Archive for the ‘Oncogenes’ Category

Genes and the odds

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Posted 15 May 2012 — by James Street
Category Bioinformatics, Gene sequencing, genetic research, Oncogenes

 

 

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genetic mutation

By Amy Jeter
The Virginian-Pilot
©

Iris Byrum told her three grown daughters what she was going to do before she did it.

She broke the news in person, and though she also wanted to tell them what they should do – scream to them not to take any chances with their health – she bit her tongue and remained calm, as usual.

Her own body already had been ravaged twice by an aggressive breast cancer, weakened by the poisons needed to beat it back.

Tissue from her abdomen filled the hole in her chest left by a radical mastectomy. A scar marched across her hips, where surgeons had harvested the flesh and a small crater gaped under her collarbone, where they had carved away a second tumor.

The cancer was 10 years gone but could return at any time.

Byrum didn’t want to go through that again. She didn’t want her daughters to go through that ever – but knew full well that they might. All she could do was try to prepare them.

So Byrum, an operations supervisor with UPS, tracked down Helena and Angie at Angie’s house in Suffolk. She caught up with Mindy at home.

She announced that she was going to find out if cancer ran in the family.

If Byrum tested positive for a gene mutation, her chances of another bout with the disease would rise, and she’d have to ratchet up her defense.

A positive result also meant each of her girls would be faced with a decision: Did they want to know whether they’d inherited the curse?

“At first we were all gung-ho,” said Helena Byrum, the eldest. “Who wouldn’t want to know what their fate is?”

Byrum’s first tumor appeared in 1996 with the suddenness and severity of a gunshot wound.

She was 40 years old and had been in and out of hospitals her whole life. When she was a teenager, doctors implanted a steel rod in her back and encased her in a body cast to correct scoliosis.

Maybe that was why she became the kind of woman who always did the recommended health checks.

In this case, her fastidiousness probably saved her life. Byrum herself found the lump in her breast, less than three weeks after a clean mammogram.

The cancer, already almost the size of a golf ball, was advanced. Her doctors classified it as Stage 3, bordering on Stage 4, the most severe.

Her teenage daughters asked, “Are you going to die?”

Not if she had anything to say about it. A surgeon removed Byrum’s right breast and 19 lymph nodes, to be sure cancer cells hadn’t broken away from the tumor and traveled through her body in her lymph system. Thankfully, her lymph nodes showed no sign of the disease.

Byrum underwent seven months of chemotherapy, but her hair didn’t fall out that time. She kept working, and most people didn’t know she was sick.

In 1997, she found another lump.

This one was a monster, rising ominously under her collarbone. Byrum’s doctors warned about the possible need to remove her collarbone and cut the nerve in her right arm.

Luckily, they didn’t have to go to such extremes. When she awoke from surgery, she could still move her arm. At first, she was horrified, thinking the surgeons hadn’t cut away the tumor, but the nurses quickly assured her that it was a victory.

This time, the treatments pummeled her body for two years. She lost weight, her hair and her fingernails. High-dose chemotherapy made her vomit constantly. She moved to Richmond for six weeks to undergo an experimental stem-cell transplant.

Her husband, Ricky, was working as a longshoreman, and Helena had moved out of the house. Ricky’s brother’s wife, Kim, cared for Byrum. Angie, the middle daughter, cooked her mother’s meals, drove her to chemo treatments and to the emergency room on weekends.

“I maybe saw Mom cry twice,” Angie remembered. “If I was her, I probably would have been crying all the time.”

It took another year for Byrum to begin to feel like herself again.

Even then, she needed to monitor her body closely. Every six months, her blood was tested, her chest was X-rayed and her bones were scanned.

Around that time, Byrum heard about gene mutations that increase cancer risk and wondered if she might have one. In one in 10 patients, cancer is related to a hereditary cause.

The most common mutations causing breast cancer occur in genes called BRCA1 and BRCA2, short for breast cancer susceptibility genes one and two. The genes are supposed to work as tumor suppressors. Inherited mutations in those genes account for an estimated 5 to 10 percent of breast cancers and 10 to 15 percent of ovarian cancers among white women in the United States.

For women who inherit a harmful mutation, the lifetime risk of developing breast cancer is as high as 80 percent, compared with 12 percent for women in the general population. Risk for ovarian cancer is 15 to 40 percent, compared with 1.4 percent. Men with a deleterious mutation on either gene also are at increased risk for breast and possibly other cancers.

Additionally, cancer survivors with a damaged BRCA gene are more likely to develop a new tumor in the breast, ovaries or other site associated with those genes.

Byrum wanted to know if the odds were working against her and her daughters – or even her mother and nieces.

Her family’s cancer history would have provided a clue, but Byrum didn’t know much beyond the fact that her father had died of lung cancer the year before her first diagnosis. Her aunts and uncles were much older than her parents, and no one discussed the specifics of their health.

“It was almost like a secret,” she said.

Intrigued, but cautious, Byrum decided to wait to be tested. She wanted to be sure laws barred insurance companies from hiking premiums or dropping coverage for people with the mutations. She also wanted to be sure the tests were accurate.

In 2008, her doctor said it might be time.

Byrum knew she was opening a can of worms as far as her family was concerned. That’s why she told them all beforehand. If she tested positive, everyone from her sister to her grandsons could carry the gene mutation.

But Byrum was ready for answers. Did her cancer have a source? Was it a fluke? Was it a rogue gene? Could she have passed it on?

She had a feeling about it, and her feelings usually were right. Still, waiting for the result felt a lot like waiting to learn how far her cancer had spread.

The news, when it came, didn’t surprise her. Byrum had a BRCA1 mutation, and she immediately decided to have her left breast and ovaries cut away.

“I was armed with information that was going to prevent me from – hopefully – prevent me from ever having to go through cancer treatment again,” Byrum said. “It was life-saving information, and I was going to use it.”

Two months later, waiting outside the genetic counselor’s office at Virginia Oncology Associates, Helena told her mother about her bargain with God.

If one of the three daughters had to be positive, Helena wanted it to be her.

Angie and Mindy both had children. Helena didn’t. If her sisters were positive, they’d have to worry about whether they’d passed the mutation on to their kids. Helena’s next generation – and its genes – was still a blank slate.

Also, Helena felt she was the most like their mother. She believed she’d inherited her mother’s strength, along with her brown eyes and generous spirit. They even shared a name: Iris Helena Byrum.

They both liked concrete facts.

All three daughters supported their mom’s decision to have the surgeries. They approached the problem differently when it came to themselves.

Helena, who was in her early 30s, figured she would get the test. If the result was positive, she’d go from there.

Angie, two years younger, was caring for a fussy infant. She didn’t want anything else big to worry about: “I thought it would be weird to know.”

Mindy, four years younger than Helena, had more time before the decision became pressing.

Taking the test was easy, Helena found: you just give a little blood. Waiting for the results turned out to be much more difficult.

It was all she could think about: “If I become positive, what do I do? If I become negative, then I don’t have to worry about it. But what if, what if…,”

By the time she finally found herself in the waiting room with her mother, Helena’s anxieties boiled over in tears. Unusual; she normally didn’t cry.

The wait seemed like forever.

Let me be the only one, Helena thought. Let me be the only one.

