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Driver Mutations & Passenger Mutations on the road to cancer


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Scientists at the Wellcome Trust Sanger Institute, where one-third of the human genome was sequenced, have pioneered decoding the sequence of cancer genomes. They have carried out the broadest survey yet of the human genome in cancer by sequencing more than 250 million letters of DNA code, covering more than 500 genes and 200 cancers.

The survey, published in Nature, shows that the number of mutated genes that drive development of cancer is greater than previously thought. Significantly, as well as driver mutations for cancer, each cell type carries many more passenger mutations that have hitchhiked along for the ride. The study showed that a challenge for cancer biologists will be to distinguish the drivers from the larger number of passengers.

"The human genome is a vast place and this, our first deep systematic exploration in cancer, has thrown up many surprises", said Professor Mike Stratton, co-leader of the Cancer Genome Project at the Sanger Institute. "We have found a much larger number of mutated driver genes produced by a wider range of forces than we expected."

All cancers are believed to be due to mutations, abnormalities in genes. The availability of the human genome sequence has opened the door to analyzing hundreds to thousands of genes, which will ultimately allow us to acquire a complete catalogue of the mutations in individual cancers.

The team studied more than 500 genes of a type called kinases, some of which have been previously implicated in causing cancers. One example is the BRAF gene: the 2002 pilot phase of the team's work showed that BRAF was mutated in more than 60% of cases of malignant melanoma. That observation has driven discovery of new drugs to treat melanoma. The study was much broader and included breast, lung, colorectal and stomach cancers, which are the most common cancer types.

The new research showed that mutations in cancers can be divided into drivers or passengers. Driver mutations are the ones that cause cancer cells to grow, whereas passengers are co-travellers that make no contribution to cancer development. The team identified possible driver mutations in 120 genes, most of which had not been seen before.

"It turns out that most mutations in cancers are passengers," explained Dr Andy Futreal, co-leader of the Cancer Genome Project. "However, buried among them are much larger numbers of driver mutations than was previously anticipated. This suggests that many more genes contribute to cancer development than was thought."

Our understanding of the roles of kinase proteins sheds light on some of the mutations. Kinases can act as a series of relays, switching on and off in our cells, to control cell behaviour, such as cell division.

Dr Futreal explains: "For example, we found that a group of kinases involved in the Fibroblast Growth Factor Receptor signalling pathway was hit much more than we expected, particularly in colorectal cancers."

The team also found that the mutations carry important coded messages within them. The type of mutation found varies markedly between individual cancers, reflecting the processes that generated the mutations, some of which were active decades before the cancer showed itself.

Patterns of mutations are an archaeological record written into the DNA of each cancer telling us about the factors that caused the cancer in the first place, which were often active many decades previously. Some of the patterns can be deciphered, such as the signatures of damage from ultraviolet radiation (sunlight) or cancer-causing chemicals in tobacco, but others are currently cryptic and will require decoding in the future.

"This study vindicates all of the effort that went into the Human Genome Project," commented Dr Mark Walport, Director of the Wellcome Trust. "Understanding the mutations that cause cancer is crucial in order to develop accurately targeted treatments."

This research shows cancers in a different light and highlights many different insights into how cancers develop. The challenge will be distinguishing the drivers from the passengers.

In some cases this is straightforward. For many others, it appears, scientists will have to analyse much larger numbers of each cancer type. New, faster DNA sequencing technologies will play an important part in achieving the scale of study needed.

"The time is right to apply the powerful tools of genomics to obtain a comprehensive view of what goes wrong at the DNA level in cancer," said Francis S. Collins, MD, PhD, director of the National Human Genome Research Institute at the National Institutes of Health. "The important and interesting data on protein kinases in this report by Professor Stratton, Dr Futreal and colleagues further encourages the conclusion that a full assault on the cancer genome will yield many opportunities to revolutionize diagnosis and treatment."

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While it may be nice to think that driver mutations are around 70% predictive of a response (mutation positive patients have response rates from 53% - 100%, depending upon the study), while mutation negative response patients have a response rate generally around 10% (response rate of 0% - 25%), sometimes the genetic signal may not be the driver mutation. Other signaling pathways, like passenger mutations, could be operative.

It turns out that most mutations in cancers are passengers. It had been thought by molecular scientists that driver mutations are the ones that cause cancer cells to grow, whereas passengers are co-travellers that make no contribution to cancer development.

