Algorithms help doctors create better treatment plans for aspiring parents
More than 8 million people have been born worldwide with the help of in vitro fertilization since 1978. In IVF, an egg is fertilized by sperm in the lab; the resulting clump of cells is transferred into a patient’s uterus.
Although IVF techniques have advanced significantly in recent decades, the average success rate is still fairly low: around 45 percent. The percentage steadily declines as women age; a 40-year-old woman has a likely success rate of about 12 percent, according to Pregnancy & IVF Clinics Worldwide.
Embryonics, a startup in Haifa, Israel, aims to raise the IVF success rate with its suite of AI algorithms. The company’s system uses machine learning to help doctors create personalized treatment plans.
“Technology can help fertility doctors make data-driven decisions and answer complex questions in a smarter way,” says Dr. Yael Gold-Zamir, CEO and cofounder. She launched the company in 2018 with David Silver and IEEE Fellow Alex Bronstein.
Gold-Zamir has a medical degree from the Hebrew University of Jerusalem. Silver is a machine learning engineer who previously worked for Apple and Intel. Bronstein is a computer science professor at the Technion.
“Embryonics is tackling very unique problems—the quality of human analysis and how to analyze big data so that it is clinically relevant,” Bronstein says.
IVF PRIMER
In IVF, several mature eggs are retrieved from the patient’s ovaries. The eggs are then mixed with sperm in a clinic. The developing embryos grow in the lab for several days until an embryologist chooses one or two to be implanted. (The term embryo technically refers to the developmental stage, when the amniotic sac forms inside the uterus, around two weeks after fertilization. But fertility clinics typically refer to the clusters of cells that they evaluate and implant as embryos.)
Doctors typically choose which embryos to implant based on chromosomal testing and appearance, Silver says. Each is graded based on the number and size of its cells and its rate of development.
But there are several problems with that approach, Silver points out.
“One is that the embryologists’ ability to collect data is limited,” he says. “The amount of data about embryos, past patients, and successful live births available to any single doctor is very small, so it’s hard for them to generalize [about] what indicates that a fertilized egg is viable.”
Another problem is that not all clinics have the same grading system, so two facilities might rate the same embryo differently.
“Technology can help doctors in fertility make data-driven decisions and answer complex questions in a smarter way.”
One of the startup’s algorithms uses deep learning to classify images of the embryos and predict which ones will result in a successful pregnancy. It compares the patient’s medical information, such as age and underlying health conditions, along with images of her embryos to the same data from past patients who had successful or unsuccessful implantations.
Silver and Bronstein used thousands of medical images from around the world to train the AI system. But while developing the algorithm, the engineers found that clinics don’t have the same equipment or use the same settings on microscopes and other tools. The variation affected how the platform classified the embryos.
To overcome that problem, Bronstein and Silver developed their own data-augmentation system for the images. It cancels out environmental factors such as lighting and removes irrelevant parts of the images.
“The system only extracts information that is biologically meaningful, such as cellular structures,” Silver says.
The algorithm is currently being tested in clinics in several countries including Lithuania, Malaysia, and Spain. Doctors were hesitant to use the platform at first, Gold-Zamir says, but since testing it with patients, they have given the company positive feedback. The system has increased the success rate by more than 15 percent, Silver says.
The company has submitted its embryo-classification system to the U.S. Food and Drug Administration for approval. It already has been approved in Europe.
Embryonics is developing an algorithm to help doctors prescribe the best hormone-replacement treatment for patients who require it to increase their chances of successful implantations. There are currently no definitive guidelines to help doctors decide which medication is best for patients, Silver says.
“We found that sometimes the same patient goes to several clinics and is prescribed completely different hormone treatment plans,” he says.
To improve decision-making for the treatment plan, the Embryonics team is developing an algorithm that uses machine learning to provide customized recommendations. The algorithm is learning from information about patients as well as a collection of past treatment plans and their outcomes.
“Based on similarities among patients we can do simulations,” Silver says, “and estimate what would have happened if another treatment protocol was chosen.”
“IVF is complicated,” Gold-Zamir says. “It’s not just one decision doctors have to make; it’s a process of sequential decisions. And we need to maximize the potential for the success of all of those decisions.”
Funding
The startup emerged from Gold-Zamir’s belief that technology can help doctors make better decisions and therefore increase IVF success. She says most fertility specialists make decisions about a patient’s treatment options the same way experts did 40 years ago.
