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Posts Tagged ‘mass spectrometry’

Dana-Farber Researchers “OncoMap” The Way To Personalized Treatment For Ovarian Cancer

Posted by Paul Cacciatore on November 16, 2010

Researchers have shown that point mutations – mis-spellings in a single letter of genetic code – that drive the onset and growth of cancer cells can be detected successfully in advanced ovarian cancer using a technique called OncoMap. The finding opens the way for personalized medicine in which every patient could have their tumor screened, specific mutations identified, and the appropriate drug chosen to target the mutation and halt the growth of their cancer.

Researchers have shown that point mutations – mis-spellings in a single letter of genetic code – that drive the onset and growth of cancer cells can be detected successfully in advanced ovarian cancer using a technique called OncoMap. The finding opens the way for personalized medicine in which every patient could have their tumor screened, specific mutations identified, and the appropriate drug chosen to target the mutation and halt the growth of their cancer.

Using mass spectrometry for identifying the genetic make-up of cancer cells, OncoMap can determine the point mutations in tumors by utilizing a large panel of over 100 known cancer-causing genes (referred to as “oncogenes“). In the work to be presented today (Wednesday) at the 22nd EORTC-NCI-AACR [1] Symposium on Molecular Targets and Cancer Therapeutics in Berlin, researchers will describe how they used OncoMap to identify oncogene mutations in tumor samples obtained from women with advanced high-grade serous ovarian cancer. [2] Earlier in the year 76 mutations in 26 different genes had been found but, since then, further work in more tumor samples has found more.

Ursula A. Matulonis, M.D., Medical Director, Gynecologic Oncology, Dana-Farber Cancer Institute; Associate Professor, Medicine, Harvard Medical School

Dr. Ursula Matulonis, director/program leader in medical gynecologic oncology at the Dana-Farber Cancer Institute located in Boston, Massachusetts (USA) and Associate Professor of Medicine at Harvard Medical School, will tell the meeting:

“Epithelial ovarian cancer is the most lethal of all the gynecologic malignancies, and new treatments are needed for both newly diagnosed patients as well as patients with recurrent cancer. The success of conventional chemotherapy has reached a plateau, and new means of characterizing ovarian cancer so that treatment can be personalized are needed.

We know that many human cancers have point mutations in certain oncogenes, and that these mutations can cause cancer cells to have a dependence on just one overactive gene or signalling pathway for the cancer cell’s growth and survival – a phenomenon known as ‘oncogene addiction’. If the mutation that causes the oncogene addiction can be inhibited, then it seems that this often halts the cancer process. Examples of mutations that are successfully inhibited by targeted drugs are HER2 (for which trastuzumab [Herceptin®] is used in breast cancer), EGFR (erlotinib [Tarceva®] in lung cancer) and c-kit (imatinib [Gleevec®] in chronic myeloid leukemia). So if we know the status of specific genes in a tumor, then this enables us to choose specific treatments that are likely to work successfully against the cancer.”

Dr Matulonis and her colleagues used OncoMap to investigate the mutation status of high-grade serous ovarian tumors that were known not to be caused by inherited mutations in the BRCA 1 and BRCA 2 genes. They found mutations previously identified to be involved in ovarian cancer: KRAS, BRAF, CTNNB1 and PIK3CA. The KRAS and PIK3CA mutations were the most common, while BRAF was more rare. The researchers also identified a low frequency of mutations in many other different oncogenes.

Dr. Matulonis further noted:

“This study shows that it’s feasible to use OncoMap to identify whether a patient’s tumor has a mutation in an oncogene for which a known drug is available to target that specific gene, so as to enable us to place her on a clinical study of that drug; for instance, XL147 or GDC-0941 are inhibitors for the P13kinase mutation that are in clinical trials at present.  In addition, someone’s cancer could harbor a mutation (such as ALK) that is not known to be associated with ovarian cancer or has not yet been studied in ovarian cancer – these patients could be matched with a drug that inhibits that protein too. As new drugs get developed, this information would be used to match future drugs with patients and their cancers.”

The researchers hope that OncoMap will become a clinical test for all cancer patients at the Dana-Farber Cancer Institute before long, so that the genetic information obtained can be used to choose the best treatment for them.

