July 16, 2006
Customized Cancer Treatments With Proteonomics

Proteonomic techniques combined with gene silencing has led to the identification of yet another gene which can mutate to contribute to cancer.

In a step toward personalized medicine, Howard Hughes Medical Institute investigator Brian J. Druker and colleagues have developed a new technique to identify previously unknown genetic mutations that can trigger cancerous growth. By analyzing the proteins instead of the genes inside acute myeloid leukemia (AML) cells, the researchers have dramatically reduced the time it takes to zero in on molecular abnormalities that might be vulnerable to specific drug treatments.

The researchers are correct in arguing that this approach could lead to use to identify specific mutations for individual cancers so that cancer treatments can be tailored to the characteristics of each cancer case. But that is not the only value of this approach. They also have hit upon a faster way to find proteins whose mutations can cause or at least contribute to the development of cancer. My guess is the identification of more genes which can mutate to contribute to cancer will be the greater value.

"This approach gives us a way to figure out what's driving the growth of a cancer in an individual patient and ultimately match that patient with the right drug," said Druker, who is based at the Oregon Health & Science University in Portland. Druker's team collaborated on the research, which was published in the July 17, 2006, issue of the journal Cancer Cell, with scientists in the lab of D. Gary Gilliland, an HHMI investigator at Brigham and Women's Hospital, as well as researchers at the Portland VA Medical Center, Cell Signaling Technology, the University of Chicago, and Yale University.

Traditionally, cancer-gene hunters have scanned the genome looking for mutations that trigger out-of-control cell growth. Druker tried this approach, but found it wanting. "We were doing some high-throughput DNA sequencing, and we weren't really finding much," he said.

DNA sequencing is a hard way to look for mutations that drive cancer because cancer cells are genetically very unstable and have large numbers of mutations that are just side effects of the cancer. Also, genomes are very large and they end up sequencing lots of sequences that are not genes or that are not getting expressed even if they are genes.

They decided to instead sequence the peptides that make up proteins. This reduces the sequencing job by orders of magnitude.

Instead, the team added tools from the burgeoning field of proteomics, the study of proteins. "We decided this more functional assay would get us to the disease-causing genes more rapidly," said Druker, who has been studying a group of cell-signaling proteins called tyrosine kinases for 20 years.

Tyrosine kinases play a key role in many cancers. In healthy cells, they help form a chain of signals that prompt normal cell growth and division. Sometimes, though, a tyrosine kinase gets stuck in an "on" position, driving out-of-control cell division and, ultimately, cancer. This potentially devastating kinase activation carries a calling card in the form of a molecule called a phosphate.

"The phosphates signal activated tyrosine kinases," said Druker. "So we decided to use the phosphates as markers."

To find these markers, the team took myeloid leukemia cells and chemically digested them into a mixture of protein snippets called peptides. Next, they extracted all of the peptides carrying extra phosphates and sent them through a mass spectrometer, which precisely measured the weight of each peptide. Sophisticated software then sifted through a massive protein database at the National Library of Medicine, identifying each of the team's peptides as a segment of a specific protein. The analysis showed that many of the peptides came from tyrosine kinases. Scanning this list, Druker picked out five as likely suspects.

Sounds like they used RNA interference (RNAi) to block the candidate genes. So they used a number of fairly new techniques to do this research.

Druker's team then introduced into their leukemia cells five segments of RNA that each shut down one of the candidate kinases. Silencing four of the kinases with RNA did nothing the cells still grew out of control. But with the fifth, the cells no longer became cancerous.

"That left one gene to sequence. We found that the gene, called JAK3, had a mutation that drives the growth of leukemia cells in mice," said Druker. Analysis of additional patient samples later identified two more mutations in the JAK3 gene.

Thomas Mercher, a postdoctoral fellow in Gilliland's lab, then tested the mutation in a mouse model. "It was important to show that the JAK3 mutation, when introduced in mice, would lead to a leukemia-like illness. It did, confirming that the JAK3 mutations play a central role in leukemia," said Gilliland.

