Genome Reading and Editing Tools for All
In 2006, the cover of Scientific American was "Know Your DNA" and the inside story was "Genomes for All." Today, we are closer to that goal than ever. Making it affordable for everyone to understand and change their DNA will fundamentally alter how we manage diseases, how we conduct clinical research, and even how we select a mate.
A frequent line of questions on the topic of making genome reading affordable is: Do we need to read the whole genome in order to accurately predict disease risk?
Since 2006, we have driven the cost of reading a human genome down from $3 billion to $600. To aid interpretation and research to produce new diagnostics and therapeutics, my research team at Harvard initiated the Personal Genome Project and later, Openhumans.org. This has demonstrated international informed consent for human genomes, and diverse environmental and trait data can be distributed freely. This is done with no strings attached in a manner analogous to Wikipedia. Cell lines from that project are similarly freely available for experiments on synthetic biology, gene therapy and human developmental biology. DNA from those cells have been chosen by the US National Institute of Standards and Technology and the Food and Drug Administration to be the key federal standards for the human genome.
A frequent line of questions on the topic of making genome reading affordable is: Do we need to read the whole genome in order to accurately predict disease risk? Can we just do most commonly varying parts of the genome, which constitute only a tiny fraction of a percent? Or just the most important parts encoding the proteins or 'exome,' which constitute about one percent of the genome? The commonly varying parts of the genome are poor predictors of serious genetic diseases and the exomes don't detect DNA rearrangements which often wipe out gene function when they occur in non-coding regions within genes. Since the cost of the exome is not one percent of the whole genome cost, but nearly identical ($600), missing an impactful category of mutants is really not worth it. So the answer is yes, we should read the whole genome to glean comprehensively meaningful information.
In parallel to the reading revolution, we have dropped the price of DNA synthesis by a similar million-fold and made genome editing tools close to free.
WRITING
In parallel to the reading revolution, we have dropped the price of DNA synthesis by a similar million-fold and made genome editing tools like CRISPR, TALE and MAGE close to free by distributing them through the non-profit Addgene.org. Gene therapies are already curing blindness in children and cancer in adults, and hopefully soon infectious diseases and hemoglobin diseases like sickle cell anemia. Nevertheless, gene therapies are (so far) the most expensive class of drugs in history (about $1 million dollars per dose).
This is in large part because the costs of proving safety and efficacy in a randomized clinical trial are high and that cost is spread out only over the people that benefit (aka the denominator). Striking growth is evident in such expensive hyper-personalized therapies ever since the "Orphan Drug Act of 1983." For the most common disease, aging (which kills 90 percent of people in wealthy regions of the world), the denominator is maximal and the cost of the drugs should be low as genetic interventions to combat aging become available in the next ten years. But what can we do about rarer diseases with cheap access to genome reading and editing tools? Try to prevent them in the first place.
A huge fraction of these births is preventable if unaffected carriers of such diseases do not mate.
ARITHMETIC
While the cost of reading has plummeted, the value of knowing your genome is higher than ever. About 5 percent of births result in extreme medical trauma over a person's lifetime due to rare genetic diseases. Even without gene therapy, these cost the family and society more than a million dollars in drugs, diagnostics and instruments, extra general care, loss of income for the affected individual and other family members, plus pain and anxiety of the "medical odyssey" often via dozens of mystified physicians. A huge fraction of these births is preventable if unaffected carriers of such diseases do not mate.
The non-profit genetic screening organization, Dor Yeshorim (established in 1983), has shown that this is feasible by testing for Tay–Sachs disease, Familial dysautonomia, Cystic fibrosis, Canavan disease, Glycogen storage disease (type 1), Fanconi anemia (type C), Bloom syndrome, Niemann–Pick disease, Mucolipidosis type IV. This is often done at the pre-marital, matchmaking phase, which can reduce the frequency of natural or induced abortions. Such matchmaking can be done in such a way that no one knows the carrier status of any individual in the system. In addition to those nine tests, many additional diseases can be picked up by whole genome sequencing. No person can know in advance that they are exempt from these risks.
Furthermore, concerns about rare "false positives" is far less at the stage of matchmaking than at the stage of prenatal testing, since the latter could involve termination of a healthy fetus, while the former just means that you restrict your dating to 90 percent of the population. In order to scale this up from 13 million Ashkenazim and Sephardim to billions in diverse cultures, we will likely see new computer security, encryption, blockchain and matchmaking tools.
Once the diseases are eradicated from our population, the interventions can be said to impact not only the current population, but all subsequent generations.
THE FUTURE
As reading and writing become exponentially more affordable and reliable, we can tackle equitable distribution, but there remain issues of education and security. Society, broadly (insurers, health care providers, governments) should be able to see a roughly 12-fold return on their investment of $1800 per person ($600 each for raw data, interpretation and incentivizing the participant) by saving $1 million per diseased child per 20 families. Everyone will have free access to their genome information and software to guide their choices in precision medicines, mates and participation in biomedical research studies.