Byrum said nothing. Though she knew it was absurd, she felt responsible. It weighed on her heart, as a mother.

Let it be negative, Byrum thought. Let it be negative.

Tifany Lewis, the genetic counselor, called them back and asked Helena how she was doing.

“OK,” Helena said. “A little nervous.”

She told Lewis she was all right with whatever happened next.

Lewis told her the result:

Positive.

Helena doesn’t remember much about what happened next.

The tears struck again.

Helena knew the genetic counselor was talking. She tried to concentrate on the words but understood nothing. Lewis’ voice sounded like the muffled-horn noise of adults in Charlie Brown cartoons.

Later, Helena realized that she felt the way people often do when they learn that they have cancer. It was how her mother had felt that second time.

This time, Byrum’s heart was breaking. She wanted to cry, but she knew she needed to be a rock. She reached out her hand to her daughter as Lewis went over charts and described risk.

In later appointments, Helena’s doctors outlined her choices. She could monitor her body through extensive checks every three to four months: mammograms, MRIs, CT scans, ultrasounds, blood tests.

Or she could submit to a double mastectomy and have her ovaries taken out. That would lower her risk for both breast and ovarian cancer to that of the general population.

But concepts like risk and probability proved maddeningly esoteric when Helena tried to apply them to her own flesh and bones.

With each year she lived, she became more likely to develop cancer. That argued for bold action now.

On the other hand, some women with a deleterious mutation never developed cancer. What if she allowed herself to be cut up for no reason?

Like many in her position, Helena first chose the screening option.

For about nine months, she regularly took time off from her job as an office manager for a construction company to sit in waiting rooms and worry about test results. But the stress started to get to her.

Two major obstacles stood in the way of surgeries: Helena still wanted to have a child, so she needed her ovaries. And she wanted to breast-feed.

Only her mother could change her mind about a mastectomy. Byrum told Helena that not being able to breast-feed didn’t mean you were a bad mother. But if Helena developed breast cancer while she was pregnant, that would be a serious problem.

“Do you want to have a baby and raise it?” Byrum asked. “Or do you want to have a baby to breast-feed it?”

The answer was clear, though not easy.

Helena had always been athletic. She didn’t like the thought of mutilating her body, which was what a mastectomy seemed to do, euphemisms aside.

She was so trim that doctors couldn’t even use her own body fat to build new breasts after the surgery. She just didn’t have enough.

In September 2009, Helena had a double mastectomy.

Recovery was tougher than she expected. She rested in her Virginia Beach home for a month and, at first, wasn’t even strong enough to push a fan’s plug into a socket.

Her mind teemed with second thoughts: What did I do? Why did I do this? Did it really happen? Did they really tell me I was positive? Did I make all of this up?

depression weighed her down and stayed until her doctor called one day with news.

They had found precancerous cells in the left breast after it was removed.

“I knew right then that I definitely did the right thing.”

These days, Helena thinks a lot about timelines.

She’s 37, two years beyond her original target age for having a child. Then there’s the ovarian-cancer risk, which continues to increase as she nears 40.

Ovarian cancer is a tricky animal. It’s less common and, in its early stages, less deadly than breast cancer. But it’s also more difficult to find. By the time the disease is detected, it might be too late.

Twice a year, Helena takes time off from her job as an assistant accounting manager to get an ultrasound, and occasionally her doctors will order blood work to check on her ovaries. They don’t pressure her, but they do remind her that precious time is passing, especially after they removed a growth in her uterus last November. It turned out to be nothing.

After Helena got tested, Angie and Mindy did, too.

Angie did it suddenly, almost on a whim, one day in her gynecologist’s office.

She was relieved to learn that she tested negative but felt almost guilty telling Helena about it. Angie thought maybe her role was to be there for her mother and sister.

The more she thought about it, the more she realized that, while it was good news, it didn’t mean she was in the clear. She probably has the same chances as anyone of developing cancer – or any other health problem.

“There’s always something,” said Angie, who is 35.

Mindy, who is 32, tested positive. She is still considering her options.

Helena faces important choices. She has wanted a child ever since she can remember. Her sisters’ boys call her “NeeNee,” and she’s a natural with them, wrestling and playing Nerf darts.

But if Helena has a baby, she could pass along her gene mutation and the agony that goes along with it. Or maybe she wouldn’t.

Helena takes comfort in the thought that scientific advancements could make the whole process less painful decades from now, when her grown child could face a predicament like hers.

She has a little time to make her decision.

Her mother is standing back, ready to step in whenever needed.

“Whatever decision she would make,” Byrum said, “I completely support her, 100 percent.”

 

Amy Jeter, 757-446-2730, amy.jeter@pilotonline.com

 

More than 30 tests are available

Cancer in most patients isn’t associated with an inherited gene mutation.

However, Hampton Roads patients can find out if they carry a gene mutation that increases their cancer risk at Virginia Oncology Associates’ risk reduction clinic.

People with several family members with the same type of cancer or family members who developed the disease when they were younger than 50 might benefit from genetic testing, said Dr. Ranjit Goudar.

At the clinic, a patient first sees Goudar or another oncologist to compile a detailed profile of the patient’s health, family medical history, lifestyle and past. Based on that information, the doctor will determine whether one of more than 30 genetic tests could be appropriate.

If testing is an option, the patient generally will consult with a genetic counselor, such as Tifany Lewis. Together, they’ll discuss the test and how results could affect the patient and family members.

The tests range between a few hundred to several thousand dollars, and most health insurance plans cover most tests.

Whether or not results are positive, Goudar works with patients on ways to stay cancer-free.

“Trying to prevent cancer is the real goal,” Goudar said. “That’s ambitious, but it’s very doable.”

- Amy Jeter

 

Novel Stanford math formula can predict success of certain cancer therapies

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Posted 06 Oct 2011 — by James Street
Category Mathematical and Physical Modelling, Oncogenes, Physics and Engineering

STANFORD, Calif. — Carefully tracking the rate of response of human lung tumors during the first weeks of treatment can predict which cancers will undergo sustained regression, suggests a new study by researchers at the Stanford University School of Medicine.

The finding was made after scientists gained a new insight into therapies that target cancer-causing genes: They are successful not because they cause cell death directly, but instead because they slow the rate of tumor cell division. In other words, squelching messages promoting rampant cell growth allows already existing death signals to prevail and causes tumors to shrink.

The research highlights the emerging promise of applying mathematical and computational concepts to the study of complex biological systems.

“It’s really just advanced high-school-level math,” said associate professor of medicine and of pathology Dean Felsher, MD, PhD. “With some simple measurements, we found we can determine when a cancer is addicted to a particular cancer gene and will respond to therapy targeting that gene. I was astounded that it works.”

Felsher, the leader of the Stanford Molecular Therapeutics Program, is a senior author of the study, which will be published Oct. 5 in Science Translational Medicine. He teamed up with assistant professor of radiology David Paik, PhD, an expert in computational biology and the co-senior author of the research. Felsher and Paik are both members of the Stanford Cancer Institute and Molecular Imaging Program; Felsher is also a member of Stanford’s Center for Cancer Systems Biology.

