NBC News reports on a 16-person clinical trial of "personalized messenger RNA vaccines" which use the immune system to fight cancer cells. "The goal is not to eliminate existing tumors, but instead to stamp out lingering, undetected cancer cells, and later any new cells that form before they can cause a recurrence." Patients still have surgery to remove tumors. After that, the mRNA vaccines are personalized for each individual using genetic material taken from their unique tumor cells. In the clinical trial, after getting the vaccine, the patients also received chemotherapy, which is standard post-op treatment for operable pancreatic cancer... [The article notes that less than 13% of people diagnosed with pancreatic cancer live for more than five years, making it "one of the deadliest cancers."] [E]xperts have long believed that people with pancreatic cancer could not generate an immune response against tumors. But after nine doses of the personalized vaccine, [clinical trial participant Donna] Gustafson is one of eight people in the 16-person Phase 1 trial who did just that, producing an army of immune cells called T cells that seek out and destroy tumor cells... [Dr. Vinod Balachandran, a vaccine center director who is leading the trial, said] it was unclear whether the immune response would last and lead to the patients living longer... New data collected during the trial's six-year follow-up period shows that it may. Those findings will be presented Monday at the American Association for Cancer Research's annual meeting in San Diego. Six years after treatment, Gustafson and six others who responded to the treatment are still alive... More research is still needed. Genentech and BioNTech, the two drugmakers behind the vaccine, have already launched a larger Phase 2 clinical trial... Another team is working on an off-the-shelf vaccine that targets a protein called KRAS that is present in as many as 90% of pancreatic cancers. In a small, early trial, about 85% of the participants mounted an immune response to the protein.

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An anonymous reader quotes a report from Ars Technica: On Thursday, OpenAI announced it had developed a large language model specifically trained on common biology workflows. Called GPT-Rosalind after Rosalind Franklin, the model appears to differ from most science-focused models from major tech companies, which have generally taken a more generic approach that works for various fields. In a press briefing, Yunyun Wang, OpenAI's Life Sciences Product Lead, said the system was designed to tackle two major roadblocks faced by current biology researchers. One is the massive datasets created by decades of genome sequencing and protein biochemistry, which can be too much for any one researcher to take in. The second is that biology has many highly specialized subfields, each with its own techniques and jargon. So, for example, a geneticist who finds themselves working on a gene that's active in brain cells might struggle to understand the immense neurobiological literature. Wang said the company had taken an LLM and trained it on 50 of the most common biological workflows, as well as on how to access the major public databases of biological information. Further training has resulted in a system that can suggest likely biological pathways and prioritize potential drug targets. "We're connecting genotype to phenotype through known pathways and regulatory mechanisms, infer likely structural or functional properties of proteins, and really leveraging this mechanistic understanding," Wang said. To address LLMs' tendencies toward sycophancy and overenthusiasm, OpenAI says it has tuned the model to be more skeptical, so it's more likely to tell you when something is a bad drug target. There was a lot of talk about GPT-Rosalind's "reasoning" and "expert-level" abilities. We were told that the former was defined as being able to work through complex, multi-step processes, while the latter was derived from the model's performance on a handful of benchmarks. Access to GPT-Rosalind is currently limited "due to concerns about the model's potential for harmful outputs if asked to do something like optimize a virus's infectivity," notes Ars. Only U.S.-based organizations can request access at the moment.

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The biotech industry's engineered cells could become an $8 trillion market by 2035, notes Phys.org. But how do you keep them from being stolen? Their article notes "an uptick in the theft and smuggling of high-value biological materials, including specially engineered cells." In Science Advances, a team of U.S. researchers present a new approach to genetically securing precious biological material. They created a genetic combination lock in which the locking or encryption process scrambled the DNA of a cell so that its important instructions were non-functional and couldn't be easily read or used. The unlocking, or decryption, process involves adding a series of chemicals in a precise order over time — like entering a password — to activate recombinases, which then unscramble the DNA to their original, functional form... They created a biological keypad with nine distinct chemicals, each acting as a one-digit input. By using the same chemicals in pairs to form two-digit inputs, where two chemicals must be present simultaneously to activate a sensor, they expanded the keypad to 45 possible chemical inputs without introducing any new chemicals. They also added safety penalties — if someone tampers with the system, toxins are released — making it extremely unlikely for an unauthorized person to access the cells. "The researchers conducted an ethical hacking exercise on the test lock and found that random guessing yielded a 0.2% success rate, remarkably close to the theoretical target of 0.1%."

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