“Many complicated decisions are made based on the doctor’s gut feeling, which is based on all the cases they have seen in their career,” she says. The decisions include which embryos are viable, how many should be implanted, and what kind of hormone treatment is most appropriate.
Gold-Zamir was introduced to Bronstein and Silver through a colleague. Although their original goal was simply to publish a research paper, the trio wanted to improve fertility outcomes and decided to commercialize their first algorithm.
Initially, funding for the company came from friends and family, but the team later received a grant from the Israel Innovation Authority, a government agency that helps fund technology startups. Gold-Zamir says the grant enabled them to launch the company.
The founders also participated in the Google for Startups program, which provides companies with funding, mentoring, and networking.
Embryonics now has 17 employees including doctors, bioinformaticians and computer scientists.
Its next goal is to develop algorithms to help doctors choose which embryos to freeze for future IVF cycles as well as noninvasive genetic screening and analysis.
“I love being able to apply the latest and greatest technologies to something that impacts human life in one of the greatest ways possible: starting a family,” Silver says.
Israeli startup OncoHost predicts which specialized therapies will be most effective for late-stage cancer
Cancer care is hit or miss. While some treatments can help specific patients, the same therapy could also have no effect or even cause further damage to others. A new blood test developed by an Israeli company offers doctors an additional layer of information to help decide which cancer treatments can work for each patient.
OncoHost’s PROphet blood test is aimed at patients with late-stage non-small-cell lung cancer. New technologies for cancer treatment can be highly effective, but only for certain patients. Often, doctors have no way of knowing which will work until the patient is months into the treatment.
“A physician considering a treatment for his patients that includes immunotherapy can run the test in order to get better visibility on what the patient’s journey will look like,” says Dr. Ofer Sharon, the CEO of OncoHost. “We want to allow the clinicians to make the decisions earlier and add valuable clinical insights to support the complex decision-making process that every oncologist faces when treating a patient with advanced cancer.”
With just one blood test, PROphet analyzes over 7,000 proteins, and then uses machine-learning tools and artificial intelligence to characterize, analyze, and anticipate which treatment is likely to work based on a patient’s individual patterns. It can also help doctors decide on alternative therapies that could be used to overcome a patient’s resistance.
This information is critical for cancer patients whose doctors are considering immunotherapy — a highly effective alternative to chemotherapy — which strengthens the immune system to fight off the cancer rather than attacking the cancer cells directly. But immunotherapy only works for a minority of patients. Until now, doctors had little way of knowing which patients would respond.
The test predicts outcomes with remarkably high accuracy at three months, six months, and one-year markers. The clinical validation of the PROphet platform is based on trials of more than 700 patients at 35 sites around the world conducted by OncoHost. As the database of patient profiles expands, the tests will get even more accurate.
“They can really profile the patients into groups of patients that will respond to the treatment and those that will not respond. It looks rather promising,” says Aaron Ciechanover, Nobel laureate in chemistry and distinguished research professor in the faculty of medicine at the Technion–Israel Institute of Technology, who is a member of OncoHost’s board of scientific advisers.
“The idea is to predict ahead of time biological markers in the treatment of tumors,” Prof. Ciechanover says. “As we know, people are different from one another. Each of us reacts differently to different drugs. The idea is to find a profile of proteins and other components that predict with high certainty whether the treatment that you are giving a patient will be successful or not.”
“Until now, cancer treatment has been what I call one-size-fits-all. We bombarded the patient with chemotherapy, radiotherapy, with enormous side effects,” Ciechanover says. “Now we are narrowing it. We are going to be much more precise. We are going to provide treatment that has much fewer side effects. And we may discover new pathways that are involved in carcinogenesis, enabling us to develop new drugs.”
Ciechanover and Sharon will be among the speakers at “Investing in Precision Medicine,” an online event hosted by OurCrowd on March 28.
OncoHost is collaborating with leading academic and clinical partners including the Mayo Clinic, University of Miami, Roswell Park, Rutgers, Somalogic, and the National Health Service in the UK.
“Success with immunotherapy is not guaranteed in every patient, so this study is seeking to identify changes in proteins circulating in the blood which may help doctors to choose the best treatment for each patient,” says Dr. David Farrugia, an NHS consultant medical oncologist and chief investigator of all eight NHS clinical trial sites in the UK.
OncoHost plans to expand to providing tests for other common cancers, Sharon says, including urogenital cancers and head and neck cancers, as well as autoimmune diseases such as arthritis, cirrhosis, and lupus.