Felsher and his colleagues used a computational biology approach to characterize a phenomenon called oncogene addiction, in which a cancer is dependent on the activity of one cancer-causing gene. Tumors that are dependent on a single mutated protein for their growth regress quickly when the activity of that protein, or oncogene, is blocked. However, because individual cancers reflect the interplay of hundreds or thousands of mutations within each cell, it’s very difficult to tell which, or how many, tumors fall into that category.

“Lots of people will respond to therapy at first, but many times they don’t get better,” said Felsher. “With a new therapy, would you rather wait four months and say, ‘Well, it’s kind of working,’ or is it better to know after a couple of weeks? We’ve found that the kinetics of regression can quickly predict whether the tumor is oncogene-addicted and likely to be treated successfully by targeted therapies.”

As an oncologist specializing in the treatment of patients with lymphoma, Felsher has studied the concept of oncogene addiction in his laboratory for several years. He and his colleagues have developed a strain of laboratory mice that express a mutated version of an oncogene called K-ras when a chemical is added to the animals’ drinking water. When the chemical is present, the animals develop lung tumors; when it is removed from the water, the tumors regress.

Felsher and Paik then used this experimental model of oncogene addiction in the current study. After inducing tumor formation in the animals, they stopped the expression of the oncogene and mapped the kinetics of the tumors’ regression by precisely measuring death and survival signals. As previously reported, the cancers were undetectable within four weeks. But then they went one step further: to understand the changes in signals in the cancer they measured the phosphorylation status — shorthand for activity levels of certain important genes — of known signaling molecules involved in both cell survival and programmed cell death.

“Basically, we wanted to understand what happens to survival and death signals in the cell when you turn oncogenes off in an addicted tumor,” said Felsher. “What we saw is that the levels of both sets of signals go down dramatically over time, but the signals that promote the survival of cancer cells dissipate much more quickly. When that happens, the balance tips toward cell death and the tumors get smaller.”

Whether a cell lives or dies depends on a balance of signals. This research shows that oncogene-targeted therapies kill addicted tumors indirectly by decreasing survival signals, which allows the existing death signals to predominate.

Felsher and Paik then used a differential equation (a way to describe the relationship between interdependent variables over time) to correlate the changes in aggregate survival and death signals with the rate of tumor regression in the animals. They checked the accuracy of their equation by using it to predict the behavior of tumors in an experimental model of lymphomas in which the oncogene called Myc is activated. They also showed that the equation could identify whether a Myc-induced lymphoma would increase or decrease in size when additional pro-survival or pro-death pathways were activated.

Satisfied that the approach worked, the researchers then turned their attention to people with a type of lung cancer called adenocarcinoma. About 10 percent of these cancers have a mutation in a gene called epidermal growth factor receptor, or EGFR, and will respond to EGFR-targeted therapy.

“We realized that we could possibly use our equation to predict the kinetics of tumor cell elimination in cancer patients,” said Felsher. “An oncogene-addicted tumor will regress at a specific rate and in a different way than a tumor that is not addicted, and patients with addicted tumors will have a better prognosis when the responsible oncogene is inactivated by targeted therapy.”

Felsher and Paik discovered that their model could predict which of 43 patients enrolled in a clinical trial to test an EGFR-targeted therapy called erlotinib had tumors that were oncogene-addicted, and which did not, simply by charting the rate of tumor regression during the first four weeks of treatment. As they predicted, those patients with oncogene-addicted tumors fared better than their peers.

Although in this case the predictions were done retrospectively and therefore did not affect the patients’ treatment, it’s possible that in the future similar techniques could be used to quickly assess whether a therapy is likely to work for a particular patient, or if a different treatment should be tried. The researchers are now trying to extend their findings to include other cancers and additional variables.

“Our results may have provocative implications,” said Felsher. “We’ve learned that a key point that many people don’t realize is that it matters a lot how quickly the tumor is getting smaller. There’s a certain rate of regression where you’re never going to get rid of your cancer completely, but at another rate you will. For oncogene-addicted tumors, it’s a very predictable kinetic response.”

###

In addition to Felsher and Paik, other Stanford researchers involved in the work include Phuoc Tran, MD, PhD, now an assistant professor of radiation oncology at Johns Hopkins; Pavan Bendapudi, MD, now a resident at Massachusetts General Hospital; postdoctoral scholars H. Jill Lin, PhD, and Nicholas Hughes, PhD; graduate student Peter Choi; research associates Shan Koh and Joy Chen; and pulmonary fellow George Horng, MD, now at California Pacific Medical Center.

The research was supported by the Radiological Society of North America, the Francis Family Foundation, the Henry S. Kaplan Fund, the Howard Hughes Medical Institute, Stanford MIPS, the National Institutes of Health, the Leukemia and Lymphoma Society, Burroughs Wellcome Fund and Damon Runyon Foundation.

Information about the Department of Medicine, in which the research was conducted, is available at http://medicine.stanford.edu.

The Stanford University School of Medicine consistently ranks among the nation’s top medical schools, integrating research, medical education, patient care and community service. For more news about the school, please visit http://mednews.stanford.edu. The medical school is part of Stanford Medicine, which includes Stanford Hospital & Clinics and Lucile Packard Children’s Hospital. For information about all three, please visit http://stanfordmedicine.org/about/news.html.

PRINT MEDIA CONTACT: Krista Conger at (650) 725-5371 (kristac@stanford.edu)
BROADCAST MEDIA CONTACT: M.A. Malone at (650) 723-6912 (mamalone@stanford.edu)

Gene Mutations Associated with Lethal Prostate Cancers Found through Exome Sequencing

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Posted 28 Sep 2011 — by James Street
Category Gene sequencing, genetic research, Oncogenes, Prostate Cancer

GEN News Highlights: Sep 27, 2011

Whole-exome sequencing of advanced and lethal prostate cancer tumors has led to the identification of a number of genes that are regularly altered, researchers report. The team, at the University of Washington and the Fred Hutchinson Cancer Research Center, in addition found that a subset of prostate cancer tumors exhibit hypermutated genomes, which may be linked with drug resistance.

Reporting in PNAS, the University of Washington’s Jay Shendure, M.D., Fred Hutchinson’s Peter S. Nelson, M.D., and colleagues, say comparison of castration-resistant and castration-sensitive cells derived from the same prostate cancer has highlighted mutations in the Wnt pathway as potentially contributing to the development of castration resistance. Their results are described in a paper titled “Exome sequencing identifies a spectrum of mutation frequencies in advanced and lethal prostate cancers.”

Drs. Shendure, Nelson et al. carried out whole-exome sequencing to investigate the mutational landscape of 23 prostate cancers from 16 different lethal metastatic tumors including three high-grade primary carcinomas. They separately compared the exomes of three tumors representing castration-resistant variants of original cancers. All tissues were propagated in immunocompromised mice as tumor xenografts to model the heterogeneity in tumor growth, response to treatment, and lethality.

Because corresponding normal tissue wasn’t available for a number of the tumor samples, the researchers  only sequenced tumor tissue but discarded genes that mapped to the mouse genome, as well as all variants that had been identified in the pilot dataset of the 1,000 Genomes Project and those present in any of some 2,000 additional exomes sequenced at the University of Washington.