One of the reasons I'm optimistic that cancer will be cured within 10 to 20 years is that cancer researchers have such better tools for doing their work as compared to 10 years ago or even 5 years ago. Also, their tools will get better next year and the year after that and enormously better 15 or 20 years from now. Researchers will be able to identify mutations, introduce mutations, interfere function, sequence DNA, sequence peptides, and do other tasks with biological systems more cheaply and rapidly in the future with microfluidics and other advances in techniques. Experiments that are not even possible to do today will become possible and then increasingly easy to do.

Share |      Randall Parker, 2006 July 16 10:23 PM  Biotech Cancer

cancer man said at July 17, 2006 10:07 PM:

Good post.

I think your 10-20 year estimate is too conservative though considering in part the much more powerful computers researchers will be using (along with expanded information) by just 2010.

It is also interesting to see the 5 year survival rate curves over the past 30 years. In this sense, at least surviving cancer for many forms including breast cancer is approaching 90-100% and could reach this in just 5 years. Other cancers have had impressive gains as well although the curves for brain, stomach and lung cancer are much flatter.

A breakthrough in a few years could occur though, so I wouldn't argue these trends are deterministic.

Matt said at July 18, 2006 10:10 AM:

Sounds remarkable. I am hoping that this research and approach will produce results within the next decade.

gdpawel said at April 25, 2007 12:08 AM:

Protein Expression Analysis (Proteonomics)

Even though ovarian cancer accounts for only 4% of all cancers among women, it ranks as the fifth cause of death from it. A majority of ovarian cancer patients are not diagnosed until they reach the later stages of the disease. Once the cancer has metastasized, it is much harder to treat. There is a need to detect ovarian cancer in its early stages. There are no effective and proven tests for finding ovarian cancer in the early stages like mammography for breast cancer.

One of the most promising new approaches that may deal with early detection of ovarian cancer is Proteomics (Protein Expression Analysis), the study of proteins in the cells, tissues and body fluids. Even before a tumor can be felt, some researchers have found, the tumor begins secreting a distinctive pattern, or fingerprint of proteins. Here, you go beyond genes (DNA, the Genomic Analysis or structure of the human genome) and beyond Gene Expression (the measure of RNA content, like Her2/neu in breast cancer) to measure the actual proteins themselves.

However, Genomic Analysis is only important insofar as it influences Gene Expression Analysis, which is only important insofar as it influences Protein Expression Analysis (Proteonomics), which is only important insofar as it influences Protein Function Analysis (are proteins active or inactive), which is only important insofar as it influences Cell Function Analysis (cell culture assays), which is only important insofar as it influences Disease Analysis (doing something to treat the patient and then making a measurement on the patient with CT/PET scanning), in that order. There is an inverse hierachy between relevance and ease of measurement.

There are many pathways to altered cellular (forest) function (hence all the different "trees" which correlate in different situations). It serves to validate Whole Cell Profiling. The forest is looked at, and not the trees. Whole Cell Profiling measures what happens at the end (the effects on the forest), rather than the status of the individual trees. Cancer is a complex disease and needs to be attacked on many fronts. The best thing to do is to combine these different tests in ways which make the most sense. The future of cancer therapy will be personalized treatments for individual patients, and will require a combination of novel diagnostics and therapeutics.

Cell culture assays, using the whole cell profiling method, can assess the activity of a drug upon combined effect of all cellular process, using combined metabolic (cell metabolism) and morphologic (structure) endpoints, at the cell population level, measuring the interaction of the entire genome.

Improving cancer patient diagnosis and treatment through a combination of cellular and gene-based testing will offer predictive insight into the nature of an individual's particular cancer and enable oncologists to prescribe treatment more in keeping with the heterogeneity of the disease. The biologies are very different and the response to given drugs is very different.

Source: Cell Function Analysis

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