Genetics Of Color Cancer

Unraveling

the

Genetics of

COLON A Stanford Professor and Undergraduate’s Genomics Approach.

CANCER By Hahn Nguyen

In the 9.3 minutes it takes you to skim through this magazine, one person in the United States will have died from colon cancer. According to the American Cancer Society’s estimates, 145,000 people will be diagnosed and 56,730 people will die from colectoral cancer in our country alone each year. Colon cancer is currently the third most common form of cancer in the western world. Tumors and polyps in the colon, rectum, and appendix characterize this type of cancer. In an attempt to combat this disease, an undergraduate student working in a cancer biology lab at Stanford University School of Medicine has brought us one step closer to unlocking the genetics behind colon cancer. This lab, headed by Assistant Professor of Pathology Jonathan Pollack, uses a genomics approach to improve our knowledge of cancer and patient care. That is, they use DNA microarrays to discover gene patterns in cancer cell lines that model the disease as a whole. “Our main interest is understanding genetic instability and how it promotes cancer development and progression,” says Pollack. “And this is our first foray into colon cancer.” Craig Giacomini, who is now a Stanford first-year medical student, began leading this research in his junior year as a Stanford undergraduate. Published in Cancer Research this past October, their recent research indicates colon cancer, in terms of genes expressed and

Professor Jonathan Pollack and student Craig Giacomini prove that what we call “colon cancer” is actually two distinct diseases and suggest a source of chemoresistance in patients

gene pathways, is actually two distinct types of disease. That is, colon cancer occurs due to two scientifically distinct disease pathways. Through analysis of cancer cell lines, their research also suggests a source of chemotherapyresistance in patients. While not all mutations are harmful, those that increase the mutation rate, otherwise known as mutations in the “mutator” genes, often give rise to cancers. Mutations of this sort can be divided into two subtypes based on the class of mutator phenotypes: microsatellite instability (MSI) and chromosome instability, also known as microsatellite stable (MSS). Microsatellites consist of simple sequence repeats, such as a string of adenine nucleotides, found throughout the genome and sometimes in genes. In MSI mutations, microsatellites are copied incorrectly. As a result, the sequences become longer or shorter, and consequentially mutations occur throughout the genome. In the other mutator type, MSS, pieces of chromosomes are gained or lost in mitosis. This causes excess or lack of expression of certain genes. The phenotypic differences in MSI are a tendency for polyps to form on the right side of the colon and for patients to fair better. To further understand the role of MSI and MSS in colon cancer, Craig Giacomini in Professor Pollack’s lab looked for similarities between the Layout by Jason Shen

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Unraveling the Genetics of Colon Cancer two mutator phenotypes from a genetic standpoint. Do the two mutator phenotypes target two completely separate sets of genes or the same genes in different ways? “That was the goal in looking for signatures to distinguish between the two,” notes Pollack. The researchers working in his lab sought to find collections of specific genes that are expressed or not expressed which are unique to each form of the disease. This would result in a unique “signature” or group of certain genes whose expression significantly differ in the two causes of colon cancer.

primary gastric tumors (gastric tumors also have MSI and MSS mutations). Since the gene signature is also highly accurate for gastric tumors, this may reflect universal information about MSS and MSI mutations in other forms of cancers. These are not necessarily genes that cause colon cancer, but rather they are biomarkers of the tumors being MSI or MSS. “That’s the tip of the iceberg,” Pollack declares. “As a general conclusion, it indicates that these two types of cancer are really distinct, not just from mutation mechanism, but also from the genes involved in their pathogenesis. From an expression standpoint, they are distinct and hopefully in the future can be treated differently.” If colon cancer were simply one type of disease, the same genes would be mutated despite which mechanism caused the mutation. Pollack’s lab would have found nearly identical gene expression in MSI and MSS colon cancer. However, that was not the case. Analyzing microarray data, Giacomini identified 217 genes in total that are significantly different in the two types. Inspection of just eight of these genes is sufficient to classify whether the tumor is caused by a MSI or MSS mutation. Giacomini’s results are evidence that the term “colon cancer” actually refers to two different types of cancer, in terms of gene expression and pathways. The disease has two different mutation sources, making it, in fact, two different diseases, separable on a molecular and genetic level. Ideally, physicians and researchers will develop treatments that work on each optimally. Currently, patients diagnosed with MSI colon cancer tend

Cancer is such a complicated disorder with so many genetic DISRUPTIONS occurring. Microarray technology gives us an excellent tool to look at something so COMPLEX To explore the gene differences, Giacomini led research in profiling gene expression in 18 different colon cancer cell lines. His group used complementary-DNA microarrays to discover a statistically robust gene expression signature. The microarray represents approximately 21,000 different genes, and allows the researchers to analyze which genes are expressed in the cell lines. A microarray can be thought of as a grid of DNA spots, where each spot contains a unique DNA sequence that codes for a certain gene. These gene-specific probes contain complementary strand DNA, and will hybridize to a cell line sample which expresses that same particular gene. With fluorescent tags, researchers can determine to what degree any particular gene is expressed in their sample cell line, compared to a generic cell line. Microarray technology essentially allows thousands of experiments to be performed at once, as thousands of genes are tested for expression levels. In this project, Giacomini used this technology to profile the genes expressed in the colon cancer cells. “Cancer is such a complicated disorder with so many genetic disruptions occurring. Microarray technology gives us an excellent tool to look at something so complex,” Giacomini says. The result? Of the 21,000 genes surveyed, as few as eight differentially expressed genes can accurately classify MSI and MSS colon cancer. In other words, eight different genes are expressed in higher levels in MSI and are the best predictors that determine whether a cancer cell is MSS or MSI derived. Professor Pollack’s lab tested this signature on an independent set of colon and gastric tumors from the University of Hong Kong, which had already been identified as MSI or MSS mutated. Ignoring the tumors’ classifications, the Giacomini profiled the tumors’ gene expressions and predicted which tumors fell into which category. Their predictions were 100% accurate for colon cancer cell lines, 85% for primary colon tumors, and 84% for