The team then narrowed down the resulting set of novel nonsynonymous single nucleotide variants (nov-nsSNVs) to identify the most likely protein-altering point mutations across different tumors. This reduced the 14,705 novel variants identified in the initial sequencing phase down to  20 genes in which nov-nsSNVs were found in two or more exomes and 10 genes with nov-nsSNVs in three or more exomes.

To segregate candidate genes further, they annotated positions in terms of conservation, using the Genomic Evolutionary Rate Profiling (GERP) score. This technique predicted which variants at highly conserved positions would be functionally significant and resulted in the identification of a subset of best candidates including several previously identified mutations in advanced prostate cancer (including the top candidate TP53) and others ( including DLK2 and SDF2) that have been linked with tumorigenesis but not with prostate cancer per se.

Nov-nsSNVs in TP53 were found in 5 of the 16 independent tumors used to evaluate recurrence, and these variants were predicted to cause premature termination of the protein. Three tumors harbored nov-nsSNVs within the gene encoding DLK2, a protein that shares similarity with the delta transcription factor and has recently been shown to be involved in notch1 signaling during development, the authors note. Three tumor genomes encoded variants in the calcium-binding protein stromal-derived factor (SDF4). Non-nsSNVs found in SDF4 and DLK2 included variations in conserved regions.

Other genes that harbored nov-nsSNVs in at least two or more advanced prostate cancers included  CDH15, LAMC1, and GPC6. These were recently identified in a whole-genome sequencing study of localized primary prostate cancers.

A comparison of the exomes of castration-sensitive tumors with their castration-resistant derivatives identified 12–50 genes with nonsynonymous mutations that were only present in the castration-resistant xenografts, even though each of these genes had also been identified in at least one of the 16 independent tumors.

Of note, the castration-resistant tumors displayed a significant enrichment for genes involved in in Wnt signaling: of 86 mutations found in CRPCs but not the castration-sensitive cancer from which they were derived, each tumor had at least one mutation in a member of the Wnt pathway. These included FZD6, GSK3B, and WNT6.

The genomes of three prostate cancers displayed a nearly 10-fold higher number of nov-nsSNVs than the other advanced or lethal prostate cancer tumors. These hypermutated tumors were consequently excluded from the initial analysis and filtering and evaluated separately. Interestingly, the researchers report, none of the three tumors demonstrated distinguishing features: All were derived from Caucasian patients, one represented a primary neoplasm, one a lymph node metastasis, and one a liver metastasis.

One of the tumors was evaluated further to confirm that the hypermutator phenotype arose before passaging in mice. Further characterization of this tumor indicated that the pattern of somatic mutations was heavily dominated by transition mutations, with G→A and C→T transitions accounting for greater than 70% of mutations observed.

The mutation frequencies in these hypermutated tumors far exceed those found in primary prostate cancers and in most cancers such as breast, pancreas, and brain for which comprehensive exome or genome sequencing has been carried out, the authors point out.

However, they add, “cancers in the colon with mismatch repair gene defects and those that arise in the lung and skin, where environmental genotoxins like tobacco or UV sun exposure are implicated in disease etiology, have numbers of mutations that approach those present in these hypermutated prostate cancers.” Even so, the patterns of mutations observed in the hypermutated prostate tumors didn’t match those that occur in cancers associated with tobacco exposure.

One potential explanation for the large number of mutations in the three prostate cancer samples is the acquisition of a mutator phenotype, in which alterations in DNA polymerase or DNA repair genes result in an accelerated rate of mutations, they suggest. Supporting this notion was the finding that one of the hypermutated tumors possessed three candidate mutations in MSH6, a gene known to promote mismatch repair and microsatellite stability, and which has previously been implicated in prostate cancers with increased overall mutation rate.

However, the other two hypermutated tumors didn’t appear to harbor non-nsSNVs within DNA mismatch repair genes. “Thus, a plausible explanation for the elevated mutation frequencies in these cancers remains to be established,” the authors admit.

“Collectively, our results indicate that point mutations arising in coding regions of advanced prostate cancers are common, but with notable exceptions, very few genes are mutated in a substantial fraction of tumors,” they conclude. “Our results also suggest that increasingly deep catalogs of human germline variation may challenge the necessity of sequencing matched tumor normal pairs.”

Fox Chase to help cancer patients by mapping genes of tumor

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Posted 20 Jun 2011 — by James Street
Category Bioinformatics, Epigenetics, genetic research, Oncogenes

In a move that could one day help cancer patients mine their own DNA for new treatment options, Fox Chase Cancer Center last week announced it was striking a partnership with the California-based biotechnology giant Life Technologies Corp.

The plan is to map the genes of patients’ tumors so doctors can devise precise treatments.

Fighting cancer remains fiendishly complex. “At a genetic level, any given tumor type is different in different individuals,” says Jeff Boyd, senior vice president for molecular medicine at Fox Chase.

So the hospital in Northeast Philadelphia plans to open the Cancer Genome Institute at Fox Chase this fall to use the latest equipment from Life Technologies for “deep sequencing” tumors.

Patients will initially bear the cost of this $7,000 test, which takes about two weeks, plus pay more for operational costs. So few cancer patients will likely sign up early on.

But Boyd predicts the time and costs will drop dramatically as the technology grows more widespread.

Some patients may find immediate help if their genetic mutations are similar to known conditions with proven drug treatments. Other patients may show multiple mutations best treated with new combinations of existing drugs. But others may show mutations with no currently known treatments.

Still, over time, Fox Chase and Life Technologies hope to build a rich database of genetic information and patient outcomes that could drive development of new drugs for hard-to-treat cancers.

Because of cost, Fox Chase plans to analyze only the parts of DNA that make proteins. The rest – more than 98 percent of a person’s DNA – is a focus of research at the Kimmel Cancer Center at Jefferson, where researchers say they believe some cancers are caused by mutations in this so-called noncoding DNA. Meanwhile, scientists at the University of Pennsylvania’s Abramson Cancer Center are interested in genetic mutations that can lead to cancer-drug resistance over time.

All three efforts show how cancer care is becoming almost as personal as a signature.
- Helen Shen

