Tiny circles of DNA harbor cancer-associated oncogenes and immunomodulatory genes promoting cancer development. They arise during transformation from pre-cancer to cancer, say Stanford Medicine-led team.

Tiny circles of DNA that defy the accepted laws of genetics are key drivers of cancer formation, according to an international study led by researchers at Stanford Medicine.

The circles called extrachromosomal DNA or ecDNA, often harbor cancer-associated genes called oncogenes. Because they can exist in large numbers in a cell, they deliver a super-charged growth signal that can override a cell’s natural programming. The researchers found they also contain genes likely to dampen the immune system’s response to nascent cancer.

Previous research had suggested that the circles, which are widespread in human cancers but rarely found in healthy cells, primarily arise in advanced tumors as the abnormal cells increasingly botch the intricate steps required to copy their DNA before each cell division.

But the new study shows that the roly-poly circles can be found even in precancerous cells — and their presence jump-starts a cancerous transformation. The researchers believe that blocking their formation, or their effect on the cells that carry them, might stop cancers from developing.

“This study has profound implications for our understanding of ecDNA in tumor development,” said professor of pathology Paul Mischel, MD. “It shows the power and diversity of ecDNA as a fundamental process in cancer. It has implications for early diagnosis of precancers that put patients at risk, and it highlights the potential for earlier intervention as treatments are developed.” 

Mischel is one of six senior authors of the research, which was published in Nature. Howard Chang, MD, PhD, professor of genetics and Virginia and D.K. Ludwig Professor in Cancer Research, is also a senior author.

Other senior authors include senior staff scientist Thomas Paulson, PhD, from Seattle’s Fred Hutchison Cancer Center; assistant professor of pediatrics Sihan Wu, PhD, assistant professor at Children’s Medical Center Research Institute at the University of Texas Southwestern Medical Center; professor of computer science and engineering Vineet Bafna, PhD, from UC San Diego; and professor of cancer prevention and director of the Early Cancer Institute Rebecca Fitzgerald, MD, from the University of Cambridge.  

“People with ecDNA in their precancerous cells are 20 to 30 times more likely than others to develop cancer,” Chang said.

“This is a huge increase, and it means we really need to pay attention to this. Because we also found that some ecDNAs carry genes that affect the immune system, it suggests that they may also promote early immune escape.”

A grand challenge

Deciphering ecDNA’s role in cancer was one of four Cancer Grand Challenges awarded by the National Cancer Institute and Cancer Research UK in 2022. The grand challenges program was launched to bring together researchers from around the world to tackle complex research topics too daunting for any one group.

Mischel was awarded $25 million to lead a team of international researchers to learn more about the circles. But first they had to jettison some key genetic principles that have guided the field for nearly 200 years.

“When we think about how a tumor evolves in a patient and in response to treatment, we think of the branching trees of life proposed by Charles Darwin,” Mischel said.

“This idea is so powerful that researchers often sequence the DNA from multiple parts of a tumor and draw these trees to learn about its evolution. If a mutation is there at the trunk of the tree and in all of its branches, we assume it is a key driver event in the formation of the tumor; if it is in only some branches, we assume it happened later in tumor development and may not be a good target for drug development.”

But these assumptions hinge on the idea that all of a tumor’s DNA is neatly contained on chromosomes, which are evenly divided among daughter cells each time a cancer cell divides — ensuring that each new cell gets one, and only one, copy of each chromosome.

In contrast, the tiny ecDNA circles swirl in a dividing cell like bubbles circling a bathtub drain and are portioned willy-nilly between the new daughter cells. One may get nearly all the circles; the other, almost none. As the generations accumulate, the evolutionary tree favored by Darwin begins to look decidedly odd, with the appearance of ecDNA-bearing cells sprinkled among the branches like haphazardly hung Christmas lights.

“Some researchers have looked at the evolutionary trees and decided that, because you see it here, but not there, it must be that ecDNA formation is a late event and probably isn’t important when considering treatments,” Mischel said. “Our team thought that interpretation was wrong.”

Pinpointing a reason

To get to the bottom of the tiny circles, Mischel, Chang and their collaborators turned to a specific example of cancer development — people with a condition known as Barrett’s esophagus, which occurs when the cells lining the lower part of the esophagus are damaged by acid reflux and become more like cells lining the intestine than healthy esophageal tissue.

About 1% of these people develop esophageal cancer, which is difficult to treat and has a high mortality rate. Because the outcome is so poor, people with Barrett’s esophagus are routinely monitored with endoscopies and biopsies of the abnormal tissue. Because of these frequent biopsies, the researchers had access to tissue samples collected both before and after cancers developed.