Dr. Matulonis said:

“At present, only a few targeted therapies are being used for newly diagnosed ovarian cancer and most are being used to treat recurrent ovarian cancer, but this will change eventually. I have already referred several of our patients who are either newly diagnosed or have recurrent cancer and who have mutations (one with KRAS and one with PIK3CA) to our phase I program for drugs studies specific to these mutations.  For ovarian cancer, understanding mutational analysis is one piece of the genetic puzzle. Our group will also start looking for chromosomal and gene amplifications and deletions in patients’ tumors, which we know are important for ovarian cancer.”

Matulonis believes that OncoMap and other similar analytical tools will become mainstream practice in all cancer clinics before long. Tools for detecting genes with the incorrect numbers of copies or abnormal expression will also help doctors to choose the best treatment for individual patients.”

Source: Researchers map the way to personalised treatment for ovarian cancer, Abstract no: 35. Oral presentation in plenary session 2.  22nd EORTC-NCI-AACR Symposium on Molecular Targets and Cancer Therapeutics, Berlin, Germany, November 16- 19, 2010.

References:

[1] EORTC [European Organisation for Research and Treatment of Cancer, NCI [National Cancer Institute], AACR [American Association for Cancer Research].

[2] The study was funded by the Madeline Franchi Ovarian Cancer Research Fund, twoAM Fund and the Sally Cooke Ovarian Cancer Research Fund.

Related Information:

Posted in Conferences, Discoveries, Genetics, Medical Study Results, Meeting Highlights, Pipeline Drugs | Tagged: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , | 1 Comment »

The Cancer Biomarker Conundrum: Too Many False Discoveries

Posted by Paul Cacciatore on August 12, 2010

The boom in cancer [including ovarian] biomarker investments over the past 25 years has not translated into major clinical success. The reasons for biomarker failures include problems with study design and interpretation, as well as statistical deficiencies, according to an article published online August 12 in The Journal of the National Cancer Institute.

The boom in cancer [including ovarian] biomarker investments over the past 25 years has not translated into major clinical success. The reasons for biomarker failures include problems with study design and interpretation, as well as statistical deficiencies, according to an article published online August 12 in The Journal of the National Cancer Institute.

The National Institutes of Health defines a biomarker as “a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.” In the past decade, there have been numerous biomarker discoveries, but most initially promising biomarkers have not been validated for clinical use.

Eleftherios P. Diamandis, M.D., Ph.D., Head, Section of Clinical Biochemistry, Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, Ontario, Canada

To understand why so-called biomarker “breakthroughs” have not made it to the clinic, Eleftherios P. Diamandis, M.D., Ph.D., professor of pathology and laboratory medicine at Mount Sinai Hospital in Toronto and associate scientist at the Samuel Lunenfeld Research Institute of Mount Sinai Hospital, reviewed some biomarkers initially hailed as breakthroughs and their subsequent failings.

Diamandis first describes the requirements for biomarkers to be approved for clinical use: A biomarker must be released into circulation in easily detectable amounts by a small asymptomatic tumor or its micro-environment; and it should preferably be specific for the tissue of origin. Also, if the biomarker is affected by a non-cancer disease, its utility for cancer detection may be compromised. For example, the prostate-specific antigen (PSA) biomarker, which is used to detect prostate cancer, is also elevated in benign prostatic hyperplasia.

Diamandis looks at seven biomarkers that have emerged in the past 25 years, all of which were considered promising when they were first described. These include nuclear magnetic resonance of serum for cancer diagnosis; lysophosphatidic acid for ovarian cancer; four- and six-parameter diagnostic panels for ovarian cancer; osteopontin for ovarian cancer; early prostate cancer antigen-2 (EPCA-2) for prostate cancer detection; proteomic profiling of serum by mass spectrometry for ovarian cancer diagnosis; and peptidomic patterns for cancer diagnosis. Problems ranged from inappropriate statistical analysis to biases in case patient and control subject selection. For example, the problems with EPCA-2 included reporting values that were beyond the detection limit of the assay and using inappropriate reagents to test EPCA-2, such as solid surfaces coated with undiluted serum.

Diamandis concludes that “problems with pre-analytical, analytical, and post-analytical study design could lead to the generation of data that could be highly misleading.”

Sources:

The Cancer Biomarker Conundrum: Too Many False Discoveries, Journal of the National Cancer Institute Advance Access,  published on August 12, 2010, DOI 10.1093/jnci/djq335.

Eleftherios P. Diamandis. Cancer Biomarkers: Can We Turn Recent Failures into Success? Commentary, Journal of the National Cancer Institute Advance Access published on August 12, 2010, DOI 10.1093/jnci/djq306.

