A retrovirus illustration.
Even with groundbreaking advances in cancer treatment and research over the past two centuries, the problem remains that some cancer does not respond to treatment. A subset of patients experience recurrence or metastasis, even when the original tumor is detected at an early stage.
"Why do some tumors evolve into metastatic disease that is then capable of spreading, while other tumors do not?"
Moreover, doctors are not able to tell in advance which patients will respond to treatment and which will not. This means that many patients endure conventional cancer therapies, like countless rounds of chemo and radiation, that do not ultimately increase their likelihood of survival.
Researchers are beginning to understand why some tumors respond to treatment and others do not. The answer appears to lie in the strange connection between human life at its earliest stages — and retroviruses. A retrovirus is different than a regular virus in that its RNA is reverse-transcribed into DNA, which makes it possible for its genetic material to be integrated into a host's genome, and passed on to subsequent generations.
Researchers have shown that reactivation of retroviral sequences is associated with the survival of developing embryos. Certain retroviral sequences must be expressed around the 8-cell stage for successful embryonic development. Active expression of retroviral sequences is required for proper functioning of human embryonic stem cells. These sequences must then shut down at the later state, or the embryo will fail to develop. And here's where things get really interesting: If specific stem cell-associated retroviral sequences become activated again later in life, they seem to play a role in some cancers becoming lethal.
"Eight to 10 million years ago, at the time when we became primates, the population was infected with a virus."
While some retroviral sequences in our genome contribute to the restriction of viral infection and appear to have contributed to the development of the placenta, they can also, if expressed at the wrong time, drive the development of cancer stem cells. Described as the "beating hearts" of treatment-resistant tumors, cancer stem cells are robust and long-living, and they can maintain the ability to proliferate indefinitely.
This apparent connection has inspired Gennadi V. Glinsky, a research scientist at the Institute of Engineering in Medicine at UC San Diego, to find better ways to diagnose and treat metastatic cancer. Glinsky specializes in the development of new technologies, methods, and system integration approaches for personalized genomics-guided prevention and precision therapy of cancer and other common human disorders. We spoke with him about his work and the exciting possibilities it may open up for cancer patients. This interview has been edited and condensed for clarity.
What key questions have driven your research in this area?
I was thinking for years that the major mysteries are: Why do some tumors evolve into metastatic disease that is then capable of spreading, while other tumors do not? What explains some cancer cells' ability to get into the blood or lymph nodes and be able to survive in this very foreign, hostile environment of circulatory channels, and then be able to escape and take root elsewhere in the body?
"If you detect conventional cancer early, and treat it early, it will be cured. But with cancer involving stem cells, even if you diagnose it early, it will come back."
When we were able to do genomic analysis on enough early stage cancers, we arrived at an alternative concept of cancer that starts in the stem cells. Stem cells exist throughout our bodies, so in the case of cancer starting in stem cells you will have metastatic properties … because that's what stem cells do. They can travel throughout the body, they can make any other type of cell or resemble them.
So there are basically two types of cancer: conventional non-stem cell cancer and stem cell-like cancer. If you detect conventional cancer early, and treat it early, it will be cured. But with cancer involving stem cells, even if you diagnose it early, it will come back.
What causes some cancer to originate in stem cells?
Cancer stem cells possess stemness [or the ability to self-renew, differentiate, and survive chemical and physical insults]. Stemness is driven by the reactivation of retroviral sequences that have been integrated into the human genome.
Tell me about these retroviral sequences.
Eight to 10 million years ago, at the time when we became primates, the population was infected with a virus. Part of the population survived and the virus was integrated into our primate ancestors' genome. These are known as human endogenous retroviruses, or HERVs. The DNA of the host cells became carriers of these retroviral sequences, and whenever the host cells multiply, they carry the sequences in them and pass them on to future generations.
This pattern of infection and integration of retroviral sequences has happened thousands of times during our evolutionary history. As a result, eight percent of the human genome is derived from these different retroviral sequences.
We've found that some HERVs are expressed in some cancers. For example, 10-15 percent of prostate cancer is stem cell-like. But at first it was not understood what this HERV expression meant.
Gennadi V. Glinsky, a research scientist at the Institute of Engineering in Medicine at UC San Diego.
(Courtesy)
How have you endeavored to solve this in your lab?
We were trying to track down metastatic prostate cancer. We found a molecular signature of prostate cancer that made the prostate tumors look like stem cells. And those were the ones likely to fail cancer therapy. Then we applied this signature to other types of cancers and we found that uniformly, tumors that exhibit stemness fail therapy.
Then in 2014, several breakthrough papers came out that linked the activation of the retroviral sequences in human embryonic stem cells and in human embryo development. When I read these papers, it occurred to me that if these retroviral sequences are required for pluripotency in human embryonic stem cells, they must be involved in stem cell-resembling human cancer that's likely to fail therapy.
What was one of the biggest aha moments in your cancer research?
Several major labs around the U.S. took advantage of The Cancer Genome Anatomy Project, which made it possible to have access to about 12,000 individual human tumors across a spectrum of 30 or so cancer types. This is the largest set of tumors that's ever been made available in a comprehensive and state of the art way. So we now know all there is to know about the genetics of these tumors, including the long-term clinical outcome.
