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Old 09-02-2014, 06:42 PM
gdpawel gdpawel is offline
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Default Quest for Targeted Therapeutic 'Cocktails' Hits Roadblocks

Mark Pegram, M.D.

The use of cutting-edge technology and bioinformatics to inform clinical decision-making in oncology is still a ways off, according to Mark Pegram, MD, the Susy Yuan-Huey Hung Professor of Oncology and Director of the Stanford Breast Oncology Program, Stanford University, Palo Alto, California. At the 9th Annual New Orleans Summer Cancer Meeting, Dr. Pegram said the “lofty goal” of targeted therapeutic “cocktails”—which will be needed to address the molecular diversity of tumors—is proving hard to achieve.

Circulating Tumor Cells

Circulating tumor cells as an alternative to serial biopsies of metastaticterm lesions has great appeal, but the uptake of this technology has been somewhat anemic.

One problem is obtaining a consistent definition of a circulating tumor cell. The cell must be positive for cytokeratin, must have a nucleus, must have a negative control, must be negative for leukocyte cytoplasm (white cell markers), and the nucleus must fit inside the cytoplasm.

“These are the things measured using a huge variety of different approaches for defining and capturing [circulating tumor cells],” he said. The most clinically advanced is the CellSearch System, which uses an antibodyterm/ferrofluid combination to attach specifically to circulating tumor cells, and magnets to draw those cells out of the blood sample to be stained and identified. The test enumerates the number of circulating tumor cells in a patient with metastatic disease, and this is correlated with overall survival.

“The problem with this assay is that it is not sensitive enough to capture [circulating tumor cells] in early stages of disease. While enumeration of [circulating tumor cells] is prognostic, let’s be honest: that’s not what we are interested in,” Dr. Pegram said. “We are interested in predicting response to treatment.”

He has observed that while some clinicians are “enamored” of this technology and do use it, others realize that it holds little value over routine restaging with radiographic studies. The assay also reveals little as to what is happening in these cells, and the small number of circulating tumor cells captured—five or so—is insufficient for fully deciphering the tumor, he said.

Capturing more cells could help, and that is what microfluidics-based cell separation does. This new, simpler technology passes blood through a membrane, separating larger tumor cells from other blood elements and yielding thousands of cells upon which clinically relevant tests can be performed.

“The approaches that have much higher yields will be more useful because they will be informative as to what cells are doing at a molecular level,” he predicted.

Even more sophisticated blood-based technology will someday be better able to capture the genetic heterogeneity of advanced solid tumors at a gene-expression level so they can be compared with the primary tumor. This, however, will present other challenges.

“In one blood sample there are multiple populations of [circulating tumor cells] that are different from another. This will pose a diagnostic challenge and a treatment challenge, as well, if we find unique targets within the same patient at the same time,” he said. “Until we can come to terms with the complexity of solid tumor malignancies, we can’t make informed decisions.”

At this point, guideline committees “have not latched on to [circulating tumor cells] as a ‘must’ in clinical practice,” he indicated, calling circulating tumor cell determination a “consideration,” but one lacking in great value until emerging technologies can interrogate circulating tumor cells at a molecular level.

Genomics and Drug Development

The promise of genomics was to identify mutations within a tumor and thus allow the clinician to concoct a tailored therapeutic cocktail. In reality, however, the scenario is infinitely complex. Within a single MCF-7 human breast cancer cell, for instance, 157 chromosomal break points have been found.

“We have rich genomic information in a tumor cell, but this does not tell the doctor how to treat the patient,” he said.

The Cancer Genome Atlas (TCGA) Network, in its examination of its first 507 breast cancer samples, revealed only four frequently mutated genes out of 50 that were identified: PIK3CA, TP53, MAP3K1, and GATA3.

“This was a stunning observation,” commented Dr. Pegram. “We thought we would discover multiple new therapeutic targets in breast cancer and therefore have home runs in drug development, but we found only four, and all four were already known to be common mutations.”

