Computation to Improve Therapy & Finances Inside Cancer Careers

In this week’s episode, we discuss how the revolution in computational science is changing what we can learn from clinical trials and biological samples to improve immunotherapy in a conversation with Dr. Peng Jiang. Then we turn to the practical matter of personal finances and taxes for graduate students and postdocs in a conversation with Dr. Emily Roberts of Personal Finances for PhDs.
 
Segment 1: Computation to Improve Therapy
Peng Jiang, Ph.D.
Cancer Data Science Laboratory
NIH Earl Stadtman Program
 
Ad: NCI Rising Scholars: Cancer Research Seminar Series
 
Segment 2: Finances
Emily Rodgers, Ph.D.
NIH Intramural Research Training Award (IRTA)
Strength Finders Assessment
Personal Finance for PhDs Podcast Apple or Spotify
PhDstipends.com
Postdocsalaries.com
 
Your Turn:
Let Go, Move On Between Ignorance and Enlightenment by Venerable Master Hsing Yun  
Die With Zero: Getting All You Can from Your Money and Your Life by Bill Perkins  
Tig Notaro LIVE
[UPBEAT MUSIC]
OLIVER BOGLER: Hello and welcome to Inside Cancer Careers, a podcast from the National Cancer Institute. I'm your host Oliver Bogler. I work at the NCI in the Center for Cancer Training. On Inside Cancer Careers, we explore all the different ways that people join the fight against disease and hear their stories. Today, we're talking to Dr. Peng Jiang, a computational scientist in NCI's Center for Cancer Research who is focusing on using big data to understand how cancer can become resistant to immunotherapy and overcoming it. After the break, we are speaking to Dr. Emily Roberts of Personal Financial for PhDs. She joins us to talk about how to make things work financially during grad school and post-doc years. Then stay tuned to hear recommendations for interesting things from our guests and learn how you can take your turn.
OLIVER: It's a pleasure to welcome Dr. Peng Jiang, a Stadtman Investigator in the NCI's Cancer Data Science Laboratory, where he leads the section on Computational Cancer Immunology to the show. Welcome.
PENG JIANG: Yes, thank you, Oliver, for the introduction.
OLIVER: It's a great time in the history of cancer research with the explosion of knowledge about many cancers leading to the development of a good number of new therapy approaches. But in many cases, resistance arises to these new therapies. And we're learning resistance itself may be mediated by many different mechanisms, so making it very hard to predict the response to a treatment and its durability. How can a computational scientist like yourself address this challenge?
PENG: Yes, Oliver, this is a really great question. As you know, like for almost any type of an effective therapy, in most cases -- I will say for more than 70% of cases for any therapy resistance will eventually come one day. It's really a bottle neck for the entire field. And a unique angle from our data science is that we utilized the large amount of clinical data from patients. Realize that patients' data tells us what is going on under that resistance. Like, you know, the cause for resistance could be really complicated, right? Like one clinical trial, one cohort patient may only tell you part of the story, because it's so complicated. However, if you aggregate a lot of data sites from different centers, from different studies, and really look at the behavior of tumors with developing resistance, you know, really big data mining, you can see lots of like new mechanisms as a price. This is a unique advantage of data scientists because we can utilize the big data-driven approach to aggregate lots of resources from different publications from different centers to get a holistic overview of that resistance process, and then develop some hypothesis and gather some key experimental validation. So in short, like I think our data sciences are an advantage in this process is the big data, the size.
OLIVER: So what kind of data are you analyzing, clinical data but also biolo