OncoHost’s technology is part of the groundbreaking field of precision medicine, where treatment is individualized to each patient. Previously, precision medicine was focused on DNA markers and mutations, but this is a static picture, Sharon explains. By focusing on blood proteins, the PROphet test allows doctors a much more dynamic, real-time analysis of how a course of medicine is expected to interact with a patient over time.
“When we run a blood test before treatment, we can predict the disease’s trajectory,” Sharon says. “When we measure proteins, by proxy we’re measuring biological processes. It’s that downstream view that allows us to get a very wide picture on the interaction between the tumor, the patient’s body, and the therapy.”
OncoHost opened a new laboratory facility in North Carolina in January and is planning to launch commercially in the US this year. The company is also in talks to open a regional lab in Abu Dhabi. Its financial trajectory is also looking up, with revenues projected to increase sevenfold over the next two years.
Meet the army of cells that make up the body’s health-protection squad
Your immune system is probably something you ignore, at least until you get ill. Then you realize how important the immune system is. It’s all the various organs, cells and proteins spread throughout the body that protect us from bacteria, viruses and other potentially harmful invaders.
Cells of the immune system can be split into two closely related military squads: innate and adaptive. Troops belonging to the first — innate — patrol the body to detect intruders, such as bacteria and viruses. These troops don’t trust anyone, not even their own body’s cells. But they don’t have to fend off bad guys alone. When faced by a tough adversary, they can call in back-ups — the adaptive troops — that are skilled in even heavy combat.
Naama Geva-Zatorsky works at the Rappaport Technion Integrated Cancer Center in Haifa, Israel. There, she studies microbes and the immune system. The mission of the body’s innate immune system, she explains, is to distinguish between friendly cells (the body’s own cells) and intruders (non-self). Friendlies have specific structures on their surface, like a flag, that the innate troops recognize. They know to ignore these cells. Intruders lack those familiar surface “flags” found on the body’s healthy cells.
When innate troops detect “non-self” structures — such as a virus — they set off alarms. These call out other troops to help eliminate the intruders as quickly as possible. The three most important types of innate troops are immune cells known as neutrophils (NEW-troh-fils), macrophages (MAK-roh-faeges) and dendritic cells.
Neutrophils survey their neighborhood by “tasting” microbes. When they find an intruder, these troops release small signalling molecules called cytokines (SY-toh-kynes). Cytokines quickly recruit help to the developing fight. They tell other immune cells what type of help they need and where to send it. Sometimes neutrophils also change shape. They sprout long arms and form a web-like net to trap invaders.
Macrophages are bigger, curly-shaped cells that respond to the neutrophil alarms. They hang out in the tissues longer than the neutrophils do. While there, they gobble up as many invaders as possible through a process called phagocytosis (Fag-oh-sy-TOH-sis). Macrophages won’t stop eating until nothing is left.
Dendritic cells arrive around the same time as macrophages. Dendritic cells digest pieces of microbes and then show them out on their long arms. In this way they recruit a back-up squad into the battle: the adaptive immune system.
These are false-colored images made by a scanning electron microscope of cells extracted from blood. Red blood cells (red), or RBCs, sit amidst some platelets (light blue) and cells of the immune system. Between the RBCs is a macrophage (purple). To the right are a neutrophil (deep blue) and a lymphocyte (yellow). STEVE GSCHMEISSNER/SCIENCE PHOTO LIBRARY/GETTY IMAGES
The back-up forces
The heavy forces of the adaptive immune system don’t get involved with every little invader. Most of the time, innate immune cells can win the battle by themselves. We don’t even notice it happening. However, when worrisome pathogens invade our bodies, cells of the adaptive immune system take over. They tailor their particular response to each invader but need a few days to make much headway.
Sometimes intruders sneak into the body and take over some healthy cell. That’s where it will multiply (or replicate). But once inside, that invader is also hidden. The innate cells can no longer find it.
Helper T cells, a type of white blood cell —or lymphocyte — now step in. They collect info on the enemy, regardless of whether those attackers are inside of a cell or out. Then they’ll pass this intel along to another team, the killer T cells.
Killer T cells are another type of lymphocyte. They can kill anything that looks suspicious.