Drugs are already targeting PI3K, the other three frequent mutations are not druggable, and the rest of the 50 genes are low-frequency mutations (affecting about 2% of breast cancers) for which pharmaceutical companies are unlikely to invest. “This will pose a challenge because our current models of drug development will not survive this reality,” he predicted.

Furthermore, according to Dr. Pegram, deep sequencing identifies even more heterogeneity, revealing individual clones with different mutational profiles within the same tumor. The current next-generation diagnostics are not performing deep sequencing and therefore are not demonstrating the molecular heterogeneity that is critical for selecting the best targeted agent, he said.

Even “more sobering,” he continued, is that this complexity is present at the time of diagnosis, with further alterations piled on due to drug resistance. Cancer and genomes are not static; they are a moving target, he reiterated.

While the situation is clinically frustrating now, there is the potential to tease apart the molecular evolution of cancers with future sequencing technology, and this “extraordinary” achievement could give insights into prevention strategies.

Adding Proteomic Data

Even more complex than genomics is proteomics, the large-scale analysis of protein-expression profiles through mass spectrometry. Proteomic information on post-translational modifications in the tumor (ie, phosphorylation, glycolisation, etc) could be a useful adjunct to genomic information, producing a more “holistic view” of pathway regulation.

“The hope is that mixing proteomic work along with genomic work will facilitate our understanding of what is going on in the dynamic tumor cell,” Dr. Pegram said. “But the problem with proteomics is size: the proteome is much larger than the genome, due to alternative splicing and protein modification.”

The information desired from proteomics includes all protein-to-protein interactions, protein functions and their regulation, protein modifications, subcellular location, and protein concentrations. Current approaches do not provide all this information.

While polymerase chain reaction (PCR) testing determines gene amplification, there is no PCR equivalent for proteomics. Sequencing tools are robust in genomics, but mass spectrometry is still emerging in proteomics. Furthermore, proteomic data is “big data,” and huge servers are needed just to store the data. Novel approaches are currently being pioneered to address these issues, he said.

In summary, Dr. Pegram said, “Mutational events in cancer can yield complex and deranged pathways, but they are still highly functional and they can take the lives of our patients. We need to understand them.”

Disclosure: Dr. Pegram reported no potential conflicts of interest.

The ASCO Post, September 1, 2014, Volume 5, Issue 14

[url]http://www.ascopost.com/issues/september-1,-2014/quest-for-targeted-therapeutic-%E2%80%98cocktails%E2%80%99-hits-roadblocks.aspx
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Last edited by gdpawel : 10-07-2014 at 12:55 AM. Reason: Post full article in forum board
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Old 09-07-2014, 11:08 PM
gdpawel gdpawel is offline
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Default Employing multi-dimensional analyses of both genomic and phenotypic platforms

Robert A. Nagourney, M.D.

Oncologists confront numerous hurdles as they attempt to apply the new cancer prognostic and predictive tests. Among them are the complexities of gene arrays that introduce practicing physicians to an entirely new lexicon of terms like “splice variant, gene-rearrangement, amplification and SNP.”

Although these phrases may roll of the tongue of the average molecular biologists (mostly PhDs), they are foreign and opaque to the average oncologist (mostly MDs). To address this communication shortfall laboratory service providers provide written addenda (some quite verbose) to clarify and illuminate the material. Some institutions have taken to convening “molecular tumor boards” where physicians most adept at genomics serve as “translators.” Increasingly, organizations like ASCO offer symposia on modern gene science to the rank and file, a sort of Cancer Genomics for Dummies. If we continue down this path, oncologists may soon know more but understand less than any other medical sub-specialists.

However well intended these educational efforts may be, none of them are prepared to address the more fundamental question: How well do genomic profiles actually predict response? This broader issue lays bare our tendency to confuse data with results and big data with big results. To wit, we must remember that our DNA, originally provided to each of us in the form of a single cell (the fertilized ovum) carries all of the genetic information that makes us, us. From the hair follicles on our heads to the acid secreting cells in our stomach, every cell in our body carries exactly the same genetic data neatly scripted onto our nuclear hard-drives.

