It’s fundamental to life and fascinating to study. Our cells are often adorned with a variety of carbohydrate sugars. O-glycosylation refers to the biochemical process through which these sugar molecules, either found at the cell surface or secreted, get added to proteins. The presence or absence of these sugars on certain proteins plays fundamental roles in normal tissue development and first-line human immunity. It also is associated with various diseases, including cancer.

Our lab recently joined a team of NIH scientists led by my NIDCR colleague Kelly Ten Hagen to demonstrate how O-glycosylation can influence SARS-CoV-2, the coronavirus that causes COVID-19, and its ability to fuse to cells, which is a key step in infecting them. In fact, our data, published in the journal Proceedings of the National Academy of Sciences, indicate that some variants, seem to have mutated to exploit the process to their advantage [1].

The work builds on the virus’s reliance on the spike proteins that crown its outer surface to attach to human cells. Once there, the spike protein must be activated to fuse and launch an infection. That happens when enzymes produced by our own cells make a series of cuts, or cleavages, to the spike protein.

The first cut comes from an enzyme called furin. We and others had earlier evidence that O-glycosylation can affect the way furin makes those cuts. That got us thinking: Could O-glycosylation influence the interaction between furin and the spike protein? The furin cleavage area of the viral spike was indeed adorned with sugars, and their presence or absence might influence spike activation by furin.

We also noticed the Alpha and Delta variants carry a mutation that removes the amino acid proline in a specific spot. That was intriguing because we knew from earlier work that enzymes called GALNTs, which are responsible for adding bulky sugar molecules to proteins, prefer prolines near O-glycosylation sites.

It also suggested that loss of proline in the new variants could mean decreased O-glycosylation, which might then influence the degree of furin cleavage and SARS-CoV-2’s ability to enter cells. I should note that the recent Omicron variant was not examined in the current study.

After detailed studies in fruit fly and mammalian cells, we demonstrated in the original SARS-CoV-2 virus that O-glycosylation of the spike protein decreases furin cleavage. Further experiments then showed that the GALNT1 enzyme adds sugars to the spike protein and this addition limits the ability of furin to make the needed cuts and activate the spike protein.

Importantly, the spike protein change found in the Alpha and Delta variants lowers GALNT1 activity, making it easier for furin to start its activating cuts. It suggests that glycosylation of the viral spike by GALNT1 may limit infection with the original virus, and that the Alpha and Delta variant mutation at least partially overcomes this effect, to potentially make the virus more infectious.

Building on these studies, our teams looked for evidence of GALNT1 in the respiratory tracts of healthy human volunteers. We found that the enzyme is indeed abundantly expressed in those cells. Interestingly, those same cells also express the ACE2 receptor, which SARS-CoV-2 depends on to infect human cells.

It’s also worth noting here that the Omicron variant carries the very same spike mutation that we studied in Alpha and Delta. Omicron also has another nearby change that might further alter O-glycosylation and cleavage of the spike protein by furin. The Ten Hagen lab is looking into these leads to learn how this region in Omicron affects spike glycosylation and, ultimately, the ability of this devastating virus to infect human cells and spread.

Reference:

[1] Furin cleavage of the SARS-CoV-2 spike is modulated by O-glycosylation. Zhang L, Mann M, Syed Z, Reynolds HM, Tian E, Samara NL, Zeldin DC, Tabak LA, Ten Hagen KG. PNAS. 2021 Nov 23;118(47).

Links:

COVID-19 Research (NIH)

Kelly Ten Hagen (National Institute of Dental and Craniofacial Research/NIH)

Lawrence Tabak (NIDCR)

NIH Support: National Institute of Dental and Craniofacial Research


Teaching the Immune System to Attack Cancer with Greater Precision

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Needle in a vial. Cancer cell in the background
Credit: PhotobyTawat/Shutterstock/Tom Deerink, National Institute of General Medical Sciences, NIH

To protect humans from COVID-19, the Pfizer and Moderna mRNA vaccines program human cells to translate the injected synthetic messenger RNA into the coronavirus spike protein, which then primes the immune system to arm itself against future appearances of that protein. It turns out that the immune system can also be trained to spot and attack distinctive proteins on cancer cells, killing them and leaving healthy cells potentially untouched.

While these precision cancer vaccines remain experimental, researchers continue to make basic discoveries that move the field forward. That includes a recent NIH-funded study in mice that helps to refine the selection of protein targets on tumors as a way to boost the immune response [1]. To enable this boost, the researchers first had to discover a possible solution to a longstanding challenge in developing precision cancer vaccines: T cell exhaustion.

The term refers to the immune system’s complement of T cells and their capacity to learn to recognize foreign proteins, also known as neoantigens, and attack them on cancer cells to shrink tumors. But these responding T cells can exhaust themselves attacking tumors, limiting the immune response and making its benefits short-lived.

In this latest study, published in the journal Cell, Tyler Jacks and Megan Burger, Massachusetts Institute of Technology, Cambridge, help to explain this phenomenon of T cell exhaustion. The researchers found in mice with lung tumors that the immune system initially responds as it should. It produces lots of T cells that target many different cancer-specific proteins.