Targeting the cancer kinome through polypharmacology

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Posted 07 Jun 2011 — by James Street
Category Breast Cancer, Chronic myeloid leukaemia (CML), GIST, Kinome, Lung Cancer, Oncogenes
Zachary A. Knight, Henry Lin, and Kevan M. Shokat
Zachary A. Knight, The Rockefeller University, New York, New York 10065, USA.
Correspondence to Z.A.K. and K.M.S. Email: zknight@rockefeller.edu; Email: shokat@cmp.ucsf.edu
Small right arrow pointing to: The publisher’s final edited version of this article is available at Nat Rev Cancer
Small right arrow pointing to: See other articles in PMC that cite the published article.
Abstract
Kinase inhibitors are the largest class of new cancer drugs. However, it is already apparent that most tumours can escape from the inhibition of any single kinase. If it is necessary to inhibit multiple kinases, how do we choose which ones? In this Opinion article, we discuss some of the strategies that are currently being used to identify new therapeutic combinations of kinase targets.
More than 10,000 patent applications for kinase inhibitors have been filed since 2001 in the United States alone1. This massive investment has been fuelled by the realization that kinases are intimately involved in cancer cell growth, proliferation and survival. Indeed, kinases and their direct regulators are among the most frequently mutated oncogenes and tumour suppressors24. Well known examples include the oncogenic kinases PIK3CA (the p110α subunit of PI3K), epidermal growth factor receptor (EGFR) and BRAF; the Ras family of oncogenes, which activate both PI3Ks and Raf; and the PTEN tumour suppressor, which inhibits PI3K signalling.
Despite the excitement surrounding these targets, clinical progress has been uneven. Kinase inhibitors have revolutionized the treatment of a select group of diseases, such as chronic myeloid leukaemia (CML) and gastrointestinal stromal tumours (GIST), which are driven by a single oncogenic kinase; for these conditions, kinase inhibitors have achieved multi-year increases in survival57. Smaller but significant responses have been observed for some cancers that are highly dependent on angiogenesis, and therefore sensitive to inhibitors of vascular endothelial growth factor (VEGF) signalling, such as renal cell carcinoma811.
Kinase inhibitors have been least effective in treating the types of cancer that have the highest mortality rates, such as lung, breast, colorectal, pancreatic and prostate cancer. Clinical trials show that the most effective kinase inhibitors prolong survival by only a few months for these cancers1217. Results have been improved by identifying markers for patients that are more likely to respond to kinase inhibitor therapy — such as EGFR mutations in lung cancer18, ERBB2 overexpression in breast cancer19, and wild-type KRAS in lung and colorectal cancer20,21 — but even among these subgroups, relapse is inevitable for patients with disseminated disease.
Why has clinical progress been so challenging? One reason is that most tumours can escape from the inhibition of any single kinase (FIG. 1). This first became clear when resistance mutations in BCR–ABL were discovered in patients with CML who were resistant to imatinib22; similar mutations have now been detected in other kinases following treatment with kinase inhibitors2326. Alternatively, tumours can acquire drug resistance through mechanisms that do not involve mutation of the target (FIG. 1a). These mechanisms include the activation of surrogate kinases that substitute for the drug target27 and the inactivation of phosphatases to amplify the residual kinase activity that persists during drug treatment28. It is also clear that many tumours possess intrinsic resistance to kinase inhibitors at the time of initial therapy (FIG. 1b). This can result from the activation of multiple, redundant kinase signalling pathways29 or the presence of activating mutations in downstream pathway components, such as KRAS or PTEN, which enable the tumour to bypass the drug target20,21,30.
Figure 1

Resistance to kinase inhibitors
Overcoming these resistance mechanisms will require targeting tumour cells at multiple levels, through either single drugs that bind to multiple proteins31 or cocktails of highly selective inhibitors32. The challenge for the cancer research community is to learn how to predict the best combinations of targets and then prioritize those combinations for clinical testing. This is a daunting task, because the number of possible target combinations is almost limitless, but clinical trials are slow and expensive.
Targeting one kinase with multiple drugs
If a tumour depends on the activity of a single kinase, then using multiple drugs to target that kinase can be effective. This was first demonstrated in CML, in which early clinical trials showed that more than 90% of patients with chronic phase disease responded to the BCR–ABL inhibitor imatinib5 (TABLE 1), but that a subset of those patients relapsed while on the drug. Disease progression was associated with the emergence of leukaemic cells bearing mutations in BCR–ABL that block imatinib binding22, suggesting that drugs targeting these BCR–ABL mutants would be effective. Two second-generation BCR–ABL inhibitors were developed (dasatinib and nilotinib) that retain activity against most of the more than 50 clinically observed BCR–ABL resistance mutations, and these drugs are highly effective against imatinib-resistant disease33,34. However, a common BCR–ABL mutation (T315I) prevents the binding of all three drugs, and this has emerged as the default allele for many patients on long-term inhibitor therapy22,35. To address this problem, third-generation drugs have been developed that potently inhibit BCR–ABL T315I. These agents are effective in preclinical models of drug-resistant CML3639, and four such compounds are currently in clinical trials. Some patients have now survived more than 10 years since starting treatment by undergoing sequential therapy with three generations of BCR–ABL inhibitors40, proving that it is possible to extend the therapeutic response in CML by repeatedly targeting the same kinase.
Table 1
Table 1

US FDA-approved kinase inhibitors
A similar approach has been used to target the ERBB2 receptor tyrosine kinase in breast cancer. Trastuzumab is a monoclonal antibody that binds to the extracellular domain of ERBB2, thereby both inhibiting ERBB2 signalling and recruiting immune cells to the tumour19; however, patients with metastatic cancer who are treated with trastuzumab invariably relapse. The mechanism of trastuzumab resistance is not understood, but it is clear that resistant tumours remain dependent on ERBB2 signalling. This is because patients with breast cancer who have progressed on trastuzumab therapy nonetheless respond to lapatinib14, a small molecule inhibitor of the tyrosine kinase domain of ERBB2. Therefore, it is possible to induce a second response in these patients by targeting ERBB2 with a drug that binds to a different site on the protein. Unlike CML, however, the clinical response to lapatinib in metastatic breast cancer is brief, and disease progression typically occurs within a few months14.
These examples show that in certain cases sequential targeting of a single kinase with multiple drugs can prolong the therapeutic response. It is unclear how broadly this model applies, because it is unclear how many tumours are truly dependent on a single oncogene (a state referred to as ‘oncogene addiction’ (REF. 41)). The strongest evidence in favour of this hypothesis is the discovery of resistance mutations after kinase inhibitor treatment in CML, GIST, lung cancer and a myeloproliferative disorder known as hypereosinophilic syndrome2226. Such mutations are definitive proof that the mutated kinase was required for the survival of that tumour. The oncogene addiction model is also supported by many preclinical studies showing that tumour cell lines containing an activating mutation or amplification of a kinase can be more sensitive to inhibitors of that kinase in vitro4244.
Conversely, the detection of an oncogenic kinase mutation does not guarantee sensitivity to the corresponding kinase inhibitor. For example, mutations in PIK3CA or PTEN are poor predictors of the sensitivity of tumour cell lines to PI3K inhibitors31,45. Mutations in KRAS do not, in general, sensitize tumour cells to inhibitors of Raf or Mek43,46. Indeed, the response of most tumours to inhibition of an oncogene is much less dramatic than the response in CML, in which even transient inhibition of BCR–ABL irreversibly commits cells to apoptosis44 (FIG. 2). In this respect, it is worth noting that the term oncogene addiction gained widespread use because it describes a paradox: inhibiting an oncogene would be predicted to reverse the gain of function caused by the oncogene, not kill all the tumour cells. It is only recently that this term has been conflated with the idea that oncogenes should be expected to be required for tumour survival. For this reason, there is clearly a need to identify additional vulnerabilities in tumours beyond the genes that are directly mutated.
Figure 2

Degrees of oncogene addiction
Targeting nodes in a signalling network
Three sets of targets collectively account for a large proportion of current efforts in kinase inhibitor drug discovery. These are the receptor tyrosine kinases (for example, EGFR, ERBB2, platelet-derived growth factor receptor (PDGFR) and VEGF receptor 2 (VEGFR2)), the kinases in the MAPK pathway (for example, BRAF, MEK1 and MEK2) and the kinases in the PI3K pathway (for example, PIK3CA, Akt and mTOR). These three groups of targets are mechanistically linked because most receptor tyrosine kinases activate the MAPK and PI3K pathways as their primary signalling function (FIG. 3).
Figure 3