However, buried among them are much larger numbers of driver mutations than was previously anticipated. This suggests that many more genes contribute to cancer development than was thought.

Cells speak to each other and the messages they send are interpreted via intracellular pathways. You wouldn't know this using analyte-based genomic and proteomic methodologies. However, functional (cytometric) profiling provides the window. It can test various cell-death signaling pathways downstream.

While most scientists use genomic or proteomic platforms to detect mutations in these pathways that might result in response to chemicals, functional (cytometric) profiling platforms have taken a different tack. By applying functional analysis, to measure the end result of pathway activation or deactivation, they can predict whether patients will actually respond.

The functional (cytometric) profiling platform has the capacity to measure genetic and epigenetic events as a functional, real-time adjunct to static genomic and proteomic platforms.

As virtually every presentation at the 2012 AACR meeting made obligatory reference to genomic analysis, almost every one of them then doubled back to metabolism as the principal driver of human cancer.

It may be very important to zero in on different genes and proteins. However, when actually taking the "targeted" drugs, do the drugs even enter the cancer cell? Once entered, does it immediately get metabolized or pumped out, or does it accumulate? In other words, will it work for every patient?

All the validations of this gene or that protein provides us with a variety of sophisticated techniques to provide new insights into the tumorigenic process, but if the "targeted" drug either won't "get in" in the first place or if it gets pumped out/extruded or if it gets immediately metabolized inside the cell, it just isn't going to work.

To overcome the problems of heterogeneity in cancer and prevent rapid cellular adaptation, oncologists are able to tailor chemotherapy in individual patients. This can be done by testing "live" tumor cells to see if they are susceptible to particular drugs, before giving them to the patient. DNA microarray work will prove to be highly complementary to the parellel breakthrough efforts in targeted therapy through cell function analysis.

Gene Mutation vs Chromosomal Theory of Cancer


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In molecular testing of lung cancer, there are patients that have driver mutations such as EGFR and ALK. Some of these patients treated with appropriate targeted drugs like Tarceva and Xalkori, respectively, often have responses and somewhat improved survival.

The problem, according to Dr. Joan Schiller, Professor of Hematology and Oncology, and Deputy Director of the Simmons Cancer Center at the University of Texas Southwestern Medical School, is that those two mutations only represent a small minority of lung cancers.

The Lung Cancer Mutation Consortium (LCMC), a group of 14 institutions that banded together to find more driver mutations that are actionable in lung cancer, collected and analyzed about 1,200 patients with stage IV adenocarcinoma of the lung, of which 1,000 usable tumors and data were obtained.

Of those, KRAS represented about 23% of all the mutations, EGFR about 20% and EML4-ALK about 10%. The remainder 1%-2% were PI-3 kinase, RAS-1, RET, MEC, MET, ALT, BRAF, and HER2. Therefore, in 45% of patients, they were not able to identify a targetable mutation. This does not include squamous cell carcinoma, although they hope to extend the data collection to include this segment.

In the future, they hope to be able to have a physician order genomic testing on a patient's lung cancer and have it come back with appropriate mutations identified so that they can target the right drug to the right patient.

Medical research has focused a great deal on developing DNA (genomic) tests to identify gene expressions, amplifications and mutations relevant to cancer. The hope is that genetic information will enable researchers to better predict how you will respond to various treatment options.

However, when it comes to predicting the best treatment, unlocking the complexities of your DNA is simply not the answer. In fact, a March 2010 study in the Journal of the National Cancer Institute looked at the value of a number of gene tests and concluded none of the studies showed “clear usefulness.”

While genomic analysis can provide a veneer of information, unraveling the complexity of human tumor biology is beyond the scope of these analyses. Gene tests cannot capture the myriad of factors that ultimately determine how tumor cells will behave inside the body. Simply put, the human body is much more complex than the sum of its genes.

For example: a flower seed may have the genetic instructions to become a rose. But, its genes will not necessarily determine its size, number of blooms, etc. These features are heavily influenced by non-genetic and environmental factors, such as the soil, nutrients, water, sun exposure, pathogens and the climate in which the seed is nurtured.

No good gardener would attempt to tell you how your future bouquet will look by simply examining a packet of flower seeds. Similarly, no good doctor should attempt to choose drugs based solely on genomic analyses. Most physicians realize that genotype does not equal phenotype.