Yet there’s a problem: various subsets of T cells get in each other’s way. They compete until, eventually, one of those subsets becomes the dominant T cell type. Even when those dominant T cells grow exhausted, they still remain in the tumor and keep out other T cells, which might otherwise attack different neoantigens in the cancer.

Building on this basic discovery, the researchers came up with a strategy for developing cancer vaccines that can “awaken” T cells and reinvigorate the body’s natural cancer-fighting abilities. The strategy might seem counterintuitive. The researchers vaccinated mice with neoantigens that provide a weak but encouraging signal to the immune cells responsible for presenting the distinctive cancer protein target, or antigen, to T cells. It’s those T cells that tend to get suppressed in competition with other T cells.

When the researchers vaccinated the mice with one of those neoantigens, the otherwise suppressed T cells grew in numbers and better targeted the tumor. What’s more, the tumors shrank by more than 25 percent on average.

Research on this new strategy remains in its early stages. The researchers hope to learn if this approach to cancer vaccines might work even better when used in combination with immunotherapy drugs, which unleash the immune system against cancer in other ways.

It’s also possible that the recent and revolutionary success of mRNA vaccines for preventing COVID-19 actually could help. An important advantage of mRNA is that it’s easy for researchers to synthesize once they know the specific nucleic acid sequence of a protein target, and they can even combine different mRNA sequences to make a multiplex vaccine that primes the immune system to recognize multiple neoantigens. Now that we’ve seen how well mRNA vaccines work to prompt a desired immune response against COVID-19, this same technology can be used to speed the development and testing of future vaccines, including those designed precisely to fight cancer.

Reference:

[1] Antigen dominance hierarchies shape TCF1+ progenitor CD8 T cell phenotypes in tumors. Burger ML, Cruz AM, Crossland GE, Gaglia G, Ritch CC, Blatt SE, Bhutkar A, Canner D, Kienka T, Tavana SZ, Barandiaran AL, Garmilla A, Schenkel JM, Hillman M, de Los Rios Kobara I, Li A, Jaeger AM, Hwang WL, Westcott PMK, Manos MP, Holovatska MM, Hodi FS, Regev A, Santagata S, Jacks T. Cell. 2021 Sep 16;184(19):4996-5014.e26.

Links:

Cancer Treatment Vaccines (National Cancer Institute/NIH)

The Jacks Lab (Massachusetts Institute of Technology, Cambridge)

NIH Support: National Cancer Institute; National Heart, Lung, and Blood Institute


New Microscope Technique Provides Real-Time 3D Views

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Most of the “cool” videos shared on my blog are borne of countless hours behind a microscope. Researchers must move a biological sample through a microscope’s focus, slowly acquiring hundreds of high-res 2D snapshots, one painstaking snap at a time. Afterwards, sophisticated computer software takes this ordered “stack” of images, calculates how the object would look from different perspectives, and later displays them as 3D views of life that can be streamed as short videos.

But this video is different. It was created by what’s called a multi-angle projection imaging system. This new optical device requires just a few camera snapshots and two mirrors to image a biological sample from multiple angles at once. Because the device eliminates the time-consuming process of acquiring individual image slices, it’s up to 100 times faster than current technologies and doesn’t require computer software to construct the movie. The kicker is that the video can be displayed in real time, which isn’t possible with existing image-stacking methods.

The video here shows two human melanoma cells, rotating several times between overhead and side views. You can see large amounts of the protein PI3K (brighter orange hues indicate higher concentrations), which helps some cancer cells divide and move around. Near the cell’s perimeter are small, dynamic surface protrusions. PI3K in these “blebs” is thought to help tumor cells navigate and survive in foreign tissues as the tumor spreads to other organs, a process known as metastasis.

The new multi-angle projection imaging system optical device was described in a paper published recently in the journal Nature Methods [1]. It was created by Reto Fiolka and Kevin Dean at the University of Texas Southwestern Medical Center, Dallas.

Like most technology, this device is complicated. Rather than the microscope and camera doing all the work, as is customary, two mirrors within the microscope play a starring role. During a camera exposure, these mirrors rotate ever so slightly and warp the acquired image in such a way that successive, unique perspectives of the sample magically come into view. By changing the amount of warp, the sample appears to rotate in real-time. As such, each view shown in the video requires only one camera snapshot, instead of acquiring hundreds of slices in a conventional scheme.

The concept traces to computer science and an algorithm called the shear warp transform method. It’s used to observe 3D objects from different perspectives on a 2D computer monitor. Fiolka, Dean, and team found they could implement a similar algorithm optically for use with a microscope. What’s more, their multi-angle projection imaging system is easy-to-use, inexpensive, and can be converted for use on any camera-based microscope.

The researchers have used the device to view samples spanning a range of sizes: from mitochondria and other tiny organelles inside cells to the beating heart of a young zebrafish. And, as the video shows, it has been applied to study cancer and other human diseases.

In a neat, but also scientifically valuable twist, the new optical method can generate a virtual reality view of a sample. Any microscope user wearing the appropriately colored 3D glasses immediately sees the objects.

