Wednesday, November 13, 2013

A Different View

It was six months ago this week that I was diagnosed with cancer.

Today I visited Dana-Farber. But not as a patient.

Before cancer I regularly attended a scientific seminar series presented by Dana-Farber's Center for Cancer Computational Biology (CCCB). Today was the first one I've attended since diagnosis.

As a side note, the CCCB is led by a former astrophysicist named John Quackenbush. If you'd like to read a short book written for laymen about genomics, I recommend his "The Human Genome".

Today's seminar featured Gavin Sherlock, a professor of Genetics from Stanford, and was titled "Tracking Adaptive Evolution in Real Time and at High Resolution".

A long time ago I intended to write a post about cancer genomics. I'll give a really brief version here.

Cancer is a disease of the DNA. The roots of cancer are in changes to DNA that are acquired during life. These are called somatic mutations because they occur in the body during life, as opposed to mutations that are inherited (called germline mutations because you get them via your parents' germ cells: sperm and egg). Somatic mutations can be caused by things like radiation, chemicals (in food, cigarette smoke, the environment), etc.

There are two kinds of mutations that are involved in cancer. Some genes act as oncogenes. When they acquire mutations that cause them to be overactive (they express an excess amount of their associated protein(s)) they cause cells to become cancer-like. They might grow beyond their normal size or location, fail to die when they should, divide more quickly than normal or take on other properties. The other kind of gene involved in cancer is a tumor suppressor gene. When these acquire mutations that cause them to be expressed less than normal (called "loss of function"), it's like taking the brakes off.

It typically takes multiple mutations in the same cell population to produce cancer.

It's also an accelerating process. Once cancer cells start dividing rapidly, they start acquiring mutations faster, too. One thing that goes wrong in cancer cells is the DNA repair process. So cells can acquire large and numerous DNA changes. Also, since they are dividing quickly, evolution occurs faster in them. Cancer cells adapt to their environment. This has implications for patients, because drugs that are targeted at a particular genetic mutation (called targeted therapies) might work well initially in a patient but then the cancer cells might adapt in such a way the drug no longer works.

The research that Dr. Sherlock presented today was performed in yeast but it has clear implications for cancer. His group used clever molecular biology tools (DNA barcodes) to trace yeast cells through hundreds of generations and then be able to pull out all of the mutations that occurred along that time line. Some mutations occur but are not advantageous so they disappear in a few generations. Other mutations are neutral, so they remain for a while but then also die out because other mutations are more advantageous. And finally a few mutations are advantageous and eventually spread throughout the population of cells over multiple generations.

We know the same thing happens in cancer. It's referred to as tumor heterogeneity. So far we treat each tumors as though all its cells have the same DNA but we know that they don't. We can't really watch evolution in a real tumor in the same way as this research did in yeast: it would require collecting cells from throughout a tumor at a whole bunch of time points. Not many patients are going to put up with that.

Biologists and cancer researchers are very clever and will eventually figure out a way to observe this in real tumors. And once we do, treatment regimens will change. We'll learn particular mutation patterns in particular types of cancer and drugs will be given in specific sequences to head off tumor evolution and kill the cancer cells before they can adapt.

Dr. Sherlock's lab's research shows how evolution actually works in cell populations. He showed some cool plots of mutation distribution over time. Unfortunately the figures were from work that is not yet published so I can't find them online to include here.

Here's a sketch. Each letter is a mutation. If you draw a vertical line through the plot at any time point, the plot shows the distribution of mutations in the population at that time. During the talk, Dr. Sherlock showed real data that looks like this sketch.

The sketch is from this presentation, which was similar to today's but doesn't include the latest experimental data.

Anyway, it felt great to think about cancer from a scientific point of view again instead of as a patient.

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