What makes this all work, however, isn’t the DNA on the hard drive, but instead the software that judiciously extracts exactly what it needs, exactly when it needs it. It’s this next level of complexity that makes us who we are. While it is true that you can’t grow hair or secrete stomach acid without the requisite DNA, simply having that DNA does not mean you will grow hair or make acid. Our growing reliance upon informatics has created a “forest for the trees” scenario, focusing our gaze upon nearby details at the expense of larger trends and insights.

What is desperately needed is a better approximation of the next level of complexity. In biology that moves us from the genotype (informatics) to the phenotype (function). To achieve this, our group now regularly combines genomic, transcriptomic or proteomic information with functional analyses. This enables us to interrogate whether the presence or absence of a gene, transcript or protein will actually confer that behavior or response at the system level.

I firmly believe that the future of cancer therapeutics will combine genomic, transcriptomic and/or proteomic analyses with functional (phenotypic) analyses.

Recent experiences come to mind. A charming patient in her 50s underwent a genomic analysis that identified a PI3K mutation. She sought an opinion. We conducted an EVA-PCD assay on biopsied tissue that confirmed sensitivity to the drugs that target PI3K. Armed with this information, we administered Everolimus at a fraction of the normal dose. The response was prompt and dramatic with resolution of liver function abnormalities, normalization of her performance status and a quick return to normal activities. A related case occurred in a young man with metastatic colorectal cancer. He had received conventional chemotherapies but at approximately two years out, his disease again began to progress.

A biopsy revealed that despite prior exposure to Cetuximab (the antibody against EGFR) there was persistent activity for the small molecule inhibitor, Erlotinib. Consistent with prior work that we had reported years earlier, we combined Cetuximab with Erlotinib, and the patient responded immediately.

Each of these patients reflects the intelligent application of available technologies. Rather than treat individuals based on the presence of a target, we can now treat based on the presence of a response. The identification of targets and confirmation of response has the potential to achieve ever higher levels of clinical benefit. It may ultimately be possible to find effective treatments for every patient if we employ multi-dimensional analyses that incorporate the results of both genomic and phenotypic platforms.
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Old 09-11-2014, 04:29 PM
gdpawel gdpawel is offline
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Default Genomic analysis has proven to be harder to realize than expected

Therapies targeted at the specific genetics of a patient's lung cancer have proved harder to realize than expected.

Ramaswamy Govindan vividly remembers the first time he treated his patients with the cancer drug gefitinib. It was the start of the millennium, and the outlook for patients with metastatic non-small-cell lung cancer (NSCLC) was dire: less than 40% survived a year after diagnosis.

“The second patient I treated was about to go into hospice care,” recalls Govindan, a medical oncologist at Washington University School of Medicine in St Louis, Missouri. “But she went on to live three years before dying of a heart attack.”

Gefitinib was approved by the US Food and Drug Administration (FDA) in 2003. Marketed as Iressa by AstraZeneca, its arrival was a watershed moment in the treatment of NSCLC, the most common type of lung cancer. The drug blocks a protein called epidermal growth factor receptor (EGFR), which transmits signals that help to control the division and migration of cancer cells.

However, although some patients responded well to the treatment, many others did not. The same was true for another drug that targets EGFR: erlotinib (Tarceva), developed by Genentech and OSI Pharmaceuticals and approved by the FDA in 2004. The only apparent trend was that non-smokers were more likely than smokers to respond to erlotinib. “Back in the day, you would give Tarceva to somebody because they didn't smoke, but in the vast majority of those people it didn't help,” says Mark Kris, a thoracic oncologist at Memorial Sloan Kettering Cancer Center in New York City.

In 2004, two research teams — one of which included Kris — discovered the secret1, 2. Both gefitinib and erlotinib were selectively active against lung cancers with hyperactive, mutated versions of the EGFR gene, but ineffective against tumours in which the gene was not mutated. Mutated EGFR is predom-inantly found in a type of NSCLC called adenocarcinoma, which accounts for 40% of lung cancers and is the most common form of the disease in people who have never smoked.