B cells release weapons called antibodies that seek out the enemy. Antibodies, which are families of Y-shaped proteins, are sticky. They glom onto everything that resembles the intruder. In many cases this will be the actual invader. Other times it might be pieces left behind when the intruders are killed. They might even be invader look-alikes created by vaccines. These invader mimics will allow B cells to respond quicker if ever a real microbial invader comes along.
This computer illustration shows B cells (orange) releasing antibodies (white Y shapes) designed to attach to a targeted virus (blue). Those antibodies will mark their target for destruction by other immune cells. JUAN GAERTNER/SCIENCE PHOTO LIBRARY/GETTY IMAGES
The antibodies tag their target cells so that other immune-system teams can later go in and take them out. Antibodies will also stalk escaping enemies throughout the body. Those antibodies seek out surface patterns on cells or cell bits that identify specific intruders.
After a battle is over, invader-specific B cells remain behind. They form a pool of veterans. They preserve a memory of the former invasion. Based on that living memory, they’ll be able to help the body react faster and better the next time the same type of invader arrives. This process is called immunological memory and it’s the key to how vaccines work.
“When an invader comes in the body, it’s great to have an alert immune system,” says Naama. “But it’s also important that it doesn’t overdo.” There are several ways to stop such an overblown response.
Regulatory T cells, for instance, tamp down the activity of other T cells, before they get out of control. During a skirmish, T cells can get so keyed up that they risk getting out of control. That’s where T-reg squads come in. They help calm down the T-cell combat troops so that the immune system can return to normal.
The immune system helps keep us safe. We can also exploit it against deadly diseases thanks to vaccines and immune-boosting drugs. “The immune system is cool, but we need to keep it healthy,” says Naama. How? Look after yourself. A healthy body means a healthy immune system.
This video explains how cells in the innate and adaptive immune squads cooperate to fight invading pathogens — such as disease-causing microbes.
Power Words
agent: A person or thing (it can be a chemical or even a form of energy) that plays some role in getting something done.
antibodies: Any of a large number of proteins that the body produces from B cells and releases into the blood supply as part of its immune response. The production of antibodies is triggered when the body encounters an antigen, some foreign material. Antibodies then lock onto antigens as a first step in disabling the germs or other foreign substances that were the source of those antigens.
B cell: A type of small white blood cell (also known as a B lymphocyte), which plays an important role in the immune system. Made in the bone marrow, these cells mature into plasma cells, and serve as the source of antibodies.
bacteria: (singular: bacterium) Single-celled organisms. These dwell nearly everywhere on Earth, from the bottom of the sea to inside other living organisms (such as plants and animals). Bacteria are one of the three domains of life on Earth.
cell: (in biology) The smallest structural and functional unit of an organism. Typically too small to see with the unaided eye, it consists of a watery fluid surrounded by a membrane or wall. Depending on their size, animals are made of anywhere from thousands to trillions of cells. Most organisms, such as yeasts, molds, bacteria and some algae, are composed of only one cell.
cytokine: A small protein secreted by certain cells of the immune system which the body uses to have some particular controlling effect on other cells. Examples include interferons, interleukins and growth factors.
dendritic cell: A type of immune system cell that initiates the primary response to a foreign substance.
digest: (noun: digestion) To break down food into simple compounds that the body can absorb and use for growth. Some sewage-treatment plants harness microbes to digest — or degrade — wastes so that the breakdown products can be recycled for use elsewhere in the environment.
force: Some outside influence that can change the motion of a body, hold bodies close to one another, or produce motion or stress in a stationary body.
germ: Any one-celled microorganism, such as a bacterium or fungal species, or a virus particle. Some germs cause disease. Others can promote the health of more complex organisms, including birds and mammals. The health effects of most germs, however, remain unknown.
honey: A viscous (gooey) material that honeybees store in their honeycombs. The bees make it from nectar.
immune: (adj.) Having to do with immunity. (v.) Able to ward off a particular infection. Alternatively, this term can be used to mean an organism shows no impacts from exposure to a particular poison or process. More generally, the term may signal that something cannot be hurt by a particular drug, disease or chemical.
immune system: The collection of cells and their responses that help the body fight off infections and deal with foreign substances that may provoke allergies.
innate: An adjective for some behavior, attitude or response that is natural, or inborn, and doesn’t have to be learned.
macrophage: A type of white blood cell dispatched by the immune system. Like janitors of the body, they gobble up germs, wastes and debris for disposal. These cells also stimulate other immune cells by exposing them to small bits of the invaders.