Strategies for multi-targeted kinase inhibition
There is a compelling biological rationale for targeting each of these groups in combination. For example, clinical resistance to tyrosine kinase inhibitors is often associated with reactivation of PI3K signalling28. Therefore, the effectiveness of tyrosine kinase inhibitors might be increased by combination with an inhibitor of the PI3K pathway. This combination has been shown to be effective in animal models and is undergoing extensive clinical testing: at least 21 clinical trials are currently evaluating the combination of a tyrosine kinase inhibitor and an mTOR inhibitor in several types of cancer. There has been a particular emphasis on the use of PI3K inhibitors to sensitize tumours to inhibitors of EGFR or ERBB2 (such as erlotinib47, lapatinib48 and trastuzumab49). This is because the anti-tumour activity of EGFR and/or ERBB2 inhibitors has been correlated with their ability to inhibit the phosphorylation of ERBB3, a kinase-inactive receptor that primarily functions to activate the PI3K pathway28.
Other target combinations are suggested by the connectivity of the signalling network. For example, mTOR activates a well-characterized negative feedback loop that inhibits the activity of PI3K (FIG. 3a). mTOR inhibitors such as rapamycin block this negative feedback loop, resulting in hyper-activation of PI3K that may counteract the anti-proliferative effect of mTOR inhibition. For this reason, it has been proposed that the dual inhibition of PI3K and mTOR may be more effective than inhibiting either target alone. Preclinical experiments support this idea45, and drugs such as PP121 that target multiple steps in this pathway have been designed31 (FIG. 3a). Several dual PI3K and mTOR inhibitors are currently being evaluated in clinical trials (for example, NVP-BEZ235, BGT226 and XL765) alongside agents that selectively target either PI3Ks (for example, XL147 and GDC-0941) or mTOR (for example, OSI-027, AZD8055 and rapamycin analogues). As there are practical challenges associated with developing both multi-targeted single agents and multi-drug cocktails50, it will be interesting to see which approach emerges as the most successful from these clinical trials.
Combination therapy can be used in other cases to target an otherwise undruggable protein. For example, KRAS is one of the most commonly mutated oncogenes, but efforts to find Ras inhibitors have been unsuccessful. It was long believed that the MAPK pathway was the primary Ras effector in most tumours51, but Raf and Mek inhibitors have inconsistent activity against tumour cells with Ras mutations43,46. Ras also directly binds to and activates PI3K52, and the disruption of this interaction prevents KRAS-driven tumorigenesis in the mouse53. For this reason, it may be necessary to inhibit both the MAPK and PI3K pathways to block the growth of tumours with Ras mutations. This conclusion is supported by data showing that resistance to Mek inhibitors in some KRAS-mutant cells is caused by mutations in PIK3CA or PTEN, and that this resistance is reversed by PI3K inhibition54. Moreover, the combination of PI3K and Mek inhibitors is active in a mouse model of KRAS-driven lung cancer55. The rationale for this combination is so compelling that Merck and AstraZeneca recently announced a joint Phase I clinical trial that will test the combination of an Akt inhibitor (MK-2206) and a Mek inhibitor (AZD6244) against solid tumours (FIG. 3b).
Limitations of rational drug combinations
The challenge associated with developing these types of rationally designed drug cocktails is that preclinical experiments do not predict their efficacy in humans. This is true even when the individual agents have already shown clinical anticancer activity. For example, preclinical experiments supported the combination of gefitinib and trastuzumab in breast cancer56,57, erlotinib and bevacizumab in renal cell carcinoma58, and cetuximab and bevacizumab in colorectal cancer59, but all of these failed in clinical trials58,60,61. In the case of cetuximab and bevacizumab, the drug combination reduced survival compared with the single agents60.
In some cases, these discrepancies may be due to misinterpretation of the preclinical data, rather than a failure of the preclinical model itself. For example, careful studies have shown that the addition of gefitinib to trastuzumab therapy in xenograft models of breast cancer results in only modest additional efficacy62, and that this additional benefit requires gefitinib concentrations that may be toxic in humans63. In other cases, subtle changes in the dosing regimen can have a large effect on the activity of the combination. For example, preclinical studies of the cyclin-dependent kinase inhibitor flavopiridol and the topoisomerase inhibitor irinotecan showed that this combination can effectively induce apoptosis in colon cancer cells when administered in a specific sequence (irinotecan followed by flavopiridol, resulting in apoptosis in 43% of the cells)64. The reverse sequence of drugs (15% apoptosis) and concurrent therapy (30% apoptosis) were both less effective. This finding was rationalized by a model in which pretreatment with flavopiridol arrested cells in the G1 phase of the cell cycle and thereby reduced the number of cells progressing through S phase and therefore irinotecan sensitivity64. A subsequent clinical trial validated the safety and preliminary efficacy of this sequential dosing regimen65.
Preclinical studies of drug combinations are probably biased towards validating the targets that are already believed to be important, and this bias limits their ability to prioritize new drug combinations for clinical testing. For example, all kinase inhibitors have some toxicity to cells, and for this reason two kinase inhibitors can usually be shown to be more toxic than either compound alone. For these comparisons it is often unclear what should be used as the normal cell to measure therapeutic index66, and in many cases the survival of the mouse in a xenograft experiment is the only evidence of differential toxicity. This can be addressed to some degree by correlating lethality with genotype across many tumour cell lines42 or by using pairs of isogenic cell lines that differ at a single locus66, but this becomes challenging when comparing drug combinations.
For a small group of kinase targets with an undisputed role in cancer — such as the oncogenic receptor tyrosine kinases and the core components of the PI3K and MAPK pathways — numerous clinical trials of drug combinations are planned or underway. It is uncertain, however, that these kinases are the best cancer drug targets67, and the route to clinical testing for combinations of drugs that target other kinases is less straight-forward. One major obstacle is that it is difficult to conduct clinical trials combining two investigational drugs, and even more difficult if the two drugs originate from different pharmaceutical companies68,69. Companies are reluctant to conduct joint clinical trials of early-stage compounds because of fears about loss of intellectual property and the possibility of an unforeseen side effect from the combination68,69. This creates a Catch-22 scenario: many kinase inhibitors are likely to be effective only as part of a combination therapy, but it will be difficult to test those combinations until after the drugs are approved as single agents. Indeed, the joint venture mentioned above between AstraZeneca and Merck to test Akt and Mek inhibitors in combination was reported in national media, such as The Wall Street Journal, partly because such early stage collaborations are so rare70. In the field of AIDS research, this problem was addressed in 1993 by the formation of the Inter-Company Collaboration for AIDS Drug Development that coordinated the testing of drug cocktails by 15 pharmaceutical companies71. However, there is not yet a comparable mechanism for companies to collaborate to test new combinations of investigational drugs in oncology, where there is arguably the greatest need and opportunity.
Using RNAi to discover new targets
The development of RNA interference (RNAi) has made it possible to directly screen for the genes required for tumour proliferation in mammalian cells. These screens have two advantages. First, they can identify new drug targets, as any gene that selectively blocks tumour growth when knocked down by RNAi is a candidate. Second, these screens provide an unbiased test of models of tumour signalling, because they directly examine which genes are most important to the tumour. This perspective is valuable, because most combination therapies are based on simple models of tumour signalling; however, there is little evidence that such models capture the most crucial interactions in the tumour cell, which could be highly indirect and inaccessible to simple reasoning.
Three recent papers illustrate the power of large-scale RNAi screens to address this problem by looking for genes that are selectively required for the growth of tumour cells expressing an activated KRAS mutant7274. Luo et al.72 screened ~75,000 short hairpin RNAs (shRNAs) and found 83 shRNAs targeting 77 genes that preferentially impaired the growth of KRASG13D cells compared with control cells in which the KRASG13D allele had been disrupted by homologous recombination72. Analysis of these hits revealed an unexpected enrichment of a network of genes involved in mitosis. A small molecule inhibitor of the mitotic kinase polo-like kinase 1 (PLK1) had increased cytotoxicity to KRAS-mutant cells in vitro and in vivo72.
Scholl et al.73 screened a smaller set of shRNAs (5,024 targeting 1,011 genes) against a broader panel of cells that included 8 tumour cell lines (4 KRASG13D mutant and 4 KRAS wild-type) and 2 control cell lines73. The top hit was STK33, a serine threonine kinase in the calmodulin kinase family with no previous connection to Ras signalling or cancer. shRNAs targeting STK33 induced KRAS mutation-dependent toxicity in a broad panel of tumour cell lines, through a mechanism that may involve modulation of S6K1 kinase activity73.
Barbie et al.74 screened a panel of shRNAs targeting kinases, phosphatases and oncogenes against a panel of 19 tumour cell lines and then extracted from these data the genes selectively required for the survival of KRAS-mutant cells74. The top hit from this screen was TBK1, a protein kinase that activates nuclear factor-κB (NF-κB) signalling by phosphorylating the NF-κB inhibitory protein IκBα. A companion paper showed that genetic inhibition of NF-κB signalling was sufficient to block tumour development in a mouse model of KRAS-driven lung adenocarcinoma75.
A common finding from all three papers was that, although many genes were required for the survival of KRAS-mutant cells, few of those genes could have been predicted in advance on the basis of known biochemical interactions or models of Ras signalling. Among the three kinases (PLK1, STK33 and TBK1) that were the focus of follow-up experiments, only TBK1 had been previously linked to Ras (through a pathway involving the exocyst complex, RALB and RALGDS), and this protein could hardly be described as a well-known Ras effector. This is even more remarkable when we consider that Ras and its downstream targets are among the most intensely studied proteins in biology.
Similar results were described in a series of papers that attempted to define the ‘essential kinome’ that is required for cell proliferation and survival67,76,77. This was done by carrying out kinome-wide shRNA screens on a large panel of tumour cell lines, primary cells and pairs of isogenic cells that differed in the expression of a single gene. The primary conclusion from these papers was that there was little overlap in the kinases that are required for cell proliferation across many different cell lines. Indeed, there was no correlation between the number of PubMed citations for a kinase and the likelihood that the kinase was important for tumour cell proliferation. In the words of the authors77: “Although the regulation of cell proliferation and survival are heavily studied areas, we did not see a bias in these screens toward the identification of previously known and well studied kinases, suggesting that our knowledge of the molecular events in these areas is still meager.” (D. A. Grueneberg et al, 2008).
Given the unpredictable sensitivities of tumour cells to shRNAs targeting a single kinase, it may be possible to identify new pairs of targets by screening shRNAs in the presence of a drug. An early experiment in this area looked for shRNAs that synergistically killed cancer cells in the presence of A-443654, a small molecule inhibitor of Akt78. This was motivated by the surprisingly weak anti-tumour activity of A-443654 as a single agent in preclinical models79. Two kinases were identified in this screen: casein kinase 1, γ3 (CSNK1G3) and inositol polyphosphate multikinase (IPMK). Neither of these kinases had previously been linked to Akt signalling or cancer. However, knock down of both genes potentiated the inhibition of phosphorylation of Akt and ribosomal protein S6, suggesting that these kinases may have a cryptic role in regulating signalling through the PI3K pathway.
Barriers to translating RNAi into drugs
RNAi screens can help challenge our assumptions about the genes that are most important in cancer. However, there are considerable obstacles to translating any hit from one of these screens into a new drug. Most RNAi screens measure only cell proliferation in vitro, which ignores most of the capabilities of a tumour. Therefore, it will be necessary to validate the large number of genes that emerge from these screens in more complex and time-consuming models. Once these hits are validated, they become subject to the same caveats that accompany potential drug targets identified in any other way.
In this respect, it is important to emphasize that there is not a direct correlation between RNAi knockdown of a gene and the identification of a potential drug target. Most drugs cannot be replicated by an shRNA because, for example, the drug interferes with multiple targets or inhibits a single domain of a multidomain protein only. Likewise, most shRNAs cannot be replicated by a drug, because most proteins are undruggable. Indeed, there are many examples in which an shRNA (or gene knockout) and a drug targeting the same protein give different phenotypes, and the reasons for these differences have been extensively documented (for a review of this topic see REF. 80). As a result, RNAi screens may be more likely to expose the gaps in our knowledge of cancer biology than to directly point the way to new therapeutic approaches.
Using drugs to discover kinase targets
Historically, most drugs were discovered because they possessed activity in cells or animals, and their targets and mechanism of action were elucidated only later. This is sometimes called ‘phenotype-based’ drug discovery because the phenotype was discovered before the target. By contrast, almost all modern drug discovery is ‘target-based’, meaning that the target is selected first, on the basis of a hypothesis about its role in disease.
Nonetheless, there are instances in which phenotype-based drug discovery has contributed to the development of kinase inhibitors for cancer, albeit unintentionally. One example is sorafenib, which was originally designed as an inhibitor of Raf based on the logic that Raf inhibition might be effective for Ras-mutant tumours81. Sorafenib has yet to show clinical benefit for tumours that contain frequent Ras mutations, such as lung and pancreatic cancer, and has also failed in clinical trials for the treatment of melanoma82, a disease that has a high rate of BRAF mutations43,83. However, in early clinical trials of sorafenib (which were designed to establish safety and therefore contained a diverse patient population) responses were observed in two unexpected tumour types84: renal cell (kidney) and hepatocellular (liver) cancer. One patient with kidney cancer in an early Phase I trial achieved stable disease for 2 years84, leading to the broader testing and approval of sorafenib for kidney cancer (and more recently liver cancer). The efficacy of sorafenib in kidney cancer is now attributed to the inhibition of VEGFR2 in endothelial cells, which blocks angiogenesis, rather than the inhibition of Raf in the tumour. Preclinical studies have shown that the inhibition of an additional target, PDGFR in pericytes, may be important85. Therefore, sorafenib probably blocks tumour growth through the inhibition of two kinases, expressed in different tissues, neither of which was the intended target of the drug.
Imatinib provides a second example of serendipitious target discovery. After its initial approval for the treatment of CML, imatinib was tested in five patients with hypereosinophilic syndrome, a disease of unknown molecular origin, based on the reasoning that treatments that are effective in CML are sometimes also effective in patients with hypereosinophilia86 (even though the mechanism of action of those other treatments, such as hydroxyurea and interferon-α, is unrelated to the mechanism of imatinib). Remarkably, four of the five patients treated with imatinib showed a complete haematological response (normalized eosinophil counts), such that they were able to discontinue other therapies. Analysis of DNA from the leukocytes of these patients led to the discovery of a chromosomal rearrangement that generated a fusion between PDGFRA and FIP1L1, producing a constitutively active PDGFR kinase24. As PDGFR is one of a small number of kinases inhibited by imatinib, this suggested that PDGFR activation was probably the cause of the disease. This was confirmed by the discovery of a T674I resistance mutation in PDGFR in a patient who had relapsed from imatinib therapy24.
As these examples show, the advantage of using drugs to identify cancer targets is that they can reveal in an unbiased way the proteins most essential to the tumour. The major limitation of this approach is that it is difficult to identify the targets of a molecule that has an unknown mechanism of action87. If the target is unknown, then it is difficult to increase the potency of the compound by medicinal chemistry. It can also be challenging to determine whether the efficacy and toxicity of the drug are linked (because they reside in the same target) or separable (because they reside in different targets). For these reasons, it is often impossible to improve compounds that are identified in a screen but have an unknown mechanism of action.
Targeted polypharmacology
In the case of sorafenib and imatinib, it was straightforward to identify the relevant targets of those drugs, because the targets were almost certain to be kinases. As these two drugs have a relatively small number of high-affinity targets in the human kinome (fewer than 20), the possibilities could be rapidly tested. Could this approach be generalized, so that kinase inhibitors could be used to search in an unbiased way for new combinations of therapeutic targets?
A unique feature of kinase inhibitors is that they have the potential for greater target promiscuity than almost any other type of drug. This is because the kinase superfamily (including the structurally related protein, lipid and small molecule kinases) is the largest family of druggable genes that binds to a common substrate (ATP). Kinases differ in this respect from other large gene families, such as G protein-coupled receptors, which interact in their druggable site with a wide range of structurally diverse ligands, including both peptides and small molecules. This fact has been emphasized50 by noting that the kinase inhibitor sunitinib inhibits at least 79 kinases at low micromolar concentrations, whereas all the other approved drugs combined target only 320 proteins. Therefore, individual kinase inhibitors have an enormous potential for unpredicted target combinations and so new biological activities.
Despite this potential for promiscuity, it is increasingly feasible to enumerate the targets of kinase inhibitors in a systematic way. This is because most kinases can be heterologously expressed, either as a soluble kinase domain or on the surface of phage, and assayed for drug binding in a purified format. Although there are exceptions, the activity of most kinase inhibitors in cells correlates with biochemical parameters that can be measured in vitro, such as the dissociation constant (KD) of the drug and the Michaelis–Menten constant for ATP binding (KM,ATP) of the kinase88. As kinases have become increasingly important drug targets, the measurement of these biochemical parameters has been industrialized, and there are now many vendors that offer to screen compounds against panels of kinases that approach or exceed half of the kinome (FIG. 4a). As the cost of assaying compounds against these panels has decreased, it has transformed the types of experiments that are feasible (FIG. 4b). For example, a widely cited paper from 2000 reported the specificity of 24 commonly used kinase inhibitors against 28 kinases (approximately 700 kinase–drug pairs)89. In 2007, the same group published a follow-up paper that analysed the specificity of 65 common kinase inhibitors against 70 kinases (approximately 4,500 kinase–drug pairs)90. In 2008, scientists from GlaxoSmithKline reported the testing of a panel of 577 diverse kinase inhibitors against 203 kinases (more than 117,000 unique kinase–drug pairs)91; in this case, the aim was not to evaluate any specific compound but to characterize the selectivity properties of kinase inhibitors as a drug class.
Figure 4