"When we cross-referenced these 10,713 human cancer survival genes to see how many are part of the retroviral network in human cells, we found that the answer was 97 percent!"
These labs identified 10,713 human genes that were associated with the likelihood of patients surviving or dying after [cancer] treatment. I call them the human cancer survival genes, and there are two classes of them: one whose high expression in tumors correlates with an increased likelihood of survival and one whose high expression in tumors correlates with a decreased likelihood of survival.
When we cross-referenced these 10,713 human cancer survival genes to see how many are part of the retroviral network in human cells, we found that the answer was 97 percent!
How will all of this new knowledge change how cancer is treated?
To make cancer stem cells vulnerable to treatment, you need to interfere with stemness and the stemness network. And to do this, you would need to identify the retroviral component of the network, and interfere with this component therapeutically.
The real breakthrough will come when we start to treat these early stage stem cell-like cancers with stem cell-targeting therapy that we are trying to develop. And with our ability to detect the retroviral genome activation, we will be able to detect stem cell-like cancer very early on.
How far away are we from being able to apply this information clinically?
We have two molecule [treatment] candidates. We know that they efficiently interfere with the stemness program in the cells. The road to clinical trials is typically a long one, but since we're clear about our targets, it's a shorter road. We would like to say it's two to three years until we can start a human trial.
AI was integral to creating Moderna's mRNA vaccine against COVID.
But the truth is that before ChatGPT nabbed the public’s attention, AI was already here, and it was doing more important things than writing essays for lazy college students. Case in point: It was key to saving the lives of tens of millions of people.
AI-designed mRNA vaccines
As Dave Johnson, chief data and AI officer at Moderna, told MIT Technology Review‘s In Machines We Trust podcast in 2022, AI was integral to creating the company’s highly effective mRNA vaccine against COVID. Moderna and Pfizer/BioNTech’s mRNA vaccines collectively saved between 15 and 20 million lives, according to one estimate from 2022.
Johnson described how AI was hard at work at Moderna, well before COVID arose to infect billions. The pharmaceutical company focuses on finding mRNA therapies to fight off infectious disease, treat cancer, or thwart genetic illness, among other medical applications. Messenger RNA molecules are essentially molecular instructions for cells that tell them how to create specific proteins, which do everything from fighting infection, to catalyzing reactions, to relaying cellular messages.
Johnson and his team put AI and automated robots to work making lots of different mRNAs for scientists to experiment with. Moderna quickly went from making about 30 per month to more than one thousand. They then created AI algorithms to optimize mRNA to maximize protein production in the body — more bang for the biological buck.
For Johnson and his team’s next trick, they used AI to automate science, itself. Once Moderna’s scientists have an mRNA to experiment with, they do pre-clinical tests in the lab. They then pore over reams of data to see which mRNAs could progress to the next stage: animal trials. This process is long, repetitive, and soul-sucking — ill-suited to a creative scientist but great for a mindless AI algorithm. With scientists’ input, models were made to automate this tedious process.
“We don’t think about AI in the context of replacing humans,” says Dave Johnson, chief data and AI officer at Moderna. “We always think about it in terms of this human-machine collaboration, because they’re good at different things. Humans are really good at creativity and flexibility and insight, whereas machines are really good at precision and giving the exact same result every single time and doing it at scale and speed.”
All these AI systems were in put in place over the past decade. Then COVID showed up. So when the genome sequence of the coronavirus was made public in January 2020, Moderna was off to the races pumping out and testing mRNAs that would tell cells how to manufacture the coronavirus’s spike protein so that the body’s immune system would recognize and destroy it. Within 42 days, the company had an mRNA vaccine ready to be tested in humans. It eventually went into hundreds of millions of arms.
Biotech harnesses the power of AI
Moderna is now turning its attention to other ailments that could be solved with mRNA, and the company is continuing to lean on AI. Scientists are still coming to Johnson with automation requests, which he happily obliges.
“We don’t think about AI in the context of replacing humans,” he told the Me, Myself, and AI podcast. “We always think about it in terms of this human-machine collaboration, because they’re good at different things. Humans are really good at creativity and flexibility and insight, whereas machines are really good at precision and giving the exact same result every single time and doing it at scale and speed.”
Moderna, which was founded as a “digital biotech,” is undoubtedly the poster child of AI use in mRNA vaccines. Moderna recently signed a deal with IBM to use the company’s quantum computers as well as its proprietary generative AI, MoLFormer.
Moderna’s success is encouraging other companies to follow its example. In January, BioNTech, which partnered with Pfizer to make the other highly effective mRNA vaccine against COVID, acquired the company InstaDeep for $440 million to implement its machine learning AI across its mRNA medicine platform. And in May, Chinese technology giant Baidu announced an AI tool that designs super-optimized mRNA sequences in minutes. A nearly countless number of mRNA molecules can code for the same protein, but some are more stable and result in the production of more proteins. Baidu’s AI, called “LinearDesign,” finds these mRNAs. The company licensed the tool to French pharmaceutical company Sanofi.
Writing in the journal Accounts of Chemical Research in late 2021, Sebastian M. Castillo-Hair and Georg Seelig, computer engineers who focus on synthetic biology at the University of Washington, forecast that AI machine learning models will further accelerate the biotechnology research process, putting mRNA medicine into overdrive to the benefit of all.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