microbe: Short for microorganism. A living thing that is too small to see with the unaided eye, including bacteria, some fungi and many other organisms such as amoebas. Most consist of a single cell.
molecule: An electrically neutral group of atoms that represents the smallest possible amount of a chemical compound. Molecules can be made of single types of atoms or of different types.
neutrophil: A type of white blood cell released by the immune system. It gobbles up wastes and release chemicals that can digest cells, including germs.
pathogen: An organism that causes disease.
primary: An adjective meaning major, first or most important.
protein: A compound made from one or more long chains of amino acids. Proteins are an essential part of all living organisms. They form the basis of living cells, muscle and tissues; they also do the work inside of cells. Among the better-known, stand-alone proteins are the hemoglobin (in blood) and the antibodies (also in blood) that attempt to fight infections. Medicines frequently work by latching onto proteins.
recruit: (verb) To enroll a new member into some group. It could be into the military. Or it could be into participating in a research group to test some drug, behavior or environmental condition.
replicate: (in biology) To copy something. When viruses make new copies of themselves — essentially reproducing — this process is called replication.
risk: The chance or mathematical likelihood that some bad thing might happen.
survey: To view, examine, measure or evaluate something, often land or broad aspects of a landscape.
SWAT: Acronym for special weapons and tactics. It’s a term for the type of heavily armored, combat troops who can support or even take over for patrol officers and detectives in challenging conditions.
system: A network of parts that together work to achieve some function. For instance, the blood, vessels and heart are primary components of the human body’s circulatory system. Similarly, trains, platforms, tracks, roadway signals and overpasses are among the potential components of a nation’s railway system. System can even be applied to the processes or ideas that are part of some method or ordered set of procedures for getting a task done.
T cells: A family of white blood cells, also known as lymphocytes, that are primary actors in the immune system. They fight disease and can help the body deal with harmful substances.
tag: (in immunology) A chemical change that allows the immune system to identify cells or other material that it should attack and disable or remove.
vaccine: (v. vaccinate) A biological mixture that resembles a disease-causing agent. It is given to help the body create immunity to a particular disease. The injections used to administer most vaccines are known as vaccinations.
virus: Tiny infectious particles consisting of genetic material (RNA or DNA) surrounded by protein. Viruses can reproduce only by injecting their genetic material into the cells of living creatures. Although scientists frequently refer to viruses as live or dead, in fact many scientists argue that no virus is truly alive. It doesn’t eat like animals do, or make its own food the way plants do. It must hijack the cellular machinery of a living cell in order to survive.
An Israeli research group says its artificially intelligent antibiotic-prescribing algorithm can cut the risk of antibiotic resistance by half.
Antibiotics are essential to curing bacterial infections, but their overuse promotes the appearance and proliferation of antibiotic-resistant bacteria.
“We wanted to understand how antibiotic resistance emerges during treatment and find ways to better tailor antibiotic treatment for each patient to not only correctly match the patient’s current infection susceptibility but also to minimize their risk of infection recurrence and … resistance to treatment,” said Roy Kishony from the Technion – Israel Institute of Technology.
The group focused on two common bacterial infections — urinary tract infections and wound infections — to show how each patient’s past infection history can be used to choose the best antibiotic to reduce the chance of antibiotic resistance emerging.
“As most infections are seeded from a patient’s own microbiota, these resistance-gaining recurrences can be predicted using the patient’s past infection history and minimized by machine learning [with] personalized antibiotic recommendations, offering a means to reduce the emergence and spread of resistant pathogens,” the researchers said.
They used genomic sequencing techniques and machine learning analysis of patient records to develop this approach, described in the journal Science.
“We found that the antibiotic susceptibility of the patient’s past infections could be used to predict their risk of returning with a resistant infection following antibiotic treatment,” said lead author Mathew Stracy.
“Using this data, together with the patient’s demographics like age and gender, allowed us to develop the algorithm.”
The study was a collaboration involving Kishony and physicians Varda Shalev, Gabriel Chodick and Jacob Kuint at Maccabi KSM Research and Innovation Center. Maccabi is one of Israel’s four national health maintenance organizations.
The hope is that this algorithm could be used at the point of care to improve treatment and minimize the spread of resistant bacteria.
The CDC describes antibiotic resistance as “one of the world’s most urgent public health problems,” noting that it “has the potential to affect people at any stage of life, as well as the healthcare, veterinary and agriculture industries.”