Selectivity profiling of kinase inhibitors
Extrapolating from these trends, it is plausible that some drug discovery programmes in the near future will profile every kinase inhibitor that is synthesized against most of the kinome. This would occur before any biological testing, as a component of routine compound characterization. The availability of selectivity data on this scale would enable medicinal chemistry to focus on optimizing drug profiles against complex patterns of kinases that gave a desired phenotype, rather than attempting to maximize specificity for a single target. It is likely that drug discovery at some pharmaceutical companies already operates in this way to some degree, although it may not be explicitly acknowledged.
What would be the advantage of this approach? The primary advantage is that it allows for target serendipity — the discovery of target combinations that could not have been predicted, but that are optimal for killing tumour cells — while allowing medicinal chemists to optimize compounds based on biochemical measurements against purified proteins. This has the potential to address the limitations of both target-based drug discovery, which often fails because the target is wrong, and phenotype-based drug discovery, which often fails because the compounds cannot be optimized.
This type of ‘targeted polypharmacology’ would represent a considerable challenge to medicinal chemists, who would be asked to carry out chemical optimization against multi-dimensional target profiles. However, there is already evidence that this is possible for certain target combinations31,92, and kinase drug discovery seems to be the ideal setting to test this model. We analysed a large database of kinase inhibitor selectivity data93 to discover whether certain combinations of kinase targets are enriched among known kinase inhibitors; whether the preference for these target combinations could be rationalized on the basis of sequence analysis; and whether this could be used to estimate the combinatorial druggability of most of the kinome that has not yet been targeted by a small molecule (FIG. 5; see Supplementary information S1 (figure)). We have found, consistent with previous analyses93, that there are clearly clusters of kinases that tend to be inhibited by similar drugs, but that there are also many target combinations that should be accessible but remain undiscovered. We interpret this to mean that there is an important opportunity to discover multi-targeted kinase inhibitors with new biological activities.
Figure 5