By testing your tumor in its native state, “functional profiling” takes not just your genomic make-up into consideration, but your cells’ entire biology. Treatment based on genetic testing is still a guessing game. Selecting drugs and combinations through the functional profiling of a tumor sample can predict response to treatment.


Kris MG, Johnson BE, Kwiatkowski DJ, et al. Identification of driver mutations in tumor specimens from 1,000 patients with lung adenocarcinoma: The NCI's Lung Cancer Mutation Consortium (LCMC). Program and abstracts of the American Society of Clinical Oncology Annual Meeting; June 3-7, 2011; Chicago, Illinois. Abstract CRA7506.

Varella-Garcia M, Berry LD, Su PF, et al. ALK and MET genes in advanced lung adenocarcinomas: The Lung Cancer Mutation Consortium experience. Program and abstracts of the American Society of Clinical Oncology Annual Meeting and Exposition; June 1-5, 2012; Chicago, Illinois. Abstract 7589.


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DNA deletions promote cancer, collateral damage makes it vulnerable

Passenger deletions that deactivate essential genes provide new opportunity to strike cancer

Genomic deletions promote cancer by carving up or eliminating tumor-suppressor genes, but now scientists report in the journal Nature that the collateral damage they inflict on neighboring genes exposes cancer cells to vulnerabilities and new avenues for attack.

Working with cell lines of glioblastoma multiforme, the most lethal type of brain tumor, researchers from the Dana-Farber Cancer Institute at Harvard Medical School, and some now at The University of Texas MD Anderson Cancer Center, found that collateral deletion of a gene vital to tumor metabolism allowed them to kill malignant cells by blocking another gene that redundantly performs the same function.

"Cancer-driving deletions disable tumor suppressors, and so far efforts to restore or replace the function of these deactivated genes, or turn them against cancer cells, have yet to show promising results," said co-lead author Florian Muller, Ph.D., an instructor in MD Anderson's Department of Genomic Medicine.

Passengers matter - a new approach for targeted therapy

"In this case, we looked at passenger deletions - genes co-deleted along with tumor-suppressor genes, but not directly involved in cancer promotion - as a starting point for identifying potential targets and therapies," Muller said.

The researchers wiped out glioblastoma cells that had deletions of the metabolic gene ENO1 on both copies of chromosome one when they also inhibited the function of ENO2, which is located on chromosome seven. Both genes encode for the enzyme enolase, which carries out a crucial step in glycolysis, the processing of glucose into energy that is particularly important to solid tumors. Cells can tolerate the loss of either ENO1 or ENO2, but not both.

"The principle of collateral vulnerability caused by passenger deletions of redundant essential genes provides the basis for a new approach to identify potential targets and develop targeted therapies," said MD Anderson President Ronald DePinho, M.D., senior author of the paper.

"These deletions are found in hundreds of genes in many types of cancer, so our model for glioblastoma multiforme should apply to developing personalized treatments for other cancers as well," DePinho said.

Targeting gene loss of function in tumor cells stands in contrast to the more frequent search for molecular targeted therapies to stymie active cancer-driving genes that are amplified or dysfunctional due to mutations.

Finding ENO

DNA deletions that take out tumor-suppressing genes tend to be large events that affect many other neighboring genes. "Most metabolic genes come in duplicates; they back one another up," Muller said. The hypothesis: Deletions create vulnerabilities by deactivating both copies of one essential gene, providing an opportunity to treat cancer by deactivating the second gene

A search of The Cancer Genome Atlas for glioblastoma multiforme yielded a variety of candidates, including ENO1, which resides on a stretch of chromosome one that includes several candidate tumor-suppressing genes. That portion of the chromosome is deleted in 1 to 5 percent of glioblastomas and in scattered cases of other tumor types.

In mammals, the enzyme enolase is encoded by ENO1 in all types of tissues, by ENO2 in neural cells, and by ENO3 in muscle cells. In theory, with ENO1 missing, inhibiting ENO2 would thwart glioblastoma cells without harming normal brain cells, which would still have both genes.

shRNA knock down of ENO2 confirms hypothesis

In work conducted by co-first authors Simona Colla, Ph.D., also an instructor in Genomic Medicine, and Elisa Aquilanti, a medical student at Albert Einstein College of Medicine in New York, the team used short hairpin RNA to knock down ENO2 in glioblastoma cell lines either missing ENO1 or with ENO1 intact.