The researchers assessed the prevalence of ecDNA, and identified the genes they carried, in biopsies from nearly 300 people with Barrett’s esophagus or esophageal cancer treated at the University of Cambridge or at Seattle’s Fred Hutchison Cancer Center, where individual patients were studied as the cancer developed.

They found that the prevalence of ecDNA increased from 24% to 43% in early- versus late-stage esophageal cancer, indicating the continual formation of the DNA circles during cancer progression. More tellingly, they found that 33% of people with Barrett’s esophagus who developed esophageal cancer had ecDNA in their precancerous cells.

In contrast, only one out of 40 people who didn’t develop cancer had cells with ecDNA, and that individual passed away due to another cause.

“The conclusions were remarkable,” Mischel said. “We see that ecDNA can arise in these precancerous cells, and that if it is there, the patient is going to get cancer. We also saw the continuous formation of ecDNA as the cancer progresses, indicating that it is advantageous to cancer growth. Finally, we saw that the ecDNA can contain immune-modulatory genes in addition to oncogenes.”

“If a gene is carried on ecDNA, it is very likely to be important for cancer,” Chang said. “These circles are not only giving us new targets for cancer diagnosis and drug development; they are also teaching us what is important for tumor growth.”

What to look at next

The researchers are planning to explore more about how ecDNAs arise in cancer cells and how they work together to make proteins that drive cancer cell growth.

They saw that cancers with ecDNA were likely to also have mutations in a protein called p53. Sometimes called “the guardian of the genome,” p53 temporarily halts the cell cycle to allow cells to repair damage or mutations to their DNA before beginning to divide.

“We want to learn more about the landscape of ecDNA in precancers and the risks it confers,” Mischel said.

“We also want to know if we can stop its formation or activity; how to improve our ability to detect their presence; how they affect the immune system; and whether there are opportunities for new, novel therapies. There is much more to learn, and our team is excited to tackle all these issues. But what we do know for certain is that these tiny DNA circles are a very big deal in cancer.” 

Source: Stanford University

Stanford researchers lay out a framework for engineers as they expand and build new medical artificial intelligence models.

A patient lies on the operating table as the surgical team reaches an impasse. They can’t find the intestinal rupture. A surgeon asks aloud: “Check whether we missed a view of any intestinal section in the visual feed of the last 15 minutes.”

An artificial intelligence medical assistant gets to work reviewing the patient’s past scans and highlighting video streams of the procedure in real time. It alerts the team when they’ve skipped a step in the procedure and reads out relevant medical literature when surgeons encounter a rare anatomical phenomenon.

Doctors across all disciplines, with assistance from artificial intelligence, may soon have the ability to quickly consult a patient’s entire medical file against the backdrop of all medical healthcare data and every published piece of medical literature online. This potential versatility in the doctor’s office is only now possible due to the latest generation of AI models.

“We see a paradigm shift coming in the field of medical AI,” said Jure Leskovec, professor of computer science at Stanford Engineering. “Previously, medical AI models could only address very small, narrow pieces of the health care puzzle. Now we are entering a new era, where it’s much more about larger pieces of the puzzle in this high stakes field.”

Stanford researchers and their collaborators describe generalist medical artificial intelligence, or GMAI, as a new class of medical AI models that are knowledgeable, flexible, and reusable across many medical applications and data types. Their perspective on this advance is published in the issue of Nature.

Leskovec and his collaborators chronicle how GMAI will interpret varying combinations of data from imaging, electronic health records, lab results, genomics, and medical text well beyond the abilities of concurrent models like ChatGPT. These GMAI models will provide spoken explanations, offer recommendations, draw sketches, and annotate images.

“A lot of inefficiencies and errors that happen in medicine today occur because of the hyper-specialization of human doctors and the slow and spotty flow of information,” said co-first author Michael Moor, an MD and now postdoctoral scholar at Stanford Engineering. “The potential impact of generalist medical AI models could be profound because they wouldn’t be just an expert in their own narrow area, but would have more abilities across specialties.”

Medicine without borders

Of the more than 500 AI models for clinical medicine approved by the FDA, most only perform one or two narrow tasks, such as scanning a chest X-ray for signs of pneumonia. But recent advances in foundation model research promise to solve more diverse and challenging tasks.

“The exciting and the groundbreaking part is that generalist medical AI models will be able to ingest different types of medical information – for example, imaging studies, lab results, and genomics data – to then perform tasks that we instruct them to do on the fly,” said Leskovec.

