I’m working on developing innovative cancer immunotherapy approaches to address key challenges in the field. Immunotherapy is an exceptionally powerful strategy for curing cancer because it harnesses the body’s immune system—our internal army—and empowers it to recognize and eliminate cancer cells. In this effort, we are focusing on engineering T cells, the immune system’s soldiers and generals, through synthetic biology.
However, significant challenges remain, especially in treating solid tumors like breast cancer. Within the tumor microenvironment, T cells often become exhausted due to the overwhelming number of cancer cells and the suppressive environment created by the tumor. This exhaustion severely limits the effectiveness of these therapies.
To tackle this issue, we employ a cutting-edge model system using 3D bioprinted breast cancer tissue integrated with engineered human T cells. These T cells are reprogrammed through advanced synthetic biology techniques to test and develop solutions for overcoming exhaustion.
Prompt to O1-Pro:
Building on work I’ve previously done and tested with o1-Preview and GPT-4o, I posed the following prompt:
“I’d like you to focus on 3D bioprinted solid tumors as a model to address the T cell exhaustion problem. Specifically, the model should incorporate stroma, as seen in breast cancer, to replicate the tumor microenvironment and explore potential solutions. These solutions could involve technologies like T cell reprogramming, synthetic biology circuits, cytokines, transcription factors related to exhaustion, or metabolic programming. Draw inspiration from other fields, such as Battle Royale games or the immune system’s ability to clear infected cells without triggering autoimmunity. Identify potential pitfalls in developing these therapies and propose alternative approaches. Think outside the box and outline iterative goals that could evolve into full-scale projects. Focus exclusively on in vitro human systems and models.”
Why Battle Royale Games?
You might wonder why I referenced Battle Royale games. That’s precisely the point—I wanted to push the model to think beyond conventional approaches and draw from completely different systems for inspiration. While o1-Preview and GPT-4o were able to generate some interesting ideas based on this concept, but they were mostly what I could also conceive though better most PhD students. In contrast, o1-Pro came up with far more creative and innovative solutions, that left me in awe!
Idea #9: A Remarkable Paradigm
Here, I’m sharing one specific idea, which I’ll call Idea #9 based on its iteration sequence. This idea was exceptional because it proposed an extraordinary paradigm inspired by Battle Royale games but more importantly within the context of deep temporal understanding of biological processes. This was the first time any model explicitly considered the time-dependent nature of biological events—an insight that reflects a remarkably advanced and nuanced understanding!
“Adapt or Fail” Under Escalating Challenges:
Another remarkable aspect of idea #9 was that conceptually it drew from the idea of “adapt or fail” in escalating challenges, directly inspired by Battle Royale mechanics. This was the first time any model could think of it from this perspective. It also emphasized the importance of temporal intervals in reversing or eliminating exhausted T cells. Indeed, this approach mirrors the necessity for T cells to adapt dynamically under pressure and survive progressively tougher challenges, something we would love to model in in vitro systems! One particularly further striking insight was the role of stimulation intervals in preventing exhaustion. Idea #9 suggested that overly short intervals between stimuli might be a key factor driving T cell exhaustion in current therapies. This observation really amazed me with its precision and relevance—because it pinpointed a subtle but critical aspect of T cell activations and development of exhaustion mechanisms.
There’s more behind the link. I have no relevant expertise that would allow me to evaluate how novel this actually was. But immunology is the author’s specialty with his work having close to 30 000 citations on Google Scholar, so I’d assume him to know what he’s talking about.
Of indirect relevance here is that Derya Unutmaz is an avid OpenAI fan who they trust enough to be an early tester. So while I’m not saying that he’s deliberately dissembling, he is known to be overly enthusiastic about AI, and so any of his vibes-y impressions should be taken with a pound of salt.
Certainly he seems impressed with the models understanding, but did it actually solve a standing problem? Did its suggestions actually work?
This is (also) outside my area of expertise, so need to see the idea verified by reality—or at least by professional consensus outside the project.
Mathematics (and mathematical physics, theoretical computer science, etc.) would be more clear-cut examples because any original ideas from the model could be objectively verified (without actually running experiments). Not to move the goalposts—novel insights in biology or chemistry would also count, its just hard for me to check whether they are significant, or whether models propose hundreds of ideas and most of them fail (e.g. the bottleneck is experimental resources).
Derya Unutmaz reported that o1-pro came up with a novel idea in the domain of immunotherapy:
There’s more behind the link. I have no relevant expertise that would allow me to evaluate how novel this actually was. But immunology is the author’s specialty with his work having close to 30 000 citations on Google Scholar, so I’d assume him to know what he’s talking about.
Of indirect relevance here is that Derya Unutmaz is an avid OpenAI fan who they trust enough to be an early tester. So while I’m not saying that he’s deliberately dissembling, he is known to be overly enthusiastic about AI, and so any of his vibes-y impressions should be taken with a pound of salt.
Thanks!
Certainly he seems impressed with the models understanding, but did it actually solve a standing problem? Did its suggestions actually work?
This is (also) outside my area of expertise, so need to see the idea verified by reality—or at least by professional consensus outside the project.
Mathematics (and mathematical physics, theoretical computer science, etc.) would be more clear-cut examples because any original ideas from the model could be objectively verified (without actually running experiments). Not to move the goalposts—novel insights in biology or chemistry would also count, its just hard for me to check whether they are significant, or whether models propose hundreds of ideas and most of them fail (e.g. the bottleneck is experimental resources).