In its 2019 Antibiotic Resistance Threats Report, it said more than 2.8 million antibiotic-resistant infections occur each year in the United States, resulting in the deaths of more than 35,000 people.
When Clostridioides difficile — a bacterium associated with antibiotic use that is not typically resistant but can cause deadly bouts of diarrhea — is added to these figures, the reported U.S. toll exceeds 3 million infections and 48,000 deaths.
Cells contain certain chaperone proteins that can break down the protein clumps found in amyotrophic lateral sclerosis (ALS) and Huntington’s disease, but don’t always activate the right proteins at the right time, a recent study shows.
“[The cells] do not always realize there is a problem, or know how to solve it, even when they do in fact have the tools to do so,” Reut Shalgi, PhD, a professor at Technion Israel Institute of Technology and the study’s principal investigator, said in a press release.
“The good news is that since the ability is there, we hope future treatments can be developed to activate it and employ the body’s own tools to cure these debilitating neurodegenerative diseases,” Shalgi said.
The study, “Differential roles for DNAJ isoforms in HTT-polyQ and FUS aggregation modulation revealed by chaperone screens,” was published in Nature Communications.
Neurodegenerative diseases, including ALS and Huntington’s, are characterized by protein aggregation (clumping) in the nerves’ cells, impairing their function.
Normally, when a protein is made in the body, it is folded into the 3D shape it needs to perform its function. In neurodegenerative diseases, however, certain proteins fail to fold properly, and instead stick to each other, forming aggregates.
Chaperone proteins help other proteins fold into the correct shape. Sometimes, when proteins aggregate, chaperones are activated to correct the mistake.
The researchers sought to investigate the ability of specific chaperones to break down ALS or Huntington’s-associated aggregates. To do so, they tested 66 chaperones in cultures with either aggregates of the Huntington’s-related Huntingtin protein, or with FUS protein aggregates, which are found in many cases of familial ALS.
Overall, eight individual chaperones were able to prevent ALS aggregate formation, and four provided significant protection against Huntington’s aggregates, although there was no overlap between the two diseases, the researchers noted.
One chaperone that protected against ALS aggregates — DNAJB14 — exists in two versions, called isoforms. The isoforms are similar, but one is shorter and lacks some important protein domains that are present on the longer version.
The researchers found that, contrary to the long version, the short version could not break down the FUS aggregates. According to the researchers, this could be because the long isoform contains a region responsible for interacting with HSP70 proteins — an important family of chaperones — that the researchers hypothesized may be important for the protein’s ability to break down aggregates.
Indeed, when the researchers blocked the HSP70-binding domain on the long version, it also lost its ability to prevent aggregates.
In Huntington’s aggregates, another chaperone, DNAJB12, significantly worsened aggregate formation in its long isoform, but was protective in its short isoform, which also lacked the HSP70 binding domain.
Although DNAJB12 did not independently influence ALS aggregates, a physical interaction was sometimes observed between DNAJB14 and DNAJB12. When the team prevented this interaction, DNAJB14 no longer was able to clear FUS aggregates, suggesting that the interaction between the two proteins likely contributes to DNAJB14’s ability to remove aggregates.
Overall, “these results collectively support the notion that the DNAJB14–DNAJB12–HSP70 complex is essential for providing substantial protection from [ALS-associated aggregates],” the researchers wrote.
Furthermore, when DNAJB14’s long version was added to cell cultures containing FUS aggregates, the expression of more chaperones and other proteins important for maintaining protein function — which had been diminished by aggregate formation — was restored.
“This represented a fine-tuned, apparently well-suited response to address the challenges of [FUS aggregate-containing] cells,” the researchers wrote.
However, when the team compared overall production of chaperone proteins in cells with and without the protein clumps, they found that the cells with FUS aggregates failed to naturally increase levels of the protective chaperones in response to the aggregates. In fact, many chaperones, including those in the HSP70 family, were repressed.
Overall, this suggests that while cells have the tools to break down ALS aggregates, they don’t always respond properly, and may fail to activate the right chaperones at the right time.
“It is not enough that the tools exist in the cell’s toolbox. The cell needs to realize there is a problem, and then it needs to know which, out of the many tools available to it, it should use to solve the problem,” said Shalgi.
The team noted, however, that identifying the key chaperones involved provides a target for the development of future therapeutic interventions.
What makes it possible for cancer cells to spread and flourish despite radiotherapy, surgery to remove the initial tumor, chemotherapy and immunotherapy?