Knocking down ENO2:

Had no effect in glioblastoma cells with ENO1 intact.

Sharply inhibited growth of glioblastoma cells with ENO1 deleted and caused complete loss of tumor-forming potential when injected into the brains of mice.

As expected, growth could be restored in ENO1-deleted glioblastoma cells by either expressing hairpin-resistant ENO2 or by artificially expressing ENO1 in the cells.

"In cells with ENO1 deletions, the backup is gone. We then hit ENO2 with a gene-specific shRNA, no enolase is left and the cells cannot survive," Muller said.

An enolase inhibitor is selectively toxic to ENO1-deficient cancer cells

Because ENO1 accounts for 75 to 90 percent of total enolase activity, ENO1-deleted glioblastomas cells have much lower overall enolase activity than either cancer cells with ENO1 intact or normal non-cancerous cells. With ENO1-deleted cells already deficient in the enzyme, the investigators reckoned that low doses of a small-molecule enolase inhibitor would be sufficient to block glycolysis and reach a toxic threshold.

The enolase inhibitor phosphonoacetohydroxamate (PHAH) was found to be highly toxic to ENO1-deleted cancer cells while having minimal effect on ENO1-intact cancer cells or normal human brain cells. This toxicity could be completely reversed by artificial re-expression of ENO1.

PHAH isn't approved for human use, Muller said, and based on its structure is unlikely to penetrate tissues and tumors effectively. Researchers are working with MD Anderson's Institute for Applied Cancer Science and others to develop a potential drug.

Concept applicable to other passenger deletions, other cancers

Although ENO1 deletions only occur in a small subset of glioblastoma patients, passenger deletions are quite frequent in the cancer genome and occur in most cancer types, the investigators noted. Because genes critical for cell survival often come in ENO1/ENO2 type duplicate pairs, the concept of collateral lethality is likely to be applicable to passenger deletions beyond those affecting ENO1.

This research began at Dana-Farber in Boston and continued at MD Anderson after DePinho became the institution's president last September.

This research was funded by grants from the National Cancer Institute of the National Institutes of Health (NIH grant numbers CA95616-10 and CA009361), the American Cancer Society and the Howard Hughes Medical Institute; also a Harvard PRISE fellowship, the Dana-Farber Cancer Center/Harvard Cancer Center Myeloma SPORE, and the Ben and Catherine Ivy Foundation.

Co-authors with DePinho, Muller, Colla and Aquilanti are Giannicola Genovese, M.D., Pingna Deng, M.D., Luigi Nezi, Ph.D., Baoli Hu, Ph.D., Jian Hu, Ph.D., Derrick Ong, Ph.D, Eliot Fletcher-Sananikone, Lawrence Kwong, Ph.D., Y. Alan Wang, Ph.D., and Lynda Chin, M.D., all of MD Anderson's Department of Genomic Medicine and formerly of Dana-Farber Cancer Institute; Veronica Manzo, Jaclyn Lee, Daniel Eisenson, Rujuta Narurkar, Michelle A. Lee, M.D., Ph.D., Ergun Sahin, M.D. Ph.D., Dennis Ho, Ph.D, of Dana-Farber Cancer Institute; and Cameron Brennan, M.D., of Memorial Sloan-Kettering Cancer Center in New York.

Source: MD Anderson

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Why don't all the mutation positive patients respond and why do some mutation negative patients respond? Cancer biology is complex. Molecular biologists can only seek and identify that which they know about a priori. There are numerous mutations, insertions and deletions. A gene mutation, deletion, translocation or amplification could disrupt many cell functions, leading to drug resistance, or could inactivate or damage the doors through which a drug enters a cell.

So far, efforts to restore or replace the function of these deactivated genes, or turn them against cancer cells, have yet to show promising results. The number of mutated genes that drive development of cancer is greater than previously though. Significantly, as well as driver mutations for cancer, each cell type carries many more passenger mutations that have hitchhiked along for the ride. A challenge for cancer biologists has been to distinguish the drivers from the larger number of passengers.

Even the leader of the Cancer Genome Project at the Sanger Institute has admitted that the human genome is a vast place and deep systemic exploration in cancer has thrown up many surprises. They had found a much larger number of mutated driver genes produced by a wider range of forces than they expected. As it turns out, most mutations in cancers are passengers, however, buried among them are much larger numbers of driver mutations than was previously anticipated. This suggests that many more genes contribute to cancer development than was thought.

