Summer Research Highlight

Seven fantastic undergrads & recent-grads are working with me this summer, and we’ve already made a ton of progress.  We have a separate student blog, The Pathway Not Taken, which was established as part of a Computing Research Association Collaborative REU grant (I hope that program comes back, it was great).

First up is Amy Rose Lazarte, who just graduated from Reed. Before heading to Puppet Labs as a software engineer, she’s working to build models of phytoplankton fitness in freshwater lakes.  Read her post for more info:

Ecology Modeling: Thermal Variation and Phytoplankton Fitness

(If you want to learn a bit about all projects, read my summer kickstarter post).


Gender & Racial Disparities in Big Cancer Data

As a researcher who works with large publicly available biological datasets, I was reminded of the potential biases in big data when I came across this blog post from the University of Michigan Health Lab:

How Genomic Sequencing May Be Widening Racial Disparities in Cancer Care .  Nicole Fawcett, Aug 17, 2106.

Cancer is a notoriously heterogeneous disease, meaning that different patients with the same cancer type may harbor different sets of mutations.  Further, many genes associated with cancer tend to be mutated at very low frequencies in tumors [1].   In order to gain enough statistical power to confidently identify these rare “driver” mutations, we need data from hundreds to thousands of tumor samples.  Obtaining such a large number of samples often requires collecting tissues whenever possible.

The Cancer Genome Atlas (TCGA) is a massive data repository for dozens of cancers, containing data from hundreds to thousands of individuals for most cancer types.  The post above describes a recent study that determined the racial breakdown of tumor samples in 10 of the 31 tumor types from TCGA.  They found that while the samples were racially diverse — even, in some cases, matching the U.S. population — the number of African-American, Asian, and Hispanic samples were too small to identify group-specific mutations with 10% frequency for any tumor type except breast cancer in African-Americans. On the other hand, there were enough Caucasian samples in every tumor type to identify mutations with 10% frequency in the population (and 5% frequency for 8 of the 10 tumor types assessed).  Consequently, we identify more “rare” mutations that pertain to Caucasians simply because we have more data to support the findings.  Further, only 3% of the total samples were Hispanic, while Hispanics comprise 16% of the U.S. population.

This disparity is not limited to a race.  Gender representation in big cancer data has also been in the press.  The under-representation of women in sex-nonspecific cancer over the past 15 years has been reviewed by Hoyt and Rubin (Cancer 2012), who noted that this gap may be widening.

Want to see the discrepancies for yourself?  The data is easy enough to obtain, but Enpicom has a fantastic interactive visualization of the entire TCGA data repository by patient gender, race, and age.


Consider glioma, for example – while the incidence rate of brain tumors is higher in women than in men [2], women comprised only 41.4% of the over 1,100 samples.


Even more alarmingly,  over 88% of the samples are Caucasian.screen-shot-2016-09-14-at-1-50-03-pm

There is evidence of higher incidence rates of brain cancer in Caucasians compared African-Americans and Hispanics, but surely this doesn’t justify the over-representation in this dataset.

So, what should we do?

On one hand, we need to carefully design data collection efforts to ensure that different racial/ethnic groups are adequately represented – not simply to reflect the proportion in the U.S. population but to gain enough statistical power to confidently identify rare mutations.   On the other hand,  “convenience sampling” methods of obtaining tumors from the most convenient places, even if the population is homogenous, have enabled consortia to collect enough data in the first place.  In fact, we better understand the “rare mutation” concept due to the mostly-white patient data collected by TCGA and others.

The only clear answer is that we need more data.

[1] This is often called the “long tail” distribution of cancer gene mutations.  For more information, see, for example,  Lessons from the Cancer Genome. Garraway and Lander.  Cell 2013.

[2] All primary malignant and non-malignant brain and CNS tumors.  In fact, the incidence rate of malignant brain tumors is slightly higher in men.  Cancer statistics from the Central Brain Tumor Registry of the United States.



Yep, cancer is still complicated

Image from

If you haven’t read The Emperor of All Maladies: A Biography of Cancer by Siddhartha Mukherjee, I would highly recommend it. And if you would rather watch it, Ken Burns produced a documentary focusing on the book that recently aired on PBS.   While we have come a long way in cancer research, it is alarming how little we still know about it.  In the age of personalized medicine and the plethora of cancer datasets, you would think that understanding cancer is getting to be, at the very least, more understandable.  This New York Times opinion article gives a few examples where finding a druggable mutation is not as easy as one would hope.

Trying to Fool Cancer –


This WIRED article resonated with the New Media Seminar I’m taking at Virginia Tech.

Big Data: One Thing to Think About When Buying Your Apple Watch | WIRED.

I hadn’t heard of  the term ephemeralization coined by Buckminster Fuller before, which is the promise of technology to do “more and more with less and less until eventually you can do everything with nothing.” Fuller cites Ford’s assembly line as one example of ephemeralization.  Ali Rebaie, the author of the WIRED article, writes that the Big Data movement is another form of it.  Our ability to analyze huge datasets has lead to designing more efficient technology.  All in all, Fuller seems to fit right in with the others we have been reading in the seminar.