Frin Left: Prof. Naama Brenner, Prof. Omri Barak and Aseel Shomar. They have proposed that cancer cells learn and adapt to their environment, enabling them to develop drug resistance. (photo credit: Rami Shelush, Technion spokesperson’s office)
Cancer cells may be brainless, but they are as clever as chess players who want to win. They know how to spread (metastasize) to other parts of the body. It is this “skill” that makes malignant tumors the most common cause of death in Israel.
But what makes it possible for such cells to spread and flourish despite radiotherapy, surgery to remove the initial tumor, chemotherapy and immunotherapy?
A novel explanation
Researchers at the Technion-Israel Institute of Technology in Haifa have just published an article on the subject in iScience, an interdisciplinary open-access journal with continuous publication of research across the life, physical, and earth sciences, titled “Cancer progression as a learning process.”
Aseel Shomar, a Nazareth-born doctoral student in biochemical engineering who is on an Adams Fellowship, together with Prof. Omri Barak and Prof. Naama Brenner, suggested a novel explanation in the hope that better understanding should lead to better treatment.
They propounded the idea that cancer cells are able to learn and adapt to changing environments by actively searching for solutions that would enable them to survive. Studying cancer using this approach and tools of learning theory will advance our understanding of these phenomena, they said.
Scan photos of a tumor; in it you can see cancerous cells that are colored in purple. (credit: Nucleai)
It is commonly thought that both drug resistance and the ability to metastasize appear in cancer cells as random mutations. Since such a mutation gives cancer cells an advantage, making it possible for them to survive in an environment that struggles to fight them, these mutations become dominant.
However, mounting evidence from research groups around the world does not seem to match this hypothesis, and treatment plans based on it did not significantly increase patients’ life expectancy.
But now, the Technion team members have proposed a new hypothesis that matches the evidence at hand: cancer cells learn and adapt to their environment, enabling them to develop drug resistances and conform to the new environments of metastasis locations.
How does a cell learn without a brain? Brenner explained that when sensing stress, the cell seeks to reduce that pressure and launches a trial-and-error process within the gene regulatory network, changing the way existing genes are expressed. An interaction that reduces the stress gets strengthened.
Even so, considering the number of possible configurations the cell can try, it seems unlikely that the process would work. However, using computer simulations based on learning theory, the group showed that cells could in fact learn and adapt in this fashion.
One element of what makes this feasible is that more than one solution can be found to solve the same problem faced by the cell. Another element is the way the gene regulatory network is structured, with regulatory “hubs” that control parts of it.
Malignant cells are not unique in their learning ability, they said. Brenner, Prof. Erez Braun and others have shown in the past that yeast cells can adapt to new environments and develop abilities they did not initially possess.
Few other labs around the world have demonstrated this effect in simple organisms.
A rare type of discovery
Learning theory – a process that brings together personal and environmental experiences and influences for acquiring, enriching or modifying one’s knowledge, skills, values and behavior – develops hypotheses that describe how this process takes place and provides the mathematical tools to study these phenomena.
The Technion’s Network Biology Research Lab studies the way various biological systems adapt, which is a process that is not fully understood.
Its researchers – who come from a variety of faculties including physics, electrical and computer engineering, chemical engineering and medicine – seek to connect theoretical models to complex and dynamic biological systems.
While tumors that learn and adapt might sound alarming, the Haifa authors were optimistic. While cancer cells have the capacity for learning, normally something holds it back.
In fact, the same mutations found to promote cancer in our body can be carried by cells that still remain healthy. Even cells from active tumors that wander into healthy tissue were in some experiments “cured,” reverting to their non-cancerous state.
“There is an interaction between the individual cell and the tissue,” Brenner noted. “The cell has the capacity to explore, but the tissue imposes order and stability. We propose that using the approach and methods of learning theory will help investigate this interaction in greater depth.
“Cancer could perhaps be treated through strengthening the tissue’s ability to calm and control the pre-cancerous cell.”
Most scientific studies add a brick to build the wall of discovery, but this finding is one of a rare type that reexamines existing data and proposes a new framework, offering answers to questions that had until now remained unanswered and opening up new avenues of exploration, they concluded.
Now Dr Hodaya Oliel is speaking out about her incredible journey and how the Israel Institute of Technology helped her fulfil her dreams
A Technion alumnus has become the first person in Israel with cerebral palsy to graduate with a medical degree.