Cell speak to each other and the messages they send are interpreted via intracellular and extracellular pathways. You wouldn't know this using analyte-based genomic and proteomic methodologies. You need the capacity to measure genetic and epigenetic events as a functional, real-time adjunct to static genomic and proteomic platforms.

A recent article in Biotechniques, "Will the Real Cancer Cell Please Stand Up," has suggested the cancer cells are not individual entities but networks. A harmonic oscillation develops between tumor, stroma, vasculature and cytokines. In this mix, the cancer cell is but one piece of the puzzle. The value of using cancer cell-lines for drug sensitivity investigation has come under fire. Some of the tumor promotion signals in the form of small interfering RNAs may arise not from the cancer cells but instead from the surrounding stroma. How then will genomic analyses of cancer cells play out in the real world of human tumor biology and clinical response prediction?

Cancer cell-lines date back to the 1950s (Hela Cells). In the decades since, approximately 1000 cancer cell-lines have become essential tools for cancer cell biology and to test drugs. Almost every anti-cancer drug now in use gained early traction through testing on cancer cell-lines. Now, researchers are calling for new models that can assay primary cancer cells directly from patients to sketch a more realistic molecular portrait of primary tumors. In other words, using "live" fresh cells instead of cell-lines.

http://www.biotechniques.com/news/Will- ... 29467.html

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In recent years, personalized care has come to be considered synonymous with genomic profiling. While breakthroughs in human genomics is applauded, there is no molecular platform that can match patients to treatments. The objective response rate of just 10 percent, almost all in breast and ovarian cancer patients in one study (Von Hoff J Clin Oncol 2010 Nov 20:28(33): 4877-83), suggests that cancer biology is demonstrably more complex than an enumeration of its constituent DNA base pairs.

The Microarray (gene chips) is a device that measures differences in gene sequence, gene expression or protein expression in biological samples. Microarrays may be used to compare gene or protein expression under different conditions, such as cells found in cancer.

Hence the headlong rush to develop tests to identify molecular predisposing mechansims whose presence still does not guarantee that a drug will be effective for an individual patient. Nor can they, for any patient or even large group of patients, discriminate the potential for clinical activity among different agents of the same class.

Genetic profiles are able to help doctors determine which patients will probably develop cancer, and those who will most likely relapse. However, it cannot be suitable for specific treatments for "individual" patients.

Cancer cells often have many mutations in many different pathways, so even if one route is shut down by a targeted treatment, the cancer cell may be able to use other routes. Targeting one pathway may not be as effective as targeting multiple pathways in a cancer cell.

Another challenge is to identify for which patients the targeted treatment will be effective. Tumors can become resistant to a targeted treatment, or the drug no longer works, even if it has previously been effective in shrinking a tumor. Drugs are combined with existing ones to target the tumor more effectively. Most cancers cannot be effectively treated with targeted drugs alone.

Gene profiling tests, important in order to identify new therapeutic targets and thereby to develop useful drugs, are still years away from working successfully in predicting treatment response for "individual" patients. Perhaps this is because they are performed on dead, preserved cells that were never actually exposed to the drugs whose activity they are trying to assess.

It will never be as effective as the cell "function" method, which exists today and is not hampered by the problems associated with gene expression tests. That is because they measure the net effect of all processes within the cancer, acting with and against each other in real time, and it tests living cells actually exposed to drugs and drug combinations of interest.

The key to understanding the genome is understanding how cells work. The ultimate driver is a "functional" assay (is the cell being killed regardless of the mechanism) as opposed to a "target" assay (does the cell express a particular target that the drug is supposed to be attacking). While a "target" assay tells you whether or not to give "one" drug, a "functional" assay can find other compounds and combinations and can recommend them from the one assay.

The core of the functional assay is the cell, composed of hundreds of complex molecules that regulate the pathways necessary for vital cellular functions. If a "targeted" drug could perturb any one of these pathways, it is important to examine the effects of the drug within the context of the cell. Both genomics and proteomics can identify potential new therapeutic targets, but these targets require the determination of cellular endpoints.

Cell-based functional assays are being used for screening compounds for efficacy and biosafety. The ability to track the behavior of cancer cells permits data gathering on functional behavior not available in any other kind of assay.

Literature Citation: Eur J Clin Invest 37 (suppl. 1):60, 2007

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