Adoptive Cell Therapy for Cancer
Ani Nejatfard's journey in T-Cell engineering & AlphaFold2s breakthrough in protein structure prediction.
Currently working on a Ph.D. in Biochemistry and a member of the Appel Lab, Ani Nejatfard shared her unique story and provided insight into the immunology space. Immunology covers the medical study of the immune system in humans, animals, and plants. Ani specifically works in adoptive cell therapy which involves manipulating T-cells to give them specific receptors. These receptors act as a weapon, allowing the T-cell to combat certain diseases. Once the T-cell has been manipulated, the lab grows millions of them before giving them back to the patient in hopes of curing some of their diseases. The primary purpose of adoptive cell therapy is for the treatment of cancers. Thus far, adoptive cell therapy has been really effective against non-solid blood cancers. However, the approach has yielded little success in treating solid tumors. These tumors possess distinct physiological features, allowing them to resist traditional treatment approaches. Solid tumors can also adapt to their environment and better protect themselves against our immune system.
Another issue that Ani mentioned is how we deliver these new T-cells to the patient's body. The new, engineered immune cells may cause an adverse immune response themselves, requiring that patients are simultaneously treated with harsh immunosuppressants. This method is extremely intensive and straining on a patient's already weak immune system.
Coming from an immigrant family, Ani was strongly encouraged to choose a field in either medicine, engineering, or science. Throughout college, Ani explored several of these options, quickly realizing that she did not do well in direct clinical settings. Ani described the initial challenges of working in a highly technical field, such as the learning curve necessary to parse dense scientific literature. She commented that her undergraduate and professional mentors, such as Professor Neal at UCSD, helped foster her love for scientific research and her confidence in her scientific ability. Ani stated that those mentors were specifically supportive and encouraging, giving her confidence and helping her develop some technical skills. When asked how she eventually developed those skills, Ani recommends students to consistently read scientific literature and embrace the discomfort that accompanies learning something new and challenging. Ani also advises students to not be afraid of talking to professors or superiors at their jobs as they are also human and will gladly share their experiences and wisdom if you approach them with enthusiasm. Ani hopes to eventually teach students about her experiences and pass on her skills. She hopes to make students less fearful of their professors and more comfortable with asking them for guidance and support. She also hopes to tell students that there is no correct career path. While it is a common belief that students need to have their whole life figured out very early, Ani argues that people should not worry about having a clear direction and should instead focus on experimenting, learning from their mistakes, and growing as an individual. Ani's story is just one example that one's path does not need to align with contemporary expectations as there are plenty of ways to get involved in the scientific space.
The final part of our interview discussed the value of computing power for biochemistry. Ani and I specifically discussed AlphaFold2 (AF2), a novel technology that produces remarkably accurate atomic structures for individual proteins. Developed by DeepMind, this technology is incredibly powerful as proteins can be characterized without the need for tedious and costly lab analysis. Having accessible information on the structure of different proteins will be crucial in studying neurodegenerative diseases such as Alzheimer's as the shape of a protein will affect which molecules can bind to it.
Understanding the structure of proteins could have implications for drug design as we will have a better idea of which molecules we need to test with the proteins. We also discussed a new adaptation of AF2 called AF2 Complex. While AF2 can only predict the structure of individual proteins (proteins that have only one polypeptide chain), AF2 Complex builds upon this by predicting the structures of multimeric protein complexes (proteins that have two or more polypeptide chains). In addition, AF2 Complex does not require paired multiple sequence alignments as it introduces new metrics for predicting direct protein-protein interactions between arbitrary protein pairs. The rapid growth of the biotechnology space has given Ani optimism that her concerns will be addressed and that our society will move one step closer to achieving a healthy population.
Sources:
https://www.future-science.com/doi/10.2144/btn-2022-0007#:~:text=Abstract,tedious%20and%20costly%20lab%20analysis
https://www.nature.com/articles/s41467-022-29394 2#:~:text=AlphaFold2%20(AF2)%2C%20a%20deep,protein%20structure%20prediction1%2C2