Posted in Biomarker, Early Detection, Proteomics | Tagged: , , , , , , , , , , , , , , , | Leave a Comment »

Georgia Tech’s Ovarian Cancer Early Detection Blood Test Exhibits High Accuracy in Small Study; Larger Study Planned

Posted by Paul Cacciatore on August 11, 2010

Scientists at the Georgia Institute of Technology have attained very promising results on their initial investigations of a new test for ovarian cancer. Using a new technique involving mass spectrometry of a single drop of blood serum, the test correctly identified women with ovarian cancer in 100 percent of the 94 patients tested. Because of the extremely low prevalence of ovarian cancer in the general population (∼0.04%), extensive prospective testing will be required to evaluate the test’s potential utility in general screening applications.

Scientists at the Georgia Institute of Technology have attained very promising results on their initial investigations of a new test for ovarian cancer. Using a new technique involving mass spectrometry of a single drop of blood serum, the test correctly identified women with ovarian cancer in 100 percent of the 94 patients tested. The results can be found online in the journal Cancer Epidemiology, Biomarkers, & Prevention Research.

John McDonald, Ph.D., Professor, Associate Dean for Biology Program Development, Georgia Institute of Technology; Chief Science Officer, Ovarian Cancer Institute

Facundo Fernandez, Ph.D., Associate Professor, School of Chemistry & Biochemistry, Georgia Institute of Technology

“Because ovarian cancer is a disease of relatively low prevalence, it’s essential that tests for it be extremely accurate. We believe we may have developed such a test,” said John McDonald, chief research scientist at the Ovarian Cancer Institute (Atlanta) and professor of biology at Georgia Tech.

The measurement step in the test, developed by the research group of Facundo Fernandez, associate professor in the School of Chemistry and Biochemistry at Tech, uses a single drop of blood serum, which is vaporized by hot helium plasma. As the molecules from the serum become electrically charged, a mass spectrometer is used to measure their relative abundance. The test looks at the small molecules involved in metabolism that are in the serum, known as metabolites. Machine learning techniques developed by Alex Gray, assistant professor in the College of Computing and the Center for the Study of Systems Biology, were then used to sort the sets of metabolites that were found in cancerous plasma from the ones found in healthy samples. Then, McDonald’s lab mapped the results between the metabolites found in both sets of tissue to discover the biological meaning of these metabolic changes.

The assay did extremely well in initial tests involving 94 subjects. In addition to being able to generate results using only a drop of blood serum, the test proved to be 100 percent accurate in distinguishing sera from women with ovarian cancer from normal controls. In addition it registered neither a single false positive nor a false negative

The group is currently in the midst of conducting the next set of assays, this time with 500 patients.

“The caveat is we don’t currently have 500 patients with the same type of ovarian cancer, so we’re going to look at other types of ovarian cancer,” said Fernandez. “It’s possible that there are also signatures for other cancers, not just ovarian, so we’re also going to be using the same approach to look at other types of cancers. We’ll be working with collaborators in Atlanta and elsewhere.”

In addition to having a relatively low prevalence, ovarian cancer is also asymptomatic in the early stages. Therefore, if further testing confirms the ability to accurately detect ovarian cancer by analyzing metabolites in the serum of women, doctors will be able detect the disease early and save many lives.

Libby’s H*O*P*E*™ Comment:

Alex Gray, Ph.D., Assistant Professor, College of Computing & Center for the Study of Systems Biology, Georgia Institute of Technology

This study involved testing the metabolite levels in blood sera from 44 women diagnosed with serous papillary ovarian cancer (stages I-IV) and 50 healthy women or women with benign conditions.  The assay distinguished between the cancer and control groups with an unprecedented 99% to 100% accuracy. The method possesses significant clinical potential as a cancer diagnostic tool.  Because of the extremely low prevalence of ovarian cancer in the general population (∼0.04%), extensive prospective testing will be required to evaluate the test’s potential utility in general screening applications.

Sources:

Initial Trials On New Ovarain Cancer Tests Exhibit Extremely High Accuracy, News Release, Georgia Institute of Technology, August 11, 2010.

Zhou M,Guan W, Walker LD, et. al. Rapid Mass Spectrometric Metabolic Profiling of Blood Sera Detects Ovarian Cancer with High Accuracy. Cancer Epidemiol Biomarkers Prev 1055-9965.EPI-10-0126; Published OnlineFirst August 10, 2010; doi:10.1158/1055-9965.EPI-10-0126

Posted in Biomarker, Early Detection, Medical Study Results | Tagged: , , , , , , , , , | 3 Comments »

 
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