Translation is the process of turning basic scientific research into therapies that cure disease, new sources of energy that heal the planet, and other things that move the world forward. The Translation podcast takes a deep dive into scientific advancements with a huge potential to improve society. We talk directly with the people advancing the science with their own hands and minds, and focus on how we can translate the science from the bench to the benefit of all.
Initially centered on biology and synthetic biology, we’ll talk with the most promising young scientists in the field. We aim to demystify the science for a general audience and to shine a light on how great science turns into great business. We hope these discussions will inspire scientists, entrepreneurs, and investors to help commercialize breakthrough research.
Follow Fifty Years on Twitter to get notified when new episodes drop. If you’re an author of an upcoming paper in bio or know of any interesting papers dropping soon and want to hear from the authors, drop us an email at translation@fiftyyears.com.
Episode Contributors: Michael Chavez, Ashton Trotman-Grant, Ayush Noori, Alfredo Andere, Kyle Giffin, Kenny Workman
Episode Summary: Imagine if every graphics design company built its own version of Photoshop in-house. That’s exactly what’s happening today in biology research. Ten-fold increases in data every two years are forcing every biology team to build out their own, in-house bioinformatics stack to store, clean, pipe, and manage the massive volumes of data generated by their experiments. All that work has to happen even before teams can analyze the results! Recognizing this obstacle to high-throughput biology research, Alfredo, Kenny and Kyle built LatchBio to bring the modern computing stack to biotech. By uniting wet lab experiments with dry lab processing, storage, and analyses, LatchBio is democratizing access to top-notch bioinformatics and empowering biologists to derive relevant insights from their data that can move our world forward. Tune in to learn more about their journey from Berkeley dropouts to entrepreneurs building no-code tools to power the biocomputing revolution.
About the Team
Alfredo Andere, CEO, was born in Mexico City and raised in Guadalajara, Mexico. He majored in Computer Science and Electrical Engineering and minored in Math at UC Berkeley before dropping out to co-found LatchBio.
Kyle Giffin, COO, attended UC Berkeley to study Cognitive Neuroscience and Data Science before dropping out to found LatchBio.
Kenny Workman, CTO, started engaging in molecular biology research when he was 15, first at local community colleges as a lab hand and then at MIT and UC Berkeley over successive summers. Prior to co-founding LatchBio, he worked at Asimov and Serotiny as a Software and Machine Learning Engineer.
Key Takeaways
After hundreds of interviews with biotech leaders to discover pain points around managing data, the founders developed the LatchAI platform.
Common biology analyses require piping gigabytes/terabytes of data, meaning data storage and retrieval require programming expertise.
Although scientists may be experts in biological theory and wet lab experimentation, programming expertise is scarce. Biologists must rely on limited computational analysts to process and visualize their data; thus, access to bioinformaticians is a bottleneck in the scientific discovery process.
On the flip side, bioinformaticians are often hampered by repetitive analysis tasks, preventing them from innovating new computational methods.
Recognizing this disconnect between biologists and bioinformaticians, Alfredo, Kenny, and Kyle launched LatchBio: an end-to-end biocomputing platform to allow both wet lab and dry lab scientists to get back to what they’re trained to do - science!
The team recently launched their SDK - a Python native developer toolkit - to bridge the divide between the computationally literate bioinformaticians and the no-code savvy biologists.
The goal of LatchBio is to become the universal cloud computing platform for academic research and industry biotech.
Impact
Company: LatchBio
First Author: Kevin Litchfield
Episode Summary: Novel drugs that boost the immune system to fight cancer have become pharma darlings in the few short years since their approval. These drugs, known as immunotherapies, have so far focused on improving T cell responses and can be used to cure a multitude of different cancer types. Yet more often than not, immunotherapies have no effect on a patient, leaving doctors guessing on whether to prescribe the drug. To find the reason why some people respond while others don’t, Kevin and his team create a huge database of sequences derived from immunotherapy-treated patients. With it, he discovers biomarkers, mutational signatures, and immune profiles that correlate to response with the hopes that one day, these measurements form a diagnostic to ensure we treat the right patients.
About the Author
Kevin is a group leader at University College London and performed this work in the lab of Charles Swanton at the Francis Crick Institute. Dr. Swanton and his group are experts in studying the genome instability and evolution of cancer.
Kevin started his career as a mathematician but was always driven to apply his skills to improving medicine.
Key Takeaways
Immunotherapies aim to cure cancer by “taking the breaks off” your immune system, supercharging it to attack tumors.
Two immunotherapies known as checkpoint inhibitors (CPI), anti-CTLA-4 and anti-PD-1, work by enhancing T cells and have recently become blockbuster drugs for the treatment of multiple different cancer types.
These immunotherapies don’t work in many patients and medicine has yet to understand why.
Kevin aggregated DNA and RNA sequencing data across multiple studies to generate a dataset that contained over 1,000 CPI treated patients who did and did not benefit from treatment.
With this data, Kevin discovers mutational signatures, biomarkers, and immune profiles that correlate to whether a patient will respond to treatment.
Translation
Kevin finds measurable signatures of a patient’s cancer that could be used to determine whether a patient should receive CPIs.
This retrospective analysis will need to be validated as a prospective study to determine whether Kevin’s findings actually predict response.
More tumor data as well as information about the patient’s genetics is being brought in to improve the accuracy of this prediction.
Collaborations between academics, medical centers, non-profits, and industry partners will enable the findings to make an impact on patient outcomes.
Paper: Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition
First Author: Kevin Parker
Episode Summary: Engineered T cells that hunt and kill blood cancers have recently obtained three landmark FDA approvals, forever changing the way we treat this disease. Even with its massive clinical success, these cells come with life-threatening neurotoxicities. But is neurotoxicity a set feature of using T cell therapies or is our engineering accidentally targeting the brain? Utilizing advances in bioinformatics and the huge sequencing datasets available to science, Kevin uncovers similarities between a cell type in our brain and the cancer we target with engineered cells. Finding this needle in a haystack, Kevin creates a link between how we engineer these cells and the neurotoxicities we see, discovering a potential root cause of the problem and generating a rule for how to engineer around it.
About the Author
Kevin recently received his PhD from Stanford University in the labs of Professor Howard Chang and Professor Ansuman Satpathy. These labs specialize in uncovering the molecular mechanisms of disease using advanced sequencing modalities.
Bridging both biology and computer science, Kevin’s background and expertise made him uniquely suited to hunt down the culprit of CAR T cell neurotoxicity.
Key Takeaways
CAR T cells are excellent at killing blood cancers but are not without side-effects -- they can cause severe neurotoxicities.
The receptor engineered into CAR T cells was thought to be specific to these blood cancers, ensuring the therapies don't attack healthy tissue.
Kevin looked at publically available single cell sequencing data to find a small subset of brain cells hiding in plain sight that the CAR T cells could attack.
In mice, engineered “blood cancer specific” T cells attack the brain, demonstrating that neurotoxicity is an off-target effect of the therapy, not a byproduct.
Translation
The finding points to the potential need for different engineered receptors to be used to target these blood cancers.
As CAR T cells expand to other cancers and malignancies, this process can be run to ensure we engineer cells that minimize the opportunity for damage to healthy tissue.
Paper: Single-Cell Analyses Identify Brain Mural Cells Expressing CD19 as Potential Off-Tumor Targets for CAR-T Immunotherapies