Polypharmacology in the protein kinome
We have focused on approaches to identify combinations of kinase targets with increased anticancer activity, but understanding the basis for kinase inhibitor-mediated toxicity to normal cells is also valuable, as this information will improve efforts to increase therapeutic index. The broad kinome profiling of clinically approved and investigational kinase inhibitors is likely to help identify such problematic kinase targets. Removing these toxicity-associated kinases from new drug candidates may allow for more complete inhibition of cancer cell targets while avoiding systemic toxicity.
Conclusions
Many different approaches will be necessary to identify the best combinations of targeted therapies for cancer. However, it is important to begin to consider the challenges that may be faced in the near future, when drugs targeting every kinase linked to cancer have been tested in clinical trials, but survival rates for most types of cancer have only marginally improved. It will not be sufficient in this case to simply pursue the next set of oncogenes, because tumour sequencing projects have already shown that such oncogenes do not exist, at least among the genes that are mutated with high frequency24. Therefore the burden will be on the cancer research community to think of more creative ways to target important proteins such as kinases that have already been identified.
Supplementary Material
S1

Acknowledgements
This work was partly supported by the US National Institutes of Health grants DK083531 (Z.A.K.) and EB001987 (K.M.S.).
Competing interests statement

The authors declare no competing financial interests.
DATABASES

National cancer institute Drug Dictionary: http://www.cancer.gov/drugdictionary/
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Contributor Information
Zachary A. Knight, The Rockefeller University, New York, New York 10065, USA.
Henry Lin, Howard Hughes Medical Institute and Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California 94158, USA.
Kevan M. Shokat, Howard Hughes Medical Institute and Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California 94158, USA.
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