While virtual reality viewing of cellular life might sound like a gimmick, Fiolka and Dean believe that it will help researchers use their current microscopes to see any sample in 3D—offering the chance to find rare and potentially important biological events much faster than is possible with even the most advanced microscopes today.

Fiolka, Dean, and team are still just getting started. Because the method analyzes tissue very quickly within a single image frame, they say it will enable scientists to observe the fastest events in biology, such as the movement of calcium throughout a neuron—or even a whole bundle of neurons at once. For neuroscientists trying to understand the brain, that’s a movie they will really want to see.

Reference:

[1] Real-time multi-angle projection imaging of biological dynamics. Chang BJ, Manton JD, Sapoznik E, Pohlkamp T, Terrones TS, Welf ES, Murali VS, Roudot P, Hake K, Whitehead L, York AG, Dean KM, Fiolka R. Nat Methods. 2021 Jul;18(7):829-834.

Links:

Metastatic Cancer: When Cancer Spreads (National Cancer Institute)

Fiolka Lab (University of Texas Southwestern Medical Center, Dallas)

Dean Lab (University of Texas Southwestern)

Microscopy Innovation Lab (University of Texas Southwestern)

NIH Support: National Cancer Institute; National Institute of General Medical Sciences


Artificial Intelligence Accurately Predicts RNA Structures, Too

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A mechanical claw grabs molecular models
Credit: Camille L.L. Townshend

Researchers recently showed that a computer could “learn” from many examples of protein folding to predict the 3D structure of proteins with great speed and precision. Now a recent study in the journal Science shows that a computer also can predict the 3D shapes of RNA molecules [1]. This includes the mRNA that codes for proteins and the non-coding RNA that performs a range of cellular functions.

This work marks an important basic science advance. RNA therapeutics—from COVID-19 vaccines to cancer drugs—have already benefited millions of people and will help many more in the future. Now, the ability to predict RNA shapes quickly and accurately on a computer will help to accelerate understanding these critical molecules and expand their healthcare uses.

Like proteins, the shapes of single-stranded RNA molecules are important for their ability to function properly inside cells. Yet far less is known about these RNA structures and the rules that determine their precise shapes. The RNA elements (bases) can form internal hydrogen-bonded pairs, but the number of possible combinations of pairings is almost astronomical for any RNA molecule with more than a few dozen bases.

In hopes of moving the field forward, a team led by Stephan Eismann and Raphael Townshend in the lab of Ron Dror, Stanford University, Palo Alto, CA, looked to a machine learning approach known as deep learning. It is inspired by how our own brain’s neural networks process information, learning to focus on some details but not others.

In deep learning, computers look for patterns in data. As they begin to “see” complex relationships, some connections in the network are strengthened while others are weakened.

One of the things that makes deep learning so powerful is it doesn’t rely on any preconceived notions. It also can pick up on important features and patterns that humans can’t possibly detect. But, as successful as this approach has been in solving many different kinds of problems, it has primarily been applied to areas of biology, such as protein folding, in which lots of data were available for researchers to train the computers.

That’s not the case with RNA molecules. To work around this problem, Dror’s team designed a neural network they call ARES. (No, it’s not the Greek god of war. It’s short for Atomic Rotationally Equivariant Scorer.)

To start, the researchers trained ARES on just 18 small RNA molecules for which structures had been experimentally determined. They gave ARES these structural models specified only by their atomic structure and chemical elements.

The next test was to see if ARES could determine from this small training set the best structural model for RNA sequences it had never seen before. The researchers put it to the test with RNA molecules whose structures had been determined more recently.

ARES, however, doesn’t come up with the structures itself. Instead, the researchers give ARES a sequence and at least 1,500 possible 3D structures it might take, all generated using another computer program. Based on patterns in the training set, ARES scores each of the possible structures to find the one it predicts is closest to the actual structure. Remarkably, it does this without being provided any prior information about features important for determining RNA shapes, such as nucleotides, steric constraints, and hydrogen bonds.

It turns out that ARES consistently outperforms humans and all other previous methods to produce the best results. In fact, it outperformed at least nine other methods to come out on top in a community-wide RNA-puzzles contest. It also can make predictions about RNA molecules that are significantly larger and more complex than those upon which it was trained.

The success of ARES and this deep learning approach will help to elucidate RNA molecules with potentially important implications for health and disease. It’s another compelling example of how deep learning promises to solve many other problems in structural biology, chemistry, and the material sciences when—at the outset—very little is known.

Reference:

[1] Geometric deep learning of RNA structure. Townshend RJL, Eismann S, Watkins AM, Rangan R, Karelina M, Das R, Dror RO. Science. 2021 Aug 27;373(6558):1047-1051.

Links:

Structural Biology (National Institute of General Medical Sciences/NIH)

The Structures of Life (National Institute of General Medical Sciences/NIH)

RNA Biology (NIH)

RNA Puzzles

Dror Lab (Stanford University, Palo Alto, CA)

NIH Support: National Cancer Institute; National Institute of General Medical Sciences


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