“We expect to see a significant change in the way medical AI will operate,” continued Moor. “Next, we will have devices that, rather than doing just a single task, can do maybe a thousand tasks, some of which were not even anticipated during model development.”

The authors, which also include Oishi Banerjee and Pranav Rajpurkar from Harvard University, Harlan Krumholz from Yale, Zahra Shakeri Hossein Abad from University of Toronto, and Eric Topol at the Scripps Research Translational Institute, outline how GMAI could tackle a variety of applications from chatbots with patients, to note-taking, all the way to bedside decision support for doctors.

In the radiology department, the authors propose, models could draft radiology reports that visually point out abnormalities, while taking the patient’s history into account. Radiologists could improve their understanding of cases by chatting with GMAI models: “Can you highlight any new multiple sclerosis lesions that were not present in the previous image?”

In their paper, the scientists describe additional requirements and capabilities that are needed to develop GMAI into a trustworthy technology. They point out that the model needs to consume all of the personal medical data, as well as historical medical knowledge, and refer to it only when interacting with authorized users. It then needs to be able to hold a conversation with a patient, much like a triage nurse, or doctor to collect new evidence and data or suggest various treatment plans.

Concerns for future development

In their research paper, the co-authors address the implications of a model capable of 1,000 medical assignments with the potential to learn even more. “We think the biggest problem for generalist models in medicine is verification. How do we know that the model is correct – and not just making things up?” Leskovec said.

They point to the flaws already being caught in the ChatGPT language model. Likewise, an AI-generated image of the pope wearing a designer puffy coat is funny. “But if there’s a high-stake scenario and the AI system decides about life and death, verification becomes really important,” said Moor.

The authors continue that safeguarding privacy is also a necessity. “This is a huge problem because with models like ChatGPT and GPT-4, the online community has already identified ways to jailbreak the current safeguards in place,” Moor said.

“Deciphering between the data and social biases also poses a grand challenge for GMAI,” Leskovec added. GMAI models need the ability to focus on signals that are causal for a given disease and ignore spurious signals that only tend to correlate with the outcome.

Assuming that model size is only going to get bigger, Moor points to early research that shows larger models tend to exhibit more social biases than smaller models. “It is the responsibility of the owners and developers of such models and vendors, especially if they’re deploying them in hospitals, to really make sure that those biases are identified and addressed early on,” said Moor.

“The current technology is very promising, but there’s still a lot missing,” Leskovec agreed. “The question is: can we identify current missing pieces, like verification of facts, understanding of biases, and explainability/justification of answers so that we give an agenda for the community on how to make progress to fully realize the profound potential of GMAI?”

Source: Stanford University

SpaceX headed by billionaire Elon Musk has finally received an official license to launch the Starship rocket to space for the first time in history. The license will be valid for five years.

The license was granted by the U.S. Federal Aviation Administration last Friday. This license was the final frontier that limited the progress of testing the novel launch technology in practice.

The launch should happen tomorrow, April 17. The precise hour was not disclosed, but it should happen from 5:30 a.m. to 2 p.m. Central time. It will be performed from the SpaceX Starbase facility in Boca Chica, Texas – this schedule has been confirmed by the company itself on Twitter.

Targeting as soon as Monday, April 17 for the first flight test of a fully integrated Starship and Super Heavy rocket from Starbase in Texas → https://t.co/bJFjLCiTbK pic.twitter.com/Ry25Uuvknh

— SpaceX (@SpaceX) April 14, 2023

“After a comprehensive license evaluation process, the FAA determined SpaceX met all safety, environmental, policy, payload, airspace integration and financial responsibility requirements,” stated FAA in its official announcement.

Previously, SpaceX targeted earlier dates for the launch of its Starship rocket, but the licensing process delayed the original plans.  The rocket is expected to demonstrate its orbital flight capabilities.

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German defense industry company Rheinmetall said it was attacked by hackers, but assured that its military business was not affected.

According to the company’s spokesman, the cyber attack was directed against its division dealing with industrial non-military customers, mainly working in the automotive sector.

“Rheinmetall is currently investigating the extent of the damages and is in close contact with the relevant authorities,” commented representatives of the company.

State prosecutors are currently investigating the origin of the attack, but do not provide any specific details, or if this incident was related to the company’s support for Ukraine.

Rheinmetall AG is a German technology group that specializes in security and mobility solutions. The company has two main business segments: Rheinmetall Automotive and Rheinmetall Defence.

Rheinmetall Automotive produces components and systems for the automotive industry, including engine systems, emission reduction technologies, and electric mobility solutions. Rheinmetall Defence offers a range of products and services for the defense sector, including armored vehicles, weapons systems, and simulation and training systems.

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