In terms of writing and editing, if delivery efficiency and accuracy keep improving, then pill or aerosol formulations of gene therapies -- even non-prescription, veterinary or home-made versions -- are not inconceivable. Preventions tends to be more affordable and more humane than cures. If gene therapies provide prevention of diseases of aging, cancer and cognitive decline, they might be considered "enhancement," but not necessarily more remarkable than past preventative strategies, like vaccines against HPV-cancer, smallpox and polio. Whether we're overcoming an internal genetic flaw or an external infectious disease, the purpose is the same: to minimize human suffering. Once the diseases are eradicated from our population, the interventions can be said to impact not only the current population, but all subsequent generations. This reminds us that we need to listen carefully, educate each other and proactively imagine and deflect likely, and even unlikely, unintended consequences, including stigmatization of the last few unprotected individuals.
A new type of cancer therapy is shrinking deadly brain tumors with just one treatment
Few cancers are deadlier than glioblastomas—aggressive and lethal tumors that originate in the brain or spinal cord. Five years after diagnosis, less than five percent of glioblastoma patients are still alive—and more often, glioblastoma patients live just 14 months on average after receiving a diagnosis.
But an ongoing clinical trial at Mass General Cancer Center is giving new hope to glioblastoma patients and their families. The trial, called INCIPIENT, is meant to evaluate the effects of a special type of immune cell, called CAR-T cells, on patients with recurrent glioblastoma.
How CAR-T cell therapy works
CAR-T cell therapy is a type of cancer treatment called immunotherapy, where doctors modify a patient’s own immune system specifically to find and destroy cancer cells. In CAR-T cell therapy, doctors extract the patient’s T-cells, which are immune system cells that help fight off disease—particularly cancer. These T-cells are harvested from the patient and then genetically modified in a lab to produce proteins on their surface called chimeric antigen receptors (thus becoming CAR-T cells), which makes them able to bind to a specific protein on the patient’s cancer cells. Once modified, these CAR-T cells are grown in the lab for several weeks so that they can multiply into an army of millions. When enough cells have been grown, these super-charged T-cells are infused back into the patient where they can then seek out cancer cells, bind to them, and destroy them. CAR-T cell therapies have been approved by the US Food and Drug Administration (FDA) to treat certain types of lymphomas and leukemias, as well as multiple myeloma, but haven’t been approved to treat glioblastomas—yet.
CAR-T cell therapies don’t always work against solid tumors, such as glioblastomas. Because solid tumors contain different kinds of cancer cells, some cells can evade the immune system’s detection even after CAR-T cell therapy, according to a press release from Massachusetts General Hospital. For the INCIPIENT trial, researchers modified the CAR-T cells even further in hopes of making them more effective against solid tumors. These second-generation CAR-T cells (called CARv3-TEAM-E T cells) contain special antibodies that attack EFGR, a protein expressed in the majority of glioblastoma tumors. Unlike other CAR-T cell therapies, these particular CAR-T cells were designed to be directly injected into the patient’s brain.
The INCIPIENT trial results
The INCIPIENT trial involved three patients who were enrolled in the study between March and July 2023. All three patients—a 72-year-old man, a 74-year-old man, and a 57-year-old woman—were treated with chemo and radiation and enrolled in the trial with CAR-T cells after their glioblastoma tumors came back.
The results, which were published earlier this year in the New England Journal of Medicine (NEJM), were called “rapid” and “dramatic” by doctors involved in the trial. After just a single infusion of the CAR-T cells, each patient experienced a significant reduction in their tumor sizes. Just two days after receiving the infusion, the glioblastoma tumor of the 72-year-old man decreased by nearly twenty percent. Just two months later the tumor had shrunk by an astonishing 60 percent, and the change was maintained for more than six months. The most dramatic result was in the 57-year-old female patient, whose tumor shrank nearly completely after just one infusion of the CAR-T cells.
The results of the INCIPIENT trial were unexpected and astonishing—but unfortunately, they were also temporary. For all three patients, the tumors eventually began to grow back regardless of the CAR-T cell infusions. According to the press release from MGH, the medical team is now considering treating each patient with multiple infusions or prefacing each treatment with chemotherapy to prolong the response.
While there is still “more to do,” says co-author of the study neuro-oncologist Dr. Elizabeth Gerstner, the results are still promising. If nothing else, these second-generation CAR-T cell infusions may someday be able to give patients more time than traditional treatments would allow.
“These results are exciting but they are also just the beginning,” says Dr. Marcela Maus, a doctor and professor of medicine at Mass General who was involved in the clinical trial. “They tell us that we are on the right track in pursuing a therapy that has the potential to change the outlook for this intractable disease.”
Since the early 2000s, AI systems have eliminated more than 1.7 million jobs, and that number will only increase as AI improves. Some research estimates that by 2025, AI will eliminate more than 85 million jobs.
But for all the talk about job security, AI is also proving to be a powerful tool in healthcare—specifically, cancer detection. One recently published study has shown that, remarkably, artificial intelligence was able to detect 20 percent more cancers in imaging scans than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.