Dr Hodaya Oliel, who’s currently a resident in the Pediatrics Bet department at the Shamir Medical Center, “always wanted to be a doctor” and views her countless surgeries as a child and teenager as God’s way of showing her “what it’s like to be a child who is hospitalised.”
Now 27, she was born prematurely at just 27 weeks and spent three months in the NICU. Being diagnosed with cerebral palsy at just six months, she lost some motor function in her legs, but her cognitive function was, fortunately, unaffected.
“It was never easy, but I remember being so curious about everything I saw, even the operating room”, she told the Jerusalem Post. “These experiences are what spurred me to succeed in high school and while I was studying for my psychometric exam. I didn’t make any backup plans for if I didn’t succeed. That was not an option.
“Everyone needs to live with the lot they were given, and not give up on their dreams when the going gets tough. These dreams are worth fighting for. There were so many moments when I felt like giving up, but my dream was too important, so I kept trudging through the hard times. Reaching my goal was what kept me going.”
“I really love the Technion and truly appreciate everyone there, many of whom are good people who helped me overcome all the difficulties I faced. I do not take any of this for granted for even one second.”
She plans to specialise in paediatric neurology so that she can help both children and families struggling with the same condition.
Between cell-grown steaks and cow-free milk, professors and graduates from the Israeli Institute of Technology are cooking up a new way forward
A whole host of innovative food companies changing the way we treat animals are the products of leading Technion minds.
Aleph Farms – the first company to grow steaks directly from the cells of cows – was co-founded by Technion Professor, Shulamit Levenberg, SavorEat, a company that produces 3D-printed burger patties via a robot chef using ingredient cartridges has as its VP a Technion alumnus and Itay Dana, another Technion alumnus, works as Head of Product Innovation at SuperMeat.
A recent investment round of $105 million went to Aleph Farms, which they say will help execute large-scale global commercialization and portfolio expansion into new types of animal protein and product lines.
Cell-based meat involves growing actual meat from cell cultures taken from a live animal and SuperMeat uses the same process to apply to chicken.
Meanwhile, food-tech innovator – Imagindairy – which develops real milk in the lab without harming animals, is making huge strides in a market that wants something better than plant-based milks.
Co-founded by Technion alumnus, Dr. Eyal Afergan, it cultivates milk proteins from animal cells, meaning the nutritional value, taste, smell and texture is the same as cow’s milk but without causing any suffering to the animal. This startup has also raised $1.5 million in funding.
Several Technion professors and graduates are responsible for oncological developments that are on course to transform the way cancer is caught, diagnosed and treated
A startup that has developed a blood test to predict how well cancer patients will react to treatment is planning to collaborate with the NHS in setting up clinical trials, while a technology to help pathologists detect cancer has been given an FDA ‘breakthrough’ nod.
OncoHost – the company behind the blood test that Prof. Yuval Shaked of the Technion Israel Institute of Technology has created – is the result of a decade’s research. The trials will focus on patients diagnosed with advanced stages of melanoma or non-small cell lung cancer and will join the company’s existing trials using diagnostic platform PROphet, which uses AI to predict patient response to immunotherapy. The result is a personalised treatment plan that will help provide clinicians with potential combination strategies to overcome treatment resistance.The Israeli startup also plans to open additional clinical trial sites around the world to expand its research to other cancers.
Changing the way cancer is detected is also being revolutionised, thanks to Ibex Medical Analytics – the maker of an AI-based cancer diagnostic software. Its Chief Scientific Officer, Dr. Daphna Laifenfeld, spent time researching personalised medicine during her tenure at the Technion.
The startup has received a breakthrough device designation by the US Food and Drug Administration (FDA), which will help expedite the clinical review and regulatory approval of its technology. In receiving this, its potential to help pathologists both detect and diagnose cancer has been formally acknowledged.
The software is already used in labs worldwide as part of everyday clinical practice, as well as continually demonstrating its positive outcomes in clinical studies.
Meanwhile, Prof. Marcelle Machluf – yet another Technion professor – has made it her life’s work to create a medicine delivery system that can defeat cancer. The co-founder and inventor of NanoGhost – a technology that targets cancer cells with modified adult stem cells loaded with medicine – is also the faculty dean of Biotechnology & Food Engineering at the Technion, and it was here that she started the research that led to NanoGhost in 2010.
NanoGhost – which has already raised $5 million – is showing promising progress, with clinical trials aimed by 2023.