The realization that specific genetic variants might help researchers to develop personalized lung-cancer treatments has launched a generation of targeted drugs that can deliver years of additional life to certain subgroups of patients. But some patients are still waiting to reap the medical benefits of the post-genomic era, and many doctors and clinical researchers fear that the low-hanging fruits of lung-cancer genetics may already have been picked.

The cancer genome is a battered and scarred landscape of DNA-sequence changes as well as swapped, duplicated and deleted regions. The therapeutic focus is on the subset of these mutated genes — 'drivers' — that are essential for aggressive cell growth. The most useful drivers from a therapeutic perspective are oncogenes, which encode proteins that promote uncontrolled cell division and have the potential to convert a normally functioning cell into a cancer cell. Drugs that target mutant oncogenes might halt or reverse tumour growth.

One major lung-cancer oncogene is EGFR. Mutations to the EGFR oncogene are detected in more than 40% of adenocarcinomas. Three drugs are commercially available for EGFR-mutant cancers, and more are in trials. In 2007, researchers uncovered a second driver oncogene that is present in 5–7% of adenocarcinomas. Called ALK, this gene encodes a poorly understood signalling protein and occasionally undergoes a genomic rearrangement that leaves the resulting protein permanently turned on. In 2011, the FDA approved crizotinib (marketed by Pfizer as Xalkori) for NSCLC patients whose tumours exhibit such rearrangements. Phase III trial data presented by Pfizer at the 2014 annual meeting of the American Society for Clinical Oncology (ASCO) indicate that crizotinib can extend the life of patients whose tumours have mutations in ALK.

However, the benefits of these targeted drugs are only temporary — after about a year of remission, most tumours acquire resistance. For example, more than half of the tumours treated with EGFR inhibitors acquire a mutation called T790M in the EGFR gene3. This blocks the drug without interfering with the mutant protein's signalling.

Tumours often contain genetically distinct cell populations, and many researchers believe that cancer recurrence may represent the evolutionary victory of an already-resistant minority. “Once we start to kill off cells that have the sensitizing mutation, the intrinsically resistant cells start to grow,” says Tony Mok, a clinical oncologist at the Chinese University of Hong Kong.

Presentations at this year's ASCO meeting revealed promising clinical-trial data on drugs being developed by Clovis Oncology and AstraZeneca that inhibit the T790M mutant receptor. One molecule induced tumour shrinkage in almost two-thirds of patients.

Patients with crizotinib-resistant tumours also received hopeful news this year. Such resistance often arises in the absence of a detectable mutation, which suggests that other mechanisms increase ALK activity to overwhelm crizotinib's modest capacity for inhibition. In April 2014, the FDA moved with unprecedented speed to approve the drug ceritinib (marketed by Novartis as Zykadia) based purely on a phase I trial4 showing a strong clinical response in resistant patients. Subsequent data suggest that ceritinib works equally well in both previously untreated and crizotinib-resistant patients.

Ceritinib is 5 to 20 times more potent than crizotinib as an ALK inhibitor, and it is also more selective, says Alice Shaw, an oncologist at Massachusetts General Hospital in Boston, whose team led the phase I trial. At least nine other ALK drugs are in development.

Targeted treatments benefit only a minority of lung-cancer patients. For the rest, the hunt continues for drivers that might prove vulnerable to therapy. Most progress has been seen in people diagnosed with adenocarcinoma and who do not smoke, many of whom have cancers that have arisen through one primary driver mutation (see page S12). By contrast, the mutational load in a smoker's tumour can be overwhelming, making it a challenge to separate the signals of likely driver mutations from the noise generated by large numbers of 'passenger' mutations that make a minimal contribution to tumour growth.

But even targeting the genetic culprit in a single driver mutation can be tricky. Take the example of the oncogene KRAS, which encodes a signalling protein involved in cell proliferation. KRAS mutations appear in as many as one-quarter of adenocarcinomas, but attempts at targeted therapy have so far failed. A study reported at the 2014 ASCO meeting suggests that a subset of patients with KRAS-mutant NSCLC may benefit from a combination of drugs that target several proteins in the same biological pathway as KRAS. So far, only 10–15% of KRAS-mutant tumours respond to combination treatment, says Vassiliki Papadimitrakopoulou, a medical oncologist at the MD Anderson Cancer Center in Houston, Texas, who helped to coordinate the study. “We would like to see more than that.”