It’s really great and a little strange at first to be able to look myself up on PUBMED

28 Stanford Scientific

to fair better overall. Stanford’s research suggests that the best treatment targets in the different types of colon cancer are likely distinct from each other, and that in the future, physicians should treat the two with drugs specific to each form of the disease. The eight genes differentially expressed in the classifier signature could reflect the effects of specific mutations or the sensing of ongoing DNA mutation activity. To address this ambiguity, the research group profiled the genes of corrected cancer cells, cells that have been genetically altered to stop the mutator phenotype. If the genes responded to ongoing mutation activity, they would no longer be expressed in corrected cells. However, Giacomini found the genes still expressed in these cells, indicating that the eight predictor genes represent the effects of prior mutations. These genes do not sense the damage; they are the result of the damage. Almost half of the eight predictor genes are metallothioneins, a family of small proteins that protects cells against many

attacks, including chemotherapy. The eight predictor genes are distinguishable because they are expressed at higher levels in MSI tumors. In parallel to the lab’s findings, it has been observed in treatments that MSI tumors tend to be resistant to some chemotherapy. “This immediately suggests a link between expression of metallothionein genes resistance,” explains Pollack. Additional tests in cultured cells can determine whether or not metallothioneins confers chemo-resistance. “That could improve treatment now because there are currently some drugs that inhibit production of metallothionein,” says Pollack. “Conceivably, we would begin to use these drugs in combination with current chemotherapy. “Our study shows the power of a genomics approach,” continues Pollack. “We have a hypothesis and we can test it by profiling expression across thousands of genes, since we don’t know for which specific gene to test… but by taking a genomics approach, we can learn something about cancer that we wouldn’t otherwise be able to see or address.” Professor Pollack has headed this cancer research lab for the past four years. His lab performs ongoing research in prostate and breast cancer, using DNA microarrays to identity and describe molecular phenotypes of tumor cells. The lab has also recently discovered a gene signature in leukemia that can predict how well a patient will fair in cancer treatment. “Although heart disease is the top cause of death in the United States, I believe as heart treatments improve cancer will take over as a leading cause of death,” notes Pollack. “There’s interesting biology underlying cancer research, and there’s a chance to make a difference in people’s lives.” Craig Giacomini began working in Pollack’s lab as an undergraduate at Stanford, and had his results accepted by premiere cancer journal Cancer Research in the spring of his senior year. “It’s really great and a little strange at first to be able to look myself up on Pubmed [the National Library of Medicine’s journal archive]. Working in Professor Pollack’s lab gave me insight into medically-based research, and it’s something I’d want to do in the future,” said Giacomini. Although his main interests consist of research, Giacomini is currently working toward his medical degree at the Stanford School of Medicine. “I want to stay in academic medicine,” he explains. “And combine research with clinical practice.” “In this research, Giacomini was 90% of the effort,” says Pollack. “One of the great things about Stanford, as opposed to Harvard or UC Berkeley, is that the Undergraduate School sits right next to the School of Medicine. There are many opportunities for undergraduates which are beneficial in both directions, and Giacomini’s research is certainly an example.” S Hanh Nguyen is a freshman majoring in Biology with a biochemistry concentration and has career goals in disease researching. When she’s not reading science articles, Hanh enjoys guitar, drinking coffee, and yoga.

Studying Cancer < A DNA microarray is a small grid on a slide. Each spot on the grid contains a unique DNA sequence that codes for a certain gene. These gene-specific probes are made up of complementary DNA— nucleic acids that are complementary, and will attach to, the DNA sequence that expresses the gene of interest. With fluorescent tags, researchers can determine where a particular DNA sample “sticks” to its complement, or hybridizes, and therefore to what degree any particular gene is expressed in their sample. Microarray technology essentially allows thousands of experiments to be performed at once, since as many as 21,000 (or 42,000 with newer technology) genes are concurrently tested for expression levels.

Giacomini analyzes > microarray data. “The great thing about microarrays is being able to look at thousands of genes simultaneously,” he says.

< Giacomini observes cancer cell lines. He first submitted his research to Proceedings in the National Academy of Sciences, which unfortunately did not review or publish his paper. “That was quite frustrating,” says Giacomini. “Although PNAS was my first choice, I was still psyched when Cancer Research accepted my paper.”

Microarray data collected by Prof. > Jon Pollack and Craig Giacomini. Each row represents a particular gene and each column represents a different sample cancer cell line. “The higher the red intensity of the square, the higher that particular gene is expressed in that specific cell line, compared to the average of all the cell lines,” explains Giacomini. “If the gene expression is low, the corresponding square is green.” Pollack continues, “We derived our eight gene signature from these above genes, because these particular genes are more highly expressed in MSI colon cancer, as seen in this microarray data.

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