Individualized cancer research

I have been intrigued for some time by Jay Tenenbaum‘s idea to forget about clinical cancer trials and focus on deep DNA and RNA (and perhaps protein) profiling of individual patients in order to optimize a treatment especially for the given patient. (See e.g. this earlier blog post about his company, CollabRx.)

Tenenbaum and Leroy Hood of the Institute for Systems Biology recently wrote about their ideas in an editorial called A Smarter War on Cancer:

One alternative to this conventional approach would be to treat a small number of highly motivated cancer patients as individual experiments, in scientific parlance an “N of 1.” Vast amounts of data could be analyzed from each patient’s tumor to predict which proteins are the most effective targets to destroy the cancer. Each patient would then receive a drug regimen specifically tailored for their tumor. The lack of “control patients” would require that each patient serve as his or her own control, using single subject research designs to track the tumor’s molecular response to treatment through repeated biopsies, a requirement that may eventually be replaced by sampling blood.

This sounds cool, but my gut feeling has been that it’s probably not a realistic concept yet. However, I came across a blogged conference report that suggests there may be some value in this approach already. MassGenomics writes about researchers in Canada who decided to try to help an 80-year-old patient with a rare type of tumor (an adenocarcinoma of the tongue). This tumor was surgically removed but metastasized to the lungs and did not respond to the prescribed drug. The researchers then sequenced the genome (DNA) and transcriptome ([messenger] RNA) of the tumor and a non-tumor control sample. They found four mutations that had occurred in the tumor, and also identified a gene that had been amplified in the tumor and against which there happened to be a drug available in the drug bank. Upon treatment with this drug, all metastases vanished – but unfortunately came back in a resistant form several months later. Still, it is encouraging to see that this type of genome studies can be used to delay the spread of tumors, even if just for a couple of months.

A while back, MIT Technology Review wrote about a microfluidic chip which is being used in a clinical trial for prostate cancer. This chip from Fluidigm is meant to analyze gene expression patterns in rare tumor cells captured from blood samples. It is hoped that the expression signatures will be predictive of how different patients respond to different medications. Another microfluidic device from Nanosphere has been approved by the U.S. Food and Drug Administration to be used to “…detect genetic variations in blood that modulate the effectiveness of some drugs.” This would take pharmacogenomics – the use of genome information to predict how individuals will respond to drugs – into the doctor’s office.

“You could have a version of our system in a molecular diagnostics lab running genetic assays, like those for cystic fibrosis and warfarin, or in a microbiology lab running virus assays, or in a stat lab for ER running tests, like the cardiac troponin test, a biomarker to diagnose heart attack, and pharmacogenomic testing for [Plavix metabolism],” says [Nanosphere CEO] Moffitt.

Update 10 Dec:

(a) Rick Anderson commented on this post and pointed to Exicon, a company that offers, among other things, personalized cancer diagnostics based on micro-RNA biomarkers.

(b) Via H+ magazine,  I learned about the Pink Army Cooperative, who do “open source personal drug development for breast cancer.” They want to use synthetic biology to make “N=1 medicines”, that is, drugs developed for one person only. They “…design our drugs computationally using public scientific knowledge and diagnostic data collected from the individual to be treated.”

Diagnostics

I finally found a meaningful way to use Wolfram Alpha beyond the demo scenarios that were presented when it launched. I did a diagnostic blood test a couple of weeks ago (blood lipids, liver profiles etc.) and Wolfram Alpha can be easily told to plot your own results along with distribution of values measured from American patients, by giving a query such as “ldl cholesterol 77 35 year old male non-smoker“.

Wolfram Alpha will become more useful now, though, because they have released an API that developers can use to integrate data from Wolfram Alpha into other applications. Unfortunately, it will cost money to use it, so it will likely only be used by semi-large to large corporations.

Diagnostics are also set to become more useful than the test I did. A company called Integrated Diagnostics, with systems biology luminary Leroy Hood as one of the founders, has officially launched. They are developing an inexpensive microfluidic chip that will allow easy and sensitive blood tests that can detect early-stage diseases. Instead of looking at single disease markers, as is often done today, the company’s chip will – in the spirit of systems biology – assay several markers at once, based on the idea that “diseases arise from a perturbed network” of genes and proteins.