For patients with non-adenocarcinoma lung cancers, targeted options are limited. Very few patients with squamous cell carcinoma (SCC) — the second most common form of lung cancer — have EGFR or ALK driver mutations. Most SCC tumours occur in smokers, and are plagued by the same extensive genomic mutation that is confounding efforts to apply targeted treatment to smokers' adenocarcinomas.

This may be about to change, thanks to the work of Govindan and his colleagues at the Cancer Genome Atlas (TCGA), which in 2012 published a detailed assessment of the SCC genomic landscape derived from tissue samples from 178 SCC tumours5. The results suggested a number of avenues for potential intervention. A mutation in the gene CDKN2A, for example, is found in 70% of SCC tumours and could be a target.

The urgent need for progress in lung-cancer treatment has inspired Papadimitrakopoulou, who is collaborating with other US investigators on the Lung Cancer Master Protocol. Launched in June, this multi-arm, multi-institutional clinical trial will use sequencing to match SCC patients with targeted drug candidates. It will also accumulate a lot of cancer genomic data. “We will be characterizing the largest set of SCCs across the United States,” says Papadimitrakopoulou.

Govindan and his colleagues are also working on large-scale genomic analysis. After a genomic survey of mutations in 230 adenocarcinoma tumours6, published in July 2014, he and fellow TCGA coordinators Louis Staudt and Matthew Meyerson are working on plans to study a larger number of tumour samples in the hope of detecting additional targetable drivers.
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Old 09-11-2014, 04:29 PM
gdpawel gdpawel is offline
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The robust performance of drugs that target ALK and EGFR has made testing for mutations in these genes routine. But as the cost of sequencing plummets, some clinicians believe that it makes more sense to survey hundreds of cancer-related genes rather than just those two to provide a larger set of potential targets. Kris is among the evangelists for extensive clinical sequencing. “If you have lung cancer in 2014, the first thing we do is a biopsy that includes a comprehensive genetic test for all potential drivers,” he says. Companies are also providing the tools to do this. Foundation Medicine, a company in Cambridge, Massachusetts, co-founded by TCGA scientists, generates oncology diagnostic reports for clinicians based on sequencing data from 236 cancer-associated genes. The company expects to do 25,000 tests in 2014, up from 9,000 in 2013. In June, the Memorial Sloan Kettering Cancer Center forged a partnership with Quest Diagnostics of Madison, New Jersey, to broaden clinician access to the centre's in-house genetic test, which also surveys numerous oncogenes in parallel.

Genetic analyses could help to identify patients with mutations that are rare in lung cancer but are common in other tumour types. For example, a subset of adenocarcinoma patients with mutations affecting the RET gene might benefit from cabozantinib, a drug that targets this alteration in thyroid cancer7. And with much of the pharmaceutical industry's oncology efforts focused on developing targeted drugs, data from sequencing the genes of lung-cancer patients can also help to direct those patients to clinical trials. To assess the impact of sequencing on lung-cancer care, Kris and other scientists — who formed a group called the Lung Cancer Mutation Consortium — sequenced as many as 10 known oncogenes in more than 1,000 patients. Kris reports that 28% of the people tested were matched to clinical trials they might not otherwise have known about8.

As with KRAS, many oncogenes are informative scientifically but are not medically useful, leading some researchers to question the short-term benefits of routine, large-scale tumour sequencing in patients — a practice Mok says is unlikely to improve lung-cancer care significantly until the next EGFR comes along. Still, he believes that genetic analysis must be embedded into the diagnostic process so that drugs can be matched to a patient as quickly as possible — he holds out hope that new drivers will soon join ALK and EGFR.

As would everyone struggling to find new weapons against this lethal disease. With such resources at hand, more doctors might look forward to experiencing the sweet satisfaction Govindan encountered on providing his patient with just the treatment she needed to buy years of additional life.

[url]http://www.nature.com/nature/journal/v513/n7517_supp/full/513S8a.html
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