Meet Prerna Jain: An Indian Trailblazer Making her Mark in STEM in America


WhatsApp Image 2023 09 30 at 6.53.55 PM

As a Senior Translational Scientist at Tempus Labs, Prerna is currently specializing in analyzing cancer patient data to support oncology drug development. Having worked previously as a graduate research assistant at Brown Lab, Georgia Institute of Technology, Prerna’s work resides at the intersection of genomics, machine learning, and data science.

In this interview, we delve into her experiences, aspirations, and the transformative impact of her work in the ever-evolving world of biotechnology and data-driven healthcare.

As a woman in STEM, can you share what it means to you to be a role model for others who may be underrepresented in this field?

I currently work as a Translational Scientist and I help pharmaceutical companies make decisions about their cancer drugs in a data-driven manner. I analyze large datasets of thousands of cancer patients from the US during this process. A key realization for me was the lack of equal representation of genders, ethnicities and race in the data itself – for example, minority patients have been found to be less likely to be eligible to enroll in clinical trials not just because of their socio-economic status, but because of the eligibility criteria defined while designing the trial. As a result major decisions about how to treat these fatal illnesses are being made without satisfactorily taking our biological uniqueness into account. I have been fortunate enough to dedicate my professional time to investigate potential solutions – our publication ‘Evaluating the impact of performance status criteria on minority eligibility for oncology clinical trials’ highlights some of these findings. We have a long way to go to fix issues of underrepresentation in our datasets, but I feel honored to be a part of the solution as an Indian woman.

Could you tell us about any specific challenges you’ve faced or stereotypes you’ve encountered throughout your journey and how you’ve navigated or overcome them?

Tech/data science has lower representation of women in general, however my industry is at the intersection of tech and healthcare which might be why I have been fortunate enough to not work in a very imbalanced setting. I have not faced many instances where I was sharing my work to a (virtual) room full of men or felt out of place in general. The only incident that comes to mind is a coworker teasing another coworker when I (as a non-native English speaker) corrected their grammar.

One challenge which is prevalent across industries is pay disparity. Women or people of color are often paid less for the same job profiles, and it is very hard to realize if you are one of those unlucky people because people are not as transparent about pay. This lack of transparency only benefits the companies we work for because they can continue to disproportionately compensate some people as long as it is not widely known. I try to have open discussions with colleagues and friends in the industry to make sure I have a good understanding of what fair compensation is. Also, just being confident in what I bring to the table and being a fierce advocate for myself during negotiations has helped me deal with this issue. 

Your previous role at Intellia Therapeutics involved research into CRISPR technology. Can you describe any pivotal experiences or discoveries from your time there that shaped your career path?

I am so grateful for my stint at Intellia because it paved the way for my research career in cancer immunology. While I was a summer intern there, I worked on developing software which would help them analyze data from their CRISPR gene editing technology experiments. Through these experiments, I learnt more about how we can use genomic sequencing and machine learning to understand our immune system. In my next role at Tempus, I continued my work in this exciting space. We conducted a deep analysis of tumors from lung cancer patients and found a novel cell type which, if stimulated appropriately could attack tumors. Our paper ‘Integration of tumor extrinsic and intrinsic features associates with immunotherapy response in non-small cell lung cancer’ published in Nature Communications last year goes over this work – it shows methods to find insights from biological models which can improve cancer patient outcomes. 

Could you share any recent trends or developments in the field of genomics or data science that you find intriguing and believe have the potential to shape the future of your work?

Real world data (RWD) analysis involves using clinical and genomic data from patients in the real-world and understanding their disease journey. Typically in the past, most of the insights we got about how patients respond to treatments were from observing them in controlled clinical trials. Now, we see a shift towards the industry also using inferences from RWD to guide decisions about designing treatments. This is very exciting and will shape the future of my work, since you can work with a much larger sample set (essentially anyone who has had that disease and has an accessible electronic medical record) of patients for a fraction of the cost of running a late stage clinical trial.

A cool example is a project I worked on in collaboration with doctors at the Mayo Clinic and Johns Hopkins – we profiled the genomes of biliary tract cancer patients and found a certain subset of patients had markers which made them great candidates for immunotherapy, a cancer treatment which is extremely effective in the right kind of patient. The results are outlined in our paper Clinical, Genomic, and Transcriptomic Data Profiling of Biliary Tract Cancer Reveals Subtype-Specific Immune Signatures – it is the first of its kind to highlight characteristics of this unique population. This work contributed to ongoing trials for an immunotherapy drug in biliary cancer patients, and results from their phase III trials were successful!

Like most industries, LLMs have the potential of creating a transformative impact in this space too. You can feed these models large, messy datasets of patient information and it can be trained to find patterns which can help us in finding better treatments for patients.

Outside of your work, are there any hobbies you enjoy that help you unwind?

I am one of the lucky people who enjoys work enough to find it stimulating instead of demanding. But I love giving time to my various hobbies, some of which are very typical for people living in Southern California – I enjoy most activities which involve being active and outdoors (yoga, going to the beach, strength training, hiking), traveling, cooking and playing music. Also, does regularly trying out new cafes to get coffee and pastry count as a hobby? 

Lastly, on a more personal note, what message or legacy would you like to leave for future generations of Indian women aspiring to make their mark in the field of biotechnology. 

Aim for the stars and don’t be afraid of discomfort! Biotechnology is a rapidly evolving field and you have to be on top of new developments constantly and apply it in your work – don’t be intimidated, it is very likely that many people around you have no idea about these things either. In my opinion, the most valuable skill is being able to learn efficiently – understanding a topic you know nothing about well enough to be able to apply it. This is not a space where you can easily be confident or comfortable, but embracing that and working through it paves the way for success.


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